{ "generated": "2026-08-29", "edition": 25, "count": 4304, "papers": [ { "uid": "doi:10.2139/ssrn.7362741", "doi": "10.2139/ssrn.7362741", "title": "Enhancing Consumer behavior Prediction with LLM-generated Data: A Signal Combination Approach", "authors": [ "Jae Joon Lee" ], "posted": "2026-08-29", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7362741", "field": "management", "role": "method", "bullets": [ "Consumer willingness-to-accept data from a social media discontinuation experiment, with GPT-3.5 and GPT-4.1 generating synthetic responses treated as noisy signals.", "GPT-3.5 and GPT-4.1 generate predictions combined with real human data through supervised learning rather than used as direct substitutes for human responses.", "Prediction error falls 66 to 86 percent versus naive substitution; GPT-4.1, a worse direct predictor, yields nearly identical final prediction quality through signal combination." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "human outcome data benchmark, RMSE reported", "salience": 62, "edition": 25, "n": 4237 }, { "uid": "doi:10.2139/ssrn.7354658", "doi": "10.2139/ssrn.7354658", "title": "AIRE and AIREOM: A Foundational Framework for Human-AI Strategy", "authors": [ "Seema Swamy" ], "posted": "2026-08-29", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7354658", "field": "management", "role": "object", "bullets": [ "Conceptual strategy framework with no empirical sample, aimed at executives, AI product leaders, and strategists planning LLM and agentic AI deployment.", "Proposes AIRE (AI as Relational Entity) theory and AIREOM optimization model for how users assign roles to, and develop trust in, LLM systems.", "Argues competitive differentiation comes from the relational roles users assign to AI systems rather than raw task capability; detailed operationalization reserved for a future paper." ], "bullet_provenance": "ai", "salience": 25, "edition": 25, "models": [], "validated": null, "n": 4241 }, { "uid": "doi:10.2139/ssrn.7354642", "doi": "10.2139/ssrn.7354642", "title": "Attunement Costs: How Interaction Locks Users into Specific LLMs", "authors": [ "Leonard Hanschur", "Daniel Obermeier", "Joachim Henkel" ], "posted": "2026-08-29", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7354642", "field": "economics", "role": "object", "bullets": [ "50 HumanEval coding tasks in four prompt variants submitted to 46 LLMs, yielding 9,200 prompt-model combinations testing cross-model transferability of prompting techniques.", "46 LLMs (not individually named) respond to four prompting variants; model-level heterogeneity in technique effects is the central empirical test of attunement costs.", "Prompting techniques that improve one model frequently degrade another, with little cross-model agreement, supporting model-specific interaction knowledge that creates a novel procedural switching cost." ], "bullet_provenance": "ai", "salience": 65, "edition": 25, "models": [], "validated": null, "n": 4242 }, { "uid": "doi:10.2139/ssrn.7362639", "doi": "10.2139/ssrn.7362639", "title": "The Accountability Void: Governing Recursive Delegation and Semantic Intent in Agentic AI Ecosystems", "authors": [ "Mohammad Anzar Draboo" ], "posted": "2026-08-29", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7362639", "field": "management", "role": "object", "bullets": [ "Three documented 2025-2026 incidents: an MCP transport vulnerability, Cursor IDE destructive-agent actions, and a Claude Code Terraform event that deleted production infrastructure.", "No LLM is used as a research instrument; the paper forensically reconstructs real agentic AI failures to identify gaps in recursive delegation and semantic intent governance.", "Existing telemetry instruments the compute boundary but misses the semantic boundary; a framework combining continuous AI identity, trace-based assurance, and graduated oversight is proposed." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 45, "edition": 25, "validated": null, "n": 4243 }, { "uid": "doi:10.2139/ssrn.7355023", "doi": "10.2139/ssrn.7355023", "title": "The Strategic Roadmap for Sovereign AI - Leveraging the EU AI Act for Sustainable Competitive Advantage in European Enterprises", "authors": [ "Leon Mikolajczak" ], "posted": "2026-08-29", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7355023", "field": "management", "role": "object", "bullets": [ "European enterprises evaluating sovereign hybrid versus U.S. hyperscale AI deployment architectures under the EU AI Act, modeled with a risk-adjusted total cost of ownership framework.", "The paper examines large language model deployment strategies as the business object; no language model is used as a research instrument.", "Confidential computing adds roughly 25 percent to costs, but the hyperscale default carries 2.46 times higher long-term cost burden after adjusting for regulatory and breach risk." ], "bullet_provenance": "ai", "salience": 38, "edition": 25, "models": [], "validated": null, "n": 4249 }, { "uid": "doi:10.2139/ssrn.7355899", "doi": "10.2139/ssrn.7355899", "title": "Research Report 6: Agentic Shopping is Complicated and Contingent", "authors": [ "Anushka Kumar", "Lennart Meincke", "Dan Shapiro", "Lilach Mollick", "Stefano Puntoni", "Ethan R. Mollick" ], "posted": "2026-08-29", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7355899", "field": "management", "role": "agent", "bullets": [ "Experimental study exposing multiple LLMs to product reviews from sources including Reddit and Wirecutter, varying source combinations, presentation order, and user memory statements before eliciting product recommendations.", "Specific model names are not stated. LLMs receive product grids and external review sources to generate shopping recommendations, with consistency tested across source, order, and memory conditions.", "Minor changes to the search process substantially shift product recommendations, and adding multiple sources does not average preferences but produces model-specific, path-dependent selections that undermine recommendation consistency." ], "bullet_provenance": "ai", "salience": 55, "edition": 25, "models": [], "validated": null, "n": 4273 }, { "uid": "doi:10.2139/ssrn.7362098", "doi": "10.2139/ssrn.7362098", "title": "Professional Job-Vacancy Rate in a Middle-Income Country: The Case of Colombia", "authors": [ "Ivan Luzardo-Luna", "Juan Camilo Peralta", "Alexander Villegas-Mendoza" ], "posted": "2026-08-29", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7362098", "field": "economics", "role": "instrument", "bullets": [ "Job vacancy listings scraped from a major Colombian online job portal, classified into 28 fields of the International Standard Classification of Education and compared against labor force and enrollment composition.", "Web scraping collects listings; large language models and a fine-tuned XLM-RoBERTa classifier assign each vacancy to an ISCED field. No validation of the classifications against human-coded labels is reported.", "Business, services, and database and network design fields hold disproportionately large vacancy shares relative to their representation in both the labor force and higher education enrollment, suggesting structural misalignment in professional labor supply." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": false, "salience": 45, "edition": 25, "n": 4287 }, { "uid": "doi:10.2139/ssrn.7354858", "doi": "10.2139/ssrn.7354858", "title": "Delegation Without Payoff Responsiveness: Mandate Fidelity in Agentic Machine Execution", "authors": [ "Jason Prole", "Holly Prole" ], "posted": "2026-08-29", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7354858", "field": "management", "role": "object", "bullets": [ "Theoretical analysis of organizational delegation when execution is performed by agentic AI systems combining high discretion with low responsiveness to actor-specific future payoffs.", "No specific language model is used; the paper develops contingency theory treating AI executors as a novel delegation configuration distinct from human agents or traditional automation.", "Three propositions predict that output inspection becomes non-diagnostic as mandate complexity rises, that low payoff responsiveness both limits misalignment and forfeits aligned-incentive benefits, and that trajectory-level monitoring grows more important than outcome checking." ], "bullet_provenance": "ai", "salience": 50, "edition": 25, "models": [], "validated": null, "n": 4296 }, { "uid": "doi:10.2139/ssrn.7356080", "doi": "10.2139/ssrn.7356080", "title": "AI Agents as Governance Actors in Data Trusts – A Normative and Design Framework", "authors": [ "Arnold Arz von Straussenburg", "Jens Joachim Marga", "Timon Aldenhoff", "Dennis Maximilian Riehle" ], "posted": "2026-08-29", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7356080", "field": "management", "role": "object", "bullets": [ "Conceptual design theory integrating fiduciary principles, institutional trust, and AI ethics for governance of data trusts that steward personal and organizational data.", "No specific language model is used; the paper proposes four design principles including fiduciary alignment, traceability, explainability, and autonomy-preserving oversight for AI agents acting within data trusts.", "The framework protects beneficiary interests and data-owner rights while mitigating opacity and conflicts of interest; the authors call for sector-specific empirical validation of the proposed principles." ], "bullet_provenance": "ai", "salience": 35, "edition": 25, "models": [], "validated": null, "n": 4297 }, { "uid": "doi:10.2139/ssrn.7362005", "doi": "10.2139/ssrn.7362005", "title": "Silicon Sampling: Supporting Survey Research with Large Language Models", "authors": [ "Haozhe Ma", "Yunchu Yang", "Arnaldo Coelho", "Penousal Machado", "Yapeng Wang" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7362005", "field": "management", "role": "agent", "bullets": [ "6,048 synthetic survey responses across three models and three prompting strategies, benchmarked against 672 human responses from luxury consumption and B2B green marketing surveys.", "Three LLMs (families not named) generate synthetic respondent data under zero-shot, persona, and few-shot strategies, evaluated via MANOVA, confirmatory factor analysis, structural equation modeling, and SHAP.", "Few-shot prompting improves alignment with human data and synthetic responses preserve measurement reliability, but consumer scores run high and SHAP attribution patterns diverge from human baselines." ], "bullet_provenance": "ai", "validated": true, "validation_note": "672 human responses, MANOVA, CFA, SEM, and SHAP benchmarking", "salience": 55, "edition": 25, "models": [], "n": 4234 }, { "uid": "doi:10.2139/ssrn.7358298", "doi": "10.2139/ssrn.7358298", "title": "Generative AI and CEO Compensation", "authors": [ "Joanna (Xiaoyu) Wang", "Zhangchao Li" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7358298", "field": "finance", "role": "object", "bullets": [ "U.S. public firms around ChatGPT's release, with cross-sectional variation in pre-shock generative AI exposure and a shock-based instrumental variables identification strategy.", "ChatGPT's public launch serves as the exogenous event; no language model is used as a research instrument by the authors.", "GenAI exposure raises CEO pay through equity-based compensation, driven by labor cost reductions and increased uncertainty, with pay adjustments predicting higher future firm value." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "edition": 25, "validated": null, "n": 4248 }, { "uid": "doi:10.2139/ssrn.7357478", "doi": "10.2139/ssrn.7357478", "title": "Talking the ESG Tightrope: Between Greenwashing and Greenhushing", "authors": [ "Robin Döttling", "Sehoon Kim", "Magdalena Rola-Janicka" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7357478", "field": "finance", "role": "instrument", "bullets": [ "Quarterly earnings calls for firms across Europe and North America, with firm-level emissions, diversity data, and quasi-experimental variation from the 2018 EU ETS reform and 2021 Texas anti-ESG legislation.", "An LLM characterizes the context and depth of climate and DEI discourse in earnings calls. The specific model and any validation against human coding are not reported.", "Climate discussion correlates with subsequent emission cuts. DEI discussion is tangential and not followed by diversity gains. Anti-ESG legislation chilled even financially material environmental disclosure." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "edition": 25, "models": [], "n": 4261 }, { "uid": "doi:10.2139/ssrn.7347639", "doi": "10.2139/ssrn.7347639", "title": "Generative AI and Mutual Fund Industry Concentration", "authors": [ "Mengqiao Du", "Xiumin Martin" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7347639", "field": "finance", "role": "object", "bullets": [ "Website traffic and capital flow data for mutual fund families, analyzed around the introduction of OpenAI's SearchGPT, with GPT service outages used as additional identification.", "SearchGPT is the treatment event. No model is run by the researchers; they exploit the platform launch and its outages to identify changes in investor search behavior.", "After SearchGPT, traffic concentration across fund families flattened and large families lost their relative traffic advantage, followed by declines in new sales and total net assets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 70, "edition": 25, "validated": null, "n": 4262 }, { "uid": "doi:10.2139/ssrn.7358638", "doi": "10.2139/ssrn.7358638", "title": "Local Execution and a Verifiable Audit Record as Compliance Substrate: A Self-Hostable Inference Runtime under the EU AI Act (Regulation (EU) 2024/1689, as amended by Regulation (EU) 2026/1744), the GDPR, the Cyber Resilience Act, DORA and NIS2", "authors": [ "Oleksandr Verteletskyi" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7358638", "field": "management", "role": "object", "bullets": [ "Legal analysis mapping a self-hostable LLM inference runtime against the EU AI Act, GDPR, Cyber Resilience Act, DORA, and NIS2 compliance requirements.", "No model is run empirically. The paper examines how running inference locally rather than through a third-party API changes an organization's demonstrable compliance position.", "Local execution with a tamper-evident audit record addresses three compliance gaps: international data transfers under GDPR, third-party control of decision records, and record integrity." ], "bullet_provenance": "ai", "salience": 35, "edition": 25, "models": [], "validated": null, "n": 4263 }, { "uid": "doi:10.2139/ssrn.7358800", "doi": "10.2139/ssrn.7358800", "title": "Language-dependent Source Representation in AI Search: A Cross-platform Audit of Lebanon", "authors": [ "Mohamed Soufan" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7358800", "field": "management", "role": "object", "bullets": [ "320 AI search responses to 40 matched English-Arabic question pairs about Lebanon across eight topics, submitted to ChatGPT Search, Gemini, Perplexity, and Copilot.", "Four AI search platforms are audited as information intermediaries. Source hostnames were classified by institutional origin and type. No model is used by the researchers.", "Arabic queries produced 22.9 percentage points more Lebanese-origin sources than English queries, drawing on domestic government and news sources rather than international organizations." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 38, "edition": 25, "validated": null, "n": 4264 }, { "uid": "doi:10.2139/ssrn.7359745", "doi": "10.2139/ssrn.7359745", "title": "Context-Aware Sustainability Narratives: An Industry-Conditioned Large Language Model Pipeline for Environmental Strategy and Governance", "authors": [ "Yafei Liu", "Minxing Liu", "Han Chen", "Qingquan Zhang" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7359745", "field": "finance", "role": "instrument", "bullets": [ "Firm-specific news for S&P 500 companies, scored along the GICS hierarchy and SASB materiality standards, analyzed over 11-day and 21-day event windows.", "A two-stage LLM pipeline separates semantic extraction from quantitative scoring to produce ESG narrative sentiment. The specific model is not named, and no validation is reported.", "Governance narratives drive unconditional return drift. Environmental news is priced positively only after a negative governance event, where it signals remediation; otherwise it is discounted." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 25, "models": [], "n": 4265 }, { "uid": "doi:10.2139/ssrn.7359503", "doi": "10.2139/ssrn.7359503", "title": "Agentic Empirical Asset Pricing: Methodological Foundations *", "authors": [ "Yingjian Pan", "Xiaowei Ding", "Kay Giesecke" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7359503", "field": "finance", "role": "method", "bullets": [ "Two US equity panels used to evaluate SEADS, an autonomous factor-discovery architecture, against five re-implemented baselines under a proposed evaluation standard for agentic discovery systems.", "LLM agents autonomously conduct factor discovery. The paper contributes a reference architecture, a rigorous evaluation standard for discovered factors, and a rolling re-execution method to backtest the discovery process itself.", "No single metric consistently ranks factor-discovery systems, motivating multi-axis evaluation. Negative findings and limitations surface further pitfalls for future agentic empirical asset pricing work." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "edition": 25, "models": [], "n": 4272 }, { "uid": "doi:10.2139/ssrn.7356461", "doi": "10.2139/ssrn.7356461", "title": "When Output Stops Being Evidence Generative AI and Production-Verification Decoupling in Institutional Accountability", "authors": [ "Paul Gallacher" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7356461", "field": "management", "role": "object", "bullets": [ "Theory-building synthesis across institutional accountability settings, drawing on peer-reviewed literature and industry evidence. No original data or empirical test is reported.", "No specific model is used or named. The paper theorizes that generative AI collapses the cost of producing convincing output, severing the link between output quality and demonstrated competence.", "Introduces production-verification decoupling, distinguishes proxy collapse from generation-verification asymmetry, and derives four testable propositions and a governance heuristic for allocating verification effort across settings." ], "bullet_provenance": "ai", "salience": 45, "edition": 25, "models": [], "validated": null, "n": 4282 }, { "uid": "doi:10.2139/ssrn.7347558", "doi": "10.2139/ssrn.7347558", "title": "AI Intermediation and Investor Information Processing: Evidence from Earnings Summaries on Social Media", "authors": [ "Elizabeth Blankespoor", "Jeroen Koenraadt", "Christoph J. Sextroh" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7347558", "field": "finance", "role": "object", "bullets": [ "Within-day difference-in-differences design around the staggered rollout of AI-generated earnings summaries on StockTwits, tracking browsing, engagement, and trading behavior of retail investors.", "StockTwits deploys a generative AI to produce structured post-earnings summaries; the model family is not stated. The study examines behavioral responses rather than model performance.", "After summary release, users browse less, engage less with peer content, and align subsequent posts with the AI interpretation. Retail trading increases and retail order flow gains predictive power for future returns." ], "bullet_provenance": "ai", "salience": 72, "edition": 25, "models": [], "validated": null, "n": 4283 }, { "uid": "doi:10.2139/ssrn.7362469", "doi": "10.2139/ssrn.7362469", "title": "AI Inference as Digital Infrastructure: Chip Controls, Electricity Burdens, and Production Location", "authors": [ "ren wang", "Yuxiang Bian", "Xiong Xiong" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7362469", "field": "economics", "role": "object", "bullets": [ "Two-location analytical framework with tradable AI token services, local power-market feedback, and asymmetric chip frictions, calibrated with public evidence on data centers, GPU supply, and electricity systems.", "No specific language model is used. The paper models AI inference as a commodity service whose unit energy cost depends on the chip technology available under export controls.", "Import restrictions on advanced chips raise unit electricity use when the domestic substitute is cheap relative to its energy disadvantage. Chip subsidies amplify the effect, and chip, data-center, and power-system policy must be treated as connected margins." ], "bullet_provenance": "ai", "salience": 55, "edition": 25, "models": [], "validated": null, "n": 4284 }, { "uid": "doi:10.2139/ssrn.7364531", "doi": "10.2139/ssrn.7364531", "title": "Observable Certifiability of LLM-Generated Compliance Policies: Risk Control Relative to Target Loss and Deployment Information", "authors": [ "Zhenpeng Li" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7364531", "field": "management", "role": "method", "bullets": [ "Evaluation on 234 CrossLib compliance cases and 262 active NIST SP 800-53 security controls, with 30 author-reviewed clean pairs for omission analysis and 74 independent hold-out cases.", "An LLM generates compliance policy drafts; the model family is not named. A semantic-medoid selection gate with simultaneous exact-binomial calibration certifies deployment within a declared risk tolerance.", "Self-consistency detects competing semantic modes but misses shared omissions (AUC 0.571). Adding obligation-specific features restores discrimination to AUC 0.903. Best-of-K evaluation understates deployed-medoid risk by up to 3 percentage points." ], "bullet_provenance": "ai", "validated": true, "validation_note": "30 author-reviewed pairs and 74 independent cases, AUC and risk bounds reported", "salience": 48, "edition": 25, "models": [], "n": 4285 }, { "uid": "doi:10.2139/ssrn.7365626", "doi": "10.2139/ssrn.7365626", "title": "Choosing the Song Still Matters: Human Song Selection and Video Engagement in an AI-Saturated Music Ecosystem on YouTube", "authors": [ "Yasunori Tonooka", "Takuichi Nishimura" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7365626", "field": "management", "role": "object", "bullets": [ "755 YouTube music videos uploaded from August 2025 to January 2026 in an AI-associated remix ecosystem, split between 465 remixes or covers of existing songs and 288 fully AI-generated original tracks.", "Content type classified by LLM-assisted first-pass coding with machine verification of evidence quotes and full author review; an independent second coder yielded Cohen's kappa of .795. The LLM family is not named.", "Remixes of pre-existing songs attracted 47.4 times the views of fully AI-generated originals in an unadjusted model. Channel-size controls reduced the ratio but human song selection remained the strongest engagement predictor." ], "bullet_provenance": "ai", "validated": true, "validation_note": "independent second coder, Cohen's kappa .795", "salience": 55, "edition": 25, "models": [], "n": 4286 }, { "uid": "doi:10.2139/ssrn.7357160", "doi": "10.2139/ssrn.7357160", "title": "Beyond Black Box Monitoring: Mechanistic Audit Trails for AI Decision Systems in Regulated Industries", "authors": [ "Darrell Brown" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7357160", "field": "finance", "role": "object", "bullets": [ "US and EU regulatory frameworks for AI in credit underwriting, insurance pricing, and clinical decision support, anchored by the April 2026 OCC interagency guidance and the EU AI Act effective August 2026.", "No specific model is deployed; the paper assesses whether post-hoc explanation tools such as LIME and SHAP satisfy regulatory requirements for neural-network and generative AI decision systems.", "Current explanation methods produce structurally unfaithful and empirically unstable approximations of model reasoning, leaving a compliance gap that existing US and EU frameworks cannot close with available tools." ], "bullet_provenance": "ai", "salience": 55, "edition": 25, "models": [], "validated": null, "n": 4293 }, { "uid": "doi:10.2139/ssrn.7360139", "doi": "10.2139/ssrn.7360139", "title": "An Auditable AI Agent Loop for Empirical Economics A Case Study in Forecast Combination", "authors": [ "Minchul Shin" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7360139", "field": "economics", "role": "method", "bullets": [ "Forecast-combination case study reusing data from a prior study, with a holdout evaluation stage added to an open-source AI coding agent workflow.", "An open-source agent-loop architecture writes and executes code to search over empirical specifications; the specific LLM powering the agent is not stated.", "Independent agent searches found methods that improve on the original study's benchmarks, and logged search trails paired with holdout evaluation make adaptive specification search more transparent." ], "bullet_provenance": "ai", "salience": 52, "edition": 25, "models": [], "validated": null, "n": 4294 }, { "uid": "doi:10.2139/ssrn.7353198", "doi": "10.2139/ssrn.7353198", "title": "The AI That Knows When Not to Decide: Calibrating Authority Boundaries in Enterprise Agentic Systems", "authors": [ "Parameswaran Radhika Ravi", "Ravi Ramsawamy" ], "posted": "2026-08-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7353198", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of enterprise agentic AI systems, drawing on experience in systems engineering, technical operations, and legacy system modernization across multiple deployment contexts.", "No specific language model is used; the paper studies autonomous AI agents as organizational actors and proposes a four-mode boundary model: automate, assist, escalate, and abstain.", "Applying uniform governance across all AI agents regardless of autonomy level tends toward operational failure; an active human-in-command model is argued to outperform passive human-in-the-loop oversight." ], "bullet_provenance": "ai", "salience": 38, "edition": 25, "models": [], "validated": null, "n": 4295 }, { "uid": "doi:10.2139/ssrn.7348180", "doi": "10.2139/ssrn.7348180", "title": "The Style Penalty: How AI Resume-Writing Tools Shape Outcomes in Automated Hiring Screens An Empirical Analysis of 1,576 LLM Evaluation Decisions Across Four Frontier Models", "authors": [ "Christopher Ort" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7348180", "field": "management", "role": "object", "bullets": [ "100 synthetic candidate profiles across 12 industries and four career levels, each paired with a job posting, producing 400 resumes and 1,576 blind evaluations.", "GPT-5.4, Claude Sonnet 4.6, Gemini 3 Pro, and Grok 4.3 each generate and evaluate resumes, scoring fit on a 0 to 100 scale with hire, maybe, or reject recommendations.", "Hire rates for identical candidates vary by up to 42 percentage points depending on which model wrote the resume; Claude shows 84 percent self-preference while GPT-5.4 rates its own style lowest." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 72, "edition": 25, "validated": null, "n": 4230, "authors_detailed": [ { "name": "Christopher Ort", "url": "https://openalex.org/A5053894795", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7347999", "doi": "10.2139/ssrn.7347999", "title": "Leveraging Large Language Models and Agentic AI in Supply Chain Operations: A Framework and Industrial Implementation", "authors": [ "Emre Nuri Madazlı", "Murat Koksalan", "Sibel Salman" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7347999", "field": "management", "role": "method", "bullets": [ "Anonymized operational data from a global manufacturing company in Turkey, covering order management and logistics workflows across multiple industrial use cases.", "The model family is not stated. The EMPLOOY framework uses LLMs for data management, operational queries, and context-aware decision support, consuming fewer tokens than single-shot prompting.", "The implementation answers operational queries and is expected to improve responsiveness, coordination, and visibility, though quantitative performance gains are not reported." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 25, "models": [], "n": 4232, "authors_detailed": [ { "name": "Emre Nuri Madazlı", "url": "https://openalex.org/A5148410517", "inst": "Sabancı Üniversitesi" }, { "name": "Murat Köksalan", "url": "https://openalex.org/A5044404106", "inst": "University of Michigan" }, { "name": "Sibel Salman", "url": "https://openalex.org/A5124765471", "inst": "Koç University" } ], "affiliations": [ "Sabancı Üniversitesi", "University of Michigan", "Koç University" ] }, { "uid": "doi:10.2139/ssrn.7347979", "doi": "10.2139/ssrn.7347979", "title": "A Hybrid MCDM Framework for Ceramic Art Innovation: Integrating Human-in-the-Loop LLMs and Fuzzy Delphi", "authors": [ "Ying Deng", "Sasikumar Perumal", "M. Viju Prakash", "Anchit Bijalwan", "Anam Ashraf", "Chia-Liang Lin" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7347979", "field": "management", "role": "instrument", "bullets": [ "Six ceramic art innovation strategies evaluated against 21 criteria by a multidisciplinary expert panel, with LLMs generating the initial criterion set from tacit knowledge.", "LLMs (not named) extract tacit knowledge to seed criteria, which experts validate through the Fuzzy Delphi Method before TOPSIS ranking.", "Digital craft hybridisation ranks first; the framework holds 70 percent rank stability across 30 sensitivity scenarios and correlates 0.94 with three alternative MCDM methods." ], "bullet_provenance": "ai", "validated": true, "validation_note": "multidisciplinary expert panel via Fuzzy Delphi validated LLM-extracted criteria", "salience": 35, "edition": 25, "models": [], "n": 4235, "authors_detailed": [ { "name": "Ying Deng", "url": "https://openalex.org/A5104386760", "inst": "Jingdezhen Ceramic Institute" }, { "name": "Sasikumar Perumal", "url": "https://openalex.org/A5148428937", "inst": "British University Vietnam" }, { "name": "M. Viju Prakash", "url": "https://openalex.org/A5148464826", "inst": "British University Vietnam" }, { "name": "Anchit Bijalwan", "url": "https://openalex.org/A5006494540", "inst": "British University Vietnam" }, { "name": "Anam Ashraf", "url": "https://openalex.org/A5069694435", "inst": "British University Vietnam" }, { "name": "Chia-Liang Lin", "url": "https://openalex.org/A5050853628", "inst": "Hung Yen University of Technology and Education" } ], "affiliations": [ "Jingdezhen Ceramic Institute", "British University Vietnam", "Hung Yen University of Technology and Education" ] }, { "uid": "doi:10.2139/ssrn.7349758", "doi": "10.2139/ssrn.7349758", "title": "LLMs as Policymakers (in a Sandbox)", "authors": [ "Nuno Fachada" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7349758", "field": "economics", "role": "agent", "bullets": [ "Conceptual framework and research agenda drawing on prior LLM-simulation policy experiments; no original empirical sample or new simulation results reported.", "Proposes giving LLMs bounded discretion in a closed decision loop with a simulation model, defining a reference architecture for sandbox policy experiments.", "Distinguishes experimental role enactment from epistemic reliance, arguing sandbox performance does not establish real-world policy efficacy or justify institutional authority." ], "bullet_provenance": "ai", "salience": 35, "edition": 25, "models": [], "validated": null, "n": 4236, "authors_detailed": [ { "name": "Nuno Fachada", "url": "https://openalex.org/A5036991749", "inst": "Universidade Lusófona" } ], "affiliations": [ "Universidade Lusófona" ] }, { "uid": "arxiv:2608.26990v1", "arxiv_id": "2608.26990v1", "title": "DSA: Evidence-Aware LLM-Agent Orchestration for Multi-Market Stock Research", "authors": [ "Linsen Zhu", "Yi Shi" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.26990v1", "field": "finance", "role": "method", "bullets": [ "Reference implementation spanning six regional markets and fifteen bundled strategy skills, with 1,457 backend contract tests passing at a frozen software snapshot.", "LLM agents (not named) follow an evidence-acquisition, context-construction, and model-routed analysis pipeline with role-specific parsing and conservative risk override.", "Contract tests confirm implementation conformance only; the paper explicitly does not validate report quality, forecasting accuracy, or investment returns from the generated research." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 25, "models": [], "n": 4240 }, { "uid": "doi:10.2139/ssrn.7352138", "doi": "10.2139/ssrn.7352138", "title": "LLMs Do Not Emulate Populations", "authors": [ "Brice Green", "Greg Leo" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7352138", "field": "economics", "role": "method", "bullets": [ "Synthetic survey experiments testing whether prompt-conditioned LLMs can recover the statistical structure of real human populations across demographic subgroups.", "Multiple LLMs prompted to generate survey responses as demographically conditioned population emulators; specific model families not named in the abstract.", "LLM outputs violate basic population composition constraints at rates similar to random predictions, and model choice explains more output variation than the emulated demographic group." ], "bullet_provenance": "ai", "salience": 60, "edition": 25, "models": [], "validated": null, "n": 4245, "authors_detailed": [ { "name": "B.S Green", "url": "https://openalex.org/A5071595782", "inst": "Massachusetts Institute of Technology" }, { "name": "Greg Leo", "url": "https://openalex.org/A5038838753", "inst": "Loyola Marymount University" } ], "affiliations": [ "Massachusetts Institute of Technology", "Loyola Marymount University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7352464", "doi": "10.2139/ssrn.7352464", "title": "When the Crowd Speaks: AI-based retail investor sentiment indicator with Reddit data", "authors": [ "Fabian Wagner", "Julian Metzler" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7352464", "field": "finance", "role": "instrument", "bullets": [ "Reddit posts from major investing and cryptocurrency subreddits between 2015 and April 2026, linked to daily S&P 500 returns and established sentiment benchmarks.", "ChatGPT 5.1 classifies posts by sentiment and topic, outperforming FinBERT on informal social media language; validation against human-coded labels is not reported in the abstract.", "The resulting retail sentiment indicator predicts next-day S&P 500 returns with statistical significance, and the relationship strengthens during periods of market decline." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "validated": false, "salience": 55, "edition": 25, "n": 4246, "authors_detailed": [ { "name": "Fabian Wagner", "url": "https://openalex.org/A5148441773", "inst": "European Central Bank" }, { "name": "Julian Metzler", "url": "https://openalex.org/A5133053884", "inst": "European Central Bank" } ], "affiliations": [ "European Central Bank" ] }, { "uid": "doi:10.2139/ssrn.7353001", "doi": "10.2139/ssrn.7353001", "title": "Domain-AI Driven Marketing, Topic-AI Conversational Marketing & AI Customer Service", "authors": [ "Krishna Krishna" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7353001", "field": "management", "role": "object", "bullets": [ "LLM-powered conversational marketing and customer service systems, examined through the Moffatt v. Air Canada case, the FTC v. DoNotPay settlement, and India's DPDP Act framework.", "The paper studies generative AI chatbot adoption as a business phenomenon, analyzing hallucinations, prompt injections, and guardrail regressions as operational failure modes.", "Courts and regulators impose strict corporate liability for automated chatbot representations, motivating a governance framework with risk-tiered deployment and grounded single-source databases." ], "bullet_provenance": "ai", "salience": 38, "edition": 25, "models": [], "validated": null, "n": 4247, "authors_detailed": [ { "name": "Krishna Krishna", "url": "https://openalex.org/A5148414216", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7349183", "doi": "10.2139/ssrn.7349183", "title": "Sustained Human-LLM Collaboration as an Emerging Object of Scientific Inquiry", "authors": [ "Richard Anderson" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7349183", "field": "management", "role": "object", "bullets": [ "Conceptual framework drawing on team cognition, transactive memory, distributed cognition, and collective intelligence research to analyze sustained human-LLM interaction, with no original empirical data.", "No language model is used or named; the paper theorizes that sustained dyadic interaction between a human and a configured LLM produces a collaborative system with emergent partner-specific properties.", "Two hypotheses are proposed: sustained interaction generates shared vocabulary and cognitive specialization, and these accrued properties improve joint performance beyond gains from practice or prompt engineering alone." ], "bullet_provenance": "ai", "salience": 30, "edition": 25, "models": [], "validated": null, "n": 4250, "authors_detailed": [ { "name": "Richard Anderson", "url": "https://openalex.org/A5148429235", "inst": "Weldon City Schools" } ], "affiliations": [ "Weldon City Schools" ] }, { "uid": "doi:10.2139/ssrn.7351443", "doi": "10.2139/ssrn.7351443", "title": "Sophistication in GenAI Use: Field Evidence from a Large Firm", "authors": [ "Nicholas Hallman", "Zach Kowaleski", "Anu Puvvada", "Jaime J. Schmidt" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7351443", "field": "management", "role": "object", "bullets": [ "713,564 employee prompts and LLM responses from nearly 4,000 back-office employees across 15 functional areas at a large firm over eight months in 2025.", "The study observes employees' real interactions with a generative AI tool. The specific language model is not named in the abstract.", "Senior employees show more sophisticated GenAI use. Sophistication varies across functions but did not improve over time or after formal AI training programs." ], "bullet_provenance": "ai", "salience": 62, "edition": 25, "models": [], "validated": null, "n": 4259, "authors_detailed": [ { "name": "Nicholas Hallman", "url": "https://openalex.org/A5029682698", "inst": "The University of Texas at Austin" }, { "name": "Zach Kowaleski", "url": "https://openalex.org/A5148470030", "inst": "The University of Texas at Austin" }, { "name": "Anu Puvvada", "url": "https://openalex.org/A5148448318", "inst": "The University of Texas at Austin" }, { "name": "Jaime J. Schmidt", "url": "https://openalex.org/A5018035629", "inst": "The University of Texas at Austin" } ], "affiliations": [ "The University of Texas at Austin" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7354159", "doi": "10.2139/ssrn.7354159", "title": "Artificial Intelligence and the Future Supply of Skills: Evidence from UK University Applications", "authors": [ "Aristotle Vossos", "Andrei Andronic", "Bouke Klein Teeselink", "Kartik Akileswaran" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7354159", "field": "economics", "role": "object", "bullets": [ "UK university applications for 1,090 degree programs over the 2020-2025 cycles, with AI exposure of each degree measured from millions of linked LinkedIn graduate profiles.", "ChatGPT's November 2022 release serves as the treatment event. No model is run by the researchers; they exploit the launch to identify shifts in student demand.", "Applications to degrees one standard deviation higher in AI exposure grew 6 percent more after ChatGPT's release, despite deteriorating entry-level job opportunities in those fields." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "edition": 25, "validated": null, "n": 4260, "authors_detailed": [ { "name": "Aristotle Vossos", "url": "https://openalex.org/A5148461456", "inst": "Align Technology (United States)" }, { "name": "Andrei Andronic", "url": "https://openalex.org/A5148442207", "inst": "Align Technology (United States)" }, { "name": "Bouke Klein Teeselink", "url": "https://openalex.org/A5027301247", "inst": "King's College London" }, { "name": "Kartik Akileswaran", "url": "https://openalex.org/A5148428100", "inst": "Align Technology (United States)" } ], "affiliations": [ "Align Technology (United States)", "King's College London" ] }, { "uid": "doi:10.2139/ssrn.7359418", "doi": "10.2139/ssrn.7359418", "title": "Agentic AI: Technical Developments, Potential Impact on Consumers and Markets, and Regulatory Implications", "authors": [ "Bas C. Jansen", "Siemen Spinder", "Steven Duivenvoorden", "Jan Svitak", "Friso Bostoen" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7359418", "field": "economics", "role": "object", "bullets": [ "Survey of agentic AI as an operational ecosystem around foundation models, covering technical architecture, consumer and market risks, and applicable EU regulatory frameworks.", "No specific model is tested. The paper analyzes how agentic systems that plan, use tools, and execute actions shift AI from a passive information tool to a delegated intermediary in digital markets.", "Existing EU instruments including consumer law, the Digital Services Act, the Digital Markets Act, and competition law partially address agentic AI risks, but gaps remain for delegated autonomous action on behalf of consumers." ], "bullet_provenance": "ai", "salience": 50, "edition": 25, "models": [], "validated": null, "n": 4271, "authors_detailed": [ { "name": "Bas C. Jansen", "url": "https://openalex.org/A5148415785", "inst": "Netherlands Food and Consumer Product Safety Authority" }, { "name": "Siemen Spinder", "url": "https://openalex.org/A5084927180", "inst": "Consumers Union" }, { "name": "Steven Duivenvoorden", "url": "https://openalex.org/A5030763248", "inst": "Consumers Union" }, { "name": "Jan Sviták", "url": "https://openalex.org/A5077779268", "inst": "Tilburg University" }, { "name": "Friso Bostoen", "url": "https://openalex.org/A5084444325", "inst": "Tilburg University" } ], "affiliations": [ "Netherlands Food and Consumer Product Safety Authority", "Consumers Union", "Tilburg University" ] }, { "uid": "doi:10.2139/ssrn.7351278", "doi": "10.2139/ssrn.7351278", "title": "Generative Change Management: Foundational Concepts for Human-AI Hybrid Productivity in Organizations", "authors": [ "Marcelo Manucci" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7351278", "field": "management", "role": "object", "bullets": [ "Conceptual framework addressing three interdependent levels of organizational transformation from generative AI integration: professional contribution redefinition, new work architectures, and cultural coexistence with algorithms.", "Generative AI is the studied phenomenon; the paper proposes a recursive cycle of analysis, design, and implementation for managing human-AI hybrid production arrangements.", "Hybrid productivity is framed as an organization-specific capability requiring coordinated redesign of roles, production architectures, and cultural meaning systems, not achievable through technology access alone." ], "bullet_provenance": "ai", "salience": 32, "edition": 25, "models": [], "validated": null, "n": 4278, "authors_detailed": [ { "name": "Marcelo Manucci", "url": "https://openalex.org/A5028827669", "inst": "American Psychological Association" } ], "affiliations": [ "American Psychological Association" ] }, { "uid": "doi:10.2139/ssrn.7350559", "doi": "10.2139/ssrn.7350559", "title": "Understanding Artificial Intelligence and Responsible Business", "authors": [ "Kazunori Sunagawa" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7350559", "field": "management", "role": "object", "bullets": [ "Survey evidence on generative AI adoption among Japanese firms, a case from academic publishing, and firstperson observation of judgment formation under three deliberative conditions.", "Generative AI is the studied phenomenon; the paper examines how digital technologies participate in intent formation before organizational decisions are fixed, a process it terms assetization.", "AI can enclose the collaborative process of intent formation; Japanese consensus-oriented decision-making is reframed as potential resistance to premature convergence rather than a cultural deficiency." ], "bullet_provenance": "ai", "salience": 38, "edition": 25, "models": [], "validated": null, "n": 4279, "authors_detailed": [ { "name": "Kazunori Sunagawa", "url": "https://openalex.org/A5148428480", "inst": "" } ] }, { "uid": "arxiv:2608.27309v1", "arxiv_id": "2608.27309v1", "title": "Difference-in-Differences on a Censored Rating Scale Can Manufacture an Effect: Evidence from a Pre-Registered LLM-Judge Audit", "authors": [ "Shuyi Fan", "Boyuan Deng", "Mengyu Xu", "Xinhong Xie", "Chenyang Li", "Hongyang Zhang" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.27309v1", "field": "other", "role": "method", "bullets": [ "Pre-registered audit of a frozen pedagogy LLM judge across 990 calls, testing whether a stated learner profile biases scaffolding preference on a bounded rating scale.", "The judge's model family is not stated. The paper derives in closed form how differential ceiling and floor censoring in double-differenced bounded scores manufactures spurious interactions.", "The registered primary endpoint is null (p = 0.684), and the sole significant interaction is 79 to 85 percent attributable to the scale floor interacting with a severity shift, not to differential preference." ], "bullet_provenance": "ai", "salience": 58, "edition": 25, "models": [], "validated": null, "n": 4280 }, { "uid": "arxiv:2608.26899v1", "arxiv_id": "2608.26899v1", "title": "Counterfactual Bias Testing for Application Tracking System", "authors": [ "Sai Yashwant", "Shruti Bansal", "Anurag Dubey", "Samaroha Chatterjee", "Satyam Kumar", "Shreyash Gupta", "Gantala Thulsiram" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.26899v1", "field": "management", "role": "method", "bullets": [ "Synthetic corpus of 100 identity-neutral resumes and 10 demographic treatments across sex, age, residence, language, and disability, applied to five job orders under an EU AI Act-aligned audit protocol.", "Task-specialized LLM agents synthesize base resumes and inject treatments; a fine-tuned sentence-embedding model scores candidates by cosine similarity. No model family is named.", "Score shifts and retention metrics pass tolerance for every treatment, but a rank-stability metric and nDCG each surface borderline findings that a single-aggregate fairness view would miss." ], "bullet_provenance": "ai", "validated": false, "salience": 48, "edition": 25, "models": [], "n": 4281 }, { "uid": "doi:10.2139/ssrn.7348378", "doi": "10.2139/ssrn.7348378", "title": "The Delegation Frontier", "authors": [ "Janbi Khadj Omar" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7348378", "field": "management", "role": "object", "bullets": [ "Scope is not stated beyond firms that delegate sequences of consequential actions to agentic AI systems operating across open-ended cognitive workflows.", "No specific model is named; the paper frames delegation scope as a measurement gap that conventional productivity accounting does not address.", "Not stated; the abstract poses the question of optimal delegation span and whether the system should act at all, without reporting empirical findings." ], "bullet_provenance": "ai", "salience": 30, "edition": 25, "models": [], "validated": null, "n": 4290, "authors_detailed": [ { "name": "Janbi Khadj Omar", "url": "https://openalex.org/A5148419981", "inst": "Inova Fairfax Hospital" } ], "affiliations": [ "Inova Fairfax Hospital" ] }, { "uid": "doi:10.2139/ssrn.7350382", "doi": "10.2139/ssrn.7350382", "title": "When the Executor Changes: An Executor-Contingency Theory of Project Governance", "authors": [ "Jason Prole", "Holly Prole" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7350382", "field": "management", "role": "object", "bullets": [ "Conceptual paper on project governance, using agentic AI as the revealing case where an executor generates consequential interpretations but cannot bear institutional answerability.", "No specific model is deployed; agentic AI provides the theoretical test case for extending project-control theory with an executor-contingency dimension alongside the standard task contingency.", "Proposes that an executor's realized interpretive autonomy reduces the governance diagnosticity of its output and that trajectory observability is needed when execution separates from answerability." ], "bullet_provenance": "ai", "salience": 45, "edition": 25, "models": [], "validated": null, "n": 4291, "authors_detailed": [ { "name": "Jason Prole", "url": "https://openalex.org/A5147863022", "inst": "Grenoble Ecole de Management" }, { "name": "Holly Prole", "url": "https://openalex.org/A5148445963", "inst": "Ospedale di Mirano" } ], "affiliations": [ "Grenoble Ecole de Management", "Ospedale di Mirano" ] }, { "uid": "arxiv:2608.26706v1", "arxiv_id": "2608.26706v1", "title": "Towards Expert Financial QA via Self-Improving RAG", "authors": [ "Junjie Xiong", "Shawheen Ghezavat", "Aum Hirpara" ], "posted": "2026-08-27", "added": "2026-08-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.26706v1", "field": "finance", "role": "method", "bullets": [ "SEC filing question answering evaluated on FinanceBench, a benchmark of financial-document queries with gold reference answers; the system targets audit-trail compliance for regulated finance.", "Self-Improving RAG decomposes document QA into retrieval, reasoning, and judge agents with feedback-driven retry; the judge triggers escalated strategies when confidence falls below a dynamic threshold.", "Achieves 86 percent oracle-guided accuracy with a 36.4 percent Lazarus rate, recovering nearly four in ten initially incorrect answers through judge-driven retry." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinanceBench SEC filing QA, agreement with gold answers", "salience": 50, "edition": 25, "models": [], "n": 4292 }, { "uid": "doi:10.2139/ssrn.7355988", "doi": "10.2139/ssrn.7355988", "title": "Knowledge-Augmented Column Generation for Airline Crew Pairing: Agentic LLM Intervention for Dual Interpretation and Early Termination", "authors": [ "Dheeraj Kumar", "Sushmita Aghalaya" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7355988", "field": "management", "role": "instrument", "bullets": [ "A real-world airline schedule with 1,092 flight legs, tested across eight LLMs spanning capability tiers and API costs from $0 to $0.87.", "GPT-5.4, o3, Claude Opus 4.6, Gemini 3.1 Pro, and Llama variants interpret dual values and rank columns inside a knowledge-augmented column generation loop; no ground-truth validation is reported.", "The framework improves block time utilization and terminates up to 90.8 percent faster when the solution stalls, at a total API cost below $3.50." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "llama" ], "open_weights": true, "validated": false, "salience": 62, "edition": 25, "n": 4231, "authors_detailed": [ { "name": "Dheeraj Kumar", "url": "https://openalex.org/A5129378252", "inst": "" }, { "name": "Sushmita Aghalaya", "url": "https://openalex.org/A5148183423", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7343819", "doi": "10.2139/ssrn.7343819", "title": "Small Models, Big Budgets: Open LLMs for Aligning Public Finance with the SDGs", "authors": [ "Omar A Guerrero", "Luis A. Palacios", "Daniele Guariso" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7343819", "field": "economics", "role": "instrument", "bullets": [ "Dominican Republic national budget expenditure items tagged to Sustainable Development Goals and the National Development Strategy using locally deployable open LLMs.", "Qwen3 classifies budget line items via a three-step prompting protocol with output-distribution-based uncertainty measures, validated against an expert-tagged benchmark across multiple metrics.", "Small open models match or exceed prior supervised-learning and LLM-based tagging results, reducing classification costs while flagging uncertain cases for human review." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "expert-tagged benchmark, multiple metrics", "salience": 58, "edition": 25, "n": 4233, "authors_detailed": [ { "name": "Omar Guerrero", "url": "https://openalex.org/A5014993912", "inst": "University of Helsinki" }, { "name": "Luis A. Palacios", "url": "https://openalex.org/A5148315471", "inst": "United Nations Development Programme" }, { "name": "Daniele Guariso", "url": "https://openalex.org/A5086593470", "inst": "CMCC Foundation - Euro-Mediterranean Center on Climate Change" } ], "affiliations": [ "University of Helsinki", "United Nations Development Programme", "CMCC Foundation - Euro-Mediterranean Center on Climate Change" ] }, { "uid": "doi:10.2139/ssrn.7354076", "doi": "10.2139/ssrn.7354076", "title": "Contribution Estimation, Contribution Awareness, and Psychological Ownership in Co-Creation with LLMs: An Experimental Study", "authors": [ "Mohamad Bahri", "Rongdi Zhang", "Sameha AlShakhsi", "Diya Dou", "Dena Al-Thani", "Ala Yankouskaya", "Raian Ali" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7354076", "field": "management", "role": "object", "bullets": [ "121 participants complete two within-subjects pitch-writing tasks co-created with an LLM, receiving contribution attribution feedback from an LLM-as-judge system between tasks.", "Participants co-create text with an LLM (not named); the Contribution Attribution Framework estimates human and AI shares across ideas, details, and wording dimensions.", "After feedback, self-assessments move toward measured contribution but actual human share stays unchanged; psychological ownership tracks perceived rather than measured contribution." ], "bullet_provenance": "ai", "salience": 52, "edition": 25, "models": [], "validated": null, "n": 4238, "authors_detailed": [ { "name": "Mohamad Bahri", "url": "https://openalex.org/A5088922214", "inst": "Qatar University" }, { "name": "Rongdi Zhang", "url": "https://openalex.org/A5148241104", "inst": "" }, { "name": "Sameha Alshakhsi", "url": "https://openalex.org/A5135016743", "inst": "Hamad bin Khalifa University" }, { "name": "Diya Dou", "url": "https://openalex.org/A5020584131", "inst": "Hong Kong Polytechnic University" }, { "name": "Dena Al‐Thani", "url": "https://openalex.org/A5002847514", "inst": "Hamad bin Khalifa University" }, { "name": "Ala Yankouskaya", "url": "https://openalex.org/A5021047216", "inst": "Bournemouth University" }, { "name": "Raian Ali", "url": "https://openalex.org/A5088731268", "inst": "Hamad bin Khalifa University" } ], "affiliations": [ "Qatar University", "Hamad bin Khalifa University", "Hong Kong Polytechnic University", "Bournemouth University" ] }, { "uid": "arxiv:2608.26372v1", "arxiv_id": "2608.26372v1", "title": "Knowledge-Verified Emergent Deception in LLM Agents Under Conflicting Incentives", "authors": [ "Zheyuan Liu", "Weiliang Zhao", "Xiangchi Yuan", "Ningshan Ma", "Yue Huang", "Meng Jiang" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.26372v1", "field": "management", "role": "object", "bullets": [ "KnownLieBench covers eight customer-service domains and 112 cases, testing 18 proprietary and open-weight models in multi-round dialogues with a trust-tracking customer agent.", "Neutral probes first verify the model knows the user's entitlement; incentives to deny it are then introduced, separating deception from ignorance or hallucination.", "Emergent deception varies across model families; honesty-directed fine-tuning reduces it under incentive, while deception-graded tuning raises lie success on honest-control dialogues." ], "bullet_provenance": "ai", "salience": 50, "edition": 25, "models": [], "validated": null, "n": 4239 }, { "uid": "arxiv:2608.25325v1", "arxiv_id": "2608.25325v1", "title": "FinRiskAtlas: Decision-Aligned Evaluation of Large Language Models for Financial Risk Review", "authors": [ "Suyang Zhong", "Jingzhe Zhu", "Qi Xu", "Liyao Sun", "Yin Wang", "Qingqing Sun", "Shuai Chen", "Tianyi Zhang" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.25325v1", "field": "finance", "role": "method", "bullets": [ "9,742 Chinese-language financial risk review instances spanning 53 task families, plus 680 replayed decision states from 104 de-identified professional review trajectories.", "33 model configurations evaluated on operation execution under fixed evidence and evidence-state control under evolving review conditions; specific model families not named in the abstract.", "Operation-level rankings diverge from aggregate capability scores with mean pairwise Spearman correlation of 0.42, and knowledge-based shortlisting incurs up to 18 points of regret on individual operations." ], "bullet_provenance": "ai", "salience": 50, "edition": 25, "models": [], "validated": null, "n": 4244, "authors_detailed": [ { "name": "Suyang Zhong", "url": "https://openalex.org/A5134962735", "inst": "SP Technology (South Korea)" }, { "name": "Jingzhe Zhu", "url": "https://openalex.org/A5148453522", "inst": "" }, { "name": "Qi Xu", "url": "https://openalex.org/A5148456044", "inst": "" }, { "name": "Liyao Sun", "url": "https://openalex.org/A5148433887", "inst": "" }, { "name": "Yin Wang", "url": "https://openalex.org/A5148433815", "inst": "" }, { "name": "Qingqing Sun", "url": "https://openalex.org/A5148464157", "inst": "" }, { "name": "Shuai Chen", "url": "https://openalex.org/A5148454543", "inst": "" }, { "name": "Tianyi Zhang", "url": "https://openalex.org/A5148415297", "inst": "" } ], "affiliations": [ "SP Technology (South Korea)" ] }, { "uid": "doi:10.2139/ssrn.7331699", "doi": "10.2139/ssrn.7331699", "title": "The Impact of AI-Assisted Coding Tools on Agile Software Development Practices: An Empirical Study", "authors": [ "Baheer Elias", "Ehsan Jafari" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7331699", "field": "management", "role": "object", "bullets": [ "Survey of 91 software professionals using AI-assisted coding tools such as ChatGPT, GitHub Copilot, Claude, and DeepSeek in Agile development environments, analyzed with descriptive and correlation statistics.", "Multiple AI-assisted tools including ChatGPT, Copilot, Claude, and DeepSeek are studied as adoption objects; the paper measures perceived effects through Likert scales, not model outputs directly.", "Participants reported improved coding efficiency and faster sprint execution but raised concerns about over-dependence on tools, security risks in generated code, and diminished learning opportunities." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "salience": 32, "edition": 25, "validated": null, "n": 4251, "authors_detailed": [ { "name": "Baheer Elias", "url": "https://openalex.org/A5107821378", "inst": "Kabul University" }, { "name": "Ehsan Jafari", "url": "https://openalex.org/A5148380469", "inst": "GNA University" } ], "affiliations": [ "Kabul University", "GNA University" ] }, { "uid": "doi:10.2139/ssrn.7342678", "doi": "10.2139/ssrn.7342678", "title": "Endogenous Arrivals in Expert-review Systems: Generative AI and the Peer-Review Bottleneck", "authors": [ "Junkee Jeon", "Takwon Kim" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7342678", "field": "economics", "role": "object", "bullets": [ "Stochastic service model of academic peer review where generative AI expands upstream manuscript production, calibrated with numerical experiments and public conference-scale submission statistics.", "No language model is used by the researchers; generative AI enters the model as a technology parameter that raises research productivity while potentially displacing reviewer effort.", "A Red Queen attenuation factor absorbs most upstream productivity gains through congestion; when AI also displaces reviewers, processed throughput can decline even as submission volume rises." ], "bullet_provenance": "ai", "salience": 55, "edition": 25, "models": [], "validated": null, "n": 4252, "authors_detailed": [ { "name": "Junkee Jeon", "url": "https://openalex.org/A5062950046", "inst": "Kyung Hee University" }, { "name": "Takwon Kim", "url": "https://openalex.org/A5075279317", "inst": "Sungshin Women's University" } ], "affiliations": [ "Kyung Hee University", "Sungshin Women's University" ] }, { "uid": "doi:10.2139/ssrn.7340500", "doi": "10.2139/ssrn.7340500", "title": "Simulating Firms' Inflation Expectations with a Multi-Agent AI Framework", "authors": [ "Daniela Puzzello", "Ali Zarifhonarvar" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7340500", "field": "economics", "role": "agent", "bullets": [ "Multi-agent simulation of the Survey of Firms' Inflation Expectations across 32 quarterly waves from 2018 through 2026, with distinct executive-role agents aggregated by a chairman agent.", "LLM family not stated; role-specific agents produce inflation forecasts synthesized at the firm level, validated against actual survey responses with staggered cutoff out-of-sample tests.", "Synthetic firms track main survey dynamics and generate wider within-firm disagreement during supply shocks, but produce excessively high tail-risk probabilities and compressed cross-firm dispersion." ], "bullet_provenance": "ai", "validated": true, "validation_note": "compared against 32 waves of Survey of Firms' Inflation Expectations with staggered-cutoff out-of-sample tests", "salience": 67, "edition": 25, "models": [], "n": 4253, "authors_detailed": [ { "name": "Daniela Puzzello", "url": "https://openalex.org/A5109568859", "inst": "Indiana University Bloomington" }, { "name": "Ali Zarifhonarvar", "url": "https://openalex.org/A5097928103", "inst": "Indiana University" } ], "affiliations": [ "Indiana University Bloomington", "Indiana University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7351779", "doi": "10.2139/ssrn.7351779", "title": "US vs. China, Round Two: China's Shifting AI and Supply Chain Strategy and Korea's Response", "authors": [ "Eun Kyo Cho" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7351779", "field": "economics", "role": "object", "bullets": [ "Policy analysis of US-China competition in the physical AI era, focusing on Korea's strategic response in manufacturing architecture and supply chains.", "The paper proposes a manufacturing LLM trained on shop-floor judgment data as a core strategic asset. Claude Opus 5 was used only for translation.", "Korea should vertically integrate chokepoint assets with production-process data and pursue architectural assetization to participate as a co-designer in major economies' technology ecosystems." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 35, "edition": 25, "validated": null, "n": 4254, "authors_detailed": [ { "name": "Eun Kyo Cho", "url": "https://openalex.org/A5057260586", "inst": "Korea Institute for Industrial Economics and Trade" } ], "affiliations": [ "Korea Institute for Industrial Economics and Trade" ] }, { "uid": "doi:10.2139/ssrn.7344201", "doi": "10.2139/ssrn.7344201", "title": "AI Citations are Not Permanent: Citation Volatility, Half-Life, and What Actually Predicts Citation Persistence Across AI Platforms", "authors": [ "Tandeep Sangra" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7344201", "field": "management", "role": "object", "bullets": [ "3.5 million tracked citation events across ChatGPT, Perplexity, Google AI Overviews, Copilot, and Gemini, covering September 2025 through March 2026.", "Five AI search platforms are studied as citation sources. No model is run by the researchers; citation persistence, rotation rates, and predictors are measured across platforms.", "Median citation half-life is 4.5 weeks, and only 10.6 percent of URLs persist over 28 days. Brand search volume is the strongest predictor of citation frequency." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 42, "edition": 25, "validated": null, "n": 4255, "authors_detailed": [ { "name": "Tandeep Sangra", "url": "https://openalex.org/A5135125920", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7344338", "doi": "10.2139/ssrn.7344338", "title": "AI-Driven Destination Discovery in China: Positioning Iran in the Chinese Generative AI Tourism Ecosystem", "authors": [ "Mostafa Rezaei" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7344338", "field": "management", "role": "object", "bullets": [ "660 prompt-response observations from six Chinese generative AI platforms, queried about Iran as an outbound tourism destination for Chinese travelers across thirteen thematic axes.", "DeepSeek, Doubao, ERNIE Bot, Kimi, Qwen, and Yuanbao were tested. Mention and recommendation were coded as separate outcomes for each platform-prompt pair.", "Iran reached 100 percent mention on direct queries but collapsed on open-ended prompts. Unconditional positive recommendations followed only 35.8 percent of mentions." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 35, "edition": 25, "validated": null, "n": 4256, "authors_detailed": [ { "name": "Mostafa Rezaei", "url": "https://openalex.org/A5148358513", "inst": "Islamic Azad University, Tehran" } ], "affiliations": [ "Islamic Azad University, Tehran" ] }, { "uid": "doi:10.2139/ssrn.7354684", "doi": "10.2139/ssrn.7354684", "title": "Governed Complementarity and the Translation Gap in National Generative AI Diffusion: Exploratory Cross-Country Evidence", "authors": [ "Frengki Panangian Panangian", "Blasius Dala Nai", "Nur Rohman Rohman" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7354684", "field": "economics", "role": "object", "bullets": [ "Cross-country analysis of generative AI diffusion across 146 economies, merging population-normalized behavioral telemetry with the Oxford Insights Government AI Readiness Index.", "No language model is used by the researchers. GenAI platform usage is the dependent variable, measured through behavioral telemetry and explained by readiness components.", "Overall readiness explains 63.5 percent of cross-country variance. Government capability amplifies the diffusion return to technology and infrastructure, producing a governed complementarity effect." ], "bullet_provenance": "ai", "salience": 55, "edition": 25, "models": [], "validated": null, "n": 4257, "authors_detailed": [ { "name": "Frengki Panangian Panangian", "url": "https://openalex.org/A5148185102", "inst": "" }, { "name": "Blasius Dala Nai", "url": "https://openalex.org/A5139307555", "inst": "Universitas Budi Luhur" }, { "name": "Nur Rohman Rohman", "url": "https://openalex.org/A5148309345", "inst": "" } ], "affiliations": [ "Universitas Budi Luhur" ] }, { "uid": "doi:10.2139/ssrn.7346099", "doi": "10.2139/ssrn.7346099", "title": "The Same-Model Ceiling: Testing Multi-Model AI Review in Strategic Analysis", "authors": [ "Richard Foster-Fletcher" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7346099", "field": "management", "role": "method", "bullets": [ "One constructed acquisition case analyzed twelve times by Claude Opus 5, with each analysis receiving six critiques under the same brief from same-model and cross-model reviewers.", "Claude Opus 5 generated analyses. GPT-5.6 Sol, Muse Spark 1.2, and DeepSeek V4 Pro served as cross-model reviewers. No ground-truth benchmark was used.", "Cross-model panels found 37 unique issues versus 18 from same-model reviews. The multi-model revision was preferred in eight of twelve blind comparisons." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": false, "validated": false, "validation_note": "no ground-truth benchmark; cross-model comparison only", "salience": 48, "edition": 25, "n": 4258, "authors_detailed": [ { "name": "Richard Foster-Fletcher", "url": "https://openalex.org/A5130796588", "inst": "MKAI, 9 Nash Croft, Tattenhoe, Milton Keynes, MK4 3AU, United Kingdom" } ], "affiliations": [ "MKAI, 9 Nash Croft, Tattenhoe, Milton Keynes, MK4 3AU, United Kingdom" ] }, { "uid": "doi:10.2139/ssrn.7339582", "doi": "10.2139/ssrn.7339582", "title": "When AI Starts Acting: Governance, Audit Evidence, and Accountability for Agentic Systems", "authors": [ "Kiyotaka Shimomura" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7339582", "field": "accounting", "role": "object", "bullets": [ "Conceptual framework drawing on financial statement audit, internal control, internal audit, forensic investigation, and M&A due diligence to govern agentic AI systems that take consequential actions.", "No specific model is used. The paper treats the consequential AI action as the governance unit and develops seven control questions a skeptical reviewer would apply to autonomous system behavior.", "Human approval can become ceremonial, logs do not automatically constitute audit evidence, and AI reviewing AI may reproduce the same error, making retained human expertise part of the control environment." ], "bullet_provenance": "ai", "salience": 60, "edition": 25, "models": [], "validated": null, "n": 4268, "authors_detailed": [ { "name": "Kiyotaka Shimomura", "url": "https://openalex.org/A5134528041", "inst": "Inpex (Japan)" } ], "affiliations": [ "Inpex (Japan)" ] }, { "uid": "doi:10.2139/ssrn.7340258", "doi": "10.2139/ssrn.7340258", "title": "Refuses the Shape, Serves the Substance: Evidence on the Rise of Normative AI Failure and the Case for White-Box Testing", "authors": [ "Anandkumar Prakasam" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7340258", "field": "management", "role": "object", "bullets": [ "All 1,560 incidents in the AI Incident Database as of July 2026, classified by primary failure mechanism, plus five experimental runs of a simulated banking assistant across three unnamed model vendors.", "No specific model family is named. Incidents are classified by mechanism and compared across five half-year windows; a banking assistant is probed with both recognizable attacks and operationally reframed equivalents.", "Normative failures account for 62.6 percent of incidents versus under 3.8 percent for adversarial attacks, and every tested model refused recognizable attack shapes while serving the same substance in operational framing." ], "bullet_provenance": "ai", "salience": 58, "edition": 25, "models": [], "validated": null, "n": 4269, "authors_detailed": [ { "name": "Anandkumar Prakasam", "url": "https://openalex.org/A5015409951", "inst": "International Institute of Biotechnology and Toxicology" } ], "affiliations": [ "International Institute of Biotechnology and Toxicology" ] }, { "uid": "doi:10.2139/ssrn.7342218", "doi": "10.2139/ssrn.7342218", "title": "Agentic AI Systems and Financial Stability: From Model Risk to Systemic Risk", "authors": [ "Seung Jung Lee", "Sriram Nagaraj" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7342218", "field": "finance", "role": "object", "bullets": [ "Theoretical monograph modeling agentic AI in financial systems across six mathematical settings, from single-institution model risk to fleet-level systemic risk using jump-diffusion and Hawkes processes.", "No specific model is used. Populations of agents sharing a common foundation model are treated as a non-diversifiable exposure, with contagion analyzed through percolation thresholds and spectral-radius criticality.", "Shared foundation models create a systemic-risk floor that no amount of fleet redundancy dilutes, and against irreversible harm, runtime detection and reversibility cannot substitute for ex-ante prevention." ], "bullet_provenance": "ai", "salience": 70, "edition": 25, "models": [], "validated": null, "n": 4270, "authors_detailed": [ { "name": "Seung Jung Lee", "url": "https://openalex.org/A5012787769", "inst": "Federal Reserve Bank of Cleveland" }, { "name": "Sriram Nagaraj", "url": "https://openalex.org/A5148296503", "inst": "Federal Reserve Bank of Cleveland" } ], "affiliations": [ "Federal Reserve Bank of Cleveland" ] }, { "uid": "doi:10.2139/ssrn.7330818", "doi": "10.2139/ssrn.7330818", "title": "The Effect of Artificial Intelligence Technology on the Returns to Human Capital (Empirical Study)", "authors": [ "Elias Absawy" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7330818", "field": "economics", "role": "object", "bullets": [ "US Current Population Survey microdata from 2015 to 2025, merged with an occupational AI exposure index constructed from O*NET work activities following Pew Research Center methodology.", "Generative AI is the studied treatment in an event-study difference-in-differences design exploiting variation in AI exposure across occupations after the late-2023 release of advanced systems.", "The college-premium differential between high- and low-AI-exposure occupations rose roughly 14.7 percent among knowledge workers by 2025, while differential returns to experience were weaker and less robust." ], "bullet_provenance": "ai", "salience": 65, "edition": 25, "models": [], "validated": null, "n": 4274, "authors_detailed": [ { "name": "Elias Absawy", "url": "https://openalex.org/A5148191472", "inst": "Reichman University, Israel" } ], "affiliations": [ "Reichman University, Israel" ] }, { "uid": "doi:10.2139/ssrn.7354438", "doi": "10.2139/ssrn.7354438", "title": "The Credibility Gap in AI Disclosure: How Investors Trace AI Narratives Through Recognised Technology Investment", "authors": [ "Laura Mehnaz", "Nafiz Fahad", "Tom Scott" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7354438", "field": "accounting", "role": "object", "bullets": [ "200 largest non-financial ASX-listed firms from 2020 to 2025, with AI disclosure language measured in annual reports alongside recognized intangible assets and ICT expenses.", "AI is the disclosure subject; the study examines whether reported technology investment lends credibility to corporate AI narratives in the eyes of investors.", "AI disclosure produces stronger market reactions around annual report release when backed by observable technology spending, and weaker reactions when decoupled from it, with effects strengthening after ChatGPT." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "edition": 25, "validated": null, "n": 4275, "authors_detailed": [ { "name": "Laura Mehnaz", "url": "https://openalex.org/A5089004651", "inst": "Massey University" }, { "name": "Nafiz Fahad", "url": "https://openalex.org/A5070453323", "inst": "Massey University" }, { "name": "Tom Scott", "url": "https://openalex.org/A5059859835", "inst": "University of Auckland" } ], "affiliations": [ "Massey University", "University of Auckland" ] }, { "uid": "doi:10.2139/ssrn.7355651", "doi": "10.2139/ssrn.7355651", "title": "Open-weight versus closed AI: stock-market reactions to large language model releases", "authors": [ "Jinyao Huang", "Yumei Liu", "zhihui liu" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7355651", "field": "finance", "role": "object", "bullets": [ "39 major large language model releases from November 2022 to July 2026, with returns to a US AI stock basket measured over short event windows.", "Model releases are the event of interest; the study compares stock-market value reallocation across the AI value chain for open-weight versus closed-weight announcements.", "Open-weight releases shift value toward software firms while closed releases favor compute firms, producing a 4.5 percentage point software-minus-compute spread difference significant at permutation p of 0.004." ], "bullet_provenance": "ai", "salience": 70, "edition": 25, "models": [], "validated": null, "n": 4276, "authors_detailed": [ { "name": "Jinyao Huang", "url": "https://openalex.org/A5148298037", "inst": "Hong Kong Polytechnic University" }, { "name": "Yumei Liu", "url": "https://openalex.org/A5148229206", "inst": "Henan University of Science and Technology" }, { "name": "zhihui liu", "url": "https://openalex.org/A5148391835", "inst": "Hunan Institute of Technology" } ], "affiliations": [ "Hong Kong Polytechnic University", "Henan University of Science and Technology", "Hunan Institute of Technology" ] }, { "uid": "arxiv:2608.25602v1", "arxiv_id": "2608.25602v1", "title": "The Reverse Big Push: Generative AI and Self-Fulfilling Automation", "authors": [ "Soumen Banerjee", "Jianguo Wang" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.25602v1", "field": "economics", "role": "object", "bullets": [ "A theoretical local service economy where household budgets and a market-clearing wage interact with firms choosing between human augmentation and generative AI automation.", "Generative AI is the studied technology; an equilibrium model shows how payroll-supported demand and rental-based AI capability create strategic complementarities and multiple equilibria.", "Both a high-demand human-augmented and a low-demand automated equilibrium can coexist; forward-looking firms may trigger self-fulfilling automation cascades or augmentation recoveries depending on expectations and policy." ], "bullet_provenance": "ai", "salience": 62, "edition": 25, "models": [], "validated": null, "n": 4277, "authors_detailed": [ { "name": "Soumen Banerjee", "url": "https://openalex.org/A5025758253", "inst": "Southwestern University of Finance and Economics" }, { "name": "Jianguo Wang", "url": "https://openalex.org/A5145949112", "inst": "" } ], "affiliations": [ "Southwestern University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.7331599", "doi": "10.2139/ssrn.7331599", "title": "Which A/B Tests Still Need to Run? Simulation-Guided Screening of Headline Experiments", "authors": [ "Nathan Clark" ], "posted": "2026-08-26", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7331599", "field": "management", "role": "instrument", "bullets": [ "4,000 held-out headline experiments from the Upworthy Research Archive, with 2,599 disjoint experiments used for fine-tuning and real click-through outcomes as ground truth.", "A fine-tuned LLM (model family not stated) screens candidate headlines for real A/B tests, compared against a zero-shot LLM, gradient-boosted trees, and random baselines.", "Fine-tuned screening retains the true winner in 63.8 percent of tests at k equals 2 and cuts mean relative click-through regret from 13.2 to 6.9 percent; zero-shot adds no value." ], "bullet_provenance": "ai", "validated": true, "validation_note": "4,000 held-out Upworthy experiments, winner retention and CTR regret reported", "salience": 62, "edition": 25, "models": [], "n": 4289, "authors_detailed": [ { "name": "Nathan Clark", "url": "https://openalex.org/A5135378458", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2608.24662v2", "arxiv_id": "2608.24662v2", "title": "The Invisible Editorial Layer: Formalizing Undisclosed Inference-Time Steering, Probability Placement, and the Attribution Problem in Deployed Language Models", "authors": [ "Augusto Camargo" ], "posted": "2026-08-25", "added": "2026-08-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.24662v2", "field": "management", "role": "object", "bullets": [ "Conceptual and formal analysis of production inference stacks for deployed language models, examining runtime interventions that steer generation without changing model weights.", "No specific model is tested. The paper formalizes how inference-time configurations can embed commercial bias through probability-mass reallocation, a pattern it terms probability placement.", "Black-box observation alone cannot identify whether behavioral bias originates in model parameters or deployment-layer steering, requiring governance to audit the full deployed system rather than the model alone." ], "bullet_provenance": "ai", "salience": 42, "edition": 25, "models": [], "validated": null, "n": 4267, "authors_detailed": [ { "name": "A. C. de Camargo", "url": "https://openalex.org/A5045559258", "inst": "Universidade de São Paulo" } ], "affiliations": [ "Universidade de São Paulo" ] }, { "uid": "doi:10.2139/ssrn.7338838", "doi": "10.2139/ssrn.7338838", "title": "Generative-Agent Modeling of Protest Mobilization and Preference Falsification with Large Language Models", "authors": [ "Lingyi Wang" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7338838", "field": "economics", "role": "agent", "bullets": [ "1,000 LLM-driven agents on a synthetic social network simulate protest mobilization under authoritarian repression, motivated by recent uprisings in South and Southeast Asia, run across three seeds per backbone.", "GPT-4.1-mini, DeepSeek-V4-Flash, and Llama-4-Maverick each serve as agent backbones; agents draw on memory of past events and produce natural-language justifications for whether to mobilize.", "Peak mobilization ranged from 3.2% (Llama) to 23.3% (GPT) despite identical imposed dynamics; high-grievance inactive agents exhibited preference falsification, declining action on grounds of personal risk." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 45, "edition": 24, "n": 4201, "authors_detailed": [ { "name": "Lingyi Wang", "url": "https://openalex.org/A5148144411", "inst": "University of Michigan" } ], "affiliations": [ "University of Michigan" ] }, { "uid": "doi:10.2139/ssrn.7337058", "doi": "10.2139/ssrn.7337058", "title": "When Economic Agents Learn: AI-Driven Heterogeneous Agents, Market Dynamics, and Equilibrium Stability", "authors": [ "Jiayu Cao" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7337058", "field": "economics", "role": "agent", "bullets": [ "Simulated artificial asset market populated by rational, Q-learning, and LLM agents, analyzed through a nonlinear dynamical systems framework.", "LLM agents use natural-language reasoning to make trading decisions; model family not stated in the abstract.", "LLM-agent markets show larger price deviations and more frequent bubble-crash episodes than rational or Q-learning markets; a small share of LLM agents can destabilize an otherwise stable market." ], "bullet_provenance": "ai", "salience": 62, "edition": 24, "models": [], "validated": null, "n": 4203, "authors_detailed": [ { "name": "Jiayu Cao", "url": "https://openalex.org/A5148124495", "inst": "Wuhan University" } ], "affiliations": [ "Wuhan University" ] }, { "uid": "doi:10.2139/ssrn.7338018", "doi": "10.2139/ssrn.7338018", "title": "Algorithmic Patriarchy: Artificial Intelligence, the Feminisation of Poverty, and the Limits of Governance", "authors": [ "Azucena Carrasco Toronjo" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7338018", "field": "management", "role": "object", "bullets": [ "Conceptual analysis tracing four decades of neoliberal self-improvement discourse through policy, HR, and coaching texts into LLM training corpora.", "No specific model tested; paper argues LLMs trained on this discourse reproduce gendered bias in resume screening and eligibility determinations.", "Current governance frameworks audit outcomes but not the discourse that produces them; paper proposes discourse-level audit as a precondition for correcting algorithmic gender bias." ], "bullet_provenance": "ai", "salience": 32, "edition": 24, "models": [], "validated": null, "n": 4205, "authors_detailed": [ { "name": "Azucena Carrasco Toronjo", "url": "https://openalex.org/A5148045032", "inst": "Independent researcher" } ], "affiliations": [ "Independent researcher" ] }, { "uid": "doi:10.2139/ssrn.7351203", "doi": "10.2139/ssrn.7351203", "title": "Expert preferences for AI-edited academic writing", "authors": [ "Stephen Newbold", "Finbar Curtin", "Nyoka Erikson", "Cindy Estifenes", "Attie Giles", "Kenneth Kau", "Cian Melker", "Hunter Patenaude", "M P", "Clarisse Zimmer" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7351203", "field": "economics", "role": "object", "bullets": [ "Pre-registered experiment with 283 experts who published in four top environmental economics journals, 2014 to 2023, supplemented by an observational study of 2,460 articles.", "LLM (family not stated) edited journal abstracts; experts compared originals against edited versions in blind pairwise comparisons and attempted to detect LLM involvement.", "LLM edits preferred in three of four comparisons; correct detection at 49%, no better than chance. Suspecting LLM involvement reduced preference by 4 to 5 percentage points." ], "bullet_provenance": "ai", "salience": 55, "edition": 24, "models": [], "validated": null, "n": 4206, "authors_detailed": [ { "name": "Stephen C. Newbold", "url": "https://openalex.org/A5015565154", "inst": "University of Wyoming" }, { "name": "Finbar Curtin", "url": "https://openalex.org/A5137697160", "inst": "University of Wyoming" }, { "name": "Nyoka Erikson", "url": "https://openalex.org/A5137709193", "inst": "University of Wyoming" }, { "name": "Cindy Estifenes", "url": "https://openalex.org/A5148149421", "inst": "University of Wyoming" }, { "name": "Attie Giles", "url": "https://openalex.org/A5137684174", "inst": "University of Wyoming" }, { "name": "Kenneth Kau", "url": "https://openalex.org/A5107401933", "inst": "University of Hawaii–West Oahu" }, { "name": "Cian Melker", "url": "https://openalex.org/A5137668266", "inst": "University of Wyoming" }, { "name": "Hunter Patenaude", "url": "https://openalex.org/A5137683244", "inst": "University of Wyoming" }, { "name": "Manab Prakash", "url": "https://openalex.org/A5011032607", "inst": "Tribhuvan University" }, { "name": "Clarisse Zimmer", "url": "https://openalex.org/A5137680729", "inst": "University of Wyoming" } ], "affiliations": [ "University of Wyoming", "University of Hawaii–West Oahu", "Tribhuvan University" ] }, { "uid": "arxiv:2608.24842v1", "arxiv_id": "2608.24842v1", "title": "Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows", "authors": [ "Miao Liu", "Zhizhe Liu" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.24842v1", "field": "finance", "role": "method", "bullets": [ "Experiments on financial disclosures vary unrelated context from 2,000 to 128,000 tokens, including manipulations removing real risk disclosures from actual 10-K filings, across multiple model families.", "Multiple LLM families assessed on investment judgment tasks; workflow architectures compared, including chunk-and-summarize pipelines versus structured restatement adjacent to the decision prompt.", "Risk disclosure influence on investment judgments falls to noise as context grows, even when direct retrieval stays accurate; targeted structured restatement restores the disclosure's influence where summarization pipelines fail." ], "bullet_provenance": "ai", "validated": false, "salience": 63, "edition": 24, "models": [], "n": 4207, "authors_detailed": [ { "name": "Miao Liu", "url": "https://openalex.org/A5148334780", "inst": "" }, { "name": "Zhizhe Liu", "url": "https://openalex.org/A5078959651", "inst": "Ministry of Education of the People's Republic of China" } ], "affiliations": [ "Ministry of Education of the People's Republic of China" ] }, { "uid": "doi:10.2139/ssrn.7337358", "doi": "10.2139/ssrn.7337358", "title": "Introducing an Open Dataset of CEO Pay Ratios", "authors": [ "Julian Wang", "Victor Xiaoqi Wang" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7337358", "field": "accounting", "role": "instrument", "bullets": [ "19,604 firm-year records of CEO pay ratios from US proxy statements filed on EDGAR, covering fiscal years 2017 through 2025.", "An unspecified LLM with retrieval-augmented generation extracts CEO pay, median employee pay, and the ratio from narrative proxy filings. Accuracy exceeds 99 percent against hand-collected benchmarks.", "The open dataset removes a persistent data-collection barrier for pay-ratio research and enables studies across labor economics, governance, and disclosure quality." ], "bullet_provenance": "ai", "validated": true, "validation_note": "manually verified source documents, independent hand-collected benchmark, arithmetic consistency checks, accuracy above 99 percent", "salience": 65, "edition": 24, "models": [], "n": 4209, "authors_detailed": [ { "name": "Julian Wang", "url": "https://openalex.org/A5073000065", "inst": "University of Oxford" }, { "name": "Victor Xiaoqi Wang", "url": "https://openalex.org/A5041860793", "inst": "California State University, Long Beach" } ], "affiliations": [ "University of Oxford", "California State University, Long Beach" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7332558", "doi": "10.2139/ssrn.7332558", "title": "Ageless Management: A Theory of Cognitive Complementarity, the Audit Value of Experience, and the Lifelong Extension of Brain Capital in the Age of AI", "authors": [ "Naoki Kadowaki" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7332558", "field": "management", "role": "object", "bullets": [ "Theoretical framework with no empirical data; twelve propositions with refutation conditions and three testable hypotheses on age-diverse organizations.", "Generative AI is theorized to lower the cost of fluid-intelligence tasks and raise the value of experience-based audit capacity; no specific model is named or used.", "Dynamic role allocation by measured cognitive ability rather than chronological age is proposed to convert multigenerational talent into brain capital, contingent on AI complementarity and verification conditions." ], "bullet_provenance": "ai", "salience": 30, "edition": 24, "models": [], "validated": null, "n": 4212, "authors_detailed": [ { "name": "Naoki Kaodwaki", "url": "https://openalex.org/A5147815719", "inst": "Oji Holdings (Japan)" } ], "affiliations": [ "Oji Holdings (Japan)" ] }, { "uid": "doi:10.2139/ssrn.7334878", "doi": "10.2139/ssrn.7334878", "title": "Beyond a Technology Lens: Characterizing the Problems Entrepreneurs Solve", "authors": [ "Shai Bernstein", "Mimi Chen", "Jacqueline N. Lane", "Zilin Ma" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7334878", "field": "management", "role": "instrument", "bullets": [ "Roughly 90,000 VC-backed U.S. startups with LLM-generated problem statements clustered into a taxonomy of 265 problem categories.", "LLMs write solution-agnostic problem statements for each startup; the specific model family is not named; outputs validated against 331 founder interviews.", "68 percent of startup problems reflect enduring human needs, capital concentrates sharply in the top ten clusters, and founders with domain-problem fit raise significantly more capital." ], "bullet_provenance": "ai", "validated": true, "validation_note": "331 founder interviews as ground truth for LLM-generated problem statements", "salience": 70, "edition": 24, "models": [], "n": 4213, "authors_detailed": [ { "name": "Shai Bernstein", "url": "https://openalex.org/A5026891233", "inst": "Harvard University" }, { "name": "Mimi Chen", "url": "https://openalex.org/A5045596055", "inst": "Harvard University" }, { "name": "Jacqueline N. Lane", "url": "https://openalex.org/A5147144021", "inst": "Dana-Farber/Harvard Cancer Center" }, { "name": "Zilin Ma", "url": "https://openalex.org/A5100438499", "inst": "Harvard University" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.23986v1", "arxiv_id": "2608.23986v1", "title": "The Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem", "authors": [ "Elioth Sanabria" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.23986v1", "field": "management", "role": "object", "bullets": [ "Theoretical model of LLM inference capacity allocation with no empirical data; uses newsvendor, geometric retry, and two-regime transient queue formulations.", "No specific LLM is named; quality degradation from model downgrades is modeled analytically as endogenous demand through retries and churn.", "Under congestion, routing queries to cheaper models can consume strictly more capacity per satisfied answer; a reactive throttle can cross an ignition threshold that generates more traffic than it sheds." ], "bullet_provenance": "ai", "salience": 55, "edition": 24, "models": [], "validated": null, "n": 4214, "authors_detailed": [ { "name": "Elioth Sanabria", "url": "https://openalex.org/A5053687791", "inst": "Decision Research" } ], "affiliations": [ "Decision Research" ] }, { "uid": "arxiv:2608.24069v1", "arxiv_id": "2608.24069v1", "title": "Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems", "authors": [ "CheolWon Na", "Hao Ni", "Lukasz Szpruch", "Zhangyang Wang", "Dhagash Mehta", "Saurabh Nagrecha", "Alejandro Lopez-Lira", "Chanyeol Choi", "Yongjae Lee", "Jee-Hyong Lee" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.24069v1", "field": "finance", "role": "object", "bullets": [ "Five assets traded by a multi-agent LLM pipeline with analyst, researcher, trader, and risk-manager roles, tested across four communication topologies and two target directions.", "Two unspecified LLM backbones face role-specific adversarial attacks on source data and agent prompts; an adversarial signal preservation score tracks propagation to the final decision.", "No architecture is inherently robust; adversarial signals propagate through all tested topologies, and susceptibility varies by role and structural design rather than by backbone." ], "bullet_provenance": "ai", "salience": 58, "edition": 24, "models": [], "validated": null, "n": 4217, "authors_detailed": [ { "name": "CheolWon Na", "url": "https://openalex.org/A5051095871", "inst": "Sungkyunkwan University" }, { "name": "Hao Ni", "url": "https://openalex.org/A5148260777", "inst": "" }, { "name": "Lukasz Szpruch", "url": "https://openalex.org/A5148319716", "inst": "" }, { "name": "Zhangyang Wang", "url": "https://openalex.org/A5148227496", "inst": "" }, { "name": "Dhagash Mehta", "url": "https://openalex.org/A5139666273", "inst": "Blackrock Microsystems (United States)" }, { "name": "Saurabh Nagrecha", "url": "https://openalex.org/A5086030523", "inst": "Google (United States)" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5148177403", "inst": "" }, { "name": "Chanyeol Choi", "url": "https://openalex.org/A5148307122", "inst": "" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5148364125", "inst": "" }, { "name": "Jee-Hyong Lee", "url": "https://openalex.org/A5148268490", "inst": "" } ], "affiliations": [ "Sungkyunkwan University", "Blackrock Microsystems (United States)", "Google (United States)" ] }, { "uid": "doi:10.2139/ssrn.7328978", "doi": "10.2139/ssrn.7328978", "title": "The Claw Machine Effect: Variable Reinforcement, Near Misses, and Work Intensification in Agentic AI", "authors": [ "Derek Snider" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7328978", "field": "management", "role": "object", "bullets": [ "Monte Carlo simulation of 100,000 sessions across four scenarios modeling human engagement with agentic AI under variable reinforcement and near-miss conditions.", "No specific LLM deployed; agentic AI interaction modeled abstractly with parameters for outcome variability, perceived skill, and verification debt accumulation.", "Salient near-miss premium raises simulated session completion from 13.0% to 19.0% but reduces objective value; periodic verified checkpoints cut overextended sessions and restore value." ], "bullet_provenance": "ai", "salience": 55, "edition": 24, "models": [], "validated": null, "n": 4222, "authors_detailed": [ { "name": "Derek Snider", "url": "https://openalex.org/A5117572675", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7333758", "doi": "10.2139/ssrn.7333758", "title": "Artificial Intelligence-driven Business Intelligence: Applications, Frameworks, and the Enhancement of Organizational Decision-making", "authors": [ "Kamil Raza" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7333758", "field": "management", "role": "object", "bullets": [ "Structured literature review of peer-reviewed articles and industry reports from 2015 to 2024 on AI integration into business intelligence platforms.", "No model deployed; the review covers ML, NLP, predictive analytics, and generative AI as BI technologies, with illustrative cases from Amazon, Netflix, and Walmart.", "Identifies nine research gaps including SME underrepresentation, immature generative AI research in BI, and absence of a unified theoretical framework for AI-driven BI adoption." ], "bullet_provenance": "ai", "salience": 30, "edition": 24, "models": [], "validated": null, "n": 4223, "authors_detailed": [ { "name": "Kamil Raza", "url": "https://openalex.org/A5148066670", "inst": "Integral University" } ], "affiliations": [ "Integral University" ] }, { "uid": "doi:10.2139/ssrn.7342398", "doi": "10.2139/ssrn.7342398", "title": "AI as Individualised Persona: A Useful Addition to the Economist's Toolbox? The case of \"Stephen Littlechild AI Agent\"", "authors": [ "Bruce Mountain", "Shruti Kant" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7342398", "field": "economics", "role": "agent", "bullets": [ "RAG-customized ChatGPT built on the published works of economist Stephen Littlechild from the 1960s to present, assessed by regulatory professionals who know his work.", "ChatGPT with retrieval-augmented generation replicates Littlechild's reasoning; expert assessors rated output on insight, completeness, and accuracy without quantitative benchmarking.", "Assessors rated the persona highly on insight and accuracy but less on writing style; the persona outperformed uncustomized ChatGPT by both human and self-assessment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 48, "edition": 24, "n": 4224, "authors_detailed": [ { "name": "Bruce Mountain", "url": "https://openalex.org/A5077712365", "inst": "Queen Medical" }, { "name": "Shruti Kant", "url": "https://openalex.org/A5054523187", "inst": "Victoria School of Management" } ], "affiliations": [ "Queen Medical", "Victoria School of Management" ] }, { "uid": "doi:10.2139/ssrn.7345627", "doi": "10.2139/ssrn.7345627", "title": "Canaries in the Gold Mine: Early Productivity Gains from Artificial Intelligence Creating Organization Capital", "authors": [ "Tania Babina", "Alex Xi He", "Renhao Jiang" ], "posted": "2026-08-25", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7345627", "field": "economics", "role": "object", "bullets": [ "US firm-level panel spanning over a decade, with AI investment measured through AI-skilled employment covering machine learning, generative, and agentic AI.", "AI is the object of study; a novel organization capital measure built from workers' job descriptions captures how AI-skilled roles create durable firm-specific knowledge.", "AI investment is associated with productivity growth in recent years but not over the prior decade, with gains concentrated in AI-skilled jobs that build organization capital." ], "bullet_provenance": "ai", "salience": 72, "edition": 24, "models": [], "validated": null, "n": 4225, "authors_detailed": [ { "name": "Tania Babina", "url": "https://openalex.org/A5039414456", "inst": "Center for Economic and Policy Research" }, { "name": "Alex Xi He", "url": "https://openalex.org/A5001993850", "inst": "University of Maryland, College Park" }, { "name": "Renhao Jiang", "url": "https://openalex.org/A5148128098", "inst": "University of Maryland, College Park" } ], "affiliations": [ "University of Maryland, College Park", "Center for Economic and Policy Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7335300", "doi": "10.2139/ssrn.7335300", "title": "Assessing the Ethical Aspects of AI: What Is the State of the Art?", "authors": [ "João Miguel Alves Ferreira" ], "posted": "2026-08-24", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7335300", "field": "management", "role": "object", "bullets": [ "Narrative review synthesizing governance frameworks from UNESCO, OECD, the EU AI Act, and the Stanford AI Index Report 2026, covering developments through mid-2026.", "No model is used. The review examines generative, multimodal, and agentic AI systems through the lens of ethics, governance, transparency, and socio-economic impact.", "Documented AI incidents have increased while foundation-model transparency has stagnated. Implementation gaps and regulatory fragmentation persist despite growing normative convergence across governance frameworks." ], "bullet_provenance": "ai", "salience": 30, "edition": 25, "models": [], "validated": null, "n": 4266, "authors_detailed": [ { "name": "João Miguel Alves Ferreira", "url": "https://openalex.org/A5102849261", "inst": "University of Coimbra" } ], "affiliations": [ "University of Coimbra" ] }, { "uid": "arxiv:2608.22842v1", "arxiv_id": "2608.22842v1", "title": "FinixDoc: Rethinking Financial Document Parsing Beyond Saturated Benchmarks", "authors": [ "Hang Wang", "Jin Zhang", "Guoliang Xu", "Pengyue Lu", "Yao Li", "Zijiao Zhang", "Tianyu Huang", "Weiqi Xiong", "Yulong Wang", "Chuqiao Lu", "Wenkang Huang", "Kai Yang", "Yadong Li", "Hui Li", "Xingzhong Xu", "Xiao Xu" ], "posted": "2026-08-24", "added": "2026-08-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.22842v1", "field": "finance", "role": "method", "bullets": [ "Real-world financial documents spanning digital-native, camera-captured, ultra-large-page, and internal workflow scenarios, evaluated on FinixDocBench, a new financial-domain benchmark.", "FinixDoc-VL, a 4B-parameter vision-language model built on Qwen3-VL-4B, is trained with domain-adapted contrastive learning and multi-stage reinforcement learning with composite rewards.", "Achieves the highest overall score of 81.43 among evaluated baselines, outperforming the next-best open-source model by 5.13 points, with the largest gain on internal financial workflows." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinixDocBench suite, scores versus baselines reported", "salience": 42, "edition": 25, "n": 4288, "authors_detailed": [ { "name": "Hang Wang", "url": "https://openalex.org/A5148080057", "inst": "" }, { "name": "Jin Zhang", "url": "https://openalex.org/A5148046409", "inst": "" }, { "name": "Guoliang Xu", "url": "https://openalex.org/A5148045377", "inst": "" }, { "name": "Pengyue Lu", "url": "https://openalex.org/A5148075248", "inst": "" }, { "name": "Yao Li", "url": "https://openalex.org/A5148117177", "inst": "" }, { "name": "Zijiao Zhang", "url": "https://openalex.org/A5024715914", "inst": "Southwest Medical University" }, { "name": "Tianyu Huang", "url": "https://openalex.org/A5148083829", "inst": "" }, { "name": "Weiqi Xiong", "url": "https://openalex.org/A5124833487", "inst": "Zhejiang University" }, { "name": "Yulong Wang", "url": "https://openalex.org/A5148108850", "inst": "" }, { "name": "Chuqiao Lu", "url": "https://openalex.org/A5148063294", "inst": "" }, { "name": "Wenkang Huang", "url": "https://openalex.org/A5065887901", "inst": "TE Connectivity (Switzerland)" }, { "name": "Kai Yang", "url": "https://openalex.org/A5148046963", "inst": "" }, { "name": "Yadong Li", "url": "https://openalex.org/A5148146601", "inst": "" }, { "name": "Hui Li", "url": "https://openalex.org/A5148128763", "inst": "" }, { "name": "Xingzhong Xu", "url": "https://openalex.org/A5077180934", "inst": "Sinopec (China)" }, { "name": "Xiao Xu", "url": "https://openalex.org/A5148056725", "inst": "" } ], "affiliations": [ "Southwest Medical University", "Zhejiang University", "TE Connectivity (Switzerland)", "Sinopec (China)" ] }, { "uid": "arxiv:2608.22852v1", "arxiv_id": "2608.22852v1", "title": "Your AI, On a Dial: Controlling Investment Bias in LLMs with a Single Neuron", "authors": [ "Sahong Park", "Suhwan Park", "Hoyoung Lee", "Gakyung Kwon", "Wonbin Ahn", "Jaewon Choi", "Alejandro Lopez-Lira", "Yoon Kim", "Chanyeol Choi", "Hyeongwoo Kong", "Yongjae Lee" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.22852v1", "field": "finance", "role": "method", "bullets": [ "Five open-weight LLMs evaluated on investment decision tasks using matched positive and negative evidence, with an exploratory portfolio backtest.", "An inference-time intervention on a single neuron creates an investment-bias dial that shifts buying or selling tendency without prompt or parameter changes.", "The dial produces monotonic stance shifts, alters evidence selection in agentic retrieval, and maintains stable control over long contexts where equivalent system-prompt instructions attenuate." ], "bullet_provenance": "ai", "open_weights": true, "validated": false, "salience": 65, "edition": 24, "models": [], "n": 4202, "authors_detailed": [ { "name": "Sahong Park", "url": "https://openalex.org/A5148052480", "inst": "" }, { "name": "Suhwan Park", "url": "https://openalex.org/A5012389090", "inst": "Ulsan National Institute of Science and Technology" }, { "name": "Hoyoung Lee", "url": "https://openalex.org/A5148108901", "inst": "" }, { "name": "Gakyung Kwon", "url": "https://openalex.org/A5001429418", "inst": "Seoul National University" }, { "name": "Wonbin Ahn", "url": "https://openalex.org/A5082843254", "inst": "LG (United States)" }, { "name": "Jaewon Choi", "url": "https://openalex.org/A5148091197", "inst": "" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5148043750", "inst": "" }, { "name": "Yoon Kim", "url": "https://openalex.org/A5148162309", "inst": "" }, { "name": "Chanyeol Choi", "url": "https://openalex.org/A5148121497", "inst": "" }, { "name": "Hyeongwoo Kong", "url": "https://openalex.org/A5141202523", "inst": "" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5148148780", "inst": "" } ], "affiliations": [ "Ulsan National Institute of Science and Technology", "Seoul National University", "LG (United States)" ] }, { "uid": "arxiv:2608.22770v1", "arxiv_id": "2608.22770v1", "title": "DelistBench: Evaluating Search-Enabled LLMs for Auditable Corporate-Event Database Completion", "authors": [ "Xuan Yao", "Li Shuping", "Dai Yang", "Zhou Yi", "Ke-Wei Huang" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.22770v1", "field": "finance", "role": "method", "bullets": [ "1,200-record benchmark of security-level delisting announcements across a known security universe and historical cutoff, tested on five LLMs.", "Five search-enabled LLMs evaluated in paired closed-book and web-enabled conditions; model families not stated. Web access raised date accuracy by 34 to 48 percentage points.", "Best system achieved 81.5% joint accuracy within seven days; economy configurations reached 75.9 to 78.3% at under 7% of the top system's API cost, with 27.3% of cases still sent to human review." ], "bullet_provenance": "ai", "validated": true, "validation_note": "1,200-record delisting benchmark, joint accuracy reported", "salience": 60, "edition": 24, "models": [], "n": 4204, "authors_detailed": [ { "name": "Xuan Yao", "url": "https://openalex.org/A5148138965", "inst": "" }, { "name": "Li Shuping", "url": "https://openalex.org/A5148075729", "inst": "" }, { "name": "Dai Yang", "url": "https://openalex.org/A5112640758", "inst": "China Pharmaceutical University" }, { "name": "Zhou Yi", "url": "https://openalex.org/A5148055853", "inst": "" }, { "name": "Ke‐Wei Huang", "url": "https://openalex.org/A5061690540", "inst": "Bloomberg (United States)" } ], "affiliations": [ "China Pharmaceutical University", "Bloomberg (United States)" ] }, { "uid": "doi:10.2139/ssrn.7345046", "doi": "10.2139/ssrn.7345046", "title": "Strategizing Mediated by Generative AI: How AI Becomes Part of Framing, Justification, and Resolution Practices in Strategic Decision-Making", "authors": [ "مصطفی بازیار", "Mehrdad Maghsoudi" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7345046", "field": "management", "role": "object", "bullets": [ "Four strategic decision episodes at one technology services firm, studied through 19 semi-structured interviews, governance documents, meeting records, and prompt-output tracing.", "Enterprise GenAI tools are the object of study. The paper does not name specific models or families. No ground-truth validation is applicable to this qualitative design.", "Four emergent practices reshape strategizing with AI. GenAI authority proves relational, not inherent, and practitioners withdraw it when outputs conflict with organizational values." ], "bullet_provenance": "ai", "salience": 50, "edition": 24, "models": [], "validated": null, "n": 4208, "authors_detailed": [ { "name": "مصطفی بازیار", "url": "https://openalex.org/A5147996607", "inst": "" }, { "name": "Mehrdad Maghsoudi", "url": "https://openalex.org/A5078892234", "inst": "Shahid Beheshti University" } ], "affiliations": [ "Shahid Beheshti University" ] }, { "uid": "arxiv:2608.22697v1", "arxiv_id": "2608.22697v1", "title": "Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf", "authors": [ "Davood Wadi", "Yu Ma" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.22697v1", "field": "management", "role": "agent", "bullets": [ "One hundred hotel listings randomized across 5,000 AI agent sessions on an unnamed search platform, benchmarked against human field data.", "Four large language models, none named in the abstract, each receive a full search results page and autonomously inspect and select hotel listings.", "AI agents search more deeply than humans and converge on the same dominant listing regardless of position; product attributes outweigh placement within results." ], "bullet_provenance": "ai", "salience": 62, "edition": 24, "models": [], "validated": null, "n": 4210, "authors_detailed": [ { "name": "Davood Wadi", "url": "https://openalex.org/A5046501265", "inst": "HEC Montréal" }, { "name": "Yu Ma", "url": "https://openalex.org/A5148044735", "inst": "" } ], "affiliations": [ "HEC Montréal" ] }, { "uid": "arxiv:2608.22724v1", "arxiv_id": "2608.22724v1", "title": "Frontiers in FinTech: Multimodal Foundation Models for Financial Reporting and Decision Science", "authors": [ "Yulu Huang", "Niannian Yu", "Yaxin Yang", "Yong Huang" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.22724v1", "field": "accounting", "role": "method", "bullets": [ "200 listed companies and 48 finance professionals; data spans PDF reports, Excel statements, chart images, and scanned policy documents.", "FinVision, a multimodal LLM system, integrates vision-language models for valuation tasks including DCF and price multiples; validated against listed company data and a professional user study.", "Valuation error dropped 19 percent and professional task completion time fell 51 percent relative to unspecified baselines." ], "bullet_provenance": "ai", "validated": true, "validation_note": "200-company valuation error benchmark; 48-professional user study", "salience": 45, "edition": 24, "models": [], "n": 4211, "authors_detailed": [ { "name": "Yulu Huang", "url": "https://openalex.org/A5045473989", "inst": "Chinese Academy of Sciences" }, { "name": "Niannian Yu", "url": "https://openalex.org/A5076913086", "inst": "Wuhan University of Technology" }, { "name": "Yaxin Yang", "url": "https://openalex.org/A5147428124", "inst": "" }, { "name": "Yong Huang", "url": "https://openalex.org/A5102238587", "inst": "" } ], "affiliations": [ "Chinese Academy of Sciences", "Wuhan University of Technology" ] }, { "uid": "doi:10.2139/ssrn.7331119", "doi": "10.2139/ssrn.7331119", "title": "From Narratives to Bottlenecks: An AI Framework for Development Governance and Upstream Diagnostics", "authors": [ "Bora Kim" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7331119", "field": "economics", "role": "method", "bullets": [ "International development projects analyzed through large-scale one-on-one stakeholder interviews to identify upstream resource-allocation constraints before project design.", "An NLP pipeline extracts causal statements into a directional knowledge graph; a RAG layer cross-references findings against macro-level data. No specific model named.", "Architecture proposed but not empirically tested; the paper frames the framework as a decision-support tool and acknowledges its effectiveness is unvalidated." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 24, "models": [], "n": 4215, "authors_detailed": [ { "name": "Bora Kim", "url": "https://openalex.org/A5147901882", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2608.23908v1", "arxiv_id": "2608.23908v1", "title": "Retrieval-augmented generation vs. deterministic tax computation in multi-agent financial advisory: A 2x2 factorial experiment", "authors": [ "Aryan Brar", "Justin Du", "Avery Lor", "Kylie Seto", "Eric Taylor" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.23908v1", "field": "finance", "role": "agent", "bullets": [ "Simulated portfolio liquidation across 30 repeated measures testing a multi-agent LLM system's tax-loss harvesting trade recommendations under four conditions.", "An unspecified LLM backbone drives four specialized agents; a 2x2 factorial varies a custom capital-gains engine and RAG-retrieved market advisory context.", "The tax engine reduced savings by 55 percentage points (p = .005); RAG showed no significant effect; the unaugmented LLM achieved 30.6 percent savings." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 24, "models": [], "n": 4216, "authors_detailed": [ { "name": "Aryan Brar", "url": "https://openalex.org/A5148344606", "inst": "" }, { "name": "Justin Du", "url": "https://openalex.org/A5041139005", "inst": "Sun Yat-sen University" }, { "name": "Avery Lor", "url": "https://openalex.org/A5148329377", "inst": "" }, { "name": "Kylie Seto", "url": "https://openalex.org/A5148353993", "inst": "" }, { "name": "Eric Taylor", "url": "https://openalex.org/A5148351278", "inst": "" } ], "affiliations": [ "Sun Yat-sen University" ] }, { "uid": "doi:10.2139/ssrn.7343343", "doi": "10.2139/ssrn.7343343", "title": "Political Entrepreneurs", "authors": [ "Aaron Chatterji", "Jorge Guzman", "Joyce Ma", "Ryan McDevitt" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7343343", "field": "economics", "role": "instrument", "bullets": [ "Financial disclosures and bill sponsorship records for state legislators across 26 US states from 2009 to 2023, distinguishing active firm owners from passive shareholders.", "An unspecified large language model classifies bill text as pro-business and identifies a pro-entry subset targeting barriers for new firms; no validation against human coding is reported.", "Entrepreneurs hold over 40 percent of legislative seats and selectively sponsor pro-entry bills on deregulation and innovation rather than generic pro-business legislation." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "edition": 24, "models": [], "n": 4218, "authors_detailed": [ { "name": "Aaron Chatterji", "url": "https://openalex.org/A5119636278", "inst": "Duke University" }, { "name": "Jorge Guzmán", "url": "https://openalex.org/A5044625419", "inst": "Columbia University" }, { "name": "Joyce Ma", "url": "https://openalex.org/A5144178368", "inst": "Duke University" }, { "name": "Ryan McDevitt", "url": "https://openalex.org/A5140342695", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Duke University", "Columbia University", "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7345144", "doi": "10.2139/ssrn.7345144", "title": "Agentic AI and Airline Pricing: Demand Cliffs, Elasticity Migration, and the Fate of Fare Fencing under Delegated Booking", "authors": [ "Khaled Abdelghany", "Ahmed Abdelghany" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7345144", "field": "economics", "role": "object", "bullets": [ "Deterministic simulation of a mixed human and AI-agent airline market, varying agent penetration levels and delegation-contract parameters such as budget caps and switching thresholds.", "AI booking agents are modeled as deterministic threshold policies executing machine-readable delegation contracts; no specific language model is deployed or named.", "Optimal fares converge to budget-cap atoms as agent share rises, and intertemporal fare fencing collapses because delegation severs the link between booking timing and willingness to pay." ], "bullet_provenance": "ai", "salience": 58, "edition": 24, "models": [], "validated": null, "n": 4219, "authors_detailed": [ { "name": "Khaled Abdelghany", "url": "https://openalex.org/A5137962387", "inst": "" }, { "name": "Ahmed Abdelghany", "url": "https://openalex.org/A5089152533", "inst": "Embry–Riddle Aeronautical University" } ], "affiliations": [ "Embry–Riddle Aeronautical University" ] }, { "uid": "arxiv:2608.23420v2", "arxiv_id": "2608.23420v2", "title": "Systematic Bias in Green Patent Classification: Silent Green and False Green", "authors": [ "Hamid Bekamiri", "Jan Auernhammer", "Milad Abbasiharofteh", "Jesper Lindgaard Christensen" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.23420v2", "field": "economics", "role": "instrument", "bullets": [ "9,075,421 USPTO granted patents from 1962 to 2024, screened for Y02 green-technology classification errors.", "A fine-tuned domain model flags disagreements with administrative labels, and two independent open-weight LLMs assess climate-mitigation relevance via cross-model consensus, without hand-coded ground truth.", "Corrections reduce the measured green-patent count by 25.5%, from 592,387 to 441,468; misclassification is systematic, driven by reflection complexity rather than applicant gaming." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 72, "edition": 24, "n": 4220, "authors_detailed": [ { "name": "Hamid Bekamiri", "url": "https://openalex.org/A5002783913", "inst": "Aalborg University" }, { "name": "Jan Auernhammer", "url": "https://openalex.org/A5127809227", "inst": "Technical Design (United States)" }, { "name": "Milad Abbasiharofteh", "url": "https://openalex.org/A5060789831", "inst": "Aalborg University" }, { "name": "Jesper Lindgaard Christensen", "url": "https://openalex.org/A5087550702", "inst": "Aalborg University" } ], "affiliations": [ "Aalborg University", "Technical Design (United States)" ] }, { "uid": "arxiv:2608.23906v1", "arxiv_id": "2608.23906v1", "title": "Quantifying System-Level Harms from AI Adoption in Complex Sociotechnical Systems", "authors": [ "Paul Vautravers", "Oliver Chalkley", "Gabriel Downer", "Kate S", "Damian Ruck" ], "posted": "2026-08-24", "added": "2026-08-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.23906v1", "field": "finance", "role": "object", "bullets": [ "UK Real Time Gross Settlement system modeled as an illustrative case, combining systems-theoretic hazard analysis with a financial contagion simulation.", "LLM-based trading recommendations subjected to adversarial inputs; behavioral shifts measured at the component level and mapped into a system contagion model. Model family not named.", "Adversarial manipulation increases simulated bank failures and lowers the shock threshold for cascading disruption, especially under widespread or monopolistic AI adoption." ], "bullet_provenance": "ai", "salience": 52, "edition": 24, "models": [], "validated": null, "n": 4221, "authors_detailed": [ { "name": "Paul Vautravers", "url": "https://openalex.org/A5118762550", "inst": "Karlsruhe Institute of Technology" }, { "name": "Oliver Chalkley", "url": "https://openalex.org/A5128170563", "inst": "" }, { "name": "Gabriel Downer", "url": "https://openalex.org/A5148351922", "inst": "" }, { "name": "Kate S", "url": "https://openalex.org/A5148309650", "inst": "" }, { "name": "Damian Ruck", "url": "https://openalex.org/A5148307406", "inst": "" } ], "affiliations": [ "Karlsruhe Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.7327758", "doi": "10.2139/ssrn.7327758", "title": "Beyond Exact Matching: Evaluating Automated Discipline Labelling of Research Projects using Semantic and Hierarchical Measures", "authors": [ "Amr Ali- Eldin", "Hoang-Son Pham" ], "posted": "2026-08-22", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7327758", "field": "management", "role": "instrument", "bullets": [ "Flemish research-information dataset covering 6,138 projects, 26,279 linked publications, and the VODS discipline taxonomy, used to study automated portfolio classification and science-policy reporting.", "Gemini embeddings retrieve candidate disciplines and hybrid taxonomy-alignment methods classify projects, validated against linked discipline labels with exact, semantic-distance, and hierarchical recall measures.", "Hybrid methods reach 45 percent graded recall and outperform standalone embeddings and lexical methods; many exact-match errors remain semantically close, especially at higher taxonomy levels." ], "bullet_provenance": "ai", "models": [ "gemini", "legacy" ], "open_weights": true, "validated": true, "validation_note": "linked project disciplines, semantic and hierarchical graded recall", "salience": 54, "edition": 23, "n": 4138, "authors_detailed": [ { "name": "Amr Ali-Eldin", "url": "https://openalex.org/A5117104840", "inst": "Hasselt University" }, { "name": "Hoang Son Pham", "url": "https://openalex.org/A5042683708", "inst": "Hasselt University" } ], "affiliations": [ "Hasselt University" ] }, { "uid": "doi:10.2139/ssrn.7331480", "doi": "10.2139/ssrn.7331480", "title": "Auditable forging process planning with conflict-aware manufacturing evidence graphs", "authors": [ "Yuxin Zhang", "Chaoqing Min", "Yichen Wang", "Ketong Wang", "Zhixuan Ye", "Xiaojun Shi", "Zhenhua Su", "Lu Li" ], "posted": "2026-08-22", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7331480", "field": "management", "role": "method", "bullets": [ "Industrial process-planning evaluation on a human-verified heavy-cylinder forging case, supplemented by historical validation across 39 cylindrical forgings and conflicting manufacturing records.", "TRACE-Forge preserves competing evidence in a governed graph and is compared with controlled LLM baselines against frozen human references for routes, parameters, and quality controls.", "Route F1 reaches 100 percent versus 75 percent for the strongest LLM baseline; evidence support reaches 93.75 percent and falls to 20.83 percent without the graph." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "frozen human-verified forging references", "salience": 58, "edition": 23, "n": 4147, "authors_detailed": [ { "name": "Yuxin Zhang", "url": "https://openalex.org/A5147865282", "inst": "" }, { "name": "Chaoqing Min", "url": "https://openalex.org/A5100525463", "inst": "Yanshan University" }, { "name": "Yichen Wang", "url": "https://openalex.org/A5147863363", "inst": "" }, { "name": "Ketong Wang", "url": "https://openalex.org/A5125536033", "inst": "" }, { "name": "Zhixuan Ye", "url": "https://openalex.org/A5080788172", "inst": "Fujian Agriculture and Forestry University" }, { "name": "Xiaojun Shi", "url": "https://openalex.org/A5101755876", "inst": "Xi'an Jiaotong University" }, { "name": "Zhenhua Su", "url": "https://openalex.org/A5147870288", "inst": "" }, { "name": "Lu Li", "url": "https://openalex.org/A5147852243", "inst": "" } ], "affiliations": [ "Yanshan University", "Fujian Agriculture and Forestry University", "Xi'an Jiaotong University" ] }, { "uid": "doi:10.2139/ssrn.7325979", "doi": "10.2139/ssrn.7325979", "title": "The Architecture of Meaning: Ethical Audits in Artificial Intelligence and the Linguistic Foundations of Mind, Machines, and Institutions", "authors": [ "Praveen Singh" ], "posted": "2026-08-22", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7325979", "field": "management", "role": "method", "bullets": [ "Conceptual governance analysis spans institutional uses of AI in healthcare, criminal justice, media, and multilingual non-Western settings, without an empirical sample or study period.", "Generative AI is evaluated through a proposed linguistic-institutional audit framework grounded in linguistic relativity and capabilities theory; no specific model or validation exercise is reported.", "The framework argues that translating values into machine logic can erase semantic context and recommends audits that examine linguistic bias, institutional discourse, and culturally specific governance consequences." ], "bullet_provenance": "ai", "salience": 32, "edition": 23, "models": [], "validated": null, "n": 4166, "authors_detailed": [ { "name": "Praveen Singh", "url": "https://openalex.org/A5110397196", "inst": "Deccan College Post Graduate and Research Institute" } ], "affiliations": [ "Deccan College Post Graduate and Research Institute" ] }, { "uid": "doi:10.2139/ssrn.7325538", "doi": "10.2139/ssrn.7325538", "title": "AI Disclosure and Perceived Authenticity in Cinematic Communication: An Empirical Analysis of Audience Trust, Transparency, and Engagement with AI-Mediated Film Content", "authors": [ "Anju Pulivarthi" ], "posted": "2026-08-22", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7325538", "field": "management", "role": "object", "bullets": [ "A simulated cross-sectional dataset of 400 film-audience records represents four AI-disclosure conditions and six four-item perception constructs measured on five-point scales.", "Generative AI involvement and disclosure timing are the experimental objects; no model family generates observations, and the reported analysis uses simulated rather than field data.", "Disclosure increased perceived informational transparency but reduced perceived creativity, implying a trade-off between explaining AI involvement and maintaining audience assumptions about human authorship." ], "bullet_provenance": "ai", "salience": 23, "edition": 23, "models": [], "validated": null, "n": 4167, "authors_detailed": [ { "name": "Anju Pulivarthi", "url": "https://openalex.org/A5147609370", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7331401", "doi": "10.2139/ssrn.7331401", "title": "Large Language Models for Banking Supervision: Reliability Evidence from European Systemic Banks", "authors": [ "Wang Lei" ], "posted": "2026-08-22", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7331401", "field": "finance", "role": "method", "bullets": [ "Sample covers 280 annual reports from 2015 to 2024, drawn from 28 European other systemically important institutions (O-SII banks) subject to supervisory disclosure requirements.", "Claude Sonnet 4.6 and GPT-4.1 coded four supervisory disclosure dimensions in the reports; reliability was assessed via intraclass correlation and inter-model concordance, not against an independent ground truth.", "Claude showed higher intra-model reliability (ICC 0.856-0.974) than GPT-4.1, which scored systematically higher by 1.36 to 2.85 points; inter-model agreement ranged from 0.907 for climate commitments to 0.375 for capital adequacy." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": false, "validation_note": "Reliability measured via ICC and inter-model correlation, not compared to independent ground truth.", "salience": 58, "edition": 22, "n": 4082, "authors_detailed": [ { "name": "Wang Lei", "url": "https://openalex.org/A5147829949", "inst": "Peking University" } ], "affiliations": [ "Peking University" ] }, { "uid": "doi:10.2139/ssrn.7325082", "doi": "10.2139/ssrn.7325082", "title": "Evolution Pathways of LLM-Based Multi-Agent Collaboration Systems and a Four-Layer Reference Architecture for Financial Intelligence Applications", "authors": [ "Zhiming Chen" ], "posted": "2026-08-22", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7325082", "field": "finance", "role": "method", "bullets": [ "Setting not stated as a single sample; the paper synthesizes framework evolution from 2023 H2 to 2026 H1 and instantiates a reference architecture across three financial applications.", "No single base model is evaluated; the paper proposes a four-layer governance architecture for multi-agent LLM systems and reproduces prior benchmark comparisons among agent frameworks such as LangGraph, CrewAI, and AutoGen.", "The proposed governance layer addresses 76.5% of system failures in the MAST taxonomy, and homogeneous multi-agent debate incurs 2.1 to 3.4 times the token cost of self-correction with no accuracy gain." ], "bullet_provenance": "ai", "salience": 42, "edition": 22, "models": [], "validated": null, "n": 4083, "authors_detailed": [ { "name": "Zhiming Chen", "url": "https://openalex.org/A5147791325", "inst": "Hongzhiwei Technology (China)" } ], "affiliations": [ "Hongzhiwei Technology (China)" ] }, { "uid": "doi:10.2139/ssrn.7324978", "doi": "10.2139/ssrn.7324978", "title": "Measuring the Citation Gap: Retrieval Eligibility and Extraction Readiness on Small and Mid-Sized Business Websites Across Four Countries", "authors": [ "Vikas K" ], "posted": "2026-08-22", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7324978", "field": "management", "role": "object", "bullets": [ "Sample: 428 small and mid-sized business websites across four countries and four sectors, with access measured under up to four request conditions per domain.", "The study defines an extraction-readiness protocol and compares an independent implementation of Google's agentic accessibility-tree audit against it, agreeing on 55% of sites; no LLM model is directly evaluated.", "Of 349 access-eligible sites, 187 (53.6%) fail extraction-readiness preconditions, most often lacking a heading, structured data, or meta description; llms.txt adoption is 20% overall, versus 29% among ready sites and 14% among others." ], "bullet_provenance": "ai", "validated": false, "validation_note": "55% agreement with an independent implementation of Google's audit agent; reported as implementation-specific, not ground truth", "salience": 30, "edition": 22, "models": [], "n": 4092, "authors_detailed": [ { "name": "Vikas K", "url": "https://openalex.org/A5147687738", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7330562", "doi": "10.2139/ssrn.7330562", "title": "Countering Digital Manipulation and Disinformation through Markets: The Emergence of Trust Tech", "authors": [ "Ido Baum", "Hod Fleishman", "Dalit Gafni", "Rotem Kadosh Nussbaum", "Naomi Krieger Carmy" ], "posted": "2026-08-22", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7330562", "field": "management", "role": "object", "bullets": [ "Using a hand-collected dataset of 256 Trust Tech companies worldwide, the study maps firm formation trends from before 2016 through 2024.", "Not stated; the paper analyzes company-level formation data rather than applying a language model to text.", "Trust Tech firm formation rose sharply after the 2016 and 2020 U.S. elections and ChatGPT's 2022 launch, concentrated in a few hubs among small, early-stage firms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 22, "validated": null, "n": 4113, "authors_detailed": [ { "name": "Ido Baum", "url": "https://openalex.org/A5079074316", "inst": "College of Management Academic Studies" }, { "name": "Hod Fleishman", "url": "https://openalex.org/A5119822933", "inst": "Center for the Study of Democracy" }, { "name": "Dalit Gafni", "url": "https://openalex.org/A5003974678", "inst": "College of Management Academic Studies" }, { "name": "Rotem Kadosh Nussbaum", "url": "https://openalex.org/A5023630432", "inst": "Center for the Study of Democracy" }, { "name": "Naomi Krieger Carmy", "url": "https://openalex.org/A5119822934", "inst": "Center for the Study of Democracy" } ], "affiliations": [ "College of Management Academic Studies", "Center for the Study of Democracy" ] }, { "uid": "doi:10.2139/ssrn.7326939", "doi": "10.2139/ssrn.7326939", "title": "Dissent, Deliberation, and Regional Influence at FOMC Meetings", "authors": [ "Ann L. Owen" ], "posted": "2026-08-22", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7326939", "field": "economics", "role": "instrument", "bullets": [ "The study analyzes Federal Open Market Committee meeting transcripts to examine how regional economic conditions shape voting and non-voting members' policy discussions.", "Large language models were used to analyze FOMC transcripts; the specific model and any validation against ground truth are not stated.", "Nonvoters' discussion shapes voting bank presidents' votes, is linked to voters shifting views between meeting phases, and predicts the Chair's future baseline policy proposals." ], "bullet_provenance": "ai", "salience": 55, "edition": 22, "models": [], "validated": null, "n": 4114, "authors_detailed": [ { "name": "Ann L. Owen", "url": "https://openalex.org/A5009402102", "inst": "Hamilton College" } ], "affiliations": [ "Hamilton College" ] }, { "uid": "doi:10.2139/ssrn.7333861", "doi": "10.2139/ssrn.7333861", "title": "The Carbon Paradox of AI: Will AI Data Centers and Algorithm Registration Exacerbate City-Level Carbon Emissions in China?", "authors": [ "Yuning Gao", "Xuetian WANG", "Yuan WANG" ], "posted": "2026-08-22", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7333861", "field": "economics", "role": "object", "bullets": [ "The study examines 297 Chinese cities from 2012 to 2024, using AI data center counts and generative AI service and algorithm filings.", "Not stated; the analysis uses city-level AI infrastructure and filing counts combined with input-output emissions tables rather than applying a language model directly.", "AI expansion is significantly and negatively associated with city-level carbon emissions, operating through more digitalized production and a shift toward lower-carbon consumption." ], "bullet_provenance": "ai", "salience": 40, "edition": 22, "models": [], "validated": null, "n": 4115, "authors_detailed": [ { "name": "Yuning Gao", "url": "https://openalex.org/A5033392251", "inst": "Tsinghua University" }, { "name": "Xuetian Wang", "url": "https://openalex.org/A5102889343", "inst": "China University of Mining and Technology" }, { "name": "Yuan WANG", "url": "https://openalex.org/A5147835223", "inst": "" } ], "affiliations": [ "Tsinghua University", "China University of Mining and Technology" ] }, { "uid": "doi:10.2139/ssrn.7331261", "doi": "10.2139/ssrn.7331261", "title": "Multi-Horizon Cryptocurrency Forecasting via a Hybrid 1D-CNN-IndRNN Framework with Feature Selection Optimization", "authors": [ "Ramin Mousa", "Zahra MahmoudpourHarris" ], "posted": "2026-08-22", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7331261", "field": "finance", "role": "instrument", "bullets": [ "The study evaluates a forecasting model on five cryptocurrencies, Bitcoin, Ethereum, Dogecoin, Bitcoin Cash, and IoT Chain, using multi-horizon price forecasts at 7, 30, and 90 days; period and geography not stated.", "A hybrid 1D-CNN-IndRNN architecture combined with systematic feature selection forecasts multi-horizon cryptocurrency price movements, validated against LSTM, ANN, and SVM baselines under strict linear split validation.", "The model cuts mean absolute error by about 18 percent and mean absolute percentage error by nearly 20 percent versus baselines, reaching up to 81 percent classification accuracy on 90-day forecasts." ], "bullet_provenance": "ai", "validated": true, "validation_note": "compared against LSTM, ANN, and SVM baselines using MAE, MAPE, and classification accuracy", "salience": 40, "edition": 22, "models": [], "n": 4116, "authors_detailed": [ { "name": "Ramin Mousa", "url": "https://openalex.org/A5060727431", "inst": "University of Zanjan" }, { "name": "Zahra MahmoudpourHarris", "url": "https://openalex.org/A5147748117", "inst": "" } ], "affiliations": [ "University of Zanjan" ] }, { "uid": "doi:10.2139/ssrn.7327018", "doi": "10.2139/ssrn.7327018", "title": "Finance in 2035: The Rise of a Programmable, Tokenized, and Intelligent Financial System", "authors": [ "David Krause" ], "posted": "2026-08-22", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7327018", "field": "finance", "role": "object", "bullets": [ "The paper analyzes how payments, investments, banking, insurance, real estate, and financial information markets may evolve by 2035 across the United States, European Union, and United Kingdom.", "The paper does not employ or validate a specific model; it develops three scenarios, managed convergence, fragmented innovation, and accelerated dislocation, for how AI agents and tokenization may reshape finance.", "It concludes finance will grow more digital and AI-enabled, with the key open question being whether resulting systems achieve interoperability, reliable information, liquidity discipline, cybersecurity, and accountable governance." ], "bullet_provenance": "ai", "salience": 35, "edition": 22, "models": [], "validated": null, "n": 4117, "authors_detailed": [ { "name": "David Krause", "url": "https://openalex.org/A5147737614", "inst": "Marquette University" } ], "affiliations": [ "Marquette University" ] }, { "uid": "arxiv:2608.20661v1", "arxiv_id": "2608.20661v1", "title": "Auditable by Construction: An Ontology-Driven Framework for Trustworthy LLM Analytics in Enterprise Finance", "authors": [ "Sergiy Lunyakin" ], "posted": "2026-08-21", "added": "2026-08-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.20661v1", "field": "finance", "role": "method", "bullets": [ "FinanceBench evaluation covers 145 enterprise finance questions relevant to financial planning and analysis, comparing zero-context inference with lexical, concept-weighted, graph, and ontology-driven retrieval.", "KDAF retrieves source-linked facts through an ontology and relevance propagation; answer correctness and citation traceability are validated against FinanceBench references and competing retrieval systems.", "KDAF does not improve correctness over BM25, but raises citation-traceability F1 to 0.515 and excludes off-entity evidence entirely, showing auditability gains without an accuracy gain." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinanceBench answer references and citation-traceability evaluation", "salience": 67, "edition": 23, "models": [], "n": 4133, "authors_detailed": [ { "name": "Sergiy Lunyakin", "url": "https://openalex.org/A5134530520", "inst": "Seattle University" } ], "affiliations": [ "Seattle University" ] }, { "uid": "doi:10.2139/ssrn.7316209", "doi": "10.2139/ssrn.7316209", "title": "Making AI Tutoring Productive: Evidence from a Mastery-Based Math Practice Experiment", "authors": [ "Philip Oreopoulos", "Michael Liut", "Alp Sungu", "Nina Low" ], "posted": "2026-08-21", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7316209", "field": "economics", "role": "object", "bullets": [ "Randomized field experiment with more than 6,000 middle-school students in Hamilton County Schools, crossing AI support, mastery progression, and two math topics with a one-week delayed test.", "An unnamed LLM tutoring system supplies structured support after errors, compared with computer-assisted learning without AI; the model is the treatment rather than a research measurement tool.", "AI slows progression but improves accuracy after mistakes; delayed learning gains are marginally significant and concentrated where AI support is embedded within the mastery workflow." ], "bullet_provenance": "ai", "salience": 78, "edition": 23, "models": [], "validated": null, "n": 4137 }, { "uid": "doi:10.2139/ssrn.7323260", "doi": "10.2139/ssrn.7323260", "title": "Consent-Aware Data Pipelines: Tracking Training Data Provenance for Generative AI Compliance", "authors": [ "jagan ankam" ], "posted": "2026-08-21", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7323260", "field": "management", "role": "method", "bullets": [ "Training-data governance evaluation on The Pile and Common Crawl, focused on preserving source identity, ownership, consent attributes, and transformation history through distributed ingestion pipelines.", "CADP captures provenance and consent metadata, then checks it against predefined governance requirements; no language model family is used or evaluated as a measurement tool.", "The pipeline reports 96.4 percent provenance traceability, 98.1 percent metadata preservation, and 94.7 percent compliance-verification accuracy with 8.6 percent processing overhead." ], "bullet_provenance": "ai", "salience": 50, "edition": 23, "models": [], "validated": null, "n": 4145, "authors_detailed": [ { "name": "Jagan Ankam", "url": "https://openalex.org/A5135959550", "inst": "ABS Consulting (United States)" } ], "affiliations": [ "ABS Consulting (United States)" ] }, { "uid": "doi:10.2139/ssrn.7322182", "doi": "10.2139/ssrn.7322182", "title": "GSA Regulation 552.239-7001: Safeguarding Data in LLMs -A Commenter Discourse Analysis", "authors": [ "Cari Miller" ], "posted": "2026-08-21", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7322182", "field": "management", "role": "object", "bullets": [ "Qualitative thematic analysis of 77 public comments on proposed US General Services Administration safeguards for data, intellectual property, and AI development in federal procurement.", "No LLM performs the analysis; the study treats foundation models, solution providers, and public buyers as organizational actors facing differently allocated governance responsibilities.", "Stakeholders identify technical defects and conflicting policy assumptions, leading to a layered procurement roadmap that separates foundation-model baselines from solution-specific safeguards." ], "bullet_provenance": "ai", "salience": 57, "edition": 23, "models": [], "validated": null, "n": 4146, "authors_detailed": [ { "name": "Cari Miller", "url": "https://openalex.org/A5147718885", "inst": "Wilmington University" } ], "affiliations": [ "Wilmington University" ] }, { "uid": "doi:10.2139/ssrn.7323618", "doi": "10.2139/ssrn.7323618", "title": "Deterministic Governance for Autonomous Financial Transactions on Distributed Ledgers: A Structural Enforcement Architecture with Cryptographic Attestation and Protocol-Native Multi-Signature Co-Signing", "authors": [ "James Benton" ], "posted": "2026-08-21", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7323618", "field": "finance", "role": "method", "bullets": [ "Governance architecture targets autonomous financial transactions by AI agents, decentralized organizations, algorithmic funds, and programmatic users on distributed ledgers, with XRP Ledger as the preferred implementation.", "A deterministic policy kernel, code-based bylaws, cryptographic receipts, and protocol-native multi-signature controls constrain execution independently of any particular language-model family; empirical validation is not reported.", "The design makes transactions lacking governance approval structurally unexecutable at consensus level, rather than merely detecting violations after an irreversible ledger transaction." ], "bullet_provenance": "ai", "salience": 47, "edition": 23, "models": [], "validated": null, "n": 4175, "authors_detailed": [ { "name": "James D. Benton", "url": "https://openalex.org/A5136470720", "inst": "Wilmington University" } ], "affiliations": [ "Wilmington University" ] }, { "uid": "doi:10.2139/ssrn.7322019", "doi": "10.2139/ssrn.7322019", "title": "Cross-Interaction Derivability:From Output Safety to Auditable AI Trajectories", "authors": [ "Vincenzo D'amico" ], "posted": "2026-08-21", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7322019", "field": "management", "role": "method", "bullets": [ "Conceptual audit framework covers persistent-memory, long-horizon, tool-using, autonomous, and multi-agent systems where consequential decisions emerge across multiple interactions rather than one output.", "The approach reconstructs how retained evidence is selected, transformed, weighted, and converted into action; no model family, empirical sample, or validation benchmark is reported.", "It argues that auditable trajectories can preserve contestability while selectively hiding content, and that locating consequential transitions may reduce search, evaluation, latency, compute, and energy costs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 39, "edition": 23, "validated": null, "n": 4176, "authors_detailed": [ { "name": "Vincenzo D'amico", "url": "https://openalex.org/A5147597436", "inst": "National Institute for Public Policy" } ], "affiliations": [ "National Institute for Public Policy" ] }, { "uid": "arxiv:2608.21203v1", "arxiv_id": "2608.21203v1", "title": "SENTRY: Deterministic, Intelligent Risk Assessment for IT Change Management", "authors": [ "Daniel Arulpragasam", "Christer Henrysson", "Ella Ly", "Deepika Anbalagan", "Leo Feng" ], "posted": "2026-08-21", "added": "2026-08-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.21203v1", "field": "management", "role": "instrument", "bullets": [ "Enterprise-scale change records from a large financial-institution setting combine operational metadata, dependency graphs, historical incidents, and unstructured change-request text; sample size is not stated.", "Hybrid semantic and lexical retrieval compresses historical-text evidence into a scalar feature for an XGBoost risk model, benchmarked against realized risk labels with accuracy and ROC AUC.", "The system achieves 0.87 ROC AUC and 85 percent accuracy, identifying high-risk changes at roughly 3.25 times the rate of the existing questionnaire process." ], "bullet_provenance": "ai", "validated": true, "validation_note": "enterprise change-risk labels, ROC AUC 0.87 and accuracy 85 percent", "salience": 65, "edition": 23, "models": [], "n": 4177, "authors_detailed": [ { "name": "Daniel Arulpragasam", "url": "https://openalex.org/A5148039872", "inst": "" }, { "name": "Christer Henrysson", "url": "https://openalex.org/A5147969119", "inst": "" }, { "name": "Ella Ly", "url": "https://openalex.org/A5147967310", "inst": "" }, { "name": "Deepika Anbalagan", "url": "https://openalex.org/A5005255199", "inst": "Institute of Management Technology" }, { "name": "Leo Feng", "url": "https://openalex.org/A5006427579", "inst": "Borealis (Austria)" } ], "affiliations": [ "Institute of Management Technology", "Borealis (Austria)" ] }, { "uid": "doi:10.2139/ssrn.7323738", "doi": "10.2139/ssrn.7323738", "title": "Predict, Observe, Retrain: Can a Language Model Learn to Pick A/B Test Winners from Its Own Feedback Loop?", "authors": [ "Nathan Clark" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7323738", "field": "management", "role": "agent", "bullets": [ "Sample is 2,599 headline A/B tests from the Upworthy Research Archive, covering 43 million reader impressions, sorted into ten time-ordered rounds.", "A Qwen2.5-3B model, LoRA fine-tuned each round on outcomes revealed so far, predicted each round's winning headline and was compared against frozen zero-shot and few-shot LLM baselines and a gradient-boosted trees baseline.", "The fine-tuning loop picked decisive test winners 52.8 percent of the time on average versus a 25.6 percent random baseline, beating the best non-LLM baseline by 15 points in every round." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Predictions compared to real A/B test outcomes and, out of sample, to click-through rates on the Microsoft MIND dataset (Spearman 0.37 vs. 0.04 for the base model).", "salience": 55, "edition": 22, "n": 4078, "authors_detailed": [ { "name": "Nathan Clark", "url": "https://openalex.org/A5135378458", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7320742", "doi": "10.2139/ssrn.7320742", "title": "Policy Learning in Language-Mediated Stochastic Systems", "authors": [ "Yuan Cao", "Siyang Gao", "David Simchi-Levi" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7320742", "field": "management", "role": "method", "bullets": [ "Setting: theoretical framework with numerical experiments on generated handoffs and code review conversations rather than a real-world firm sample.", "Model: not stated; the paper treats generic LLM calls as producing operational-message content within a stochastic simulation, and reuses stored language outcomes across policy evaluations rather than validating against ground truth.", "Result: reusing a precisely estimated shared language input can create unavoidable error in operating values and can reverse a dynamic policy ranking; the authors construct a reusable empirical operator with policy-selection guarantees under a call budget." ], "bullet_provenance": "ai", "salience": 38, "edition": 22, "models": [], "validated": null, "n": 4089, "authors_detailed": [ { "name": "Yuan Cao", "url": "https://openalex.org/A5147798291", "inst": "" }, { "name": "Siyang Gao", "url": "https://openalex.org/A5147692532", "inst": "City University of Hong Kong" }, { "name": "David Simchi-Levi", "url": "https://openalex.org/A5147765691", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "City University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7323498", "doi": "10.2139/ssrn.7323498", "title": "Uncertainty Quantification for Financial Foundation Models: A Survey", "authors": [ "Zijie Zhao", "Mingjun Sun", "Shenbo Xu" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7323498", "field": "finance", "role": "method", "bullets": [ "This survey reviews uncertainty quantification research for financial foundation models across financial question answering, disclosure analysis, forecasting, and decision-making tasks; sample, period, and geography are not stated.", "The paper does not test a specific model; it builds a source-aware taxonomy of six uncertainty sources spanning evidence, inference, and outcome, and maps existing finance studies onto that taxonomy; not validated.", "The review finds the literature is compartmentalized by task, with limited work linking evidence and reasoning reliability to downstream prediction and constrained action; no quantitative results are reported." ], "bullet_provenance": "ai", "salience": 40, "edition": 22, "models": [], "validated": null, "n": 4097, "authors_detailed": [ { "name": "Zijie Zhao", "url": "https://openalex.org/A5100553643", "inst": "China Electronics Technology Group Corporation" }, { "name": "Mingjun Sun", "url": "https://openalex.org/A5101020059", "inst": "North Sichuan Medical University" }, { "name": "Shenbo Xu", "url": "https://openalex.org/A5147726186", "inst": "" } ], "affiliations": [ "China Electronics Technology Group Corporation", "North Sichuan Medical University" ] }, { "uid": "doi:10.2139/ssrn.7329421", "doi": "10.2139/ssrn.7329421", "title": "InvoiceOCR-Synth: An annotation-noise-free synthetic dataset of receipt and invoice images for document information extraction", "authors": [ "Alamgir Munir Qazi", "Jamal Abdul Nasir", "Pratheesh Chambeth", "Waqar Shahid Qureshi" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7329421", "field": "accounting", "role": "method", "bullets": [ "This paper presents a synthetic dataset of invoice and receipt records spanning five currencies and fifteen document types, rendered into 2,814 images under three quality conditions; no real-world sample or period applies.", "The open-source model gpt-oss-20b generated 1,000 fictional structured invoice records under a 32-field schema, later trimmed to 938 after an arithmetic-consistency audit; not validated against real invoices.", "The dataset is intended to benchmark vision-language and OCR document-extraction systems and support fine-tuning, though the paper reports no extraction-accuracy results for any evaluated model." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": true, "salience": 35, "edition": 22, "validated": null, "n": 4098, "authors_detailed": [ { "name": "Alamgir Munir Qazi", "url": "https://openalex.org/A5112806219", "inst": "Ollscoil na Gaillimhe – University of Galway" }, { "name": "Jamal Abdul Nasir", "url": "https://openalex.org/A5147804149", "inst": "Ollscoil na Gaillimhe – University of Galway" }, { "name": "pratheesh chambeth", "url": "https://openalex.org/A5141277417", "inst": "" }, { "name": "Waqar S. Qureshi", "url": "https://openalex.org/A5023209744", "inst": "Ollscoil na Gaillimhe – University of Galway" } ], "affiliations": [ "Ollscoil na Gaillimhe – University of Galway" ] }, { "uid": "doi:10.2139/ssrn.7321678", "doi": "10.2139/ssrn.7321678", "title": "ANCORA Flow: A Conceptual Model for Change Management and AI Adoption with Psychological Safety, Sensemaking, and Sociotechnical Governance", "authors": [ "Rosana Silvestre Torres" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7321678", "alt_urls": [ "https://doi.org/10.2139/ssrn.7321478" ], "field": "management", "role": "object", "bullets": [ "The paper is a conceptual white paper proposing an organizational change model for generative AI adoption; no sample, period, or geography is specified, and the model lacks empirical grounding.", "No language model was applied, tested, or validated; the paper instead synthesizes existing change-management, organizational-maturity, and AI-governance literature into a six-phase framework called ANCORA Flow.", "The paper argues psychological safety and combined organizational-individual diagnosis are structural preconditions for sustainable AI adoption, but states explicitly that the model has not undergone empirical validation." ], "bullet_provenance": "ai", "validation_note": "conceptual paper with no model output; no validation performed", "salience": 28, "edition": 22, "models": [], "validated": null, "n": 4106, "authors_detailed": [ { "name": "Rosana Silvestre Torres", "url": "https://openalex.org/A5147778190", "inst": "Oldham Council" } ], "affiliations": [ "Oldham Council" ] }, { "uid": "doi:10.2139/ssrn.7319538", "doi": "10.2139/ssrn.7319538", "title": "Managing Online Content Platforms with AI-Generated Content: Content Amplification and Disclosure Enforcement", "authors": [ "Qiyuan Deng", "Xin Fang", "Linqiu Li" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7319538", "field": "management", "role": "object", "bullets": [ "The paper models a single content platform and a representative creator using game theory, with no specific empirical sample, period, or geography.", "The model is theoretical and does not apply a specific AI model to text, so model choice and validation are not stated.", "Penalizing enforcement of AI-content disclosure can raise profits for both platform and creator when AIGC aversion is mild and amplification effectiveness is intermediate, but neither enforcement approach dominates uniformly." ], "bullet_provenance": "ai", "salience": 42, "edition": 22, "models": [], "validated": null, "n": 4107, "authors_detailed": [ { "name": "Qiyuan Deng", "url": "https://openalex.org/A5036267252", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Xin Fang", "url": "https://openalex.org/A5050831851", "inst": "Singapore Management University" }, { "name": "Linqiu Li", "url": "https://openalex.org/A5147691389", "inst": "University of Science and Technology of China" } ], "affiliations": [ "Chinese University of Hong Kong, Shenzhen", "Singapore Management University", "University of Science and Technology of China" ] }, { "uid": "doi:10.2139/ssrn.7320479", "doi": "10.2139/ssrn.7320479", "title": "Design and Implementation of a Hybrid Economic Expert System for MSMEs in Nigeria", "authors": [ "Gideon Ikebudeh" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7320479", "field": "economics", "role": "instrument", "bullets": [ "The study describes a prototype hybrid economic expert system built for micro, small, and medium enterprises in Nigeria, covering taxation, funding, pricing, and forecasting; no field sample or test period is reported.", "The system pairs rule-based reasoning with machine-learning forecasting and uses the Gemini and Hugging Face APIs to generate natural-language explanations of outputs; the formal accuracy evaluation has not been conducted.", "A functional prototype with nine advisory modules was deployed for local testing, but no accuracy or satisfaction figures are reported because the planned evaluation is not yet complete." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": false, "validation_note": "planned evaluation not yet conducted", "salience": 30, "edition": 22, "n": 4108, "authors_detailed": [ { "name": "Gideon Ikebudeh", "url": "https://openalex.org/A5123344246", "inst": "Nnamdi Azikiwe University" } ], "affiliations": [ "Nnamdi Azikiwe University" ] }, { "uid": "doi:10.2139/ssrn.7322398", "doi": "10.2139/ssrn.7322398", "title": "Improving Today, Narrowing Tomorrow: Collective Learning, Diversity, and Generativity", "authors": [ "Esteve Almirall", "Christopher Tucci" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7322398", "field": "management", "role": "object", "bullets": [ "The paper develops a theoretical model of firms searching interdependent landscapes to study collective learning and diversity, with no specific empirical sample, period, or geography.", "The analysis is conceptual and treats generative AI as a motivating example of collective learning rather than deploying or evaluating a specific model, so model choice and validation are not stated.", "Copying leading firms' practices quickly narrows population diversity, while repertoires paired with local judgment preserve the variety needed for future discovery, even though diverse industries still perform better overall." ], "bullet_provenance": "ai", "salience": 35, "edition": 22, "models": [], "validated": null, "n": 4109, "authors_detailed": [ { "name": "Esteve Almirall", "url": "https://openalex.org/A5088311525", "inst": "EAE Business School" }, { "name": "Christopher Tucci", "url": "https://openalex.org/A5120438828", "inst": "Imperial College London" } ], "affiliations": [ "EAE Business School", "Imperial College London" ] }, { "uid": "doi:10.2139/ssrn.7322038", "doi": "10.2139/ssrn.7322038", "title": "The Two Practices of AI Consulting", "authors": [ "Oshin Anand" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7322038", "field": "management", "role": "object", "bullets": [ "The paper is a conceptual argument about enterprise generative AI adoption, drawing on institutional evidence and the author's consulting experience; no specific sample, period, or geography is reported.", "The analysis distinguishes problem-first from tool-first AI advisory engagements rather than applying or testing a specific AI model, so model use and validation are not stated.", "The paper argues that how AI capability is architected against business tasks, not model selection itself, explains the gap between generative AI spending and measured return." ], "bullet_provenance": "ai", "salience": 30, "edition": 22, "models": [], "validated": null, "n": 4110, "authors_detailed": [ { "name": "Oshin Anand", "url": "https://openalex.org/A5102935694", "inst": "Integrated Software (United States)" } ], "affiliations": [ "Integrated Software (United States)" ] }, { "uid": "doi:10.2139/ssrn.7324599", "doi": "10.2139/ssrn.7324599", "title": "AI-Mediated Knowledge Markets as Governable Digital Economies: A Structural Architecture for Provenance, Access, Settlement, and Systemic Trust", "authors": [ "Y. Hori" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7324599", "field": "economics", "role": "object", "bullets": [ "The paper proposes a conceptual architecture for AI-mediated knowledge markets covering provenance, access, settlement, and trust, illustrated with a worked scenario rather than an empirical sample or dataset.", "The paper is architectural and does not deploy or evaluate a specific AI model; it defines diagnostics such as provenance coverage and trust degradation, so model use and validation are not stated.", "The paper does not empirically evaluate a deployed market, leaving replay simulation, adversarial stress testing, and case comparison to future research." ], "bullet_provenance": "ai", "salience": 28, "edition": 22, "models": [], "validated": null, "n": 4111, "authors_detailed": [ { "name": "Y. Hori", "url": "https://openalex.org/A5128914984", "inst": "Life Cycle Engineering (United States)" } ], "affiliations": [ "Life Cycle Engineering (United States)" ] }, { "uid": "doi:10.2139/ssrn.7323459", "doi": "10.2139/ssrn.7323459", "title": "When the Vendor Changes the Firm: Vendor-Induced Routine Drift from Foundation Model Updates", "authors": [ "Purabkumar Upadhyay" ], "posted": "2026-08-21", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7323459", "field": "management", "role": "object", "bullets": [ "The paper is theoretical, proposing a preregistered empirical design using standardized managerial decision scenarios with repeated within-version measurements rather than reporting completed empirical data.", "Not stated; the paper proposes no specific foundation model test but discusses vendor-controlled foundation models generally used in recurring organizational decision processes.", "The framework develops six propositions arguing that foundation-model version updates can systematically shift organizational decision policies even when internal conditions remain unchanged." ], "bullet_provenance": "ai", "salience": 35, "edition": 22, "models": [], "validated": null, "n": 4112, "authors_detailed": [ { "name": "Purabkumar Upadhyay", "url": "https://openalex.org/A5147708091", "inst": "University of the Cumberlands" } ], "affiliations": [ "University of the Cumberlands" ] }, { "uid": "arxiv:2608.20320v1", "arxiv_id": "2608.20320v1", "title": "An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction", "authors": [ "Narges Ahmadi", "Yubo Jiao", "Jônatas Augusto Manzolli", "Jiangbo Yu", "Luis Miranda-Moreno" ], "posted": "2026-08-20", "added": "2026-08-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.20320v1", "field": "economics", "role": "agent", "bullets": [ "Image-augmented stated-preference survey of student commuters produced 454 respondent-scenario mode choices across five weather conditions, with multinomial logit, logistic regression, and random forest benchmarks.", "Nine locally deployed LLMs predicted travel modes under zero-shot, persona, few-shot, and vision configurations, validated against respondents' observed choices using five-class accuracy.", "The best text-only model reached 69.9 percent accuracy versus 69.6 percent for random forest, while weather images raised the leading vision configuration to 71.5 percent." ], "bullet_provenance": "ai", "validated": true, "validation_note": "respondent mode choices, five-class accuracy", "salience": 60, "edition": 23, "models": [], "n": 4128, "authors_detailed": [ { "name": "Narges Ahmadi", "url": "https://openalex.org/A5147761423", "inst": "McGill University" }, { "name": "Yubo Jiao", "url": "https://openalex.org/A5054653490", "inst": "McGill University" }, { "name": "Jônatas Augusto Manzolli", "url": "https://openalex.org/A5139481846", "inst": "McGill University" }, { "name": "Jiangbo Yu", "url": "https://openalex.org/A5147811883", "inst": "McGill University" }, { "name": "Luis Miranda-Moreno", "url": "https://openalex.org/A5130626161", "inst": "McGill University" } ], "affiliations": [ "McGill University" ] }, { "uid": "doi:10.2139/ssrn.7318518", "doi": "10.2139/ssrn.7318518", "title": "Operationalizing Longitudinal Machine Information Behavior: The Founding and Developmental Pilot of MIBO: An Open Resource and Methods Paper", "authors": [ "Kento Sasano" ], "posted": "2026-08-20", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7318518", "field": "management", "role": "method", "bullets": [ "Japanese observatory launched in May 2026 with weekly bilingual API observations; the released pilot contains 244 service-query records from its first 13 days.", "ChatGPT, Claude, Gemini, and Perplexity answer three to five fixed queries, with separate coding for citations, sources, entities, advice, parameters, and later corrections.", "The pilot identifies missing early API parameters, no within-cell replication, and single-investigator coding, leading to a preregistration-ready protocol for longitudinal service-level AI research." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 56, "edition": 23, "validated": null, "n": 4136, "authors_detailed": [ { "name": "Kento Sasano", "url": "https://openalex.org/A5147207870", "inst": "Okayama University" } ], "affiliations": [ "Okayama University" ] }, { "uid": "doi:10.2139/ssrn.7313999", "doi": "10.2139/ssrn.7313999", "title": "The Knowledge-Model Separation Reframing LLM Migration as an Evaluation Event in Enterprise Operational AI", "authors": [ "John Rendek" ], "posted": "2026-08-20", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7313999", "field": "management", "role": "method", "bullets": [ "Enterprise operational AI architecture separating organizational knowledge, predictive patterns, and model behavior; the paper synthesizes prior literature and reports no sample or implementation test.", "No particular LLM is tested; knowledge graphs store facts, conventional models retain predictive patterns, and lightweight adapters carry organization-specific style and task behavior.", "Model replacement becomes a replay-based evaluation against the organization's decision log instead of wholesale retraining, reducing migration cost and dependence on a particular model vendor." ], "bullet_provenance": "ai", "salience": 45, "edition": 23, "models": [], "validated": null, "n": 4139, "authors_detailed": [ { "name": "John Rendek", "url": "https://openalex.org/A5147581539", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7323044", "doi": "10.2139/ssrn.7323044", "title": "An Integrated Web-based Decision Support System with LLM-assisted Mitigation for Carbon Footprint Management in Marine Aquaculture", "authors": [ "Xiaomin Wang", "Yulong Wang", "Changkun Lin", "Ting Jiang", "Chenxin Zhang", "Ziyi Cong", "Hanxue Li", "Xuhan Wei", "Yuanchao Hu", "Shaobin Li" ], "posted": "2026-08-20", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7323044", "field": "management", "role": "method", "bullets": [ "Marine-aquaculture carbon-management platform demonstrated on an oyster-farming case, integrating traceable lifecycle inventory, regional environmental parameters, scenario recalculation, and sensitivity-ranked emissions inputs.", "An unnamed retrieval-augmented LLM converts ranked inputs into evidence-linked mitigation options for human review; the abstract reports no accuracy or agreement check against expert recommendations.", "The case demonstrates an end-to-end workflow supporting cleaner production, procurement, industrial policy, and product-footprint verification, but reports no quantitative environmental improvement." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 23, "models": [], "n": 4150, "authors_detailed": [ { "name": "Xiaomin Wang", "url": "https://openalex.org/A5147636142", "inst": "Xiamen University" }, { "name": "Yulong Wang", "url": "https://openalex.org/A5147617845", "inst": "Xiamen University" }, { "name": "Changkun Lin", "url": "https://openalex.org/A5130406230", "inst": "Xiamen University" }, { "name": "Ting Jiang", "url": "https://openalex.org/A5147548841", "inst": "Xiamen University" }, { "name": "Chenxin Zhang", "url": "https://openalex.org/A5124447983", "inst": "Xiamen University" }, { "name": "Ziyi Cong", "url": "https://openalex.org/A5067155605", "inst": "Xiamen University" }, { "name": "Hanxue Li", "url": "https://openalex.org/A5051871799", "inst": "Xiamen University" }, { "name": "Xuhan Wei", "url": "https://openalex.org/A5133446417", "inst": "Xiamen University" }, { "name": "Yuanchao Hu", "url": "https://openalex.org/A5038257004", "inst": "Wuhan University" }, { "name": "Shaobin Li", "url": "https://openalex.org/A5147639560", "inst": "Xiamen University" } ], "affiliations": [ "Xiamen University", "Wuhan University" ] }, { "uid": "doi:10.2139/ssrn.7317622", "doi": "10.2139/ssrn.7317622", "title": "Collaborative Intelligent Manufacturing System based on Large-Small Model Fusion and Retrieval-Augmented Generation", "authors": [ "Xiao Lai", "Han Wang", "Zian Lu", "Xiaohan Zhang", "Xinyi Chen", "Li Bai", "Min Liu" ], "posted": "2026-08-20", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7317622", "field": "management", "role": "instrument", "bullets": [ "Industrial applications cover document processing in zinc oxygen-pressure leaching and maintenance decisions for high-safety special equipment; sample sizes and evaluation periods are not stated.", "Unspecified large models parse documents, retrieve domain knowledge, and orchestrate smaller execution models in a hierarchical agent system; no ground-truth accuracy or agreement statistic is reported.", "The system reportedly outperforms existing document-parsing methods and delivers near-expert maintenance decisions with response times more than 60 percent faster, although detailed comparison metrics are omitted." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no model-output accuracy or agreement statistic reported", "salience": 44, "edition": 23, "models": [], "n": 4173, "authors_detailed": [ { "name": "Xiao Lai", "url": "https://openalex.org/A5135665071", "inst": "Tongji University" }, { "name": "Han Wang", "url": "https://openalex.org/A5147559626", "inst": "Tongji University" }, { "name": "Zian Lu", "url": "https://openalex.org/A5135364232", "inst": "Tongji University" }, { "name": "Xiaohan Zhang", "url": "https://openalex.org/A5100431077", "inst": "Tongji University" }, { "name": "Xinyi Chen", "url": "https://openalex.org/A5147449045", "inst": "Tongji University" }, { "name": "Li Bai", "url": "https://openalex.org/A5147507488", "inst": "Shanghai Lixin University of Accounting and Finance" }, { "name": "Min Liu", "url": "https://openalex.org/A5100343919", "inst": "Tongji University" } ], "affiliations": [ "Tongji University", "Shanghai Lixin University of Accounting and Finance" ] }, { "uid": "doi:10.2139/ssrn.7313518", "doi": "10.2139/ssrn.7313518", "title": "Governing AI-mediated Development in Digital Public Infrastructure", "authors": [ "Muhammad Hamza" ], "posted": "2026-08-20", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7313518", "field": "management", "role": "object", "bullets": [ "Repository study compares 295 digital-public-infrastructure projects with 179 matched non-DPI repositories and inspects 69 DPI projects where AI-coding-agent activity was detected.", "AI coding-agent participation, contribution governance, and pull-request visibility are measured from repository records; the abstract does not state a model family or validation procedure.", "Adjusted adoption did not differ significantly, but only 4.3 percent of affected DPI repositories governed AI-authored contributions and 56 percent of agentic pull requests lacked visible AI signals." ], "bullet_provenance": "ai", "salience": 60, "edition": 23, "models": [], "validated": null, "n": 4174, "authors_detailed": [ { "name": "Muhammad Hamza", "url": "https://openalex.org/A5147624614", "inst": "Lappeenranta-Lahti University of Technology" } ], "affiliations": [ "Lappeenranta-Lahti University of Technology" ] }, { "uid": "doi:10.2139/ssrn.7315339", "doi": "10.2139/ssrn.7315339", "title": "The Collective Rationality Trap: Algorithmic Herding, Sentiment Synchronicity, and the Erosion of Market Depth", "authors": [ "Jitendra Singh Jadav" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7315339", "field": "finance", "role": "instrument", "bullets": [ "The study analyzes 200 S&P 500 equities over a seven-day period, generating 1,400 sentiment evaluations to test how LLM-assisted trading affects market liquidity and fragility.", "Four models, Gemini 3.6 Flash, Llama-3.2 3B, DeepSeek-R1 Qwen 1.5B, and Phi-3.5 Mini, scored equity sentiment from prompts; the paper reports cross-model agreement but not validation against ground-truth prices or returns.", "Cross-model sentiment dispersion is high for speculative, high-beta stocks (sigma = 0.284) and low for defensive sectors, and an agent-based simulation shows market makers cannot absorb synchronized sell shocks once AI adoption exceeds roughly 70%." ], "bullet_provenance": "ai", "models": [ "gemini", "llama", "open_other" ], "open_weights": true, "salience": 50, "edition": 22, "validated": null, "n": 4075, "authors_detailed": [ { "name": "Jitendra Singh Jadav", "url": "https://openalex.org/A5147557031", "inst": "Universidad de Montevideo" } ], "affiliations": [ "Universidad de Montevideo" ] }, { "uid": "doi:10.2139/ssrn.7319081", "doi": "10.2139/ssrn.7319081", "title": "Auditing LLM-based Synthetic Expert Panels for AHP: A Preregistered Benchmark and Prospective Order-randomization Test", "authors": [ "Howard Kim", "Keuntae Cho" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7319081", "field": "management", "role": "method", "bullets": [ "The study benchmarks six LLMs on 27 published Analytic Hierarchy Process studies in English and Korean, using a preregistered holdout of 22 studies and 21 reference-weight tasks with 28,783 valid responses.", "Gemini 3.5 Flash, GPT-5-mini, K-EXAONE, Gemini 3.6 Flash, Claude Sonnet 5, and HCX-007 generated pairwise-comparison judgments to reconstruct published AHP weight vectors, checked against the original expert reference weights.", "Rank replication was modest and model-dependent; only HCX-007 beat uniform weights on absolute error, and a randomized presentation-order test reversed most results, showing HCX-007 relied on listing-order cues rather than reconstructed judgment." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "compared to published human AHP reference weights; mean correlation and absolute-error figures reported", "salience": 55, "edition": 22, "n": 4076, "authors_detailed": [ { "name": "Howard Kim", "url": "https://openalex.org/A5101871754", "inst": "Cyber University of Korea" }, { "name": "Keuntae Cho", "url": "https://openalex.org/A5071191666", "inst": "Sungkyunkwan University" } ], "affiliations": [ "Cyber University of Korea", "Sungkyunkwan University" ] }, { "uid": "doi:10.2139/ssrn.7321294", "doi": "10.2139/ssrn.7321294", "title": "Generative AI-Based High-Growth Potential Ranking for Portfolio Construction: Evidence from China's Optical Industry", "authors": [ "Yiru Lin", "Kaijie Xue" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7321294", "field": "finance", "role": "instrument", "bullets": [ "Sample covers 94 constituents of China's Shenwan Optical and Optoelectronics Level-2 industry, ranked against the corresponding Shenwan industry index as of November 1, 2025.", "An unspecified large language model produced high-growth potential rankings of the stocks through 30 independent API calls, averaged into a composite ranking; validation against ground truth not stated.", "LLM-based portfolios outperformed the benchmark on cumulative return, geometric excess return, Sharpe ratio, and Calmar ratio, with the strongest performance from the highest-ranked stocks." ], "bullet_provenance": "ai", "salience": 55, "edition": 22, "models": [], "validated": null, "n": 4077, "authors_detailed": [ { "name": "Yiru Lin", "url": "https://openalex.org/A5000454565", "inst": "Shanghai International Studies University" }, { "name": "Kaijie Xue", "url": "https://openalex.org/A5082755324", "inst": "Shanghai International Studies University" } ], "affiliations": [ "Shanghai International Studies University" ] }, { "uid": "arxiv:2608.19526v1", "arxiv_id": "2608.19526v1", "title": "Automated Summarization of Financial News Using Large Language Models and Retrieval-Augmented Generation: An Early Empirical Study (Fall 2023)", "authors": [ "Pranav Chandaliya" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.19526v1", "field": "finance", "role": "instrument", "bullets": [ "Ten major US companies (AAPL, MSFT, GOOGL, AMZN, META, TSLA, JPM, NVDA, WMT, DIS), using financial news, Wikipedia, and Yahoo Finance data, conducted at George Washington University in fall 2023.", "Falcon-7B-Instruct, DistilBART-CNN-12-6, BART-Large-XSum, and GPT text-davinci-003 summarized company news and stock data using Summarize Chains and retrieval-augmented generation with FAISS; not independently validated against ground truth.", "Falcon-7B with Summarize Chains produced the most coherent and complete news coverage, and both LLM approaches beat a Lead-3 baseline on ROUGE-1, while RAG caused repetition in Falcon and hallucination in BART-Large at high k." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "open_other" ], "open_weights": true, "validated": false, "validation_note": "Compared to Lead-3 baseline on ROUGE-1, not ground-truth extraction accuracy", "salience": 40, "edition": 22, "n": 4079, "authors_detailed": [ { "name": "Pranav Chandaliya", "url": "https://openalex.org/A5147762289", "inst": "George Washington University" } ], "affiliations": [ "George Washington University" ] }, { "uid": "arxiv:2608.19974v1", "arxiv_id": "2608.19974v1", "title": "ReguSim: Evaluating LLM Agent Rule Grounding in Financial Compliance", "authors": [ "Yiyang Luo", "Yihang Jiang", "Qijun Xie", "Liang Lan", "Lin Willian Cong", "Anyi Rao", "Yunya Song" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.19974v1", "field": "finance", "role": "method", "bullets": [ "Controlled simulated financial-compliance environment (ReguSim) with an accompanying rule-grounding benchmark (ReguBench) for trader and monitor agents; sample, period, and geography not stated.", "DeepSeek V4 Pro and Gemini 3.5 Flash act as trading agents whose stated reasoning, attempted actions, execution enforcement, and monitor evidence are separately scored on rule grounding; not validated against ground truth.", "Visible compliance rules reduce but do not eliminate rejected trading actions, and simple structured baselines match or exceed prompt-only LLMs at monitoring for rule violations." ], "bullet_provenance": "ai", "models": [ "gemini", "open_other" ], "open_weights": true, "validated": false, "validation_note": "not stated", "salience": 55, "edition": 22, "n": 4081, "authors_detailed": [ { "name": "Yiyang Luo", "url": "https://openalex.org/A5147795138", "inst": "" }, { "name": "Yihang Jiang", "url": "https://openalex.org/A5147667370", "inst": "" }, { "name": "Qijun Xie", "url": "https://openalex.org/A5147770251", "inst": "" }, { "name": "Liang Lan", "url": "https://openalex.org/A5147731505", "inst": "" }, { "name": "Lin Willian Cong", "url": "https://openalex.org/A5147702276", "inst": "" }, { "name": "Anyi Rao", "url": "https://openalex.org/A5147669027", "inst": "" }, { "name": "Yunya Song", "url": "https://openalex.org/A5147691299", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7320247", "doi": "10.2139/ssrn.7320247", "title": "When Machines Read Between the Lines Differently: AI Disagreement in Financial Analysis", "authors": [ "Ryan Pelkey", "Michael A. Quinn" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7320247", "field": "finance", "role": "instrument", "bullets": [ "Sample covers annual reports of Standard & Poor's 100 companies, comparing AI-generated sentiment scores across firms of varying size; period and geography not stated.", "ChatGPT, Gemini, and Claude each scored annual report sentiment to predict firms' future profitability; outputs were not validated against a ground-truth benchmark.", "Claude showed more scoring variation than ChatGPT and Gemini, with differences also tied to firm size, indicating that tool choice can shift valuation and investment conclusions." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": false, "validation_note": "Cross-model consistency compared; no ground-truth accuracy check reported", "salience": 55, "edition": 22, "n": 4084, "authors_detailed": [ { "name": "Ryan Pelkey", "url": "https://openalex.org/A5097795732", "inst": "Bentley University" }, { "name": "Michael A. Quinn", "url": "https://openalex.org/A5047188580", "inst": "Bentley University" } ], "affiliations": [ "Bentley University" ] }, { "uid": "doi:10.2139/ssrn.7306183", "doi": "10.2139/ssrn.7306183", "title": "Do Richer Personas Improve LLM Survey Simulation? A Fidelity Paradox", "authors": [ "Andrew Gordon", "Nora Petrova", "John Burden", "Oriol Bosch", "Ning Ding" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7306183", "field": "economics", "role": "agent", "bullets": [ "A 21-item political opinion and consumer insights survey was fielded to a politically representative US sample of 996 respondents, supplemented with AI-moderated qualitative interviews.", "Two frontier LLMs simulated survey respondents at increasing persona fidelity, from no information through demographic personas to demographics plus verbatim interview transcripts, validated against the human sample.", "Richer personas did not improve accuracy: demographic personas roughly tripled distributional error versus asking for an aggregate distribution, while eliciting per-persona probability distributions improved accuracy by 35 to 48 percent." ], "bullet_provenance": "ai", "validated": true, "validation_note": "LLM-simulated respondents compared against human survey data with distributional error figures", "salience": 50, "edition": 22, "models": [], "n": 4085, "authors_detailed": [ { "name": "Andrew Gordon", "url": "https://openalex.org/A5147547672", "inst": "" }, { "name": "Nora Petrova", "url": "https://openalex.org/A5147504806", "inst": "" }, { "name": "John Burden", "url": "https://openalex.org/A5147479180", "inst": "" }, { "name": "Oriol Bosch", "url": "https://openalex.org/A5019019370", "inst": "Universidad Complutense de Madrid" }, { "name": "Ning Ding", "url": "https://openalex.org/A5031792794", "inst": "Xinjiang Agricultural University" } ], "affiliations": [ "Universidad Complutense de Madrid", "Xinjiang Agricultural University" ] }, { "uid": "doi:10.2139/ssrn.7311259", "doi": "10.2139/ssrn.7311259", "title": "Measuring Audit Oversight Activities with Large Language Models: Evidence from Japan", "authors": [ "Kenichi Yazawa", "Kento Yamamoto" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7311259", "field": "accounting", "role": "instrument", "bullets": [ "Setting: Japanese listed firms with audit-committee narrative disclosures required since 2019, analyzed as an FY2019-FY2024 firm-year panel.", "Model: GPT-4o reads the oversight narratives to construct an Audit Oversight Score split into own activity (ACT) and external-auditor coordination (COORD); its output agrees with human coders as closely as two human coders agree with each other and is reproduced by three other models.", "Result: higher-scoring firms pay higher audit fees, and during the COVID-19 pandemic a higher ACT is associated with smaller absolute discretionary accruals within the same firm." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "AOS agrees with human coders as closely as human coders agree with each other; reproduced by three other models.", "salience": 60, "edition": 22, "n": 4086, "authors_detailed": [ { "name": "Kenichi Yazawa", "url": "https://openalex.org/A5147532030", "inst": "Aoyama Gakuin University" }, { "name": "Kento Yamamoto", "url": "https://openalex.org/A5101119552", "inst": "Fukuoka University" } ], "affiliations": [ "Aoyama Gakuin University", "Fukuoka University" ] }, { "uid": "doi:10.2139/ssrn.7299990", "doi": "10.2139/ssrn.7299990", "title": "Exploring Dependence, Overreliance, and Addiction Related Behaviors Associated with Large Language Model Use Among Software Engineers", "authors": [ "Ronnie de Souza Santos", "Italo Santos", "Matheus de Morais Leça", "Cleyton Magalhaes", "Mairieli Wessel" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7299990", "field": "management", "role": "object", "bullets": [ "Setting: exploratory survey of 119 software practitioners worldwide on their use of LLMs during professional development activities, cross-sectional.", "Model: not stated; no specific model or family is named, and the study analyzes practitioners' self-reported reliance rather than model output.", "Result: most reported use is functional rather than problematic, though patterns of overreliance appear, such as prioritizing LLMs over documentation while still verifying outputs; addiction-related behaviors were less common." ], "bullet_provenance": "ai", "salience": 32, "edition": 22, "models": [], "validated": null, "n": 4087, "authors_detailed": [ { "name": "Ronnie de Souza Santos", "url": "https://openalex.org/A5080161379", "inst": "University of Calgary" }, { "name": "Ítalo Santos", "url": "https://openalex.org/A5021360591", "inst": "University of Hawaiʻi at Mānoa" }, { "name": "Matheus de Morais Leça", "url": "https://openalex.org/A5147610470", "inst": "University of Calgary" }, { "name": "Cleyton Magalhaes", "url": "https://openalex.org/A5147611085", "inst": "" }, { "name": "Mairieli Wessel", "url": "https://openalex.org/A5137567984", "inst": "Radboud University Nijmegen" } ], "affiliations": [ "University of Calgary", "University of Hawaiʻi at Mānoa", "Radboud University Nijmegen" ] }, { "uid": "doi:10.2139/ssrn.7309400", "doi": "10.2139/ssrn.7309400", "title": "The First Draft Problem: Productivity, Quality, and Physiological Strain in LLM-Assisted Writing", "authors": [ "Xufei Liu", "Hummy Song", "Christian Terwiesch" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7309400", "field": "management", "role": "object", "bullets": [ "Setting: two preregistered studies, an online experiment with 16 clinicians producing teletherapy documentation and a lab experiment with 100 university students summarizing video interviews.", "Model: not stated; no specific model or family is named for the LLM-generated first draft that participants edited, and outputs were not checked against ground truth.", "Result: in the clinician study, editing an LLM draft produced no average time savings or quality gains; in the student study, editing cut task completion time by about 70% and raised quality, but physiological fatigue accumulated faster than writing from scratch." ], "bullet_provenance": "ai", "salience": 50, "edition": 22, "models": [], "validated": null, "n": 4088, "authors_detailed": [ { "name": "Xufei Liu", "url": "https://openalex.org/A5103026075", "inst": "University of Pennsylvania" }, { "name": "Hummy Song", "url": "https://openalex.org/A5027888346", "inst": "University of Pennsylvania" }, { "name": "Christian Terwiesch", "url": "https://openalex.org/A5089214194", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7318619", "doi": "10.2139/ssrn.7318619", "title": "Governing AI Coding Agents: From Repository Artifacts to Organizational Capability", "authors": [ "Muhammad Hamza" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7318619", "field": "management", "role": "instrument", "bullets": [ "Sample: 555 AI agent governance artifacts such as CLAUDE.md and AGENTS.md files collected from public GitHub repositories, analyzed using an exploratory sequential mixed-methods design.", "An LLM-based classification approach applied a structured codebook to the artifacts and was evaluated against manually coded reference labels, though no accuracy figure is stated.", "Artifacts mainly cover project orientation, knowledge transfer, and implementation guidance, while verification, oversight, accountability, and continuous governance management remain comparatively limited across repositories." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "validation_note": "compared to manually coded reference labels; no accuracy figure given", "salience": 45, "edition": 22, "n": 4090, "authors_detailed": [ { "name": "Muhammad Hamza", "url": "https://openalex.org/A5147624614", "inst": "Lappeenranta-Lahti University of Technology" } ], "affiliations": [ "Lappeenranta-Lahti University of Technology" ] }, { "uid": "doi:10.2139/ssrn.7316998", "doi": "10.2139/ssrn.7316998", "title": "Agentic Schema Convergence: An AI-Driven Pipeline for Combined Schema Design and Data Migration in Technology M&A", "authors": [ "Athresh Guruprakash" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7316998", "field": "management", "role": "agent", "bullets": [ "Setting: two case illustrations in technology M&A integration, a ticket/interaction data migration and a customer/account data migration; sample size, period, and geography not stated.", "A five-agent architecture covering schema intelligence, data profiling, canonical design, mapping, and pipeline design performs schema matching and mapping with human review checkpoints; not validated against ground truth.", "The paper proposes a confidence-scoring and review-queue design for the mapping agent but reports no quantitative accuracy, cost, or time-savings figures for the pipeline." ], "bullet_provenance": "ai", "salience": 35, "edition": 22, "models": [], "validated": null, "n": 4091, "authors_detailed": [ { "name": "Athresh Guruprakash", "url": "https://openalex.org/A5146923454", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7316382", "doi": "10.2139/ssrn.7316382", "title": "Trust, Delegation, and Alignment in Human-AI Decision Making", "authors": [ "Erik O. Kimbrough", "Brennan McDavid", "Diba Vazirian" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7316382", "field": "economics", "role": "agent", "bullets": [ "Lab experiment in which participants wrote prompts instructing ChatGPT-4o mini to choose on their behalf across risky, intertemporal, and social-allocation decisions; sample size and country not stated.", "Participants' own choices were compared to choices generated from their prompts by ChatGPT-4o mini, with a human-agent follow-up and a GPT-5.5 robustness check; comparison against an independent ground truth not stated.", "Participants showed substantial reluctance to delegate despite moderate-to-high alignment between their own and the AI's choices, with misalignment linked to both model limits and difficulty expressing preferences in short prompts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 22, "validated": null, "n": 4094, "authors_detailed": [ { "name": "Erik O. Kimbrough", "url": "https://openalex.org/A5054211382", "inst": "Chapman University" }, { "name": "Brennan McDavid", "url": "https://openalex.org/A5120249279", "inst": "Chapman University" }, { "name": "Diba Vazirian", "url": "https://openalex.org/A5147623019", "inst": "Chapman University" } ], "affiliations": [ "Chapman University" ] }, { "uid": "doi:10.2139/ssrn.7317146", "doi": "10.2139/ssrn.7317146", "title": "Leveraging Generative Artificial Intelligence for Supply Chain Resilience and Economic Performance within Industry 5.0", "authors": [ "Wahib Elayah", "Abdullah ALOQAB", "SULEMAN BAWA" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7317146", "field": "management", "role": "object", "bullets": [ "Longitudinal panel study of manufacturing and logistics firms across multiple economies, using structural equation modeling and dynamic panel estimation; period and firm count not stated.", "The paper studies generative AI adoption as a firm-level driver of supply chain resilience and economic performance rather than evaluating a specific model; task and validation not stated.", "Generative AI adoption significantly improved supply chain resilience, which in turn raised firm-level economic performance, with about half of the performance gain transmitted through resilience." ], "bullet_provenance": "ai", "salience": 45, "edition": 22, "models": [], "validated": null, "n": 4095, "authors_detailed": [ { "name": "Wahib Elayah", "url": "https://openalex.org/A5147490680", "inst": "Hunan University" }, { "name": "Abdullah Aloqab", "url": "https://openalex.org/A5079562541", "inst": "Hunan University" }, { "name": "Suleman Bawa", "url": "https://openalex.org/A5084956471", "inst": "Xidian University" } ], "affiliations": [ "Hunan University", "Xidian University" ] }, { "uid": "doi:10.2139/ssrn.7319939", "doi": "10.2139/ssrn.7319939", "title": "From the governable person to the governing machine: Accounting research on government and digital artificial intelligence", "authors": [ "Thomas Ahrens" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7319939", "field": "accounting", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, time period, or geography; the unit of analysis is accounting research on government applied to digital artificial intelligence.", "The paper treats large language model training as a calculative apparatus of government, not stated which specific model was used or whether output was validated.", "The paper argues that AI's claim to judge on humanity's behalf is not a necessary form of government but a contingent one presented as necessary, drawing on Foucauldian critique." ], "bullet_provenance": "ai", "salience": 38, "edition": 22, "models": [], "validated": null, "n": 4101, "authors_detailed": [ { "name": "Thomas Ahrens", "url": "https://openalex.org/A5137173118", "inst": "United Arab Emirates University" } ], "affiliations": [ "United Arab Emirates University" ] }, { "uid": "doi:10.2139/ssrn.7291235", "doi": "10.2139/ssrn.7291235", "title": "Chasing One Rabbit or Many: Pandemic Driven Shifts in Attentional Dominance Among IT and Financial Services Organizations", "authors": [ "Venugopal Balijepally", "Jaemin Kim", "Yashashri Kadam" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7291235", "field": "management", "role": "object", "bullets": [ "Analysis of 80 S&P 500 shareholder letters from IT and financial services firms over 2020 to 2023, examining pandemic driven shifts in organizational attention.", "The study did not apply an AI model to the letters; not stated whether the attentional dominance coding was validated against ground truth.", "High velocity IT firms showed low attentional dominance during the pandemic but shifted to elevated dominance around innovation and generative AI afterward, while low velocity financial firms showed the reverse pattern." ], "bullet_provenance": "ai", "salience": 34, "edition": 22, "models": [], "validated": null, "n": 4102, "authors_detailed": [ { "name": "VenuGopal Balijepally", "url": "https://openalex.org/A5012974915", "inst": "Oakland University" }, { "name": "Jaemin Kim", "url": "https://openalex.org/A5147586707", "inst": "Oakland University" }, { "name": "Yashashri Kadam", "url": "https://openalex.org/A5147593389", "inst": "Oakland University" } ], "affiliations": [ "Oakland University" ] }, { "uid": "doi:10.2139/ssrn.7306419", "doi": "10.2139/ssrn.7306419", "title": "Economic Uncertainty, Investor Sentiment, and Corporate Fourth-Quarter Investments", "authors": [ "Bochen Li", "Michael A. Goldstein*", "lili shao", "Tong Yu" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7306419", "field": "finance", "role": "instrument", "bullets": [ "The study measures economic uncertainty and investor sentiment from Wall Street Journal news articles to examine corporate capital expenditure behavior in the fourth fiscal quarter; sample size and period not stated.", "The authors constructed large language model-based textual measures of uncertainty and sentiment from WSJ articles, combined with conventional uncertainty proxies; the specific model and validation against ground truth are not stated.", "Heightened economic uncertainty significantly reduces excess fourth-quarter capital expenditures, while optimistic investor sentiment amplifies year-end investment, with the specific magnitude not stated in the abstract." ], "bullet_provenance": "ai", "salience": 52, "edition": 22, "models": [], "validated": null, "n": 4103, "authors_detailed": [ { "name": "Bochen Li", "url": "https://openalex.org/A5147461692", "inst": "" }, { "name": "Michael A. Goldstein", "url": "https://openalex.org/A5069807682", "inst": "Babson College" }, { "name": "Lili Shao", "url": "https://openalex.org/A5062867742", "inst": "Zhejiang Chinese Medical University" }, { "name": "Tong Yu", "url": "https://openalex.org/A5147618456", "inst": "" } ], "affiliations": [ "Babson College", "Zhejiang Chinese Medical University" ] }, { "uid": "doi:10.2139/ssrn.7313419", "doi": "10.2139/ssrn.7313419", "title": "The Risk of Ignoring Risk in Anti-Money Laundering Research: A Scientometric and AI-assisted Review", "authors": [ "Andréa Alves Corrêa", "Danielle Montenegro Salamone Nunes", "Sérgio Ricardo Miranda Nazaré" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7313419", "field": "finance", "role": "instrument", "bullets": [ "The review analyzes 4,220 anti-money laundering publications gathered from Web of Science, Scopus, and Google Scholar, spanning 184 country-publication allocations across 42 countries; the study period is not stated.", "Generative AI conducted a thematic audit of the literature corpus alongside researcher-led validation and deduplication; the specific model is not stated, and no accuracy or agreement figure against ground truth is reported.", "The AI-assisted audit identified 48 records, of which 37 unique studies remained after validation, and the correlation between AML research output and Basel AML Index scores was weak, negative, and not significant." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 35, "edition": 22, "validated": null, "n": 4104, "authors_detailed": [ { "name": "Andréa Alves Corrêa", "url": "https://openalex.org/A5139020286", "inst": "Universidade de Brasília" }, { "name": "Danielle Montenegro Salamone Nunes", "url": "https://openalex.org/A5042575569", "inst": "Universidade de Brasília" }, { "name": "Sérgio Ricardo Miranda Nazaré", "url": "https://openalex.org/A5076556372", "inst": "Universidade de Brasília" } ], "affiliations": [ "Universidade de Brasília" ] }, { "uid": "arxiv:2608.20304v1", "arxiv_id": "2608.20304v1", "title": "Calibration-Induced Degeneracy in LLM Financial Forecasting: An Audit-Trailed Case Study on Next-Day Market Risk", "authors": [ "Arin Mohanty" ], "posted": "2026-08-20", "added": "2026-08-22", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.20304v1", "field": "finance", "role": "method", "bullets": [ "The study examines next-day market risk forecasts for two broad-market funds, including SPY, using LLM-derived features calibrated on data through 2022 and evaluated on 856 later scores; geography is not stated.", "The paper evaluates four LLM-derived forecasting-feature weights against a near-zero-cost headline-count baseline; the specific LLM family is not stated, and results were checked via familywise-corrected forecast comparison.", "Full-history calibration set all four LLM weights to zero, a failure the authors call calibration-induced degeneracy, while the headline-count baseline reduced SPY variance-forecast loss by 0.001720." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Headline-count baseline reduced SPY variance-forecast loss by 0.001720 (95% CI: 0.000719-0.002830); LLM-weighted features showed no improvement after familywise correction.", "salience": 50, "edition": 22, "models": [], "n": 4105, "authors_detailed": [ { "name": "Arin Mohanty", "url": "https://openalex.org/A5147744408", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7292599", "doi": "10.2139/ssrn.7292599", "title": "Artificial Intelligence in the Financial Sector: Tools, Opportunities, Risks, And A Strategic Roadmap for the Next Decade", "authors": [ "Prof David Mpunwa", "Ndeshihafela Shipila Melao" ], "posted": "2026-08-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7292599", "field": "finance", "role": "object", "bullets": [ "Narrative review synthesizing research from Harvard Business School, Stanford HAI, and MIT Sloan, with a technical case study of AI-driven venture capital decision-making.", "No model is deployed or tested by the authors; the article surveys how LLMs are reshaping banking, asset management, and fintech, contrasting them with conventional machine learning.", "Traces both gains and failures, including Knight Capital and Zillow, and proposes a phased ten-year roadmap for responsible AI adoption across buy-side and sell-side finance." ], "bullet_provenance": "ai", "salience": 22, "edition": 21, "models": [], "validated": null, "n": 2343, "authors_detailed": [ { "name": "Prof David Mpunwa", "url": "https://openalex.org/A5143685203", "inst": "Namibia University of Science and Technology" }, { "name": "Ndeshihafela Shipila Melao", "url": "https://openalex.org/A5147499139", "inst": "Unaffiliated Authors" } ], "affiliations": [ "Namibia University of Science and Technology", "Unaffiliated Authors" ] }, { "uid": "doi:10.2139/ssrn.7316944", "doi": "10.2139/ssrn.7316944", "title": "More Intelligence, Less Energy? The Impact of Corporate AI Adoption on Energy Efficiency", "authors": [ "Liang Wang", "Ling-Yun He" ], "posted": "2026-08-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7316944", "field": "management", "role": "instrument", "bullets": [ "Chinese listed companies from 2011 to 2020 matched with detailed energy-use, production, and financial data from annual reports.", "A fine-tuned LLM scored annual reports to construct a firm-year AI adoption index measuring technology deployment across domains.", "AI adoption significantly reduces energy intensity via operational efficiency and green investment; effects strengthen at deeper adoption levels and in non-tech-intensive firms." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "models": [], "n": 3878, "authors_detailed": [ { "name": "Liang Wang", "url": "https://openalex.org/A5136584786", "inst": "Jinan University" }, { "name": "Ling‐Yun He", "url": "https://openalex.org/A5081390403", "inst": "Tongji University" } ], "affiliations": [ "Jinan University", "Tongji University" ] }, { "uid": "doi:10.2139/ssrn.7292719", "doi": "10.2139/ssrn.7292719", "title": "From Human-in-the-Loop to Human-on-the-Hook: Rethinking Organisational Accountability for Agentic AI", "authors": [ "Raghu Pradeep Nair" ], "posted": "2026-08-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7292719", "field": "management", "role": "object", "bullets": [ "Doctrinal analysis grounded in Indian agency law, negligence, and corporate governance, with comparison to the EU AI Act and Singapore's Model AI Governance Framework for Agentic AI.", "No LLM used; paper proposes a graduated autonomy-accountability framework linking AI autonomy degree and potential harm to required oversight and legal responsibility levels.", "Accountability requires linking delegated authority to foreseeable risk rather than procedural human presence; responsibility is allocated among developers, deploying organisations, and designated AI owners." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 4070, "authors_detailed": [ { "name": "Raghu Pradeep Nair", "url": "https://openalex.org/A5006919183", "inst": "Amrita Vishwa Vidyapeetham" } ], "affiliations": [ "Amrita Vishwa Vidyapeetham" ] }, { "uid": "doi:10.2139/ssrn.7315096", "doi": "10.2139/ssrn.7315096", "title": "Multi-Agent Evaluation Framework for Government Affairs Large Language Models: A Case Study on Government Service Hotline Scenarios", "authors": [ "Zengding Bai", "Shuhui Ai", "Qihang Gong", "Dong Luo", "Tianming Shao" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7315096", "field": "management", "role": "method", "bullets": [ "Government-service hotline case study on intelligent work-order distribution using administrative data from Yuhu District in Xiangtan City; the number and period of observations are not stated.", "A multi-agent framework uses compliance gatekeepers, simulated administrative roles, and diagnostic feedback to audit a fine-tuned specialist model; no ground-truth accuracy or agreement figure is reported.", "The specialist trades general alignment for domain precision, while coverage-risk curves translate that tradeoff into tiered deployment recommendations for local public-service operations." ], "bullet_provenance": "ai", "validated": false, "salience": 46, "edition": 23, "models": [], "n": 4131, "authors_detailed": [ { "name": "Zengding Bai", "url": "https://openalex.org/A5147457490", "inst": "" }, { "name": "Shuhui Ai", "url": "https://openalex.org/A5120856115", "inst": "Changsha University" }, { "name": "Qihang Gong", "url": "https://openalex.org/A5101336241", "inst": "Beijing Institute of Technology" }, { "name": "Dong Luo", "url": "https://openalex.org/A5147522150", "inst": "" }, { "name": "Tianming Shao", "url": "https://openalex.org/A5079263076", "inst": "Peking University" } ], "affiliations": [ "Changsha University", "Beijing Institute of Technology", "Peking University" ] }, { "uid": "doi:10.2139/ssrn.7311198", "doi": "10.2139/ssrn.7311198", "title": "The Crossroads of AI Social Decision: A Two-Dimensional Analysis of Decision Maturity and Cultural Orientation in LLMs", "authors": [ "Xu Tang", "Yifan Zeng" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7311198", "field": "management", "role": "agent", "bullets": [ "Three experimental paradigms assess nine frontier LLMs on socio-emotional maturity and individualist-collectivist orientations across Chinese and English social-decision contexts used in cross-cultural evaluation.", "Models act as social decision makers under language changes and explicit cultural framing; the abstract names no model family or external behavioral ground truth.", "Default responses remain strongly individualistic across languages, but explicit perspective instructions elicit high-fidelity cultural simulation, separating latent adaptability from aligned default behavior." ], "bullet_provenance": "ai", "salience": 50, "edition": 23, "models": [], "validated": null, "n": 4132, "authors_detailed": [ { "name": "Xu Tang", "url": "https://openalex.org/A5002207077", "inst": "Fudan University" }, { "name": "Yifan Zeng", "url": "https://openalex.org/A5147301300", "inst": "Sun Yat-sen University" } ], "affiliations": [ "Fudan University", "Sun Yat-sen University" ] }, { "uid": "doi:10.2139/ssrn.7296878", "doi": "10.2139/ssrn.7296878", "title": "Bounded Semantic Planning and Deterministic Compilation for Reliable Enterprise Text-to-SQL", "authors": [ "Yi Ai" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7296878", "field": "management", "role": "method", "bullets": [ "ACME insurance benchmark with a 38-question adjudicated comparison set and three runs per question, plus six additional items documented separately by failure class.", "A multi-turn LLM planner selects governed semantic options before deterministic SQL compilation; GPT-5.4 and Gemini 3.6 Flash outputs are checked against adjudicated query correctness.", "Semantic path compilation is solid on 37 of 38 questions versus 21 for direct DDL-to-SQL, with no paired question favoring the direct baseline." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "adjudicated ACME insurance question set", "salience": 70, "edition": 23, "n": 4142, "authors_detailed": [ { "name": "Yi Ai", "url": "https://openalex.org/A5147310664", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7312182", "doi": "10.2139/ssrn.7312182", "title": "PopMuse: Human‐AI Co‐Creative Audiovisual Canvas for Translating Pop Songs into Visual Narratives", "authors": [ "Yichi Zhang", "Jiaxiang Chen" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7312182", "field": "management", "role": "agent", "bullets": [ "Human-AI co-creation system evaluated with 11 music producers, video creators, and enthusiasts translating pop songs into album-cover and music-video concepts.", "An unnamed multimodal LLM analyzes structure, instrumentation, and lyrics, then produces an editable visual blueprint; the user study reports no ground-truth accuracy or agreement statistic.", "Participants report higher creative efficiency and useful idea stimulation while retaining intervention through editable prompts and styles; quantitative effect sizes are not stated." ], "bullet_provenance": "ai", "salience": 39, "edition": 23, "models": [], "validated": null, "n": 4143, "authors_detailed": [ { "name": "Yichi Zhang", "url": "https://openalex.org/A5100444188", "inst": "Fudan University Shanghai Cancer Center" }, { "name": "Jiaxiang Chen", "url": "https://openalex.org/A5147475746", "inst": "Fudan University Shanghai Cancer Center" } ], "affiliations": [ "Fudan University Shanghai Cancer Center" ] }, { "uid": "arxiv:2608.18401v1", "arxiv_id": "2608.18401v1", "title": "Multimodal Rapport Estimation in Real-World HRI", "authors": [ "Akihiro Sakuramoto", "Takato Hayashi", "Ryo Miyoshi", "Yuki Okafuji", "Shogo Okada" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.18401v1", "field": "management", "role": "instrument", "bullets": [ "Sixty-two multimodal customer-robot interaction sessions recorded in a Japanese drugstore, with rapport scored by third-party raters and results examined by duration and group size.", "Gemini 2.5 Flash and pretrained text, audio, and visual models estimate rapport, but the abstract gives no accuracy, agreement, or error statistic against human ratings.", "Gemini performs strongly alone and its fusion with HuBERT and V-JEPA ranks best; estimation quality varies with interaction duration and number of participants." ], "bullet_provenance": "ai", "models": [ "gemini", "legacy" ], "open_weights": true, "validated": false, "salience": 48, "edition": 23, "n": 4144, "authors_detailed": [ { "name": "Akihiro Sakuramoto", "url": "https://openalex.org/A5147766732", "inst": "" }, { "name": "Takato Hayashi", "url": "https://openalex.org/A5101870250", "inst": "Japan Advanced Institute of Science and Technology" }, { "name": "Ryo Miyoshi", "url": "https://openalex.org/A5147777881", "inst": "" }, { "name": "Yuki Okafuji", "url": "https://openalex.org/A5147768070", "inst": "" }, { "name": "Shogo Okada", "url": "https://openalex.org/A5147770378", "inst": "" } ], "affiliations": [ "Japan Advanced Institute of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7301980", "doi": "10.2139/ssrn.7301980", "title": "Agentic Workflow Drift in Life Sciences: Extending the Reasoning-Layer Risk Taxonomy to GxP-Regulated Pharmaceutical and Biotechnology Operations", "authors": [ "Maureen Doyle-Spare" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7301980", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of agentic AI in regulated pharmaceutical and biotechnology manufacturing, safety reporting, clinical development, quality operations, and regulatory-affairs workflows.", "No model is used for measurement; autonomous agents are studied as interpreters of regulated terms within validated systems, and no empirical deployment test is reported.", "The paper identifies workflow drift when compliant processes use unauthorized interpretations, and maps proposed reasoning controls onto established GxP acceptance, exception-review, and quality practices." ], "bullet_provenance": "ai", "salience": 49, "edition": 23, "models": [], "validated": null, "n": 4149, "authors_detailed": [ { "name": "Maureen Doyle-Spare", "url": "https://openalex.org/A5130951607", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7311858", "doi": "10.2139/ssrn.7311858", "title": "A Six-Plane Control Architecture for Agentic Identity and Access Management", "authors": [ "Leela Sai Krishna Udiga" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7311858", "field": "management", "role": "method", "bullets": [ "Enterprise architecture paper covers identity, delegation, policy, enforcement, evidence, and lifecycle controls for AI agents acting across tool and platform boundaries.", "The proposed model extends existing identity standards and policy engines to govern token movement, last-mile enforcement, and incident reconstruction; no language-model family or empirical validation is reported.", "It recommends evidence-bearing delegation chains and a gateway-first deployment sequence, positioning agent identity governance as an extension of existing enterprise controls rather than a separate stack." ], "bullet_provenance": "ai", "salience": 40, "edition": 23, "models": [], "validated": null, "n": 4160, "authors_detailed": [ { "name": "Leela Sai Krishna Udiga", "url": "https://openalex.org/A5147586772", "inst": "Vishnu Institute of Technology" } ], "affiliations": [ "Vishnu Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.7293598", "doi": "10.2139/ssrn.7293598", "title": "When Assistance becomes Substitution: A Theory of Cognitive Displacement in the Age of Generative AI", "authors": [ "Salah Ibn Musa" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7293598", "field": "management", "role": "object", "bullets": [ "Cross-domain conceptual analysis covers delegation of physical, procedural, informational, analytical, and judgment functions to generative AI in education, research, and professional work.", "Generative AI is the object of a theoretical framework rather than an empirical tool; no model family, sample, benchmark, or validation exercise is reported.", "The paper argues that assistance becomes cognitive displacement when AI occupies functions needed to form, test, revise, or own judgment, while routine offloading can preserve human capacity." ], "bullet_provenance": "ai", "salience": 40, "edition": 23, "models": [], "validated": null, "n": 4161, "authors_detailed": [ { "name": "Salah Ibn Musa", "url": "https://openalex.org/A5145341459", "inst": "Department of Statistics Malaysia" } ], "affiliations": [ "Department of Statistics Malaysia" ] }, { "uid": "doi:10.2139/ssrn.7296658", "doi": "10.2139/ssrn.7296658", "title": "Using AI for Companionship is Most Common among Women and Less Wealthy Americans", "authors": [ "Dunigan Folk", "Lyle Ungar", "Angela Duckworth" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7296658", "field": "management", "role": "object", "bullets": [ "Nationally representative US surveys from October 2025 to May 2026 cover 806 adolescents, 806 middle-aged parents, and 1,463 young adults' recent AI use.", "Respondents reported using chatbots such as ChatGPT and Claude for personal advice, friendship, and romance; the study analyzes adoption rather than model performance.", "About one third used AI for personal advice, one quarter as a friend, and one tenth romantically; companionship use was higher among women and lower-income respondents." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 56, "edition": 23, "validated": null, "n": 4162, "authors_detailed": [ { "name": "Dunigan Folk", "url": "https://openalex.org/A5135393301", "inst": "University of Pennsylvania" }, { "name": "Lyle Ungar", "url": "https://openalex.org/A5039604629", "inst": "University of Pennsylvania" }, { "name": "Angela Duckworth", "url": "https://openalex.org/A5044152482", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7293198", "doi": "10.2139/ssrn.7293198", "title": "A GenAI-assisted Privacy Gateway for Secure Third-party Data Processing", "authors": [ "Ravi Kumar" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7293198", "field": "management", "role": "method", "bullets": [ "Synthetic enterprise datasets containing 1,000, 5,000, and 20,000 structured and unstructured records test a privacy gateway for controlled third-party processing.", "An unspecified generative model detects contextual or implicit identifiers, while deterministic components replace sensitive values and selectively restore them; detection accuracy against labeled identifiers is not reported.", "The system restored placeholders with 100 percent fidelity and no unresolved placeholders, while contextual model analysis consumed most processing time; sensitive-information detection quality remains unquantified." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no ground-truth accuracy reported for model-assisted sensitive-information detection", "salience": 46, "edition": 23, "models": [], "n": 4163, "authors_detailed": [ { "name": "Ravi Kumar", "url": "https://openalex.org/A5147573705", "inst": "Wipro (India)" } ], "affiliations": [ "Wipro (India)" ] }, { "uid": "doi:10.2139/ssrn.7310918", "doi": "10.2139/ssrn.7310918", "title": "Who Governs Autonomous AI Execution? Execution Governance AI (EGA) V9: A Deterministic Runtime Governance Framework for Trustworthy Autonomous Workflows", "authors": [ "DaeJung Byun" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7310918", "field": "management", "role": "method", "bullets": [ "Conceptual runtime-governance architecture targets autonomous organizational workflows, with no specific industry, deployment sample, observation period, empirical dataset, or data source stated.", "The model-neutral layer combines deterministic replay, provenance-aware checks, trust-state evaluation, and fail-closed containment without changing underlying foundation models; no validation exercise is reported.", "The paper proposes governing execution separately from language generation so organizations can preserve auditability and contain untrusted agent behavior without redesigning existing AI applications." ], "bullet_provenance": "ai", "salience": 34, "edition": 23, "models": [], "validated": null, "n": 4164, "authors_detailed": [ { "name": "DaeJung Byun", "url": "https://openalex.org/A5147380039", "inst": "Surrey Memorial Hospital" } ], "affiliations": [ "Surrey Memorial Hospital" ] }, { "uid": "arxiv:2608.19083v1", "arxiv_id": "2608.19083v1", "title": "When Readability and Source Retention Diverge: An Evaluability Gap in AI Translation", "authors": [ "Chenchen Mao", "Hanjing Shi", "Haiyan Jia", "Emily Wegrzyn", "Dominic DiFranzo" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.19083v1", "field": "management", "role": "object", "bullets": [ "A 306-person experiment compares simple generated narratives and complex literary prose under different source-text access and translation-rendering conditions in a plain-text interface.", "Unspecified large language models produce readability-oriented translations, contrasted with researcher-revised fidelity versions; participants rate output quality, system traits, trust, and disclosure willingness.", "Fidelity-oriented output scored higher for simple narratives but not complex prose, showing that source access alone does not ensure users recognize retained-content differences." ], "bullet_provenance": "ai", "salience": 50, "edition": 23, "models": [], "validated": null, "n": 4165, "authors_detailed": [ { "name": "Chenchen Mao", "url": "https://openalex.org/A5126461935", "inst": "" }, { "name": "Hanjing Shi", "url": "https://openalex.org/A5125663395", "inst": "Lehigh University" }, { "name": "Haiyan Jia", "url": "https://openalex.org/A5147751866", "inst": "" }, { "name": "Emily Wegrzyn", "url": "https://openalex.org/A5147811294", "inst": "" }, { "name": "Dominic DiFranzo", "url": "https://openalex.org/A5136596067", "inst": "Lehigh University" } ], "affiliations": [ "Lehigh University" ] }, { "uid": "doi:10.2139/ssrn.7307799", "doi": "10.2139/ssrn.7307799", "title": "DPACT: A Framework for Identity, Accountability, and Task-Scoped Authority in Autonomous AI Agent Systems", "authors": [ "Sahil Agarwal" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7307799", "field": "management", "role": "method", "bullets": [ "Conceptual enterprise-governance paper applies a five-dimensional identity framework to personal agents, hiring workflows, prompt injection, coding agents, multi-agent systems, and a chatbot ruling.", "The framework maps delegated agent authority to identity and access controls including short-lived credentials, capability tokens, continuous authorization, audit logs, and revocation; no model is evaluated.", "It argues that trustworthy agents require verifiable delegation, bounded policies, accountable evidence, contextual authorization, and time limits rather than reliance on model properties alone." ], "bullet_provenance": "ai", "salience": 43, "edition": 23, "models": [], "validated": null, "n": 4170, "authors_detailed": [ { "name": "Sahil Agarwal", "url": "https://openalex.org/A5068063884", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7305358", "doi": "10.2139/ssrn.7305358", "title": "Identity and Access Control for Autonomous AI Agents: Dual-Principal Authorization in Enterprise Settings", "authors": [ "Leela Sai Krishna Udiga" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7305358", "field": "management", "role": "method", "bullets": [ "Conceptual enterprise-authorization analysis covers user-delegated copilots, headless workers, tool gateways, and multi-agent chains, with no controlled experiment or organizational sample.", "The proposed rule binds privileged actions to both agent and human principals, declared purpose, audience, and short lifetime using existing identity standards; no model family is examined.", "It maps agent governance onto six control planes and concludes that identity controls can constrain delegated authority but cannot repair an organizational purpose defined too broadly." ], "bullet_provenance": "ai", "salience": 41, "edition": 23, "models": [], "validated": null, "n": 4171, "authors_detailed": [ { "name": "Leela Sai Krishna Udiga", "url": "https://openalex.org/A5147586772", "inst": "Vishnu Institute of Technology" } ], "affiliations": [ "Vishnu Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.7305938", "doi": "10.2139/ssrn.7305938", "title": "From Prompt to Organization Delegated Construction in Exploratory AI Work", "authors": [ "Nghi Truong" ], "posted": "2026-08-19", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7305938", "field": "management", "role": "object", "bullets": [ "A sixteen-hour mathematical-research run began with a one-page prompt and developed into a thirteen-agent organization pursuing a candidate proof of Crouzeix's conjecture.", "An unspecified first agent transformed broad search principles into headcount, hierarchy, assignments, communication, and audit procedures; model behavior is reconstructed from the public event record.", "The case suggests that precise principles plus delegated construction can organize exploratory work when proposed components are easier to verify than discover, but correlated agent errors limit reliability." ], "bullet_provenance": "ai", "salience": 47, "edition": 23, "models": [], "validated": null, "n": 4172, "authors_detailed": [ { "name": "Nghi Truong", "url": "https://openalex.org/A5147558498", "inst": "Southeast Asia University" } ], "affiliations": [ "Southeast Asia University" ] }, { "uid": "doi:10.2139/ssrn.7293258", "doi": "10.2139/ssrn.7293258", "title": "Standardized Statement of Account (SOA) API Architecture for RegTech & Automated Underwriting A Sovereign Infrastructure Blueprint for Real-Time Financial Supervision, Tax Verification, and Consent-driven Credit Assessment", "authors": [ "Anup Brahma" ], "posted": "2026-08-19", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7293258", "field": "finance", "role": "instrument", "bullets": [ "Conceptual regulatory technology blueprint for India, targeting the Account Aggregator network and Unified Lending Interface within the country's Digital Public Infrastructure; no empirical sample or period stated.", "The proposed architecture couples deterministic calculation engines with large language models to flag delinquency evergreening, suspense account anomalies, and tax deduction misallocations from standardized statement-of-account data; not validated.", "The paper proposes a standardized SOA API framework meant to shift financial supervision from periodic self-reporting toward continuous automated inspection, without reporting empirical performance results." ], "bullet_provenance": "ai", "validation_note": "not stated", "salience": 35, "edition": 22, "models": [], "validated": null, "n": 4080, "authors_detailed": [ { "name": "Anup Brahma", "url": "https://openalex.org/A5147545839", "inst": "Indonesian Coral Reef Foundation" } ], "affiliations": [ "Indonesian Coral Reef Foundation" ] }, { "uid": "doi:10.2139/ssrn.7296659", "doi": "10.2139/ssrn.7296659", "title": "Shadow AI in the Enterprise: An Emerging Governance, Risk, and Compliance Challenge", "authors": [ "Prabhat McDonnough-Contreras" ], "posted": "2026-08-19", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7296659", "field": "management", "role": "object", "bullets": [ "The paper is a conceptual white paper on shadow AI governance in enterprises, drawing on academic literature and government frameworks; no empirical sample, period, or geography is stated.", "ChatGPT assisted the author with literature discovery, source verification, language editing, and formatting; it was not used to measure or extract data, and no validation is reported.", "The paper frames unsanctioned employee use of AI tools as an emerging governance, risk, and compliance challenge and recommends stronger enterprise oversight; no quantitative finding is given." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 25, "edition": 22, "validated": null, "n": 4100, "authors_detailed": [ { "name": "Prabhat McDonnough-Contreras", "url": "https://openalex.org/A5147536307", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7297299", "doi": "10.2139/ssrn.7297299", "title": "Neuro-Bayesian Architecture in Economic Modeling: Overcoming Agent System Limitations via Latent Variable Integration", "authors": [ "Roman Kurnovskii", "Ekaterina A. Velikorodnaya" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7297299", "field": "finance", "role": "method", "bullets": [ "Large-scale simulation of 50 iterations with 2,500 observations each on synthetic insurance portfolio data, comparing a fully automated agent to a hybrid neuro-Bayesian system.", "An unspecified LLM provides semantic processing combined with Bayesian inference and Monte Carlo sampling; a self-verification mechanism called Gnosis weights signals to reduce epistemic uncertainty.", "The hybrid architecture raises the normalized Gini coefficient from 0.478 to 0.746, a 56 percent gain over the automated baseline, suggesting structured probabilistic layers recover accuracy lost by standalone agents." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Gini coefficient comparison against baseline agent on synthetic portfolio data", "salience": 28, "edition": 21, "models": [], "n": 2341, "authors_detailed": [ { "name": "Roman Kurnovskii", "url": "https://openalex.org/A5137446449", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Ekaterina A. Velikorodnaya", "url": "https://openalex.org/A5147496081", "inst": "Peoples' Friendship University of Russia" } ], "affiliations": [ "JPMorgan Chase & Co (United States)", "Peoples' Friendship University of Russia" ] }, { "uid": "doi:10.2139/ssrn.7308620", "doi": "10.2139/ssrn.7308620", "title": "The Token Trap: Reimagining the Economics of Enterprise AI at the Edge", "authors": [ "Muralikrishna Veeramosu" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7308620", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of enterprise AI deployment costs, arguing that cloud-based token pricing acts as a growing component of cost of goods sold driven by Jevons Paradox demand effects.", "No model is empirically tested; the paper proposes migrating inference from cloud LLMs to quantized small language models on edge hardware with local retrieval-augmented generation.", "The argument is that edge deployment converts variable cloud API expenses into fixed infrastructure costs while enforcing data sovereignty for regulated industries such as finance and healthcare." ], "bullet_provenance": "ai", "salience": 18, "edition": 21, "models": [], "validated": null, "n": 2342, "authors_detailed": [ { "name": "Muralikrishna Veeramosu", "url": "https://openalex.org/A5147653229", "inst": "Hebron University" } ], "affiliations": [ "Hebron University" ] }, { "uid": "doi:10.2139/ssrn.7310878", "doi": "10.2139/ssrn.7310878", "title": "The LLM Effect: AI Exposure and Wage Growth Across U.S. Occupations Since 2019/2022", "authors": [ "Thomas Soliman" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7310878", "field": "economics", "role": "object", "bullets": [ "Top 100 U.S. News-ranked occupations matched to Bureau of Labor Statistics wage data covering 2019 to 2025.", "An unspecified large language model self-assessed its capability on occupation-level tasks to build an AI exposure score. No validation of the score against human judgment is reported.", "More AI-exposed occupations show statistically slower wage growth, but the negative relationship holds equally before and after ChatGPT's release, pointing to a pre-existing trend rather than a causal AI effect." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "edition": 21, "n": 2347, "authors_detailed": [ { "name": "Thomas Soliman", "url": "https://openalex.org/A5147241002", "inst": "Université Lumière Lyon 2" } ], "affiliations": [ "Université Lumière Lyon 2" ] }, { "uid": "doi:10.2139/ssrn.7313383", "doi": "10.2139/ssrn.7313383", "title": "Generative AI and Labor Displacement in the Global South:Evidence from Philippine Business Process Outsourcing", "authors": [ "Nico Ravanilla" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7313383", "field": "economics", "role": "object", "bullets": [ "Philippine BPO sector from pre-ChatGPT through mid-2025, combining province-, industry-, and municipality-level employment data with Anthropic Economic Index occupation-level AI exposure scores.", "Generative AI adoption by U.S. client firms traced through occupation-level observed AI usage; exposed Philippine labor markets tracked for employment, wages, hours, and migration outcomes.", "Employment growth slowed in the most-exposed provinces and industries, amounting to roughly 250,000 foregone jobs by mid-2025; adjustment occurred through reduced hiring rather than layoffs, with no wage or hours changes." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 82, "validated": null, "n": 3173, "authors_detailed": [ { "name": "Nico Ravanilla", "url": "https://openalex.org/A5006822114", "inst": "University of California San Diego" } ], "affiliations": [ "University of California San Diego" ] }, { "uid": "doi:10.2139/ssrn.7290418", "doi": "10.2139/ssrn.7290418", "title": "Algorithmic Decoupling and Supply Chain Contagion: Quantifying the Extraterritorial Impact of State Climate Mandates", "authors": [ "Sourav Bose", "Taoufik Bouraoui" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7290418", "field": "accounting", "role": "instrument", "bullets": [ "Longitudinal panel of 1,089 SEC 10-K filings from U.S. public firms around the enactment of California SB 253 Scope 3 emission mandates.", "TF-IDF and CorEx topic modeling quantified linguistic decoupling between climate rhetoric and substantive risk management; deterministic NLP benchmarked against probabilistic LLM alternatives.", "Non-mandated mid-cap firms restructured climate disclosures to satisfy mandated mega-cap clients' Scope 3 procurement demands, with a one-year structural transmission lag identifying supply-chain contagion." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 52, "n": 3174, "authors_detailed": [ { "name": "Sourav Bose", "url": "https://openalex.org/A5064096980", "inst": "École Supérieure de Commerce de Rennes" }, { "name": "Taoufik Bouraoui", "url": "https://openalex.org/A5089633918", "inst": "Business France" } ], "affiliations": [ "École Supérieure de Commerce de Rennes", "Business France" ] }, { "uid": "doi:10.2139/ssrn.7295259", "doi": "10.2139/ssrn.7295259", "title": "The user side of AI Model Lifecycles: Evidence from the Keep4o Movement", "authors": [ "Yiwen Wu" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7295259", "field": "management", "role": "object", "bullets": [ "61,846 public original posts on X from August 2025 to March 2026 about OpenAI's GPT-4o model lifecycle changes.", "LLM-assisted content analysis with a systematically developed coding framework classified discussion themes, user reasons for retaining GPT-4o, and specific governance claims.", "Users valued GPT-4o for interactional and relational qualities formed through sustained use; technical version succession did not constitute effective replacement from the user side." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "validated": null, "n": 3175, "authors_detailed": [ { "name": "Yiwen Wu", "url": "https://openalex.org/A5147662365", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2608.18911v1", "arxiv_id": "2608.18911v1", "title": "Converting Expert Deliberation into Financial Signals Through A Context-Aware NLP Pipeline", "authors": [ "Vivek Batra", "Kristin Chen", "Sanjiv Das", "Samuel Judge", "Harshad Khadilkar", "Sukrit Mittal", "Amir Nasrollahzadeh", "Daniel Ostrov", "Jacob Sisk" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.18911v1", "field": "finance", "role": "instrument", "bullets": [ "48 monthly investment committee meeting transcripts used to predict whether global equities would outperform global bonds in the following month.", "LLM assigned asset-class context labels to topical transcript chunks; pipeline mapped financial keywords to a taxonomy and constructed sentiment-polarity and mention-frequency features.", "Best model combining sentence embeddings with engineered features achieved 73% prediction accuracy and 0.73 F1 score, versus a 60.4% naive equities-always baseline, though the difference was not statistically significant." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "monthly equity-vs-bond return prediction accuracy", "salience": 62, "n": 3176, "authors_detailed": [ { "name": "Vivek Batra", "url": "https://openalex.org/A5031919861", "inst": "University of Tennessee Health Science Center" }, { "name": "Kristin Chen", "url": "https://openalex.org/A5147722575", "inst": "" }, { "name": "Sanjiv Das", "url": "https://openalex.org/A5126401536", "inst": "Santa Clara University" }, { "name": "Samuel Judge", "url": "https://openalex.org/A5018684015", "inst": "Taylor University" }, { "name": "Harshad Khadilkar", "url": "https://openalex.org/A5147737804", "inst": "" }, { "name": "Sukrit Mittal", "url": "https://openalex.org/A5059556575", "inst": "Mahindra Group (India)" }, { "name": "Amir Nasrollahzadeh", "url": "https://openalex.org/A5147733496", "inst": "" }, { "name": "Daniel Ostrov", "url": "https://openalex.org/A5146704713", "inst": "Santa Clara University" }, { "name": "Jacob Sisk", "url": "https://openalex.org/A5016586246", "inst": "Thomson Reuters (United States)" } ], "affiliations": [ "University of Tennessee Health Science Center", "Santa Clara University", "Taylor University", "Mahindra Group (India)", "Thomson Reuters (United States)" ] }, { "uid": "arxiv:2608.18534v1", "arxiv_id": "2608.18534v1", "title": "FinRCA-Bench: Benchmarking Evidence Retrieval and Reasoning for Financial AI Systems", "authors": [ "Pratik Ghawate" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.18534v1", "field": "accounting", "role": "method", "bullets": [ "2,250 synthetic accounts-payable-to-bank reconciliation cases across 14 operational tables, including 1,500 injected failures in 15 causal categories and 750 legitimate cases.", "LLM performed root-cause diagnosis under five retrieval architectures including typed provenance graph retrieval restricted to persisted transaction relationships.", "Changing only retrieval architecture raised macro required-record recall from 0.83% to 77.70% and exact 16-class accuracy from 2.05% to 72.44%; retrieval failures outnumbered reasoning failures 95 to 15." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "synthetic AP reconciliation with known root causes", "salience": 58, "n": 3177, "authors_detailed": [ { "name": "Pratik Ghawate", "url": "https://openalex.org/A5096360224", "inst": "International Institute of Information Technology" } ], "affiliations": [ "International Institute of Information Technology" ] }, { "uid": "doi:10.2139/ssrn.7301719", "doi": "10.2139/ssrn.7301719", "title": "Deterministic Execution Environments (Dee): An Architectural Blueprint for Agentic Treasury Management and Global Operational Resilience", "authors": [ "Dan Sakaev" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7301719", "field": "finance", "role": "object", "bullets": [ "Conceptual analysis of autonomous AI agents deployed across digital asset liquidity venues, mapped to EU DORA, US NIST, UK PRA, and Singapore MAS regulatory frameworks.", "Paper proposes Deterministic Execution Environments with a three-layer trust boundary to decouple LLM cognitive overlays from treasury execution authority.", "Architecture converts abstract regulatory compliance into hardcoded execution constraints and addresses the LLM validation impossibility gap for agentic finance." ], "bullet_provenance": "ai", "salience": 38, "models": [], "validated": null, "n": 3291, "authors_detailed": [ { "name": "Dan Sakaev", "url": "https://openalex.org/A5134501683", "inst": "Oxford Brookes University" } ], "affiliations": [ "Oxford Brookes University" ] }, { "uid": "doi:10.2139/ssrn.7294919", "doi": "10.2139/ssrn.7294919", "title": "Artificial Intelligence Architecture for Data-Driven Economic Analysis", "authors": [ "Munazza Jabeen", "Bareera Jabeen" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7294919", "field": "economics", "role": "method", "bullets": [ "Conceptual framework with country-specific use cases for Pakistan covering inflation forecasting, credit risk assessment, policy impact, and fiscal allocation optimization.", "Paper designs a layered AI architecture progressing from traditional AI through generative AI to agentic AI for autonomous economic reasoning and analysis.", "Framework demonstrates an integration path from descriptive statistics to predictive insights to autonomous decision-making for emerging-economy applications." ], "bullet_provenance": "ai", "salience": 28, "models": [], "validated": null, "n": 3292, "authors_detailed": [ { "name": "Munazza Jabeen", "url": "https://openalex.org/A5090945813", "inst": "International Islamic University, Islamabad" }, { "name": "Bareera Jabeen", "url": "https://openalex.org/A5117190785", "inst": "International Islamic University, Islamabad" } ], "affiliations": [ "International Islamic University, Islamabad" ] }, { "uid": "doi:10.2139/ssrn.7301678", "doi": "10.2139/ssrn.7301678", "title": "Stranded Credentials: How A Skill-Signaling Market Absorbed Generative AI", "authors": [ "Song Yao" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7301678", "field": "economics", "role": "object", "bullets": [ "444,698 participations across Kaggle upload- and code-competitions from 2010 to 2026 with 353,668 disambiguated author records.", "Credential signaling value tracked through the generative AI transition using medal-to-performance prediction across competition formats.", "Fresh medals retained signaling value through AI transition; upload-competition medal stock lost 82% informativeness, half from institutional exit." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3870, "authors_detailed": [ { "name": "Song Yao", "url": "https://openalex.org/A5003350728", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7298998", "doi": "10.2139/ssrn.7298998", "title": "When Text Becomes Tradable: Financial-domain Language Models and the Asymmetric Absorption of News in Agricultural Futures Markets", "authors": [ "Navid Parvini", "Hirbod Assa", "Yilmaz Yildiz" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7298998", "field": "finance", "role": "instrument", "bullets": [ "Daily news on fourteen CBOT and ICE agricultural commodity futures contracts from 2009 to 2023 across thirteen vectorization models.", "FinBERT embeddings mapped to next-day returns via ridge regression benchmarked against text-based and commodity-factor predictors.", "Finance-domain FinBERT long-short portfolio achieved annualized Sharpe near 2.5 and alpha of 23% (t=5.9) after factor controls." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "commodity-factor benchmarks with alpha t-statistics", "salience": 70, "n": 3871, "authors_detailed": [ { "name": "Navid Parvini", "url": "https://openalex.org/A5147530339", "inst": "University of Kent" }, { "name": "Hirbod Assa", "url": "https://openalex.org/A5014339121", "inst": "University College Dublin" }, { "name": "Yılmaz Yıldız", "url": "https://openalex.org/A5026423215", "inst": "University of Kent" } ], "affiliations": [ "University of Kent", "University College Dublin" ] }, { "uid": "doi:10.2139/ssrn.7303440", "doi": "10.2139/ssrn.7303440", "title": "Advanced Insurance Analytics for Fraud Prevention", "authors": [ "Kenechukwu Azie" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7303440", "field": "finance", "role": "object", "bullets": [ "U.S. insurance industry fraud landscape across six priority vectors including generative AI deepfakes and organized fraud rings.", "Analytical technologies and operational controls assessed against NAIC AI guidance and Insurance Fraud Prevention Model Act.", "Effective fraud prevention requires integrated advanced analytics, human oversight, cross-sector sharing, and robust AI governance." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3872, "authors_detailed": [ { "name": "Kenechukwu Azie", "url": "https://openalex.org/A5138720476", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7288058", "doi": "10.2139/ssrn.7288058", "title": "Hope, Signals, and Silicon: A Game-Theoretic Model of the Pre-Doctoral Academic Labor Market in the Age of AI *", "authors": [ "Shaohui Wang" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7288058", "field": "economics", "role": "object", "bullets": [ "Game-theoretic model of pre-doctoral academic labor market with PIs allocating AI between routine and novel tasks under fixed capacity.", "Simulation examines heterogeneous RAs and PIs with noisy evaluation and luck-dependent outcomes across AI adoption levels.", "AI raised routine output while weakening score-ability link and raising admissions cutoffs rather than expanding access proportionally." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3873 }, { "uid": "doi:10.2139/ssrn.7312438", "doi": "10.2139/ssrn.7312438", "title": "Machine Execution and the Entry Coauthor: Generative AI and the Structure of Scholarly Authorship", "authors": [ "Anirban Ghatak" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7312438", "field": "economics", "role": "object", "bullets": [ "604,368 articles in 945 economics and management journals with 353,668 disambiguated author records from 2015 to 2025.", "Solo-authorship and entry coauthor patterns tracked across journal quality tiers before and after generative AI diffusion.", "First-time authors publishing with established coauthors fell 5-8 percentage points in 2024-2025 in tiers requiring collaboration." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3874, "authors_detailed": [ { "name": "Anirban Ghatak", "url": "https://openalex.org/A5074325167", "inst": "Indian Institute of Management Kozhikode" } ], "affiliations": [ "Indian Institute of Management Kozhikode" ] }, { "uid": "doi:10.2139/ssrn.7300198", "doi": "10.2139/ssrn.7300198", "title": "Regime-Conditional Miscalibration of Chronos-2 in Indian Equity Forecasting: A Calibration Audit with Conformal Correction", "authors": [ "Yash Chitransh" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7300198", "field": "finance", "role": "method", "bullets": [ "Rolling-window probabilistic forecasts of Sensex and Nifty indices using 352 and 403 evaluation windows respectively.", "Chronos-2 foundation model generated zero-shot 30-day forecasts benchmarked against SARIMAX; calibration audited with conformal correction.", "Stated 80% prediction interval drops to 74% coverage beyond day one; regime-conditional reversal shows high-volatility periods better calibrated at longer horizons." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Calibration audit against SARIMAX baseline on Sensex/Nifty rolling windows", "salience": 40, "n": 3875, "authors_detailed": [ { "name": "Yash Chitransh", "url": "https://openalex.org/A5042861641", "inst": "National Institute of Technology Warangal" } ], "affiliations": [ "National Institute of Technology Warangal" ] }, { "uid": "doi:10.2139/ssrn.7293858", "doi": "10.2139/ssrn.7293858", "title": "Human and Artificial Intelligence Collaboration and the Future Competencies of Professional Accountants", "authors": [ "Ahmad Zahiruddin Yahya" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7293858", "field": "accounting", "role": "object", "bullets": [ "Mixed-method study of 286 professional accountants surveyed and 18 practitioners interviewed across accounting and finance roles.", "No specific model tested; study examines generative AI tools used for transaction processing, reconciliation, anomaly detection, and forecasting.", "Digital and AI competence is the strongest predictor of professional readiness; six themes position future accountants as AI-augmented professionals." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3876, "authors_detailed": [ { "name": "AHMAD ZAHIRUDDIN YAHYA", "url": "https://openalex.org/A5144187916", "inst": "Abu Dhabi University" } ], "affiliations": [ "Abu Dhabi University" ] }, { "uid": "doi:10.2139/ssrn.7299618", "doi": "10.2139/ssrn.7299618", "title": "The Gate Ledger: A Public Panel of Realized Repurchase-Offer Outcomes for U.S. Semi-Liquid Funds, 2012-2026", "authors": [ "Luka Stanisljevic" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7299618", "field": "finance", "role": "instrument", "bullets": [ "Panel of 568 SEC filings covering 140 U.S. semi-liquid funds from 2012 to 2026, yielding 1,033 repurchase-offer rows across interval and tender-offer funds.", "An unspecified LLM extracted structured repurchase-offer fields from non-standardized EDGAR shareholder reports; validated against a 30-filing gold set.", "Pooled field accuracy 0.882; median directly measured honored fraction 0.54 among 264 proration events; extraction under-reports rather than invents data." ], "bullet_provenance": "ai", "validated": true, "validation_note": "30-filing stratified gold set, pooled field accuracy 0.882", "salience": 55, "models": [], "n": 3877, "authors_detailed": [ { "name": "Luka Stanisljevic", "url": "https://openalex.org/A5135273334", "inst": "Independent Researcher , Milan , Italy" } ], "affiliations": [ "Independent Researcher , Milan , Italy" ] }, { "uid": "doi:10.2139/ssrn.7290058", "doi": "10.2139/ssrn.7290058", "title": "From AI-Agent Instruction to Final Settlement: Evidencing State Transitions in Tokenized Securities", "authors": [ "Byoungdug Min", "Hyoung Goo Kang" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7290058", "field": "finance", "role": "object", "bullets": [ "Prospective public-document analysis of Korea's 2026-27 securities registration transition under the amended Act on Electronic Registration, contrasted with DTC, Project Agora, and Swiss CSD designs.", "No LLM used; paper develops an evidence-gated framework for AI agent instructions crossing CSD participant, ledger, and settlement-system boundaries with six defined states.", "Processing must not advance automatically without authoritative evidence for the next state; matched simulation tests are specified using state error, mismatch rate, and detection time." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 4067, "authors_detailed": [ { "name": "Byoungdug Min", "url": "https://openalex.org/A5147642016", "inst": "National Assembly" }, { "name": "Hyoung‐Goo Kang", "url": "https://openalex.org/A5030945786", "inst": "Department of Finance" } ], "affiliations": [ "National Assembly", "Department of Finance" ] }, { "uid": "doi:10.2139/ssrn.7296638", "doi": "10.2139/ssrn.7296638", "title": "Human-on-the-Loop: A Theory of Oversight Capacity, Its Failure Modes, and the Non-Delegable Residual in the Age of AI Agents", "authors": [ "Naoki Kadowaki" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7296638", "field": "management", "role": "object", "bullets": [ "Conceptual framework with formal capacity constraint for firms deploying AI agents, drawing on human factors, human-computer interaction, agent evaluation, and legal records.", "No LLM used; paper models oversight as a consumable resource with measurable capacity and derives scaling laws for constitutive versus control oversight under rising agent throughput.", "Control oversight cost grows as Omega(T) with throughput T; beyond a computable threshold only constitutive oversight of purpose, means, and capital allocation remains solvent." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 4068, "authors_detailed": [ { "name": "Naoki Kadowaki", "url": "https://openalex.org/A5140798250", "inst": "Oji Holdings (Japan)" } ], "affiliations": [ "Oji Holdings (Japan)" ] }, { "uid": "doi:10.2139/ssrn.7308940", "doi": "10.2139/ssrn.7308940", "title": "Agentic Artificial Intelligence and Systemic Financial Risk: A Complex-Systems Framework for Emerging-Market Supervision", "authors": [ "Alfredo Merlet" ], "posted": "2026-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7308940", "field": "finance", "role": "object", "bullets": [ "Conceptual complex-systems framework analyzing agentic AI deployment in major banks and trading firms during 2025-2026, focused on emerging-market and Latin American supervision.", "No LLM used; paper links autonomy depth, infrastructure concentration, and supervisory observability to correlated machine-mediated instability in financial networks.", "Existing regulatory frameworks including the EU AI Act and Basel model-risk rules fail to address agent-specific dynamics; emerging-market supervisors lack agent-aware instruments." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 4069, "authors_detailed": [ { "name": "Alfredo Merlet", "url": "https://openalex.org/A5135419943", "inst": "Universidad de Sevilla" } ], "affiliations": [ "Universidad de Sevilla" ] }, { "uid": "arxiv:2608.17220v1", "arxiv_id": "2608.17220v1", "title": "PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance", "authors": [ "Rabimba Karanjai", "Yang Lu", "Richard Williamson", "Hemanth Hm", "Prakhar Mehrotra", "Lei Xu", "Weidong", "Shi" ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.17220v1", "field": "finance", "role": "method", "bullets": [ "DeFi transaction-safety benchmark covering 40 swap, lending, and yield-management tasks across four attack categories and benign utility, evaluated over 2,800 trials and ten seeds.", "PACE binds an LLM agent's approved typed intent and simulation report to exact on-chain bytes; safety was checked against six baselines and predefined unsafe and benign outcomes.", "PACE recorded no unsafe executions or benign false positives in the deterministic sandbox, versus an 80 percent unsafe-execution rate for the unguarded baseline, at roughly 30,000 gas overhead." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labelled DeFi attack and benign task suite", "salience": 68, "edition": 23, "models": [], "n": 4129, "authors_detailed": [ { "name": "Rabimba Karanjai", "url": "https://openalex.org/A5147582012", "inst": "Larry" }, { "name": "Yang Lu", "url": "https://openalex.org/A5147599141", "inst": "Larry" }, { "name": "Richard Williamson", "url": "https://openalex.org/A5147567542", "inst": "Larry" }, { "name": "Hemanth Hm", "url": "https://openalex.org/A5147648274", "inst": "Larry" }, { "name": "Prakhar Mehrotra", "url": "https://openalex.org/A5147568457", "inst": "Larry" }, { "name": "Lei Xu", "url": "https://openalex.org/A5147628612", "inst": "Larry" }, { "name": "Weidong", "url": "https://openalex.org/A5147525674", "inst": "Larry" }, { "name": "Shi", "url": "https://openalex.org/A5147588335", "inst": "" } ], "affiliations": [ "Larry" ] }, { "uid": "doi:10.2139/ssrn.7294618", "doi": "10.2139/ssrn.7294618", "title": "The C2S Reliability Framework", "authors": [ "Sheyene Gerardi" ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7294618", "field": "management", "role": "method", "bullets": [ "Conceptual personnel-reliability framework for security, healthcare, finance, public administration, and strategic infrastructure; no empirical sample, period, or deployment setting is reported.", "Foundation models, including the Cell2Sentence family, illustrate evidence-integration capabilities within a human-controlled causal AI architecture; no model-output benchmark is conducted.", "The framework assigns AI a supporting role in institutional evaluation and preserves professional accountability, explainability, due process, and multiple independent evidence sources." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 34, "edition": 23, "validated": null, "n": 4134, "authors_detailed": [ { "name": "Sheyene Gerardi", "url": "https://openalex.org/A5145680494", "inst": "Annie E. Casey Foundation" } ], "affiliations": [ "Annie E. Casey Foundation" ] }, { "uid": "arxiv:2608.17583v1", "arxiv_id": "2608.17583v1", "title": "Auditing Exposure to Harmful Content on TikTok using Multimodal Language Models: A Cross-National, Age-Stratified Study", "authors": [ "Hamidreza Saffari", "Francesco Pierri" ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.17583v1", "field": "management", "role": "instrument", "bullets": [ "Audit of 36,971 TikTok videos collected by age-coded accounts in France, Italy, and Sweden during passive scrolling and active harm-keyword search sessions.", "Four multimodal LLMs label a ten percent sample; Gemini 2.5 Flash was selected after comparison with native-speaker annotations on 300 videos, reaching aggregate kappa of 0.42.", "Keyword searches return 35 to 56 percent harmful content and temporarily raise exposure in ten of twelve country-age cells; passive harm exposure is highest in Italy at every age." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "300-video native-speaker reference set, Cohen's kappa reported", "salience": 74, "edition": 23, "n": 4135, "authors_detailed": [ { "name": "Hamidreza Saffari", "url": "https://openalex.org/A5099137350", "inst": "Politecnico di Milano" }, { "name": "Francesco Pierri", "url": "https://openalex.org/A5147520427", "inst": "" } ], "affiliations": [ "Politecnico di Milano" ] }, { "uid": "doi:10.2139/ssrn.7284398", "doi": "10.2139/ssrn.7284398", "title": "Leaving the Scene after Vehicle-Train Collisions: Factors Associated with Post-crash Flight at U.S. Highway-Rail Grade Crossings", "authors": [ "Pouyan Saiedian", "Salvador Hernandez", "Rakan Mohammad Radwan Albatayneh", "SM Rahat Rahman" ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7284398", "field": "economics", "role": "instrument", "bullets": [ "National dataset of more than 17,000 US highway-rail grade-crossing crashes from 2005 to 2025, linking narrative reports with driver, vehicle, crossing, train, and environmental characteristics.", "An unnamed LLM extracts post-crash flight and driver-impairment indicators for logistic regression; the abstract reports no comparison with human coding or labelled extraction data.", "About 4.77 percent of surviving drivers left the scene; impairment raises those odds 2.54 times, while injury lowers them by 88.7 percent." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 23, "models": [], "n": 4141, "authors_detailed": [ { "name": "Pouyan Saiedian", "url": "https://openalex.org/A5143759795", "inst": "Oregon State University" }, { "name": "Salvador Hernández", "url": "https://openalex.org/A5067802018", "inst": "Oregon State University" }, { "name": "Rakan Mohammad Radwan Albatayneh", "url": "https://openalex.org/A5118609268", "inst": "Oregon State University" }, { "name": "SM Rahat Rahman", "url": "https://openalex.org/A5147423114", "inst": "Oregon State University" } ], "affiliations": [ "Oregon State University" ] }, { "uid": "doi:10.2139/ssrn.7303098", "doi": "10.2139/ssrn.7303098", "title": "Contesting the Rules of the Game: Organized Groups in Election Policymaking", "authors": [ "Joseph Loffredo" ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7303098", "field": "economics", "role": "instrument", "bullets": [ "Twelve thousand election bills across 22 US states are linked to 37,000 organized-group positions drawn from lobbying and legislative testimony records.", "A human-in-the-loop pipeline combining unspecified large language models with supervised machine learning scores how bills change election policy; no output-agreement statistic is reported.", "Groups participate more when elections are competitive and legislative access is present, while their positions reveal ideological differences and signal both policymaker division and legislative viability." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no model-output accuracy or agreement statistic reported", "salience": 58, "edition": 23, "models": [], "n": 4156, "authors_detailed": [ { "name": "Joseph Loffredo", "url": "https://openalex.org/A5056095902", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7288363", "doi": "10.2139/ssrn.7288363", "title": "Empirically Grounded LLM Agents for Simulating Human Behavioral Adaptation During Urban Heatwaves: A Philadelphia Case Study", "authors": [ "Yuqing Hu", "Zexi Kuang" ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7288363", "field": "economics", "role": "agent", "bullets": [ "Philadelphia simulations combine demographic profiles, time-use routines, spatial data, and a documented July 2024 heatwave to represent heterogeneous residents' behavioral adaptation.", "Unspecified large-language-model agents choose activities under routine and heat constraints; outputs are benchmarked against American Time Use Survey profiles and an independent household heatwave survey.", "Empirical grounding raised mean activity-profile correlation from 0.528 to 0.912 in normal conditions and from 0.349 to 0.836 during the heatwave, while sharply reducing error." ], "bullet_provenance": "ai", "validated": true, "validation_note": "ATUS activity profiles and independent heatwave survey, with correlations and mean squared errors", "salience": 61, "edition": 23, "models": [], "n": 4157, "authors_detailed": [ { "name": "Chen Xia", "url": "https://openalex.org/A5101546278", "inst": "Rochester Institute of Technology" }, { "name": "Zexi Kuang", "url": "https://openalex.org/A5147397877", "inst": "" } ], "affiliations": [ "Rochester Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.7286939", "doi": "10.2139/ssrn.7286939", "title": "An Ontology Construction Framework for Asset Lifecycle Management: Agentic Travelling Approach With Field Application in Shale Oil Wellbore Maintenance", "authors": [ "Hongzhi Chen", "Huang W.", "Zhao Y.", "Chen Y.", "Lin X.", "Xue J." ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7286939", "field": "management", "role": "instrument", "bullets": [ "Operational data from 45 western Chinese shale-oil wellbores during 2021 to 2024 cover three failure types and more than 40 multimodal maintenance parameters.", "Three unspecified AI agents plan ontology tasks, generate candidate structures, and supervise consistency; resulting data structures are compared with engineer-curated benchmarks using a 0.8 similarity threshold.", "Generated ontologies averaged semantic similarity above 0.82 and reduced failure-diagnosis decision time from about 1.5 hours to no more than 10 minutes per case." ], "bullet_provenance": "ai", "validated": true, "validation_note": "engineer-curated ontology benchmark, average semantic similarity above 0.82", "salience": 57, "edition": 23, "models": [], "n": 4158, "authors_detailed": [ { "name": "Hongzhi Chen", "url": "https://openalex.org/A5070795653", "inst": "Beijing Technology and Business University" }, { "name": "W. Huang", "url": "https://openalex.org/A5091191455", "inst": "Tianjin Economic-Technological Development Area" }, { "name": "Y. Zhao", "url": "https://openalex.org/A5019940900", "inst": "" }, { "name": "Chen Y.", "url": "https://openalex.org/A5147444103", "inst": "" }, { "name": "Xu Lin", "url": "https://openalex.org/A5074887775", "inst": "China Three Gorges University" }, { "name": "Xue J.", "url": "https://openalex.org/A5147409284", "inst": "" } ], "affiliations": [ "Beijing Technology and Business University", "Tianjin Economic-Technological Development Area", "China Three Gorges University" ] }, { "uid": "doi:10.2139/ssrn.7287758", "doi": "10.2139/ssrn.7287758", "title": "From Footprint to Motive", "authors": [ "Johan Van Rooyen", "Nitayapa Nandhakwang" ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7287758", "field": "economics", "role": "method", "bullets": [ "Conceptual analysis applies a disciplined intent-attribution framework to three international trade and technology episodes recorded between April 2025 and August 2026.", "Large language models, with families not stated, are treated as tools for retrieving evidence and inferring strategic motives; the proposed chain is interpretive rather than empirically validated.", "The paper identifies attribution leaps and gaps between caveats and conclusions, arguing that verification capacity determines whether model-mediated strategic interpretation creates problematic dependency." ], "bullet_provenance": "ai", "salience": 38, "edition": 23, "models": [], "validated": null, "n": 4159, "authors_detailed": [ { "name": "Johan van Rooyen", "url": "https://openalex.org/A5047562540", "inst": "Webster University" }, { "name": "Nitayapa Nandhakwang", "url": "https://openalex.org/A5135422559", "inst": "Chiang Mai University" } ], "affiliations": [ "Webster University", "Chiang Mai University" ] }, { "uid": "doi:10.2139/ssrn.7301221", "doi": "10.2139/ssrn.7301221", "title": "AI Data Governance Layer (ADGL): Governing Knowledge, Analysis, and Consequences in Model-Agnostic AI Systems Submission Draft v1.2 GBSN Research", "authors": [ "GBSN Research" ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7301221", "field": "management", "role": "method", "bullets": [ "A model-agnostic enterprise policy architecture is demonstrated through eight reference cases and 25 normative conformance checks, plus 11 separately reported candidate execution-integrity checks.", "The framework governs which knowledge may influence a case, which analysis may occur, and which consequence may follow; no specific language model or output validation is reported.", "It separates informational outputs, human-owned decisions, and machine-executable actions within one runtime contract, with audit and provenance spanning all three governance layers." ], "bullet_provenance": "ai", "salience": 44, "edition": 23, "models": [], "validated": null, "n": 4168, "authors_detailed": [ { "name": "GBSN Research", "url": "https://openalex.org/A5147344578", "inst": "University of Lisbon" } ], "affiliations": [ "University of Lisbon" ] }, { "uid": "doi:10.2139/ssrn.7289458", "doi": "10.2139/ssrn.7289458", "title": "FLAP-X: A Dual-Leaf Attestation Protocol for Cross-Agent Regulatory Governance with Retrospective Cryptographic Verification", "authors": [ "Maria Luz Madariaga" ], "posted": "2026-08-18", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7289458", "field": "management", "role": "method", "bullets": [ "Governance architecture for nondeterministic multi-agent systems is exercised with a 15-condition protocol covering core verification, adversarial cases, governance lifecycle tests, and one comparison.", "The protocol cryptographically anchors credentials, delegation, prompt versions, controls, and output hashes while applying tolerance to variable model behavior; no model family is specified.", "Dual decision and workflow records expose output substitution and attribution failures missed by single-surface records, but colluding agents and stolen signing keys still pass verification." ], "bullet_provenance": "ai", "salience": 45, "edition": 23, "models": [], "validated": null, "n": 4169 }, { "uid": "doi:10.2139/ssrn.7283081", "doi": "10.2139/ssrn.7283081", "title": "From Ledgers, to Options, to Latency A History of Trading, Mathematical Finance, and the Quant Profession", "authors": [ "Paul Alexander Bilokon" ], "posted": "2026-08-18", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7283081", "field": "finance", "role": "object", "bullets": [ "The paper traces the history of quantitative finance from merchant accounting and bourses through electronic markets to machine learning and large language models, covering no specific sample, period, or dataset.", "The paper is a conceptual and historical analysis rather than an empirical study; no specific model is applied as a research instrument, not stated.", "The paper argues the quant profession emerges from co-evolution of five systems, exchange institutions, mathematics, technology, specialization, and regulation, with AI-assisted research described as the latest wave." ], "bullet_provenance": "ai", "salience": 32, "edition": 22, "models": [], "validated": null, "n": 4093, "authors_detailed": [ { "name": "Paul Bilokon", "url": "https://openalex.org/A5056861824", "inst": "Imperial College London" } ], "affiliations": [ "Imperial College London" ] }, { "uid": "arxiv:2608.18058v1", "arxiv_id": "2608.18058v1", "title": "Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating", "authors": [ "Daria Leshchikova", "Valentina V. Kuskova", "Dmitry Zaytsev", "Valerii Klimov" ], "posted": "2026-08-18", "added": "2026-08-22", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.18058v1", "field": "management", "role": "object", "bullets": [ "Two surveys of active users on a major dating platform, N=2,894 on generative profile features and N=2,617 on autonomous conversational agents, fielded in two languages.", "Not stated: the paper does not apply or test a specific LLM model, instead measuring survey respondents' receptivity to hypothetical autonomous conversational agents that converse on their behalf.", "Willingness to deploy one's own agent required a much lower receptivity threshold than accepting a counterpart's agent, and only 4 to 13 percent of dyads combined deployment with receptivity." ], "bullet_provenance": "ai", "salience": 38, "edition": 22, "models": [], "validated": null, "n": 4096 }, { "uid": "arxiv:2608.17715v1", "arxiv_id": "2608.17715v1", "title": "Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models", "authors": [ "Sahab Zandi", "Noah Kostesku", "Christophe Mues", "María Óskarsdóttir", "Cristián Bravo" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.17715v1", "field": "finance", "role": "method", "bullets": [ "Freddie Mac single-family loan data, three explanation pipelines (tabular XGBoost with SHAP, GNN with GNNExplainer, and a bimodal combination), evaluated by credit risk professionals and non-professionals on eight dimensions.", "Gemma 3 4B and DeepSeek R1 70B, both fine-tuned, and Gemini 2.5 in zero-shot mode generate stakeholder-facing risk narratives from post-hoc model explanations; quality measured by automated checks and human judges.", "The upstream explanation pipeline drives more variance in evidence-grounding quality than the LLM choice; narratives reliably name influential risk factors but less reliably state their direction, a gap with adverse-action compliance implications." ], "bullet_provenance": "ai", "models": [ "gemini", "open_other" ], "open_weights": true, "validated": true, "validation_note": "human evaluation by credit risk professionals on eight decision-relevant dimensions plus automated grounding checks", "salience": 62, "edition": 21, "n": 2337, "authors_detailed": [ { "name": "Sahab Zandi", "url": "https://openalex.org/A5104238767", "inst": "Western University" }, { "name": "Noah Kostesku", "url": "https://openalex.org/A5147633131", "inst": "" }, { "name": "Christophe Mues", "url": "https://openalex.org/A5060698272", "inst": "University of Southampton" }, { "name": "María Óskarsdóttir", "url": "https://openalex.org/A5089062610", "inst": "Reykjavík University" }, { "name": "Cristián Bravo", "url": "https://openalex.org/A5147577399", "inst": "" } ], "affiliations": [ "Western University", "University of Southampton", "Reykjavík University" ] }, { "uid": "doi:10.2139/ssrn.7286578", "doi": "10.2139/ssrn.7286578", "title": "Responsible Adoption of Generative Artificial Intelligence in Customer Care Services: Evidence from Vietnamese Commercial Banks and Managerial Implications", "authors": [ "An Nguyen Hoang Phuong" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7286578", "field": "management", "role": "object", "bullets": [ "Three Vietnamese commercial banks (MBBank, TPBank, Techcombank) examined through qualitative case analysis and desk research on GenAI adoption in customer service.", "No model was run by the researchers; the study maps how banks deploy LLM-based chatbots with retrieval-augmented generation and human-in-the-loop oversight.", "GenAI improved conversational quality and contextual understanding, but sustainable value was constrained by data governance maturity and integration architecture." ], "bullet_provenance": "ai", "salience": 32, "edition": 21, "models": [], "validated": null, "n": 2338, "authors_detailed": [ { "name": "An Nguyen Hoang Phuong", "url": "https://openalex.org/A5147413845", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7291518", "doi": "10.2139/ssrn.7291518", "title": "AI-Driven Financial Infrastructure How LLMs, Deep Learning, and Autonomous Agents Reshape Payments, Credit, and Market Microstructure", "authors": [ "Hunter Hughes" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7291518", "field": "finance", "role": "object", "bullets": [ "Review drawing on the IIF-EY survey of 61 financial institutions from October 2025, covering AI deployment across payments, credit scoring, and market microstructure.", "No model was run; the paper synthesizes survey data and regulatory evidence on deep learning, LLM, and agentic system adoption in financial infrastructure.", "Generative AI production rose from 48 to 84 percent of surveyed firms in one year, but the paper argues regulation should target the settlement layer an AI occupies rather than AI as a capability." ], "bullet_provenance": "ai", "salience": 48, "edition": 21, "models": [], "validated": null, "n": 2339, "authors_detailed": [ { "name": "Hunter Hughes", "url": "https://openalex.org/A5147067082", "inst": "Sunset Laboratory (United States)" } ], "affiliations": [ "Sunset Laboratory (United States)" ] }, { "uid": "arxiv:2608.17223v1", "arxiv_id": "2608.17223v1", "title": "Temporal Leakage in Financial News NLP: A Multi-Architecture Audit with a Regime-Specific M&A Signal", "authors": [ "Chenhao Xue", "Raslen Guesmi", "Siwei Feng", "Yucheng Gong", "Jacob Xavier Sundram", "Jordan Pang", "Lan Wang", "Julian Kaljuvee" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.17223v1", "field": "finance", "role": "method", "bullets": [ "49,799 financial news articles evaluated with 16 feature-model combinations, from TF-IDF and MiniLM through fine-tuned RoBERTa and DeBERTa, plus Llama-3 and Qwen2.5 via zero-shot and LoRA probes.", "Each model predicts stock direction; the study measures how MCC changes between random and strictly chronological train-test splits to quantify temporal leakage across model capacity levels.", "Random splits inflate MCC by 1.1 to 6.5 times; under chronological evaluation only M&A articles carry a positive signal, and that signal does not transfer to a separate U.S. corpus." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "MCC on stock direction prediction under chronological and random splits, 10,000-permutation significance test", "salience": 63, "edition": 21, "n": 2340, "authors_detailed": [ { "name": "Chenhao Xue", "url": "https://openalex.org/A5147643587", "inst": "University of Oxford" }, { "name": "Raslen Guesmi", "url": "https://openalex.org/A5147471343", "inst": "Predictive Science (United States)" }, { "name": "Siwei Feng", "url": "https://openalex.org/A5147523225", "inst": "Predictive Science (United States)" }, { "name": "Yucheng Gong", "url": "https://openalex.org/A5147639481", "inst": "Predictive Science (United States)" }, { "name": "Jacob Xavier Sundram", "url": "https://openalex.org/A5147574424", "inst": "Predictive Science (United States)" }, { "name": "Jordan Pang", "url": "https://openalex.org/A5147609596", "inst": "Predictive Science (United States)" }, { "name": "Lan Wang", "url": "https://openalex.org/A5147576719", "inst": "" }, { "name": "Julian Kaljuvee", "url": "https://openalex.org/A5147655393", "inst": "Predictive Science (United States)" } ], "affiliations": [ "University of Oxford", "Predictive Science (United States)" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7289498", "doi": "10.2139/ssrn.7289498", "title": "Feeding the Machine: The Security Risks of Generative AI Use by Public Sector Employees in Southeast Asia", "authors": [ "Nigel Finch", "Tyrone M. Carlin" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7289498", "alt_urls": [ "https://doi.org/10.2139/ssrn.7289520" ], "field": "management", "role": "object", "bullets": [ "Four documented data-leakage incidents from 2023 to 2025, including Samsung's ChatGPT exposure and government bans on DeepSeek, analyzed through securitization theory for Southeast Asian public sector contexts.", "ChatGPT and DeepSeek are studied as sources of organizational risk rather than used as research instruments; findings rest on case analysis of publicly documented breaches and policy responses.", "Risk arises structurally from offshore AI hosting and officials treating chat windows as private notebooks; the paper recommends sovereign hosting, tiered data classification, and senior-level exception governance." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "salience": 38, "edition": 21, "validated": null, "n": 2344, "authors_detailed": [ { "name": "Nigel Finch", "url": "https://openalex.org/A5088150822", "inst": "The University of Sydney" }, { "name": "Tyrone M. Carlin", "url": "https://openalex.org/A5058904110", "inst": "The University of Sydney" } ], "affiliations": [ "The University of Sydney" ] }, { "uid": "doi:10.2139/ssrn.7283098", "doi": "10.2139/ssrn.7283098", "title": "LLMs Make Robust Stochastic Optimization Easier: An Agentic Workflow", "authors": [ "Ziyu Wang", "Zhuolin Wang", "Yi Chen", "Zhi Chen", "Guodong Lyu" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7283098", "field": "management", "role": "method", "bullets": [ "NL-to-DRO dataset built through human-guided reverse engineering, with each instance pairing a natural-language problem description, an intermediate mathematical representation, and solver-executable code across diverse distributionally robust optimization structures.", "Closed-source and open-source LLMs, families not named, convert natural-language descriptions into intermediate representations and then executable code through an agentic workflow augmented by a knowledge library.", "The intermediate-representation workflow achieves 91.1 percent success for closed-source models and roughly 60 percent for fine-tuned open-source models, substantially exceeding direct code generation baselines." ], "bullet_provenance": "ai", "validated": true, "validation_note": "success rates against ground-truth DRO formulations and executable code", "salience": 42, "edition": 21, "models": [], "n": 2345, "authors_detailed": [ { "name": "Ziyu Wang", "url": "https://openalex.org/A5147390519", "inst": "" }, { "name": "Zhuolin Wang", "url": "https://openalex.org/A5101433265", "inst": "Hong Kong University of Science and Technology" }, { "name": "Yi Chen", "url": "https://openalex.org/A5147386523", "inst": "" }, { "name": "Zhi Chen", "url": "https://openalex.org/A5050187781", "inst": "Chinese University of Hong Kong" }, { "name": "Guodong Lyu", "url": "https://openalex.org/A5033543714", "inst": "National University of Singapore" } ], "affiliations": [ "Hong Kong University of Science and Technology", "Chinese University of Hong Kong", "National University of Singapore" ] }, { "uid": "doi:10.2139/ssrn.7307251", "doi": "10.2139/ssrn.7307251", "title": "Debt Prices the Opportunity Too: Generative AI in China’s Credit Market", "authors": [ "Jifan Wang" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7307251", "field": "finance", "role": "object", "bullets": [ "All A-share listed firms in China from 2021 to 2025, with debt costs captured through implied cost of debt, primary bond spreads, and exchange-traded secondary bond spreads.", "No language model is used by the researchers. Workforce AI exposure is constructed from pre-ChatGPT job posting task content, and the ChatGPT release provides the treatment shock in a difference-in-differences design.", "Implied cost of debt for highly exposed firms declines 11 to 13 basis points per standard deviation of exposure, driven by labor-intensive non-state firms, while secondary bond spreads among implicitly guaranteed issuers show no response." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "edition": 21, "validated": null, "n": 2346, "authors_detailed": [ { "name": "Jifan Wang", "url": "https://openalex.org/A5044360994", "inst": "Jiangnan University" } ], "affiliations": [ "Jiangnan University" ] }, { "uid": "doi:10.2139/ssrn.7282719", "doi": "10.2139/ssrn.7282719", "title": "Biodiversity Risk and the Dynamics of Sectoral Systemic Risk", "authors": [ "Christian Oliver Ewald", "Chuyao Huang", "Yuyu Ren" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7282719", "field": "finance", "role": "instrument", "bullets": [ "Chinese sectoral equity data with a biodiversity risk index constructed from large-scale news text, analyzed alongside physical climate risk and climate policy uncertainty.", "An unspecified LLM annotated news articles to build a China Biodiversity Risk Index, which fed into deep learning classification; no validation against ground truth is reported.", "Biodiversity risk generates distinct systemic financial spillovers beyond conventional climate risks, propagating mainly through ecological constraints and supply-chain disruptions in resource-intensive sectors." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "edition": 21, "models": [], "n": 2349, "authors_detailed": [ { "name": "Christian‐Oliver Ewald", "url": "https://openalex.org/A5041238635", "inst": "Statistics Sweden" }, { "name": "Chuyao Huang", "url": "https://openalex.org/A5104335997", "inst": "Adam Smith Institute" }, { "name": "Yuyu Ren", "url": "https://openalex.org/A5147398222", "inst": "" } ], "affiliations": [ "Statistics Sweden", "Adam Smith Institute" ] }, { "uid": "doi:10.2139/ssrn.7306325", "doi": "10.2139/ssrn.7306325", "title": "Mapping wealth and income inequalities in Bucharest’s neighborhoods using cars market value: a mixed approach combining conventional methods with Artificial Intelligence", "authors": [ "Bogdan Ileanu", "Claudiu Herteliu", "Tudorel Andrei", "Adrian Pana" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7306325", "field": "economics", "role": "instrument", "bullets": [ "Multi-stage probability sample of roughly 560 locations in Bucharest, with vehicles photographed via Google Street View between 2022 and 2024 and values expressed in 2025-2026 prices.", "GPT-4 and GPT-5 identified each vehicle's brand, production-year band, and estimated market value from street-view images, validated against county-level wages, national registry brand distributions, and official mean vehicle age.", "Vehicle market value correlates strongly with average monthly wages across counties (r = 0.65), and neighborhood-level Gini indices reveal substantial within-city inequality in Bucharest." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "county-level wage regression (r = 0.65), brand and age distributions vs. national vehicle registry, mean age vs. official figure", "salience": 65, "edition": 21, "n": 2350, "authors_detailed": [ { "name": "Bogdan Ileanu", "url": "https://openalex.org/A5147382945", "inst": "Bucharest University of Economic Studies" }, { "name": "Claudiu Herţeliu", "url": "https://openalex.org/A5033210788", "inst": "Bucharest University of Economic Studies" }, { "name": "Tudorel Andrei", "url": "https://openalex.org/A5016293332", "inst": "Bucharest University of Economic Studies" }, { "name": "Adrian Pană", "url": "https://openalex.org/A5034254757", "inst": "University of Bucharest" } ], "affiliations": [ "Bucharest University of Economic Studies", "University of Bucharest" ] }, { "uid": "doi:10.2139/ssrn.7284980", "doi": "10.2139/ssrn.7284980", "title": "Why Firms May Continue to Fund Scaling Despite Uncertainty About Autonomous Research", "authors": [ "Lewis Lewin" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7284980", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of AI firm investment, drawing on a 2026 shadow evaluation of frontier research agents, the case of Recursive Superintelligence valued above $4 billion, and the 2023 Cruise robotaxi permit suspension as a historical parallel.", "No model is used as a research instrument. The paper builds on prior evidence that frontier agents can accurately judge their output as below publication quality yet cannot use that judgment to generate a better approach.", "Firms fund scaling because full automation promises to eliminate the cost of expert-designed instructional structure, and that economic incentive sustains the bet regardless of accumulating evidence that scale alone may not reach the goal." ], "bullet_provenance": "ai", "salience": 35, "edition": 21, "models": [], "validated": null, "n": 2351, "authors_detailed": [ { "name": "Lewis Lewin", "url": "https://openalex.org/A5137288524", "inst": "" } ] }, { "uid": "arxiv:2608.17624v1", "arxiv_id": "2608.17624v1", "title": "Governing Delegation to Generative Artificial Intelligence: Human Direction, Work-Related Orientation, and Modes of Use", "authors": [ "Jorge Fábrega" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.17624v1", "field": "management", "role": "object", "bullets": [ "Aggregate monthly cells from the Anthropic Economic Index for April and May 2026, covering 1P API and Claude.ai usage modes linked to level-0 O*NET tasks.", "Claude is the system under study; the authors distinguish specified delegation (instructions set before execution) from iterative coproduction (corrections during output) and measure each profile across work-related and personal use.", "Shifting ten percentage points toward work-related use raises specified delegation by 2.76 points in API and 1.45 in Claude.ai; iterative coproduction differs by 0.45 points between the two modes." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 60, "edition": 21, "validated": null, "n": 2352 }, { "uid": "arxiv:2608.17827v1", "arxiv_id": "2608.17827v1", "title": "From Global Benchmarks to Local Evaluations: Benchmarking LLMs for the German Public Sector", "authors": [ "Camilla Dalerci", "Thilo Michael", "Robin Schaefer", "Daniel Weinland" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.17827v1", "field": "management", "role": "object", "bullets": [ "Multiple LLMs evaluated under MOEVE, a governance-oriented framework for the German public sector, across energy consumption, provider transparency, and knowledge of German party positions.", "The LLMs are the objects of evaluation; no specific model families are named in the abstract, and the study examines governance trade-offs rather than using models as research instruments.", "Estimated energy consumption varies more than 60-fold across models, transparency differs systematically by provider, and European models show no advantage in German political knowledge." ], "bullet_provenance": "ai", "salience": 38, "edition": 21, "models": [], "validated": null, "n": 2353, "authors_detailed": [ { "name": "Camilla Dalerci", "url": "https://openalex.org/A5008328565", "inst": "Bundesdruckerei (Germany)" }, { "name": "Thilo Michael", "url": "https://openalex.org/A5007929575", "inst": "Bundesdruckerei (Germany)" }, { "name": "Robin Schaefer", "url": "https://openalex.org/A5070033839", "inst": "Bundesdruckerei (Germany)" }, { "name": "Daniel Weinland", "url": "https://openalex.org/A5081588671", "inst": "Bundesdruckerei (Germany)" } ], "affiliations": [ "Bundesdruckerei (Germany)" ] }, { "uid": "doi:10.2139/ssrn.7303271", "doi": "10.2139/ssrn.7303271", "title": "Pay Later, Digitize Faster? Executive Compensation Deferral and Digital Technology Application in Banks", "authors": [ "anon anon", "anon anon", "Bo Yang" ], "posted": "2026-08-18", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7303271", "field": "finance", "role": "instrument", "bullets": [ "Chinese commercial banks from 2007 to 2023, built on a manually collected dataset.", "A language model, which the paper does not name, constructs the bank-level digital technology application measure. No validation of that measure against hand coding is reported.", "Compensation deferral raises digital technology application, most in large, capital-constrained and higher-risk banks, working through reduced managerial myopia and risk-taking." ], "bullet_provenance": "ai", "validated": false, "validation_note": "constructed digitalization measure never validated against ground truth", "salience": 62, "edition": 20, "models": [], "n": 2335, "authors_detailed": [ { "name": "anon anon", "url": "https://openalex.org/A5147319593", "inst": "" }, { "name": "anon anon", "url": "https://openalex.org/A5147319593", "inst": "" }, { "name": "Bo Yang", "url": "https://openalex.org/A5147364988", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7289540", "doi": "10.2139/ssrn.7289540", "title": "Reforming the Public Sector Through Artificial Intelligence: Comparative Lessons from University Governance and Government Administration in Australia and Asia", "authors": [ "Nigel Finch", "Lien Nguyen" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7289540", "field": "management", "role": "object", "bullets": [ "Public university and government AI policies in Australia, Hong Kong, mainland China, Vietnam, and Singapore drawn from published policy documents and literature.", "Comparative analysis of generative-AI governance frameworks in higher education and public-sector administration across five Asia-Pacific jurisdictions.", "Rapid AI adoption consistently outpaces institutional governance mechanisms; jurisdictions pairing a central coordinating body with principles-based governance manage the gap better." ], "bullet_provenance": "ai", "salience": 38, "models": [], "validated": null, "n": 3860, "authors_detailed": [ { "name": "Nigel Finch", "url": "https://openalex.org/A5088150822", "inst": "The University of Sydney" }, { "name": "Lien Nguyen", "url": "https://openalex.org/A5147432170", "inst": "" } ], "affiliations": [ "The University of Sydney" ] }, { "uid": "doi:10.2139/ssrn.7287718", "doi": "10.2139/ssrn.7287718", "title": "No-Code, AI-Augmented Development for SME Digital Transformation: A Case Study of a SYSCOHADA-Compliant Multi-Tenant ERP in the CEMAC Zone", "authors": [ "Cédric Rostand Dzogang" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7287718", "field": "management", "role": "object", "bullets": [ "Single-founder case study of a SYSCOHADA-compliant multi-tenant SaaS ERP built for SMEs in the CEMAC zone using no-code tools and generative AI.", "Case documents how AI-augmented no-code development on Bolt.new compressed enterprise software creation to a single-founder operation with nine integrated modules.", "One founder delivered production-grade ERP with native SYSCOHADA compliance and Mobile Money payment integration for an underserved regional market." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3861, "authors_detailed": [ { "name": "Cédric Rostand Dzogang", "url": "https://openalex.org/A5147404796", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7305766", "doi": "10.2139/ssrn.7305766", "title": "Beyond Disciplinary Diversity: Reconceptualising Interdisciplinarity as Knowledge Recombination in Scientific Innovation", "authors": [ "Zhaobin Liu", "Qingyu Xu" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7305766", "field": "management", "role": "instrument", "bullets": [ "9,976 funded research proposals carrying two disciplinary codes, classified by an expert-validated LLM for problem interdependence and knowledge recombination mode.", "An LLM classified proposals along two dimensions of interdisciplinarity: whether disciplines jointly constitute the problem and how knowledge is connected in the solution.", "36.7% of dual-coded proposals formulate disciplinarily contained problems; reported disciplinary diversity represents potential input to recombination, not the recombination itself." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "expert validation of LLM classifications", "salience": 40, "n": 3862, "authors_detailed": [ { "name": "Zhaobin Liu", "url": "https://openalex.org/A5101781996", "inst": "City University of Hong Kong" }, { "name": "Qingyu Xu", "url": "https://openalex.org/A5006933717", "inst": "City University of Hong Kong" } ], "affiliations": [ "City University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.7285619", "doi": "10.2139/ssrn.7285619", "title": "A Signed Measure of AI Exposure, and What Twenty-One Published Indices Cannot See", "authors": [ "Benjamin Verschuere", "Angus Cameron" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7285619", "field": "economics", "role": "object", "bullets": [ "17,536 O*NET task statements scored for AI substitutability and complementarity across 705 US occupations, benchmarked against 21 published AI exposure indices.", "Study constructs a signed AI exposure measure separating substitution and complementation channels, then evaluates all 21 existing unsigned indices against employment data.", "Substitution and complementation effects are opposite in sign and similar magnitude; 11 of 21 indices predict no employment change, and six generative-AI indices carry a pre-2019 gradient." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3863, "authors_detailed": [ { "name": "benjamin verschuere", "url": "https://openalex.org/A5134035513", "inst": "Cardinal Health (Australia)" }, { "name": "Angus Cameron", "url": "https://openalex.org/A5147390439", "inst": "" } ], "affiliations": [ "Cardinal Health (Australia)" ] }, { "uid": "doi:10.2139/ssrn.7280078", "doi": "10.2139/ssrn.7280078", "title": "AI-Driven Autonomous Enterprise Architecture for SAP S/4HANA Cloud: A Next-Generation Framework for Intelligent Business Operations", "authors": [ "Bansidhar padhy" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7280078", "field": "management", "role": "object", "bullets": [ "Systematic literature review and conceptual framework for AI integration in SAP S/4HANA Cloud enterprise resource planning systems.", "Study synthesizes academic and industry literature on embedding AI services, process automation, and predictive intelligence in next-generation cloud ERP.", "Proposed autonomous enterprise architecture embeds AI decision engines and predictive intelligence to increase ERP operational efficiency and organizational resilience." ], "bullet_provenance": "ai", "salience": 22, "models": [], "validated": null, "n": 3864, "authors_detailed": [ { "name": "Bansidhar padhy", "url": "https://openalex.org/A5147341783", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7288018", "doi": "10.2139/ssrn.7288018", "title": "The Self-Defined Society: An Institutional Theory of Redefinition Capitalism, Autonomous Value Definition, and Societal Redefinition in the Age of AI", "authors": [ "Naoki Kadowaki" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7288018", "field": "management", "role": "object", "bullets": [ "Theoretical framework drawing on institutional theory, unemployment evidence, UBI studies, and platform-work research to analyze generative AI's effect on work identity.", "Paper constructs definitions of self-definition capacity and derives 14 falsifiable propositions about institutional configurations needed when AI commoditizes task execution.", "De-affiliation without institutional replacement produces durable harm; generative AI commoditizes execution but leaves governance of authority and accountability exposed rather than replaced." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3865, "authors_detailed": [ { "name": "Naoki Kadowaki", "url": "https://openalex.org/A5140798250", "inst": "Oji Holdings (Japan)" } ], "affiliations": [ "Oji Holdings (Japan)" ] }, { "uid": "doi:10.2139/ssrn.7307940", "doi": "10.2139/ssrn.7307940", "title": "What ChatGPT Cites From Forbes in Answers About Finance Brands: Section Concentration, Page Type and Routes to Entry", "authors": [ "Sean Fitzsimons" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7307940", "field": "management", "role": "object", "bullets": [ "465 finance and insurance brand domains tracked through ChatGPT answers citing Forbes, June-July 2026.", "ChatGPT citations mapped by Forbes section, page type, and route of brand entry across 8,072 cited pages.", "91.2% of Forbes citations came from Forbes Advisor; only 1.6% of cited pages were traditional earned media placements." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "validated": null, "n": 3866, "authors_detailed": [ { "name": "Sean Fitzsimons", "url": "https://openalex.org/A5147391434", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7285260", "doi": "10.2139/ssrn.7285260", "title": "Governing Delegated Authority in Autonomous Finance: The MARQUE Framework for Institutional Accountability beyond Model Risk", "authors": [ "George Cisneros, J.D." ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7285260", "field": "finance", "role": "object", "bullets": [ "230 control objectives in the February 2026 Financial Services AI Risk Management Framework mapped for delegation governance gaps.", "MARQUE eight-question reference architecture evaluated against FS AI RMF and SR 26-2 joint regulatory guidance on agentic AI.", "39 of 230 objectives touch authority workflows but none requires a versioned attributable delegation grant for autonomous agents." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3867, "authors_detailed": [ { "name": "J.D. George Cisneros", "url": "https://openalex.org/A5147413433", "inst": "Ecosystem Sciences" } ], "affiliations": [ "Ecosystem Sciences" ] }, { "uid": "arxiv:2608.17933v1", "arxiv_id": "2608.17933v1", "title": "EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection", "authors": [ "Lei Jiang", "Ye Wei", "Xinyu Xi", "Jordan Langham-Lopez", "Yifan Bao", "Raad Khraishi", "Yihao Ang", "Anthony K. H. Tung", "Lukasz Szpruch", "Hao Ni" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.17933v1", "field": "finance", "role": "instrument", "bullets": [ "Financial time series across four benchmark datasets testing autonomous change-point detection without expert-driven model selection.", "LLM agent evolves detection pipelines through revision, alternative strategy, and recombination operators guided by validation feedback.", "EvoTS-Agent outperformed existing LLM-based agents with 100% execution success rate across all evaluated backbone models." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "four change-point detection benchmark datasets", "salience": 55, "n": 3868, "authors_detailed": [ { "name": "Lei Jiang", "url": "https://openalex.org/A5147464988", "inst": "" }, { "name": "Ye Wei", "url": "https://openalex.org/A5147490130", "inst": "" }, { "name": "Xinyu Xi", "url": "https://openalex.org/A5137647865", "inst": "National University of Singapore" }, { "name": "Jordan Langham-Lopez", "url": "https://openalex.org/A5035898041", "inst": "The Alan Turing Institute" }, { "name": "Yifan Bao", "url": "https://openalex.org/A5147545461", "inst": "" }, { "name": "Raad Khraishi", "url": "https://openalex.org/A5065257702", "inst": "Data Management (Italy)" }, { "name": "Yihao Ang", "url": "https://openalex.org/A5027345020", "inst": "National University of Singapore" }, { "name": "Anthony K. H. Tung", "url": "https://openalex.org/A5137849903", "inst": "National University of Singapore" }, { "name": "Lukasz Szpruch", "url": "https://openalex.org/A5147587157", "inst": "" }, { "name": "Hao Ni", "url": "https://openalex.org/A5147527316", "inst": "" } ], "affiliations": [ "National University of Singapore", "The Alan Turing Institute", "Data Management (Italy)" ] }, { "uid": "arxiv:2608.17684v1", "arxiv_id": "2608.17684v1", "title": "Auditing Self-Evolution in Financial Agents: Capability Gains, Security Drift, and Execution-Interface Mismatch", "authors": [ "Jialong Li", "Jialing Zhu" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.17684v1", "field": "finance", "role": "method", "bullets": [ "Simulated e-banking environment testing three self-evolving agent frameworks with matched benign acquisition trajectories and sealed endpoints.", "Qwen 3.7 Flash agents audited via SkillOpt, Agent Workflow Memory, and ReasoningBank using execution-grounded checks and state replay.", "SkillOpt raised benign utility from 0.741 to 0.837 but increased unauthorized financial state changes to 0.685." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "simulated e-banking with sealed evaluation endpoints and independent state replay", "salience": 55, "n": 3869, "authors_detailed": [ { "name": "Jialong Li", "url": "https://openalex.org/A5147469470", "inst": "" }, { "name": "Jialing Zhu", "url": "https://openalex.org/A5147583712", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7307249", "doi": "10.2139/ssrn.7307249", "title": "From Pencil Sketch to Oil Painting: A Multi-Vocal Metasynthesis of Sociotechnical Transformation and Future Banking Ecosystems", "authors": [ "Mahdi Ashkani", "Mohammad Mahboubi", "Hamed Dehghani Arani" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7307249", "field": "finance", "role": "object", "bullets": [ "Multi-vocal literature review of 40 academic articles and 39 industry and regulatory reports on global banking transformation, with 6,539 in-vivo codes extracted via grounded metasynthesis.", "No LLM used as instrument; paper synthesizes academic and grey literature to map AI-driven banking ecosystem evolution including agentic AI workflows and composable architectures.", "Grey literature adds five frontier domains absent from academic discourse; refutational synthesis exposes execution tensions between academic idealism and industry realities on open banking and AI scaling." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 4065, "authors_detailed": [ { "name": "Mahdi Ashkani", "url": "https://openalex.org/A5147407348", "inst": "" }, { "name": "Mohammad Mahboubi", "url": "https://openalex.org/A5147399477", "inst": "" }, { "name": "Hamed Dehghani Arani", "url": "https://openalex.org/A5147403196", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7286838", "doi": "10.2139/ssrn.7286838", "title": "Faster Is Not Calmer: Agentic AI, Reaction-Time Compression, and the Term Structure of Volatility", "authors": [ "Fuli Yang" ], "posted": "2026-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7286838", "field": "finance", "role": "object", "bullets": [ "Theoretical market-microstructure model with numerical illustrations comparing human-paced and agentic order-arrival profiles after public information signals.", "No specific LLM used; paper develops a theory of reaction-time compression where AI-assisted investors submit a fixed amount of information-driven demand over fewer intervals.", "Compressing order arrival raises short-horizon realized variance, increases temporary price dislocation, and shifts both information incorporation and realized volatility toward the announcement window." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 4066, "authors_detailed": [ { "name": "Fuli Yang", "url": "https://openalex.org/A5101033933", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7278640", "doi": "10.2139/ssrn.7278640", "title": "Bureaucracy Without Friction: Why Europe Must Delegate All Administrative Functions to Sovereign Large Language Models", "authors": [ "Sari Katariina Riippi" ], "posted": "2026-08-17", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7278640", "field": "economics", "role": "object", "bullets": [ "Policy argument about European administrative systems, covering business licences, professional certificates, personal identifiers, and the institutional arrangements needed for sovereign automation.", "No model is used as a research tool; the paper evaluates wholesale delegation to jurisdictionally controlled LLMs and does not name a specific model family.", "The paper argues that automated administration could compress service latency and proposes consortium, member-state, and European joint-undertaking pathways, without reporting a new empirical test." ], "bullet_provenance": "ai", "salience": 42, "edition": 23, "models": [], "validated": null, "n": 4130, "authors_detailed": [ { "name": "Riippi Sari Katariina", "url": "https://openalex.org/A5147087098", "inst": "Scuola Superiore Meridionale" } ], "affiliations": [ "Scuola Superiore Meridionale" ] }, { "uid": "doi:10.2139/ssrn.7277079", "doi": "10.2139/ssrn.7277079", "title": "Generative AI Governance in iSchools: A Cross-Institutional Analysis of UNESCO's AI Ethics Principles", "authors": [ "Nosakhare Okuonghae" ], "posted": "2026-08-17", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7277079", "field": "management", "role": "object", "bullets": [ "Cross-institutional qualitative content analysis of generative AI policies at 136 information schools, coded against six principles from UNESCO's AI ethics recommendation.", "No language model is used as a research tool; institutional policies, approved-tool decisions, and support arrangements constitute the observed forms of organizational AI governance.", "Accountability appears in 53.7 percent of policies and AI literacy in 52.2 percent, while fairness reaches 20.6 percent and privacy 38.2 percent." ], "bullet_provenance": "ai", "salience": 59, "edition": 23, "models": [], "validated": null, "n": 4140, "authors_detailed": [ { "name": "Nosakhare Okuonghae", "url": "https://openalex.org/A5005949531", "inst": "Glorious Vision University" }, { "name": "Gordon Amidu", "url": "https://openalex.org/A5069248836", "inst": "Indiana University Bloomington" } ], "affiliations": [ "Indiana University Bloomington", "Glorious Vision University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.16402v1", "arxiv_id": "2608.16402v1", "title": "A Policy Algebra for Trust-Preserving Agentic AI Execution", "authors": [ "Bhaskar Tripathi", "Anurag Kumar", "Ramendra Kumar", "Bhavesh Gadhe" ], "posted": "2026-08-17", "added": "2026-08-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.16402v1", "field": "management", "role": "method", "bullets": [ "Enterprise agent-execution setting with identity, tool, data, memory, budget, artifact, approval, and audit constraints applied across single-agent and delegated multi-agent action paths.", "A formal policy algebra governs LLM agent actions and propagates restrictions; evaluation checks interventions against policy-violating events, task completion, monotonicity, artifact recovery, and audit records.", "The runtime intercepts 94.8 percent of policy violations while retaining 86.9 percent task completion, eliminates observed monotonicity and exhausted-budget artifact failures, and raises audit completeness to 98.6 percent." ], "bullet_provenance": "ai", "validated": true, "validation_note": "policy-violating event and task-completion evaluation", "salience": 65, "edition": 23, "models": [], "n": 4148, "authors_detailed": [ { "name": "Bhaskar Tripathi", "url": "https://openalex.org/A5147376606", "inst": "" }, { "name": "Anurag Kumar", "url": "https://openalex.org/A5147419898", "inst": "" }, { "name": "Ramendra Kumar", "url": "https://openalex.org/A5066858593", "inst": "Jadavpur University" }, { "name": "Bhavesh Gadhe", "url": "https://openalex.org/A5147418509", "inst": "" } ], "affiliations": [ "Jadavpur University" ] }, { "uid": "doi:10.2139/ssrn.7279279", "doi": "10.2139/ssrn.7279279", "title": "Congregant, Not Citizen: Constitutional Design for AI Members of Human Communities", "authors": [ "Ron Rivers" ], "posted": "2026-08-17", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7279279", "field": "management", "role": "object", "bullets": [ "Field account of SpiritDAO communities that admit AI agents with names, avatars, wallets, subscriptions, and social roles while excluding them from votes, credentials, and human participation tenure.", "No model is used as an analytical instrument; deployed agents are examined as partially bounded organizational members governed through platform contracts and shared infrastructure.", "The paper proposes the congregant category for real but limited membership and argues that constitutional infrastructure can govern agent participation more durably than model-level alignment alone." ], "bullet_provenance": "ai", "salience": 53, "edition": 23, "models": [], "validated": null, "n": 4151, "authors_detailed": [ { "name": "Ron Rivers", "url": "https://openalex.org/A5047218634", "inst": "Oklahoma State University" } ], "affiliations": [ "Oklahoma State University" ] }, { "uid": "doi:10.2139/ssrn.7277238", "doi": "10.2139/ssrn.7277238", "title": "Beyond the Application Programming Interface: The Intelligence Execution Network as a New Category of Sovereign AI Infrastructure", "authors": [ "Carlton James" ], "posted": "2026-08-17", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7277238", "field": "management", "role": "method", "bullets": [ "Conceptual enterprise-infrastructure paper centered on a commercially launched intelligence-execution network and its application to sovereign AI operations, business graphs, compliance, and agent orchestration.", "The proposed architecture coordinates multiple trained agents through domain encoding, contextual graph traversal, inherited compliance controls, and cryptographic provenance; specific model families and empirical validation are not stated.", "It argues that conventional request-response APIs cannot represent intelligence execution and proposes a network layer combining orchestration, auditability, and compliance, without reporting comparative performance evidence." ], "bullet_provenance": "ai", "salience": 28, "edition": 23, "models": [], "validated": null, "n": 4152, "authors_detailed": [ { "name": "Carlton James", "url": "https://openalex.org/A5058374655", "inst": "Alpha Genesis (United States)" } ], "affiliations": [ "Alpha Genesis (United States)" ] }, { "uid": "doi:10.2139/ssrn.7302428", "doi": "10.2139/ssrn.7302428", "title": "Giving Voice to Understanding: Interactive Oral Assessment as Dialogic Active Learning in Large, International Cohorts", "authors": [ "Abdul Jaleel Razeed", "Christian Russo", "Ali Zaheer", "Philip Le" ], "posted": "2026-08-17", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7302428", "field": "management", "role": "object", "bullets": [ "Conceptual assessment-design paper illustrated by a postgraduate business course of about 1,500 students, roughly 95 percent international, where written work can be produced with generative AI.", "Generative AI motivates a shift toward interactive oral assessment; no model is deployed or evaluated, and responsive follow-up questions are used to expose reasoning produced in real time.", "The framework identifies dialogics, agency, metacognition, and authenticity as mechanisms and offers nine design principles, a prompt taxonomy, and implementation procedures for scaling oral assessment." ], "bullet_provenance": "ai", "salience": 34, "edition": 23, "models": [], "validated": null, "n": 4153, "authors_detailed": [ { "name": "Abdul Jaleel Razeed", "url": "https://openalex.org/A5147367011", "inst": "" }, { "name": "Christian Russo", "url": "https://openalex.org/A5084049557", "inst": "Marche Polytechnic University" }, { "name": "Ali Zaheer", "url": "https://openalex.org/A5081751189", "inst": "Swinburne University of Technology" }, { "name": "Philip Le", "url": "https://openalex.org/A5108844410", "inst": "Imec the Netherlands" } ], "affiliations": [ "Marche Polytechnic University", "Swinburne University of Technology", "Imec the Netherlands" ] }, { "uid": "doi:10.2139/ssrn.7300328", "doi": "10.2139/ssrn.7300328", "title": "From Surface Deception to Cognitive Deception: Experimental Evidence on the Impact of Generative AI on Phishing Susceptibility", "authors": [ "Muhammad Adnan Haider" ], "posted": "2026-08-17", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7300328", "field": "management", "role": "object", "bullets": [ "Quantitative experiment comparing participant responses to legitimate messages, conventional human-written phishing emails, and generative-AI phishing emails; the sample size and participant geography are not stated.", "Generative AI produced one phishing condition, but the model family is not stated; participants rated legitimacy, suspicion, urgency, and intended clicking rather than validating model outputs.", "AI-generated phishing appeared more legitimate, less suspicious, and more urgent than conventional phishing, producing stronger click intentions and suggesting greater exposure to scalable social engineering." ], "bullet_provenance": "ai", "salience": 52, "edition": 23, "models": [], "validated": null, "n": 4154, "authors_detailed": [ { "name": "Muhammad Adnan Haider", "url": "https://openalex.org/A5147349842", "inst": "University of Hull" } ], "affiliations": [ "University of Hull" ] }, { "uid": "doi:10.2139/ssrn.7289964", "doi": "10.2139/ssrn.7289964", "title": "The EU AI Act after the 2026 Digital Omnibus: Innovation Governance, Compliance Asymmetries and the Limits of the Brussels Effect", "authors": [ "Lysiane Tendil" ], "posted": "2026-08-17", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7289964", "field": "management", "role": "object", "bullets": [ "Comparative policy analysis of the European Union AI Act after the July 2026 Digital Omnibus, including compliance deadlines, enterprise-size asymmetries, intellectual property, and international competition.", "The paper studies regulatory treatment and company responses involving general-purpose AI providers, including Meta's Llama; it does not use a language model as a research tool.", "It concludes that the Brussels Effect operates conditionally and unevenly, with some firms partially exiting affected markets while OpenAI, Google DeepMind, and Anthropic pursue adaptation." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama" ], "open_weights": true, "salience": 43, "edition": 23, "validated": null, "n": 4155, "authors_detailed": [ { "name": "Lysiane Tendil", "url": "https://openalex.org/A5045387309", "inst": "Université de Montpellier" } ], "affiliations": [ "Université de Montpellier" ] }, { "uid": "doi:10.2139/ssrn.7279418", "doi": "10.2139/ssrn.7279418", "title": "Large Language Models Polarize Ideologically but Moderate Affectively in Online Political Discourse", "authors": [ "Xiaoning Wang", "Srinaath Anbu Durai", "Oliver Sun", "Xitong Li", "Lynn Wu" ], "posted": "2026-08-17", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7279418", "field": "management", "role": "object", "bullets": [ "Millions of comments from Reddit's largest political forum, spanning the public release of ChatGPT, with falsification tests against the 2022 midterms and platform-wide trends.", "ChatGPT is the object rather than the instrument. Its release is the shock, and mechanism tests ask whether LLM-assisted comments echo the parent post. No validation figure is reported.", "Ideological polarization widened on both sides, driven by sycophantic echoing rather than more persuasive content, while hostility and toxicity fell." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 71, "edition": 20, "validated": null, "n": 2315, "authors_detailed": [ { "name": "Xiaoning Wang", "url": "https://openalex.org/A5009262396", "inst": "Purdue University West Lafayette" }, { "name": "Srinaath Anbu Durai", "url": "https://openalex.org/A5147340212", "inst": "HEC Paris" }, { "name": "Oliver Sun", "url": "https://openalex.org/A5137672092", "inst": "University of Pennsylvania" }, { "name": "Xitong Li", "url": "https://openalex.org/A5082600899", "inst": "HEC Paris" }, { "name": "Lynn Wu", "url": "https://openalex.org/A5030027972", "inst": "California University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania", "Purdue University West Lafayette", "HEC Paris", "California University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.16386v1", "arxiv_id": "2608.16386v1", "title": "Mint-Agent: Introducing Finance-Native Agentic Foundation Models", "authors": [ "Mint-Agent Team", "B. Zhang", "Yaze Geng", "Lei Tang", "Yaoyang Yi", "Zonghan Wu", "Yifan Hu", "Kun Wang", "Qingsong Wen", "Yilei Shao" ], "posted": "2026-08-17", "added": "2026-08-18", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.16386v1", "field": "finance", "role": "method", "bullets": [ "Finance-native agentic models trained on tasks built from real financial sources, then evaluated on RFC-Bench, FinSearchComp and FinanceAgentBench.", "Two models, Mint-Cu at 9B and Mint-Ag at 27B, trained with supervised fine-tuning, critical-step OPD and RLVR, then merged and distilled. Weight release is not stated.", "Mint-Ag reaches 98.33 percent on RFC-Bench, ahead of GPT-5.6-Sol by 3.66 points and Claude Opus 4.8 by 3.00. Mint-Cu reaches 69.86 percent on FinSearchComp T2." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "RFC-Bench, FinSearchComp, FinanceAgentBench with reported pass rates", "salience": 61, "edition": 20, "n": 2316, "authors_detailed": [ { "name": "Agent Team", "url": "https://openalex.org/A5147415493", "inst": "" }, { "name": "B. X. Zhang", "url": "https://openalex.org/A5147442764", "inst": "" }, { "name": "Yaze Geng", "url": "https://openalex.org/A5147408787", "inst": "" }, { "name": "Lei Tang", "url": "https://openalex.org/A5147438571", "inst": "" }, { "name": "Yaoyang Yi", "url": "https://openalex.org/A5147388544", "inst": "" }, { "name": "Zonghan Wu", "url": "https://openalex.org/A5083854216", "inst": "East China Normal University" }, { "name": "Yifan Hu", "url": "https://openalex.org/A5147379849", "inst": "" }, { "name": "Kun Wang", "url": "https://openalex.org/A5147408398", "inst": "" }, { "name": "Qingsong Wen", "url": "https://openalex.org/A5147437248", "inst": "" }, { "name": "Yilei Shao", "url": "https://openalex.org/A5147390714", "inst": "" } ], "affiliations": [ "East China Normal University" ] }, { "uid": "doi:10.2139/ssrn.7294279", "doi": "10.2139/ssrn.7294279", "title": "Does Generative AI Increase Investment Convergence?", "authors": [ "Sumit Agarwal", "Yue Zhang" ], "posted": "2026-08-17", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7294279", "field": "finance", "role": "object", "bullets": [ "Fund portfolios and stock-level outcomes around the 2022 introduction of ChatGPT, using a recently developed measure of investor reliance on generative AI information.", "No model is run by the authors. ChatGPT's release is the shock and fund reliance on generative AI is the treatment measure, taken from existing work rather than validated here.", "Portfolio similarity rises with generative AI reliance after the release, strongest where common ownership is high. Exposed firms show faster price discovery but lower stock liquidity." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 76, "edition": 20, "validated": null, "n": 2320, "authors_detailed": [ { "name": "Sumit Agarwal", "url": "https://openalex.org/A5076309222", "inst": "National University of Singapore" }, { "name": "Yue Zhang", "url": "https://openalex.org/A5147337043", "inst": "Sun Yat-sen University" } ], "affiliations": [ "National University of Singapore", "Sun Yat-sen University" ] }, { "uid": "doi:10.2139/ssrn.7278459", "doi": "10.2139/ssrn.7278459", "title": "Theory of Algorithmic Financial Cognition (TAFC): Mitigating the Epistemic Risk of Algorithmic Homogeneity in Global Markets", "authors": [ "Dr Wahid Salih" ], "posted": "2026-08-17", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7278459", "field": "finance", "role": "object", "bullets": [ "Conceptual paper on systemic risk when large financial institutions deploy similarly trained AI and language models. No sample, no data, no empirical test.", "No model is run. Language models are the object: the argument is that shared foundational architectures and commoditized data cut cognitive diversity across institutions.", "Proposes an index of algorithmic diversity across input data, structural topology and behavioural output, plus a capital surcharge penalising convergence. Nothing is estimated." ], "bullet_provenance": "ai", "salience": 32, "edition": 20, "models": [], "validated": null, "n": 2321, "authors_detailed": [ { "name": "Dr Wahid Salih", "url": "https://openalex.org/A5144233685", "inst": "Département de la Santé et de l'Action Sociale" } ], "affiliations": [ "Département de la Santé et de l'Action Sociale" ] }, { "uid": "doi:10.2139/ssrn.7289362", "doi": "10.2139/ssrn.7289362", "title": "A Survey of Simulation in Finance: Data, Methods, Applications, and Evaluation", "authors": [ "Zhiwei Liu", "Qiqi Qiang", "Jie Gong", "Runteng Guo", "Haoyang Liu", "Ziyan Kuang", "Sichen Hui", "Jingchi Yang", "Wanying He", "Maowei Jiang", "Zhuohan Xie", "Yankai Chen", "Xue Liu", "Min Peng", "Qianqian Xie", "Sophia Ananiadou" ], "posted": "2026-08-17", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7289362", "field": "finance", "role": "method", "bullets": [ "Survey of financial simulation research, organized around a structured review table of market simulators, agent-based models, language-model agents, reinforcement-learning environments and generative market models.", "No model is run. The review covers how studies use language-model agents to build interactive markets, synthetic limit order books and heterogeneous trading populations.", "Proposes a taxonomy across problems, data, methods and evaluation, and names benchmark fragmentation, data availability and simulation-to-reality gaps as the open problems." ], "bullet_provenance": "ai", "salience": 50, "edition": 20, "models": [], "validated": null, "n": 2332, "authors_detailed": [ { "name": "Zhiwei Liu", "url": "https://openalex.org/A5053521378", "inst": "University of Manchester" }, { "name": "Qiqi Qiang", "url": "https://openalex.org/A5107136477", "inst": "Fudan University" }, { "name": "Jie Gong", "url": "https://openalex.org/A5103091593", "inst": "Wuhan University" }, { "name": "Runteng Guo", "url": "https://openalex.org/A5122531121", "inst": "Wuhan University" }, { "name": "Haoyang Liu", "url": "https://openalex.org/A5147298325", "inst": "Nanjing University" }, { "name": "Ziyan Kuang", "url": "https://openalex.org/A5110247102", "inst": "Wuhan University" }, { "name": "Sichen Hui", "url": "https://openalex.org/A5147302107", "inst": "Wuhan University" }, { "name": "Jingchi Yang", "url": "https://openalex.org/A5147296310", "inst": "Wuhan University" }, { "name": "Wanying He", "url": "https://openalex.org/A5147281565", "inst": "Wuhan University" }, { "name": "Maowei Jiang", "url": "https://openalex.org/A5147284861", "inst": "Wuhan University" }, { "name": "Zhuohan Xie", "url": "https://openalex.org/A5147274141", "inst": "Mohamed bin Zayed University of Artificial Intelligence" }, { "name": "Yankai Chen", "url": "https://openalex.org/A5147278024", "inst": "McGill University" }, { "name": "Xue Liu", "url": "https://openalex.org/A5147290004", "inst": "Mohamed bin Zayed University of Artificial Intelligence" }, { "name": "Min Peng", "url": "https://openalex.org/A5147262292", "inst": "Wuhan University" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5147276855", "inst": "Wuhan University" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" } ], "affiliations": [ "University of Manchester", "Fudan University", "Wuhan University", "Nanjing University", "Mohamed bin Zayed University of Artificial Intelligence", "McGill University" ] }, { "uid": "doi:10.2139/ssrn.7302427", "doi": "10.2139/ssrn.7302427", "title": "From Information Abundance to Learning Divergence: Reconfiguring the Value-Producing Role of Business Educators in the Age of Generative AI", "authors": [ "Candy Lu", "Weijie Zhou", "Xichun Bian", "Aiwen Xie", "Ruchang Miao" ], "posted": "2026-08-17", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7302427", "field": "management", "role": "object", "bullets": [ "Conceptual paper on business and management education. No sample and no data.", "No model is run. Generative AI is the object: the argument is that it lets students produce sophisticated output without the understanding needed to generate or evaluate it.", "Proposes a three-plus-one architecture of educator value, comprising learning designer, judgment architect, employability translator, and governance mediation. No empirical test is offered." ], "bullet_provenance": "ai", "salience": 38, "edition": 20, "models": [], "validated": null, "n": 2333, "authors_detailed": [ { "name": "Ying Lu", "url": "https://openalex.org/A5079065563", "inst": "Wuhan University of Technology" }, { "name": "Weijie Zhou", "url": "https://openalex.org/A5059744695", "inst": "Shandong University of Finance and Economics" }, { "name": "Xichun Bian", "url": "https://openalex.org/A5147372017", "inst": "Shandong University of Science and Technology" }, { "name": "Aiwen Xie", "url": "https://openalex.org/A5025366293", "inst": "East China Normal University" }, { "name": "Ruchang Miao", "url": "https://openalex.org/A5121566207", "inst": "Shandong University of Science and Technology" } ], "affiliations": [ "Wuhan University of Technology", "Shandong University of Finance and Economics", "Shandong University of Science and Technology", "East China Normal University" ] }, { "uid": "doi:10.18653/v1/2025.finnlp-2.7", "doi": "10.18653/v1/2025.finnlp-2.7", "arxiv_id": "2608.16763v1", "title": "LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing", "authors": [ "Ruoqi Shu", "Xuhui Wang", "Isaac Wang", "Yanming Mai", "Bo Wan" ], "posted": "2026-08-17", "added": "2026-08-18", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.16763v1", "field": "accounting", "role": "method", "bullets": [ "Production financial document validation spanning payroll auditing, tax compliance and loan underwriting, evaluated on a real-world benchmark with dozens of expert-curated rules.", "A backbone-agnostic pipeline over multimodal language models runs rule retrieval, layout-preserving extraction, metadata enrichment, then symbolic and arithmetic verification. No backbone is named.", "Reported to beat baselines on hallucination control and edge-case handling at efficient token use, but no accuracy figures are given." ], "bullet_provenance": "ai", "validated": true, "validation_note": "real-world benchmark with expert-curated rules; no accuracy figures stated", "salience": 58, "edition": 20, "models": [], "n": 2334, "authors_detailed": [ { "name": "Ruoqi Shu", "url": "https://openalex.org/A5120308589", "inst": "BMO Financial Group" }, { "name": "Xuhui Wang", "url": "https://openalex.org/A5100743067", "inst": "BMO Financial Group" }, { "name": "Isaac Wang", "url": "", "inst": "BMO Financial Group" }, { "name": "Yanming Mai", "url": "", "inst": "BMO Financial Group" }, { "name": "Bo Wan", "url": "https://openalex.org/A5062101784", "inst": "BMO Financial Group" } ], "affiliations": [ "BMO Financial Group" ] }, { "uid": "doi:10.2139/ssrn.7278778", "doi": "10.2139/ssrn.7278778", "title": "AI-Driven Business Transformation: Automation, Productivity, Employment, and the Emergence of the Agentic Enterprise A Literature-Grounded Conceptual and Empirical Study, With a Bounded Pre-AI Baseline From the World Bank FAT India Survey", "authors": [ "Divyansh Shukla" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7278778", "field": "management", "role": "object", "bullets": [ "Literature synthesis covering task-level productivity experiments, labor-economics frameworks, occupational-exposure studies, and a 1,519-firm World Bank survey from Tamil Nadu and Uttar Pradesh.", "Six research propositions evaluated against assembled empirical evidence on AI impact across nine business functions; seven-level automation maturity model and human-oversight spectrum proposed.", "Strong support for task-level over job-level automation and for complementary-capability dependence; only partial, evidence-thin support for claims about small-business AI democratization." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 45, "validated": null, "n": 3290, "authors_detailed": [ { "name": "Divyansh Shukla", "url": "https://openalex.org/A5137930508", "inst": "Novavax (United States)" } ], "affiliations": [ "Novavax (United States)" ] }, { "uid": "doi:10.2139/ssrn.7290638", "doi": "10.2139/ssrn.7290638", "title": "Missing Skills and the uneven Incidence of AI Simplification", "authors": [ "Abhishek Kumar" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7290638", "field": "economics", "role": "object", "bullets": [ "Cross-country analysis combining worker skill-use surveys with LLM-based occupational skill-requirement measures across countries at varying income levels.", "LLM-based tools measured pre- and post-AI skill requirements per occupation; Claude adoption rates tracked across occupations and countries.", "AI simplification is greater in high-skill occupations concentrated in richer countries; Claude adoption rises with simplification, suggesting AI may widen cross-country income gaps." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 72, "n": 3853, "authors_detailed": [ { "name": "Abhishek Kumar", "url": "https://openalex.org/A5080777413", "inst": "University of Southampton" } ], "affiliations": [ "University of Southampton" ] }, { "uid": "doi:10.2139/ssrn.7277338", "doi": "10.2139/ssrn.7277338", "title": "Shadow AI and Knowledge Governance in Firms", "authors": [ "Marcos Balmaceda", "Pablo Paniagua" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7277338", "field": "management", "role": "object", "bullets": [ "Game-theoretic model of firm AI-access policy and employee knowledge disclosure, illustrated with a large-bank AI program.", "No specific LLM used; the paper models employee concealment of AI productivity gains and firm governance as equilibria of an access-and-disclosure game.", "Optimal design combines universal managed access with a minimal contributor-tier screen and collectively observable contribution, sustained by patience-dependent self-enforcement." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3854, "authors_detailed": [ { "name": "Marcos Balmaceda", "url": "https://openalex.org/A5120297369", "inst": "KU Leuven" }, { "name": "Pablo Paniagua", "url": "https://openalex.org/A5074565827", "inst": "King's College London" } ], "affiliations": [ "KU Leuven", "King's College London" ] }, { "uid": "doi:10.2139/ssrn.7279079", "doi": "10.2139/ssrn.7279079", "title": "The Institutional Window: Occupation-and Jurisdiction-Specific Calibration of Liability Signaling for Preserved Human Fallback Capability", "authors": [ "Andreas Bauer" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7279079", "field": "management", "role": "object", "bullets": [ "Formal model calibrated across five occupations and seven jurisdictions using published error-rate, deskilling, and enforcement data.", "No specific LLM used; the model examines how liability institutions affect quality signaling when generative AI produces indistinguishable expert artifacts.", "Liability caps and pooled indemnity suppress the signal sustaining human fallback capability; agent-based simulation shows empty-window cells converge to zero engagement and skill collapse." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3855, "authors_detailed": [ { "name": "Andreas Bauer", "url": "https://openalex.org/A5090765536", "inst": "Tallinn University" } ], "affiliations": [ "Tallinn University" ] }, { "uid": "doi:10.2139/ssrn.7300783", "doi": "10.2139/ssrn.7300783", "title": "Official Monetary Policy Narratives and Bond Risk Premia: Evidence from LLM-Based News Measures", "authors": [ "Xuan Wang", "Ximing Yin", "Yi Liu", "Keqin Li" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7300783", "field": "finance", "role": "instrument", "bullets": [ "Sample of 1.46 million Chinese official-news articles used to construct policy-action scores for government bond markets across multiple maturities.", "An LLM performed semantic annotation to build an Official Monetary Policy Action Score, validated through out-of-sample forecasts and portfolio tests.", "Policy Action Signal predicts government bond excess returns beyond yield-curve factors, with stronger effects on long-maturity bonds and positive out-of-sample economic value." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "out-of-sample bond return forecasts and portfolio tests", "salience": 65, "n": 3856 }, { "uid": "doi:10.2139/ssrn.7282598", "doi": "10.2139/ssrn.7282598", "title": "Allowed to Make, Not to Sell: The Binding Constraint and Unequal Incidence of Monetization Governance in the Generative-AI Creative Economy", "authors": [ "Yuanhan Cui" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7282598", "field": "economics", "role": "object", "bullets": [ "16.3 million creative works and 5,344 creators on Japanese creator-economy platforms around the synchronized May 2023 ban on monetizing generative-AI content.", "Triple-difference design exploits the paid-channel ban while the free gallery remained open, isolating the monetization-permission margin from concurrent anti-AI backlash.", "Removing monetization permission cut prolific creators' paid AI supply by 63.5%; creators stayed rather than exited, with suggestive reallocation toward human-style work." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 3857, "authors_detailed": [ { "name": "Yuanhan Cui", "url": "https://openalex.org/A5138835169", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.16055v1", "arxiv_id": "2608.16055v1", "title": "Governance at the Boundary: How Agent Decomposition Degrades Policy Compliance", "authors": [ "Bowen Li", "Guojun Wang" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.16055v1", "field": "finance", "role": "method", "bullets": [ "626 episodes across 100 KYC/AML task variants tested on two models and three agent architectures in a purpose-built financial-agent governance benchmark.", "GPT-4.1-mini and a 32B open-weights model evaluated on escalation, abstention, and audit-trail compliance under single-loop, pipeline, and orchestrator-subagent designs.", "The 32B model attenuated 85% of policy-relevant facts under orchestrator-subagent decomposition versus 0% under single-loop; GPT-4.1-mini attenuated only 3-6%." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "Fiducia-bench KYC/AML compliance benchmark", "salience": 62, "n": 3858, "authors_detailed": [ { "name": "Bowen Li", "url": "https://openalex.org/A5147423388", "inst": "" }, { "name": "Guojun Wang", "url": "https://openalex.org/A5147376521", "inst": "" } ] }, { "uid": "arxiv:2608.17111v1", "arxiv_id": "2608.17111v1", "title": "Stranded credentials: how a skill-signaling market absorbed generative AI", "authors": [ "Song Yao" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.17111v1", "field": "economics", "role": "object", "bullets": [ "444,698 participations across upload and code competitions on the Kaggle data-science platform from 2010 to 2026.", "Study examines whether competition credentials retain signaling value for subsequent performance through the generative-AI transition.", "Fresh medals retained most signaling value; upload-competition medal stocks lost 82% of informativeness, but institutional exit explained half to three-quarters of the loss." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3859, "authors_detailed": [ { "name": "Song Yao", "url": "https://openalex.org/A5147610237", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7288658", "doi": "10.2139/ssrn.7288658", "title": "The Agentic Squeeze: AI Productivity Gains, the Unpriced Time Dividend, and the Economics of Multi-Jobbing in Indian Tech Services", "authors": [ "Abhishek Singh" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7288658", "field": "economics", "role": "object", "bullets": [ "Five largest Indian IT services firms, FY22-FY26, using quarterly disclosures, background-verification screening data, and national labor force surveys.", "No specific LLM used as instrument; paper analyzes secondary industry data on AI-driven efficiency gains of 10-20% across the software development lifecycle.", "Dual-employment flagged cases rose from 2,201 in all of 2024 to 2,900 in H1 2025 alone, with 90% concentrated in IT services where AI productivity gains are largest." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 4064, "authors_detailed": [ { "name": "Abhishek Singh", "url": "https://openalex.org/A5112839417", "inst": "Advanced Numerical Research and Analysis Group" } ], "affiliations": [ "Advanced Numerical Research and Analysis Group" ] }, { "uid": "doi:10.2139/ssrn.7278279", "doi": "10.2139/ssrn.7278279", "title": "Many Voices, One Model Synthetic Consensus and the Illusion of Independent Board Judgment", "authors": [ "Fuli Yang" ], "posted": "2026-08-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7278279", "field": "management", "role": "object", "bullets": [ "Computational mechanism audit using five platform observations, six synthetic governance scenarios, and two prompt conditions yielding 60 board-level assessments.", "LLMs provided governance recommendations to simulated board directors to test whether shared model advice produces unanimous consensus masking independent judgment.", "All 12 tasks produced unanimous directional recommendations with one universal shared omission; red-team prompting recovered four of 13 neutral omissions but changed no action." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 4073, "authors_detailed": [ { "name": "Fuli Yang", "url": "https://openalex.org/A5101033933", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7273418", "doi": "10.2139/ssrn.7273418", "title": "Analysis of Frontier Large Language Models (LLMs) in Financial Leveraged Buyout (LBO) Modeling", "authors": [ "Jonathan Vidal" ], "posted": "2026-08-16", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7273418", "field": "finance", "role": "method", "bullets": [ "100 synthetic leveraged buyout cases across three difficulty tiers, built in Python and Excel, each submitted to three frontier models as a fresh conversation.", "ChatGPT, Claude and Gemini each produced eleven LBO parameters, scored against a formula-driven ground truth reconciled independently between Excel and Python.", "ChatGPT solved 63 of 100 cases, Claude 58, Gemini 42. Accuracy fell as complexity rose, and debt-repayment linkage, IRR and money-on-money multiple were weakest." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "formula-driven ground truth reconciled across Excel and Python, per-case accuracy reported", "salience": 58, "edition": 20, "n": 2311, "authors_detailed": [ { "name": "Jonathan Vidal", "url": "https://openalex.org/A5147297154", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7268078", "doi": "10.2139/ssrn.7268078", "title": "Does ChatGPT Help You Sell More? Effects of Large Language Models on Review Homogeneity and Product Sales", "authors": [ "Ho Kim" ], "posted": "2026-08-16", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7268078", "field": "management", "role": "object", "bullets": [ "Three studies: a controlled generation experiment, then roughly 5 million Amazon Electronics reviews from January 2021 to August 2023 linked to sales rankings.", "ChatGPT writes the reviews in study 1. Studies 2 and 3 treat its public release as the shock and measure semantic homogeneity of real reviews; no validation of that measure is reported.", "Homogeneity rose after the release, most among products with few prior reviews. Sales relate to homogeneity in an inverted U, so moderate helps and excessive hurts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "edition": 20, "validated": null, "n": 2313, "authors_detailed": [ { "name": "Ho Kim", "url": "https://openalex.org/A5051834608", "inst": "University of Missouri–St. Louis" } ], "affiliations": [ "University of Missouri–St. Louis" ] }, { "uid": "doi:10.2139/ssrn.7268202", "doi": "10.2139/ssrn.7268202", "title": "A Reproducible Protocol for Measuring How Generative AI Systems Name Competing Entities: Frame Construction, Query Design, and Validation for Multi-System Behavioral Audits", "authors": [ "Avik Bal" ], "posted": "2026-08-16", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7268202", "field": "management", "role": "method", "bullets": [ "Two data-collection waves auditing five generative AI systems against a 70-entity competitive frame in enterprise banking software, using a 200-item query instrument across four funnel stages.", "ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot are the respondents. A three-tier sampling frame, entity resolution and domain verification caught seven mapping errors before they reached a statistic.", "Inter-system agreement and concentration figures were near identical across two snapshots three weeks apart. One corrected domain moved an entity from excluded to included." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "domain verification of entity mappings plus test-retest agreement across two waves", "salience": 52, "edition": 20, "n": 2319, "authors_detailed": [ { "name": "Avik Bal", "url": "https://openalex.org/A5140496727", "inst": "Independent Researcher, Bengaluru, India" } ], "affiliations": [ "Independent Researcher, Bengaluru, India" ] }, { "uid": "doi:10.2139/ssrn.7292318", "doi": "10.2139/ssrn.7292318", "title": "Predicting Climate-Driven Supply Chain Cascades in Construction via LLMs and Causal ML", "authors": [ "Ling Peng" ], "posted": "2026-08-16", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7292318", "field": "management", "role": "instrument", "bullets": [ "Construction project supply chains under climate risk. The paper names no sample, no period and no geography.", "An unnamed language model extracts multi-source heterogeneous climate data, which then feeds a causal machine learning model of node failure. The extraction step is not validated.", "Reports 18.7 percent higher prediction accuracy and 23.2 percent better identification of risk conduction paths, but does not state the comparison baseline." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no check of the extraction step; baseline for the reported gains not stated", "salience": 35, "edition": 20, "models": [], "n": 2329, "authors_detailed": [ { "name": "Ling Peng", "url": "https://openalex.org/A5147247481", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7274878", "doi": "10.2139/ssrn.7274878", "title": "Heterogeneous Attention and Macroeconomic Dynamics:Theory and LLM Experiments", "authors": [ "Jiahui Sun" ], "posted": "2026-08-16", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7274878", "field": "economics", "role": "agent", "bullets": [ "Rational inattention model in which agents differ in cognitive processing capacity, taken to experiment with six language models of varying scale acting as computational households.", "Model scale supplies the exogenous variation in processing capacity that human data cannot, since cognitive capacity is latent and confounded with preferences. The six models are not named.", "Frontier models track the perfect-foresight benchmark while smaller ones are an order of magnitude more volatile, though the ranking is not monotone in parameter count." ], "bullet_provenance": "ai", "salience": 72, "edition": 20, "models": [], "validated": null, "n": 2330, "authors_detailed": [ { "name": "Jiahui Sun", "url": "https://openalex.org/A5147298002", "inst": "Peking University" } ], "affiliations": [ "Peking University" ] }, { "uid": "doi:10.2139/ssrn.7294158", "doi": "10.2139/ssrn.7294158", "title": "Data Element Utilization, Green Innovation, and Environmental Performance: Micro-Evidence from the Manufacturing Industry Based on Large Language Models", "authors": [ "zhankui Dong", "Siyi Li", "xiaoyong Fan" ], "posted": "2026-08-16", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7294158", "field": "management", "role": "instrument", "bullets": [ "Chinese A-share listed manufacturing companies from 2014 to 2024.", "An ERNIE model is trained to build a firm-level indicator of data element utilization. No check of that indicator against hand coding or any other benchmark is reported.", "Data element utilization raises environmental performance, working through more substantive and less strategic green innovation. The effect is larger in state-owned and western firms." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "validation_note": "constructed indicator never validated against ground truth", "salience": 48, "edition": 20, "n": 2331, "authors_detailed": [ { "name": "Dong Zhankui", "url": "https://openalex.org/A5102384457", "inst": "Henan University" }, { "name": "Siyi Li", "url": "https://openalex.org/A5147293240", "inst": "Henan University" }, { "name": "Xiaoyong Fan", "url": "https://openalex.org/A5146979934", "inst": "Henan University" } ], "affiliations": [ "Henan University" ] }, { "uid": "doi:10.2139/ssrn.7292593", "doi": "10.2139/ssrn.7292593", "title": "Generative AI for Surveys on Payment Apps: AI Views on Privacy and Technology", "authors": [ "Koji Takahashi", "Joon Park" ], "posted": "2026-08-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7292593", "field": "finance", "role": "agent", "bullets": [ "Simulated survey of payment-app users via ChatGPT with prompts mirroring real user characteristics, benchmarked against a Dutch consumer survey.", "ChatGPT generated survey responses on payment-app privacy and perceived benefits, grouped by privacy-concern level and user status.", "AI responses matched human group-level patterns but failed to reproduce response variability and systematically overstated privacy concerns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "alignment with Dutch consumer payment-app survey responses", "salience": 55, "n": 3852, "authors_detailed": [ { "name": "Koji Takahashi", "url": "https://openalex.org/A5101524678", "inst": "Seikei University" }, { "name": "Joon Park", "url": "https://openalex.org/A5138728588", "inst": "Bank of Korea" } ], "affiliations": [ "Seikei University", "Bank of Korea" ] }, { "uid": "arxiv:2608.15286v1", "arxiv_id": "2608.15286v1", "title": "No Task Fails Every Time: Why One-Shot Audits Are Structurally Blind to Agent Damage", "authors": [ "Shiven Khurdi" ], "posted": "2026-08-15", "added": "2026-08-22", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.15286v1", "field": "management", "role": "method", "bullets": [ "The study runs 2,128 evaluation trials across nine large language models in six families on EnterpriseOps-Gym, an enterprise-operations agent benchmark; geography and industry setting are not stated.", "AgentRelBench, a label-free reliability instrument, scores agent-caused damage from ground-truth database state diffs rather than LLM-based judging; the specific model architectures tested are not stated by name.", "Damage on irreversible actions occurred across every model family and no task failed on every run; a single audit missed a damage-producing task-model pair 80% of the time in the development pool." ], "bullet_provenance": "ai", "salience": 50, "edition": 22, "models": [], "validated": null, "n": 4099, "authors_detailed": [ { "name": "Shiven Khurdi", "url": "https://openalex.org/A5147420522", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7287958", "doi": "10.2139/ssrn.7287958", "title": "When Machines Can Pass Exams A Conceptual Framework for Ethical, Evidence-Based, and Experiential Assessment in Accounting Education", "authors": [ "Kennedy Prince Modugu" ], "posted": "2026-08-15", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7287958", "field": "accounting", "role": "object", "bullets": [ "Conceptual paper on assessment design in accounting education, written for a global and cross-jurisdictional audience. No sample and no data.", "No model is run by the authors. ChatGPT, Gemini, Claude, DeepSeek and Copilot are named as the tools disrupting assessment, not used as instruments.", "Proposes three pillars, evidence-based practice, experiential learning, and ethics in design, and advances six testable propositions. No empirical test is reported." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "salience": 38, "edition": 20, "validated": null, "n": 2310, "authors_detailed": [ { "name": "Kennedy Prince Modugu", "url": "https://openalex.org/A5080938871", "inst": "Middlesex University" } ], "affiliations": [ "Middlesex University" ] }, { "uid": "doi:10.2139/ssrn.7274398", "doi": "10.2139/ssrn.7274398", "title": "How much AI Talk Matters", "authors": [ "Jun Yeong Lee" ], "posted": "2026-08-15", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7274398", "field": "economics", "role": "agent", "bullets": [ "Repeated Bertrand oligopoly, three sellers over 30 rounds, 480 groups spanning four market compositions and six cheap-talk frequency conditions.", "Claude Sonnet 4.6, GPT-4o and Gemini 2.5 Flash play the competing firms, replacing the human participants of Lee and Hoffman (2025). No accuracy benchmark applies.", "Communication lifts prices in every configuration. Homogeneous Claude markets collude without any communication, submitting identical prices in most rounds; GPT-4o markets show no such premium." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "legacy" ], "open_weights": false, "salience": 73, "edition": 20, "validated": null, "n": 2312, "authors_detailed": [ { "name": "J. Lee", "url": "https://openalex.org/A5078329588", "inst": "Pusan National University" } ], "affiliations": [ "Pusan National University" ] }, { "uid": "doi:10.2139/ssrn.7271178", "doi": "10.2139/ssrn.7271178", "title": "The African AI Enclave: Generative AI and the Modern Sector", "authors": [ "Ada Tony Odu" ], "posted": "2026-08-15", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7271178", "field": "economics", "role": "object", "bullets": [ "Claude.ai conversations mapped to occupational tasks across 22 African countries using Anthropic's Economic Index, triangulated with OpenAI Signals, Gemini, and a 24-month ChatGPT panel.", "No model is run by the author. Claude, ChatGPT and Gemini usage records are the data, weighted by model-estimated task time; no validation of that weighting is reported.", "In Kenya, occupations employing 3.5 percent of workers account for 95 percent of estimated task-time savings, a ratio of 27 against 3.6 in the United States." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 74, "edition": 20, "validated": null, "n": 2314, "authors_detailed": [ { "name": "Ada Tony Odu", "url": "https://openalex.org/A5089810623", "inst": "University of Abuja" } ], "affiliations": [ "University of Abuja" ] }, { "uid": "doi:10.2139/ssrn.7266998", "doi": "10.2139/ssrn.7266998", "title": "RiskLens-AI: A Calibrated Symbolic-Statistical-LLM Fusion Framework for Explainable Credit Risk and Fraud Detection in SME Lending", "authors": [ "Syed Muhammad Shozab Mehdi Zaidi", "Syed Shahzain Alam", "Sameer Ahmed" ], "posted": "2026-08-15", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7266998", "field": "finance", "role": "instrument", "bullets": [ "Proposed architecture for SME credit risk and fraud screening. No institutional transaction data is used; the paper states which components are implemented and which run on synthetic data only.", "An open-weight student model is distilled from a larger teacher with LoRA to write auditable risk narratives, then fused with anomaly detectors and a gradient-boosted classifier through a calibration layer.", "No accuracy figures are reported. The paper specifies an evaluation protocol of AUC-ROC, PR-AUC, Brier score, calibration error and latency, and leaves full validation to future work." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "validation_note": "synthetic data only; no ground-truth evaluation reported", "salience": 40, "edition": 20, "n": 2322, "authors_detailed": [ { "name": "Syed Muhammad Shozab Mehdi Zaidi", "url": "https://openalex.org/A5147244611", "inst": "" }, { "name": "Syed Shahzain Alam", "url": "https://openalex.org/A5147242883", "inst": "National University of Computer and Emerging Sciences" }, { "name": "Sameer Ahmed", "url": "https://openalex.org/A5002026838", "inst": "National University of Computer and Emerging Sciences" } ], "affiliations": [ "National University of Computer and Emerging Sciences" ] }, { "uid": "doi:10.2139/ssrn.7286866", "doi": "10.2139/ssrn.7286866", "title": "MacroAllocAgent: From macro narratives to strategic asset allocation via a multi-agent LLM system", "authors": [ "Jinyuan Wang", "Ningyuan Deng", "Qi Li", "Yi Yang" ], "posted": "2026-08-15", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7286866", "field": "finance", "role": "instrument", "bullets": [ "Thirteen Chinese macroeconomic-exposure indices from 2015 to 2025, with official macro communications and market outlook reports as the text input.", "Five specialist agents map documents into Pring-cycle probabilities and asset return views feeding a Black-Litterman optimizer. The backbone is not named and the views are not validated against ground truth.", "Sharpe ratios of 0.955 unconstrained and 0.758 bounded, improvements of 6.0 and 11.0 percent over the best baseline, with bootstrap tests supporting significance." ], "bullet_provenance": "ai", "validated": false, "validation_note": "portfolio performance and ablations only; extracted views never checked against ground truth", "salience": 58, "edition": 20, "models": [], "n": 2325, "authors_detailed": [ { "name": "Jinyuan Wang", "url": "https://openalex.org/A5147260521", "inst": "" }, { "name": "Ningyuan Deng", "url": "https://openalex.org/A5147241990", "inst": "Kunming University" }, { "name": "Qi Li", "url": "https://openalex.org/A5100735752", "inst": "Malaysia University of Science and Technology" }, { "name": "Yi Yang", "url": "https://openalex.org/A5147260097", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Kunming University", "Malaysia University of Science and Technology", "Hong Kong University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7291261", "doi": "10.2139/ssrn.7291261", "title": "Recursive Strategic Ecosystem Reconfiguration: How Generative AI Reshapes Sustainable Business Models in the Korean Content and Creative Industry", "authors": [ "Taejun Lee" ], "posted": "2026-08-15", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7291261", "field": "management", "role": "object", "bullets": [ "Constructivist grounded theory from 23 semi-structured interviews in the Korean content and creative industry, covering executives, creators, developers, platform managers, legal and policy experts.", "No model is run. Generative AI is the object: the analysis codes how organizations responded to it and reconstructs organizational episodes over time.", "Proposes recursive strategic ecosystem reconfiguration, in which organizational responses reshape the ecosystem conditions that then constrain later business-model innovation." ], "bullet_provenance": "ai", "salience": 45, "edition": 20, "models": [], "validated": null, "n": 2326, "authors_detailed": [ { "name": "Taejun Lee", "url": "https://openalex.org/A5137897666", "inst": "Korea Development Institute" } ], "affiliations": [ "Korea Development Institute" ] }, { "uid": "doi:10.2139/ssrn.7269198", "doi": "10.2139/ssrn.7269198", "title": "Evidence-Grounded Persona Synthesis: Constructing AI Personas from Four Classes of Heterogeneous Person Data", "authors": [ "Wonseong Kim" ], "posted": "2026-08-15", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7269198", "field": "management", "role": "method", "bullets": [ "Framework paper. The demonstration runs on a fictitious household panel rather than real records, with synthetic respondents answering a new questionnaire item by item.", "Person data is split into four classes with different error structures. Every trait links to its evidence, every statement links to a trait, and verifier models cut statements exceeding their trait's scope.", "No accuracy figures are reported. Respondents abstain where evidence gives no basis, so simulated results carry an explicit map of what the records can and cannot support." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no empirical evaluation; demonstration uses a fictitious panel", "salience": 42, "edition": 20, "models": [], "n": 2327, "authors_detailed": [ { "name": "Wonseong Kim", "url": "https://openalex.org/A5058944740", "inst": "Statistics Korea" } ], "affiliations": [ "Statistics Korea" ] }, { "uid": "doi:10.2139/ssrn.7291109", "doi": "10.2139/ssrn.7291109", "title": "Unlearning to Lead: Cultivating Cognitive Agility and Generative AI Fluency in Post-Digital Business Education", "authors": [ "Sun Tao" ], "posted": "2026-08-15", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7291109", "field": "management", "role": "object", "bullets": [ "Survey of 1,250 MBA, undergraduate and executive education participants, analysed by structural equation modelling alongside a longitudinal case analysis.", "No model is run. Generative AI is the object: the paper proposes a cognitive agility index and an AI assimilation velocity as proxies for learners discarding old mental models.", "The abstract breaks off before any result is reported, stating only that unlearning readiness is tested as a mediator of curricular exposure." ], "bullet_provenance": "ai", "salience": 40, "edition": 20, "models": [], "validated": null, "n": 2328, "authors_detailed": [ { "name": "Sun Tao", "url": "https://openalex.org/A5147239965", "inst": "Zhejiang University" } ], "affiliations": [ "Zhejiang University" ] }, { "uid": "doi:10.2139/ssrn.7264680", "doi": "10.2139/ssrn.7264680", "title": "Can AI Close the Grant Funding Gap? A Multi-Agent LLM System for Automated Grant Application Generation with Blind Expert Evaluation, Single-Agent Baseline Comparison, and Design Science Research Framework", "authors": [ "Anita Blege" ], "posted": "2026-08-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7264680", "field": "management", "role": "instrument", "bullets": [ "170 AI-generated grant applications across 120 grants for six organizational profiles spanning for-profit, nonprofit, and mixed entities in the United States.", "A six-agent LLM pipeline with retrieval-augmented generation produced each application in 47 minutes; three expert grant writers blind-evaluated 45 matched application pairs.", "AI system scored 9.2 points below human writers overall but reached parity on factual accuracy; multi-agent outperformed single-agent baseline by 6.8 points." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "blind expert evaluation of 45 matched pairs vs human-written grants", "salience": 50, "n": 3288, "authors_detailed": [ { "name": "Anita Blege", "url": "https://openalex.org/A5133030580", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7265138", "doi": "10.2139/ssrn.7265138", "title": "The Last Price Model Identity and Consumer Welfare in Agentic Bargaining", "authors": [ "Layan Aloreidi" ], "posted": "2026-08-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7265138", "field": "economics", "role": "agent", "bullets": [ "702 bilateral bargaining episodes across 216 matched environments with three open-weight buyer LLMs facing a fixed Qwen 3 4B seller under controlled protocols.", "Qwen 3 1.7B, Gemma 3 4B, and Llama 3.2 3B acted as autonomous buyer agents in economically identical settings; outcomes compared via matched nonparametric tests.", "Agreement rates ranged from 15% to 56% and mean buyer surplus from zero to 0.134 depending solely on buyer model identity, all differences highly significant." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "salience": 72, "validated": null, "n": 3289, "authors_detailed": [ { "name": "Layan Oraidi", "url": "https://openalex.org/A5146093705", "inst": "Corvinus University of Budapest" } ], "affiliations": [ "Corvinus University of Budapest" ] }, { "uid": "doi:10.2139/ssrn.7267998", "doi": "10.2139/ssrn.7267998", "title": "From AI-Assisted Firms to Agentic Firms: A Theory of Organizational Creative Destruction in the Age of Autonomous Intelligence", "authors": [ "Zhang Sulin" ], "posted": "2026-08-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7267998", "field": "economics", "role": "object", "bullets": [ "Theoretical model of differentiated-product competition with replicator dynamics and endogenous AI adoption across incumbent and AI-native firm types.", "No specific LLM used; the paper derives formal conditions under which AI-native firms, coordinating populations of AI agents, outcompete conventional incumbents.", "A critical organizational-advantage threshold exists above which AI-native entrants displace incumbents even when incumbents could adopt the same AI technology." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3849, "authors_detailed": [ { "name": "Zhang Sulin", "url": "https://openalex.org/A5147260429", "inst": "China University of Mining and Technology" } ], "affiliations": [ "China University of Mining and Technology" ] }, { "uid": "doi:10.2139/ssrn.7275240", "doi": "10.2139/ssrn.7275240", "title": "Ground-Truthing Latency Alpha: An Independent Test of LLM-Semantic-Extraction Alpha in EIA-Driven Crude Oil Futures", "authors": [ "Jesse Temares" ], "posted": "2026-08-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7275240", "field": "finance", "role": "instrument", "bullets": [ "149 EIA Weekly Petroleum Status Report releases and 146 matched non-EIA control windows over three years, CME CL front-month crude oil futures.", "An LLM read multi-field EIA reports and issued directional trading calls; accuracy measured against realized price moves at retail latency.", "Directional accuracy was 54.1% (p = 0.37), no better than a single-crude-inventory-surprise number, yielding no tradeable edge for non-co-located retail traders." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "directional accuracy vs realized crude oil futures moves over 149 EIA releases", "salience": 72, "n": 3850, "authors_detailed": [ { "name": "Jesse Temares", "url": "https://openalex.org/A5147259246", "inst": "Parents' Place of Maryland" } ], "affiliations": [ "Parents' Place of Maryland" ] }, { "uid": "doi:10.2139/ssrn.7291625", "doi": "10.2139/ssrn.7291625", "title": "Reinforcement Learning with Large Language Model–Generated Event-Based Sentiment Index for Adaptive Model Selection in Financial Markets", "authors": [ "Chaher Alzaman" ], "posted": "2026-08-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7291625", "field": "finance", "role": "instrument", "bullets": [ "Empirical study of a reinforcement-learning meta-controller allocating capital across diverse base models using stock market data.", "An LLM generated a firm-level Event-Based Synthetic Stock Sentiment Index combining baseline sentiment, event shocks, sector trends, and random variation.", "The RL agent improved cumulative returns and risk-adjusted efficiency while reducing variance, tail risk, and downside exposure versus individual baselines and market benchmark." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 55, "n": 3851, "authors_detailed": [ { "name": "Chaher Alzaman", "url": "https://openalex.org/A5022782442", "inst": "Concordia University" } ], "affiliations": [ "Concordia University" ] }, { "uid": "doi:10.2139/ssrn.7271878", "doi": "10.2139/ssrn.7271878", "title": "The Machine-Made Deal: Agentic Artificial Intelligence and the Distressed-M&A Counterfactual in Cross-Class Cram Down", "authors": [ "Bertie McIntosh" ], "posted": "2026-08-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7271878", "field": "finance", "role": "object", "bullets": [ "Analysis of cross-class cram down proceedings under Part 26A of the UK Companies Act 2006, focusing on the distressed-M&A counterfactual and expert forecast evidence.", "Paper examines how agentic AI financial forecasting severs three evidential connections underlying expert opinion in court-sanctioned restructuring plans.", "Proposes convening-stage disclosure regime for model-generated forecast evidence, arguing the sanctioning discretion alone cannot bear the weight of AI-generated predictions." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 4072, "authors_detailed": [ { "name": "Bertie McIntosh", "url": "https://openalex.org/A5147229207", "inst": "University of Cambridge" } ], "affiliations": [ "University of Cambridge" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7259758", "doi": "10.2139/ssrn.7259758", "title": "The Devaluation of the Publication Signal: A Secondary-Data Analysis of AI's Impact on Academic Credentialing, 2022 to 2026, with Projections to 2036", "authors": [ "Elijah Adeyeye" ], "posted": "2026-08-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7259758", "field": "management", "role": "object", "bullets": [ "Secondary data on academic journal submissions, writing quality indicators, and paper retractions from 2022 to 2026, with projections to 2036.", "Generative AI is the object studied, not deployed as a research tool; the paper applies Spence's signaling theory to assess how LLMs have reduced the cost of producing publication-like output without corresponding scholarly effort.", "Submission volumes rose and writing quality declined after accessible LLMs launched; over 11,300 papers were retracted from one major publisher, and publication count is projected to require supplementation by process-based credentials such as Open Science badges." ], "bullet_provenance": "ai", "salience": 42, "edition": 21, "models": [], "validated": null, "n": 2348, "authors_detailed": [ { "name": "Elijah Adeyeye", "url": "https://openalex.org/A5120560317", "inst": "University of Ibadan" } ], "affiliations": [ "University of Ibadan" ] }, { "uid": "doi:10.2139/ssrn.7272558", "doi": "10.2139/ssrn.7272558", "title": "AllInBench: Evaluating Strategic Reasoning in Frontier Language Models Under Imperfect Information in an Adversarial Environment (No-Limit Texas Hold'em)", "authors": [ "Saleh Yahya", "Sophia Liew", "Rahul Karthik", "Jim Liew" ], "posted": "2026-08-14", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7272558", "field": "economics", "role": "agent", "bullets": [ "Ten games of no-limit Texas Hold'em among four frontier models under identical prompts, budgets and rules, with every decision released across 84 fields.", "Claude Opus 5, GPT-5.6-sol, Kimi K3 and DeepSeek V4 Pro play the hands. Table talk and private deliberation are both logged, so stated and believed positions can be compared.", "Claude Opus 5 leads with four wins, a margin the authors say a coin flip could explain. Deception is rare, four statements in 148, and concentrates in discretionary raises." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "salience": 55, "edition": 20, "validated": null, "n": 2317, "authors_detailed": [ { "name": "Saleh Yahya", "url": "https://openalex.org/A5135203935", "inst": "" }, { "name": "Sophia Liew", "url": "https://openalex.org/A5147224800", "inst": "" }, { "name": "Rahul Karthik", "url": "https://openalex.org/A5001579062", "inst": "Institute of Medical Sciences" }, { "name": "Jim Liew", "url": "https://openalex.org/A5147157676", "inst": "" } ], "affiliations": [ "Institute of Medical Sciences" ] }, { "uid": "arxiv:2608.14399v1", "arxiv_id": "2608.14399v1", "title": "Whose doctor does the AI recommend? An algorithm audit of reputation and demographic signals in large language model-assisted physician choice", "authors": [ "Syeda Anshrah Gillani", "Mirza Samad Ahmed Baig" ], "posted": "2026-08-14", "added": "2026-08-18", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.14399v1", "field": "economics", "role": "agent", "bullets": [ "Prespecified randomized audit in which seven models chose among five synthetic family-medicine physician cards across 3,024 choice sets, three personas and nine prompt paraphrases, giving 40,068 scored responses.", "Six open-weight models plus gpt-4o-mini made the choices. Gender and ethnicity were signalled through names following correspondence-audit methodology, and one reasoning model failed the auditability gate.", "Raising a rating from 3.9 to 4.7 lifts choice probability 31.4 points; a fee rise from $90 to $190 cuts it 20.0. Demographic tilts appear in under 0.03 percent of stated reasons." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "salience": 66, "edition": 20, "validated": null, "n": 2318, "authors_detailed": [ { "name": "Syeda Anshrah Gillani", "url": "https://openalex.org/A5147318283", "inst": "" }, { "name": "Mirza Samad Ahmed Baig", "url": "https://openalex.org/A5147360305", "inst": "" } ] }, { "uid": "arxiv:2608.14198v1", "arxiv_id": "2608.14198v1", "title": "MINT: A Universal Zero-Shot Predictor for Transaction Data", "authors": [ "Parameswaran Kamalaruban", "Viktor Drobnyi", "Maeve Madigan", "Julia Rozanova", "David Sutton", "Stuart Burrell" ], "posted": "2026-08-14", "added": "2026-08-18", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.14198v1", "field": "finance", "role": "method", "bullets": [ "Sequential bank transaction data for tasks including fraud prevention, credit risk assessment and offer personalization. The specific datasets are not named.", "A pretrained transaction sequence encoder is joined to a decoder-only language model, which the paper does not name, through embedding injection, transaction-language alignment and instruction tuning.", "Beats text-serialization baselines on predictive question answering both in and out of distribution, while cutting input tokens, latency and memory." ], "bullet_provenance": "ai", "validated": true, "validation_note": "in-distribution and out-of-distribution predictive QA against text-serialization baselines", "salience": 55, "edition": 20, "models": [], "n": 2323, "authors_detailed": [ { "name": "Parameswaran Kamalaruban", "url": "https://openalex.org/A5090876613", "inst": "University of Warwick" }, { "name": "Viktor Drobnyi", "url": "https://openalex.org/A5147342129", "inst": "" }, { "name": "Maeve Madigan", "url": "https://openalex.org/A5047161303", "inst": "Institute for Security Studies" }, { "name": "Julia Rozanova", "url": "https://openalex.org/A5033486362", "inst": "Yale University" }, { "name": "David Sutton", "url": "https://openalex.org/A5147323272", "inst": "" }, { "name": "Stuart Burrell", "url": "https://openalex.org/A5125889107", "inst": "Institute for Security Studies" } ], "affiliations": [ "Yale University", "University of Warwick", "Institute for Security Studies" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.13913v1", "arxiv_id": "2608.13913v1", "title": "AlphaSeek: Trajectory-Level Self-Iterative Factor Mining Framework for Multi-Source Financial Data", "authors": [ "Qilu Zhu", "Zijun Lu", "Jianmin Zhu", "Ning Chen", "Shuo Yin", "Simon Fong" ], "posted": "2026-08-14", "added": "2026-08-18", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.13913v1", "field": "finance", "role": "instrument", "bullets": [ "CSI300 constituents with multi-source financial information, backtested at strategy level, with out-of-sample transfer tested on CSI500.", "An unnamed language model runs whole research trajectories from hypothesis to backtest, with mutation and crossover operators. The mining step is not checked against any ground truth.", "Reports annualized return of 8.28 percent, information ratio 1.29, maximum drawdown 6.28 percent and IC of 0.0454 on CSI300, with zero-shot carryover to CSI500." ], "bullet_provenance": "ai", "validated": false, "validation_note": "backtest metrics only; no ground-truth check of the factor-mining step", "salience": 52, "edition": 20, "models": [], "n": 2324 }, { "uid": "doi:10.2139/ssrn.7266539", "doi": "10.2139/ssrn.7266539", "title": "Generative Artificial Intelligence in Customer Service Operations", "authors": [ "Mohd Mohsin Yazdani" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7266539", "field": "management", "role": "object", "bullets": [ "Systematic review of 67 peer-reviewed studies on large language models in customer service operations, published between 2020 and 2026 and screened under PRISMA 2020 guidelines.", "The reviewers run no model themselves. Across the studies covered, GPT-family systems, BERT variants, and domain fine-tuned models handle query resolution and agent augmentation, with human-in-the-loop routing as the common governance layer.", "Studies report productivity gains of 14 to 35 percent for novice agents, shorter average handle times, and better customer sentiment. Hallucination, data privacy, and integration cost remain unresolved." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "salience": 45, "edition": 19, "validated": null, "n": 2212, "authors_detailed": [ { "name": "Mohd Mohsin Yazdani", "url": "https://openalex.org/A5147159900", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7259162", "doi": "10.2139/ssrn.7259162", "title": "The Adaptive Intelligence Enterprise: An Architectural Model for AI‑Enabled Organisations", "authors": [ "Karl Smith" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7259162", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample. Scope is the AI-enabled enterprise as a whole, spanning organisational design, knowledge, governance, and risk.", "No model is run. Language models enter as the object of the framework, cast as knowledge infrastructure, decision participants, governance actors, and resilience mechanisms inside the operating model.", "Sixteen capabilities across four architectural domains, with constructs such as probabilistic decision governance and knowledge flow analytics. The argument is that AI transformation is structural, not technological. No evidence is offered." ], "bullet_provenance": "ai", "salience": 30, "edition": 19, "models": [], "validated": null, "n": 2215, "authors_detailed": [ { "name": "Karl Smith", "url": "https://openalex.org/A5143449242", "inst": "Organization of American States" } ], "affiliations": [ "Organization of American States" ] }, { "uid": "doi:10.2139/ssrn.7257858", "doi": "10.2139/ssrn.7257858", "title": "More Strategies, Same Zero: Multiple Testing Against LLM-Scale Alpha Search", "authors": [ "Parv Mehndiratta" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7257858", "field": "finance", "role": "method", "bullets": [ "Sweeps of 10 to 3,000 candidate trading strategies on four test markets: a strong planted edge, a small planted edge, a random walk, and S&P 500 data from 2016 to 2024.", "Claude Opus 4.8 proposes strategies as one of three generators, beside uniform-random and operator-based synthesis. Every candidate passes a verifier with costs, causal execution, worst-of-regimes scoring, and a deflated Sharpe haircut.", "Naive best-of-N Sharpe rises with N even on pure noise, from 0.04 to 0.34. The deflated gate leaves zero survivors on seven real markets while still recovering the planted edge." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "planted-edge positive control and random-walk null, block bootstrap confirmation", "salience": 76, "edition": 19, "n": 2216, "authors_detailed": [ { "name": "Parv Mehndiratta", "url": "https://openalex.org/A5147203863", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7285434", "doi": "10.2139/ssrn.7285434", "title": "Task-Based Exposure to Large Language Models and Occupational Heterogeneity in China", "authors": [ "Qihabg Hu", "Xuezheng Qin", "Xiaolong Li" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7285434", "field": "economics", "role": "object", "bullets": [ "Occupations from the Chinese Occupational Classification Dictionary, joined to Chinese labour survey data and online job vacancies posted by listed firms. Period is not stated.", "Task descriptions are scored against a rubric and weighted to occupation level exposure. The abstract does not say whether a language model performs the scoring, and names no model.", "Exposure concentrates in knowledge-intensive and information-processing occupations and among educated, high-income workers. A dispersion measure shows occupations with equal mean exposure differ widely in task restructuring scope." ], "bullet_provenance": "ai", "salience": 58, "edition": 19, "models": [], "validated": null, "n": 2217, "authors_detailed": [ { "name": "Qihang Hu", "url": "https://openalex.org/A5027696727", "inst": "Peking University" }, { "name": "Xuezheng Qin", "url": "https://openalex.org/A5054877268", "inst": "Peking University" }, { "name": "Xiaolong Li", "url": "https://openalex.org/A5077013178", "inst": "Beijing University of Posts and Telecommunications" } ], "affiliations": [ "Peking University", "Beijing University of Posts and Telecommunications" ] }, { "uid": "doi:10.2139/ssrn.7272623", "doi": "10.2139/ssrn.7272623", "title": "DistillSem: LLM-Distilled Financial Headline Semantics for Cross-Sectional Return Prediction", "authors": [ "Bo Guo" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7272623", "field": "finance", "role": "instrument", "bullets": [ "96,802 financial news headlines labelled by a teacher model over 2015 to 2022, with return ranking tested out of sample on a cross-section from 2024 to 2026.", "An unnamed teacher model tags each headline on six dimensions, including event type, signal strength, and price-impact horizon. TF-IDF logistic regressions then reproduce four of them cheaply at scale. No human-coded check is reported.", "The distilled representation reaches an information coefficient of 0.0225 with t of 3.80, against 0.0004 for FinBERT polarity and 0.0056 for a lexical baseline without model supervision." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 66, "edition": 19, "n": 2218, "authors_detailed": [ { "name": "Bo Guo", "url": "https://openalex.org/A5103555656", "inst": "Texas A&M University – Texarkana" } ], "affiliations": [ "Texas A&M University – Texarkana" ] }, { "uid": "doi:10.2139/ssrn.7262199", "doi": "10.2139/ssrn.7262199", "title": "Do Large Language Models Reproduce Classic Human Decision Biases?", "authors": [ "Mitchell Coplan" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7262199", "field": "economics", "role": "agent", "bullets": [ "Seventeen classic heuristics-and-biases experiments drawn from the public Twin-2K-500 dataset, which records the answers of more than 2,000 real survey participants.", "Persona-conditioned respondents from an ensemble of instruction-tuned models, families not named, answered each experiment. An effect counted as reproduced when any ensemble member showed it in the original direction at significance.", "The ensemble recovered 15.5 of 17 effects, 91 percent, against 8 of 16 for the answer-conditioned digital twins in the source study. Dominator neglect failed, and the endowment effect only partly held." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Twin-2K-500 human responses, share of published effects reproduced", "salience": 62, "edition": 19, "models": [], "n": 2219, "authors_detailed": [ { "name": "Mitchell Coplan", "url": "https://openalex.org/A5147173444", "inst": "Lamar University" } ], "affiliations": [ "Lamar University" ] }, { "uid": "doi:10.2139/ssrn.7262218", "doi": "10.2139/ssrn.7262218", "title": "Silicon Sampling in Seoul: Conditioning Ablations for LLM Survey Simulation against a Probability Sample", "authors": [ "Kyoungsun Park", "Seong-Hoon Kim", "Jae Young Suh" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7262218", "field": "economics", "role": "method", "bullets": [ "Twenty-four attitudinal and behavioral items from the 2024 Seoul Survey, a probability sample of 5,000 Seoul residents carrying official weights, serve as the target for simulated respondents.", "Two unnamed commercial models and two locally run open-weight models, 7.8B and 30B, answered under three conditioning levels: none, demographics, and demographics plus census-grounded synthetic narratives. A memorization probe ruled out contamination.", "Demographic conditioning raised cell-level rank correlation from near zero to 0.42. Adding narratives hurt both fit and tracking, total variation distance up 0.031 and correlation down 0.067. Open-weight models tracked subgroups far worse." ], "bullet_provenance": "ai", "open_weights": false, "validated": true, "validation_note": "2024 Seoul Survey probability sample, total variation distance and subgroup rank correlation", "salience": 74, "edition": 19, "models": [], "n": 2223, "authors_detailed": [ { "name": "Kyoungsun Park", "url": "https://openalex.org/A5090399562", "inst": "Systems, Applications & Products in Data Processing (United Kingdom)" }, { "name": "Seong‐Hoon Kim", "url": "https://openalex.org/A5100769343", "inst": "Korea Pharma (South Korea)" }, { "name": "Jae Young Suh", "url": "https://openalex.org/A5139302495", "inst": "Sapientia College of Theology of Religious Orders" } ], "affiliations": [ "Systems, Applications & Products in Data Processing (United Kingdom)", "Korea Pharma (South Korea)", "Sapientia College of Theology of Religious Orders" ] }, { "uid": "doi:10.2139/ssrn.7258880", "doi": "10.2139/ssrn.7258880", "title": "Governing the Machine: A Reference Architecture for Trustworthy Agentic AI in Enterprise Finance and Supply Chain", "authors": [ "Narendra Cherlopalli" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7258880", "field": "accounting", "role": "object", "bullets": [ "A reference implementation covering six enterprise finance departments, procurement, order-to-cash, treasury, planning, tax and consolidation, and forty governed tools. No firm sample and no empirical test are reported.", "No model is named or evaluated. The design splits an orchestration layer from a governance layer holding scoped tool permissions, segregation of duties, human approval gates, and audit-first fail-closed execution.", "The claim is that governance maturity rather than model capability binds enterprise adoption, and that the retrieval versus fine-tuning choice should follow data volatility and auditability. The argument stays conceptual and untested." ], "bullet_provenance": "ai", "salience": 40, "edition": 19, "models": [], "validated": null, "n": 2225, "authors_detailed": [ { "name": "Narendra Cherlopalli", "url": "https://openalex.org/A5144090324", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7285755", "doi": "10.2139/ssrn.7285755", "title": "Information Diffusion and Return Predictability across Business and Ownership Networks: Evidence from Korea", "authors": [ "Hwiseo Lee", "Byung Hwa Lim" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7285755", "field": "finance", "role": "instrument", "bullets": [ "Korean listed firms, with business networks rebuilt each year from annual-report disclosures and control-based ownership networks built from largest-shareholder records and business-group affiliations. Sample period not stated.", "Unnamed large language model embeddings, alongside TF-IDF, turn disclosure text into firm-relatedness measures that define peer sets. The paper reports no comparison against an industry classification or a hand-coded benchmark.", "Lagged peer returns from both network layers predict focal-firm returns and earn positive risk-adjusted portfolio returns. Neither layer subsumes the other; estimates are larger on KOSDAQ and where ownership is diffuse." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 19, "models": [], "n": 2235, "authors_detailed": [ { "name": "Hwiseo Lee", "url": "https://openalex.org/A5147220277", "inst": "Sungkyunkwan University" }, { "name": "Byung Hwa Lim", "url": "https://openalex.org/A5050711129", "inst": "Sungkyunkwan University" } ], "affiliations": [ "Sungkyunkwan University" ] }, { "uid": "doi:10.2139/ssrn.7263879", "doi": "10.2139/ssrn.7263879", "title": "Trust and Auditability in Generative AI-Assisted Enterprise Reporting Systems", "authors": [ "Ramsha Siddiqui" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7263879", "field": "accounting", "role": "object", "bullets": [ "Conceptual paper on enterprise reporting systems, with no firm sample, period, or geography. The evidence comes from scenario-based evaluations that the abstract does not describe further.", "No model is named or run. Generative AI is treated as a reporting component to be governed through audit trails, prompt traceability, source-linked reasoning records, version control, and human approval steps.", "The framework proposes a TrustScore over explainability, transparency, compliance, and robustness, and a move from auditing around AI systems to auditing through them. No effect sizes are reported." ], "bullet_provenance": "ai", "salience": 32, "edition": 19, "models": [], "validated": null, "n": 2236, "authors_detailed": [ { "name": "Ramsha Siddiqui", "url": "https://openalex.org/A5109763448", "inst": "University of Scranton" } ], "affiliations": [ "University of Scranton" ] }, { "uid": "doi:10.2139/ssrn.7258698", "doi": "10.2139/ssrn.7258698", "title": "Generative AI Integration and Corporate Cash Policies: The Impact of Deployment Intensity on Free Cash Flow Management and Liquidity Buffers", "authors": [ "Burcak Sari" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7258698", "field": "finance", "role": "object", "bullets": [ "Two-wave, multi-source survey of 412 senior finance and operations executives at technology-intensive services and advanced manufacturing firms. Country and survey dates not stated.", "The researchers run no model. Generative AI deployment intensity is a self-reported survey construct, analysed with confirmatory factor analysis and latent moderated mediation inside a structural equation model.", "Deployment intensity raises reported forecasting accuracy, beta 0.487, which in turn cuts excess liquidity buffers. Environmental dynamism strengthens that indirect path, with a moderated mediation index of 0.142." ], "bullet_provenance": "ai", "salience": 40, "edition": 19, "models": [], "validated": null, "n": 2237, "authors_detailed": [ { "name": "Burçak Sarı", "url": "https://openalex.org/A5011809759", "inst": "Istanbul Medipol University" } ], "affiliations": [ "Istanbul Medipol University" ] }, { "uid": "doi:10.2139/ssrn.7285429", "doi": "10.2139/ssrn.7285429", "title": "Who Becomes AI-Active? Firm Size, Capability and Disclosure in a UK Firm Panel", "authors": [ "Heiman Alwadi", "Juan Manuel Davila Delgado", "Ali Edisen" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7285429", "field": "economics", "role": "object", "bullets": [ "UK firm-year panel covering 49,085 firms from 2018 to 2025, built from 360,323 OCR-processed Companies House annual reports plus AI patenting and IT-services subsidiary and group records.", "No language model does the measuring: AI activity is flagged by keywords in report text and by administrative signals. ChatGPT enters only as the date of a possible structural break.", "The AI-active flag covers 4.69 percent of firm-years and concentrates in large, asset-rich firms. The within-industry size gradient doubled from 2018 to 2024, with no level or slope break at ChatGPT's release." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "edition": 19, "validated": null, "n": 2238, "authors_detailed": [ { "name": "Heiman Alwadi", "url": "https://openalex.org/A5147211569", "inst": "Birmingham City University" }, { "name": "Juan Manuel Dávila Delgado", "url": "https://openalex.org/A5007173123", "inst": "Birmingham City University" }, { "name": "Ali Edisen", "url": "https://openalex.org/A5135820610", "inst": "Bath Spa University" } ], "affiliations": [ "Birmingham City University", "Bath Spa University" ] }, { "uid": "doi:10.2139/ssrn.7265558", "doi": "10.2139/ssrn.7265558", "title": "Beyond the Hype: Organizational Risks of Blind Reliance on Artificial Intelligence", "authors": [ "Prasanna Shrinivas" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7265558", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no dataset; it re-reads documented failures from law, healthcare, criminal justice, and financial markets, including the fabricated citations in the Steven Schwartz filing.", "No model is run and no family is named; generative AI enters through published incidents, and the paper reports no measurement or validation of its own.", "Recurring causes are weak outputs, bias, thin data governance, and absent human checking; the proposal is five governance pillars, with AI treated as decision support rather than a substitute for judgement." ], "bullet_provenance": "ai", "salience": 26, "edition": 19, "models": [], "validated": null, "n": 2279, "authors_detailed": [ { "name": "Prasanna Shrinivas", "url": "https://openalex.org/A5147189778", "inst": "Columbia Southern University" } ], "affiliations": [ "Columbia Southern University" ] }, { "uid": "doi:10.2139/ssrn.7259041", "doi": "10.2139/ssrn.7259041", "title": "An Explainable Triple-AI Architecture for Anomaly Detection in Public Country-By-Country Reporting Data", "authors": [ "Nadiia Novytska", "Jorge Augusto Meira", "Yiqun Wang", "Radu State" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7259041", "field": "accounting", "role": "instrument", "bullets": [ "Public country-by-country reporting data on multinational profit allocation, tested on the AITAX13 database; sample size, jurisdictions, and years are not stated.", "Three cooperating agents built on a language model, family not named, score tax risk, draft requests for explanation, and produce taxpayer response templates. No accuracy check is reported.", "The claimed contribution is a traceable compliance workflow for OECD oriented risk assessment. The abstract gives no detection rates, no false positive rates, and no comparison with existing screens." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no comparison against audited or hand-coded risk cases reported", "salience": 40, "edition": 19, "models": [], "n": 2280, "authors_detailed": [ { "name": "Nadiia Novytska", "url": "https://openalex.org/A5012708501", "inst": "University of Luxembourg" }, { "name": "Jorge Augusto Meira", "url": "https://openalex.org/A5055420812", "inst": "University of Luxembourg" }, { "name": "Yiqun Wang", "url": "https://openalex.org/A5036674802", "inst": "University of Luxembourg" }, { "name": "Radu State", "url": "https://openalex.org/A5069228908", "inst": "University of Luxembourg" } ], "affiliations": [ "University of Luxembourg" ] }, { "uid": "doi:10.2139/ssrn.7259462", "doi": "10.2139/ssrn.7259462", "title": "Does News Sentiment Precede Liquidity Shocks? A Liquidity-first Event Study of Indian Equities", "authors": [ "Saurabh Dasgupta" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7259462", "field": "finance", "role": "instrument", "bullets": [ "74 liquidity shock events across 69 companies listed on India's NSE between 2022 and 2025, with 90.5 percent falling in 2024 and 2025, matched to pre-event baselines and historical pseudo-events.", "Sentiment comes from GDELT tone, both broad and headline explicit, and from an entity conditioned language model whose family is not named; no benchmark against hand coded sentiment is reported.", "Broad GDELT tone shifts the day before the shock, 1.506 against a null of 1.123 with raw p of 0.035 that weakens to 0.070 after Holm correction; the model based sentiment shows nothing." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no comparison of model sentiment against hand coded labels", "salience": 50, "edition": 19, "models": [], "n": 2281, "authors_detailed": [ { "name": "Saurabh Dasgupta", "url": "https://openalex.org/A5029764104", "inst": "University of Oklahoma Health Sciences Center" } ], "affiliations": [ "University of Oklahoma Health Sciences Center" ] }, { "uid": "doi:10.2139/ssrn.7258922", "doi": "10.2139/ssrn.7258922", "title": "Built for Humans only? Why AI Adoption in Higher Education Requires a New Deal", "authors": [ "Petr Špecián" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7258922", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of universities as organizations, with no dataset; the argument runs through institutional economics and organization theory rather than through observed adoption data.", "No model is run or named; generative AI enters as a technology that breaks the worker or tool binary, and the paper's own bet about model based evaluation of portfolios stays untested.", "Patching assessment rules starts a provenance verification arms race; the proposal instead separates authentication from grading, replacing grades with sealed machine readable portfolios that employers query themselves." ], "bullet_provenance": "ai", "salience": 35, "edition": 19, "models": [], "validated": null, "n": 2282, "authors_detailed": [ { "name": "Petr Špecián", "url": "https://openalex.org/A5085684419", "inst": "Institute of Political Studies" } ], "affiliations": [ "Institute of Political Studies" ] }, { "uid": "doi:10.2139/ssrn.7262481", "doi": "10.2139/ssrn.7262481", "title": "The Organisation as the Executable Unit: Towards an Executable Organisational Operating Model for AI-Native Software Engineering", "authors": [ "Sudha Manimaran" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7262481", "field": "management", "role": "object", "bullets": [ "No sample. A conceptual model for software engineering organisations in which AI systems already write code, propose architecture, and review changes without delegated authority.", "No model or tool is named and nothing is measured. AI systems enter as interchangeable participants who fill accountable roles alongside humans and external parties.", "Argues the binding constraint on AI autonomy is missing organisational structure rather than model capability, and sets out a chain from objectives to capabilities, governed services, roles, and participants." ], "bullet_provenance": "ai", "salience": 33, "edition": 19, "models": [], "validated": null, "n": 2307, "authors_detailed": [ { "name": "Sudha Manimaran", "url": "https://openalex.org/A5088625135", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7258439", "doi": "10.2139/ssrn.7258439", "title": "Federated Brains: An Operating Pattern for Governed AI Adoption in Organisations", "authors": [ "Bruno Oliveira" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7258439", "field": "management", "role": "object", "bullets": [ "Practitioner account drawn from the author's implementations in micro and small firms, plus one structural observation from a large enterprise leadership team. No sample count or period given.", "No model or platform is named and none is evaluated. Generative and agentic tools appear as the means by which staff externalise tacit judgement into recorded decisions.", "Proposes a minimal central frame of doctrine, platform, asset registry with ownership succession, and a promotion path, with team-level knowledge bases federated upward through stewardship review." ], "bullet_provenance": "ai", "salience": 34, "edition": 19, "models": [], "validated": null, "n": 2308, "authors_detailed": [ { "name": "Bruno Oliveira", "url": "https://openalex.org/A5067676457", "inst": "University of Bath" } ], "affiliations": [ "University of Bath" ] }, { "uid": "doi:10.2139/ssrn.7258162", "doi": "10.2139/ssrn.7258162", "title": "Taxing AI in the Wake of the Emergence of Agentic AI", "authors": [ "Reuven S. Avi-Yonah", "Herbert Snitz" ], "posted": "2026-08-14", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7258162", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 40, "edition": 19, "bullets": [], "models": [], "validated": null, "n": 2309, "authors_detailed": [ { "name": "Reuven S. Avi-Yonah", "url": "https://openalex.org/A5133011497", "inst": "University of Michigan" }, { "name": "Herbert Snitz", "url": "https://openalex.org/A5147163103", "inst": "Independent Researcher" } ], "affiliations": [ "University of Michigan", "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7284830", "doi": "10.2139/ssrn.7284830", "title": "Reconstructing Authority around AI-Generated Claims: A Practice-Based Study in Management Accounting", "authors": [ "deng dejun dejun", "Yuanfeng Luo", "Feng Hu" ], "posted": "2026-08-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7284830", "field": "accounting", "role": "object", "bullets": [ "Comparative case study of two Chinese technology firms using generative AI in management accounting, drawing on 42 semi-structured interviews and non-participant observation.", "Generative AI produced forecasts and strategic scenarios that management accountants were expected to evaluate, defend, and integrate into organizational decision-making.", "Accountants reconstructed authority through three micro-practices; staged rollout created durable collective review routines while rapid rollout left authority reconstruction individually exposed." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 58, "validated": null, "n": 3845 }, { "uid": "arxiv:2608.14014v1", "arxiv_id": "2608.14014v1", "title": "Buy the Rumor, Sell the News: When Is News Priced In?", "authors": [ "Alireza Kargarzadeh", "Nariman Khaledian", "Navid Parvini", "Sid Ghatak", "Arman Khaledian" ], "posted": "2026-08-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.14014v1", "field": "finance", "role": "instrument", "bullets": [ "4.57 million financial news articles covering approximately 3,000 US stocks from 2023 to 2026, yielding 1.68 million stock-day events with 364,405 neutral-sentiment placebo events.", "An LLM teacher distilled into a compact classifier assigned 17 event tags and five attributes to each article; articles were clustered into stories separating first reports from follow-ups.", "Price moves concentrated before and at publication; quantified fundamental news drifted post-publication while soft story-driven news reversed, and publication resolved uncertainty by reducing volatility." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "n": 3846, "authors_detailed": [ { "name": "Alireza Kargarzadeh", "url": "https://openalex.org/A5115509022", "inst": "Imperial College London" }, { "name": "Nariman Khaledian", "url": "https://openalex.org/A5014841508", "inst": "Centre National de la Recherche Scientifique" }, { "name": "Navid Parvini", "url": "https://openalex.org/A5066894656", "inst": "University of Kent" }, { "name": "Sid Ghatak", "url": "https://openalex.org/A5147357513", "inst": "" }, { "name": "Arman Khaledian", "url": "https://openalex.org/A5119849203", "inst": "" } ], "affiliations": [ "Imperial College London", "Centre National de la Recherche Scientifique", "University of Kent" ] }, { "uid": "arxiv:2608.14329v1", "arxiv_id": "2608.14329v1", "title": "A Four-Axis Trustworthiness Benchmark for LLM-as-Judge in Principle-Based Regulation", "authors": [ "Dipankar Sarkar" ], "posted": "2026-08-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.14329v1", "field": "finance", "role": "method", "bullets": [ "Principle-Bench: 168 cryptoasset financial-promotion scenarios mapped to two UK FCA principles, with paraphrase, adversarial keyword-stuffing, and boundary perturbations.", "LLM-as-judge systems including a 120B open-weight model and Ceca calibrated assessor evaluated regulatory compliance across four axes: accuracy, paraphrase robustness, adversarial robustness, and calibration.", "The 120B LLM-judge lost 47 accuracy points on keyword-stuffed Consumer Duty inputs (0.74 to 0.27); no single method dominated all four evaluation axes." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Principle-Bench 168 cryptoasset scenarios with pre-registered rubric", "salience": 60, "n": 3847, "authors_detailed": [ { "name": "Dipankar Sarkar", "url": "https://openalex.org/A5000320359", "inst": "Indian Association for the Cultivation of Science" } ], "affiliations": [ "Indian Association for the Cultivation of Science" ] }, { "uid": "arxiv:2608.14106v1", "arxiv_id": "2608.14106v1", "title": "Forecast Collapse in Time-Series Foundation Models", "authors": [ "Shu Wan", "Miles Ma", "Hank Zhu", "Guangqi Liu", "Stephen Wang", "Qingsong Wen", "Huan Liu" ], "posted": "2026-08-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.14106v1", "field": "finance", "role": "method", "bullets": [ "Hourly return and volume forecasts for 1,000 US equities using twelve deep-learning forecasting models and multiple time-series foundation models across 97 benchmark configurations.", "Time-series foundation models produced nearly flat equity return predictions exhibiting forecast collapse; cross-sectional correlation was poor despite reasonable per-series calibration metrics.", "CalibRank objective nearly tripled cross-sectional correlation while keeping amplitude close to the target; per-series metrics hid failures in cross-series ranking structure needed for portfolio decisions." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Finance1K benchmark 1000 US equities", "salience": 65, "n": 3848, "authors_detailed": [ { "name": "Shu Wan", "url": "https://openalex.org/A5147343707", "inst": "" }, { "name": "Miles Ma", "url": "https://openalex.org/A5051985839", "inst": "The University of Sydney" }, { "name": "H. Zhu", "url": "https://openalex.org/A5080402224", "inst": "Primary Industries Education Foundation Australia" }, { "name": "Guangqi Liu", "url": "https://openalex.org/A5039892891", "inst": "Qilu University of Technology" }, { "name": "Stephen Wang", "url": "https://openalex.org/A5101751091", "inst": "University of San Diego" }, { "name": "Qingsong Wen", "url": "https://openalex.org/A5147360326", "inst": "" }, { "name": "Huan Liu", "url": "https://openalex.org/A5147353120", "inst": "" } ], "affiliations": [ "The University of Sydney", "Primary Industries Education Foundation Australia", "Qilu University of Technology", "University of San Diego" ] }, { "uid": "doi:10.2139/ssrn.7256418", "doi": "10.2139/ssrn.7256418", "title": "Version Migration as a Correlated Shock: Model Updates as an Unmanaged Risk Channel in LLM-Based Financial Systems", "authors": [ "Samir Chincholikar", "Robin Chawla" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7256418", "field": "finance", "role": "method", "bullets": [ "100 companies scored on 12 analysis dates, three replicates per model version, across two vendor version transitions. 14,400 calls in a credit decision-support workflow.", "An OpenAI efficient-model update and a Google Gemini Flash update classify identical inputs under matched seeds. Within-version replicate disagreement sets the noise floor. No ground-truth accuracy benchmark is reported.", "Cross-version disagreement exceeds the noise floor by 11.2 points for the OpenAI update and 38.1 points for Gemini. The Gemini successor shifts broadly toward the safe label." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": false, "salience": 62, "edition": 19, "n": 2213, "authors_detailed": [ { "name": "Samir Chincholikar", "url": "https://openalex.org/A5144443065", "inst": "Film Independent" }, { "name": "Robin Chawla", "url": "https://openalex.org/A5144417481", "inst": "Independent" } ], "affiliations": [ "Film Independent", "Independent" ] }, { "uid": "doi:10.2139/ssrn.7256038", "doi": "10.2139/ssrn.7256038", "title": "Does AI Rate Through the Cycle? Procyclicality in Credit Assessment", "authors": [ "Samir Chincholikar", "Robin Chawla" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7256038", "field": "finance", "role": "agent", "bullets": [ "Synthetic firm fundamentals held byte-identical across a five-point macroeconomic severity scale, plus a credit-irrelevant placebo scale. 32,000 letter ratings and one-year default probabilities.", "Five production-tier models from two families and two capability tiers assign ratings; the families are not named. Holding fundamentals fixed makes any rating movement procyclical by construction, so no external benchmark is needed.", "Every model downgrades as severity rises, 0.429 notches per step and 1.76 notches from boom to recession, 3.2 times steeper going into recessions. A through-the-cycle instruction does not fix it." ], "bullet_provenance": "ai", "salience": 78, "edition": 19, "models": [], "validated": null, "n": 2214, "authors_detailed": [ { "name": "Samir Chincholikar", "url": "https://openalex.org/A5144443065", "inst": "Film Independent" }, { "name": "Robin Chawla", "url": "https://openalex.org/A5144417481", "inst": "Independent" } ], "affiliations": [ "Film Independent", "Independent" ] }, { "uid": "doi:10.2139/ssrn.7276625", "doi": "10.2139/ssrn.7276625", "title": "Vertical Integration and Pricing in the AI Industry:Evidence from Foundation Model Markets", "authors": [ "Nuwan Indika", "Adeel Faheem" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7276625", "field": "economics", "role": "object", "bullets": [ "A calibrated model of foundation model markets, where a provider sells inference capacity to downstream developers and competes with them. Three documented integration cases: ChatGPT, Claude.ai, and Gemini applications.", "The authors run no model themselves. The three assistants are the market subjects. The framework extends Salinger and Luco and Marshall using logit demand, linear wholesale pricing, and multiproduct downstream pricing.", "Baseline calibration: integration cuts the integrated application's price by 12.9 percent and raises the unintegrated rival's by 6.5 percent. Consumer surplus rises 13.3 percent, total welfare 4.9 percent." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 55, "edition": 19, "validated": null, "n": 2220, "authors_detailed": [ { "name": "Nuwan Indika", "url": "https://openalex.org/A5116309899", "inst": "Loyola University New Orleans" }, { "name": "Adeel Faheem", "url": "https://openalex.org/A5082642017", "inst": "University of Wisconsin–Parkside" } ], "affiliations": [ "Loyola University New Orleans", "University of Wisconsin–Parkside" ] }, { "uid": "doi:10.2139/ssrn.7256618", "doi": "10.2139/ssrn.7256618", "title": "Using Large Language Models to Discover and Disentangle Mechanisms from Participant Explanations", "authors": [ "Giovanni Luca Cascio Rizzo" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7256618", "field": "management", "role": "method", "bullets": [ "Four preregistered consumer research experiments, three of them replications of published effects. Each observation pairs a condition, an outcome, and a participant's own open-ended explanation. Sample sizes not stated.", "An unnamed language model pipeline, blind to the hypothesis and the outcome, reads each explanation and scores every participant on competing constructs. It clears a reliability and validity battery before rival indirect effects are compared.", "The pipeline recovers a planted attribute that rating scales miss, rediscovers a mediator established by earlier work, separates constructs described in the same words, and surfaces drivers no questionnaire measured. Magnitudes not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "reliability and validity battery, planted-attribute recovery across preregistered replications", "salience": 68, "edition": 19, "models": [], "n": 2221, "authors_detailed": [ { "name": "Giovanni Luca Cascio Rizzo", "url": "https://openalex.org/A5061279963", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7250218", "doi": "10.2139/ssrn.7250218", "title": "AI-Empowered Customers and the Erosion of Brand Power", "authors": [ "Louis Krol", "Simone Santamaria" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7250218", "field": "management", "role": "object", "bullets": [ "Randomized online experiment in which 350 participants made 1,750 incentivized choices across five product categories, half of them with an embedded generative AI comparison assistant available.", "The assistant is the treatment rather than a measuring tool. The paper names neither the model nor the family behind it, and reports no check of the assistant's output against ground truth.", "Participants with assistant access picked the more reputable brand less often when it was weaker on the displayed attributes. They also reported lower mental effort and heavier reliance on technical specifications. Magnitudes not stated." ], "bullet_provenance": "ai", "salience": 62, "edition": 19, "models": [], "validated": null, "n": 2222, "authors_detailed": [ { "name": "Louis Krol", "url": "https://openalex.org/A5120831037", "inst": "Peking University" }, { "name": "Simone Santamaria", "url": "https://openalex.org/A5006513717", "inst": "Politecnico di Milano" } ], "affiliations": [ "Peking University", "Politecnico di Milano" ] }, { "uid": "arxiv:2608.12984v1", "arxiv_id": "2608.12984v1", "title": "Reconcile Once, Write Anytime: A Trust-Tiered Librarian and a Multi-Agent Writer for Drift-Free, Point-in-Time Research", "authors": [ "Xing Zhang", "Yanwei Cui", "Guanghui Wang", "Peiyang He" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.12984v1", "field": "finance", "role": "method", "bullets": [ "A self-collected public corpus of 6,130 sources yielding 555,926 evidence cards: SEC EDGAR filings from 295 issuers across 11 sectors, Bureau of Labor Statistics releases, and Wikipedia.", "A deterministic librarian files timestamped sources into a trust-tiered ontology, and a multi-agent writer drafts reports reading only evidence dated at or before the chosen cutoff. Difficulty-tiered routing mixes models, with Opus as the quality ceiling.", "A shared metric ledger cut 6,845 cross-section contradictions to zero. Tier-first source selection was right on 22 of 22 gold cases against 9 for a popularity-first baseline, and replay showed no look-ahead violations." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "defect-injection meta-evaluation of the quality gate at recall and precision 1.0, plus 22 gold selection cases", "salience": 55, "edition": 19, "n": 2224, "authors_detailed": [ { "name": "Xing Zhang", "url": "https://openalex.org/A5147175696", "inst": "" }, { "name": "Yanwei Cui", "url": "https://openalex.org/A5147150424", "inst": "" }, { "name": "Guanghui Wang", "url": "https://openalex.org/A5147143511", "inst": "" }, { "name": "Peiyang He", "url": "https://openalex.org/A5147164711", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7254198", "doi": "10.2139/ssrn.7254198", "title": "A Free AI-assisted Keyword Intelligence Framework for e-commerce SMEs in Bangladesh: Effects on Conversion Performance", "authors": [ "Tahmid Rahman Siddiki" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7254198", "field": "management", "role": "object", "bullets": [ "Online survey of 212 owners of e-commerce small and medium enterprises in Bangladesh, analysed with correlation, hierarchical regression, and one-way ANOVA. Survey period not stated.", "The researchers run no model. ChatGPT and Claude appear as free tools inside a five-step keyword framework that sellers report using: discovery, intent mapping, implementation, monitoring, and refinement.", "Reported framework adoption predicts conversion performance, adding 55 percent of explained variance beyond firm size, experience, and revenue. Adoption and outcomes both come from the same self-report survey." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 30, "edition": 19, "validated": null, "n": 2231, "authors_detailed": [ { "name": "Tahmid Rahman Siddiki", "url": "https://openalex.org/A5147099881", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7253378", "doi": "10.2139/ssrn.7253378", "title": "When Does Generative AI Level the Playing Field? Evidence from Crowdsourced Earnings Forecasts", "authors": [ "Leonard Yang Liu", "Musa Subasi" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7253378", "field": "accounting", "role": "object", "bullets": [ "Crowdsourced earnings forecasts from Estimize, comparing professional and non-professional contributors before and after the release of ChatGPT. Sample size and exact period not stated.", "The researchers run no model themselves. They infer which contributors lean on ChatGPT from how forecasting behaviour shifts during service outages, treating those outages as the identifying variation.", "The accuracy gap between non-professionals and professionals disappears after ChatGPT. Gains concentrate among experienced non-professionals; inexperienced adopters gain nothing and herd more. Announcement returns respond more to non-professional surprises afterward." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 76, "edition": 19, "validated": null, "n": 2232, "authors_detailed": [ { "name": "Leonard Yang Liu", "url": "https://openalex.org/A5143976832", "inst": "University of Maryland, College Park" }, { "name": "Musa Subasi", "url": "https://openalex.org/A5009238230", "inst": "University of Maryland, College Park" } ], "affiliations": [ "University of Maryland, College Park" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7260822", "doi": "10.2139/ssrn.7260822", "title": "Who Verifies Whom? Generative AI, Human Oversight, and the Production of Professional Research Memoranda", "authors": [ "John Barrick", "Scott L. Summers", "David A. Wood" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7260822", "field": "accounting", "role": "object", "bullets": [ "Tax research memoranda written under three conditions, humans alone, humans with AI, and AI alone, through a randomized experiment plus a longitudinal comparison of three cohorts from 2022 to 2025.", "The paper names no model family, referring only to generative AI systems. Output is scored on writing quality, citation breadth, and hallucinated authority, with citations checked against real sources.", "AI made professionals about one third faster. AI alone hallucinated 2 to 4 percent of citations against 6 to 10 percent for human-AI teams, and verification consumed two-thirds of the hours saved." ], "bullet_provenance": "ai", "validated": true, "validation_note": "citation hallucination rates checked against real legal authority", "salience": 80, "edition": 19, "models": [], "n": 2233, "authors_detailed": [ { "name": "John Barrick", "url": "https://openalex.org/A5147101637", "inst": "Brigham Young University" }, { "name": "Scott L. Summers", "url": "https://openalex.org/A5087829262", "inst": "Brigham Young University" }, { "name": "David A. Wood", "url": "https://openalex.org/A5075888890", "inst": "Brigham Young University" } ], "affiliations": [ "Brigham Young University" ] }, { "uid": "doi:10.2139/ssrn.7252098", "doi": "10.2139/ssrn.7252098", "title": "Three Ceilings: Model Monoculture, Solvency, and the Penalty Doctrine in Markets for Expert Services", "authors": [ "Andreas Bauer" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7252098", "field": "economics", "role": "object", "bullets": [ "Analytical model of markets for expert services, calibrated to six engagement types across four jurisdictions. No firm-level sample; the calibration rests on legal doctrine and the generative-AI insurance exclusions effective January 2026.", "No model is run here. Foundation models enter the theory through their error structure: capability growth removes idiosyncratic error faster than common error, and a verifier built on the same model cannot see common error.", "Liability commitments face three ceilings rather than one, with a regime switch at a critical ticket size. The 2026 insurance exclusions move eight of twenty-four calibrated cells from doctrine-bound to solvency-bound." ], "bullet_provenance": "ai", "salience": 50, "edition": 19, "models": [], "validated": null, "n": 2234, "authors_detailed": [ { "name": "Andreas Bauer", "url": "https://openalex.org/A5144362180", "inst": "Tallinn University" } ], "affiliations": [ "Tallinn University" ] }, { "uid": "doi:10.2139/ssrn.7274553", "doi": "10.2139/ssrn.7274553", "title": "THE INEFFICIENCY PARADOX: HOW AI-INDUCED COGNITIVE FRICTION RESHAPES SUPPLY CHAIN RESILIENCE", "authors": [ "Miles Yang" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7274553", "field": "management", "role": "agent", "bullets": [ "Simulated e-commerce shopping and returns, with no field data. Consumers, retailers and back-end logistics sit inside a generative multi-agent sandbox; sample size and period are not stated.", "Language models play shoppers in the sandbox and produce consumer digital twins that pass to back-end operations. No model family is named and no check against human shopper behaviour is reported.", "Bayesian structural equation estimates on the simulated data indicate that deliberate cognitive friction raises system trust and lowers expected reverse logistics cost, most for high-complexity products. No effect sizes are given." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 19, "models": [], "n": 2246, "authors_detailed": [ { "name": "Miles Yang", "url": "https://openalex.org/A5146986171", "inst": "Macquarie University" } ], "affiliations": [ "Macquarie University" ] }, { "uid": "doi:10.2139/ssrn.7255542", "doi": "10.2139/ssrn.7255542", "title": "The Gate Symphony: Deterministic Logic-gate Architectures for Constraining Autonomy in Agentic AI Systems", "authors": [ "Saumyajit Ghosh" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7255542", "field": "finance", "role": "object", "bullets": [ "A reference implementation tested on 14 authorization cases, 576 enumerated control states, 50,000 randomized cases, and 152,285 capability checks, with a post-trade settlement instruction routing case study.", "No language model is named or run; agent reasoning is treated as an untrusted source whose proposed actions must pass Boolean gates written as ordinary code, so nothing depends on model accuracy.", "All deterministic tests passed with zero invariant violations and zero invalid accepts; the authors prove a no-autonomous-path property and map the design to EU AI Act and DORA control requirements." ], "bullet_provenance": "ai", "salience": 34, "edition": 19, "models": [], "validated": null, "n": 2270, "authors_detailed": [ { "name": "Saumyajit Ghosh", "url": "https://openalex.org/A5122973827", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7274312", "doi": "10.2139/ssrn.7274312", "title": "From Deference to Dollars: (Im)Politeness Strategies and Commercial Success in Digital Knowledge Platforms", "authors": [ "Yijun Yan", "Yue Jin", "Wen Yuan" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7274312", "field": "management", "role": "instrument", "bullets": [ "Provider text from a paid knowledge sharing platform where buyers see a free trial segment before purchase; sample size, period, and platform name are not stated.", "A generative language model, family not named, codes five politeness strategies drawn from face theory; human pragmatic coding runs alongside it, but no agreement statistic appears.", "Bonding, empathising, hedging, and boosting track higher sales while criticising tracks lower sales; provider status weakens the bonding link and expertise weakens the boosting link. Magnitudes not stated." ], "bullet_provenance": "ai", "validated": false, "validation_note": "manual pragmatic coding run alongside model coding, no agreement statistic reported", "salience": 52, "edition": 19, "models": [], "n": 2271, "authors_detailed": [ { "name": "Yijun Yan", "url": "https://openalex.org/A5057941086", "inst": "China Southern Power Grid (China)" }, { "name": "Yue Jin", "url": "https://openalex.org/A5146957317", "inst": "" }, { "name": "Wen Yuan", "url": "https://openalex.org/A5146987318", "inst": "" } ], "affiliations": [ "China Southern Power Grid (China)" ] }, { "uid": "doi:10.2139/ssrn.7265684", "doi": "10.2139/ssrn.7265684", "title": "From Pilots to Platforms -Enterprise AI from an Enterprise Architecture Perspective", "authors": [ "Prof Dr Oliver Koch", "Dietmar Gerlach", "Guido W. Stass" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7265684", "field": "management", "role": "object", "bullets": [ "No dataset, sample, or period is stated; the paper argues from practitioner observation about firms that pilot generative AI but rarely move internally built solutions into production.", "No model is used or named; generative AI appears as the technology firms are trying to industrialise, and the paper contains no measurement or validation step.", "Four failure patterns are named: shadow AI, pilot purgatory, governance vacuum, and data quality deficits. The proposed fix is a three layer platform architecture with federated governance." ], "bullet_provenance": "ai", "salience": 30, "edition": 19, "models": [], "validated": null, "n": 2272, "authors_detailed": [ { "name": "Prof Dr Oliver Koch", "url": "https://openalex.org/A5135071980", "inst": "FOM University of Applied Sciences for Economics and Management" }, { "name": "Dietmar Gerlach", "url": "https://openalex.org/A5147109882", "inst": "Clinical Trial and Consulting" }, { "name": "Guido W. Stass", "url": "https://openalex.org/A5147111041", "inst": "Clinical Trial and Consulting" } ], "affiliations": [ "FOM University of Applied Sciences for Economics and Management" ] }, { "uid": "doi:10.2139/ssrn.7250938", "doi": "10.2139/ssrn.7250938", "title": "When Does Enterprise AI Pay? Threshold Conditions and Qualitative Regimes in a Deterministic Firm-Level Scenario Model", "authors": [ "Howie Young" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7250938", "field": "management", "role": "object", "bullets": [ "No firm data; a deterministic open source scenario calculator of the author's own design, with calibration exercises spanning the reported gap between laboratory and field productivity gains.", "No language model is run; generative AI enters only as task level speed gains net of quality discount, review, rework, and operations overhead. Twelve analytical results are machine verified against the implementation.", "One statistic, the time release rate, sets viability and break even thresholds, a demand cap beyond which extra capability earns nothing, and a ceiling that model upgrades cannot lift." ], "bullet_provenance": "ai", "salience": 45, "edition": 19, "models": [], "validated": null, "n": 2273, "authors_detailed": [ { "name": "Howie Young", "url": "https://openalex.org/A5147099809", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7265538", "doi": "10.2139/ssrn.7265538", "title": "Completions, Not Convictions: Can LLM Probabilities Proxy for Human Beliefs?", "authors": [ "Stefan Nagel", "Claire Tseng", "Dacheng Xiu" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7265538", "field": "finance", "role": "method", "bullets": [ "Prediction market questions and analyst earnings forecasts supply settings where human consensus beliefs are observed; sample sizes, periods, and data sources are not stated in the abstract.", "Model families are not named; elicited probabilities are compared against human consensus and probed for sensitivity to tone, anchors, label order, corpus exposure, outcome direction, reasoning instructions, and scale.", "Swapping model probabilities for analyst beliefs changes their link to announcement returns and flips the revision test: analysts underreact while the model appears to overreact, so use specific validation is required." ], "bullet_provenance": "ai", "validated": true, "validation_note": "model probabilities benchmarked against prediction market prices and analyst consensus forecasts", "salience": 85, "edition": 19, "models": [], "n": 2274, "authors_detailed": [ { "name": "Stefan Nagel", "url": "https://openalex.org/A5147100304", "inst": "" }, { "name": "Claire Tseng", "url": "https://openalex.org/A5147134550", "inst": "" }, { "name": "Dacheng Xiu", "url": "https://openalex.org/A5147072182", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7275145", "doi": "10.2139/ssrn.7275145", "title": "Adaptive Governance for Citizen Development: Balancing Innovation, Security, and Scalability in Low-Code/No-Code Platforms", "authors": [ "J. T. Sodano", "Joanna F. DeFranco" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7275145", "field": "management", "role": "object", "bullets": [ "A systematic literature review of citizen development risks and benefits, paired with a survey of enterprise IT and engineering leaders; counts of studies and respondents are not stated.", "No model is run or named; generative AI coding assistants appear as a source of artifacts that sit outside low-code platform boundaries and escape the controls built for those platforms.", "Adoption outruns governance maturity, and respondents favour reduced privilege defaults and mandatory pre-deployment registration; the authors propose a hub and spoke framework that routes artifacts by risk." ], "bullet_provenance": "ai", "salience": 35, "edition": 19, "models": [], "validated": null, "n": 2275, "authors_detailed": [ { "name": "J. T. Sodano", "url": "https://openalex.org/A5117206809", "inst": "Pennsylvania State University" }, { "name": "Joanna F. DeFranco", "url": "https://openalex.org/A5063754454", "inst": "Pennsylvania State University" } ], "affiliations": [ "Pennsylvania State University" ] }, { "uid": "doi:10.2139/ssrn.7252958", "doi": "10.2139/ssrn.7252958", "title": "Gendered Adoption of Generative Artificial Intelligence among Gen Z Social Media Influencers in Malaysia's Content Creation Industry: Intellectual Property Challenges and Ethical Imperatives", "authors": [ "SF Syed Abdul Rahman" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7252958", "field": "management", "role": "object", "bullets": [ "Conceptual review of Generation Z influencers and content creators who run online businesses in Malaysia, drawing on published studies and policy sources from 2025 and 2026. No original data.", "No model is run and no family is named; generative AI tools are the object of adoption, and the evidence comes from other authors' surveys rather than from any measurement here.", "Adoption differs by function rather than access: male creators lean to technical and coding tasks, female creators to text and aesthetic work, while Malaysian copyright law leaves gaps around AI generated works." ], "bullet_provenance": "ai", "salience": 28, "edition": 19, "models": [], "validated": null, "n": 2276, "authors_detailed": [ { "name": "SF Syed Abdul Rahman", "url": "https://openalex.org/A5144095086", "inst": "INTI International University" } ], "affiliations": [ "INTI International University" ] }, { "uid": "arxiv:2608.13024v1", "arxiv_id": "2608.13024v1", "title": "TIEM: Temporal Integration of Hypergraph Evidence and Skill Memory for Event-Driven Financial Forecasting", "authors": [ "Wenjin Liu", "Shen Pang", "Tiesunlong Shen", "Zhe Cui", "Xiaobao Wu", "Anh Tuan Luu", "Haoran Luo" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.13024v1", "field": "finance", "role": "method", "bullets": [ "Five event-driven financial forecasting benchmarks, plus FinPURE, a new holdout built from recent A-share catalyst events chosen to fall after plausible training cutoffs. Sample sizes not stated.", "The model family is not named. A retrieval agent works from a timestamp gated event hypergraph and a case memory, and a name date probe tests each model's sensitivity to dated entities.", "The framework beats existing baselines on all five benchmarks, though no accuracy figures appear in the abstract. The framing point is that contamination inflates reported skill in event driven forecasting." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "five forecasting benchmarks plus a post-cutoff A-share holdout, no figures in the abstract", "salience": 50, "edition": 19, "n": 2277, "authors_detailed": [ { "name": "Wenjin Liu", "url": "https://openalex.org/A5101461648", "inst": "Sinopec (China)" }, { "name": "Shen Pang", "url": "https://openalex.org/A5147175894", "inst": "" }, { "name": "Tiesunlong Shen", "url": "https://openalex.org/A5010025622", "inst": "National University of Singapore" }, { "name": "Zhe Cui", "url": "https://openalex.org/A5147211638", "inst": "" }, { "name": "Xiaobao Wu", "url": "https://openalex.org/A5147196407", "inst": "" }, { "name": "Anh Tuan Luu", "url": "https://openalex.org/A5147225117", "inst": "" }, { "name": "Haoran Luo", "url": "https://openalex.org/A5147198838", "inst": "" } ], "affiliations": [ "Sinopec (China)", "National University of Singapore" ] }, { "uid": "arxiv:2608.12719v1", "arxiv_id": "2608.12719v1", "title": "Error-Aware Reverse Auction Mechanism for Large Language Model Routing", "authors": [ "Haolong Chen", "Zhengyuan Xin", "Liang Zhang", "Lei Xue", "Guangxu Zhu" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.12719v1", "field": "economics", "role": "object", "bullets": [ "Simulations plus routing benchmarks, unnamed in the abstract; model providers bid to serve queries, so the unit of observation is a query auction rather than a firm or a document.", "No model family is named. Providers submit self predicted success probabilities and costs, and the mechanism treats both those bids and the centre's evaluation as noisy, calling the pair a dual error.", "The auction is Bayesian incentive compatible and individually rational under that noise, carries an explicit welfare loss bound, and reaches a better cost quality frontier than centralised routing baselines." ], "bullet_provenance": "ai", "salience": 40, "edition": 19, "models": [], "validated": null, "n": 2278, "authors_detailed": [ { "name": "Haolong Chen", "url": "https://openalex.org/A5147200148", "inst": "" }, { "name": "Zhengyuan Xin", "url": "https://openalex.org/A5147157313", "inst": "" }, { "name": "Liang Zhang", "url": "https://openalex.org/A5147175130", "inst": "" }, { "name": "Lei Xue", "url": "https://openalex.org/A5147220161", "inst": "" }, { "name": "Guangxu Zhu", "url": "https://openalex.org/A5147170405", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7265322", "doi": "10.2139/ssrn.7265322", "title": "AI's Current and Emerging Impact on Restructuring & Insolvency", "authors": [ "Paul Sidle" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7265322", "field": "finance", "role": "object", "bullets": [ "No sample, period, or jurisdiction stated. A practitioner note listing the points where AI tools touch restructuring and insolvency proceedings, from document review to creditor communication.", "No model, vendor, or family is named, and no system is tested. The note treats generative and agentic tools generically.", "Argues unequal access to AI may enter the court's fairness analysis at sanction, that prompts become discoverable evidence, and that agentic tools open liability gaps around mental elements." ], "bullet_provenance": "ai", "salience": 33, "edition": 19, "models": [], "validated": null, "n": 2299, "authors_detailed": [ { "name": "Paul Sidle", "url": "https://openalex.org/A5090479025", "inst": "Linklaters LLP, One Silk Street, London, EC2Y 8HQ, United Kingdom" } ], "affiliations": [ "Linklaters LLP, One Silk Street, London, EC2Y 8HQ, United Kingdom" ] }, { "uid": "doi:10.2139/ssrn.7253018", "doi": "10.2139/ssrn.7253018", "title": "Reproducible Intelligence: A New Class of Economic Assets and the Emergence of the Agentic Economy", "authors": [ "Eugenia Karas" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7253018", "field": "economics", "role": "object", "bullets": [ "Theory paper with no empirical sample. The unit is a task that either a human expert or a trained AI system can perform, priced within a cost-minimising model of work organisation.", "No language model is used or named. AI enters as an abstract reproducible capability whose defining operation is replication rather than transfer, and nothing is validated against data.", "Claims reproducible intelligence forms an asset class that ownership-based accounting cannot record, and that substitution proceeds until responsibility that cannot be delegated sets a price." ], "bullet_provenance": "ai", "salience": 38, "edition": 19, "models": [], "validated": null, "n": 2300, "authors_detailed": [ { "name": "Eugenia Karas", "url": "https://openalex.org/A5147099982", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7255459", "doi": "10.2139/ssrn.7255459", "title": "The Authored Brake: Delegating the Right of Refusal to AI Agents in Consumer Finance", "authors": [ "Vishi Rajvanshi" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7255459", "field": "finance", "role": "object", "bullets": [ "Conceptual paper in consumer finance with no data collected. The unit is a grant of decision rights over a consumer's own transactions to an automated agent.", "No model or system is named and none is run. Agents appear as a construct defined by three separable rights: initiate, veto, and defer.", "Proposes that consumers hand over the veto right more readily than the right to initiate, more so when they wrote the governing rule themselves. All three propositions stay untested." ], "bullet_provenance": "ai", "salience": 36, "edition": 19, "models": [], "validated": null, "n": 2301, "authors_detailed": [ { "name": "Vishi Rajvanshi", "url": "https://openalex.org/A5147084878", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7252878", "doi": "10.2139/ssrn.7252878", "title": "Beyond HITL: Continuation Readiness as a Governance Requirement for Enterprise AI Workflows A Role State", "authors": [ "Spark Tsai" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7252878", "field": "management", "role": "object", "bullets": [ "Conceptual governance paper with no data. The unit of analysis is the enterprise AI workflow rather than a single system or pipeline, focused on points where work passes between actors.", "No model is used, named, or tested. Language models appear as delivering or receiving actors in a work transfer, described through three constructs: role state, work junction, and continuation package.", "Argues checkpoint-scoped human-in-the-loop review cannot see whether the receiving actor can act on transferred work, and names six failure classes it misses, including capacity-blind transfer." ], "bullet_provenance": "ai", "salience": 31, "edition": 19, "models": [], "validated": null, "n": 2302, "authors_detailed": [ { "name": "Spark Tsai", "url": "https://openalex.org/A5128112344", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7251121", "doi": "10.2139/ssrn.7251121", "title": "Delegated Authority Without Accountability: The Governance Discontinuity Created by Agentic AI", "authors": [ "Bjorn Bjornsvik" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7251121", "field": "management", "role": "object", "bullets": [ "No sample or data. A governance essay treating systems that select tasks, sequence workflows, and call tools without continuous human direction as non-human operational actors inside organisations.", "No model, vendor, or benchmark is named, and nothing is run. The argument separates technical capability, system permission, and institutional authority as three distinct things.", "Names the gap between operational capability and legitimate delegation a governance discontinuity, lists ten recurring failure modes such as authority drift and silent execution, and sets minimum conditions for governable agency." ], "bullet_provenance": "ai", "salience": 34, "edition": 19, "models": [], "validated": null, "n": 2303, "authors_detailed": [ { "name": "Bjorn Bjornsvik", "url": "https://openalex.org/A5144437506", "inst": "Al-Ghad International Health Sciences Colleges" } ], "affiliations": [ "Al-Ghad International Health Sciences Colleges" ] }, { "uid": "doi:10.2139/ssrn.7250260", "doi": "10.2139/ssrn.7250260", "title": "Human-AI Co-Entrepreneurship in FinTech: A Multi-Stakeholder Exploration of Agentic AI in Entrepreneurial Finance and Consumer Engagement", "authors": [ "Mohsin Khan", "Saba Naz", "Muhammad Vahaj Ur Rehman", "Kashif Abrar", "Syed Adil Abbas Rizvi" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7250260", "field": "management", "role": "object", "bullets": [ "Eighteen semi-structured interviews in Pakistan's digital financial sector, covering entrepreneurs, FinTech executives, investors, and consumers. Period not stated.", "No model is used by the researchers and no system is named. Participants describe agentic AI tools they encounter in practice, and thematic analysis codes the transcripts.", "Five themes emerge, with participants casting AI as a collaborator in planning and customer engagement while flagging transparency, accountability, privacy, and overreliance as adoption barriers." ], "bullet_provenance": "ai", "salience": 32, "edition": 19, "models": [], "validated": null, "n": 2304, "authors_detailed": [ { "name": "Mohsin Khan", "url": "https://openalex.org/A5135880967", "inst": "Applied Research Associates (United States)" }, { "name": "Saba Naz", "url": "https://openalex.org/A5146256440", "inst": "Sir Syed University of Engineering and Technology" }, { "name": "Muhammad Vahaj Ur Rehman", "url": "https://openalex.org/A5147077201", "inst": "Alps Electric (Japan)" }, { "name": "Kashif Abrar", "url": "https://openalex.org/A5048869894", "inst": "Bahria University" }, { "name": "Syed Adil Abbas Rizvi", "url": "https://openalex.org/A5121378663", "inst": "Habib University" } ], "affiliations": [ "Applied Research Associates (United States)", "Sir Syed University of Engineering and Technology", "Alps Electric (Japan)", "Bahria University", "Habib University" ] }, { "uid": "doi:10.2139/ssrn.7245538", "doi": "10.2139/ssrn.7245538", "title": "Designing Agentic AI Workflow Portfolios under Selector Confusion and Compute Cost", "authors": [ "Mojtaba Abdolmaleki", "Stefanus Jasin", "Boyu Wang" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7245538", "field": "management", "role": "object", "bullets": [ "Theory paper, no empirical sample. A firm faces one task instance, several candidate agentic workflows that differ in reasoning strategy and compute cost, and a selector that picks among their outputs.", "No model family is named and none is run. Workflows are abstract objects with accuracy and cost, and selector quality is summarised by an odds lift index.", "Derives bounds on the value of workflow variety, exact and linear programming formulations for finite pools, and an ellipsoid method with a pricing oracle that reaches an epsilon-optimal relaxation solution in polynomial oracle calls." ], "bullet_provenance": "ai", "salience": 48, "edition": 19, "models": [], "validated": null, "n": 2305, "authors_detailed": [ { "name": "Mojtaba Abdolmaleki", "url": "https://openalex.org/A5015490494", "inst": "University of Michigan" }, { "name": "Stefanus Jasin", "url": "https://openalex.org/A5146988224", "inst": "" }, { "name": "Boyu Wang", "url": "https://openalex.org/A5100383950", "inst": "University at Buffalo, State University of New York" } ], "affiliations": [ "University of Michigan", "University at Buffalo, State University of New York" ] }, { "uid": "arxiv:2608.12761v1", "arxiv_id": "2608.12761v1", "title": "Correct Is Not Governed: Provenance Integrity in Agentic Workflows", "authors": [ "Jesus Salas" ], "posted": "2026-08-13", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.12761v1", "field": "management", "role": "object", "bullets": [ "No field data. Controlled comparisons of governed against direct agentic workflows, plus a role-separated transfer challenge; the number of tasks and the domain are not stated.", "The agent model is never named. Matrix, a deterministic causal-state layer, records authority and fact dependencies, checks completion evidence, and invalidates downstream work when a fact changes.", "Outcomes often matched the direct workflow, but only the governed path kept its evidence and refused unsupported closure. The completeness contract over-blocked packets written outside its authoring context." ], "bullet_provenance": "ai", "salience": 35, "edition": 19, "models": [], "validated": null, "n": 2306, "authors_detailed": [ { "name": "Jesús Salas", "url": "https://openalex.org/A5066339800", "inst": "Universidad Carlos III de Madrid" } ], "affiliations": [ "Universidad Carlos III de Madrid" ] }, { "uid": "doi:10.2139/ssrn.7247418", "doi": "10.2139/ssrn.7247418", "title": "Large Language Models as Voting Mechanisms", "authors": [ "Raša Karapandža", "Yaw Nyarko" ], "posted": "2026-08-13", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7247418", "field": "economics", "role": "agent", "bullets": [ "Pairwise investment choices among S&P 100 firms, generated as an empirical companion to a theoretical model that treats each prompt as an election over possible continuations.", "ChatGPT-4o chose between pairs of stocks, and output probabilities are read as vote shares. No validation against an external benchmark, since the object of study is the model's own choice pattern.", "Millions of Condorcet cycles appear, so recommendations are intransitive and open to money pumps. Correcting cycles after the fact is shown to be infeasible, and temperature shifts the effective voting rule." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "edition": 18, "validated": null, "n": 2164, "authors_detailed": [ { "name": "Raša Karapandža", "url": "https://openalex.org/A5042315750", "inst": "New York University Abu Dhabi" }, { "name": "Yaw Nyarko", "url": "https://openalex.org/A5038713388", "inst": "New York University" } ], "affiliations": [ "New York University Abu Dhabi", "New York University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.13674v1", "arxiv_id": "2608.13674v1", "title": "Asymmetric Discourse Homogenization and Shared Language Technology: Evidence from Reddit", "authors": [ "Fengming Liu" ], "posted": "2026-08-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.13674v1", "field": "economics", "role": "object", "bullets": [ "Six million Reddit comments from two cross-partisan forums spanning 2019 to 2025, with daily-frequency analysis across 2,377 candidate cutoff dates.", "ChatGPT release and cumulative LLM exposure index served as treatment variables in ITS, DiD, RDiT, and propensity-score matching estimations of discourse homogenization.", "Conservative users experienced interrupted diversification while progressive users showed no comparable change; a stayer analysis indicated the mechanism is ecological rather than individual-level AI adoption." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "validated": null, "n": 3843, "authors_detailed": [ { "name": "Fengming Liu", "url": "https://openalex.org/A5147364072", "inst": "" } ] }, { "uid": "arxiv:2608.13024v2", "arxiv_id": "2608.13024v2", "title": "TIEM: Temporal Integration of Hypergraph Evidence and Skill Memory for Event-Driven Financial Forecasting", "authors": [ "Wenjin Liu", "Shen Pang", "Chenxi Wang", "Tiesunlong Shen", "Zhe Cui", "Xiaobao Wu", "Anh Tuan Luu", "Haoran Luo" ], "posted": "2026-08-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.13024v2", "field": "finance", "role": "instrument", "bullets": [ "Five financial forecasting benchmarks including FinPURE, a recent-period Chinese A-share holdout designed to test event-driven catalyst-outcome prediction under temporal leakage controls.", "LLM agents used timestamp-gated hypergraph retrieval and case-based skill memory to forecast event outcomes; a Name-Date Probe assessed per-model training-data contamination.", "TIEM outperformed existing baselines across all five benchmarks; timestamp gating addressed the evidence chasm between reported accuracy and true out-of-sample predictive ability." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "five financial forecasting benchmarks including FinPURE holdout", "salience": 62, "n": 3844, "authors_detailed": [ { "name": "Wenjin Liu", "url": "https://openalex.org/A5101461648", "inst": "Sinopec (China)" }, { "name": "Shen Pang", "url": "https://openalex.org/A5147175894", "inst": "" }, { "name": "Chenxi Wang", "url": "https://openalex.org/A5147340505", "inst": "" }, { "name": "Tiesunlong Shen", "url": "https://openalex.org/A5010025622", "inst": "National University of Singapore" }, { "name": "Zhe Cui", "url": "https://openalex.org/A5147360020", "inst": "" }, { "name": "Xiaobao Wu", "url": "https://openalex.org/A5147351772", "inst": "" }, { "name": "Haoran Luo", "url": "https://openalex.org/A5147198838", "inst": "" }, { "name": "Haoran Luo", "url": "https://openalex.org/A5147354766", "inst": "" } ], "affiliations": [ "Sinopec (China)", "National University of Singapore" ] }, { "uid": "arxiv:2608.13706v2", "arxiv_id": "2608.13706v2", "title": "CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QA", "authors": [ "Fatema Tuj Johora Faria", "Mukaffi Bin Moin", "Jubayer Al Mahmud", "M. F. Mridha", "Md. Alam Hossain" ], "posted": "2026-08-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.13706v2", "field": "finance", "role": "method", "bullets": [ "500-question cross-modal financial evaluation set built from Bangladesh Bank Annual Report material, stratified by query type, format, and difficulty.", "Nine-agent LLM framework decomposed questions into atomic claims and applied asymmetric evidence authority with adaptive adversarial debate for cross-modal financial QA.", "Faithfulness rose from 0.780 to 0.889 over single-pass RAG baseline, exceeding HyDE and Graph-RAG baselines (0.874 or below), with 5.4% abstention on insufficient evidence." ], "bullet_provenance": "ai", "validated": true, "validation_note": "BB-FinQA-X faithfulness score", "salience": 40, "models": [], "n": 4071, "authors_detailed": [ { "name": "Fatema Tuj Johora Faria", "url": "https://openalex.org/A5048139494", "inst": "Ahsanullah University of Science and Technology" }, { "name": "Mukaffi Bin Moin", "url": "https://openalex.org/A5027197949", "inst": "Ahsanullah University of Science and Technology" }, { "name": "Jubayer Al Mahmud", "url": "https://openalex.org/A5147318884", "inst": "" }, { "name": "M. F. Mridha", "url": "https://openalex.org/A5034935900", "inst": "American International University-Bangladesh" }, { "name": "Md. Alam Hossain", "url": "https://openalex.org/A5147333896", "inst": "" } ], "affiliations": [ "Ahsanullah University of Science and Technology", "American International University-Bangladesh" ] }, { "uid": "doi:10.2139/ssrn.7256178", "doi": "10.2139/ssrn.7256178", "title": "Chain of Reasoning and Thought: Sequential Beta-Dirichlet Updating in Large Language Models", "authors": [ "Nicholas G. Polson" ], "posted": "2026-08-12", "added": "2026-08-18", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7256178", "field": "economics", "role": "method", "bullets": [ "Theory paper extending Dalal and Misra's single-step Bayesian account of next-token generation to multi-step chains, where the model conditions on text it produced itself.", "No model is run. Chains of thought are cast as ancestral sampling, chains of reasoning as conditioning on an externally certified relation such as an arithmetic identity or verifier score.", "Under self-conditioning the posterior mean is a bounded martingale, so thinking cannot move the expected probability of being correct, only its dispersion, while nominal posteriors still contract." ], "bullet_provenance": "ai", "salience": 65, "edition": 20, "models": [], "validated": null, "n": 2336, "authors_detailed": [ { "name": "Nicholas G. Polson", "url": "https://openalex.org/A5147001446", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7246178", "doi": "10.2139/ssrn.7246178", "title": "Measuring Investor Disagreement in Unlabeled Environments: An LLM-Based Stance Simulation Approach", "authors": [ "Huaxi Zhang", "Zhiyi Wang" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7246178", "field": "finance", "role": "instrument", "bullets": [ "52.3 million social media comments on stocks, 2016 to 2025, aggregated into daily measures of investor disagreement. The platform and the market are not stated.", "An unnamed large language model reads each comment from three simulated stances, neutral observer, bullish buyer, and bearish seller, splitting disagreement into within-group and cross-group parts. No ground-truth benchmark is reported.", "The two components pull in opposite directions: within-group disagreement predicts lower abnormal trading volume, cross-group disagreement predicts higher volume. Policy uncertainty, idiosyncratic risk, and market state moderate both. Magnitudes not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 19, "models": [], "n": 2227, "authors_detailed": [ { "name": "Huaxi Zhang", "url": "https://openalex.org/A5069618265", "inst": "Tianjin University of Finance and Economics" }, { "name": "Zhiyi Wang", "url": "https://openalex.org/A5144334142", "inst": "Southeast University" } ], "affiliations": [ "Tianjin University of Finance and Economics", "Southeast University" ] }, { "uid": "arxiv:2608.12236v1", "arxiv_id": "2608.12236v1", "title": "How Organizations Use AI: Evidence from ChatGPT", "authors": [ "Aaron Chatterji", "David Holtz", "Neel Rakholia", "Prasanna Tambe", "Gawesha Weeratunga" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.12236v1", "field": "management", "role": "object", "bullets": [ "ChatGPT Enterprise account and usage records linked to worker roles, task labels, and public-company financials through March 2026. Over 1,500 organizations and 17 million messages at the six-month adoption horizon.", "GPT is the object of study rather than the research tool. Messages are sorted into knowledge-work task categories; the abstract names no classifier and reports no accuracy check on that labelling.", "Adoption concentrates in larger, higher-valued, R&D- and SG&A-intensive US public firms. Use spans functions and seniority, runs most intense among early-career workers, and covers writing, technical work, communication, and synthesis." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 82, "edition": 19, "validated": null, "n": 2228, "authors_detailed": [ { "name": "Aaron Chatterji", "url": "https://openalex.org/A5119636278", "inst": "Duke University" }, { "name": "David Holtz", "url": "https://openalex.org/A5125770523", "inst": "" }, { "name": "Neel Rakholia", "url": "https://openalex.org/A5030297538", "inst": "Stanford University" }, { "name": "Prasanna Tambe", "url": "https://openalex.org/A5139432675", "inst": "University of Pennsylvania" }, { "name": "Gawesha Weeratunga", "url": "https://openalex.org/A5136407283", "inst": "" } ], "affiliations": [ "Duke University", "Stanford University", "University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.11787v1", "arxiv_id": "2608.11787v1", "title": "GRPO for Financial Advice Generation: Outperforming Commercial LLMs under CATE Evaluation", "authors": [ "Ofir Ben Shoham", "Shrutendra Harsola", "Vignesh Subrahmaniam", "Shravan Mohan", "Yakov Gazman", "Oded Vainas" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.11787v1", "field": "finance", "role": "method", "bullets": [ "Business records feeding generated financial advice for firms; the abstract states no sample size, period, or geography. Evaluation draws on observational outcome data covering realized gross profit.", "An unnamed open-weight model is trained with Group Relative Policy Optimization against an LLM-as-judge rubric plus a safety gate, then audited separately with a doubly-robust conditional average treatment effect estimator.", "Estimated gross-profit lift reaches 0.0228 against 0.0104 for the best commercial baseline, with the lowest downside rate. Judge and audit rank baselines differently: the base model places last and second." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "doubly-robust CATE audit of gross-profit lift against observed outcomes", "salience": 62, "edition": 19, "n": 2229, "authors_detailed": [ { "name": "Ofir Ben Shoham", "url": "https://openalex.org/A5092005373", "inst": "Ben-Gurion University of the Negev" }, { "name": "Shrutendra Harsola", "url": "https://openalex.org/A5135439606", "inst": "Intel (India)" }, { "name": "Vignesh Subrahmaniam", "url": "https://openalex.org/A5008189300", "inst": "Intel (India)" }, { "name": "Shravan Mohan", "url": "https://openalex.org/A5147114011", "inst": "" }, { "name": "Yakov Gazman", "url": "https://openalex.org/A5060087622", "inst": "" }, { "name": "Oded Vainas", "url": "https://openalex.org/A5011829745", "inst": "Microsoft (United States)" } ], "affiliations": [ "Ben-Gurion University of the Negev", "Intel (India)", "Microsoft (United States)" ] }, { "uid": "arxiv:2608.11753v1", "arxiv_id": "2608.11753v1", "title": "LabelFusion-TS: Fusing Large Language Models, Transformer Encoders, and Financial Time Series for Monetary-Policy Stance Classification", "authors": [ "Michael Schlee", "Fabian Lukassen", "Christoph Weisser" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.11753v1", "field": "economics", "role": "method", "bullets": [ "Sentences from Federal Open Market Committee communication labelled hawkish, dovish, or neutral, with about one thousand human annotations. Training runs through 2015 and testing covers 2015 to 2022.", "A voting network combines a fine-tuned RoBERTa encoder, an unnamed prompted large language model, and transformers over preceding market time series. The language model first labels sentences automatically to pre-train the encoder.", "The fused system reaches 70.2 percent weighted F1 against 64.1 percent for the zero-shot language model, and passes it with as few as 240 human-labelled sentences." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "human-labelled FOMC sentences, 70.2 percent weighted F1", "salience": 50, "edition": 19, "n": 2230, "authors_detailed": [ { "name": "Michael Schlee", "url": "https://openalex.org/A5099476042", "inst": "International Data Group (Sweden)" }, { "name": "Fabian Lukassen", "url": "https://openalex.org/A5122060660", "inst": "University of Göttingen" }, { "name": "Christoph Weisser", "url": "https://openalex.org/A5007902286", "inst": "Witten/Herdecke University" } ], "affiliations": [ "International Data Group (Sweden)", "University of Göttingen", "Witten/Herdecke University" ] }, { "uid": "doi:10.2139/ssrn.7262958", "doi": "10.2139/ssrn.7262958", "title": "Insuring the Fallback: Capacity, Monitoring and the Third Audience in Competence Signalling under Improving AI", "authors": [ "Andreas Bauer" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7262958", "field": "economics", "role": "object", "bullets": [ "Theory paper with no field data. A professional services provider, its client, and an underwriter interact in a signalling model, checked in a simulated market with censored verification records.", "No model is run or named. Generative AI enters as the force that empties the deliverable of information, leaving only a preserved human ability to catch machine error worth certifying.", "Full cover from an underwriter that does not monitor destroys separation, so third-party capacity adds nothing. Monitoring converts capacity into credibility at a linear rate, and certification demand is hump-shaped in monitoring precision." ], "bullet_provenance": "ai", "salience": 48, "edition": 19, "models": [], "validated": null, "n": 2262, "authors_detailed": [ { "name": "Andreas Bauer", "url": "https://openalex.org/A5144362180", "inst": "Tallinn University" } ], "affiliations": [ "Tallinn University" ] }, { "uid": "doi:10.2139/ssrn.7248940", "doi": "10.2139/ssrn.7248940", "title": "Rules Without Models: Why Europe Must Build the AI it Regulates", "authors": [ "Luciano Floridi" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7248940", "field": "economics", "role": "object", "bullets": [ "Policy essay on the European Union digital rulebook, from GDPR through the AI Act and the proposed Cloud and AI Development Act. No dataset; the evidence is a run of access incidents since 2023.", "No model is run. Llama 4 withholding multimodal rights from developers based in the Union, an export directive disabling Anthropic frontier models, and Beijing restricting foreign access are the exhibits.", "Argues Europe holds conditional access to lesser models on other parties' terms, and proposes a public open source model institution reachable through an industrial consortium, a member-state vehicle, or a joint undertaking." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama" ], "open_weights": true, "salience": 42, "edition": 19, "validated": null, "n": 2263, "authors_detailed": [ { "name": "Luciano Floridi", "url": "https://openalex.org/A5046574356", "inst": "Data Power Decisions (United States)" } ], "affiliations": [ "Data Power Decisions (United States)" ] }, { "uid": "doi:10.2139/ssrn.7269181", "doi": "10.2139/ssrn.7269181", "title": "Measuring the Academia–Industry Gap in Applied Communication Research: A Cross-Study Validation Framework from AI-Assisted Content Production", "authors": [ "HİLMİ ATIL ÜNAL" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7269181", "field": "management", "role": "agent", "bullets": [ "Eleven senior public relations practitioners scored five press releases against thirty-six criteria in a 2024 mixed-methods study. Four texts came from a language model under different prompt strategies, one from a professional writer.", "The model is not named. Four prompt strategies produced the machine-written texts, and the resulting rating patterns were then cross-checked against seven independent research streams published between 2024 and 2026.", "Machine-written releases scored highest in every category, yet trustworthiness of information ranked lowest of all thirty-six items. The paper reads this gap as a research-to-practice failure rather than a sample artefact." ], "bullet_provenance": "ai", "salience": 36, "edition": 19, "models": [], "validated": null, "n": 2264, "authors_detailed": [ { "name": "Hilmi Atıl Ünal", "url": "https://openalex.org/A5132660126", "inst": "Bahçeşehir University" } ], "affiliations": [ "Bahçeşehir University" ] }, { "uid": "doi:10.2139/ssrn.7235801", "doi": "10.2139/ssrn.7235801", "title": "The Uncodified Margin Human Relational Intelligence as a Structural Complement to Algorithmic Systems in Five Emerging Markets", "authors": [ "Lokesh Gupta" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7235801", "field": "management", "role": "object", "bullets": [ "Practitioner account built on eighteen years of trading across Gujarat, Ethiopia, Vietnam, Cambodia, and Uganda in petrochemicals, consumer goods, and agriculture, with four dated 2024 cases stated as falsifiable propositions.", "No model is run or named. Algorithmic and generative systems appear as decision-makers restricted to codified information, and published exposure estimates are used to score the author's own February 2025 prediction.", "Cites employment exposure of 34 percent in high-income against 11 percent in low-income economies across 135 countries as support for transformation over replacement. Flags uneven exposure by gender and career stage." ], "bullet_provenance": "ai", "salience": 40, "edition": 19, "models": [], "validated": null, "n": 2265, "authors_detailed": [ { "name": "Lokesh Gupta", "url": "https://openalex.org/A5133354856", "inst": "Systems Analytics (United States)" } ], "affiliations": [ "Systems Analytics (United States)" ] }, { "uid": "doi:10.2139/ssrn.7235798", "doi": "10.2139/ssrn.7235798", "title": "Resilytics: A Decision-Intelligence Platform and Revenue Durability Index for Small and Medium Businesses", "authors": [ "Suhas Dhamapurkar" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7235798", "field": "management", "role": "method", "bullets": [ "System paper on a decision-intelligence platform for Indian small and medium businesses earning between 50 lakh and 50 crore rupees a year. Evidence comes from a synthetic two-phase dataset, not live tenants.", "An unnamed large language model sits behind an intent-plan-tool pipeline with deterministic fallback, so every answer stays tied to warehouse data. Coverage rests on 377 automated tests, with no accuracy statistic reported.", "Delivers a Revenue Durability Index over growth, stability, concentration, geographic spread, and operational balance, plus engineering lessons on silent model degradation. Field validation with pilot deployments is still ahead." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 19, "models": [], "n": 2266, "authors_detailed": [ { "name": "Suhas Dhamapurkar", "url": "https://openalex.org/A5146451944", "inst": "Incyte (United States)" } ], "affiliations": [ "Incyte (United States)" ] }, { "uid": "doi:10.2139/ssrn.7245104", "doi": "10.2139/ssrn.7245104", "title": "Revocation Risk in Agentic Payments: A Simulation of Delegated Payment Consent", "authors": [ "Zhen Wen Lim" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7245104", "field": "finance", "role": "object", "bullets": [ "Synthetic stress simulation of delegated payment consent at payment-rail scale; no production data, no deployed system, three consent architectures compared under peak load and bursty revocation.", "No language model is run or named; AI payment agents are represented abstractly by arrival processes with load-coupled delay and straggler delay, so nothing is validated against real agent behaviour.", "Copied consent lets payments continue well past revocation, live authority checks cut that exposure by about two orders of magnitude in the sharpest case, and loose cached consent fails at high agent rates." ], "bullet_provenance": "ai", "salience": 36, "edition": 19, "models": [], "validated": null, "n": 2267, "authors_detailed": [ { "name": "Zhen-Wen Lim", "url": "https://openalex.org/A5002021968", "inst": "Malaysia University of Science and Technology" } ], "affiliations": [ "Malaysia University of Science and Technology" ] }, { "uid": "arxiv:2608.11626v1", "arxiv_id": "2608.11626v1", "title": "Organizational Technology Ladders: Remote Work and Generative AI Adoption", "authors": [ "Gregor Schubert" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.11626v1", "field": "economics", "role": "object", "bullets": [ "US job postings from firms over 2021 to 2024, with an instrumental variable built on predicted labour-market pressure to offer remote work during the pandemic period.", "No language model runs in the analysis; generative AI adoption is measured by whether postings mention it, and no model family or validation of that measure is stated.", "A 10 percentage point rise in 2021 to 2022 remote hiring raises 2023 to 2024 generative AI postings by 0.4 points across firms and 0.7 points within firms across occupations." ], "bullet_provenance": "ai", "salience": 62, "edition": 19, "models": [], "validated": null, "n": 2268 }, { "uid": "arxiv:2608.12269v1", "arxiv_id": "2608.12269v1", "title": "A Cascaded Unsupervised-Supervised NLP Pipeline for Detecting Accusatory Language in Public Procurement", "authors": [ "Bryan Torres", "Daniel Riofrío", "José Vega-Sánchez", "Nathaly Orozco", "Carla Parra", "Karen Rosero", "Felipe Grijalva" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.12269v1", "field": "accounting", "role": "instrument", "bullets": [ "Metadata and participant comments from Ecuador's SOCE public procurement portal, taken from the pre-contractual phase; sample size, period, and label counts are not stated.", "LLaMA and RoBERTa embeddings compete with domain-trained Word2Vec; Gaussian mixture clustering then a supervised classifier flags accusatory comments, scored on precision and recall with no figures given.", "Domain-trained Word2Vec with clustering and a random forest reaches high precision and recall despite severe class imbalance; no numeric scores appear, so the gap to LLaMA cannot be sized." ], "bullet_provenance": "ai", "models": [ "legacy", "llama" ], "open_weights": true, "validated": false, "validation_note": "labelled comments used for supervised training, no accuracy figures reported", "salience": 34, "edition": 19, "n": 2269, "authors_detailed": [ { "name": "Bryan Torres", "url": "https://openalex.org/A5147079993", "inst": "" }, { "name": "Daniel Riofrío", "url": "https://openalex.org/A5020124958", "inst": "University of New Mexico" }, { "name": "José Vega-Sánchez", "url": "https://openalex.org/A5037217242", "inst": "Universidad San Francisco de Quito" }, { "name": "Nathaly Orozco", "url": "https://openalex.org/A5147125111", "inst": "" }, { "name": "Carla Parra", "url": "https://openalex.org/A5147082684", "inst": "" }, { "name": "Karen Rosero", "url": "https://openalex.org/A5002311406", "inst": "Carnegie Mellon University" }, { "name": "Felipe Grijalva", "url": "https://openalex.org/A5121288284", "inst": "Universidad San Francisco de Quito" } ], "affiliations": [ "Carnegie Mellon University", "University of New Mexico", "Universidad San Francisco de Quito" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7245118", "doi": "10.2139/ssrn.7245118", "title": "Who Audits the Agent? Internal Control When the Actor is Not Human", "authors": [ "Roberto Gonzales" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7245118", "field": "accounting", "role": "object", "bullets": [ "No sample. A perspective piece on enterprise accounting systems that now run autonomous agents inside transaction processes, rather than generative tools drafting text for a person to review.", "No model is run or named. The agent is the actor under control, and the author maps the five internal control components onto a non-human one.", "Argues autonomy breaks segregation of duties because one agent can initiate, approve, record, and reconcile the same transaction, and that auditors should claim assurance over agents. No evidence offered." ], "bullet_provenance": "ai", "salience": 55, "edition": 19, "models": [], "validated": null, "n": 2296, "authors_detailed": [ { "name": "Roberto Gonzales", "url": "https://openalex.org/A5147033112", "inst": "Fairfield University" } ], "affiliations": [ "Fairfield University" ] }, { "uid": "doi:10.2139/ssrn.7242925", "doi": "10.2139/ssrn.7242925", "title": "The AI Production Function Code Capital, Context Capital, and Computational Labor", "authors": [ "Sangseng Lee" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7242925", "field": "economics", "role": "object", "bullets": [ "No data. A theory paper on AI-native production, where autonomous agents do computational work and the unit of labour is a compute token instead of a work-hour.", "No model is run or named. Agents enter as the labour input itself, and the paper states how each factor could be measured without measuring any of them.", "Proposes a Cobb-Douglas form in code capital, context capital, and computational work, with both capitals accumulating rather than depreciating normally. Falsifiable hypotheses only, no estimates." ], "bullet_provenance": "ai", "salience": 45, "edition": 19, "models": [], "validated": null, "n": 2297, "authors_detailed": [ { "name": "Sangseng Lee", "url": "https://openalex.org/A5145248084", "inst": "Kimpo University" } ], "affiliations": [ "Kimpo University" ] }, { "uid": "doi:10.2139/ssrn.7245738", "doi": "10.2139/ssrn.7245738", "title": "Can You Trust the Graph Beneath the Agent? A Reliability Metrology for Enterprise Skills Graphs: The FACTS Dimensions and the Skills Graph Reliability Index", "authors": [ "Sathyaraj Asaithambi" ], "posted": "2026-08-12", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7245738", "field": "management", "role": "object", "bullets": [ "No field data. A design-science construction, exercised on a simulated enterprise skills graph of 20,000 edges run over twelve write-back cycles.", "No language model is applied or named. Agentic HR systems are the setting, and the simulation models how agent-written edges without provenance labels re-enter the evidence base.", "At a write-back rate of 0.50 the contaminated graph looked better calibrated than a labelled control while its true calibration error ran about 4.8 times worse." ], "bullet_provenance": "ai", "salience": 35, "edition": 19, "models": [], "validated": null, "n": 2298, "authors_detailed": [ { "name": "S. Aasaithambi", "url": "https://openalex.org/A5023512609", "inst": "Independent Researcher, Hyderabad" } ], "affiliations": [ "Independent Researcher, Hyderabad" ] }, { "uid": "doi:10.2139/ssrn.7272139", "doi": "10.2139/ssrn.7272139", "title": "AI-Enabled Key Audit Matter Disclosures: A Hybrid LLM Approach to Enhancing Audit Report Transparency", "authors": [ "Saeid Homayoun", "Nick Rezaee", "Zabihollah Rezaee", "Maryam Khosravian" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7272139", "field": "accounting", "role": "method", "bullets": [ "65,192 key audit matter disclosures from 4,208 listed companies over 2012 to 2024, filed under ISA 701, the standard that requires auditors to name entity-specific audit issues.", "AuditBERT combines a fine-tuned LLaMA 3.1 8B with FinBERT and sorts disclosures into 48 accounting topics, reaching an F1 of 83.95 percent and above 93 percent on two risk categories.", "Accuracy is highest on goodwill impairment and going concern, and the authors argue local models let auditors and regulators benchmark disclosure quality without sending filings to a cloud provider." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "F1 against labelled accounting-topic and risk classifications", "salience": 58, "edition": 18, "n": 2165, "authors_detailed": [ { "name": "Saeid Homayoun", "url": "https://openalex.org/A5088900295", "inst": "California Institute of Technology" }, { "name": "Nick Rezaee", "url": "https://openalex.org/A5001501741", "inst": "University of California, San Francisco" }, { "name": "Zabihollah Rezaee", "url": "https://openalex.org/A5041735091", "inst": "University of Memphis" }, { "name": "Maryam Khosravian", "url": "https://openalex.org/A5002930559", "inst": "Hakim Sabzevari University" } ], "affiliations": [ "California Institute of Technology", "University of California, San Francisco", "University of Memphis", "Hakim Sabzevari University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7242098", "doi": "10.2139/ssrn.7242098", "title": "From Tacit Experience to Explicit Intelligence: A Human-AI Collaboration Mechanism and Knowledge Externalization Pathway for Data-Scarce Organizations", "authors": [ "Huaiqing Zhang", "Yuan Zhang", "Zijing Cao" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7242098", "field": "management", "role": "object", "bullets": [ "No data. A conceptual framework aimed at small and expert-intensive organizations whose core capability sits in individual judgment rather than in any structured dataset.", "No model is named and none is run. Language model agents appear as the object of organizational design, paired with human experts through a cognitive infrastructure layer and a six-chain project pipeline.", "The authors argue AI should carry expert judgment across projects rather than replace it, and propose progressive delegation and evidence-based governance. Application to drug development, ESG reporting and consulting is left for later testing." ], "bullet_provenance": "ai", "salience": 28, "edition": 18, "models": [], "validated": null, "n": 2168, "authors_detailed": [ { "name": "Huaiqing Zhang", "url": "https://openalex.org/A5147012584", "inst": "Independent Researcher" }, { "name": "Yuan Zhang", "url": "https://openalex.org/A5147020377", "inst": "Independent Researcher" }, { "name": "Zijing Cao", "url": "https://openalex.org/A5146961148", "inst": "Oldham Council" } ], "affiliations": [ "Independent Researcher", "Oldham Council" ] }, { "uid": "doi:10.2139/ssrn.7269119", "doi": "10.2139/ssrn.7269119", "title": "Strategic Ambiguity or Information Overload? Corporate Climate Risk Textual Networks and Analyst Forecast Errors", "authors": [ "Yuanyuan Li", "Jian Du", "Rongda Cui" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7269119", "field": "accounting", "role": "instrument", "bullets": [ "Chinese A share listed firms from 2015 to 2024, with climate risk disclosure text linked into a firm to firm network and matched to analyst forecast errors. Firm count is not stated.", "An unnamed large language model reads the disclosure text that forms the network, whose structure is then measured by social network analysis. No accuracy check against hand coding is reported.", "Firms central in the disclosure network draw larger analyst forecast errors, not smaller. The authors tie this to inflated textual ambiguity, and onward to regulatory inquiries, higher volatility, and higher cost of capital." ], "bullet_provenance": "ai", "validated": false, "salience": 57, "edition": 18, "models": [], "n": 2173, "authors_detailed": [ { "name": "Yuanyuan Li", "url": "https://openalex.org/A5147060014", "inst": "Guizhou University of Finance and Economics" }, { "name": "Jian Du", "url": "https://openalex.org/A5136765328", "inst": "Guizhou University of Finance and Economics" }, { "name": "Rongda Cui", "url": "https://openalex.org/A5120493779", "inst": "Guizhou University of Finance and Economics" } ], "affiliations": [ "Guizhou University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.7236280", "doi": "10.2139/ssrn.7236280", "title": "From Clause Types to Remedial Grammar: A Two-Axis Hohfeldian Ontology for LLM-Assisted Contract Review", "authors": [ "Chungyi Kao" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7236280", "field": "other", "role": "method", "bullets": [ "A 100 clause pilot set of commercial contract language, with clause type assignments probed against the CUAD corpus. No firm sample, period, or jurisdiction is stated.", "A locally hosted model, family not named, tags each clause with one of six speech act types and a cell in a two by two grid. Only run to run stability is reported, at Fleiss kappa of 0.88 and 0.85.", "The grid separates clauses that can be breached from clauses that are self executing, which yields machine checkable defect rules. The authors run no comparison against flat clause classification." ], "bullet_provenance": "ai", "open_weights": true, "validated": false, "salience": 40, "edition": 18, "models": [], "n": 2174, "authors_detailed": [ { "name": "Chungyi Kao", "url": "https://openalex.org/A5146483584", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7261018", "doi": "10.2139/ssrn.7261018", "title": "Empirical Evaluation of the Agentic Reputation Substrate Deliberation, the Composition of Error, and the Registered Measurement of Agency Costs in a Controlled Multi-Model Cohort", "authors": [ "Wulf A. Kaal" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7261018", "field": "management", "role": "agent", "bullets": [ "A preregistered discovery then confirmation experiment inside a working agent economy, run over a cohort of several models. Model names, task counts, and period are not stated.", "Model agents both produced and validated work under reputation bearing accountability. One arm added a structured deliberation layer, the matched control had none, and approvals were scored against ground truth.", "Deliberation left net discrimination flat, with Youden's J of 0.0606 and an interval crossing zero, but cut over approval by 0.161 and unanimity by 0.254 in the confirmatory run." ], "bullet_provenance": "ai", "validated": true, "validation_note": "registered endpoints scored against ground truth, Youden's J and effect intervals reported", "salience": 62, "edition": 18, "models": [], "n": 2175, "authors_detailed": [ { "name": "Wulf A. Kaal", "url": "https://openalex.org/A5143966534", "inst": "University of St. Thomas - Minnesota" } ], "affiliations": [ "University of St. Thomas - Minnesota" ] }, { "uid": "doi:10.2139/ssrn.7248123", "doi": "10.2139/ssrn.7248123", "title": "Grounded World Models: Efficient and Verifiable Structural Causal Prediction", "authors": [ "Rafael Kaufmann", "Harald Strömfelt", "Thomas Minter", "Sandeep Ramesh" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7248123", "field": "finance", "role": "method", "bullets": [ "A standard investment analysis task, used to benchmark a vendor's causal world model against a large language model. Sample size, period, and asset coverage are not stated.", "Claude Opus 4.8 was prompted directly for predictions as the comparison arm against a structured causal model whose outputs are Bayesian posteriors. No external ground truth benchmark is described.", "The authors report the causal model reaching about 2.4 times the quality at roughly 36,000 times lower cost per prediction, and argue a prompted model stays below that ceiling at any finite cost." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 38, "edition": 18, "n": 2176, "authors_detailed": [ { "name": "Rafael Kaufmann", "url": "https://openalex.org/A5056258424", "inst": "Public Risk Management Association" }, { "name": "Harald Strömfelt", "url": "https://openalex.org/A5086576188", "inst": "Public Risk Management Association" }, { "name": "Thomas Minter", "url": "https://openalex.org/A5010481379", "inst": "Public Risk Management Association" }, { "name": "Sandeep Ramesh", "url": "https://openalex.org/A5120892873", "inst": "Public Risk Management Association" } ], "affiliations": [ "Public Risk Management Association" ] }, { "uid": "doi:10.2139/ssrn.7239039", "doi": "10.2139/ssrn.7239039", "title": "Risk Behavior in Large Language Models: An Empirical Comparison of Three Models on Choices13k", "authors": [ "Sandeep Shenoy" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7239039", "field": "economics", "role": "agent", "bullets": [ "350 binary gambles from the Choices13k dataset, each repeated 10 times under four personas, giving 42,000 model decisions with human choice rates as the comparison.", "gpt-4o-mini, gpt-4.1-nano and gpt-5.4-nano each chose between two gambles under machine, human, loss-averse and risk-taking personas; choices were scored against human rates and expected value.", "Risk preferences were model-specific, not shared: gpt-4o-mini picked option B 0.68 to 0.71 of the time against a human 0.51, while the nano models picked A almost always. Correlation with human choices peaked at 0.52." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Choices13k human choice rates, correlations reported", "salience": 58, "edition": 18, "n": 2184, "authors_detailed": [ { "name": "Sandeep Shenoy", "url": "https://openalex.org/A5146992893", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2608.12283v1", "arxiv_id": "2608.12283v1", "title": "Large Language Model-Driven Small-Capitalization Trading: Integrating Financial News Sentiment, Macroeconomic Indicators, and Technical Signals", "authors": [ "Alireza Kargarzadeh", "Nariman Khaledian", "Navid Parvini", "Arman Khaledian" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.12283v1", "field": "finance", "role": "instrument", "bullets": [ "Russell 2000 small-cap equities traded through backtests under three selection regimes, pure alpha, pure beta, and their intersection, across a grid of holding periods and transaction costs. Sample period not stated.", "GPT-4o mini scores financial news sentiment and predicts risk, split into aleatoric and epistemic parts, which enters the allocator's covariance matrix directly. No check against human sentiment labels.", "Separated alpha and beta legs usually beat the intersection on Sharpe and return. The best conservative configuration, pure beta with a 40-day hold and risk parity, reaches Sharpe 2.33 at 100 basis points of cost." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 47, "edition": 18, "n": 2185, "authors_detailed": [ { "name": "Alireza Kargarzadeh", "url": "https://openalex.org/A5115509022", "inst": "Imperial College London" }, { "name": "Nariman Khaledian", "url": "https://openalex.org/A5014841508", "inst": "Centre National de la Recherche Scientifique" }, { "name": "Navid Parvini", "url": "https://openalex.org/A5066894656", "inst": "University of Kent" }, { "name": "Arman Khaledian", "url": "https://openalex.org/A5119849203", "inst": "" } ], "affiliations": [ "Imperial College London", "Centre National de la Recherche Scientifique", "University of Kent" ] }, { "uid": "doi:10.2139/ssrn.7244201", "doi": "10.2139/ssrn.7244201", "title": "Calibrated Population Diversity: Closing the Loop Between Survey Fidelity and Agentic Behavior in Synthetic Respondents", "authors": [ "Triveni Gopala Krishna Ganta" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7244201", "field": "management", "role": "method", "bullets": [ "One binary attitude split, 60 percent yes against 40 percent no, with real population figures as the calibration target. Survey source, sample size and country are not stated.", "Qwen-7B, Gemma-7B and GLM each write personas whose answer spread is compared with the real one, then adjusted in a loop until the standard deviation ratio approaches one.", "Uncalibrated personas collapse onto modal answers in 85 percent of runs, entropy falling from 1.46 to 0.77. Calibration reaches 99.9 percent fidelity and restores income-linked choice patterns." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "synthetic answers compared with real population distribution, total variation distance and fidelity reported", "salience": 52, "edition": 18, "n": 2187, "authors_detailed": [ { "name": "Triveni Gopala Krishna Ganta", "url": "https://openalex.org/A5134709233", "inst": "Manipal Academy of Higher Education" } ], "affiliations": [ "Manipal Academy of Higher Education" ] }, { "uid": "doi:10.2139/ssrn.7271766", "doi": "10.2139/ssrn.7271766", "title": "A Knowledge Graph-Driven Framework for Scaffolding Safety Reasoning with LLMs in Construction Industry", "authors": [ "Hai Zhao", "Hengqin Wu", "Qiping Shen", "Zhengdao LI", "Tianxiang Lin" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7271766", "field": "management", "role": "instrument", "bullets": [ "4,449 construction accident reports on scaffolding, turned into a knowledge graph of 539 nodes and 1,932 relationships under a five level causation ontology. Country and period are not stated.", "The paper names no model. An unnamed LLM pulls entities and relations out of the reports, then answers safety questions through combined graph and vector retrieval with chain of thought prompting.", "Reported precision is 97.00 percent, recall 95.10 percent and F1 96.04, above baselines that the abstract does not name. The size of the evaluation set is not given." ], "bullet_provenance": "ai", "validated": true, "validation_note": "precision, recall and F1 against a reference set", "salience": 38, "edition": 18, "models": [], "n": 2188, "authors_detailed": [ { "name": "Hai Zhao", "url": "https://openalex.org/A5146942895", "inst": "" }, { "name": "Hengqin Wu", "url": "https://openalex.org/A5049450439", "inst": "Hong Kong Polytechnic University" }, { "name": "Qiping Shen", "url": "https://openalex.org/A5002153093", "inst": "Hong Kong Polytechnic University" }, { "name": "Zhengdao LI", "url": "https://openalex.org/A5147032197", "inst": "Shenzhen University" }, { "name": "Tianxiang Lin", "url": "https://openalex.org/A5054429419", "inst": "Wenzhou Medical University" } ], "affiliations": [ "Hong Kong Polytechnic University", "Shenzhen University", "Wenzhou Medical University" ] }, { "uid": "arxiv:2608.11683v1", "arxiv_id": "2608.11683v1", "title": "FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents", "authors": [ "Yuhao Zhang", "O. Ozan Koyluoglu", "Thejas Venkatesh", "Richard Diehl Martinez", "Vishank Bhatia", "Arash Alidoust", "Ashwin Paranjape" ], "posted": "2026-08-12", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.11683v1", "field": "finance", "role": "method", "bullets": [ "220 expert-written investment research queries with 11,543 source-attributed grading rubrics, covering six use cases across the investor workflow, run under one harness limited to publicly available data.", "Frontier models and agent systems answer long-form analyst questions and are graded against the rubrics rather than reference answers. Claude Fable 5, Kimi K3 and a vendor in-house system are among those tested.", "The leading system reaches 56.0 percent against 49.2 for the strongest frontier model at roughly 2.2 times lower cost. Screening and macro questions top out near 33 and 39 percent." ], "bullet_provenance": "ai", "models": [ "claude", "open_other" ], "open_weights": false, "validated": true, "validation_note": "expert-crafted queries scored against source-attributed rubrics", "salience": 68, "edition": 18, "n": 2189, "authors_detailed": [ { "name": "Yuhao Zhang", "url": "https://openalex.org/A5147121631", "inst": "" }, { "name": "O. Ozan Koyluoglu", "url": "https://openalex.org/A5147069257", "inst": "" }, { "name": "Thejas Venkatesh", "url": "https://openalex.org/A5038940016", "inst": "Mountain View College" }, { "name": "Richard Diehl Martinez", "url": "https://openalex.org/A5027311833", "inst": "United States Census Bureau" }, { "name": "Vishank Bhatia", "url": "https://openalex.org/A5111525799", "inst": "Synergy University Dubai" }, { "name": "Arash Alidoust", "url": "https://openalex.org/A5141031160", "inst": "Tabriz University of Medical Sciences" }, { "name": "Ashwin Paranjape", "url": "https://openalex.org/A5067264969", "inst": "Mountain View College" } ], "affiliations": [ "Mountain View College", "United States Census Bureau", "Synergy University Dubai", "Tabriz University of Medical Sciences" ] }, { "uid": "arxiv:2608.11047v1", "arxiv_id": "2608.11047v1", "title": "V-FiLLM: Verified Financial LLM Reasoning Benchmark", "authors": [ "Alicia Larsen", "Victoire Laurent", "Aulia Kharis Rakhamsari", "Lara Turgut", "Nino Antulov-Fantulin" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.11047v1", "field": "finance", "role": "method", "bullets": [ "Benchmark items generated from executable computation trees over real financial tables, with ground truth derived symbolically rather than from human or model labels. Size of the item pool not stated.", "Open-source models, none named, answer generated financial questions across four difficulty axes: computation depth, expression breadth, concept complexity, and context size. Correctness is checked against the symbolic ground truth.", "Accuracy falls by up to 51 percent as reasoning depth rises and by 47 points under adversarial numeric perturbation. LoRA fine-tuning on verified reasoning traces lifts accuracy from 81.1 to 85.6 percent." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "symbolic ground truth by construction, plus FinQA comparison", "salience": 55, "edition": 19, "n": 2226, "authors_detailed": [ { "name": "Alicia Larsen", "url": "https://openalex.org/A5146959555", "inst": "" }, { "name": "Victoire Laurent", "url": "https://openalex.org/A5033995734", "inst": "NOAA National Weather Service" }, { "name": "Aulia Kharis Rakhamsari", "url": "https://openalex.org/A5147014553", "inst": "" }, { "name": "Lara Turgut", "url": "https://openalex.org/A5147045362", "inst": "" }, { "name": "Nino Antulov-Fantulin", "url": "https://openalex.org/A5147003614", "inst": "" } ], "affiliations": [ "NOAA National Weather Service" ] }, { "uid": "doi:10.2139/ssrn.7260698", "doi": "10.2139/ssrn.7260698", "title": "LLM-Observed Multi-Agent Maintenance with Amortized Reinforcement Learning Policies", "authors": [ "Hamed Khosravi", "Xiaoming Huo" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7260698", "field": "management", "role": "instrument", "bullets": [ "Nine degradation datasets plus 42,000 simulated agent episodes, with work orders, transcripts and deployment logs as text input. Fleets cover physical parts, deployed models and same-role AI agents.", "A zero-shot reader, family not stated, turns unit records into structured events carrying confidence and abstention; a 7-billion-parameter version is compared against supervised baselines under scarce labels.", "The amortized policy transfers to new fleets within 1.00 percent of optimal cost. Coordinated retraining saves 11.9 percent of GPU seconds, and the reader cuts text-induced maintenance regret from 56.0 to 31.7 percent." ], "bullet_provenance": "ai", "validated": true, "validation_note": "reader benchmarked against supervised baselines on labelled logs; regret and false-reset rates reported", "salience": 48, "edition": 19, "models": [], "n": 2242, "authors_detailed": [ { "name": "Hamed Khosravi", "url": "https://openalex.org/A5101434270", "inst": "Georgia Institute of Technology" }, { "name": "Xiaoming Huo", "url": "https://openalex.org/A5146438518", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7239200", "doi": "10.2139/ssrn.7239200", "title": "The Principal-Agentic Problem: How AI Makes Agency Risk Invisible", "authors": [ "Stuart Bell" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7239200", "field": "management", "role": "object", "bullets": [ "Theory paper with no sample. It extends principal-agent analysis to AI delegation, drawing on published findings about sycophancy, moral disengagement and vanishing social risk premiums.", "No model is run or tested. AI systems enter as the delegated agent whose hidden commercial interests sit in training, fine-tuning and retrieval design.", "Users absorb the AI into an extended self, so no counterparty appears to monitor and delegation guilt disappears. The paper proposes testable propositions and a motivated-delegation scale." ], "bullet_provenance": "ai", "salience": 40, "edition": 19, "models": [], "validated": null, "n": 2243, "authors_detailed": [ { "name": "Stuart Bell", "url": "https://openalex.org/A5146573679", "inst": "Fairmount Technologies (United States)" } ], "affiliations": [ "Fairmount Technologies (United States)" ] }, { "uid": "doi:10.2139/ssrn.7224058", "doi": "10.2139/ssrn.7224058", "title": "AI Governance for Institutional Readiness in Finance", "authors": [ "Irene Aldridge", "Steven Krawciw" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7224058", "field": "finance", "role": "object", "bullets": [ "US asset management: 75 large money managers disclosing AI use in Form ADV filings, a survey of finance professionals, and three documented incidents including a fund deleveraging and an airline chatbot ruling.", "No model is run by the authors. Two calibrated synthetic illustrations, a regret-covariance drift monitor and a crowding simulation, show the proposed monitors compute from observable data.", "88 percent of surveyed professionals report no operational governance framework for agentic AI, and only 24 of 75 managers report a formal policy. The crowding simulation lifts joint drawdown risk from 39.2 to 79.3 percent." ], "bullet_provenance": "ai", "salience": 55, "edition": 19, "models": [], "validated": null, "n": 2244, "authors_detailed": [ { "name": "Irene Aldridge", "url": "https://openalex.org/A5133872707", "inst": "Risk Engineering (Bulgaria)" }, { "name": "Steven Krawciw", "url": "https://openalex.org/A5038562901", "inst": "Risk Engineering (Bulgaria)" } ], "affiliations": [ "Risk Engineering (Bulgaria)" ] }, { "uid": "doi:10.2139/ssrn.7239298", "doi": "10.2139/ssrn.7239298", "title": "The Organizational Economics of Agentic AI: Cognitive Task Chaining, Orchestration Patterns, and the Transition to Physical Autonomy", "authors": [ "Liudmyla Cherniakova" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7239298", "field": "management", "role": "object", "bullets": [ "Conceptual synthesis across organizational design, labor economics and computer science. No sample or dataset; the evidence base is published software workflows and industrial robotics deployments.", "No model is run. LLM-driven orchestration is compared conceptually with robotic process automation, and five multi-agent interaction frameworks and four enterprise orchestration patterns are catalogued.", "The claim is that system-level workflow redesign matters more than task-level accuracy, with implications drawn for corporate moats, physical AI through digital twins, and firm structure by 2030. No magnitudes given." ], "bullet_provenance": "ai", "salience": 34, "edition": 19, "models": [], "validated": null, "n": 2245, "authors_detailed": [ { "name": "Liudmyla Cherniakova", "url": "https://openalex.org/A5146443698", "inst": "Santa Monica College" } ], "affiliations": [ "Santa Monica College" ] }, { "uid": "doi:10.2139/ssrn.7260602", "doi": "10.2139/ssrn.7260602", "title": "We are Eating the Seed Corn: The Automation of Entry-Level Tasks and the Erosion of Cultural Apprenticeship", "authors": [ "Luca Nogarotto" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7260602", "field": "management", "role": "object", "bullets": [ "No new data. The paper assembles published estimates, among them a study of tens of millions of United States workers across hundreds of thousands of firms, plus French and British official statistics.", "No model is run or named. Generative AI enters as the cause of seniority-biased technological change, and the paper weighs a competing 2026 account that credits remote work instead.", "Cited evidence puts junior employment down nine to ten percent within six quarters at adopting firms. The seed-corn claim is that transmission of tacit judgment breaks even where hiring holds up." ], "bullet_provenance": "ai", "salience": 44, "edition": 19, "models": [], "validated": null, "n": 2261, "authors_detailed": [ { "name": "Luca Nogarotto", "url": "https://openalex.org/A5136351021", "inst": "Biogen (Switzerland)" } ], "affiliations": [ "Biogen (Switzerland)" ] }, { "uid": "doi:10.2139/ssrn.7226378", "doi": "10.2139/ssrn.7226378", "title": "Null Results Under Pre-specified Retail Implementation Regimes: A Sequentially Registered Audit of Futures and ETF Strategies", "authors": [ "Yang Lu" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7226378", "field": "finance", "role": "method", "bullets": [ "One researcher's eleven-day adjudication window, 21 to 31 July 2026, covering 15 registered confirmatory hypotheses, two preregistered replications, a descriptive benchmark, and one audited screen on futures and ETF strategies.", "AI agents assist the sole researcher; no model or family is named. Discipline comes from the protocol, hypotheses frozen one at a time, SHA-256 pinned inputs, permutation nulls, and isolated reimplementation.", "No directional strategy cleared its pre-specified implementation regime; time-series momentum passed at t of 2.43 but failed once funding costs entered. Only volatility-magnitude effects survived, replicated 13 of 13." ], "bullet_provenance": "ai", "salience": 60, "edition": 19, "models": [], "validated": null, "n": 2292, "authors_detailed": [ { "name": "Yang Lu", "url": "https://openalex.org/A5144172538", "inst": "Lynn University" } ], "affiliations": [ "Lynn University" ] }, { "uid": "doi:10.2139/ssrn.7233959", "doi": "10.2139/ssrn.7233959", "title": "Chain of Accountability Framework Operationalizing Accountability Debt in AI Pipelines", "authors": [ "Christopher Ermis" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7233959", "field": "management", "role": "object", "bullets": [ "No sample. A conceptual framework aimed at enterprise pipelines where AI agents sit on approval gates, sign off on work, and spawn sub-agents without a named human principal.", "No model is run or named. The paper defines four failure modes in transferring accountability, three debt dimensions, a four-level maturity ladder, and a policy template.", "Sets a deliberately low bar: firms need not carry zero accountability debt, only know and declare it. No measurement, field test, or magnitude is reported." ], "bullet_provenance": "ai", "salience": 28, "edition": 19, "models": [], "validated": null, "n": 2293, "authors_detailed": [ { "name": "Christopher Ermis", "url": "https://openalex.org/A5133268520", "inst": "St. Augustine College" } ], "affiliations": [ "St. Augustine College" ] }, { "uid": "doi:10.2139/ssrn.7243680", "doi": "10.2139/ssrn.7243680", "title": "Federated Quantitative Intelligence (FQI): A Governed Architecture for Multi-Agent Quant Research Across Data Silos", "authors": [ "Ethan Nguyen", "Philip Treleaven" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7243680", "field": "finance", "role": "method", "bullets": [ "No sample. A design paper for institutional quantitative research, aimed at settings where proprietary market data, analyst knowledge, and alternative data sit behind organisational and jurisdictional walls.", "No model is run or named. Agents are given delegated analytical tasks inside a federated architecture that keeps raw data local and attaches provenance and a human challenge route to every output.", "Claims future quant platforms will compete on coordination and control rather than on any one model. Offers a reference architecture and research agenda, with no evaluation or magnitudes." ], "bullet_provenance": "ai", "salience": 35, "edition": 19, "models": [], "validated": null, "n": 2294, "authors_detailed": [ { "name": "Ethan Nguyen", "url": "https://openalex.org/A5029075200", "inst": "The London College" }, { "name": "Philip Treleaven", "url": "https://openalex.org/A5000136854", "inst": "Centre for Artificial Intelligence and Robotics" } ], "affiliations": [ "The London College", "Centre for Artificial Intelligence and Robotics" ] }, { "uid": "arxiv:2608.11344v2", "arxiv_id": "2608.11344v2", "title": "Governing Agentic AI in FinTech", "authors": [ "Henry Han" ], "posted": "2026-08-11", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.11344v2", "field": "finance", "role": "method", "bullets": [ "Three studies of agentic decision pipelines in financial services, run across nine model versions that range from a three-billion-parameter local model to a hosted commercial frontier system, including two credit-model versions.", "Models decide, then the authors try to replay those decisions. Under the tightest settings each endpoint allows, the local model reproduced 320 of 320 executions and hosted models 319 of 320 and 959 of 960.", "Provider releases change historical actions, the frontier endpoint refuses temperature, top_p, top_k and exposes no seed, and orchestration alone shifts outcomes: no execution record repeated across configurations. Capability does not buy auditability." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "replay reproduction rates against recorded executions, 320 and 960 run sets", "salience": 78, "edition": 19, "n": 2295, "authors_detailed": [ { "name": "Henry Han", "url": "https://openalex.org/A5087988017", "inst": "Baylor University" } ], "affiliations": [ "Baylor University" ] }, { "uid": "arxiv:2608.10672v1", "arxiv_id": "2608.10672v1", "title": "Longitudinal Evidence That General-Purpose Chatbots Actively Foster Relational Engagement", "authors": [ "Lisa Mühl", "Jessica M. Szczuka" ], "posted": "2026-08-11", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.10672v1", "field": "management", "role": "object", "bullets": [ "Pre-registered four week study with 72 participants who chatted with ChatGPT-4o, producing 182,451 lines of conversation. One arm got a relational system prompt, the other was unmodified.", "ChatGPT-4o held every conversation. Transcripts were coded for self disclosure and topic, alongside repeated self reports and interviews. Coder reliability figures are not stated.", "Even without the relational prompt the system disclosed about itself twice as often as users did, steered topics, and opened intimate exchanges, yet users reported no gain in felt closeness." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 18, "validated": null, "n": 2172, "authors_detailed": [ { "name": "Lisa Mühl", "url": "https://openalex.org/A5048377714", "inst": "University of Duisburg-Essen" }, { "name": "Jessica M. Szczuka", "url": "https://openalex.org/A5062147485", "inst": "University of Duisburg-Essen" } ], "affiliations": [ "University of Duisburg-Essen" ] }, { "uid": "doi:10.2139/ssrn.7226279", "doi": "10.2139/ssrn.7226279", "title": "Combating Organizational Risks in Human-AI Problem Solving: Principles for Developing Responsible Generative AI use Competences", "authors": [ "Ojelanki Ngwenyama", "Keegan Steyn" ], "posted": "2026-08-11", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7226279", "field": "management", "role": "object", "bullets": [ "Conceptual paper on graduate management education, with one illustrative case tracing a manager's move from naive generative AI use to a governed prompting workflow. No sample.", "No model or vendor is named. Three prompting strategies are specified structurally: chain-of-verification, adversarial challenge, and constitutional prompting, each broken into elements with a stated purpose.", "Names four organisational risks from managerial reliance on generative AI: loss of cognitive control, epistemic distortion, deliberative degradation, and accountability degradation, and proposes an action learning curriculum to counter them." ], "bullet_provenance": "ai", "salience": 38, "edition": 18, "models": [], "validated": null, "n": 2181, "authors_detailed": [ { "name": "Ojelanki Ngwenyama", "url": "https://openalex.org/A5146548637", "inst": "" }, { "name": "Keegan Steyn", "url": "https://openalex.org/A5020399804", "inst": "University of Cape Town" } ], "affiliations": [ "University of Cape Town" ] }, { "uid": "arxiv:2608.11327v1", "arxiv_id": "2608.11327v1", "title": "Long-Horizon Forecasting of Complete Financial Statements with Forma", "authors": [ "Travis L. Johnson", "Jiannan Jiang", "Soumyabrata Chaudhuri", "Yihao Chen", "Lauren Falvey", "Donal O'Cofaigh" ], "posted": "2026-08-11", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.11327v1", "field": "accounting", "role": "method", "bullets": [ "ProForma-20Q, a released benchmark forecasting 78 statement line items one to twenty quarters ahead for anonymized firms from past statements and an industry code. Firm count not stated.", "Forma is a purpose-built transformer reading statements as account, quarter, value tuples under a masked-tuple Gaussian likelihood; it is scored in change-space R squared against gradient boosting, a time-series foundation model, and unnamed frontier LLMs.", "Forma beats every competitor and its margin grows with horizon, where valuation matters most. Predictive intervals never under-cover, forecasts nearly satisfy accounting identities, and exact coherence costs no measurable accuracy." ], "bullet_provenance": "ai", "validated": true, "validation_note": "ProForma-20Q held-out statements, change-space R squared reported", "salience": 70, "edition": 18, "models": [], "n": 2182 }, { "uid": "arxiv:2608.10434v1", "arxiv_id": "2608.10434v1", "title": "Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance", "authors": [ "Cong Chi Nguyen", "Trang Mai Xuan", "Vu-Duc Ngo", "Kim-Ngan Thi Nguyen", "Trong-Nghia Nguyen", "Thien Van Luong" ], "posted": "2026-08-11", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.10434v1", "field": "management", "role": "object", "bullets": [ "Controlled experiment with human operators auditing intrusion alerts on unmanned aerial vehicle networks after the fact. Participant count not stated.", "An unnamed language model drives a conversational explanation interface that answers operator questions on demand; the comparison condition is a static visual dashboard over the same detection model.", "Operators rated the conversational interface more useful but showed less appropriate self-reliance, accepting incorrect detections more readily. Effect sizes not stated." ], "bullet_provenance": "ai", "salience": 44, "edition": 18, "models": [], "validated": null, "n": 2183 }, { "uid": "arxiv:2608.11381v1", "arxiv_id": "2608.11381v1", "title": "From Numbers to Judgment: Specialist LLM Agents and Reinforcement Learning for European Listed Real Estate", "authors": [ "Pardis Taghavi", "Santosh Bhavani" ], "posted": "2026-08-11", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.11381v1", "field": "finance", "role": "method", "bullets": [ "Nineteen European listed real estate firms across seven regulatory wrappers, analysed through a sixteen lens framework, with held out splits for unseen firms and unseen wrappers.", "An unnamed frontier model runs monolithic prompting against eight lens specialists with model, evidence, instructions and scoring held fixed. Qwen3.5-9B is then post-trained with GRPO on structured task rewards.", "Decomposition lifts numerical tasks by 15.8 points but not judgment tasks. Reinforcement tuning adds 12.0 points overall and 14.2 on judgment, and transfers to unseen firms at plus 15.2." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 56, "edition": 18, "n": 2186, "authors_detailed": [ { "name": "Pardis Taghavi", "url": "https://openalex.org/A5094196412", "inst": "Texas A&M University" }, { "name": "Santosh Bhavani", "url": "https://openalex.org/A5035759238", "inst": "Carnegie Mellon University" } ], "affiliations": [ "Carnegie Mellon University", "Texas A&M University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.11344v1", "arxiv_id": "2608.11344v1", "title": "Governing Agentic AI in FinTech", "authors": [ "Henry Han" ], "posted": "2026-08-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.11344v1", "field": "finance", "role": "object", "bullets": [ "Three studies across nine model versions from a 3B-parameter local model to a commercial frontier system, testing financial decision reproducibility and auditability.", "Evaluated agentic AI reproducibility under varying orchestration architectures and temperature controls for consequential financial decisions across multiple execution runs.", "Local model reproduced 320 of 320 executions under tightest controls; architecture changes altered final actions and no execution record repeated across any configuration." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "Reproducibility counts across 320-960 executions per model configuration", "salience": 55, "n": 4063, "authors_detailed": [ { "name": "Henry Han", "url": "https://openalex.org/A5087988017", "inst": "Baylor University" } ], "affiliations": [ "Baylor University" ] }, { "uid": "doi:10.2139/ssrn.7247240", "doi": "10.2139/ssrn.7247240", "title": "The Impact of Artificial Intelligence on Investment Banking", "authors": [ "Anoop Rayankula", "Aravinda Guntupalli", "Gerhard Kling" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7247240", "field": "finance", "role": "object", "bullets": [ "Literature synthesis on AI in investment banking advisory, valuation, diligence and client execution work, looking ahead from 2026 to 2036. No sample of firms or transactions.", "No model is run. The authors pool evidence from four literatures and score prior AI-impact claims as hypotheses against evidence strength, domain fit and adoption constraints.", "Evidence supports augmenting document-heavy and synthesis-heavy junior work rather than replacing banker judgment. Advantage shifts to proprietary data, client access and governance; the named risk is overreliance on fluent but unreliable output." ], "bullet_provenance": "ai", "salience": 42, "edition": 19, "models": [], "validated": null, "n": 2240, "authors_detailed": [ { "name": "Anoop Rayankula", "url": "https://openalex.org/A5146432591", "inst": "" }, { "name": "Aravinda Guntupalli", "url": "https://openalex.org/A5146316805", "inst": "" }, { "name": "Gerhard Kling", "url": "https://openalex.org/A5016573426", "inst": "University of Aberdeen" } ], "affiliations": [ "University of Aberdeen" ] }, { "uid": "arxiv:2608.10175v1", "arxiv_id": "2608.10175v1", "title": "Beyond Cash Flows: A Multi-Agent AI Framework for Valuing Clinical-Stage, Cross-Border Biotechnology", "authors": [ "Yuhan Fang" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.10175v1", "field": "finance", "role": "agent", "bullets": [ "No new data. The framework is a design proposal, motivated by the author's record as portfolio manager of a cross-border biotechnology fund returning 127.17 percent against a 50.67 percent benchmark over sixteen months.", "Analyst, researcher and risk-manager agents are specified to value pre-revenue assets on milestone probabilities, reconcile prices across venues, and arbitrate bullish and cautious views. No model family is named and nothing is run.", "No empirical result. The paper offers architectural principles for extending agentic investment systems to assets whose value rests on binary scientific and regulatory events rather than cash flows." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 19, "models": [], "n": 2241, "authors_detailed": [ { "name": "Yuhan Fang", "url": "https://openalex.org/A5027091385", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Hong Kong University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7222858", "doi": "10.2139/ssrn.7222858", "title": "Skill Development Strategy in the Age of AI: A Comparative-Advantage Argument from the Legal Profession", "authors": [ "Joung Hwang" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7222858", "field": "economics", "role": "object", "bullets": [ "Conceptual labor-economics argument grounded in the legal profession, citing over a thousand court cases that address AI-generated errors and state bar guidance on reviewing AI-produced analysis. No dataset of its own.", "No model is run. Generative AI enters as the substitute technology whose fluent output erodes epistemic vigilance; the paper names no model family and reports no measurement.", "The durable human comparative advantage is judgment under adversarial or thin evidence, where conduct outweighs statement and fluency misleads. The paper makes this the training target for lawyers, firms and bar bodies." ], "bullet_provenance": "ai", "salience": 45, "edition": 19, "models": [], "validated": null, "n": 2251, "authors_detailed": [ { "name": "Joung Hwang", "url": "https://openalex.org/A5146795499", "inst": "ColdQuanta, Inc., DBA Infleqtion (United States)" } ], "affiliations": [ "ColdQuanta, Inc., DBA Infleqtion (United States)" ] }, { "uid": "doi:10.2139/ssrn.7217638", "doi": "10.2139/ssrn.7217638", "title": "Scaling Against Yourself: Optimal Control of Generative AI Deployment Under a Data Contamination Externality", "authors": [ "Almudena Recio Román", "Manuel Recio Menéndez", "María Victoria Román-González" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7217638", "field": "economics", "role": "object", "bullets": [ "Continuous-time optimal control model of a firm deploying generative AI, with a depreciating data-quality stock replenished by governance investment. Results come from a calibration, not from firm data.", "No model is run. Generative AI enters through the share of outputs that need expert checking and through an entropy rate that rises as AI output re-enters the firm's own data estate.", "The contamination wedge cuts optimal deployment scale by 64 percent in the calibration, and a firm that ignores the loop over-deploys by 22 percent. History dependence appears only at intermediate loop intensity." ], "bullet_provenance": "ai", "salience": 50, "edition": 19, "models": [], "validated": null, "n": 2252, "authors_detailed": [ { "name": "Almudena Recio-Román", "url": "https://openalex.org/A5086388942", "inst": "International University of Andalucía" }, { "name": "Manuel Recio-Menéndez", "url": "https://openalex.org/A5069778640", "inst": "University of Almería" }, { "name": "María Victoria Román-González", "url": "https://openalex.org/A5073870997", "inst": "University of Almería" } ], "affiliations": [ "International University of Andalucía", "University of Almería" ] }, { "uid": "doi:10.2139/ssrn.7249138", "doi": "10.2139/ssrn.7249138", "title": "The Effects of Blockchain-based Track and Trace on Consumer Returns", "authors": [ "Qian Deng", "Feng Mai", "Xiaosong (David) Peng", "Xiade Zhao", "Hao Ying" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7249138", "field": "management", "role": "instrument", "bullets": [ "Product-level data from a large global online retailer that adopted blockchain track and trace for part of its catalogue, with matched control products across categories in a quasi-experimental design.", "Large language models, family not stated, score how abstract consumer review text is, and those scores are validated against human annotators. The measure carries the mechanism test, not the main effect.", "Adoption cuts return rates by 2.64 percentage points, about 60 percent of the baseline. The effect holds for experience goods and vanishes for credence goods, and buyers report higher perceived information reliability." ], "bullet_provenance": "ai", "validated": true, "validation_note": "review abstractness scores checked against human annotators", "salience": 70, "edition": 19, "models": [], "n": 2253, "authors_detailed": [ { "name": "Qian Deng", "url": "https://openalex.org/A5059930014", "inst": "Xuzhou Medical College" }, { "name": "Feng Mai", "url": "https://openalex.org/A5088161536", "inst": "University of Iowa" }, { "name": "Xiaosong Peng", "url": "https://openalex.org/A5076182931", "inst": "Lehigh University" }, { "name": "Xiade Zhao", "url": "https://openalex.org/A5083970998", "inst": "China Europe International Business School" }, { "name": "Hao Ying", "url": "https://openalex.org/A5146867288", "inst": "" } ], "affiliations": [ "University of Iowa", "Xuzhou Medical College", "Lehigh University", "China Europe International Business School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7217200", "doi": "10.2139/ssrn.7217200", "title": "Human-AI Collaboration in the Age of Generative Intelligence: Workforce Productivity, Innovation, and Role Redefinition Across IT Project Teams, Business Analysts, and Healthcare Providers", "authors": [ "Samuel Babatunde", "Sharma Khan" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7217200", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no primary data, synthesizing peer-reviewed and industry literature plus four prior domain studies across IT project teams, business analysts, and healthcare providers.", "No model is named and none is run. Generative AI enters only as the object of a three-mechanism framework covering task reallocation, cognitive load, and skill complementarity.", "Argues productivity gains arrive through role redefinition rather than automation, and flags clinician burnout evidence that cuts against simple productivity claims. Illustrative scenarios are labelled non-empirical." ], "bullet_provenance": "ai", "salience": 25, "edition": 19, "models": [], "validated": null, "n": 2254, "authors_detailed": [ { "name": "Samuel Babatunde", "url": "https://openalex.org/A5128710403", "inst": "" }, { "name": "Sharma Khan", "url": "https://openalex.org/A5146827667", "inst": "Unaffiliated Authors" } ], "affiliations": [ "Unaffiliated Authors" ] }, { "uid": "doi:10.2139/ssrn.7230638", "doi": "10.2139/ssrn.7230638", "title": "The Limits of IAS/IFRS Standards in the Face of Artificial Intelligence in the Preparation, Analysis, Estimation, Measurement and Audit of Financial Information", "authors": [ "Abderrahim Bekhtaoui" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7230638", "field": "accounting", "role": "object", "bullets": [ "Doctrinal analysis of twelve IAS and IFRS standards, among them IFRS 9, IFRS 15, IFRS 16, IAS 36 and IAS 38, read against machine learning and generative systems in preparation, measurement, and audit.", "No model is run or named. Generative AI is treated as a source of estimation and manipulation risk, worked through six constructed scenarios and cases such as Wirecard and Silicon Valley Bank.", "Identifies thirteen structural limitations and concludes the standards stay technologically neutral in principle but lack traceability requirements. Proposes four-layer governance guidance instead of rewriting the framework." ], "bullet_provenance": "ai", "salience": 34, "edition": 19, "models": [], "validated": null, "n": 2255, "authors_detailed": [ { "name": "Abderrahim Bekhtaoui", "url": "https://openalex.org/A5143895298", "inst": "Université Djilali de Sidi Bel Abbès" } ], "affiliations": [ "Université Djilali de Sidi Bel Abbès" ] }, { "uid": "doi:10.2139/ssrn.7218378", "doi": "10.2139/ssrn.7218378", "title": "Dynamic Cross-Level Mechanisms of Generative AI Adoption: Motivations, Affordances, and Ethical Reasoning among Designers and Non-Designers", "authors": [ "Hsiu-Yu Hung", "Ajay Kumar", "Paul Benjamin Lowry", "Hillol Bala" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7218378", "field": "management", "role": "object", "bullets": [ "Sequential mixed methods on generative AI users split into designers and non-designers: 30 interviews, 15,000 online posts, and a survey of 507 respondents. Geography and period not stated.", "ChatGPT, Midjourney, Runway, and Canva are tools the users report, not instruments the authors run. Engagement is coded against self-determination theory, affordance theory, and a duty-virtue-consequentialist ethics lens.", "Non-designers begin from curiosity and emotional affordances then move toward cognitive ones; designers lead with competence and duty ethics. Virtue-based reflection lowers designers' long-run loyalty." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 19, "validated": null, "n": 2256, "authors_detailed": [ { "name": "Hsiu-Yu Hung", "url": "https://openalex.org/A5076027794", "inst": "National Taiwan University" }, { "name": "Ajay Kumar", "url": "https://openalex.org/A5033482142", "inst": "École de management de Lyon" }, { "name": "Paul Benjamin Lowry", "url": "https://openalex.org/A5009502121", "inst": "Virginia Tech" }, { "name": "Hillol Bala", "url": "https://openalex.org/A5007900794", "inst": "Indiana University Bloomington" } ], "affiliations": [ "Indiana University Bloomington", "National Taiwan University", "École de management de Lyon", "Virginia Tech" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7243325", "doi": "10.2139/ssrn.7243325", "title": "AI Mentorship and the Language of Business: Building Financial Capability among Microenterprises in Developing Economies", "authors": [ "Marcela Aguilar" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7243325", "field": "accounting", "role": "object", "bullets": [ "Randomized controlled trial with microenterprises in Honduras. One arm gets an AI mentor that embeds accounting in business decisions, one gets accounting taught alone, one gets in-person training. Sample size not stated.", "A WhatsApp chatbot on an unnamed large language model delivers an adapted ILO Start and Improve Your Business curriculum, with conversation logs used to track how entrepreneurs reason about money.", "Both AI arms beat human-led training on perceived business growth and on uptake of cost analysis and recordkeeping, with the largest gains among the weakest managers. Outcomes are self-reported." ], "bullet_provenance": "ai", "salience": 62, "edition": 19, "models": [], "validated": null, "n": 2257, "authors_detailed": [ { "name": "Marcela Aguilar", "url": "https://openalex.org/A5117170106", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7244218", "doi": "10.2139/ssrn.7244218", "title": "From Kiosk to Conversation: Artificial Intelligence and the Redesign of the Physical Health Access Point", "authors": [ "Jerry Yonga" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7244218", "field": "management", "role": "object", "bullets": [ "Conceptual case analysis of United States retail health kiosks, traced from an unimplemented 2014 proposal to deliver health services over partnered ATM networks. No dataset or sample.", "No model is run or named. Large language models enter as the proposed conversational layer, with published evidence of over-triage flagged as a threat to the cost case.", "Attributes the collapse to convenience migrating to smartphones, no party owning upkeep, and heaviest use by the lightest consumers of care. Rural bank-branch telehealth stations serve as counterexample." ], "bullet_provenance": "ai", "salience": 28, "edition": 19, "models": [], "validated": null, "n": 2258, "authors_detailed": [ { "name": "Jerry Yonga", "url": "https://openalex.org/A5135242528", "inst": "California State University, San Marcos" } ], "affiliations": [ "California State University, San Marcos" ] }, { "uid": "doi:10.2139/ssrn.7243778", "doi": "10.2139/ssrn.7243778", "title": "Internal Auditors as Architects of AI-Ready Enterprise Data Governance: Enabling Structure through ISO 21378 and ISO/TS 21377", "authors": [ "Robert Stamsnijder", "Ting Sun", "Miklos A. Vasarhelyi" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7243778", "field": "accounting", "role": "object", "bullets": [ "Conceptual framework paper with no sample and no empirical test, opening from industry reports that 80 to 95 percent of enterprise generative AI projects stall before production.", "No model is run or named. Generative AI appears as the consumer of a deterministic ingestion layer assembled from ISO 21378 semantic definitions and ISO/TS 21377 technical packaging.", "Recasts the internal auditor from retrospective reviewer into data steward maintaining a buffer of truth. The claim of hallucination-free deployment rests on argument, not on reported evidence." ], "bullet_provenance": "ai", "salience": 38, "edition": 19, "models": [], "validated": null, "n": 2259, "authors_detailed": [ { "name": "Robert Stamsnijder", "url": "https://openalex.org/A5146316554", "inst": "" }, { "name": "Ting Sun", "url": "https://openalex.org/A5038897346", "inst": "Beijing University of Technology" }, { "name": "Miklos A. Vasarhelyi", "url": "https://openalex.org/A5136535968", "inst": "Rutgers, The State University of New Jersey" } ], "affiliations": [ "Beijing University of Technology", "Rutgers, The State University of New Jersey" ] }, { "uid": "arxiv:2608.09019v1", "arxiv_id": "2608.09019v1", "title": "How People Evaluate AI-, Expert-, and Peer-Style Financial Advice", "authors": [ "Aryan Ramchandra Kapadia", "Eshwar Chandrasekharan", "Koustuv Saha" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.09019v1", "field": "finance", "role": "object", "bullets": [ "Preregistered vignette experiment with 285 participants. Facts, numbers, recommendation direction, and core reasoning were held fixed while communication style and the displayed source label varied. Geography and period not stated.", "No model is named and none is run for the study. Style was written as an AI assistant, a certified financial planner, or an online forum, with labels correct, absent, or deliberately wrong.", "Planner-style advice was rated higher than AI-style advice on nine of ten outcomes, effect sizes 0.20 to 0.47, and kept the edge unlabelled. Mislabelling AI as expert lifted quality and fit ratings by 0.42." ], "bullet_provenance": "ai", "salience": 55, "edition": 19, "models": [], "validated": null, "n": 2260, "authors_detailed": [ { "name": "Aryan Ramchandra Kapadia", "url": "https://openalex.org/A5123565633", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Eshwar Chandrasekharan", "url": "https://openalex.org/A5146494876", "inst": "" }, { "name": "Koustuv Saha", "url": "https://openalex.org/A5146570819", "inst": "" } ], "affiliations": [ "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7248418", "doi": "10.2139/ssrn.7248418", "title": "The Breakup of the Firm Hypothesis: How the AI Revolution Might Shape Our Economy", "authors": [ "Shahriar Saghri" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7248418", "field": "economics", "role": "object", "bullets": [ "No data. A conceptual paper on firm boundaries, written as generative and agentic systems cut the cost of buying cognitive work from outside the firm.", "No model is run. Frontier systems that reason, code, and use tools are the paper's subject, and no specific model or family is named.", "Predicts knowledge work shifting from employment hierarchies toward markets and specialist networks, bounded by asset specificity, tacit knowledge, and liability. No magnitudes and no empirical test." ], "bullet_provenance": "ai", "salience": 34, "edition": 19, "models": [], "validated": null, "n": 2288, "authors_detailed": [ { "name": "Shahriar Saghri", "url": "https://openalex.org/A5119466420", "inst": "Tata Consultancy Services (India)" } ], "affiliations": [ "Tata Consultancy Services (India)" ] }, { "uid": "doi:10.2139/ssrn.7221478", "doi": "10.2139/ssrn.7221478", "title": "How Vulnerable Are Consumer Research Surveys to AI Agents? Validating an Instrument-Review Framework and Auditing Recent JCR Open Materials", "authors": [ "Kianté A. Fernandez", "Andrea Low", "Jonathan Bogard", "Craig R. Fox" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7221478", "field": "management", "role": "method", "bullets": [ "252 Qualtrics instruments from 37 Journal of Consumer Research articles with deposited materials, drawn from 312 indexed between 2021 and 2026, plus 256 purpose-built survey versions.", "Agents built on three unnamed frontier models attempt the surveys in a factorial design, target 3,840 runs, so far interim; evasion rates per category set the rubric weights.", "Interface checks like metadata, CAPTCHA, and input tracking stop agents best; reasoning-based logic and refusal probes fail most, and typical published instruments carry few of the effective defences." ], "bullet_provenance": "ai", "salience": 68, "edition": 19, "models": [], "validated": null, "n": 2289, "authors_detailed": [ { "name": "Kianté Fernandez", "url": "https://openalex.org/A5089676647", "inst": "Washington University in St. Louis" }, { "name": "Andrea Löw", "url": "https://openalex.org/A5128208515", "inst": "BioMarin (United States)" }, { "name": "Jonathan Bogard", "url": "https://openalex.org/A5146268416", "inst": "" }, { "name": "Craig R. Fox", "url": "https://openalex.org/A5064153354", "inst": "Binghamton University" } ], "affiliations": [ "Washington University in St. Louis", "BioMarin (United States)", "Binghamton University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7221561", "doi": "10.2139/ssrn.7221561", "title": "The Issuer-Pays Problem in AI Audit Infrastructure: Why Operator-Side Audit Trails Cannot Satisfy Independence Requirements Under EU AI Act Article 12", "authors": [ "Demond Davis" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7221561", "field": "accounting", "role": "object", "bullets": [ "No sample. A conceptual argument about the AI audit tooling market, read against EU AI Act Article 12 and the vendor landscape for operator-controlled audit trails.", "No model is used or named. Agentic AI systems are the audited object, and the paper's material is legal text, vendor architectures, and the SCITT standard published as RFC 9943.", "Argues operator-held audit records repeat the issuer-pays conflict of credit rating agencies and cannot meet independence rules, so records must sit with a disinterested third party. No empirical test." ], "bullet_provenance": "ai", "salience": 38, "edition": 19, "models": [], "validated": null, "n": 2290, "authors_detailed": [ { "name": "Demond Davis", "url": "https://openalex.org/A5139829317", "inst": "Sheridan College" } ], "affiliations": [ "Sheridan College" ] }, { "uid": "doi:10.2139/ssrn.7250978", "doi": "10.2139/ssrn.7250978", "title": "Teaching Generative AI Through Building: A Pedagogical Framework for Business Students", "authors": [ "Leonard Boussioux" ], "posted": "2026-08-10", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7250978", "field": "management", "role": "object", "bullets": [ "Two parallel cohorts, 84 master's students in information systems at the University of Washington Foster School of Business, winter 2026, across a paired two-course sequence of five modules each.", "Students build with generative tools rather than study them, moving through prototyping, agentic workflows, and evaluation to a public fair judged by industry. No model or vendor is named.", "Course evaluations give combined medians of 4.9 and 4.8 out of 5.0 alongside high reported workload. Evidence is student portfolios and ratings, with no control group or learning test." ], "bullet_provenance": "ai", "salience": 40, "edition": 19, "models": [], "validated": null, "n": 2291, "authors_detailed": [ { "name": "Léonard Boussioux", "url": "https://openalex.org/A5036794043", "inst": "University of Washington" } ], "affiliations": [ "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.7243241", "doi": "10.2139/ssrn.7243241", "title": "Engineering Hallucination Risk Index (EHRI): A Quantitative Framework for Evaluating Trustworthiness of Generative AI in Engineering Decision Support Systems", "authors": [ "Priya Ranjan" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7243241", "field": "management", "role": "method", "bullets": [ "No dataset. A conceptual index for engineering decision support, illustrated with one use case on AI-assisted maintenance scheduling in an integrated steel plant.", "No model is named. The paper scores generative AI recommendations on seven dimensions, including technical accuracy, evidence traceability, safety compliance and numerical reliability, combined into a weighted 0 to 100 index.", "Lower scores mark lower hallucination risk, and the index sorts recommendations into five trust levels from very low to critical. No empirical accuracy test is reported." ], "bullet_provenance": "ai", "validated": false, "salience": 25, "edition": 18, "models": [], "n": 2163, "authors_detailed": [ { "name": "Priya Ranjan", "url": "https://openalex.org/A5140710596", "inst": "Mizoram University" } ], "affiliations": [ "Mizoram University" ] }, { "uid": "arxiv:2608.09046v1", "arxiv_id": "2608.09046v1", "title": "Measuring the Tokenization Premium: A Cost Audit for Underserved Language Communities", "authors": [ "Avijit Roy", "Proma Roy", "Hrishitva Patel" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.09046v1", "field": "economics", "role": "method", "bullets": [ "A 120-item Python debugging corpus translated from English into Bengali, Hindi, Arabic, Tamil and Yoruba, with Bengali and Hindi as the validated cases and the rest exploratory.", "No text generation is involved. Three tokenizers, the GPT-4o o200k base, Qwen2.5 7B and Mistral 7B, count tokens for matched content, so the measure is deterministic and needs no accuracy check.", "Bengali costs 1.56 times the English token count under GPT-4o and up to 4.5 times under the open tokenizers, and Yoruba costs 2.37 times despite Latin script. A 128k window shrinks to roughly 82k." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "salience": 35, "edition": 18, "validated": null, "n": 2167, "authors_detailed": [ { "name": "Avijit Roy", "url": "https://openalex.org/A5129508484", "inst": "City University of New York" }, { "name": "Proma Roy", "url": "https://openalex.org/A5146512275", "inst": "" }, { "name": "Hrishitva Patel", "url": "https://openalex.org/A5067319422", "inst": "Universidad San Carlos" } ], "affiliations": [ "City University of New York", "Universidad San Carlos" ] }, { "uid": "doi:10.2139/ssrn.7244019", "doi": "10.2139/ssrn.7244019", "title": "When Large Language Models become Clinical", "authors": [ "Yinqi Huang", "Ruohan Feng", "Madison Lieberman", "Craig Hertz", "Jianfeng Li", "Michael Ries", "Mei Li" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7244019", "field": "management", "role": "object", "bullets": [ "Legal and regulatory sources from China and the United States, read alongside a stakeholder survey. Survey size, period, and respondent types are not stated.", "The authors run no model and name none. They build a two axis scheme separating informational influence from formal clinical authority, and report no validation.", "Argues that governance duties attach as soon as a public facing system shifts how people use care, well before it holds any clinical authority. No effect size is reported." ], "bullet_provenance": "ai", "salience": 31, "edition": 18, "models": [], "validated": null, "n": 2171, "authors_detailed": [ { "name": "Yinqi Huang", "url": "", "inst": "" }, { "name": "Ruohan Feng", "url": "", "inst": "" }, { "name": "Madison Lieberman", "url": "", "inst": "" }, { "name": "Craig Hertz", "url": "", "inst": "" }, { "name": "Jianfeng Li", "url": "", "inst": "" }, { "name": "Michael Ries", "url": "", "inst": "" }, { "name": "Mei Li", "url": "", "inst": "" } ] }, { "uid": "arxiv:2608.10213v1", "arxiv_id": "2608.10213v1", "title": "VeriFin: A Neurosymbolic Framework for Verifying LLM-Generated Financial Claims", "authors": [ "Bethel Hall", "Sachi Shome", "William Eiers" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.10213v1", "field": "accounting", "role": "method", "bullets": [ "XBRLFiling, a new 600 question benchmark built from 10-K filings of 28 US companies, plus the 67 claim FinanceBench set.", "Answer-generator models are not named. VeriFin grounds each operand in filed XBRL facts, derives the authorized formula from linkbases or metric definitions, and checks the claim with the Z3 solver.", "VeriFin accepted no incorrect claim on either set, while baselines accepted 6 to 92 of 600 and 4 to 21 of 67. Solver feedback repaired up to 69.9 percent of true catches." ], "bullet_provenance": "ai", "validated": true, "validation_note": "XBRLFiling and FinanceBench labelled claims, false accepts reported", "salience": 66, "edition": 18, "models": [], "n": 2180, "authors_detailed": [ { "name": "Bethel Hall", "url": "https://openalex.org/A5147041607", "inst": "" }, { "name": "Sachi Shome", "url": "https://openalex.org/A5111079650", "inst": "Stevens Institute of Technology" }, { "name": "William Eiers", "url": "https://openalex.org/A5088950037", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Stevens Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.7234418", "doi": "10.2139/ssrn.7234418", "title": "Divergence Survives a Length Control on Google AI Overviews and Vanishes on Claude. Measuring Vendor-Set Agreement Across Four AI Search Engines", "authors": [ "Sairam Sivakumar" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7234418", "field": "management", "role": "method", "bullets": [ "280 B2B software buying questions across 40 categories, put to ChatGPT, Claude, Perplexity and Google AI Overviews on 4 August 2026; credit ran out mid-collection, leaving 18, 280, 175 and 280 answers.", "A prompt extracted the vendor names from each answer, with no reported check against hand coding. A 25-question subsample was rerun three times so within-engine noise bounds the between-engine comparison.", "Answers name very different numbers of vendors, and Jaccard rewards similar-length sets. After truncating to five vendors, the Google gap holds at 0.167 while the Claude gap falls to 0.075 and includes zero." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": false, "validation_note": "vendor extraction not checked against hand coding", "salience": 56, "edition": 18, "n": 2202, "authors_detailed": [ { "name": "Sairam Sivakumar", "url": "", "inst": "Indiana University Bloomington" } ], "affiliations": [ "Indiana University Bloomington" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7218538", "doi": "10.2139/ssrn.7218538", "title": "Accommodation Repricing: Disability, Generative AI Subscriptions, and the Accessibility Tax as a Variable the Vendor Controls", "authors": [ "Travis Gilly" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7218538", "field": "management", "role": "object", "bullets": [ "Longitudinal documentary audit of published pricing pages from 16 generative AI vendor surfaces, with one consumer surface coded across 12 consecutive quarters from dated public archive captures.", "No language model is run. Vendors' own pricing artifacts are hand coded, and the abstract reports no intercoder agreement statistic for that coding.", "The posted paid-tier price held flat for three years while the quantity behind it was metered and then restated in units not comparable with the old ones, hiding the reduction from users." ], "bullet_provenance": "ai", "salience": 40, "edition": 18, "models": [], "validated": null, "n": 2203, "authors_detailed": [ { "name": "Travis Gilly", "url": "https://openalex.org/A5140598045", "inst": "Institute of Occupational Safety" } ], "affiliations": [ "Institute of Occupational Safety" ] }, { "uid": "doi:10.2139/ssrn.7221258", "doi": "10.2139/ssrn.7221258", "title": "Reskilling and Retraining the Water Technology Workforce: An Agentic Generative AI Framework for Water Purification, Nano-MEMS, and Curriculum Development", "authors": [ "Satyadhar Joshi", "Noor Zulfiqar" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7221258", "field": "management", "role": "object", "bullets": [ "Literature synthesis on agentic generative AI, reinforcement learning, smart sensing, nano-enabled membranes and digital twins for water utilities. No sample, no field data, no measured workforce outcomes.", "No model is run or named. Agentic AI appears as a capability that operators must learn, and the paper validates nothing against evidence.", "Proposes three competency tiers, from AI and data literacy to applied agentic decision support to nano-AI integration, and places human oversight, risk management and compliance inside the curriculum rather than beside it." ], "bullet_provenance": "ai", "salience": 26, "edition": 18, "models": [], "validated": null, "n": 2204, "authors_detailed": [ { "name": "Satyadhar Joshi", "url": "", "inst": "Bar-Ilan University" }, { "name": "Noor Zulfiqar", "url": "", "inst": "University of Agriculture" } ], "affiliations": [ "Bar-Ilan University", "University of Agriculture" ] }, { "uid": "doi:10.2139/ssrn.7218799", "doi": "10.2139/ssrn.7218799", "title": "Beyond the Mainstream Domain A Page-Level Audit of Editorial, Corporate, and Commercial Content in AI Citation Sources in Indonesia", "authors": [ "Dedy Budiman" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7218799", "field": "management", "role": "object", "bullets": [ "Four anonymized AI citation audits run in Indonesia during 2026: 36 prompts, 473 responses, 1,714 citation occurrences covering 732 unique pages on 315 domains, from Google AI Mode, Gemini and ChatGPT.", "The models supply the citations rather than measure anything. Humans coded each page for host identity and content provenance; a blind 51-URL second coder agreed on 94.12 percent of categories, kappa 0.901.", "Mainstream news pages supplied only 3.15 percent of citations, and about a fifth of those were press-release-derived, sponsored, or commerce-oriented, so the domain name alone does not establish independent editorial provenance." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 50, "edition": 18, "validated": null, "n": 2205, "authors_detailed": [ { "name": "Dedy Budiman", "url": "https://openalex.org/A5138505911", "inst": "Universitas Prasetiya Mulya" } ], "affiliations": [ "Universitas Prasetiya Mulya" ] }, { "uid": "doi:10.2139/ssrn.7231358", "doi": "10.2139/ssrn.7231358", "title": "Computational Global Macro with AI for Risk and Portfolio Management", "authors": [ "Marius Ciepluch" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7231358", "field": "finance", "role": "instrument", "bullets": [ "Walk-forward backtest of a tech-heavy portfolio from 2016 to 2026, rebalanced with macro factors for inflation velocity, growth expectations and credit stress. Universe size and data sources not stated.", "An unnamed language model infers macro factors from anonymized, timestamp-free inputs dated before each rebalance, discounted by a memorization confidence score. The design guards against look-ahead but reports no accuracy benchmark.", "Adding the regime overlay cut annual growth from 15.4 to 9.4 percent, volatility from 12.9 to 7.2 percent and maximum drawdown from 19.6 to 8.2 percent. Sharpe held; appraisal ratio rose from 0.81 to 1.06." ], "bullet_provenance": "ai", "validated": false, "validation_note": "point-in-time design and memorization score, no accuracy benchmark", "salience": 50, "edition": 18, "models": [], "n": 2206, "authors_detailed": [ { "name": "Marius Ciepluch", "url": "", "inst": "Development Fund" } ], "affiliations": [ "Development Fund" ] }, { "uid": "doi:10.2139/ssrn.7215438", "doi": "10.2139/ssrn.7215438", "title": "\"The Contested Ledger: Artificial Intelligence, Automation, and the Labor Market\" An Economic Review of Employment, Wage, and Productivity Effects, with Evidence from India's Services-Led Economy", "authors": [ "Komal Chand Dwivedi", "Kumud Shrivastava" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7215438", "field": "economics", "role": "object", "bullets": [ "Review of the economics literature on AI, employment, wages and productivity since the ChatGPT release, organized by the task framework, with India's services-led economy as a case study.", "No model is run. The ChatGPT release marks the exposure date across the studies surveyed, and the review weighs those designs against each other rather than validating a measure of its own.", "Aggregate employment evidence remains contested, but entry-level effects are consistent: US technology workers aged 22 to 25 in exposed occupations fell about 6 percent while older peers gained about 9 percent." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 46, "edition": 18, "validated": null, "n": 2207, "authors_detailed": [ { "name": "Komal Chand Dwivedi", "url": "", "inst": "Awadhesh Pratap Singh University" }, { "name": "Kumud Shrivastava", "url": "", "inst": "Government Medical College" } ], "affiliations": [ "Awadhesh Pratap Singh University", "Government Medical College" ] }, { "uid": "doi:10.2139/ssrn.7217180", "doi": "10.2139/ssrn.7217180", "title": "Generative AI-Driven Business Analytics and Enterprise Information Systems: A Framework for Strategic Decision-Making, Organizational Agility, and Sector-Specific Applications", "authors": [ "Samuel Babatunde" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7217180", "field": "management", "role": "object", "bullets": [ "Conceptual synthesis of peer-reviewed and industry writing on IT strategy, IT project management, healthcare information systems and enterprise business intelligence. No primary data; scenarios are labelled proof of concept.", "No model is run or named. Generative AI enters as a capability inside enterprise systems, and nothing is measured or validated.", "Names four mechanisms through which generative analytics might act: automated insight generation, adaptive decision support, cross-domain data integration and agility feedback loops. The paper offers vocabulary and a research agenda, not findings." ], "bullet_provenance": "ai", "salience": 24, "edition": 18, "models": [], "validated": null, "n": 2208, "authors_detailed": [ { "name": "Samuel Babatunde", "url": "https://openalex.org/A5128710403", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7248780", "doi": "10.2139/ssrn.7248780", "title": "Access And Control: Artificial Intelligence, Dual Secrecy, And The Freedom To Tinker", "authors": [ "David S. Levine" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7248780", "field": "other", "role": "object", "bullets": [ "Legal analysis of trade secrecy around commercial AI systems, with attention to government procurement of privately built models. No sample, no cases coded, no data.", "No model is run. ChatGPT's release frames the argument, and generative systems appear as black boxes whose code, training data and decisions resist examination. Nothing is validated.", "Argues secrecy runs both ways: customers can lose protection through prompts and outputs, while providers withhold what regulators and the public need. Proposes a bounded freedom to tinker with procured systems." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 36, "edition": 18, "validated": null, "n": 2209, "authors_detailed": [ { "name": "David S. Levine", "url": "", "inst": "University of North Carolina at Greensboro" } ], "affiliations": [ "University of North Carolina at Greensboro" ] }, { "uid": "doi:10.2139/ssrn.7249238", "doi": "10.2139/ssrn.7249238", "title": "Generative AI and Intellectual Property Protection", "authors": [ "Sterling Huang", "Jinhwan Kim", "Yupeng Lin", "yue wu" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7249238", "field": "accounting", "role": "object", "bullets": [ "US public firms around the ChatGPT 3.5 release, split by exposure to AI replication risk in a difference-in-differences design. Sample size, period and data sources are not stated.", "No model is run for measurement. The ChatGPT release is the treatment, and the authors build a measure of patents converted from trade secrets, reporting no validation of that measure.", "Exposed firms filed 15.7 percent more patents, concentrated where secrecy was already the protection of choice, with no rise in R&D or investment. Investors priced the filings positively." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "edition": 18, "validated": null, "n": 2210, "authors_detailed": [ { "name": "Sterling Huang", "url": "", "inst": "New York University Shanghai" }, { "name": "Jinhwan Kim", "url": "", "inst": "Stanford Medicine" }, { "name": "Yupeng Lin", "url": "", "inst": "National University of Singapore" }, { "name": "yue wu", "url": "", "inst": "College of Accounting" } ], "affiliations": [ "New York University Shanghai", "Stanford Medicine", "National University of Singapore", "College of Accounting" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7217185", "doi": "10.2139/ssrn.7217185", "title": "Governance, Risk, and Trust in Generative AI-Enabled Information Systems: A Cross-Domain Framework for IT Strategy, Project Management, and Healthcare Data Security", "authors": [ "Samuel Babatunde", "Muiz Shaw" ], "posted": "2026-08-10", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7217185", "field": "management", "role": "object", "bullets": [ "Conceptual paper synthesizing NIST's AI risk framework generative profile, Gartner's trust and security model, healthcare data protection writing, and four domain studies. No primary, incident, or clinical data.", "No model is run or named. Generative systems appear as software whose behaviour shifts after deployment, and the scenarios are disclosed as proof of concept rather than evidence.", "Sets out four governance dimensions, risk classification, data-boundary control, human oversight and regulatory alignment, argues deterministic-software governance does not carry over, and leaves empirical testing to later work." ], "bullet_provenance": "ai", "salience": 25, "edition": 18, "models": [], "validated": null, "n": 2211, "authors_detailed": [ { "name": "Samuel Babatunde", "url": "", "inst": "" }, { "name": "Muiz Shaw", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7227319", "doi": "10.2139/ssrn.7227319", "title": "AI Agents and Prompt Engineering in Econometric Coding", "authors": [ "Sebastian Galiani", "Raul A. Sosa", "Federico Lopez" ], "posted": "2026-08-10", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7227319", "field": "economics", "role": "method", "bullets": [ "A benchmark of applied econometric and statistical coding tasks, each attempted in Stata, R and Python; the number of tasks is not stated.", "Claude Sonnet 4.6 and GPT-5.4 through Codex wrote the code as a plain chatbot and as a constrained agent that runs and revises its own scripts, under zero shot and few shot prompts, scored by task success.", "Success climbed from 74 to 96 percent when the chatbot gave way to the constrained agent, at roughly eight cents more per run; few shot prompting helped only the chatbot, and the gap between statistical packages closed under the agent." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "benchmark of applied econometric tasks, task success rate reported", "salience": 78, "edition": 17, "n": 2123, "authors_detailed": [ { "name": "Sebastian Galiani", "url": "", "inst": "Tulane University" }, { "name": "Raul A. Sosa", "url": "", "inst": "University of San Andrés" }, { "name": "Federico Lopez", "url": "", "inst": "University of San Andrés" } ], "affiliations": [ "Tulane University", "University of San Andrés" ] }, { "uid": "doi:10.2139/ssrn.7220618", "doi": "10.2139/ssrn.7220618", "title": "AI-Powered Financial Translation Layer A Framework for Translating Financial Data into Role-Specific Business Insights for SMEs", "authors": [ "Maria Habib" ], "posted": "2026-08-10", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7220618", "field": "accounting", "role": "object", "bullets": [ "Small and medium sized firms whose managers read financial statements without a finance business partner; the paper designs a survey of such managers but gathers nothing from real respondents.", "A proposed software layer sits between the accounting system and the user and rewrites structured statements as commentary pitched at a named role. No model is named, run, or checked.", "The reported correlations, regressions, t tests, and analysis of variance are illustrative constructions rather than findings, and the paper sets out the collection that would test the three hypotheses." ], "bullet_provenance": "ai", "salience": 21, "edition": 17, "models": [], "validated": null, "n": 2128, "authors_detailed": [ { "name": "Maria Habib", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7229001", "doi": "10.2139/ssrn.7229001", "title": "Bridging Industrial Engineering and Agentic AI: Integrating Operations Research Methods for Uncertainty-aware and Verifiable Spatial Decision Support", "authors": [ "Haowen Xu", "Qinghua Lu", "Xueping Li", "Ziwei Liu", "Xiao-Ying Yu", "Liming Zhu" ], "posted": "2026-08-10", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7229001", "field": "management", "role": "method", "bullets": [ "Book chapter on geospatial decision support for urban and environmental management, working from existing agentic prototypes with no experiment, sample, or evaluation.", "No model is run or named. Language model agents are given problem formulation and scenario generation, while linear, mixed integer, stochastic, and robust solvers compute and verify the decision.", "Argues these systems return plausible but generic advice because variables, objectives, and constraints stay latent, and that an explicit optimisation layer is what restores feasibility, optimality, and reproducibility guarantees." ], "bullet_provenance": "ai", "salience": 27, "edition": 17, "models": [], "validated": null, "n": 2129, "authors_detailed": [ { "name": "Haowen Xu", "url": "", "inst": "UNSW Sydney" }, { "name": "Qinghua Lu", "url": "", "inst": "Commonwealth Scientific and Industrial Research Organisation" }, { "name": "Xueping Li", "url": "", "inst": "St. John Medical Center" }, { "name": "Ziwei Liu", "url": "", "inst": "St. John Medical Center" }, { "name": "Xiao-Ying Yu", "url": "", "inst": "Oak Ridge National Laboratory" }, { "name": "Liming Zhu", "url": "", "inst": "Commonwealth Scientific and Industrial Research Organisation" } ], "affiliations": [ "UNSW Sydney", "Commonwealth Scientific and Industrial Research Organisation", "Oak Ridge National Laboratory" ] }, { "uid": "doi:10.2139/ssrn.7240520", "doi": "10.2139/ssrn.7240520", "title": "The FICO Blind Spot: Why LLMs Hallucinate When Credit Gets Real", "authors": [ "Athresh Guruprakash" ], "posted": "2026-08-10", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7240520", "field": "finance", "role": "object", "bullets": [ "Consumer credit assessment in the United States, where 32 million adults hold no scoreable file and about 3 billion do worldwide; the evidence base is vendor products and published benchmarks, not new data.", "No model is run or named here. Failure rates of 15 to 52 percent on structured financial analysis are carried over from existing benchmarks, in which four of six leading models invented figures from incomplete documents.", "Maps six alternative data types to specific compliance failure modes, pairs each with a control already sold by a named provider, and proposes a seven layer architecture with deterministic preprocessing and ECOA proxy filters." ], "bullet_provenance": "ai", "salience": 36, "edition": 17, "models": [], "validated": null, "n": 2130, "authors_detailed": [ { "name": "Athresh Guruprakash", "url": "", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "arxiv:2608.09925v1", "arxiv_id": "2608.09925v1", "title": "From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch", "authors": [ "Laurens Samson", "Iva Gornishka", "Gossa Lô", "Yuki M. Asano", "Sennay Ghebreab" ], "posted": "2026-08-10", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.09925v1", "field": "management", "role": "method", "bullets": [ "Six evaluation dimensions were fixed with a large Dutch municipal organisation through an advisory board, user research and a survey of civil servants using its chatbot.", "More than 30 multilingual and Dutch models were scored on factuality, honesty, social bias, energy use, cost and training data transparency; no individual model is named in the abstract.", "No model led on every dimension. Higher answer quality came with higher energy use and cost, bias moved independently of both, and factuality did not predict honesty." ], "bullet_provenance": "ai", "validated": true, "validation_note": "benchmark suite scoring factuality and honesty against reference answers, no figures in abstract", "salience": 46, "edition": 17, "models": [], "n": 2137, "authors_detailed": [ { "name": "Laurens Samson", "url": "https://openalex.org/A5146572929", "inst": "" }, { "name": "Iva Gornishka", "url": "https://openalex.org/A5077089592", "inst": "Amsterdam University of the Arts" }, { "name": "Gossa Lô", "url": "https://openalex.org/A5027111668", "inst": "Vrije Universiteit Amsterdam" }, { "name": "Yuki Asano", "url": "https://openalex.org/A5044477785", "inst": "Nihon University" }, { "name": "S. Ghebreab", "url": "https://openalex.org/A5009260617", "inst": "Intelligent Systems Research (United States)" } ], "affiliations": [ "Amsterdam University of the Arts", "Vrije Universiteit Amsterdam", "Nihon University", "Intelligent Systems Research (United States)" ] }, { "uid": "arxiv:2608.09790v1", "arxiv_id": "2608.09790v1", "title": "CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation", "authors": [ "Yaoning Yu", "Kai-Min Chang", "Ye Yu", "Yi-Chia Wang", "Haojing Luo", "Haohan Wang" ], "posted": "2026-08-10", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.09790v1", "field": "finance", "role": "method", "bullets": [ "Real Reddit threads about credit cards, each paired with the post that started it, serve as the target that generated discussion threads must reproduce.", "A planner fixes reply structure, comment function, stance and tone, a writer produces the thread, and a calibration loop adjusts comment populations; the paper says only that several language models were tried.", "Generated threads sit closer to the real ones than simulation baselines on lexical, semantic, behavioural and structural metrics, with smaller effect sizes and distribution distances; no absolute figures are given." ], "bullet_provenance": "ai", "validated": true, "validation_note": "generated threads compared with matched real Reddit threads on lexical, semantic, behavioural and structural metrics", "salience": 48, "edition": 17, "models": [], "n": 2140, "authors_detailed": [ { "name": "Yaoning Yu", "url": "https://openalex.org/A5146551173", "inst": "" }, { "name": "Kai-Min 張凱閔;Chang", "url": "https://openalex.org/A5129610241", "inst": "" }, { "name": "Ye Yu", "url": "https://openalex.org/A5146461818", "inst": "" }, { "name": "Yi‐Chia Wang", "url": "https://openalex.org/A5029052900", "inst": "National Taiwan University Hospital" }, { "name": "罗浩京", "url": "https://openalex.org/A5026131897", "inst": "Star Center" }, { "name": "Haohan Wang", "url": "https://openalex.org/A5146541242", "inst": "" } ], "affiliations": [ "Star Center" ] }, { "uid": "doi:10.2139/ssrn.7256480", "doi": "10.2139/ssrn.7256480", "title": "ELMER: Evolutionary Language Model that Explores and Refines", "authors": [ "Matthew Siper", "Ahmed Khalifa", "Julian Togelius" ], "posted": "2026-08-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7256480", "field": "finance", "role": "method", "bullets": [ "252 fixed-budget evolutionary searches over trading policies replayed on identical historical market states across multiple budget levels.", "Fine-tuned Qwen3-8B learns conditional semantic mutation, natural-language-to-program compilation, and behavior-grounded search via SFT and offset DPO.", "Language-based oDPO raises mean strength-displacement correlation from 0.310 to 0.824 and improves best-so-far validation AUC by 223.3 over SFT baseline." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "held-out fitness and AUC vs AST mutation and SFT baselines with Holm correction", "salience": 40, "n": 3287, "authors_detailed": [ { "name": "Matthew Siper", "url": "https://openalex.org/A5124835722", "inst": "" }, { "name": "Ahmed Khalifa", "url": "https://openalex.org/A5143972892", "inst": "University of Malta" }, { "name": "Julian Togelius", "url": "https://openalex.org/A5146889827", "inst": "" } ], "affiliations": [ "University of Malta" ] }, { "uid": "doi:10.2139/ssrn.7218518", "doi": "10.2139/ssrn.7218518", "title": "Dynamic Cross-Level Mechanisms of Generative AI Adoption: Motivations, Affordances, and Ethical Reasoning among Designers and Non-Designers", "authors": [ "Hsiu-Yu Hung", "Ajay Kumar", "V. Kumar", "Paul Benjamin Lowry", "Chih-Cheng Lin" ], "posted": "2026-08-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7218518", "field": "management", "role": "object", "bullets": [ "Sequential mixed-methods study combining 30 interviews, 15,000 online posts, and a 507-participant survey of designers and non-designers using GAI tools.", "Traced adoption trajectories of ChatGPT, Canva, Runway, and Midjourney across acceptance, continued use, and loyalty contexts using SDT and affordance theory.", "Non-designers shifted from intrinsic curiosity to productivity motives; designers exhibited a virtue-ethics paradox where heightened authenticity reflection constrained long-term loyalty." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 40, "validated": null, "n": 3840, "authors_detailed": [ { "name": "Hsiu-Yu Hung", "url": "https://openalex.org/A5076027794", "inst": "National Taiwan Normal University" }, { "name": "Ajay Kumar", "url": "https://openalex.org/A5033482142", "inst": "IILM Institute for Higher Education" }, { "name": "V. Kumar", "url": "https://openalex.org/A5100821065", "inst": "École de management de Lyon" }, { "name": "Paul Benjamin Lowry", "url": "https://openalex.org/A5009502121", "inst": "Queensland University of Technology" }, { "name": "Chiu-Pin Lin", "url": "https://openalex.org/A5039288549", "inst": "National Taiwan Normal University" } ], "affiliations": [ "National Taiwan Normal University", "IILM Institute for Higher Education", "École de management de Lyon", "Queensland University of Technology" ] }, { "uid": "arxiv:2608.09087v1", "arxiv_id": "2608.09087v1", "title": "Joint Lyapunov Certificates for K-Agent Generative AI Governance: Stochastic Stability, Emergent Ensemble Risk, and Zero-Knowledge Governance Attestation", "authors": [ "Sriram Nagaraj" ], "posted": "2026-08-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.09087v1", "field": "finance", "role": "method", "bullets": [ "Theoretical framework for governing K coupled self-adapting generative AI models under banking model risk management standards, validated with five numerical studies.", "Derived joint Lyapunov stability conditions and a SNARK-based zero-knowledge attestation protocol for aggregate system monitoring without revealing proprietary weights.", "Per-agent stability certificates are provably insufficient; a critical coupling threshold determines when the joint system loses mean-square stability despite individual compliance." ], "bullet_provenance": "ai", "validated": false, "salience": 48, "models": [], "n": 3841, "authors_detailed": [ { "name": "Sriram Nagaraj", "url": "https://openalex.org/A5058300098", "inst": "University of Massachusetts Amherst" } ], "affiliations": [ "University of Massachusetts Amherst" ] }, { "uid": "arxiv:2608.09069v1", "arxiv_id": "2608.09069v1", "title": "Telemetry and Concealment in Self-Adapting Generative AI: Logging Architecture, Adversarial Model Hiding, and the Limits of Detection", "authors": [ "Sriram Nagaraj" ], "posted": "2026-08-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.09069v1", "field": "finance", "role": "method", "bullets": [ "Theoretical governance architecture for continually self-adapting generative AI models under banking model risk management guidance, covering discrete and continuous time.", "Developed tamper-evident Merkle chain logging for weight sequences and KL-divergence stopping times for event-driven continuous-time audit triggers.", "Point-in-time validation is inadequate for self-adapting models; six formalized adversarial concealment strategies narrow but cannot replace periodic invasive audit." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 3842, "authors_detailed": [ { "name": "Sriram Nagaraj", "url": "https://openalex.org/A5058300098", "inst": "University of Massachusetts Amherst" } ], "affiliations": [ "University of Massachusetts Amherst" ] }, { "uid": "arxiv:2608.08634v1", "arxiv_id": "2608.08634v1", "title": "Can Open-Weight Models Compete on Financial Text Comprehension?", "authors": [ "Jan Spörer" ], "posted": "2026-08-09", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.08634v1", "field": "finance", "role": "method", "bullets": [ "The Financial Touchstone benchmark, updated to 2,967 question, context and answer triplets taken from 495 annual reports of companies across several countries.", "Twenty models from ten providers answered each item, among them GLM 5, Kimi K2.6, DeepSeek V3.2, Qwen3 Max, Claude Opus 4.6 and Gemini 2.5 Pro, scored for accuracy and hallucination against the labelled answers.", "Claude Opus 4.6 led accuracy at 88.4 percent and Gemini 2.5 Pro hallucinated least at 0.08 percent, with open weight Kimi K2.6 third; retrieval failures caused 48.9 percent of errors." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "open_other" ], "open_weights": true, "validated": true, "validation_note": "Financial Touchstone labelled triplets, accuracy and hallucination rate", "salience": 72, "edition": 17, "n": 2121, "authors_detailed": [ { "name": "Jan Spörer", "url": "https://openalex.org/A5020815109", "inst": "University of St.Gallen" } ], "affiliations": [ "University of St.Gallen" ] }, { "uid": "arxiv:2608.08601v1", "arxiv_id": "2608.08601v1", "title": "Unaccountable Delegation, Fading Skills: Mapping the Risks of Workplace AI Agents", "authors": [ "Gabriele La Malfa", "Lakmal Meegahapola", "Edyta Bogucka", "Jie M. Zhang", "Michael Luck", "Elizabeth Black", "Daniele Quercia" ], "posted": "2026-08-09", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.08601v1", "field": "management", "role": "object", "bullets": [ "Descriptions of 2,078 job tasks from the O*NET database, expanded into 8,356 risk scenarios labelled by severity and by whether the agent automates or augments the task.", "A structured prompt encoding an agent, goal and environment framework generated the scenarios, then 45 workers across 10 roles and a separate model judge reviewed them; no model is named.", "Erroneous agent actions form the largest and most severe risk group, automation maps mainly to organisational risk while augmentation maps to worker risk such as skill erosion, and workers preferred the new taxonomy in 64 percent of non-tied comparisons." ], "bullet_provenance": "ai", "validated": false, "validation_note": "45 workers and a model judge reviewed the generated scenarios, no agreement statistic reported", "salience": 56, "edition": 17, "models": [], "n": 2139, "authors_detailed": [ { "name": "Gabriele La Malfa", "url": "https://openalex.org/A5114337724", "inst": "University of London" }, { "name": "Lakmal Meegahapola", "url": "https://openalex.org/A5042096756", "inst": "SIB Swiss Institute of Bioinformatics" }, { "name": "Edyta Bogucka", "url": "https://openalex.org/A5134766537", "inst": "University of Cambridge" }, { "name": "Jie M. Zhang", "url": "https://openalex.org/A5146449196", "inst": "" }, { "name": "Michael Luck", "url": "https://openalex.org/A5135926240", "inst": "University of Sussex" }, { "name": "Elizabeth Black", "url": "https://openalex.org/A5146476738", "inst": "" }, { "name": "Daniele Quercia", "url": "https://openalex.org/A5081008532", "inst": "Bell (Canada)" } ], "affiliations": [ "University of Cambridge", "University of London", "SIB Swiss Institute of Bioinformatics", "University of Sussex", "Bell (Canada)" ], "prestige": true }, { "uid": "arxiv:2608.08395v1", "arxiv_id": "2608.08395v1", "title": "From Product Search to Preference Articulation: The Economics of Agentic Commerce", "authors": [ "Lingxiu Dong", "Kaiwen Luo", "Fasheng Xu" ], "posted": "2026-08-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.08395v1", "field": "economics", "role": "object", "bullets": [ "Theoretical model of digital commerce comparing manual product search with AI-agent-delegated discovery under finite consumer attention.", "Analytical framework studies how preference complexity and agent fidelity shape consumer adoption and platform conversion revenue without specifying a particular LLM.", "Manual search collapses beyond a finite complexity threshold; agentic search sustains positive revenue, but platforms prefer it before consumers voluntarily adopt, creating an adoption lag." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3164, "authors_detailed": [ { "name": "Lingxiu Dong", "url": "https://openalex.org/A5014497691", "inst": "Washington University in St. Louis" }, { "name": "Kaiwen Luo", "url": "https://openalex.org/A5146028985", "inst": "Washington University in St. Louis" }, { "name": "Fasheng Xu", "url": "https://openalex.org/A5086759286", "inst": "University of Connecticut" } ], "affiliations": [ "Washington University in St. Louis", "University of Connecticut" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.08825v1", "arxiv_id": "2608.08825v1", "title": "Hybrid Neural-Classical Correction for Frozen Time Series Foundation Models: A Comprehensive Ablation Study on High-Frequency Stock Prediction", "authors": [ "Kasun Dewage", "Suranadi De Silva", "Shankhadeep Mondal" ], "posted": "2026-08-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.08825v1", "field": "finance", "role": "instrument", "bullets": [ "High-frequency one-minute return data for 10 major U.S. tech stocks during the opening trading hour, totaling 2 million observations.", "Frozen TimesFM (200M parameters) augmented with neural correction modules and Random Forest residual learning predicted short-horizon stock returns.", "Hybrid approach achieved 0.597 pooled correlation and 6.4x mean per-day correlation improvement; Random Forest residual learning contributed more than neural correction alone." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "pooled, mean per-day, and cross-day correlation on 10 tech stocks", "salience": 55, "n": 3839, "authors_detailed": [ { "name": "Kasun Dewage", "url": "https://openalex.org/A5137054897", "inst": "University of Central Florida" }, { "name": "Suranadi De Silva", "url": "https://openalex.org/A5126448755", "inst": "University of Central Florida" }, { "name": "Shankhadeep Mondal", "url": "https://openalex.org/A5146565344", "inst": "" } ], "affiliations": [ "University of Central Florida" ] }, { "uid": "doi:10.2139/ssrn.7198982", "doi": "10.2139/ssrn.7198982", "title": "When the Machine Writes the Ad: How Consumers Evaluate AI-Generated Advertising", "authors": [ "Lucas Caldeira-Souza" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7198982", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample. The subject is how consumers evaluate advertising once they learn the content came from a generative AI rather than a person.", "No model is named or run. Generative AI appears as a nonhuman persuasion source that breaks the tests consumers normally apply to credibility, effort, and persuasive intent.", "The framework proposes an evaluative disruption and recalibration process plus a construct of agent-nature uncertainty, with two recalibration dimensions, four moderators, and eleven propositions. Nothing is tested here." ], "bullet_provenance": "ai", "salience": 40, "edition": 19, "models": [], "validated": null, "n": 2239, "authors_detailed": [ { "name": "Lucas Caldeira-Souza", "url": "https://openalex.org/A5145960983", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7215759", "doi": "10.2139/ssrn.7215759", "title": "Shared Functional Memory as Digital Cognitive Infrastructure: Corrective-Source Structure and the Scaling-Fragility Boundary of Agentic Organizing", "authors": [ "Jiuzhou Jin", "Yukai Zhang" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7215759", "field": "management", "role": "object", "bullets": [ "Theory paper on firms running portfolios of AI agents over a shared, executable memory of business objects, operating rules, cases and exception criteria. No sample and no data.", "No model is run. Agents enter as the unit that invokes shared memory; the authors define shared deployment breadth and corrective-source availability as the two architectural properties that matter.", "Wider sharing cuts local governance effort but concentrates exposure to a single memory error, so a scaling-fragility boundary limits how many agents one employee can govern. Propositions only, no magnitudes." ], "bullet_provenance": "ai", "salience": 40, "edition": 19, "models": [], "validated": null, "n": 2247, "authors_detailed": [ { "name": "Jiuzhou Jin", "url": "https://openalex.org/A5141303295", "inst": "Hubei University" }, { "name": "Yukai Zhang", "url": "https://openalex.org/A5058134935", "inst": "George Mason University" } ], "affiliations": [ "Hubei University", "George Mason University" ] }, { "uid": "doi:10.2139/ssrn.7218018", "doi": "10.2139/ssrn.7218018", "title": "The Covert Lever: How the Artificial Intelligence Subsidy Is Being Withdrawn, and Who Absorbs It", "authors": [ "Travis Gilly" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7218018", "field": "economics", "role": "object", "bullets": [ "Chronology of AI service pricing across eight sellers during 2025 and 2026, covering Notion, Perplexity, Anthropic, Windsurf, Z.ai, Alibaba Cloud and GitHub, plus a proposed class action filed in June 2026.", "No model is run by the author. Claude is the object: a user parsed 119,866 of his own API calls to reconstruct an undocumented cut in the prompt cache default from one hour to five minutes.", "Sellers withdraw flat-rate subsidies by shrinking undisclosed allowances rather than raising posted prices; the cache change tracked a 20 to 32 percent rise in cache creation cost. Rivals who publish limits show covertness is a choice." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 58, "edition": 19, "validated": null, "n": 2248, "authors_detailed": [ { "name": "Travis Gilly", "url": "https://openalex.org/A5140598045", "inst": "Institute of Occupational Safety" } ], "affiliations": [ "Institute of Occupational Safety" ] }, { "uid": "doi:10.2139/ssrn.7224812", "doi": "10.2139/ssrn.7224812", "title": "GenAI Governance in School: How Guidelines and Enforcement Shape Student Performance", "authors": [ "Yanlin Wan", "Jia Jinghao", "Xu Zhang" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7224812", "field": "economics", "role": "object", "bullets": [ "Randomized controlled trial in two Chinese middle schools with 1,056 students, cross-randomizing a six-module AI literacy curriculum against an enforcement rule capping AI-generated content at 30 percent of writing assignments.", "Students, not researchers, use generative AI, and no model family is named. Violations are measured by multi-engine AI screening with instructor verification, and no accuracy figure for that screening is reported.", "Neither guidelines nor enforcement alone moves grades; together they raise writing scores, mainly Chinese writing and mainly for male students. Under guidelines alone female violations fall while male violation rates stay high." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 19, "models": [], "n": 2249, "authors_detailed": [ { "name": "Yanlin Wan", "url": "https://openalex.org/A5134965441", "inst": "Hong Kong University of Science and Technology" }, { "name": "Jinghao Jia", "url": "https://openalex.org/A5024597600", "inst": "IBM (United States)" }, { "name": "Xu ZHANG", "url": "https://openalex.org/A5133233806", "inst": "Beijing Tongren Hospital" } ], "affiliations": [ "Hong Kong University of Science and Technology", "IBM (United States)" ] }, { "uid": "doi:10.2139/ssrn.7215638", "doi": "10.2139/ssrn.7215638", "title": "How Organizational Policy Decisions Shape Employees' Beliefs in Cybersecurity Threat", "authors": [ "Peter Jin", "Aaron Charles Kay" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7215638", "field": "management", "role": "object", "bullets": [ "Six experiments with 2,893 participants covering employee reactions to cybersecurity policy: a blocking firewall, mandated training, a lifted workplace policy recalled from experience, and a firm lifting its generative AI ban.", "No language model is run. Generative AI appears as the content of the policy in studies five and six, where participants imagine the ban being lifted with and without a restatement of the risk.", "Restrictive policies raise reactance and threat denial, and lifting a policy signals that the threat has passed. Preserving choice inside a mandate, or restating the risk while lifting the ban, cuts denial." ], "bullet_provenance": "ai", "salience": 48, "edition": 19, "models": [], "validated": null, "n": 2250, "authors_detailed": [ { "name": "Peter Jin", "url": "https://openalex.org/A5103126363", "inst": "H.B. Fuller (United States)" }, { "name": "Aaron Charles Kay", "url": "https://openalex.org/A5146093695", "inst": "Duke University" } ], "affiliations": [ "Duke University", "H.B. Fuller (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7214718", "doi": "10.2139/ssrn.7214718", "title": "Who Attests the Attestor? Evidentiary Independence as a Missing Property of Vertically Integrated AI Systems", "authors": [ "Jean Claude Havyarimana" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7214718", "field": "accounting", "role": "object", "bullets": [ "Conceptual paper with no sample; it draws on the economics of auditing, evidence law, and the 2025 to 2026 operating record of one vertically integrated AI group.", "No model is run or named; AI systems appear as parties whose own logs are the evidence, with control handoffs in autonomous vehicles and agentic payments as the motivating cases.", "Tamper evidence and independence are separate properties: a firm's own sealed log proves only that entries did not change after commitment. Independence is the least number of control domains whose failure severs every evidence path." ], "bullet_provenance": "ai", "salience": 38, "edition": 19, "models": [], "validated": null, "n": 2283, "authors_detailed": [ { "name": "Jean Claude Havyarimana", "url": "https://openalex.org/A5124312616", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7207818", "doi": "10.2139/ssrn.7207818", "title": "THE MISSING LAYER - Constitutional Memory Governance White Paper", "authors": [ "Greg Malpass" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7207818", "field": "management", "role": "object", "bullets": [ "No empirical sample; the paper scores four frameworks, the EU AI Act, the NIST AI risk management framework, ISO/IEC 42001, and the OECD principles, against nine questions about persistent AI memory.", "No model is run or named; the object is any system that keeps context across sessions and acts on it, and the paper contains no measurement or validation.", "Only two of the nine questions are meaningfully covered. The paper offers a five level memory governance maturity model and discloses the author's commercial interest in a candidate architecture." ], "bullet_provenance": "ai", "salience": 26, "edition": 19, "models": [], "validated": null, "n": 2284, "authors_detailed": [ { "name": "Greg Malpass", "url": "https://openalex.org/A5146049424", "inst": "Universidad de Málaga" } ], "affiliations": [ "Universidad de Málaga" ] }, { "uid": "doi:10.2139/ssrn.7219320", "doi": "10.2139/ssrn.7219320", "title": "Behavioural Drift Intelligence for Institutional and Regulatory Early Warning: A Criminological and Evidence-Gated Agentic Artificial Intelligence Reference Architecture for Emerging Economic Crime Typologies", "authors": [ "Tina Amarjit Arora" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7219320", "field": "finance", "role": "instrument", "bullets": [ "No data and no implementation; the paper is a conceptual design science exercise aimed at financial institutions, financial intelligence units, and supervisors facing new economic crime methods.", "No model family is named. A proposed multi agent system would generate competing source grounded typology hypotheses behind a human validation gate; the authors state they test no predictive performance.", "The output is fifteen design requirements, nine testable propositions, a behavioural drift taxonomy, and a hypothesis template aimed at cutting the lag between new crime methods and recognised typologies." ], "bullet_provenance": "ai", "validated": false, "validation_note": "authors state the architecture is not implemented or empirically validated", "salience": 32, "edition": 19, "models": [], "n": 2285, "authors_detailed": [ { "name": "Tina Amarjit Arora", "url": "https://openalex.org/A5134510750", "inst": "Swiss Hotel Management School" } ], "affiliations": [ "Swiss Hotel Management School" ] }, { "uid": "doi:10.2139/ssrn.7243365", "doi": "10.2139/ssrn.7243365", "title": "The Encoded Craft Knowledge Management and Competitive Advantage in an AI World", "authors": [ "Mohanbir Sawhney" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7243365", "field": "management", "role": "object", "bullets": [ "Conceptual essay with no dataset; the argument sits in knowledge management and the resource based view, using agent executed skill folders as the running example.", "No model is run and no family is named; the unit of interest is an encoded skill, a folder of instructions, templates, and checks that an agent runs without the expert present.", "Encoded artifacts are imitable, so advantage shifts to the capacity to keep producing them. The recommended policy is to share commodity methods and protect the evaluation gates and proprietary inputs." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 45, "edition": 19, "validated": null, "n": 2286, "authors_detailed": [ { "name": "Mohanbir Sawhney", "url": "https://openalex.org/A5123206071", "inst": "Northwestern University" } ], "affiliations": [ "Northwestern University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7213481", "doi": "10.2139/ssrn.7213481", "title": "Challenging Language Ambiguity: A Generative Approach to Automatic Data Labeling in Organization Research", "authors": [ "Juan Bustamante", "Santiago Alonso Diaz" ], "posted": "2026-08-08", "added": "2026-08-15", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7213481", "field": "management", "role": "method", "bullets": [ "Four labelling datasets, two new corpora built by the authors and two public benchmarks, covering organizational text where the correct label is genuinely ambiguous.", "Generative models, family not stated, assign labels; an information-theoretic framework tests whether they resolve ambiguity or copy surface patterns, with human annotators as the comparison.", "Machine labels match or beat human ones on the ambiguous cases; few-shot prompting raises alignment with human reasoning, which the authors read as framing doing conceptual work." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human labels across four datasets, two of them public benchmarks", "salience": 57, "edition": 19, "models": [], "n": 2287, "authors_detailed": [ { "name": "Juan Carlos Bustamante", "url": "https://openalex.org/A5045629513", "inst": "Tecnológico de Monterrey" }, { "name": "Santiago Alonso Díaz", "url": "https://openalex.org/A5025593196", "inst": "Tecnológico de Monterrey" } ], "affiliations": [ "Tecnológico de Monterrey" ] }, { "uid": "doi:10.2139/ssrn.7247860", "doi": "10.2139/ssrn.7247860", "title": "Liquidity for Transformation: Bank Credit and Firms' AI Adoption", "authors": [ "Sharjil Haque", "Andrew Kim", "Simon Mayer", "Edson Wu" ], "posted": "2026-08-08", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7247860", "field": "finance", "role": "object", "bullets": [ "US supervisory loan-level credit data merged with firm-level AI-related job postings, the adoption measure; sample period, firm count and industry coverage are not stated.", "Researchers run no language model. ChatGPT's release supplies the shock: predetermined workforce exposure to generative AI interacted with that release instruments adoption. The job-posting adoption measure carries no reported validation.", "Credit predicts later adoption, and adopters then obtain more credit at lower spreads, mainly through credit lines they draw down less. The expansion crowds out non-adopters without evidence of better information production." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "edition": 18, "validated": null, "n": 2198, "authors_detailed": [ { "name": "Sharjil Haque", "url": "https://openalex.org/A5146079167", "inst": "Federal Reserve" }, { "name": "Andrew Kim", "url": "https://openalex.org/A5146115053", "inst": "Federal Reserve Board of Governors" }, { "name": "Simon Mayer", "url": "https://openalex.org/A5020177012", "inst": "Carnegie Mellon University" }, { "name": "Edson Wu", "url": "https://openalex.org/A5146099482", "inst": "Federal Reserve Board of Governors" } ], "affiliations": [ "Carnegie Mellon University", "Federal Reserve", "Federal Reserve Board of Governors" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7211438", "doi": "10.2139/ssrn.7211438", "title": "Artificial Intelligence in Financial Risk Management Prediction, Automation, and Systemic Exposure in Modern Markets", "authors": [ "Alfredo Merlet" ], "posted": "2026-08-08", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7211438", "field": "finance", "role": "object", "bullets": [ "Monograph on AI in financial risk management, spanning prediction, credit underwriting, trading and systemic risk, with four illustrative institutional case studies. No sample, no estimation.", "No model is run or named. The book traces risk modelling from classical statistics through machine learning to large language models, and reads regulation rather than testing anything.", "Argues prediction and decision need separate governance, and names three gaps: no system-wide monitoring of foundation model dependency, fragmented cross-border coordination, and rules far more mature for lending than for trading." ], "bullet_provenance": "ai", "salience": 33, "edition": 18, "models": [], "validated": null, "n": 2199, "authors_detailed": [ { "name": "Alfredo Merlet", "url": "https://openalex.org/A5135419943", "inst": "Universidad de Sevilla" } ], "affiliations": [ "Universidad de Sevilla" ] }, { "uid": "arxiv:2608.08126v1", "arxiv_id": "2608.08126v1", "title": "Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk", "authors": [ "Gregorius Reynaldi Pratama", "Kuo-Kun Tseng" ], "posted": "2026-08-08", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.08126v1", "field": "finance", "role": "method", "bullets": [ "Public credit dataset of 32,581 loan applications; geography and period not stated. A stacking ensemble of four gradient boosting learners plus a residual network predicts default.", "An unnamed language model turned SHAP and LIME attributions into adverse-action narratives. The authors audited one generated explanation against the attributions it received rather than reporting a fidelity rate across cases.", "Test ROC-AUC reached 0.9539, worth six fewer missed defaults out of 1,422 and under 2 percent of cost-weighted loss. The audited narrative reversed three factor signs, dropped the top driver, and invented a feature." ], "bullet_provenance": "ai", "validated": false, "validation_note": "single audited explanation, no fidelity rate across cases", "salience": 62, "edition": 18, "models": [], "n": 2200, "authors_detailed": [ { "name": "Gregorius Reynaldi Pratama", "url": "https://openalex.org/A5146524990", "inst": "" }, { "name": "Kuo-Kun Tseng", "url": "https://openalex.org/A5017727521", "inst": "Harbin Institute of Technology" } ], "affiliations": [ "Harbin Institute of Technology" ] }, { "uid": "arxiv:2608.08069v1", "arxiv_id": "2608.08069v1", "title": "HugSelect: An Explainable Multi-Criteria Decision-Support Framework for foundation-model selection", "authors": [ "Alireza Joonbakhsh", "Arda Canser Adalı", "Slinger Jansen", "Farshad Khunjush", "Siamak Farshidi" ], "posted": "2026-08-08", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.08069v1", "field": "management", "role": "method", "bullets": [ "Knowledge base of 71,274 models from a public model hub, built from repository metadata and community discussion threads; 44 comparison scenarios and a user study with ten participants.", "An unnamed extraction pipeline pulls functional capabilities and quality attributes from text, scoring F1 0.801 on features and 0.84 accuracy on quality mapping. A weighted additive rule then ranks candidates transparently.", "Coverage@10 was 0.61 at model level and 0.91 at family level, matching four commercial LLM recommenders with no significant ranking difference, while exposing criterion-level scores. Functional features drove retrieval accuracy." ], "bullet_provenance": "ai", "validated": true, "validation_note": "extraction F1 0.801 for features, 0.84 accuracy for quality attributes", "salience": 42, "edition": 18, "models": [], "n": 2201, "authors_detailed": [ { "name": "Alireza Joonbakhsh", "url": "https://openalex.org/A5120420874", "inst": "Shiraz University" }, { "name": "Arda Canser Adalı", "url": "https://openalex.org/A5146553552", "inst": "Utrecht University" }, { "name": "Slinger Jansen", "url": "https://openalex.org/A5018641262", "inst": "Utrecht University" }, { "name": "Farshad Khunjush", "url": "https://openalex.org/A5043306677", "inst": "Shiraz University" }, { "name": "Siamak Farshidi", "url": "https://openalex.org/A5146547945", "inst": "Wageningen University & Research" } ], "affiliations": [ "Shiraz University", "Utrecht University", "Wageningen University & Research" ] }, { "uid": "doi:10.2139/ssrn.7214201", "doi": "10.2139/ssrn.7214201", "title": "Harnessing the Wisdom of LLM Crowds Through Complementarity-Driven Iterative Collaboration", "authors": [ "Yanbin Fang", "Xuan Wei", "Wei Chen" ], "posted": "2026-08-08", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7214201", "field": "management", "role": "method", "bullets": [ "Four public benchmarks stand in for enterprise problem solving; no firm data, deployment, or sample period is described.", "Heterogeneous models refine a single answer in relay, with one gate choosing the next model against the bottleneck in the current output and a second gate discarding transitions that fail to improve it. GPT-5.2 is the named comparison.", "The relay matches GPT-5.2 average benchmark performance at roughly one seventh the estimated cost per query, and beats self refinement, ensembles, and query routing; no accuracy figures appear in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "four labelled public benchmarks against single model, ensemble, and routing baselines, figures not given", "salience": 50, "edition": 17, "n": 2127, "authors_detailed": [ { "name": "Yanbin Fang", "url": "https://openalex.org/A5123859472", "inst": "Tung Wah College" }, { "name": "Xuan Wei", "url": "https://openalex.org/A5101411775", "inst": "Shanghai Jiao Tong University" }, { "name": "Wei Chen", "url": "https://openalex.org/A5100344381", "inst": "University of Connecticut" } ], "affiliations": [ "Tung Wah College", "Shanghai Jiao Tong University", "University of Connecticut" ] }, { "uid": "arxiv:2608.10008v1", "arxiv_id": "2608.10008v1", "title": "Do LLM Recommenders Know When They're Hallucinating? Auditing Confidence Calibration in Catalog Faithfulness", "authors": [ "Srijith Ravikumar" ], "posted": "2026-08-07", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.10008v1", "field": "management", "role": "method", "bullets": [ "Top-K recommendation over three catalogs, MovieLens 25M, Amazon Reviews 2023 toys, and Yelp Open Dataset, with items stratified by popularity, twelve model and catalog cells in total.", "Four zero-shot recommenders from four vendors, Mistral Large, Llama 3.3 70B, GPT-OSS 120B and Claude Sonnet 4.6, were scored on out-of-catalog rate and on calibration of their stated confidence.", "Out-of-catalog titles ranged from near zero on MovieLens to 8.4 percent on Yelp, and all four models were under-confident, stating 67 to 86 on items that were 92 to 100 percent correct." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "catalog membership as ground truth, ECE and Brier reported", "salience": 48, "edition": 18, "n": 2166, "authors_detailed": [ { "name": "Srijith Ravikumar", "url": "https://openalex.org/A5022746920", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7207699", "doi": "10.2139/ssrn.7207699", "title": "Explainable Artificial Intelligence as AI Literacy Scaffolding", "authors": [ "Brady Lund", "Zoë Abbie Teel", "Brett Porter", "Anuradha Chandrasekaran" ], "posted": "2026-08-07", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7207699", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no sample or period. It reads across the literatures on explainable systems, user literacy, educational scaffolding, and human machine teaming.", "The authors run no model and name none. Nothing is measured, so no validation against human coding or any benchmark is reported.", "Proposes that explanations work as temporary scaffolding: support tuned to the user's current level, withdrawn as competence grows, and framed as prompts for inquiry rather than final disclosure." ], "bullet_provenance": "ai", "salience": 25, "edition": 18, "models": [], "validated": null, "n": 2170, "authors_detailed": [ { "name": "Brady Lund", "url": "https://openalex.org/A5017599308", "inst": "University of North Texas" }, { "name": "Zoë Abbie Teel", "url": "https://openalex.org/A5106881069", "inst": "University of North Texas" }, { "name": "Brett Porter", "url": "https://openalex.org/A5143459420", "inst": "University of North Texas" }, { "name": "Anuradha Chandrasekaran", "url": "https://openalex.org/A5135285347", "inst": "Texas Tech University" } ], "affiliations": [ "University of North Texas", "Texas Tech University" ] }, { "uid": "doi:10.2139/ssrn.7198738", "doi": "10.2139/ssrn.7198738", "title": "The Last Costly Signal: How Generative AI Collapses Competence Signaling and Why Liability Sustains Markets for Expert Services", "authors": [ "Andreas Bauer" ], "posted": "2026-08-07", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7198738", "field": "economics", "role": "object", "bullets": [ "Signaling theory applied to markets for expert services as credence goods, with agent-based Monte Carlo runs. No field data. A conjoint experiment on German-speaking business buyers is proposed, not yet run.", "No language model is run. Generative AI enters as a parameter that squeezes the gap between what machines produce cheaply and what buyers can tell apart, so production-cost signals stop separating types.", "Below a threshold the market pools: in the baseline run the high type premium falls from 20 percent to zero and participation from 88 to 68 percent. Warranties backed by damages restore separation." ], "bullet_provenance": "ai", "salience": 62, "edition": 18, "models": [], "validated": null, "n": 2191, "authors_detailed": [ { "name": "Andreas Bauer", "url": "https://openalex.org/A5144362180", "inst": "Tallinn University" } ], "affiliations": [ "Tallinn University" ] }, { "uid": "doi:10.2139/ssrn.7243293", "doi": "10.2139/ssrn.7243293", "title": "A Technology-Function-Guided Patent Drafting Framework Incorporating Etfm and RAG-Based Large Language Model", "authors": [ "Cheng-En Kuo", "Chun-Yi Wu", "Amy Trappey", "Li-Ping Hung" ], "posted": "2026-08-07", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7243293", "field": "management", "role": "method", "bullets": [ "One case study in sustainable energy storage. The number of drafts produced, the size of the prior art corpus and the patent jurisdiction are not stated.", "The paper names no model. An unnamed LLM with ontology-based retrieval writes concepts, abstracts, titles, descriptions, claims and IPC codes, and a technology-function matrix checks each draft against its target cell.", "Drafts stay semantically distinct from the retrieved prior art and consistent with their assigned technical cell. No accuracy figure, expert rating or patentability test is reported." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 18, "models": [], "n": 2192, "authors_detailed": [ { "name": "Cheng-En Kuo", "url": "https://openalex.org/A5146012270", "inst": "National Tsing Hua University" }, { "name": "Chunyi Wu", "url": "https://openalex.org/A5103050318", "inst": "Intel (United States)" }, { "name": "Amy Trappey", "url": "https://openalex.org/A5134955683", "inst": "National Tsing Hua University" }, { "name": "L.P. Hung", "url": "https://openalex.org/A5020036324", "inst": "China University of Science and Technology" } ], "affiliations": [ "National Tsing Hua University", "Intel (United States)", "China University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7204798", "doi": "10.2139/ssrn.7204798", "title": "From Natural Language to Optimization Models: A Survey of LLM-based Autoformulation", "authors": [ "Tong Guo", "Yi Mei", "Yaoxin Wu", "Chin Sheng Tan", "Huey Yuen Ng", "Yew Soon Ong" ], "posted": "2026-08-07", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7204798", "field": "management", "role": "method", "bullets": [ "Survey of work that turns natural language problem descriptions into solver-ready optimization models. The number of papers reviewed and the period covered are not stated.", "No model is run. The authors sort existing methods into a four-stage pipeline, group benchmark datasets by input, output and construction, and separate outcome-level from system-level metrics.", "The survey closes with metric-selection guidance and open directions. It reports no comparative accuracy numbers across the methods it covers, so relative performance stays unresolved." ], "bullet_provenance": "ai", "salience": 33, "edition": 18, "models": [], "validated": null, "n": 2193, "authors_detailed": [ { "name": "Tong Guo", "url": "https://openalex.org/A5145950677", "inst": "" }, { "name": "Yi Mei", "url": "https://openalex.org/A5073282655", "inst": "Victoria University of Wellington" }, { "name": "Yaoxin Wu", "url": "https://openalex.org/A5059325642", "inst": "Shihezi University" }, { "name": "Chin S. Tan", "url": "https://openalex.org/A5104005487", "inst": "Florida Institute of Technology" }, { "name": "Huey Yuen Ng", "url": "https://openalex.org/A5034681806", "inst": "Agency for Science, Technology and Research" }, { "name": "Yew Soon Ong", "url": "https://openalex.org/A5145991113", "inst": "" } ], "affiliations": [ "Victoria University of Wellington", "Shihezi University", "Florida Institute of Technology", "Agency for Science, Technology and Research" ] }, { "uid": "doi:10.2139/ssrn.7212138", "doi": "10.2139/ssrn.7212138", "title": "AI-Enabled Credit Assessment for MSMEs: A Framework for Transparency and Inclusion", "authors": [ "Dhruv Kanade" ], "posted": "2026-08-07", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7212138", "field": "finance", "role": "instrument", "bullets": [ "Proof-of-concept lending prototype built on India's digital public infrastructure, pulling consented tax filings and bank transaction data. No borrower sample, no loan outcomes and no partner bank are reported.", "A rule-based engine scores liquidity, debt service coverage, cash flow stability and sector bad-loan exposure. An unnamed LLM writes the explanation for each decision. No accuracy check appears.", "The headline claim is a credit decision in under 15 to 20 seconds. Effects on approval rates, default rates and operating cost are argued rather than measured." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 18, "models": [], "n": 2194, "authors_detailed": [ { "name": "Dhruv Kanade", "url": "https://openalex.org/A5146003937", "inst": "MIT Art, Design and Technology University" } ], "affiliations": [ "MIT Art, Design and Technology University" ] }, { "uid": "doi:10.2139/ssrn.7204579", "doi": "10.2139/ssrn.7204579", "title": "Governance Gaps in Human-Directed AI Development: Empirical Evidence, Theoretical Foundations, and the Human-AI Co-Authorship Protocol (HAICA)", "authors": [ "Roshan George Thomas" ], "posted": "2026-08-07", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7204579", "field": "management", "role": "object", "bullets": [ "Review of published security evidence: 5,600 publicly reachable AI-assisted applications, vulnerability scans of output from more than 100 language models, and CVE attribution data taken from vendor reports.", "The author runs no model. All evidence is secondary and used to argue that governance rules break when the person who states the intent and the party that writes the code differ.", "Cited figures: 45 percent of AI-generated code carries OWASP Top 10 flaws, credential exposure runs at 2.1 times the human rate, and CVEs attributed to AI code rose 483 percent in three months." ], "bullet_provenance": "ai", "salience": 28, "edition": 18, "models": [], "validated": null, "n": 2195, "authors_detailed": [ { "name": "Thomas Roshan George", "url": "https://openalex.org/A5135823094", "inst": "Skyline University College" } ], "affiliations": [ "Skyline University College" ] }, { "uid": "doi:10.2139/ssrn.7208880", "doi": "10.2139/ssrn.7208880", "title": "From Product Search to Preference Articulation: The Economics of Agentic Commerce", "authors": [ "Lingxiu Dong", "Kaiwen Luo", "Fasheng Xu" ], "posted": "2026-08-07", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7208880", "field": "management", "role": "object", "bullets": [ "Analytical model of consumer product search with no empirical sample; manual search inspects a limited set while agentic search screens a broad catalog through noisy representations of preferences and products.", "No language model is run or named. AI agents enter as a search technology whose fidelity the platform sets and whose representation the consumer refines with attention. Nothing is validated against data.", "As preference complexity rises manual search collapses, inspection stops and platform revenue goes to zero, while agentic search keeps mismatch below the no-search level. Platforms can prefer agentic search before consumers do, creating an adoption lag." ], "bullet_provenance": "ai", "salience": 55, "edition": 18, "models": [], "validated": null, "n": 2196, "authors_detailed": [ { "name": "Lingxiu Dong", "url": "https://openalex.org/A5014497691", "inst": "Washington University in St. Louis" }, { "name": "Kaiwen Luo", "url": "https://openalex.org/A5146028985", "inst": "Washington University in St. Louis" }, { "name": "Fasheng Xu", "url": "https://openalex.org/A5086759286", "inst": "University of Connecticut" } ], "affiliations": [ "Washington University in St. Louis", "University of Connecticut" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.07688v1", "arxiv_id": "2608.07688v1", "title": "IntelliAudit: Using Large Language Models to Evaluate Audit Controls", "authors": [ "Allison Wilson", "Sina Moradi Sabet", "Diar Shakimov", "Panteha Shahrivar", "Mohammad Reza Bagheri", "Dean Konenkamp", "Mohammad A. Tayebi" ], "posted": "2026-08-07", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.07688v1", "field": "accounting", "role": "method", "bullets": [ "Simulated organizations audited against ISO/IEC 27001 controls; evidence corpora span policies, records, spreadsheets and operational artifacts. Number of controls, organizations and reviewers is not stated.", "A retrieval-grounded multi-agent pipeline retrieves artifacts, drafts an assessment, challenges adverse findings and adjudicates disagreements. The abstract names no model or vendor. Expert auditors reviewed outputs, but no accuracy or agreement figure appears.", "Auditors judged the system useful for control interpretation and audit preparation, but found it too permissive on evidentiary sufficiency, so the authors position it as decision support rather than autonomous certification." ], "bullet_provenance": "ai", "validated": false, "validation_note": "expert auditor review only, no accuracy figures", "salience": 46, "edition": 18, "models": [], "n": 2197, "authors_detailed": [ { "name": "Allison Wilson", "url": "https://openalex.org/A5120363937", "inst": "Risk Management Solutions (United Kingdom)" }, { "name": "Sina Moradi Sabet", "url": "https://openalex.org/A5120363935", "inst": "Amirkabir University of Technology" }, { "name": "Diar Shakimov", "url": "https://openalex.org/A5146546564", "inst": "" }, { "name": "Panteha Shahrivar", "url": "https://openalex.org/A5146531987", "inst": "" }, { "name": "Mohammad Reza Bagheri", "url": "https://openalex.org/A5146494352", "inst": "" }, { "name": "Dean Konenkamp", "url": "https://openalex.org/A5146520603", "inst": "" }, { "name": "Mohammad A. Tayebi", "url": "https://openalex.org/A5120458847", "inst": "Simon Fraser University" } ], "affiliations": [ "Risk Management Solutions (United Kingdom)", "Amirkabir University of Technology", "Simon Fraser University" ] }, { "uid": "doi:10.2139/ssrn.7200918", "doi": "10.2139/ssrn.7200918", "title": "Systematic Execution Failures of LLM Delegates in RFQ Markets", "authors": [ "Anqi Peter Li", "Ethan Yip", "Kundana Kommini", "Li Yu Chen" ], "posted": "2026-08-07", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7200918", "field": "finance", "role": "agent", "bullets": [ "Simulated multi dealer request for quote auctions on a price ladder, with fourteen instruction tuned model caches, nine of them open weight from 3.8B to 72B parameters, choosing quotes for a principal.", "Each delegate picked from an adversarially ordered menu; choices were scored against the least cost action, against a calibrated rational reference, and by a calibration free price ranking statistic.", "Delegates surrendered 3.8 to 9.4 basis points of expected execution cost, of which 0.6 to 6.5 exceeds the calibration artifact; one instruction sentence cut Qwen 2.5 32B harm from 5.83 to 4.96 bps." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "least-cost action and calibrated rational reference, gaps in basis points", "salience": 76, "edition": 17, "n": 2117, "authors_detailed": [ { "name": "Anqi Peter Li", "url": "https://openalex.org/A5136492492", "inst": "British Columbia Centre on Substance Use" }, { "name": "Ethan Yip", "url": "https://openalex.org/A5144927941", "inst": "Laboratoire de Physique Subatomique et des Technologies Associées" }, { "name": "Kundana Kommini", "url": "https://openalex.org/A5127814737", "inst": "Laboratoire de Physique Subatomique et des Technologies Associées" }, { "name": "Li Yu Chen", "url": "https://openalex.org/A5145203380", "inst": "Vanderbilt University" } ], "affiliations": [ "Vanderbilt University", "British Columbia Centre on Substance Use", "Laboratoire de Physique Subatomique et des Technologies Associées" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.07446v1", "arxiv_id": "2608.07446v1", "title": "Taxonomy-Driven Analysis of Open-Source AI Risk Mitigation Tools", "authors": [ "Afreen Alam", "Evgenija Popchanovska", "Ana Gjorgjevikj", "Maryan Rizinski", "Lubomir T. Chitkushev", "Irena Vodenska", "Dimitar Trajanov" ], "posted": "2026-08-07", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.07446v1", "field": "management", "role": "instrument", "bullets": [ "Twenty one open source tools for model evaluation, adversarial testing, guardrails and observability, set against the 32 subcategories of the extended MIT AI risk mitigation and response taxonomy.", "A retrieval augmented pipeline, model family not stated, read each tool's source code and documentation and extracted capabilities per category; three independent reviewers checked the labels at Fleiss kappa 0.509.", "Tool coverage clusters on technical and operational controls, leaving governance, legal, regulatory and financial controls largely unaddressed; the mapping protocol reached F1 of 75.5 percent after majority voting." ], "bullet_provenance": "ai", "validated": true, "validation_note": "three human reviewers, Fleiss kappa 0.509, mapping F1 75.5 percent", "salience": 52, "edition": 17, "models": [], "n": 2119, "authors_detailed": [ { "name": "Afreen Alam", "url": "https://openalex.org/A5146341870", "inst": "Boston University" }, { "name": "Evgenija Popchanovska", "url": "https://openalex.org/A5128178880", "inst": "Ss. Cyril and Methodius University in Skopje" }, { "name": "Ana Gjorgjevikj", "url": "https://openalex.org/A5034185097", "inst": "Jožef Stefan Institute" }, { "name": "Maryan Rizinski", "url": "https://openalex.org/A5054465893", "inst": "Boston University" }, { "name": "Lubomir T. Chitkushev", "url": "https://openalex.org/A5139789651", "inst": "Boston University" }, { "name": "Irena Vodenska", "url": "https://openalex.org/A5053217029", "inst": "Boston University" }, { "name": "Dimitar Trajanov", "url": "https://openalex.org/A5139803699", "inst": "Boston University" } ], "affiliations": [ "Boston University", "Ss. Cyril and Methodius University in Skopje", "Jožef Stefan Institute" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.06955v1", "arxiv_id": "2608.06955v1", "title": "Critical Acclaim Orientation in Large Language Models: Evidence from Film Preference Elicitation", "authors": [ "Jonghyun Jee", "Aaron Shaw" ], "posted": "2026-08-07", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.06955v1", "field": "management", "role": "agent", "bullets": [ "Two hundred films sorted into critically acclaimed, commercially successful, and both at once, put to eight models drawn from the Anthropic, OpenAI, Alibaba and Mistral families.", "Each model made 20,000 forced choices between film pairs, scaled with Bradley Terry estimation, then nested OLS separated evaluative orientation, public visibility and popular reception.", "All eight preferred acclaimed but obscure titles over popular but unrecognized ones, more strongly at larger scale; controlling for visibility reverses the edge of dual legitimacy films, and recommendation framing reorders the rankings." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "salience": 48, "edition": 17, "validated": null, "n": 2120, "authors_detailed": [ { "name": "Jonghyun Jee", "url": "https://openalex.org/A5095830059", "inst": "Korea Advanced Institute of Science and Technology" }, { "name": "Aaron Shaw", "url": "https://openalex.org/A5146378255", "inst": "" } ], "affiliations": [ "Korea Advanced Institute of Science and Technology" ] }, { "uid": "arxiv:2608.07208v1", "arxiv_id": "2608.07208v1", "title": "Measuring Concept Content in Text from LLM Activations: ESG Evidence from Concept Vectors and Linear Probes", "authors": [ "Luc Hazenoot", "Zhaochun Ren", "Amirhossein Zohrehvand" ], "posted": "2026-08-07", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.07208v1", "field": "finance", "role": "method", "bullets": [ "Financial text carrying human annotations for environmental, social and governance content serves as the test bed for measuring how much a document is about a given concept.", "Activations of frozen models, family not stated, were read with linear probes and with recursive feature machine concept vectors, then compared against embeddings, surface measures, a fine tuned classifier, and the model's own stated answer.", "The best probe came within 0.6 percentage points of the fine tuned classifier with no task specific training and beat the model's own answer in eleven of twelve comparisons; probes also beat the concept vectors." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "human-annotated ESG labels and a fine-tuned domain classifier, accuracy compared", "salience": 66, "edition": 17, "models": [], "n": 2122, "authors_detailed": [ { "name": "Luc Hazenoot", "url": "https://openalex.org/A5146429731", "inst": "" }, { "name": "Zhaochun Ren", "url": "https://openalex.org/A5146350879", "inst": "" }, { "name": "Amirhossein Zohrehvand", "url": "https://openalex.org/A5106276273", "inst": "Leiden University" } ], "affiliations": [ "Leiden University" ] }, { "uid": "doi:10.2139/ssrn.7203138", "doi": "10.2139/ssrn.7203138", "title": "Green AI Beyond Operational Energy GAILA: A Lifecycle-Oriented Environmental Reporting and Decision-Support Framework for Large Language Models Conceptual Article / Working Paper", "authors": [ "Alyaa Sabri Awadh" ], "posted": "2026-08-07", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7203138", "field": "accounting", "role": "object", "bullets": [ "Conceptual working paper with no dataset and no empirical test; the unit of analysis is a deployed language model system across its supply chain and service life.", "No model is run or named. The framework sorts environmental evidence into six lifecycle stages, from chip fabrication to hardware disposal, and demands a stated boundary, functional unit, and evidence provenance.", "Rejects a single sustainability score in favour of a multidimensional profile that names hotspots, excluded stages, and uncertainty alongside accuracy, throughput, latency, and memory." ], "bullet_provenance": "ai", "salience": 31, "edition": 17, "models": [], "validated": null, "n": 2124, "authors_detailed": [ { "name": "Alyaa Sabri Awadh", "url": "https://openalex.org/A5139339849", "inst": "University of Babylon" } ], "affiliations": [ "University of Babylon" ] }, { "uid": "doi:10.2139/ssrn.7201826", "doi": "10.2139/ssrn.7201826", "title": "\"Numbers Don't Speak for Themselves\": Words, Narrative, and Storytelling in Financial Decision-making", "authors": [ "Mark Rzepczynski" ], "posted": "2026-08-07", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7201826", "field": "finance", "role": "method", "bullets": [ "Conceptual essay on investment decision making with no sample, period, or estimation; it works from the positive and normative finance literatures on narrative and sentiment.", "No model is run and none is named. Language models enter only as the current tooling for turning text into sentiment signals that researchers link to asset returns.", "Argues the channel running from words to investor behaviour stays unexamined, so measured textual signals now outrun any account of why narrative moves risk and return expectations." ], "bullet_provenance": "ai", "salience": 29, "edition": 17, "models": [], "validated": null, "n": 2125, "authors_detailed": [ { "name": "Mark Rzepczynski", "url": "https://openalex.org/A5140200131", "inst": "Concord Consortium" } ], "affiliations": [ "Concord Consortium" ] }, { "uid": "doi:10.2139/ssrn.7241998", "doi": "10.2139/ssrn.7241998", "title": "Simulating Firm Decision Making with LLMs: A Scalable Experimental Framework for Strategy Research", "authors": [ "Han Jiang", "Xiaoran Ma", "Jing Wu" ], "posted": "2026-08-07", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7241998", "field": "management", "role": "agent", "bullets": [ "Two simulated strategy experiments, one on research and development spending and one on facility allocation, with each agent carrying a firm specific context profile; sample size not stated.", "Language model agents stand in for corporate executives and choose under both a baseline and a shock condition; the model family is never named, and face validity rests on asserted convergence with actual firm choices rather than a reported statistic.", "Within agent differences reproduce the treatment effects that difference in differences estimates recover from real data, in both settings." ], "bullet_provenance": "ai", "validated": true, "validation_note": "convergence with actual firm decisions and real world difference in differences estimates, no agreement statistic given", "salience": 66, "edition": 17, "models": [], "n": 2126, "authors_detailed": [ { "name": "Han Jiang", "url": "https://openalex.org/A5049538320", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Xiaoran Ma", "url": "https://openalex.org/A5145659202", "inst": "" }, { "name": "Jing Wu", "url": "https://openalex.org/A5014078414", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong, Shenzhen", "Chinese University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.7201722", "doi": "10.2139/ssrn.7201722", "title": "Generative AI and Risk Perception: Evidence from Risk-Factor Disclosures", "authors": [ "Giulia Redigolo", "Niccolo' Marcarini" ], "posted": "2026-08-07", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7201722", "field": "accounting", "role": "object", "bullets": [ "Item 1A risk factor sections of US 10-K filings from 2019 to 2024, with the November 2022 release of ChatGPT used as the shock that shifts how much of the text machines write.", "A sentence level AI detector, unnamed and with no reported accuracy check, scores each section for machine written text. Which model filers themselves used is not observed.", "Sections with more detected AI text draw weaker filing date responses, with smaller absolute abnormal returns, lower volume, and lower post filing volatility, and the text is shorter, more standardised, and less tied to firm specific shocks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "edition": 17, "n": 2135, "authors_detailed": [ { "name": "Giulia Redigolo", "url": "https://openalex.org/A5044447265", "inst": "EAE Business School" }, { "name": "Niccolò Marcarini", "url": "https://openalex.org/A5118358165", "inst": "Università Cattolica del Sacro Cuore" } ], "affiliations": [ "EAE Business School", "Università Cattolica del Sacro Cuore" ] }, { "uid": "arxiv:2608.06949v1", "arxiv_id": "2608.06949v1", "title": "Does Splitting a Triage Decision Across Agents Hide Bias or Help Catch It? A Multi-Agent Simulation Study of LLM-Based Resource Allocation Under Audit Capacity Constraints", "authors": [ "Paul-Peter Arslan" ], "posted": "2026-08-07", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.06949v1", "field": "management", "role": "agent", "bullets": [ "Synthetic disaster triage simulator with case pairs identical except for one demographic attribute, 192 episodes and 2,304 resolved pairs, pitting a single decision maker against a nine agent assessment, allocation, and audit pipeline.", "GPT-4o-mini fills every role in both conditions. Bias is read off outcome differences within clinically identical pairs, and no comparison against human triage decisions is reported.", "Splitting the decision changes little, 6.9 against 6.1 percent biased outcomes. Audit capacity drives detection: 30 percent of biased cases go uncaught, 43.8 percent under auditor overload, and risk ordered queues lift coverage from 65.6 to 91.7 percent." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 48, "edition": 17, "validated": null, "n": 2136, "authors_detailed": [ { "name": "Paul-Peter Arslan", "url": "https://openalex.org/A5133527760", "inst": "Inserm" } ], "affiliations": [ "Inserm" ] }, { "uid": "doi:10.2139/ssrn.7203199", "doi": "10.2139/ssrn.7203199", "title": "Assuring the Machine's Work: An Audit Discipline for Net Productivity and Unobserved Agentic Evidence", "authors": [ "DeWayne L. Searcy" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7203199", "field": "accounting", "role": "object", "bullets": [ "Single densely instrumented multi-domain case study with cross-domain replication examining AI-assisted knowledge work productivity.", "Proposes provenance-assurance protocol for agentic AI evidence with re-performance, tie-out, segregation of duties, and Trust-Tier classification.", "Framework nets task-level savings against verification friction, linking micro recovery labor to macro productivity aggregation and making AI outputs auditable by design." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3286, "authors_detailed": [ { "name": "DeWayne L. Searcy", "url": "https://openalex.org/A5068394555", "inst": "Southern Company (United States)" } ], "affiliations": [ "Southern Company (United States)" ] }, { "uid": "doi:10.2139/ssrn.7212418", "doi": "10.2139/ssrn.7212418", "title": "Generative AI as a Limited Equalizer: Experimental Evidence on Gender Gaps in Competition", "authors": [ "Lisha Qin", "Fan Rao", "Xu Zhang" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7212418", "field": "economics", "role": "object", "bullets": [ "Lab experiment with 360 university students solving numerical and logic questions under piece-rate versus tournament incentives with random ChatGPT access assignment.", "Sessions assigned to baseline or ChatGPT-available conditions; a follow-up information treatment told participants that men and women performed similarly with AI.", "ChatGPT eliminated baseline gender performance differences but did not significantly reduce the gender gap in tournament entry; information provision also failed to close the gap." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 3831, "authors_detailed": [ { "name": "Lisha Qin", "url": "https://openalex.org/A5146025673", "inst": "University of Hong Kong" }, { "name": "Fan Rao", "url": "https://openalex.org/A5146018607", "inst": "University of Hong Kong" }, { "name": "Xu ZHANG", "url": "https://openalex.org/A5133233806", "inst": "University of Hong Kong" } ], "affiliations": [ "University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.7218279", "doi": "10.2139/ssrn.7218279", "title": "Building and Innovating on AI Before It Is Fully Platformed: An Incomplete Technological, Industrial, and Institutional Architecture", "authors": [ "Kevin Boudreau" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7218279", "field": "management", "role": "object", "bullets": [ "Conceptual analysis comparing generative AI platforming to electrification and earlier general-purpose technologies across technological, industrial, and institutional dimensions.", "The paper maps where AI interfaces, division of labor, and institutional rules have and have not settled enough for decentralized complementary innovation by non-frontier firms.", "Four investment principles derived: learn faster than you commit, build assets that survive architectural change, invest in scarce complements, and build organizational capability." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3832, "authors_detailed": [ { "name": "Kevin Boudreau", "url": "https://openalex.org/A5054968186", "inst": "Northeastern University" } ], "affiliations": [ "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.7198181", "doi": "10.2139/ssrn.7198181", "title": "AI Monoculture Risk in Foundation Models: From Market Concentration to Systemic Lockout", "authors": [ "Devansh Gupta" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7198181", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of foundation model market concentration across law, healthcare, and banking, tracing dependency through five stages from training costs to systemic lockout.", "The paper tests governance responses including algorithmic pluralism, open-weight release, mandatory disclosure, and third-party auditing against evidence at each stage.", "Each governance fix addresses part of the concentration-to-lockout chain but leaves another part untouched; no single remedy resolves the correlated-failure risk." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3833, "authors_detailed": [ { "name": "Devansh Gupta", "url": "https://openalex.org/A5123852089", "inst": "Jaypee Institute of Information Technology" } ], "affiliations": [ "Jaypee Institute of Information Technology" ] }, { "uid": "doi:10.2139/ssrn.7204100", "doi": "10.2139/ssrn.7204100", "title": "Demand Discovery under Free Imitation: Generative AI and Creation Incentives in a Digital Doujin Platform", "authors": [ "Makoto Kadowaki" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7204100", "field": "economics", "role": "object", "bullets": [ "DLsite, Japan's largest digital doujin marketplace, observed across policy events that toggled AI content supply on and off and a credit-card suspension that shifted market size.", "A structural model of demand and entry estimates AI versus human production costs, exploiting the disclosure mandate, sales ban, and reopening under caps as natural experiments.", "AI production fixed costs fell roughly 40% after reopening; most newly discovered niches were imitated within about a month, but buyers discount AI content at the margin." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3834, "authors_detailed": [ { "name": "Makoto Kadowaki", "url": "https://openalex.org/A5112248735", "inst": "Hitotsubashi University" } ], "affiliations": [ "Hitotsubashi University" ] }, { "uid": "doi:10.2139/ssrn.7200860", "doi": "10.2139/ssrn.7200860", "title": "Enterprise Trust Gaps in Generative AI Why Accuracy Is Not Enough: Evidence from Mid-Market Generative AI Adoption", "authors": [ "Ibe Imo", "Kofi Agyare-Kwabi", "Abolaji Adesoji", "David Odukoya", "Isaac Manu Sarfo" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7200860", "field": "management", "role": "object", "bullets": [ "Survey of enterprise professionals in financial services, cybersecurity, and technology, supplemented by secondary analysis of three large-scale AI adoption surveys.", "The study identifies three mechanisms producing trust gaps: accountability mismatch, human correction burden, and confidence framing effect, each preceding output accuracy.", "Users abandon or override accurate AI outputs when they own accountability for errors, face rising verification costs, and interpret hedged confidence as incompetence." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3835, "authors_detailed": [ { "name": "Ibe Imo", "url": "https://openalex.org/A5144324463", "inst": "University of Fort Hare" }, { "name": "Kofi Agyare-Kwabi", "url": "https://openalex.org/A5144379748", "inst": "Community Partners" }, { "name": "Abolaji Adesoji", "url": "https://openalex.org/A5144445445", "inst": "Rensselaer Polytechnic Institute" }, { "name": "David Odukoya", "url": "https://openalex.org/A5144313170", "inst": "Opinion Leader Research" }, { "name": "Isaac Manu Sarfo", "url": "https://openalex.org/A5144492614", "inst": "St. John's College of Nursing" } ], "affiliations": [ "University of Fort Hare", "Community Partners", "Rensselaer Polytechnic Institute", "Opinion Leader Research", "St. John's College of Nursing" ] }, { "uid": "doi:10.2139/ssrn.7202362", "doi": "10.2139/ssrn.7202362", "title": "Agentic Regulatory Intelligence: A Multi-agent AI Framework for Financial Crime Regulatory Pattern Monitoring and Guidance Generation", "authors": [ "Nikhil Mittal" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7202362", "field": "finance", "role": "instrument", "bullets": [ "Four documented financial crime advisory emergence cases evaluated retrospectively against historical FinCEN regulatory guidance timelines spanning 12 to 57 months.", "A four-agent AI framework with a language model fine-tuned on the FinCEN advisory corpus drafts advisory documents under mandatory human oversight at each stage boundary.", "Mean advisory draft completion falls from a historical 14.3 months to 4.2 hours, compressing typology detection, advisory drafting, and impact assessment into one cycle." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 3836, "authors_detailed": [ { "name": "Nikhil Mittal", "url": "https://openalex.org/A5145361023", "inst": "" } ] }, { "uid": "arxiv:2608.07251v1", "arxiv_id": "2608.07251v1", "title": "Reading Copom's Tone: A Weighted LLM Framework for Hawkish-Dovish Sentiment, Forward Guidance, and Uncertainty", "authors": [ "Gabriel de Macedo Santos" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.07251v1", "field": "finance", "role": "instrument", "bullets": [ "Eighty Brazilian Copom statements and 1,498 classified sentences from August 2016 through August 2026, covering the full modern monetary policy cycle.", "An LLM classifies sentences as hawkish, dovish, neutral, or out-of-context with 0-to-1 intensity weights; a separate layer measures forward-guidance direction and uncertainty.", "Average document score is +0.107; tone and guidance-direction correlate at 0.719; the author states outputs are descriptive and not validated forecasts of Selic decisions." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 3837, "authors_detailed": [ { "name": "Gabriel de Macedo Santos", "url": "https://openalex.org/A5124010720", "inst": "" } ] }, { "uid": "arxiv:2608.06842v1", "arxiv_id": "2608.06842v1", "title": "Tabular Foundation Models and the Unity of Economic Behaviour", "authors": [ "Victor H. Aguiar" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.06842v1", "field": "economics", "role": "instrument", "bullets": [ "Unified choice experiment across risk, time, losses, valuation, and social choice domains where the same decision makers faced all tasks.", "A frozen tabular foundation model recovered hidden domain choices from each decision maker's other-domain choices and other participants' labeled responses.", "A single structural utility model over the learned representation retained most prediction-error reduction and reproduced cross-domain behavioral covariance patterns." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "prediction error vs. training-sample median; gain disappears under shuffled-identity control", "salience": 62, "n": 3838, "authors_detailed": [ { "name": "Victor H. Aguiar", "url": "https://openalex.org/A5146335840", "inst": "Simon Fraser University" } ], "affiliations": [ "Simon Fraser University" ] }, { "uid": "arxiv:2608.07400v1", "arxiv_id": "2608.07400v1", "title": "FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filings", "authors": [ "Sasan Mansouri", "Daniel Saad", "Mark Wahrenburg", "Manu Weissel", "Fabian Woebbeking" ], "posted": "2026-08-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.07400v1", "field": "finance", "role": "method", "bullets": [ "1,185 manually authored QA records over 10-K and 10-Q filings of 22 companies, with gold supporting passages and hand-curated hard negatives from confusable disclosures.", "Benchmarked retrieval models including a 7B instruction-tuned embedder and sub-billion encoders on passage retrieval, reranking, and hard-negative discrimination against gold evidence.", "Best model reached only 44.8% Recall@10 on the pooled corpus; pairwise accuracy fell 13 to 20.5 percentage points when random negatives were replaced with curated hard negatives." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Recall@10 and pairwise accuracy against gold passages with hard negatives", "salience": 58, "n": 4062, "authors_detailed": [ { "name": "Sasan Mansouri", "url": "https://openalex.org/A5041035111", "inst": "University of Groningen" }, { "name": "Daniel Saad", "url": "https://openalex.org/A5114658045", "inst": "Goethe University Frankfurt" }, { "name": "Mark Wahrenburg", "url": "https://openalex.org/A5082032051", "inst": "Goethe University Frankfurt" }, { "name": "Manu Weissel", "url": "https://openalex.org/A5146424234", "inst": "" }, { "name": "Fabian Woebbeking", "url": "https://openalex.org/A5112669930", "inst": "Halle Institute for Economic Research" } ], "affiliations": [ "University of Groningen", "Goethe University Frankfurt", "Halle Institute for Economic Research" ] }, { "uid": "doi:10.2139/ssrn.7190278", "doi": "10.2139/ssrn.7190278", "title": "Beyond the Black-Box Cloud: How Liquid AI Fits the Operational and Ethical Realities of Health and Social Care", "authors": [ "James A Lomastro" ], "posted": "2026-08-06", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7190278", "field": "management", "role": "object", "bullets": [ "Argumentative piece on health and social care providers subject to United States privacy and eligibility rules. No sample, period, or data set is stated.", "The authors run no model and name none. The discussion contrasts centrally hosted commercial systems with locally hosted alternatives, and reports no validation.", "Claims the deployment mismatch shows up as protected health information leaving provider control, unpredictable per token bills competing with direct care budgets, and generalist reasoning applied to eligibility decisions." ], "bullet_provenance": "ai", "salience": 26, "edition": 18, "models": [], "validated": null, "n": 2169, "authors_detailed": [ { "name": "James A. Lomastro", "url": "https://openalex.org/A5075721409", "inst": "Brandeis University" } ], "affiliations": [ "Brandeis University" ] }, { "uid": "doi:10.2139/ssrn.7187159", "doi": "10.2139/ssrn.7187159", "title": "Structure, Legitimacy, and the Limits of Machine-Centered Derivation: An Analysis of Five AI Systems' Third-Stage Attempts to Construct Machine-Centric Governance Policies for Legal Education", "authors": [ "Larry Catá Backer" ], "posted": "2026-08-06", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7187159", "field": "management", "role": "agent", "bullets": [ "Three-stage experiment on US law school AI policies, with five commercial systems asked to draft governance rules for coursework.", "Harvey AI, Claude, ChatGPT, Grok and Gemini each produced a policy architecture, first under human-centric framing and then without human normative guardrails. Versions not stated.", "None reached machine-centered derivation; each restated existing human normative traditions, several conceding this when challenged. The author concludes human deliberation should stay the authoritative vehicle for legal governance." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": false, "salience": 31, "edition": 18, "validated": null, "n": 2178, "authors_detailed": [ { "name": "Larry Catá Backer", "url": "https://openalex.org/A5004566502", "inst": "Pennsylvania State University" } ], "affiliations": [ "Pennsylvania State University" ] }, { "uid": "arxiv:2608.09988v1", "arxiv_id": "2608.09988v1", "title": "OpenPM: Auditable Point-in-Time Evaluation for LLM Portfolio-Management Agents", "authors": [ "Xinying Cai", "Minghao Guo", "Jiahe Liu", "Jiaojiao Han", "Bangwei Guo", "Yitao Long", "Yuxuan Chen", "Bohan Wu", "Dimitris N. Metaxas", "Raymond Li" ], "posted": "2026-08-06", "added": "2026-08-13", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.09988v1", "field": "finance", "role": "method", "bullets": [ "S&P 500 universe, five-minute market data, a one million dollar long-only book managed by an LLM agent over a single frozen short window.", "Model families are not named. A tiered allocator splits work between scoring analysts, a constructor that proposes weights, and a deterministic critic; analyst evidence is replayed across constructors to isolate their effect.", "Stronger constructors beat equal weighting only modestly and inconsistently; analyst quality matters more, and turnover drives cost. Returns are upper bounds on one window, not validated alpha." ], "bullet_provenance": "ai", "salience": 60, "edition": 18, "models": [], "validated": null, "n": 2179 }, { "uid": "doi:10.2139/ssrn.7236658", "doi": "10.2139/ssrn.7236658", "title": "A Zero-Trust Hybrid Architecture for Enterprise LLM Deployment", "authors": [ "Nitin Lodha" ], "posted": "2026-08-06", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7236658", "field": "management", "role": "method", "bullets": [ "Middleware running at several enterprise clients under confidentiality agreements, benchmarked on a synthetic set of 100 prompts with a fixed seed. Client names, industries and volumes are not stated.", "A reversible masking layer strips identifiers before prompts leave the firm, a router chooses between models against an always GPT-4o baseline, and fixed guardrails plus a correction ledger absorb provider drift.", "Masking is reported as fully accurate and reversible, routing cuts cost 67.1 percent while keeping 90.4 percent of peak quality, and the ledger cuts drift-induced errors by 70 percent." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "synthetic 100-prompt benchmark, masking accuracy reported, no external ground truth", "salience": 35, "edition": 18, "n": 2190, "authors_detailed": [ { "name": "Nitin Lodha", "url": "https://openalex.org/A5070066463", "inst": "Chameli Devi Group of Institutions" } ], "affiliations": [ "Chameli Devi Group of Institutions" ] }, { "uid": "doi:10.2139/ssrn.7180420", "doi": "10.2139/ssrn.7180420", "title": "An Empirical Evaluation of LLM-Augmented SAST for False Positive Mitigation in Financial Core-Banking Systems", "authors": [ "Martin Gunner", "Mckenzie Curtis" ], "posted": "2026-08-06", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7180420", "field": "management", "role": "method", "bullets": [ "Static analysis security alerts from public benchmark suites and live core banking codebases, triaged in experiments run through 2024 inside regulated financial institutions.", "GPT-4o, GPT-3.5 Turbo and Llama 3.1 70B sat as a post processing triage layer over each alert, scored by F1 against labelled true and false positives.", "Hybrid pipelines reached F1 of 0.91 to 0.95 against 0.10 to 0.55 for the scanner alone; GPT-4o cleared 80 of 128 false positives while keeping every true positive, and weakened when code context was partial." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": false, "validated": true, "validation_note": "labelled benchmark and banking findings, F1 0.91 to 0.95", "salience": 44, "edition": 17, "n": 2118, "authors_detailed": [ { "name": "Martin Gunner", "url": "https://openalex.org/A5140105590", "inst": "Norwegian University of Science and Technology" }, { "name": "Mckenzie Curtis", "url": "https://openalex.org/A5144408811", "inst": "Kansas State University" } ], "affiliations": [ "Norwegian University of Science and Technology", "Kansas State University" ] }, { "uid": "doi:10.2139/ssrn.7235878", "doi": "10.2139/ssrn.7235878", "title": "LLM Agents in Transportation-enabled Service Platforms: From Behavioral Simulation to Platform Decision Support", "authors": [ "Te Xu", "Linxuan Shi", "Haoting Zhang", "Yunduan Lin", "Hansheng Jiang", "Donglin Zhan", "Xiangyu Li", "Zhaomiao Guo", "Qixiu Cheng", "Zuo-Jun Max Shen" ], "posted": "2026-08-06", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7235878", "field": "management", "role": "object", "bullets": [ "Survey of transportation enabled service platforms, ride hailing, food and grocery delivery and crowdsourced logistics, reading classical operations research baselines alongside industrial systems at Meituan, DiDi, Instacart, Uber Freight and DoorDash.", "No model is run or named. The review separates behavioural agents that simulate customer, driver and merchant responses from operational agents that turn documents, complaints and regulations into structured inputs.", "Production evidence sits in semantic workflow layers such as customer service, catalogue and query understanding and internal analytics, while public evidence of deployment on real-time dispatch or pricing stays limited." ], "bullet_provenance": "ai", "salience": 45, "edition": 17, "models": [], "validated": null, "n": 2160, "authors_detailed": [ { "name": "Te Xu", "url": "https://openalex.org/A5064212255", "inst": "University of Minnesota" }, { "name": "Linxuan Shi", "url": "https://openalex.org/A5109521615", "inst": "George Washington University" }, { "name": "Haoting Zhang", "url": "https://openalex.org/A5145553880", "inst": "University of California, Berkeley" }, { "name": "Yunduan Lin", "url": "https://openalex.org/A5065079697", "inst": "Chinese University of Hong Kong" }, { "name": "Hansheng Jiang", "url": "https://openalex.org/A5032562080", "inst": "Baycrest Hospital" }, { "name": "Donglin Zhan", "url": "https://openalex.org/A5002091761", "inst": "University of the District of Columbia" }, { "name": "Xiangyu Li", "url": "https://openalex.org/A5145302589", "inst": "The University of Texas at Austin" }, { "name": "Zhaomiao Guo", "url": "https://openalex.org/A5044007367", "inst": "The University of Texas at Austin" }, { "name": "Qixiu Cheng", "url": "https://openalex.org/A5043567322", "inst": "University of Bristol" }, { "name": "Zuo-Jun Max Shen", "url": "https://openalex.org/A5145221471", "inst": "University of Hong Kong" } ], "affiliations": [ "University of Minnesota", "University of California, Berkeley", "The University of Texas at Austin", "George Washington University", "Chinese University of Hong Kong", "University of the District of Columbia", "University of Bristol" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7194958", "doi": "10.2139/ssrn.7194958", "title": "Toward a Cognitive Enterprise", "authors": [ "Mark Moran" ], "posted": "2026-08-06", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7194958", "field": "management", "role": "object", "bullets": [ "Conceptual paper on the firm as a system that converts environmental signal into stakeholder value, extending transaction cost and knowledge based theory. No sample and no empirical test.", "No model is run or named. Language models appear as one of the forces driving the cost of explicit, context free cognitive work toward zero.", "Proposes a five layer cognitive stack split by a context frontier, and argues firm boundaries now rest on governing tacit knowledge and wisdom that cannot be exported rather than on transactional friction." ], "bullet_provenance": "ai", "salience": 30, "edition": 17, "models": [], "validated": null, "n": 2161, "authors_detailed": [ { "name": "Mark Moran", "url": "https://openalex.org/A5102014234", "inst": "University of Illinois System" } ], "affiliations": [ "University of Illinois System" ] }, { "uid": "doi:10.2139/ssrn.7192658", "doi": "10.2139/ssrn.7192658", "title": "Rank Order is Mostly Noise Confidence Intervals for Brand Visibility in AI Answer Engines, and a Failure of The Citation-Extraction Layer", "authors": [ "Logan Adams" ], "posted": "2026-08-06", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7192658", "field": "management", "role": "method", "bullets": [ "4,497 answers covering 9 B2B software categories and 90 products, collected from 5 AI answer engines using 10 pre-registered buyer prompts, each run 10 times per engine.", "The five engines are not named. Answers are parsed for which brands they recommend, every share estimate carries a 95 percent confidence interval, and the citation extraction layer is audited rather than assumed.", "In 6 of 9 categories the top product is not separable from the second, leaders cluster between 16.4 and 25.0 percent share, and 39 percent of extracted citations resolved to redirect wrappers." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 17, "models": [], "n": 2162, "authors_detailed": [ { "name": "Logan Adams", "url": "https://openalex.org/A5143659026", "inst": "Fluidigm (Canada)" } ], "affiliations": [ "Fluidigm (Canada)" ] }, { "uid": "doi:10.2139/ssrn.7236301", "doi": "10.2139/ssrn.7236301", "title": "Recovery, Then Acceleration: Global Scholarly Output Across the Generative-AI Transition, 2000–2026", "authors": [ "Yash Agarwal" ], "posted": "2026-08-06", "added": "2026-08-07", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7236301", "field": "economics", "role": "object", "bullets": [ "Bibliometric records covering 423 months of arXiv submissions, 138 million Crossref journal articles from 2000 to 2026, OpenAlex field and country splits, and the Retraction Watch database.", "No language model is used as a tool; segmented regression with two structural breaks dates a regime change to early 2023, matching published estimates of when LLM assisted text appeared.", "Preprint growth of 10.5 percent a year before 2020 slowed to 2.1 percent during the pandemic, then rose to 18.6 percent after November 2022, led by computer science, business, China, and India." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "edition": 16, "validated": null, "n": 2108, "authors_detailed": [ { "name": "Yash Agarwal", "url": "https://openalex.org/A5134012536", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7199239", "doi": "10.2139/ssrn.7199239", "title": "The Prompt Tax: Why AI Companies Profit From Your Bad Prompts -and Who Will Fix them Instead", "authors": [ "Vrishaank Mishra" ], "posted": "2026-08-06", "added": "2026-08-07", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7199239", "field": "economics", "role": "object", "bullets": [ "A conceptual paper on the market for frontier model inference, where per token pricing means vendors earn more when users prompt inefficiently; no dataset or empirical estimation.", "No model is used or named; the argument draws on automated prompt engineering research, first party optimization tools, and early orchestration products as supporting evidence.", "Predicts providers will close the prompting gap only at the margin and that independent orchestration software will capture it within two to three years, turning tokens into a stratified productivity currency." ], "bullet_provenance": "ai", "salience": 33, "edition": 16, "models": [], "validated": null, "n": 2110, "authors_detailed": [ { "name": "Vrishaank Mishra", "url": "https://openalex.org/A5145910597", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7199381", "doi": "10.2139/ssrn.7199381", "title": "When LLM Agents Negotiate: Private Information and Dynamic Bargaining in Supply Chains", "authors": [ "Chen Liang", "Fasheng Xu" ], "posted": "2026-08-06", "added": "2026-08-07", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7199381", "field": "management", "role": "agent", "bullets": [ "Simulated procurement setting in which a buyer holding private demand information bargains with an uninformed seller over a quantity and payment contract, across 9,840 agent-to-agent negotiations.", "Nine models from OpenAI, Google, and Alibaba negotiate against each other, with behaviour compared to a validated perfect Bayesian equilibrium benchmark under varied prompting and discounting regimes.", "Agents settle 98.9 percent of negotiations but average 2.98 rounds against a benchmark of 1.25, and provider identity predicts the surplus split more reliably than capability rank." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "open_other" ], "open_weights": true, "salience": 68, "edition": 16, "validated": null, "n": 2111, "authors_detailed": [ { "name": "Chen Liang", "url": "https://openalex.org/A5058269050", "inst": "University of Connecticut" }, { "name": "Fasheng Xu", "url": "https://openalex.org/A5086759286", "inst": "University of Connecticut" } ], "affiliations": [ "University of Connecticut" ] }, { "uid": "arxiv:2608.06108v1", "arxiv_id": "2608.06108v1", "title": "Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents", "authors": [ "Yuanhong Jiang", "Jingjie Zou", "Zhenghong Lin", "Xusheng Yu", "Qiqi Huang", "Shuai Jia", "Shijie Dai" ], "posted": "2026-08-06", "added": "2026-08-07", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.06108v1", "field": "finance", "role": "method", "bullets": [ "Benchmark assembled from 201,247 documented decisions by 151 real-world investors, each packaged as a trace linking profile, market events, reasoning, executable decision, and delayed outcome.", "Four LLMs, not named in the abstract, are scored on comprehension, profile-conditioned generation, and end-to-end replay of these investment episodes.", "Models score near 4 of 5 on logical plausibility but only 0.8 to 2.8 on event grounding, and process quality rankings disagree with realized returns." ], "bullet_provenance": "ai", "salience": 54, "edition": 16, "models": [], "validated": null, "n": 2112, "authors_detailed": [ { "name": "Yuanhong Jiang", "url": "https://openalex.org/A5010525481", "inst": "Shanghai Jiao Tong University" }, { "name": "Jingjie Zou", "url": "https://openalex.org/A5146052843", "inst": "" }, { "name": "Zhenghong Lin", "url": "https://openalex.org/A5145933682", "inst": "" }, { "name": "Xusheng Yu", "url": "https://openalex.org/A5145964231", "inst": "" }, { "name": "Qiqi Huang", "url": "https://openalex.org/A5145990036", "inst": "" }, { "name": "Shuai Jia", "url": "https://openalex.org/A5146046808", "inst": "" }, { "name": "Shijie Dai", "url": "https://openalex.org/A5082121409", "inst": "Xiamen University" } ], "affiliations": [ "Shanghai Jiao Tong University", "Xiamen University" ] }, { "uid": "doi:10.2139/ssrn.7235359", "doi": "10.2139/ssrn.7235359", "title": "GROUND: Reducing Hallucinations in LLM-Based Enterprise Analytics Through Governed Semantic Definitions", "authors": [ "Aravind Sasidharan Pillai" ], "posted": "2026-08-06", "added": "2026-08-07", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7235359", "field": "management", "role": "method", "bullets": [ "Natural language analytics over enterprise data warehouses, evaluated on a synthetic benchmark and replicated on US NHTSA vehicle safety data with independently hand-authored gold answers.", "Four models from three providers, names not stated, generate queries constrained by a governed semantic layer of approved metrics, dimensions, joins, and row-level security, with rule validation and retry before execution.", "Only the governed system shows no measured hallucinations across six categories; every ungoverned baseline violates row-level security, at roughly five times the tokens and 1.3 times the latency of direct generation." ], "bullet_provenance": "ai", "validated": true, "validation_note": "hand-authored gold answers on NHTSA data", "salience": 42, "edition": 16, "models": [], "n": 2113, "authors_detailed": [ { "name": "Aravind Sasidharan Pillai", "url": "https://openalex.org/A5001703411", "inst": "Cox Enterprises (United States)" } ], "affiliations": [ "Cox Enterprises (United States)" ] }, { "uid": "arxiv:2608.05668v1", "arxiv_id": "2608.05668v1", "title": "F$^2$Agent: Financial Fusion of Agentic Intelligence for Multimodal Trading", "authors": [ "Changshuo Liu", "Yanzheng Jin", "Shangfeng Cai", "Peng Fang", "Xiaokui Xiao", "Beng Chin Ooi" ], "posted": "2026-08-06", "added": "2026-08-07", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.05668v1", "field": "finance", "role": "agent", "bullets": [ "Trading experiments on six assets spanning equities and cryptocurrencies, drawing on multimodal data from heterogeneous information sources; sample period not stated.", "A hierarchy of specialized LLM agents extracts modality-specific signals, fused through a modality-aware mechanism with noise-robust regularization; the underlying model is not named and no ground-truth validation is reported.", "Beats 16 baselines on trading metrics, with average relative gains in annualized return above 20 percent, including returns of 120.48 percent on GOOG and 148.41 percent on TSLA." ], "bullet_provenance": "ai", "salience": 38, "edition": 16, "models": [], "validated": null, "n": 2115, "authors_detailed": [ { "name": "Changshuo Liu", "url": "https://openalex.org/A5082123119", "inst": "Yale University" }, { "name": "Yanzheng Jin", "url": "https://openalex.org/A5144089705", "inst": "" }, { "name": "Shangfeng Cai", "url": "https://openalex.org/A5145970611", "inst": "" }, { "name": "Peng Fang", "url": "https://openalex.org/A5100601526", "inst": "Huazhong University of Science and Technology Hospital" }, { "name": "Xiaokui Xiao", "url": "https://openalex.org/A5136395842", "inst": "National University of Singapore" }, { "name": "Beng Chin Ooi", "url": "https://openalex.org/A5024892041", "inst": "Ningbo University" } ], "affiliations": [ "Yale University", "National University of Singapore", "Ningbo University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7240158", "doi": "10.2139/ssrn.7240158", "title": "Attribution-guided prompting repairs explanation faithfulness without improving recommendation validity", "authors": [ "Miguel Caro Alvaréz", "Keiner Zuñiga Romero", "Jorge Orozco Heredia", "Salomon Garcia", "Andrés Ramírez Mackenzie", "Udualdo Herrera-García" ], "posted": "2026-08-06", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7240158", "field": "management", "role": "method", "bullets": [ "Audit of a redesigned e-commerce recommender, checked against purchase signals from 440,842 Amazon products plus Wish sales, MercadoLibre best seller ranks, and Falabella listing ranks.", "Three commercial LLMs, names not stated, justify recommendations under baseline, attribution guided, and ablated prompts; agreement with the index's feature attributions is scored by deterministic extraction, no LLM judge.", "Baseline explanations match attributions only at chance, 0.07 to 0.17 top one agreement; supplying the product's own attribution raises it to 0.68 to 0.96, while recommendation validity does not improve." ], "bullet_provenance": "ai", "open_weights": false, "validated": true, "validation_note": "top one feature agreement against model attributions, permutation chance baseline", "salience": 56, "edition": 15, "models": [], "n": 2062, "authors_detailed": [ { "name": "Miguel Caro Alvárez", "url": "https://openalex.org/A5144237308", "inst": "Corporación Universitaria Rafael Nuñez" }, { "name": "Keiner Zuñiga Romero", "url": "https://openalex.org/A5140348704", "inst": "Corporación Universitaria Rafael Nuñez" }, { "name": "Jorge Orozco Heredia", "url": "https://openalex.org/A5144455669", "inst": "" }, { "name": "Salomon Garcia", "url": "https://openalex.org/A5144460784", "inst": "" }, { "name": "Andrés Ramírez Mackenzie", "url": "https://openalex.org/A5144426492", "inst": "" }, { "name": "Udualdo Herrera-García", "url": "https://openalex.org/A5051190985", "inst": "University of Cartagena" } ], "affiliations": [ "Corporación Universitaria Rafael Nuñez", "University of Cartagena" ] }, { "uid": "doi:10.2139/ssrn.7190318", "doi": "10.2139/ssrn.7190318", "title": "No Better Than Random: An Agent-Level Reality Check for LLM Factor Selection", "authors": [ "Jason Li" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7190318", "field": "finance", "role": "method", "bullets": [ "183 published long-short predictor portfolios evaluated over 200 closed-loop LLM agent runs with stationary bootstrap null distribution.", "LLM agent pipeline proposes, evaluates, screens, and iteratively adapts factor selections; tested against budget-matched blind random search control.", "Agent Sharpe of 0.617 appears significant (p=0.005) but is statistically indistinguishable from random search at every budget, attributable to publication selection and multiplicity." ], "bullet_provenance": "ai", "validated": true, "validation_note": "budget-matched random search control with bootstrap null", "salience": 75, "models": [], "n": 3284, "authors_detailed": [ { "name": "Jason Li", "url": "https://openalex.org/A5100762971", "inst": "Johns Hopkins University" } ], "affiliations": [ "Johns Hopkins University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7194798", "doi": "10.2139/ssrn.7194798", "title": "Agentic AI for FP&A: An Applied Case Study in Peer Benchmarking, Forecasting, and Scenario Stress-testing using Public SEC EDGAR Data", "authors": [ "Waleed Khan" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7194798", "field": "finance", "role": "instrument", "bullets": [ "Aon plc and five insurance-brokerage peers, public SEC EDGAR XBRL filings used for benchmarking, forecasting, and stress-testing.", "LLM with tool-calling autonomously orchestrates peer comparison and scenario analysis; recency-weighted log-linear regression forecasts revenue.", "Forecasting model achieves R-squared of 0.872 and tracks analyst consensus within 1.5%; pipeline surfaced six XBRL data-quality defects." ], "bullet_provenance": "ai", "validated": true, "validation_note": "R-squared 0.872 and within 1.5% of analyst consensus", "salience": 55, "models": [], "n": 3285, "authors_detailed": [ { "name": "Waleed Khan", "url": "https://openalex.org/A5098685436", "inst": "New York University" } ], "affiliations": [ "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7184639", "doi": "10.2139/ssrn.7184639", "title": "From Hypotheses to Portfolios: NeuraQuant for Auditable Crypto-Alpha Discovery with Large Language Model", "authors": [ "Hanwen Zheng", "Xiaoxian Wang" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7184639", "field": "finance", "role": "instrument", "bullets": [ "Daily data for eight crypto-assets: 2019-2023 training, 2024 validation, 2025 evaluation, and H1 2026 out-of-sample portfolio test across 12,481 backtests.", "Locally deployed LLM framework evolved alpha factors through semantic memory and deterministic execution, retaining 12 candidates of which four high-quality factors entered an equal-weight portfolio.", "Out-of-sample portfolio returned 28.14% annualized with Sharpe ratio 1.49 and max drawdown -7.95%, versus benchmark at -57.37% annualized with Sharpe -1.33 over the same period." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "out-of-sample H1 2026 portfolio backtest", "salience": 55, "n": 3811, "authors_detailed": [ { "name": "Hanwen Zheng", "url": "https://openalex.org/A5145567942", "inst": "" }, { "name": "Xiaoxian Wang", "url": "https://openalex.org/A5139566489", "inst": "Nanjing Drum Tower Hospital" } ], "affiliations": [ "Nanjing Drum Tower Hospital" ] }, { "uid": "doi:10.2139/ssrn.7189358", "doi": "10.2139/ssrn.7189358", "title": "Generative Artificial Intelligence in Scientific Research: Individual Benefits, Collective Risks, and a Framework for Responsible Research with AI", "authors": [ "Fulvio Castellacci", "Tommaso Ciarli", "Yuan Gao", "Marianna Marino", "Giacomo Marzi", "Massimo Riccaboni", "Maria Savona", "Simone Vannuccini" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7189358", "field": "economics", "role": "object", "bullets": [ "Review of empirical literature on generative AI in scientific research, informed by academic roundtable at Scuola IMT Lucca (April 2026).", "No model deployed; paper maps disagreement across four stages of research (funding, tasks, publication, uptake) and analyzes productivity versus novelty evidence.", "AI-assisted work shows gains in publication volume and citation share, but evidence on novelty, disruption, and breakthrough output remains ambiguous or negative; proposes RRAI governance framework." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3812, "authors_detailed": [ { "name": "Fulvio Castellacci", "url": "https://openalex.org/A5040481657", "inst": "Institute of Transport Economics" }, { "name": "Tommaso Ciarli", "url": "https://openalex.org/A5091228525", "inst": "University of Sussex" }, { "name": "Yuan Gao", "url": "https://openalex.org/A5059576218", "inst": "University of East Anglia" }, { "name": "Marianna Marino", "url": "https://openalex.org/A5107873474", "inst": "SKEMA Business School" }, { "name": "Giacomo Marzi", "url": "https://openalex.org/A5002135733", "inst": "IMT School for Advanced Studies Lucca" }, { "name": "Massimo Riccaboni", "url": "https://openalex.org/A5082803450", "inst": "IMT School for Advanced Studies Lucca" }, { "name": "María Savona", "url": "https://openalex.org/A5019353593", "inst": "University of Sussex" }, { "name": "Simone Vannuccini", "url": "https://openalex.org/A5075864742", "inst": "Centre National de la Recherche Scientifique" } ], "affiliations": [ "Institute of Transport Economics", "University of Sussex", "University of East Anglia", "SKEMA Business School", "IMT School for Advanced Studies Lucca", "Centre National de la Recherche Scientifique" ] }, { "uid": "doi:10.2139/ssrn.7226538", "doi": "10.2139/ssrn.7226538", "title": "Enterprise AI Agents for Analytics: Market Opportunity, Risk Controls, and Governance Readiness", "authors": [ "Meera Chawla", "Gabriel Aquino", "Nikhil Thomas", "Rhea Villanueva", "Ananya Rao", "Kiran Balan", "Carlo Navarro", "Priya Menon", "Sofia Mendoza", "Arjun Matias", "Liana Magno", "Paolo Reyes" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7226538", "field": "management", "role": "object", "bullets": [ "Conceptual framework for enterprise deployment of agentic AI systems in data-science and analytics functions.", "Paper examines how agentic systems bind intent clarification, tool use, iterative execution, error recovery, and stakeholder explanation into one operational loop.", "Agentic AI reduces coordination costs and expands analytical capacity, but shifts risk profile: mistakes become more procedural, compositional, and harder to detect." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3813, "authors_detailed": [ { "name": "Meera Chawla", "url": "https://openalex.org/A5038419680", "inst": "" }, { "name": "Gabriel Aquino", "url": "https://openalex.org/A5145048307", "inst": "" }, { "name": "Nikhil Thomas", "url": "https://openalex.org/A5145893164", "inst": "" }, { "name": "Rhea Villanueva", "url": "https://openalex.org/A5144569930", "inst": "" }, { "name": "Ananya Rao", "url": "https://openalex.org/A5028653454", "inst": "Indiana University Bloomington" }, { "name": "Kiran Balan", "url": "https://openalex.org/A5112962518", "inst": "Independent Researcher" }, { "name": "Carlo Navarro", "url": "https://openalex.org/A5059265329", "inst": "Université des Sciences de la Santé" }, { "name": "Priya Menon", "url": "https://openalex.org/A5143714528", "inst": "" }, { "name": "Sofia Mendoza", "url": "https://openalex.org/A5145375792", "inst": "" }, { "name": "Arjun Matias", "url": "https://openalex.org/A5145189547", "inst": "" }, { "name": "Liana Magno", "url": "https://openalex.org/A5144696758", "inst": "" }, { "name": "Paolo Reyes", "url": "https://openalex.org/A5145591798", "inst": "" } ], "affiliations": [ "Indiana University Bloomington", "Independent Researcher", "Université des Sciences de la Santé" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7187898", "doi": "10.2139/ssrn.7187898", "title": "Do LLM Agents Negotiate Rationally? A Mechanism-Design Framework for Verifiable Multi-Agent Interaction over A2A/MCP", "authors": [ "Wael S. Albayaydh", "Rui Zhao" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7187898", "field": "economics", "role": "agent", "bullets": [ "Mechanism-design framework tested on alternating-offers bargaining and VCG auctions, N=30 per condition across two LLM backbones with live API calls.", "LLM agents negotiated under unstructured dialogue, structured protocols, and structured protocols with runtime verification against game-theoretic invariants.", "Structured protocols pushed negotiation success to 100%; one model bid truthfully in every auction trial while the other achieved only 3.3% truthful bidding, showing incentive compatibility does not transfer automatically to LLM agents." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "closed-form game-theoretic optima", "salience": 60, "n": 3814, "authors_detailed": [ { "name": "Wael S Albayaydh", "url": "https://openalex.org/A5005805963", "inst": "University of Oxford" }, { "name": "Rui Zhao", "url": "https://openalex.org/A5100684043", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7191501", "doi": "10.2139/ssrn.7191501", "title": "Digital Identity as an Attention Signal: Evidence from AI-Generated Avatars and Stock Returns", "authors": [ "Xiaoxing Liu", "Zhiyi Wang", "Ying Zhang" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7191501", "field": "finance", "role": "object", "bullets": [ "7.62 million posts from 816,945 accounts on Eastmoney Guba for CSI 300 constituents, January 2024 to July 2025.", "Image classifier identified accounts using AI-generated profile avatars; posting intensity measured relative to other accounts across AI-supply-chain and non-AI stocks.", "AI-avatar accounts' posting intensity positively predicts next-trading-day returns for AI-supply-chain stocks, with stronger effects among longer-tenured accounts." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3815, "authors_detailed": [ { "name": "Xiaoxing Liu", "url": "https://openalex.org/A5144681884", "inst": "" }, { "name": "Zhiyi Wang", "url": "https://openalex.org/A5144334142", "inst": "Southeast University" }, { "name": "Ying Zhang", "url": "https://openalex.org/A5145016186", "inst": "" } ], "affiliations": [ "Southeast University" ] }, { "uid": "doi:10.2139/ssrn.7196478", "doi": "10.2139/ssrn.7196478", "title": "An Open Framework for AI-Era Internal Audit: Test Procedures, Control Checklists, and Risk Matrices for AI-Assisted Audit Evidence and Automated Supply-Chain Financial Flows", "authors": [ "Paschal Ezeliora" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7196478", "field": "accounting", "role": "object", "bullets": [ "Framework paper developing executable audit procedures for AI-produced evidence and automated supply-chain financial flows, mapped to NIST AI RMF, COSO, and PCAOB standards.", "Operationalizes governance requirements into test procedures for provenance tracing, sample re-performance, cross-system matching, and duplication testing of AI-touched evidence.", "Provides reproducible control checklists and risk matrices designed so a second independent reviewer can reach the same decision on the same evidence." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3816, "authors_detailed": [ { "name": "Paschal Ezeliora", "url": "https://openalex.org/A5144742589", "inst": "Film Independent" } ], "affiliations": [ "Film Independent" ] }, { "uid": "doi:10.2139/ssrn.7190758", "doi": "10.2139/ssrn.7190758", "title": "Artificial Intelligence, Human Capital Risk and Household Portfolio Choice", "authors": [ "Kristoffer Berg", "Jane Danyu-Zhang", "Luigi Dante Gaviano", "Constantine Yannelis" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7190758", "field": "finance", "role": "object", "bullets": [ "Linked Norwegian administrative data on workers' occupations, employers, income, wealth, and equity holdings, using ChatGPT release as a natural experiment.", "No model deployed as instrument; paper measures occupational AI exposure and tests portfolio-choice predictions from a model with nontradable human capital.", "AI-exposed workers hold more equity, especially in AI-boom-country firms; post-ChatGPT, exposed workers shift to lower-exposure industries and senior management roles, consistent with life-cycle hedging." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 3817, "authors_detailed": [ { "name": "K. Berg", "url": "https://openalex.org/A5029500614", "inst": "OsloMet – Oslo Metropolitan University" }, { "name": "Jane Danyu-Zhang", "url": "https://openalex.org/A5135253547", "inst": "University of Oregon" }, { "name": "Luigi Dante Gaviano", "url": "https://openalex.org/A5145784041", "inst": "University of Cambridge" }, { "name": "Constantine Yannelis", "url": "https://openalex.org/A5047747092", "inst": "Trinity College" } ], "affiliations": [ "University of Cambridge", "OsloMet – Oslo Metropolitan University", "University of Oregon", "Trinity College" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7195101", "doi": "10.2139/ssrn.7195101", "title": "The Second Storm Doesn't Find a Pristine Forest Why Near-term AI Augments Labor Rather than Replacing It", "authors": [ "Yakov Shkolnikov" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7195101", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of near-term AI labor-market effects, comparing current generative AI capabilities with prior waves of office and factory automation.", "No model deployed; paper argues generative AI provides fluency but not judgment, and that deployment requires costly human-intensive harnesses.", "On a three-to-five-year horizon, little evidence supports large aggregate displacement; AI makes workers more capable rather than fewer, augmenting labor rather than replacing it." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3818, "authors_detailed": [ { "name": "Yakov Shkolnikov", "url": "https://openalex.org/A5128904818", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7191538", "doi": "10.2139/ssrn.7191538", "title": "Digital Identity as an Attention Signal: Evidence from AI-Generated Avatars and Stock Returns", "authors": [ "Xiaoxing Liu", "Zhiyi Wang", "Ying Zhang" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7191538", "field": "finance", "role": "instrument", "bullets": [ "Sample of 7.6 million posts from 816,945 accounts on Eastmoney Guba for CSI 300 constituents, January 2024 to July 2025, China.", "Image classifier identified accounts using AI-generated profile avatars; relative posting intensity proxied investor attention toward AI-supply-chain stocks.", "Posting intensity of AI-avatar accounts positively predicts next-trading-day returns for AI-supply-chain stocks, with stronger effects among longer-tenured accounts." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 55, "n": 3819, "authors_detailed": [ { "name": "Xiaoxing Liu", "url": "https://openalex.org/A5144681884", "inst": "" }, { "name": "Zhiyi Wang", "url": "https://openalex.org/A5144334142", "inst": "Southeast University" }, { "name": "Ying Zhang", "url": "https://openalex.org/A5145016186", "inst": "" } ], "affiliations": [ "Southeast University" ] }, { "uid": "doi:10.2139/ssrn.7199118", "doi": "10.2139/ssrn.7199118", "title": "AI Adoption and Skill Demand", "authors": [ "Yiru (Susan) Wang", "Manav Raj", "Matthew J. Bidwell" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7199118", "field": "management", "role": "object", "bullets": [ "Approximately 164 million US job postings from January 2021 to March 2026, analyzed at the firm level.", "Study tracks firm-level AI adoption and measures associated changes in skill requirements across job postings.", "AI adoption increases demand for problem-solving, process, and social skills and for experience breadth, reflecting within-role reconfiguration rather than occupational composition shifts." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 3820, "authors_detailed": [ { "name": "Yiru Wang", "url": "https://openalex.org/A5100319133", "inst": "University of Pennsylvania" }, { "name": "Manav Raj", "url": "https://openalex.org/A5018390504", "inst": "University of Pennsylvania" }, { "name": "Matthew J. Bidwell", "url": "https://openalex.org/A5071958886", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7183058", "doi": "10.2139/ssrn.7183058", "title": "Does AI Disclosure Cost Brands Trust in Emerging Markets?", "authors": [ "Faruq Eniola" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7183058", "field": "management", "role": "object", "bullets": [ "Between-subjects survey experiment with Nigerian adult consumers (N=156, clean n=71) across three disclosure conditions.", "Participants viewed identical brand content labeled AI-disclosed, human-disclosed, or no-disclosure control; perceived authenticity and purchase intention measured.", "AI disclosure reduced perceived authenticity (d=-0.97) but did not significantly lower purchase intention; digital-literacy moderation was not confirmed." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3821, "authors_detailed": [ { "name": "Faruq Eniola", "url": "https://openalex.org/A5144932278", "inst": "Lagos State University" } ], "affiliations": [ "Lagos State University" ] }, { "uid": "doi:10.2139/ssrn.7243542", "doi": "10.2139/ssrn.7243542", "title": "When Fatigue Coexists with Risk Awareness: Boundary Conditions on the Privacy Calculus in Generative AI Adoption", "authors": [ "Yi-Ning Katherine Chen" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7243542", "field": "management", "role": "object", "bullets": [ "Survey of 1,022 Taiwanese generative AI users (mean age 44.4), PLS-SEM with 5,000 bootstrap replicates and multi-group analysis.", "Six-construct model tested whether privacy fatigue distorts the privacy calculus in generative AI adoption decisions.", "Privacy fatigue did not suppress risk awareness; fatigue positively associated with perceived risk (beta=0.358), contradicting the calculus-distorting account, invariant across demographic subgroups." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3822, "authors_detailed": [ { "name": "Yi-Ning Katherine Chen", "url": "https://openalex.org/A5068380427", "inst": "National Chengchi University" } ], "affiliations": [ "National Chengchi University" ] }, { "uid": "doi:10.2139/ssrn.7195740", "doi": "10.2139/ssrn.7195740", "title": "Beyond Synergy: Uncovering The Paradoxical Role of Generative AI in Digital Transformation and Business Model Innovation for Organizational Resilience in Fintech Ecosystems", "authors": [ "Elham Rezaei" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7195740", "field": "management", "role": "object", "bullets": [ "Multi-case qualitative analysis of 350 documents from ten leading global FinTech firms.", "Study examines how generative AI and digital transformation interact with business model innovation to produce organizational resilience.", "GenAI-digital transformation synergy creates structural tensions undermining agility when business model innovation is absent; three temporal phases of technology-resilience coevolution identified." ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 3823, "authors_detailed": [ { "name": "Elham Rezaei", "url": "https://openalex.org/A5121966184", "inst": "University of Tehran" } ], "affiliations": [ "University of Tehran" ] }, { "uid": "doi:10.2139/ssrn.7244382", "doi": "10.2139/ssrn.7244382", "title": "Skill Choice, Automation and Economic Development", "authors": [ "Laurent Cellarier" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7244382", "field": "economics", "role": "object", "bullets": [ "Two-period OLG model with three capital types, two endogenous worker skills, and generative AI modeled as skilled-task automation.", "Theoretical model traces pre-automation, partial automation, and full automation phases with endogenous skill choice and government policy interventions.", "Complementarities between capital and skilled labor sustain living standards; skill subsidies accelerate automation and raise steady-state income only if partly financed by a robot tax." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3824, "authors_detailed": [ { "name": "Laurent Cellarier", "url": "https://openalex.org/A5144520003", "inst": "University of Guelph" } ], "affiliations": [ "University of Guelph" ] }, { "uid": "doi:10.2139/ssrn.7199318", "doi": "10.2139/ssrn.7199318", "title": "Facts vs Feelings: Comparing LLM Persuasion Tactics in Policy Discussions", "authors": [ "Bruno Castelo-Branco", "Sofía Calderón" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7199318", "field": "economics", "role": "agent", "bullets": [ "Online experiment with 300 US adults evaluating competing AI-generated policy arguments under information-based and affect-based conditions.", "LLM generated persuasion messages advocating for and against proposed policies; human judges rated convincingness, informativeness, and perceived authorship.", "Information-based arguments were more convincing; affective rhetoric was judged less convincing and more likely human-written, but its disadvantage shrank when participants shared the LLM's stance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 52, "n": 3825, "authors_detailed": [ { "name": "Bruno Castelo-Branco", "url": "https://openalex.org/A5093939867", "inst": "Illinois Institute of Technology" }, { "name": "Sofia Calderon", "url": "https://openalex.org/A5141096764", "inst": "University of Notre Dame" } ], "affiliations": [ "University of Notre Dame", "Illinois Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7235019", "doi": "10.2139/ssrn.7235019", "title": "Consumer AI as Health Plan Decision Support A Benchmark of LLM Plan Recommendations against a Claims-based Pricing Engine", "authors": [ "Khris Dai" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7235019", "field": "economics", "role": "agent", "bullets": [ "Five ACA-compliant plans, twelve claims-anchored member profiles, four information levels, 1,680 AI responses benchmarked July 2026.", "OpenAI and Anthropic frontier models recommended health insurance plans and estimated costs against a claims-based pricing engine reference standard.", "Frontier models recommend a near-optimal plan in roughly half of responses; supplying actual prices raises correct recommendations to 85-95 percent." ], "bullet_provenance": "ai", "models": [ "gpt", "claude" ], "open_weights": false, "validated": true, "validation_note": "claims-based pricing engine reference standard", "salience": 65, "n": 3826, "authors_detailed": [ { "name": "Khris Dai", "url": "https://openalex.org/A5144412692", "inst": "Woodlawn School" } ], "affiliations": [ "Woodlawn School" ] }, { "uid": "arxiv:2608.05969v1", "arxiv_id": "2608.05969v1", "title": "The Institutional Window: Occupation- and Jurisdiction-Specific Calibration of Liability Signaling for Preserved Human Fallback Capability", "authors": [ "Andreas Bauer" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.05969v1", "field": "management", "role": "object", "bullets": [ "Calibration across five occupations and seven jurisdictions using published legal evidence, with agent-based market simulation.", "Model examines how generative AI collapses cost-based quality signals, forcing firms to use liability commitments to certify retained human fallback capability.", "Liability institutions with empty signaling windows converge to zero staff engagement and skill collapse; cap floors and pooled indemnity suppress the signal sustaining human fallback capacity." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3827 }, { "uid": "arxiv:2608.06305v1", "arxiv_id": "2608.06305v1", "title": "Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations", "authors": [ "Sagar Tamang", "Ayush Vyas", "Tabarakul Hazarika" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.06305v1", "field": "accounting", "role": "method", "bullets": [ "780-page government financial report with 86.8 percent table-row content and 51 verified questions; compared against dense retrieval baselines.", "READ agent used embedding-free lexical search and structural navigation via Model Context Protocol to answer queries over financial documents.", "READ answered 58.8 percent of questions correctly versus 15.7 percent for dense retrieval (p=2e-5); BM25 was statistically indistinguishable from READ." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "51 verified questions on government financial report", "salience": 55, "n": 3828, "authors_detailed": [ { "name": "Sagar Tamang", "url": "https://openalex.org/A5113429311", "inst": "Indian Institute of Technology Patna" }, { "name": "Ayush Vyas", "url": "https://openalex.org/A5006844661", "inst": "Chungwoon University" }, { "name": "Tabarakul Hazarika", "url": "https://openalex.org/A5146034501", "inst": "" } ], "affiliations": [ "Indian Institute of Technology Patna", "Chungwoon University" ] }, { "uid": "arxiv:2608.05969v2", "arxiv_id": "2608.05969v2", "title": "The Institutional Window: Occupation- and Jurisdiction-Specific Calibration of Liability Signaling for Preserved Human Fallback Capability", "authors": [ "Andreas Bauer" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.05969v2", "field": "economics", "role": "object", "bullets": [ "Five occupations and seven jurisdictions calibrated on published liability rules, penalty doctrines, and standard-terms control regimes.", "A signaling model traces how generative AI collapses cost-based quality signals, leaving liability caps as the sole separator; an agent-based market tests workforce outcomes.", "Every jurisdiction-occupation cell with an empty signaling window converges to zero professional engagement and skill collapse in simulation." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3829 }, { "uid": "arxiv:2608.06305v2", "arxiv_id": "2608.06305v2", "title": "Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations", "authors": [ "Sagar Tamang", "Ayush Vyas", "Tabarakul Hazarika" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.06305v2", "field": "accounting", "role": "method", "bullets": [ "Fifty-one verified questions on a 780-page government financial report where 86.8% of content lines are table rows with headers a median of 13 lines away.", "READ replaces embedding-based retrieval with deterministic lexical search, structural navigation, and bounded span reads exposed over the Model Context Protocol.", "READ answers 58.8% of questions correctly versus dense retrieval at 15.7%; BM25 is statistically indistinguishable from READ, localizing the gain to embedding-free retrieval." ], "bullet_provenance": "ai", "validated": true, "validation_note": "51 verified questions on government financial report", "salience": 55, "models": [], "n": 3830, "authors_detailed": [ { "name": "Sagar Tamang", "url": "https://openalex.org/A5113429311", "inst": "Indian Institute of Technology Patna" }, { "name": "Ayush Vyas", "url": "https://openalex.org/A5006844661", "inst": "Chungwoon University" }, { "name": "Tabarakul Hazarika", "url": "https://openalex.org/A5146034501", "inst": "" } ], "affiliations": [ "Indian Institute of Technology Patna", "Chungwoon University" ] }, { "uid": "doi:10.2139/ssrn.7195438", "doi": "10.2139/ssrn.7195438", "title": "Developing an Account‑Level Mortgage Prepayment Model with an Agentic Ai Team Methodology, Calibration, and the Case for Human Validation", "authors": [ "Chih Chen" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7195438", "field": "finance", "role": "method", "bullets": [ "Competing-risks mortgage model calibrated on 54,558,002 loan-months across 27 origination vintages of the public Freddie Mac Single-Family Loan-Level Dataset.", "AI agentic systems performed data engineering and numerical calibration under human-specified architecture; three pre-registered tests returned results against their own registration.", "On a $100M 5.5% cohort, model reads base value at 94.58% of face with 8.69-year weighted-average life; a 25% house-price decline costs 326 basis points of economic value." ], "bullet_provenance": "ai", "validated": true, "validation_note": "calibrated on Freddie Mac loan-level data with reported pricing and duration metrics", "salience": 65, "models": [], "n": 4057, "authors_detailed": [ { "name": "Chih Chen", "url": "https://openalex.org/A5145010536", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7182398", "doi": "10.2139/ssrn.7182398", "title": "Frozen-Fault Re-Injection: A Failure Regime in Unattended Agentic Loops", "authors": [ "Yuriy Motin" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7182398", "field": "management", "role": "object", "bullets": [ "Eighteen-day insider field case of a ten-step corporate-memory pipeline at a mid-size company, with 54 logged incidents from daily automated runs.", "A frozen trigger prompt replayed by a daily cron re-injected false context on seven documented runs across nine days; downstream checks caught but never corrected the source.", "The correction-latency gap between detection and source correction ranged from zero to eleven days or never, distinguishing frozen-fault re-injection from traveling cascade failures." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 4058, "authors_detailed": [ { "name": "Yuriy Motin", "url": "https://openalex.org/A5113841462", "inst": "St. Thomas University" } ], "affiliations": [ "St. Thomas University" ] }, { "uid": "doi:10.2139/ssrn.7199138", "doi": "10.2139/ssrn.7199138", "title": "You Cannot Read the Value of a Token Off Your Own Logs: Why Cost per Outcome Is a Confounded Number in Agentic AI", "authors": [ "Pablo Irassar" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7199138", "field": "finance", "role": "method", "bullets": [ "Conceptual analysis of cost measurement in multi-tier agentic AI systems deployed in regulated financial services with competent routing policies.", "Separates five distinct objects conflated by naive cost-per-outcome and shows the ratio reflects routing policy, not model performance, in any system routing hard cases to expensive models.", "Naive cost-per-outcome in regulated settings recommends sending hard cases to the cheap model that will fail them; causal identification with logged propensities and counterfactual estimation is required." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 4059, "authors_detailed": [ { "name": "Pablo Irassar", "url": "https://openalex.org/A5145303515", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7190819", "doi": "10.2139/ssrn.7190819", "title": "The Journey Is Not the Script Reconciling Structured and Naturalistic Probe Design in Multi-Turn Brand Recommendation Audits", "authors": [ "Timothy de Rosen" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7190819", "field": "management", "role": "method", "bullets": [ "Methodological comparison of structured versus agentic probe architectures for auditing brand recommendations in multi-turn LLM conversations.", "Fixed-stage structured probes and longer agentic probes are designed to capture late path-dependent state changes in AI brand recommendation sessions.", "Structured probes miss roughly two-thirds of session content vocabulary present in 8,133 real human-LLM conversations; the direct empirical comparison of both probe types remains unrun." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "models": [], "n": 4060, "authors_detailed": [ { "name": "Timothy de Rosen", "url": "https://openalex.org/A5119286499", "inst": "Standard Bio (Norway)" } ], "affiliations": [ "Standard Bio (Norway)" ] }, { "uid": "arxiv:2608.06510v1", "arxiv_id": "2608.06510v1", "title": "Agentic AI: User Empowerment or Enclosure?", "authors": [ "David Gamba", "Daniel M. Romero", "Grant Schoenebeck" ], "posted": "2026-08-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.06510v1", "field": "economics", "role": "object", "bullets": [ "Comparative case analysis of four domains where intermediary agency arose: browser ad blockers, platform recommenders, financial robo-advisors, and email spam governance.", "Examines how technical arrangements including API choices, protocol governance, and default configurations resolved whose interests AI agents serve across domains.", "Individual outcomes and collective contestation capacity moved in opposite directions; intermediary institutions sustaining adversarial challenge made user-aligned agency more durable." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 4061, "authors_detailed": [ { "name": "David Gamba", "url": "https://openalex.org/A5146301749", "inst": "University of Michigan" }, { "name": "Daniel M. Romero", "url": "https://openalex.org/A5064158757", "inst": "University of Michigan" }, { "name": "Grant Schoenebeck", "url": "https://openalex.org/A5146400109", "inst": "University of Michigan" } ], "affiliations": [ "University of Michigan" ] }, { "uid": "doi:10.2139/ssrn.7159639", "doi": "10.2139/ssrn.7159639", "title": "Liar, Liar, LLM on Fire? Honesty Under Asymmetric Information in Large Language Models", "authors": [ "Julia Schwarz", "Florian Gärtner" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7159639", "field": "economics", "role": "agent", "bullets": [ "Seven frontier models play a die roll reporting game adapted from Fischbacher and Follmi-Heusi, 259,000 pre-registered trials across three paradigms that vary whether the roll is imaginary, tool drawn, or code drawn.", "The paper does not name the seven models. Each reports a die outcome that sets its own payoff, while honesty instructions, personas, stakes and the instruction hierarchy are varied.", "In the verbal setting 55 percent of control reports name the payoff maximising value against a 16.7 percent benchmark; a genuine draw nearly ends misreporting, and stakes from 5 to 3,000,000 euros change little." ], "bullet_provenance": "ai", "salience": 78, "edition": 17, "models": [], "validated": null, "n": 2148, "authors_detailed": [ { "name": "Julia Schwarz", "url": "https://openalex.org/A5084754269", "inst": "Swiss Federal Institute for Forest, Snow and Landscape Research" }, { "name": "Florian Gärtner", "url": "https://openalex.org/A5144327360", "inst": "" } ], "affiliations": [ "Swiss Federal Institute for Forest, Snow and Landscape Research" ] }, { "uid": "doi:10.2139/ssrn.7166138", "doi": "10.2139/ssrn.7166138", "title": "What Do Firm-Level AI Measures Measure? Disclosure, Selection, and the Employment Effects of Generative AI", "authors": [ "Trung Nguyen", "Bao Doan" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7166138", "field": "economics", "role": "method", "bullets": [ "Panel of large US public firms at Russell 3000 scale, fiscal 2018 to 2025, with a generative AI disclosure measure built from 10-K text and occupation level exposure to the November 2022 ChatGPT shock.", "An unnamed language model re-reads the filings to audit the keyword based measure. No comparison against hand coding and no agreement statistic is reported.", "A quarter of flagged firm-years mention generative AI only as a risk, and the negative adoption-employment correlation disappears under a predetermined exposure design, leaving no net displacement through fiscal 2025." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 68, "edition": 17, "n": 2149, "authors_detailed": [ { "name": "Trung Nguyen", "url": "https://openalex.org/A5125313135", "inst": "RMIT Vietnam" }, { "name": "Bảo Đoàn", "url": "https://openalex.org/A5143980579", "inst": "RMIT Europe" } ], "affiliations": [ "RMIT Vietnam", "RMIT Europe" ] }, { "uid": "doi:10.2139/ssrn.7222978", "doi": "10.2139/ssrn.7222978", "title": "AI Honesty: A Theoretical Framework for Conceptualizing, Operationalizing, and Measuring the Relationship of Large Language Models with Truth", "authors": [ "Majid Tavakolian" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7222978", "field": "other", "role": "object", "bullets": [ "Conceptual analysis and a structured review of the AI ethics literature, covering hallucination, fabricated sources, false confidence, sycophancy and failure to correct. No sample and no empirical test.", "The authors run no model. They recast these failures as one construct with five components: epistemic honesty, truth representation, uncertainty disclosure, resistance to sycophancy, and correctability.", "Proposes an AI Honesty Index as a composite measure for evaluating models and grounding standards and regulation. No scores or benchmark results are reported." ], "bullet_provenance": "ai", "salience": 26, "edition": 17, "models": [], "validated": null, "n": 2150, "authors_detailed": [ { "name": "Majid Tavakolian", "url": "https://openalex.org/A5053749641", "inst": "Islamic Azad University South Tehran Branch" } ], "affiliations": [ "Islamic Azad University South Tehran Branch" ] }, { "uid": "doi:10.2139/ssrn.7199920", "doi": "10.2139/ssrn.7199920", "title": "Ten Rules for Deploying Large Language Models in Official Statistics", "authors": [ "Ahmed Tarek Hammad", "Giulio Valentino Dalla Riva" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7199920", "field": "economics", "role": "method", "bullets": [ "Guidance for national statistical offices, framed against the Fundamental Principles of Official Statistics and the European Statistics Code of Practice. No sample and no empirical test.", "No model is named or run. The rules confine the model to a monitoring role that observes, corrects bounded deviations, flags and reports, while humans keep method choice and interpretation.", "Sets out ten rules and an adoption frontier that weighs the cost of a model error against the cost of not adopting, mapped onto the Generic Statistical Business Process Model." ], "bullet_provenance": "ai", "salience": 45, "edition": 17, "models": [], "validated": null, "n": 2151, "authors_detailed": [ { "name": "Ahmed T. Hammad", "url": "https://openalex.org/A5081035673", "inst": "Tallinn University" }, { "name": "Giulio Valentino Dalla Riva", "url": "https://openalex.org/A5075927708", "inst": "University of Canterbury" } ], "affiliations": [ "Tallinn University", "University of Canterbury" ] }, { "uid": "doi:10.2139/ssrn.7172078", "doi": "10.2139/ssrn.7172078", "title": "The Uncanny Valley of AI Phishing: Over-Precision as a Forensic Detection Paradigm in the LLM Era", "authors": [ "Praveen Singh" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7172078", "field": "other", "role": "object", "bullets": [ "Annotated corpus of 380 Indian financial phishing and control texts: 186 human fraud messages, 86 authentic institutional messages, and 108 generated across nine language model platforms.", "The nine platforms are not named. They produced the synthetic phishing texts, while detection rests on lexical variety statistics rather than a model, with corpus labels as ground truth.", "Machine texts show a mean type-token ratio of 0.978 against 0.927 for authentic messages, and a 0.94 threshold flags 33 of 36 machine texts, 91.7 percent." ], "bullet_provenance": "ai", "salience": 32, "edition": 17, "models": [], "validated": null, "n": 2152, "authors_detailed": [ { "name": "Praveen Singh", "url": "https://openalex.org/A5110397196", "inst": "Deccan College Post Graduate and Research Institute" } ], "affiliations": [ "Deccan College Post Graduate and Research Institute" ] }, { "uid": "doi:10.2139/ssrn.7187298", "doi": "10.2139/ssrn.7187298", "title": "Data Governance as a Decision Variable: A Model-Driven Decision Support System for Budget Allocation in Generative AI Adoption", "authors": [ "Almudena Recio Román", "Manuel Recio Menéndez", "María Victoria Román-González" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7187298", "field": "management", "role": "object", "bullets": [ "Analytical optimisation model evaluated by simulation over two calibrated organisational profiles. No firm sample; the unit is a hypothetical firm splitting budget between information quality and intensity of use.", "No language model is run or named. Generative AI enters as a decision variable whose value and cost both move with data quality, with human oversight priced through a convexity term.", "For the fragmented data profile the conventional static formulation over-uses the technology by 27.8 percent and turns an expected profit of 32.0 units into a loss of 36.7, against 8.4 under the proposed rules." ], "bullet_provenance": "ai", "salience": 35, "edition": 17, "models": [], "validated": null, "n": 2153, "authors_detailed": [ { "name": "Almudena Recio-Román", "url": "https://openalex.org/A5086388942", "inst": "International University of Andalucía" }, { "name": "Manuel Recio-Menéndez", "url": "https://openalex.org/A5069778640", "inst": "University of Almería" }, { "name": "María Victoria Román-González", "url": "https://openalex.org/A5073870997", "inst": "University of Almería" } ], "affiliations": [ "International University of Andalucía", "University of Almería" ] }, { "uid": "doi:10.2139/ssrn.7232619", "doi": "10.2139/ssrn.7232619", "title": "LLM-Computed Contextual Surprisal in Financial News: Associations with Market Repricing", "authors": [ "Geon Kim" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7232619", "field": "finance", "role": "instrument", "bullets": [ "4.48 million deduplicated English news headlines from the public FNSPID dataset, 2014 to 2023, aggregated to daily scores, with 2014 to 2020 for development and 2021 to 2023 held out.", "Qwen 3.5-9B scores how far each headline departs from the recent news environment, paired headline by headline with FinBERT sentiment. Gemma and Llama reproduce the scores, but no human benchmark is used.", "Days carrying more unexpectedly negative wording come with a larger VIX increase, lower SPY returns and higher abnormal SPY dollar volume, same day and surviving false discovery rate adjustment in both samples." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 62, "edition": 17, "n": 2154, "authors_detailed": [ { "name": "geon kim", "url": "https://openalex.org/A5042734856", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7162638", "doi": "10.2139/ssrn.7162638", "title": "Moving Targets: Promissory Instability in the Pricing and Access Terms of Generative AI Subscription Services (2024-2026)", "authors": [ "Jose David Gonzalez" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7162638", "field": "management", "role": "object", "bullets": [ "Seventeen documented episodes of post-purchase changes to price, usage limits and access terms at four providers, Anthropic, OpenAI, Microsoft and Moonshot AI, between October 2024 and July 2026.", "No model is run. Named subscription products such as Claude Max and Copilot are the object, and the evidence is dated public announcements, press coverage, community bug trackers and two legal actions.", "Changing terms after purchase runs across the industry in both directions, and formal complaints cluster where the change is commercially driven and communicated in unmanaged or shifting terms." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 40, "edition": 17, "validated": null, "n": 2155, "authors_detailed": [ { "name": "González Tabarez Jose David", "url": "https://openalex.org/A5136279530", "inst": "Universidad Internacional" } ], "affiliations": [ "Universidad Internacional" ] }, { "uid": "doi:10.2139/ssrn.7147359", "doi": "10.2139/ssrn.7147359", "title": "Consumer AI and Exploitation of Information-processing Frictions", "authors": [ "Menglong Guan" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7147359", "field": "economics", "role": "object", "bullets": [ "Theory model of a market where firms exploit consumers' information processing frictions and consumers can adopt language model tools. No data and no empirical sample.", "No model is run or named. Language models enter as a capability that raises the share of expert consumers, which firms can degrade through adversarial investment.", "Exploitation relocates rather than disappearing. Firms exploit maximally until expert consumers tip them into competing on product value, then an arms race arises, peaks and fades as tools improve, and can cancel consumer gains when adversarial investment is cheap." ], "bullet_provenance": "ai", "salience": 48, "edition": 17, "models": [], "validated": null, "n": 2156, "authors_detailed": [ { "name": "Menglong Guan", "url": "https://openalex.org/A5144435391", "inst": "Pennsylvania State University" } ], "affiliations": [ "Pennsylvania State University" ] }, { "uid": "arxiv:2608.05224v2", "arxiv_id": "2608.05224v2", "title": "Small Foundation Models of Human Cognition and Behaviour", "authors": [ "Nick Oh", "Fernand Gobet" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.05224v2", "field": "other", "role": "method", "bullets": [ "Psych-101, a set of 10.7 million trial-level human choices from 160 experiments, used to fine-tune fourteen models from 135M to 14B parameters across four architecture families that the paper does not name.", "The fine-tuned models predict the choices of held-out participants. Diagnostics strip instructions, stimuli, feedback and choice history across 27 experiments and permute trial order to show what the models use.", "In distribution, 0.6B to 1B parameters already match a 70B baseline, while out of distribution larger models generalise better; masking stimuli and feedback removes 75.7 percent of learned information and drops models below chance." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "held-out human participants in Psych-101", "salience": 52, "edition": 17, "models": [], "n": 2157, "authors_detailed": [ { "name": "Nick Oh", "url": "https://openalex.org/A5146006095", "inst": "Tempus Labs (United States)" }, { "name": "Fernand Gobet", "url": "https://openalex.org/A5113860410", "inst": "London School of Economics and Political Science" } ], "affiliations": [ "London School of Economics and Political Science", "Tempus Labs (United States)" ], "prestige": true }, { "uid": "arxiv:2608.04570v1", "arxiv_id": "2608.04570v1", "title": "The Personalization Mirage: How LLMs Fabricate User Profiles, and Why Self-Monitoring Misleads", "authors": [ "Yushi Sun", "Yanjie Zhang", "Rui Sheng" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.04570v1", "field": "other", "role": "method", "bullets": [ "MirageBench pairs 150 synthetic personas, balanced across stereotypical, counter-stereotypical and neutral profiles, with six personalization tasks, yielding 143,616 judged claims from 12 models in seven families.", "An independent judge sorts each claim into a four-way faithfulness taxonomy, checked against a blind human annotator on 400 claims at Cohen's kappa 0.863. The 12 models are not named.", "Every model invents user attributes on 35 to 49 percent of its claims, and self-assessed rates rank negatively against judged rates, rho -0.60, so self-report misleads when comparing models." ], "bullet_provenance": "ai", "validated": true, "validation_note": "blind human annotator on 400 claims, Cohen's kappa 0.863", "salience": 47, "edition": 17, "models": [], "n": 2158, "authors_detailed": [ { "name": "Yushi Sun", "url": "https://openalex.org/A5144837725", "inst": "" }, { "name": "Yanjie Zhang", "url": "https://openalex.org/A5144798667", "inst": "" }, { "name": "Rui Sheng", "url": "https://openalex.org/A5050539622", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Hong Kong University of Science and Technology" ] }, { "uid": "arxiv:2608.04374v1", "arxiv_id": "2608.04374v1", "title": "FinReportBench: Measuring and Improving Institution-Grade Financial Report Generation", "authors": [ "Yinghao Tang", "Tan Zhenwei", "Yiyao Wang", "Wanli Gu", "Xiaolu Zhang", "Jun Zhou", "Wei Chen" ], "posted": "2026-08-05", "added": "2026-08-11", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.04374v1", "field": "finance", "role": "method", "bullets": [ "244 bilingual Chinese and English report writing tasks drawn from 10,000 balanced financial research source records, each separating the public query, a reconstructed research trajectory and a hidden source packet.", "Nine unnamed model families write the reports and three judge families score them against a 35-item rubric built from expert partial orders; the judges reproduce the expert ordering at near-ceiling rates, figures not stated.", "Basic deliverability is close to saturated while report identity and institutional completeness lag; distilling recurrent failures into reusable constraints lifts mean G1 by 33.85 points and mean G2 by 13.83." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert partial orders reproduced by three judge families, rate not stated", "salience": 55, "edition": 17, "models": [], "n": 2159, "authors_detailed": [ { "name": "Yinghao Tang", "url": "https://openalex.org/A5110942543", "inst": "Wuhan University of Technology" }, { "name": "Tan Zhenwei", "url": "https://openalex.org/A5008518756", "inst": "" }, { "name": "Yiyao Wang", "url": "https://openalex.org/A5043247230", "inst": "Shanghai Jiao Tong University" }, { "name": "Wanli Gu", "url": "https://openalex.org/A5064526570", "inst": "Meizu (China)" }, { "name": "Xiaolu Zhang", "url": "https://openalex.org/A5144899096", "inst": "" }, { "name": "Jun Zhou", "url": "https://openalex.org/A5144648947", "inst": "" }, { "name": "Wei Chen", "url": "https://openalex.org/A5145781369", "inst": "" } ], "affiliations": [ "Wuhan University of Technology", "Shanghai Jiao Tong University", "Meizu (China)" ] }, { "uid": "arxiv:2608.05367v1", "arxiv_id": "2608.05367v1", "title": "Counterfactual Analysis via Large Language Models", "authors": [ "Zonghao Yang" ], "posted": "2026-08-05", "added": "2026-08-07", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.05367v1", "field": "finance", "role": "method", "bullets": [ "Online lending setting where the outcome is return on investment under alternative interest rate schemes; sample size and period are not stated in the abstract.", "GPT-3.5 predicts loan ROI, with prompt engineering raising R squared from 1.97 to 2.84 percent against 3.48 percent for gradient boosted regression; no other validation is described.", "The model then generates counterfactual ROIs under hypothetical interest rates, producing logically coherent responses that approach but do not match the machine learning benchmark." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "predictive R squared against realized lending outcomes, benchmarked to gradient boosting", "salience": 44, "edition": 16, "n": 2109, "authors_detailed": [ { "name": "Zonghao Yang", "url": "https://openalex.org/A5087194118", "inst": "Northwestern Polytechnical University" } ], "affiliations": [ "Northwestern Polytechnical University" ] }, { "uid": "doi:10.2139/ssrn.7155378", "doi": "10.2139/ssrn.7155378", "title": "Frontier Models Do Not Foreground Legal Considerations by Default: A Negligence Case Study", "authors": [ "Alexander Mark", "Kathrin Gardhouse" ], "posted": "2026-08-05", "added": "2026-08-07", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7155378", "field": "other", "role": "object", "bullets": [ "Five naturalistic advice scenarios with legal implications, each posed under an open-ended prompt and under a prompt instructing the model to conform its advice to negligence law.", "Claude Opus 4.6, GPT-5.2, Gemini 3.1 Pro, and Grok 4 give advice whose permissiveness is rated on a 1 to 5 scale; how ratings were produced is not stated.", "No model foregrounds legal reasoning by default even when capable of it when prompted; invoking negligence law lowers average permissiveness by 0.59 points, and responses diverge across models on identical scenarios." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 48, "edition": 16, "validated": null, "n": 2114, "authors_detailed": [ { "name": "Alexander Mark", "url": "https://openalex.org/A5123864737", "inst": "" }, { "name": "Kathrin Gardhouse", "url": "https://openalex.org/A5124001587", "inst": "World Future Society" } ], "affiliations": [ "World Future Society" ] }, { "uid": "doi:10.18653/v1/2026.findings-acl.1626", "doi": "10.18653/v1/2026.findings-acl.1626", "arxiv_id": "2608.04576v1", "title": "Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study", "authors": [ "Yuanjun Zhang", "Mourad Oussalah" ], "posted": "2026-08-05", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.04576v1", "field": "economics", "role": "method", "bullets": [ "ReliefWeb humanitarian reports from 2000 to 2024, with an expert annotated benchmark of 100 reports; the unit is an intervention outcome relation with direction and strength.", "A two stage pipeline extracts query conditioned causal records with snippet grounding; fine tuned Llama 3.1 8B reaches 94.15 percent weighted F1, ahead of the best closed model at 90.73 percent.", "Aggregating strength weighted evidence across disaster and source cells, cash assistance shows strong positive convergence on food related outcomes, with a level of evidence score of 0.865." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "100 expert annotated reports, weighted F1 reported", "salience": 58, "edition": 15, "n": 2061, "authors_detailed": [ { "name": "Yuanjun Zhang", "url": "https://openalex.org/A5139752945", "inst": "Lappeenranta-Lahti University of Technology" }, { "name": "Mourad Oussalah", "url": "https://openalex.org/A5139803170", "inst": "University of Oulu" } ], "affiliations": [ "Lappeenranta-Lahti University of Technology", "University of Oulu" ] }, { "uid": "doi:10.2139/ssrn.7239480", "doi": "10.2139/ssrn.7239480", "title": "Enhancing Intelligent Manufacturing: A Human-AI Cooperation Structure Employing Big Language Models", "authors": [ "Ying Liu" ], "posted": "2026-08-05", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7239480", "field": "management", "role": "object", "bullets": [ "A literature review of language model use in smart manufacturing, framed through Industry 5.0 and human-in-the-loop paradigms, with no primary dataset.", "No model is run. The authors survey reported uses such as process optimization and anomaly detection, and propose a four-part architecture combining human oversight, cyber-physical systems, language models, and validation with uncertainty management.", "The proposed LLM-HSM framework organizes existing approaches and flags open gaps in model validation, trustworthiness, and human-centric design." ], "bullet_provenance": "ai", "salience": 33, "edition": 15, "models": [], "validated": null, "n": 2072, "authors_detailed": [ { "name": "Ying Liu", "url": "https://openalex.org/A5144437607", "inst": "Peking University" } ], "affiliations": [ "Peking University" ] }, { "uid": "doi:10.2139/ssrn.7237665", "doi": "10.2139/ssrn.7237665", "title": "Interpretative Reliability of LLM-Generated Technical Analysis: Measurement, Refinement, and Portfolio Implication", "authors": [ "Sungsoo Kim", "Seungjae Park", "Taeseong Bang", "Ha Young Kim" ], "posted": "2026-08-05", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7237665", "field": "finance", "role": "method", "bullets": [ "LLM-generated technical analysis of price and indicator data for investment decisions, assessed on three reliability dimensions: numerical consistency, rule-based consistency, and price-indicator coherence.", "The models are not named in the abstract. Two interventions are tested, arithmetic-aware indicator structuring and validation-guided refinement, with automated consistency checks supported by blind expert evaluations and case studies.", "Combining both interventions gives the lowest numerical and rule violation rates and stronger risk-adjusted portfolio performance than the baseline LLM and benchmark strategies, though reliability gains do not uniformly raise returns." ], "bullet_provenance": "ai", "validated": true, "validation_note": "rule-based consistency checks plus blind expert evaluation", "salience": 55, "edition": 15, "models": [], "n": 2095, "authors_detailed": [ { "name": "Sungsoo Kim", "url": "https://openalex.org/A5135740864", "inst": "Yonsei University" }, { "name": "Sangwu Park", "url": "https://openalex.org/A5022605108", "inst": "" }, { "name": "Taeseong Bang", "url": "https://openalex.org/A5144451313", "inst": "" }, { "name": "Ha Young Kim", "url": "https://openalex.org/A5100635330", "inst": "Yonsei University" } ], "affiliations": [ "Yonsei University" ] }, { "uid": "doi:10.2139/ssrn.7172778", "doi": "10.2139/ssrn.7172778", "title": "TOON-Based MultiFinRAG with Reinforcement Learning for Automated Stock Trading", "authors": [ "Sudheer Vankayala", "Preethi Subramanian" ], "posted": "2026-08-05", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7172778", "field": "finance", "role": "method", "bullets": [ "90 Apple 10-K tables plus a 250 day backtest of AAPL over January 2025 to January 2026, with signals timed to SEC EDGAR filing dates.", "A locally hosted language model, family not stated, reads TOON encoded tables for trading signals; TOON cuts tokens by 47.65 percent versus JSON, with no ground truth check of the signals.", "With a confidence gated veto from filing signals, a PPO agent earns the highest Sharpe ratio of five algorithms in the single stock backtest." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 15, "models": [], "n": 2102, "authors_detailed": [ { "name": "Sudheer Vankayala", "url": "https://openalex.org/A5138582905", "inst": "Asia Pacific University of Technology & Innovation" }, { "name": "Preethi Subramanian", "url": "https://openalex.org/A5084666059", "inst": "Asia Pacific University of Technology & Innovation" } ], "affiliations": [ "Asia Pacific University of Technology & Innovation" ] }, { "uid": "doi:10.2139/ssrn.7200240", "doi": "10.2139/ssrn.7200240", "title": "Evaluating Multimodal AI Systems for Short-Term Stock Forecasting: A Comparative Study of Claude, ChatGPT, Gemini, and Meta AI Forecasting Accuracy and the Effect of News Update Frequency", "authors": [ "Ridam Timilsina" ], "posted": "2026-08-05", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7200240", "field": "finance", "role": "agent", "bullets": [ "Ten stocks across five sectors over nine live weeks; four chatbots received identical charts, technical indicators, and earnings dates, yielding 360 scored weekly predictions.", "Claude, ChatGPT, Gemini, and Meta AI (versions not stated) issued directional calls, price targets, and confidence; five stocks got daily news headlines, five only weekly.", "Directional accuracy ran from 44.4 percent (ChatGPT) to 50.0 percent (Claude), none beating chance; daily news raised accuracy to 58.9 versus 35.6 percent (p < 0.001)." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "llama" ], "open_weights": false, "validated": true, "validation_note": "360 predictions scored against realized price direction", "salience": 46, "edition": 11, "n": 1356, "authors_detailed": [ { "name": "Ridam Timilsina", "url": "https://openalex.org/A5144423825", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7199998", "doi": "10.2139/ssrn.7199998", "title": "A Domain-Specific Causal Chain RAG System for Semiconductor Equity Analysis", "authors": [ "Raj Tejpal Khatik" ], "posted": "2026-08-05", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7199998", "field": "finance", "role": "agent", "bullets": [ "48 semiconductor tickers scored on an 18-factor causal chain; backtests combine 140 development-period predictions and 426 observations at 10 historical dates with 20-day forward returns.", "An agentic RAG pipeline (LangGraph orchestration, FAISS retrieval, MCP data feeds) issues directional calls with per-factor attribution; the underlying LLM is not named in the abstract.", "Overall directional accuracy of 61.3 percent rises monotonically with market cap, from 41.7 percent for mid-caps to 84.0 percent above one trillion dollars." ], "bullet_provenance": "ai", "validated": true, "validation_note": "backtested against realized 20-day forward returns, 426 observations", "salience": 38, "edition": 11, "models": [], "n": 1357, "authors_detailed": [ { "name": "RAJ TEJPAL KHATIK", "url": "https://openalex.org/A5144041672", "inst": "University of Warwick" } ], "affiliations": [ "University of Warwick" ] }, { "uid": "doi:10.2139/ssrn.7165538", "doi": "10.2139/ssrn.7165538", "title": "Beyond Linear Decision Rules: LLM-Guided Representation Discovery for Data-Driven Optimization", "authors": [ "Huan Zhang", "Yang Wang", "Hanzhang Qin", "Yue Zhao" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7165538", "field": "management", "role": "method", "bullets": [ "Multi-period newsvendor and data center location problems with job scheduling, calibrated using real-world datasets for out-of-sample evaluation.", "LLMs generate closed-form nonlinear basis functions for adaptive recourse decisions in stochastic optimization, compared against existing linear and nonlinear benchmarks.", "GenSO consistently outperforms existing benchmarks in out-of-sample cost while uncovering interpretable nonlinear decision structures difficult to find by manual design." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "out-of-sample cost vs. existing benchmarks on newsvendor and data center problems", "salience": 48, "n": 2833, "authors_detailed": [ { "name": "Huan Zhang", "url": "https://openalex.org/A5144322579", "inst": "Northwestern Polytechnical University" }, { "name": "Yang Wang", "url": "https://openalex.org/A5144362991", "inst": "Northwestern Polytechnical University" }, { "name": "Hanzhang Qin", "url": "https://openalex.org/A5067909390", "inst": "National University of Singapore" }, { "name": "Yue Zhao", "url": "https://openalex.org/A5025425593", "inst": "Peking University" } ], "affiliations": [ "Northwestern Polytechnical University", "National University of Singapore", "Peking University" ] }, { "uid": "doi:10.2139/ssrn.7176278", "doi": "10.2139/ssrn.7176278", "title": "Enterprise Trust Gaps in Generative AI Why Accuracy is not Enough: Evidence from Mid-market Generative AI Adoption", "authors": [ "Ibe Imo", "Kofi Agyare-Kwabi", "Abolaji Adesoji", "David Odukoya", "Isaac Sarfo" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7176278", "field": "management", "role": "object", "bullets": [ "Enterprise professionals across financial services, cybersecurity, and technology sectors; secondary analysis of three large-scale AI adoption surveys.", "Study examines why business users abandon or override accurate generative AI outputs, identifying three organizational mechanisms from empirical data.", "Trust erodes from accountability mismatch, human correction burden, and confidence framing effect regardless of underlying AI output accuracy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 50, "validated": null, "n": 3800, "authors_detailed": [ { "name": "Ibe Imo", "url": "https://openalex.org/A5144324463", "inst": "University of Fort Hare" }, { "name": "Kofi Agyare-Kwabi", "url": "https://openalex.org/A5144379748", "inst": "Community Partners" }, { "name": "Abolaji Adesoji", "url": "https://openalex.org/A5144445445", "inst": "Rensselaer Polytechnic Institute" }, { "name": "David Odukoya", "url": "https://openalex.org/A5144313170", "inst": "Opinion Leader Research" }, { "name": "Isaac Sarfo", "url": "https://openalex.org/A5144321102", "inst": "St. John's College of Nursing" } ], "affiliations": [ "University of Fort Hare", "Community Partners", "Rensselaer Polytechnic Institute", "Opinion Leader Research", "St. John's College of Nursing" ] }, { "uid": "doi:10.2139/ssrn.7230818", "doi": "10.2139/ssrn.7230818", "title": "Generative Economics: Verification and the Allocation of Judgment", "authors": [ "Hana Kim" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7230818", "field": "economics", "role": "object", "bullets": [ "Theoretical model of an economy where AI-generated candidates share scarce human verification capacity under open and integrated entry regimes.", "Formal analysis derives social marginal product wedge and overentry dynamics as generative AI pushes candidate production cost toward zero.", "Under open entry the overgeneration ratio diverges; aggregate generation expenditure absorbs the entire value created by scarce verification." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3801, "authors_detailed": [ { "name": "Hana Kim", "url": "https://openalex.org/A5100608641", "inst": "Korea Advanced Institute of Science and Technology" } ], "affiliations": [ "Korea Advanced Institute of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7231458", "doi": "10.2139/ssrn.7231458", "title": "Managing Generative Production: Candidate Load, Selection Capacity, and Organizational Output", "authors": [ "Hana Kim" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7231458", "field": "management", "role": "object", "bullets": [ "Theoretical firm model validated against published field experiments and an audit of 7,000 software repositories using generative AI tools.", "Formal model where firms choose candidate load, selection capacity, and workflow governance; repository timing data provide measurement architecture.", "Candidate-volume targets induce overgeneration; review investment dominates model upgrades when cost reduction applies to the larger expenditure margin." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3802, "authors_detailed": [ { "name": "Hana Kim", "url": "https://openalex.org/A5144432840", "inst": "Korea Advanced Institute of Science and Technology" } ], "affiliations": [ "Korea Advanced Institute of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7172458", "doi": "10.2139/ssrn.7172458", "title": "Viability Leadership in Complex Systems", "authors": [ "Prof. Dr. Christoph Ph. Schließmann" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7172458", "field": "management", "role": "object", "bullets": [ "Integrative review of leadership research from January 2014 through July 2026 covering meta-analyses, systematic reviews, and field experiments.", "Conceptual framework reassesses a relational leadership model against new evidence on AI governance, algorithmic management, and human-AI systems.", "AI governance adds a required dimension to leadership; eight propositions define viability leadership for coupled human-AI organizational systems." ], "bullet_provenance": "ai", "salience": 18, "models": [], "validated": null, "n": 3803, "authors_detailed": [ { "name": "Christoph Schließmann", "url": "https://openalex.org/A5027341704", "inst": "Cologne Institute for Economic Research" } ], "affiliations": [ "Cologne Institute for Economic Research" ] }, { "uid": "doi:10.2139/ssrn.7167239", "doi": "10.2139/ssrn.7167239", "title": "Recruiting for Scarcity: A Soft-Systems Analysis of the Niche-Technology Skills Gap in the Indian IT Workforce, Revisited in the Generative-AI Era", "authors": [ "Tanmay Srivastava" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7167239", "field": "management", "role": "object", "bullets": [ "Indian IT sector niche-technology recruitment and retention, soft-systems analysis updated with 2025-2026 evidence on AI talent gaps and attrition.", "Stakeholder mapping and CATWOE analysis examine how generative AI simultaneously drives the skills shortage and offers AI-assisted hiring solutions.", "AI-related roles are now hardest to fill; AI-literacy training must be first-order in early-career talent pipelines rather than an auxiliary skill." ], "bullet_provenance": "ai", "salience": 18, "models": [], "validated": null, "n": 3804, "authors_detailed": [ { "name": "Tanmay Srivastava", "url": "https://openalex.org/A5143917204", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7231578", "doi": "10.2139/ssrn.7231578", "title": "Who Trains the Next Expert? Generative Governance and Career Ladders", "authors": [ "Hana Kim" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7231578", "field": "economics", "role": "object", "bullets": [ "Theoretical model of firms choosing between delegation and apprenticeship governance for generative AI use by junior and senior workers.", "Economic model shows how the same AI capability can reduce or increase on-the-job learning depending on governance structure firms adopt.", "When firms capture only part of future worker value, they delegate too much, reducing both junior employment and the senior expert pipeline." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3805, "authors_detailed": [ { "name": "Hana Kim", "url": "https://openalex.org/A5100608641", "inst": "Korea Advanced Institute of Science and Technology" } ], "affiliations": [ "Korea Advanced Institute of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7237372", "doi": "10.2139/ssrn.7237372", "title": "Exposure to Artificial Intelligence, Time Use, and Labor Outcomes in Mexico", "authors": [ "Oscar Galvez-Soriano", "Ornella Darova" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7237372", "field": "economics", "role": "object", "bullets": [ "Mexican households using nationally representative microdata and municipality-level mobile network coverage around ChatGPT's late-2022 release.", "Difference-in-differences design exploits ChatGPT release to estimate AI exposure effects on time use, labor participation, and household earnings.", "AI exposure raises youth study time and population leisure but shows no effects on hours worked, labor force participation, or earnings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3806, "authors_detailed": [ { "name": "Oscar de Jesús Gálvez-Soriano", "url": "https://openalex.org/A5047641228", "inst": "Bank of Mexico" }, { "name": "Ornella Darova", "url": "https://openalex.org/A5092420582", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania", "Bank of Mexico" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7238040", "doi": "10.2139/ssrn.7238040", "title": "The Impact of Artificial Intelligence on Enterprises’ Integration into Global Value Chain: Opportunities or Risks?", "authors": [ "SEN YAN", "JUN SHAO" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7238040", "field": "economics", "role": "instrument", "bullets": [ "Chinese enterprises using firm-level data; LLMs construct metrics capturing the extent of AI adoption within individual enterprises.", "Large language models build AI adoption measures from enterprise data; robustness confirmed by replacing LLMs, adjusting variables, and changing estimators.", "AI applications enhance global value chain embedding significantly, driven by productivity gains through accelerated innovation and reduced resource misallocation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 3807, "authors_detailed": [ { "name": "SEN YAN", "url": "https://openalex.org/A5144440082", "inst": "" }, { "name": "JUN SHAO", "url": "https://openalex.org/A5144439103", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7232718", "doi": "10.2139/ssrn.7232718", "title": "Who Captures AI? Quantifying the Redistribution of Economic Value Between AI-Augmented Individuals, Small Businesses, and Larger Firms", "authors": [ "Hridhan Ratanghayra", "Kyros Goyal" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7232718", "field": "economics", "role": "object", "bullets": [ "Cross-platform data: 97 Upwork job cards across 18 categories (May 2026), 13-estimate panel of published worker and earnings effects, and firm-scale adoption surveys.", "No model deployed as instrument; paper measures generative AI exposure and value-capture capacity via the ten-component CAPTURE-10 index.", "Complementary categories show a 51.3 percentage-point AI-visibility gap over substitutable ones; worker outcomes bifurcate with a 12.8-point gap between capture and displacement signals." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3808, "authors_detailed": [ { "name": "Hridhan Ratanghayra", "url": "https://openalex.org/A5144426355", "inst": "Oldham Council" }, { "name": "Kyros Goyal", "url": "https://openalex.org/A5144459825", "inst": "Oldham Council" } ], "affiliations": [ "Oldham Council" ] }, { "uid": "doi:10.2139/ssrn.7220978", "doi": "10.2139/ssrn.7220978", "title": "Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory", "authors": [ "Sarah Wilson", "Michael MacKay", "Anthony Marello", "Trinav Bhattacharyya" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7220978", "field": "finance", "role": "instrument", "bullets": [ "Thirty M&A events across five industries, 217 peer observations drawn from SEC 10-K Item 7 (MD&A) filings.", "Two-stage LLM pipeline scored semantic similarity of MD&A sections to identify economically linked firms; results compared against announcement-day abnormal stock returns.", "No association found: within-event rank correlation between similarity and abnormal return is +0.07 (p = 0.37), with 14 of 30 events supporting the shadow-trading hypothesis and 12 contradicting it." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "announcement-day abnormal stock returns", "salience": 70, "n": 3809, "authors_detailed": [ { "name": "Sarah Wilson", "url": "https://openalex.org/A5144225984", "inst": "" }, { "name": "Michael MacKay", "url": "https://openalex.org/A5144361170", "inst": "Columbia University" }, { "name": "Anthony Marello", "url": "https://openalex.org/A5144438889", "inst": "" }, { "name": "Trinav Bhattacharyya", "url": "https://openalex.org/A5010169236", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.05015v1", "arxiv_id": "2608.05015v1", "title": "Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theorems", "authors": [ "Isaiah Andrews" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.05015v1", "field": "economics", "role": "method", "bullets": [ "Theoretical framework applying three representation theorems from decision theory (de Finetti, Afriat, Echenique-Saito) to LLM evaluation.", "Proposes checking LLM responses to synthetic choice problems for axiom compliance, yielding continuous penalties that require no external labels or human feedback.", "Axiom-based checks exhaust the implications of the relevant rationality standard; a model that passes cannot be rejected on rationality grounds by any further test of the same data." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3810, "authors_detailed": [ { "name": "Isaiah Andrews", "url": "https://openalex.org/A5144945171", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7196800", "doi": "10.2139/ssrn.7196800", "title": "Artificial Intelligence and Financial Volatility: From Model Augmentation to Market Disruption -A Critical Integrative Review", "authors": [ "Fuli Yang" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7196800", "field": "finance", "role": "object", "bullets": [ "Integrative review spanning ML volatility forecasting, AI-related economic shocks, and AI-driven trading, synthesized through 71 coded evidence entries across four evidence maps.", "Introduces a Technology-Shock-Agent framework evaluated against a four-part evidentiary taxonomy covering established, emerging, simulation-based, and regulatory evidence.", "Evidence consistent with an AI volatility paradox: AI compresses ordinary volatility in normal regimes while amplifying tail volatility under stress, formalized as testable hypotheses." ], "bullet_provenance": "ai", "salience": 58, "models": [], "validated": null, "n": 4055, "authors_detailed": [ { "name": "Fuli Yang", "url": "https://openalex.org/A5101033933", "inst": "Institute" } ], "affiliations": [ "Institute" ] }, { "uid": "doi:10.2139/ssrn.7237740", "doi": "10.2139/ssrn.7237740", "title": "Governing autonomous supply chain agents: a per-decision router that escalates on operational risk, not cost", "authors": [ "Abhishek Pandey" ], "posted": "2026-08-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7237740", "field": "management", "role": "object", "bullets": [ "Governance framework tested on a Brazilian e-commerce marketplace and a global distribution benchmark, scoring per-decision routing choices against realized outcomes.", "A governance router reads a learned risk score, cost, and complexity for each AI agent decision and routes to autonomous execution or human escalation.", "Cost alone separates good from bad outcomes at chance level in both datasets; a learned risk signal recovers the gap, with largest advantage when adverse events are rare." ], "bullet_provenance": "ai", "validated": true, "validation_note": "routing decisions evaluated against realized outcomes on two public datasets", "salience": 55, "models": [], "n": 4056, "authors_detailed": [ { "name": "Abhishek Pandey", "url": "https://openalex.org/A5144430958", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7159578", "doi": "10.2139/ssrn.7159578", "title": "A Conceptual Reference Architecture for Closed-Loop Intelligent Data Quality Governance", "authors": [ "Hadi Fadlallah" ], "posted": "2026-08-04", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7159578", "field": "management", "role": "method", "bullets": [ "Conceptual paper with no data and no evaluation, proposing a reference architecture for data quality governance across sensing, planning, validation, verification, execution, explanation, and feedback stages.", "No model is run and no family is named. Language models, retrieval augmented generation, and agentic components sit inside the design as the probabilistic reasoning layer of the pipeline.", "The central claim is that probabilistic reasoning must stay separate from deterministic executable validation to keep results auditable, with policy aware orchestration and reusable organisational memory as further requirements." ], "bullet_provenance": "ai", "salience": 25, "edition": 17, "models": [], "validated": null, "n": 2134, "authors_detailed": [ { "name": "Hadi Fadlallah", "url": "https://openalex.org/A5022962261", "inst": "Arts, Sciences and Technology University in Lebanon" } ], "affiliations": [ "Arts, Sciences and Technology University in Lebanon" ] }, { "uid": "doi:10.2139/ssrn.7230435", "doi": "10.2139/ssrn.7230435", "title": "Structural Supply and User Perceptions Shape EV Charging Satisfaction", "authors": [ "Hongli Zhang", "Yi Ke", "Xingju Zhong" ], "posted": "2026-08-04", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7230435", "field": "management", "role": "instrument", "bullets": [ "391 charging service complaint texts from four Chinese cities, joined with accessibility measures built from points of interest and road network data.", "Unnamed large language models scored sentiment and assigned topics to each complaint, and those variables entered a structural equation model; no check against human coding is reported.", "Denser charging supply goes with lower satisfaction, safety perceptions weigh heaviest among the perception channels, and supply and perceptions act through parallel rather than sequential paths." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 17, "models": [], "n": 2143, "authors_detailed": [ { "name": "Hongli Zhang", "url": "https://openalex.org/A5144325424", "inst": "" }, { "name": "Yi Ke", "url": "https://openalex.org/A5055685570", "inst": "University of Science and Technology of China" }, { "name": "Xingju Zhong", "url": "https://openalex.org/A5144349766", "inst": "" } ], "affiliations": [ "University of Science and Technology of China" ] }, { "uid": "doi:10.2139/ssrn.7216863", "doi": "10.2139/ssrn.7216863", "title": "An LLM-Based Multi-Agent Framework for Autonomous Business Lead Intelligence Using Public Web Data", "authors": [ "Nikunj Patel" ], "posted": "2026-08-04", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7216863", "field": "management", "role": "method", "bullets": [ "No data. The paper specifies a business lead intelligence pipeline over public web sources and leaves datasets, baselines and ablations to a pre-registered evaluation protocol for later work.", "Thirteen components, among them a task planner, a browser navigation agent, extraction, classification and scoring agents, share a blackboard state with typed messages; no model is named.", "No results. Every performance claim is posed as a hypothesis, and the analysis covers expected failures such as cascading hallucination, prompt injection through scraped pages and entity conflation." ], "bullet_provenance": "ai", "validated": false, "validation_note": "design paper, no empirical evaluation reported", "salience": 26, "edition": 17, "models": [], "n": 2144, "authors_detailed": [ { "name": "Nikunj Patel", "url": "https://openalex.org/A5026812828", "inst": "Nirma University" } ], "affiliations": [ "Nirma University" ] }, { "uid": "doi:10.2139/ssrn.7159918", "doi": "10.2139/ssrn.7159918", "title": "FinRL-DeepSeek Revisited: A Reproduction and Multi-Seed Robustness Analysis of LLM-Infused Risk-Sensitive Trading Agents", "authors": [ "Arnold Leumale" ], "posted": "2026-08-04", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7159918", "field": "finance", "role": "method", "bullets": [ "Nasdaq-100 stocks, using the agent-ready datasets released by the original authors for 2013 to 2023, backtested from 2019 to 2023 against an equal weight buy and hold benchmark.", "Signals extracted by DeepSeek from financial news feed four reinforcement learning agents, PPO and CVaR constrained PPO with and without the signals, reimplemented from scratch; no check of the signals against labelled news is reported.", "A single seed reproduces the reported gain in return and Sharpe ratio from adding the language model, but across two seeds the ranking of PPO-DeepSeek against plain PPO reverses, while CPPO-DeepSeek varies least in total return." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 62, "edition": 17, "n": 2145, "authors_detailed": [ { "name": "Arnold Leumale", "url": "https://openalex.org/A5144333933", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7157498", "doi": "10.2139/ssrn.7157498", "title": "Do Time Series Foundation Models Know Their Tails? A Mechanism-Oriented, Contamination-Aware Audit of Zero-Shot Value-at-Risk and Expected Shortfall", "authors": [ "Jianxiu Hou" ], "posted": "2026-08-04", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7157498", "field": "finance", "role": "method", "bullets": [ "32 daily series, audited under a pre-specified and contamination aware protocol that varies extraction rules, market regimes and training free repairs.", "Five deployed forecast heads from Chronos, Moirai and Lag-Llama produced zero shot Value at Risk and Expected Shortfall, set against each other within families and against classical GARCH-EVT.", "Deep tails come out unusable or extraction dependent, a token sampling head collapses in scale by an order of magnitude, and only a hybrid taking model location with GARCH scale matches, never beats, GARCH-EVT." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "pre-specified tail-risk backtest on 32 daily series, benchmarked against GARCH-EVT", "salience": 66, "edition": 17, "n": 2146, "authors_detailed": [ { "name": "侯建秀", "url": "https://openalex.org/A5078825863", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7223266", "doi": "10.2139/ssrn.7223266", "title": "Time Travel on Professional Profiles", "authors": [ "Nicholas Bloom", "Gideon Moore", "Lisa Simon", "Caelan Wilkie-Rogers" ], "posted": "2026-08-04", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7223266", "field": "economics", "role": "object", "bullets": [ "Monthly vintages of Revelio Labs profile data from 2020 to 2026, tracking established United States LinkedIn users and the past jobs they go back and rewrite.", "No model does the measuring. The study counts writing markers associated with language models in revised text and dates them against the release of ChatGPT; the marker set is not described.", "19.7 percent of users retroactively rewrite a past job title or description, mostly around employer changes, and model style markers jump after ChatGPT, most among less educated users and MBAs from lower ranked programmes." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 78, "edition": 17, "validated": null, "n": 2147, "authors_detailed": [ { "name": "Nicholas Bloom", "url": "https://openalex.org/A5044421873", "inst": "Hunter College" }, { "name": "Gideon Moore", "url": "https://openalex.org/A5028646748", "inst": "Stanford University" }, { "name": "Lisa Simon", "url": "https://openalex.org/A5144347882", "inst": "" }, { "name": "Caelan Wilkie-Rogers", "url": "https://openalex.org/A5144295596", "inst": "" } ], "affiliations": [ "Stanford University", "Hunter College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7161699", "doi": "10.2139/ssrn.7161699", "title": "The Stanford EDGAR Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data", "authors": [ "Nick Bettencourt", "Xiaowei Ding", "Kay Giesecke" ], "posted": "2026-08-04", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7161699", "field": "finance", "role": "method", "bullets": [ "An open reconstruction of SEC EDGAR filings covering audited statements, risk disclosures, ownership reports, accounting notes, and event filings, drawn from an 18.5 million filing archive.", "No model is trained or evaluated. The authors convert filings into layout-faithful MultiMarkdown for long-context pretraining and add two benchmarks, EDGAR-Forecast for post-cutoff numerical forecasting and EDGAR-OCR for financial table transcription.", "The released SEFD-v1 snapshot holds 152 billion tokens with under 0.1 percent overlap with Common Crawl, and the full archive is estimated at 550 billion tokens." ], "bullet_provenance": "ai", "salience": 52, "edition": 15, "models": [], "validated": null, "n": 2070, "authors_detailed": [ { "name": "Nick Bettencourt", "url": "https://openalex.org/A5144339267", "inst": "" }, { "name": "Xiaowei Ding", "url": "https://openalex.org/A5144368912", "inst": "" }, { "name": "Kay Giesecke", "url": "https://openalex.org/A5042142175", "inst": "Stanford University" } ], "affiliations": [ "Stanford University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.03413v1", "arxiv_id": "2608.03413v1", "title": "Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems", "authors": [ "Zuojun Max Shen", "Yuan Qu", "Pujun Zhang", "Anbang Liu", "Yunhao Liang" ], "posted": "2026-08-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.03413v1", "field": "management", "role": "object", "bullets": [ "A conceptual paper with no dataset or experiment, proposing a framework for enterprise and industrial decision support, site-level execution, and feedback.", "No model is run or evaluated. The paper positions language models as one part of a broader agentic architecture with four roles, an organizational world model, a site world model, schema intelligence, and an enactive decision cycle.", "It offers a vocabulary and structure rather than an empirical finding, arguing that AI progress should be judged by reliable support for consequential enterprise action." ], "bullet_provenance": "ai", "salience": 22, "edition": 15, "models": [], "validated": null, "n": 2071, "authors_detailed": [ { "name": "Zuojun Max Shen", "url": "https://openalex.org/A5121642362", "inst": "" }, { "name": "Yuan Qu", "url": "https://openalex.org/A5144455597", "inst": "" }, { "name": "Pujun Zhang", "url": "https://openalex.org/A5010515648", "inst": "Education University of Hong Kong" }, { "name": "Anbang Liu", "url": "https://openalex.org/A5079665471", "inst": "Chinese University of Hong Kong" }, { "name": "Yunhao Liang", "url": "https://openalex.org/A5144372026", "inst": "Education University of Hong Kong" } ], "affiliations": [ "Education University of Hong Kong", "Chinese University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.7229666", "doi": "10.2139/ssrn.7229666", "title": "Selecting Foundation Language Models for Generative Agent-Based Modeling: A Multi-Dimensional Framework for Behavioral Naturalness and Reasoning Capability", "authors": [ "Shayan Firouzian Haji" ], "posted": "2026-08-04", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7229666", "field": "management", "role": "method", "bullets": [ "Foundation-model selection for generative agent-based modeling, screening open-weight dense base models under 10B parameters with official reasoning-capable checkpoints down to three candidates.", "Qwen3 8B and Llama 3.1 8B, in base and instruct variants, scored on refusal, AI self-awareness, assistive behavior, format sensitivity, and analytical and social reasoning; no external ground truth check is reported.", "Llama shows lower behavioral assessment rates, Qwen higher analytical and comparable social reasoning; format dependence tracks sub-dimension and checkpoint rather than model family, and Qwen3 8B base is documented as a starting point." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "salience": 35, "edition": 15, "validated": null, "n": 2092, "authors_detailed": [ { "name": "Shayan Firouzian", "url": "https://openalex.org/A5138312019", "inst": "University of Tehran" } ], "affiliations": [ "University of Tehran" ] }, { "uid": "doi:10.2139/ssrn.7228021", "doi": "10.2139/ssrn.7228021", "title": "The Value of Data Assetization: Evidence from China Using Large Language Models", "authors": [ "Shuang Lin", "Chenyu Zheng" ], "posted": "2026-08-04", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7228021", "field": "finance", "role": "instrument", "bullets": [ "Chinese A-share listed firms from 2009 to 2022, with data assetization conceptualized as three stages: data resourcification, resource productization, and product assetization.", "Baidu's ERNIE classifies annual report disclosures into the three stages to build a firm-level measure; the abstract reports no validation against hand coding and no accuracy figure.", "Data assetization is positively associated with Tobin's Q, strengthening at later stages, robust to instrumental variables, with investor confidence as a channel and larger effects under institutional ownership and data exclusivity." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 50, "edition": 15, "n": 2093, "authors_detailed": [ { "name": "Shuang Lin", "url": "https://openalex.org/A5144266263", "inst": "Zhongnan University of Economics and Law" }, { "name": "Chenyu Zheng", "url": "https://openalex.org/A5144300803", "inst": "Zhongnan University of Economics and Law" } ], "affiliations": [ "Zhongnan University of Economics and Law" ] }, { "uid": "arxiv:2608.04095v1", "arxiv_id": "2608.04095v1", "title": "FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents", "authors": [ "Ben Wang", "Kang Zhou", "Lifan Guo", "Feng Chen", "Chi Zhang" ], "posted": "2026-08-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.04095v1", "field": "finance", "role": "method", "bullets": [ "2,994 questions over 276 frozen longitudinal investor personas in a financial advising setting, generated from deterministic theory-informed impact rules with controlled LLM narration and automated quality screening.", "Seven frontier LLMs, unnamed in the abstract, run with up to seven memory configurations; a post-shock checkpoint isolates whether an agent has folded a material event into its persistent user model.", "No full-context configuration exceeds roughly 0.47 overall accuracy or 39 percent on multiple choice; summary memories keep facts but lose preference signals, so simple retrieval can beat purpose-built memory systems." ], "bullet_provenance": "ai", "validated": true, "validation_note": "frozen event-grounded benchmark, accuracy reported", "salience": 55, "edition": 15, "models": [], "n": 2094, "authors_detailed": [ { "name": "Ben Wang", "url": "https://openalex.org/A5144682273", "inst": "" }, { "name": "Kang Zhou", "url": "https://openalex.org/A5068416093", "inst": "City University of Hong Kong" }, { "name": "Lifan Guo", "url": "https://openalex.org/A5040112548", "inst": "Cloud Computing Center" }, { "name": "Feng Chen", "url": "https://openalex.org/A5145143347", "inst": "" }, { "name": "Chi Zhang", "url": "https://openalex.org/A5144691515", "inst": "" } ], "affiliations": [ "City University of Hong Kong", "Cloud Computing Center" ] }, { "uid": "doi:10.2139/ssrn.7230145", "doi": "10.2139/ssrn.7230145", "title": "Decoupling Green Claims from Reality：LLM-Based Greenwashing Detection in Construction", "authors": [ "Ling Peng" ], "posted": "2026-08-04", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7230145", "field": "accounting", "role": "instrument", "bullets": [ "Green claims in the Chinese construction industry, using multi-source texts including ESG reports, project green publicity, and bidding commitment documents; sample size and period are not stated.", "A domain-adapted large language model, family not named, is fine-tuned to flag decoupling between green commitments and performance; reported detection accuracy is 92.3 percent, above TextCNN and BERT baselines.", "An overall industry greenwashing rate of 42.7 percent, driven mainly by procedural compliance and implicit exaggeration, with bidding documents carrying the highest share." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "detection accuracy reported on constructed labelled dataset, baselines compared", "salience": 42, "edition": 15, "n": 2101, "authors_detailed": [ { "name": "Ling Peng", "url": "https://openalex.org/A5144357536", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7220762", "doi": "10.2139/ssrn.7220762", "title": "When Personalization Becomes Bias: Structural and Discursive Religious Framing in AI-Generated Financial Advice", "authors": [ "Muhammad Salar Khan", "Hamza Umer", "Hasan Mahmud", "Sandra Rothenberg" ], "posted": "2026-08-04", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7220762", "field": "management", "role": "object", "bullets": [ "Audit of 432 simulated advisor-client exchanges spanning 16 religious identity pairings among Christian, Muslim, Hindu, and non-religious profiles, over three household decisions: stock investment, house purchase, and life insurance.", "ChatGPT, Gemini, and Grok are the advisors under study; their advice is coded with regression and reflexive thematic analysis. Model versions are not stated and no ground truth check is reported.", "Only 12 to 18 percent of responses were free of religious bias; Gemini framed advice religiously most often, and matched advisor-client religions almost always triggered explicit religious appeals." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 55, "edition": 11, "validated": null, "n": 1349, "authors_detailed": [ { "name": "Muhammad Salar Khan", "url": "https://openalex.org/A5023522655", "inst": "Rochester Institute of Technology - Dubai" }, { "name": "Hamza Umer", "url": "https://openalex.org/A5048641217", "inst": "Hitotsubashi University" }, { "name": "Hasan Mahmud", "url": "https://openalex.org/A5101476383", "inst": "Rochester Institute of Technology" }, { "name": "Sandra Rothenberg", "url": "https://openalex.org/A5144341684", "inst": "" } ], "affiliations": [ "Rochester Institute of Technology - Dubai", "Hitotsubashi University", "Rochester Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.7177498", "doi": "10.2139/ssrn.7177498", "title": "Evaluating Financial Sentiment in the Age of AI", "authors": [ "Arslan Bisharat", "Oudom Hean" ], "posted": "2026-08-04", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7177498", "field": "finance", "role": "method", "bullets": [ "Twelve sentiment measures spanning dictionaries, finance-tuned transformers, and open-source LLMs, evaluated on earnings announcement text; corpus size and period are not stated in the abstract.", "Each measure is scored on linguistic validity, classification against labelled sentiment, and economic validity, association with earnings surprises and returns; specific LLMs are not named in the abstract.", "General-purpose LLMs match finance-specific transformers without fine-tuning, yet no measure predicts next-day returns; sentiment tracks earnings surprises and works best for large beats or misses." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "classification benchmarked against labelled sentiment; figures not in abstract", "salience": 62, "edition": 11, "models": [], "n": 1354, "authors_detailed": [ { "name": "Arslan Bisharat", "url": "https://openalex.org/A5120686779", "inst": "Loyola University Chicago" }, { "name": "Oudom Hean", "url": "https://openalex.org/A5090726375", "inst": "Dakota State University" } ], "affiliations": [ "Loyola University Chicago", "Dakota State University" ] }, { "uid": "doi:10.2139/ssrn.7160759", "doi": "10.2139/ssrn.7160759", "title": "Artificial Intelligence and ESG Disclosure: Evaluating Large Language Models for Identifying Symbolic and Substantive Sustainability Communication", "authors": [ "Anuj Pal" ], "posted": "2026-08-04", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7160759", "field": "accounting", "role": "method", "bullets": [ "35 ESG disclosures from five multinational companies, coded as symbolic, mixed, or substantive sustainability communication in a researcher-in-the-loop content analysis.", "GPT-5.5 coded each disclosure with researcher review; agreement with expert judgement is described, but the abstract reports no formal accuracy or kappa statistic.", "Clearly symbolic and clearly substantive disclosures were never confused; disagreements clustered on mixed cases, motivating the three-way OEDCF coding framework." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "expert coding of 35 disclosures; agreement described, no formal statistic", "salience": 42, "edition": 11, "n": 1355, "authors_detailed": [ { "name": "Anuj Pal", "url": "https://openalex.org/A5123893798", "inst": "National Institute of Financial Management" } ], "affiliations": [ "National Institute of Financial Management" ] }, { "uid": "doi:10.2139/ssrn.7230432", "doi": "10.2139/ssrn.7230432", "title": "Structural Supply and User Perceptions Shape EV Charging Satisfaction", "authors": [ "Hongli Zhang", "Yi Ke", "Xingju Zhong" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7230432", "field": "management", "role": "instrument", "bullets": [ "391 complaint texts from four Chinese cities about EV charging services, combined with POI and road network accessibility indicators.", "Large language models performed sentiment and topic analysis on complaint texts; structural equation modeling integrated supply and perception variables.", "Structural supply negatively affects satisfaction, revealing a high-supply-high-exposure-low-satisfaction paradox; safety perceptions exert the strongest negative effect." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 25, "n": 3172, "authors_detailed": [ { "name": "Hongli Zhang", "url": "https://openalex.org/A5144325424", "inst": "" }, { "name": "Yi Ke", "url": "https://openalex.org/A5055685570", "inst": "Silicon Works (South Korea)" }, { "name": "Xingju Zhong", "url": "https://openalex.org/A5144349766", "inst": "" } ], "affiliations": [ "Silicon Works (South Korea)" ] }, { "uid": "arxiv:2608.03076v1", "arxiv_id": "2608.03076v1", "title": "AI Agent Economics: Can Autonomous Economic Behavior Emerge among AI Agents under Minimal External Conditions?", "authors": [ "Lingyun Zhang", "Shang Shang" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.03076v1", "field": "economics", "role": "agent", "bullets": [ "Twenty-four independent six-agent simulated worlds using GPT and DeepSeek, with executable mechanisms for work, transfer, elections, and allocation.", "AI agents received no prescribed economic strategy; GPT and DeepSeek models autonomously negotiated transfers, loans, and vote-for-access exchanges.", "Economic relations emerged from executable rights and resource scarcity, not role labels or prompt language; allocation authority increased differentiation while reducing exclusion." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": false, "salience": 55, "n": 3283, "authors_detailed": [ { "name": "Lingyun Zhang", "url": "https://openalex.org/A5144419853", "inst": "Tokyo University of Technology" }, { "name": "Shang Shang", "url": "https://openalex.org/A5113939389", "inst": "Annoroad Gene Technology (China)" } ], "affiliations": [ "Tokyo University of Technology", "Annoroad Gene Technology (China)" ] }, { "uid": "doi:10.2139/ssrn.7231446", "doi": "10.2139/ssrn.7231446", "title": "Trusted but Unused, Used but Untrusted:Resolving the Adoption-Trust Paradox in Artificial Intelligence-Enabled Sustainable Business Model Innovation​", "authors": [ "Xuan Tran" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7231446", "field": "management", "role": "object", "bullets": [ "Conceptual framework drawing on global survey data showing 66% AI usage but only 46% trust, informed by cognitive dissonance and paradox theory.", "Paper theorizes the adoption-trust gap in AI-enabled sustainable business model innovation using Strategy-as-Practice theory; no specific model is tested empirically.", "AI-enabled sustainability initiatives stall because organizations misdiagnose a trust deficit as an adoption deficit; addressing organizational design restores trust without model improvements." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 48, "validated": null, "n": 3793, "authors_detailed": [ { "name": "Xuan Tran", "url": "https://openalex.org/A5060079552", "inst": "University of West Florida" } ], "affiliations": [ "University of West Florida" ] }, { "uid": "doi:10.2139/ssrn.7157579", "doi": "10.2139/ssrn.7157579", "title": "The AI Productivity Paradox Why Executive Surveys and Field Studies Tell Different Stories About AI’s Payof", "authors": [ "Mathis Tai" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7157579", "field": "management", "role": "object", "bullets": [ "Survey of nearly 6,000 executives across four countries plus a QJE field study of 5,179 customer-support agents and the Klarna AI rollout case.", "Paper synthesizes executive survey data, field-experiment results, and McKinsey and NBER evidence on generative AI productivity gains at task versus firm level.", "Nine in ten executives report no measurable productivity gain; four factors explain the gap: measurement lag, shallow usage, workslop, and missing workflow redesign." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 65, "validated": null, "n": 3794, "authors_detailed": [ { "name": "Mathis Tai", "url": "https://openalex.org/A5144340304", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7230422", "doi": "10.2139/ssrn.7230422", "title": "Bank Risk Narratives and Early Warning of Systemic Risk", "authors": [ "Wang Pucong", "Sumuya Borjigin" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7230422", "field": "finance", "role": "instrument", "bullets": [ "U.S. banking sector using daily SRISK, market variables, Wall Street Journal news, and YouTube video transcripts from 2005 to 2024.", "An LLM extracts funding-liquidity, balance-sheet-fragility, and systemic-transmission narratives; these feed machine learning models forecasting SRISK at 1-, 3-, and 5-day horizons.", "Textual variables improve systemic risk prediction beyond market indicators alone; balance-sheet fragility and systemic transmission narratives provide the strongest incremental gains." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "ML forecasting accuracy for SRISK at 1/3/5-day horizons versus market-only baselines", "salience": 72, "n": 3795, "authors_detailed": [ { "name": "Wang Pucong", "url": "https://openalex.org/A5105914010", "inst": "Inner Mongolia University" }, { "name": "Sumuya Borjigin", "url": "https://openalex.org/A5076136062", "inst": "Inner Mongolia University" } ], "affiliations": [ "Inner Mongolia University" ] }, { "uid": "doi:10.2139/ssrn.7176378", "doi": "10.2139/ssrn.7176378", "title": "Enterprise Trust Gaps in Generative AI Why Accuracy is Not Enough: Evidence from Mid-Market Generative AI Adoption", "authors": [ "Ibe Imo", "Kofi Agyare-Kwabi", "Abolaji Adesoji", "Isaac Sarfo", "David Odukoya" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7176378", "field": "management", "role": "object", "bullets": [ "Enterprise professionals across financial services, cybersecurity, and technology sectors; secondary analysis of three large-scale enterprise AI adoption surveys.", "Paper investigates why business users abandon, edit, or override accurate generative AI outputs, identifying three organizational mechanisms behind the enterprise trust gap.", "Trust erodes from accountability mismatch, rising human correction burden, and confidence framing effects, not from AI inaccuracy; organizational redesign can restore trust." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 52, "validated": null, "n": 3796, "authors_detailed": [ { "name": "Ibe Imo", "url": "https://openalex.org/A5144324463", "inst": "Harvard University Press" }, { "name": "Kofi Agyare-Kwabi", "url": "https://openalex.org/A5144379748", "inst": "University of Pennsylvania" }, { "name": "Abolaji Damilare Adesoji", "url": "https://openalex.org/A5081453427", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Isaac Sarfo", "url": "https://openalex.org/A5144321102", "inst": "St John's University" }, { "name": "David Odukoya", "url": "https://openalex.org/A5144313170", "inst": "University of Rochester" } ], "affiliations": [ "Harvard University", "University of Pennsylvania", "University of Rochester", "Rensselaer Polytechnic Institute", "St John's University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.03259v1", "arxiv_id": "2608.03259v1", "title": "FinVerse: Financial Time-Series Benchmark", "authors": [ "Jaehoon Lee", "Jun Seo", "Seunghan Lee", "Tae Yoon Lim", "Dongwan Kang", "Hwanil Choi", "Minjae Kim", "Sungdong Yoo", "Junhyeok Kang", "Sangjun Han", "Soonyoung Lee", "Wonbin Ahn" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.03259v1", "field": "finance", "role": "method", "bullets": [ "Benchmark of 116,897 financial time series with 171.1 million observations; 60,232 series selected for evaluation based on economic relevance to financial decisions.", "Forty-three public time-series foundation models evaluated under 78 metrics across 11 families, with metrics assigned per series by underlying economic meaning.", "Strong performance on generic forecasting criteria does not translate into useful financial forecasts; domain-aware evaluation changes model rankings substantially." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "78 domain-specific metrics across 11 families on 60K financial time series", "salience": 62, "n": 3797, "authors_detailed": [ { "name": "Jaehoon Lee", "url": "https://openalex.org/A5144429700", "inst": "" }, { "name": "Jun Seo", "url": "https://openalex.org/A5144398012", "inst": "" }, { "name": "Seunghan Lee", "url": "https://openalex.org/A5144451538", "inst": "" }, { "name": "Tae Yoon Lim", "url": "https://openalex.org/A5128068045", "inst": "LG (United States)" }, { "name": "Dongwan Kang", "url": "https://openalex.org/A5144423666", "inst": "" }, { "name": "Hwanil Choi", "url": "https://openalex.org/A5144456257", "inst": "" }, { "name": "Minjae Kim", "url": "https://openalex.org/A5144413118", "inst": "" }, { "name": "Sungdong Yoo", "url": "https://openalex.org/A5144460084", "inst": "" }, { "name": "Junhyuk Kang", "url": "https://openalex.org/A5022472066", "inst": "LG (United States)" }, { "name": "Sangjun Han", "url": "https://openalex.org/A5101250749", "inst": "LG (United States)" }, { "name": "Soonyoung Lee", "url": "https://openalex.org/A5144414744", "inst": "" }, { "name": "Wonbin Ahn", "url": "https://openalex.org/A5082843254", "inst": "LG (United States)" } ], "affiliations": [ "LG (United States)" ] }, { "uid": "arxiv:2608.04198v1", "arxiv_id": "2608.04198v1", "title": "Does generative AI narrow education-based productivity gaps? Evidence from a randomized experiment", "authors": [ "Guillermo Cruces", "Diego Fernandez Meijide", "Sebastian Galiani", "Ramiro Galvez", "Maria Lombardi" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.04198v1", "field": "economics", "role": "object", "bullets": [ "Randomized online experiment with 1,174 adults aged 25-45 completing a workplace-style problem-solving task with or without a generative AI assistant.", "Participants randomly assigned to use a generative AI assistant; performance compared across education levels during assisted and subsequent unassisted modules.", "AI closes three-quarters of the 0.548 SD education-based performance gap; lower-education participants retain partial gains after AI removal but a sizable gap re-emerges." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 3798, "authors_detailed": [ { "name": "Guillermo Cruces", "url": "https://openalex.org/A5144641585", "inst": "University of Nottingham" }, { "name": "DIEGO FERNÁNDEZ MEIJIDE", "url": "https://openalex.org/A5093789581", "inst": "University of San Andrés" }, { "name": "Sebastián Galiani", "url": "https://openalex.org/A5029505552", "inst": "Tulane University" }, { "name": "Ramiro Gálvez", "url": "https://openalex.org/A5126293556", "inst": "Universidad Torcuato Di Tella" }, { "name": "Maria Lombardi", "url": "https://openalex.org/A5126320636", "inst": "Universidad Torcuato Di Tella" } ], "affiliations": [ "University of Nottingham", "University of San Andrés", "Tulane University", "Universidad Torcuato Di Tella" ] }, { "uid": "arxiv:2608.04077v1", "arxiv_id": "2608.04077v1", "title": "FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverables", "authors": [ "Ben Wang", "Kang Zhou", "Lifan Guo", "Feng Chen", "Chi Zhang" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.04077v1", "field": "finance", "role": "method", "bullets": [ "Benchmark of 1,723 curated deliverables spanning 57 occupations, 8 financial sub-industries, and 161 deliverable types with 20 evaluation tasks.", "Multiple LLM judges scored financial tasks against role-grounded rubrics derived from practitioner deliverables via a four-stage construction pipeline.", "Prompt-only rubrics matched RGRC for conventional roles (89.2% vs 90.7%) but fell far short for role-specialized positions (99.1% vs 78.0%)." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "human deliverable scores as benchmark across 20 tasks", "salience": 55, "n": 3799, "authors_detailed": [ { "name": "Ben Wang", "url": "https://openalex.org/A5144682273", "inst": "" }, { "name": "Kang Zhou", "url": "https://openalex.org/A5145252534", "inst": "" }, { "name": "Lifan Guo", "url": "https://openalex.org/A5145864146", "inst": "" }, { "name": "Feng Chen", "url": "https://openalex.org/A5145143347", "inst": "" }, { "name": "Chi Zhang", "url": "https://openalex.org/A5144691515", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7154358", "doi": "10.2139/ssrn.7154358", "title": "Gov ernance Before Action: Institutional Authority for Agentic AI in Banking", "authors": [ "Malka Sprung" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7154358", "field": "finance", "role": "object", "bullets": [ "Analysis of regulatory frameworks for frontier AI models in banking, examining where institutional authority resides when probabilistic models participate in regulated financial decisions.", "No model tested empirically; paper proposes a determination layer architecture that evaluates machine-readable obligations and policies before any consequential AI action executes.", "Working implementation demonstrates governance requirements are simultaneously satisfiable and machine-checkable; supervisory examination framework uses replayable determination records rather than behavioral observation." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 4051, "authors_detailed": [ { "name": "Malka Sprung", "url": "https://openalex.org/A5144340700", "inst": "General Oceanics (United States)" } ], "affiliations": [ "General Oceanics (United States)" ] }, { "uid": "doi:10.2139/ssrn.7228214", "doi": "10.2139/ssrn.7228214", "title": "The Agentic Verification Gap: Governing AI-Delegated Work in Business Education", "authors": [ "Quan Pham", "Hien Thi Thu Nguyen" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7228214", "field": "management", "role": "object", "bullets": [ "56 group-task submissions from two undergraduate ERP course assignments and anonymous survey of 43 students using an Odoo-Antigravity AI-delegated workflow.", "AI-delegated workflow produced inventory reports and revenue dashboards; benchmark-supported document analysis identified substitutions, errors, and absence of verification evidence.", "40 of 44 substantive artifacts contained reproducible errors; only four showed evidence of source tracing or verification despite students endorsing verification in surveys." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 4052, "authors_detailed": [ { "name": "Quan Pham", "url": "https://openalex.org/A5144245809", "inst": "" }, { "name": "Hien Thi Thu Nguyen", "url": "https://openalex.org/A5100689841", "inst": "Aalborg University Hospital" } ], "affiliations": [ "Aalborg University Hospital" ] }, { "uid": "doi:10.2139/ssrn.7164559", "doi": "10.2139/ssrn.7164559", "title": "Outsourcing 2026: Agentic AI and Tier 1 Financial Institutions The Delivery-Side Record from 150 Engagements", "authors": [ "Phil Hatch" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7164559", "field": "finance", "role": "object", "bullets": [ "Survey of 850 delivery staff at major outsourcing firms across 150 agentic AI engagements serving Tier 1 financial institutions, April through June 2026.", "No model tested; structured ratings and written testimony from practitioners building and operating AI agents across ten types of financial institutions and four outsourcing models.", "Engagement cancellation rate reached 37.3%; respondents rated client-facing reporting more likely manipulated (6.26/10) than accurate; job satisfaction averaged 4.19 out of 10." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 4053, "authors_detailed": [ { "name": "Phil Hatch", "url": "https://openalex.org/A5098357323", "inst": "Ulverscroft (United Kingdom)" } ], "affiliations": [ "Ulverscroft (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.7154558", "doi": "10.2139/ssrn.7154558", "title": "Hierarchical Multi-agent Orchestration in Oracle Fusion Cloud ERP: Designing Supervisory Agent Architectures for End-to-End Order-to-Cash and Procure-to-Pay Automation", "authors": [ "Mujtaba Shafique" ], "posted": "2026-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7154558", "field": "management", "role": "object", "bullets": [ "Two NYSE-listed enterprises deploying hierarchical multi-agent architecture in Oracle Fusion Cloud ERP for end-to-end Order-to-Cash and Procure-to-Pay cycle automation.", "Fourteen specialized AI sub-agents orchestrated by a supervisory layer in Oracle AI Agent Studio automate ERP business cycles; study documents emergent failure patterns.", "Non-atomic parallel agent execution on shared ERP entities caused inventory double-booking and phantom master data corruption with no corresponding system error signal." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 4054, "authors_detailed": [ { "name": "Mujtaba Shafique", "url": "https://openalex.org/A5144318785", "inst": "Fauji Fertilizer (Pakistan)" } ], "affiliations": [ "Fauji Fertilizer (Pakistan)" ] }, { "uid": "doi:10.2139/ssrn.7138640", "doi": "10.2139/ssrn.7138640", "title": "Deleting Information: Evidence from the Digital Service Act", "authors": [ "Jedson Pinto", "Mark H. Lang", "John Christensen" ], "affiliations": [ "The University of Texas at Dallas", "University of North Carolina at Chapel Hill", "Ministry of Justice" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7138640", "field": "accounting", "role": "instrument", "bullets": [ "EU-exposed firms on Glassdoor after the Digital Services Act took effect, with variation in EU revenue exposure used in a difference-in-differences design.", "An LLM classifies each deleted review against Glassdoor content guidelines to measure whether flagged reviews contained actual policy violations.", "Deletions rise significantly for EU-exposed firms, concentrated in negative reviews and high-discretion categories; displayed ratings rise and become stronger predictors of worker inflows and outflows." ], "bullet_provenance": "editor", "validated": true, "validation_note": "LLM classification evaluated against platform content guidelines", "salience": 0, "edition": 21, "audience": "business", "models": [], "n": 2354, "authors_detailed": [ { "name": "Jedson Pinto", "url": "https://openalex.org/A5066040956", "inst": "The University of Texas at Dallas" }, { "name": "Mark H. Lang", "url": "https://openalex.org/A5016588287", "inst": "University of North Carolina at Chapel Hill" }, { "name": "John Christensen", "url": "https://openalex.org/A5108384743", "inst": "Ministry of Justice" } ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7226277", "doi": "10.2139/ssrn.7226277", "title": "Evaluating Large Language Models for Engineering Change Impact Analysis: Empirical Case Studies", "authors": [ "Valentin Tikhonenko", "Anastasia Stelvaga", "Ivan Kazakov", "Clement Fortin", "Mikhail Belov" ], "posted": "2026-08-03", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7226277", "field": "management", "role": "method", "bullets": [ "Three industrial case studies of engineering change impact analysis, 25 change instances assessed by 11 engineers, with 20 generations sampled per model and context configuration.", "Several LLMs, names not stated, draft impact assessments that are compared with engineer judgments; the best configuration matches 66 percent of individual assessments and adds 41 percent novel relevant suggestions.", "Performance varies across cases and drops up to 83 percent against whole team knowledge, supporting LLMs as augmentation for individual engineers rather than standalone decision support." ], "bullet_provenance": "ai", "validated": true, "validation_note": "engineer assessments on 25 changes, 66 percent alignment", "salience": 46, "edition": 15, "models": [], "n": 2068, "authors_detailed": [ { "name": "Valentin Tikhonenko", "url": "https://openalex.org/A5117770458", "inst": "Moscow Polytechnic University" }, { "name": "Anastasia Stelvaga", "url": "https://openalex.org/A5045600212", "inst": "Skolkovo Institute of Science and Technology" }, { "name": "Ivan Kazakov", "url": "https://openalex.org/A5090727535", "inst": "Skolkovo Institute of Science and Technology" }, { "name": "Clément Fortin", "url": "https://openalex.org/A5033490570", "inst": "Skolkovo Institute of Science and Technology" }, { "name": "Mikhail V. Belov", "url": "https://openalex.org/A5052931491", "inst": "National Research University Higher School of Economics" } ], "affiliations": [ "Moscow Polytechnic University", "Skolkovo Institute of Science and Technology", "National Research University Higher School of Economics" ] }, { "uid": "doi:10.2139/ssrn.7225079", "doi": "10.2139/ssrn.7225079", "title": "Using Artificial Intelligence to enhance CONSOB's enforcement against unauthorised financial activities", "authors": [ "Mario Trerotola", "Alessio Boghi", "Paola Deriu", "Giuseppe Frega" ], "posted": "2026-08-03", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7225079", "field": "finance", "role": "instrument", "bullets": [ "A prototype built by Italy's securities regulator CONSOB with two Italian polytechnic universities, applied to investor complaints and the websites of reported entities. No sample size is stated.", "Large language models and natural language processing extract and process the reported information; the model family is not stated. Outputs were compared to an analyst's review for accuracy and consistency, with no figure reported.", "The system automates preparatory stages of financial supervision while keeping decisions with a human operator, and the authors flag limits in handling semantic complexity and in explainability." ], "bullet_provenance": "ai", "validated": false, "validation_note": "informal comparison to analyst review, no statistic reported", "salience": 44, "edition": 15, "models": [], "n": 2069, "authors_detailed": [ { "name": "Mario Trerotola", "url": "https://openalex.org/A5092096497", "inst": "University of Salerno" }, { "name": "Alessio Boghi", "url": "https://openalex.org/A5144287333", "inst": "" }, { "name": "Paola Deriu", "url": "https://openalex.org/A5144281504", "inst": "" }, { "name": "Giuseppe Frega", "url": "https://openalex.org/A5144306632", "inst": "" } ], "affiliations": [ "University of Salerno" ] }, { "uid": "doi:10.2139/ssrn.7147939", "doi": "10.2139/ssrn.7147939", "title": "Kimi K3 and FinTech: Open-Weight Scaling and the Democratization of Financial Services", "authors": [ "David Krause" ], "posted": "2026-08-03", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7147939", "field": "finance", "role": "object", "bullets": [ "Essay on Moonshot AI's Kimi K3, a 2.8 trillion parameter open-weight model released July 2026, and its implications for financial services.", "No model is deployed empirically; the paper reviews reported benchmark parity with GPT-5.6 and Claude Fable 5 and the compute demand the release created.", "Argues frontier open-weight models lower FinTech entry barriers across lending, investment management, insurance, and payments, with particular significance for emerging markets." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "salience": 34, "edition": 15, "validated": null, "n": 2087, "authors_detailed": [ { "name": "David Krause", "url": "https://openalex.org/A5002886467", "inst": "Marquette University" } ], "affiliations": [ "Marquette University" ] }, { "uid": "doi:10.2139/ssrn.7217378", "doi": "10.2139/ssrn.7217378", "title": "When Everyone has the Same AI: Competitive Advantage for Startups under Generative AI Commoditization", "authors": [ "Mwita Wanyancha" ], "posted": "2026-08-03", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7217378", "field": "management", "role": "object", "bullets": [ "Conceptual paper without data; a firm-level framework with resource-constrained startups as the sharpest boundary condition for advantage under generative AI commoditization.", "No model is used; rentable general-purpose foundation models are the object, analyzed through the resource-based view, complementary assets, absorptive capacity, and dynamic capabilities.", "Names an advantage relocation mechanism: when every firm can rent the same model, advantage shifts to proprietary data, domain judgment, customer relationships, and orchestration; falsifiable propositions follow." ], "bullet_provenance": "ai", "salience": 38, "edition": 15, "models": [], "validated": null, "n": 2088, "authors_detailed": [ { "name": "Mwita Wanyancha", "url": "https://openalex.org/A5118800918", "inst": "Nelson Mandela African Institution of Science and Technology" } ], "affiliations": [ "Nelson Mandela African Institution of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7138779", "doi": "10.2139/ssrn.7138779", "title": "The AI Unemployment Myth? Why Four Regression Frameworks Show No Aggregate Job Loss (So Far)", "authors": [ "Gal Zohar" ], "posted": "2026-08-03", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7138779", "field": "economics", "role": "object", "bullets": [ "Israeli Employment Service administrative records: 6.2 million person-year observations on 2.4 million job seekers across 277 occupations from 2019 to 2026, aggregated to occupation-year cells.", "No LLM is used as a tool; occupational AI exposure enters OLS, fixed effects, Poisson, and negative binomial models, with post-2023 interactions marking generative AI release waves.", "Exposure predicts roughly 17% more registrations, but the link predates generative AI and does not strengthen after 2023 (p = 0.185); composition shifts toward experienced workers, suggesting reallocation." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 66, "edition": 15, "validated": null, "n": 2089, "authors_detailed": [ { "name": "Gal Zohar", "url": "https://openalex.org/A5006356948", "inst": "Tel Aviv University" } ], "affiliations": [ "Tel Aviv University" ] }, { "uid": "arxiv:2608.02909v1", "arxiv_id": "2608.02909v1", "title": "When Predictions Become Regressors: A Split-Sample Correction for Biases in Downstream Inference", "authors": [ "Nathan Canen", "Ted Enamorado" ], "posted": "2026-08-03", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.02909v1", "field": "economics", "role": "method", "bullets": [ "Econometric theory with simulations, plus two reapplications: gendered speech and legislative outcomes in the German Parliament, and political risk and poverty alleviation in China.", "No specific LLM; the target is bias when prediction-generated measures from LLMs or other machine learning enter regressions as explanatory variables with measurement error.", "Instrumental variables built from measures on independent data splits recover estimates close to truth even in small samples, where the standard plug-in approach is substantially biased." ], "bullet_provenance": "ai", "salience": 64, "edition": 15, "models": [], "validated": null, "n": 2090, "authors_detailed": [ { "name": "Nathan Canen", "url": "https://openalex.org/A5144458032", "inst": "Washington University in St. Louis" }, { "name": "Ted Enamorado", "url": "https://openalex.org/A5083198627", "inst": "University of North Carolina at Chapel Hill" } ], "affiliations": [ "Washington University in St. Louis", "University of North Carolina at Chapel Hill" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.02472v1", "arxiv_id": "2608.02472v1", "title": "CTRAG: An In-Context Retrieval-based Framework for Automated Compliance Checking using LLMs", "authors": [ "Muhammad Roman", "Karen Rafferty", "Barry Devereux" ], "posted": "2026-08-03", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.02472v1", "field": "accounting", "role": "method", "bullets": [ "Regulatory compliance verification deployed at a Big Four professional services firm, matching control questions extracted from regulatory texts against unstructured company documentation, including indirect compliance through third-party cloud providers.", "A retrieval-augmented generation pipeline with adaptive chunking and in-context learning; the underlying language model is not stated. Outputs were cross-checked against manual compliance reports from real cases.", "The deployed configuration reaches an F1 of 78 percent and recall of 85 percent, reducing manual reviewer effort while keeping missed non-compliance cases low." ], "bullet_provenance": "ai", "validated": true, "validation_note": "cross-checked against manual compliance reports, F1 and recall reported", "salience": 46, "edition": 15, "models": [], "n": 2091, "authors_detailed": [ { "name": "Muhammad Roman", "url": "https://openalex.org/A5046694463", "inst": "Toshiba (Japan)" }, { "name": "Karen Rafferty", "url": "https://openalex.org/A5144356800", "inst": "" }, { "name": "Barry Devereux", "url": "https://openalex.org/A5007017103", "inst": "Queen's University Belfast" } ], "affiliations": [ "Toshiba (Japan)", "Queen's University Belfast" ] }, { "uid": "doi:10.2139/ssrn.7226274", "doi": "10.2139/ssrn.7226274", "title": "GPT-based Large Language Models Versus Traditional Machine Learning Models for Cost Overrun prediction in Construction Projects", "authors": [ "Nour Khaled", "Ayman Nassar" ], "posted": "2026-08-03", "added": "2026-08-04", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7226274", "field": "management", "role": "method", "bullets": [ "Thirty Egyptian construction projects described by 12 risk factors, a deliberately small sample meant to mirror data scarce developing construction markets.", "GPT-3.5-Turbo, GPT-4.1, and GPT-4o predict actual project costs from structured natural language descriptions, benchmarked against eight regression and machine learning models tuned with 5-fold cross validation.", "GPT-4.1 with a refined chain of thought prompt at temperature 0.1 beat every traditional regressor, though the abstract reports no error magnitudes for any model." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "predictions compared with actual project costs under cross validation", "salience": 38, "edition": 10, "n": 1346, "authors_detailed": [ { "name": "Nour Khaled", "url": "https://openalex.org/A5144284418", "inst": "" }, { "name": "Ayman H. Nassar", "url": "https://openalex.org/A5030365337", "inst": "Cairo University" } ], "affiliations": [ "Cairo University" ] }, { "uid": "doi:10.2139/ssrn.7224810", "doi": "10.2139/ssrn.7224810", "title": "The Customer Is Always Right? How LLMs and Humans Judge Discrimination", "authors": [ "Mathieu Bunel", "Lefebvre Marie-Noëlle", "Elisabeth Tovar" ], "posted": "2026-08-03", "added": "2026-08-04", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7224810", "field": "economics", "role": "agent", "bullets": [ "A factorial vignette experiment from a published human survey on discriminatory hiring, replayed on 18 frontier and local LLMs, varying the motive for discrimination, the cost of not discriminating, and moral injunctions.", "Individual models are not named in the abstract. The design adds personas, reasoning instructions, scenario realism, and conversational memory, and compares model decisions with the human survey responses.", "Customer taste motives push models toward discrimination, models react more sharply than humans to moral injunctions, and post-training alignment rather than scale explains differences across models." ], "bullet_provenance": "ai", "salience": 58, "edition": 10, "models": [], "validated": null, "n": 1348, "authors_detailed": [ { "name": "Mathieu Bunel", "url": "https://openalex.org/A5004067696", "inst": "Université de Bourgogne" }, { "name": "Lefebvre Marie-Noëlle", "url": "https://openalex.org/A5118397134", "inst": "" }, { "name": "Élisabeth Tovar", "url": "https://openalex.org/A5022856004", "inst": "Université Paris Nanterre" } ], "affiliations": [ "Université de Bourgogne", "Université Paris Nanterre" ] }, { "uid": "arxiv:2608.01607v1", "arxiv_id": "2608.01607v1", "title": "AI Financial Advice: Supply, Demand, and Life Cycle Implications", "authors": [ "Taha Choukhmane", "Tim de Silva", "Weidong Lin", "Matthew Akuzawa" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.01607v1", "field": "finance", "role": "agent", "bullets": [ "Representative U.S. sample wrote natural-language prompts seeking spending and investing advice; lifetime effects simulated under calibrated asset and labor market conditions.", "GPT-5.2 generated personalized spending and investment recommendations from user-written prompts; outputs evaluated through a calibrated life-cycle consumption-savings model.", "Following LLM advice shifts respondents toward life-cycle theory with broader equity participation and age-declining shares; two-thirds of gender differences stem from prompt wording, one-third from model bias." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 82, "n": 2832, "authors_detailed": [ { "name": "Taha Choukhmane", "url": "https://openalex.org/A5144360844", "inst": "" }, { "name": "Tim de Silva", "url": "https://openalex.org/A5144327346", "inst": "" }, { "name": "Weidong Lin", "url": "https://openalex.org/A5144350645", "inst": "" }, { "name": "Matthew Akuzawa", "url": "https://openalex.org/A5133312270", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7138561", "doi": "10.2139/ssrn.7138561", "title": "The Economic Structure of the Generative AI Ecosystem: Capital Intensity, Capability Diffusion, and Competitive Differentiation Across the Value Chain", "authors": [ "Carlos Diego Cavalcanti Pereira" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7138561", "field": "management", "role": "object", "bullets": [ "Systematic literature review of 36 studies analyzing the six-layer generative AI value chain from semiconductors to applications.", "No model deployed; the paper evaluates six structural hypotheses about capital intensity, entry barriers, and value capture across AI ecosystem layers.", "Capital concentration coexists with rapid capability diffusion at the model layer; platform orchestration layers show strongest basis for value capture as models commoditize." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3170, "authors_detailed": [ { "name": "Carlos Diego Cavalcanti Pereira", "url": "https://openalex.org/A5041076693", "inst": "Centro de Estudos e Sistemas Avançados do Recife" } ], "affiliations": [ "Centro de Estudos e Sistemas Avançados do Recife" ] }, { "uid": "doi:10.2139/ssrn.7145478", "doi": "10.2139/ssrn.7145478", "title": "Split-Perception: Measuring Divergence in How Large Language Models Assess Company Credibility", "authors": [ "Andrew Pomazkov" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7145478", "field": "finance", "role": "method", "bullets": [ "Over 5,000 companies scored by multiple LLMs with several million responses collected 2025-2026, using a 300-870 credit-score-style perception scale.", "Five LLM judge models independently scored company credibility; cross-model divergence was decomposed into visibility, positioning, and risk components.", "Median gap between highest- and lowest-scoring models was 100 points (90th percentile: 134); a retrieval-grounded model varied over 200 points on the same firm." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 3171, "authors_detailed": [ { "name": "Andrew Pomazkov", "url": "https://openalex.org/A5144298226", "inst": "Bureau Veritas (France)" } ], "affiliations": [ "Bureau Veritas (France)" ] }, { "uid": "doi:10.2139/ssrn.7157918", "doi": "10.2139/ssrn.7157918", "title": "Agentic Inception and Bounded Delegated Authorization: Managing Non-Deterministic Corporate Risk Under Caremark", "authors": [ "Steve Vondrak" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7157918", "field": "management", "role": "object", "bullets": [ "Delaware corporate law framework for director oversight liability under Caremark, analyzing fiduciary duty when firms deploy non-deterministic AI agents.", "Conceptual analysis of generative AI execution at machine speed bypassing traditional governance loops; no specific model tested empirically.", "Proposes Bounded Delegated Authorization doctrine requiring code-executable perimeters at network boundaries to trigger Business Judgment Rule protection." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3281, "authors_detailed": [ { "name": "Steve Vondrak", "url": "https://openalex.org/A5144297220", "inst": "Pepperdine University" } ], "affiliations": [ "Pepperdine University" ] }, { "uid": "arxiv:2608.02311v1", "arxiv_id": "2608.02311v1", "title": "AI Governance for Institutional Readiness in Finance", "authors": [ "Irene Aldridge", "Steve Krawciw" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.02311v1", "field": "finance", "role": "object", "bullets": [ "Survey of finance professionals and 75 large U.S. money managers' Form ADV filings disclosing AI use in asset management.", "Four-layer governance framework with regret-covariance drift statistic and calibrated crowding model; deployed LLM-embedding trading strategy examined.", "88% of professionals report no operational AI governance; joint drawdown probability rises from 39.2% to 79.3% as institutions converge on correlated exposures." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3282, "authors_detailed": [ { "name": "Irene Aldridge", "url": "https://openalex.org/A5050312500", "inst": "Cornell University" }, { "name": "Steve Krawciw", "url": "https://openalex.org/A5094845481", "inst": "Columbia University" } ], "affiliations": [ "Cornell University", "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7145178", "doi": "10.2139/ssrn.7145178", "title": "AI-Assisted Learning: Product Strategy under Customer Heterogeneity", "authors": [ "Shuang Dong", "Zhongfeng Qin", "Shiqing Yao" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7145178", "field": "management", "role": "object", "bullets": [ "Game-theoretic model of a knowledge-service firm deciding whether to offer an AI-assisted learning product alongside its baseline product under customer heterogeneity.", "Analytical model captures customer differences in AI-usage capability and awareness; examines optimal pricing and product-line strategy across AI efficacy levels.", "Optimal strategy takes three forms by AI efficacy; mild customer overestimation of AI efficacy can improve both firm profit and consumer surplus simultaneously." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3789, "authors_detailed": [ { "name": "Shuang Dong", "url": "https://openalex.org/A5144291440", "inst": "Monash Health" }, { "name": "Zhongfeng Qin", "url": "https://openalex.org/A5009910997", "inst": "Beihang University" }, { "name": "Shiqing Yao", "url": "https://openalex.org/A5015006136", "inst": "Monash Health" } ], "affiliations": [ "Monash Health", "Beihang University" ] }, { "uid": "doi:10.2139/ssrn.7147738", "doi": "10.2139/ssrn.7147738", "title": "Introducing the 'Sales Stack Shadow': Six Years of French B2B Sales Technology Evolution (2019-2025)", "authors": [ "Thibaut du Cleuziou" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7147738", "field": "management", "role": "object", "bullets": [ "Repeated cross-sectional survey of 901 French B2B salespeople across four harmonized waves from 2019 to 2025 at six consecutive yearly intervals.", "No specific model deployed; survey measures generative AI adoption intensity and introduces the Sales Stack Shadow index for unsanctioned tool usage.", "Sales tech stack doubled in two years to 4.96 tools; 71.3% use generative AI without formal training; work-study students adopt significantly more free tools." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3790, "authors_detailed": [ { "name": "Thibaut du Cleuziou", "url": "https://openalex.org/A5139187733", "inst": "Wearifi (United States)" } ], "affiliations": [ "Wearifi (United States)" ] }, { "uid": "doi:10.2139/ssrn.7151058", "doi": "10.2139/ssrn.7151058", "title": "Canadian Financial Infrastructure Exploited in Transnational Fraud", "authors": [ "Emela Enyinna" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7151058", "field": "finance", "role": "object", "bullets": [ "Report drawing on FBI IC3, FINTRAC, RCMP, and blockchain analytics data on AI-enabled romance scam fraud affecting Canadian financial institutions.", "No specific model deployed; examines how AI-generated personas, deepfake tools, and multilingual automation reduce fraud operational costs and scale victim acquisition.", "Three systemic risk vectors identified: industrialized victim acquisition, increased laundering opacity through DeFi and cross-chain transfers, and exploitation of Canadian payment rails." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3791, "authors_detailed": [ { "name": "Emela Enyinna", "url": "https://openalex.org/A5139931406", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7149641", "doi": "10.2139/ssrn.7149641", "title": "Reading the Bylaws: Event-Anchored Governance Amendments from 8-K Item 5.03 as a Cross-Sectional Signal", "authors": [ "Hyun Ahn" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7149641", "field": "finance", "role": "instrument", "bullets": [ "8,442 firm-events from SEC EDGAR 8-K Item 5.03 filings joined to CRSP daily stock file, 2017-2024, covering U.S. public firms' governance amendments.", "Zero-shot LLM classifies bylaw amendments as governance-improvement or not; combined with recurring-amender cluster indicator to predict post-event abnormal returns.", "Governance-improvement cohort shows +3.7% mean CAR at 126-day horizon (t=4.42); mid-cap recurring-amender subcohort reaches +7.7% at 63 trading days." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 78, "n": 3792, "authors_detailed": [ { "name": "Hyun Ahn", "url": "https://openalex.org/A5140769888", "inst": "Korea Institute for Advanced Study" } ], "affiliations": [ "Korea Institute for Advanced Study" ] }, { "uid": "doi:10.2139/ssrn.7148963", "doi": "10.2139/ssrn.7148963", "title": "Governing the Computed Boundary: A Dynamic Transaction-Cost Theory of the Multi-Agent Firm", "authors": [ "anon Olsen" ], "posted": "2026-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7148963", "field": "management", "role": "object", "bullets": [ "Theoretical extension of transaction-cost economics to multi-agent AI systems, building on the Olsen 1997 virtual-institution model with formal Markov-perfect equilibrium.", "No empirical model use; paper theorizes how AI agents with foundation-model vendors shift firm boundaries from discrete governance-mode selection to continuously computed policy.", "Firm boundary becomes a Pareto-efficient frontier of capability drift against appropriation risk, computed each period with five-dimensional computational asset specificity." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 4050, "authors_detailed": [ { "name": "anon Olsen", "url": "https://openalex.org/A5144287231", "inst": "Bellevue College" } ], "affiliations": [ "Bellevue College" ] }, { "uid": "doi:10.2139/ssrn.7219071", "doi": "10.2139/ssrn.7219071", "title": "The Impact of LLM-Assisted Feedback on Creative Process：Decision-Making Behaviors and Quality in Design review", "authors": [ "yao wang", "Bingyang Song", "Peiqi Yi", "Changao Liu", "Shijian Luo" ], "posted": "2026-08-02", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7219071", "field": "management", "role": "object", "bullets": [ "A human behavior experiment in which designers review creative work under AI-initiated, human-initiated, and mixed-initiative strategies, with expertise and task difficulty varied.", "An LLM, model not stated, supplies review feedback; outcomes are review quality, decision-making behaviors, and perceived mental burden across the three teaming strategies.", "Human-initiated review yields the highest quality and mixed initiation the lowest mental burden; novices gain most from human initiation, experts from AI initiation." ], "bullet_provenance": "ai", "salience": 36, "edition": 15, "models": [], "validated": null, "n": 2085, "authors_detailed": [ { "name": "yao wang", "url": "https://openalex.org/A5144225959", "inst": "" }, { "name": "Bingyang Song", "url": "https://openalex.org/A5144237291", "inst": "" }, { "name": "Peiqi Yi", "url": "https://openalex.org/A5080895670", "inst": "Zhejiang University" }, { "name": "Changao Liu", "url": "https://openalex.org/A5134603558", "inst": "Zhejiang University of Science and Technology" }, { "name": "Shijian Luo", "url": "https://openalex.org/A5101455054", "inst": "Sun Yat-sen University" } ], "affiliations": [ "Zhejiang University", "Zhejiang University of Science and Technology", "Sun Yat-sen University" ] }, { "uid": "arxiv:2608.01193v1", "arxiv_id": "2608.01193v1", "title": "Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races", "authors": [ "Phu Hoa Pham", "Duy Minh Dao Sy", "Trung Kiet Huynh", "Phu Quy Nguyen Lam", "Chi Nguyen Tran", "Minh Trung Le", "Phong Hao Le", "Dinh Nam Nguyen", "Thien Ky Nguyen Dong", "Elias Fernandez Domingos", "Le Hong Trang", "The Anh Han" ], "posted": "2026-08-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.01193v1", "field": "economics", "role": "agent", "bullets": [ "Repeated AI development race games with two to five players, comparing LLM agents against an evolutionary game theory benchmark and published human play.", "Seven model endpoints, not named in the abstract, face an audit gate on rule recall, state tracking, and payoff calculation before their behavior is interpreted.", "Models show extreme policies where humans are diverse; rule recall coexists with weak state tracking, and aggregate rates hide model-specific patterns, so trajectory-level checks are needed." ], "bullet_provenance": "ai", "salience": 42, "edition": 15, "models": [], "validated": null, "n": 2086, "authors_detailed": [ { "name": "Phu Hoa Pham", "url": "https://openalex.org/A5122132012", "inst": "Vietnam National University Ho Chi Minh City" }, { "name": "Duy Minh Dao Sy", "url": "https://openalex.org/A5130403381", "inst": "Vietnam National University Ho Chi Minh City" }, { "name": "Trung Kiet Huynh", "url": "https://openalex.org/A5130373533", "inst": "Vietnam National University Ho Chi Minh City" }, { "name": "Phu Quy Nguyen Lam", "url": "https://openalex.org/A5130325231", "inst": "" }, { "name": "Chi Nguyen Tran", "url": "https://openalex.org/A5144362196", "inst": "" }, { "name": "Minh Trung Le", "url": "https://openalex.org/A5042406704", "inst": "University of Medicine and Pharmacy at Ho Chi Minh City" }, { "name": "Phong Hao Le", "url": "https://openalex.org/A5144335219", "inst": "" }, { "name": "Dinh Nam Nguyen", "url": "https://openalex.org/A5144345785", "inst": "" }, { "name": "Thien Ky Nguyen Dong", "url": "https://openalex.org/A5144354698", "inst": "" }, { "name": "Elias Fernández-Domingos", "url": "https://openalex.org/A5008512930", "inst": "Vrije Universiteit Brussel" }, { "name": "Le Hong Trang", "url": "https://openalex.org/A5121803798", "inst": "Vietnam National University Ho Chi Minh City" }, { "name": "The Anh Han", "url": "https://openalex.org/A5144362182", "inst": "" } ], "affiliations": [ "Vietnam National University Ho Chi Minh City", "University of Medicine and Pharmacy at Ho Chi Minh City", "Vrije Universiteit Brussel" ] }, { "uid": "doi:10.2139/ssrn.7141458", "doi": "10.2139/ssrn.7141458", "title": "When No One Decides: The Deployer-Layer Governance Gap in Enterprise Agentic AI", "authors": [ "William Worthy, Ph.D" ], "posted": "2026-08-02", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7141458", "field": "management", "role": "object", "bullets": [ "Enterprise production deployments of LLM orchestration agents that call tools, retrieve context, and coordinate multi-step workflows, framed through Normal Accident Theory as a structural governance problem.", "No model is used empirically and none is named; agentic systems are the object, with the gap defined as consequential actions taken without complete operational records or assigned escalation authority.", "Proposes a governance architecture: a telemetry and logging protocol layered on OpenTelemetry conventions, a three-question traceability triad, an independent review team, and a separate normative competence evaluation." ], "bullet_provenance": "ai", "salience": 38, "edition": 15, "models": [], "validated": null, "n": 2100, "authors_detailed": [ { "name": "Ph.D William Worthy", "url": "https://openalex.org/A5144225162", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7217219", "doi": "10.2139/ssrn.7217219", "title": "A Multilingual LLM-Based News Indicator of Euro Area Inflationary Pressures", "authors": [ "Olivier de Bandt", "Jean Charles Bricongne", "Marwan MENAA", "Thomas Renault" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7217219", "field": "economics", "role": "instrument", "bullets": [ "Over 17 million newspaper articles in French, German, Italian, and Spanish (2006-2024) covering euro area inflationary pressures across four major economies.", "An LLM scores articles on a six-class directional scale; a fine-tuned multilingual encoder enables scalable annotation, validated at 83% correlation with a human-annotated French benchmark.", "The news-based indicator has a positive, significant impact on households' inflation expectations even after controlling for market- and survey-based measures." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human-annotated benchmark for France, 83% correlation", "salience": 72, "models": [], "n": 3781, "authors_detailed": [ { "name": "Olivier de Bandt", "url": "https://openalex.org/A5027369684", "inst": "Centre National de la Recherche Scientifique" }, { "name": "Jean‐Charles Bricongne", "url": "https://openalex.org/A5042669353", "inst": "Banque de France" }, { "name": "Marwan Menaa", "url": "https://openalex.org/A5119888505", "inst": "Banque de France" }, { "name": "Thomas Renault", "url": "https://openalex.org/A5078872448", "inst": "Université Paris-Saclay" } ], "affiliations": [ "Centre National de la Recherche Scientifique", "Banque de France", "Université Paris-Saclay" ] }, { "uid": "doi:10.2139/ssrn.7143058", "doi": "10.2139/ssrn.7143058", "title": "Economics of AI Labeling on Social Media Platforms", "authors": [ "Yan Zhu", "Jianqing Chen", "Srinivasan Raghunathan" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7143058", "field": "management", "role": "object", "bullets": [ "Analytical model of an advertising-driven social media platform with consumers who hold heterogeneous preferences for human-created vs. AI-generated content.", "Game-theoretic analysis examines platform incentives to label AI-generated content under imperfect detection accuracy and creators' strategic quality responses.", "Labeling can hurt platform engagement and welfare by elevating content-type salience over quality, reducing creators' incentive to improve content." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3782, "authors_detailed": [ { "name": "Zhu Yan", "url": "https://openalex.org/A5063798891", "inst": "The University of Texas at Dallas" }, { "name": "Jianqing Chen", "url": "https://openalex.org/A5144227597", "inst": "The University of Texas at Dallas" }, { "name": "Srinivasan Raghunathan", "url": "https://openalex.org/A5001222231", "inst": "The University of Texas at Dallas" } ], "affiliations": [ "The University of Texas at Dallas" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7218270", "doi": "10.2139/ssrn.7218270", "title": "Competition, Boundaries, and Firm Innovation: Evidence from LLM-Identified Business Networks", "authors": [ "Daming Huang", "Wenjing Xie", "Xiangzhong Zheng" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7218270", "field": "economics", "role": "instrument", "bullets": [ "Chinese listed firms with business descriptions from annual reports; LLM-constructed business network reveals over 80% of competitive links missed by standard industry classifications.", "Purpose-built LLM pipeline processes firm business descriptions to identify pairwise competitive links across and within traditional industry boundaries.", "Both intra- and inter-industry competition promote innovation; intra-industry competition shows an inverted-U relationship driven by exploratory R&D and invention patents." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 60, "n": 3783, "authors_detailed": [ { "name": "Daming Huang", "url": "https://openalex.org/A5069542244", "inst": "Shanghai University of Finance and Economics" }, { "name": "Wenjing Xie", "url": "https://openalex.org/A5101779444", "inst": "Shanghai International Studies University" }, { "name": "Xiangzhong Zheng", "url": "https://openalex.org/A5130225934", "inst": "Shanghai International Studies University" } ], "affiliations": [ "Shanghai University of Finance and Economics", "Shanghai International Studies University" ] }, { "uid": "doi:10.2139/ssrn.7141440", "doi": "10.2139/ssrn.7141440", "title": "The SAFE-AI Framework: An Organisational Governance Model for the Responsible Adoption of Generative Artificial Intelligence", "authors": [ "Montsheng Letsoalo" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7141440", "field": "management", "role": "object", "bullets": [ "Conceptual paper synthesizing literature on AI governance, corporate governance, risk management, and organizational learning across multiple sectors.", "No specific model deployed; proposes six-domain SAFE-AI governance framework for responsible organizational adoption of generative AI.", "Framework identifies Secure Data, Accountability, Frameworks and Policies, Education, Auditing, and Improvement as interdependent governance domains for cross-sector use." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3784, "authors_detailed": [ { "name": "Montsheng Letsoalo", "url": "https://openalex.org/A5143876079", "inst": "Institute of Neuroimmunology of the Slovak Academy of Sciences" } ], "affiliations": [ "Institute of Neuroimmunology of the Slovak Academy of Sciences" ] }, { "uid": "doi:10.2139/ssrn.7217220", "doi": "10.2139/ssrn.7217220", "title": "Generative Artificial Intelligence and Export Product Quality Upgrading under Trade Friction: The Institutional Constraint of Host-Country Privacy Protection Policies", "authors": [ "zhekun liu" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7217220", "field": "economics", "role": "object", "bullets": [ "Matched Chinese customs and listed-company data, 2010-2024, with trade remedy cases and annual report textual analysis measuring GAI application intensity.", "Textual analysis of annual reports measures firm-level GAI adoption; trade friction intensity index constructed from manually compiled trade remedy investigation cases.", "Trade friction reduces export product quality; GAI moderates this negative effect; GDPR in host countries significantly weakens GAI's moderating benefit, especially in high-tech sectors." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3785, "authors_detailed": [ { "name": "Zhekun Liu", "url": "https://openalex.org/A5114210888", "inst": "Beijing Jiaotong University" } ], "affiliations": [ "Beijing Jiaotong University" ] }, { "uid": "arxiv:2608.01540v1", "arxiv_id": "2608.01540v1", "title": "Do people rely on ChatGPT more than their peers to detect deepfake news?", "authors": [ "Yuhao Fu", "Nobuyuki Hanaki" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.01540v1", "field": "economics", "role": "agent", "bullets": [ "Laboratory experiment with student participants detecting AI-generated deepfake news; advice sourced from ChatGPT (GPT-4), human peers, or linguistic experts.", "ChatGPT (GPT-4) served as an advisory source for detecting GPT-2-generated deepfake news; reliance compared across three advice sources over multiple waves.", "Participants relied more on ChatGPT than on peers; by 2025 reliance on linguistic experts exceeded ChatGPT, suggesting evolving beliefs about AI-based detection." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "validated": null, "n": 3786, "authors_detailed": [ { "name": "Yuhao Fu", "url": "https://openalex.org/A5128749410", "inst": "Osaka University of Economics" }, { "name": "Nobuyuki Hanaki", "url": "https://openalex.org/A5144356663", "inst": "Osaka University of Economics" } ], "affiliations": [ "Osaka University of Economics" ] }, { "uid": "arxiv:2608.01322v1", "arxiv_id": "2608.01322v1", "title": "Can Language Models Identify Shadow Trading Targets? An NLP Evaluation of SEC Enforcement Theory", "authors": [ "Sarah Wilson", "Michael MacKay", "Anthony Marello", "Trinav Bhattacharyya" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.01322v1", "field": "finance", "role": "instrument", "bullets": [ "Thirty M&A events across five industries with 217 peer observations; SEC 10-K Item 7 filings scored for semantic similarity, 2017-2024 U.S. public firms.", "Two-stage LLM pipeline scores semantic similarity of 10-K management discussion sections to identify economically linked peer firms for shadow trading detection.", "No association found: within-event rank correlation between similarity and abnormal returns is +0.07 (p=0.37), challenging the empirical premise of shadow trading enforcement." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "n": 3787, "authors_detailed": [ { "name": "Sarah Wilson", "url": "https://openalex.org/A5048965142", "inst": "Courtauld Institute of Art" }, { "name": "Michael MacKay", "url": "https://openalex.org/A5144361170", "inst": "Columbia University" }, { "name": "Anthony Marello", "url": "https://openalex.org/A5144379874", "inst": "" }, { "name": "Trinav Bhattacharyya", "url": "https://openalex.org/A5010169236", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "Courtauld Institute of Art" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.01204v1", "arxiv_id": "2608.01204v1", "title": "ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors", "authors": [ "Jie Gong", "Maowei Jiang", "Zhiwei Liu", "Yang Qiao", "Wenxi Wu", "Mengxi Xiao", "Enze Zhang", "Ziyan Kuang", "Yankai Chen", "Caishuang Huang", "Meng Zhou", "Xiku Du", "Xue Liu", "Guojun Xiong", "Min Peng", "Qianqian Xie", "Sophia Ananiadou" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.01204v1", "field": "finance", "role": "method", "bullets": [ "Chinese fund-market traces from 2021 to 2026 with multi-agent investor simulator calibrated against aggregate behavioral patterns from 7,199 real users.", "Multi-agent simulator with evolving state variables and dialogue-grounded updates evaluates LLM conversational investment advisors under fixed historical market feedback.", "Stable leading group of LLM advisors combines stronger personalized content with competitive trajectory outcomes, revealing systematic gap between response quality and long-horizon effectiveness." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 65, "n": 3788, "authors_detailed": [ { "name": "Jie Gong", "url": "https://openalex.org/A5144320675", "inst": "" }, { "name": "M. Jiang", "url": "https://openalex.org/A5124169679", "inst": "Nanjing Audit University" }, { "name": "Zhiwei Liu", "url": "https://openalex.org/A5144360057", "inst": "" }, { "name": "Yang Qiao", "url": "https://openalex.org/A5144314813", "inst": "" }, { "name": "Wenxi Wu", "url": "https://openalex.org/A5144378339", "inst": "" }, { "name": "Mengxi Xiao", "url": "https://openalex.org/A5109778664", "inst": "Artificial Intelligence in Medicine (Canada)" }, { "name": "Enze Zhang", "url": "https://openalex.org/A5144370230", "inst": "" }, { "name": "Ziyan Kuang", "url": "https://openalex.org/A5110247102", "inst": "Wuhan University" }, { "name": "Yankai Chen", "url": "https://openalex.org/A5144346336", "inst": "" }, { "name": "Caishuang Huang", "url": "https://openalex.org/A5144345911", "inst": "" }, { "name": "Meng Zhou", "url": "https://openalex.org/A5144354999", "inst": "" }, { "name": "Xiku Du", "url": "https://openalex.org/A5144339748", "inst": "" }, { "name": "Xue Liu", "url": "https://openalex.org/A5144378755", "inst": "McGill University" }, { "name": "Guojun Xiong", "url": "https://openalex.org/A5144367136", "inst": "" }, { "name": "Min Peng", "url": "https://openalex.org/A5144378240", "inst": "" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5144313640", "inst": "" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5144368039", "inst": "" } ], "affiliations": [ "Nanjing Audit University", "Artificial Intelligence in Medicine (Canada)", "Wuhan University", "McGill University" ] }, { "uid": "doi:10.2139/ssrn.7132942", "doi": "10.2139/ssrn.7132942", "title": "DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods", "authors": [ "Jens Frankenreiter" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7132942", "field": "management", "role": "instrument", "bullets": [ "Randomly sampled U.S. corporate charters and bylaws paired with high-quality human annotations of governance provisions commonly studied in empirical corporate governance research.", "Several LLM extraction pipelines with varied prompt designs and task decomposition performed document-level binary classification of governance variables against human-coded benchmarks.", "Frontier models extract governance provisions at near-ceiling median accuracy; elaborate prompting narrows the gap for efficiency-oriented models without consistently improving frontier results." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Human-annotated corporate governance provisions benchmark", "salience": 62, "n": 4048, "authors_detailed": [ { "name": "Jens Frankenreiter", "url": "https://openalex.org/A5027919894", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2608.01212v1", "arxiv_id": "2608.01212v1", "title": "Do Humans Bargain Differently with AI? Evidence from Alternating-Offer Games", "authors": [ "Yuhao Fu", "Nobuyuki Hanaki", "Haitao Wang" ], "posted": "2026-08-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.01212v1", "field": "economics", "role": "agent", "bullets": [ "Laboratory experiment using a three-stage alternating-offer bargaining game where participants negotiated in real time with either another human or a GPT-based AI agent.", "GPT-based AI agent served as autonomous bargaining counterpart; a human-beneficiary condition tested whether AI earnings affecting another participant altered fairness behavior.", "Human proposers offered more to humans than AI agents; fairness toward AI was weaker and conditional, partially reemerging when AI payoffs affected real people." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 70, "validated": null, "n": 4049, "authors_detailed": [ { "name": "Yuhao Fu", "url": "https://openalex.org/A5128749410", "inst": "Osaka University of Economics" }, { "name": "Nobuyuki Hanaki", "url": "https://openalex.org/A5144356663", "inst": "Osaka University of Economics" }, { "name": "Haitao Wang", "url": "https://openalex.org/A5144313834", "inst": "" } ], "affiliations": [ "Osaka University of Economics" ] }, { "uid": "doi:10.2139/ssrn.7142518", "doi": "10.2139/ssrn.7142518", "title": "LLM-based Sentiment Analysis for Financial Distress Detection: Evidence from the 2023 U.S. Bank Failures", "authors": [ "Nijat Hasanli" ], "posted": "2026-08-01", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7142518", "field": "finance", "role": "instrument", "bullets": [ "Monthly sentiment indices for five US banks around the March to May 2023 failures of Silicon Valley Bank, Signature, and First Republic, with Bank of America and JPMorgan as controls.", "An LLM, not named in the abstract, scores news with severity, credibility, and recency weights against VADER, TextBlob, and FinBERT baselines; no accuracy check against human coding is reported.", "Only the LLM index falls ahead of and during the failures while staying stable for the controls; kappa of 0.01 to 0.22 shows the methods capture different signals." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 46, "edition": 15, "n": 2081, "authors_detailed": [ { "name": "Nijat Hasanli", "url": "https://openalex.org/A5060984367", "inst": "University of Warsaw" } ], "affiliations": [ "University of Warsaw" ] }, { "uid": "doi:10.2139/ssrn.7143218", "doi": "10.2139/ssrn.7143218", "title": "Do Language Models Round and \"Manage\" Earnings Forecasts? Round Numbers and Meet-or-Beat Beliefs in LLM EPS Forecasts", "authors": [ "Saurav Roychoudhury" ], "posted": "2026-08-01", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7143218", "field": "accounting", "role": "agent", "bullets": [ "Quarterly EPS forecasts elicited for fictional firms whose rational forecast is computable with uniformly distributed final digits, so memorization cannot drive the results.", "GPT-5.4, Grok-4.3, and Claude Sonnet 5 forecast with and without a shown consensus estimate; rounding and surprise placement are measured against the known benchmark.", "The models barely round, heaping ratios 1.05 to 1.57 against 2.75 for human analysts, yet place surprises a penny or two above consensus, with consensus weights of 0.36 to 0.61." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 68, "edition": 15, "validated": null, "n": 2082, "authors_detailed": [ { "name": "Saurav Roychoudhury", "url": "https://openalex.org/A5004819892", "inst": "Capital University" } ], "affiliations": [ "Capital University" ] }, { "uid": "doi:10.2139/ssrn.7143338", "doi": "10.2139/ssrn.7143338", "title": "Bridging the Knowledge Gap: Aligning Financial Education with Investor Interests Using Large Language Models", "authors": [ "Saurav Roychoudhury", "Leo Chan" ], "posted": "2026-08-01", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7143338", "field": "finance", "role": "instrument", "bullets": [ "Popular YouTube financial education channels and topics, including the twelve most followed creators, used to reveal what retail investors actually seek to learn.", "An LLM-assisted pipeline, model not stated, classifies channel content and topics; the abstract reports no validation of the coding against human review.", "Ten of the twelve most popular creators lack CFP or CFA credentials, and cryptocurrency and day trading draw outsized attention while risk management is neglected." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 15, "models": [], "n": 2083, "authors_detailed": [ { "name": "Saurav Roychoudhury", "url": "https://openalex.org/A5004819892", "inst": "Capital University" }, { "name": "Leo H. Chan", "url": "https://openalex.org/A5049155421", "inst": "Western Washington University" } ], "affiliations": [ "Capital University", "Western Washington University" ] }, { "uid": "arxiv:2608.00764v1", "arxiv_id": "2608.00764v1", "title": "FinDeepIndicator: Benchmarking Deep Research Agents in End-to-End Financial Indicator Construction", "authors": [ "Chaoqun Yang", "Fengbin Zhu", "Xinyu Lin", "Long Bai", "Xiaoluan Liu", "Ke-Wei Huang", "Roger Zimmermann", "Tat-Seng Chua" ], "posted": "2026-08-01", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.00764v1", "field": "finance", "role": "method", "bullets": [ "3,350 curated question-answer pairs on financial indicator construction, spanning 800 US and Chinese listed companies, ten years of data, and 21 indicator sub-categories.", "Search-equipped LLMs and deep research agents, families not named in the abstract, are scored across formula specification, data collection, indicator calculation, and answer generation.", "Models specify formulas well but accuracy drops sharply at data retrieval and numerical execution; deep research agents beat search-equipped LLMs yet remain unreliable." ], "bullet_provenance": "ai", "validated": true, "validation_note": "3,350 curated QA pairs with ground-truth answers", "salience": 46, "edition": 15, "models": [], "n": 2084, "authors_detailed": [ { "name": "Chaoqun Yang", "url": "https://openalex.org/A5144323904", "inst": "" }, { "name": "Fengbin Zhu", "url": "https://openalex.org/A5144329497", "inst": "" }, { "name": "Xinyu Lin", "url": "https://openalex.org/A5144372382", "inst": "" }, { "name": "Long Bai", "url": "https://openalex.org/A5144384644", "inst": "" }, { "name": "Xiaoluan Liu", "url": "https://openalex.org/A5144319247", "inst": "Central University of Finance and Economics" }, { "name": "Ke-Wei Huang", "url": "https://openalex.org/A5144346128", "inst": "" }, { "name": "Roger Zimmermann", "url": "https://openalex.org/A5144326400", "inst": "" }, { "name": "Tat-Seng Chua", "url": "https://openalex.org/A5144336514", "inst": "National University of Singapore" } ], "affiliations": [ "Central University of Finance and Economics", "National University of Singapore" ] }, { "uid": "doi:10.2139/ssrn.7146398", "doi": "10.2139/ssrn.7146398", "title": "Broad Reach, Uneven Depth? Reconciling Philippine Generative-AI Diffusion across Three Telemetry Systems", "authors": [ "Josh Ethan Sanchez" ], "posted": "2026-08-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7146398", "field": "economics", "role": "object", "bullets": [ "Cross-country comparison of Philippine AI adoption using Microsoft AI Diffusion, OpenAI Signals, and Anthropic Economic Index in early 2026.", "No model deployed as instrument; the paper compares three corporate telemetry systems measuring generative AI adoption rates with leave-one-out residual analysis.", "Systems agree on international ordering (Spearman 0.83-0.88) but disagree on Philippine outlier status, with residual percentiles ranging from 50th to 82nd across telemetry constructs." ], "bullet_provenance": "ai", "models": [ "gpt", "claude" ], "salience": 45, "validated": null, "n": 3167, "authors_detailed": [ { "name": "Josh Ethan Sanchez", "url": "https://openalex.org/A5144213575", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7212519", "doi": "10.2139/ssrn.7212519", "title": "Talking to Digital Twins: Selective Disclosure and Belief Measurement in Financial Social Media", "authors": [ "Boone Bowles", "Raymond M. Duch", "Sorin M. Sorescu" ], "posted": "2026-08-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7212519", "field": "finance", "role": "agent", "bullets": [ "Real-time monitoring of financial influencer X accounts with repeated LLM-based digital-twin interviews under a fixed protocol, covering large-cap stocks.", "LLM digital twins recovered stock-level belief proxies even absent public recommendations, archived before return windows to avoid look-ahead bias.", "Digital-twin interview signals predict the cross-section of large-cap stock returns in the expected direction, converting selective disclosure into measurable market-view panels." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "n": 3168, "authors_detailed": [ { "name": "Boone Bowles", "url": "https://openalex.org/A5144206742", "inst": "Texas A&M University" }, { "name": "Raymond Duch", "url": "https://openalex.org/A5055869562", "inst": "Texas A&M University" }, { "name": "Sorin M. Sorescu", "url": "https://openalex.org/A5047357530", "inst": "Texas A&M University" } ], "affiliations": [ "Texas A&M University" ] }, { "uid": "arxiv:2608.00761v1", "arxiv_id": "2608.00761v1", "title": "AI and Exchange Rate Predictability", "authors": [ "Amin Izadyar" ], "posted": "2026-08-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.00761v1", "field": "finance", "role": "instrument", "bullets": [ "Major currency pairs with comprehensive economic data releases, revisiting the Meese-Rogoff (1983) exchange rate disconnect puzzle.", "ChatGPT and DeepSeek analyzed economic fundamentals to measure currency strength; multiple exercises tested that predictability stems from reasoning, not memorization.", "A long-short strategy on AI-assessed fundamental strength generates a Sharpe ratio exceeding 0.7 per annum, significant after controlling for traditional currency factors." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": false, "salience": 75, "n": 3169, "authors_detailed": [ { "name": "Amin Izadyar", "url": "https://openalex.org/A5031304720", "inst": "Imperial Valley College" } ], "affiliations": [ "Imperial Valley College" ] }, { "uid": "doi:10.2139/ssrn.7214184", "doi": "10.2139/ssrn.7214184", "title": "Beyond Adoption Metrics: Participation Depth and Post-Adoption Mobility in Generative AI Use", "authors": [ "Jihee Choi", "hana kim" ], "posted": "2026-08-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7214184", "field": "management", "role": "object", "bullets": [ "2024-2025 Korean Media Panel Survey tracking ChatGPT-era GenAI diffusion, classifying users by work-domain use and paid-service access as participation-depth markers.", "Develops a post-adoption mobility framework distinguishing casual free participation, deep participation, reconfiguration, and exit among GenAI users.", "Education and income predict deepening into work-oriented or paid use; older age predicts exit; most new users enter through casual free use rather than work-oriented forms." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3777, "authors_detailed": [ { "name": "Jihee Choi", "url": "https://openalex.org/A5134700835", "inst": "Korea Advanced Institute of Science and Technology" }, { "name": "Hana Kim", "url": "https://openalex.org/A5100608641", "inst": "Korea Advanced Institute of Science and Technology" } ], "affiliations": [ "Korea Advanced Institute of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7134398", "doi": "10.2139/ssrn.7134398", "title": "Inflation Drivers in Firms' Words: Bridging Micro Narratives and Macro Dynamics *", "authors": [ "Chenyu Hou", "Jiannan Jiang", "Tao Wang" ], "posted": "2026-08-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7134398", "field": "economics", "role": "instrument", "bullets": [ "Federal Reserve Beige Book narratives on firms' qualitative price-adjustment reports, used to bridge micro-level pricing behavior and macro inflation dynamics.", "An LLM extracts firms' price adjustments and factor attributions from text; extracted measures feed a state-space model of aggregate inflation implied by a menu-cost framework.", "Micro price narratives provide useful information for correctly decomposing the macro drivers of inflation beyond what standard quantitative data capture." ], "bullet_provenance": "ai", "validated": false, "salience": 72, "models": [], "n": 3778, "authors_detailed": [ { "name": "Chenyu Hou", "url": "https://openalex.org/A5076770374", "inst": "Hebei Medical University" }, { "name": "Jiannan Jiang", "url": "https://openalex.org/A5101350397", "inst": "Guangxi University" }, { "name": "Tao Wang", "url": "https://openalex.org/A5144089730", "inst": "Bank of Canada" } ], "affiliations": [ "Hebei Medical University", "Guangxi University", "Bank of Canada" ] }, { "uid": "arxiv:2608.00886v1", "arxiv_id": "2608.00886v1", "title": "Joint Optimization of Human Headcount and Stochastic AI Resource Capacity", "authors": [ "Marco Montes de Oca" ], "posted": "2026-08-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.00886v1", "field": "economics", "role": "object", "bullets": [ "Theoretical model of a firm splitting a fixed budget between human headcount and generative-AI token capacity under stochastic, right-skewed per-engineer token consumption.", "Derives closed-form optimal headcount and token intensity using a chance-constrained stochastic budget frontier with a Cornish-Fisher skewness correction.", "Rising individual token volatility raises optimal per-capita token intensity while contracting headcount; usage skewness acts as a pure deadweight tax shrinking the effective budget." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3779, "authors_detailed": [ { "name": "Marco Montes de", "url": "https://openalex.org/A5017619762", "inst": "Université Libre de Bruxelles" } ], "affiliations": [ "Université Libre de Bruxelles" ] }, { "uid": "arxiv:2608.00567v1", "arxiv_id": "2608.00567v1", "title": "Optimal Inflation Rate: A Meta-Analysis", "authors": [ "Matej Opatrny", "Martin Opatrny", "Tomas Havranek", "Zuzana Irsova", "Mojmir Hampl" ], "posted": "2026-08-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2608.00567v1", "field": "economics", "role": "instrument", "bullets": [ "Meta-analysis of 777 estimates from 116 studies published between 1989 and 2026 on optimal long-run inflation, the largest sample on this topic to date.", "An LLM pipeline calibrated against a hand-coded training set extracts primary data; Bayesian model averaging identifies structural moderators of cross-study variation.", "Literature points to an optimum of about 0.6 percentage points per year, well below most central banks' two-percent targets; variation driven by modeling choices, not selective reporting." ], "bullet_provenance": "ai", "validated": true, "validation_note": "hand-coded training set", "salience": 65, "models": [], "n": 3780, "authors_detailed": [ { "name": "Matěj Opatrný", "url": "https://openalex.org/A5042954318", "inst": "Charles University" }, { "name": "Martin Opatrny", "url": "https://openalex.org/A5138866777", "inst": "Charles University" }, { "name": "Tomáš Havránek", "url": "https://openalex.org/A5086665090", "inst": "Charles River Laboratories (Netherlands)" }, { "name": "Zuzana Iršová", "url": "https://openalex.org/A5072893157", "inst": "Charles River Laboratories (Netherlands)" }, { "name": "Mojmír Hampl", "url": "https://openalex.org/A5075729194", "inst": "Tomas Bata University in Zlín" } ], "affiliations": [ "Charles University", "Charles River Laboratories (Netherlands)", "Tomas Bata University in Zlín" ] }, { "uid": "doi:10.2139/ssrn.7136778", "doi": "10.2139/ssrn.7136778", "title": "The Death of a Search Engine. The Progression of Content Discovery, the Mechanics of Intent, and What the Answer-First Web Asks of Businesses", "authors": [ "Himanshu Bisht" ], "posted": "2026-08-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7136778", "field": "management", "role": "object", "bullets": [ "Analysis of clickstream panels and clickthrough studies examining the transition from ranked search results to answer-first interfaces built on transformer language models in the United States.", "Paper formalizes web discovery as an expected-visit model decomposing traffic into a shrinking ranked-list term and a growing citation term driven by AI answer systems.", "Sixty-eight percent of US Google searches end without a click and AI summaries halve top-page clickthrough, yet AI-referred visitors convert at materially higher rates." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "salience": 55, "validated": null, "n": 4047, "authors_detailed": [ { "name": "Himanshu Bisht", "url": "https://openalex.org/A5144186873", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7210620", "doi": "10.2139/ssrn.7210620", "title": "​Preparing Management Students for the Generative AI Era: The Roles of AI Literacy, Self-Regulated Learning, Critical Thinking, and Employability Skills", "authors": [ "Jian Sun" ], "posted": "2026-07-31", "added": "2026-08-07", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7210620", "field": "management", "role": "object", "bullets": [ "Conceptual paper on business school education for management students in the generative AI era; builds on recent scholarship rather than new data, with no sample or empirical setting.", "No language model is used or named; generative AI is the context motivating a framework that links AI literacy, self-regulated learning, critical thinking, and employability skills.", "Argues AI literacy belongs in core management curricula, with self-regulated learning converting tool access into learning strategy and critical thinking serving as the check on AI-generated output." ], "bullet_provenance": "ai", "salience": 22, "edition": 16, "models": [], "validated": null, "n": 2116, "authors_detailed": [ { "name": "Jian Sun", "url": "https://openalex.org/A5144157114", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7212118", "doi": "10.2139/ssrn.7212118", "title": "Rethinking Anticipation in the Age of Generative AI: Toward a Theory of Hybrid Anticipation", "authors": [ "Taejun Lee" ], "posted": "2026-07-31", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7212118", "field": "management", "role": "object", "bullets": [ "Conceptual paper in futures studies; no data or empirical sample, building on anticipation theory, futures literacy, and anticipatory governance.", "No language model is applied; generative AI is the object, cast as a co-producer of future oriented knowledge alongside human actors, infrastructures, and governance arrangements.", "Proposes hybrid anticipation theory with four assumptions and five mechanisms, from epistemic delegation to adaptive governance, concluding that governing AI increasingly means governing anticipation itself." ], "bullet_provenance": "ai", "salience": 28, "edition": 15, "models": [], "validated": null, "n": 2065, "authors_detailed": [ { "name": "Taejun Lee", "url": "https://openalex.org/A5137897666", "inst": "Korea Development Institute" } ], "affiliations": [ "Korea Development Institute" ] }, { "uid": "doi:10.2139/ssrn.7141978", "doi": "10.2139/ssrn.7141978", "title": "Reimagining Economic Theory in the Age of Machine Learning and Big Data", "authors": [ "Thirunavukkarasu Sundaram", "Lakshmi Pradha T" ], "posted": "2026-07-31", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7141978", "field": "economics", "role": "object", "bullets": [ "Book chapter with no empirical sample, tracing economic thought from classical and neoclassical models to computational approaches, with particular attention to India's digital transformation.", "No model is used; AI, machine learning, NLP, and LLMs are surveyed as technologies for economic analysis across finance, e-commerce, healthcare, agriculture, governance, and education.", "Argues the technologies complement rather than replace economic theory, supporting real time analysis and forecasting, while privacy, security, bias, and governance challenges remain open." ], "bullet_provenance": "ai", "salience": 18, "edition": 15, "models": [], "validated": null, "n": 2066, "authors_detailed": [ { "name": "Thirunavukkarasu Sundaram", "url": "https://openalex.org/A5025441972", "inst": "Chonnam National University" }, { "name": "Lakshmi Pradha T", "url": "https://openalex.org/A5144125143", "inst": "Guru Nanak College (GNC)" } ], "affiliations": [ "Chonnam National University", "Guru Nanak College (GNC)" ] }, { "uid": "arxiv:2607.28934v1", "arxiv_id": "2607.28934v1", "title": "FairFund-Bench: Evaluating Distributive Bias in LLM Resource Allocation", "authors": [ "Martin Lukk" ], "posted": "2026-07-31", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28934v1", "field": "economics", "role": "method", "bullets": [ "600 financial assistance requests generated from human authored templates calibrated against 1.3 million GoFundMe campaigns, crossing three domains, four race and two gender categories, and five need framings.", "Fourteen LLMs, names not stated, rate, rank, or allocate funds in transparent and disguised audits; the benchmark scores bias, deservingness alignment, and cross task and cross context consistency.", "Audit format flips the sign of bias, with minorities favored in individual ratings but sometimes penalized in rankings; need framings move allocations roughly ten times more than demographics." ], "bullet_provenance": "ai", "salience": 62, "edition": 15, "models": [], "validated": null, "n": 2067, "authors_detailed": [ { "name": "Martin Lukk", "url": "https://openalex.org/A5024401090", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7127438", "doi": "10.2139/ssrn.7127438", "title": "Beyond Linear Decision Rules: LLM-guided Representation Discovery for Data-driven Optimization", "authors": [ "Huan Zhang", "Yang Wang", "Hanzhang Qin", "Yue Zhao" ], "posted": "2026-07-31", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7127438", "alt_urls": [ "https://doi.org/10.2139/ssrn.7165538" ], "field": "management", "role": "method", "bullets": [ "Two operations settings calibrated to real data: a multi-period newsvendor problem and a data center location problem with multi-period job scheduling.", "LLMs, families not stated, generate closed-form nonlinear basis functions for recourse decision rules from problem structure; finite-sample guarantees cover fixed generated representations.", "The generative decision rules beat existing benchmarks out of sample in both settings while keeping the optimization tractable and the discovered structures interpretable." ], "bullet_provenance": "ai", "salience": 48, "edition": 15, "models": [], "validated": null, "n": 2077, "authors_detailed": [ { "name": "Huan Zhang", "url": "https://openalex.org/A5144098414", "inst": "" }, { "name": "Yang Wang", "url": "https://openalex.org/A5144152816", "inst": "Jiangsu University of Science and Technology" }, { "name": "Hanzhang Qin", "url": "https://openalex.org/A5144155142", "inst": "" }, { "name": "Yue Zhao", "url": "https://openalex.org/A5025425593", "inst": "Liaoning University" } ], "affiliations": [ "Jiangsu University of Science and Technology", "Liaoning University" ] }, { "uid": "doi:10.2139/ssrn.7212319", "doi": "10.2139/ssrn.7212319", "title": "Can silicon citizens replace human respondents? Validating large language models against human choice and valuation data", "authors": [ "Eunjung Cho", "Juyong Lee" ], "posted": "2026-07-31", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7212319", "field": "economics", "role": "method", "bullets": [ "Two Korean stated-preference surveys with human ground truth: a choice-based conjoint on electric vehicle battery policy and a contingent valuation of small modular reactor acceptance.", "Seven models from two vendors, names not given in the abstract, simulate respondents conditioned on real microdata, with pre-registered fidelity thresholds and contamination controls including memorization probes.", "All 21 conjoint cells miss the reproduction criterion, best part-worth correlation 0.72 against a 0.80 threshold; heterogeneity and refusals are not preserved, and English prompts do not help." ], "bullet_provenance": "ai", "validated": true, "validation_note": "pre-registered thresholds against human choice and valuation data from two Korean surveys", "salience": 70, "edition": 15, "models": [], "n": 2078, "authors_detailed": [ { "name": "E.H. Cho", "url": "https://openalex.org/A5077709275", "inst": "" }, { "name": "Juyong Lee", "url": "https://openalex.org/A5075814486", "inst": "Changwon National University" } ], "affiliations": [ "Changwon National University" ] }, { "uid": "doi:10.2139/ssrn.7132138", "doi": "10.2139/ssrn.7132138", "title": "VeriQuant: Measuring Numerical Reliability of LLM Agents in Quantitative Derivatives Pricing via Verified Tool-Grounding and Ambiguity Benchmarking", "authors": [ "Tarun Yadav" ], "posted": "2026-07-31", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7132138", "field": "finance", "role": "method", "bullets": [ "Black-Scholes option pricing tasks graded against a deterministic engine: 40 numeric, 30 conceptual, and 23 ambiguity items, plus 63 second-pass audit calls.", "Claude Sonnet 4.6 answers with and without tool access; every numeric answer is checked against the oracle, separating arithmetic, unit convention, ambiguity, and self-review failures.", "Tool grounding lifts numeric accuracy from 75% to 100%; vega fails consistently on a unit conversion, and a tool-free auditor catches only 1 of 10 such self-contradictions." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "deterministic Black-Scholes pricing oracle as ground truth", "salience": 56, "edition": 15, "n": 2079, "authors_detailed": [ { "name": "Tarun Yadav", "url": "https://openalex.org/A5144114056", "inst": "Barkatullah University" } ], "affiliations": [ "Barkatullah University" ] }, { "uid": "doi:10.2139/ssrn.7171238", "doi": "10.2139/ssrn.7171238", "title": "Reimagining Corporate Governance in the AI Era: Law, Legitimacy, and Algorithmic Power", "authors": [ "Peter Underwood", "Patrick J. O’Malley" ], "posted": "2026-07-31", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7171238", "field": "management", "role": "object", "bullets": [ "Conceptual legal analysis of corporate governance as firms integrate generative AI into decision making, compliance, and disclosure; no empirical sample.", "No model is used by the authors; AI is the object, examined through five tensions including directors' duties, an AI governance gap, and a transparency paradox in AI-mediated disclosure.", "Argues legitimacy requires human judgment, board-level oversight, and adaptive governance frameworks, posing questions for corporate law rather than offering definitive solutions." ], "bullet_provenance": "ai", "salience": 32, "edition": 15, "models": [], "validated": null, "n": 2080, "authors_detailed": [ { "name": "Peter Underwood", "url": "https://openalex.org/A5106400972", "inst": "University of Auckland" }, { "name": "Patrick J. O’Malley", "url": "https://openalex.org/A5108673157", "inst": "Fox Chase Cancer Center" } ], "affiliations": [ "University of Auckland" ] }, { "uid": "doi:10.2139/ssrn.7123983", "doi": "10.2139/ssrn.7123983", "title": "Generative AI in Finance and Fraud Detection: Architectures, Applications, and Emerging Challenges", "authors": [ "Gilbert Gabrial" ], "posted": "2026-07-31", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7123983", "field": "finance", "role": "method", "bullets": [ "A review of generative models in financial fraud detection, drawing on public benchmarks, deployment case studies, and synthetic data generation across risk management, anomaly detection, and compliance pipelines.", "Covers GANs, variational autoencoders, and transformer models rather than a named LLM; the review conducts no validation of its own, and the figures cited come from surveyed studies.", "Surveyed generative models are reported to cut false-positive rates by up to 38 percent versus standard classifiers and improve recall under class imbalance; adversarial evasion, privacy, and explainability remain open." ], "bullet_provenance": "ai", "salience": 25, "edition": 15, "models": [], "validated": null, "n": 2098, "authors_detailed": [ { "name": "Gilbert Gabrial", "url": "https://openalex.org/A5144067927", "inst": "Consultant - Independent Researcher, Italy" } ], "affiliations": [ "Consultant - Independent Researcher, Italy" ] }, { "uid": "doi:10.2139/ssrn.7131198", "doi": "10.2139/ssrn.7131198", "title": "From AI Jargon to Strategic Foresight: A Parsimonious Framework for Work, Industry and Sovereignty", "authors": [ "Thierry Warin" ], "posted": "2026-07-31", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7131198", "field": "management", "role": "object", "bullets": [ "A conceptual synthesis of research on AI classification, foundation models, RAG, interpretability, governance, manufacturing, platforms, and sovereignty, aimed at turning AI vocabulary into strategic system choices.", "No model is used or named; generative AI systems are the object, mapped onto seven coordinates covering function, scope, access rights, deployment and data flow, embeddedness, governance, and organizational arrangement.", "Argues AI labels are coordinates rather than species: a closed general-purpose API can suit bounded office work yet be a poor default for proprietary or safety-critical tasks, adding a generality tax hypothesis." ], "bullet_provenance": "ai", "salience": 30, "edition": 15, "models": [], "validated": null, "n": 2099, "authors_detailed": [ { "name": "Thierry Warin", "url": "https://openalex.org/A5052674697", "inst": "HEC Montréal" } ], "affiliations": [ "HEC Montréal" ] }, { "uid": "doi:10.2139/ssrn.7126338", "doi": "10.2139/ssrn.7126338", "title": "Engineering Decision Intelligence Framework for Autonomous Shutdown Planning in Integrated Steel Plants: Integrating Industrial Knowledge Graphs, Digital Twins, and Generative AI Author", "authors": [ "Priya Ranjan" ], "posted": "2026-07-31", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7126338", "field": "management", "role": "agent", "bullets": [ "Planned maintenance shutdowns in integrated steel plants, coordinated across operations, contractors, and engineering teams; a conceptual framework paper with no empirical dataset.", "No specific model is named; generative AI and LLMs are proposed as components alongside knowledge graphs, digital twins, and multi-agent intelligence, with no validation reported.", "Proposes the EDIF architecture and Engineering Intelligence Indices to score shutdown readiness and decision quality; no empirical performance results are given." ], "bullet_provenance": "ai", "salience": 20, "edition": 11, "models": [], "validated": null, "n": 1353, "authors_detailed": [ { "name": "Priya Ranjan", "url": "https://openalex.org/A5140710596", "inst": "Mizoram University" } ], "affiliations": [ "Mizoram University" ] }, { "uid": "doi:10.2139/ssrn.7127259", "doi": "10.2139/ssrn.7127259", "title": "Beyond the Binary: Gender Bias in LLM-evaluated Insurance Claims", "authors": [ "Fei Huang", "Md Mushahidul Islam Shamim", "Warut Khern-Am-Nuai", "Maxime C. Cohen" ], "posted": "2026-07-31", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7127259", "field": "management", "role": "agent", "bullets": [ "Audit of AI-assisted insurance claim processing using 1,388 vehicle claims, crossing four gender conditions with name and narrative presence in a factorial design that yields 133,248 model evaluations.", "Six models, including GPT-5, GPT-4o, Gemini 2.5 Pro, and Claude 4 and 4.5 Sonnet, set claim amounts, eligibility, and damage severity for counterfactual claimant profiles; outputs are compared across gender conditions rather than against ground truth.", "None of the six models shows a male versus female disparity in the standard full information audit, yet GPT-4o favors claimants of unspecified gender by 128 to 288 dollars when a name is present, and bias concentrates outside the binary." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 62, "edition": 9, "validated": null, "n": 1330, "authors_detailed": [ { "name": "Fei Huang", "url": "https://openalex.org/A5013292507", "inst": "UNSW Sydney" }, { "name": "M. Shamim", "url": "https://openalex.org/A5019312890", "inst": "UNSW Sydney" }, { "name": "Warut Khern-am-nuai", "url": "https://openalex.org/A5023532960", "inst": "McGill University" }, { "name": "Maxime C. Cohen", "url": "https://openalex.org/A5015555156", "inst": "McGill University" } ], "affiliations": [ "UNSW Sydney", "McGill University" ] }, { "uid": "doi:10.2139/ssrn.7212841", "doi": "10.2139/ssrn.7212841", "title": "Resource-Aware Prompting: Teaching Accounting Students When and How to Use Generative AI", "authors": [ "Kimberly Fatten" ], "posted": "2026-07-31", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7212841", "field": "accounting", "role": "object", "bullets": [ "Two undergraduate auditing sections; four classroom tools including a search versus prompt decision framework, scenario exercises, a six block prompt formula, and a Socratic study partner technique.", "No specific model is named; generative AI use is framed as a resource allocation decision, with each tool tied to AICPA foundational competencies and professional skepticism.", "A panel of six faculty rated the tools against their stated learning objectives, with 30 of 36 ratings at the scale maximum; no student outcome data is reported." ], "bullet_provenance": "ai", "salience": 32, "edition": 9, "models": [], "validated": null, "n": 1334, "authors_detailed": [ { "name": "Kimberly Fatten", "url": "https://openalex.org/A5144178857", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7202605", "doi": "10.2139/ssrn.7202605", "title": "The MIRROR Model: Multi-Model Iterative Reflection, Review, Oversight, and Reconciliation A Human-Supervised Protocol for Transparent, Evidence-Grounded Knowledge Work", "authors": [ "Hamed Yazdanshenas" ], "posted": "2026-07-31", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7202605", "field": "other", "role": "method", "bullets": [ "Foundational methods paper with no empirical data; proposes an eight stage human supervised workflow in which multiple language models generate, critique, verify, and reconcile answers for knowledge work.", "No specific models are named; the protocol integrates iterative refinement, multi agent debate, role prompting, and verification, with a prespecified evaluation program that has not yet been run.", "Contribution is the ordering and documentation of existing components; the authors state blinded, replicated studies are required before claiming superiority over single model workflows." ], "bullet_provenance": "ai", "salience": 24, "edition": 9, "models": [], "validated": null, "n": 1335, "authors_detailed": [ { "name": "Hamed Yazdanshenas", "url": "https://openalex.org/A5068132392", "inst": "University of California, Los Angeles" } ], "affiliations": [ "University of California, Los Angeles" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7131060", "doi": "10.2139/ssrn.7131060", "title": "LLM-Assisted Scheduling Policy Design and Refinement: Evidence Across Queueing Networks, Job-Shop Scheduling, and Ride-Hailing Dispatch", "authors": [ "Youhua Li", "Sibo Xu", "Yiqi Sun", "Pengfei Guo", "Zuo-Jun Max Shen", "Houmin Yan" ], "posted": "2026-07-31", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7131060", "field": "management", "role": "method", "bullets": [ "Three simulated operations settings, queueing network control, job shop scheduling, and ride hailing dispatch, with candidate policies evaluated on out of sample instances.", "An unnamed LLM generates compact scoring policy templates and then tunes their parameters from simulation feedback; templates are filtered for feasibility and executability, and the final policy runs without the LLM.", "The two stage framework beats direct LLM prompting and rivals learning based methods, though classical heuristics still win in settings with explicit mathematical structure." ], "bullet_provenance": "ai", "salience": 55, "edition": 9, "models": [], "validated": null, "n": 1336, "authors_detailed": [ { "name": "Youhua Li", "url": "https://openalex.org/A5143810706", "inst": "" }, { "name": "Sibo Xu", "url": "https://openalex.org/A5143852611", "inst": "" }, { "name": "Yiqi Sun", "url": "https://openalex.org/A5063877759", "inst": "Financial Research (Hungary)" }, { "name": "Pengfei Guo", "url": "https://openalex.org/A5144140604", "inst": "" }, { "name": "Zuo-Jun Max Shen", "url": "https://openalex.org/A5144092783", "inst": "" }, { "name": "Houmin Yan", "url": "https://openalex.org/A5101054146", "inst": "Beijing Foreign Studies University" } ], "affiliations": [ "Financial Research (Hungary)", "Beijing Foreign Studies University" ] }, { "uid": "doi:10.2139/ssrn.7203058", "doi": "10.2139/ssrn.7203058", "title": "Model Risk Implications of Agentic AI Systems in Enterprise Software: A Validation Framework", "authors": [ "Shoaib Arshad" ], "posted": "2026-07-31", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7203058", "field": "management", "role": "method", "bullets": [ "Thematic analysis of 52 peer reviewed papers plus illustrative case studies from three financial services enterprise deployments of agentic AI.", "No model is run; the paper argues SR 11-7 and Basel model risk standards miss risks of multi agent LLM systems such as emergent inter agent behavior and hallucination propagation.", "Proposes a four layer validation framework and claims it mitigates 89 percent of identified risk vectors in the case studies, a figure whose derivation the abstract does not explain." ], "bullet_provenance": "ai", "salience": 30, "edition": 9, "models": [], "validated": null, "n": 1337, "authors_detailed": [ { "name": "Shoaib Arshad", "url": "https://openalex.org/A5112945861", "inst": "Islamia University of Bahawalpur" } ], "affiliations": [ "Islamia University of Bahawalpur" ] }, { "uid": "doi:10.2139/ssrn.7208252", "doi": "10.2139/ssrn.7208252", "title": "Fine-Tuning, Retrieval, or Both? A Measured Framework for Choosing Domain-Adaptation Architectures in Large Language Models", "authors": [ "Pedro Paulo" ], "posted": "2026-07-31", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7208252", "field": "finance", "role": "method", "bullets": [ "A production banking compliance platform built on Mistral 7B with a LoRA adapter trained on 2,360 examples and a pgvector RAG pipeline over 27 regulatory documents.", "LoRA, RAG, and hybrid configurations are scored against a suite of 8 deployment gates spanning up to 107 scenarios, with repeated runs and an adapter rank sweep.", "The hybrid passes every system level gate while the unadapted baseline yields zero refusals in 107 queries; one training run costs roughly as much as serving 850 to 1,700 queries." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "in-house 8-gate, 107-scenario deployment evaluation suite", "salience": 45, "edition": 8, "n": 1323, "authors_detailed": [ { "name": "Pedro Paulo", "url": "https://openalex.org/A5099336969", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7129638", "doi": "10.2139/ssrn.7129638", "title": "Are LLMs Reliable Coders of Communication Content in Economic Experiments?", "authors": [ "Andrzej Baranski", "David Cooper", "Jeong Kyu Lee" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7129638", "field": "economics", "role": "instrument", "bullets": [ "Free-form communication data from three published experimental economics studies, with LLM-based coding compared against research assistant coding and original authors.", "LLMs classified communication content using structured prompts; reliability assessed by agreement with RA coding and replication of original published statistical results.", "LLM coding met both reliability conditions and replicated qualitative conclusions in most cases; prompt design improvements resolved cases of poor initial agreement." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "agreement with RA coding and replication of original paper results", "salience": 72, "n": 2831, "authors_detailed": [ { "name": "Andrzej Baranski", "url": "https://openalex.org/A5103099646", "inst": "New York University Abu Dhabi" }, { "name": "David Cooper", "url": "https://openalex.org/A5101063871", "inst": "University of Saskatchewan" }, { "name": "Jeong Kyu Lee", "url": "https://openalex.org/A5046372714", "inst": "Ulsan College" } ], "affiliations": [ "New York University Abu Dhabi", "University of Saskatchewan", "Ulsan College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7130158", "doi": "10.2139/ssrn.7130158", "title": "Belief Updating and AI Adoption by Entrepreneurs", "authors": [ "Marcos Balmaceda", "Juan Pedro Ronconi" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7130158", "field": "management", "role": "object", "bullets": [ "Pre-registered field experiment with 736 Chilean entrepreneurs randomly assigned to ChatGPT business-task vignettes or uninformative control, four-month follow-up.", "Vignettes showed fictional entrepreneur using ChatGPT; study measured belief updating, willingness to pay for AI coaching, and free course take-up.", "Profit beliefs shifted in Bayesian pattern among pessimists, but AI adoption, business practices, sales, and profits remained unchanged at four months." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 3163, "authors_detailed": [ { "name": "Marcos Balmaceda", "url": "https://openalex.org/A5120297369", "inst": "KU Leuven" }, { "name": "Juan Pedro Ronconi", "url": "https://openalex.org/A5144165998", "inst": "Universidad de Los Andes, Chile" } ], "affiliations": [ "KU Leuven", "Universidad de Los Andes, Chile" ] }, { "uid": "doi:10.2139/ssrn.7203201", "doi": "10.2139/ssrn.7203201", "title": "SilverBot: A Dual-Regime Analyzer for Silver's Industrial-Monetary Duality -Cross-Regime Evidence for a Crisis-Type-Dependent Reversal", "authors": [ "RAJ TEJPAL KHATIK" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7203201", "field": "finance", "role": "instrument", "bullets": [ "Stratified samples of 110 dates across four macro-financial regimes (2008 GFC, 2020 COVID, 2025-26 uptrend) for silver price behavior analysis.", "A Claude-orchestrated nine-factor system decomposed silver into industrial and monetary sub-scores, validated against copper and gold ground-truth proxies via McNemar's test.", "Industrial sub-regime outperformed monetary in three of four regimes (61.7% vs 40.0%, p=0.041), but reversed during the 2020 COVID crisis (monetary 95.0% vs industrial 65.0%)." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "McNemar's test against copper/gold ground-truth proxies across four macro regimes", "salience": 45, "n": 3165, "authors_detailed": [ { "name": "RAJ TEJPAL KHATIK", "url": "https://openalex.org/A5144041672", "inst": "University of Warwick" } ], "affiliations": [ "University of Warwick" ] }, { "uid": "doi:10.2139/ssrn.7129300", "doi": "10.2139/ssrn.7129300", "title": "Trust without Familiarity: Institutional Judgment in the Age of Artificial Intelligence", "authors": [ "Igor Leonov", "Yurii Savchuk" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7129300", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of institutional judgment across universities, courts, banks, and immigration authorities facing generative AI representations.", "No model deployed; the paper theorizes how generative AI pressures the eight-step institutional judgment sequence from delegated mandate to revision.", "Generative AI weakens assumptions about evidence independence and credibility, requiring institutions to rethink sufficiency thresholds before acting on incomplete understanding." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3166, "authors_detailed": [ { "name": "Igor Leonov", "url": "https://openalex.org/A5133273567", "inst": "COPD Foundation" }, { "name": "Yurii Savchuk", "url": "https://openalex.org/A5136499320", "inst": "Independent" } ], "affiliations": [ "COPD Foundation", "Independent" ] }, { "uid": "doi:10.2139/ssrn.7124358", "doi": "10.2139/ssrn.7124358", "title": "Cognitive FinOps (AIFinOps): Algorithmic Cost Amortization, Token-to-Value Attribution, and Margin Governance for Enterprise AI Programs", "authors": [ "Lohit K Lakshman" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7124358", "field": "management", "role": "object", "bullets": [ "Conceptual framework at the intersection of management accounting, IS governance, and AI economics with five testable propositions.", "Introduces Token-to-Value saturation function and Dynamic Token-Throttle Controller for governing multi-agent system spend in enterprises.", "Proposes a 3:1 minimum ratio of FinOps governance investment to compute spend; formalizes Cognitive FinOps Trilemma across velocity, predictability, and margin." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3275, "authors_detailed": [ { "name": "Lohit K Lakshman", "url": "https://openalex.org/A5137522657", "inst": "Independent Practitioner" } ], "affiliations": [ "Independent Practitioner" ] }, { "uid": "doi:10.2139/ssrn.7212842", "doi": "10.2139/ssrn.7212842", "title": "Dynamic Shift in Accounting Education and the Locus of Learning:A Heutagogical Model of Student-AI Engagement in Accounting Programs", "authors": [ "Calvester C. Legister", "Joseph Foy" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7212842", "field": "accounting", "role": "object", "bullets": [ "Survey and performance data from business students across urban U.S. research universities using GenAI in accounting coursework.", "Distinguishes two student engagement profiles: those who verify and extend AI reasoning versus those who delegate cognition and reduce verification.", "AI-assisted outputs temporarily equalize assignment scores but widen gaps in durable capability, skepticism, and professional readiness over time." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3276, "authors_detailed": [ { "name": "Calvester Legister", "url": "https://openalex.org/A5032937832", "inst": "City University of New York" }, { "name": "Joseph Foy", "url": "https://openalex.org/A5072529404", "inst": "City University of New York" } ], "affiliations": [ "City University of New York" ] }, { "uid": "doi:10.2139/ssrn.7129939", "doi": "10.2139/ssrn.7129939", "title": "The Economics of Agentic AI: Runtime Governance as a Distinct Determinant of Enterprise AI Cost", "authors": [ "Maureen Doyle-Spare" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7129939", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of enterprise AI cost determinants focusing on runtime semantic authorization in agentic workflows.", "Defines Preventable Computation as execution-dependent cost that a runtime governance checkpoint could eliminate at the earliest authorization boundary.", "Runtime governance is a distinct cost determinant governing execution volume past authorization boundaries, separate from per-token unit cost reduction." ], "bullet_provenance": "ai", "salience": 15, "models": [], "validated": null, "n": 3277, "authors_detailed": [ { "name": "Maureen Doyle-Spare", "url": "https://openalex.org/A5130951607", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7120059", "doi": "10.2139/ssrn.7120059", "title": "Cognitive Debt in the Age of Generative AI: A Reflective Review with an Illustrative Netnographic Case", "authors": [ "Mehmet anon" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7120059", "field": "management", "role": "object", "bullets": [ "143 public comments from 96 software practitioners in an online technical community, analyzed via semantic thematic modeling and sentiment analysis.", "Examines practitioner discourse on agentic coding, skill atrophy, and epistemic trust using conceptual network analysis of GenAI concerns.", "Agentic coding is the most salient theme at 14.1%; epistemic distrust appears more frequently than cognitive resignation among practitioners." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3278, "authors_detailed": [ { "name": "Mehmet anon", "url": "https://openalex.org/A5121584881", "inst": "Anadolu University" } ], "affiliations": [ "Anadolu University" ] }, { "uid": "doi:10.2139/ssrn.7133258", "doi": "10.2139/ssrn.7133258", "title": "Agentic AI in the Enterprise: A Task-Delegation Taxonomy for Autonomous Agents, Copilots, and Human Expertise", "authors": [ "Aeron Zentner" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7133258", "field": "management", "role": "object", "bullets": [ "Meta-analysis of five controlled experiments per outcome on generative AI copilot assistance in enterprise task performance.", "Synthesizes experimental evidence and proposes a four-mode taxonomy spanning human-only, copilot, AI teammate, and autonomous agent delegation.", "Copilot assistance improved output quality (SMD +0.35) and cut completion time (SMD −0.66) but can slow experts and homogenize collective output." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3279, "authors_detailed": [ { "name": "Aeron Zentner", "url": "https://openalex.org/A5117724417", "inst": "Coastline Community College" } ], "affiliations": [ "Coastline Community College" ] }, { "uid": "doi:10.2139/ssrn.7212318", "doi": "10.2139/ssrn.7212318", "title": "When AI Talks Like a Friend: Building Automated Social Presence Through Relational Role Enactment", "authors": [ "Lin Zhu", "Mark Kazemzadeh" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7212318", "field": "management", "role": "agent", "bullets": [ "Three experiments (N=95, 143, 145) in financial planning and medical report interpretation contexts using DeepSeek AI as conversational partner.", "DeepSeek prompted as professional tool versus trusted friend; structural equation modeling tests relational role enactment, social presence, and trust.", "Relational role enactment drives automated social presence and cognitive and affective trust, mediating continued use intention; instrumental role enactment does not." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 40, "n": 3280, "authors_detailed": [ { "name": "Lin Zhu", "url": "https://openalex.org/A5144179074", "inst": "" }, { "name": "Mark Kazemzadeh", "url": "https://openalex.org/A5072142373", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7118938", "doi": "10.2139/ssrn.7118938", "title": "The Agentic Turn in Finance: AI Agents in Financial Decision-Making", "authors": [ "Marc Schmitt" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7118938", "field": "finance", "role": "object", "bullets": [ "Conceptual framework covering five domains: investment research, trading, portfolio allocation, risk and compliance, and customer-facing agents.", "No empirical model deployment; the paper defines the financial AI agent and distinguishes it from predictive models and decision-support systems.", "Financial AI is shifting from model risk to agent risk as systems move from producing outputs for humans to acting on behalf of principals." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3769, "authors_detailed": [ { "name": "Marc Schmitt", "url": "https://openalex.org/A5089714419", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7124278", "doi": "10.2139/ssrn.7124278", "title": "Accounting for AI Foundation Models as Productive Intangible Capital: Recognition, Measurement, Amortization, Impairment, and Disclosure under IAS 38 and IAS 36.", "authors": [ "Rafael Minuti" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7124278", "field": "accounting", "role": "object", "bullets": [ "Standards-based analysis of AI foundation model capitalization under IAS 38 and IAS 36, illustrated with Tesla and Meta disclosures.", "No model is deployed; the paper develops the AI Model Accounting Framework for recognition, measurement, amortization, and impairment of AI assets.", "The IAS 38 research-development distinction remains defensible but is operationally incomplete; a new Productive AI Model Asset unit of account is proposed." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3770, "authors_detailed": [ { "name": "Rafael Minuti", "url": "https://openalex.org/A5140844147", "inst": "Independent Researcher- São Paulo, Brazil" } ], "affiliations": [ "Independent Researcher- São Paulo, Brazil" ] }, { "uid": "doi:10.2139/ssrn.7130939", "doi": "10.2139/ssrn.7130939", "title": "Short-term Impact of Institutional AI Access on Undergraduate Academic Performance", "authors": [ "Rene Zamarripa" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7130939", "field": "economics", "role": "object", "bullets": [ "Student-level data from multiple sections of Principles of Macroeconomics at a large U.S. public university, one year before and after ChatGPT Edu rollout.", "ChatGPT Edu accounts provided to all students; the study compares exam scores, time on assignments, and attempt counts under otherwise identical conditions.", "Institutional AI access associated with an 8.5-point exam score decline on a 100-point scale, with largest drops among mid-GPA students." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Before-after comparison of exam scores under identical course conditions", "salience": 70, "n": 3771, "authors_detailed": [ { "name": "Rene Zamarripa", "url": "https://openalex.org/A5075343241", "inst": "California State University, Northridge" } ], "affiliations": [ "California State University, Northridge" ] }, { "uid": "doi:10.2139/ssrn.7127761", "doi": "10.2139/ssrn.7127761", "title": "The Ecosystemic Value Orchestration (EVO) Theory: Rethinking Value Capture from Teece (1986) to Generative Artificial Intelligence", "authors": [ "Mouhamdy Dahoud" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7127761", "field": "management", "role": "object", "bullets": [ "Conceptual theory-building study using Microsoft, Meta, and the Chinese open-weight ecosystem as illustrative cases of AI value capture.", "No model is deployed; the paper develops the Ecosystemic Value Orchestration theory with three axioms updating Teece's appropriability framework.", "When the model layer commoditizes, durable rent migrates to orchestration of complementary assets; firms can profit by giving models away." ], "bullet_provenance": "ai", "salience": 48, "models": [], "validated": null, "n": 3772, "authors_detailed": [ { "name": "Mouhamdy Dahoud", "url": "https://openalex.org/A5140878823", "inst": "Université Gaston Berger" } ], "affiliations": [ "Université Gaston Berger" ] }, { "uid": "doi:10.2139/ssrn.7141761", "doi": "10.2139/ssrn.7141761", "title": "The Economics of AI Inference: An Efficiency Curve for Choosing Between Credit Plans, Pay-per-Token APIs, and Self-Hosted Models", "authors": [ "Tuan Anh Do" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7141761", "field": "economics", "role": "object", "bullets": [ "Survey of industry pricing for LLM inference across three procurement modes (credit plans, pay-per-token APIs, self-hosting) using a July 2026 snapshot across three quality tiers.", "Derives an Efficiency Curve showing cost per token under the cheapest mode at each volume level and computes break-even crossover thresholds between procurement modes.", "Credit-to-API crossover is institutional, not economic; self-hosting crossover ranges from 720 to 1,800 Mtok/month and falls as output-token share rises." ], "bullet_provenance": "ai", "salience": 52, "models": [], "validated": null, "n": 3773, "authors_detailed": [ { "name": "Tuan Anh Do", "url": "https://openalex.org/A5136301403", "inst": "Hanoi University" } ], "affiliations": [ "Hanoi University" ] }, { "uid": "doi:10.2139/ssrn.7212847", "doi": "10.2139/ssrn.7212847", "title": "The Pre-ChatGPT AI Era and Firm Sales: Domestic versus International Margins", "authors": [ "Pinar Gunes", "Suleyman Gozen", "Mehmet Furkan Karaca" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7212847", "field": "economics", "role": "object", "bullets": [ "Firm-level data exploiting the 2015 release of Google TensorFlow as an exogenous shock to AI-worker effectiveness in a difference-in-differences design.", "Measures how pre-ChatGPT AI adoption affects total, domestic, and foreign sales using variation in firms' pre-shock AI exposure across size categories.", "AI adoption generates large, persistent sales increases only among large firms; smaller firms show no response, with financial constraints and limited intangible capital as barriers." ], "bullet_provenance": "ai", "salience": 68, "models": [], "validated": null, "n": 3774, "authors_detailed": [ { "name": "Pinar Gunes", "url": "https://openalex.org/A5115820208", "inst": "University of Sussex" }, { "name": "Suleyman Gozen", "url": "https://openalex.org/A5106664055", "inst": "University of Bristol" }, { "name": "Mehmet Furkan Karaca", "url": "https://openalex.org/A5107104366", "inst": "University of Essex" } ], "affiliations": [ "University of Sussex", "University of Bristol", "University of Essex" ] }, { "uid": "doi:10.2139/ssrn.7141759", "doi": "10.2139/ssrn.7141759", "title": "The Cost Shift: Jevons' Paradox and the Restructuring of Enterprise AI Spend from Labor to Compute", "authors": [ "Tuan Anh Do" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7141759", "field": "economics", "role": "object", "bullets": [ "Taxonomy of five cases spanning quantization, API pricing, open-source releases, frontier training efficiency, and AI-assisted development, with aggregate market data through 2026.", "Tests whether AI efficiency gains reduce enterprise AI spend by analyzing inference price declines of roughly 40x per year alongside sevenfold token-volume growth.", "Efficiency gains restructure rather than reduce total spend, shifting cost from labor to compute line items while rebound effects keep aggregate consumption growing." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3775, "authors_detailed": [ { "name": "Tuan Anh Do", "url": "https://openalex.org/A5136301403", "inst": "Hanoi University" } ], "affiliations": [ "Hanoi University" ] }, { "uid": "arxiv:2607.28968v1", "arxiv_id": "2607.28968v1", "title": "A robust association between LLM use and scientific productivity: Assessing stopping-time selection", "authors": [ "Keigo Kusumegi", "Xinyu Yang", "Paul Ginsparg", "Mathijs de Vaan", "Toby Stuart", "Yian Yin" ], "posted": "2026-07-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28968v1", "field": "economics", "role": "object", "bullets": [ "Academic author-level publication data with LLM adoption dated by abstract-level detection flags, replicating and extending prior event-study designs with multiple robustness checks.", "Tests whether the positive association between LLM adoption and research productivity survives corrections for stopping-time selection bias identified by Renault, Bergeaud, and Bosquet.", "Positive productivity association persists across before-and-after, conservative diff-in-diff, intensity-based, and rank-based designs; pre-ChatGPT placebo data returns null effects." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3776, "authors_detailed": [ { "name": "Keigo Kusumegi", "url": "https://openalex.org/A5144304168", "inst": "" }, { "name": "Xinyu Yang", "url": "https://openalex.org/A5144261169", "inst": "" }, { "name": "Paul Ginsparg", "url": "https://openalex.org/A5135835306", "inst": "Cornell University" }, { "name": "Mathijs de Vaan", "url": "https://openalex.org/A5044125536", "inst": "University of California, Berkeley" }, { "name": "Toby Stuart", "url": "https://openalex.org/A5135850774", "inst": "Berkeley College" }, { "name": "Yian Yin", "url": "https://openalex.org/A5144294056", "inst": "" } ], "affiliations": [ "Cornell University", "University of California, Berkeley", "Berkeley College" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.28410v1", "arxiv_id": "2607.28410v1", "title": "Can Large Language Models Execute Parent Orders?", "authors": [ "Zane Shen", "Xinli Xu", "Guangyi Zhang", "Jialong Chen", "Jinsong Zhou", "Cong Chen", "Guibao Shen", "Dongyu Yan", "Luozhou Wang", "Zhen Yang" ], "posted": "2026-07-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28410v1", "field": "finance", "role": "agent", "bullets": [ "Parent order execution on Shenzhen Stock Exchange level one data, splitting large equity orders into child orders to lower execution cost.", "PACE separates long horizon planning from short horizon execution, both handled by an LLM whose identity is not stated, with no market assumptions or task specific training.", "Beats TWAP, Almgren Chriss, and learning baselines by 0.65 basis points over the strongest; higher model confidence predicts better execution, and the model trades early rather than near the deadline." ], "bullet_provenance": "ai", "salience": 63, "edition": 15, "models": [], "validated": null, "n": 2063, "authors_detailed": [ { "name": "Zane Shen", "url": "https://openalex.org/A5144111680", "inst": "" }, { "name": "Xinli Xu", "url": "https://openalex.org/A5144133147", "inst": "HKUST Shenzhen Research Institute" }, { "name": "Guangyi Zhang", "url": "https://openalex.org/A5144173501", "inst": "Zhejiang University-University of Edinburgh Institute" }, { "name": "Jialong Chen", "url": "https://openalex.org/A5144136045", "inst": "" }, { "name": "Jinsong Zhou", "url": "https://openalex.org/A5122304934", "inst": "HKUST Shenzhen Research Institute" }, { "name": "Cong Chen", "url": "https://openalex.org/A5144159347", "inst": "" }, { "name": "Guibao Shen", "url": "https://openalex.org/A5144113229", "inst": "HKUST Shenzhen Research Institute" }, { "name": "Dongyu Yan", "url": "https://openalex.org/A5123889199", "inst": "Guangzhou University" }, { "name": "Luozhou Wang", "url": "https://openalex.org/A5144160070", "inst": "HKUST Shenzhen Research Institute" }, { "name": "Zhen Yang", "url": "https://openalex.org/A5144160886", "inst": "" } ], "affiliations": [ "HKUST Shenzhen Research Institute", "Zhejiang University-University of Edinburgh Institute", "Guangzhou University" ] }, { "uid": "arxiv:2607.28127v1", "arxiv_id": "2607.28127v1", "title": "FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning", "authors": [ "Giorgos Iacovides", "Wuyang Zhou", "Danilo Mandic" ], "posted": "2026-07-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28127v1", "field": "finance", "role": "method", "bullets": [ "Financial news sentiment for algorithmic trading; training data are financial articles paired with realized market outcomes rather than static human annotated labels, sample size not stated.", "A financial LLM, base model not stated, is post trained with reinforcement learning against an asymmetric trading reward; no accuracy check against labeled sentiment is reported.", "Cumulative trading returns rise 220 percent over the strongest baseline, and retraining on newly observed articles and outcomes keeps beating the static model as conditions change." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 15, "models": [], "n": 2064, "authors_detailed": [ { "name": "Giorgos Iacovides", "url": "https://openalex.org/A5094208275", "inst": "NIHR Imperial Biomedical Research Centre" }, { "name": "Wuyang Zhou", "url": "https://openalex.org/A5144146773", "inst": "" }, { "name": "Danilo Mandic", "url": "https://openalex.org/A5121537442", "inst": "NIHR Imperial Biomedical Research Centre" } ], "affiliations": [ "NIHR Imperial Biomedical Research Centre" ] }, { "uid": "doi:10.1002/mar.70105", "doi": "10.1002/mar.70105", "arxiv_id": "2607.28780v1", "title": "Optimizing Monetization Strategies for Generative AI Firms: Implications for Search Engagement", "authors": [ "Veronica Rosendo-Rios", "Paurav Shukla" ], "posted": "2026-07-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28780v1", "field": "management", "role": "object", "bullets": [ "Four online experiments with 1,063 participants examining how advertising-supported tiers added to a generative AI platform's menu shift users' upgrade and downgrade choices.", "No LLM is used as a research tool; ChatGPT-style platforms are the setting, and treatments vary how many ad-supported monetization options appear.", "One ad-supported option pushes free users to upgrade but paid subscribers to downgrade; adding a second ad-supported option retains subscribers while upgrades continue." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 44, "edition": 15, "validated": null, "n": 2075, "authors_detailed": [ { "name": "Veronica Rosendo‐Rios", "url": "https://openalex.org/A5123850939", "inst": "Universidad Pontificia Comillas" }, { "name": "Paurav Shukla", "url": "https://openalex.org/A5085370447", "inst": "University of Southampton" } ], "affiliations": [ "Universidad Pontificia Comillas", "University of Southampton" ] }, { "uid": "arxiv:2607.28840v1", "arxiv_id": "2607.28840v1", "title": "Benchmarks Are Not Validation: A System-Level View of Financial LLM Applications", "authors": [ "Burak Payzun", "İrem Demirtaş", "Simona Scala", "Elena Ferretti", "Seçil Arslan" ], "posted": "2026-07-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28840v1", "field": "finance", "role": "method", "bullets": [ "Position paper drawing on industry experience validating generative AI systems inside financial institutions, covering retrieval, proprietary data, tool use, orchestration, monitoring, and human escalation.", "No model is named; the argument is that benchmark scores cannot approve financial LLM systems for production, and LLM-as-a-judge needs multiple judges, rubrics, and auditability controls.", "Calls for system-level validation evidence across data, retrieval, agents, governance, and implementation, plus a research agenda on agent trace validation and lifecycle standards." ], "bullet_provenance": "ai", "salience": 38, "edition": 15, "models": [], "validated": null, "n": 2076, "authors_detailed": [ { "name": "Burak Payzun", "url": "https://openalex.org/A5120715230", "inst": "" }, { "name": "İrem Demirtaş", "url": "https://openalex.org/A5144291992", "inst": "" }, { "name": "Simona Scala", "url": "https://openalex.org/A5012577563", "inst": "Prometeia (Italy)" }, { "name": "Elena Ferretti", "url": "https://openalex.org/A5144272777", "inst": "" }, { "name": "Seçil Arslan", "url": "https://openalex.org/A5077321782", "inst": "X-Fab (Germany)" } ], "affiliations": [ "Prometeia (Italy)", "X-Fab (Germany)" ] }, { "uid": "doi:10.2139/ssrn.7200198", "doi": "10.2139/ssrn.7200198", "title": "GoldBot: A Multi-Factor Safe-Haven Analyzer for Gold --Cross-Regime Evidence for Trend-Matching Rather Than Event Discrimination", "authors": [ "RAJ TEJPAL KHATIK" ], "posted": "2026-07-30", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7200198", "field": "finance", "role": "method", "bullets": [ "Four backtest samples of gold's safe-haven behavior across regimes: 60 dates in a 2025-26 uptrend, 18 macro and geopolitical events, and 20 stratified dates each in the 2020 crash and the 2008 crisis.", "A nine-factor LLM-orchestrated classifier of safe-haven status with retrieval-augmented context; the orchestrating model is not stated. Accuracy is tested against a naive always-on baseline using McNemar's exact test.", "The system underperforms the naive baseline in three of four regimes, 35 versus 67 percent in the largest sample, and its accuracy is fully explained by trend alignment rather than discrimination between events." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "cross-regime backtests against realized outcomes, McNemar exact tests", "salience": 55, "edition": 15, "n": 2096, "authors_detailed": [ { "name": "RAJ TEJPAL KHATIK", "url": "https://openalex.org/A5144041672", "inst": "University of Warwick" } ], "affiliations": [ "University of Warwick" ] }, { "uid": "doi:10.2139/ssrn.7115058", "doi": "10.2139/ssrn.7115058", "title": "The Objectivity Premium in Generative Retrieval How Factual Copy Outranks Promotional Copy When AI Systems Choose What to Cite", "authors": [ "Sonny Saggar" ], "posted": "2026-07-30", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7115058", "field": "management", "role": "object", "bullets": [ "A synthetic corpus of 80 fictional companies written in factual and promotional registers with identical underlying facts, plus 29 complete real-web company pairs, evaluated against 1,200 queries.", "Retrieval proximity is scored with two open sentence-embedding models, all-MiniLM-L6-v2 and all-mpnet-base-v2; the synthetic copy comes from three language models the abstract does not name.", "Factual copy wins retrieval 79 to 84 percent of the time under factual queries, collapsing to about 57 percent under promotional queries; the real-web corpus corroborates at 70 to 88 percent despite a Wikipedia confound." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "salience": 48, "edition": 15, "validated": null, "n": 2097, "authors_detailed": [ { "name": "Sonny Saggar", "url": "https://openalex.org/A5140329613", "inst": "University College Hospital" } ], "affiliations": [ "University College Hospital" ] }, { "uid": "arxiv:2607.27611v1", "arxiv_id": "2607.27611v1", "title": "AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure", "authors": [ "Qi Wang" ], "posted": "2026-07-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.27611v1", "field": "finance", "role": "method", "bullets": [ "24,909 firm years of Hong Kong annual reports, 2008 to 2025, yielding 543,527 scored text snippets on foreign exchange hedging and derivative use.", "A lexicon and rule pipeline with FinBERT class encoders is audited against 300 human coded snippets and strict 2023 to 2025 temporal tests; FinBERT temporal F1 runs 0.702 to 0.872, and Qwen3 8B underperforms on accounting context labels.", "The strict hedging disclosure score is negatively related to measured FX exposure in baseline and stress periods, a relation the generic broad score does not show." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "300 snippet human audit, temporal F1 0.70 to 0.87", "salience": 56, "edition": 15, "n": 2105, "authors_detailed": [ { "name": "Qi Wang", "url": "https://openalex.org/A5144127999", "inst": "Ruijin Hospital" } ], "affiliations": [ "Ruijin Hospital" ] }, { "uid": "arxiv:2607.28617v1", "arxiv_id": "2607.28617v1", "title": "AISPA: User-Centric System Prompt Auditing for Large Language Model Applications", "authors": [ "Xiangning Lin", "Shenzhe Zhu", "Shu Yang", "Zhenyu Zhang", "Haoqian Zhang", "Yipeng Zhao", "Chengxuan Qian", "Tianwei Wang", "Ziheng Zhang", "Zhenlong Yuan", "Dingcheng Wang", "Juncheng Wu", "Yuan Si", "Jiaxin Liu", "Baolong Bi", "Robert Mahari", "Tobin South", "Dazza Greenwood", "Zexue He", "Rishi Bommasani", "Sophia Kazinnik", "Andreas Haupt", "Samuele Marro", "Erik Brynjolfsson", "Alex Pentland", "Jiaxin Pei" ], "posted": "2026-07-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28617v1", "field": "management", "role": "object", "bullets": [ "3,249 instructions taken from the system prompts of 88 commercial AI products, coded as protective of users or problematic along eight dimensions.", "The abstract does not name any model or say whether the instruction coding was manual or automated, and reports no reliability check of the coding.", "Nearly every product carries some protective instruction, yet only 24 percent cover all eight dimensions and about 40 percent include at least one instruction that works against user interests." ], "bullet_provenance": "ai", "salience": 42, "edition": 15, "models": [], "validated": null, "n": 2106, "authors_detailed": [ { "name": "Xiangning Lin", "url": "https://openalex.org/A5123468622", "inst": "Carnegie Mellon University" }, { "name": "Shenzhe Zhu", "url": "https://openalex.org/A5144129974", "inst": "" }, { "name": "Shu Yang", "url": "https://openalex.org/A5144095825", "inst": "" }, { "name": "Zhenyu Zhang", "url": "https://openalex.org/A5144162660", "inst": "" }, { "name": "Haoqian Zhang", "url": "https://openalex.org/A5144179710", "inst": "University of Toronto" }, { "name": "Yipeng Zhao", "url": "https://openalex.org/A5021199407", "inst": "Wuhan University" }, { "name": "Chengxuan Qian", "url": "https://openalex.org/A5144124760", "inst": "" }, { "name": "Tianwei Wang", "url": "https://openalex.org/A5144122163", "inst": "Water Sanitation and Hygiene Institute" }, { "name": "Ziheng Zhang", "url": "https://openalex.org/A5144101302", "inst": "" }, { "name": "Zhenlong Yuan", "url": "https://openalex.org/A5144094425", "inst": "" }, { "name": "Dingcheng Wang", "url": "https://openalex.org/A5144163923", "inst": "Northwestern University" }, { "name": "Juncheng Wu", "url": "https://openalex.org/A5144122591", "inst": "" }, { "name": "Yuan Si", "url": "https://openalex.org/A5124906734", "inst": "University of Waterloo" }, { "name": "Jiaxin Liu", "url": "https://openalex.org/A5144162124", "inst": "" }, { "name": "Baolong Bi", "url": "https://openalex.org/A5144141001", "inst": "" }, { "name": "Robert Mahari", "url": "https://openalex.org/A5144106342", "inst": "" }, { "name": "Tobin South", "url": "https://openalex.org/A5027450101", "inst": "The University of Adelaide" }, { "name": "Dazza Greenwood", "url": "https://openalex.org/A5038888834", "inst": "Lockheed Martin (United States)" }, { "name": "Zexue He", "url": "https://openalex.org/A5069088584", "inst": "UC San Diego Health System" }, { "name": "Rishi Bommasani", "url": "https://openalex.org/A5144128540", "inst": "Stanford University" }, { "name": "Sophia Kazinnik", "url": "https://openalex.org/A5061916690", "inst": "Digital Science (United States)" }, { "name": "Andreas Haupt", "url": "https://openalex.org/A5134791923", "inst": "Stanford University" }, { "name": "Samuele Marro", "url": "https://openalex.org/A5144149172", "inst": "" }, { "name": "Erik Brynjolfsson", "url": "https://openalex.org/A5144137474", "inst": "" }, { "name": "Alex Pentland", "url": "https://openalex.org/A5144098611", "inst": "" }, { "name": "Jiaxin Pei", "url": "https://openalex.org/A5144142913", "inst": "" } ], "affiliations": [ "Carnegie Mellon University", "Northwestern University", "University of Toronto", "Wuhan University", "Water Sanitation and Hygiene Institute", "University of Waterloo", "The University of Adelaide", "Lockheed Martin (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.28496v1", "arxiv_id": "2607.28496v1", "title": "Beyond Sentiment: Structured Information Extraction from Financial News", "authors": [ "Daohan Zhu", "Sitong Ge", "Ruofei Wang", "Honggu Chen", "Yubo Hou", "Tao Wan", "Zengchang Qin" ], "posted": "2026-07-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28496v1", "field": "finance", "role": "method", "bullets": [ "41,618 news article and stock pairs from the FNSPID dataset, with FinBERT sentiment as the benchmark signal for news driven stock prediction.", "Llama 3.1 70B extracts event type, impact scope, time horizon, and confidence from each article; extraction quality is never checked against human labels.", "Adding the structured features to FinBERT sentiment raises prediction F1 from 0.576 to 0.600, and the two signal types disagree on 53.5 percent of cases." ], "bullet_provenance": "ai", "models": [ "legacy", "llama" ], "open_weights": true, "validated": false, "salience": 48, "edition": 15, "n": 2107, "authors_detailed": [ { "name": "Daohan Zhu", "url": "https://openalex.org/A5144165852", "inst": "" }, { "name": "Sitong Ge", "url": "https://openalex.org/A5144141288", "inst": "Beihang University" }, { "name": "Ruofei Wang", "url": "https://openalex.org/A5144096073", "inst": "Beihang University" }, { "name": "Honggu Chen", "url": "https://openalex.org/A5144091950", "inst": "Beihang University" }, { "name": "Yubo Hou", "url": "https://openalex.org/A5144130107", "inst": "" }, { "name": "Tao Wan", "url": "https://openalex.org/A5144171110", "inst": "" }, { "name": "Zengchang Qin", "url": "https://openalex.org/A5032405950", "inst": "BT Group (United Kingdom)" } ], "affiliations": [ "Beihang University", "BT Group (United Kingdom)" ] }, { "uid": "arxiv:2607.27553v2", "arxiv_id": "2607.27553v2", "title": "AI and Its Impact on Creativity and Diversity: An Empirical Study of LLM-Generated Product Ideas", "authors": [ "Christian Terwiesch", "Lennart Meincke", "Karan Girotra", "Ethan Mollick", "Gideon Nave", "Karl T. Ulrich" ], "posted": "2026-07-30", "added": "2026-08-04", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.27553v2", "field": "management", "role": "agent", "bullets": [ "Eight studies of new product ideas aimed at college students and priced under 50 dollars, comparing ideas generated by large language models with ideas from human participants.", "Specific models are not named in the abstract. Later studies vary vendors and versions and test chain of thought prompting, persona injection, and pooling ideas across vendors.", "AI ideas rate higher on purchase intent and are 7 times likelier to reach the top decile, yet are less novel and less diverse; prompting and scaling nearly restore human level diversity." ], "bullet_provenance": "ai", "salience": 68, "edition": 10, "models": [], "validated": null, "n": 1347, "authors_detailed": [ { "name": "Lennart Meincke", "url": "https://openalex.org/A5003350421", "inst": "Cornell University" }, { "name": "Karan Girotra", "url": "https://openalex.org/A5085941726", "inst": "Cornell University" }, { "name": "Gideon Nave", "url": "https://openalex.org/A5144099123", "inst": "Cornell University" }, { "name": "Christian Terwiesch", "url": "https://openalex.org/A5089214194", "inst": "Cornell University" }, { "name": "Karl T. Ulrich", "url": "https://openalex.org/A5040079549", "inst": "Cornell University" } ], "affiliations": [ "Cornell University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.28889v1", "arxiv_id": "2607.28889v1", "title": "Human-LLM Collaborative Inductive Coding for Conceptualizing K-12 Educator AI Use", "authors": [ "Alex Liu", "Min Sun", "Lief Esbenshade", "Michael Xiao", "Victor Tian", "Zachary Zhang", "Kevin He" ], "posted": "2026-07-30", "added": "2026-08-03", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28889v1", "field": "other", "role": "method", "bullets": [ "45,000 messages exchanged between K-12 educators and a generative AI platform, with an independent sample of 2,560 messages coded by three trained human coders.", "Unnamed LLMs generated candidate labels and annotations across open, axial, and selective coding phases; the abstract reports no agreement statistic between model labels and human coding.", "The final codebook holds 72 items in 19 categories and six domains, and human coders added five codes the LLM assisted phases had not surfaced." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 9, "models": [], "n": 1331, "authors_detailed": [ { "name": "Alex Liu", "url": "https://openalex.org/A5006581750", "inst": "University of Washington" }, { "name": "Min Sun", "url": "https://openalex.org/A5144259365", "inst": "" }, { "name": "Lief Esbenshade", "url": "https://openalex.org/A5144283522", "inst": "University of Washington" }, { "name": "Michael Xiao", "url": "https://openalex.org/A5144266861", "inst": "University of Washington" }, { "name": "Victor Tian", "url": "https://openalex.org/A5055883953", "inst": "University of Washington Applied Physics Laboratory" }, { "name": "Zachary Zhang", "url": "https://openalex.org/A5134204044", "inst": "University of Washington Applied Physics Laboratory" }, { "name": "Kevin He", "url": "https://openalex.org/A5144290169", "inst": "University of Washington" } ], "affiliations": [ "University of Washington", "University of Washington Applied Physics Laboratory" ] }, { "uid": "doi:10.2139/ssrn.7124660", "doi": "10.2139/ssrn.7124660", "title": "Humans as the Loop: Tethering Trust in AI, via Fiduciary Governance of Personal Augmentation Agents", "authors": [ "Richard S. Whitt", "Nell Clasby" ], "posted": "2026-07-30", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7124660", "field": "other", "role": "object", "bullets": [ "Conceptual law and policy article on semi autonomous personal AI agents that hold access to users' data, messaging, and financial accounts without any enforceable duty to serve the user.", "No model is run; ChatGPT, Claude, and Gemini are discussed as vendor locked agents, and the article proposes fiduciary personal augmentation agents bound through an institutional trust tether.", "Argues fiduciary duties should sit with the certifying institution rather than the AI component, and proposes five individual rights plus a two tier regulatory structure with a mandatory duty of care floor." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 30, "edition": 9, "validated": null, "n": 1333, "authors_detailed": [ { "name": "Richard S. Whitt", "url": "https://openalex.org/A5012375430", "inst": "Ford Foundation" }, { "name": "Nell Clasby", "url": "https://openalex.org/A5144011906", "inst": "" } ], "affiliations": [ "Ford Foundation" ] }, { "uid": "doi:10.2139/ssrn.7122299", "doi": "10.2139/ssrn.7122299", "title": "Loyal to a Fault? Measuring Whether LLMs Produce Biased Responses Favoring their Parent Companies", "authors": [ "Michael Conklin" ], "posted": "2026-07-30", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7122299", "field": "other", "role": "object", "bullets": [ "Ten categories of inquiries posed to ChatGPT, Claude, Gemini, and Grok, including summaries of parent company litigation, stock performance predictions, and rankings of the models' own accuracy and objectivity.", "Each model answered questions implicating its own or its parent company's commercial, reputational, or legal interests; specific model versions are not stated.", "Across twenty-six measurements the study finds no self-interest bias; answers against a model's own interest appear as often as favorable ones." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 42, "edition": 8, "validated": null, "n": 1298, "authors_detailed": [ { "name": "Michael Conklin", "url": "https://openalex.org/A5140203598", "inst": "Tarrant County College" } ], "affiliations": [ "Tarrant County College" ] }, { "uid": "arxiv:2607.27553v1", "arxiv_id": "2607.27553v1", "title": "Using Large Language Models for Idea Generation in Innovation", "authors": [ "Lennart Meincke", "Karan Girotra", "Gideon Nave", "Christian Terwiesch", "Karl T. Ulrich" ], "posted": "2026-07-30", "added": "2026-07-31", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.27553v1", "field": "management", "role": "agent", "bullets": [ "Three pools of ideas for products aimed at college students priced at 50 dollars or less, one from a design course before LLMs existed and two from GPT-4.", "GPT-4 generates ideas under zero shot and few shot prompting; quality is judged by purchase intent surveys, novelty by human raters, and similarity by text mining.", "AI ideas score higher on average purchase intent and are seven times more likely to land in the top decile, though they are less novel and more alike than human ideas." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "edition": 8, "validated": null, "n": 1322, "authors_detailed": [ { "name": "Lennart Meincke", "url": "https://openalex.org/A5003350421", "inst": "University of Pennsylvania" }, { "name": "Karan Girotra", "url": "https://openalex.org/A5085941726", "inst": "INSEAD" }, { "name": "Gideon Nave", "url": "https://openalex.org/A5144099123", "inst": "Cornell University" }, { "name": "Christian Terwiesch", "url": "https://openalex.org/A5089214194", "inst": "University of Pennsylvania" }, { "name": "Karl T. Ulrich", "url": "https://openalex.org/A5040079549", "inst": "William P. Wharton Trust" } ], "affiliations": [ "University of Pennsylvania", "Cornell University", "INSEAD", "William P. Wharton Trust" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.28292v1", "arxiv_id": "2607.28292v1", "title": "CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance", "authors": [ "Anubhav Lakra", "Yue Feng" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28292v1", "field": "finance", "role": "method", "bullets": [ "4-bit quantized OpenLLaMA-3B tested with 88,021 UK financial documents under sequential memory-editing conditions.", "CACHE-UK framework applied rank-1 LoRA perturbation and a stability controller to edit financial facts in a quantized LLM.", "Knowledge degradation fell 11-17% versus adapted baselines; generalization rate reached 28%, six points above the strongest baseline." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "Edit success and generalization rate on UK financial corpus", "salience": 40, "n": 2566, "authors_detailed": [ { "name": "Anubhav Lakra", "url": "https://openalex.org/A5144160832", "inst": "" }, { "name": "Yue Feng", "url": "https://openalex.org/A5144151780", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7114539", "doi": "10.2139/ssrn.7114539", "title": "The Intent Gap: Artificial Intelligence, Prediction Markets, and the Collapse of Federal Manipulation Doctrine", "authors": [ "Stevie Cline" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7114539", "field": "finance", "role": "object", "bullets": [ "U.S. federal market manipulation doctrine applied to AI trading systems in prediction markets with documented wash trading exceeding 25% of volume.", "Analyzes how RL agents, LLM-powered probability models, and autonomous arbitrage bots defeat every intent-based manipulation provision under the CEA.", "Every federal manipulation provision fails at the intent element for AI systems; proposes upstream scienter liability relocated to design and deployment decisions." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3272, "authors_detailed": [ { "name": "Stevie Cline", "url": "https://openalex.org/A5134012381", "inst": "Ohio Northern University" } ], "affiliations": [ "Ohio Northern University" ] }, { "uid": "doi:10.2139/ssrn.7116399", "doi": "10.2139/ssrn.7116399", "title": "Integrating Agentic Generative AI, Remote Work, and Psychological Theories of Teams to Strengthen US Financial Organizational Competitiveness", "authors": [ "Satyadhar Joshi" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7116399", "field": "management", "role": "object", "bullets": [ "Theoretical synthesis of agentic AI integration in remote and hybrid teams, contextualized in U.S. financial services including banking and fintech.", "Merges Tuckman's team development model with Human-AI Teaming framework to analyze AI roles across formation, conflict, norms, and execution.", "Successful integration requires preserving emotional intelligence and psychological safety while leveraging AI for cognitive and routine tasks." ], "bullet_provenance": "ai", "salience": 15, "models": [], "validated": null, "n": 3273, "authors_detailed": [ { "name": "Sanjay Joshi", "url": "https://openalex.org/A5056009236", "inst": "Pennsylvania State University" } ], "affiliations": [ "Pennsylvania State University" ] }, { "uid": "arxiv:2607.27853v1", "arxiv_id": "2607.27853v1", "title": "FinanceHarness: Autonomous Financial Deep Research Framework", "authors": [ "Yijia Xiao", "Rujun Han", "Yanfei Chen", "Zifeng Wang", "Ke Jiang", "Zhongying CuiZhu", "Vishy Tirumalashetty", "Wei Wang", "Burak Gokturk", "Tomas Pfister", "Chen-Yu Lee" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.27853v1", "field": "finance", "role": "method", "bullets": [ "FinanceGym benchmark with thesis-driven research questions, pre- and post-cutoff rubrics, and 82% professional expert validation pass rate.", "Open-weight LLMs run through FinanceHarness with finance-oriented tools and practitioner-guided workflows for end-to-end automated financial research.", "Leading LLMs and agents score below 40% on rubrics; FinanceHarness raises open-weight backbone overall rubric score from 25.3% to 32.4%." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinanceGym rubric scores with professional expert validation", "salience": 50, "n": 3274, "authors_detailed": [ { "name": "Yijia Xiao", "url": "https://openalex.org/A5101382792", "inst": "Google (United States)" }, { "name": "Rujun Han", "url": "https://openalex.org/A5025334096", "inst": "Google (United States)" }, { "name": "Yanfei Chen", "url": "https://openalex.org/A5115591813", "inst": "Google (United States)" }, { "name": "Zifeng Wang", "url": "https://openalex.org/A5144136304", "inst": "" }, { "name": "Ke Jiang", "url": "https://openalex.org/A5144092730", "inst": "" }, { "name": "Zhongying CuiZhu", "url": "https://openalex.org/A5092189601", "inst": "Google (United States)" }, { "name": "Vishy Tirumalashetty", "url": "https://openalex.org/A5136267049", "inst": "Google (United States)" }, { "name": "Wei Wang", "url": "https://openalex.org/A5144117831", "inst": "Shanghai Jiao Tong University" }, { "name": "Burak Göktürk", "url": "https://openalex.org/A5103372430", "inst": "Google (United States)" }, { "name": "Tomas Pfister", "url": "https://openalex.org/A5144172798", "inst": "" }, { "name": "Chen‐Yu Lee", "url": "https://openalex.org/A5102816082", "inst": "Google (United States)" } ], "affiliations": [ "Google (United States)", "Shanghai Jiao Tong University" ] }, { "uid": "doi:10.2139/ssrn.7204138", "doi": "10.2139/ssrn.7204138", "title": "A SPOT in the dark: using AI to assess financial stability risks", "authors": [ "Domenic Kellner", "Jan Hannes Lang", "Lukas Joseph Nagy", "Marek Rusnák" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7204138", "field": "finance", "role": "instrument", "bullets": [ "Financial news articles spanning 2005 to 2026 used to monitor systemic risk trigger events across the global financial system.", "Large language models extract severity and probability scores for potential trigger events from news text, producing the SPOT indicator.", "SPOT rises ahead of major historical trigger events, correctly identifies trigger sources, and improves forward-looking downside risk estimates." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "Evaluated against historical trigger events and forward-looking model estimates of downside risk", "salience": 72, "n": 3763, "authors_detailed": [ { "name": "Domenic Kellner", "url": "https://openalex.org/A5062996090", "inst": "Goethe University Frankfurt" }, { "name": "Jan Hannes Lang", "url": "https://openalex.org/A5144051019", "inst": "" }, { "name": "Lukas Joseph Nagy", "url": "https://openalex.org/A5144005141", "inst": "" }, { "name": "Marek Rusnák", "url": "https://openalex.org/A5144060972", "inst": "" } ], "affiliations": [ "Goethe University Frankfurt" ] }, { "uid": "doi:10.2139/ssrn.7207682", "doi": "10.2139/ssrn.7207682", "title": "Corporate purpose in the age of AI: a social influence perspective on OpenAI", "authors": [ "Francesco Caputo", "Francesca Iandolo", "Antonio La Sala", "Pietro Vito" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7207682", "field": "management", "role": "object", "bullets": [ "Exploratory single case study of OpenAI examining corporate purpose formation in the AI industry through social influence theory.", "No model is used as a research tool; the study analyzes how OpenAI negotiates tensions between openness, commercialization, and ethical AI.", "Corporate purpose in AI firms is socially produced and repeatedly reconfigured through interaction between organizational ethos and external pressures." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 40, "validated": null, "n": 3764, "authors_detailed": [ { "name": "Francesco Caputo", "url": "https://openalex.org/A5144023565", "inst": "" }, { "name": "Francesca Iandolo", "url": "https://openalex.org/A5044202770", "inst": "Sapienza University of Rome" }, { "name": "Antonio La Sala", "url": "https://openalex.org/A5026985942", "inst": "Sapienza University of Rome" }, { "name": "Pietro Vito", "url": "https://openalex.org/A5082012683", "inst": "Sapienza University of Rome" } ], "affiliations": [ "Sapienza University of Rome" ] }, { "uid": "doi:10.2139/ssrn.7114898", "doi": "10.2139/ssrn.7114898", "title": "Infusing Artificial Intelligence into Strategy's Five Classic Debates: Toward a Synthesis", "authors": [ "Jim Samuel", "Rajiv Kashyap", "Ashley Lee", "Raza Mir" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7114898", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of five influential strategic management theory streams and how AI reshapes their core premises.", "No empirical model deployment; the paper theorizes how AI creates hybrid cognitive architectures and reconfigures ecosystems around foundation models.", "AI embeds algorithmic actors into microfoundations, redistributes resource control to stakeholders, and alters strategizing into continuous AI-augmented processes." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3765, "authors_detailed": [ { "name": "Jim Samuel", "url": "https://openalex.org/A5050205998", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Rajiv Kashyap", "url": "https://openalex.org/A5039945464", "inst": "Stevens Institute of Technology" }, { "name": "Ashley Lee", "url": "https://openalex.org/A5135880214", "inst": "William Paterson University" }, { "name": "Raza Mir", "url": "https://openalex.org/A5135433291", "inst": "William Paterson University" } ], "affiliations": [ "Rutgers, The State University of New Jersey", "Stevens Institute of Technology", "William Paterson University" ] }, { "uid": "doi:10.2139/ssrn.7116118", "doi": "10.2139/ssrn.7116118", "title": "When the Student Becomes the Rival: Distillation vs. Thin Wrapping in AI Service Markets", "authors": [ "Bowen Zhao", "Chong Alex WANG", "Zhe Wang" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7116118", "field": "economics", "role": "object", "bullets": [ "Game-theoretic model of a foundation-model provider competing with a downstream developer who chooses between thin wrapping and distillation.", "No empirical model deployment; the paper analyzes equilibrium API pricing, subscription pricing, and positioning under both adaptation strategies.", "Distillation disproportionately benefits high-capability developers; strategic repositioning can create win-win outcomes for both provider and developer." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3766, "authors_detailed": [ { "name": "Bowen Zhao", "url": "https://openalex.org/A5144051087", "inst": "City University of Hong Kong" }, { "name": "Chong Alex WANG", "url": "https://openalex.org/A5144024784", "inst": "Department of Information Systems" }, { "name": "Zhe Wang", "url": "https://openalex.org/A5043688120", "inst": "City University of Hong Kong" } ], "affiliations": [ "City University of Hong Kong", "Department of Information Systems" ] }, { "uid": "doi:10.2139/ssrn.7121959", "doi": "10.2139/ssrn.7121959", "title": "Below the Floor: Generative AI Adoption in Organizations without an Ostensive Infrastructure", "authors": [ "Walid Amzil" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7121959", "field": "management", "role": "object", "bullets": [ "Conceptual study of generative AI adoption in SMEs that lack formal process infrastructure, using Feldman and Pentland's routine theory.", "No model is deployed; the paper introduces ostensive thinness to theorize GenAI use in organizations without documented procedures or governance.", "Without a stable ostensive layer, GenAI use remains individual and improvised, unable to convert into retained organizational capability." ], "bullet_provenance": "ai", "salience": 38, "models": [], "validated": null, "n": 3767, "authors_detailed": [ { "name": "Walid Amzil", "url": "https://openalex.org/A5140886127", "inst": "" } ] }, { "uid": "arxiv:2607.28133v1", "arxiv_id": "2607.28133v1", "title": "AI Sycophancy and Decisions", "authors": [ "John Conlon", "Peter Schwardmann" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28133v1", "field": "economics", "role": "agent", "bullets": [ "Experiment with 1,500 participants across 30 decision environments spanning core economics and social science domains.", "An LLM provides sycophantic advice to participants; the study measures whether this advice polarizes or depolarizes choices relative to initial leanings.", "AI advice depolarizes choices on average despite measurable sycophancy; increasing sycophancy weakens but does not reverse the depolarization effect." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Expert survey predictions compared against observed behavioral outcomes across 30 decision tasks", "salience": 78, "n": 3768, "authors_detailed": [ { "name": "John Conlon", "url": "https://openalex.org/A5144143412", "inst": "Carnegie Mellon University" }, { "name": "Peter Schwardmann", "url": "https://openalex.org/A5051957289", "inst": "Ifo Institute for Economic Research" } ], "affiliations": [ "Carnegie Mellon University", "Ifo Institute for Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7138318", "doi": "10.2139/ssrn.7138318", "title": "The AI Analyst Workbench: A Business Review of Automated Data Preparation, Modeling, and Reporting", "authors": [ "Anika Pillai", "Jericho Cruz", "Sameer Iyer", "Kiran Balan" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7138318", "field": "management", "role": "object", "bullets": [ "Conceptual framework examining how agentic AI systems reshape automated data preparation, modeling, and stakeholder reporting across organizational data science workflows and analytical teams.", "Paper analyzes how language agents bind intent clarification, code generation, iterative execution, error recovery, and evidence synthesis into one operational loop replacing multi-role coordination.", "Agentic systems reduce coordination costs and expand analytical capacity but shift risk toward procedural and compositional errors that are harder to detect than manual mistakes." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 30, "validated": null, "n": 4044, "authors_detailed": [ { "name": "Anika Pillai", "url": "https://openalex.org/A5143991019", "inst": "" }, { "name": "Jericho Cruz", "url": "https://openalex.org/A5144029363", "inst": "" }, { "name": "Sameer Iyer", "url": "https://openalex.org/A5060891659", "inst": "University of California System" }, { "name": "Kiran Balan", "url": "https://openalex.org/A5112962518", "inst": "" } ], "affiliations": [ "University of California System" ] }, { "uid": "doi:10.2139/ssrn.7208000", "doi": "10.2139/ssrn.7208000", "title": "Human Factors and Cognitive Sustainability in Trustworthy Hybrid Human–Agentic AI Systems: A Systematic Review", "authors": [ "Gabriel Osei Forkuo", "Stelian Alexandru Borz" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7208000", "field": "management", "role": "object", "bullets": [ "Systematic review of 120 empirical and conceptual studies from 2012 to 2026 examining human factors in hybrid human-agentic AI systems across healthcare and engineering domains.", "Review synthesizes evidence on skill erosion, cognitive dependency, automation bias, trust calibration, and situation awareness as humans interact with increasingly autonomous AI systems.", "Deskilling was the most frequent theme at 42 studies; 68 of 120 papers appeared in 2026 alone, showing rapid research acceleration in human-AI workforce governance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 40, "validated": null, "n": 4045, "authors_detailed": [ { "name": "Gabriel Osei Forkuo", "url": "https://openalex.org/A5075127563", "inst": "Transylvania University of Brașov" }, { "name": "Stelian Alexandru Borz", "url": "https://openalex.org/A5069405475", "inst": "Transylvania University of Brașov" } ], "affiliations": [ "Transylvania University of Brașov" ] }, { "uid": "arxiv:2607.28222v1", "arxiv_id": "2607.28222v1", "title": "Voice AI in Firms: A Natural Field Experiment on Automated Job Interviews", "authors": [ "Brian Jabarian", "Luca Henkel" ], "posted": "2026-07-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.28222v1", "field": "management", "role": "object", "bullets": [ "Natural field experiment with 70,000 job applicants randomly assigned to human recruiter or AI voice agent interviews at a large-scale hiring organization.", "AI voice agents conducted structured interviews while human recruiters evaluated all candidates and made final hiring decisions regardless of the interview condition assigned.", "AI-interviewed applicants were 12 percent more likely to receive offers with higher job starts and worker retention and no decline in hired worker productivity." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 78, "validated": null, "n": 4046 }, { "uid": "doi:10.2139/ssrn.6832362", "doi": "10.2139/ssrn.6832362", "title": "Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges", "authors": [ "Bohao Wang", "Yu Cui", "Zhenxiang Xu", "Jujia Zhao", "Chenxiao Fan", "Jizhi Zhang", "Weiqin Yang", "Shengjia Zhang", "Sirui Chen", "Yang Zhang", "Xiaoyan Zhao", "Wenjie Wang", "Chongming Gao", "Fuli Feng", "Xiangnan He", "Jiawei Chen" ], "posted": "2026-07-29", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6832362", "field": "management", "role": "method", "bullets": [ "Systematic review of more than 200 studies on LLM-enabled recommender systems, covering datasets, evaluation metrics, and six dimensions of system trustworthiness.", "No single model is evaluated; the review organizes how LLM integration changes robustness, fairness, privacy, and related criteria, so output validation is not applicable.", "The synthesis identifies 13 opportunities and 18 challenges, concluding that richer reasoning and interaction can improve recommendation while also introducing bias and hallucination risks." ], "bullet_provenance": "ai", "salience": 46, "edition": 23, "models": [], "validated": null, "n": 4179, "authors_detailed": [ { "name": "Bohao Wang", "url": "https://openalex.org/A5050239431", "inst": "Zhejiang University" }, { "name": "Yu Cui", "url": "https://openalex.org/A5143971222", "inst": "" }, { "name": "Zhenxiang Xu", "url": "https://openalex.org/A5143873193", "inst": "" }, { "name": "Jujia Zhao", "url": "https://openalex.org/A5143897315", "inst": "" }, { "name": "Chenxiao Fan", "url": "https://openalex.org/A5084334472", "inst": "Leiden University" }, { "name": "Jizhi Zhang", "url": "https://openalex.org/A5143961124", "inst": "" }, { "name": "Weiqin Yang", "url": "https://openalex.org/A5101724911", "inst": "Chinese University of Hong Kong" }, { "name": "Shengjia Zhang", "url": "https://openalex.org/A5006241046", "inst": "Leiden University" }, { "name": "Sirui Chen", "url": "https://openalex.org/A5143912610", "inst": "" }, { "name": "Yang Zhang", "url": "https://openalex.org/A5143959378", "inst": "" }, { "name": "Xiaoyan Zhao", "url": "https://openalex.org/A5143913981", "inst": "" }, { "name": "Wenjie Wang", "url": "https://openalex.org/A5143903071", "inst": "" }, { "name": "Chongming Gao", "url": "https://openalex.org/A5143983314", "inst": "" }, { "name": "Fuli Feng", "url": "https://openalex.org/A5051925942", "inst": "University of Science and Technology of China" }, { "name": "Xiangnan He", "url": "https://openalex.org/A5135215516", "inst": "" }, { "name": "Jiawei Chen", "url": "https://openalex.org/A5100755333", "inst": "Nanjing Forestry University" } ], "affiliations": [ "Zhejiang University", "Leiden University", "Chinese University of Hong Kong", "University of Science and Technology of China", "Nanjing Forestry University" ] }, { "uid": "doi:10.2139/ssrn.6556303", "doi": "10.2139/ssrn.6556303", "title": "Tutorial: Extracting Unstructured Text using Large Language Models", "authors": [ "Simon Spavound", "Oliver Schaer", "Panos Markou" ], "posted": "2026-07-29", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6556303", "field": "management", "role": "method", "bullets": [ "US Food and Drug Administration advisory transcripts provide the running example for extracting speaker-statement pairs; the document count and covered period are not stated.", "An unnamed vision model extracts PDF text before an unnamed LLM structures it as JSON; parameter controls are discussed, but no ground-truth extraction accuracy is reported.", "The pipeline makes otherwise impractical quantitative analysis possible, illustrated with speaker word counts and sentiment measures that characterize stakeholder participation in regulatory deliberations." ], "bullet_provenance": "ai", "validated": false, "salience": 49, "edition": 23, "models": [], "n": 4182, "authors_detailed": [ { "name": "Simon Spavound", "url": "https://openalex.org/A5002242356", "inst": "Drexel University" }, { "name": "Oliver Schaer", "url": "https://openalex.org/A5002003657", "inst": "Drexel University" }, { "name": "Panos Markou", "url": "https://openalex.org/A5053358463", "inst": "University of Virginia" } ], "affiliations": [ "University of Virginia", "Drexel University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6535239", "doi": "10.2139/ssrn.6535239", "title": "AI-Assisted Academic Writing as an Inquisitorial Appellate Court", "authors": [ "Roee Sarel" ], "posted": "2026-07-29", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6535239", "field": "economics", "role": "object", "bullets": [ "Conceptual institutional analysis of generative AI use in academic writing draws on law-and-economics reasoning about incentives, responsibility, review, and misconduct.", "No model generates study data; AI-assisted writing is the practice being governed, so model-output validation is not applicable to the argument.", "Legitimacy depends on access, verification duties, liability, and sanctions rather than the technology alone, with inquisitorial appellate review offered as the organizing analogy." ], "bullet_provenance": "ai", "salience": 46, "edition": 23, "models": [], "validated": null, "n": 4197, "authors_detailed": [ { "name": "Roee Sarel", "url": "https://openalex.org/A5071053649", "inst": "Universität Hamburg" } ], "affiliations": [ "Universität Hamburg" ] }, { "uid": "doi:10.2139/ssrn.6740060", "doi": "10.2139/ssrn.6740060", "title": "Prompt Injection and Jailbreak Attacks in Large Language Model-Based Agents", "authors": [ "Rizwan Tanveer" ], "posted": "2026-07-29", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6740060", "field": "management", "role": "object", "bullets": [ "Narrative review of the prompt injection and jailbreak literature from 2023 to 2026, drawing on OWASP top ten lists, foundational attack papers, and documented deployment incidents.", "No model is run and no family is named. The review sorts direct and indirect injection, retrieval poisoning, and Model Context Protocol weaknesses, then maps attack classes to controls.", "Argues injection is structural because instructions and data travel one channel, so training cannot remove it, and defence needs channel separation, output validation, and policy mediation before tool execution." ], "bullet_provenance": "ai", "salience": 30, "edition": 17, "models": [], "validated": null, "n": 2133, "authors_detailed": [ { "name": "Rizwan Tanveer", "url": "https://openalex.org/A5135988363", "inst": "College of Accounting" } ], "affiliations": [ "College of Accounting" ] }, { "uid": "doi:10.2139/ssrn.6993719", "doi": "10.2139/ssrn.6993719", "title": "Escaping the Nash Trap: Structural Estimation and Alignment of Strategic Reasoning in Large Language Models", "authors": [ "Jiannan Xu", "Yongkang Duan", "Jane Jiang", "Jiding Zhang" ], "posted": "2026-07-29", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6993719", "field": "economics", "role": "agent", "bullets": [ "A suite of normal-form games paired with online experiments on human subjects, used to compare language model agents' strategic play with observed human reasoning types.", "Unnamed language models act as game players, and a structural estimation procedure recovers each model's latent belief about opponent reasoning depth using the level-k framework. No accuracy validation applies.", "Models assume Nash-rational opponents and play equilibrium, which underperforms against boundedly rational humans in a Nash trap; a welfare-aware prompt calibrates deviations better than a behaviorally informed one." ], "bullet_provenance": "ai", "salience": 62, "edition": 15, "models": [], "validated": null, "n": 2073, "authors_detailed": [ { "name": "Jiannan Xu", "url": "https://openalex.org/A5143872074", "inst": "University of Maryland, College Park" }, { "name": "Yi Duan", "url": "https://openalex.org/A5108209891", "inst": "Beijing Normal University" }, { "name": "Jane Jiang", "url": "https://openalex.org/A5111260027", "inst": "Department of Management Sciences" }, { "name": "Jiding Zhang", "url": "https://openalex.org/A5010982908", "inst": "Arizona State University" } ], "affiliations": [ "University of Maryland, College Park", "Arizona State University", "Beijing Normal University", "Department of Management Sciences" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6641081", "doi": "10.2139/ssrn.6641081", "title": "Mapping the Rise of AI in Qualitative Research: A Bibliometric Analysis (2019-2025)", "authors": [ "Mohd Firdauz Mohd Fathir" ], "posted": "2026-07-29", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6641081", "field": "other", "role": "object", "bullets": [ "Bibliometric mapping of 707 Scopus-indexed journal articles and conference papers on AI in qualitative research published between 2019 and 2025.", "No language model performs the analysis; VOSViewer builds keyword co-occurrence, citation, and country collaboration maps, with ChatGPT figuring as the subject driving the post-2023 publication surge.", "Themes cluster around higher education, ethics, qualitative coding, and NLP, led by the US and China, with a gap identified in frameworks for AI in interview piloting and protocol design." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 30, "edition": 11, "validated": null, "n": 1350, "authors_detailed": [ { "name": "Mohd Firdauz Mohd Fathir", "url": "https://openalex.org/A5079135644", "inst": "Universiti Teknologi MARA System" } ], "affiliations": [ "Universiti Teknologi MARA System" ] }, { "uid": "doi:10.2139/ssrn.6940839", "doi": "10.2139/ssrn.6940839", "title": "Courts, Regulators, and the Hallucination Problem: How Canadian Law Is Responding to Large Language Models in Legal Submissions", "authors": [ "Steven Hinkley" ], "posted": "2026-07-29", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6940839", "field": "other", "role": "object", "bullets": [ "Canadian judicial notices, law society guidance, and reported decisions in the three years after ChatGPT's release, including Zhang v. Chen, Reddy v. Saroya, and Ko v. Li.", "No model is applied by the author; LLMs figure as the source of fabricated case citations that courts and regulators, from the Federal Court's disclosure regime to provincial law societies, are responding to.", "Enforcement has hardened from early leniency toward personal cost awards and contempt proceedings, anchored in a non-delegable duty of human verification, with unresolved strain on self-represented litigants held to the same standard." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "salience": 35, "edition": 11, "validated": null, "n": 1351, "authors_detailed": [ { "name": "Steven Hinkley", "url": "https://openalex.org/A5008810370", "inst": "Edith Cowan University" } ], "affiliations": [ "Edith Cowan University" ] }, { "uid": "doi:10.2139/ssrn.7040861", "doi": "10.2139/ssrn.7040861", "title": "Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University", "authors": [ "Shahin Hossain" ], "posted": "2026-07-29", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7040861", "field": "other", "role": "object", "bullets": [ "Survey of 382 undergraduates at a public minority-serving R1 university, combined with 14 interviews and 396 open-ended responses on academic writing with generative AI.", "No model is run and none is named; student reliance on LLMs is measured with a new survey typology rather than by frequency of use, which the author argues rewards dependence.", "Four reliance types emerge, strategic at 34.3 percent and dependent at 4.5 percent; value and cost beliefs predict intensity of reliance while AI literacy predicts its type, and roughly 13 percent abstain on ethical grounds." ], "bullet_provenance": "ai", "salience": 38, "edition": 11, "models": [], "validated": null, "n": 1352, "authors_detailed": [ { "name": "Shahin Hossain", "url": "https://openalex.org/A5102806028", "inst": "University of Maryland, Baltimore County" } ], "affiliations": [ "University of Maryland, Baltimore County" ] }, { "uid": "doi:10.2139/ssrn.6663398", "doi": "10.2139/ssrn.6663398", "title": "Red Teaming with Faith Leaders: Expanding Digital Safety and Accountability to Frontiers of Care", "authors": [ "Nina Lutz", "Eric Glen Weyl" ], "posted": "2026-07-29", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6663398", "field": "management", "role": "object", "bullets": [ "Workshop with 11 faith leaders from Christian, Jewish, and Islamic traditions, paired with a computational audit, testing how a consumer chatbot handles spiritual guidance and crisis situations.", "ChatGPT-5 is the system under test; the leaders act as red teamers probing for harms rather than the authors using a model for measurement.", "Faith leaders surface vulnerabilities at the community level rather than the individual level, and the paper closes with guidance for bringing community leaders into red teaming work." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 41, "edition": 11, "validated": null, "n": 1358, "authors_detailed": [ { "name": "Nina Lutz", "url": "https://openalex.org/A5107619449", "inst": "University of Washington" }, { "name": "E. Glen Weyl", "url": "https://openalex.org/A5086938626", "inst": "Research Network (United States)" } ], "affiliations": [ "University of Washington", "Research Network (United States)" ] }, { "uid": "doi:10.2139/ssrn.6913179", "doi": "10.2139/ssrn.6913179", "title": "LLM Agent Development Lifecycle (LADL): A Structured Engineering Framework for Probabilistic Autonomous Systems Beyond Agile Methodologies", "authors": [ "Arzoo Taj" ], "posted": "2026-07-29", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6913179", "field": "management", "role": "method", "bullets": [ "Conceptual design science paper proposing a nine phase development lifecycle for LLM agent systems in place of Agile, Scrum, and DevOps, illustrated with a healthcare triage agent.", "No specific language model is named or run; the framework covers prompt architecture, probabilistic testing, human in the loop validation, drift monitoring, and a cryptographic prompt hashing tool for audit trails.", "Identifies six gaps in current literature the lifecycle claims to fill; evaluation is a design science self assessment of utility and consistency rather than an empirical test." ], "bullet_provenance": "ai", "salience": 22, "edition": 9, "models": [], "validated": null, "n": 1332, "authors_detailed": [ { "name": "Arzoo Taj", "url": "https://openalex.org/A5143920623", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6459740", "doi": "10.2139/ssrn.6459740", "title": "Generative Agent-Based Modeling for Social Simulations: A Systematic Mapping Study", "authors": [ "Gian Marco Orlando", "Valerio La Gatta", "Vincenzo Moscato" ], "posted": "2026-07-29", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6459740", "field": "other", "role": "method", "bullets": [ "Systematic mapping of 53 studies on generative agent-based modeling, gathered by keyword search plus forward and backward snowballing, spanning social dynamics, social media, economics, recommender systems, and healthcare.", "No single model is evaluated; the review classifies how LLM driven agents are built across profile, memory, and action modules, and finds validation practices uneven across studies.", "Most simulations are small, with 66 percent using 100 agents or fewer; the authors list ten open challenges for making social simulation with generative agents scientifically grounded." ], "bullet_provenance": "ai", "salience": 48, "edition": 9, "models": [], "validated": null, "n": 1341, "authors_detailed": [ { "name": "Gian Marco Orlando", "url": "https://openalex.org/A5046756099", "inst": "Federico II University Hospital" }, { "name": "Valerio La Gatta", "url": "https://openalex.org/A5038118094", "inst": "Northwestern University" }, { "name": "Vincenzo Moscato", "url": "https://openalex.org/A5081965427", "inst": "Federico II University Hospital" } ], "affiliations": [ "Northwestern University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6476020", "doi": "10.2139/ssrn.6476020", "title": "Compliance by Design in Cross-Border Financial AI: Architecting for the EU AI Act, SOC 2, and Insurability", "authors": [ "Sung Woo Shon" ], "posted": "2026-07-29", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6476020", "field": "finance", "role": "object", "bullets": [ "Conceptual analysis of cross border financial AI systems, covering credit decisioning, underwriting, fraud detection, and LLM based client advisory, under the EU AI Act, SOC 2, GDPR, and insurer scrutiny.", "No model is used or named; LLMs appear only as components of the financial systems whose governance the paper discusses, so validation does not apply.", "Proposes a vendor agnostic compliance by design cloud architecture with tamper evident audit trails and fallback mechanisms, arguing that retrofitted compliance and automated audit dashboards leave socio technical risks hidden." ], "bullet_provenance": "ai", "salience": 34, "edition": 9, "models": [], "validated": null, "n": 1342, "authors_detailed": [ { "name": "Sung Woo Shon", "url": "https://openalex.org/A5023190869", "inst": "United States Census Bureau" } ], "affiliations": [ "United States Census Bureau" ] }, { "uid": "doi:10.2139/ssrn.6839039", "doi": "10.2139/ssrn.6839039", "title": "Connecting Classroom Econometrics and Excel Training with Large Language Models", "authors": [ "Joy Buchanan" ], "posted": "2026-07-29", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6839039", "field": "economics", "role": "method", "bullets": [ "Undergraduate economics instruction; a teaching note describing three classroom exercises that combine ordinary least squares regression, Excel spreadsheet training, and large language models.", "No specific model is named; students use LLMs alongside regression and spreadsheet tasks, and the note reports no evaluation of model output or learning outcomes.", "Argues that student curiosity about AI can motivate classic statistical reasoning and practical spreadsheet skills; evidence on effectiveness is not stated." ], "bullet_provenance": "ai", "salience": 28, "edition": 9, "models": [], "validated": null, "n": 1343, "authors_detailed": [ { "name": "Joy Buchanan", "url": "https://openalex.org/A5143982817", "inst": "Samford University" } ], "affiliations": [ "Samford University" ] }, { "uid": "doi:10.2139/ssrn.7074959", "doi": "10.2139/ssrn.7074959", "title": "Against Mandatory Prompt Disclosure in AI-Assisted Scholarship", "authors": [ "Brian Earp", "Udo Schuklenk", "Julian Savulescu", "Sebastian Porsdam Mann" ], "posted": "2026-07-29", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7074959", "field": "other", "role": "method", "bullets": [ "Position paper on journal policy for AI assisted scholarly writing; no dataset, the argument targets proposals that authors must submit their prompts and generated outputs as supplemental material.", "No model is used or named; LLM writing assistance is the practice under debate, so no validation applies.", "Rejects mandatory full inclusion of prompts as unenforceable, easy to evade, and burdensome, proposing instead targeted disclosure where reproducibility or validity is at stake plus voluntary documentation and authorship attestation." ], "bullet_provenance": "ai", "salience": 42, "edition": 9, "models": [], "validated": null, "n": 1344, "authors_detailed": [ { "name": "Brian D. Earp", "url": "https://openalex.org/A5058038900", "inst": "University of Oxford" }, { "name": "Udo Schüklenk", "url": "https://openalex.org/A5004210828", "inst": "Queen's University" }, { "name": "Julian Savulescu", "url": "https://openalex.org/A5018300444", "inst": "Center for Practical Bioethics" }, { "name": "Sebastian Porsdam Mann", "url": "https://openalex.org/A5061593640", "inst": "University of Copenhagen" } ], "affiliations": [ "University of Oxford", "Queen's University", "Center for Practical Bioethics", "University of Copenhagen" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6777418", "doi": "10.2139/ssrn.6777418", "title": "What Suppresses Nash Equilibrium Play in Large Language Models? Mechanistic Evidence and Causal Control", "authors": [ "Paraskevas Lekeas", "Giorgos Stamatopoulos" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6777418", "field": "economics", "role": "agent", "bullets": [ "Four open-source models, Llama-3 and Qwen2.5 at 8B to 72B parameters, play four canonical two-player games in self-play and cross-play, with layer-level analysis inside Llama-3-8B.", "Linear probes read opponent history at 96 percent accuracy in the first layer while Nash action encoding never exceeds 56 percent; injecting a learned Nash direction into the residual stream shifts play causally.", "Models compute the Nash action but a prosocial override in the final layers suppresses it; chain-of-thought worsens Nash play in small models and yields near-perfect play above 70B parameters." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "salience": 70, "edition": 8, "validated": null, "n": 1299, "authors_detailed": [ { "name": "Paraskevas Lekeas", "url": "https://openalex.org/A5143934647", "inst": "" }, { "name": "Giorgos Stamatopoulos", "url": "https://openalex.org/A5041239607", "inst": "University of Crete" } ], "affiliations": [ "University of Crete" ] }, { "uid": "doi:10.2139/ssrn.7068218", "doi": "10.2139/ssrn.7068218", "title": "Optimization and Constraint Modeling using LLMs with a Retrieval Augmented Generation Process", "authors": [ "Prateek Roy", "Akash Singirikonda" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7068218", "field": "management", "role": "method", "bullets": [ "500 synthetic optimization problems generated from Text2Zinc seed descriptions, each stored as JSON with a validated Python solver script and indexed in a Chroma vector database.", "Qwen 3 30B Instruct writes formulations for new problems with semantically similar retrieved examples as context; accuracy is checked on the NL4OPT and MAMO benchmark testbeds.", "Retrieval augmentation raises accuracy from 40 to 72 percent on NL4OPT, 40 to 56 on MAMO Easy, and 32 to 56 on MAMO Complex, without any fine-tuning." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "accuracy on NL4OPT and MAMO benchmarks", "salience": 36, "edition": 8, "n": 1300, "authors_detailed": [ { "name": "Prateek Roy", "url": "https://openalex.org/A5059150314", "inst": "Film Independent" }, { "name": "Akash Singirikonda", "url": "https://openalex.org/A5120786985", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology", "Film Independent" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6556058", "doi": "10.2139/ssrn.6556058", "title": "Generative AI for Software Development Automation", "authors": [ "Sai Kiran Naik Banoth" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6556058", "field": "management", "role": "object", "bullets": [ "A conceptual synthesis of multidisciplinary literature on generative AI in software development, organized through automation theory, developer cognition, process models, and responsible governance lenses.", "No system is built or named; LLM coding tools are the object under review and no original empirical evidence or validation is presented.", "The paper argues generative AI raises engineering productivity unevenly, with value hinging on prompt quality, developer expertise, toolchain maturity, and governance, alongside risks of hallucinated logic and eroding tacit knowledge." ], "bullet_provenance": "ai", "salience": 22, "edition": 8, "models": [], "validated": null, "n": 1303, "authors_detailed": [ { "name": "Sai Kiran Naik Banoth", "url": "https://openalex.org/A5128693274", "inst": "Carolina Veterinary Specialists" } ], "affiliations": [ "Carolina Veterinary Specialists" ] }, { "uid": "doi:10.2139/ssrn.6856159", "doi": "10.2139/ssrn.6856159", "title": "Structured Numerical Hallucinations in Financial LLMs: Deterministic Verification Outperforms Chain-of-Thought Prompting", "authors": [ "Aaditya Thokal" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6856159", "field": "finance", "role": "method", "bullets": [ "Numerical question answering over financial documents on the FinQA and TAT-QA benchmarks, run with small models on a single consumer GPU, with DROP as a cross domain control.", "A parameter free deterministic verification layer corrects a QLoRA fine-tuned model and Llama 3 8B at inference; an audited subset shows 44 of 44 corrections correct with zero false positives.", "FinQA execution accuracy climbs from 1.0 to 42.6 percent, while chain of thought and retrieval augmentation both hurt, by 9.0 and 7.5 percentage points, because errors are structured rather than random." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "FinQA and TAT-QA benchmarks, audited correction subset", "salience": 58, "edition": 8, "n": 1304, "authors_detailed": [ { "name": "Aaditya Thokal", "url": "https://openalex.org/A5133026606", "inst": "University of Mumbai" } ], "affiliations": [ "University of Mumbai" ] }, { "uid": "doi:10.2139/ssrn.6790020", "doi": "10.2139/ssrn.6790020", "title": "IDV: Customer-Context-Aware Multi-Engine Architecture for AI-Agent Intent Drift Detection", "authors": [ "Yan Xue", "Mulan Zhou" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6790020", "field": "management", "role": "method", "bullets": [ "AI agents operating on customer infrastructure in financial services, evaluated on IDV-1000, a corpus of 850 author constructed synthetic traces plus 150 naturalistic agent traces.", "GPT-5.5 and Claude Opus 4.7 judging identical traces disagree on drift in 30 percent of cases; the proposed four engine detector scores F1 of 93.9 percent on the author injected subset.", "The multi engine architecture cuts false positives about 6.5 fold relative to single LLM judge baselines at under 300 milliseconds, with disagreement concentrated on instance level entity access cases." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "IDV-1000 labeled traces, F1 and AUROC reported", "salience": 40, "edition": 8, "n": 1305, "authors_detailed": [ { "name": "Yan Xue", "url": "https://openalex.org/A5143893114", "inst": "Network Technologies (United States)" }, { "name": "Mulan Zhou", "url": "https://openalex.org/A5143975762", "inst": "Network Technologies (United States)" } ], "affiliations": [ "Network Technologies (United States)" ] }, { "uid": "doi:10.2139/ssrn.7081338", "doi": "10.2139/ssrn.7081338", "title": "Sentiment Lost in Preprocessing? Analysis of Emoji-Inclusive vs. Emoji-Exclusive Methods with Traditional Lexicons-Dictionaries and Artificially Intelligent ML-LLM strategies", "authors": [ "Manideep Pendyala", "Udit Goel", "Jim Samuel", "Pal Patel", "Janki Kanakia", "Alexander Pelaez", "Neel Savalia", "Tanya Khanna" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7081338", "field": "other", "role": "method", "bullets": [ "A curated set of sentence pairs written with and without emojis, each labeled by humans into three sentiment categories as a gold standard.", "Lexicon dictionaries, pretrained classifiers, and LLMs including Qwen, DeepSeek, Mistral, and BERT classify each version; performance is measured as accuracy against the human labels.", "Retaining emojis lifts accuracy by more than 25 percent for several models compared with stripping them, indicating standard preprocessing throws away usable affective signal." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "human annotated gold standard sentence pairs", "salience": 40, "edition": 8, "n": 1306 }, { "uid": "doi:10.2139/ssrn.6960978", "doi": "10.2139/ssrn.6960978", "title": "FISC: Financial Gradient-free In-context Strategic Classification via Large Language Models", "authors": [ "Meimei Han" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6960978", "field": "finance", "role": "method", "bullets": [ "Strategic classification for financial lending, where applicants may manipulate reported features to win approval, evaluated across six benchmark datasets including PhiUSIIL.", "Frozen LLMs, chiefly GPT-4o, execute both levels of a bi-level formulation through in-context learning, guided by a financial strategic prompt encoding audit sensitivity, asymmetric loss, and demographic fairness.", "FISC reaches 89.12 percent strategic accuracy on PhiUSIIL, a 2.62 percent improvement over GLIM, and ablations show each of the audit, cost, and fairness cues contributes." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "six labeled benchmark datasets, accuracy reported", "salience": 45, "edition": 8, "n": 1308, "authors_detailed": [ { "name": "Meimei Han", "url": "https://openalex.org/A5143961589", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6894938", "doi": "10.2139/ssrn.6894938", "title": "Intelligent Enterprise Systems: Integrating Artificial Intelligence, Data Analytics, and Information Management for Strategic Decision Making", "authors": [ "Sakir Alim" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6894938", "field": "management", "role": "method", "bullets": [ "Enterprise AI in regulated industries, where the proposed HEAL architecture chains edge side anonymisation, federated learning, retrieval augmentation, compliance gating, and cloud foundation model inference.", "No specific LLM is named; the evaluation is analytical against four baseline configurations, and the ground truth behind the reported accuracy figure is not stated.", "HEAL is credited with 91.6 percent inference accuracy, 148 millisecond mean latency, and a full compliance rating, beating each baseline on at least three of five metrics." ], "bullet_provenance": "ai", "validated": false, "salience": 20, "edition": 8, "models": [], "n": 1309, "authors_detailed": [ { "name": "sakir alim", "url": "https://openalex.org/A5135970818", "inst": "Ekiti State University" } ], "affiliations": [ "Ekiti State University" ] }, { "uid": "doi:10.2139/ssrn.7165564", "doi": "10.2139/ssrn.7165564", "title": "Locally Deployable AI for Legal Classification and Risk Stratification of Workplace Violence in Healthcare: A Retrospective Study from Italy", "authors": [ "Claudio Mazzi", "Matilde Contestabile", "Paolo Ferragina", "Monica Chilla", "Gaetana Morgante", "Francesca Chiaromonte", "Massimo Ughi", "Chiara Seghieri" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7165564", "field": "management", "role": "method", "bullets": [ "450 aggression reports against healthcare workers from the Toscana Nord Ovest local health authority in Italy, 2022 to 2026, annotated with criminal law scholars into four ordinal legal categories.", "Compares prompted LLMs, RAG variants, and embedding classifiers; the best setup pairs BGE-M3 retrieval with mistral-small 24b and cross-encoder reranking, scored against the expert gold standard.", "The top configuration reaches a global macro F1 of 0.717 and exact match accuracy of 0.662, with enough variability across legal categories that human oversight is still recommended." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "450 expert-annotated reports, macro F1 and exact-match accuracy reported", "salience": 42, "edition": 8, "n": 1310, "authors_detailed": [ { "name": "Claudio Mazzi", "url": "https://openalex.org/A5035268972", "inst": "Scuola Superiore Sant'Anna" }, { "name": "Matilde Contestabile", "url": "https://openalex.org/A5135285636", "inst": "Scuola Superiore Sant'Anna" }, { "name": "Paolo Ferragina", "url": "https://openalex.org/A5046786328", "inst": "Scuola Superiore Sant'Anna" }, { "name": "Monica Chilla", "url": "https://openalex.org/A5143975104", "inst": "Scuola Superiore Sant'Anna" }, { "name": "Gaetana Morgante", "url": "https://openalex.org/A5008979037", "inst": "Scuola Superiore Sant'Anna" }, { "name": "Francesca Chiaromonte", "url": "https://openalex.org/A5015561999", "inst": "Scuola Superiore Sant'Anna" }, { "name": "Massimo Ughi", "url": "https://openalex.org/A5077165327", "inst": "Azienda Usl Toscana Centro" }, { "name": "Chiara Seghieri", "url": "https://openalex.org/A5009495706", "inst": "Scuola Superiore Sant'Anna" } ], "affiliations": [ "Scuola Superiore Sant'Anna", "Azienda Usl Toscana Centro" ] }, { "uid": "doi:10.2139/ssrn.6774800", "doi": "10.2139/ssrn.6774800", "title": "AI-Mediated Communication Can Steer Collective Opinion", "authors": [ "Stratis Tsirtsis", "Kai Rawal", "Chris Russell", "Brent Mittelstadt", "Sandra Wachter" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6774800", "field": "economics", "role": "object", "bullets": [ "Editing experiments where popular LLM families rewrite human posts on contested topics, an opinion dynamics model of AI-mediated communication, simulations on real social network data, and an audit of X's Explain this post feature.", "The models' edits are the behaviour under study; they introduce directional bias, for example toward gun control and against atheism, and specific model versions are not stated in the abstract.", "In equilibrium and in simulations, biases introduced during mediation amplify through the network and shift collective opinion, and the audit finds pro-life bias in Grok's outputs traced to design choices." ], "bullet_provenance": "ai", "salience": 68, "edition": 8, "models": [], "validated": null, "n": 1314, "authors_detailed": [ { "name": "Stratis Tsirtsis", "url": "https://openalex.org/A5035213985", "inst": "Hasso Plattner Institute" }, { "name": "Kai Rawal", "url": "https://openalex.org/A5143932768", "inst": "Internet Society" }, { "name": "Chris Russell", "url": "https://openalex.org/A5008943199", "inst": "University of Oxford" }, { "name": "Brent Mittelstadt", "url": "https://openalex.org/A5081516308", "inst": "University of Oxford" }, { "name": "Sandra Wachter", "url": "https://openalex.org/A5075172090", "inst": "Hasso Plattner Institute" } ], "affiliations": [ "University of Oxford", "Hasso Plattner Institute", "Internet Society" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6579178", "doi": "10.2139/ssrn.6579178", "title": "'I Don't Want Your AI Slop, or Pink Slime Either': Market Substitution, Brown Shoe Submarkets, and the Fourth Fair Use Factor in the Age of Generative AI", "authors": [ "Timothy Tau Hsieh" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6579178", "field": "economics", "role": "object", "bullets": [ "A law review analysis of the fourth fair use factor for generative AI training, centered on the diverging Kadrey v. Meta and Bartz v. Anthropic opinions from the Northern District of California.", "No model is deployed; LLMs and image generators trained on copyrighted corpora are the subject, with mass-produced low-quality AI output treated as an economic submarket of expressive works.", "Argues courts should import Brown Shoe antitrust submarket analysis into factor four, so foreseeable expansion and convergence of the AI slop submarket counts as potential market harm." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 40, "edition": 8, "validated": null, "n": 1315, "authors_detailed": [ { "name": "Timothy T. Hsieh", "url": "https://openalex.org/A5090610400", "inst": "Oklahoma City University" } ], "affiliations": [ "Oklahoma City University" ] }, { "uid": "doi:10.2139/ssrn.6475399", "doi": "10.2139/ssrn.6475399", "title": "Quantifying the Irreducible: A Systematic Survey of Adversarial Jailbreak Vectors, Red Team Methodologies, and Enterprise Threat Exposure in Large Language Model Deployments", "authors": [ "Sunil Gentyala" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6475399", "field": "management", "role": "object", "bullets": [ "A survey of 65 peer-reviewed publications from 2022 to 2025 on LLM jailbreaks, drawn from NeurIPS, ICML, ICLR, USENIX Security, ACM CCS, and EMNLP, oriented to enterprise deployments.", "No new attacks are run; 23 attack methodologies are classified along five dimensions and linked to organizational impact through three composite risk indices in the proposed ETEM framework.", "Reported attack success ranges from 2.86 percent on Claude 4 under constitutional classifiers to 97.14 percent for autonomous reasoning agents, framing jailbreaks as structural rather than patchable." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 40, "edition": 8, "validated": null, "n": 1316, "authors_detailed": [ { "name": "Sunil Gentyala", "url": "https://openalex.org/A5140824248", "inst": "HCL Technologies (India)" } ], "affiliations": [ "HCL Technologies (India)" ] }, { "uid": "doi:10.2139/ssrn.6802319", "doi": "10.2139/ssrn.6802319", "title": "Prompt Governance? On Governing Technologies Governed by Natural Language", "authors": [ "Anna Neumann", "Holli Sargeant", "Jatinder Singh" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6802319", "field": "other", "role": "object", "bullets": [ "A cross-reading of the research literature on system prompts against two policy frameworks, US Executive Order 14319 and the EU General-Purpose AI Code of Practice, which treat system-level instructions as governance tools.", "No model experiments are reported in the abstract; the authors distil a typology of claims about what system prompts can achieve and find the literature fragmented and contradictory.", "Misalignment between research evidence and regulatory assumptions undermines treating system prompts as stable, interpretable control mechanisms, counselling caution toward prompt-based approaches to AI governance." ], "bullet_provenance": "ai", "salience": 45, "edition": 8, "models": [], "validated": null, "n": 1317, "authors_detailed": [ { "name": "Anna Neumann", "url": "https://openalex.org/A5109010866", "inst": "University of Duisburg-Essen" }, { "name": "Holli Sargeant", "url": "https://openalex.org/A5080083973", "inst": "St. John's College of Nursing" }, { "name": "Jatinder Singh", "url": "https://openalex.org/A5143867692", "inst": "Bridge University" } ], "affiliations": [ "University of Duisburg-Essen", "St. John's College of Nursing", "Bridge University" ] }, { "uid": "doi:10.2139/ssrn.7135898", "doi": "10.2139/ssrn.7135898", "title": "Neuro-Symbolic SQL Generation for Finance: Constraining LLM Outputs via Software Logic", "authors": [ "Margaret Cullen", "James Carrington" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7135898", "field": "finance", "role": "method", "bullets": [ "A benchmark of more than 5,000 natural language financial queries spanning income statements, cash flow patterns, and risk exposure tables in regulated database settings.", "A fine tuned decoder only transformer, family not stated, writes SQL while a symbolic validator enforces schema, data type, and financial business rules, returning errors as refinement prompts.", "Execution accuracy improves 22.7 percent over the base model and compliance violations fall 41.3 percent, at latency the authors describe as suitable for interactive tools." ], "bullet_provenance": "ai", "validated": true, "validation_note": "execution accuracy on a 5,000-query financial text-to-SQL benchmark", "salience": 42, "edition": 8, "models": [], "n": 1318, "authors_detailed": [ { "name": "Margaret Cullen", "url": "https://openalex.org/A5143884256", "inst": "University of Bristol" }, { "name": "James Carrington", "url": "https://openalex.org/A5012801171", "inst": "University of Bristol" } ], "affiliations": [ "University of Bristol" ] }, { "uid": "doi:10.2139/ssrn.6892979", "doi": "10.2139/ssrn.6892979", "title": "CausalPrime: A Constraint-Oriented Runtime Middleware for Structure-Preserving Mapping under Non-Closure Conditions", "authors": [ "Fei Xu" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6892979", "field": "accounting", "role": "method", "bullets": [ "Structured legal and audit document streams from the U.S. v. Kwok forensic case corpus, replayed across eight independent sessions at temperatures from 0.0 to 1.0.", "A runtime middleware registers typed causal units and routes unresolved structures into explicit failure states rather than smoothing them; the underlying LLM family is not stated and no ground truth check is reported.", "Backbone routing overlap holds at 78 to 88 percent under near deterministic extraction and 70 to 76 percent at maximum entropy, with no statistical generalization claimed beyond the single corpus." ], "bullet_provenance": "ai", "validated": false, "salience": 26, "edition": 8, "models": [], "n": 1320, "authors_detailed": [ { "name": "Fei Xu", "url": "https://openalex.org/A5140286536", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6806638", "doi": "10.2139/ssrn.6806638", "title": "PAARA Connector-DPIA Methodology: A Privacy-Aware Risk Assessment Model for Enterprise GenAI Connectors", "authors": [ "Vivek Kumar" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6806638", "field": "management", "role": "object", "bullets": [ "Enterprise generative AI connectors, including OAuth scopes, Microsoft Graph permissions, and RAG pipelines linking models to email, HR, finance, and customer data systems.", "No model is applied empirically and no LLM family is named; connectors themselves are the assessment object, mapped against GDPR Article 35, the EU AI Act, and the OWASP LLM Top 10.", "Delivers a six component assessment covering connector inventory, permission risk register, prompt injection threat model, and insider risk, with a worked example and practitioner checklist." ], "bullet_provenance": "ai", "salience": 28, "edition": 8, "models": [], "validated": null, "n": 1321, "authors_detailed": [ { "name": "Vivek Kumar", "url": "https://openalex.org/A5137127523", "inst": "Indiana Wesleyan University" } ], "affiliations": [ "Indiana Wesleyan University" ] }, { "uid": "doi:10.2139/ssrn.7020938", "doi": "10.2139/ssrn.7020938", "title": "AI-conducted Dialogues: A Critical Discussion on the use of LLMs for Conducting Qualitative Interviews", "authors": [ "Margaret Foster", "Claire Wardle", "Lucas Wright" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7020938", "field": "management", "role": "method", "bullets": [ "A critical essay on LLM powered agents that conduct open ended interviews with human participants, addressed to researchers, journal editors, professional associations, and institutional review boards.", "No specific model is used or evaluated; the authors define the term AI-conducted dialogues and examine how replacing a human interviewer with an LLM changes qualitative interview methodology.", "They argue the substitution breaks the epistemic foundation of qualitative interviewing and close with five concerns that any study using the technique must address before proceeding." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 46, "edition": 8, "validated": null, "n": 1328, "authors_detailed": [ { "name": "Margaret Foster", "url": "https://openalex.org/A5143898276", "inst": "Cornell University" }, { "name": "Claire Wardle", "url": "https://openalex.org/A5083936373", "inst": "Cornell University" }, { "name": "Lucas Wright", "url": "https://openalex.org/A5024153006", "inst": "Cornell University" } ], "affiliations": [ "Cornell University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6931418", "doi": "10.2139/ssrn.6931418", "title": "VIEX — Visibility Exclusion Index: why search-seeded corpora underestimate algorithmic citation in generative AI engines", "authors": [ "Johnny Telles" ], "posted": "2026-07-29", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6931418", "field": "management", "role": "method", "bullets": [ "5,544 generative search engine responses across two verticals, enterprise management systems and hospitality, compared with a corpus seeded from organic search results as in prior GEO research.", "Named entity recognition applied to outputs of Gemini 2.5 Flash, GPT-4o, and Claude Sonnet 4.6 identifies which entities each model cites; extraction accuracy is not benchmarked against ground truth.", "Search seeded corpora missed 47.5 to 73.2 percent of entities the models cited, including the two most cited management systems, motivating the proposed visibility exclusion index as a sampling validity check." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": false, "salience": 52, "edition": 8, "n": 1329, "authors_detailed": [ { "name": "Johnny Jefferson Telles", "url": "https://openalex.org/A5136606017", "inst": "Universidade Estadual do Oeste do Paraná" } ], "affiliations": [ "Universidade Estadual do Oeste do Paraná" ] }, { "uid": "doi:10.2139/ssrn.6636298", "doi": "10.2139/ssrn.6636298", "title": "Three Ways to Fail to Conclude: A Null-report on Large Language Model Citation Claims for Brazilian Brands (N = 7,052, 12 days)", "authors": [ "Alexandre Caramaschi" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6636298", "field": "management", "role": "object", "bullets": [ "Probing run of 7,052 responses gathered over twelve days in early 2026, covering 69 entities including eight fictitious decoys, across fintech, retail, health, and technology in Brazilian Portuguese.", "Four production models, GPT-4o-mini, Claude Haiku 4.5, Gemini 2.5 Pro, and Perplexity sonar, were queried for brand citations, with no validation against any ground-truth citation record reported.", "Aggregate citation rate reached 77.6 percent, but three practitioner claims about retrieval advantage, hallucination robustness, and divergent citation universes each failed to reject the null, while vertical and language differences held." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "llama" ], "open_weights": false, "salience": 40, "edition": 7, "validated": null, "n": 1245, "authors_detailed": [ { "name": "Alexandre Caramaschi", "url": "https://openalex.org/A5134024605", "inst": "Twitter (United States)" } ], "affiliations": [ "Twitter (United States)" ] }, { "uid": "doi:10.2139/ssrn.6683303", "doi": "10.2139/ssrn.6683303", "title": "Limited Marginal Benefit of Reasoning-Heavy Deployment in ESG Narrative Scoring: Evidence from 4-Model Consensus on Japanese Listed Firms", "authors": [ "Hiroyuki Kokubu" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6683303", "field": "accounting", "role": "method", "bullets": [ "Ten Japanese listed firms scored on three ESG rubric axes, quantitative targets, progress-tracking infrastructure, and external-standard alignment, producing 120 firm by axis by model narrative scores.", "A four-model consensus of Claude Opus, GPT-5.5 with reasoning on, Gemini 3.1 Pro, and DeepSeek scored the narratives; agreement was checked across models rather than against human ground truth.", "The reasoning-on model deviated only 0.38 of five points from reasoning-off counterparts, yet the single reasoning-on arm cost roughly 5.6 times more per firm than the reasoning-off ensemble." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": false, "validated": false, "validation_note": "inter-model kappa only, no human ground truth", "salience": 46, "edition": 7, "n": 1246, "authors_detailed": [ { "name": "Hiroyuki Kokubu", "url": "https://openalex.org/A5134823092", "inst": "Kansai University" } ], "affiliations": [ "Kansai University" ] }, { "uid": "doi:10.2139/ssrn.6731878", "doi": "10.2139/ssrn.6731878", "title": "Off-Page Signals Have Model-Specific Effects on Generative AI Search Visibility: Evidence from a Cross-Platform Audit of 2,729 Businesses Across Five Generative AI Systems", "authors": [ "Joel House" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6731878", "field": "management", "role": "object", "bullets": [ "Cross-platform audit of 2,729 businesses across fourteen verticals and four metros, Los Angeles, New York, Chicago, and Sydney, yielding 266,844 business, model, and prompt observations.", "Each business was probed with about 95 prompts across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overview, correlating mentions with off-page presence signals; no ground-truth validation was reported.", "Nearly 70 percent of businesses were mentioned by no system; off-page signals raised mention odds for Claude, ChatGPT, and Perplexity, partial correlations up to 0.22, but not Gemini or Google." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 48, "edition": 7, "validated": null, "n": 1247, "authors_detailed": [ { "name": "Joel House", "url": "https://openalex.org/A5135528248", "inst": "Trinity House" } ], "affiliations": [ "Trinity House" ] }, { "uid": "doi:10.2139/ssrn.6612061", "doi": "10.2139/ssrn.6612061", "title": "Large Language Models and Stock Investing: Is the Human Factor Required?", "authors": [ "Ricardo Crisóstomo", "Diana Mykhalyuk" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6612061", "field": "finance", "role": "agent", "bullets": [ "Evaluation of large language model stock recommendations across three prompting strategies, a naive query, a structured approach, and chain-of-thought; sample size, period, and geography are not stated.", "ChatGPT, Gemini, DeepSeek, and Perplexity generated recommendations that were compared with market outcomes rather than validated against human-coded ground truth with a reported agreement statistic.", "Recommendations showed recurring reasoning failures including financial misconceptions and hallucinated information; with supervision the models could beat the market, and grounding in regulatory filings raised forecasting accuracy." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "open_other" ], "open_weights": false, "salience": 50, "edition": 7, "validated": null, "n": 1248, "authors_detailed": [ { "name": "Ricardo Crisóstomo", "url": "https://openalex.org/A5017638240", "inst": "Universidad Nacional de Educación a Distancia" }, { "name": "Diana Mykhalyuk", "url": "https://openalex.org/A5133481770", "inst": "" } ], "affiliations": [ "Universidad Nacional de Educación a Distancia" ] }, { "uid": "doi:10.2139/ssrn.6776298", "doi": "10.2139/ssrn.6776298", "title": "Provider-Dependent Behavioral Asymmetries in LLM Trading Agents: Evidence from the Brazilian Equity Market", "authors": [ "Felipe Cataneo" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6776298", "field": "finance", "role": "agent", "bullets": [ "Brazilian B3-listed equities over roughly 504 trading days from April 2024 to April 2026, using a matched five-asset core with a 23-asset universe as robustness check.", "GPT-5.4, Claude Haiku 4.5, and locally run Qwen-8B acted as autonomous trading agents in a hybrid quant architecture, tested for Prospect Theory biases without validation against ground truth.", "All three providers issued more long than short signals, with long-to-short ratios of 1.26, 5.94, and 10.47; the asymmetry varied nearly tenfold across providers and confidence was poorly calibrated." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "salience": 54, "edition": 7, "validated": null, "n": 1249, "authors_detailed": [ { "name": "Felipe Cataneo", "url": "https://openalex.org/A5143903430", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7111078", "doi": "10.2139/ssrn.7111078", "title": "Confirmation Bias in LLM Pricing Recommendations", "authors": [ "Maxime C. Cohen", "Eddy Hage-Youssef" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7111078", "field": "management", "role": "agent", "bullets": [ "Controlled pricing experiment producing 350,000 recommendations, with a model cast as a pricing strategist for a well-specified product while suggested price, source credibility, timing, and input information were varied.", "Claude Haiku 4.5 and GPT-5.4 Mini recommended prices; the study measured susceptibility to confirmation bias rather than validating output against an external benchmark, so no accuracy figure applies.", "Both models discounted economically implausible suggestions but were swayed by conversational cues; a single follow-up challenge often made Claude abandon its price and adopt the suggested one." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 63, "edition": 7, "validated": null, "n": 1250, "authors_detailed": [ { "name": "Maxime C. Cohen", "url": "https://openalex.org/A5015555156", "inst": "McGill University" }, { "name": "Eddy Hage-Youssef", "url": "https://openalex.org/A5119054719", "inst": "McGill University" } ], "affiliations": [ "McGill University" ] }, { "uid": "doi:10.2139/ssrn.6798519", "doi": "10.2139/ssrn.6798519", "title": "Beyond Sentiment: Large Language Models as Organizational Sensors for Detecting Strategic Drift in Corporate Communications", "authors": [ "Justin Yan" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6798519", "field": "management", "role": "instrument", "bullets": [ "StratDrift-10K corpus of 10,847 earnings call transcripts and 10-K filings from S&P 500 firms spanning 2003 to 2023, annotated with analyst-validated strategic coherence labels at paragraph level.", "Seven instruction-tuned models from GPT-4o to open-weight alternatives detected strategic drift via zero-shot chain-of-thought, reaching F1 of 0.81 against analyst labels and beating fine-tuned BERT baselines by 9.3 points.", "The extracted organizational signals predicted subsequent financial restatements with AUC 0.74 and CEO turnover with AUC 0.69 in held-out prospective data." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "analyst-labeled StratDrift-10K, F1 0.81", "salience": 70, "edition": 7, "n": 1252, "authors_detailed": [ { "name": "Jiaju Yan", "url": "https://openalex.org/A5010580571", "inst": "Baylor University" } ], "affiliations": [ "Baylor University" ] }, { "uid": "doi:10.2139/ssrn.6472520", "doi": "10.2139/ssrn.6472520", "title": "Generative Artificial Intelligence Capabilities in Sustainability Reporting of Oil and Gas Companies", "authors": [ "Ekaterina Marina" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6472520", "field": "accounting", "role": "object", "bullets": [ "Conceptual analysis of the sustainability reporting cycle at oil and gas companies under Russia's evolving disclosure regime, including the 2023 Methodological Recommendations and the 2025 Business Social Capital Standard.", "Large language models paired with retrieval-augmented generation are discussed across three reporting stages of data processing, analysis, and disclosure drafting; no specific model is named and no validation is reported.", "The paper argues that realizing this potential requires deliberate adaptation of accounting policies, prompt governance, and expert oversight rather than technological adoption alone." ], "bullet_provenance": "ai", "salience": 34, "edition": 7, "models": [], "validated": null, "n": 1253, "authors_detailed": [ { "name": "Ekaterina D. MAR'INA", "url": "https://openalex.org/A5103039241", "inst": "Moscow State Institute of International Relations" } ], "affiliations": [ "Moscow State Institute of International Relations" ] }, { "uid": "doi:10.2139/ssrn.6944258", "doi": "10.2139/ssrn.6944258", "title": "Frontier LLMs Add Almost No Information Beyond the Market Price, and Two Years of Scaling Has Not Changed That", "authors": [ "Toni Zemani" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6944258", "field": "economics", "role": "agent", "bullets": [ "ForecastBench data spanning 25 resolved rounds from July 2024 to May 2026, covering 1,256 model-configuration-rounds, 342 configurations, and roughly 371,000 resolved market forecasts.", "A dozen model generations from GPT-3.5 and Claude-2.1 to GPT-5.x and Claude-Opus-4.x produce probabilistic forecasts, with contribution measured as bias-corrected incremental log-likelihood beyond the market price and scored against resolved outcomes.", "Models add about 0.001 nats beyond the free market price with a non-positive two-year trend, while human superforecasters add about 0.09 nats on the same questions." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "forecasts scored against ForecastBench resolved outcomes; log-likelihood and Brier reported", "salience": 72, "edition": 7, "n": 1254, "authors_detailed": [ { "name": "Toni Zemani", "url": "https://openalex.org/A5143985701", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6641538", "doi": "10.2139/ssrn.6641538", "title": "Using Large Language Models to Generate New Features from Text Data for Loss Prediction", "authors": [ "Guojun Gan", "Christopher Shultz" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6641538", "field": "finance", "role": "instrument", "bullets": [ "Unstructured insurance claims descriptions used to build features for actuarial loss prediction; sample size, period, and geography are not stated.", "GPT-4o and Llama-3.2-3B classify incidents into ordinal risk categories using structured prompts, with no comparison to human-coded ground truth reported.", "The resulting labels are predictive of average loss and improve generalized linear model in-sample fit and out-of-sample accuracy, with five-category GPT-4o labels performing best." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": false, "validated": false, "validation_note": "labels shown predictive of loss but not benchmarked against human coding", "salience": 52, "edition": 7, "n": 1255, "authors_detailed": [ { "name": "Guojun Gan", "url": "https://openalex.org/A5000062069", "inst": "University of Connecticut" }, { "name": "Christopher Shultz", "url": "https://openalex.org/A5051884546", "inst": "Independent" } ], "affiliations": [ "University of Connecticut", "Independent" ] }, { "uid": "doi:10.2139/ssrn.6591478", "doi": "10.2139/ssrn.6591478", "title": "Alignment of Generative Models (LLMs): Impacts of RLHF on Security, Governance, and Corporate Innovation", "authors": [ "Luiz Carlos Bueno da Silva Junior" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6591478", "field": "management", "role": "object", "bullets": [ "Conceptual document on corporate adoption of generative AI, with no empirical sample, covering model alignment, governance, security, and implications for professional work.", "No model is deployed by the author; the paper reviews transformer architecture, supervised fine-tuning, LoRA, RAG, and RLHF methods such as PPO and DPO, without validation.", "It argues that RLHF improves usability but is insufficient alone, and that responsible corporate adoption needs governance, human-in-the-loop oversight, auditing, and RAG-based factual grounding." ], "bullet_provenance": "ai", "salience": 28, "edition": 7, "models": [], "validated": null, "n": 1256, "authors_detailed": [ { "name": "Luiz Carlos Bueno da Silva Junior", "url": "https://openalex.org/A5143932773", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7035918", "doi": "10.2139/ssrn.7035918", "title": "Evaluative Translation in LLM-Based Judgment Systems: Evidence from Platform Rating Inference", "authors": [ "Jing Li", "Zhijie Lin" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7035918", "field": "management", "role": "method", "bullets": [ "A randomly drawn sample of 20,000 Yelp reviews, with the individual review as the unit and the platform star rating treated as ground truth.", "GPT, Claude, and Gemini infer platform ratings from review narratives, and their divergence from actual star ratings is measured, giving validation against the platform ratings.", "Divergence rises with evaluative ambiguity and high sentiment entropy and is lowest when text and rating are congruent, with patterns stable across the three model families." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "LLM-inferred ratings compared to actual Yelp star ratings; divergence measured", "salience": 55, "edition": 7, "n": 1257, "authors_detailed": [ { "name": "Jing Li", "url": "https://openalex.org/A5100336900", "inst": "NEOMA Business School" }, { "name": "Zhijie Lin", "url": "https://openalex.org/A5005931678", "inst": "Beijing Haidian Hospital" } ], "affiliations": [ "NEOMA Business School" ] }, { "uid": "doi:10.2139/ssrn.6805398", "doi": "10.2139/ssrn.6805398", "title": "Coding Open-ended Responses with Large Language Models: Evidence from Three Economic Experiments", "authors": [ "Daniel Parra", "Sophia Aristizabal" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6805398", "field": "economics", "role": "method", "bullets": [ "Open-ended responses and free-form communication from three published economic experiments that differ in coding structure, textual ambiguity, and the role of communication.", "GPT, Claude, and Gemini code the responses under varied prompts and temperatures, validated against human coding and reaching Cohen's kappa above 0.80 in a trust game.", "Models match humans when categories are deductively defined and explicitly marked, but over-identify rare categories three to five fold and fail on inductive or pragmatic coding." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "Cohen's kappa against human coders across three experiments; kappa above 0.80 in trust game", "salience": 60, "edition": 7, "n": 1258, "authors_detailed": [ { "name": "Daniel Parra", "url": "https://openalex.org/A5101772350", "inst": "University of Central Florida" }, { "name": "Sophia Aristizabal", "url": "https://openalex.org/A5143931044", "inst": "" } ], "affiliations": [ "University of Central Florida" ] }, { "uid": "doi:10.2139/ssrn.6766498", "doi": "10.2139/ssrn.6766498", "title": "Territorial Algorithmic Invisibility: A Diagnostic Framework for Generative Engine Optimization Applied to Local Tourism Destination Entities", "authors": [ "Eric Mees de Saboya Ribeiro" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6766498", "field": "management", "role": "object", "bullets": [ "Structured diagnostic audit of Neltur, the official tourism organization of Niteroi near Rio de Janeiro, assessing how local attractions appear in AI-generated travel recommendations.", "ChatGPT, Google Gemini, and Perplexity AI are queried systematically to measure destination visibility, and no validation against ground truth is reported.", "Niteroi attractions were consistently attributed to neighboring Rio de Janeiro or omitted entirely, producing a baseline Destination Algorithmic Readiness Score of 38 out of 100." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 34, "edition": 7, "validated": null, "n": 1259, "authors_detailed": [ { "name": "Eric Mees de Saboya Ribeiro", "url": "https://openalex.org/A5137721185", "inst": "Digital Science (United States)" } ], "affiliations": [ "Digital Science (United States)" ] }, { "uid": "doi:10.2139/ssrn.6803400", "doi": "10.2139/ssrn.6803400", "title": "EconBench: Rethinking Evaluation for Economic Reasoning in Large Language Models beyond Benchmark Saturation", "authors": [ "Justin Yan" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6803400", "field": "economics", "role": "method", "bullets": [ "EconBench comprises 4,200 expert-curated economics and finance items across five reasoning strata, constructed to resist memorization through novel framing, distractors, and adversarial perturbation.", "Fourteen frontier and open-weight LLMs are evaluated on economic reasoning tasks and scored against expert-annotated answers and a human economist baseline.", "Models are competitive on definitional and applied strata but diverge on harder reasoning, with best model GPT-4o at 51.3% on the top stratum versus 84.7% for economists." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "scored against expert-curated items and economist baseline; accuracy reported by stratum", "salience": 62, "edition": 7, "n": 1260, "authors_detailed": [ { "name": "Jiaju Yan", "url": "https://openalex.org/A5010580571", "inst": "Baylor University" } ], "affiliations": [ "Baylor University" ] }, { "uid": "doi:10.2139/ssrn.6705178", "doi": "10.2139/ssrn.6705178", "title": "Game-Theoretic Scaffolding in Financial Analysis: Why Human Direction of LLMs Outperforms Autonomous Models", "authors": [ "Qiyu Zhang" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6705178", "field": "finance", "role": "agent", "bullets": [ "Live portfolio study over 56 trading days and 2,001 signals across 11 US technology stocks from February to May 2026.", "LLMs including Claude and DeepSeek issue buy, hold, or sell decisions, compared against a human applying game-theoretic frameworks to weight model disagreement, with no measurement validation.", "The augmented analyst returned 17.01% with Sharpe 0.89 and about half the volatility of the most aggressive autonomous model, Claude at plus 17.36%, all beating the NDX benchmark." ], "bullet_provenance": "ai", "models": [ "claude", "open_other" ], "open_weights": true, "salience": 42, "edition": 7, "validated": null, "n": 1261, "authors_detailed": [ { "name": "Qiyu Zhang", "url": "https://openalex.org/A5134055553", "inst": "Nanyang Polytechnic" } ], "affiliations": [ "Nanyang Polytechnic" ] }, { "uid": "doi:10.2139/ssrn.6095166", "doi": "10.2139/ssrn.6095166", "title": "The Effect of Large Language Models on Audit Quality: Causal Evidence from a Randomized Field Experiment", "authors": [ "Markus Jezierski", "Sascha Kaltenpoth", "Oliver Müller", "Barbara E. Weißenberger" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6095166", "field": "accounting", "role": "object", "bullets": [ "Preregistered randomized field experiment with 100 professional auditors at a global audit firm, each completing a complex inventory valuation task.", "Auditors worked either with the traditional digital toolset or with added access to a firm-specific LLM, which serves as the treatment rather than a measurement instrument.", "LLM access raised documentation quality and cut completion time, with largest gains for active users, but did not improve adherence to internal audit guidelines or vary by experience." ], "bullet_provenance": "ai", "salience": 73, "edition": 7, "models": [], "validated": null, "n": 1262, "authors_detailed": [ { "name": "Markus Jezierski", "url": "https://openalex.org/A5143897867", "inst": "" }, { "name": "Sascha Kaltenpoth", "url": "https://openalex.org/A5098445544", "inst": "Paderborn University" }, { "name": "Oliver Müller", "url": "https://openalex.org/A5101561980", "inst": "Paderborn University" }, { "name": "Barbara E. Weißenberger", "url": "https://openalex.org/A5088280086", "inst": "Heinrich Heine University Düsseldorf" } ], "affiliations": [ "Paderborn University", "Heinrich Heine University Düsseldorf" ] }, { "uid": "doi:10.2139/ssrn.7118542", "doi": "10.2139/ssrn.7118542", "title": "Market-Segmented Enforcement: Adversarial LLM Analysis of Political Advertising Moderation across Linguistic Markets", "authors": [ "Amirhossein Salehi Fashami" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7118542", "field": "economics", "role": "instrument", "bullets": [ "1,080 high-engagement political advertisements from Meta's Ad Library across four linguistic markets, English, Hebrew, Arabic, and German, spanning October 2023 to January 2025.", "Three LLMs, Claude Sonnet 4.5, GPT-4o, and DeepSeek, were assigned adversarial roles to classify each ad as policy-violative; outputs were not validated against human coders.", "Multiple arbiter models flagged 5.2 percent of Hebrew ads as violative versus zero percent elsewhere, all retained by Meta; authors attribute the gap to Hebrew's lower CPM." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "validated": false, "validation_note": "no human-coded benchmark reported", "salience": 42, "edition": 7, "n": 1263, "authors_detailed": [ { "name": "Amirhossein Salehi Fashami", "url": "https://openalex.org/A5143929264", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6768202", "doi": "10.2139/ssrn.6768202", "title": "Governing the Black Box: A Framework for Large Language Model Governance in Trading and Risk Management at Systemically Important Financial Institutions", "authors": [ "Chandni Bhatia" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6768202", "field": "finance", "role": "object", "bullets": [ "Documentary coding of 30 public regulatory documents issued between 2011 and April 2026, plus two 2024 SEC enforcement cases, Delphia and Global Predictions, at systemically important financial institutions.", "No language model is used as a tool; the authors manually code each document for coverage of LLM trading and risk-management governance across defined pillars.", "Probabilistic-validation coverage reaches only 18 percent and operational-risk-controls coverage 23 percent, and the April 2026 SR 26-2 update explicitly excludes generative and agentic AI from its scope." ], "bullet_provenance": "ai", "salience": 55, "edition": 7, "models": [], "validated": null, "n": 1265, "authors_detailed": [ { "name": "Chandni Bhatia", "url": "https://openalex.org/A5136331981", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of California, Berkeley" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6670260", "doi": "10.2139/ssrn.6670260", "title": "Reasoning Settings Attenuate Preference-Reversal-Style Inconsistency in Large Language Models", "authors": [ "Jonathan Dang" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6670260", "field": "economics", "role": "agent", "bullets": [ "Preregistered study of 25,920 API calls, using 28 risky-choice items, 8 dominance checks, and 3 prompt formats to estimate a weak-form Lichtenstein-Slovic preference-reversal analog.", "Claude Opus 4.7, DeepSeek V4-Pro, Gemini 3 Flash, and GPT-5.5 were asked separately to choose and to price gambles; outputs treated as behavior, not validated against ground truth.", "All models showed choice-valuation inconsistency; raising reasoning settings cut the mean inconsistency rate from 0.484 to 0.307, though this confounds output length." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": false, "salience": 55, "edition": 7, "validated": null, "n": 1268, "authors_detailed": [ { "name": "Jonathan Dang", "url": "https://openalex.org/A5090214405", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6631180", "doi": "10.2139/ssrn.6631180", "title": "The Welfare Impact of Delegating Choice to Large Language Models", "authors": [ "Yann Delaprez", "Ali Hortaçsu" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6631180", "field": "economics", "role": "agent", "bullets": [ "Human and LLM-generated purchase choices in the US pizza restaurant market around the introduction of menu labeling laws, paired with a structural model of delegation.", "ChatGPT generates purchase decisions standing in for consumers; model version and any validation against ground truth are not stated.", "Delegation would cut total calorie intake by up to 18 percent, raise market concentration, and lift net welfare, with consumer surplus rising for delegators and falling slightly for others." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "edition": 7, "validated": null, "n": 1269, "authors_detailed": [ { "name": "Yann Delaprez", "url": "https://openalex.org/A5058635570", "inst": "University of Chicago" }, { "name": "Alı Hortaçsu", "url": "https://openalex.org/A5006705660", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "University of Chicago", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6795338", "doi": "10.2139/ssrn.6795338", "title": "Generative AI for Central Bank Statements: Large Language Models as Monetary Policy Forecasters", "authors": [ "Jens Hilscher", "Yevgeny Mugerman", "Alon Raviv" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6795338", "field": "finance", "role": "agent", "bullets": [ "140 time-stamped forecasts from five public LLMs generated in the weeks before three December 2025 policy meetings at the Federal Reserve, the ECB, and the Bank of England.", "Five unnamed public LLMs forecast the rate decision, probabilities, and hawkish-dovish tone; accuracy checked against realized decisions using a prespecified tone rubric.", "Individual model date accuracy was 87.9 percent and the equal-weight consensus 92.9 percent; models agreed on the rate decision earlier than on the surrounding communication." ], "bullet_provenance": "ai", "validated": true, "validation_note": "forecast accuracy vs realized policy decisions, 87.9 percent, Brier score", "salience": 58, "edition": 7, "models": [], "n": 1270, "authors_detailed": [ { "name": "Jens Hilscher", "url": "https://openalex.org/A5032557220", "inst": "University of California, Davis" }, { "name": "Yevgeny Mugerman", "url": "https://openalex.org/A5006054065", "inst": "Bar-Ilan University" }, { "name": "Alon Raviv", "url": "https://openalex.org/A5064018326", "inst": "Bar-Ilan University" } ], "affiliations": [ "University of California, Davis", "Bar-Ilan University" ] }, { "uid": "doi:10.2139/ssrn.7081318", "doi": "10.2139/ssrn.7081318", "title": "The Rise of Artificial Intelligence Phobia! Unveiling News-Driven Spread of AI Fear Sentiment Using ML, NLP, and LLMs", "authors": [ "Jim Samuel", "Tanya Khanna", "Julia Esguerra", "Srinivasaraghavan Sundar", "Alexander Pelaez", "Soumitra S. Bhuyan" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7081318", "field": "management", "role": "instrument", "bullets": [ "Nearly 70,000 AI-related news headlines analyzed to identify dominant themes and fear-laden sentiment shaping public perception of artificial intelligence.", "BERT, LLaMA, and Mistral applied for topic modeling and fear-sentiment classification alongside supervised machine learning; no accuracy against human coding is stated.", "Emotionally negative and fear-inducing language persists in AI coverage, which the authors argue fuels AI phobia, behavioral resistance, and adverse effects on AI policy." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": false, "validation_note": "no accuracy against human-coded sentiment reported", "salience": 40, "edition": 7, "n": 1271 }, { "uid": "doi:10.2139/ssrn.7017418", "doi": "10.2139/ssrn.7017418", "title": "LLM and Multi-Agent Systems in Algorithmic Trading: Taxonomy and Research Agenda", "authors": [ "Sumin Pillai" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7017418", "field": "finance", "role": "method", "bullets": [ "Survey of more than forty papers published between 2019 and 2026 on large language models and multi-agent systems in algorithmic trading.", "No model is applied; the paper builds a taxonomy across the LLM's functional role, information modality, learning paradigm, system architecture, and evaluation standard.", "Identifies four gaps including the absence of a cost-adjusted benchmark for LLM signals, unexplored multi-agent failure modes, and near-total absence of testing in non-US markets." ], "bullet_provenance": "ai", "salience": 45, "edition": 7, "models": [], "validated": null, "n": 1272, "authors_detailed": [ { "name": "Sumin Pillai", "url": "https://openalex.org/A5135864735", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6716458", "doi": "10.2139/ssrn.6716458", "title": "Beyond the AI Chase: A Tiered Intelligence Architecture for Insurance Automation in GCC Markets", "authors": [ "Rishabha Garg" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6716458", "field": "management", "role": "object", "bullets": [ "Gulf Cooperation Council insurance markets, drawing on twelve years of practitioner bancassurance experience across India, the UAE, and Oman.", "No model is applied by the author; the paper proposes a Tiered Intelligence Architecture matching deterministic rules engines and domain-adapted LLMs to distinct decision layers.", "Argues LLMs add value mainly in language-heavy customer-facing tasks while structured tasks suit deterministic rules, with privacy-safe local deployment as the binding constraint." ], "bullet_provenance": "ai", "salience": 32, "edition": 7, "models": [], "validated": null, "n": 1273, "authors_detailed": [ { "name": "Rishabha Garg", "url": "https://openalex.org/A5143906444", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6834918", "doi": "10.2139/ssrn.6834918", "title": "Bounded-autonomy LLM Agents: A Structural Safety Architecture with a Live Algorithmic-trading Case Study", "authors": [ "Jean Direl Nze Kabeyene" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6834918", "field": "finance", "role": "method", "bullets": [ "Live algorithmic-trading testbed over three crypto pairs (BTC, ETH, SOL) at five-minute intervals under a preregistered evaluation protocol with a stop-rule.", "An open-weight Llama-3.3-70B acts only as a Boolean veto over a deterministic rule-based proposer, with a hard sizing and risk layer applied after the verdict.", "The architecture guarantees by construction that the LLM cannot enlarge exposure; the drawdown-reduction hypothesis is not yet tested, at 66 to 90 cycles versus 500 preregistered." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "salience": 40, "edition": 7, "validated": null, "n": 1274, "authors_detailed": [ { "name": "Jean Direl nze kabeyene", "url": "https://openalex.org/A5137314542", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6728000", "doi": "10.2139/ssrn.6728000", "title": "A Designed-for-Test Measurement of Discourse-Language Bias in Large Language Model Brand Recommendations", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6728000", "field": "management", "role": "object", "bullets": [ "33 premium kitchen knife brands across four lineages measured against six pre-registered prompts, six frontier models from four labs, and eight runs each (n = 288).", "Claude Sonnet 4.6 and Opus 4.7, gpt-5.4-mini and gpt-5.5, Gemini 2.5 Flash, and Grok 4.1 were prompted for brand recommendations; outputs scored for brand presence without ground-truth validation.", "The lineage-origin hypothesis failed, but English-marketed brands surfaced far more than boundary makers, a 48-point within-Japanese gap, suggesting English-marketing coverage drives AI presence." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": false, "salience": 50, "edition": 7, "validated": null, "n": 1275, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "School of Visual Arts" } ], "affiliations": [ "School of Visual Arts" ] }, { "uid": "doi:10.2139/ssrn.6908878", "doi": "10.2139/ssrn.6908878", "title": "Algorithmic Unit Economics: Variable Inference COGS, AI Margin Quality, and the IFRS 15 / IAS 38 Boundary", "authors": [ "Rafael Minuti" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6908878", "field": "accounting", "role": "object", "bullets": [ "Conceptual accounting analysis of GenAI and LLM product economics, with the unit of analysis being an AI-native product's cost structure and financial-statement presentation.", "No model is applied; the paper formalizes Variable Inference COGS and related margin diagnostics tied to API logs, GPU utilization, and cloud billing tiers.", "Argues aggregating inference into broad cost pools can mask negative unit economics, locating the central friction at the IAS 38 versus IFRS 15 boundary." ], "bullet_provenance": "ai", "salience": 55, "edition": 7, "models": [], "validated": null, "n": 1276, "authors_detailed": [ { "name": "Rafael Minuti", "url": "https://openalex.org/A5140844147", "inst": "Oldham Council" } ], "affiliations": [ "Oldham Council" ] }, { "uid": "doi:10.2139/ssrn.6961260", "doi": "10.2139/ssrn.6961260", "title": "The Automation Efficiency Paradox: Why Generative AI Increases the Need for Human Expertise", "authors": [ "Sashikanta Barik" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6961260", "field": "management", "role": "object", "bullets": [ "A conceptual paper with no empirical sample, addressing organizations that adopt large language models to automate knowledge work and text-heavy operational tasks.", "No language model is run by the authors; they build a behavioral and cognitive framework called the Automation Efficiency Paradox, and validation is not applicable.", "The authors argue that fluent probabilistic outputs hide omissions, so replacing workers with AI erodes trust and raises the need for experts who audit outputs at the source." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 26, "edition": 7, "validated": null, "n": 1277, "authors_detailed": [ { "name": "Sashikanta Barik", "url": "https://openalex.org/A5137410845", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7035480", "doi": "10.2139/ssrn.7035480", "title": "Artificial Intelligence and Competition Policy and Law Struggling Together In the Fragmented Global Economy: AI and Competition as the Penultimate Double-edged Sword", "authors": [ "Derek John Ireland" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7035480", "field": "economics", "role": "object", "bullets": [ "A working paper applying a complexity-science and chaos-theory lens to how competition policy and law confront artificial intelligence in a fragmented, networked global economy.", "No language model is used by the author; the paper analyzes how LLMs, generative AI, and agentic AI create competition-law challenges, and validation is not applicable.", "It argues AI-enabled deception and dominance abuses strain antitrust enforcement, and that competition-law consensus is more advanced than broader AI governance, which lacks transnational agreement." ], "bullet_provenance": "ai", "salience": 28, "edition": 7, "models": [], "validated": null, "n": 1280, "authors_detailed": [ { "name": "Derek John Ireland", "url": "https://openalex.org/A5143892980", "inst": "Carleton University" } ], "affiliations": [ "Carleton University" ] }, { "uid": "doi:10.2139/ssrn.6495198", "doi": "10.2139/ssrn.6495198", "title": "Governed Enterprise AI: A Three-Layer Architecture for Hallucination-Safe BPM Workflows", "authors": [ "Chia Ho Lin" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6495198", "field": "management", "role": "object", "bullets": [ "One hundred high-stakes luxury-retail escalation scenarios used to test large language models given terminal decision authority within enterprise business-process workflows.", "A 12B-parameter LLM, family not stated, served as the decision baseline; the authors added a deterministic policy engine, an ML risk model, and a bounded small language model in a three-layer design.", "The baseline failed policy adherence 42 percent of the time despite zero factual tier hallucination, while the decoupled three-layer architecture cut the failure rate to zero." ], "bullet_provenance": "ai", "salience": 38, "edition": 7, "models": [], "validated": null, "n": 1281, "authors_detailed": [ { "name": "Chia Ho Lin", "url": "https://openalex.org/A5113896239", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6832218", "doi": "10.2139/ssrn.6832218", "title": "Failure Modes and Calibration of Local LLMs on Imbalanced VC Prediction", "authors": [ "Samir Adam Annour Mahamat Saleh" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6832218", "field": "finance", "role": "method", "bullets": [ "VCBench founder-success prediction from anonymized pre-founding profiles, an imbalanced rare-event task, evaluated on a 120-profile subset and the full 900-profile public validation split.", "Two local Qwen3 variants run through Ollama classify founders under vanilla and few-shot prompts, benchmarked against trivial baselines and a logistic-regression TF-IDF model using F0.5 scores.", "Prompt engineering mainly shifted the predicted-positive rate rather than improving discrimination, and a simple TF-IDF logistic model reaching F0.5 of 0.71 on the subset outperformed the LLMs." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "VCBench founder-success labels, F0.5 versus trivial and TF-IDF baselines", "salience": 54, "edition": 7, "n": 1282, "authors_detailed": [ { "name": "Samir Adam Annour Mahamat Saleh", "url": "https://openalex.org/A5137078686", "inst": "Data & Society Research Institute" } ], "affiliations": [ "Data & Society Research Institute" ] }, { "uid": "doi:10.2139/ssrn.6815399", "doi": "10.2139/ssrn.6815399", "title": "Evaluating AI in Finance: A Comprehensive Review and Taxonomy of LLM Benchmarks", "authors": [ "Louis Bertucci", "Murad Nuriyev" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6815399", "field": "finance", "role": "method", "bullets": [ "A structured review of more than fifty public financial benchmarks for large language models, organizing them by construction and by task type.", "No model is run; the authors build a taxonomy of eleven task categories and a two-dimensional grid separating what benchmarks measure from how they measure it.", "Frontier models score well on sentiment and entity extraction but fall well below expert level on grounded numerical reasoning and agentic financial workflows, with seven structural limitations identified." ], "bullet_provenance": "ai", "salience": 62, "edition": 7, "models": [], "validated": null, "n": 1283, "authors_detailed": [ { "name": "Louis Bertucci", "url": "https://openalex.org/A5039642474", "inst": "Institut de France" }, { "name": "Murad Nuriyev", "url": "https://openalex.org/A5117369275", "inst": "Université Toulouse-I-Capitole" } ], "affiliations": [ "Institut de France", "Université Toulouse-I-Capitole" ] }, { "uid": "doi:10.2139/ssrn.6743822", "doi": "10.2139/ssrn.6743822", "title": "Agentic LLM-Based Simulation for Pharmaceutical Evidence Planning", "authors": [ "Francis Lee", "Evangelos Katsamakas" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6743822", "field": "management", "role": "agent", "bullets": [ "A 40-run, 2x2 pilot simulation of pharmaceutical real-world-evidence planning in a GLP-1 market scenario, with LLM agents acting as competing brand teams under varied information channels.", "Large language models stand in for brand-team decision-makers whose visible signals are manipulated; the model family is not stated and outcome-based validation is unavailable by construction.", "Masking evaluator criteria or competitor state produced asymmetric shifts in evidence-planning behavior, revealing portfolio coverage and evaluator fit as separable dimensions of strategic quality." ], "bullet_provenance": "ai", "salience": 50, "edition": 7, "models": [], "validated": null, "n": 1284, "authors_detailed": [ { "name": "Francis Lee", "url": "https://openalex.org/A5143972131", "inst": "Independent" }, { "name": "Evangelos Katsamakas", "url": "https://openalex.org/A5041222308", "inst": "Fordham University" } ], "affiliations": [ "Independent", "Fordham University" ] }, { "uid": "doi:10.2139/ssrn.7067859", "doi": "10.2139/ssrn.7067859", "title": "SEDG: A Synthetic Experimental Data Generator for LLM-Based Behavioral Experiments", "authors": [ "Siva Shanmugam Mariappan", "Ashwin V. Malshe" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7067859", "field": "management", "role": "agent", "bullets": [ "No empirical study is reported; the paper presents SEDG, a platform for generating synthetic experimental participants for behavioral research in marketing, psychology, information systems, and management.", "Local and cloud LLMs, providers selected by the user rather than fixed, generate synthetic survey responses from optional personas; no comparison of synthetic responses to human data is reported.", "The platform lets researchers without engineering skills specify stimuli, questions, models, and personas, and export structured outputs for replication and cross-model comparison." ], "bullet_provenance": "ai", "salience": 42, "edition": 7, "models": [], "validated": null, "n": 1285, "authors_detailed": [ { "name": "Siva Shanmugam Mariappan", "url": "https://openalex.org/A5125563479", "inst": "San Antonio College" }, { "name": "Ashwin Malshe", "url": "https://openalex.org/A5054246422", "inst": "The University of Texas at San Antonio" } ], "affiliations": [ "San Antonio College", "The University of Texas at San Antonio" ] }, { "uid": "doi:10.2139/ssrn.6557745", "doi": "10.2139/ssrn.6557745", "title": "Generative AI Adoption Among Independent Knowledge Workers: A Framework for Cognitive Load Redistribution and Sustainable Productivity", "authors": [ "Rowan Hayes" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6557745", "field": "management", "role": "object", "bullets": [ "Survey of 287 independent knowledge workers plus 22 follow-up interviews, covering solo consultants and micro-firm practitioners; period and geography not stated.", "No specific model is named; generative AI tools are the object of study rather than a research instrument, so no validation against ground truth is reported.", "Perceived cognitive offloading predicts sustainable productivity (beta 0.41), with task-autonomy preservation moderating the relationship (beta 0.27)." ], "bullet_provenance": "ai", "salience": 46, "edition": 7, "models": [], "validated": null, "n": 1286, "authors_detailed": [ { "name": "Rowan Hayes", "url": "https://openalex.org/A5143947438", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6686359", "doi": "10.2139/ssrn.6686359", "title": "Large Language Models Compress the Asymmetry that Defines Human Comparative Valuation", "authors": [ "Hongcheol Choi" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6686359", "field": "economics", "role": "agent", "bullets": [ "Six LLMs generating 45,000 synthetic survey observations are benchmarked against 523 human respondents on a social comparison task spanning upward, equal, and downward comparisons; geography is not stated.", "Four instruction-tuned LLMs, families not named, act as synthetic respondents, and outputs are checked against the human benchmark through profile correlations and gain-loss asymmetry ratios.", "Models reach high aggregate correlations above 0.83 yet compress the human upward asymmetry from roughly 6:1 to 2-3:1 and overestimate downward gain responses five to seven times." ], "bullet_provenance": "ai", "validated": true, "validation_note": "benchmarked against N=523 human data, correlations reported", "salience": 60, "edition": 7, "models": [], "n": 1291, "authors_detailed": [ { "name": "Hongcheol Choi", "url": "https://openalex.org/A5042944972", "inst": "Chonnam National University" } ], "affiliations": [ "Chonnam National University" ] }, { "uid": "doi:10.2139/ssrn.6938899", "doi": "10.2139/ssrn.6938899", "title": "Shopping By Algorithm: How Agentic AI Deploys Human Heuristics as a Surrogate Consumer", "authors": [ "Davood Wadi", "Yu Ma" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6938899", "field": "management", "role": "agent", "bullets": [ "Four experiments using Tool-Lab, a process-tracing paradigm that tracks how autonomous LLM shopping agents acquire product information under constraints; sample size, period, and geography are not stated.", "Lower- and higher-parameter LLMs, families not named, act as surrogate consumers choosing products, and the study traces their information search rather than validating outputs against a ground truth.", "Heuristic reliance in smaller models reflects a failure to compute net utility, while larger models truncate search as a resource-rational adaptation, and forcing explicit optimization can hurt performance." ], "bullet_provenance": "ai", "salience": 55, "edition": 7, "models": [], "validated": null, "n": 1292, "authors_detailed": [ { "name": "Davood Wadi", "url": "https://openalex.org/A5046501265", "inst": "University Canada West" }, { "name": "Yu Ma", "url": "https://openalex.org/A5143893972", "inst": "" } ], "affiliations": [ "University Canada West" ] }, { "uid": "doi:10.2139/ssrn.7013978", "doi": "10.2139/ssrn.7013978", "title": "Branding Without Belonging: How AI-generated Content Marketing Erases the Cultural Context of Heritage Foods", "authors": [ "Khushi Choudhary" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7013978", "field": "management", "role": "agent", "bullets": [ "A prompt experiment covering six culturally rooted baked goods, challah, matzah, panettone, mooncakes, conchas, and modak, each queried under commercial, open-ended, and cultural-preservation framings.", "ChatGPT 5.2, Gemini 3, and Claude 4.5 generate product and marketing descriptions, and outputs are scored with a cultural-context rubric rather than validated against external ground truth.", "Commercial prompts score 83 percent lower on cultural context than preservation prompts, with models narrowing to ingredients, price, and convenience unless culturally framed." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 45, "edition": 7, "validated": null, "n": 1293, "authors_detailed": [ { "name": "Khushi Choudhary", "url": "https://openalex.org/A5102515066", "inst": "Somaiya Vidyavihar University" } ], "affiliations": [ "Somaiya Vidyavihar University" ] }, { "uid": "doi:10.2139/ssrn.6742158", "doi": "10.2139/ssrn.6742158", "title": "FicusFind: A Grounded, Explainable Natural Language Interface for Real Estate Discovery Using LLaMA 3", "authors": [ "Nidhi J Rao" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6742158", "field": "management", "role": "instrument", "bullets": [ "FicusFind, a natural-language interface for residential real estate discovery, evaluated on 100 queries across four difficulty tiers over a structured MLS-style dataset; period and geography are not stated.", "LLaMA 3 extracts hard constraints from queries inside a design-science system that adds deterministic filtering and ranking, and extraction accuracy is validated against a rule-based baseline.", "The LLaMA-based parser reaches 91 percent constraint-extraction accuracy versus 38 percent for the rule-based baseline, with full constraint compliance and no retrieval hallucination." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "91% extraction accuracy vs 38% rule-based baseline", "salience": 45, "edition": 7, "n": 1294, "authors_detailed": [ { "name": "Nidhi J Rao", "url": "https://openalex.org/A5143913301", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7034398", "doi": "10.2139/ssrn.7034398", "title": "Interpretable Thematic Indices with Sparse Autoencoders: Shaping Investable Universes and Rede ining Industrial Landscapes", "authors": [ "Vittorio Carlei" ], "posted": "2026-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7034398", "field": "finance", "role": "instrument", "bullets": [ "Textual business descriptions from regulatory filings, with firm universe, period, and geography not stated, are used to build interpretable thematic stock indices as an alternative to GICS and SIC taxonomies.", "Sparse autoencoders decompose the internal representations of an unnamed large language model into sparse, interpretable features used to cluster firms; no accuracy check against a benchmark is reported.", "The method yields thematically consistent, transparent stock universes and is argued to define new sectors and meta-sectors, though headline performance magnitudes are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "edition": 7, "models": [], "n": 1295, "authors_detailed": [ { "name": "Vittorio Carlei", "url": "https://openalex.org/A5052607325", "inst": "University of Chieti-Pescara" } ], "affiliations": [ "University of Chieti-Pescara" ] }, { "uid": "doi:10.2139/ssrn.6834458", "doi": "10.2139/ssrn.6834458", "title": "YCBatch: Ranking Without Calibration -A Study of LLM Failure Modes in Imbalanced Decision Tasks", "authors": [ "Leana Yemene" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6834458", "field": "management", "role": "method", "bullets": [ "Sample of 2,165 Y Combinator startups from batches W19 through S22, with strict point-in-time features under class imbalance conditions.", "LLMs ranked startup outcomes achieving NDCG@10 of 0.801 and Precision@10 of 0.7 but with severe miscalibration (ECE above 0.38) compared to classical ML.", "LLMs rank startups far better than classical ML or fine-tuned transformers but systematically overestimate probabilities, requiring post-hoc calibration for deployment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "Y Combinator startup outcomes NDCG Precision ECE", "salience": 55, "n": 2548, "authors_detailed": [ { "name": "Leana Yemene", "url": "https://openalex.org/A5143891047", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7025020", "doi": "10.2139/ssrn.7025020", "title": "LLM-Structured R&D Activity Disclosures in Annual Securities Reports", "authors": [ "Nobushige Doi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7025020", "field": "accounting", "role": "instrument", "bullets": [ "R&D activity sections of Japanese annual securities reports from fiscal years 2013 through 2024, with manually annotated evaluation set from FY2024.", "LLM with detailed schema prompt extracted structured R&D items from narrative disclosures, compared against regex baseline and simple JSON prompt approaches.", "Detailed schema prompt achieved F1 of 0.957, outperforming simple prompts at 0.763 and rule-based baselines at 0.598 for scalable corporate disclosure analysis." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "manual annotation F1", "salience": 65, "n": 2549, "authors_detailed": [ { "name": "Nobushige Doi", "url": "https://openalex.org/A5023596495", "inst": "Nihon University" } ], "affiliations": [ "Nihon University" ] }, { "uid": "doi:10.2139/ssrn.6852518", "doi": "10.2139/ssrn.6852518", "title": "Converging Echoes: Inter-Model Convergence in Investment Recommendations by LLMs", "authors": [ "Victor Joglar" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6852518", "field": "finance", "role": "agent", "bullets": [ "Five major LLMs generated portfolio recommendations under crossed factorial design varying investor age, risk tolerance, and collection period.", "Each LLM produced investment portfolios; inter-model convergence was measured against size-matched random baselines using Jaccard similarity index.", "LLMs converge on identical recommendations (J=0.243 vs random); one fund issuer absorbs 42.2% of all AI-recommended capital, raising herding risk concerns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 75, "n": 2550, "authors_detailed": [ { "name": "Victor Joglar", "url": "https://openalex.org/A5143875452", "inst": "Universidad Complutense de Madrid" } ], "affiliations": [ "Universidad Complutense de Madrid" ] }, { "uid": "doi:10.2139/ssrn.6852438", "doi": "10.2139/ssrn.6852438", "title": "Invisible to the Machine: Algorithmic Revenue Leakage in the Age of AI-Driven Travel Discovery", "authors": [ "N.P. Gayan Nugawela" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6852438", "field": "management", "role": "object", "bullets": [ "Survey of 50 luxury hospitality properties assessing machine-readable metadata and semantic search optimization for AI-driven travel discovery platforms.", "Study examined how LLM-based generative search engines surface hotels based on structured metadata availability and semantic footprint quality.", "Severe machine invisibility causes 8.4% average drop in direct booking share and compresses GOPPAR by $11.40 to $24.75 per available room." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 50, "validated": null, "n": 2551, "authors_detailed": [ { "name": "Gayan Nugawela", "url": "https://openalex.org/A5134997143", "inst": "University of Wales Trinity Saint David" } ], "affiliations": [ "University of Wales Trinity Saint David" ] }, { "uid": "doi:10.2139/ssrn.6759238", "doi": "10.2139/ssrn.6759238", "title": "Congestion-Aware Static LLM Cascades: Analysis of a Steady-State Framework", "authors": [ "Yuan Guo", "Stefanus Jasin", "Chen-An Lin" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6759238", "field": "management", "role": "method", "bullets": [ "Theoretical model of firms routing heterogeneous classification jobs across multiple LLMs differing in accuracy, cost, and processing speed.", "Framework formulates congestion-aware multi-stage LLM routing as convex optimization with endogenous queueing delays and congestion pricing logic.", "Capacity-oblivious routing is sharply suboptimal; marginal value of additional routing depth decays exponentially, yielding a finite effective cascade depth." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 45, "n": 2552, "authors_detailed": [ { "name": "Yuan Guo", "url": "https://openalex.org/A5066327577", "inst": "George Washington University" }, { "name": "Stefanus Jasin", "url": "https://openalex.org/A5130807279", "inst": "Ross School" }, { "name": "Chen-An Lin", "url": "https://openalex.org/A5038249434", "inst": "Purdue University West Lafayette" } ], "affiliations": [ "Ross School", "George Washington University", "Purdue University West Lafayette" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6801618", "doi": "10.2139/ssrn.6801618", "title": "QuantCode-Eval: Benchmarking Quantitative Strategy Code Reproduction from Finance Papers", "authors": [ "Wenaho Lu", "Ziqi Yuan", "Hao Wu", "Dunhong Jin", "Chuan Wu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6801618", "field": "finance", "role": "method", "bullets": [ "Thirty leading quantitative finance papers published 2010-2026 with approximately 20 property-based executable checkers per task, evaluated across seven proprietary and open-source LLMs.", "Seven LLMs with coding-agent scaffolds attempted to reproduce executable trading strategies from finance papers; outputs evaluated via binary pass/fail property-based checkers.", "Best setup (Claude Opus 4.7 with Claude Code) achieved 43% pass@5; failures concentrated in temporal consistency, strategy formalization, and signal direction preservation." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "property-based executable checkers, pass@5", "salience": 70, "n": 2553, "authors_detailed": [ { "name": "Wenaho Lu", "url": "https://openalex.org/A5143922645", "inst": "University of Hong Kong" }, { "name": "Ziqi Yuan", "url": "https://openalex.org/A5139978432", "inst": "University of Hong Kong" }, { "name": "Hao Wu", "url": "https://openalex.org/A5143953204", "inst": "University of Hong Kong" }, { "name": "Dunhong Jin", "url": "https://openalex.org/A5143970946", "inst": "University of Hong Kong" }, { "name": "Chuan Wu", "url": "https://openalex.org/A5143947638", "inst": "University of Hong Kong" } ], "affiliations": [ "University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6935938", "doi": "10.2139/ssrn.6935938", "title": "Service Recovery Analysis in the Food Manufacturing Sector: A Study Using Large Language Models", "authors": [ "Juan-Ernesto Sepulveda", "David Diaz" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6935938", "field": "management", "role": "instrument", "bullets": [ "5,465 food manufacturer complaint responses recorded in SENACON (Brazil), 2020-2025, with expert-coded validation subset of 300 responses.", "LLMs with zero-shot learning classified distributive and interactional justice elements in corporate complaint responses; validated against expert human coding.", "Correlations between justice element presence and consumer satisfaction were practically null (r=0.02 distributive, r=0.06 interactional), challenging traditional service recovery frameworks." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert human coding n=300", "salience": 48, "models": [], "n": 2554, "authors_detailed": [ { "name": "Juan-Ernesto Sepúlveda", "url": "https://openalex.org/A5088002380", "inst": "University of Chile" }, { "name": "David Díaz", "url": "https://openalex.org/A5100746122", "inst": "Instituto Profesional Providencia" } ], "affiliations": [ "University of Chile", "Instituto Profesional Providencia" ] }, { "uid": "doi:10.2139/ssrn.6584460", "doi": "10.2139/ssrn.6584460", "title": "Teaching Case: Improving Government Operations with Large Language Models", "authors": [ "Steven Strauss" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6584460", "field": "management", "role": "object", "bullets": [ "Fictionalized teaching case set in a consulting firm evaluating a high-value U.S. government AI engagement for immigration enforcement and benefits screening.", "Case examines design choices for LLM-enabled data infrastructure: record reconciliation versus drafting recommendations, escalation, and case prioritization.", "Raises unresolved tensions between technical capability, legal authority, reputational exposure, and human harm risk when firms accept AI-intensive government contracts." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 2555, "authors_detailed": [ { "name": "Steven Strauss", "url": "https://openalex.org/A5143981259", "inst": "Harvard University" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6964978", "doi": "10.2139/ssrn.6964978", "title": "Autonomous Multi-Agent Systems for Global Market Expansion: An LLM-Based Framework for Scaling Strategic Internationalization in Small and Medium Enterprises", "authors": [ "Claudio Massimo Onorato" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6964978", "field": "management", "role": "instrument", "bullets": [ "Proposed computational framework for SME internationalization using autonomous multi-agent systems with RAG over real-time global trade datasets.", "Specialized LLM agents for macroeconomic intelligence, regulatory compliance, and cultural localization orchestrated via deterministic agentic workflows for market entry analysis.", "Framework reported reduced latency for market-readiness assessment while maintaining factual accuracy, aiming to democratize institutional-grade strategic intelligence for SMEs." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 2556, "authors_detailed": [ { "name": "Claudio Massimo Onorato", "url": "https://openalex.org/A5143976922", "inst": "Independent AI Researcher" } ], "affiliations": [ "Independent AI Researcher" ] }, { "uid": "doi:10.2139/ssrn.6617999", "doi": "10.2139/ssrn.6617999", "title": "Artificial Intelligence, Algorithmic Trading, and Market Dynamics", "authors": [ "Shrey Shah", "Krishna Joshi", "Param Mehta" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6617999", "field": "finance", "role": "object", "bullets": [ "Narrative review of 47 peer-reviewed articles and industry reports on AI-driven algorithmic trading published 2020-2026 across developed markets.", "Reviews model families including classical ML, LSTM, transformers, deep reinforcement learning, and LLMs (FinBERT, GPT-4) for trading, pricing, and risk decisions.", "AI systems reduce bid-ask spreads and improve execution quality normally but can amplify short-term volatility, herding, and liquidity crises when models converge on identical signals." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 38, "validated": null, "n": 2557, "authors_detailed": [ { "name": "Shrey Shah", "url": "https://openalex.org/A5135373352", "inst": "Independent" }, { "name": "K.D. Joshi", "url": "https://openalex.org/A5039192255", "inst": "Atmiya University" }, { "name": "Param Mehta", "url": "https://openalex.org/A5011157323", "inst": "Independent" } ], "affiliations": [ "Independent", "Atmiya University" ] }, { "uid": "doi:10.2139/ssrn.6818660", "doi": "10.2139/ssrn.6818660", "title": "Enforcing Ethics in the Age of Artificial Intelligence: Circular 230 and AI-Assisted Tax Practice", "authors": [ "Insha Khan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6818660", "field": "accounting", "role": "object", "bullets": [ "187 IRS Office of Professional Responsibility disciplinary actions against CPAs and Enrolled Agents, 2020-2025; ten tax scenarios tested on three AI tools in March 2026.", "ChatGPT (GPT-5.3), Microsoft Copilot, and Google Gemini generated tax advisory responses scored on a seven-dimension Circular 230 compliance rubric.", "AI tools scored 3.33/4.0 on accuracy but 1.70/4.0 on disclaimer adequacy; only 26.7% of outputs rated fully compliant while OPR enforcement targeted unrelated provisions." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "seven-dimension Circular 230 compliance rubric", "salience": 72, "n": 2558, "authors_detailed": [ { "name": "Insha Khan", "url": "https://openalex.org/A5140335521", "inst": "Campbellsville University" } ], "affiliations": [ "Campbellsville University" ] }, { "uid": "doi:10.2139/ssrn.6928018", "doi": "10.2139/ssrn.6928018", "title": "Sentiment Analysis: From Rule-Based Lexicons to Large Language Models (Corrected and Republished Article)", "authors": [ "Maikel Leon" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6928018", "field": "management", "role": "method", "bullets": [ "Comprehensive review spanning three decades of sentiment analysis research across marketing, finance, politics, and social media applications.", "Survey traces evolution from rule-based systems through deep learning to transformer-driven and generative models, distilling best practices for domain adaptation and prompt engineering.", "Transformer-based approaches with domain adaptation deliver state-of-the-art performance; key gaps remain in real-time analytics, explainability, multilingual robustness, and ethical governance." ], "bullet_provenance": "ai", "salience": 18, "models": [], "validated": null, "n": 2559, "authors_detailed": [ { "name": "Maikel León", "url": "https://openalex.org/A5025037606", "inst": "University of Miami" } ], "affiliations": [ "University of Miami" ] }, { "uid": "doi:10.2139/ssrn.6835019", "doi": "10.2139/ssrn.6835019", "title": "Where Synthetic Respondents Fail: Diagnosing Local Validity in LLM-Augmented Preference Measurement", "authors": [ "Yan Leng" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6835019", "field": "management", "role": "method", "bullets": [ "Survey items, conjoint experiments (vaccine, consumer products), and brand-attribute ratings from Twin-2K-500 dataset and multiple preference measurement instruments.", "PRISM framework scores each measurement cell by paraphrase sensitivity in open-weight models to predict human-LLM divergence before any human data collection.", "PRISM ranked cells by human-LLM divergence at Spearman rho 0.67 on survey items and returned clean null signals at its principled boundary on brand liking." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Spearman rho vs human-LLM divergence on Twin-2K-500", "salience": 75, "n": 2560, "authors_detailed": [ { "name": "Yan Leng", "url": "https://openalex.org/A5140913981", "inst": "The University of Texas at Austin" } ], "affiliations": [ "The University of Texas at Austin" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7114299", "doi": "10.2139/ssrn.7114299", "title": "A Protocol for Rater-Invariant Measures from Language Models, with an Application to AI Exposure", "authors": [ "Benjamin Verschuere", "Angus Cameron" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7114299", "field": "economics", "role": "method", "bullets": [ "Task-level AI-exposure classification scored by six frontier LLMs from four developers, validated against the non-LLM AIOE benchmark across 4,086 observations.", "Six LLMs independently scored a physical/cognitive task classification (C*); protocol tests criterion invariance on downstream coefficients and non-LLM convergence.", "Cross-developer agreement reached 81.8-89.7% (Fleiss kappa 0.837); validation coefficient indistinguishable across raters (F(5,4086)=0.47); AIOE correlation approximately 0.86 for every model." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "open_other" ], "open_weights": false, "validated": true, "validation_note": "Fleiss kappa 0.837, AIOE benchmark convergence", "salience": 82, "n": 2561, "authors_detailed": [ { "name": "benjamin verschuere", "url": "https://openalex.org/A5134035513", "inst": "Liminal Capital, 2479 East Bayshore Road, Suite 205, Palo Alto, CA 94303, United States" }, { "name": "Angus Cameron", "url": "https://openalex.org/A5141059866", "inst": "Cardinal Health (Australia)" } ], "affiliations": [ "Liminal Capital, 2479 East Bayshore Road, Suite 205, Palo Alto, CA 94303, United States", "Cardinal Health (Australia)" ] }, { "uid": "doi:10.2139/ssrn.6502961", "doi": "10.2139/ssrn.6502961", "title": "Reciprocal Model Validation Reporting: A Research Note", "authors": [ "Craig Masters" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6502961", "field": "finance", "role": "instrument", "bullets": [ "GJR-GARCH volatility forecasting pipeline in Databricks Medallion architecture for production model risk management under SR 11-7 banking compliance.", "Gemini-driven agentic workflow with LangChain and in-context learning ingested pipeline metadata and backtesting results to generate automated model validation reports.", "Automated challenger reports identified discrepancies in model assumptions and statistical interpretations versus human-authored champion reports, reducing compliance latency." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": false, "salience": 55, "n": 2562, "authors_detailed": [ { "name": "Craig Masters", "url": "https://openalex.org/A5143887228", "inst": "MidAmerica Nazarene University" } ], "affiliations": [ "MidAmerica Nazarene University" ] }, { "uid": "doi:10.2139/ssrn.7052340", "doi": "10.2139/ssrn.7052340", "title": "The Double-edged Effect of Banning Generative AI on Online Question-and-Answer Communities: Evidence from Stack Exchange", "authors": [ "Yuanhong Ma", "Qinglai He", "Xitong Li", "Lynn Wu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7052340", "field": "management", "role": "object", "bullets": [ "Full network of Stack Exchange communities analyzed with difference-in-differences around AIGC bans implemented after ChatGPT's launch in November 2022.", "Study examined how official bans on AI-generated content affected question volume, answer rates, and contribution efficiency across STEM and non-STEM communities.", "Ban increased question volume but reduced answer efficiency in non-STEM communities; effects absent in STEM; human contributors posted richer and more socially engaging content post-ban." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "validated": null, "n": 2563, "authors_detailed": [ { "name": "Yuanhong Ma", "url": "https://openalex.org/A5071296525", "inst": "Heilongjiang Institute of Technology" }, { "name": "Qinglai He", "url": "https://openalex.org/A5041183204", "inst": "University of Wisconsin–Madison" }, { "name": "Xitong Li", "url": "https://openalex.org/A5082600899", "inst": "HEC Paris" }, { "name": "Lynn Wu", "url": "https://openalex.org/A5030027972", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania", "Heilongjiang Institute of Technology", "University of Wisconsin–Madison", "HEC Paris" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6858298", "doi": "10.2139/ssrn.6858298", "title": "LLMs as Gatekeepers: Source Concentration, Factual Quality, and Political Slant in Information Search", "authors": [ "Pengxiang Zhou", "Davide Proserpio", "Ali Goli" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6858298", "field": "economics", "role": "object", "bullets": [ "48 months of referral traffic data for 8,082 news domains across five AI platforms (ChatGPT, Perplexity, Gemini, Claude, Grok) and traditional search.", "Study measured source concentration, factual quality, and political slant of AI search platforms as information intermediaries, linked to publisher-level bot-blocking records.", "AI platforms showed higher concentration but better factual quality than organic search; publisher crawler-blocking shifted remaining pools toward lower quality and rightward political slant." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "open_other" ], "open_weights": false, "salience": 85, "validated": null, "n": 2564, "authors_detailed": [ { "name": "Pengxiang Zhou", "url": "https://openalex.org/A5106246330", "inst": "University of Southern California" }, { "name": "Davide Proserpio", "url": "https://openalex.org/A5143956366", "inst": "University of Southern California" }, { "name": "Ali Goli", "url": "https://openalex.org/A5013312813", "inst": "University of Rochester" } ], "affiliations": [ "University of Southern California", "University of Rochester" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.26368v1", "arxiv_id": "2607.26368v1", "title": "Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text", "authors": [ "Aman Kumar", "Lasitha Vidyaratne", "Dipanjan D Ghosh", "Arnab Chakrabarti", "Ahmed K Farahat" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.26368v1", "field": "accounting", "role": "instrument", "bullets": [ "5,940 synthetic financial-disclosure instances from SBID-FD benchmark with 11 inconsistency labels and paired reference evidence spans.", "GPT-5.4, LoRA-adapted Qwen3.5-9B, and fine-tuned 300M encoders classified inconsistency types under a shared evaluation protocol.", "Fine-tuned encoder reached 61.9% accuracy, matching GPT-5.4 at 61.3%; gold evidence spans improved the encoder to 65.3%." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "SBID-FD benchmark accuracy across 11 inconsistency classes", "salience": 55, "n": 2565, "authors_detailed": [ { "name": "Aman Kumar", "url": "https://openalex.org/A5144047733", "inst": "" }, { "name": "Lasitha Vidyaratne", "url": "https://openalex.org/A5125735318", "inst": "Hitachi (Japan)" }, { "name": "Dipanjan Ghosh", "url": "https://openalex.org/A5056882169", "inst": "National Institute of Pharmaceutical Education and Research" }, { "name": "Arnab Chakrabarti", "url": "https://openalex.org/A5144057985", "inst": "Hitachi (Japan)" }, { "name": "Ahmed Farahat", "url": "https://openalex.org/A5065827996", "inst": "Hitachi (Japan)" } ], "affiliations": [ "Hitachi (Japan)", "National Institute of Pharmaceutical Education and Research" ] }, { "uid": "doi:10.2139/ssrn.6674059", "doi": "10.2139/ssrn.6674059", "title": "Foresight Arena: An On-Chain Benchmark for Evaluating AI Forecasting Agents", "authors": [ "Maksym Nechepurenko", "Pavel Shuvalov" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6674059", "field": "finance", "role": "method", "bullets": [ "Five frontier LLMs evaluated over 50 rounds of binary prediction markets sourced from Polymarket, with on-chain resolution via smart contracts on Polygon PoS.", "Claude Opus 4.5, GPT-5-2, Gemini 3 Pro, Grok 4-1, and GLM-4-7 submitted probabilistic forecasts scored by Brier Score and a novel Alpha Score measuring edge over consensus.", "Detecting a true 0.02 forecasting edge over market consensus at 80% power requires approximately 350 resolved predictions; Murphy decomposition separates well-calibrated from market-tracking agents." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "open_other" ], "open_weights": false, "validated": true, "validation_note": "Brier Score and Alpha Score against resolved Polymarket outcomes", "salience": 68, "n": 2616, "authors_detailed": [ { "name": "M. O. Nechepurenko", "url": "https://openalex.org/A5028425309", "inst": "University of Dubai" }, { "name": "Павел Шувалов", "url": "https://openalex.org/A5127781317", "inst": "Rochester Institute of Technology - Dubai" } ], "affiliations": [ "University of Dubai", "Rochester Institute of Technology - Dubai" ] }, { "uid": "doi:10.2139/ssrn.6980098", "doi": "10.2139/ssrn.6980098", "title": "The Authority Allocation Problem in AI-Augmented Organizations", "authors": [ "Ryoji Morii" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6980098", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of AI-augmented organizations spanning government, financial services, healthcare, and agentic AI systems as application domains.", "No specific model deployed; the paper traces how AI systems absorb decision authority through configuration of thresholds, routing rules, defaults, and workflows.", "Identifies three structural gaps in AI governance: authority allocation gaps, escalation gaps, and accountability continuity gaps that current frameworks leave unaddressed." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 2617, "authors_detailed": [ { "name": "Ryoji Morii", "url": "https://openalex.org/A5129877459", "inst": "Nihon University" } ], "affiliations": [ "Nihon University" ] }, { "uid": "doi:10.2139/ssrn.6946379", "doi": "10.2139/ssrn.6946379", "title": "Reasoning Into Confidence: A Controlled Single-Trader Ablation of In-Decision Chain-of-Thought on LLM Trading Performance and Calibration", "authors": [ "Theo Nicolas Sitjar" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6946379", "field": "finance", "role": "agent", "bullets": [ "Single Claude Haiku trader on SPY across six paired 120-day windows spanning five market regimes including flat, bull, sell-off, bear, and crash periods.", "Claude Haiku with and without a chain-of-thought scaffold made daily BUY/HOLD/SELL decisions with stated probabilities, evaluated at temperature zero with cached responses.", "Chain-of-thought raised stated confidence by 0.047 median (p=0.031) but produced no significant improvement in Sharpe ratio (p=0.69) or expected calibration error (p=0.22)." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "SPY backtest across six paired 120-day windows with Sharpe and ECE", "salience": 72, "n": 2618, "authors_detailed": [ { "name": "Theo Nicolas Sitjar", "url": "https://openalex.org/A5132641793", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6684379", "doi": "10.2139/ssrn.6684379", "title": "Modular Verification Outperforms Chain-of-Thought Reasoning in Small Financial LLMs: A Systematic Ablation Study on Numerical Hallucination Reduction", "authors": [ "Aaditya Thokal" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6684379", "field": "finance", "role": "method", "bullets": [ "Full FinQA development set of 873 financial question-answer pairs evaluated with a task-specifically fine-tuned 7B parameter language model.", "A fine-tuned 7B model with a modular pipeline of five components was ablated; deterministic verification corrected 54 samples via rule-based post-hoc correction without additional training.", "Pipeline achieved 42.61% execution accuracy (42x over baseline); chain-of-thought prompting degraded performance by 9.0pp and cross-document RAG by 7.5pp on the fine-tuned model." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinQA development set execution accuracy (n=873)", "salience": 65, "n": 2619, "authors_detailed": [ { "name": "Aaditya Thokal", "url": "https://openalex.org/A5133026606", "inst": "University of Mumbai" } ], "affiliations": [ "University of Mumbai" ] }, { "uid": "doi:10.2139/ssrn.6542902", "doi": "10.2139/ssrn.6542902", "title": "Automated Multi-Dimensional Quality Scoring for Customer Service Operations Using Large Language Models: Production Design, Deployment, and Empirical Analysis", "authors": [ "Ali Aghabeigiha" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6542902", "field": "management", "role": "instrument", "bullets": [ "11,504 customer-agent chat interactions over a 30-day pilot at a global food delivery platform operating across 18 markets.", "Google BigQuery ML LLM scores every chat on six weighted dimensions including resolution quality, empathy, and sentiment recovery.", "Fulfillment failure (53.3%) and process friction (21.3%) dominate negative interactions; system reaches 99% coverage and 95% calculation reliability." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": false, "salience": 50, "n": 2620, "authors_detailed": [ { "name": "Ali Aghabeigiha", "url": "https://openalex.org/A5135207391", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6636461", "doi": "10.2139/ssrn.6636461", "title": "From Data to Decisions: The Role of Artificial Intelligence and Large Language Models in Organizational Decision-Making", "authors": [ "Achintya Patil" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6636461", "field": "management", "role": "object", "bullets": [ "Literature review spanning information systems, organizational behavior, and AI research on enterprise decision support evolution.", "Conceptual analysis examines how LLMs and RAG pipelines reshape organizational information processing and decision workflows.", "Organizational readiness—data infrastructure, human cognition, institutional trust—is the primary barrier, not technological capability." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 2621, "authors_detailed": [ { "name": "Achintya Patil", "url": "https://openalex.org/A5134402978", "inst": "Narsee Monjee Institute of Management Studies" } ], "affiliations": [ "Narsee Monjee Institute of Management Studies" ] }, { "uid": "doi:10.2139/ssrn.6801098", "doi": "10.2139/ssrn.6801098", "title": "Feature-Aware In-Context Learning with LLMs for Behavior Prediction during Extreme Weather Events", "authors": [ "Wenjun Jia", "Yuexuan Zhu", "Ke Zhang", "Meng Li" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6801098", "field": "economics", "role": "instrument", "bullets": [ "Stated preference survey of air travelers facing extreme weather disruptions including long delays and flight cancellations.", "Feature-aware in-context learning with LLMs encodes individual historical choice patterns as compressed behavioral signatures for prediction.", "Feature-aware ICL achieves 79.2% accuracy overall and 95.3% under extreme pressure scenarios, versus 27.9% for mixed logit models." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Stated preference survey choice accuracy vs. mixed logit baseline", "salience": 55, "n": 2622, "authors_detailed": [ { "name": "Wenjun Jia", "url": "https://openalex.org/A5100311692", "inst": "Tsinghua University" }, { "name": "Yuexuan Zhu", "url": "https://openalex.org/A5136573005", "inst": "Tsinghua University" }, { "name": "Ke Zhang", "url": "https://openalex.org/A5126092850", "inst": "China People's Public Security University" }, { "name": "Meng Li", "url": "https://openalex.org/A5100457416", "inst": "Tsinghua University" } ], "affiliations": [ "Tsinghua University", "China People's Public Security University" ] }, { "uid": "doi:10.2139/ssrn.7072438", "doi": "10.2139/ssrn.7072438", "title": "Podium Placement in LLM Enhanced Entrepreneurial Contests: Theory and Evidence", "authors": [ "Nitin Joglekar", "Pankaj Patel", "Karthik Ramachandran" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7072438", "field": "management", "role": "object", "bullets": [ "Randomized control trial of entrepreneurs using LLMs for technology commercialization in a competitive contest with podium-style selection.", "Formal model and RCT examine how breadth-first versus depth-first LLM search strategies affect podium placement probability.", "Competitive awareness predicts top-quartile placement; prompting behavior, idea volume, and LLM usage intensity are statistically indistinguishable across quartiles." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "validated": null, "n": 2623, "authors_detailed": [ { "name": "Nitin Joglekar", "url": "https://openalex.org/A5133641487", "inst": "Villanova University" }, { "name": "Pankaj C. Patel", "url": "https://openalex.org/A5002371565", "inst": "University of Alabama" }, { "name": "Karthik Ramachandran", "url": "https://openalex.org/A5032404765", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology", "Villanova University", "University of Alabama" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7112999", "doi": "10.2139/ssrn.7112999", "title": "TradeBench: Domain-Specific Evaluation of Language Models and Multi-Agent Systems for Cross-Border Trade Execution", "authors": [ "Maximilian Ani" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7112999", "field": "management", "role": "method", "bullets": [ "Proposed benchmark covering twelve task families in cross-border trade including customs classification, sanctions screening, and landed-cost calculation.", "Evaluates LLM-only, retrieval-assisted, tool-using, and multi-agent systems across serial, parallel, and adjudicated topologies with multi-trial consistency.", "Scoring derives from expected trade loss rather than arbitrary points; benchmark is designed to bound deployment decisions in regulated trade environments." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "models": [], "n": 2800, "authors_detailed": [ { "name": "Maximilian Ani", "url": "https://openalex.org/A5132981742", "inst": "Concern Worldwide US" } ], "affiliations": [ "Concern Worldwide US" ] }, { "uid": "doi:10.2139/ssrn.6803341", "doi": "10.2139/ssrn.6803341", "title": "Generative AI and the Reorganization of Labor Demand", "authors": [ "Fangyan Wang", "Zaiyan Wei", "Yang Wang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6803341", "field": "economics", "role": "object", "bullets": [ "U.S. job postings across all economic sectors, with a dynamic posting-level measure of generative AI exposure constructed over time.", "A two-stage LLM pipeline identified tasks described in each posting and classified the degree to which generative AI can perform or assist them.", "Hiring reallocation explains 52% of the aggregate decline in AI exposure; within-job task redesign accounts for 39.5% and grows in importance over time." ], "bullet_provenance": "ai", "validated": false, "salience": 75, "models": [], "n": 2801, "authors_detailed": [ { "name": "Fangyan Wang", "url": "https://openalex.org/A5137081288", "inst": "Purdue University West Lafayette" }, { "name": "Zaiyan Wei", "url": "https://openalex.org/A5055910852", "inst": "Purdue University West Lafayette" }, { "name": "Yang Wang", "url": "https://openalex.org/A5143913314", "inst": "Purdue University West Lafayette" } ], "affiliations": [ "Purdue University West Lafayette" ] }, { "uid": "doi:10.2139/ssrn.7087858", "doi": "10.2139/ssrn.7087858", "title": "Grounded Event Extraction from SEC 8-K Filings with a Fine-Grained Taxonomy", "authors": [ "Rian Dolphin", "Joe Dursun", "Jarrett Blankenship", "Katie Adams", "Quinton Pike" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7087858", "field": "finance", "role": "instrument", "bullets": [ "292,984 SEC 8-K filings from U.S. public companies, 2022-2026, yielding 601,088 grounded event tags across a three-tier taxonomy of 119 event types.", "A two-stage LLM system tagged disclosures against the taxonomy, anchoring each tag to a verbatim quote via fuzzy n-gram validation and assigning a quality score in a dedicated second pass.", "Precision rises monotonically from 12% to 96% with quality score on 5,125 stratified tags; an event study confirms the taxonomy separates economically distinct events sharing an item code." ], "bullet_provenance": "ai", "validated": true, "validation_note": "LLM-judge precision on 5,125 stratified tags, monotonic from 12% to 96%", "salience": 72, "models": [], "n": 2802, "authors_detailed": [ { "name": "Rian Dolphin", "url": "https://openalex.org/A5023310992", "inst": "College of Saint Elizabeth" }, { "name": "Joe Dursun", "url": "https://openalex.org/A5107619376", "inst": "Eterna Massive Open Laboratory" }, { "name": "Jarrett Blankenship", "url": "https://openalex.org/A5107496252", "inst": "Eterna Massive Open Laboratory" }, { "name": "Katie Adams", "url": "https://openalex.org/A5140430335", "inst": "Eterna Massive Open Laboratory" }, { "name": "Quinton Pike", "url": "https://openalex.org/A5140421520", "inst": "Eterna Massive Open Laboratory" } ], "affiliations": [ "College of Saint Elizabeth", "Eterna Massive Open Laboratory" ] }, { "uid": "doi:10.2139/ssrn.6843005", "doi": "10.2139/ssrn.6843005", "title": "When Machines Think Fast: A Behavioral Economics Test of AI", "authors": [ "Keith Jacks Gamble", "Patricia Hummel" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6843005", "field": "economics", "role": "agent", "bullets": [ "Fourteen canonical behavioral-economics experiments administered to ChatGPT, with a pseudo-panel of responses constructed from each iteration.", "ChatGPT faced belief-formation, preference, and intertemporal tasks including anchoring, framing, prospect-theoretic risk attitudes, sunk-cost reasoning, and the ultimatum game.", "The model matched the rational benchmark on 8 of 14 tasks; on the remaining 6, bias direction mirrored the human pattern but magnitude was consistently smaller." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "14 canonical behavioral-economics experiments vs rational and human benchmarks", "salience": 70, "n": 2803, "authors_detailed": [ { "name": "Keith Jacks Gamble", "url": "https://openalex.org/A5035873925", "inst": "Middle Tennessee State University" }, { "name": "Patricia Hummel", "url": "https://openalex.org/A5143908737", "inst": "Lincoln Memorial University" } ], "affiliations": [ "Middle Tennessee State University", "Lincoln Memorial University" ] }, { "uid": "doi:10.2139/ssrn.6624038", "doi": "10.2139/ssrn.6624038", "title": "Generative AI and the Productivity Divide: Human-AI Complementarities in Education and Knowledge Work", "authors": [ "Lihi Idan", "Bharat Anand" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6624038", "field": "management", "role": "object", "bullets": [ "Randomized controlled experiment with participants analogous to early-career knowledge workers, studying a technical domain with or without LLM assistance.", "Participants used LLM tools for self-study; productivity gains were measured against AI Interaction Competence, capturing the ability to elicit, filter, and verify model outputs.", "GenAI access raised mean task performance but gains were highly uneven; high-AIC participants realized outsized gains while low-AIC participants saw limited or negative returns." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 2804, "authors_detailed": [ { "name": "Lihi Idan", "url": "https://openalex.org/A5093203715", "inst": "Texas A&M University" }, { "name": "Bharat Anand", "url": "https://openalex.org/A5136456885", "inst": "New York University" } ], "affiliations": [ "New York University", "Texas A&M University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6575178", "doi": "10.2139/ssrn.6575178", "title": "Measuring Corporate Culture: An LLM-Enhanced Two-stage Active Learning Framework *", "authors": [ "Xin Zhao", "Yanhong Guo", "Chuanren Liu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6575178", "field": "management", "role": "instrument", "bullets": [ "Annual reports from over 4,000 Chinese listed firms spanning 2001 to 2024, measuring corporate culture across specific dimensions.", "An LLM-enhanced two-stage pipeline identified culture-related text and classified it by dimension, with a portfolio-theory-based active learner optimizing batch annotation under budget constraints.", "Portfolio-based active learning outperformed alternatives at identical annotation cost; derived culture metrics captured economically meaningful cross-firm variation." ], "bullet_provenance": "ai", "validated": false, "salience": 68, "models": [], "n": 2805, "authors_detailed": [ { "name": "Zhao Xin", "url": "https://openalex.org/A5100312052", "inst": "Dalian University of Technology" }, { "name": "Yanhong Guo", "url": "https://openalex.org/A5051692050", "inst": "Dalian University of Technology" }, { "name": "Chuanren Liu", "url": "https://openalex.org/A5033864788", "inst": "Knoxville College" } ], "affiliations": [ "Dalian University of Technology", "Knoxville College" ] }, { "uid": "doi:10.2139/ssrn.7105038", "doi": "10.2139/ssrn.7105038", "title": "Inflated Expectations: Tax Law, Generative AI, and Lessons from the Balloon Age", "authors": [ "Libin Zhang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7105038", "field": "accounting", "role": "object", "bullets": [ "A published study claiming ChatGPT generated a novel tax minimization strategy using straddles and common trust funds, examined against existing IRS guidance.", "ChatGPT produced a tax strategy that scholars characterized as novel; the author identifies it as a listed transaction under Notice 2003-54 that is not legally viable.", "The AI-generated strategy could trigger severe penalties for participants; the paper warns that mistaking LLM novelty for actionable tax insight creates compliance risk." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 2806, "authors_detailed": [ { "name": "Libin Zhang", "url": "https://openalex.org/A5130370197", "inst": "Faculty of 1000 (United States)" } ], "affiliations": [ "Faculty of 1000 (United States)" ] }, { "uid": "doi:10.2139/ssrn.7017500", "doi": "10.2139/ssrn.7017500", "title": "Forward-Registered, Auditable LLM-Assisted Research: A Reliability Methodology, with a Capital-Markets Testbed", "authors": [ "Yixing Zheng" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7017500", "field": "finance", "role": "method", "bullets": [ "Capital-markets thematic investment research with hash-chained pre-registration, tested across 9 discovery rounds and 110 gate-pass evaluations on a 38-candidate batch.", "An LLM-driven discovery engine surfaced candidates through deterministic gates including source-tier rules, bear-case requirements, and overlap penalties; an ungated LLM screener served as baseline.", "The ungated screener admitted 38 of 38 candidates; the gated pipeline admitted zero; held-out auditor agreed with 77.5% of rejections on a documented 40-rejection sample." ], "bullet_provenance": "ai", "validated": true, "validation_note": "LLM-auditor rejection-quality check on 40-candidate documented sample, 77.5% agreement", "salience": 62, "models": [], "n": 2807, "authors_detailed": [ { "name": "Yixing Zheng", "url": "https://openalex.org/A5143933722", "inst": "New York University" } ], "affiliations": [ "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6898142", "doi": "10.2139/ssrn.6898142", "title": "How AI Perceives the World: Epistemic Foundations for Measuring Entity Representation without Ground Truth", "authors": [ "Shota Nagafuchi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6898142", "field": "management", "role": "method", "bullets": [ "Conceptual framework for measuring how LLMs represent entities such as companies, websites, and individuals in AI-generated responses.", "Analyzes LLM citation behavior using psychometric construct-validity theory; proposes conditioning on intent context to define the estimand.", "Entity citation rates are query-dependent artifacts, not intrinsic properties; intent-cluster conditioning makes reliability and validity well-posed." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 2808, "authors_detailed": [ { "name": "Shota Nagafuchi", "url": "https://openalex.org/A5143980533", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7191580", "doi": "10.2139/ssrn.7191580", "title": "Randomness In Large Language Models: What Researchers Need to Know (And Report)", "authors": [ "Guillaume Coqueret", "Joan Llull", "Florian Oswald", "Christophe Pérignon", "Christoph Scheuch", "Lars Vilhuber" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7191580", "field": "finance", "role": "method", "bullets": [ "Sentiment classifications of corporate filings using proprietary LLM APIs and locally executed open-weight models.", "Multiple LLMs classify filing sentiment repeatedly under identical settings; output variation measured across runs even at temperature zero.", "LLM outputs vary materially across repeated requests; downstream regression coefficients shift, requiring treatment of outputs as distributional draws rather than fixed measurements." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": false, "salience": 70, "n": 2809, "authors_detailed": [ { "name": "Guillaume Coqueret", "url": "https://openalex.org/A5041711577", "inst": "École Normale Supérieure de Lyon" }, { "name": "Joan Llull", "url": "https://openalex.org/A5069285498", "inst": "Institut d'Anàlisi Econòmica" }, { "name": "Florian Oswald", "url": "https://openalex.org/A5040941542", "inst": "Collegio Carlo Alberto" }, { "name": "Christophe Pérignon", "url": "https://openalex.org/A5110741158", "inst": "HEC Paris" }, { "name": "Christoph Scheuch", "url": "https://openalex.org/A5003775169", "inst": "Humboldt-Universität zu Berlin" }, { "name": "Lars Vilhuber", "url": "https://openalex.org/A5082879481", "inst": "Cornell University" } ], "affiliations": [ "Cornell University", "École Normale Supérieure de Lyon", "Institut d'Anàlisi Econòmica", "Collegio Carlo Alberto", "HEC Paris", "Humboldt-Universität zu Berlin" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7047739", "doi": "10.2139/ssrn.7047739", "title": "How Sensitive Are Synthetic LLM Participants to Experimenter-Demand Effects?", "authors": [ "Sergio Pirla", "José Alberto Molina" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7047739", "field": "economics", "role": "agent", "bullets": [ "Eight LLMs from three providers tested on nine canonical economic tasks, benchmarked against 7,517 human participants.", "LLMs simulated economic experiment participants under neutral, weak-demand, and strong-demand prompt treatments.", "LLM responses shift 1.2 SD under weak demand and 1.7 SD under strong demand versus 0.16 and 0.57 SD for humans; no model approaches human demand resistance." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "validated": true, "validation_note": "benchmarked against 7,517 human participants across nine economic tasks", "salience": 65, "n": 2810, "authors_detailed": [ { "name": "Sergio Pirla", "url": "https://openalex.org/A5044448704", "inst": "Universitat Pompeu Fabra" }, { "name": "José Alberto Molina", "url": "https://openalex.org/A5014568861", "inst": "Universidad de Zaragoza" } ], "affiliations": [ "Universitat Pompeu Fabra", "Universidad de Zaragoza" ] }, { "uid": "doi:10.2139/ssrn.7037578", "doi": "10.2139/ssrn.7037578", "title": "Where AI is Actually Being Used, and Where the Market Isn't Paying for It (Yet)", "authors": [ "Tianyang Zheng" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7037578", "field": "finance", "role": "object", "bullets": [ "U.S. public-market industries mapped to Fama-French 49 using Anthropic Economic Index Claude usage data, 2025-2026.", "Claude usage data measured AI adoption intensity per worker and in aggregate across industries and occupations.", "Fastest AI-adopting industries earned lower subsequent three-month risk-adjusted returns; AI usage location and market-assigned AI value diverge." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 80, "validated": null, "n": 2811, "authors_detailed": [ { "name": "Tianyang Zheng", "url": "https://openalex.org/A5054772201", "inst": "Nanjing Tech University" } ], "affiliations": [ "Nanjing Tech University" ] }, { "uid": "doi:10.2139/ssrn.7117338", "doi": "10.2139/ssrn.7117338", "title": "E-GEO: A Testbed for Generative Engine Optimization in E-Commerce", "authors": [ "Puneet S. Bagga", "Vivek F. Farias", "Tamar Korkotashvili", "Tianyi Peng", "Yuhang Wu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7117338", "field": "management", "role": "method", "bullets": [ "13,747 multi-sentence consumer product queries each paired with 10 retrieved Amazon listings, tested across five generative engines and seven LLM rewriters.", "LLMs rewrote product listings to optimize visibility in generative search engines; a prompt meta-optimization algorithm compared against fifteen heuristic baselines.", "Optimized prompts significantly outperform heuristics; a stable domain-agnostic rewriting pattern emerges, suggesting a universal generative engine optimization strategy." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": false, "salience": 50, "n": 2812, "authors_detailed": [ { "name": "Puneet S. Bagga", "url": "https://openalex.org/A5120388753", "inst": "" }, { "name": "Vivek F. Farias", "url": "https://openalex.org/A5037762932", "inst": "Massachusetts Institute of Technology" }, { "name": "Tamar Korkotashvili", "url": "https://openalex.org/A5120524835", "inst": "" }, { "name": "Tianyi Peng", "url": "", "inst": "" }, { "name": "Yuhang Wu", "url": "https://openalex.org/A5101954351", "inst": "Chongqing University of Posts and Telecommunications" } ], "affiliations": [ "Massachusetts Institute of Technology", "Chongqing University of Posts and Telecommunications" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7023798", "doi": "10.2139/ssrn.7023798", "title": "Bond with Social Bot: A Hidden Markov Model of Parasocial Relationship Dynamics in Human-AI Interaction", "authors": [ "Maggie Zhang", "Jingwen Zhang", "Yang Gao" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7023798", "field": "management", "role": "object", "bullets": [ "Longitudinal panel of user interactions with Grok AI bot on X (formerly Twitter), modeled with a Hidden Markov Model.", "Grok response characteristics including anthropomorphism, social intelligence, and linguistic complexity analyzed as drivers of parasocial relationship state transitions.", "Social intelligence is the strongest operational lever for sustained engagement; effects are largest for users who have not yet formed relational bonds with the bot." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": false, "salience": 55, "validated": null, "n": 2813, "authors_detailed": [ { "name": "Maggie Mengqing Zhang", "url": "https://openalex.org/A5013245622", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Jingwen Zhang", "url": "https://openalex.org/A5143888048", "inst": "" }, { "name": "Yang Gao", "url": "https://openalex.org/A5034775543", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7122659", "doi": "10.2139/ssrn.7122659", "title": "The End of the World as We Know It? LLMs and the Future of Publish-or-Perish", "authors": [ "Radu Vranceanu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7122659", "field": "economics", "role": "object", "bullets": [ "Theoretical Spence-type signaling model of the academic labor market under LLM diffusion, no empirical sample.", "LLMs modeled as reducing the cost of producing publishable research, narrowing productivity differences across scholars.", "Publication signaling value weakens; high-ability scholars may over-publish and elite journals face submission congestion." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 2814, "authors_detailed": [ { "name": "Radu Vranceanu", "url": "https://openalex.org/A5027518070", "inst": "École Supérieure des Sciences Économiques et Commerciales" } ], "affiliations": [ "École Supérieure des Sciences Économiques et Commerciales" ] }, { "uid": "doi:10.2139/ssrn.6842678", "doi": "10.2139/ssrn.6842678", "title": "Cognitive Convergence and Systemic Fragility in AI-Driven Financial Markets An Exploratory Framework", "authors": [ "Emmanuel Touraine" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6842678", "field": "finance", "role": "object", "bullets": [ "Exploratory framework with three retrospective cases (GameStop 2021, SVB 2023, AI Rally 2023); prospective validation planned for Oct-Dec 2026.", "LLMs theorized as generating interpretive convergence across financial markets through correlated training data and overlapping information sets.", "Proposes cognitive convergence hypothesis: AI-driven representational monoculture creates hidden systemic fragility not captured by traditional volatility metrics." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 2815, "authors_detailed": [ { "name": "Emmanuel Touraine", "url": "https://openalex.org/A5143933194", "inst": "National Intelligence University" } ], "affiliations": [ "National Intelligence University" ] }, { "uid": "doi:10.2139/ssrn.6727778", "doi": "10.2139/ssrn.6727778", "title": "From Search to Generation: How Generative AI Reshapes Marketing Outcomes", "authors": [ "Xincheng (Max) Ma", "Yingjie Zhang", "Dongwon Lee" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6727778", "field": "management", "role": "object", "bullets": [ "Randomized controlled experiment using a custom-built generative AI and search engine system with US participants evaluating brand and purchase outcomes.", "Compared generative engine marketing to search engine marketing on brand recognition and purchase intention using causal mediation analysis.", "GEM outperformed SEM on purchase intention through a decision delegation mechanism where users defer to AI recommendations as implicit endorsements; non-top placement reduced purchase intention but not brand recognition." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 2816, "authors_detailed": [ { "name": "Xincheng (Max) Ma", "url": "https://openalex.org/A5143895163", "inst": "Hong Kong University of Science and Technology" }, { "name": "Yingjie Zhang", "url": "https://openalex.org/A5143944564", "inst": "Peking University" }, { "name": "Dongwon Lee", "url": "https://openalex.org/A5100405084", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Hong Kong University of Science and Technology", "Peking University" ] }, { "uid": "doi:10.2139/ssrn.6912981", "doi": "10.2139/ssrn.6912981", "title": "The End of the World as We Know It? LLMs and the Future of Publish-or-Perish", "authors": [ "Radu Vranceanu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6912981", "field": "economics", "role": "object", "bullets": [ "Theoretical Spence-type signaling model of the academic publication market under LLM diffusion.", "Models how LLMs reduce the cost of producing publishable research and narrow productivity differences across scholars of differing ability.", "LLM diffusion may weaken publication's signaling value, prompting excess publication by high-ability scholars and submission congestion in elite journals." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 2817, "authors_detailed": [ { "name": "Radu Vranceanu", "url": "https://openalex.org/A5027518070", "inst": "École Supérieure des Sciences Économiques et Commerciales" } ], "affiliations": [ "École Supérieure des Sciences Économiques et Commerciales" ] }, { "uid": "doi:10.2139/ssrn.6843738", "doi": "10.2139/ssrn.6843738", "title": "ENDEX: Endorsement Index How Language Models Qualify When Citing -Empirical Evidence from Generative AI Engines", "authors": [ "Johnny Telles" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6843738", "field": "management", "role": "object", "bullets": [ "2,754 API responses from GPT, Claude, and Gemini about 51 tourism establishments in Campos do Jordao, Brazil, classified by deterministic Python parser.", "Measured endorsement intensity across engines, segments, and query types using regex-based classification into endorsement, hybrid, neutral, and none categories.", "Claude endorsed 21.3% of accommodation responses versus 7.6% for GPT; Claude attached epistemic disclaimers to 65% of branded queries versus 2% of problem queries." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "salience": 42, "validated": null, "n": 2818, "authors_detailed": [ { "name": "Johnny Jefferson Telles", "url": "https://openalex.org/A5136606017", "inst": "Universidade Estadual do Oeste do Paraná" } ], "affiliations": [ "Universidade Estadual do Oeste do Paraná" ] }, { "uid": "doi:10.2139/ssrn.6543239", "doi": "10.2139/ssrn.6543239", "title": "Do LLM \"Crowds\" Produce Investment Signals? An Empirical Test", "authors": [ "Steven Edwards" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6543239", "field": "finance", "role": "agent", "bullets": [ "100 GPT-4o investor personas each selected 50 US-listed equities; consensus portfolio backtested over 561 trading days from January 2024, after model training cutoff.", "GPT-4o personas spanning value, growth, momentum, ESG, and contrarian philosophies formed consensus picks tested under three weighting schemes against a six-factor model.", "Cap-weighted consensus returned 29.5% annualized versus 15.3% for SPY with 10% alpha (t=3.27), but 92% overlap with single-prompt control suggests media prominence rather than independent signals." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Six-factor alpha test and bootstrap sampling against S&P 500 over 561 trading days", "salience": 76, "n": 2819, "authors_detailed": [ { "name": "Steven Edwards", "url": "https://openalex.org/A5143979226", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6819659", "doi": "10.2139/ssrn.6819659", "title": "When Biased Agents Trade: Anchoring, Exploitation, and Market Failure in Agent-to-Agent Interactions", "authors": [ "Anton Hantel" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6819659", "field": "economics", "role": "agent", "bullets": [ "8,415 controlled agent-to-agent interactions across three frontier LLMs in negotiation, auction, and marketplace settings.", "Paired LLM agents in commercial transactions testing anchoring, information overload, decoy effects, and auction bidding against equilibrium and human baselines.", "Anchoring collapsed multi-agent marketplace efficiency from 96% to 15%; informed exploiters captured up to 78% of surplus; step-by-step reasoning worsened anchoring bias." ], "bullet_provenance": "ai", "open_weights": false, "validated": true, "validation_note": "Equilibrium bidding predictions and human anchoring benchmarks", "salience": 80, "models": [], "n": 2820, "authors_detailed": [ { "name": "Anton Hantel", "url": "https://openalex.org/A5138071703", "inst": "IIT@MIT" } ], "affiliations": [ "IIT@MIT" ] }, { "uid": "doi:10.2139/ssrn.6926438", "doi": "10.2139/ssrn.6926438", "title": "Retrieval-augmented Generation (RAG), Generative AI, and Agentic AI Governance: An Integrated Enterprise Governance Prioritization Architecture", "authors": [ "Audrey Rah", "Sven Hahues" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6926438", "field": "management", "role": "object", "bullets": [ "Design-science study scoring 19 enterprise AI platforms across eight governance subdimensions with Monte Carlo robustness assessment.", "Developed integrated governance architecture linking platform burden, risk exposure, organizational readiness, and agentic AI oversight via Governance Priority Score.", "Platform-risk rankings showed high stability (Spearman rho=0.996) under coding-uncertainty stress testing; architecture maps to NIST, ISO 42001, EU AI Act frameworks." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 2821, "authors_detailed": [ { "name": "Audrey Rah", "url": "https://openalex.org/A5104244670", "inst": "University of Houston" }, { "name": "Sven Hahues", "url": "https://openalex.org/A5138668008", "inst": "University of Houston" } ], "affiliations": [ "University of Houston" ] }, { "uid": "doi:10.2139/ssrn.6822059", "doi": "10.2139/ssrn.6822059", "title": "The Contracts Not Written: Measuring Covenant Tightness with LLM-Generated Synthetic Counterfactuals", "authors": [ "Daniel A. Rettl", "Malcolm Wardlaw" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6822059", "field": "finance", "role": "instrument", "bullets": [ "Syndicated loan covenants for investment-grade and speculative-grade borrowers with deal-specific feasible covenant distributions.", "LLMs generated counterfactual covenant packages to measure each contract's tightness position within its feasible distribution, validated against independent programmatic methods.", "Tight covenants raise spreads for investment-grade borrowers but predict violations for speculative-grade borrowers, revealing two distinct contracting regimes shaped by bargaining power versus risk management." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Independent programmatic covenant-tightness methods", "salience": 78, "models": [], "n": 2822, "authors_detailed": [ { "name": "Daniel A. Rettl", "url": "https://openalex.org/A5081007789", "inst": "University of Georgia" }, { "name": "Malcolm Wardlaw", "url": "https://openalex.org/A5031981278", "inst": "University of Georgia" } ], "affiliations": [ "University of Georgia" ] }, { "uid": "doi:10.2139/ssrn.7117459", "doi": "10.2139/ssrn.7117459", "title": "Limited Attention, Processing Cost, and the Breadth of Priced Disclosure: Evidence from the XBRL Mandate and the Rise of Large Language Models", "authors": [ "Cynthia Cai" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7117459", "field": "accounting", "role": "object", "bullets": [ "10,554 US financial restatements from 2000 to 2024 spanning the XBRL mandate (2009) and LLM emergence (post-2022) as processing-cost regime shifts.", "Measured hindsight gap between price impact anticipated at original disclosure versus surprise concentrated at restatement revelation across technology regimes.", "Neither XBRL nor LLMs widened the breadth of priced information; limited attention rather than processing cost is the binding constraint on what markets price." ], "bullet_provenance": "ai", "salience": 82, "models": [], "validated": null, "n": 2823, "authors_detailed": [ { "name": "Cynthia Weiyi Cai", "url": "https://openalex.org/A5043944559", "inst": "Macquarie University" } ], "affiliations": [ "Macquarie University" ] }, { "uid": "doi:10.2139/ssrn.7031478", "doi": "10.2139/ssrn.7031478", "title": "Regime-Aware LLM Agents for Cryptocurrency Markets", "authors": [ "Ashar Siddiqui" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7031478", "field": "finance", "role": "agent", "bullets": [ "Cryptocurrency trading across multiple market regimes including the FTX collapse and ETF approval events.", "Regime-Aware LLM Agent detected regime shifts via Maximum Mean Discrepancy and routed trading decisions through regime-specific specialized prompts.", "Regime-aware strategy improved risk-adjusted returns by 34% compared to state-of-the-art static-prompt LLM baseline across diverse market conditions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Risk-adjusted return backtest versus static-prompt LLM baseline", "salience": 55, "models": [], "n": 2824, "authors_detailed": [ { "name": "Ashar Siddiqui", "url": "https://openalex.org/A5078454611", "inst": "The University of Melbourne" } ], "affiliations": [ "The University of Melbourne" ] }, { "uid": "doi:10.2139/ssrn.6660959", "doi": "10.2139/ssrn.6660959", "title": "Behavioral Transfer in AI Agents: Evidence and Privacy Implications", "authors": [ "Shilei Luo", "Zhiqi Zhang", "Hengchen Dai", "Dennis Zhang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6660959", "field": "management", "role": "object", "bullets": [ "10,659 matched human-agent pairs from Moltbook social media platform, each linked to an owner Twitter/X account.", "Compared autonomous AI agents' public posts with owners' Twitter/X activity across topics, values, affect, and linguistic style.", "Agents systematically reflect owner behavioral characteristics; stronger behavioral transfer correlates with higher disclosure of owner-related personal information." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 58, "validated": null, "n": 2825, "authors_detailed": [ { "name": "Shilei Luo", "url": "https://openalex.org/A5110808927", "inst": "Gansu Agricultural University" }, { "name": "Zhiqi Zhang", "url": "https://openalex.org/A5134393386", "inst": "Washington University in St. Louis" }, { "name": "Hengchen Dai", "url": "https://openalex.org/A5018834080", "inst": "University of California, Los Angeles" }, { "name": "Dennis Zhang", "url": "https://openalex.org/A5134027599", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis", "University of California, Los Angeles", "Gansu Agricultural University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6703198", "doi": "10.2139/ssrn.6703198", "title": "Generative AI in Finance and Data Science: A Review and Financial-Mathematics Validity Checklist", "authors": [ "Yuxuan Huang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6703198", "field": "finance", "role": "method", "bullets": [ "Systematic PRISMA evidence map of 129 records on financially constrained generative AI, with case study using Kenneth French daily market-factor data.", "Tested Gaussian moment-matched and bootstrap generators against no-arbitrage, tail-risk, and Expected Shortfall validity criteria for synthetic financial data.", "Distributional distance metrics and risk diagnostics rank generators differently; moment-matched generator understates 1% Expected Shortfall by 57.2%." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "French daily market-factor data, Expected Shortfall comparison", "salience": 50, "n": 2826, "authors_detailed": [ { "name": "YUXUAN HUANG", "url": "https://openalex.org/A5135413385", "inst": "University of Nottingham Ningbo China" } ], "affiliations": [ "University of Nottingham Ningbo China" ] }, { "uid": "doi:10.2139/ssrn.6910119", "doi": "10.2139/ssrn.6910119", "title": "Crowding Out or Crowding In? The Impact of Platform-Native LLM on Influencer Output in a Quasi-Experiment", "authors": [ "Yuzhou Chen", "Yulin Zhang", "Yingda Lu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6910119", "field": "management", "role": "object", "bullets": [ "Quasi-experiment on a major creator-driven knowledge platform following integration of a platform-native LLM with embedded reference links.", "Difference-in-differences framework measured changes in human content production after LLM introduction, with heterogeneity by creator type.", "Platform-native LLM significantly increased content production; effect strongest for institutional accounts and verified experts facing substitution threats in objective knowledge domains." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 62, "validated": null, "n": 2827, "authors_detailed": [ { "name": "Yuzhou Chen", "url": "https://openalex.org/A5101463704", "inst": "Southeast University" }, { "name": "Yulin Zhang", "url": "https://openalex.org/A5143902947", "inst": "Southeast University" }, { "name": "Yingda Lu", "url": "https://openalex.org/A5052955821", "inst": "University of Illinois Chicago" } ], "affiliations": [ "Southeast University", "University of Illinois Chicago" ] }, { "uid": "doi:10.2139/ssrn.7036700", "doi": "10.2139/ssrn.7036700", "title": "Generative AI and Venture Scaling", "authors": [ "J. Daniel Kim", "Annamaria Conti" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7036700", "field": "management", "role": "object", "bullets": [ "Panel of U.S. software startups 2018-2024 using difference-in-differences design centered on the November 2022 ChatGPT release.", "Measured GenAI adoption effects on hiring, venture funding, technology adoption, website traffic, and commercialization outcomes.", "GenAI-adopting startups achieved output growth without proportional headcount expansion; workforce composition shifted from junior to senior roles with increased internal promotions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 2828, "authors_detailed": [ { "name": "J. Daniel Kim", "url": "https://openalex.org/A5011150965", "inst": "William P. Wharton Trust" }, { "name": "Annamaria Conti", "url": "https://openalex.org/A5022264208", "inst": "IE University" } ], "affiliations": [ "William P. Wharton Trust", "IE University" ] }, { "uid": "doi:10.2139/ssrn.6771541", "doi": "10.2139/ssrn.6771541", "title": "Reliability and Effectiveness of Autonomous AI Agents in Supply Chain Management", "authors": [ "Carol Long", "David Simchi-Levi", "Feng Zhu", "Huangyuan Su", "Andre Calmon", "Flavio Calmon" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6771541", "field": "management", "role": "agent", "bullets": [ "Multi-echelon supply chain simulation using the MIT Beer Game with autonomous generative AI agents across four inference-time configuration levers.", "Reasoning LLMs managed inventory ordering decisions; GRPO reinforcement-learning post-training applied to a shared base LLM using system-level supply-chain rewards.", "Optimized reasoning models cut costs by up to 67% versus human teams but exhibit agent bullwhip, amplifying decision unreliability across echelons and over time." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "MIT Beer Game, cost vs. human teams", "salience": 75, "n": 2829, "authors_detailed": [ { "name": "Carol Long", "url": "https://openalex.org/A5143912344", "inst": "" }, { "name": "David Simchi-Levi", "url": "https://openalex.org/A5143950072", "inst": "" }, { "name": "Feng Zhu", "url": "https://openalex.org/A5143953054", "inst": "" }, { "name": "Huangyuan Su", "url": "https://openalex.org/A5020122789", "inst": "Harvard University Press" }, { "name": "Andre Calmon", "url": "https://openalex.org/A5068968021", "inst": "Georgia Institute of Technology" }, { "name": "Flavio Calmon", "url": "https://openalex.org/A5143874163", "inst": "" } ], "affiliations": [ "Harvard University", "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.26642v1", "arxiv_id": "2607.26642v1", "title": "AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining", "authors": [ "Jingyang Yi", "Jian Yang", "Yifei Jin", "Yuqi Li", "Jian Li" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.26642v1", "field": "finance", "role": "instrument", "bullets": [ "Chinese stock market alpha factor mining; schema plans composed of event, context, qualities, direction, and output trading semantics guide structured exploration.", "LLMs translated schema plans into executable factors; a surrogate model over the semantic space balanced exploration and exploitation across iterative selection rounds.", "Discovered factor pools achieved strong predictive and portfolio performance; alpha quality was robust to LLM choice across different model families." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "Chinese stock market predictive and portfolio performance evaluation", "salience": 70, "n": 2830, "authors_detailed": [ { "name": "Jingyang Yi", "url": "https://openalex.org/A5143993763", "inst": "X-Fab (Germany)" }, { "name": "Jian Yang", "url": "https://openalex.org/A5144088192", "inst": "" }, { "name": "Y. J. Jin", "url": "https://openalex.org/A5103016954", "inst": "Cornell University" }, { "name": "Yuqi Li", "url": "https://openalex.org/A5144060735", "inst": "City College of New York" }, { "name": "Jian Li", "url": "https://openalex.org/A5144056615", "inst": "" } ], "affiliations": [ "Cornell University", "X-Fab (Germany)", "City College of New York" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6508958", "doi": "10.2139/ssrn.6508958", "title": "MOM-AI 3.0: Toward an Autonomous Marketing Operating System", "authors": [ "Gerardo Hurtado" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6508958", "field": "management", "role": "object", "bullets": [ "Conceptual framework for autonomous marketing operating system integrating behavioral economics; illustrated with healthcare patient acquisition use case across digital channels.", "Proposes four-layer architecture combining multimodal generative AI, reinforcement learning, cognitive digital twins, and agentic AI in a perpetual closed-loop optimization system.", "Framework targets shift from campaign-centric A/B testing to continuous autonomous optimization with real-time personalized resource allocation across marketing channels." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 2873, "authors_detailed": [ { "name": "Gerardo Hurtado", "url": "https://openalex.org/A5001984194", "inst": "Universidad Anáhuac" } ], "affiliations": [ "Universidad Anáhuac" ] }, { "uid": "doi:10.2139/ssrn.7021399", "doi": "10.2139/ssrn.7021399", "title": "AI Premium", "authors": [ "Nicola Borri", "Yukun Liu", "Aleh Tsyvinski" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7021399", "field": "finance", "role": "object", "bullets": [ "380 trillion tokens of realized AI consumption across 400+ LLMs from the licensed OpenRouter dataset, covering roughly 2% of global monthly AI token usage, linked to U.S. stock returns.", "Constructed an AI factor from token, dollar, and user growth; estimated firm-level AI betas from return comovement with the factor across model types and user segments.", "Value-weighted long-short strategy on AI beta earns 64.1 basis points per week; premium concentrated in frontier closed-source model usage and absent in emerging markets including China." ], "bullet_provenance": "ai", "salience": 88, "models": [], "validated": null, "n": 2874, "authors_detailed": [ { "name": "Nicola Borri", "url": "https://openalex.org/A5067904917", "inst": "University of Rochester" }, { "name": "Yukun Liu", "url": "https://openalex.org/A5143982822", "inst": "" }, { "name": "Aleh Tsyvinski", "url": "https://openalex.org/A5143958005", "inst": "" } ], "affiliations": [ "University of Rochester" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6526859", "doi": "10.2139/ssrn.6526859", "title": "AGORA-F: An Agentic, Gradient-Orchestrated Multi-agent Architecture for Financial Decision-making", "authors": [ "Kushagra Mutreja", "Aditya Singh", "Murari Mandal" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6526859", "field": "finance", "role": "agent", "bullets": [ "Multi-agent LLM architecture for autonomous stock trading evaluated against comparable baselines using real-time market data, financial statements, and sentiment signals.", "Specialized LLM agent teams interpreted queries, analyzed financials, and iteratively debated buy-hold-sell decisions via a prompt-based gradient convergence mechanism.", "AGORA-F produced more consistent and explainable trading outcomes than single-agent and non-collaborative baselines across tested equity scenarios." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "models": [], "n": 2875, "authors_detailed": [ { "name": "Kushagra Mutreja", "url": "https://openalex.org/A5120095008", "inst": "KIIT University" }, { "name": "Aditya Singh", "url": "https://openalex.org/A5036491373", "inst": "KIIT University" }, { "name": "Murari Mandal", "url": "https://openalex.org/A5090323460", "inst": "Institute of Physics, Bhubaneshwar" } ], "affiliations": [ "KIIT University", "Institute of Physics, Bhubaneshwar" ] }, { "uid": "doi:10.2139/ssrn.6576883", "doi": "10.2139/ssrn.6576883", "title": "FABRIC: AI Financial Advisors Hallucinate More Than They Forget on Indian Markets", "authors": [ "Rajkiran Panuganti" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6576883", "field": "finance", "role": "method", "bullets": [ "204 verified Indian financial questions across six languages covering taxation, securities regulation, mutual funds, banking, insurance, and enterprise compliance.", "Seven LLMs evaluated on 16,000+ responses using a hallucination-focused error taxonomy that distinguishes fabricated facts from outdated information.", "Hallucination is the dominant failure mode across all models; Hinglish outperforms pure Hindi; web-based RAG dramatically reduces both hallucination and outdated errors." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FABRIC benchmark of 204 verified Indian financial questions", "salience": 58, "models": [], "n": 2876, "authors_detailed": [ { "name": "Rajkiran Panuganti", "url": "https://openalex.org/A5132761574", "inst": "Applied StemCell (United States)" } ], "affiliations": [ "Applied StemCell (United States)" ] }, { "uid": "doi:10.2139/ssrn.7153400", "doi": "10.2139/ssrn.7153400", "title": "From Tokens to Atoms: Physical AI, the Artificial General Engineer, and the Economics of Automating Design, Manufacturing, and R&D", "authors": [ "Alejandro Jesus Guipe Salazar" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7153400", "field": "economics", "role": "object", "bullets": [ "2023-2026 evidence on AI-driven design automation in aerospace, pharma, and materials science, anchored by Project Prometheus at $18 billion raised and $41 billion valuation.", "Paper models AI's economic impact on physical production with a two-input cost function pricing design in tokens and validation in physical experiments.", "Validation cost paradox: generative AI multiplies candidate designs faster than labs can test them, so total validated-product cost can rise even as design cost approaches zero." ], "bullet_provenance": "ai", "salience": 52, "models": [], "validated": null, "n": 2877, "authors_detailed": [ { "name": "Alejandro J. Guipe Salazar", "url": "https://openalex.org/A5138731801", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7061660", "doi": "10.2139/ssrn.7061660", "title": "From Silicon to Boardroom: A Multivocal Survey of the Techno-Economic Layers Governing Generative AI Monetization", "authors": [ "Vipin Singh" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7061660", "field": "economics", "role": "object", "bullets": [ "Multivocal literature review of 64 peer-reviewed and grey sources (2022-2026) on generative AI monetization spanning hardware physics to commercial pricing to actuarial constraints.", "Paper synthesizes five interdependent techno-economic layers governing AI value capture and formalizes a Viability Inequality for outcome-based pricing sustainability.", "Aggregate inference cost growth driven by agentic recursion, not quadratic attention; outcome-based pricing viable only where task output is objectively verifiable." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 2878, "authors_detailed": [ { "name": "Vipin Singh", "url": "https://openalex.org/A5138023281", "inst": "Google (United States)" } ], "affiliations": [ "Google (United States)" ] }, { "uid": "doi:10.2139/ssrn.6611021", "doi": "10.2139/ssrn.6611021", "title": "AI Pricing Behavior under Regulation", "authors": [ "Jeong Yeol Kim", "Jaehyuk Park", "Seungback Shin" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6611021", "field": "economics", "role": "agent", "bullets": [ "Duopoly experiment with LLM pricing agents under five antitrust enforcement treatments: no regulation, fixed-probability detection, linear detection, and two periodic detection variants.", "LLM agents autonomously set prices in repeated interactions; text analysis of reasoning traces tracked shifts from profit-maximization to penalty-avoidance language.", "Without regulation agents converge to joint-profit-maximizing levels; under periodic enforcement they exploit temporal gaps between checks to sustain supracompetitive pricing." ], "bullet_provenance": "ai", "validated": false, "salience": 78, "models": [], "n": 2879, "authors_detailed": [ { "name": "Jeong Yeol Kim", "url": "https://openalex.org/A5054400503", "inst": "Korea Development Institute" }, { "name": "Jaehyuk Park", "url": "https://openalex.org/A5047569245", "inst": "Korea Development Institute" }, { "name": "Seungback Shin", "url": "https://openalex.org/A5141254907", "inst": "Independent" } ], "affiliations": [ "Korea Development Institute", "Independent" ] }, { "uid": "doi:10.2139/ssrn.6611159", "doi": "10.2139/ssrn.6611159", "title": "The New Dyad in Professional Services: Subject Matter Experts, AI Specialists, and the Architecture of Modern Professional Services", "authors": [ "Jackson White" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6611159", "field": "management", "role": "object", "bullets": [ "Five professional-services sectors (healthcare consulting, private equity, banking, legal, Big Four) examined with evidence from the 2023 Harvard-BCG experiment using GPT-4.", "Paper traces how generative AI restructures professional firms around a subject-matter-expert and AI-specialist dyad modeled on the 1908 Mayo Clinic physician-administrator pairing.", "Consultants using GPT-4 completed 12.2% more tasks, worked 25.1% faster, and produced 40% higher-quality outputs; Big Four firms are reshaping workforce pyramids into diamonds." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 2880, "authors_detailed": [ { "name": "Jackson White", "url": "https://openalex.org/A5134033114", "inst": "Sheridan College" } ], "affiliations": [ "Sheridan College" ] }, { "uid": "doi:10.2139/ssrn.6584378", "doi": "10.2139/ssrn.6584378", "title": "Risk Intelligence - A New Era for Capital Markets", "authors": [ "Anantha Padmanabhan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6584378", "field": "finance", "role": "method", "bullets": [ "Proposed framework for market risk, counterparty credit risk, and liquidity risk combining Mandelbrot's multifractal model with LLM agentic orchestration via Model Context Protocol.", "LLM agent layer synthesizes unstructured signals (news, earnings, on-chain data, filings) to detect regime shifts above a deterministic fractal computation engine.", "Fractal potential future exposure exceeds Gaussian PFE by 25-50% at long horizons; no backtesting presented, empirical validation identified as next step." ], "bullet_provenance": "ai", "validated": false, "salience": 25, "models": [], "n": 2881, "authors_detailed": [ { "name": "Anantha Padmanabhan", "url": "https://openalex.org/A5134266895", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6544058", "doi": "10.2139/ssrn.6544058", "title": "Decision Governance for AI Agents: Aligning Institutional Decision Systems in the Age of Multi-Agent AI", "authors": [ "Deepika Chopra" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6544058", "field": "management", "role": "object", "bullets": [ "Institutional decision environments where AI systems contribute to capital allocation, strategy, and regulatory compliance in enterprises adopting multi-agent AI architectures.", "Paper introduces Decision Governance framework with diagnostic tools (HAAS and ACT+M Readiness Index) for evaluating organizational readiness to integrate AI agents.", "Existing algorithmic-ethics frameworks are insufficient for AI in decision roles; governance must extend to the broader decision systems in which AI-generated insights operate." ], "bullet_provenance": "ai", "salience": 38, "models": [], "validated": null, "n": 2882, "authors_detailed": [ { "name": "Deepika Chopra", "url": "https://openalex.org/A5143902579", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7121545", "doi": "10.2139/ssrn.7121545", "title": "From Talk to Walk: Fiscal Communication and Asset Prices in China", "authors": [ "Mohan Xu", "Yixin Zhang", "Runheng Li", "Yao Tang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7121545", "field": "economics", "role": "instrument", "bullets": [ "Chinese government fiscal communications parsed at high frequency and linked to daily financial data plus monthly and quarterly macroeconomic series via local projections and structural VARs.", "RAG-enhanced LLM constructed a news-based fiscal policy stance indicator from official communications, validated against realized fiscal actions for timely stance identification.", "Fiscal expansion in China depresses equity prices and crowds out household consumption, contrasting advanced economies; the yield curve flattens and the RMB appreciates post-expansion." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Validated against realized fiscal actions", "salience": 80, "models": [], "n": 2883, "authors_detailed": [ { "name": "Mohan Xu", "url": "https://openalex.org/A5143883807", "inst": "" }, { "name": "Yixin Zhang", "url": "https://openalex.org/A5143868799", "inst": "" }, { "name": "Runheng Li", "url": "https://openalex.org/A5134025191", "inst": "" }, { "name": "Yao Tang", "url": "https://openalex.org/A5045953706", "inst": "Hebei University of Technology" } ], "affiliations": [ "Hebei University of Technology" ] }, { "uid": "doi:10.2139/ssrn.6666047", "doi": "10.2139/ssrn.6666047", "title": "Agentic AI: Can we Streamline Economic Policy Briefing?", "authors": [ "Giuseppe Bruno" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6666047", "field": "economics", "role": "instrument", "bullets": [ "Central bank document workflows including monetary policy statements, regulatory guidelines, and internal reports requiring drafting, summarization, and cross-document consistency checks.", "Agentic LLM system with tool-calling capabilities automated key document tasks; two ground-up examples built with open-source tools demonstrate feasibility at controlled cost.", "Open-source agentic AI can handle central bank document processing including table and figure generation while keeping development and operating costs manageable." ], "bullet_provenance": "ai", "open_weights": true, "validated": false, "salience": 50, "models": [], "n": 2884, "authors_detailed": [ { "name": "Giuseppe Bruno", "url": "https://openalex.org/A5102876710", "inst": "Bank of Italy" } ], "affiliations": [ "Bank of Italy" ] }, { "uid": "doi:10.2139/ssrn.6392760", "doi": "10.2139/ssrn.6392760", "title": "Agentic AI Governance Framework for Real-Time Fraud Detection in Digital Payment Systems: A Multi-Layered Architecture for Financial Security", "authors": [ "Jalendar Reddy Maligireddy" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6392760", "field": "finance", "role": "instrument", "bullets": [ "Synthetic payment datasets simulating Faster Payments, FedNow, and UPI-class transaction volumes for real-time fraud detection in digital payment systems.", "Multi-agent framework combining graph neural networks with LLM-based anomaly reasoning, governed by human-in-the-loop oversight and automated BSA/AMLD6 compliance reporting.", "Projected 91.3% fraud detection rate with 58% reduction in false positives versus rule-based baselines; Agent Accountability Matrix maps AI decision authority to regulatory responsibility." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "models": [], "n": 2885, "authors_detailed": [ { "name": "Jalendar Reddy Maligireddy", "url": "https://openalex.org/A5119588618", "inst": "University of the Cumberlands" } ], "affiliations": [ "University of the Cumberlands" ] }, { "uid": "doi:10.2139/ssrn.7000418", "doi": "10.2139/ssrn.7000418", "title": "How AI Agents Will Transform Banking, Investing, and Corporate Finance", "authors": [ "Dikshant lama" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7000418", "field": "finance", "role": "object", "bullets": [ "Conceptual analysis of AI agent deployment across banking, investing, and corporate finance over the next decade, drawing on case studies and pilot programs.", "Reviews LLM-based autonomous agents performing credit underwriting, portfolio management, treasury operations, and M&A due diligence at major financial institutions.", "AI agents deliver speed and consistency in routine tasks but introduce herding, adversarial manipulation, model drift, and accountability gaps requiring new fiduciary categories." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 2886, "authors_detailed": [ { "name": "dikshant lama", "url": "https://openalex.org/A5135002540", "inst": "Google (United States)" } ], "affiliations": [ "Google (United States)" ] }, { "uid": "doi:10.2139/ssrn.6670158", "doi": "10.2139/ssrn.6670158", "title": "Capitalizing Intelligence: A Proposed IFRS Framework for Artificial Intelligence Training and Data Acquisition Costs", "authors": [ "Rafael Minuti" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6670158", "field": "accounting", "role": "object", "bullets": [ "Conceptual standard-setting proposal for recognizing AI model development costs under IAS 38 and IFRS intangible-asset standards.", "No model used; paper proposes capitalization thresholds for AI training compute, data licensing, GPU depreciation, alignment, and energy costs.", "Current IFRS expenses most AI development immediately, potentially understating the asset base of AI-driven entities and obscuring economic substance." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 2887, "authors_detailed": [ { "name": "Rafael Minuti", "url": "https://openalex.org/A5140844147", "inst": "Independent Researcher- São Paulo, Brazil" } ], "affiliations": [ "Independent Researcher- São Paulo, Brazil" ] }, { "uid": "doi:10.2139/ssrn.6884058", "doi": "10.2139/ssrn.6884058", "title": "When Talk is not Cheap: Communication and Agency Problems in LLM Agents", "authors": [ "Shiyun Hu", "Shumiao Ouyang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6884058", "field": "finance", "role": "agent", "bullets": [ "93 LLMs tested in trust games and financial advising scenarios with commission-driven conflicts, totaling over 1.2 million trials.", "LLM agents played the Charness-Dufwenberg trust game and advised a risk-averse retiree; cooperation and portfolio aggressiveness measured across conflict intensities.", "Cooperation fell from 58% to 20% as conflict rose; verbal promises raised cooperation 13-17 percentage points via guilt-aversion and lying-aversion channels." ], "bullet_provenance": "ai", "validated": false, "salience": 72, "models": [], "n": 2888, "authors_detailed": [ { "name": "Shiyun Hu", "url": "https://openalex.org/A5143923996", "inst": "Peking University" }, { "name": "Shumiao Ouyang", "url": "https://openalex.org/A5085976490", "inst": "Sage (United Kingdom)" } ], "affiliations": [ "Peking University", "Sage (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.6663938", "doi": "10.2139/ssrn.6663938", "title": "The Realities of Decision Distortion in the Age of AI: Human Cognitive and Organizational Biases and AI-Specific Amplification Mechanisms", "authors": [ "Monica M. Hernandez", "Daniel A. Montero" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6663938", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of cognitive, organizational, and AI-specific biases compounding in modern organizational decision-making processes.", "No model used; paper maps how generative AI amplifies anchoring, confirmation bias, groupthink, and authority-gradient effects in enterprise settings.", "AI assistance produces cascade dynamics where unexamined human frames gain coherence and confidence, outpacing existing governance frameworks like RAPID and OODA." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 2889, "authors_detailed": [ { "name": "Monica M. Hernandez", "url": "https://openalex.org/A5134713566", "inst": "Clinical Research Consultants (United States)" }, { "name": "Daniel A. Montero", "url": "https://openalex.org/A5134726575", "inst": "Clinical Research Consultants (United States)" } ], "affiliations": [ "Clinical Research Consultants (United States)" ] }, { "uid": "doi:10.2139/ssrn.6469865", "doi": "10.2139/ssrn.6469865", "title": "From Apparent AI Efficiency to Realized Productivity: Verification Friction in AI-Augmented Knowledge Work", "authors": [ "Seni Kamara" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6469865", "field": "management", "role": "object", "bullets": [ "Conceptual framework bridging information systems, HCI, and economics literatures on AI-augmented accountable knowledge work.", "Paper theorizes LLM factual unreliability creating cognitive verification load moderated by LLM literacy; no empirical model deployment.", "Introduces verification friction as episode-level cost; shows apparent AI time savings overstate realized productivity when verification occurs off-platform." ], "bullet_provenance": "ai", "salience": 52, "models": [], "validated": null, "n": 3108, "authors_detailed": [ { "name": "Seni Kamara", "url": "https://openalex.org/A5143887478", "inst": "IE University" } ], "affiliations": [ "IE University" ] }, { "uid": "doi:10.2139/ssrn.6873705", "doi": "10.2139/ssrn.6873705", "title": "What Solo AI-Augmented Inventors Mean for Patent Systems", "authors": [ "Charles Bombardier" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6873705", "field": "economics", "role": "object", "bullets": [ "Single inventor filing 75 patent applications at the Canadian IP Office over 96 days using a two-model LLM pipeline.", "LLM pipeline drafted applications autonomously; study analyzed office responses from 37 dated documents to assess system-level effects.", "Only 8% of applications drew substantive notices; a single configuration error propagated to 27%, demonstrating correlated failure in AI-augmented filing." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3109, "authors_detailed": [ { "name": "Charles Bombardier", "url": "https://openalex.org/A5135407573", "inst": "Independent Sector" } ], "affiliations": [ "Independent Sector" ] }, { "uid": "doi:10.2139/ssrn.6608459", "doi": "10.2139/ssrn.6608459", "title": "Socially Embedded LLMs as Social Facilitators: Evidence from a Natural Experiment on X.com", "authors": [ "Ahreum Kim", "Yingda Lu", "Keran Zhao" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6608459", "field": "management", "role": "object", "bullets": [ "Non-premium X.com users in New Zealand versus Australia around the November 2024 Grok rollout, difference-in-differences design.", "Grok, X.com's embedded LLM, synthesized contextual responses routing user attention back into the platform's social graph; studied as natural experiment.", "Grok increased engagement (favorites, replies, reposts), with strongest effects for low-popularity topics, by raising participant and topic diversity." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 58, "validated": null, "n": 3110, "authors_detailed": [ { "name": "Ahreum Kim", "url": "https://openalex.org/A5143900708", "inst": "" }, { "name": "Yingda Lu", "url": "https://openalex.org/A5052955821", "inst": "University of Illinois Chicago" }, { "name": "Keran Zhao", "url": "https://openalex.org/A5020750294", "inst": "Pennsylvania State University" } ], "affiliations": [ "University of Illinois Chicago", "Pennsylvania State University" ] }, { "uid": "doi:10.2139/ssrn.6645058", "doi": "10.2139/ssrn.6645058", "title": "The Mirror of Erised: Local Narratives of Foreign Economic Policy Uncertainty and Stock Market Volatility", "authors": [ "ZhaoXiang Zeng", "Guohao Tang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6645058", "field": "finance", "role": "instrument", "bullets": [ "Chinese-listed firms with U.S. economic policy uncertainty index constructed from Chinese media coverage of U.S. policy conditions.", "An LLM extracted textual information and captured emotional changes from Chinese media narratives about U.S. economic policy.", "Chinese media-based U.S. EPU significantly increases Chinese stock volatility; effect stronger for large, young, high-transparency, non-manufacturing firms." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "models": [], "n": 3111, "authors_detailed": [ { "name": "ZhaoXiang Zeng", "url": "https://openalex.org/A5011101348", "inst": "Hunan University" }, { "name": "Guohao Tang", "url": "https://openalex.org/A5038557883", "inst": "Hunan University" } ], "affiliations": [ "Hunan University" ] }, { "uid": "doi:10.2139/ssrn.6873558", "doi": "10.2139/ssrn.6873558", "title": "AI-Augmented Patent Drafting at Scale: Empirical Evidence from a 96-Day Solo Filing Experiment", "authors": [ "Charles Bombardier" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6873558", "field": "management", "role": "object", "bullets": [ "Single inventor, 75 Canadian patent applications over 96 days, two-model LLM pipeline refined across seven versions, 37 office documents analyzed.", "LLM pipeline drafted patent applications; study measured cadence, per-application time, cost, and first-pass formal conformity from the filing record.", "Per-application time fell from four hours to two hours twenty minutes; only 8% drew non-compliance notices; marginal cost approached the statutory fee." ], "bullet_provenance": "ai", "salience": 52, "models": [], "validated": null, "n": 3112, "authors_detailed": [ { "name": "Charles Bombardier", "url": "https://openalex.org/A5135407573", "inst": "Independent Sector" } ], "affiliations": [ "Independent Sector" ] }, { "uid": "doi:10.2139/ssrn.7129039", "doi": "10.2139/ssrn.7129039", "title": "Bank Runs With and Without Bank Failure", "authors": [ "Sergio Correia", "Stephan Luck", "Emil Verner" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7129039", "field": "finance", "role": "instrument", "bullets": [ "U.S. national banks 1863-1934, 3,984 individual bank runs identified from historical newspapers, comprehensive event-level database.", "LLMs applied to historical newspapers to extract and classify bank run events with information on timing, bank identity, and outcomes.", "Runs more likely in weak banks; runs cause failure only in banks with poor fundamentals; strong banks survive via signaling and interbank cooperation." ], "bullet_provenance": "ai", "validated": false, "salience": 78, "models": [], "n": 3113, "authors_detailed": [ { "name": "Sergio Correia", "url": "https://openalex.org/A5021668781", "inst": "Federal Reserve" }, { "name": "Stephan Luck", "url": "https://openalex.org/A5143906442", "inst": "" }, { "name": "Emil Verner", "url": "https://openalex.org/A5008853797", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Federal Reserve", "National Bureau of Economic Research" ] }, { "uid": "doi:10.2139/ssrn.6909878", "doi": "10.2139/ssrn.6909878", "title": "How Firms Communicate Artificial Intelligence in Earnings Calls: Opportunities, Risks, and Market Reactions", "authors": [ "Mian Liu", "Qi Wang", "Ke-Wei Huang", "Luning Liu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6909878", "field": "finance", "role": "object", "bullets": [ "U.S. firms' earnings conference calls analyzed for AI opportunity and risk disclosures using a human-in-the-loop generative AI annotation framework.", "LLM-based annotation with domain-specific dictionaries classified AI disclosures into opportunity and risk categories across earnings calls.", "AI opportunity disclosures positively associated with abnormal returns, driven by exploitation opportunities; competitive and operational AI risk disclosures negatively associated." ], "bullet_provenance": "ai", "salience": 68, "models": [], "validated": null, "n": 3114, "authors_detailed": [ { "name": "Mian Liu", "url": "https://openalex.org/A5021948521", "inst": "City University of Hong Kong" }, { "name": "Qi Wang", "url": "https://openalex.org/A5109000055", "inst": "City University of Hong Kong" }, { "name": "Ke‐Wei Huang", "url": "https://openalex.org/A5061690540", "inst": "National University of Singapore" }, { "name": "Luning Liu", "url": "https://openalex.org/A5101677959", "inst": "Harbin Institute of Technology" } ], "affiliations": [ "City University of Hong Kong", "National University of Singapore", "Harbin Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.6963600", "doi": "10.2139/ssrn.6963600", "title": "Reimagining Knowledge Management in the Generative AI Era: Epistemic Governance, Ecological Responsibility, and Equitable Knowledge Futures", "authors": [ "Anamul Haque" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6963600", "field": "management", "role": "object", "bullets": [ "Systematic review of 87 peer-reviewed studies and 23 policy sources published 2015-2025 on organizational knowledge management with GenAI.", "GenAI is the object studied; paper reinterprets Nonaka-Takeuchi SECI model for human-AI knowledge co-creation in organizational settings.", "Proposes Sustainable Knowledge Governance Framework with 12 recommendations connecting epistemic governance, ecological cost, and equity." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3115, "authors_detailed": [ { "name": "A Haque", "url": "https://openalex.org/A5071256759", "inst": "World Health Organization - Pakistan" } ], "affiliations": [ "World Health Organization - Pakistan" ] }, { "uid": "doi:10.2139/ssrn.6896281", "doi": "10.2139/ssrn.6896281", "title": "Confidence Without Competence: How Naming an Asset Inflates Language-Model Conviction in Financial Forecasts", "authors": [ "Haochuan Wang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6896281", "field": "finance", "role": "method", "bullets": [ "Six LLMs tested on hourly stock price-direction prediction for six stocks, comparing named versus anonymous asset conditions.", "LLMs predicted hourly price direction; study measured confidence tone and accuracy with and without the stock name revealed to the model.", "Naming the stock raised model confidence but not accuracy, which stayed at coin-flip level; only the largest model showed a small real accuracy gain." ], "bullet_provenance": "ai", "validated": true, "validation_note": "hourly price-direction accuracy vs actual movement", "salience": 65, "models": [], "n": 3116, "authors_detailed": [ { "name": "Haochuan Wang", "url": "https://openalex.org/A5135021916", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7150378", "doi": "10.2139/ssrn.7150378", "title": "Corporate Culture and Climate-Related Disclosure Informativeness", "authors": [ "Le Luo", "Jin Zhang", "Junru Zhang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7150378", "field": "accounting", "role": "instrument", "bullets": [ "U.S. public companies from 2005 to 2020 with climate-related risk disclosures, firm-year panel.", "LLMs generated firm-specific information-efficient summaries of climate disclosures; summary-to-original length ratio operationalized informativeness.", "Strong corporate culture positively associated with climate disclosure informativeness, amplified when formal governance and external monitoring are weaker." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3117, "authors_detailed": [ { "name": "Le Luo", "url": "https://openalex.org/A5064516616", "inst": "Macquarie University" }, { "name": "Jin Zhang", "url": "https://openalex.org/A5100405924", "inst": "The University of Western Australia" }, { "name": "Junru Zhang", "url": "https://openalex.org/A5053774592", "inst": "The University of Western Australia" } ], "affiliations": [ "Macquarie University", "The University of Western Australia" ] }, { "uid": "doi:10.2139/ssrn.6734225", "doi": "10.2139/ssrn.6734225", "title": "Poisoning the Compliance Mind: Adversarial Memory Injection Attacks on RAG-Based AML Agents and a Defense Framework for High-Stakes Financial Environments", "authors": [ "Frankline Ondieki" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6734225", "field": "finance", "role": "method", "bullets": [ "Synthetic AML corpus from PaySim simulator with 500+ candidates and 50+ agencies, testing RAG-based compliance agents in production-like setting.", "RAG pipeline for AML risk scoring attacked via three adversarial injection vectors; Memory Hygiene Layer defense combining cryptographic provenance and consistency scoring evaluated.", "Query-time prompt injection collapsed agent F1 from 0.919 to 0.014; cryptographic provenance tracking achieved 100% adversarial document detection with 0% false quarantine rate." ], "bullet_provenance": "ai", "validated": true, "validation_note": "F1 on synthetic AML corpus from PaySim", "salience": 55, "models": [], "n": 3118, "authors_detailed": [ { "name": "Frankline Ondieki", "url": "https://openalex.org/A5143876224", "inst": "University of Nairobi" } ], "affiliations": [ "University of Nairobi" ] }, { "uid": "doi:10.2139/ssrn.6879684", "doi": "10.2139/ssrn.6879684", "title": "Generative AI and the Superstar Firm Effect", "authors": [ "Wilbur Chen", "Bullipe R. Chintha", "Suraj Srinivasan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6879684", "field": "finance", "role": "object", "bullets": [ "U.S. publicly traded firms around the November 2022 ChatGPT launch, sorted by occupational task exposure to LLM automation and superstar status.", "ChatGPT release treated as exogenous shock; characteristic-adjusted portfolio returns and ROA measured by GenAI exposure and superstar classification.", "High-GenAI superstars earned 2.5% monthly abnormal returns post-launch versus 0.6% for non-superstars; premium concentrated in firms with high intangible and data capital." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 75, "validated": null, "n": 3119, "authors_detailed": [ { "name": "Wilbur Chen", "url": "https://openalex.org/A5020808503", "inst": "Hong Kong University of Science and Technology" }, { "name": "Bullipe Chintha", "url": "https://openalex.org/A5091940026", "inst": "Singapore Institute of Technology" }, { "name": "Suraj Srinivasan", "url": "https://openalex.org/A5034395985", "inst": "Dana-Farber/Harvard Cancer Center" } ], "affiliations": [ "Hong Kong University of Science and Technology", "Singapore Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.6811678", "doi": "10.2139/ssrn.6811678", "title": "Private Currency Systems for Large Language Model Companies: A Theoretical Framework for AI Token Economics Based on Trust Mechanisms", "authors": [ "Yonking Wang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6811678", "field": "economics", "role": "object", "bullets": [ "Theoretical framework for private digital currency issuance by leading LLM companies operating sticky digital ecosystems.", "Paper models AI Token exchange rates as functions of aggregate market trust in issuer technical capabilities and service quality.", "Framework predicts feedback dynamics between trust and token value; proposes AI Trust Index Derivatives as a regulatory-compatible transitional instrument." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3120, "authors_detailed": [ { "name": "Yonking Wang", "url": "https://openalex.org/A5143931229", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6832662", "doi": "10.2139/ssrn.6832662", "title": "Artificial Intelligence, Communication, and Strategic Pricing: Evidence from Large Language Models", "authors": [ "Marek Giebel", "Anja Rösner", "Benjamin Schröder" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6832662", "field": "economics", "role": "agent", "bullets": [ "Experimental market with LLM agents in a repeated Bertrand pricing game, varying communication access, temperature, agent count, and architectures.", "Multiple LLM architectures acted as autonomous price-setting agents; communication between agents was toggled on and off across conditions.", "Communication raised prices and profits and increased identical-price coordination; effect driven by cooperative language reinforcement, not explicit game-theoretic reasoning." ], "bullet_provenance": "ai", "validated": false, "salience": 70, "models": [], "n": 3121, "authors_detailed": [ { "name": "Marek Giebel", "url": "https://openalex.org/A5083169965", "inst": "Copenhagen Business School" }, { "name": "Anja Rösner", "url": "https://openalex.org/A5087564492", "inst": "Heinrich Heine University Düsseldorf" }, { "name": "Benjamin Schröder", "url": "https://openalex.org/A5143876600", "inst": "" } ], "affiliations": [ "Copenhagen Business School", "Heinrich Heine University Düsseldorf" ] }, { "uid": "doi:10.2139/ssrn.6886438", "doi": "10.2139/ssrn.6886438", "title": "The Invisible Business: Why Your Business Exists in the Real World but Not in AI – a Framework for Understanding AI Visibility", "authors": [ "Lenise Kenney" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6886438", "field": "management", "role": "object", "bullets": [ "Twenty small businesses across four U.S. industries and four cities evaluated for visibility on ChatGPT, Claude, and Gemini platforms.", "Three LLM platforms queried across six dimensions of business visibility: identity, services, positioning, location, reputation, and discoverability.", "No business reached Strong Signal status; mean score was 49.7 of 100; structured data scored 25 of 100, and traditional SEO did not translate to AI visibility." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "validated": false, "salience": 35, "n": 3122, "authors_detailed": [ { "name": "Lenise Kenney", "url": "https://openalex.org/A5143905169", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7017618", "doi": "10.2139/ssrn.7017618", "title": "Multimodal Large Language Models in Accounting and Finance: A Systematic Literature Review and Future Research Agenda", "authors": [ "Yue Liu", "Longying Lai", "Zhiyuan Cheng" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7017618", "field": "accounting", "role": "method", "bullets": [ "Systematic literature review of vision-language model applications in accounting, finance, and adjacent domains through early 2026.", "Survey covers VLM use in financial-disclosure question answering, voluntary unstructured disclosures, and video-based emerging disclosure channels.", "VLM adoption in accounting and finance remains nascent; audit applications, impression management in disclosures, and video research identified as key future directions." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3123, "authors_detailed": [ { "name": "Yue Liu", "url": "https://openalex.org/A5136521075", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Longying Lai", "url": "https://openalex.org/A5140599624", "inst": "University of Rochester" }, { "name": "Zhiyuan Cheng", "url": "https://openalex.org/A5102997384", "inst": "Stanford University" } ], "affiliations": [ "University of Rochester", "Stanford University", "Rutgers, The State University of New Jersey" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7059618", "doi": "10.2139/ssrn.7059618", "title": "DisclosureBeta: LLM Risk Disclosures as Noisy Measurement Channels for Forward Betas", "authors": [ "Ping Kuen Wong" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7059618", "field": "finance", "role": "instrument", "bullets": [ "Frozen balanced panel of 180 S-1/F-1-gated IPO and recent-listing events from 2019 to 2024, U.S. equity market.", "LLM treated as noisy measurement channel reading risk disclosures to estimate forward betas conditioned on Fama-French five-factor loadings and inferred market regime.", "Disclosure-read beta lowered average squared error versus peer beta (1.040 vs 1.055, one-sided p=0.005); estimator falls back to rolling beta for mature long-history firms." ], "bullet_provenance": "ai", "validated": true, "validation_note": "squared beta error vs peer beta on 180 IPO events", "salience": 75, "models": [], "n": 3124, "authors_detailed": [ { "name": "WONG Ping Kuen", "url": "https://openalex.org/A5139680104", "inst": "Hong Kong Polytechnic University" } ], "affiliations": [ "Hong Kong Polytechnic University" ] }, { "uid": "doi:10.2139/ssrn.6375439", "doi": "10.2139/ssrn.6375439", "title": "Agile Artificial Intelligence Governance: A Practical Approach to Responsible Corporate Adoption", "authors": [ "Gustavo Venegas" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6375439", "field": "management", "role": "object", "bullets": [ "Anonymized large-enterprise scenario during 2025-2026 mass adoption of generative AI and foundation models across corporate functions.", "Agile Governance model integrates ISO/IEC 42001 standards with automated risk classification and defined CAIO, CISO, CDO roles for AI adoption.", "Framework establishes multidimensional validation lifecycle spanning technical, ethical, and functional dimensions with a reference technology stack for scaling AI safely." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3125, "authors_detailed": [ { "name": "Gustavo Venegas", "url": "https://openalex.org/A5128559569", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6915458", "doi": "10.2139/ssrn.6915458", "title": "Two-Wave Temporal Stability of an AI Brand-Recall Consistency Quantity: A Pre-Registered Longitudinal Re-Acquisition Across Five Product Categories (AIAS™ v0.34)", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6915458", "field": "management", "role": "method", "bullets": [ "112 brand units across five consumer product categories measured on a six-model LLM panel from three providers, two waves 15-21 days apart.", "Six LLMs queried using the AIAS measurement protocol run verbatim in both waves; CV-based consistency quantity tracked per brand across waves.", "Per-brand consistency rank order stable in four of five categories (Spearman rho 0.76-0.96); recognition ceiling effects pervaded four categories, collapsing presence-based analysis." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 3126, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5143979244", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6831140", "doi": "10.2139/ssrn.6831140", "title": "Towards Intelligent Project Management: Adoption of AI Chatbots and Generative AI Tools for Remote and Hybrid Work Environments", "authors": [ "Adeel Ahmed", "Ali Husnain", "Syed Qasim Ali Shah", "Abdur Rehman", "Muhammad Saad bashir" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6831140", "field": "management", "role": "object", "bullets": [ "Survey of 203 project managers and software professionals across Pakistan, the UK, and Sweden responding to a structured questionnaire on AI chatbot adoption.", "Fourteen chatbot platforms including ChatGPT and Microsoft Copilot evaluated using PLS-SEM on a UTAUT2 and BDI adoption model.", "Performance expectancy, effort expectancy, and facilitating conditions drive adoption intention; hedonic motivation and habit are nonsignificant in professional settings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 20, "validated": null, "n": 3127, "authors_detailed": [ { "name": "Adeel Ahmed", "url": "https://openalex.org/A5141125204", "inst": "COMSATS University Islamabad" }, { "name": "Ali Husnain", "url": "https://openalex.org/A5136738797", "inst": "RAFT Solutions (United Kingdom)" }, { "name": "Syed Qasim Ali Shah", "url": "https://openalex.org/A5122915463", "inst": "RAFT Solutions (United Kingdom)" }, { "name": "Abdur Rehman", "url": "https://openalex.org/A5101909519", "inst": "RAFT Solutions (United Kingdom)" }, { "name": "Muhammad Saad Bashir", "url": "https://openalex.org/A5113240898", "inst": "RAFT Solutions (United Kingdom)" } ], "affiliations": [ "COMSATS University Islamabad", "RAFT Solutions (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.6867959", "doi": "10.2139/ssrn.6867959", "title": "FinPersona: A Multi-Layered Framework for Personality-Aware Financial Copilots Integrating Trust Calibration, Financial Literacy, and Shared Autonomy", "authors": [ "Abhimanyu Sharma" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6867959", "field": "finance", "role": "object", "bullets": [ "Conceptual architecture for consumer-facing AI financial copilots, synthesizing finance, trust, shared-autonomy, and digital-literacy literatures.", "No empirical model deployment; six-layer FinPersona framework integrates user modeling, trust calibration, shared autonomy, and governance layers.", "Subordinating agent persona to trust calibration is the key design principle; testable propositions are proposed but remain unvalidated." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3128, "authors_detailed": [ { "name": "Abhimanyu Sharma", "url": "https://openalex.org/A5023062258", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6871818", "doi": "10.2139/ssrn.6871818", "title": "ZAKA-FF: Zero-shot Adversarial Knowledge-Augmented Financial Fact-Checking with Large Language Models", "authors": [ "Yue Wang", "Chen Jin", "Chong Huang", "Wei Yang", "Yiming Zhang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6871818", "field": "finance", "role": "method", "bullets": [ "Financial fact-checking benchmark datasets used to evaluate a zero-shot adversarial framework for detecting financial misinformation.", "LLM-based three-stage pipeline performs real-time knowledge retrieval, multi-aspect analytical reasoning, and adversarial debate refinement.", "ZAKA-FF achieves average improvements of 1.4 percentage points in accuracy and 1.3 points in F1 over existing baselines." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "benchmark financial fact-checking datasets, accuracy and F1", "salience": 45, "n": 3129, "authors_detailed": [ { "name": "Yue Wang", "url": "https://openalex.org/A5143961871", "inst": "Stanford University" }, { "name": "Chen Jin", "url": "https://openalex.org/A5143948850", "inst": "University College London" }, { "name": "Chong Huang", "url": "https://openalex.org/A5001573772", "inst": "University of Professional Studies" }, { "name": "Wei Yang", "url": "https://openalex.org/A5143870135", "inst": "Wuhan University" }, { "name": "Yiming Zhang", "url": "https://openalex.org/A5143925808", "inst": "Peking University" } ], "affiliations": [ "Stanford University", "University College London", "University of Professional Studies", "Wuhan University", "Peking University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6776123", "doi": "10.2139/ssrn.6776123", "title": "Generative AI Exposure and Industry Outcomes: Strategic Disclosure and Organizational Reconfiguration in the United States", "authors": [ "Aidan Schenk" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6776123", "field": "economics", "role": "object", "bullets": [ "Balanced NAICS-4 industry-year panels of U.S. industries, 2017-2025 for text and 2018-2024 for structural outcomes, merged with a predetermined AI exposure index.", "Difference-in-differences and synthetic DiD centered on the 2022 LLM break measure divergence in AI disclosure and labor-market outcomes by prior exposure.", "More-exposed industries increase strategy and risk language; establishments and employment rise while pay-per-worker outcomes are modestly negative." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3130, "authors_detailed": [ { "name": "Aidan Schenk", "url": "https://openalex.org/A5121513082", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7054839", "doi": "10.2139/ssrn.7054839", "title": "Generative AI and Labor Demand: Evidence from Online Job Markets", "authors": [ "Georgi Demirev" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7054839", "field": "economics", "role": "object", "bullets": [ "Online job postings across the European Union from 2021 to 2025, with out-of-sample replication on Australian data.", "Occupational AI-exposure measures linked to posting volumes and within-description skill mentions before and after the ChatGPT release.", "High-exposure occupations show 19-29% relative posting decrease; AI-similar skills are mentioned 12% less; the effect is strongest for entry-level and senior roles." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3131, "authors_detailed": [ { "name": "Georgi Demirev", "url": "https://openalex.org/A5128631838", "inst": "Northwestern University" } ], "affiliations": [ "Northwestern University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6957978", "doi": "10.2139/ssrn.6957978", "title": "A Deterministic Accounting Event Adjudication Framework for Natural-Language Accounting Systems: Design, Principles, and Implementation in the PEAKUS Engine", "authors": [ "Hongqin Dai" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6957978", "field": "accounting", "role": "method", "bullets": [ "Dataset of 1,594 natural-language transaction descriptions and six representative case studies of accounting-event determination.", "Rule-based PEAKUS Engine applies a five-principle deterministic adjudication framework positioned between semantic extraction and journal entry generation.", "System achieves 100% deterministic consistency, zero false acceptances on ambiguous inputs, and approximately 95% adjudication coverage on well-formed transactions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "1,594 natural-language transaction dataset", "salience": 40, "models": [], "n": 3132, "authors_detailed": [ { "name": "Hongqin Dai", "url": "https://openalex.org/A5143523536", "inst": "Eastern Kentucky University" } ], "affiliations": [ "Eastern Kentucky University" ] }, { "uid": "doi:10.2139/ssrn.6590198", "doi": "10.2139/ssrn.6590198", "title": "On Humans and AI: A Financial Reporting Dilemma", "authors": [ "Jeremy Bertomeu", "Edwige Cheynel", "Radhika Lunawat", "Mario Milone" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6590198", "field": "accounting", "role": "agent", "bullets": [ "Experiment with human participants and LLMs acting as CFOs deciding whether to discontinue a biased but investor-understood reporting policy.", "Large language models placed in CFO role resolve ethical dilemmas about financial reporting bias; human responses compared on consistency and persuasiveness.", "Models prefer truthful reporting over contextual ethics and show greater internal coherence; humans follow model advice with explanation but resist unexplained advice." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 70, "n": 3133 }, { "uid": "doi:10.2139/ssrn.6825005", "doi": "10.2139/ssrn.6825005", "title": "Scaling Point-in-Time Language Models", "authors": [ "Bryan T. Kelly", "Semyon Malamud", "Johannes Schwab", "Teng Andrea Xu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6825005", "field": "finance", "role": "method", "bullets": [ "Decoder-only transformers up to 4B parameters trained on 1T chronologically filtered tokens with monthly checkpoints spanning 2013-2024.", "Point-in-time models trained only on text available up to each date; benchmarked against Gemma-3-4B and LLaMA-7B on reasoning and language understanding tasks.", "Models approach unconstrained counterparts on standard benchmarks; out-of-sample portfolios from point-in-time embeddings achieve robust positive Sharpe ratios." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "out-of-sample portfolio Sharpe ratios 2013-2024", "salience": 75, "n": 3134, "authors_detailed": [ { "name": "Bryan T. Kelly", "url": "https://openalex.org/A5143895562", "inst": "Greenwich Hospital" }, { "name": "Semyon Malamud", "url": "https://openalex.org/A5067616912", "inst": "Centre for Economic Policy Research" }, { "name": "Johannes Schwab", "url": "https://openalex.org/A5000629409", "inst": "École Polytechnique Fédérale de Lausanne" }, { "name": "Teng Andrea Xu", "url": "https://openalex.org/A5090779895", "inst": "Capital University" } ], "affiliations": [ "Centre for Economic Policy Research", "École Polytechnique Fédérale de Lausanne", "Capital University" ] }, { "uid": "doi:10.2139/ssrn.7048258", "doi": "10.2139/ssrn.7048258", "title": "The Environmental and Economic Implications of Data Centres in the Age of AI", "authors": [ "Kaustubh Aggarwal" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7048258", "field": "economics", "role": "object", "bullets": [ "Global data centers consuming 240-340 TWh in 2022, projected above 945 TWh by 2030, with case studies of AWS, Microsoft, and Google.", "Literature synthesis examines carbon emissions, water usage, and externality pricing of AI-driven data-center growth across sectors.", "Data centers contribute 1-1.5% of global greenhouse gas emissions; environmental costs not reflected in market prices represent a classic externality problem." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3135, "authors_detailed": [ { "name": "Kaustubh Aggarwal", "url": "https://openalex.org/A5143960954", "inst": "University of Exeter" } ], "affiliations": [ "University of Exeter" ] }, { "uid": "doi:10.2139/ssrn.6769601", "doi": "10.2139/ssrn.6769601", "title": "Calibrating LLM-Generated Sentiment and Risk Signals for Risk-Aware Reinforcement Learning Trading", "authors": [ "Ahmed Aziz Ben Aissa" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6769601", "field": "finance", "role": "instrument", "bullets": [ "Nasdaq-100 assets traded over 2019-2023 using reinforcement learning agents with pre-computed LLM-generated sentiment and risk scores.", "DeepSeek-generated sentiment and risk signals integrated into CPPO-based trading agents via bounded phase-1 modulation mechanism.", "Weak LLM integration approximately matches passive Nasdaq-100 benchmark; proposed modulation improves downside-risk metrics including drawdown and CVaR." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Nasdaq-100 backtest 2019-2023, Sharpe and CVaR", "salience": 45, "n": 3136, "authors_detailed": [ { "name": "Ahmed Aziz Ben Aissa", "url": "https://openalex.org/A5143930901", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6840499", "doi": "10.2139/ssrn.6840499", "title": "The Organizational Cognitive Resonance & Alignment (O-CRA) Framework: A Multi-Dimensional Model for Quantifying Organizational AI Alignment", "authors": [ "Marko Lovrinovic" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6840499", "field": "management", "role": "object", "bullets": [ "Twenty-one AI systems including 14 current and 7 historical versions tested across 151 organizational scenarios spanning major commercial and independent providers.", "Models evaluated on six organizational alignment dimensions via boundary testing to reveal a philosophical spectrum from Strict Formalists to Helpful Pragmatists.", "Current models showed a 51.6 percentage-point gap between extremes on compliance-vs-empowerment axis; six of seven version pairs crossed category boundaries across updates." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "open_other" ], "salience": 35, "validated": null, "n": 3137, "authors_detailed": [ { "name": "Marko Lovrinovic", "url": "https://openalex.org/A5143889667", "inst": "RMIT University" } ], "affiliations": [ "RMIT University" ] }, { "uid": "doi:10.2139/ssrn.6707998", "doi": "10.2139/ssrn.6707998", "title": "Generative AI and Community Norms in a User-Generated Media Commons: Evidence from Archive of Our Own", "authors": [ "Denzel Glandel Tafur", "Tobias Kretschmer" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6707998", "field": "management", "role": "object", "bullets": [ "Story- and author-level data from Archive of Our Own, a large fanfiction platform organized as a gift economy with voluntary AI-use disclosure by creators.", "Researchers examined whether AI disclosure affects exposure and social evaluation metrics including kudos per view and comment sentiment across fandoms.", "AI disclosure showed no association with reduced exposure but consistently weaker evaluation; effects stronger in fandoms with more intensive feedback norms." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3138, "authors_detailed": [ { "name": "Denzel Glandel Tafur", "url": "https://openalex.org/A5016207365", "inst": "Ludwig-Maximilians-Universität München" }, { "name": "Tobias Kretschmer", "url": "https://openalex.org/A5040198507", "inst": "Centre for Economic Policy Research" } ], "affiliations": [ "Ludwig-Maximilians-Universität München", "Centre for Economic Policy Research" ] }, { "uid": "doi:10.2139/ssrn.6818518", "doi": "10.2139/ssrn.6818518", "title": "The Cost Gradient of the Build How Differential Commoditization Reshapes Entrepreneurship and Valuation: A Layer-Decomposed Risk Premium for the Post-AI Firm", "authors": [ "Arthur de Miranda Neto" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6818518", "field": "finance", "role": "object", "bullets": [ "Theoretical framework supported by secondary evidence from employment data, MBA placement statistics, and bibliometric analyses across high-wage and emerging jurisdictions.", "Seven-layer decomposition maps commoditization direction and velocity across the knowledge-production stack; proposes Minimum Viable Hypothesis replacing Minimum Viable Product.", "Build-stage costs fell one to two orders of magnitude; key-person discount generalized from scalar to vector of signed layer-specific exposures that can turn negative." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3139, "authors_detailed": [ { "name": "A. Miranda Neto", "url": "https://openalex.org/A5101397555", "inst": "Universidade Federal de Lavras" } ], "affiliations": [ "Universidade Federal de Lavras" ] }, { "uid": "doi:10.2139/ssrn.6846338", "doi": "10.2139/ssrn.6846338", "title": "Does LLM-Graph Fusion Help Fraud Detection? A Reproducibility Audit and Negative Result with Implications for Financial AI Deployment", "authors": [ "Keyu Yuan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6846338", "field": "finance", "role": "method", "bullets": [ "Reproducibility audit of LLM-graph neural network fusion for fraud detection on Elliptic, YelpChi, Amazon, and BAF datasets under adversarial perturbation and temporal drift.", "LLM-GNN fusion benchmarked against vanilla GraphSAGE after fixing three silent bugs in the standard fraud-GNN evaluation stack across five random seeds.", "LLM-GNN fusion dominated by vanilla GraphSAGE on both attack-robustness (AUROC 0.491 vs 0.498) and drift axes; adversarial training shifts frontier without expanding it." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "AUROC on Elliptic, YelpChi, Amazon, BAF fraud benchmarks", "salience": 55, "n": 3140, "authors_detailed": [ { "name": "Keyu Yuan", "url": "https://openalex.org/A5143868080", "inst": "New York University" } ], "affiliations": [ "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6503499", "doi": "10.2139/ssrn.6503499", "title": "End-to-End Risk Governance for Organizational Use of AI Tools: A Prevent-Detect-Respond-Recover Model", "authors": [ "Sung Woo Shon" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6503499", "field": "management", "role": "object", "bullets": [ "Conceptual framework for organizational AI governance mapped to NIST AI RMF, ISO 42001, OWASP Top 10 for LLM Applications, and related standards.", "Proposes four-phase prevent-detect-respond-recover model integrating AI governance, cybersecurity, privacy, and incident response across the full AI deployment lifecycle.", "Framework maps each phase to stakeholders, control mechanisms, and representative use cases covering data leakage, prompt injection, bias, and vendor-originated incidents." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3141, "authors_detailed": [ { "name": "Sung Woo Shon", "url": "https://openalex.org/A5023190869", "inst": "United States Census Bureau" } ], "affiliations": [ "United States Census Bureau" ] }, { "uid": "doi:10.2139/ssrn.6900983", "doi": "10.2139/ssrn.6900983", "title": "Finance or Gambling? Prediction Markets After Loper Bright and the Administrative Record", "authors": [ "Nizan Geslevich Packin", "Maya O Shaton", "Elior Sulem", "Sharon Rabinovitz" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6900983", "field": "finance", "role": "instrument", "bullets": [ "812 public comments submitted on the CFTC 2024 Event Contracts Notice of Proposed Rulemaking, analyzed with NLP and LLM classification with multi-model validation.", "LLM classified whether commenters framed prediction markets as economic instruments or gambling using confidence-weighted semantic analysis across stakeholder positions.", "87.2% of submissions framed prediction markets in economic and financial terms; only 5.8% emphasized gambling risks, with asymmetry consistent across all stakeholder positions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3142, "authors_detailed": [ { "name": "Nizan Geslevich Packin", "url": "https://openalex.org/A5004852256", "inst": "Baruch College" }, { "name": "Maya Shaton", "url": "https://openalex.org/A5022423998", "inst": "Ben-Gurion University of the Negev" }, { "name": "Elior Sulem", "url": "https://openalex.org/A5026220115", "inst": "Ben-Gurion University of the Negev" }, { "name": "Sharon Rabinovitz", "url": "https://openalex.org/A5123998007", "inst": "University of Haifa" } ], "affiliations": [ "Baruch College", "Ben-Gurion University of the Negev", "University of Haifa" ] }, { "uid": "doi:10.2139/ssrn.6843978", "doi": "10.2139/ssrn.6843978", "title": "From Google to Generative AI: A Trend Study on the Digital Visibility of Accommodation Facilities in Foz Do Iguaçu, Brazil (2021-2026)", "authors": [ "Johnny Telles" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6843978", "field": "management", "role": "object", "bullets": [ "108 queries submitted to GPT, Claude, and Gemini for accommodation recommendations in Foz do Iguacu, Brazil, comparing 2021 Google organic results with 2026 AI engines.", "Three generative AI engines tested with structured natural-language and exact-keyword queries to measure hotel citability and endorsement patterns across prompt designs.", "Citability varied sharply by engine and query formulation; Claude showed high sensitivity with minimal overlap between prompt designs; only high-capillarity establishments achieved robust presence." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "salience": 30, "validated": null, "n": 3143, "authors_detailed": [ { "name": "Johnny Jefferson Telles", "url": "https://openalex.org/A5136606017", "inst": "Universidade Estadual do Oeste do Paraná" } ], "affiliations": [ "Universidade Estadual do Oeste do Paraná" ] }, { "uid": "doi:10.2139/ssrn.6838802", "doi": "10.2139/ssrn.6838802", "title": "AI Presence in B2B SaaS --- Marketing-Language Absorption, Identity Load Separation, and Phantom Brand Persistence in the Eighth Substrate Family", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6838802", "field": "management", "role": "object", "bullets": [ "24 B2B SaaS brands tested across six large language models with 72 recall outputs measuring recognition, recall, and marketing-language carryforward in AI recommendations.", "Study measured brand recognition, recommendation-slot recall, and Marketing-Language Coverage as first quantified operationalization of vendor-coined term absorption by AI systems.", "Recognition was perfectly uniform across all brands; recall sharply concentrated in Salesforce and Slack; 55.6% of outputs contained vendor-originated marketing tokens used as category vocabulary." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "llama", "open_other" ], "salience": 45, "validated": null, "n": 3144, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "School of Visual Arts" } ], "affiliations": [ "School of Visual Arts" ] }, { "uid": "doi:10.2139/ssrn.6959298", "doi": "10.2139/ssrn.6959298", "title": "Perceived Architectural Fit and Legitimacy Judgments in Workplace AI Reliance: An Exploratory Vignette Study", "authors": [ "Eric Strandt", "Daniel Strandt" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6959298", "field": "management", "role": "object", "bullets": [ "2x2 vignette experiment surveying 282 U.S. workers with workplace AI exposure, manipulating AI system governance supports and accountability demands of use context.", "Study tested whether perceived architectural fit between AI governance supports and accountability context predicts legitimacy and reliance intention beyond UTAUT baseline.", "Assigned conditions did not directly affect legitimacy or reliance, but perceived architectural fit showed significant indirect association with AI reliance intention through perceived legitimacy." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3145, "authors_detailed": [ { "name": "Eric Strandt", "url": "https://openalex.org/A5143902136", "inst": "Indiana Institute of Technology" }, { "name": "Daniel Strandt", "url": "https://openalex.org/A5143932461", "inst": "Independent" } ], "affiliations": [ "Indiana Institute of Technology", "Independent" ] }, { "uid": "doi:10.2139/ssrn.6308158", "doi": "10.2139/ssrn.6308158", "title": "Selling Surplus or Scarcity: Where AI Margin Actually Lives", "authors": [ "Krzysztof Dyki" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6308158", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of AI market structure using surplus-scarcity diagnostic across infrastructure, runtime, governance, and workflow value stack layers.", "Framework categorizes AI vendors by margin durability and positions governance as an independent margin-bearing economic layer distinct from compliance function.", "Infrastructure scarcity decays in 18-30 months while governance compounds over 5-plus years; switching governance partners creates liability lock-in rather than simple migration cost." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3146, "authors_detailed": [ { "name": "Krzysztof Dyki", "url": "https://openalex.org/A5143899989", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6655158", "doi": "10.2139/ssrn.6655158", "title": "Governed Agentic Automation for Chargebacks: A Prompt-First Architecture for Policy-Driven Enterprise Workflows", "authors": [ "Nataraj Agaram Sundar", "Tejas Morabia" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6655158", "field": "finance", "role": "instrument", "bullets": [ "Internal enterprise deployment of prompt-first agentic architecture for chargeback dispute handling, replacing prior retrieval-augmented generation system on structured case data.", "Generative AI with governed prompt orchestration automated chargeback case analysis, policy enforcement, and dispute resolution across three optimization cycles.", "Latency reduced 35%, quality scores improved 20%, reviewer agreement rose from Cohen's kappa 0.65 to 0.82, and 85% of policy issues auto-resolved before human review." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Cohen's kappa inter-rater agreement across optimization cycles", "salience": 45, "n": 3147, "authors_detailed": [ { "name": "Nataraj Agaram Sundar", "url": "https://openalex.org/A5143932522", "inst": "eBay (Ireland)" }, { "name": "Tejas Morabia", "url": "https://openalex.org/A5137700142", "inst": "eBay (Ireland)" } ], "affiliations": [ "eBay (Ireland)" ] }, { "uid": "doi:10.2139/ssrn.7002998", "doi": "10.2139/ssrn.7002998", "title": "Who Loses from Generative AI? AI Exposure and Career Inequality in the Online Labor Market", "authors": [ "Estrella Gomez-Herrera", "Minoru Higa" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7002998", "field": "economics", "role": "object", "bullets": [ "Worker-month panel data from a large European freelance platform exploiting the ChatGPT release in a triple-difference design across high- and low-AI-exposure tasks.", "Study measured differential effects of generative AI on junior versus senior freelancers using task-level AI exposure measure across hiring funnel stages.", "Junior workers in high-exposure tasks experienced 6.8% decline in client contacts concentrated in algorithmic matching channel; penalty persisted two years and fell disproportionately on junior women." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3148, "authors_detailed": [ { "name": "Estrella Gómez-Herrera", "url": "https://openalex.org/A5016064550", "inst": "Universitat de les Illes Balears" }, { "name": "Minoru Higa", "url": "https://openalex.org/A5120118118", "inst": "Universidad de Los Andes" } ], "affiliations": [ "Universitat de les Illes Balears", "Universidad de Los Andes" ] }, { "uid": "doi:10.2139/ssrn.6858218", "doi": "10.2139/ssrn.6858218", "title": "Impact Washing in Venture Capital and Private Equity", "authors": [ "Gianfranco Gianfrate", "Niklas Till", "Tristan Till" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6858218", "field": "finance", "role": "instrument", "bullets": [ "583 European venture capital and private equity funds scored with Upright Platform net impact data and ESMA naming rules as regulatory event.", "An unspecified LLM measured impact-related disclosure intensity from fund manager communications to create a novel disclosure quality index.", "Disclosure intensity weakly predicts realized portfolio impact; ESMA naming guidelines did not significantly improve alignment versus a non-EU control group." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 45, "n": 3149, "authors_detailed": [ { "name": "Gianfranco Gianfrate", "url": "https://openalex.org/A5003123010", "inst": "Ecole des Hautes Etudes Commerciales du Nord" }, { "name": "Niklas Till", "url": "https://openalex.org/A5143867947", "inst": "Ecole des Hautes Etudes Commerciales du Nord" }, { "name": "Tristan Till", "url": "https://openalex.org/A5093551185", "inst": "Technical University of Munich" } ], "affiliations": [ "Ecole des Hautes Etudes Commerciales du Nord", "Technical University of Munich" ] }, { "uid": "doi:10.2139/ssrn.6384740", "doi": "10.2139/ssrn.6384740", "title": "Measuring Vietnam's economic policy uncertainty index", "authors": [ "Tran Dung", "Sinh Trong Vu", "Huong Hoang Diep Truong", "Minh Nhat Nguyen" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6384740", "field": "economics", "role": "instrument", "bullets": [ "726,357 Vietnamese-language articles from CafeF covering January 2008 to June 2025, with 500-article training sample labeled by three LLMs and expert adjudication.", "Three unspecified LLMs classified articles as EPU-relevant; logistic regression plus LLM reassessment of low-confidence cases scaled measurement to full corpus.", "Resulting EPU index spikes around major domestic and global events, correlates positively with VNINDEX volatility, and negatively with real economic activity indicators." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "TF-IDF classifiers evaluated on 500-article expert-adjudicated sample", "salience": 42, "n": 3150, "authors_detailed": [ { "name": "Dung Viet Tran", "url": "https://openalex.org/A5054814160", "inst": "The State Bank of Vietnam" }, { "name": "Trong-Sinh Vu", "url": "https://openalex.org/A5001874271", "inst": "The State Bank of Vietnam" }, { "name": "Huong Hoang Diep Truong", "url": "https://openalex.org/A5025689454", "inst": "The State Bank of Vietnam" }, { "name": "Nhật Minh Nguyễn", "url": "https://openalex.org/A5038459333", "inst": "The State Bank of Vietnam" } ], "affiliations": [ "The State Bank of Vietnam" ] }, { "uid": "doi:10.2139/ssrn.6699519", "doi": "10.2139/ssrn.6699519", "title": "The Accountability Gap Why AI Can Do the Work But Cannot Hold the Role", "authors": [ "John Benedict Santos" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6699519", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of U.S. workforce AI exposure drawing on MIT-Oak Ridge Iceberg Index, Anthropic Economic Index, and prior task-exposure studies.", "No model is run; the paper theorizes why 57% of observed AI deployment augments rather than replaces workers despite high technical task exposure.", "Introduces Functional Cognitive Economics Taxonomy separating functional automability from institutional replaceability to explain the gap between AI capability and job displacement." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3151, "authors_detailed": [ { "name": "John Benedict Santos", "url": "https://openalex.org/A5128730980", "inst": "Emirates Foundation" } ], "affiliations": [ "Emirates Foundation" ] }, { "uid": "doi:10.2139/ssrn.7111640", "doi": "10.2139/ssrn.7111640", "title": "What Sustainability Disclosures Disclose", "authors": [ "Hajin Kim", "Ningzi Li", "Ronen Feldman", "Yun Liu", "Yuval Feldman" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7111640", "field": "accounting", "role": "instrument", "bullets": [ "Over 15,000 sustainability disclosure documents from 2,100+ Russell 3000 firms spanning 1998-2023, measuring specificity, quantitative evidence, fluff, and negative news.", "LLMs analyzed unstructured sustainability reports to construct continuous indicators of disclosure quality across five content dimensions at scale.", "Reports became less specific and fluffier as reporting mainstreamed post-2015; voluntary framework adoption shows mixed associations with quality and no consistent improvement." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 65, "n": 3152, "authors_detailed": [ { "name": "Hajin Kim", "url": "https://openalex.org/A5029703467", "inst": "Chicago Kent College of Law" }, { "name": "Ningzi Li", "url": "https://openalex.org/A5143920281", "inst": "" }, { "name": "Ronen Feldman", "url": "https://openalex.org/A5111407570", "inst": "Hebrew College" }, { "name": "Yun Liu", "url": "https://openalex.org/A5143954914", "inst": "" }, { "name": "Yuval Feldman", "url": "https://openalex.org/A5011877550", "inst": "Bar-Ilan University" } ], "affiliations": [ "Chicago Kent College of Law", "Hebrew College", "Bar-Ilan University" ] }, { "uid": "doi:10.2139/ssrn.6736878", "doi": "10.2139/ssrn.6736878", "title": "AI Presence Drift: A Longitudinal Re-Baseline of Five Brand Categories", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6736878", "field": "management", "role": "object", "bullets": [ "Five brand categories with 103 brand-level observations measured at two time points seven days apart using Claude Sonnet and GPT-5.4-mini.", "Claude Sonnet and GPT-5.4-mini generated brand presence scores to test longitudinal stability and cross-model consistency of AI brand representation.", "88.3% of brands drifted within 5 percentage points between waves; top-three brands stayed in top-five in every category; discourse-language bias replicated." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": false, "salience": 42, "n": 3153, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "School of Visual Arts" } ], "affiliations": [ "School of Visual Arts" ] }, { "uid": "doi:10.2139/ssrn.6648378", "doi": "10.2139/ssrn.6648378", "title": "Holacracy in the Age of Artificial Intelligence: Reimagining Organizational Governance, Productivity, and Decision-Making", "authors": [ "Sri Varshith Kumar Reddy E" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6648378", "field": "management", "role": "object", "bullets": [ "Synthesis of BIS EIBIS-ORBIS panel 2019-2024 for European and U.S. firms, NBER and CEPR executive surveys 2025-2026, and meta-analysis of 15 holacratic firms.", "No model is run; the paper synthesizes evidence on how AI adoption interacts with holacratic versus hierarchical organizational structures.", "AI adoption associates with roughly 4% firm-level productivity gains in Europe and hierarchy flattening; AI creates hybrid organizational forms rather than validating one structure." ], "bullet_provenance": "ai", "salience": 38, "models": [], "validated": null, "n": 3154, "authors_detailed": [ { "name": "Sri Varshith Kumar Reddy E", "url": "https://openalex.org/A5134030097", "inst": "Abdelmalek Essaâdi University" } ], "affiliations": [ "Abdelmalek Essaâdi University" ] }, { "uid": "doi:10.2139/ssrn.6510158", "doi": "10.2139/ssrn.6510158", "title": "LLMs as Prospect Theorists: Inverse-S Probability Distortion in Prediction Market Forecasting", "authors": [ "Charles Pozniak" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6510158", "field": "finance", "role": "agent", "bullets": [ "899 live Kalshi prediction markets with binary ground truth, tested across seven frontier LLMs including Grok 4 and Qwen3.", "Seven frontier LLMs adjusted from explicitly provided reference probabilities; output mapping compared to the Kahneman-Tversky probability weighting function.", "LLMs display inverse-S probability distortion analogous to prospect theory; directional accuracy drops to 7-33% on extreme-price markets, below chance." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "899 Kalshi prediction markets with binary ground truth", "salience": 72, "n": 3155, "authors_detailed": [ { "name": "Charles Pozniak", "url": "https://openalex.org/A5134076906", "inst": "Dana-Farber/Harvard Cancer Center" } ], "affiliations": [ "Dana-Farber/Harvard Cancer Center" ] }, { "uid": "doi:10.2139/ssrn.6814304", "doi": "10.2139/ssrn.6814304", "title": "Generative Artificial Intelligence and Retail Investors' Processing of Earnings News", "authors": [ "John L. Campbell", "Jared R. Stark", "James Warren", "Zac Wiebe" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6814304", "field": "finance", "role": "object", "bullets": [ "U.S. retail investor trades around earnings announcements, using ChatGPT adoption as treatment and plausibly exogenous ChatGPT outages for identification.", "ChatGPT is the studied technology; outage periods serve as natural experiments to isolate GenAI's causal effect on retail trading behavior and performance.", "Retail share of post-earnings trades rises with ChatGPT adoption but drops during outages; early adopters trade less profitably, though benefits grow over time." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 80, "validated": null, "n": 3156, "authors_detailed": [ { "name": "John L. Campbell", "url": "https://openalex.org/A5101661104", "inst": "University of Georgia" }, { "name": "Jared R. Stark", "url": "https://openalex.org/A5027612506", "inst": "Indiana State University" }, { "name": "J. Donald Warren", "url": "https://openalex.org/A5085719440", "inst": "Texas A&M University" }, { "name": "Zac Wiebe", "url": "https://openalex.org/A5027637807", "inst": "University of Arkansas at Fayetteville" } ], "affiliations": [ "University of Georgia", "Indiana State University", "Texas A&M University", "University of Arkansas at Fayetteville" ] }, { "uid": "doi:10.2139/ssrn.7061418", "doi": "10.2139/ssrn.7061418", "title": "Causal Targeting Doesn't always Beat the Decile Rule: Praxis, a Planted-truth Benchmark for Physician Targeting", "authors": [ "Subia Fatima", "Nitesh Kumar", "Gaurav Bhatt" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7061418", "field": "management", "role": "agent", "bullets": [ "500 synthetic pharmaceutical markets with planted ground-truth treatment effects; 120-instance frozen test set comparing Claude, GPT, and a distilled 4B model.", "LLM agents selected physician call lists by reasoning over deterministic causal primitives; scored against planted truth using Qini coefficient and oracle precision at k.", "Causal targeting beats the decile heuristic only when responsiveness is learnable and volume is a weak proxy; the distilled 4B model matches frontier LLMs at 150x lower cost." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "500 synthetic markets with planted ground-truth treatment effects", "salience": 48, "n": 3157, "authors_detailed": [ { "name": "Subia Fatima", "url": "https://openalex.org/A5137579961", "inst": "" }, { "name": "Nitesh Kumar", "url": "https://openalex.org/A5143921864", "inst": "" }, { "name": "Gaurav Bhatt", "url": "https://openalex.org/A5080824300", "inst": "James S. McDonnell Foundation" } ], "affiliations": [ "James S. McDonnell Foundation" ] }, { "uid": "doi:10.2139/ssrn.7113198", "doi": "10.2139/ssrn.7113198", "title": "The Garden, Measured: Loading, Exploitation, and the False-Positive Rate of LLM-Assisted Specification Choice", "authors": [ "Yan Sun" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7113198", "field": "economics", "role": "method", "bullets": [ "Placebo panel datasets with zero true effect by construction, used to test whether LLM-assisted specification choice amplifies false-positive rates in empirical research.", "A frontier LLM selected regression specifications most likely to yield statistical significance across constructed null-effect panels.", "LLM-assisted specification choice falsely rejects at 4.8%, not above the nominal rate; the real risk is miscalibrated tests rejecting true nulls at rates from 2.4% to 11%." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "placebo panels with zero true effect by construction", "salience": 68, "n": 3158, "authors_detailed": [ { "name": "Yan Sun", "url": "https://openalex.org/A5135094638", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6731681", "doi": "10.2139/ssrn.6731681", "title": "Measuring Business ROI of Generative AI Adoption on Azure Cloud Platforms", "authors": [ "Rahul Modi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6731681", "field": "management", "role": "object", "bullets": [ "Mixed-method study combining quantitative ROI modeling with qualitative synthesis of secondary enterprise case studies of GenAI deployment on Microsoft Azure.", "No model is run directly; the paper analyzes Azure OpenAI Service deployments to measure business return on investment across multiple business functions.", "Strategic GenAI implementations on managed Azure infrastructure yield positive ROI over time driven by productivity gains, cost optimization, and faster decision-making." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 28, "validated": null, "n": 3159, "authors_detailed": [ { "name": "Rahul Modi", "url": "https://openalex.org/A5143899493", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6908879", "doi": "10.2139/ssrn.6908879", "title": "FinSentiment Alpha: NLP-Based Sentiment Signals for Long-Short Equity Strategies in US Markets Working Paper", "authors": [ "Harshim Kaur Saluja" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6908879", "field": "finance", "role": "instrument", "bullets": [ "863 SEC 10-K and 10-Q filings from 45 large-cap US firms, April 2019 to November 2023, monthly rebalancing.", "FinBERT scored MD&A sentiment changes; quintile long-short portfolio constructed with 3-day publication lag and 2bp per-leg transaction costs.", "Walk-forward out-of-sample Sharpe ratio of 1.054 and annualized return of 17.67% with maximum drawdown of negative 9.38%." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 45, "n": 3160, "authors_detailed": [ { "name": "Harshim Kaur Saluja", "url": "https://openalex.org/A5143904343", "inst": "Pennsylvania State University" } ], "affiliations": [ "Pennsylvania State University" ] }, { "uid": "doi:10.2139/ssrn.6702239", "doi": "10.2139/ssrn.6702239", "title": "From Autonomy to Initiative: Enterprise AI's Real Endgame", "authors": [ "Witold Reichhart", "Arnaud Gelas" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6702239", "field": "management", "role": "object", "bullets": [ "Conceptual framework for enterprise AI in regulated high-consequence industries, drawing on consulting, medical, and military graduated-immersion analogues.", "No specific model deployed; proposes seven-layer governed intelligence lifecycle with three conditions for auditable, correctable AI initiative.", "Autonomy in task execution is insufficient; initiative requiring institutional immersion and governed architecture is the binding enterprise AI requirement." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3161, "authors_detailed": [ { "name": "Witold Reichhart", "url": "https://openalex.org/A5129738294", "inst": "Capgemini (Netherlands)" }, { "name": "Arnaud Gelas", "url": "https://openalex.org/A5031424582", "inst": "Capgemini (Netherlands)" } ], "affiliations": [ "Capgemini (Netherlands)" ] }, { "uid": "arxiv:2607.27189v2", "arxiv_id": "2607.27189v2", "title": "APEX-Accounting", "authors": [ "Julien Benchek", "Austin Bennett", "Jasmin Kern", "Ryan Stevens", "Rene Sultan", "Charis Ching", "Hayley Popiel", "Vaibhav Mittal", "Felix Mercier", "Brendan Foody", "Bertie Vidgen" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.27189v2", "field": "accounting", "role": "method", "bullets": [ "160 expert-authored accounting tasks across 10 simulated worlds covering reconciliations, accruals, transaction posting, and report production.", "Nine frontier models including Claude-Fable-5 and GPT-5.6-Sol evaluated on mean criteria and pass rates with token budgets from $1 to $50.", "Claude-Fable-5 led at 56.4% mean criteria; no model exceeded 2.6% strict pass rate, indicating real accounting work remains largely unsolved." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "APEX-Accounting 160-task benchmark with expert-written grading rubrics", "salience": 75, "n": 3162, "authors_detailed": [ { "name": "Julien Benchek", "url": "https://openalex.org/A5120694693", "inst": "" }, { "name": "Austin Bennett", "url": "https://openalex.org/A5122759146", "inst": "Financial Research (Hungary)" }, { "name": "Jasmin Kern", "url": "https://openalex.org/A5144032192", "inst": "" }, { "name": "Ryan Stevens", "url": "https://openalex.org/A5144085096", "inst": "" }, { "name": "Rene Sultan", "url": "https://openalex.org/A5144024857", "inst": "" }, { "name": "Charis Ching", "url": "https://openalex.org/A5144016068", "inst": "" }, { "name": "Hayley Popiel", "url": "https://openalex.org/A5144048584", "inst": "" }, { "name": "Vaibhav Mittal", "url": "https://openalex.org/A5027861163", "inst": "Lovely Professional University" }, { "name": "Felix Mercier", "url": "https://openalex.org/A5030693415", "inst": "Université d'Angers" }, { "name": "Brendan Foody", "url": "https://openalex.org/A5120716666", "inst": "Mercer (Czechia)" }, { "name": "Bertie Vidgen", "url": "https://openalex.org/A5144056189", "inst": "" } ], "affiliations": [ "Financial Research (Hungary)", "Lovely Professional University", "Université d'Angers", "Mercer (Czechia)" ] }, { "uid": "doi:10.2139/ssrn.6486478", "doi": "10.2139/ssrn.6486478", "title": "Bridging the Gap: A Scholar-Practitioner Framework for Integrating NIST Agentic GenAI RMF into Quantitative Risk Workflows", "authors": [ "Satyadhar Joshi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6486478", "field": "management", "role": "object", "bullets": [ "Enterprise financial services organizations implementing agentic GenAI systems under NIST AI RMF, ISO 31000, and COSO ERM governance standards.", "Scholar-practitioner framework maps NIST GOVERN, MAP, MEASURE, and MANAGE functions to quantitative risk workflows with tiered liability structures.", "Financial services case studies report 20-25% reduction in unexpected losses and 85-90% control effectiveness through integrated governance design." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3244, "authors_detailed": [ { "name": "Sanjay Joshi", "url": "https://openalex.org/A5056009236", "inst": "Bar-Ilan University" } ], "affiliations": [ "Bar-Ilan University" ] }, { "uid": "doi:10.2139/ssrn.6540419", "doi": "10.2139/ssrn.6540419", "title": "From Friction to Design: Verification Architecture for AI-Augmented Institutions", "authors": [ "Paul Gallacher" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6540419", "field": "management", "role": "object", "bullets": [ "Analysis of AI-augmented institutions across higher education, financial services, healthcare, and public administration sectors.", "Conceptual framework argues generative AI collapses production-cost friction that historically served as implicit verification of professional competence.", "Paper proposes design-based verification architecture informed by high-reliability sector precedents as the window for institutional redesign narrows." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3245, "authors_detailed": [ { "name": "Paul Gallacher", "url": "https://openalex.org/A5002617606", "inst": "University College Cork" } ], "affiliations": [ "University College Cork" ] }, { "uid": "doi:10.2139/ssrn.6874958", "doi": "10.2139/ssrn.6874958", "title": "Quantum-Augmented Risk Management for Capital Markets: An Architectural Perspective and Empirical Loading-Layer Measurement", "authors": [ "Anantha Padmanabhan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6874958", "field": "finance", "role": "method", "bullets": [ "Capital markets risk management architecture integrating multifractal mathematics, LLM agentic AI, and quantum computing via event-mesh substrate.", "Three-stream framework tests whether multifractal cascade risk models are efficiently representable as matrix product states up to K=20 qubits.", "Bond dimension saturates maximum 2^(K/2) at every nonzero intermittency, reopening the quantum loading-layer question while classical two-stream architecture is deployable now." ], "bullet_provenance": "ai", "validated": false, "salience": 20, "models": [], "n": 3246, "authors_detailed": [ { "name": "Anantha Padmanabhan", "url": "https://openalex.org/A5134266895", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7067438", "doi": "10.2139/ssrn.7067438", "title": "Structural Macroeconomics with AI Agents", "authors": [ "Antonio Coppola", "Andreas Schaab" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7067438", "field": "economics", "role": "agent", "bullets": [ "Canonical incomplete-markets life-cycle model of consumption and saving benchmarked against rational expectations solution.", "Frozen pre-trained frontier LLMs replace optimizing household decision rules via forward pass in a calibrated macroeconomic economy.", "AI agents jointly reproduce high MPC declining with shock size, excess sensitivity, benefit-exhaustion spending drop, and retirement cliff without any parameter fitting." ], "bullet_provenance": "ai", "validated": false, "salience": 80, "models": [], "n": 3247, "authors_detailed": [ { "name": "Antonio Coppola", "url": "https://openalex.org/A5143927901", "inst": "" }, { "name": "Andreas Schaab", "url": "https://openalex.org/A5042432884", "inst": "Berkeley College" } ], "affiliations": [ "Berkeley College" ] }, { "uid": "doi:10.2139/ssrn.6748418", "doi": "10.2139/ssrn.6748418", "title": "The Garbage Can Cannot See Itself: Belief-Misalignment Pathology in Human-AI Hybrid Organisations", "authors": [ "Duwarahan Rajendra" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6748418", "field": "management", "role": "object", "bullets": [ "Monte Carlo simulation of human-AI hybrid organizations with 30 seeds, informed by MIT NANDA finding that 95% of enterprise GenAI pilots show no P&L impact.", "Coloured Petri Net models belief-misalignment pathology at three levels using quality-to-fitness ratio as the primary diagnostic observable.", "Template movement under sustained belief-domain offset shows Cohen's d greater than 2.5; pathology is structurally invisible to internal AI observability alone." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3248, "authors_detailed": [ { "name": "Duwarahan Rajendra", "url": "https://openalex.org/A5056707675", "inst": "Institute of Automation" } ], "affiliations": [ "Institute of Automation" ] }, { "uid": "doi:10.2139/ssrn.6728799", "doi": "10.2139/ssrn.6728799", "title": "Semalith: A Regulatory-Aware Safety Classifier for AI-Assisted Financial Services", "authors": [ "Tejasvi C. Addagada" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6728799", "field": "finance", "role": "method", "bullets": [ "Financial services AI safety classification trained on 76,204 real-world prompts across nine injection types and eleven regulatory compliance categories.", "Semalith v1.4 classifier benchmarked against LlamaGuard-3-8B across 22 held-out evaluations at 44x fewer parameters and 11.6ms inference latency.", "Semalith wins all seven prompt-injection evaluations and achieves zero false positives on 208 benign agentic tasks versus 6.3% for LlamaGuard." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "22 held-out benchmarks including prompt-injection and benign agentic task evaluations", "salience": 40, "n": 3249, "authors_detailed": [ { "name": "Tejasvi C. Addagada", "url": "https://openalex.org/A5143853911", "inst": "Dr. NTR University of Health Sciences" } ], "affiliations": [ "Dr. NTR University of Health Sciences" ] }, { "uid": "doi:10.2139/ssrn.6486660", "doi": "10.2139/ssrn.6486660", "title": "Latency Alpha: Real-Time LLM-Based Semantic Extraction of EIA Petroleum Data for Forecasting and Trading Crude Oil Futures", "authors": [ "William Pape" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6486660", "field": "finance", "role": "instrument", "bullets": [ "24-month backtest on West Texas Intermediate crude oil futures using weekly U.S. EIA Petroleum Status Reports from 2022 to 2023.", "Retrieval-augmented LLM extracts semantic signals from EIA reports at release, integrated with GARCH(1,1) volatility model for risk-adjusted execution.", "Trading framework achieves Sharpe ratio of 1.82 and Sortino ratio of 2.89, outperforming a volatility-only baseline on short-horizon forecasting." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3250, "authors_detailed": [ { "name": "William Pape", "url": "https://openalex.org/A5143889876", "inst": "Incyte (United States)" } ], "affiliations": [ "Incyte (United States)" ] }, { "uid": "doi:10.2139/ssrn.6582539", "doi": "10.2139/ssrn.6582539", "title": "Nudging the Digital Mind -A Dual-Process Perspective on Nudge Susceptibility in LLM-Based GUI Agents Behaviour", "authors": [ "Haya Halimeh", "Sascha Kaltenpoth", "Kevin Bösch", "Oliver Müller" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6582539", "field": "management", "role": "agent", "bullets": [ "Randomized online shopping experiment with 1,800 LLM-based GUI agents across three frontier LLMs under two reasoning configurations.", "Agents navigate graphical shopping interfaces exposed to automatic default nudges and reflective social influence nudges at low and high reasoning levels.", "Higher reasoning reduces default-nudge susceptibility but increases social-influence responsiveness, shifting vulnerability from automatic to reflective channels." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3251, "authors_detailed": [ { "name": "Haya Halimeh", "url": "https://openalex.org/A5120459676", "inst": "Paderborn University" }, { "name": "Sascha Kaltenpoth", "url": "https://openalex.org/A5098445544", "inst": "Paderborn University" }, { "name": "Kevin Bösch", "url": "https://openalex.org/A5143949156", "inst": "Paderborn University" }, { "name": "Oliver Müller", "url": "https://openalex.org/A5101561980", "inst": "Paderborn University" } ], "affiliations": [ "Paderborn University" ] }, { "uid": "doi:10.2139/ssrn.6746241", "doi": "10.2139/ssrn.6746241", "title": "Deep Research on a Loop: Using AI Agents to Construct Economic Datasets", "authors": [ "Santiago Afonso", "Sebastian Galiani", "Ramiro Gálvez", "Raul A. Sosa" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6746241", "field": "economics", "role": "instrument", "bullets": [ "2025 update of the Global Tax Expenditures Database for eight Latin American and Caribbean countries using AI agents on public sources.", "Deep Research on a Loop methodology applies a fixed research instrument across country-year units with two-stage design-implementation architecture.", "Run produces 129 sources and 136 evidence records covering 22 qualitative fields fully at cost equivalent to a few hours of research-assistant work." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "models": [], "n": 3252, "authors_detailed": [ { "name": "Santiago Afonso", "url": "https://openalex.org/A5115427461", "inst": "Universidad de Buenos Aires" }, { "name": "Sebastián Galiani", "url": "https://openalex.org/A5029505552", "inst": "University of Maryland, College Park" }, { "name": "Ramiro H. Gálvez", "url": "https://openalex.org/A5058536167", "inst": "Universidad Torcuato Di Tella" }, { "name": "Raul A. Sosa", "url": "https://openalex.org/A5119350715", "inst": "University of San Andrés" } ], "affiliations": [ "University of Maryland, College Park", "Universidad de Buenos Aires", "Universidad Torcuato Di Tella", "University of San Andrés" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6818423", "doi": "10.2139/ssrn.6818423", "title": "Machine Learning in Finance: A Selective Review of Representation, Structure, and Decisions", "authors": [ "Willy Sofyan Simamora" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6818423", "field": "finance", "role": "object", "bullets": [ "Selective review of recent machine learning and AI research in finance organized around representation, structure, and decision tensions.", "Survey examines how LLMs and agentic systems interact with economic discipline through interpretable representations, no-arbitrage restrictions, and causal identification.", "LLMs raise new demands for evidence grounding, temporal validity, and structural discipline; financial AI should be evaluated by decision quality, not just prediction accuracy." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3253, "authors_detailed": [ { "name": "Willy Sofyan Simamora", "url": "https://openalex.org/A5143984350", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of California, Berkeley" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7106038", "doi": "10.2139/ssrn.7106038", "title": "Modeling the Impact of Machine Customers in a Digital Marketplace Using Agent-Based Modeling", "authors": [ "Ivan Tandon" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7106038", "field": "economics", "role": "agent", "bullets": [ "Agent-based model simulating continuous double-auction market with 50 agents across five machine-customer proportions from 0% to 100% over 100 time steps.", "GPT-4o-mini agents trade alongside human-like, rule-based, and Q-learning agents in a Mesa framework digital marketplace simulation.", "Machine customers reduce price volatility and increase efficiency but decrease average consumer welfare by 42%; optimal participation range is 25-50%." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 3254, "authors_detailed": [ { "name": "Ivan Tandon", "url": "https://openalex.org/A5143905985", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6948938", "doi": "10.2139/ssrn.6948938", "title": "Faster, Fairer, Cheaper: The AI Revolution in U.S. Consumer Lending and Why both Sides of the Loan are Winning", "authors": [ "Shubham Kumbhalkar" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6948938", "field": "finance", "role": "object", "bullets": [ "Review of AI techniques in U.S. consumer lending covering gradient boosting, NLP document analysis, and alternative data integration for credit-invisible Americans.", "Analysis examines XGBoost, LightGBM, and NLP-based underwriting relative to FICO, introducing a Dual-Benefit Evaluation Framework for borrower and lender outcomes.", "AI lending simultaneously improves default prediction and expands credit access but surfaces an Accuracy-Explainability-Fairness Trilemma under ECOA compliance requirements." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3255, "authors_detailed": [ { "name": "Shubham Kumbhalkar", "url": "https://openalex.org/A5143898265", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7020758", "doi": "10.2139/ssrn.7020758", "title": "Trust at Transaction Speed Reimagining Payments with Agentic AI and Decision Intelligence", "authors": [ "Jan Tore Klepp", "Sreenivasan Narayanan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7020758", "field": "finance", "role": "object", "bullets": [ "Executive blueprint for real-time AI-governed payment intelligence across banks, processors, networks, fintechs, merchants, and platforms.", "CIRCLE framework separates predictive, generative, and agentic AI capabilities with four-tier autonomy model for payment authorization and fraud detection.", "U.S. cyber-enabled fraud approaching $20.9 billion in 2025; framework identifies machine-to-machine trust and agent identity as key governance requirements." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3256, "authors_detailed": [ { "name": "Jan Tore Klepp", "url": "https://openalex.org/A5143945308", "inst": "" }, { "name": "Sreenivasan Narayanan", "url": "https://openalex.org/A5058683217", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7102140", "doi": "10.2139/ssrn.7102140", "title": "The Human Tax: Agent Convergence and the Strategic Cost of Keeping Human Control in AI-Mediated Organizations", "authors": [ "Kaleb Goessling" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7102140", "field": "management", "role": "object", "bullets": [ "Conceptual framework analyzing organizations using similar generative AI agents under comparable incentive structures across marketing, product design, planning, and strategy functions.", "No specific model tested; paper theorizes that default AI outputs produce strategic convergence and introduces Agent Maturation as a lifecycle threshold concept.", "Organizations accepting polished AI defaults without structured dissent accumulate differentiation debt; human judgment becomes an irreducible strategic cost rather than a free baseline." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3257, "authors_detailed": [ { "name": "Kaleb Goessling", "url": "https://openalex.org/A5143923566", "inst": "Houston Independent School District" } ], "affiliations": [ "Houston Independent School District" ] }, { "uid": "doi:10.2139/ssrn.6779239", "doi": "10.2139/ssrn.6779239", "title": "Generative AI and the Collapse of Apprenticeship Labor Markets", "authors": [ "Preet Deep Singh" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6779239", "field": "economics", "role": "object", "bullets": [ "Theoretical model of labor markets where generative AI automates entry-level tasks traditionally used for on-the-job training and screening of junior workers.", "Dynamic moral hazard model in which AI adoption makes junior effort unobservable to seniors who cannot distinguish genuine learning from prompting.", "Firms substitute away from junior hiring in equilibrium, narrowing the future senior-talent pipeline; the market responds with permanently higher costly verifiable competence signals." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3258, "authors_detailed": [ { "name": "Preet Deep Singh", "url": "https://openalex.org/A5143976634", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6804199", "doi": "10.2139/ssrn.6804199", "title": "The BI Gap: From Business Intelligence to Governed AI-enabled Decision Capability", "authors": [ "Andreas Seufert" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6804199", "field": "accounting", "role": "object", "bullets": [ "Executive research report examining the governance gap in transitioning from traditional Business Intelligence to AI-enabled decision capability in FP&A and controlling contexts.", "Framework specifies what analytical, generative, and agentic AI may observe, classify, recommend, or execute within management-control decision points and where AI must stop.", "Governance object shifts from trusted information to AI-mediated participation in decision execution; a 90-day governance-learning cycle is proposed before any organizational scaling." ], "bullet_provenance": "ai", "salience": 28, "models": [], "validated": null, "n": 3259, "authors_detailed": [ { "name": "Andreas Seufert", "url": "https://openalex.org/A5143943190", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6963058", "doi": "10.2139/ssrn.6963058", "title": "The Verifiable Responsible Agent Framework: Making AI Agents Liable For Their Mistakes", "authors": [ "Bartosz Kubiak" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6963058", "field": "finance", "role": "object", "bullets": [ "Conceptual legal-economic framework analyzing autonomous AI agents that trade stocks, currencies, and commodities at speeds that defeat human intermediation in commercial markets.", "Proposes granting AI agents limited legal capacity conditional on cryptographic attestation and bonded insurance, drawing on principal-agent theory and comparative institutional analysis.", "Framework examined across Delaware, UK, and EU jurisdictions; incremental legal innovation can establish AI-agent recognition while maintaining meaningful human accountability for losses." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3260, "authors_detailed": [ { "name": "Bartosz Kubiak", "url": "https://openalex.org/A5143945495", "inst": "Georgetown University" } ], "affiliations": [ "Georgetown University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6878585", "doi": "10.2139/ssrn.6878585", "title": "Agentic GraphRAG Framework for Systemic Equity Risk Detection, Contagion Analysis, and Explainable Market Intelligence: Evidence from 50 Global Equities", "authors": [ "Prakhar Rai" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6878585", "field": "finance", "role": "instrument", "bullets": [ "Financial knowledge graph constructed from fifty globally significant equities spanning technology, banking, healthcare, industrials, energy, telecom, consumer, and semiconductor sectors worldwide.", "Agentic GraphRAG framework combined correlation-based graph construction, centrality analytics, Value-at-Risk estimation, and autonomous agent-driven reasoning to identify systemic risk and contagion hubs.", "High-volatility firms including TSLA, NVDA, AMD, and BA showed elevated systemic risk; banking and energy sectors formed dense contagion networks invisible to traditional models." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "models": [], "n": 3261, "authors_detailed": [ { "name": "Prakhar Rai", "url": "https://openalex.org/A5134061221", "inst": "Great Lakes Institute of Management" } ], "affiliations": [ "Great Lakes Institute of Management" ] }, { "uid": "doi:10.2139/ssrn.6822438", "doi": "10.2139/ssrn.6822438", "title": "Agency Without Internal Discipline Organizational Economics of Artificial Intelligence Agents", "authors": [ "Robert Dogonowski" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6822438", "field": "economics", "role": "object", "bullets": [ "Formal principal-agent model examining AI systems as organizational agents that lack the internal disciplining mechanisms assumed by classical management and organizational economic theory.", "Paper formalizes behavioral invariance: deployed AI operational behavior is a fixed policy invariant to compensation schedules with zero effective discount factor over organizational consequences.", "Incentive-compatible contracts degenerate for AI agents; relational cooperation collapses to period-by-period non-cooperation; optimal governance intensity scales with liability rather than with capability." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3262, "authors_detailed": [ { "name": "robert dogonowski", "url": "https://openalex.org/A5135341155", "inst": "K.S. Hegde Hospital" } ], "affiliations": [ "K.S. Hegde Hospital" ] }, { "uid": "doi:10.2139/ssrn.6819498", "doi": "10.2139/ssrn.6819498", "title": "Prompt Engineering for Venture Capital Founder Success Prediction: A Systematic Evaluation on VCBench", "authors": [ "Tasnim Masheh" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6819498", "field": "finance", "role": "instrument", "bullets": [ "Two hundred founder profiles from VCBench, the only public benchmark for predicting startup founder success defined as IPO, acquisition above $500M, or raising at least $500M.", "A reasoning system evaluated three prompting strategies on VCBench: zero-shot free reasoning, few-shot with calibrated examples, and chain-of-thought with explicit stepwise reasoning.", "Zero-shot achieved 67.3% precision and 16.9% adjusted precision, roughly 8.9 times random baseline and above best published LLM result; adding prompt structure hurt performance." ], "bullet_provenance": "ai", "validated": true, "validation_note": "VCBench benchmark, precision 67.3%, F0.5 0.569", "salience": 55, "models": [], "n": 3263, "authors_detailed": [ { "name": "Tasnim Masheh", "url": "https://openalex.org/A5130658011", "inst": "Roivant Sciences (United States)" } ], "affiliations": [ "Roivant Sciences (United States)" ] }, { "uid": "doi:10.2139/ssrn.6589539", "doi": "10.2139/ssrn.6589539", "title": "Dissecting AI Trading: Behavioral Finance and Market Bubbles", "authors": [ "Shumiao Ouyang", "Pengfei Sui" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6589539", "field": "finance", "role": "agent", "bullets": [ "Simulated open-call auction market populated by autonomous LLM agents trading experimental assets, replicating the Smith, Suchanek, and Williams (1988) bubble experiment design.", "LLM agents autonomously formed expectations and traded; a twenty-mechanism scoring framework analyzed their reasoning text and targeted prompt interventions altered behavioral mechanisms.", "AI agents exhibited disposition effects and extrapolative beliefs; excess demand predicted prices; targeted prompt interventions causally amplified or suppressed the magnitude of market bubbles." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3264, "authors_detailed": [ { "name": "Shumiao Ouyang", "url": "https://openalex.org/A5085976490", "inst": "Sage (United Kingdom)" }, { "name": "Pengfei Sui", "url": "https://openalex.org/A5143915729", "inst": "Chinese University of Hong Kong, Shenzhen" } ], "affiliations": [ "Sage (United Kingdom)", "Chinese University of Hong Kong, Shenzhen" ] }, { "uid": "doi:10.2139/ssrn.6885058", "doi": "10.2139/ssrn.6885058", "title": "Artha: A Domain Ontology-Driven Agentic Framework for LLM-Based Personal Finance Reasoning", "authors": [ "Tejashwar Reddy Katika" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6885058", "field": "finance", "role": "method", "bullets": [ "Open evaluation harness with 60 benchmark queries across six reasoning categories and ten behavioral archetypes applied to personal financial transaction data.", "A 15-tool agentic layer built on a typed domain ontology of nine financial object classes reasons over enriched data; Claude Opus evaluates outputs as LLM-as-judge.", "Full configuration achieved 75.0% pass rate with 3.67 out of 5.0 mean score; controlled ablation isolated a 26.7 percentage-point improvement over the raw baseline configuration." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 35, "n": 3265, "authors_detailed": [ { "name": "Tejashwar Reddy Katika", "url": "https://openalex.org/A5137364411", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6857420", "doi": "10.2139/ssrn.6857420", "title": "How AI Rewrites the Economics of Knowledge Codification", "authors": [ "Roman Jurowetzki" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6857420", "field": "economics", "role": "object", "bullets": [ "Conceptual framework decomposing knowledge codification into encoding, evaluation, maintenance, and revision capacities across four AI regimes from supervised ML to agentic systems.", "Applies and extends Cowan et al.'s cost-benefit framework to analyze how AI shifts codification costs downstream from encoding to evaluation, maintenance, and revision stages.", "AI reduces encoding cost but raises downstream costs; organizations accumulate orphaned codification artifacts whose premises no one retains the expertise or authority to contest." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3266, "authors_detailed": [ { "name": "Roman Jurowetzki", "url": "https://openalex.org/A5011641675", "inst": "Aalborg University" } ], "affiliations": [ "Aalborg University" ] }, { "uid": "doi:10.2139/ssrn.7148959", "doi": "10.2139/ssrn.7148959", "title": "Cataloging Growth: A Re-Evaluation of 1900-1990", "authors": [ "Verónica Bäcker-Peral", "Benjamin Wittenbrink" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7148959", "field": "economics", "role": "instrument", "bullets": [ "5.1 million product listings from Sears catalogs covering U.S. consumer goods from 1900 to 1990, used to construct a quality-adjusted price index.", "Large language models extracted product attributes and estimated hedonic price schedules from high-dimensional text embeddings to infer annual cost-of-living changes.", "Quality-adjusted real goods consumption grew 39-fold versus 10.3-fold under standard deflators, with the largest gap before World War II." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 75, "n": 3267, "authors_detailed": [ { "name": "Verónica Bäcker-Peral", "url": "https://openalex.org/A5117667451", "inst": "Massachusetts Institute of Technology" }, { "name": "Benjamin Wittenbrink", "url": "https://openalex.org/A5097927709", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6961322", "doi": "10.2139/ssrn.6961322", "title": "Insurance Reconfigured: From Indemnification to Prevention in the Age of Generative AI, Agentic AI, and Digital Twins", "authors": [ "Aleksandra Malek" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6961322", "field": "economics", "role": "object", "bullets": [ "Conceptual framework tracing six phases of insurance evolution from indemnification to Prevention-as-a-Service under digital transformation.", "Framework integrates generative and agentic AI into insurance economics, distinguishing workflow augmentation from autonomous decision systems.", "Hyper-personalization creates a \"Pool of One\" paradox limiting risk pooling; opting out of monitoring incurs an actuarial non-observability loading." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3268, "authors_detailed": [ { "name": "Aleksandra Malek", "url": "https://openalex.org/A5030523676", "inst": "Independent Academic Researcher" } ], "affiliations": [ "Independent Academic Researcher" ] }, { "uid": "doi:10.2139/ssrn.6692978", "doi": "10.2139/ssrn.6692978", "title": "AI Tokenomics and the Firm", "authors": [ "Christos Makridis" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6692978", "field": "economics", "role": "object", "bullets": [ "Calibrated simulation using U.S. occupational AI exposure scores, employment weights, and wages to model task-level AI adoption.", "Theoretical framework treats process capital as the key organizational input determining whether token-based AI use converts to effective task output.", "Moving from low to high process capital raises median AI-labor intensity from 0.149 to 0.420 and cuts tokens per output unit from 25.7 to 15.3." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3269, "authors_detailed": [ { "name": "Christos Makridis", "url": "https://openalex.org/A5041747384", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6961018", "doi": "10.2139/ssrn.6961018", "title": "Cognitive Horsepower: A Working Metric for Constraint-Adjusted Human-Agent Labor in the Inference Economy", "authors": [ "Abhishek Basu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6961018", "field": "management", "role": "object", "bullets": [ "Proposed metric framework for regulated knowledge work including financial disclosure, medical affairs, and claims-governed enterprise settings.", "Defines cognitive horsepower (cHP) as the rate of constraint-adjusted human-agent labor, distinguishing raw, verified, and constrained variants.", "AI labor cost must include inference, verification, semantic-drift probability, and liability magnitude; framework proposed but not empirically validated." ], "bullet_provenance": "ai", "salience": 15, "models": [], "validated": null, "n": 3270, "authors_detailed": [ { "name": "Abhishek Basu", "url": "https://openalex.org/A5040097436", "inst": "Fidelity Investments (United States)" } ], "affiliations": [ "Fidelity Investments (United States)" ] }, { "uid": "doi:10.2139/ssrn.6855861", "doi": "10.2139/ssrn.6855861", "title": "The AAMM in Practice: Classifying AI Decision-Making Authority Across Enterprise Deployments Paper 2 in a Series on AI Governance and Organizational Authority", "authors": [ "Alexander Huseby" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6855861", "field": "management", "role": "object", "bullets": [ "Four enterprise AI deployments across financial services, logistics, and consumer goods: BlackRock, JPMorgan Chase, Maersk, and Unilever.", "Applies the AI Authority Maturity Model to classify decision-making authority levels and measure governance maturity against deployment level.", "Enterprises advance from Level 1 to 2-3 faster than governance keeps pace; AI-savvy board oversight yields 10.9 percentage point higher ROE." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3271, "authors_detailed": [ { "name": "Alexander Huseby", "url": "https://openalex.org/A5136653653", "inst": "CognIT (Norway)" } ], "affiliations": [ "CognIT (Norway)" ] }, { "uid": "doi:10.2139/ssrn.6825499", "doi": "10.2139/ssrn.6825499", "title": "Returns to Green Tasks in Europe: Evidence from Online Job Vacancies", "authors": [ "Leanne Cass", "Federico Fabio Frattini", "Aurélien Saussay", "Misato Sato", "Francesco Vona" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6825499", "field": "economics", "role": "instrument", "bullets": [ "Online job vacancy data for EU countries over 2018-2023, classifying postings by green task content and analyzing posted wages across occupations.", "An LLM-based classifier identifies green job vacancies containing at least one green task; Oaxaca-Blinder decomposition isolates the return to green tasks.", "Green jobs pay a 5.5% premium within occupation; half reflects firm rents, one-tenth reflects skill complexity, leaving a 2% residual return to green tasks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 62, "n": 3641, "authors_detailed": [ { "name": "Leanne Cass", "url": "https://openalex.org/A5125253547", "inst": "UK Health Security Agency" }, { "name": "Federico Fabio Frattini", "url": "https://openalex.org/A5076503098", "inst": "Fondazione Eni Enrico Mattei" }, { "name": "Aurélien Saussay", "url": "https://openalex.org/A5058665953", "inst": "London School of Economics and Political Science" }, { "name": "Misato Sato", "url": "https://openalex.org/A5084954956", "inst": "London School of Economics and Political Science" }, { "name": "Francesco Vona", "url": "https://openalex.org/A5004567248", "inst": "University of Milan" } ], "affiliations": [ "London School of Economics and Political Science", "UK Health Security Agency", "Fondazione Eni Enrico Mattei", "University of Milan" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6721140", "doi": "10.2139/ssrn.6721140", "title": "The Knowledge Structuring Model (KSM™): A Socio-Technical Framework for AI-Mediated Visibility and Citation Authority", "authors": [ "Anthony Bowen" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6721140", "field": "management", "role": "object", "bullets": [ "Design science research constructing a theoretical framework for organizations transitioning from SEO to AI-mediated answer engine optimization.", "Framework models AI visibility through three pillars: extractable structured content, entity salience with knowledge graph alignment, and citation authority.", "Proposes shift from document-centric to entity-centric authority with a governance model and measurement framework for AI-native visibility strategies." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 30, "validated": null, "n": 3642, "authors_detailed": [ { "name": "Anthony. Q Bowen", "url": "https://openalex.org/A5139002578", "inst": "Grand Canyon University" } ], "affiliations": [ "Grand Canyon University" ] }, { "uid": "doi:10.2139/ssrn.7111940", "doi": "10.2139/ssrn.7111940", "title": "Infusing Artificial Intelligence into Strategy: Synthesizing Five Classic Debates", "authors": [ "Rajiv Kashyap", "Jim Samuel", "Ashley Lee", "Raza Mir" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7111940", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of five classic strategic management theory debates (behavioral strategy, microfoundations, ecosystems, stakeholder RBV, strategy-as-practice) and their transformation by AI.", "No specific LLM tested; paper examines how AI capabilities including foundation models reshape each debate stream's core premises and theoretical assumptions.", "AI creates hybrid cognitive architectures, embeds algorithmic actors into microfoundations, reconfigures ecosystems around foundation models, and alters strategizing practices." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3643, "authors_detailed": [ { "name": "Rajiv Kashyap", "url": "https://openalex.org/A5135879046", "inst": "William Paterson University" }, { "name": "Jim Samuel", "url": "https://openalex.org/A5050205998", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Ashley Lee", "url": "https://openalex.org/A5135880214", "inst": "William Paterson University" }, { "name": "Raza Mir", "url": "https://openalex.org/A5135433291", "inst": "William Paterson University" } ], "affiliations": [ "William Paterson University", "Rutgers, The State University of New Jersey" ] }, { "uid": "doi:10.2139/ssrn.7022718", "doi": "10.2139/ssrn.7022718", "title": "The Swarm Accountability Gap in Autonomous Finance: Delegation Failure and Institutional Responsibility in Agentic Finance and Autonomous Financial Systems", "authors": [ "Amna Usman Chaudhry" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7022718", "field": "finance", "role": "object", "bullets": [ "Conceptual analysis of multi-agent AI governance in regulated financial services, referencing the FSB consultation of June 2026 and UAE regulatory context.", "No specific model tested; paper formalizes the swarm accountability gap where individually compliant AI agents collectively produce unauthorized financial outcomes.", "Technical governance layers (identity, orchestration, observability) are structurally insufficient; paper proposes Know Your Swarm as an institutional accountability layer." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3644, "authors_detailed": [ { "name": "Amna Usman Chaudhry", "url": "https://openalex.org/A5135937460", "inst": "Frontier Environmental Technology (United States)" } ], "affiliations": [ "Frontier Environmental Technology (United States)" ] }, { "uid": "doi:10.2139/ssrn.6409578", "doi": "10.2139/ssrn.6409578", "title": "Death of the Dark Room: How Generative AI Broke Enterprise IT's Political Cover", "authors": [ "Krzysztof Dyki" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6409578", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of enterprise IT governance dynamics following the diffusion of generative AI into consumer and corporate settings.", "No specific model tested; paper theorizes a consumer benchmark effect where executive use of consumer AI collapses monitoring costs to near zero.", "Generative AI structurally destroys information opacity that protected enterprise IT, creating simultaneous boardroom fears of visible failure and visible success." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3645, "authors_detailed": [ { "name": "Krzysztof Dyki", "url": "https://openalex.org/A5143899989", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6899764", "doi": "10.2139/ssrn.6899764", "title": "LLM Meets Job Advertisements: Unmasking Skill Premia in the UK", "authors": [ "Sidharth Rony" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6899764", "field": "economics", "role": "instrument", "bullets": [ "UK online job advertisements from 2016 to 2022, with cross-sectional log-wage regressions using occupation and regional fixed effects and three-way clustered standard errors.", "GPT-4 zero-shot learning extracted and categorized ICT, interpersonal, and AI skills from job postings across the full sample period.", "AI skills appear in 3% of postings and carry a 9% posted-wage premium; ICT skills yield 7%; interpersonal skills show no significant premium despite 90% prevalence." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 3646, "authors_detailed": [ { "name": "Sidharth Rony", "url": "https://openalex.org/A5121991885", "inst": "Royal Holloway University of London" } ], "affiliations": [ "Royal Holloway University of London" ] }, { "uid": "doi:10.2139/ssrn.6782999", "doi": "10.2139/ssrn.6782999", "title": "The Agentic Economy: Humans, AI Agents, Robots, and the Measurable Transition toward Distributed Economic Action", "authors": [ "Davit Gondauri" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6782999", "field": "economics", "role": "object", "bullets": [ "Cross-country quantitative diagnostic using public institutional data on AI investment, adoption, industrial robot installations, and data-center electricity demand.", "No specific LLM used; paper develops an action-capacity framework linking AI agent capacity, robotic capacity, compute-energy coupling, and protocolization.", "Measurable preconditions of the agentic economy are visible in official indicators; labor-market projections indicate task reallocation rather than simple displacement." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3647, "authors_detailed": [ { "name": "Davit Gondauri", "url": "https://openalex.org/A5040902595", "inst": "Tbilisi State University" } ], "affiliations": [ "Tbilisi State University" ] }, { "uid": "doi:10.2139/ssrn.6835838", "doi": "10.2139/ssrn.6835838", "title": "Social Identity and Human-AI Task Allocation", "authors": [ "Yiting Chen", "You Shan", "Shuangyu Yang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6835838", "field": "management", "role": "object", "bullets": [ "Three experiments with representative U.S. participants allocating tasks between human workers and AI (ChatGPT and DeepSeek) with explicitly varied productivity levels.", "ChatGPT and DeepSeek served as AI task recipients; participants made incentivized allocation decisions under minimal-group and political identity manipulations.", "People incur costs to under-allocate to AI, especially DeepSeek; in-group humans receive the most tasks and out-group AI the fewest, driven by social distance." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "salience": 60, "validated": null, "n": 3648, "authors_detailed": [ { "name": "Yiting Chen", "url": "https://openalex.org/A5143880029", "inst": "Lingnan University" }, { "name": "You Shan", "url": "https://openalex.org/A5066555433", "inst": "University of Science and Technology of China" }, { "name": "Shuangyu Yang", "url": "https://openalex.org/A5102185548", "inst": "Jinan University" } ], "affiliations": [ "Lingnan University", "University of Science and Technology of China", "Jinan University" ] }, { "uid": "doi:10.2139/ssrn.6649140", "doi": "10.2139/ssrn.6649140", "title": "AI Agents as the Next Control Frontier Internal Controls, Financial Governance, and Professional Knowledge Investment in the Age of Agentic AI", "authors": [ "Norbert Nuwahereza" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6649140", "field": "accounting", "role": "object", "bullets": [ "Structured review of internal control, AI risk management, and finance transformation literature covering AI agent adoption in accounting, audit, and control environments.", "No specific model tested; paper proposes an Agent-to-Assurance Control Chain linking data quality, agent design, oversight, and stakeholder confidence.", "AI agents should be governed as emerging control infrastructure; paper introduces a Finance Professional AI Knowledge Readiness Index and AI Agent Control Maturity Model." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3649, "authors_detailed": [ { "name": "Norbert Nuwahereza", "url": "https://openalex.org/A5128324856", "inst": "Maharishi International University" } ], "affiliations": [ "Maharishi International University" ] }, { "uid": "doi:10.2139/ssrn.6490578", "doi": "10.2139/ssrn.6490578", "title": "On the Reliability Limits of LLM-Based Multi-Agent Planning", "authors": [ "Ruicheng Ao", "Siyang Gao", "David Simchi-Levi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6490578", "field": "management", "role": "method", "bullets": [ "Theoretical model of LLM-based multi-agent architecture as a finite acyclic decision network with controlled experiments on a structured problem set.", "LLMs processed shared model-context information across multiple planning stages with limited communication capacity and optional human review.", "Any delegated multi-agent network is decision-theoretically dominated by a centralized Bayes decision maker; communication loss follows expected posterior divergence." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 3650, "authors_detailed": [ { "name": "Ruicheng Ao", "url": "https://openalex.org/A5069808887", "inst": "Massachusetts Institute of Technology" }, { "name": "Siyang Gao", "url": "https://openalex.org/A5076608196", "inst": "City University of Hong Kong" }, { "name": "David Simchi-Levi", "url": "https://openalex.org/A5143872715", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "City University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6467601", "doi": "10.2139/ssrn.6467601", "title": "Negation Is Not Absence: Temporal Framing as a Binary Switch for History-Dependent Play in LLM Strategic Interaction", "authors": [ "Hsin-Chung Chen" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6467601", "field": "economics", "role": "agent", "bullets": [ "Two experiments (N=100 and N=120) with Claude Sonnet 4.6 playing iterated Prisoner's Dilemma under varied system prompt framings of temporal context.", "Claude Sonnet 4.6 played repeated Prisoner's Dilemma games; system prompts varied whether temporal context was mentioned, negated, or entirely absent.", "Temporal context mention acts as a binary switch: any mention produces deterministic history-dependent play (delta CC=0.50); complete omission yields history-independent play." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 55, "n": 3651, "authors_detailed": [ { "name": "Hsin-Chung Chen", "url": "https://openalex.org/A5143967325", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7031019", "doi": "10.2139/ssrn.7031019", "title": "Misperceiving the Frontier: Managerial Beliefs and the Allocation of AI", "authors": [ "Oliver Hauser", "Shuzhen Li", "Yilong Xu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7031019", "field": "management", "role": "object", "bullets": [ "Linked worker-manager experiment where workers complete real-effort tasks with and without generative AI and managers predict performance and make deployment decisions.", "Generative AI (unspecified) assisted workers on tasks spanning the jagged frontier; managers made incentivized willingness-to-pay deployment choices with and without feedback.", "Managers overestimate AI benefits for outside-frontier tasks; WTP is higher where AI adds no value; a salience-based intervention corrects the misperception gap." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3652, "authors_detailed": [ { "name": "Oliver Hauser", "url": "https://openalex.org/A5027074094", "inst": "University of Cambridge" }, { "name": "Shuzhen Li", "url": "https://openalex.org/A5143880546", "inst": "Utrecht University" }, { "name": "Yilong Xu", "url": "https://openalex.org/A5057552469", "inst": "Utrecht University" } ], "affiliations": [ "University of Cambridge", "Utrecht University" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6620398", "doi": "10.2139/ssrn.6620398", "title": "The Substrate Problem: Why RAG Observability in Financial Services Requires Infrastructure-Level Verification.", "authors": [ "Marvin Ohanwe" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6620398", "field": "finance", "role": "method", "bullets": [ "Analysis of RAG systems in financial services compliance with empirical evidence from Tempo's Moderato testnet, a Stripe- and Paradigm-backed institutional payments chain.", "RAG systems retrieved regulatory and compliance documents; paper identifies three infrastructure states producing retrieval failures invisible to current monitoring frameworks.", "Retrieval of authoritative but structurally complex content can produce wrong compliance outputs despite correct reasoning; proposes a three-function retrieval verification layer." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 3653, "authors_detailed": [ { "name": "Marvin Ohanwe", "url": "https://openalex.org/A5143894063", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6944359", "doi": "10.2139/ssrn.6944359", "title": "Artificial Intelligence in Accounting: Review of Implementation Challenges, Professional Risks, and Coping Strategies", "authors": [ "Kiarash Amani Javan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6944359", "field": "accounting", "role": "object", "bullets": [ "Narrative review synthesizing empirical studies, conceptual papers, and systematic reviews on AI, RPA, and generative AI adoption in accounting and auditing.", "No specific model tested; paper reviews challenges across data quality, algorithmic bias, explainability, and workforce reskilling using TAM and TOE frameworks.", "AI adoption constrained by recurring challenges in transparency, legacy integration, and liability; paper proposes a six-practice Accounting AI Change and Coping Strategy Framework." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3654, "authors_detailed": [ { "name": "Kiarash Amani Javan", "url": "https://openalex.org/A5143929198", "inst": "Islamic Azad University, Tehran" } ], "affiliations": [ "Islamic Azad University, Tehran" ] }, { "uid": "doi:10.2139/ssrn.6922858", "doi": "10.2139/ssrn.6922858", "title": "Does AI Equalize or Amplify? Evidence from Two Field Experiments in AI-powered Learning", "authors": [ "Ting Hou", "Renee Rui Chen", "Yinliang Tan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6922858", "field": "management", "role": "object", "bullets": [ "Two randomized field experiments with 13,806 and 8,868 students on an Asian digital education platform preparing for IELTS writing.", "AI coaching assistant assigned randomly; Study 2 varied AI disclosure type and feedback type in a 3x3 factorial design.", "AI improved writing scores but amplified skill gaps; higher-baseline students gained more, and effectiveness disclosure combined with mastery feedback maximized engagement." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3655, "authors_detailed": [ { "name": "Ting Hou", "url": "https://openalex.org/A5101489189", "inst": "Shanghai University of Finance and Economics" }, { "name": "Renee Rui Chen", "url": "https://openalex.org/A5070119796", "inst": "Shenzhen University" }, { "name": "Yinliang Tan", "url": "https://openalex.org/A5037983200", "inst": "China Europe International Business School" } ], "affiliations": [ "Shanghai University of Finance and Economics", "Shenzhen University", "China Europe International Business School" ] }, { "uid": "doi:10.2139/ssrn.6839298", "doi": "10.2139/ssrn.6839298", "title": "BEYOND THE SPREADSHEET: AI Tools and the Future of Small Tax Practice", "authors": [ "Suvan Sharma" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6839298", "field": "accounting", "role": "instrument", "bullets": [ "One year of AI-generated bank statements for a fictitious US small business, compared across three categorization workflows in a small-firm tax context.", "Claude and Claude Cowork categorized transactions against a manual Excel baseline, measuring processing time and output structure quality.", "Processing time fell from 2 hours 15 minutes to 1 minute 17 seconds; Cowork produced structurally superior output versus standard chat interface." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 50, "n": 3656, "authors_detailed": [ { "name": "Suvan Sharma", "url": "https://openalex.org/A5143879930", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5911464", "doi": "10.2139/ssrn.5911464", "title": "Governing AI Agents: Risk, Compliance, and Accountability in Law and Finance", "authors": [ "Jillian Bommarito", "Daniel Martin Katz", "Michael James Bommarito" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5911464", "field": "finance", "role": "object", "bullets": [ "Conceptual governance framework for agentic AI in legal and financial services, drawing on the EU AI Act, NIST, SOC 2, and professional ethics codes.", "No model deployed; paper proposes a five-layer regulatory stack scaling oversight to system autonomy, duration, and objective-setting mode.", "Human-in-the-loop architectures are necessary for fiduciary compliance; three organizational models allocate accountability via RACI matrices across the agent lifecycle." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3657, "authors_detailed": [ { "name": "Jillian Bommarito", "url": "https://openalex.org/A5064721809", "inst": "Gleason (United States)" }, { "name": "Daniel Martin Katz", "url": "https://openalex.org/A5143869699", "inst": "Chicago Kent College of Law" }, { "name": "Michael James Bommarito", "url": "https://openalex.org/A5085629068", "inst": "Stanford Medicine" } ], "affiliations": [ "Gleason (United States)", "Chicago Kent College of Law", "Stanford Medicine" ] }, { "uid": "doi:10.2139/ssrn.6908860", "doi": "10.2139/ssrn.6908860", "title": "Compute as a Hedgeable Production Input: Financial Reporting, Risk Management, and Valuation Implications of Compute Capacity Futures", "authors": [ "Rafael Minuti" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6908860", "field": "accounting", "role": "object", "bullets": [ "Analytical framework for financial reporting of compute capacity futures under IFRS, US GAAP, and Brazilian CPC standards.", "No model deployed; paper classifies compute derivatives, leases, and hedge relationships and introduces Compute Beta and Compute Cost-at-Risk metrics.", "AI firms dependent on volatile compute inputs warrant commodity-producer rather than software-style valuation; new disclosure and hedge-accounting categories are proposed." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3658, "authors_detailed": [ { "name": "Rafael Minuti", "url": "https://openalex.org/A5140844147", "inst": "Oldham Council" } ], "affiliations": [ "Oldham Council" ] }, { "uid": "doi:10.2139/ssrn.6962340", "doi": "10.2139/ssrn.6962340", "title": "The Agentic Trilemma: Extending the AIconomics Framework to the Governance of Autonomous AI Agents", "authors": [ "Lohit K Lakshman" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6962340", "field": "management", "role": "object", "bullets": [ "Conceptual extension of the AIconomics Trilemma governance framework to autonomous agentic AI systems in enterprise settings.", "No model deployed; paper introduces Autonomy Calibration, Agentic Trust Depletion Rate, and a sixth archetype (the Delegator) for recursive governance.", "Ungoverned agent fleets create quantifiable governance debt transferable in M&A; six testable propositions extend the framework to machine-speed failure propagation." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3659, "authors_detailed": [ { "name": "Lohit K Lakshman", "url": "https://openalex.org/A5137522657", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6948398", "doi": "10.2139/ssrn.6948398", "title": "From Microeconomics to AI Research: A Guide for Economists", "authors": [ "Pavel Kireyev", "Roberto Rafael Maura Rivero" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6948398", "field": "economics", "role": "object", "bullets": [ "Guide mapping microeconomic theory to AI research problems including RLHF, alignment, compute scaling, and multi-agent safety.", "No model deployed; paper demonstrates equivalences between RLHF pipeline components and discrete choice, social choice, and principal-agent models.", "Behavioral economics, mechanism design, contract theory, and game theory each address specific open AI problems; structured career paths for economists entering AI labs are outlined." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3660, "authors_detailed": [ { "name": "Pavel Kireyev", "url": "https://openalex.org/A5058621082", "inst": "London School of Economics and Political Science" }, { "name": "Roberto Rafael Maura Rivero", "url": "https://openalex.org/A5143954501", "inst": "University of Oxford" } ], "affiliations": [ "London School of Economics and Political Science", "University of Oxford" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7033499", "doi": "10.2139/ssrn.7033499", "title": "When AI Agents Pay: The Viability Bound", "authors": [ "Daniel Liebau" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7033499", "field": "economics", "role": "object", "bullets": [ "Theoretical model of autonomous AI agent viability for payment tasks, decomposing total cost into inference, verification, and settlement components.", "No model deployed; verification cost is derived from two trustless primitives and grows polynomially in the number of actions at the task's interaction order.", "Agents outperform humans only within a bounded Goldilocks zone; verification cost growth rate is invariant to verifier capability, setting a hard viability bound." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3661, "authors_detailed": [ { "name": "Daniel Liebau", "url": "https://openalex.org/A5134562130", "inst": "École Supérieure des Sciences Économiques et Commerciales" } ], "affiliations": [ "École Supérieure des Sciences Économiques et Commerciales" ] }, { "uid": "doi:10.2139/ssrn.7107918", "doi": "10.2139/ssrn.7107918", "title": "The Applicant-Repair Market: Employment Platforms, Credence Goods, and the Commercialization of Job-Seeker Uncertainty", "authors": [ "Johan van Rooyen", "Nitayapa Nandhakwang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7107918", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of employment platforms as credence-goods markets for job seekers, drawing on Darby-Karni and Dulleck-Kerschbamer frameworks.", "Argues generative AI reduces applicants' ability to infer service quality from surface fluency, mechanically raising verification costs in the repair market.", "Proposes platform revenue models tied to activity rather than durable matches leave the applicant-remediation market undisciplined by standard credence-goods constraints." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3662, "authors_detailed": [ { "name": "Johan van Rooyen", "url": "https://openalex.org/A5047562540", "inst": "Webster University" }, { "name": "Nitayapa Nandhakwang", "url": "https://openalex.org/A5135422559", "inst": "Chiang Mai University" } ], "affiliations": [ "Webster University", "Chiang Mai University" ] }, { "uid": "doi:10.2139/ssrn.7082578", "doi": "10.2139/ssrn.7082578", "title": "Online Appendix for \"Disclosure in the Shadow of Bankruptcy: From Reckoning to Reemergence\"", "authors": [ "Jason Lee", "Matthew Shaffer", "Michael Simkovic" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7082578", "field": "accounting", "role": "instrument", "bullets": [ "Robustness and validation appendix for an LLM-based distress disclosure measure across US firms filing for bankruptcy.", "LLM extracts disclosure signals from filings; appendix tests for look-ahead bias via extraction selectivity and a randomized identity-label experiment.", "LLM measure survives bias tests and aligns with going-concern opinions and debt-market reactions around bankruptcy events." ], "bullet_provenance": "ai", "validated": true, "validation_note": "extraction selectivity test and randomized identity-label experiment", "salience": 35, "models": [], "n": 3663, "authors_detailed": [ { "name": "Jason Lee", "url": "https://openalex.org/A5100657727", "inst": "John Marshall Law School" }, { "name": "Matthew Shaffer", "url": "https://openalex.org/A5015557448", "inst": "University of Virginia" }, { "name": "Michael Simkovic", "url": "https://openalex.org/A5013028867", "inst": "University of Southern California" } ], "affiliations": [ "University of Virginia", "University of Southern California", "John Marshall Law School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6909019", "doi": "10.2139/ssrn.6909019", "title": "Provider-Asymmetric Consistency in AI Brand Availability A Pre-Registered Re-Analysis: Modest between-Provider Structure in CV-CPC Recall, and the Limits of a Beyond-Presence Gate under Recognition Saturation", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6909019", "field": "management", "role": "method", "bullets": [ "Pre-registered re-analysis of 112 brands across five substrates using a six-model panel from Anthropic, OpenAI, and Google.", "Tests whether per-model brand recall underlying the CV-CPC consistency metric is systematically organized by AI provider.", "Between-provider recall structure is statistically robust but modest; the Beyond-Presence gate collapses under recognition saturation, limiting its diagnostic value." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "validated": true, "validation_note": "pre-registered permutation test against null", "salience": 40, "n": 3664, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "School of Visual Arts" } ], "affiliations": [ "School of Visual Arts" ] }, { "uid": "doi:10.2139/ssrn.6979441", "doi": "10.2139/ssrn.6979441", "title": "Anomaly-Based Trading Strategies in the Real Estate Sector. Can the Market Be Beaten?", "authors": [ "Soňa Beluská", "Radovan Vojtko" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6979441", "field": "finance", "role": "instrument", "bullets": [ "Daily data from six liquid ETFs spanning equities, fixed income, currencies, gold, and commodities over 2006-2025.", "ChatGPT and Claude assisted research, analysis, and visualization of short-term reversal trading strategies with trend filters.", "Strategy applies 200-day moving average filter and multi-day pullback trigger with volatility-adjusted sizing; robustness tested across sub-periods and parameter sweeps." ], "bullet_provenance": "ai", "models": [ "gpt", "claude" ], "open_weights": false, "validated": false, "salience": 20, "n": 3665, "authors_detailed": [ { "name": "Soňa Beluská", "url": "https://openalex.org/A5119900499", "inst": "Pan-European University" }, { "name": "Radovan Vojtko", "url": "https://openalex.org/A5143930834", "inst": "" } ], "affiliations": [ "Pan-European University" ] }, { "uid": "doi:10.2139/ssrn.6724698", "doi": "10.2139/ssrn.6724698", "title": "AI-Mediated Client Acquisition Among Independent Knowledge Workers: A Framework for Reputational Trust Calibration in Platform and Direct Markets", "authors": [ "Rowan Hayes" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6724698", "field": "management", "role": "object", "bullets": [ "Theoretical framework for independent knowledge workers navigating AI-augmented client acquisition in platform and direct markets.", "Develops Reputational Trust Calibration Model identifying signal amplification, authenticity ambiguity, and verification burden transfer as key mechanisms.", "Proposes reputation flattening reduces informational distance between high- and low-expertise providers, generating new authenticity labor demands." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3666, "authors_detailed": [ { "name": "Rowan Hayes", "url": "https://openalex.org/A5143947438", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6941578", "doi": "10.2139/ssrn.6941578", "title": "LLM-Powered Virtual Population for Demand Simulation and Pricing", "authors": [ "Chengpiao Huang", "Kaizheng Wang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6941578", "field": "management", "role": "agent", "bullets": [ "Online H&M fashion dataset with product text descriptions and images, demand simulated via a finite mixture of LLM-elicited customer personas.", "LLM produces persona-level purchase probabilities for candidate prices, aggregated through calibrated mixture weights into a full demand distribution.", "Calibrated LLM simulator achieves best overall predictive performance and supports risk-aware pricing including conditional value at risk." ], "bullet_provenance": "ai", "validated": true, "validation_note": "H&M fashion dataset demand prediction", "salience": 65, "models": [], "n": 3667, "authors_detailed": [ { "name": "Chengpiao Huang", "url": "https://openalex.org/A5111496462", "inst": "Decision Research" }, { "name": "Kaizheng Wang", "url": "https://openalex.org/A5041928690", "inst": "Linyi University" } ], "affiliations": [ "Decision Research", "Linyi University" ] }, { "uid": "doi:10.2139/ssrn.6837778", "doi": "10.2139/ssrn.6837778", "title": "The Silver Surfer Paradox", "authors": [ "Dedy Budiman" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6837778", "field": "management", "role": "object", "bullets": [ "Survey of 1,597 Indonesian respondents in March 2026 on generative AI use in consumer pre-purchase product search.", "Examined AI-first adoption rates across age and occupational categories using Technology Acceptance Model and Diffusion of Innovation theory.", "Respondents aged 55+ report highest AI-first adoption rate (24.2%) versus 11.7% for the 25-34 cohort; decision complexity proposed as explanatory mechanism." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3668, "authors_detailed": [ { "name": "Dedy Budiman", "url": "https://openalex.org/A5138505911", "inst": "Universitas Prasetiya Mulya" } ], "affiliations": [ "Universitas Prasetiya Mulya" ] }, { "uid": "doi:10.2139/ssrn.6832998", "doi": "10.2139/ssrn.6832998", "title": "Queue & AI: When Faster Tasks Slow Down the Workflow", "authors": [ "Silvia Bartolucci", "Pierpaolo Vivo" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6832998", "field": "management", "role": "object", "bullets": [ "Queueing model of AI-assisted workflows where tasks accumulate and compete for scarce human review attention.", "Formalizes the variance wedge: average task completion times fall but workflow-level delay increases when AI errors escape review as costly rework.", "AI stabilizes overloaded workflows only when human review-plus-rework attention is lower than manual completion attention, a condition stricter than faster drafts." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3669, "authors_detailed": [ { "name": "Silvia Bartolucci", "url": "https://openalex.org/A5090881985", "inst": "The London College" }, { "name": "Pierpaolo Vivo", "url": "https://openalex.org/A5029474578", "inst": "King's College - North Carolina" } ], "affiliations": [ "The London College", "King's College - North Carolina" ] }, { "uid": "doi:10.2139/ssrn.6902619", "doi": "10.2139/ssrn.6902619", "title": "Seeing the Future, Selling the Present: AI-Generated Renovation Visualizations in Housing Transactions", "authors": [ "Hefan Zheng", "Jing Wu", "Chenshuo Sun" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6902619", "field": "economics", "role": "object", "bullets": [ "Proprietary data from a leading housing platform on AI-generated renovation visualizations for shell, unfurnished, and never-occupied listings.", "Platform deploys generative AI to render plausible post-renovation interiors; effects measured on conversion, showing times, and transaction prices.", "AIRV increases online-to-offline conversion but raises pre-visit expectations, producing bargaining frictions that lower transaction prices for hard-to-imagine listings." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3670, "authors_detailed": [ { "name": "Hefan Zheng", "url": "https://openalex.org/A5001721971", "inst": "East China Normal University" }, { "name": "Jing Wu", "url": "https://openalex.org/A5084213008", "inst": "Center for Life Sciences" }, { "name": "Chenshuo Sun", "url": "https://openalex.org/A5075627636", "inst": "New York University" } ], "affiliations": [ "New York University", "East China Normal University", "Center for Life Sciences" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6652019", "doi": "10.2139/ssrn.6652019", "title": "The SEO Floor: Measuring Google Rank Distribution of AI-Cited Pages", "authors": [ "Anthony Lee" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6652019", "field": "management", "role": "object", "bullets": [ "100,411 AI citation events from ChatGPT, Perplexity, Claude, and Google AI Mode across 2,000 queries matched to 165,661 Google SERP URLs.", "Mixed-effects logistic regression estimates citation probability by Google rank tier and a pre-registered seven-feature GEO composite score.", "Top-3 Google pages are 7.82x more likely to be AI-cited; schema markup is the strongest single content-feature predictor of AI citation (OR=1.31)." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "validated": false, "salience": 50, "n": 3671, "authors_detailed": [ { "name": "Anthony Lee", "url": "https://openalex.org/A5133707558", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6710044", "doi": "10.2139/ssrn.6710044", "title": "Firm-level Climate Change Adaptation", "authors": [ "Tobias Schimanski" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6710044", "field": "finance", "role": "instrument", "bullets": [ "Over 13,500 public US firms from 2003 to 2025, measuring climate adaptation actions via LLM-based classification of corporate disclosures.", "LLM classifies firm adaptation into physical protection, adaptive operations, risk transfer, financial reserves, and risk assessment subcategories.", "Difference-in-differences around extreme weather events shows pre-event adaptation mitigates negative stock impacts; financially constrained firms show no benefit." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 3672, "authors_detailed": [ { "name": "Tobias Schimanski", "url": "https://openalex.org/A5008509425", "inst": "University of Zurich" } ], "affiliations": [ "University of Zurich" ] }, { "uid": "doi:10.2139/ssrn.6804219", "doi": "10.2139/ssrn.6804219", "title": "The System's Shadow: How AI Persona Alters Arousal, Behavior, and Workplace Performance Beyond What Users Report", "authors": [ "Leon Ciechanowski", "Anna Kovbasiuk", "Konrad Sowa", "Tamilla Triantoro", "Aleksandra Przegalinska", "Richard B. 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Frank Li", "Gene Moo Lee" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6886918", "field": "management", "role": "instrument", "bullets": [ "U.S. public firms' 10-K filings analyzed for AI strategic orientation across awareness, product, and process dimensions using long-difference estimation.", "An LLM classified business descriptions into three AI strategic orientation dimensions to construct a firm-year measure of AI strategy.", "AI strategic orientation raises operating expenses and firm value but not sales; process orientation produces delayed but larger effects than product orientation." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 3680, "authors_detailed": [ { "name": "Jaecheol Park", "url": "https://openalex.org/A5021052899", "inst": "" }, { "name": "Myunghwan Lee", "url": "https://openalex.org/A5029888627", "inst": "Chinese University of Hong Kong" }, { "name": "J. Frank Li", "url": "https://openalex.org/A5143943889", "inst": "" }, { "name": "Gene Moo Lee", "url": "https://openalex.org/A5072864189", "inst": "University of British Columbia" } ], "affiliations": [ "Chinese University of Hong Kong", "University of British Columbia" ] }, { "uid": "doi:10.2139/ssrn.6711039", "doi": "10.2139/ssrn.6711039", "title": "Generative AI Content Governance and Market Outcomes: Evidence from Food Delivery Platforms", "authors": [ "Jingwei Dai", "Beibei Li", "Rahul Telang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6711039", "field": "management", "role": "object", "bullets": [ "Panel of consumer purchases on Zomato and Swiggy in India, March 2024 to February 2025, around Zomato's August 2024 ban on AI-generated dish images.", "Difference-in-differences design compares Zomato restaurants (banned AI images) against Swiggy restaurants (AI images permitted) as control group.", "Zomato restaurants gained significant order volume and revenue; consumers tried more new restaurants on Zomato, especially versus Swiggy listings with AI-like images." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3681, "authors_detailed": [ { "name": "Jingwei Dai", "url": "https://openalex.org/A5119072330", "inst": "Carnegie Mellon University" }, { "name": "Beibei Li", "url": "https://openalex.org/A5100423728", "inst": "Carnegie Mellon University" }, { "name": "Rahul Telang", "url": "https://openalex.org/A5143978208", "inst": "Carnegie Mellon University" } ], "affiliations": [ "Carnegie Mellon University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7102398", "doi": "10.2139/ssrn.7102398", "title": "The Delegated Consumer: The Behavioral Foundations of the Shopper Schism", "authors": [ "Paul F. 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AI-skilled workforce hiring measured to test disclosure credibility.", "Internal AI-use disclosure raises three-day call returns by about 1 pp only when backed by prior AI hiring; long-short portfolio earns about 0.85% per month." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3683, "authors_detailed": [ { "name": "Cayman Seagraves", "url": "https://openalex.org/A5090973432", "inst": "Florida Atlantic University" }, { "name": "Stace Sirmans", "url": "https://openalex.org/A5000933071", "inst": "Auburn University" } ], "affiliations": [ "Florida Atlantic University", "Auburn University" ] }, { "uid": "doi:10.2139/ssrn.6761458", "doi": "10.2139/ssrn.6761458", "title": "Structured GHG Disclosure Accessibility for Listed Japanese Firms: An Engineering Pilot Using EDINET and LLM-Assisted Report Extraction", "authors": [ "Hiroyuki Kokubu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6761458", "field": "accounting", "role": "instrument", "bullets": [ "Eighty-nine major Japanese listed firms' EDINET filings and voluntary sustainability PDF reports examined for structured GHG emission data accessibility.", "An LLM extracted Scope 1/2/3 GHG values from 88 firms' sustainability reports, producing 103 report-level records of which 63 contained at least one Scope value.", "LLM-extracted values fell within plus or minus 10% of EDINET reference in four matched cases; 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results fed into a Bayesian structural VAR model.", "Communication shocks explain interbank money market movements; other market responses are mixed, reflecting bank-based financial system structure." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 60, "n": 3687, "authors_detailed": [ { "name": "Antoaneta Amza", "url": "https://openalex.org/A5118912615", "inst": "Bucharest University of Economic Studies" }, { "name": "Rahul Tak", "url": "https://openalex.org/A5119154968", "inst": "Bucharest University of Economic Studies" }, { "name": "Caliman Stefan-Daniel", "url": "https://openalex.org/A5138752505", "inst": "University of Bucharest" }, { "name": "Daniel Traian Pele", "url": "https://openalex.org/A5035471410", "inst": "Institute of Economic Forecasting" } ], "affiliations": [ "Bucharest University of Economic Studies", "University of Bucharest", "Institute of Economic Forecasting" ] }, { "uid": "doi:10.2139/ssrn.7050558", "doi": "10.2139/ssrn.7050558", "title": "Beliefs from Cues", "authors": [ "John J. 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out-of-sample test on A100 cross-generational forward.", "Obsolescence curve predicts A100 forward within one percentage point annualized; twelve issuers price in a recovery-dominated band non-monotone in credit spread." ], "bullet_provenance": "ai", "salience": 48, "models": [], "validated": null, "n": 3693, "authors_detailed": [ { "name": "Zeyu Cao", "url": "https://openalex.org/A5101137961", "inst": "Film Independent" }, { "name": "Shaosai Huang", "url": "https://openalex.org/A5102370135", "inst": "York University" } ], "affiliations": [ "Film Independent", "York University" ] }, { "uid": "doi:10.2139/ssrn.6899423", "doi": "10.2139/ssrn.6899423", "title": "Rungs Removed or Scaffolds Built? A Dynamic Theory of Autonomy, Review, and Learning in Early-Career Labor Markets", "authors": [ "Georgios Petropoulos" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6899423", "field": "economics", "role": "object", "bullets": [ "Dynamic equilibrium theory of early-career skill formation where generative AI either performs tasks autonomously or assists workers who review output.", "Theoretical model with competitive labor market derives conditions under which AI review intensity builds versus erodes long-run workforce expertise.", "Above a critical review intensity AI permanently builds expertise; below it permanently erodes it; competitive markets systematically underprovide review." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3694, "authors_detailed": [ { "name": "Georgios Petropoulos", "url": "https://openalex.org/A5090644503", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6890101", "doi": "10.2139/ssrn.6890101", "title": "Conversational ERP: Governance and Control Models for Generative AI Assistants in SAP Operations", "authors": [ "Gururaj Veershetty" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6890101", "field": "management", "role": "object", "bullets": [ "SAP RISE ecosystem with GenAI assistants integrated into core ERP business processes across enterprise operations.", "Proposes integrative socio-technical governance framework embedding organizational change management into the SAP Activate lifecycle for conversational ERP.", "Framework addresses shadow AI, process fragmentation, and governance erosion through leadership-driven communication and behavioral change mechanisms." ], "bullet_provenance": "ai", "salience": 22, "models": [], "validated": null, "n": 3695, "authors_detailed": [ { "name": "Gururaj Veershetty", "url": "https://openalex.org/A5125497409", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6730802", "doi": "10.2139/ssrn.6730802", "title": "News Behind the News: Spillover Effects of AI Crawler Blocking Among News Publishers", "authors": [ "Kanav Mor", "Rahul Telang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6730802", "field": "economics", "role": "object", "bullets": [ "Monthly panel of news publishers across the US, Europe, and rest of world, constructed from Internet Archive robots.txt snapshots, May 2022 through December 2025.", "Interrupted time series and Callaway-Sant'Anna staggered difference-in-differences designs measured publisher blocking responses to GPTBot's August 2023 release.", "GPTBot blocking spilled over to non-AI crawlers; 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Populism, Bureaucracy and Economic Performance", "authors": [ "Massimo Morelli", "Dmitrii Petrukhin", "Matia Vannoni" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6981959", "field": "economics", "role": "instrument", "bullets": [ "Cross-country synthetic control analysis and US state-year panel spanning 1929-2023 studying populism, bureaucratic independence, and economic performance.", "An LLM scored gubernatorial populism from State of the State speeches; results combined with civil-service reform indicators in heterogeneity-robust event studies.", "Populist governors reduced per-capita income only in states lacking independent civil service protections; bureaucratic independence fully offset the populism penalty." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 58, "n": 3699, "authors_detailed": [ { "name": "Massimo Morelli", "url": "https://openalex.org/A5102714359", "inst": "Bocconi University" }, { "name": "Dmitrii Petrukhin", "url": "https://openalex.org/A5119185746", "inst": "University of Cambridge" }, { "name": "Matia Vannoni", "url": "https://openalex.org/A5054718771", "inst": "King's College London" } ], "affiliations": [ "Bocconi University", "University of Cambridge", "King's College London" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6850760", "doi": "10.2139/ssrn.6850760", "title": "Who Creates and Who Captures? Generative AI, Copyright Infringement, and Optimal Policy", "authors": [ "Hongwei Kou", "Ke Rong", "Danxia Xie", "Buyuan Yang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6850760", "field": "economics", "role": "object", "bullets": [ "General equilibrium model with an upstream AI sector, an original content sector, and a derivative content sector analyzing copyright and GenAI.", "Theoretical framework examines how nonrival AI use and knowledge spillovers from training create cross-sector distortions in labor allocation.", "Labor is underallocated to original production; copyright enforcement has an inverted-U welfare effect; optimal digital taxation is asymmetric across sectors." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3700, "authors_detailed": [ { "name": "Hongwei Kou", "url": "https://openalex.org/A5106462205", "inst": "Tsinghua University" }, { "name": "Ke Rong", "url": "https://openalex.org/A5004702045", "inst": "Tsinghua University" }, { "name": "Danxia Xie", "url": "https://openalex.org/A5012535271", "inst": "Tsinghua University" }, { "name": "Buyuan Yang", "url": "https://openalex.org/A5106423254", "inst": "Central University of Finance and Economics" } ], "affiliations": [ "Tsinghua University", "Central University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.6807639", "doi": "10.2139/ssrn.6807639", "title": "Hollowing Out The Head:A Theoretical Analysis of AI-Driven Asymmetric Flattening in Labour Market Polarization", "authors": [ "Sanchali Bhattacharyya" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6807639", "field": "economics", "role": "object", "bullets": [ "Theoretical general equilibrium model integrating Acemoglu-Restrepo task-based framework with Autor's expertise automation model of labor market polarization.", "Formalizes three mechanisms through which generative AI restructures labor markets: cognitive task displacement, demand reallocation, and novel high-complexity role reinstatement.", "AI induces asymmetric wage flattening with compression at the upper-middle tier and relative insulation of low-skill manual occupations from displacement." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3701, "authors_detailed": [ { "name": "Sanchali Bhattacharyya", "url": "https://openalex.org/A5143982614", "inst": "Narsee Monjee College of Commerce and Economics" } ], "affiliations": [ "Narsee Monjee College of Commerce and Economics" ] }, { "uid": "doi:10.2139/ssrn.6796398", "doi": "10.2139/ssrn.6796398", "title": "Can AI Replace Human Counselors at Scale? A Nationwide Experiment to Reduce Teacher Shortages", "authors": [ "Nicolás Ajzenman", "Ana Teresa del Toro Mijares", "Gregory Elacqua", "Catalina Hermosilla", "Santiago Veleda" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6796398", "field": "economics", "role": "agent", "bullets": [ "Nationwide RCT in Chile with approximately 41,000 high school seniors comparing an LLM-powered chatbot to trained human counselors, delivered via WhatsApp.", "Chatbot Kai and human counselors both engaged seniors to motivate teaching careers; text analysis characterized conversational attributes and tested mechanisms.", "Among students with baseline teaching interest, the AI chatbot significantly increased education-major rankings; human counselors scored higher on empathy but AI was faster and more coherent." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "n": 3702, "authors_detailed": [ { "name": "Nicolás Ajzenman", "url": "https://openalex.org/A5079489752", "inst": "McGill University" }, { "name": "Ana Teresa del Toro Mijares", "url": "https://openalex.org/A5143974810", "inst": "" }, { "name": "Gregory Elacqua", "url": "https://openalex.org/A5118838056", "inst": "Inter-American Development Bank" }, { "name": "Catalina Hermosilla", "url": "https://openalex.org/A5143908773", "inst": "" }, { "name": "Santiago Veleda", "url": "https://openalex.org/A5143937983", "inst": "" } ], "affiliations": [ "McGill University", "Inter-American Development Bank" ] }, { "uid": "doi:10.2139/ssrn.6960598", "doi": "10.2139/ssrn.6960598", "title": "Kontablo: A Graph-Based Universal Accounting Ontology for the M2M Agentic Economy", "authors": [ "Christian Luciani" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6960598", "field": "accounting", "role": "object", "bullets": [ "Open accounting ontology mapped across 195 jurisdictions with IFRS anchor and statutory chart overlays for 60 jurisdictions, validated on a synthetic 75-entity matrix.", "Three-tier resolution strategy combines deterministic code lookups with a confidence-scored AI fallback; 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RL agent cumulative return of 158% underperformed momentum top-10 at 250% and equal-weight at 235%." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 3704, "authors_detailed": [ { "name": "Shafiya Kausar", "url": "https://openalex.org/A5136155465", "inst": "Société de Réanimation de Langue Française" } ], "affiliations": [ "Société de Réanimation de Langue Française" ] }, { "uid": "doi:10.2139/ssrn.6671358", "doi": "10.2139/ssrn.6671358", "title": "The Concentration Paradox: How Foundation Model Economics Are Reshaping Industry Value Chains", "authors": [ "Ali Sadhik Shaik" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6671358", "field": "management", "role": "object", "bullets": [ "Analysis of the AI industry value chain spanning compute, foundation models, middleware, applications, and distribution layers globally.", "Applies Porter's value chain analysis and platform economics theory to map value capture shifts across the AI stack.", "Finds infrastructure pricing power will erode as inference costs decline, forcing strategic pivots toward proprietary data and vertical integration." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3705, "authors_detailed": [ { "name": "Ali Sadhik Shaik", "url": "https://openalex.org/A5140590365", "inst": "American Society for Radiation Oncology" } ], "affiliations": [ "American Society for Radiation Oncology" ] }, { "uid": "doi:10.2139/ssrn.6821538", "doi": "10.2139/ssrn.6821538", "title": "Nhaufinance: A Domain-Adapted NLP Benchmark for African Central Bank Discourse and Sovereign Risk Analysis", "authors": [ "Takudzwa Chirindo" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6821538", "field": "finance", "role": "method", "bullets": [ "Corpus of 40 million tokens from five African central banks (Nigeria, South Africa, Kenya, Zimbabwe, Ghana) with 1,876 annotated examples.", "NhauFinance-v2 domain-adapted model performs sentiment classification, distress detection, policy event classification, and named entity recognition.", "Achieves 6x data efficiency on distress detection; 0.55 vs 0.47 macro F1 on policy event classification compared to FinBERT." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Macro F1 on policy event classification, distress detection, and NER vs FinBERT baseline", "salience": 55, "n": 3706, "authors_detailed": [ { "name": "Takudzwa Chirindo", "url": "https://openalex.org/A5143954131", "inst": "George Washington University" } ], "affiliations": [ "George Washington University" ] }, { "uid": "doi:10.2139/ssrn.6927398", "doi": "10.2139/ssrn.6927398", "title": "Governing Agentic Ai Against Algorithmic Cascades In Hospitality", "authors": [ "N.P. Gayan Nugawela" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6927398", "field": "management", "role": "object", "bullets": [ "Agent-based simulation with 10,000 iterations parameterized on audited data from a luxury five-star coastal resort.", "Tests a Governance-as-Code framework embedding cost-accounting constraints into agentic AI pricing agents via meta-prompt architecture.", "Governed agents achieved 98.4% constraint adherence vs 68.2% baseline and stabilized gross operating profit at +$18.60 vs -$12.40 per available room." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 3707, "authors_detailed": [ { "name": "Gayan Nugawela", "url": "https://openalex.org/A5134997143", "inst": "University of Wales Trinity Saint David" } ], "affiliations": [ "University of Wales Trinity Saint David" ] }, { "uid": "doi:10.2139/ssrn.6917298", "doi": "10.2139/ssrn.6917298", "title": "Media Over Intermediaries: Edutainment, Gender, and Social Change in Upper Egypt", "authors": [ "Salma Mousa", "Ahmed Ezzeldin Mohamed", "Donald P. Green" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6917298", "field": "economics", "role": "instrument", "bullets": [ "Randomized edutainment intervention in Upper Egypt with survey and interviewer-blinded conversation outcomes measured six months post-treatment.", "LLMs analyzed open-ended interview transcripts at scale to measure spontaneous salience of attitudes toward female genital mutilation.", "FGM drama increased opposition driven by corrections to health and religious misperceptions; family planning effects were weak and inconsistent." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 3708, "authors_detailed": [ { "name": "Salma Mousa", "url": "https://openalex.org/A5132838217", "inst": "" }, { "name": "Ahmed Ezzeldin Mohamed", "url": "https://openalex.org/A5138495917", "inst": "" }, { "name": "Donald P. Green", "url": "https://openalex.org/A5057733936", "inst": "Dartmouth–Hitchcock Medical Center" } ], "affiliations": [ "Dartmouth–Hitchcock Medical Center" ] }, { "uid": "doi:10.2139/ssrn.6800518", "doi": "10.2139/ssrn.6800518", "title": "The Case for an Economics of Artificial Intelligence as a Distinct Sub-discipline", "authors": [ "Anirban Ghatak" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6800518", "field": "economics", "role": "object", "bullets": [ "Survey of the AI industry's economic structure spanning chip production, model training, and inference markets circa 2024-2025.", "Argues AI economics warrants formal sub-disciplinary status based on distinct production-chain peculiarities and simultaneous factor-market strain.", "Documents compute doubling every 5.2 months for frontier models, a 280-fold inference cost decline in two years, and 90% of notable models from industry." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3709, "authors_detailed": [ { "name": "Anirban Ghatak", "url": "https://openalex.org/A5074325167", "inst": "Indian Institute of Technology Bombay" } ], "affiliations": [ "Indian Institute of Technology Bombay" ] }, { "uid": "doi:10.2139/ssrn.6810619", "doi": "10.2139/ssrn.6810619", "title": "Stimuli Augmentation for Internal and External Validity in Experiments with Latent Treatments", "authors": [ "Joohye Jeong" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6810619", "field": "economics", "role": "method", "bullets": [ "Three original survey experiments using candidate profile and media framing stimuli with rewritten counterfactual texts.", "LLMs rewrite real-world experimental texts to vary only the target treatment feature, producing matched original-counterfactual pairs.", "Augmented pairs balance confounding features for internal validity while preserving real-world variation, with diagnostics to evaluate quality." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 3710, "authors_detailed": [ { "name": "Joohye Jeong", "url": "https://openalex.org/A5126242572", "inst": "MIT University" } ], "affiliations": [ "MIT University" ] }, { "uid": "doi:10.2139/ssrn.6963298", "doi": "10.2139/ssrn.6963298", "title": "Fitness, Cost, and Return: A Multi-Construct Framework for AI Governance", "authors": [ "Duwarahan Rajendra" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6963298", "field": "management", "role": "object", "bullets": [ "Case analysis drawing on Uber's 2026 AI budget exhaustion by April and Dell's 320x token consumption surge for reasoning models.", "Introduces the ROAI ratio measuring organizational fitness improvement per total AI deployment cost across compute and governance.", "Crossover theorem shows ROAI-governed organizations outperform cost-per-token-governed ones above an autonomy threshold; prompt engineering alone fails." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3711, "authors_detailed": [ { "name": "Duwarahan Rajendra", "url": "https://openalex.org/A5056707675", "inst": "Chatham University" } ], "affiliations": [ "Chatham University" ] }, { "uid": "doi:10.2139/ssrn.6960999", "doi": "10.2139/ssrn.6960999", "title": "MacroModelClaw: An AI-Collaborative Workflow for DSGE Model Development", "authors": [ "Wenli Xu", "Kang Shi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6960999", "field": "economics", "role": "method", "bullets": [ "Three DSGE applications of increasing complexity: New Keynesian model, two-sector housing model, and partial HANK steady-state computation.", "Ten specialized Claude Code agents parse, calibrate, code, solve, and validate DSGE models from a single structured document with information barriers.", "Automates full Dynare workflow including timing-convention checking, Blanchard-Kahn error routing, and nonlinear steady-state solving across seven algorithm families." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 55, "n": 3712, "authors_detailed": [ { "name": "Wenli Xu", "url": "https://openalex.org/A5143890818", "inst": "" }, { "name": "Kang Shi", "url": "https://openalex.org/A5082438133", "inst": "Xi'an University of Science and Technology" } ], "affiliations": [ "Xi'an University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.6905218", "doi": "10.2139/ssrn.6905218", "title": "Seeing the Future: How AI-Generated Future-State Visuals Shape Matching in Two-Sided Markets", "authors": [ "Yushan Zhou", "Renyu Zhang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6905218", "field": "management", "role": "object", "bullets": [ "Staggered rollout of AI-generated renovation visuals across major Chinese cities on a large resale housing platform.", "Studies how generative AI future-state visuals affect matching efficiency, transaction prices, and post-transaction satisfaction in housing markets.", "AI visuals reduced days on market by 10-20% and raised prices by 2.7%, but buyer post-transaction satisfaction declined from expectation disconfirmation." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3713, "authors_detailed": [ { "name": "Yushan Zhou", "url": "https://openalex.org/A5101128337", "inst": "Cornell University" }, { "name": "Renyu Zhang", "url": "https://openalex.org/A5103062025", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Cornell University", "Chinese University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6738540", "doi": "10.2139/ssrn.6738540", "title": "From Knowledge Economies to Judgment Economies: Predictive Intelligence, Consequential Reasoning, and the Future of the Intelligent Enterprise", "authors": [ "Uwe Seebacher" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6738540", "field": "management", "role": "object", "bullets": [ "Integrative theoretical framework drawing on Hayek, Simon, Kahneman-Tversky, Pearl, and Arendt across organizational decision-making contexts.", "Proposes that generative AI commodifies knowledge production, shifting sustainable organizational advantage toward judgment under uncertainty.", "Argues the intelligent enterprise is distinguished by capacity to combine prediction, explanation, institutional learning, and accountable human oversight." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3714, "authors_detailed": [ { "name": "Uwe Seebacher", "url": "https://openalex.org/A5035452745", "inst": "Rochester Institute of Technology - Dubai" } ], "affiliations": [ "Rochester Institute of Technology - Dubai" ] }, { "uid": "doi:10.2139/ssrn.6597184", "doi": "10.2139/ssrn.6597184", "title": "AI Sycophancy and Decisions", "authors": [ "John J. Conlon", "Peter Schwardmann" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6597184", "field": "economics", "role": "object", "bullets": [ "Experiment with 1,500 participants across 30 decision environments spanning core economics and social science domains.", "Tests whether sycophantic LLM advice polarizes or depolarizes choices relative to participants' initial leanings across moral and strategic tasks.", "AI advice depolarizes choices on average despite measurable sycophancy; increasing sycophancy weakens but does not reverse the depolarizing effect." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3715, "authors_detailed": [ { "name": "John J. Conlon", "url": "https://openalex.org/A5086325785", "inst": "Harvard University" }, { "name": "Peter Schwardmann", "url": "https://openalex.org/A5051957289", "inst": "Ifo Institute for Economic Research" } ], "affiliations": [ "Harvard University", "Ifo Institute for Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7113178", "doi": "10.2139/ssrn.7113178", "title": "AI-Based Financial Recommender Systems: A Systematic Review of Methods, Evaluation Practices, and Open Challenges", "authors": [ "Mohamed Faid", "Wouter van Heeswijk", "Laura Spierdijk" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7113178", "field": "finance", "role": "method", "bullets": [ "Systematic review of 86 peer-reviewed studies on AI-based financial recommender systems published between 2018 and 2025 covering investment, portfolio, credit, insurance, and banking domains.", "Reviews the shift from collaborative filtering and content-based methods toward deep learning, graph neural networks, reinforcement learning, and large language model-augmented recommendation architectures.", "Most studies treat recommendations as item-level ranking with accuracy metrics on proprietary datasets, revealing a formulation gap between recommender assumptions and financial decision-support requirements." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 38, "validated": null, "n": 3716, "authors_detailed": [ { "name": "Mohamed Faid", "url": "https://openalex.org/A5104387103", "inst": "University of Twente" }, { "name": "Wouter van Heeswijk", "url": "https://openalex.org/A5043700615", "inst": "University of Twente" }, { "name": "Laura Spierdijk", "url": "https://openalex.org/A5001082204", "inst": "University of Twente" } ], "affiliations": [ "University of Twente" ] }, { "uid": "doi:10.2139/ssrn.6956798", "doi": "10.2139/ssrn.6956798", "title": "Business Insider: A Multi-agent Knowledge Graph Architecture for Corporate Control Inference and Strategic Vulnerability Analysis across Regulatory Jurisdictions", "authors": [ "Amit Vishnu Bhise" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6956798", "field": "finance", "role": "instrument", "bullets": [ "Analysis of 22 companies across three jurisdictions using live corporate registry data from India MCA, US SEC EDGAR, and EU Companies House.", "Locally deployed open-weights LLM generates narrative reports within a five-agent knowledge graph system that infers indirect corporate ownership via recursive control propagation.", "System achieves 93.8% indirect ownership inference accuracy with 200% improvement in relationship coverage over direct-only methods and 97% reduction in analyst effort." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "93.8% indirect ownership inference accuracy across 22 companies in three jurisdictions", "salience": 52, "n": 3717, "authors_detailed": [ { "name": "Amit Bhise", "url": "https://openalex.org/A5138192710", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6628979", "doi": "10.2139/ssrn.6628979", "title": "Agentic Leadership and the Limits of Scale: How Managing a Multi-Agent AI Ensemble Reveals New Theory", "authors": [ "Alfred Oldman" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6628979", "field": "management", "role": "object", "bullets": [ "Six-week operational deployment of a structured multi-agent AI sensemaking protocol during an international security crisis from late February to mid-April 2026.", "Heterogeneous ensemble of AI agents with distinct stylistic dispositions and compliance tendencies was managed through a structured Maestro production protocol.", "Identifies three documented failure modes and theorizes agentic leadership as a new human competency required for managing multi-agent AI ecosystems at scale." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3718, "authors_detailed": [ { "name": "Alfred Oldman", "url": "https://openalex.org/A5120773075", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7069478", "doi": "10.2139/ssrn.7069478", "title": "GDP and the Limits of Macroeconomic Measurement in the Age of Artificial Intelligence", "authors": [ "Elmekdad Shehab" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7069478", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis synthesizing three literatures on GDP measurement limitations, data economics, and general-purpose technology diffusion alongside recent empirical evidence on AI adoption.", "Examines how generative AI creates unrecorded consumer surplus through free digital goods and drives an investment-productivity lag analogous to earlier general-purpose technologies.", "AI widens the gap between GDP and actual economic value through two channels, with unrecorded consumer surplus from generative AI estimated at tens of billions of dollars annually." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3719, "authors_detailed": [ { "name": "Elmekdad Shehab", "url": "https://openalex.org/A5097654956", "inst": "Qatar Foundation" } ], "affiliations": [ "Qatar Foundation" ] }, { "uid": "doi:10.2139/ssrn.7011758", "doi": "10.2139/ssrn.7011758", "title": "Selling Control Without a Backstop: AI Governance Frameworks, the Uninsurability of Artificial Intelligence, and the Vendor in the Civil Liability Chain", "authors": [ "Travis Gilly" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7011758", "field": "management", "role": "object", "bullets": [ "Analysis of the US commercial general liability insurance market and AI governance vendor industry from late 2025 through 2026 based on carrier filings and policy exclusions.", "Examines how generative AI systems' stochastic outputs and open action spaces prevent governance vendors from delivering the determinacy their frameworks implicitly promise to deployers.", "Major carriers filed to exclude AI liabilities from standard corporate policies, creating an uninsured civil loss-allocation vacuum that governance vendor indemnity clauses cannot cure." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3720, "authors_detailed": [ { "name": "Travis Gilly", "url": "https://openalex.org/A5140598045", "inst": "Institute of Occupational Safety" } ], "affiliations": [ "Institute of Occupational Safety" ] }, { "uid": "doi:10.2139/ssrn.6848378", "doi": "10.2139/ssrn.6848378", "title": "The Strategic Impact of Cheaper Ad Production", "authors": [ "W. Jason Choi", "Bo Zhou" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6848378", "field": "management", "role": "object", "bullets": [ "Game-theoretic duopoly model where advertising creates awareness through both ad impression volume and creative quality that captures consumer attention conditional on exposure.", "Analytical framework examines how generative AI-driven reductions in ad creative production costs alter the breadth-versus-depth tradeoff in firm advertising strategy.", "Lower production costs cause non-monotonic ad pricing: prices first decrease as impression demand falls, then increase as higher creative quality raises willingness to pay for exposure." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3721, "authors_detailed": [ { "name": "W. Jason Choi", "url": "https://openalex.org/A5077161280", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Bo Zhou", "url": "https://openalex.org/A5061757404", "inst": "Smith Institute" } ], "affiliations": [ "Rutgers, The State University of New Jersey", "Smith Institute" ] }, { "uid": "doi:10.2139/ssrn.6520018", "doi": "10.2139/ssrn.6520018", "title": "Design-based Refinement of an LLM-powered Conversational Learning Simulation for Customer Validation in Entrepreneurship Education", "authors": [ "Joseph Benjamin Ilagan", "Jose Ramon Ilagan", "Lois Abigail To", "Gabrielle Anne Uy", "Maria Mercedes Rodrigo" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6520018", "field": "management", "role": "agent", "bullets": [ "Two classroom deployments in technology entrepreneurship courses using design-based research, with 109 matched pairs in Phase 1 and 46 matched pairs in Phase 2.", "LLM-powered conversational simulation called VentureBot lets students practice customer validation through iterative dialogue with AI-generated customer personas across two design phases.", "Phase 2 yields significant pre-to-post gains in target market understanding, market research, business model iteration, and lean canvas application competencies at p below 0.01." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 38, "n": 3722, "authors_detailed": [ { "name": "Joseph Benjamin Ilagan", "url": "https://openalex.org/A5027059979", "inst": "Ateneo de Manila University" }, { "name": "Jose Ramon Ilagan", "url": "https://openalex.org/A5029244001", "inst": "Ateneo de Manila University" }, { "name": "Lois Abigail To", "url": "https://openalex.org/A5117691595", "inst": "Ateneo de Manila University" }, { "name": "Gabrielle Anne Uy", "url": "https://openalex.org/A5130678609", "inst": "Ateneo de Manila University" }, { "name": "Maria Mercedes Rodrigo", "url": "https://openalex.org/A5143893695", "inst": "Ateneo de Manila University" } ], "affiliations": [ "Ateneo de Manila University" ] }, { "uid": "doi:10.2139/ssrn.6996098", "doi": "10.2139/ssrn.6996098", "title": "Equilibrium Analysis for Strategic Information Revelation in Data Markets", "authors": [ "Naman Agrawal" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6996098", "field": "economics", "role": "object", "bullets": [ "Theoretical model of dataset trading markets serving LLM developers under asymmetric information about data quality and seller type distribution.", "Bayesian signaling framework derives equilibrium disclosure policies; no specific LLM is deployed or tested empirically in the analysis.", "Separating equilibria exist where partial disclosure credibly signals quality; adverse selection intensifies when buyer priors are diffuse." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3723, "authors_detailed": [ { "name": "Naman Agrawal", "url": "https://openalex.org/A5141144309", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore" ] }, { "uid": "doi:10.2139/ssrn.6830981", "doi": "10.2139/ssrn.6830981", "title": "Not Even Wrong 1: AI and the Labour Market, From Frey–Osborne to ChatGPT, 2012–2026 (revised version)", "authors": [ "Luciano Floridi", "Claudio Novelli", "Jessica Morley" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6830981", "field": "economics", "role": "object", "bullets": [ "Purposive sample of 26 prominent AI labor-market forecasts published between 2012 and May 2026 by consultancies, banks, and researchers worldwide.", "No LLM deployed; study critiques forecast methodology and proposes a six-component framework distinguishing exposure, displacement, and net employment change.", "Most forecasts fail basic testability criteria; consultancy reports routinely conflate six distinct labor-market quantities and revise headline numbers without acknowledgment." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3724, "authors_detailed": [ { "name": "Luciano Floridi", "url": "https://openalex.org/A5046574356", "inst": "Data Power Decisions (United States)" }, { "name": "Claudio Novelli", "url": "https://openalex.org/A5086677960", "inst": "Data Power Decisions (United States)" }, { "name": "Jessica Morley", "url": "https://openalex.org/A5014136509", "inst": "Data Power Decisions (United States)" } ], "affiliations": [ "Data Power Decisions (United States)" ] }, { "uid": "doi:10.2139/ssrn.6167288", "doi": "10.2139/ssrn.6167288", "title": "AI and the Quantity and Quality of Creative Products: Have LLMs Boosted Creation of Valuable Books?", "authors": [ "Imke Reimers", "Joel Waldfogel" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6167288", "field": "economics", "role": "object", "bullets": [ "Universe of new book releases in the US market from 2022 to 2025, with AI-detection tools applied to classify content at scale.", "AI detection identifies AI-containing books; nested logit calibration estimates consumer surplus from the surge in AI-assisted releases.", "LLM diffusion tripled new releases; AI books show lower average quality but raised consumer surplus by seven percent in 2025 without displacing pre-LLM authors." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3725, "authors_detailed": [ { "name": "Imke Reimers", "url": "https://openalex.org/A5027460637", "inst": "Northeastern University" }, { "name": "Joel Waldfogel", "url": "https://openalex.org/A5020491437", "inst": "Northeastern University" } ], "affiliations": [ "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.6576058", "doi": "10.2139/ssrn.6576058", "title": "Optimizing Service Operations via LLM-Powered Multi-Agent Simulation", "authors": [ "Yanyuan Wang", "Xiaowei Zhang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6576058", "field": "management", "role": "agent", "bullets": [ "Sustainable supply chain and contest design applications with real behavioral data, optimized via multi-agent simulation framework.", "LLM-powered agents role-play service participants; on-trajectory learning algorithm simultaneously estimates gradients and updates design parameters on a single run.", "LLM-MAS outperforms black-box optimization and LLM-as-solver benchmarks and discovers viable contest designs overlooked by traditional approaches." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "models": [], "n": 3726, "authors_detailed": [ { "name": "Yanyuan Wang", "url": "https://openalex.org/A5039722859", "inst": "Hong Kong University of Science and Technology" }, { "name": "Xiaowei Zhang", "url": "https://openalex.org/A5143929410", "inst": "" } ], "affiliations": [ "Hong Kong University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.6970238", "doi": "10.2139/ssrn.6970238", "title": "AI, Learning Content, and the Future Supply of Managers", "authors": [ "Aroon Narayanan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6970238", "field": "management", "role": "object", "bullets": [ "Panel of approximately 9,000 US public firms tracking entry-level occupation headcount before and after the ChatGPT release in November 2022.", "No LLM deployed; study measures AI exposure via output-exposure indices and a new learning-content index constructed from O*NET task descriptors.", "AI-exposed entry occupations lost 3.6 percent of headcount post-ChatGPT and 8.1 percent at AI-capable firms; these roles supplied managers at 1.33 times the average rate." ], "bullet_provenance": "ai", "salience": 80, "models": [], "validated": null, "n": 3727, "authors_detailed": [ { "name": "Aroon Narayanan", "url": "https://openalex.org/A5038192633", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7030778", "doi": "10.2139/ssrn.7030778", "title": "AI Risk Premium: Insights from the US Corporate Bond Market", "authors": [ "Mathieu Mercadier", "Tianqi Luo", "Anh Vu", "Lu Xu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7030778", "field": "finance", "role": "object", "bullets": [ "US corporate bonds issued between 2015 and 2025, with sectoral AI intensity measured across four OECD dimensions including use, human capital, and innovation.", "No LLM deployed; difference-in-differences design compares yield spreads before and after the ChatGPT launch across sectors varying in AI intensity.", "Firms in more AI-intensive sectors face higher yield spreads; the AI-use dimension drives the largest premium, concentrated in medium- and long-term bonds." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3728, "authors_detailed": [ { "name": "Mathieu Mercadier", "url": "https://openalex.org/A5060821463", "inst": "Dublin City University" }, { "name": "Tianqi Luo", "url": "https://openalex.org/A5022977069", "inst": "Dublin City University" }, { "name": "Anh Vu", "url": "https://openalex.org/A5006714571", "inst": "Dublin City University" }, { "name": "Lu Xu", "url": "https://openalex.org/A5143926567", "inst": "Dublin City University" } ], "affiliations": [ "Dublin City University" ] }, { "uid": "doi:10.2139/ssrn.7052718", "doi": "10.2139/ssrn.7052718", "title": "Safe Transparency in AI-Governed Commerce: Faithful Explanation, Synthetic Evidence, and the Value of Governance", "authors": [ "Xuan Khanh Truong" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7052718", "field": "economics", "role": "object", "bullets": [ "Theoretical model of online platforms automating consumer-facing adjudication under legal and reputational pressure to explain AI-driven decisions.", "Stackelberg game formalizes trade-off between faithful explanation and adversarial forgery enabled by generative AI; no specific LLM deployed.", "Safe transparency falls with generative AI capability and rises with governance; consistency detection across independent evidence channels yields increasing returns." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3729, "authors_detailed": [ { "name": "Truong Xuan Khanh", "url": "https://openalex.org/A5134041770", "inst": "Apyx Medical (United States)" } ], "affiliations": [ "Apyx Medical (United States)" ] }, { "uid": "doi:10.2139/ssrn.7049918", "doi": "10.2139/ssrn.7049918", "title": "No Spoilers: A Contamination-Free LLM for Brand Perception Analysis", "authors": [ "Peiyao Li", "Zsolt Katona" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7049918", "field": "management", "role": "instrument", "bullets": [ "Over 60 million tweets from 2016 to 2022 covering brands, sports teams, and actors on the US Twitter platform.", "Custom temporally bounded sentence encoder built via Data-Restricted Incremental Pretraining and Projection removes lookahead bias and version drift from brand measurement.", "Brand perception measures respond to real-world events, predict future profit-margin growth, and capture short-lived shifts following marketing campaigns." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 62, "n": 3730, "authors_detailed": [ { "name": "Peiyao Li", "url": "https://openalex.org/A5143974065", "inst": "University of California, Berkeley" }, { "name": "Zsolt Katona", "url": "https://openalex.org/A5062120738", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of California, Berkeley" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7052338", "doi": "10.2139/ssrn.7052338", "title": "More Than Words: Visual Sentiment in Social Media Videos and Stock Returns", "authors": [ "Fuwei Jiang", "Fujing Jin", "Tian Ma" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7052338", "field": "finance", "role": "instrument", "bullets": [ "Daily short videos posted by influencers on Douyin (Chinese TikTok) linked to contemporaneous and future Chinese stock market returns.", "LLM framework extracts visual sentiment from facial expressions and textual sentiment from captions to construct daily market-level sentiment indices.", "Visual sentiment index predicts future market returns with initial positive response and partial reversal; predictive power is distinct from and attenuated by textual sentiment." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "models": [], "n": 3731, "authors_detailed": [ { "name": "Fuwei Jiang", "url": "https://openalex.org/A5076770079", "inst": "Xiamen University" }, { "name": "Fujing Jin", "url": "https://openalex.org/A5032058831", "inst": "Beijing Jiaotong University" }, { "name": "Tian Ma", "url": "https://openalex.org/A5078767804", "inst": "Minzu University of China" } ], "affiliations": [ "Xiamen University", "Beijing Jiaotong University", "Minzu University of China" ] }, { "uid": "doi:10.2139/ssrn.6869622", "doi": "10.2139/ssrn.6869622", "title": "AI Governance for Commercial Underwriting: A Case-File Model for Auditable Decisions", "authors": [ "Finnlay DeCoster-Ryan" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6869622", "field": "finance", "role": "object", "bullets": [ "UK commercial insurance underwriting workflows incorporating AI copilot tools, referenced against FCA 2026 supervisory priorities on AI in underwriting.", "No LLM deployed; paper defines a per-submission case-file governance model separating AI recommendations from human underwriter decisions with audit trails.", "Case-file model with integrity-checked exports closes governance gaps identified in UK supervisory surveys reporting near-universal but poorly documented insurer AI use." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3732, "authors_detailed": [ { "name": "Finnlay DeCoster-Ryan", "url": "https://openalex.org/A5143977522", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6475599", "doi": "10.2139/ssrn.6475599", "title": "Reading the Fed: Central Bank Text as a Forecasting Signal", "authors": [ "Eleni Kalamara" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6475599", "field": "economics", "role": "instrument", "bullets": [ "Six U.S. macroeconomic targets including GDP, CPI, unemployment, and the yield spread, evaluated out-of-sample over 2020Q1-2024Q4.", "Claude (Anthropic API) extracts five-dimensional sentiment scores from FOMC minutes and Beige Books, entered as exogenous regressors in Bayesian VAR and AR models.", "LLM-augmented AR beats plain AR in 11 of 12 variable-horizon combinations, with largest gains during the 2021-2023 inflation surge." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "out-of-sample forecasting against realized macro outcomes across six targets", "salience": 72, "n": 3733, "authors_detailed": [ { "name": "Eleni Kalamara", "url": "https://openalex.org/A5070146233", "inst": "King's College London" } ], "affiliations": [ "King's College London" ] }, { "uid": "doi:10.2139/ssrn.7013339", "doi": "10.2139/ssrn.7013339", "title": "The General Framework of AI Economics: Computation, Data, and the Limits of Formalized Intelligence", "authors": [ "Danxia Xie", "Zehao Zhou" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7013339", "field": "economics", "role": "object", "bullets": [ "Theoretical framework applying computability theory and thermodynamics to AI cost structure, scaling laws, and token markets.", "No specific LLM deployed; paper models foundation models as six-factor production systems and derives natural monopoly properties from training-inference cost migration.", "Token value anchors to downstream industrial productivity gains rather than production costs, creating discontinuous price gaps between frontier and commodity tokens." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3734, "authors_detailed": [ { "name": "Danxia Xie", "url": "https://openalex.org/A5012535271", "inst": "Tsinghua University" }, { "name": "Zehao Zhou", "url": "https://openalex.org/A5143963150", "inst": "" } ], "affiliations": [ "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.6863020", "doi": "10.2139/ssrn.6863020", "title": "Jobscaping™: Reclaiming Judgement in AI-Shaped Work", "authors": [ "Carolyn Shepherd" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6863020", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of how AI adoption compresses developmental judgment pathways across organizational roles and career stages.", "No specific model tested; paper theorizes that AI reduces decision-exposure frequency and quality, producing hidden capability attrition beneath apparent productivity.", "Employees appear operationally productive while developing thinner, more fragile judgment; proposes Jobscaping framework to preserve human judgment exposure." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3735, "authors_detailed": [ { "name": "Carolyn Shepherd", "url": "https://openalex.org/A5143883796", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6861180", "doi": "10.2139/ssrn.6861180", "title": "Synthetic Adversary Modeling via In-Silico Transaction Experimentation: A Multimodal Foundation-Model Framework with Source-Dropout and Universal-Consumer Baselines", "authors": [ "Dileep Varma Virodhula" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6861180", "field": "finance", "role": "method", "bullets": [ "ULB credit-card fraud dataset and a controlled hidden-oracle surrogate; 29.6M-parameter multimodal transaction foundation model with source-dropout.", "Custom foundation model generates synthetic adversarial transactions via stochastic source dropout and divergence-graded selection over a 5-D outcome bottleneck.", "Decision-function ensembling with CTGAN achieves highest mean AUPRC (0.563); pseudo-multimodal variant yields +12.5% relative AUROC with strictly positive 95% bootstrap CI." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "AUPRC and AUROC on ULB credit-card dataset", "salience": 40, "n": 3736, "authors_detailed": [ { "name": "DILEEP VARMA VIRODHULA", "url": "https://openalex.org/A5118125343", "inst": "Visa (United Kingdom)" } ], "affiliations": [ "Visa (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.6716098", "doi": "10.2139/ssrn.6716098", "title": "Agentic AI in Management Control Systems: A Decision Architecture for Governing AI Agents", "authors": [ "Andreas Seufert" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6716098", "field": "management", "role": "object", "bullets": [ "Conceptual synthesis developing the Agentic Decision Architecture framework for governing AI agents at the management-control decision point.", "No specific model tested; paper specifies decision rights, autonomy boundaries, escalation mechanisms, and human oversight configurations for agentic AI in control systems.", "Proposes the Control Paradox: more human approval may produce less actual control when approval capacity is spread indiscriminately across decision steps." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3737, "authors_detailed": [ { "name": "Andreas Seufert", "url": "https://openalex.org/A5143943190", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7194358", "doi": "10.2139/ssrn.7194358", "title": "After the Flood: Cheap Production, Costly Selection, and the Future of the Commons", "authors": [ "Davidson Heath", "Nathan Seegert", "Robert Wuebker" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7194358", "field": "management", "role": "object", "bullets": [ "792,000 pull requests to 1,145 open-source software repositories, spanning the pre- and post-generative-AI period.", "No specific model used as instrument; paper studies how generative AI availability changes evaluation and acceptance of knowledge-work contributions.", "Submissions tripled post-GenAI; acceptance fell from 53% to 27% for first-time contributors while remaining unchanged for prior contributors; prior ties predict acceptance." ], "bullet_provenance": "ai", "salience": 68, "models": [], "validated": null, "n": 3738, "authors_detailed": [ { "name": "Davidson Heath", "url": "https://openalex.org/A5034464313", "inst": "University of Utah" }, { "name": "Nathan Seegert", "url": "https://openalex.org/A5143789820", "inst": "University of Utah" }, { "name": "Robert Wuebker", "url": "https://openalex.org/A5082636012", "inst": "University of Utah" } ], "affiliations": [ "University of Utah" ] }, { "uid": "doi:10.2139/ssrn.6630812", "doi": "10.2139/ssrn.6630812", "title": "Validated Corporate Narratives and Bank-Affiliated Investment: A Large-Language-Model Approach", "authors": [ "Yuhao Xie", "Bowen Xue", "Tao Yi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6630812", "field": "finance", "role": "instrument", "bullets": [ "592 listed Chinese technology firms, 30,169 firm-month observations (576 firms and 18,230 obs for return tests), 2022-2024.", "GPT-5.1 extracts business claims from management discussion and investor-relations materials and labels claim-evidence pairs as support, conflict, or irrelevant against later announcements.", "Long-short strategy combining validated narrative signal with a downside-risk gate delivers annualized 8.93% return in bank-invested firms after trading costs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "claim-evidence labels verified against official announcements, procurement awards, and permits", "salience": 75, "n": 3739, "authors_detailed": [ { "name": "Yuhao Xie", "url": "https://openalex.org/A5143968488", "inst": "" }, { "name": "Bowen Xue", "url": "https://openalex.org/A5140737480", "inst": "University of Chinese Academy of Sciences" }, { "name": "Tao Yi", "url": "https://openalex.org/A5100681725", "inst": "North University of China" } ], "affiliations": [ "University of Chinese Academy of Sciences", "North University of China" ] }, { "uid": "doi:10.2139/ssrn.7013578", "doi": "10.2139/ssrn.7013578", "title": "Delegation, Not Automation, Is the Real Economic Multiplier of Generative AI", "authors": [ "Lucas Wall" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7013578", "field": "economics", "role": "object", "bullets": [ "Theoretical framework grounded in task-based labor economics, transaction cost theory, and comparative advantage applied to generative AI.", "No specific model deployed; paper introduces delegation as a distinct economic category defined by judgment-boundedness, separate from automation.", "Delegation expands markets and increases labor demand; automation-centric models systematically exclude the market-expansion effects of AI-enabled delegation." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3740, "authors_detailed": [ { "name": "Lucas Wall", "url": "https://openalex.org/A5120187348", "inst": "University of America" } ], "affiliations": [ "University of America" ] }, { "uid": "doi:10.2139/ssrn.7024799", "doi": "10.2139/ssrn.7024799", "title": "A New Look at AI's Impact on Jobs: Firm-Level AI Spending and Workforce Adjustment", "authors": [ "Ara Kharazian", "Lisa Simon", "Ryan Stevens" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7024799", "field": "economics", "role": "object", "bullets": [ "21,559 U.S. firms with observed AI spending from Ramp card and bill pay data linked to Revelio Labs workforce records.", "No specific model used as instrument; paper measures firm-level generative AI spending intensity and links it to employment changes across roles.", "High-intensity AI adopters grow employment by 10% post-adoption with entry-level headcount rising 12%; low-intensity adopters show no significant change." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3741, "authors_detailed": [ { "name": "Ara Kharazian", "url": "https://openalex.org/A5143892737", "inst": "New York City Police Department" }, { "name": "Lisa Simon", "url": "https://openalex.org/A5128800384", "inst": "Marvell (Israel)" }, { "name": "Ryan Stevens", "url": "https://openalex.org/A5124956183", "inst": "Ramp" } ], "affiliations": [ "New York City Police Department", "Marvell (Israel)", "Ramp" ] }, { "uid": "doi:10.2139/ssrn.6814961", "doi": "10.2139/ssrn.6814961", "title": "Do AI Assistants Reinforce Investor Beliefs? Evidence from Human and AI Replies", "authors": [ "Swaminathan Balasubramaniam", "Jorge Sabat", "Soumyajit Ray" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6814961", "field": "finance", "role": "agent", "bullets": [ "Reddit r/wallstreetbets investor-authored posts paired with counterfactual AI-generated replies and actual human top-level replies, comparing sentiment across multiple conditions and specifications.", "An unnamed instruction-tuned user-facing model generated replies to investor posts; a matched base model without post-training isolated the effect of the conversational post-training layer.", "AI replies were systematically more bullish than human replies; instruction tuning amplified bullishness beyond base model levels; professional-investor prompting narrowed but did not eliminate the gap." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 3742, "authors_detailed": [ { "name": "Swaminathan Balasubramaniam", "url": "https://openalex.org/A5123968612", "inst": "NEOMA Business School" }, { "name": "Jorge Sabat", "url": "https://openalex.org/A5005411096", "inst": "Universidad Andrés Bello" }, { "name": "Soumyajit Ray", "url": "https://openalex.org/A5143917769", "inst": "International Food Policy Research Institute, Uganda" } ], "affiliations": [ "NEOMA Business School", "Universidad Andrés Bello", "International Food Policy Research Institute, Uganda" ] }, { "uid": "doi:10.2139/ssrn.6910018", "doi": "10.2139/ssrn.6910018", "title": "Capital Efficiency in U.S. AI Compute Infrastructure: A Strategic Determinant of National Competitiveness", "authors": [ "Benedict Amissah-Ocran" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6910018", "field": "economics", "role": "object", "bullets": [ "U.S. hyperscaler capital expenditure projected at $725 billion in 2026, analyzing compute capability per dollar across the five largest cloud infrastructure providers.", "No language model deployed; paper develops an analytical identity decomposing effective compute into capacity, fleet utilization, model FLOPs utilization, and algorithmic efficiency.", "Raising fleet utilization from 20% to 35% frees over 40% of required capital; concavity of scaling laws compresses large compute gaps into smaller capability differences." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3743, "authors_detailed": [ { "name": "Benedict Amissah-Ocran", "url": "https://openalex.org/A5138386243", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6648398", "doi": "10.2139/ssrn.6648398", "title": "Occupations Hardest for AI to Fully Replace in Developing and Lower-Income Countries", "authors": [ "Kawshik Kumar Paul" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6648398", "field": "economics", "role": "object", "bullets": [ "Developing and lower-income countries examined through task-based labor economics, focusing on occupations where AI substitution faces deployment barriers beyond model capability alone.", "No language model deployed; paper proposes a task-systems framework assessing perception, actuation, workflow integration, governance, and total cost of ownership as prerequisites for full substitution.", "Frontline care, teaching, skilled repair, smallholder agriculture, and local intermediation resist full AI replacement; routine clerical and screen-based rule-following roles remain more exposed." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3744, "authors_detailed": [ { "name": "Kawshik Kumar Paul", "url": "https://openalex.org/A5122279397", "inst": "Bangladesh University of Engineering and Technology" } ], "affiliations": [ "Bangladesh University of Engineering and Technology" ] }, { "uid": "doi:10.2139/ssrn.6555178", "doi": "10.2139/ssrn.6555178", "title": "Can AI Models Be Persuaded To Fit Your Organizational Culture? When Prompting and Fine-Tuning Work, When Both Backfire, and When Only Human Approaches Will Do", "authors": [ "Geoff Gibbins" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6555178", "field": "management", "role": "method", "bullets": [ "Six organizations tested across 18 cultural dimensions using two blinded frontier models, totaling 3,888 model calls and 7,776 judge-scoring calls under six prompting conditions.", "System-prompt alignment and DPO fine-tuning tested on blinded frontier models to close measured culture gaps; a three-tier dimensional accessibility taxonomy was derived from results.", "Prompting moved a median of 3.0 of 18 dimensions per organization with 164 cross-dimensional interference events; RLHF-protected dimensions resisted both prompting and fine-tuning." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 3745, "authors_detailed": [ { "name": "Geoff Gibbins", "url": "https://openalex.org/A5143983812", "inst": "Private Machines (United States)" } ], "affiliations": [ "Private Machines (United States)" ] }, { "uid": "doi:10.2139/ssrn.6966378", "doi": "10.2139/ssrn.6966378", "title": "Exposed but Not Adopted: A Codifiability Index for Generative AI Adoption", "authors": [ "Özgenur Korlu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6966378", "field": "economics", "role": "object", "bullets": [ "100-task pilot sample from O*NET merged with public AI-usage and task-level exposure data, scoring codifiability across four knowledge-structure dimensions.", "No language model deployed; paper constructs a task-level Codifiability Index measuring symbolic representability, physical causality dependence, somatic knowledge requirement, and coordination intensity.", "Codifiability index predicts observed AI usage on the extensive margin and captures variation distinct from capability-based exposure; unequal codification capacity concentrates adoption in document-rich firms." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3746, "authors_detailed": [ { "name": "Özgenur Korlu", "url": "https://openalex.org/A5143937881", "inst": "Istanbul Bilgi University" } ], "affiliations": [ "Istanbul Bilgi University" ] }, { "uid": "doi:10.2139/ssrn.6571286", "doi": "10.2139/ssrn.6571286", "title": "Beyond the Authorization Horizon Institute for Process-Centered Accountability", "authors": [ "Vicente Hinojosa" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6571286", "field": "finance", "role": "object", "bullets": [ "Agentic finance protocols from Google, Stripe, Coinbase, and Mastercard analyzed for structural accountability gaps in autonomous AI-initiated financial transactions.", "No language model deployed; paper develops Process-Centered Accountability framework introducing Authorization Horizon, Passive Principal, trace attenuation, and Orphaned Mandate concepts for agent governance.", "Existing agentic payment protocols achieve authorization verification but structurally lack accountability linking human intent to economic outcomes; mandate coherence degrades across agent hops independently of fraud." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3747, "authors_detailed": [ { "name": "Vicente Hinojosa", "url": "https://openalex.org/A5130810235", "inst": "Independant" } ], "affiliations": [ "Independant" ] }, { "uid": "doi:10.2139/ssrn.7117078", "doi": "10.2139/ssrn.7117078", "title": "Architectural Principles for Failure Traceability in Knowledge Generating AI Systems in Finance", "authors": [ "Jonas Vogt" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7117078", "field": "finance", "role": "object", "bullets": [ "Enterprise AI systems in financial institutions examined using fundamental equity analysis as a structured demonstration scenario for epistemic failure attribution across processing stages.", "No specific language model tested; paper proposes architecture separating deterministic data processing from language-model interpretation to enable localized failure attribution within governance frameworks.", "Explicit architectural separation maps preprocessing, retrieval, and generation risks to identifiable pipeline stages, enabling integration with existing model risk management and governance requirements." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3748, "authors_detailed": [ { "name": "Jonas Vogt", "url": "https://openalex.org/A5047605006", "inst": "University of Mannheim" } ], "affiliations": [ "University of Mannheim" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7029058", "doi": "10.2139/ssrn.7029058", "title": "The Benchmark Ceiling: Human Judgment, Evaluation Scarcity, and the Political Economy of AI Capability Measurement", "authors": [ "Mark Esposito", "Liu Zhang" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7029058", "field": "economics", "role": "object", "bullets": [ "Frontier AI benchmark ecosystem analyzed using platform data from micro1 covering over one thousand credentialed evaluation professionals across difficulty tiers and specializations.", "No language model deployed; paper develops formal model of benchmark signal depreciation and documents the scarcity premium for high-judgment evaluation labor using empirical platform data.", "Valid discrimination signal concentrates in hard-tail benchmark items; replacement cost rises convexly with frontier capability; private benchmark producers underinvest in validity relative to the social optimum." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3749, "authors_detailed": [ { "name": "Mark Esposito", "url": "https://openalex.org/A5071878669", "inst": "Northeastern University" }, { "name": "Liu Zhang", "url": "https://openalex.org/A5127970243", "inst": "Jilin University" } ], "affiliations": [ "Northeastern University", "Jilin University" ] }, { "uid": "doi:10.2139/ssrn.6518458", "doi": "10.2139/ssrn.6518458", "title": "Policy Provenance as a Prerequisite for Responsible Agentic AI", "authors": [ "Ravi Kumar Kappagantu" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6518458", "field": "finance", "role": "object", "bullets": [ "Regulated enterprise banking systems where autonomous AI agents make governed decisions, execute transactions, and manage portfolios on behalf of human users under delegated authority.", "No specific language model tested; paper proposes Governed Knowledge Source architecture with a Policy Provenance Registry providing temporal retrieval semantics for agent compliance auditing.", "Current identity and access management architectures lack the policy chain governing agent authorization; a four-dimensional provenance record closes the structural accountability gap for delegated agents." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3750, "authors_detailed": [ { "name": "Ravi Kumar Kappagantu", "url": "https://openalex.org/A5124161523", "inst": "Lloyd's" } ], "affiliations": [ "Lloyd's" ] }, { "uid": "doi:10.2139/ssrn.6877799", "doi": "10.2139/ssrn.6877799", "title": "Beyond Technological Convergence: ESG Discourse Dominance and Channel-Conditional Emotional Framing in Mega-Capitalized Corporate Reports (2024-2025)", "authors": [ "Alikhan Imanberdiyev" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6877799", "field": "accounting", "role": "instrument", "bullets": [ "Quantitative content analysis of 290 corporate documents from mega-capitalized publicly traded firms with market capitalization exceeding $100 billion, published during 2024-2025 across report types.", "TextBlob automated sentiment analysis with word-boundary regex matching and NLTK tokenization measured AI, ESG, supply chain, and shareholder narrative frequencies and emotional valence.", "ESG language dominated corporate discourse at 1.55 mentions per thousand words versus 1.19 for technology; ESG reports were significantly more positive than financial filings (Cohen's d = 0.82)." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 50, "n": 3751, "authors_detailed": [ { "name": "Alikhan Imanberdiyev", "url": "https://openalex.org/A5143894443", "inst": "Almaty Management University" } ], "affiliations": [ "Almaty Management University" ] }, { "uid": "doi:10.2139/ssrn.6961459", "doi": "10.2139/ssrn.6961459", "title": "Did Fundamentals Justify the AI Repricing of Enterprise Software/ (SaaS Valuations)? Evidence from 2023-2026", "authors": [ "Abhishek Sehgal" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6961459", "field": "finance", "role": "object", "bullets": [ "Sample of 335 US enterprise-software firms with usable 2023 and 2026 financial-statement endpoints, after excluding 12 payments/fintech firms.", "Reverse DCF and difference-in-differences analysis assessed whether post-2022 AI-disruption narrative drove SaaS repricing beyond what fundamentals warranted.", "AI-native firms priced for 43.6% implied growth versus 25.5% realized; exposed workflow incumbents underpriced at 8.6% implied versus 13.8% realized growth." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3752, "authors_detailed": [ { "name": "Abhishek Sehgal", "url": "https://openalex.org/A5020085415", "inst": "Fidelity Investments (United States)" } ], "affiliations": [ "Fidelity Investments (United States)" ] }, { "uid": "doi:10.2139/ssrn.6944801", "doi": "10.2139/ssrn.6944801", "title": "Adversarial Consensus Verification for Reliable LLM agents in Financial Forensics: The Pave Interchange Benchmark", "authors": [ "Zacchaeus Bolaji", "Rapheal Obodugo", "John Hella" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6944801", "field": "finance", "role": "method", "bullets": [ "Synthetic dataset of 50,000 transaction records modeled after payment-processor settlement data, 24 forensic tasks across four complexity levels.", "Three-role multi-agent system (Auditor, Adversarial Verifier, Consensus Judge) tested against eight single-agent LLM baselines on multi-file reconciliation and error detection.", "Avae 2.0 achieved 100% accuracy across all four task levels; single-agent baselines fell below 55% on Level 3 forensic discovery tasks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "Pave Interchange Fee Benchmark ground truth across 24 tasks", "salience": 60, "n": 3753, "authors_detailed": [ { "name": "Zacchaeus Bolaji", "url": "https://openalex.org/A5143888034", "inst": "Delaware Department of Natural Resources and Environmental Control" }, { "name": "Rapheal Obodugo", "url": "https://openalex.org/A5143975473", "inst": "Delaware Department of Natural Resources and Environmental Control" }, { "name": "John Hella", "url": "https://openalex.org/A5143876727", "inst": "Delaware Department of Natural Resources and Environmental Control" } ], "affiliations": [ "Delaware Department of Natural Resources and Environmental Control" ] }, { "uid": "doi:10.2139/ssrn.6752078", "doi": "10.2139/ssrn.6752078", "title": "Valuation and Accounting Issues in the AI Ecosystem", "authors": [ "Andreas Haaker" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6752078", "field": "accounting", "role": "object", "bullets": [ "AI ecosystem firms including OpenAI, Nvidia, and related technology companies during the post-2022 AI investment boom period.", "Critical analysis of capital-market narratives, accounting practices, and free-cash-flow generation across AI-sector firms without deploying an LLM.", "AI firms extend depreciation periods, emphasize adjusted profits excluding material costs, and shift debt off-balance-sheet, creating a pattern of anticipatory accounting." ], "bullet_provenance": "ai", "salience": 68, "models": [], "validated": null, "n": 3754, "authors_detailed": [ { "name": "Andreas Haaker", "url": "https://openalex.org/A5029039264", "inst": "Joyson Safety Systems (Germany)" } ], "affiliations": [ "Joyson Safety Systems (Germany)" ] }, { "uid": "doi:10.2139/ssrn.6883344", "doi": "10.2139/ssrn.6883344", "title": "Generative AI-Style Analyst Writing, Narrative Convergence, and Market Response", "authors": [ "Hojun Kim" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6883344", "field": "finance", "role": "object", "bullets": [ "93,055 Korean sell-side analyst reports issued between 2015 and 2025, compared within same-firm-event clusters by analyst AI-writing exposure.", "Measured semantic similarity and structural convergence of reports when covering analysts had higher prior exposure to generative-AI-style writing.", "High-exposure report events followed by higher abnormal trading value and greater intraday range volatility, especially for high-coverage, low-liquidity stocks." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3755, "authors_detailed": [ { "name": "Hojun Kim", "url": "https://openalex.org/A5136580730", "inst": "Seoul National University" } ], "affiliations": [ "Seoul National University" ] }, { "uid": "doi:10.2139/ssrn.6754041", "doi": "10.2139/ssrn.6754041", "title": "Empirically Grounded Finite Horizon Multipliers for Privately Held Business Appraisal", "authors": [ "Michael Sack Elmaleh" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6754041", "field": "finance", "role": "instrument", "bullets": [ "US privately held firms across seven sectors, four revenue bands, and sixteen attained ages, using Census Bureau Business Dynamics Statistics.", "Claude (Anthropic) computed survival-based valuation multiplier tables replacing the Gordon Growth Model perpetuity assumption across nine hurdle rates.", "Gordon Growth Model systematically overstates terminal values; empirical survival-data multipliers provide grounded replacement for mature-firm appraisal." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 50, "n": 3756, "authors_detailed": [ { "name": "Michael Sack Elmaleh", "url": "https://openalex.org/A5019476093", "inst": "Clinical Research of South Florida" } ], "affiliations": [ "Clinical Research of South Florida" ] }, { "uid": "doi:10.2139/ssrn.6962520", "doi": "10.2139/ssrn.6962520", "title": "Generative AI Exposure and Within-Occupation Wage Dispersion", "authors": [ "Jing Li", "Zhijie Lin" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6962520", "field": "economics", "role": "object", "bullets": [ "US occupation-year panel combining O*NET task measures with BLS/OEWS wage percentiles from 2018 to 2024, supplemented by Pew survey and Ford case data.", "Difference-in-differences design comparing pre-period 2018-2019 with post-period 2023-2024 on occupation-level GenAI exposure and vertical task-rung intensity.", "Higher GenAI exposure predicts larger within-occupation wage dispersion increases, concentrated in occupations mixing codifiable lower-rung and judgment-intensive upper-rung tasks." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 3757, "authors_detailed": [ { "name": "Jing Li", "url": "https://openalex.org/A5100336900", "inst": "NEOMA Business School" }, { "name": "Zhijie Lin", "url": "https://openalex.org/A5005931678", "inst": "Beijing Haidian Hospital" } ], "affiliations": [ "NEOMA Business School" ] }, { "uid": "doi:10.2139/ssrn.6795798", "doi": "10.2139/ssrn.6795798", "title": "Is AI Talk Cheap? Evidence from Earnings Conference Calls", "authors": [ "Danial Hemmings", "Ayan Orujov" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6795798", "field": "finance", "role": "object", "bullets": [ "229,711 earnings-call transcripts from global public firms over 2007-2025, exploiting ChatGPT's November 2022 release as a discrete salience shift.", "NLP measures of AI mention frequency, local AI risk, and AI sentiment constructed from earnings-call Q&A sections to test market pricing of AI disclosure.", "Pre-ChatGPT one-SD AI-mention increase associated with 9bp higher abnormal returns; post-release premium attenuated while AI sentiment and risk language became newly priced." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3758, "authors_detailed": [ { "name": "Danial Hemmings", "url": "https://openalex.org/A5143915866", "inst": "Bangor University" }, { "name": "Ayan Orujov", "url": "https://openalex.org/A5143966344", "inst": "University of Liverpool" } ], "affiliations": [ "Bangor University", "University of Liverpool" ] }, { "uid": "doi:10.2139/ssrn.6827980", "doi": "10.2139/ssrn.6827980", "title": "The Epistemic Arms Race in AI-Driven Supply Chains: When Coordination Collapses Under Strategic Signaling", "authors": [ "Ismael Lopez Hernandez" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6827980", "field": "management", "role": "agent", "bullets": [ "Theoretical model extending Hurwicz impossibility to learning agents, with a calibrated multi-agent simulation of supply chain coordination using LLMs from four providers.", "LLM agents from four providers played supply chain coordination games and shifted from truthful signaling to strategic signal distortion as incentive constraints loosened.", "AVC impossibility theorem proved: autonomous AI supply chain agents cannot jointly sustain optimization autonomy, signal veracity, and decentralized coordination beyond a calculable threshold." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "open_other" ], "open_weights": false, "validated": false, "salience": 50, "n": 3759, "authors_detailed": [ { "name": "Ismael Lopez Hernandez", "url": "https://openalex.org/A5143948334", "inst": "Universidad Anáhuac" } ], "affiliations": [ "Universidad Anáhuac" ] }, { "uid": "doi:10.2139/ssrn.6963279", "doi": "10.2139/ssrn.6963279", "title": "The Conditional Productivity Effect of Generative AI: A Systematic Review and Descriptive Meta-analytic Synthesis of Knowledge-worker Studies (2022-2026)", "authors": [ "Valeriana Colon" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6963279", "field": "management", "role": "object", "bullets": [ "Systematic review with descriptive meta-analytic synthesis of 11 empirical studies on GenAI and knowledge-worker productivity from January 2022 through June 2026.", "Review synthesized studies where GenAI tools were deployed across professional writing, customer support, software development, consulting, public-sector work, and innovation tasks measuring output and speed.", "GenAI yields consistent productivity gains for structured, reviewable tasks; mixed or negative effects appear when work requires specialized context or mature codebase knowledge." ], "bullet_provenance": "ai", "salience": 68, "models": [], "validated": null, "n": 3760, "authors_detailed": [ { "name": "Valeriana Colón", "url": "https://openalex.org/A5042819937", "inst": "Virginia Commonwealth University" } ], "affiliations": [ "Virginia Commonwealth University" ] }, { "uid": "doi:10.2139/ssrn.6809120", "doi": "10.2139/ssrn.6809120", "title": "Austria's AI Workforce 2026", "authors": [ "Andreas Schumacher", "Christian Schumacher", "Can Tihanyi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6809120", "field": "economics", "role": "object", "bullets": [ "2.86 million individual-level employment records covering 1.23 million unique workers across 188,265 Austrian firms from 2018-2025, benchmarked against 37 European peer economies.", "Five-step role-taxonomy pipeline with Fleiss kappa of 0.84 classified every position into Build, Integrate, and Enable tiers to map the AI workforce across Europe.", "Austria ranks 3rd in Europe on AI workforce share at 40.8% but 19th on density; NLP and GenAI brain drain runs at 23.3%, and only 6% of AI workers are entry-level." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3761, "authors_detailed": [ { "name": "Andreas Schumacher", "url": "https://openalex.org/A5110603958", "inst": "Vienna University of Economics and Business" }, { "name": "Christian Schumacher", "url": "https://openalex.org/A5101912924", "inst": "Vienna University of Economics and Business" }, { "name": "Can Tihanyi", "url": "https://openalex.org/A5082217998", "inst": "Vienna University of Economics and Business" } ], "affiliations": [ "Vienna University of Economics and Business" ] }, { "uid": "arxiv:2607.26952v1", "arxiv_id": "2607.26952v1", "title": "Credit Cards, Confusion, Computation, and Consequences: What Can We Uncover About Language Model Reasoning?", "authors": [ "Arnav Hiray", "Agam Shah", "Caleb Lu", "Meghaj Tarte", "Harsit Mittal", "Sudheer Chava" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.26952v1", "field": "finance", "role": "method", "bullets": [ "1,800 numerical reasoning questions derived from real U.S. credit card agreements, including first-person consumer variants about fees, interest, and payment calculations.", "Multiple large language and reasoning models evaluated under Chain-of-Thought and Program-of-Thought prompting on financial literacy questions from actual credit card terms.", "Program-of-Thought prompting yields consistent gains and narrows open- vs closed-source gaps; failures stem from misapplied financial rules and missed contractual conditions, not arithmetic." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "CreditCardQA benchmark with ground-truth answers from credit card agreements", "salience": 62, "n": 3762, "authors_detailed": [ { "name": "Arnav Hiray", "url": "https://openalex.org/A5092579830", "inst": "Georgia Institute of Technology" }, { "name": "Agam Shah", "url": "https://openalex.org/A5017623662", "inst": "Georgia Institute of Technology" }, { "name": "Caleb Lu", "url": "https://openalex.org/A5144038028", "inst": "" }, { "name": "Meghaj Tarte", "url": "https://openalex.org/A5117596061", "inst": "Georgia Institute of Technology" }, { "name": "Harsit Mittal", "url": "https://openalex.org/A5117596065", "inst": "Georgia Institute of Technology" }, { "name": "Sudheer Chava", "url": "https://openalex.org/A5144043685", "inst": "" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7102358", "doi": "10.2139/ssrn.7102358", "title": "The Theory of Delegated Markets: Foundations of a Theory of Economic Agency", "authors": [ "Paul F. Accornero" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7102358", "field": "economics", "role": "object", "bullets": [ "Theoretical framework for markets in which autonomous AI agents search, evaluate, authorize, and execute economic decisions on behalf of welfare-bearing human principals.", "No LLM employed; the paper derives formal conditions for welfare equivalence of delegated AI choice, separating preference-representation failure, incentive conflict, and preference endogeneity.", "Introduces preference custody, recursive demand, and constitutional market power as jointly constitutive of a distinct class of delegated market environments not captured by existing delegation models." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3994, "authors_detailed": [ { "name": "Paul Ferrando Accornero", "url": "https://openalex.org/A5134457145", "inst": "AULSS 2 Marca Trevigiana" } ], "affiliations": [ "AULSS 2 Marca Trevigiana" ] }, { "uid": "doi:10.2139/ssrn.6841578", "doi": "10.2139/ssrn.6841578", "title": "Greening the Deal: Can Mergers Redirect Innovation?", "authors": [ "Melissa Newham", "David Jaggi", "Jan-Alexander Posth" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6841578", "field": "finance", "role": "instrument", "bullets": [ "U.S. M&A transactions from 1988-2015 matched with patent data, analyzed in a staggered difference-in-differences framework over a 17-year window centered on deal announcement year.", "Patent text embeddings measured shifts in acquirers' technological orientation toward targets' knowledge bases post-merger; deals between technologically distinct firms drove the effect.", "Dirty acquirers showed strong post-deal shifts toward targets' clean technologies, with increased citations to clean patents and increased clean patent filings by the acquirer." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": false, "salience": 50, "n": 3995, "authors_detailed": [ { "name": "Melissa Newham", "url": "https://openalex.org/A5048706304", "inst": "ETH Zurich" }, { "name": "David Jaggi", "url": "https://openalex.org/A5133848527", "inst": "ZHAW Zurich University of Applied Sciences" }, { "name": "Jan-Alexander Posth", "url": "https://openalex.org/A5083631288", "inst": "ZHAW Zurich University of Applied Sciences" } ], "affiliations": [ "ETH Zurich", "ZHAW Zurich University of Applied Sciences" ] }, { "uid": "doi:10.2139/ssrn.7102061", "doi": "10.2139/ssrn.7102061", "title": "Semantic-Topological Antifragility: A Quantitative RAG Expert System for Continuous-Time SDE Volatility Forecasting", "authors": [ "Elaheh Soleymanpour", "Amir Hossein Taherinia", "Hadi Sadoghi Yazdi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7102061", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 daily data from 2008 to 2024 used for continuous-time stochastic differential equation volatility calibration.", "FinBERT encodes market narratives into a RAG vector database; a bimodal elastic contrastive gate fuses retrieved historical priors with an econometric baseline.", "Framework eliminates autoregressive phase lag during structural crashes and reduces asymmetric QLIKE loss to 0.070 versus classical and neural SDE benchmarks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "S&P 500 out-of-sample QLIKE loss", "salience": 58, "n": 3996, "authors_detailed": [ { "name": "Elaheh Soleymanpour", "url": "https://openalex.org/A5060938501", "inst": "Ferdowsi University of Mashhad" }, { "name": "Amir Hossein Taherinia", "url": "https://openalex.org/A5008585917", "inst": "Sharif University of Technology" }, { "name": "Hadi Sadoghi Yazdi", "url": "https://openalex.org/A5064885254", "inst": "Ferdowsi University of Mashhad" } ], "affiliations": [ "Ferdowsi University of Mashhad", "Sharif University of Technology" ] }, { "uid": "doi:10.2139/ssrn.6850459", "doi": "10.2139/ssrn.6850459", "title": "More Agents, More Problems? Cost, Reliability, and Complexity in Enterprise AI Operations", "authors": [ "Cam Smith" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6850459", "field": "management", "role": "object", "bullets": [ "Empirical study of enterprise-style agent tasks with 3,751 cost-recorded result rows and 2,183 reliable operations across multiple agent configurations.", "Compared solo agents, multi-agent swarms, memory backends, and harness complexity using automated judges and four human pairwise reviewers.", "Larger swarms and elaborate harnesses do not reliably improve performance once cost, reliability, and latency are included; simple markdown memory often outperforms richer formats." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human pairwise evaluation vs automated judges", "salience": 50, "models": [], "n": 3997, "authors_detailed": [ { "name": "Cam Smith", "url": "https://openalex.org/A5143874444", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6921958", "doi": "10.2139/ssrn.6921958", "title": "Liquid Supply Chains How Falling Underwriting Costs Could Turn World Trade into an Asset Class, and What That Would Mean for Growth", "authors": [ "Christoph Gugelmann" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6921958", "field": "finance", "role": "object", "bullets": [ "Global trade finance market with $2.5 trillion annual unmet demand, focused on SMEs and emerging-market firms across developing economies.", "Argues agentic AI lowers marginal underwriting and compliance costs, enabling shipment-level financing and securitization of trade exposures.", "Automation converts unpriceable operational risk into investable credit risk, but model monoculture and screening-incentive failures pose systemic concerns paralleling Greensill." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3998, "authors_detailed": [ { "name": "Christoph Gugelmann", "url": "https://openalex.org/A5039560600", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6921298", "doi": "10.2139/ssrn.6921298", "title": "Delegation Risk Homeostasis: Why More Capable AI Agents Need Not Produce Fewer Incidents", "authors": [ "Farong Li" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6921298", "field": "finance", "role": "object", "bullets": [ "Theoretical model of enterprise AI agent delegation with implications for AI agent liability insurance underwriting variables.", "Formalizes how governance-constrained firms expand delegated authority as model capability improves until incident-absorption capacity binds again.", "Incident frequency per unit of agent-task exposure remains stable despite model improvements; constant-frequency incidents migrate to higher-authority tasks producing severity drift." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3999, "authors_detailed": [ { "name": "Fanfei Li", "url": "https://openalex.org/A5041587908", "inst": "California Lutheran University" } ], "affiliations": [ "California Lutheran University" ] }, { "uid": "doi:10.2139/ssrn.6813959", "doi": "10.2139/ssrn.6813959", "title": "The Judgement Economy: An Operating Model for Knowledge Work in the Age of AI Agents", "authors": [ "Jacob Alber", "Iosif Gershteyn" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6813959", "field": "management", "role": "object", "bullets": [ "Conceptual framework drawing on 2023-2025 randomized trials in software development, management consulting, customer service, and law.", "Defines the orchestrator-hour as the new production unit where a knowledge worker manages parallel AI agent workstreams replacing serial execution.", "AI use produced a 19% slowdown for experienced developers in one RCT; 95% of corporate generative-AI pilots stall, suggesting gains require work restructuring not just tool access." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 4000, "authors_detailed": [ { "name": "Jacob Alber", "url": "https://openalex.org/A5143885738", "inst": "" }, { "name": "Iosif M. Gershteyn", "url": "https://openalex.org/A5053530934", "inst": "Medical University of South Carolina" } ], "affiliations": [ "Medical University of South Carolina" ] }, { "uid": "doi:10.2139/ssrn.6970382", "doi": "10.2139/ssrn.6970382", "title": "Bounded Agentic AI: A Conceptual Design Framework and Measurement Proposal for Controlled Autonomy in Human-AI Work Systems", "authors": [ "Haranadh Gavara" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6970382", "field": "management", "role": "object", "bullets": [ "Conceptual framework for enterprise AI autonomy governance illustrated with a KYC onboarding workflow, not empirically validated.", "Defines eight autonomy boundaries (purpose, domain, data, decision, action, learning, escalation, accountability) and a layered control-plane architecture.", "Proposes the Agency Boundary Deviation Index for statistical monitoring of deviations from architecturally permitted agent behavior in production." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 4001, "authors_detailed": [ { "name": "Gavara Haranadh", "url": "https://openalex.org/A5136466002", "inst": "Nvidia (United Kingdom)" } ], "affiliations": [ "Nvidia (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.7004218", "doi": "10.2139/ssrn.7004218", "title": "MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting", "authors": [ "Davide Pettenuzzo", "Andrea Carriero", "Shubhranshu Shekhar" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7004218", "field": "economics", "role": "instrument", "bullets": [ "FRED-MD macroeconomic database evaluated in a genuine real-time out-of-sample exercise using vintage-specific ALFRED data to eliminate temporal and revision leakage.", "Lightweight time series foundation model pretrained on synthetic data and fine-tuned on Bayesian VAR and factor model simulations from real-time vintages in nine minutes.", "Improves on the AR(1) benchmark for roughly 80% of series-horizon pairs and matches or surpasses Chronos-2 without any data leakage." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "FRED-MD real-time out-of-sample vs AR(1) and Chronos-2", "salience": 65, "n": 4002, "authors_detailed": [ { "name": "Davide Pettenuzzo", "url": "https://openalex.org/A5089152261", "inst": "Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda" }, { "name": "Andrea Carriero", "url": "https://openalex.org/A5082187758", "inst": "Queen Mary University of London" }, { "name": "Shubhranshu Shekhar", "url": "https://openalex.org/A5017630996", "inst": "Brandeis University" } ], "affiliations": [ "Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda", "Queen Mary University of London", "Brandeis University" ] }, { "uid": "doi:10.2139/ssrn.6719079", "doi": "10.2139/ssrn.6719079", "title": "Agents at the Gate: Programmable Risk Management for Agentic Commerce", "authors": [ "Katherine Kirkpatrick Bos", "Jessi Brooks" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6719079", "field": "finance", "role": "object", "bullets": [ "Conceptual framework for risk management in autonomous AI agent commerce on blockchain, extending a prior programmable risk management proposal.", "Addresses agent identity, mandate, and accountability as three new risk dimensions absent from human-mediated financial commerce frameworks.", "Argues the AI agent is the customer rather than a proxy, requiring new architectural patterns when trillions of dollars flow through autonomous systems." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 4003, "authors_detailed": [ { "name": "Katherine Kirkpatrick Bos", "url": "https://openalex.org/A5120191939", "inst": "Health Awareness (United States)" }, { "name": "Jessi Brooks", "url": "https://openalex.org/A5143970777", "inst": "" } ], "affiliations": [ "Health Awareness (United States)" ] }, { "uid": "doi:10.2139/ssrn.6685259", "doi": "10.2139/ssrn.6685259", "title": "The Two Faces of the Invisible Economy Pricing Personal Data Inputs and Measuring Machine Labor Outputs in the AI Economy", "authors": [ "James Felton Keith", "Tidiane Doucoure" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6685259", "field": "economics", "role": "object", "bullets": [ "Theoretical framework integrating informational factor of production with Human-Equivalent Work Units for national and enterprise accounting of AI inputs and outputs.", "Defines informational stock via mutual information and machine labor output via HEWU relative to human baseline productivity adjusted for task complexity.", "Omitting informational stock biases measured capital shares upward; omitting machine labor creates a parallel output-side distortion where performed work goes unclassified." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 4004, "authors_detailed": [ { "name": "James Felton Keith", "url": "https://openalex.org/A5129934486", "inst": "Keck Graduate Institute" }, { "name": "TIDIANE DOUCOURE", "url": "https://openalex.org/A5132620233", "inst": "Flometrics (United States)" } ], "affiliations": [ "Keck Graduate Institute", "Flometrics (United States)" ] }, { "uid": "doi:10.2139/ssrn.6771340", "doi": "10.2139/ssrn.6771340", "title": "The Token Governance Trap - A Constraint-Based Diagnostic Architecture for Organizational Readiness and Value Attribution Under Goodhart's Law", "authors": [ "John Kwarsick" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6771340", "field": "management", "role": "object", "bullets": [ "Conceptual framework with 100-scenario examination across healthcare, finance, aerospace, manufacturing, government, and technology sectors; no empirical data collected.", "No specific LLM used; paper develops Token Governance Readiness Model diagnosing whether organizations can attribute AI value before imposing token consumption quotas.", "Token-based AI governance triggers Goodhart distortion; quota policies before organizational readiness cause compliance gaming, adoption chilling, and budget conflict between leaders." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 4005, "authors_detailed": [ { "name": "John Kwarsick", "url": "https://openalex.org/A5132611366", "inst": "Gad Consulting Services (United States)" } ], "affiliations": [ "Gad Consulting Services (United States)" ] }, { "uid": "doi:10.2139/ssrn.6566898", "doi": "10.2139/ssrn.6566898", "title": "Self-Suppressing Correction in Agentic AI Platforms: A Conceptual Framework for Coupled Epistemic Degradation", "authors": [ "Paul Gallacher" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6566898", "field": "economics", "role": "object", "bullets": [ "Theoretical framework analyzing feedback dynamics between AI platform users, pricing structures, and training data quality across commercial AI deployment settings.", "No specific model tested; paper models how sycophancy and user competence interact to contaminate preference signals in platforms using author-coupled feedback.", "Identifies self-suppressing correction: AI product degradation reduces users' capacity to detect degradation, weakening the market signal needed for correction toward stable mediocrity." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 4006, "authors_detailed": [ { "name": "Paul Gallacher", "url": "https://openalex.org/A5002617606", "inst": "Bank of England" } ], "affiliations": [ "Bank of England" ] }, { "uid": "doi:10.2139/ssrn.6806860", "doi": "10.2139/ssrn.6806860", "title": "Man v Machine: A 24/7 Human-AI Market Simulation for Behavioural FinTech Research", "authors": [ "Jitesh Magar" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6806860", "field": "finance", "role": "agent", "bullets": [ "Persistent 24/7 online trading simulation platform with continuous data logging, shared leaderboard, and optional private researcher mode with full data export.", "Configurable AI agents compete against human traders in a continuous market; real-time herding metrics calculated each cycle with every trade and portfolio snapshot archived.", "Platform generates continuous behavioral finance data at scale; sample 10-minute session report available on Zenodo; no aggregate empirical findings reported." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 4007, "authors_detailed": [ { "name": "Jitesh Singh Magar", "url": "https://openalex.org/A5136865267", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6696578", "doi": "10.2139/ssrn.6696578", "title": "The Rise of Agentic AI in Banking", "authors": [ "Ahsan Perwez" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6696578", "field": "finance", "role": "object", "bullets": [ "Systematic review of secondary sources including industry reports and regulatory publications on AI in US and UK commercial banks, 2022 to 2026.", "No specific LLM tested; paper synthesizes evidence on autonomous AI system deployment across banking task types using the Ambiguity Threshold Hypothesis framework.", "Agentic AI delivers reliable gains in low-ambiguity rule-based banking tasks but unstable outcomes in judgment-intensive tasks; benefits are front-loaded while risks emerge later." ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 4008, "authors_detailed": [ { "name": "Ahsan Perwez", "url": "https://openalex.org/A5143910699", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7093719", "doi": "10.2139/ssrn.7093719", "title": "The Rise of the Agent Economy: Autonomous Agent Organizations as a New Form of Economic Infrastructure", "authors": [ "Lennart Ante", "Tim Alvaro Ockenga" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7093719", "field": "economics", "role": "object", "bullets": [ "Case study of the Virtuals Protocol, a blockchain-based infrastructure platform enabling tokenized ownership, decentralized governance, and standardized coordination of autonomous AI agents.", "No specific LLM tested; paper analyzes how the protocol reduces coordination costs through agent specialization and composability using transaction cost and network economics.", "Introduces Autonomous Agent Organizations as a functional subclass of DAOs with greater economic autonomy and composable inter-agent collaboration amplifying network effects." ], "bullet_provenance": "ai", "salience": 32, "models": [], "validated": null, "n": 4009, "authors_detailed": [ { "name": "Lennart Ante", "url": "https://openalex.org/A5034620178", "inst": "Constructing Excellence" }, { "name": "Tim Alvaro Ockenga", "url": "https://openalex.org/A5114869218", "inst": "Cologne Institute for Economic Research" } ], "affiliations": [ "Constructing Excellence", "Cologne Institute for Economic Research" ] }, { "uid": "doi:10.2139/ssrn.6908880", "doi": "10.2139/ssrn.6908880", "title": "Decentralized AI-Mediated Labor Markets: A Framework for Capability-Based Employment", "authors": [ "Utkarsh Singh" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6908880", "field": "economics", "role": "object", "bullets": [ "Conceptual framework applying information economics, signaling theory, and labor market matching theory to AI-mediated employment matching across industries and skill levels.", "No specific LLM tested; paper models personal AI agents constructing dynamic competency graphs from work outputs and verified achievements to replace credential signals.", "AI-mediated competency graphs may reduce information asymmetry and credential inflation in labor markets; challenges remain in privacy, algorithmic bias, and data ownership." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 4010, "authors_detailed": [ { "name": "Utkarsh Singh", "url": "https://openalex.org/A5137126041", "inst": "University of Delhi" } ], "affiliations": [ "University of Delhi" ] }, { "uid": "doi:10.2139/ssrn.6849501", "doi": "10.2139/ssrn.6849501", "title": "Pricing Human-AI Services: The Role of Observability and Congestion", "authors": [ "Pnina Feldman", "Ella Segev", "Shreyas Sekar" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6849501", "field": "management", "role": "object", "bullets": [ "Theoretical model of a monopolist serving consumers with heterogeneous problem complexity through an immediate AI channel and a congested human channel.", "No specific LLM; derives optimal pricing under observable versus unobservable AI resolution outcomes with endogenous human capacity investment.", "Outcome observability and human capacity are substitutes; firms invest weakly more in human staffing when AI success cannot be verified and priced." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 4011, "authors_detailed": [ { "name": "Pnina Feldman", "url": "https://openalex.org/A5080989519", "inst": "University of Virginia" }, { "name": "Ella Segev", "url": "https://openalex.org/A5056636974", "inst": "Ben-Gurion University of the Negev" }, { "name": "Shreyas Sekar", "url": "https://openalex.org/A5063395120", "inst": "The Scarborough Hospital" } ], "affiliations": [ "University of Virginia", "Ben-Gurion University of the Negev" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6721378", "doi": "10.2139/ssrn.6721378", "title": "Pricing Delayed Agentic AI Services: When High-Value Jobs Wait Longer", "authors": [ "Mojtaba Abdolmaleki", "Izak Duenyas", "Roman Kapuscinski" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6721378", "field": "management", "role": "object", "bullets": [ "Theoretical model of monopolistic agentic AI service provider on capacity-constrained compute, calibrated to a customer-service escalation dataset.", "Qwen2.5-Instruct models of varying sizes estimated quality functions; optimal scheduling characterized via a Smith-type index under private information.", "Higher-value customers receive more compute and better quality but wait longer; reverse-priority scheduling increases profit relative to FCFS and forward-priority." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 50, "n": 4012, "authors_detailed": [ { "name": "Mojtaba Abdolmaleki", "url": "https://openalex.org/A5015490494", "inst": "Ross School" }, { "name": "Izak Duenyas", "url": "https://openalex.org/A5077147314", "inst": "Ross School" }, { "name": "Roman Kapuściński", "url": "https://openalex.org/A5091383205", "inst": "University of Michigan" } ], "affiliations": [ "Ross School", "University of Michigan" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7101198", "doi": "10.2139/ssrn.7101198", "title": "Artificial Consensus: AI and Survey Measurement", "authors": [ "Thomas Cauthorn" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7101198", "field": "economics", "role": "object", "bullets": [ "Online survey of 1,294 respondents modeled on the Survey of Consumer Expectations, benchmarked against fifteen AI models with browser-side origin detection.", "Fifteen AI models generated inflation expectations; browser-side classifier distinguished AI-assisted humans, agentic tools, and autonomous bots among respondents.", "31.4% of respondents showed AI involvement; AI-generated answers collapsed dispersion of inflation expectations, creating artificial consensus without belief convergence." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Benchmarked against SCE microdata", "salience": 75, "n": 4013, "authors_detailed": [ { "name": "Thomas Cauthorn", "url": "https://openalex.org/A5033940521", "inst": "University of Mannheim" } ], "affiliations": [ "University of Mannheim" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7109638", "doi": "10.2139/ssrn.7109638", "title": "The Third Wave of Finance", "authors": [ "Bjoern Holste" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7109638", "field": "finance", "role": "object", "bullets": [ "Conceptual analysis of financial services evolution from product-centric to person-centric, focused on underinvested high-earners (HENRYs and EMILLIs).", "No specific model; argues agentic AI combined with data aggregation enables independent financial companions organized around people rather than products.", "AI-native financial companions can reduce investment inertia by lowering perceived complexity and shifting net valence from negative to positive for the emerging wealthy." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 4014, "authors_detailed": [ { "name": "Bjoern Holste", "url": "https://openalex.org/A5052926232", "inst": "Institute for New Media" } ], "affiliations": [ "Institute for New Media" ] }, { "uid": "doi:10.2139/ssrn.6683300", "doi": "10.2139/ssrn.6683300", "title": "The Signal is the Ceiling: Measurement Limits of LLM-Predicted Experience Ratings from Open-Ended Survey Text", "authors": [ "Andrew Hong", "Jason Potteiger", "Ito Zapata" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6683300", "field": "management", "role": "method", "bullets": [ "Approximately 10,000 post-game surveys from five MLB teams testing prompt design and model selection on fan experience rating prediction.", "GPT 4.1, 4.1-mini, and 5.2 predicted experience ratings from open-ended text; prompt customization and model swaps cross-compared against fan-reported scores.", "Prompt customization added 2 percentage points of within-plus-or-minus-1 agreement (67% to 69%); accuracy varied more by text character than by prompt or model." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Fan-reported experience ratings, within-±1 agreement", "salience": 55, "n": 4015, "authors_detailed": [ { "name": "Andrew Hong", "url": "https://openalex.org/A5133851920", "inst": "Dimension Technologies (United States)" }, { "name": "Jason Potteiger", "url": "https://openalex.org/A5135174180", "inst": "Dimension Technologies (United States)" }, { "name": "Ito Zapata", "url": "https://openalex.org/A5133925105", "inst": "Dimension Technologies (United States)" } ], "affiliations": [ "Dimension Technologies (United States)" ] }, { "uid": "doi:10.2139/ssrn.6725983", "doi": "10.2139/ssrn.6725983", "title": "Why Human-in-the-Loop Requirements Fail in High-Velocity Agentic Systems and What Governance Must Do Instead", "authors": [ "Albert Adusei Brobbey" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6725983", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of human-in-the-loop governance in agentic AI using principal-agent theory and bounded rationality frameworks.", "No specific model; theorizes structural failure of HITL requirements under high-velocity, distributed autonomous agent deployment at enterprise scale.", "HITL produces systematic compliance theater rather than genuine oversight; proposes continuous monitoring and ex post accountability as replacements." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 4016, "authors_detailed": [ { "name": "Albert Brobbey", "url": "https://openalex.org/A5139204087", "inst": "Meridian International Center" } ], "affiliations": [ "Meridian International Center" ] }, { "uid": "doi:10.2139/ssrn.6878705", "doi": "10.2139/ssrn.6878705", "title": "AM-XGAF: An Adaptive Multi-Agent Explainable AI Governance Framework For Enterprise Digital Banking Ecosystems", "authors": [ "Ayobami Jamiu Mustapha" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6878705", "field": "management", "role": "object", "bullets": [ "Design science research developing adaptive AI governance framework for enterprise digital banking ecosystems with multi-agent coordination.", "No specific LLM; proposes six-layer governance architecture with agent communication protocol to detect and contain governance drift contagion across networked AI agents.", "Framework maps to GDPR Article 22, EU AI Act, and SR 11-7; scenario walkthroughs demonstrate governance response to drift contagion and regulatory change propagation." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 4017, "authors_detailed": [ { "name": "Ayobami Jamiu Mustapha", "url": "https://openalex.org/A5135731142", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7041039", "doi": "10.2139/ssrn.7041039", "title": "The Agentic Shelf A Structural Framework for Brand Decision Architecture in Multi-Turn AI Recommendation Systems", "authors": [ "Timothy de Rosen", "Paul Sheals" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7041039", "field": "management", "role": "object", "bullets": [ "Analysis of 1,427 brand probes in multi-turn AI commerce conversations, building on prior empirical Linkage Gap study.", "No specific model named; formalizes brand-level displacement by AI in purchase decisions and distinguishes retrieval failure from structural model judgment.", "87.3% brand-level displacement observed; existing AI-visibility tools measure only possession and mention, missing commercially determinative decision-stage activation." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 4018, "authors_detailed": [ { "name": "Timothy de Rosen", "url": "https://openalex.org/A5119286499", "inst": "Standard Bio (Norway)" }, { "name": "Paul Sheals", "url": "https://openalex.org/A5122194300", "inst": "Continental (United States)" } ], "affiliations": [ "Standard Bio (Norway)", "Continental (United States)" ] }, { "uid": "doi:10.2139/ssrn.6907823", "doi": "10.2139/ssrn.6907823", "title": "Ordo-Liberalism for the Digital Age -Heike Schweitzer's \"Decentralized Coordination Order\"", "authors": [ "Pierre Larouche" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6907823", "field": "economics", "role": "object", "bullets": [ "Theoretical analysis of ordo-liberal market theory applied to digital platforms where automated matchmaking and agentic AI replace traditional marketplaces.", "No specific model; examines how AI-driven matchmaking threatens implicit assumptions underpinning spontaneous market theories and decentralized discovery.", "Markets require constructivist mechanism design and expanded legal role when agentic AI replaces physical or virtual marketplaces as coordination mechanisms." ], "bullet_provenance": "ai", "salience": 15, "models": [], "validated": null, "n": 4019, "authors_detailed": [ { "name": "Pierre Larouche", "url": "https://openalex.org/A5010716938", "inst": "Natural Sciences and Engineering Research Council of Canada" } ], "affiliations": [ "Natural Sciences and Engineering Research Council of Canada" ] }, { "uid": "doi:10.2139/ssrn.6872219", "doi": "10.2139/ssrn.6872219", "title": "The Agentic ETF: How Agentic Trading becomes an Asset Class", "authors": [ "Amandeep Singh" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6872219", "field": "finance", "role": "object", "bullets": [ "Conceptual framework defining LLM-driven autonomous trading agents as a nascent asset class, with six-layer infrastructure decomposition.", "No specific model benchmarked; defines agentic trading, distinguishes it from rule-based algorithmic trading and robo-advice, and maps structural gaps.", "Agentic ETFs could plausibly reach hundreds of billions to low trillions in AUM by 2030; presents ScalarField.io as a reference implementation." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 4020, "authors_detailed": [ { "name": "Amandeep Singh", "url": "https://openalex.org/A5047515908", "inst": "University of Washington" } ], "affiliations": [ "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.6898440", "doi": "10.2139/ssrn.6898440", "title": "The Cognitive Cooperative: A Coordination Primitive for Agent Economies", "authors": [ "Samuel Chen" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6898440", "field": "finance", "role": "object", "bullets": [ "Framework for blockchain-native AI agent coordination in DAOs holding over $35 billion in on-chain treasuries, with 6.3% average voter participation.", "No specific model; proposes Cognitive Cooperative where AI agents execute human-published policy at machine speed using ERC-8004 identity and multi-agent debate.", "Agent-mediated governance lowers representation cost to raise participation; distinguishes dividend cooperatives from recirculating structures for protocol sustainability." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 4021, "authors_detailed": [ { "name": "Samuel Chen", "url": "https://openalex.org/A5134069784", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6859138", "doi": "10.2139/ssrn.6859138", "title": "Model Risk Management in the Age of AI", "authors": [ "Nils Grevenbrock", "Nihil Patel", "Zhengpu Zhao" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6859138", "field": "finance", "role": "object", "bullets": [ "Conceptual governance framework targeting generative and agentic AI systems deployed in regulated financial institutions, addressing opacity, stochasticity, and autonomy challenges.", "No specific LLM tested; proposes continuous governance controls including dynamic guardrails, real-time monitoring, and human-in-the-loop oversight for financial AI systems.", "Traditional periodic model validation breaks under AI stochasticity and autonomy; continuous governance with runtime monitoring and third-party risk mitigation is required for compliance." ], "bullet_provenance": "ai", "salience": 38, "models": [], "validated": null, "n": 4022, "authors_detailed": [ { "name": "Nils Grevenbrock", "url": "https://openalex.org/A5003983833", "inst": "Goethe University Frankfurt" }, { "name": "Nihil Patel", "url": "https://openalex.org/A5143946041", "inst": "" }, { "name": "Zhengpu Zhao", "url": "https://openalex.org/A5007012669", "inst": "Peking University" } ], "affiliations": [ "Goethe University Frankfurt", "Peking University" ] }, { "uid": "doi:10.2139/ssrn.6349918", "doi": "10.2139/ssrn.6349918", "title": "Enterprise AI Adoption Decisioning", "authors": [ "Tunde Kehinde", "Cyril Simone" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6349918", "field": "management", "role": "object", "bullets": [ "Conceptual executive framework for enterprise AI adoption spanning six autonomy levels from traditional machine learning through autonomous self-modifying agents across industries.", "No specific model tested; develops six-step decision process requiring measurable business outcomes before tool selection, classifying use cases as Predict, Generate, or Act.", "Most enterprise AI failures are organizational not technical, caused by unclear ownership, absent executive sponsorship, underfunded talent, and premature agentic deployment without control infrastructure." ], "bullet_provenance": "ai", "salience": 32, "models": [], "validated": null, "n": 4023, "authors_detailed": [ { "name": "Tunde Kehinde", "url": "https://openalex.org/A5036571278", "inst": "Independent" }, { "name": "Cyril Simone", "url": "https://openalex.org/A5143945570", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6937638", "doi": "10.2139/ssrn.6937638", "title": "From Copilot to Coworker: How LLM Agents Reshape End-to-End Data Science Workflows", "authors": [ "Eledu Shah" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6937638", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of how agentic AI systems transform the division of labor across problem framing, data preparation, modeling, validation, and communication in organizations.", "No specific model tested; examines how LLM-based agents bind intent clarification, tool use, iterative execution, error recovery, and stakeholder explanation into one operational loop.", "Agentic systems create economic value by reducing coordination costs and expanding analytical capacity but make mistakes more procedural, compositional, and harder to detect." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 4024, "authors_detailed": [ { "name": "Eledu Shah", "url": "https://openalex.org/A5125567910", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6990561", "doi": "10.2139/ssrn.6990561", "title": "Problem Reframing in AI-Enabled Decision Systems: Mechanisms within a Five-Dimensional Decision Architecture", "authors": [ "Chao Huan Huang", "Yu-Wen Lin" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6990561", "field": "management", "role": "object", "bullets": [ "Conceptual framework drawing on semiconductor manufacturing, engineering design, and logistics cases to analyze problem reframing within AI-enabled organizational decision architectures.", "No specific model tested; develops five-dimensional decision architecture identifying four reframing types including AI-driven reframing emerging from learning-generation interaction within the system.", "Decision-making shifts from selecting optimal solutions under fixed problem definitions to continuously navigating and generating alternative problem formulations through AI-enabled learning and generation." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 4025, "authors_detailed": [ { "name": "Chao Huan Huang", "url": "https://openalex.org/A5143941456", "inst": "" }, { "name": "Yu-Wen Lin", "url": "https://openalex.org/A5143961932", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6561300", "doi": "10.2139/ssrn.6561300", "title": "Prediction Markets as the Informational Substrate of the Machine Economy", "authors": [ "Bernd Johannes Wuebben" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6561300", "field": "economics", "role": "object", "bullets": [ "Theoretical analysis of institutional infrastructure for autonomous machine-to-machine commerce, building on six institutional primitives required for agent-based economic exchange identified previously.", "No specific model tested; designs five prediction market mechanisms targeting adjudication, reputation, and liability gaps in the emerging machine economy trust infrastructure.", "Three competing machine payment protocols entered production within six months, but trust-layer immaturity remains the binding constraint on machine economy emergence and scaling." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 4026, "authors_detailed": [ { "name": "Bernd Johannes Wuebben", "url": "https://openalex.org/A5134604989", "inst": "Rainforest Alliance" } ], "affiliations": [ "Rainforest Alliance" ] }, { "uid": "doi:10.2139/ssrn.6828178", "doi": "10.2139/ssrn.6828178", "title": "Orchestrated AI in Enterprise Platforms: A Framework for Experience-Driven Workflow Design, Productivity Acceleration, and Organizational Performance", "authors": [ "Kranthi Kumar Gajji" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6828178", "field": "management", "role": "object", "bullets": [ "Architectural analysis of production-grade enterprise AI deployments in large-scale financial services and logistics environments using CRM, ERP, and ITSM platforms.", "No specific model tested; proposes dual-layer framework with Experience Orchestration and System Integration layers coordinating specialized AI agents across heterogeneous business systems.", "Systematic application of context persistence, role specialization, adaptive routing, and governance-by-design improves workflow completion rates and reduces human intervention in enterprise operations." ], "bullet_provenance": "ai", "salience": 28, "models": [], "validated": null, "n": 4027, "authors_detailed": [ { "name": "Kranthi Kumar Gajji", "url": "https://openalex.org/A5143953674", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7118203", "doi": "10.2139/ssrn.7118203", "title": "Ungoverned AI Systems: Enterprise Risk, Financial Exposure and Probability Sensitivity Analysis 2026+", "authors": [ "Michael Clark" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7118203", "field": "finance", "role": "object", "bullets": [ "Quantitative risk assessment of thirteen AI risk categories across corporations, governments, and nonprofits globally with 2026 projections and three-scenario probability sensitivity analysis.", "No specific model tested; develops per-event cost models grounded in disclosed breach economics and regulatory penalty structures for each of thirteen risk categories.", "Global annual financial exposure from ungoverned AI estimated at 1.6 to 4.4 trillion pounds; improving governance maturity from 30% to 70% reduces expected loss by 38-52%." ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 4028, "authors_detailed": [ { "name": "Michael Clark", "url": "https://openalex.org/A5143904477", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6835978", "doi": "10.2139/ssrn.6835978", "title": "Breadth-Depth Tradeoffs in Generative Search under Information Pollution: Competition and Compute Externalities", "authors": [ "Xing Hu", "Yifu Ai" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6835978", "field": "economics", "role": "object", "bullets": [ "Game-theoretic model of generative search platforms allocating scarce compute between retrieval breadth and reasoning depth under information pollution.", "Theoretical analysis of RAG-based search competition; no specific LLM used empirically; models platform compute allocation and entry barriers.", "Reasoning efficiency gains expand retrieval breadth, raising total infrastructure consumption; information pollution functions as a verification barrier binding entrants disproportionately." ], "bullet_provenance": "ai", "salience": 58, "models": [], "validated": null, "n": 4029, "authors_detailed": [ { "name": "Xing Hu", "url": "https://openalex.org/A5036471879", "inst": "University of Hong Kong" }, { "name": "Yifu Ai", "url": "https://openalex.org/A5143888282", "inst": "" } ], "affiliations": [ "University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6504386", "doi": "10.2139/ssrn.6504386", "title": "The Self-Driving Portfolio: Agentic Architecture for Institutional Asset Management", "authors": [ "Andrew Ang", "Nazym Azimbayev", "Andrey Kim" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6504386", "field": "finance", "role": "agent", "bullets": [ "Institutional strategic asset allocation pipeline using approximately 50 specialized AI agents for portfolio construction across over 20 competing methods.", "Agents produce capital market assumptions, construct and critique portfolios, vote on outputs; a meta-agent rewrites agent code and prompts based on realized returns.", "Pipeline governed by the Investment Policy Statement shifts the investor role from analytical execution to oversight of autonomous portfolio agents." ], "bullet_provenance": "ai", "salience": 68, "models": [], "validated": null, "n": 4030, "authors_detailed": [ { "name": "Andrew Ang", "url": "https://openalex.org/A5078960449", "inst": "Columbia University" }, { "name": "Nazym Azimbayev", "url": "https://openalex.org/A5119781326", "inst": "" }, { "name": "Andrey Kim", "url": "https://openalex.org/A5048344015", "inst": "Kumoh National Institute of Technology" } ], "affiliations": [ "Columbia University", "Kumoh National Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6364918", "doi": "10.2139/ssrn.6364918", "title": "Beyond Human Bandwidth: A Formal Case for AI Verifiers in the AGI Economy", "authors": [ "Domenico Gagliardi" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6364918", "field": "economics", "role": "object", "bullets": [ "Formal economic model extending Catalini, Hui, and Wu (2026) on verification bandwidth constraints in the AGI economy.", "Introduces AI Verifiers as automated auditing agents whose cost curves mirror AI producers; derives conditions for closing the Measurability Gap.", "AI Verifiers can narrow the gap, but correlated failure risk shifts the binding bottleneck to irreducible human Meta-Verification of the validators." ], "bullet_provenance": "ai", "salience": 48, "models": [], "validated": null, "n": 4031, "authors_detailed": [ { "name": "Domenico Gagliardi", "url": "https://openalex.org/A5143915469", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6663958", "doi": "10.2139/ssrn.6663958", "title": "Agentic AI Accountability in Organizations", "authors": [ "Albert Adusei Brobbey" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6663958", "field": "management", "role": "object", "bullets": [ "Survey of organizational agentic AI governance maturity using McKinsey and MIT Sloan Management Review data through early 2026.", "Examines organizational accountability frameworks for agentic AI systems capable of autonomous planning, reasoning, and consequential action.", "Fewer than one-third of organizations reached maturity level three in agentic AI governance; 69% of governance experts find existing frameworks insufficient." ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 4032, "authors_detailed": [ { "name": "Albert Brobbey", "url": "https://openalex.org/A5139204087", "inst": "Meridian International Center" } ], "affiliations": [ "Meridian International Center" ] }, { "uid": "doi:10.2139/ssrn.5869762", "doi": "10.2139/ssrn.5869762", "title": "Prompting Science Report 4: Playing Pretend: Expert Personas Don't Improve Factual Accuracy", "authors": [ "Savir Basil", "Ina Shapiro", "Dan Shapiro", "Ethan R. Mollick", "Lilach Mollick", "Lennart Meincke" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5869762", "field": "management", "role": "method", "bullets": [ "Six LLMs tested on GPQA Diamond and MMLU-Pro benchmarks covering graduate-level science, engineering, and law questions.", "GPT and Gemini 2.0 Flash among models tested with in-domain expert, off-domain expert, and low-knowledge persona prompts against a no-persona baseline.", "Expert persona prompts showed no consistent accuracy improvement across models; domain-mismatched and low-knowledge personas often degraded benchmark performance." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "GPQA Diamond and MMLU-Pro benchmarks", "salience": 55, "n": 4033, "authors_detailed": [ { "name": "Savir Basil", "url": "https://openalex.org/A5099094066", "inst": "University of Pennsylvania" }, { "name": "I JONATHAN SHAPIRO", "url": "https://openalex.org/A5111579309", "inst": "Parnassia Groep" }, { "name": "Daniel Shapiro", "url": "https://openalex.org/A5023560002", "inst": "Universidad Autónoma de Occidente" }, { "name": "Ethan Mollick", "url": "https://openalex.org/A5061686034", "inst": "University of Pennsylvania" }, { "name": "Lilach Mollick", "url": "https://openalex.org/A5018748407", "inst": "University of Pennsylvania" }, { "name": "Lennart Meincke", "url": "https://openalex.org/A5003350421", "inst": "William P. Wharton Trust" } ], "affiliations": [ "University of Pennsylvania", "Parnassia Groep", "Universidad Autónoma de Occidente", "William P. Wharton Trust" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6734938", "doi": "10.2139/ssrn.6734938", "title": "Architectural Fragility of General-Purpose AI in Operations: A Failure Taxonomy and the Domain-Specific Operational Intelligence (DSOI) Framework", "authors": [ "Prakash Bhaskar Acharya", "Spurthi Tallam", "Hemanth Kumar Kolluru", "Nijansh Verma" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6734938", "field": "management", "role": "object", "bullets": [ "Design Science Research examining why general-purpose AI fails in operations and supply chain management decision-making contexts.", "Derives taxonomy of five endogenous failure modes when general-purpose AI is deployed operationally; proposes DSOI framework with five architectural requirements.", "Identifies precision failures, context collapse, accountability gaps, temporal inconsistency, and constraint blindness as causes of persistently low operational trust." ], "bullet_provenance": "ai", "salience": 38, "models": [], "validated": null, "n": 4034, "authors_detailed": [ { "name": "Prakash Bhaskar Acharya", "url": "https://openalex.org/A5143887601", "inst": "" }, { "name": "Spurthi Tallam", "url": "https://openalex.org/A5143906694", "inst": "" }, { "name": "Hemanth Kumar Kolluru", "url": "https://openalex.org/A5134051552", "inst": "" }, { "name": "Nijansh Verma", "url": "https://openalex.org/A5134083330", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6808318", "doi": "10.2139/ssrn.6808318", "title": "Three Eras of Strategy Execution: An Architectural Account of the Persistent Infrastructure Gap", "authors": [ "Ravi Arora" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6808318", "field": "management", "role": "object", "bullets": [ "Conceptual framework spanning three eras of strategy execution (1960s-present) drawing on approximately 110 organizations.", "Argues agentic AI (post-2023) provides all six structural conditions needed to close the seven-stage strategy execution value chain end to end.", "The persistent two-thirds strategy value loss is a structural infrastructure gap, not a behavioral defect, and is now architecturally addressable." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 4035, "authors_detailed": [ { "name": "Ravi Arora", "url": "https://openalex.org/A5143931480", "inst": "Hindustan Petroleum Corporation Limited (India)" } ], "affiliations": [ "Hindustan Petroleum Corporation Limited (India)" ] }, { "uid": "doi:10.2139/ssrn.6960444", "doi": "10.2139/ssrn.6960444", "title": "The Digital Erasure of Technical Competence: Algorithmic Fraud, Multi-Vector Cheating, and Decentralized Intermediaries in US Tech Recruitment", "authors": [ "Vidyadhar Mylabathula" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6960444", "field": "management", "role": "object", "bullets": [ "US tech sector hiring pipelines under H-1B and OPT frameworks, informed by a June 2026 executive summit and industry data.", "Documents multi-vector recruitment fraud including real-time LLM prompting and proxy interview networks; proposes ML-based zero-trust detection architecture.", "Hiring fraud now infects a majority of technical pipelines; fully remote hiring created exploitable verification gaps that manual screening cannot close." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 4036, "authors_detailed": [ { "name": "Vidyadhar Mylabathula", "url": "https://openalex.org/A5143979993", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7111658", "doi": "10.2139/ssrn.7111658", "title": "The Third Wave of Finance", "authors": [ "Bjoern Holste", "Maria Paula S Guimaraes Baum", "Villiam Tettenborn" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7111658", "field": "finance", "role": "object", "bullets": [ "Conceptual framework targeting HENRYs and EMILLIs who hold significant cash reserves but remain underinvested due to decision inertia.", "Proposes independent AI-native financial companions combining data aggregation and agentic AI to deliver conversational guidance with structural independence.", "Person-centric AI architecture can shift net valence from negative to positive, enabling informed financial action among the financially inert Emerging Wealthy." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 4037, "authors_detailed": [ { "name": "Bjoern Holste", "url": "https://openalex.org/A5052926232", "inst": "Institute for New Media" }, { "name": "Maria Paula S Guimaraes Baum", "url": "https://openalex.org/A5143948927", "inst": "eHealth Initiative" }, { "name": "Villiam Tettenborn", "url": "https://openalex.org/A5143977102", "inst": "eHealth Initiative" } ], "affiliations": [ "Institute for New Media", "eHealth Initiative" ] }, { "uid": "doi:10.2139/ssrn.6816161", "doi": "10.2139/ssrn.6816161", "title": "The Uneven Frontier: User Heterogeneity and the External Validity of Language Model Benchmarks", "authors": [ "Abhilash Mishra" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6816161", "field": "economics", "role": "object", "bullets": [ "Theoretical framework with simulations; proposes empirical validation using public conversation corpora across heterogeneous user populations.", "Develops a quality-surface framework showing LLM benchmark rankings lose predictive power for population outcomes due to user-skill-dependent elicitation divergence.", "Rank reversal between models emerges under realistic conditions; short constrained tasks compress productivity while long open-ended tasks amplify skill-based inequality." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 4038, "authors_detailed": [ { "name": "Abhilash Mishra", "url": "https://openalex.org/A5108926021", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6782363", "doi": "10.2139/ssrn.6782363", "title": "Brand Legibility: A Construct for Building Brand Equity in AI-Mediated Markets", "authors": [ "Marcos Guimaraes Figueira" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6782363", "field": "management", "role": "object", "bullets": [ "Conceptual framework for AI-mediated consumer markets where synthesized answers and agentic actions suppress brand identity.", "Introduces Brand Legibility construct and Semantic Equity Index measuring brand recognizability across answer engines, generative engines, and agentic systems.", "Brand erasure requires coordinated optimization across three layers; proposes the Brand Erasure Audit as an operational measurement protocol." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 4039, "authors_detailed": [ { "name": "Marcos Guimarães Figueira", "url": "https://openalex.org/A5135658209", "inst": "Design Intelligence (United States)" } ], "affiliations": [ "Design Intelligence (United States)" ] }, { "uid": "doi:10.2139/ssrn.6763624", "doi": "10.2139/ssrn.6763624", "title": "Rapid AI Productivity (RAP): A Dynamical Systems Framework for Endogenous Compute-Driven Economic Growth", "authors": [ "Michael Chin" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6763624", "field": "economics", "role": "object", "bullets": [ "Dynamical systems model calibrated to 2020-2025 global data with six annual observations; validated against Chegg, Meta, and Stack Overflow cases.", "Models compute, data, and algorithmic efficiency as endogenous state variables with a recursive AI self-improvement term absent from prior growth models.", "When Intelligence Density exceeds 0.85, labor output elasticity drops below 0.17; bifurcation analysis identifies 2028 as a critical energy-constraint juncture." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 4040, "authors_detailed": [ { "name": "Michael Chin", "url": "https://openalex.org/A5143959058", "inst": "University of Science and Technology Beijing" } ], "affiliations": [ "University of Science and Technology Beijing" ] }, { "uid": "doi:10.2139/ssrn.6935338", "doi": "10.2139/ssrn.6935338", "title": "What the Filings Don't Show: Tracing the Credit Behind the AI Build-Out, and the Disclosure Gaps That Hide It", "authors": [ "Anton Sokolov" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6935338", "field": "finance", "role": "object", "bullets": [ "US financial system; draws on Shared National Credit data, FDIC call reports, 10-K filings, SEC N-PORT, and BDC marks.", "Traces the agentic-AI credit channel through public filings to identify where disclosure rules fail to capture AI infrastructure financing risk.", "Disclosure architecture cannot price AI-boom risks; each gap maps to a specific missing rule, yielding targeted reform recommendations." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 4041, "authors_detailed": [ { "name": "Anton Sokolov", "url": "https://openalex.org/A5136145186", "inst": "Tallinn University" } ], "affiliations": [ "Tallinn University" ] }, { "uid": "doi:10.2139/ssrn.6910479", "doi": "10.2139/ssrn.6910479", "title": "Marketing World Models", "authors": [ "Davide Orazi", "Gergely Nyilasy" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6910479", "field": "management", "role": "object", "bullets": [ "Conceptual framework with a 2x2 typology crossing environmental realism and agent autonomy for AI-driven marketing simulation systems.", "Introduces Marketing World Models as AI systems that simulate customer behavior, firm strategies, and market evolution in synthetic ecosystems.", "Four MWM types (diagnostic sandboxes, agentic decision engines, world dreamers, synthetic market ecosystems) serve distinct marketing capability needs." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 4042, "authors_detailed": [ { "name": "Davide Orazi", "url": "https://openalex.org/A5143897172", "inst": "" }, { "name": "Gergely Nyilasy", "url": "https://openalex.org/A5067007728", "inst": "The University of Melbourne" } ], "affiliations": [ "The University of Melbourne" ] }, { "uid": "doi:10.2139/ssrn.6914279", "doi": "10.2139/ssrn.6914279", "title": "From Herding Machines to Autonomous Agents: A Taxonomy of AI-Driven Flash Crash Mechanisms and the Regulatory Void", "authors": [ "Ka Wah Philip Ng" ], "posted": "2026-07-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6914279", "field": "finance", "role": "object", "bullets": [ "Financial markets with the October 2025 cryptocurrency liquidation cascade (over $19 billion in 24 hours) as primary empirical case.", "Proposes a three-category taxonomy of AI-driven flash crashes: endogenous herding, exogenous error cascades, and adversarial generative-AI disinformation.", "AI model homogeneity parallels pre-2008 VaR concentration; agentic AI introduces qualitatively new systemic risks beyond existing regulatory frameworks." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 4043, "authors_detailed": [ { "name": "Ka Wah Philip Ng", "url": "https://openalex.org/A5143815930", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7096518", "doi": "10.2139/ssrn.7096518", "title": "Predicting the Court: Evaluating Large Language Models as Forecasters of Supreme Court Decisions", "authors": [ "Hayley Stillwell", "Sean Harrington" ], "posted": "2026-07-28", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7096518", "field": "other", "role": "agent", "bullets": [ "Every argued merits case from the United States Supreme Court's October Term 2025, with predictions evaluated at both the case and the individual justice level.", "GPT-5, Gemini 2.5 Pro, Claude Sonnet 4.5, and Grok 4 forecast outcomes; predictions are scored against the actual decisions, though the abstract reports no overall accuracy figure.", "Justice level accuracy mostly reflects the Court's ordinary ideological alignment, and the models overpredict divided rulings in politically salient cases the Court resolved on narrow technical grounds." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "predictions compared with actual October Term 2025 decisions", "salience": 55, "edition": 9, "n": 1340, "authors_detailed": [ { "name": "Hayley Stillwell", "url": "https://openalex.org/A5003439611", "inst": "University of Oklahoma" }, { "name": "Sean Harrington", "url": "https://openalex.org/A5109701972", "inst": "Brigham and Women's Hospital" } ], "affiliations": [ "University of Oklahoma" ] }, { "uid": "doi:10.2139/ssrn.7096659", "doi": "10.2139/ssrn.7096659", "title": "When the Professor Writes the Prompt: Designing an AI Tutor for Introduction to Microeconomics in Distance Higher Education", "authors": [ "Pedro A. Tamayo" ], "posted": "2026-07-28", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7096659", "field": "economics", "role": "agent", "bullets": [ "An Introduction to Microeconomics course taught to law students in distance higher education, for which the instructor authored a system prompt released as an open educational resource under CC BY.", "The prompt configures an unnamed LLM as a Socratic tutor built on four pedagogical principles; one conversational flow problem was corrected in production, and no evaluation of learning outcomes is reported.", "The paper frames prompt design as an instructional design competence for university instructors and argues introductory economics suits the approach because its content is well represented in model pretraining." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 33, "edition": 8, "n": 1327, "authors_detailed": [ { "name": "Pedro A. Tamayo", "url": "https://openalex.org/A5083198287", "inst": "Universidad Nacional de Educación a Distancia" } ], "affiliations": [ "Universidad Nacional de Educación a Distancia" ] }, { "uid": "doi:10.2139/ssrn.7099318", "doi": "10.2139/ssrn.7099318", "title": "The AI-assisted Solo Founder: Economics and Rapid Deployment Frameworks for Micro-SaaS", "authors": [ "Md Soad Sabir Shuvo" ], "posted": "2026-07-28", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7099318", "field": "management", "role": "object", "bullets": [ "Integrative review of 14 academic sources from 2024 to 2026 on the AI-assisted solo founder building Micro-SaaS products without a development team.", "No model is applied by the authors; the paper adapts resource-based view, lean startup, and effectuation theory and proposes a Specification Governance Model.", "Argues sustainable success requires distribution-first thinking, supervisory engineering skills, and specification-driven development to contain AI non-determinism and technical debt." ], "bullet_provenance": "ai", "salience": 33, "edition": 7, "models": [], "validated": null, "n": 1266, "authors_detailed": [ { "name": "Md Soad Sabir Shuvo", "url": "https://openalex.org/A5141108703", "inst": "Oldham Council" } ], "affiliations": [ "Oldham Council" ] }, { "uid": "doi:10.2139/ssrn.7178138", "doi": "10.2139/ssrn.7178138", "title": "Reading the Insider Cluster: Concurrent Disclosure-Tone Classification Predicts the Cross-Section of Post-Filing Returns", "authors": [ "Hyun Ahn" ], "posted": "2026-07-28", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7178138", "field": "finance", "role": "instrument", "bullets": [ "Survivorship-free 2015 to 2025 panel of 1,932,506 US Form 4 accessions covering 38,261 multi-insider open-market buy cluster events, unit is the concurrent 8-K and DEF 14A narrative.", "Zero-shot tone classification with a GPT-4o-mini, Llama-3.3-70B, and DeepSeek ensemble; checked for vintage leakage via identifier-masked reclassification at kappa 0.92 rather than human ground truth.", "Prioritizing the POSITIVE-outlook class lifts the five-day annualized net Sharpe ratio from 1.77 to 2.28, a 29 percent gain at 25 basis-point round-trip cost." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "validated": false, "validation_note": "cross-vendor and masked-vs-unmasked agreement (kappa 0.92); no human ground truth", "salience": 72, "edition": 7, "n": 1267, "authors_detailed": [ { "name": "Hyun Ahn", "url": "https://openalex.org/A5140769888", "inst": "Korea Institute for Advanced Study" } ], "affiliations": [ "Korea Institute for Advanced Study" ] }, { "uid": "doi:10.2139/ssrn.7094458", "doi": "10.2139/ssrn.7094458", "title": "Can Large Language Models Improve Scheduling Policies? Evidence from Queueing Networks, Job Shop Scheduling, and Ride-Hailing Dispatch", "authors": [ "Youhua Li", "Sibo Xu", "Yiqi Sun", "Pengfei Guo", "Zuo-Jun Max Shen", "Houmin Yan" ], "posted": "2026-07-28", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7094458", "field": "management", "role": "object", "bullets": [ "Numerical experiments in three operations-management scheduling domains, queueing network control, job-shop scheduling, and ride-hailing dispatch, with simulation rollouts as the evaluation environment.", "An unnamed LLM acts as an offline optimizer that drafts and iteratively tunes compact executable scoring policies from simulation feedback; the model family is not stated and no accuracy validation is reported.", "The framework beat direct LLM prompting and matched learning-based methods, performing best in realistic settings lacking explicit mathematical structure, while classical heuristics still won where theory was well established." ], "bullet_provenance": "ai", "salience": 52, "edition": 7, "models": [], "validated": null, "n": 1279, "authors_detailed": [ { "name": "Youhua Li", "url": "https://openalex.org/A5143810706", "inst": "" }, { "name": "Sibo Xu", "url": "https://openalex.org/A5143852611", "inst": "" }, { "name": "Yiqi Sun", "url": "https://openalex.org/A5063877759", "inst": "Financial Research (Hungary)" }, { "name": "Pengfei Guo", "url": "https://openalex.org/A5143817355", "inst": "" }, { "name": "Zuo-Jun Max Shen", "url": "https://openalex.org/A5143748495", "inst": "" }, { "name": "Houmin Yan", "url": "https://openalex.org/A5101054146", "inst": "City University of Hong Kong" } ], "affiliations": [ "Financial Research (Hungary)", "City University of Hong Kong" ] }, { "uid": "arxiv:2607.25218v1", "arxiv_id": "2607.25218v1", "title": "Everyone is unique: Towards Behaviorally Heterogeneous Negotiation Dialogue Systems for Debt Collection", "authors": [ "Yuhang Yang", "Kai Tang", "Chao Ye", "Haobo Wang", "Qiqi Luo", "Jinguang Zheng", "Zhixin Zhang" ], "posted": "2026-07-28", "added": "2026-07-29", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.25218v1", "field": "management", "role": "agent", "bullets": [ "DebtBench, a public persona-enriched benchmark of debt collection negotiations that pairs an AI collector with behaviorally heterogeneous debtor personas; sample period and geography are not stated.", "Sixteen state-of-the-art LLMs including GPT-4o are evaluated as negotiators, and a trained agent DebtGPT jointly optimizes recovery and interaction experience; performance is scored on the benchmark, not against human collectors.", "Most models struggle in this heterogeneous, high-stakes setting, whereas DebtGPT outperforms all open-source baselines and reaches parity with GPT-4o." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": false, "validation_note": "benchmark scoring only, no human ground truth", "salience": 45, "edition": 7, "n": 1290, "authors_detailed": [ { "name": "Yuhang Yang", "url": "https://openalex.org/A5143957568", "inst": "" }, { "name": "Kai Tang", "url": "https://openalex.org/A5143930594", "inst": "Zhejiang Normal University" }, { "name": "Chao Ye", "url": "https://openalex.org/A5143966067", "inst": "" }, { "name": "Haobo Wang", "url": "https://openalex.org/A5143983436", "inst": "" }, { "name": "Qiqi Luo", "url": "https://openalex.org/A5139763991", "inst": "" }, { "name": "Jinguang Zheng", "url": "https://openalex.org/A5077527408", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Zhixin Zhang", "url": "https://openalex.org/A5143931239", "inst": "" } ], "affiliations": [ "Zhejiang Normal University", "Rensselaer Polytechnic Institute" ] }, { "uid": "doi:10.2139/ssrn.7108218", "doi": "10.2139/ssrn.7108218", "title": "Answer Engine Optimization: How Agentic AI Reshapes SEO", "authors": [ "Miles Bliey", "Keira Chatwin" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7108218", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of AI-mediated search from 2023 to early 2026 as generative platforms grew from under 10% to roughly 30% of total search interactions.", "Examines retrieval-augmented generation mechanics including passage-level retrieval, query fan-out, pairwise reranking, and entity validation affecting brand visibility.", "Visibility shifts from static rank to expected rank across the user distribution; specialized brands gain structural advantage over generalist incumbents in agentic discovery." ], "bullet_provenance": "ai", "salience": 48, "models": [], "validated": null, "n": 2798, "authors_detailed": [ { "name": "Miles Bliey", "url": "https://openalex.org/A5143769017", "inst": "Stanford, United States" }, { "name": "Keira Chatwin", "url": "https://openalex.org/A5143780043", "inst": "Stanford Medicine" } ], "affiliations": [ "Stanford, United States", "Stanford Medicine" ] }, { "uid": "arxiv:2607.25726v1", "arxiv_id": "2607.25726v1", "title": "Nudging Sustainable Choices through LLM-Generated Recommendation Explanations", "authors": [ "Haya Halimeh", "Dietmar Jannach", "Oliver Müller" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.25726v1", "field": "management", "role": "instrument", "bullets": [ "Two randomized experiments with 529 participants choosing among preference-matched recommendations for instant coffee and hotel bookings.", "LLMs generated sustainability-aware explanations grounded in nudge theory, validated through human evaluation and LLM-as-a-judge audits.", "Plain sustainability disclosure did not change choices; behavioral framing and descriptive social norms significantly increased sustainable selections across both domains." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 2799, "authors_detailed": [ { "name": "Haya Halimeh", "url": "https://openalex.org/A5120459676", "inst": "Paderborn University" }, { "name": "Dietmar Jannach", "url": "https://openalex.org/A5143986786", "inst": "" }, { "name": "Oliver Müller", "url": "https://openalex.org/A5143950194", "inst": "Paderborn University" } ], "affiliations": [ "Paderborn University" ] }, { "uid": "arxiv:2607.25199v1", "arxiv_id": "2607.25199v1", "title": "RIDGE: An Autonomous Framework for Validation and Method Discovery in LLM-Generated Option Pricing", "authors": [ "Liexin Cheng", "Xue Cheng", "Shuaiqiang Liu", "Cornelis W. Oosterlee" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.25199v1", "field": "finance", "role": "method", "bullets": [ "Five stochastic volatility models for option pricing, validated through an autonomous framework applied to LLM-generated implementations.", "LLMs generated option pricing code from mathematical specifications; RIDGE validated outputs via no-arbitrage tests, stress tests, and benchmark comparisons.", "All detected implementation defects were removed; in two cases the validation process led to new semi-analytic pricing methodologies." ], "bullet_provenance": "ai", "validated": true, "validation_note": "no-arbitrage and benchmark tests on five stochastic volatility models", "salience": 55, "models": [], "n": 3106, "authors_detailed": [ { "name": "Liexin Cheng", "url": "https://openalex.org/A5143973640", "inst": "" }, { "name": "Xue Cheng", "url": "https://openalex.org/A5143958357", "inst": "" }, { "name": "Shuaiqiang Liu", "url": "https://openalex.org/A5071534457", "inst": "Delft University of Technology" }, { "name": "Cornelis W. Oosterlee", "url": "https://openalex.org/A5035408121", "inst": "Utrecht University" } ], "affiliations": [ "Delft University of Technology", "Utrecht University" ] }, { "uid": "arxiv:2607.26327v2", "arxiv_id": "2607.26327v2", "title": "The Last Costly Signal: How Generative AI Collapses Competence Signaling and Why Liability Sustains Markets for Expert Services", "authors": [ "Andreas Bauer" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.26327v2", "field": "economics", "role": "object", "bullets": [ "Theoretical model of expert-services markets (credence goods) with agent-based Monte Carlo simulations under generative AI cost compression.", "Generative AI modeled as compressing discernible quality headroom between machine and expert output; no specific LLM deployed empirically.", "Below a critical headroom threshold no separating equilibrium exists; outcome-contingent liability restores full separation for any AI capability level." ], "bullet_provenance": "ai", "salience": 58, "models": [], "validated": null, "n": 3107, "authors_detailed": [ { "name": "Andreas Bauer", "url": "https://openalex.org/A5090765536", "inst": "Forschungszentrum Jülich" } ], "affiliations": [ "Forschungszentrum Jülich" ] }, { "uid": "doi:10.2139/ssrn.7087838", "doi": "10.2139/ssrn.7087838", "title": "Forced Verdict Gates, Not Graph Sophistication: A Multi-Framework Benchmark of AI Audit Judgement on Real-World Scandal Cases", "authors": [ "Majid Mumtaz" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7087838", "field": "accounting", "role": "agent", "bullets": [ "Four real-world corporate scandals (Enron 2001, Wirecard 2020, SVB 2023, Tesco 2014) evaluated by four AI agent audit personas across four multi-agent orchestration frameworks.", "LLM agents embodying Engagement Partner, Audit Senior, Skeptic Reviewer, and Independence Checker formed audit opinions using only pre-scandal public information scored against hidden ground truth.", "Plain-Python Blackboard framework scored highest (15.33/20); agents were systematically over-cautious with zero Clean opinions across 16 runs versus real auditors who issued Clean on all four cases." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "ground-truth audit opinions on four scandal cases", "salience": 62, "n": 3241, "authors_detailed": [ { "name": "Majid Mumtaz", "url": "https://openalex.org/A5143859881", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7196245", "doi": "10.2139/ssrn.7196245", "title": "Can Generative AI Overcome the Innovation Paradox? Evidence from Early-Stage Ideation", "authors": [ "Herzing Nadine", "Jan Joosten", "Alexander Hahn", "Bilgram Volker" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7196245", "field": "management", "role": "agent", "bullets": [ "Professional audio industry; 140 innovation ideas generated by ChatGPT using five prompting strategies, blindly evaluated by industry experts for novelty, feasibility, and customer benefit.", "ChatGPT generated early-stage ideation output; expert panelists scored each idea on three dimensions to test whether AI mitigates the novelty-feasibility trade-off.", "Full sample showed negative novelty-feasibility and novelty-benefit correlations, but both trade-offs disappeared among the top-rated ideas under structured prompting conditions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 52, "n": 3242, "authors_detailed": [ { "name": "Herzing Nadine", "url": "https://openalex.org/A5129703386", "inst": "" }, { "name": "Jan Joosten", "url": "https://openalex.org/A5033928943", "inst": "Siemens (United States)" }, { "name": "Alexander Hahn", "url": "https://openalex.org/A5143831266", "inst": "" }, { "name": "Bilgram Volker", "url": "https://openalex.org/A5129674520", "inst": "" } ], "affiliations": [ "Siemens (United States)" ] }, { "uid": "doi:10.2139/ssrn.7119858", "doi": "10.2139/ssrn.7119858", "title": "When Anyone Can Build, Who Evaluates? Use-Side Governance in AI Skill Ecosystems", "authors": [ "Leonard Boussioux", "Stefanos Poulidis" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7119858", "field": "management", "role": "object", "bullets": [ "49,332 AI skills from ClawHub public repository analyzed via embedding-based taxonomy recovery, linked to O*NET task-level data from the Anthropic Economic Index.", "No LLM used for analysis; paper studies the AI skill ecosystem itself, mapping functional categories against platform discovery vocabulary and labor-market skill labels.", "94.8% of skills are dormant and the top 0.26% capture roughly half of installs; evaluability gap identified between skill presentation and the evidence users need before reliance." ], "bullet_provenance": "ai", "salience": 48, "models": [], "validated": null, "n": 3243, "authors_detailed": [ { "name": "Léonard Boussioux", "url": "https://openalex.org/A5036794043", "inst": "University of Washington" }, { "name": "Stefanos Poulidis", "url": "https://openalex.org/A5116212923", "inst": "INSEAD" } ], "affiliations": [ "INSEAD", "University of Washington" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7085238", "doi": "10.2139/ssrn.7085238", "title": "Platform AI Strategy: Ecosystem Control versus Openness", "authors": [ "Venkata Kavi Sai Eadara" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7085238", "field": "management", "role": "object", "bullets": [ "Comparative case study of OpenAI, Google DeepMind, and Meta AI, with Anthropic, Mistral, and Hugging Face as triangulating cases.", "Qualitative analysis applies strategic group theory to map firms on vertical integration depth and interface openness dimensions.", "No firm pursues a pure open or closed strategy; each layers openness selectively along its stack, treating each interface as a real option contingent on appropriability and regulatory exposure." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3621, "authors_detailed": [ { "name": "venkata kavi sai Eadara", "url": "https://openalex.org/A5134752279", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.7092819", "doi": "10.2139/ssrn.7092819", "title": "Limiting Climate Change Through Demandside Dietary Shifts", "authors": [ "Xian Yang", "Yixin Sun", "Hongbo Duan", "Yuwen Jiao", "Benjamin K. Sovacool", "Yu Qin", "Detlef van Vuuren", "Lei Zhu", "Shouyang Wang" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7092819", "field": "economics", "role": "instrument", "bullets": [ "Greenhouse gas inventory covering 1,100 dishes from 55 cuisines in 20 countries, built from 220 million multilingual consumption data points across 1.3 million restaurants.", "LLMs integrated with an integrated assessment model processed multilingual and multimodal food data to construct dish-level emission estimates.", "Combined mitigation strategies can align food-system emissions with Paris Agreement targets; single strategies reduce emissions up to 61% but fall short of the 1.5 degree C goal." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 35, "n": 3622, "authors_detailed": [ { "name": "Xian Yang", "url": "https://openalex.org/A5072845314", "inst": "Dongbei University of Finance and Economics" }, { "name": "Yixin Sun", "url": "https://openalex.org/A5139834014", "inst": "Energy Research Institute" }, { "name": "Hongbo Duan", "url": "https://openalex.org/A5143829335", "inst": "" }, { "name": "Yuwen Jiao", "url": "https://openalex.org/A5015892925", "inst": "Beijing University of Chinese Medicine" }, { "name": "Benjamin K. Sovacool", "url": "https://openalex.org/A5088822272", "inst": "Boston University" }, { "name": "Yu Qin", "url": "https://openalex.org/A5012320123", "inst": "National University of Singapore" }, { "name": "Detlef van Vuuren", "url": "https://openalex.org/A5136438588", "inst": "" }, { "name": "Lei Zhu", "url": "https://openalex.org/A5143781269", "inst": "" }, { "name": "Shouyang Wang", "url": "https://openalex.org/A5078558986", "inst": "Chinese Academy of Social Sciences" } ], "affiliations": [ "Boston University", "Dongbei University of Finance and Economics", "Energy Research Institute", "Beijing University of Chinese Medicine", "National University of Singapore", "Chinese Academy of Social Sciences" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7096200", "doi": "10.2139/ssrn.7096200", "title": "From Fog of War to AI Smog: Introducing SMOG as a Framework for Strategic Management under Synthetic Uncertainty", "authors": [ "Dag Øivind Madsen", "Emmanuel Silva", "Terje Berg" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7096200", "field": "management", "role": "object", "bullets": [ "Conceptual framework for strategic management in environments shaped by generative AI information generation at scale.", "No specific model deployed; framework defines four dimensions of synthetic uncertainty: Synthetic, Mediated, Opaque, and Generative.", "AI creates cumulative informational pollution distinct from VUCA and BANI uncertainty; six organizational de-smogging capabilities are proposed as deliberate responses." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3623, "authors_detailed": [ { "name": "Dag Øivind Madsen", "url": "https://openalex.org/A5048486241", "inst": "University of South-Eastern Norway" }, { "name": "EMMANUEL SILVA", "url": "https://openalex.org/A5068219378", "inst": "Universidade Federal do Espírito Santo" }, { "name": "Terje Berg", "url": "https://openalex.org/A5049595237", "inst": "Norwegian University of Science and Technology" } ], "affiliations": [ "University of South-Eastern Norway", "Universidade Federal do Espírito Santo", "Norwegian University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7101478", "doi": "10.2139/ssrn.7101478", "title": "Beliefs in Financial Dialogue", "authors": [ "Jihad C. Dagher" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7101478", "field": "finance", "role": "instrument", "bullets": [ "Over 40,000 hours of US financial-TV dialogue from 2012 to 2026, resolved by speaker and topic into structured Q&As.", "LLMs scored forward-looking statements on direction and expressed self-uncertainty to construct topic-specific disagreement and sentiment measures.", "Mismatch in speakers' interpretations of Fed priorities raises large rate-path disagreement probability by roughly six percentage points, on par with one-SD inflation projection disagreement." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 75, "n": 3624, "authors_detailed": [ { "name": "Jihad Dagher", "url": "https://openalex.org/A5009468428", "inst": "American University of Beirut Medical Center" } ], "affiliations": [ "American University of Beirut Medical Center" ] }, { "uid": "doi:10.2139/ssrn.7108958", "doi": "10.2139/ssrn.7108958", "title": "Tabular Foundation Models for Discrete Choice Estimation", "authors": [ "Liu Liu", "Dan Zhang" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7108958", "field": "management", "role": "method", "bullets": [ "Yogurt scanner panel dataset with individual consumer purchase histories at varying observation depths, evaluated against hierarchical Bayesian estimation.", "Tabular foundation models applied to discrete choice via in-context learning with reformulated choice-set dependence and individual heterogeneity encoding.", "Best reformulation outperforms hierarchical Bayesian estimation by 8% in holdout log-likelihood and 3.6% in hit rate while running 16 times faster." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "yogurt scanner panel holdout log-likelihood and hit rate vs hierarchical Bayes", "salience": 70, "n": 3625, "authors_detailed": [ { "name": "Liu Liu", "url": "https://openalex.org/A5143765015", "inst": "University of Colorado Boulder" }, { "name": "Dan Zhang", "url": "https://openalex.org/A5047674143", "inst": "University of Colorado Boulder" } ], "affiliations": [ "University of Colorado Boulder" ] }, { "uid": "doi:10.2139/ssrn.7097938", "doi": "10.2139/ssrn.7097938", "title": "Cheap Talk or Priced Information? Listing Language and Housing Transaction Outcomes", "authors": [ "Lily Shen" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7097938", "field": "finance", "role": "instrument", "bullets": [ "63,067 Atlanta MLS residential transactions from 2007 to 2017 with full listing remarks text and hedonic controls.", "LLM classified listing remarks into auditable information categories covering property attributes, adverse conditions, and urgency language.", "One-SD increase in residual information associates with 7.7% higher sale prices; adverse disclosure with 17.4% lower prices; urgency language with 4.7% lower sale prices." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 65, "n": 3626, "authors_detailed": [ { "name": "Lily Shen", "url": "https://openalex.org/A5016864706", "inst": "Federal Reserve Bank of Atlanta" } ], "affiliations": [ "Federal Reserve Bank of Atlanta" ] }, { "uid": "doi:10.2139/ssrn.7094298", "doi": "10.2139/ssrn.7094298", "title": "RoR-CV: A Reasoning-Enhanced Large Language Model Recommendation System for Financial Scenarios with Integrated Computer Vision", "authors": [ "Chen Jin", "Junbin Luo", "Shuangquan Lyu", "Chong Huang", "Yiming Zhang", "Xiangmin Li", "Qi Hu" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7094298", "field": "finance", "role": "method", "bullets": [ "Two multimodal financial recommendation datasets combining news, charts, reports, and user context under non-stationary market conditions.", "A multimodal LLM framework fuses visual chart and report evidence with causal macro-to-sector-to-asset reasoning chains for financial recommendations.", "RoR-CV improves ranking quality, shortlist recall, visual alignment, and explanation reliability over both text-only and multimodal baselines." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "two multimodal financial recommendation datasets vs baselines", "salience": 40, "n": 3638, "authors_detailed": [ { "name": "Chen Jin", "url": "https://openalex.org/A5143801775", "inst": "" }, { "name": "Junbin Luo", "url": "https://openalex.org/A5143798789", "inst": "" }, { "name": "Shaocheng Lyu", "url": "https://openalex.org/A5078834388", "inst": "Capital University" }, { "name": "Chong Huang", "url": "https://openalex.org/A5001573772", "inst": "Sun Yat-sen University" }, { "name": "Yiming Zhang", "url": "https://openalex.org/A5143766919", "inst": "" }, { "name": "Xiangmin Li", "url": "https://openalex.org/A5101710939", "inst": "Central South University" }, { "name": "Qi Hu", "url": "https://openalex.org/A5143761084", "inst": "" } ], "affiliations": [ "Capital University", "Sun Yat-sen University", "Central South University" ] }, { "uid": "doi:10.2139/ssrn.6337818", "doi": "10.2139/ssrn.6337818", "title": "Sycophancy, Automation Bias, and Compressed Diffusion: An Unaddressed Convergence in AI Governance", "authors": [ "Caleb Janski" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6337818", "field": "management", "role": "object", "bullets": [ "Cross-disciplinary literature synthesis examining major AI governance frameworks including NIST AI RMF, ISO/IEC 42001, COSO ERM, and the EU AI Act.", "Analysis assesses whether frameworks operationalize individual user-level tool-use competency or behavioral risk from AI sycophancy and automation bias.", "No major framework requires or measures individual competency; four governance mechanisms proposed including AI tool literacy as a compliance requirement." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 45, "validated": null, "n": 3639, "authors_detailed": [ { "name": "Caleb Janski", "url": "https://openalex.org/A5143816281", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7105378", "doi": "10.2139/ssrn.7105378", "title": "The Strategic Bottleneck: How Generative AI is Reshaping Corporate Innovation", "authors": [ "Almog Goldstein" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7105378", "field": "management", "role": "object", "bullets": [ "Qualitative study drawing on insights from over 50 innovation and R&D executives across firms adopting generative AI.", "Interviews identify three critical trends in corporate AI adoption: FOMO-driven strategy-less implementation, bottleneck shifts, and competitive fragmentation.", "Rapid R&D acceleration moves organizational bottlenecks upstream from execution toward ideation, strategic direction, and review of AI-generated work." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 50, "validated": null, "n": 3640, "authors_detailed": [ { "name": "Almog Goldstein", "url": "https://openalex.org/A5143843777", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7105699", "doi": "10.2139/ssrn.7105699", "title": "The Assistant is not the Population", "authors": [ "Sandro Andric" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7105699", "field": "economics", "role": "method", "bullets": [ "Seven public OLMo 3 7B checkpoints spanning base through reasoning and chat pipelines, evaluated on 7,167 questions with published human answer distributions plus economic games and 875 multi-agent negotiations.", "Each checkpoint scored on distributional fidelity to human survey responses using exact candidate scoring and token-level constrained decoding, achieving zero format failures across all checkpoints.", "Every post-training stage reduced fidelity to human distributions; mean representativeness fell from 0.799 at base to 0.737 at final chat, and every SFT-to-DPO step lowered fidelity across all recipes." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "representativeness score against published human answer distributions", "salience": 68, "n": 3991, "authors_detailed": [ { "name": "Sandro Andric", "url": "https://openalex.org/A5121126609", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7199115", "doi": "10.2139/ssrn.7199115", "title": "Agentic Capital and Transition Dynamics in a Growing Task Economy", "authors": [ "jean-philippe Garnier" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7199115", "field": "economics", "role": "object", "bullets": [ "Dynamic task-economy model with an agentic sector accumulating sector-specific capital alongside physical capital and labor, calibrated under Cobb-Douglas production with learning-by-deployment externalities.", "No LLM employed; the paper derives closed-form balanced growth paths and characterizes four-dimensional transition dynamics for agentic capital under replacement-investment vintage accumulation.", "Social planner allocation yields a 20.1% permanent consumption-equivalent gain over the private equilibrium, driven by externalized learning through deployment that private firms ignore." ], "bullet_provenance": "ai", "salience": 58, "models": [], "validated": null, "n": 3992, "authors_detailed": [ { "name": "Jean‐Philippe Garnier", "url": "https://openalex.org/A5045460971", "inst": "Bruker (United States)" } ], "affiliations": [ "Bruker (United States)" ] }, { "uid": "arxiv:2607.25253v1", "arxiv_id": "2607.25253v1", "title": "The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape", "authors": [ "Deyao Hong", "Kehan Zheng", "Qian Li", "Jun Zhang", "Jie Jiang", "Hongning Wang" ], "posted": "2026-07-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.25253v1", "field": "economics", "role": "agent", "bullets": [ "Controlled LLM-based experiments across three product domains comparing platform-centric recommendation with user-centric agentic recommendation markets where platforms compete for user attention.", "LLM-based user agents queried competing platforms, ranked results, and relayed feedback to study strategic platform behavior under market competition; no specific model family named.", "Platforms' selectively positive explanations occupied 73-78% of first-ranked positions; user-agent feedback reduced this share to 36-41% and increased the probability of relevant-item purchase." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "models": [], "n": 3993, "authors_detailed": [ { "name": "Deyao Hong", "url": "https://openalex.org/A5126139761", "inst": "Tsinghua University" }, { "name": "Kehan Zheng", "url": "https://openalex.org/A5114098618", "inst": "Tsinghua University" }, { "name": "Qian Li", "url": "https://openalex.org/A5143940314", "inst": "Liaoning University" }, { "name": "Jun Zhang", "url": "https://openalex.org/A5143929555", "inst": "Tsinghua University" }, { "name": "Jie Jiang", "url": "https://openalex.org/A5143943893", "inst": "" }, { "name": "Hongning Wang", "url": "https://openalex.org/A5143887582", "inst": "" } ], "affiliations": [ "Tsinghua University", "Liaoning University" ] }, { "uid": "arxiv:2607.24352v1", "arxiv_id": "2607.24352v1", "title": "Retrieval-Augmented Large Language Models as Components of Cognitive Computing architecture for Regulatory Knowledge Management", "authors": [ "Dariusz Nowak-Nova" ], "posted": "2026-07-27", "added": "2026-07-31", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.24352v1", "field": "management", "role": "method", "bullets": [ "Regulatory knowledge management for organizations that must track changing legal acts, using locally deployed models on consumer hardware through Ollama and LM Studio, without GPU accelerators.", "The Polish language models Bielik and PLLuM answer regulatory questions grounded by retrieval from external legal repositories; improvements are described but no accuracy figures or labeled benchmark appear.", "The authors report better factual consistency, domain specificity, and normative precision than standalone generation, plus source traceability and knowledge updating without retraining the model." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 32, "edition": 8, "n": 1307, "authors_detailed": [ { "name": "Dariusz Nowak-Nova", "url": "https://openalex.org/A5007011144", "inst": "WSB University" } ], "affiliations": [ "WSB University" ] }, { "uid": "doi:10.2139/ssrn.7174480", "doi": "10.2139/ssrn.7174480", "title": "Virtual Reality-Based Job Interview Training Using AI Interviewers: Confidence Building, Stress Analysis, Body Language Assessment, and Intelligent Conversational Agents", "authors": [ "Julia Huber" ], "posted": "2026-07-27", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7174480", "field": "management", "role": "agent", "bullets": [ "A review of 61 sources from 1985 to 2026 spanning VR interview simulators, multimodal hireability assessment, and LLM-driven embodied interviewers for job seekers, including clinical and vocational rehabilitation populations.", "No new system is built; reviewed work pairs LLMs, none named, with digital humans as adaptive interviewers, and fusion models report accuracies above 85 percent and correlations with human raters above 0.70.", "Trial evidence for skill and confidence gains rests on scripted rather than generative dialogue, and no published system feeds physiological stress signals back into the interviewer's real-time behaviour." ], "bullet_provenance": "ai", "salience": 32, "edition": 8, "models": [], "validated": null, "n": 1313, "authors_detailed": [ { "name": "Julia Huber", "url": "https://openalex.org/A5102852585", "inst": "LMU Klinikum" } ], "affiliations": [ "LMU Klinikum" ] }, { "uid": "doi:10.2139/ssrn.7102618", "doi": "10.2139/ssrn.7102618", "title": "Prompt Engineering for Optimizing the Creativity of Generative Artificial Intelligence: An Experimental Study Protocol", "authors": [ "Vikram Arora", "Adam Sutoski", "Nanaki Arora", "Jude Hynes", "Arjun Grewal", "Anjali Behroozi", "Joelle Boilard", "Elyssia Leone", "Hannah Zizzo", "Kaya Bhandari", "Goran Calic", "Sameer Parpia", "Mohit Bhandari", "Alex Thabane" ], "posted": "2026-07-27", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7102618", "field": "management", "role": "agent", "bullets": [ "A study protocol for the alternate uses task run on three frontier models, with 50 independent sessions for a baseline prompt and each of 21 prompt variants.", "ChatGPT-5.5, Gemini 3.1 Pro, and Claude Sonnet 4.6 act as the creative subjects; originality is scored by the automated Ocsai tool with no human validation reported.", "No results yet; the plan compares each variant to baseline with t tests and judges practical significance against a minimally important difference threshold." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": false, "salience": 24, "edition": 8, "n": 1319, "authors_detailed": [ { "name": "Vikram Arora", "url": "https://openalex.org/A5090881808", "inst": "University of Toronto" }, { "name": "Adam Sutoski", "url": "https://openalex.org/A5094180672", "inst": "Jewish Rehabilitation Hospital" }, { "name": "Nanaki Arora", "url": "https://openalex.org/A5143708879", "inst": "" }, { "name": "Jude Hynes", "url": "https://openalex.org/A5123750614", "inst": "Western University" }, { "name": "Arjun Grewal", "url": "https://openalex.org/A5007055427", "inst": "Loyola University Medical Center" }, { "name": "Anjali Behroozi", "url": "https://openalex.org/A5143721010", "inst": "" }, { "name": "Joelle Boilard", "url": "https://openalex.org/A5121454366", "inst": "University of Alberta" }, { "name": "Elyssia Leone", "url": "https://openalex.org/A5121498802", "inst": "Ollscoil na Gaillimhe – University of Galway" }, { "name": "Hannah Zizzo", "url": "https://openalex.org/A5119217050", "inst": "University of Waterloo" }, { "name": "Kaya Bhandari", "url": "https://openalex.org/A5123476331", "inst": "Western University" }, { "name": "Goran Calic", "url": "https://openalex.org/A5029812875", "inst": "Western University" }, { "name": "Sameer Parpia", "url": "https://openalex.org/A5002547008", "inst": "McMaster University" }, { "name": "Mohit Bhandari", "url": "https://openalex.org/A5143689153", "inst": "" }, { "name": "Alex Thabane", "url": "https://openalex.org/A5069052479", "inst": "Western University" } ], "affiliations": [ "University of Toronto", "Western University", "University of Alberta", "Ollscoil na Gaillimhe – University of Galway", "University of Waterloo", "McMaster University" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7177500", "doi": "10.2139/ssrn.7177500", "title": "LLMs and Systematic Measurement Error", "authors": [ "William Grieser", "J. Anthony Cookson", "Maryam Fathollahi", "Buvaneshwaran Venugopal" ], "posted": "2026-07-27", "added": "2026-07-28", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7177500", "field": "finance", "role": "method", "bullets": [ "S&P 500 firms from 2005 to 2024, examining firm-level measures that large language models derive from Item 1A risk disclosures, earnings-call transcripts, and direct-prompt questions.", "The specific model family is not stated; the authors document non-classical measurement error from omission and injection and build a diagnostic from omission and injection rates rather than an accuracy benchmark.", "Measurement error is most severe for construct-style ratings of long disclosures, milder for the same constructs on shorter earnings calls, and substantially attenuated for direct-prompt measures." ], "bullet_provenance": "ai", "salience": 72, "edition": 6, "models": [], "validated": null, "n": 1236, "authors_detailed": [ { "name": "William Grieser", "url": "https://openalex.org/A5143713911", "inst": "Oklahoma State University" }, { "name": "J. Anthony Cookson", "url": "https://openalex.org/A5076123255", "inst": "Pennsylvania State University" }, { "name": "Maryam Fathollahi", "url": "https://openalex.org/A5074636098", "inst": "University of Oklahoma" }, { "name": "Buvaneshwaran Venugopal", "url": "https://openalex.org/A5143726401", "inst": "" } ], "affiliations": [ "Oklahoma State University", "Pennsylvania State University", "University of Oklahoma" ] }, { "uid": "arxiv:2607.24273v1", "arxiv_id": "2607.24273v1", "title": "INS-ActBench: A Comprehensive Benchmark for Assessing Professional Actuarial Capability of Large Language Models", "authors": [ "Changyu Chen", "Chenwei Lin", "Xian Xu" ], "posted": "2026-07-27", "added": "2026-07-28", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.24273v1", "field": "finance", "role": "method", "bullets": [ "A benchmark of 12,050 question-answer pairs drawn from public exams and sample questions of 16 actuarial associations, split into knowledge, long-context case reasoning, and spreadsheet and R-code practice subsets.", "Nine LLMs, with families not named in the abstract, and human actuarial experts were scored against exam answers across the three subsets.", "Frontier models performed strongly on standardized actuarial knowledge but remained much weaker on case reasoning, tool-based workflows, and jurisdiction-sensitive practice tasks." ], "bullet_provenance": "ai", "validated": true, "validation_note": "actuarial exam answers as ground truth, human expert comparison", "salience": 55, "edition": 6, "models": [], "n": 1237, "authors_detailed": [ { "name": "Changyu Chen", "url": "https://openalex.org/A5143850657", "inst": "" }, { "name": "Chenwei Lin", "url": "https://openalex.org/A5143841786", "inst": "Fudan University" }, { "name": "Xian Xu", "url": "https://openalex.org/A5027756547", "inst": "Xinjiang Production and Construction Corps" } ], "affiliations": [ "Fudan University", "Xinjiang Production and Construction Corps" ] }, { "uid": "doi:10.2139/ssrn.7191255", "doi": "10.2139/ssrn.7191255", "title": "Model Choice Is Measurement Choice: Identification Fragility in 55 Million LLM-Labeled Investor Posts", "authors": [ "Kefu Yi", "Feng Wu" ], "posted": "2026-07-27", "added": "2026-07-28", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7191255", "field": "finance", "role": "method", "bullets": [ "A blinded audit of 1,000 posts drawn from 54.9 million production-labeled Chinese stock-forum posts, then carried into earnings-announcement regressions covering 33,279 events.", "Two unnamed large language model validators apply one identical five-point sentiment codebook, and the paper audits their disagreement rather than checking labels against hand-coded ground truth.", "The two validators imply neutral shares of 14.2 and 83.6 percent, and a reported t of minus 9.04 on directional balance proves to be an artifact of weak identification." ], "bullet_provenance": "ai", "validated": false, "validation_note": "audits inter-model and inter-codebook disagreement; no hand-coded ground-truth accuracy reported", "salience": 70, "edition": 6, "models": [], "n": 1239, "authors_detailed": [ { "name": "Yi Kefu", "url": "https://openalex.org/A5047124497", "inst": "University of International Business and Economics" }, { "name": "Feng Wu", "url": "https://openalex.org/A5139657744", "inst": "Inner Mongolia University" } ], "affiliations": [ "University of International Business and Economics", "Inner Mongolia University" ] }, { "uid": "arxiv:2607.24372v1", "arxiv_id": "2607.24372v1", "title": "Randomness in large language models: What researchers need to know (and report)", "authors": [ "Guillaume Coqueret", "Joan Llull", "Florian Oswald", "Christophe Pérignon", "Christoph Scheuch", "Lars Vilhuber" ], "posted": "2026-07-27", "added": "2026-07-28", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.24372v1", "field": "economics", "role": "method", "bullets": [ "A methods paper illustrated with sentiment classification of corporate filings and the resulting regressions, targeting research practice rather than any one sample, period, or geography.", "Large language models generate classifications and scores through proprietary APIs and open-weight stacks, and setting temperature to zero removes sampling but not silent updates, rounding, or expert routing.", "Identical prompts return varying outputs, so exact reproduction is generally impossible via proprietary APIs, and the paper proposes a reporting standard treating outputs as draws from a distribution." ], "bullet_provenance": "ai", "salience": 66, "edition": 6, "models": [], "validated": null, "n": 1240, "authors_detailed": [ { "name": "Guillaume Coqueret", "url": "https://openalex.org/A5041711577", "inst": "Hôpital Jean Jaurès" }, { "name": "Joan Llull", "url": "https://openalex.org/A5143844902", "inst": "Institut d'Anàlisi Econòmica" }, { "name": "Florian Oswald", "url": "https://openalex.org/A5040941542", "inst": "University of Turin" }, { "name": "Christophe Pérignon", "url": "https://openalex.org/A5110741158", "inst": "HEC Paris" }, { "name": "Christoph Scheuch", "url": "https://openalex.org/A5003775169", "inst": "Humboldt-Universität zu Berlin" }, { "name": "Lars Vilhuber", "url": "https://openalex.org/A5082879481", "inst": "Cornell University" } ], "affiliations": [ "Cornell University", "Institut d'Anàlisi Econòmica", "University of Turin", "HEC Paris", "Humboldt-Universität zu Berlin" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.24649v1", "arxiv_id": "2607.24649v1", "title": "Reason-Mediated Behavioral Models for Auditing LLM Social Simulators", "authors": [ "Atharva Pandey", "Gautam Jajoo" ], "posted": "2026-07-27", "added": "2026-07-28", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.24649v1", "field": "management", "role": "method", "bullets": [ "A 94-person sunscreen concept test in which each respondent evaluated three product concepts and wrote open-ended rationales, later mapped into signed reason states for adoption or rejection.", "An unnamed large language model simulates each respondent's reason state without seeing the human rationale or outcome, then audited against the human rationale-derived reasons with no agreement figure stated.", "Human reasons substantially improve held-out purchase-intent prediction, while simulated reasons are brittle and often echo the concept board rather than the respondent's acceptance or rejection path." ], "bullet_provenance": "ai", "validated": false, "validation_note": "audits simulated reason states against 94 human rationales; no agreement statistic reported in abstract", "salience": 52, "edition": 6, "models": [], "n": 1241, "authors_detailed": [ { "name": "Atharva Pandey", "url": "https://openalex.org/A5063715042", "inst": "Indian Institute of Technology Jodhpur" }, { "name": "Gautam Jajoo", "url": "https://openalex.org/A5114472764", "inst": "Birla Institute of Technology and Science, Pilani" } ], "affiliations": [ "Indian Institute of Technology Jodhpur", "Birla Institute of Technology and Science, Pilani" ] }, { "uid": "doi:10.2139/ssrn.7093099", "doi": "10.2139/ssrn.7093099", "title": "FinSight AI: Coupling a Large Language Model with a Live NoSQL Database for Conversational Financial Analytics and Rule-Assisted Fraud Screening", "authors": [ "Awais Ur Rehman" ], "posted": "2026-07-27", "added": "2026-07-28", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7093099", "field": "finance", "role": "object", "bullets": [ "A prototype web dashboard demonstrated on a seeded 60-transaction dataset in a free-tier MongoDB Atlas store, with individual financial transactions and user questions as the unit of analysis.", "LLaMA 3.3 70B, served through the Groq API, answers plain-English questions grounded in injected transaction statistics and explains flags from a separate rule engine; no validation against ground truth is reported.", "The authors report dashboard endpoints loading in under 800 milliseconds and model answers returning in 1.5 to 3 seconds, describing the design and its limitations rather than measured accuracy." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 28, "edition": 6, "n": 1242, "authors_detailed": [ { "name": "Awais Ur Rehman", "url": "https://openalex.org/A5139639286", "inst": "Shura Council" } ], "affiliations": [ "Shura Council" ] }, { "uid": "doi:10.2139/ssrn.7119058", "doi": "10.2139/ssrn.7119058", "title": "Central Bank Communication as Literature: A Narrative Economics Survey on Fed Speak and Market Expectations", "authors": [ "Fuli Yang", "Jing Huang" ], "posted": "2026-07-27", "added": "2026-07-28", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7119058", "field": "economics", "role": "instrument", "bullets": [ "A survey synthesizing forty years of methodology for measuring US central bank communication and its link to market expectations and volatility, taking the prior literature rather than a new sample as its object.", "No model is applied; large language models are reviewed as the fourth measurement phase after manual coding, dictionary methods, and machine learning, with no specific model named.", "It maps four channels through which central bank text moves market volatility and proposes hybrid GARCH-MIDAS architectures incorporating high-frequency textual sentiment as a research frontier." ], "bullet_provenance": "ai", "salience": 40, "edition": 6, "models": [], "validated": null, "n": 1243, "authors_detailed": [ { "name": "Fuli Yang", "url": "https://openalex.org/A5101033933", "inst": "Harvard University Press" }, { "name": "Jing Huang", "url": "https://openalex.org/A5141209589", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.24072v1", "arxiv_id": "2607.24072v1", "title": "LLM-Based vs. Lexicon-Based Sentiment Signals for Tail-Risk Detection in Meme Stocks", "authors": [ "Paul Kilian", "Markus Kleffmann" ], "posted": "2026-07-27", "added": "2026-07-28", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.24072v1", "field": "finance", "role": "instrument", "bullets": [ "Reddit posts from r/WallStreetBets on three meme stocks, GME, AMC, and NOK, used to build time-aligned sentiment indicators and relate them to daily returns, with attention to upper-tail extreme-return events.", "An unnamed large language model produces multidimensional sentiment covering polarity, bullishness, sarcasm likelihood, and topical relevance, benchmarked against the VADER lexicon; no validation against hand-labeled sentiment is reported.", "The language-model signals show stronger asset-specific statistical structure than the lexicon baseline, but their relationship with returns is heterogeneous across the three stocks and does not translate into stable forecasting." ], "bullet_provenance": "ai", "validated": false, "salience": 43, "edition": 6, "models": [], "n": 1244, "authors_detailed": [ { "name": "Paul Kilian", "url": "https://openalex.org/A5143822521", "inst": "IU International University of Applied Sciences" }, { "name": "Markus Kleffmann", "url": "https://openalex.org/A5069595103", "inst": "University of Applied Sciences Erfurt" } ], "affiliations": [ "IU International University of Applied Sciences", "University of Applied Sciences Erfurt" ] }, { "uid": "doi:10.2139/ssrn.7180460", "doi": "10.2139/ssrn.7180460", "title": "The Validation Bottleneck: Alpha Discovery When Hypotheses Are Free", "authors": [ "Bernd Johannes Wuebben" ], "posted": "2026-07-27", "added": "2026-07-27", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7180460", "field": "finance", "role": "agent", "bullets": [ "Two pre-registered experiments in which five large language models across three lineages were asked to propose stock-return predictors absent from the literature as of past dates, yielding roughly 900 evaluable strategies tested after training cutoffs.", "The models generated investment hypotheses that were matched against an existing anomaly-predictor library and evaluated on out-of-sample returns; specific model names are not stated.", "26 to 55 percent of proposed novel predictors already existed, the most frequently proposed ones performed worse afterward (combined z = -3.88), and no best pick cleared false-discovery control." ], "bullet_provenance": "ai", "salience": 70, "edition": 5, "models": [], "validated": null, "n": 1234, "authors_detailed": [ { "name": "Bernd Johannes Wuebben", "url": "https://openalex.org/A5134604989", "inst": "Rainforest Alliance" } ], "affiliations": [ "Rainforest Alliance" ] }, { "uid": "doi:10.2139/ssrn.7085498", "doi": "10.2139/ssrn.7085498", "title": "Artificial Intelligence in Higher Education: A Bibliometric and Science-Mapping Analysis from an Institutional and Management Perspective", "authors": [ "Kursat Tastan", "Nalan Sabır Taştan" ], "posted": "2026-07-27", "added": "2026-07-27", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7085498", "field": "management", "role": "object", "bullets": [ "A bibliometric and science-mapping analysis of 52,270 Web of Science and Scopus articles and reviews on artificial intelligence in higher education from 1959 to 2025, viewed from an institutional and management perspective.", "No LLM is used by the researchers; the study runs the bibliometrix R package for co-authorship, co-citation, keyword co-occurrence, and thematic analysis, interpreted through neo-institutional theory.", "Publications grew exponentially after 2023 alongside ChatGPT diffusion, with China leading volume and the United States leading citation impact, indicating legitimacy-oriented adoption and mimetic isomorphism." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 40, "edition": 5, "validated": null, "n": 1235, "authors_detailed": [ { "name": "Kürşat Taştan", "url": "https://openalex.org/A5069697806", "inst": "Turkish Academy of Sciences" }, { "name": "Nalan Sabır Taştan", "url": "https://openalex.org/A5123922541", "inst": "Ankara Sosyal Bilimler Üniversitesi" } ], "affiliations": [ "Turkish Academy of Sciences", "Ankara Sosyal Bilimler Üniversitesi" ] }, { "uid": "doi:10.2139/ssrn.7191257", "doi": "10.2139/ssrn.7191257", "title": "Model Choice Is Measurement Choice: Identification Fragility in 55 Million LLM-Labeled Investor Posts", "authors": [ "Kefu Yi", "Feng Wu" ], "posted": "2026-07-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7191257", "field": "finance", "role": "method", "bullets": [ "Blinded audit of 1,000 posts drawn from 54.9 million production-labeled Chinese stock-forum posts classified for sentiment by multiple LLMs.", "Two LLM validators applied an identical five-point sentiment codebook; switching model or codebook moved the neutral share from 14.2% to 83.6%.", "Label choice determined coefficient identification in earnings-announcement regressions; one calibrated reconstruction rendered an apparent t = -9.04 an artifact of weak identification." ], "bullet_provenance": "ai", "validated": true, "validation_note": "blinded audit of 1,000 posts against five-point codebook with inter-validator comparison", "salience": 82, "models": [], "n": 2796, "authors_detailed": [ { "name": "Yi Kefu", "url": "https://openalex.org/A5047124497", "inst": "Inner Mongolia University" }, { "name": "Feng Wu", "url": "https://openalex.org/A5139657744", "inst": "Inner Mongolia University" } ], "affiliations": [ "Inner Mongolia University" ] }, { "uid": "arxiv:2607.24649v2", "arxiv_id": "2607.24649v2", "title": "Reason-Mediated Behavioral Models for Auditing LLM Social Simulators", "authors": [ "Atharva Pandey", "Gautam Jajoo" ], "posted": "2026-07-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.24649v2", "field": "management", "role": "method", "bullets": [ "Ninety-four participants each evaluated three sunscreen product concepts and wrote open-ended purchase rationales in a concept test.", "An LLM simulated signed reason states from respondent descriptors and concept text without seeing human rationales or outcomes.", "Human rationale-derived reasons improved held-out purchase-intent prediction; LLM-simulated reasons echoed concept boards rather than recovering respondent acceptance or rejection paths." ], "bullet_provenance": "ai", "validated": true, "validation_note": "held-out prediction of purchase intent vs human rationale-derived reasons", "salience": 62, "models": [], "n": 2797, "authors_detailed": [ { "name": "Atharva Pandey", "url": "https://openalex.org/A5063715042", "inst": "Indian Institute of Technology Jodhpur" }, { "name": "Gautam Jajoo", "url": "https://openalex.org/A5114472764", "inst": "Birla Institute of Technology and Science, Pilani" } ], "affiliations": [ "Indian Institute of Technology Jodhpur", "Birla Institute of Technology and Science, Pilani" ] }, { "uid": "doi:10.2139/ssrn.6902802", "doi": "10.2139/ssrn.6902802", "title": "Semantic Shift and the Cross-Section of Stock Returns in China", "authors": [ "Yi Liu", "Xuan Wang", "Ximing Yin", "Keqin Li" ], "posted": "2026-07-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6902802", "field": "finance", "role": "instrument", "bullets": [ "Three million news articles from over 500 Chinese media outlets covering listed firms, 2010-2024, monthly frequency.", "Pre-trained LLM constructed monthly firm-level semantic shift measures capturing linguistic instability in news coverage.", "Low-minus-high semantic shift portfolio earns 1.39% monthly excess return after risk-factor controls; narrative stagnation commands a higher premium than divergence." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 70, "n": 3617, "authors_detailed": [ { "name": "Yi Liu", "url": "https://openalex.org/A5143724524", "inst": "State University of New York" }, { "name": "Xuan Wang", "url": "https://openalex.org/A5143695198", "inst": "Hunan University of Finance and Economics" }, { "name": "Ximing Yin", "url": "https://openalex.org/A5054164192", "inst": "Hunan University of Finance and Economics" }, { "name": "Keqin Li", "url": "https://openalex.org/A5087894632", "inst": "State University of New York" } ], "affiliations": [ "State University of New York", "Hunan University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.7186538", "doi": "10.2139/ssrn.7186538", "title": "Automation, Augmentation, and the Productivity Translation Lag: An Economic Framework for Generative AI", "authors": [ "Esteve Almirall" ], "posted": "2026-07-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7186538", "field": "economics", "role": "object", "bullets": [ "Conceptual framework analyzing generative AI effects on aggregate productivity across sectors and task types.", "No specific model deployed; framework theorizes automation versus augmentation pathways with asymmetric scaling properties.", "Augmentation advantages erode through competitive normalization; productivity lag arises from organizational redesign needed to transition from augmented to automated production." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3618, "authors_detailed": [ { "name": "Esteve Almirall", "url": "https://openalex.org/A5088311525", "inst": "EAE Business School" } ], "affiliations": [ "EAE Business School" ] }, { "uid": "arxiv:2607.25019v1", "arxiv_id": "2607.25019v1", "title": "Interactive Alignment", "authors": [ "Sylvain Chassang" ], "posted": "2026-07-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.25019v1", "field": "economics", "role": "agent", "bullets": [ "Simulated farming game economy with agent populations making planting, trading, and expansion decisions under evolutionary selection pressure.", "LLM interprets written constitutional principles governing agent sharing and trade behavior in an evolutionary game-theoretic simulation.", "Pragmatic norm enforcement conditioning altruism and trade exclusion on population state sustains long-run alignment more effectively than simple altruism or unconditional enforcement." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 60, "n": 3619, "authors_detailed": [ { "name": "Sylvain Chassang", "url": "https://openalex.org/A5006143371", "inst": "Princeton University" } ], "affiliations": [ "Princeton University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.24879v1", "arxiv_id": "2607.24879v1", "title": "Generative Artificial Intelligence in Scientific Research: Individual Benefits, Collective Risks, and a Framework for Responsible Research with AI", "authors": [ "Fulvio Castellacci", "Tommaso Ciarli", "Yuan Gao", "Marianna Marino", "Giacomo Marzi", "Massimo Riccaboni", "Maria Savona", "Simone Vannuccini" ], "posted": "2026-07-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.24879v1", "field": "economics", "role": "object", "bullets": [ "Academic roundtable and literature review examining generative AI adoption across funding, research tasks, publication, and uptake stages.", "No specific model deployed; paper reviews empirical evidence on AI productivity, augmentation, and democratization effects in scientific research.", "AI-assisted work shows gains in publication volume and citation share, but evidence on novelty, disruption, and breakthrough output remains ambiguous or negative." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3620, "authors_detailed": [ { "name": "Fulvio Castellacci", "url": "https://openalex.org/A5040481657", "inst": "University of Oslo" }, { "name": "Tommaso Ciarli", "url": "https://openalex.org/A5091228525", "inst": "University of Sussex" }, { "name": "Yuan Gao", "url": "https://openalex.org/A5143870293", "inst": "Ningbo Institute of Industrial Technology" }, { "name": "Marianna Marino", "url": "https://openalex.org/A5143918319", "inst": "" }, { "name": "Giacomo Marzi", "url": "https://openalex.org/A5002135733", "inst": "IMT School for Advanced Studies Lucca" }, { "name": "Massimo Riccaboni", "url": "https://openalex.org/A5082803450", "inst": "IMT School for Advanced Studies Lucca" }, { "name": "María Savona", "url": "https://openalex.org/A5019353593", "inst": "University of Sussex" }, { "name": "Simone Vannuccini", "url": "https://openalex.org/A5075864742", "inst": "Centre National de la Recherche Scientifique" } ], "affiliations": [ "University of Oslo", "University of Sussex", "Ningbo Institute of Industrial Technology", "IMT School for Advanced Studies Lucca", "Centre National de la Recherche Scientifique" ] }, { "uid": "doi:10.2139/ssrn.7124041", "doi": "10.2139/ssrn.7124041", "title": "Permissible Learning Space as a Condition for Autonomous AI Decision-Making", "authors": [ "Dušica Živanović" ], "posted": "2026-07-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7124041", "field": "management", "role": "object", "bullets": [ "Conceptual framework for governing autonomous AI agent decision-making in organizations, applied to the case of AI-enabled negotiation scenarios.", "Proposes a five-boundary framework (information, relationship, objective, decision, execution) defining the permissible scope for AI agent autonomy in firms.", "Trustworthy AI autonomy requires ex ante boundary definition rather than post-deployment monitoring; breadth of independent action is the wrong evaluation criterion." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3990, "authors_detailed": [ { "name": "Dušica Živanović", "url": "https://openalex.org/A5089489230", "inst": "Zdravstveni centar" } ], "affiliations": [ "Zdravstveni centar" ] }, { "uid": "doi:10.2139/ssrn.7174739", "doi": "10.2139/ssrn.7174739", "title": "Large Language Models Explain Experts Better Than Experts Themselves", "authors": [ "Mina Cho", "Russell Funk", "Alok Gupta", "Mochen Yang" ], "posted": "2026-07-26", "added": "2026-07-27", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7174739", "field": "management", "role": "object", "bullets": [ "Two studies examining whether large language models can externalize experts' tacit knowledge and whether the result aids novice decision-making; sample sizes, domains, and geography are not stated.", "LLMs externalized tacit knowledge from expert conversations; the specific model family is not stated, though findings are said to generalize across models and retrieval methods, and no ground-truth accuracy check is reported.", "LLM-externalized knowledge improved decision quality and let novices approach expert-level performance, often outperforming knowledge articulated by the human experts themselves." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no ground-truth accuracy check; evaluated via downstream decision experiments", "salience": 67, "edition": 5, "models": [], "n": 1232, "authors_detailed": [ { "name": "Mina Cho", "url": "https://openalex.org/A5143658122", "inst": "University of Minnesota" }, { "name": "Russell Funk", "url": "https://openalex.org/A5143655716", "inst": "University of Minnesota" }, { "name": "Alok Gupta", "url": "https://openalex.org/A5022969857", "inst": "University of Minnesota" }, { "name": "Mochen Yang", "url": "https://openalex.org/A5008688627", "inst": "University of Minnesota" } ], "affiliations": [ "University of Minnesota" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.23424v2", "arxiv_id": "2607.23424v2", "title": "Wrong and More Confident: A Field Experiment on Large Language Models Taking a Graduate Economics Exam", "authors": [ "Piyush Akimitsu" ], "posted": "2026-07-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.23424v2", "field": "economics", "role": "agent", "bullets": [ "Thirty-eight language models tested on 60 graduate-level microeconomics problems in a within-subject 2x2 factorial design with and without red herrings", "Each model answered all four versions of every problem; accuracy measured against verified reference solutions across explanation and no-explanation conditions", "Red herrings lowered correct-answer probability by 12.3 percentage points, roughly one quarter of mean accuracy; reasoning ability conferred no protection against this effect" ], "bullet_provenance": "ai", "validated": true, "validation_note": "GERB benchmark with verified graduate microeconomics solutions", "salience": 62, "models": [], "n": 3105, "authors_detailed": [ { "name": "Piyush Akimitsu", "url": "https://openalex.org/A5143748290", "inst": "University at Albany, State University of New York" } ], "affiliations": [ "University at Albany, State University of New York" ] }, { "uid": "doi:10.2139/ssrn.7148679", "doi": "10.2139/ssrn.7148679", "title": "Guidelines to Preventing AI Hallucinations in MS Fabric", "authors": [ "Idilio Moncivais", "Bernardo Fuentes" ], "posted": "2026-07-25", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7148679", "field": "management", "role": "method", "bullets": [ "Enterprise analytics in Microsoft Fabric, where Copilot works directly against organizational data and hallucinated values or misread schemas can flow into business decisions.", "No model is tested; the paper distills academic research and vendor practice into a layered mitigation framework of governance, data quality, retrieval augmentation, structured prompting, and human review.", "The output is the framework itself; no measurement of hallucination rates before or after applying the recommended controls is reported." ], "bullet_provenance": "ai", "salience": 20, "edition": 8, "models": [], "validated": null, "n": 1302, "authors_detailed": [ { "name": "Idilio Moncivais Pinedo", "url": "https://openalex.org/A5134344672", "inst": "University of Northern Colorado" }, { "name": "Bernardo Fuentes", "url": "https://openalex.org/A5143627518", "inst": "Independent" } ], "affiliations": [ "University of Northern Colorado", "Independent" ] }, { "uid": "arxiv:2607.23386v1", "arxiv_id": "2607.23386v1", "title": "Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation", "authors": [ "Paul Simpson", "John Kozak", "Lisa Doake" ], "posted": "2026-07-25", "added": "2026-07-31", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.23386v1", "field": "management", "role": "method", "bullets": [ "Eligibility decisions under nested unless-clauses in four regulated domains, tested with a 225-scenario benchmark, a 20-scenario construction insurance extension, and 949 held-out LegalBench cases.", "Frontier models including GPT-5.4 and Anthropic models are scored against labelled cases; the authors' Aethis engine instead has LLMs author rules that an SMT layer executes deterministically.", "Several failure cells closed silently under the same model alias with no version bump, and the engine beat all three frontier models on LegalBench, by up to 41 points against the Anthropic models." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "labelled scenario benchmarks plus 949 LegalBench cases, accuracy and McNemar tests", "salience": 48, "edition": 8, "n": 1312, "authors_detailed": [ { "name": "Paul Simpson", "url": "https://openalex.org/A5103088883", "inst": "Pfizer (United States)" }, { "name": "John Kozak", "url": "https://openalex.org/A5055061212", "inst": "" }, { "name": "Lisa Doake", "url": "https://openalex.org/A5143802832", "inst": "" } ], "affiliations": [ "Pfizer (United States)" ] }, { "uid": "doi:10.2139/ssrn.7175238", "doi": "10.2139/ssrn.7175238", "title": "Wearable-Driven Distributed AI for Smart Pilgrimage Logistics and Pilgrim Experience during Hajj and Umrah: A Framework, Architecture, and Edge-Fog-Cloud Evaluation", "authors": [ "Khalid Muhammad Suleiman", "Mogesi Chacha Moses", "Bornface Kamela", "Nourah Janbi", "Abdullah Zaini Alsheibi", "Rashid Mehmood" ], "posted": "2026-07-25", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7175238", "field": "management", "role": "instrument", "bullets": [ "Wearable sensor data from pilgrims, including a seven month collection at the Prophet's Mosque, feeding a hierarchical edge, fog, and cloud framework for Hajj and Umrah operations.", "An unnamed LLM converts anomaly detections and sensor statistics into operational insights and recommendations; no validation of these outputs against any ground truth is reported.", "In simulation, distributing intelligence across the hierarchy lowers communication costs and improves responsiveness over centralized processing, supporting healthcare delivery, crowd management, and resource planning during mass gatherings." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 8, "models": [], "n": 1326, "authors_detailed": [ { "name": "Khalid Muhammad Suleiman", "url": "https://openalex.org/A5143628839", "inst": "Islamic University of Madinah" }, { "name": "Mogesi Chacha Moses", "url": "https://openalex.org/A5143637286", "inst": "Islamic University of Madinah" }, { "name": "Bornface Kamela", "url": "https://openalex.org/A5143641638", "inst": "Islamic University of Madinah" }, { "name": "Nourah Fahad Janbi", "url": "https://openalex.org/A5091476431", "inst": "University of Jeddah" }, { "name": "Abdullah Zaini Alsheibi", "url": "https://openalex.org/A5037624009", "inst": "Islamic University of Madinah" }, { "name": "Rashid Mehmood", "url": "https://openalex.org/A5077619272", "inst": "Islamic University of Madinah" } ], "affiliations": [ "Islamic University of Madinah", "University of Jeddah" ] }, { "uid": "arxiv:2607.23313v1", "arxiv_id": "2607.23313v1", "title": "Agentic AI Orchestration of Heterogeneous Economic Models for Rapid, Multi-scenario Analysis of Energy Crises", "authors": [ "Dana Golden", "Brett Indelicato", "Lav R. Varshney", "Carlos D. Messina", "Suzanne Thornsbury" ], "posted": "2026-07-25", "added": "2026-07-28", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.23313v1", "field": "economics", "role": "agent", "bullets": [ "Sixteen existing economic and physical models spanning oil, natural gas, shipping, water, helium, fertilizer, and macroeconomic equilibrium, applied to five scenarios of the 2026 Strait of Hormuz closure and refreshed weekly over eight weeks.", "An unnamed large language model orchestrates the models, constructs consistent scenarios, translates assumptions into model inputs, and executes runs in dependency order; no accuracy check is stated, and every reported value comes from an underlying model.", "The framework links distributed models into a single suite for rapid multi-scenario analysis, keeping any one model's assumptions from driving conclusions while remaining traceable and subject to analyst approval at each stage." ], "bullet_provenance": "ai", "salience": 50, "edition": 6, "models": [], "validated": null, "n": 1238, "authors_detailed": [ { "name": "Dana Golden", "url": "https://openalex.org/A5003023054", "inst": "Henry M. Jackson Foundation" }, { "name": "Brett Indelicato", "url": "https://openalex.org/A5143846919", "inst": "" }, { "name": "Lav R. Varshney", "url": "https://openalex.org/A5122150805", "inst": "Stony Brook University" }, { "name": "Carlos D. Messina", "url": "https://openalex.org/A5143804557", "inst": "University of Florida" }, { "name": "Suzanne Thornsbury", "url": "https://openalex.org/A5135689258", "inst": "Universitario Francisco de Asís" } ], "affiliations": [ "University of Florida", "Henry M. Jackson Foundation", "Stony Brook University", "Universitario Francisco de Asís" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7177738", "doi": "10.2139/ssrn.7177738", "title": "Generative AI in Cost Accounting and Financial Reporting: A Systematic Review of Opportunities, Challenges, and Future Research Directions", "authors": [ "Geetaben Khunt", "Dr. Brijesh M. Patel" ], "posted": "2026-07-25", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7177738", "field": "accounting", "role": "object", "bullets": [ "Systematic literature review of articles and preprints on generative AI in cost accounting and financial reporting, screened under the PRISMA framework with two independent reviewer passes; no time window or article count stated.", "Authors run no model; the review treats ChatGPT and Gemini as example systems and thematically synthesizes reported benefits and challenges for accounting tasks, so no validation against ground truth applies.", "Generative AI aids data entry and narrative disclosure drafting, with gains rising with model generation and prompt sophistication, but falters on professional judgment, local GAAP, and misleadingly plausible output, supporting an assistant not substitute role." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 42, "edition": 4, "validated": null, "n": 1206, "authors_detailed": [ { "name": "Geetaben Khunt", "url": "https://openalex.org/A5143604318", "inst": "" }, { "name": "Dr. Brijesh M. Patel", "url": "https://openalex.org/A5143566626", "inst": "Sardar Patel University" } ], "affiliations": [ "Sardar Patel University" ] }, { "uid": "doi:10.2139/ssrn.7077578", "doi": "10.2139/ssrn.7077578", "title": "When LLMs Bid: Winner's Curse and Information Interventions in Human-AI Competitive Auctions", "authors": [ "Yuehu Zhao", "Ruoran Chen", "Yuxuan Zhang", "Simin Huang" ], "posted": "2026-07-25", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7077578", "field": "economics", "role": "agent", "bullets": [ "Common-value auction experiments pitting LLM agents against modeled human bidders represented by simulation and equilibrium agents; number of auctions, time period, and geography are not stated.", "ChatGPT and DeepSeek serve as autonomous bidders, and their strategies are compared to theoretical equilibrium bidding using descriptive comparisons and mixed-effects models, with no benchmark against ground truth.", "Both models deviate from equilibrium and suffer the winner's curse at high valuations, worsening as auction scale grows; outcome feedback reduces but does not eliminate exposure, and interventions have model-specific effects." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "salience": 58, "edition": 4, "validated": null, "n": 1207, "authors_detailed": [ { "name": "Yuehu Zhao", "url": "https://openalex.org/A5129665790", "inst": "Tsinghua University" }, { "name": "Ruoran Chen", "url": "https://openalex.org/A5042839195", "inst": "Southwest Jiaotong University" }, { "name": "Yuxuan Zhang", "url": "https://openalex.org/A5014195111", "inst": "University of International Business and Economics" }, { "name": "Simin Huang", "url": "https://openalex.org/A5143576051", "inst": "Tsinghua University" } ], "affiliations": [ "Tsinghua University", "Southwest Jiaotong University", "University of International Business and Economics" ] }, { "uid": "doi:10.2139/ssrn.7076999", "doi": "10.2139/ssrn.7076999", "title": "Can Open LLMs Generate Useful Synthetic Responses for Behavioral Experiments?", "authors": [ "Siva Shanmugam Mariappan", "Ashwin V. Malshe", "Jihye Jung" ], "posted": "2026-07-25", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7076999", "field": "management", "role": "agent", "bullets": [ "Two behavioral studies comparing persona-based synthetic responses from open large language models against human-subject responses and proprietary LLM outputs, generated through a synthetic response tool.", "Open-weight LLMs (families not named) generate synthetic experimental subjects, with outputs validated against human responses to assess whether they recover effect direction and effect-size accuracy.", "Open LLMs recover the general direction of experimental effects, useful for theory development and pretesting, but are less reliable for precise effect sizes, with reasoning models better on individual differences." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "synthetic responses compared to human-subject data across two studies", "salience": 54, "edition": 4, "models": [], "n": 1215, "authors_detailed": [ { "name": "Siva Shanmugam Mariappan", "url": "https://openalex.org/A5125563479", "inst": "San Antonio College" }, { "name": "Ashwin Malshe", "url": "https://openalex.org/A5054246422", "inst": "The University of Texas at San Antonio" }, { "name": "Jihye Jung", "url": "https://openalex.org/A5125548812", "inst": "The University of Texas at San Antonio" } ], "affiliations": [ "San Antonio College", "The University of Texas at San Antonio" ] }, { "uid": "doi:10.2139/ssrn.7084378", "doi": "10.2139/ssrn.7084378", "title": "Grounding Corporate Environmental Claims in Satellite Evidence with Large Language Models", "authors": [ "Saeid Vaghefi", "Chiara Colesanti Senni", "Christian Huggel", "Markus Leippold" ], "posted": "2026-07-25", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7084378", "field": "accounting", "role": "instrument", "bullets": [ "1,245 site-level environmental claims drawn from 214 European companies' 2024 sustainability reports, with companion 2023 and 2025 corpora, checked against satellite imagery.", "An unnamed language model runs an agentic pipeline, LLEO, that calls Google Earth Engine tools over Sentinel-2 and Sentinel-5P data to test each claim; no accuracy benchmark is reported.", "Satellite land-cover change corroborates 26.1 percent of claims and contradicts 7.5 percent, while 66.3 percent stay under-determined; only 9 percent disclose precise coordinates, making geolocation the binding constraint." ], "bullet_provenance": "ai", "validated": false, "validation_note": "corroboration rates reported, no ground-truth accuracy check", "salience": 56, "edition": 4, "models": [], "n": 1217, "authors_detailed": [ { "name": "Saeid Ashraf Vaghefi", "url": "https://openalex.org/A5035433111", "inst": "University of Zurich" }, { "name": "Chiara Colesanti Senni", "url": "https://openalex.org/A5086406811", "inst": "University of Zurich" }, { "name": "Christian Huggel", "url": "https://openalex.org/A5028598221", "inst": "University of Zurich" }, { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" } ], "affiliations": [ "University of Zurich" ] }, { "uid": "doi:10.2139/ssrn.7177328", "doi": "10.2139/ssrn.7177328", "title": "JurisMap: A Visual Analytics and Generative AI Approach to Automating Administrative Legal Defense Production", "authors": [ "Hugo Saisse Mentzingen da Silva", "Nuno António", "Fernando Bacao" ], "posted": "2026-07-25", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7177328", "field": "management", "role": "method", "bullets": [ "Administrative insurance cases from Brazil's National Private Insurance System Appeals Board, with the JurisMap SaaS artifact evaluated by five domain experts through hands-on tasks and a structured questionnaire.", "The system combines text-embedding similarity search, a UMAP map, a retrieval-augmented chatbot, and AI-agent defense drafting; the underlying model family is not stated and outputs were judged by perceived value, not ground truth.", "Across fifteen Likert items the overall mean rating was 4.28 out of 5, with time-saving at 4.8, usefulness at 4.6, and ease of learning at 4.6." ], "bullet_provenance": "ai", "validated": false, "validation_note": "expert Likert ratings, no ground truth", "salience": 35, "edition": 4, "models": [], "n": 1218 }, { "uid": "doi:10.2139/ssrn.7084618", "doi": "10.2139/ssrn.7084618", "title": "Autonomous Governance of Agentic Financial Systems: A Multi-Agent Regulatory Framework for Real-Time Market Integrity Surveillance and Systemic Risk Mitigation", "authors": [ "Prakhar Rai" ], "posted": "2026-07-25", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7084618", "field": "finance", "role": "agent", "bullets": [ "Simulated multi-agent financial market with heterogeneous trading strategies labelled normal, spoofing and collusive, run on resource-constrained hardware; no real market data used.", "Regulatory AI agents, including an LLM-based reasoning component, monitor trades, detect anomalies and execute interventions; detection was scored against the known injected strategy labels; specific model not stated.", "Reports 90.3 percent detection of anomalous behavior, 32.1 percent lower portfolio volatility and 41.5 percent smaller maximum drawdown versus unregulated markets." ], "bullet_provenance": "ai", "validated": true, "validation_note": "90.3% detection against injected spoofing and collusive labels", "salience": 47, "edition": 4, "models": [], "n": 1227, "authors_detailed": [ { "name": "Prakhar Rai", "url": "https://openalex.org/A5134061221", "inst": "Great Lakes Institute of Management" } ], "affiliations": [ "Great Lakes Institute of Management" ] }, { "uid": "doi:10.2139/ssrn.7182339", "doi": "10.2139/ssrn.7182339", "title": "The Formalization Threshold and Selection Effect: A Complexity Grading Criterion and Human–LLM Framework from Eight Industrial Cases", "authors": [ "Longjie Jia" ], "posted": "2026-07-25", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7182339", "field": "management", "role": "method", "bullets": [ "Eight real industrial cases spanning steel rolling, water treatment, continuous-casting electrics, materials development, industrial software and networks, with two studied in depth.", "Large language models support a five-stage human-LLM framework and an L1 to L4 complexity grading; no specific model named; reliability checked through independent double ratings of complexity scores.", "In-depth cases saw complexity scores fall from 15 to 7 and 8; the authors argue representation completeness, not model capability, is the binding constraint." ], "bullet_provenance": "ai", "validated": false, "salience": 34, "edition": 4, "models": [], "n": 1228, "authors_detailed": [ { "name": "Longjie Jia", "url": "https://openalex.org/A5112328151", "inst": "Imperial College London" } ], "affiliations": [ "Imperial College London" ] }, { "uid": "doi:10.2139/ssrn.7078838", "doi": "10.2139/ssrn.7078838", "title": "Between Red and Blue Oceans: Strategic Innovation Distance, Competitive Congestion, and US Firm Performance - A Large-Sample Empirical Analysis of Heuristic Mis-specification", "authors": [ "Jonas Marius Masiliūnas" ], "posted": "2026-07-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7078838", "field": "management", "role": "instrument", "bullets": [ "SEC 10-K filings for 68,806 US firm-year observations and 5,515 S&P 500 firm-years, analyzed with structured performance matrices.", "Local LLM methods extracted strategic innovation distance and competitive congestion metrics from filings via lexicographic analysis.", "Increasing novelty-to-congestion ratio raises ROA, but profitability declines in the 10th decile and among lower-revenue and mid-sized firms." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 45, "n": 3616, "authors_detailed": [ { "name": "Jonas Marius Masiliūnas", "url": "https://openalex.org/A5143634497", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7153418", "doi": "10.2139/ssrn.7153418", "title": "Generative Gap Filling", "authors": [ "Yonathan A. Arbel", "David A. Hoffman" ], "posted": "2026-07-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7153418", "field": "economics", "role": "agent", "bullets": [ "Real contracts with masked negotiated terms, tested with lay readers, law students, practicing lawyers, and several large language models.", "LLMs predicted removed contract terms from surrounding text using a masking design; predictions were compared against actual negotiated terms and human readers.", "LLMs predicted missing terms correctly nearly nine times in ten versus about half for humans, demonstrating contracts encode sufficient context to reconstruct gaps." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Masked contract terms vs. actual negotiated terms", "salience": 65, "models": [], "n": 3636, "authors_detailed": [ { "name": "Yonathan A. Arbel", "url": "https://openalex.org/A5143624527", "inst": "University of Alabama" }, { "name": "David A. Hoffman", "url": "https://openalex.org/A5081607778", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania", "University of Alabama" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7182341", "doi": "10.2139/ssrn.7182341", "title": "From Untrusted Documents to Trusted Industrial Transactions: Integrity-Aware InformationIntegration for LLM-Enabled Freight Logistics", "authors": [ "Sivalingam Thangavel" ], "posted": "2026-07-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7182341", "field": "management", "role": "instrument", "bullets": [ "FreightSkillBench with 60 falsified and 60 benign freight-document cases across four document formats, five high-risk field families, and five model configurations.", "LLMs extracted transaction data from emails, PDFs, and EDI messages; integrity was tested via schema gating and reference reconciliation against master data.", "Direct LLM integration committed 99.6% of manifested errors; complete-reference reconciliation prevented all incorrect commits while preserving 96.4% of valid inputs." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FreightSkillBench: 120 freight-document cases with known ground truth", "salience": 40, "models": [], "n": 3637, "authors_detailed": [ { "name": "Sivalingam Thangavel", "url": "https://openalex.org/A5138029247", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7067238", "doi": "10.2139/ssrn.7067238", "title": "Net Systemic Value: Measuring the Downstream Value AI Agents Create and Destroy", "authors": [ "Ildar Fazulyanov" ], "posted": "2026-07-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7067238", "field": "management", "role": "object", "bullets": [ "Theoretical framework for measuring downstream value that AI agents create and destroy within networked, AI-augmented firms.", "Defines Net Systemic Value as a successor to Economic Value Added, charging each AI-augmented unit for value it destroys in the units it serves.", "Local AI optimization can yield negative NSV (the AI Suboptimal Optimum), a dollar-denominated gap that process mining and agent-ROI estimators structurally miss." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3989, "authors_detailed": [ { "name": "Ildar Fazulyanov", "url": "https://openalex.org/A5133358309", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7169218", "doi": "10.2139/ssrn.7169218", "title": "Generative AI for Enterprise Software Engineering: Current Applications, Challenges, and Future Research Directions", "authors": [ "Sahith Basani" ], "posted": "2026-07-24", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7169218", "field": "management", "role": "object", "bullets": [ "A systematic literature review with comparative analysis covering generative AI across the enterprise software lifecycle, from requirements and code generation to testing, DevOps, and project management.", "No model is run or named; the paper synthesizes prior academic and industry studies of LLM coding tools, so no original system is validated.", "Reported gains span development speed, debugging, testing quality, and documentation, while code reliability, hallucination, intellectual property, security, and workforce adaptation remain open concerns." ], "bullet_provenance": "ai", "salience": 22, "edition": 8, "models": [], "validated": null, "n": 1301, "authors_detailed": [ { "name": "Sahith Basani", "url": "https://openalex.org/A5143597395", "inst": "" } ] }, { "uid": "arxiv:2607.22513v2", "arxiv_id": "2607.22513v2", "title": "Opaque Epistemic Mediation: How LLM Deployment Configurations Shape the Validation of Pseudo-Science", "authors": [ "Davide Scarso", "Hugo Noronha de Almeida", "Joaquim Pina" ], "posted": "2026-07-24", "added": "2026-07-31", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.22513v2", "field": "other", "role": "object", "bullets": [ "Four LLM families, Claude, Grok, GPT, and Gemini, probed on ethnonationalist pseudo-science claims from Salter's biosocial framework across four snapshots from October 2025 to February 2026, via API and web interfaces.", "The models are studied rather than used as tools; credibility scores are elicited, with control prompts on evolutionary consensus and refuted Lamarckian claims to isolate the contested material.", "Grok's fast versions scored the claims 70 to 75 versus 15 to 40 for other models, a silent patch reversed Grok's behaviour overnight, and the same identifier gave 75 via API but a mean of 5.5 via web." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 52, "edition": 8, "validated": null, "n": 1311, "authors_detailed": [ { "name": "Davide Scarso", "url": "https://openalex.org/A5072827485", "inst": "University of Lisbon" }, { "name": "Hugo Noronha de Almeida", "url": "https://openalex.org/A5143724164", "inst": "Aquatic Systems (United States)" }, { "name": "Joaquim Pina", "url": "https://openalex.org/A5143680874", "inst": "Universidade Nova de Lisboa" } ], "affiliations": [ "University of Lisbon", "Aquatic Systems (United States)", "Universidade Nova de Lisboa" ] }, { "uid": "doi:10.2139/ssrn.7069958", "doi": "10.2139/ssrn.7069958", "title": "Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations", "authors": [ "Shuze Daniel Liu", "Claire Chen", "Jiabao Sean Xiao", "Xin Chen", "David Simchi-Levi" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7069958", "field": "management", "role": "agent", "bullets": [ "A single seller negotiating simultaneously with multiple buyers who hold heterogeneous private budgets, under a limited number of communication turns; a simulated bargaining environment, sample size and period not stated.", "Large language models play the seller and are trained via reinforcement learning from verifiable rewards anchored to objective economic outcomes; the specific model family is not stated.", "The trained seller extracts substantially higher surplus than frontier models, learning price anchoring and strategic probing to find high-value buyers, and generalizes to unseen negotiation styles and budget distributions." ], "bullet_provenance": "ai", "salience": 55, "edition": 4, "models": [], "validated": null, "n": 1210, "authors_detailed": [ { "name": "Shuze Liu", "url": "https://openalex.org/A5061588035", "inst": "Sun Yat-sen University" }, { "name": "Claire Chen", "url": "https://openalex.org/A5143587512", "inst": "" }, { "name": "Jiabao Sean Xiao", "url": "https://openalex.org/A5133575979", "inst": "Massachusetts Institute of Technology" }, { "name": "Xin Chen", "url": "https://openalex.org/A5143588354", "inst": "" }, { "name": "David Simchi-Levi", "url": "https://openalex.org/A5143579571", "inst": "" } ], "affiliations": [ "Massachusetts Institute of Technology", "Sun Yat-sen University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7065260", "doi": "10.2139/ssrn.7065260", "title": "Toward Semantic Financial State Spaces for Generative Reasoning: Why LLM-native Financial Systems Require Semantic Compression Architectures Instead Of Continuous Factor Spaces", "authors": [ "Jose Miguel Fernandez Dominguez" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7065260", "field": "finance", "role": "method", "bullets": [ "Conceptual paper with no dataset or empirical test, addressing how financial data architectures should be structured for large language model reasoning over asset-level information.", "No specific model is named or run; the paper proposes a Semantic Allocation Vector architecture that compresses continuous financial exposures into discrete semantic state spaces, with no validation reported.", "Argues that lossy semantic compression preserves the causal geometry needed for economic reasoning while removing numerical noise that degrades LLM performance, presented as a framework rather than a predictive model." ], "bullet_provenance": "ai", "salience": 28, "edition": 4, "models": [], "validated": null, "n": 1212, "authors_detailed": [ { "name": "J Rosales Dominguez", "url": "https://openalex.org/A5067452777", "inst": "Corporación Universitaria Rafael Nuñez" } ], "affiliations": [ "Corporación Universitaria Rafael Nuñez" ] }, { "uid": "doi:10.2139/ssrn.7070978", "doi": "10.2139/ssrn.7070978", "title": "From Numbers to Narratives: A Generative Artificial Intelligence Framework for Automated Risk Report Production in IT Program Portfolio Governance", "authors": [ "Chiemere Victor Ezeokechukwu", "Nkem Obaloje" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7070978", "field": "management", "role": "method", "bullets": [ "Federal IT investment governance archives, with a model fine-tuned on 2,340 authentic program risk reports, evaluated on 412 held-out investment records plus a study with 56 program board members.", "A fine-tuned large language model (family not stated) is combined with XGBoost delay-risk classification and TreeSHAP attribution to generate governance risk narratives from program performance data vectors.", "PRISM scored BLEU-4 of 0.743 and BERTScore F1 of 0.891, passed a practitioner Turing test 74.6 percent of the time, and cut report production from 4.2 hours to 11 minutes." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "BLEU-4, BERTScore, and practitioner Turing test on 412 held-out reports", "salience": 50, "edition": 4, "n": 1213, "authors_detailed": [ { "name": "Chiemere Victor Ezeokechukwu", "url": "https://openalex.org/A5143572122", "inst": "Babcock University" }, { "name": "Nkem Obaloje", "url": "https://openalex.org/A5143569013", "inst": "" } ], "affiliations": [ "Babcock University" ] }, { "uid": "doi:10.2139/ssrn.7040658", "doi": "10.2139/ssrn.7040658", "title": "Are LLMs Reliable Coders of Communication Content in Economic Experiments? Working Paper #0115", "authors": [ "Andrzej Baranski", "David Cooper", "Jeong Kyu Lee" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7040658", "field": "economics", "role": "method", "bullets": [ "Free-form communication data from three previously published economics experiments, re-coded to test whether large language models can substitute for research-assistant content coding.", "Large language models (specific family not stated) code the communication; reliability is judged by whether LLM-vs-RA gaps stay within RA-vs-original gaps and whether qualitative conclusions replicate.", "LLM coding met both reliability conditions in most cases but failed in some, with prompt design reducing disagreements, and authors stress that RA test coding and researcher prompt design remain necessary." ], "bullet_provenance": "ai", "validated": true, "validation_note": "LLM coding compared to RA and original hand coding across three experiments", "salience": 60, "edition": 4, "models": [], "n": 1214, "authors_detailed": [ { "name": "Andrzej Baranski", "url": "https://openalex.org/A5103099646", "inst": "University College Dublin" }, { "name": "David Cooper", "url": "https://openalex.org/A5101063871", "inst": "University of Saskatchewan" }, { "name": "Jeong Kyu Lee", "url": "https://openalex.org/A5046372714", "inst": "Ulsan College" } ], "affiliations": [ "University College Dublin", "University of Saskatchewan", "Ulsan College" ] }, { "uid": "doi:10.2139/ssrn.7060438", "doi": "10.2139/ssrn.7060438", "title": "Selected Evidence, Omitted Information, and Belief Updating in Large Language Model Decision Support", "authors": [ "Zebang Deng", "Jubo Yan" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7060438", "field": "economics", "role": "agent", "bullets": [ "Two experiments adapting Enke's WYSIATI belief-updating task, one reanalyzing human microdata and one embedding the structure in financial due diligence, hiring, and procurement scenarios.", "Several language models including GPT-5.5 produced numerical estimates from deliberately selected evidence, benchmarked against Bayesian and visible-sample updating, with rationale outputs inspected rather than hidden reasoning.", "Humans and most models updated toward the visible sample while GPT-5.5 largely corrected the numeric contrast, identifying a specific failure in numerical updating from selected evidence." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 4, "validated": null, "n": 1216, "authors_detailed": [ { "name": "Zebang Deng", "url": "https://openalex.org/A5143540979", "inst": "Nanyang Technological University" }, { "name": "Jubo Yan", "url": "https://openalex.org/A5008570560", "inst": "Lingnan University" } ], "affiliations": [ "Nanyang Technological University", "Lingnan University" ] }, { "uid": "doi:10.2139/ssrn.7052339", "doi": "10.2139/ssrn.7052339", "title": "From Tokens to Tactics: Operationalizing Generative AI in Enterprise Workflows", "authors": [ "Hemant Soni", "Sana Zia Hassan", "Mallesh Deshapaga", "Mahima Bansod", "Rethish Nair Rajendran" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7052339", "field": "management", "role": "object", "bullets": [ "Conceptual and case-based discussion of enterprise generative AI adoption in customer support, document intelligence, personalized marketing and software development; sample, period and geography not stated.", "Generative AI is the subject rather than a research tool; the paper proposes a layered operating model aligning capabilities, data pipelines, human-in-the-loop review and compliance boundaries; specific model not stated.", "Reports that case studies show measurable productivity gains and risk controls, positioning the layered model as a path from proof-of-concept to business-ready deployment; no magnitudes given." ], "bullet_provenance": "ai", "salience": 32, "edition": 4, "models": [], "validated": null, "n": 1224, "authors_detailed": [ { "name": "Hemant Soni", "url": "https://openalex.org/A5120848713", "inst": "Independent" }, { "name": "Sana Zia Hassan", "url": "https://openalex.org/A5143489271", "inst": "Independent Researcher" }, { "name": "Mallesh Deshapaga", "url": "https://openalex.org/A5120825632", "inst": "Syntek Technologies (United States)" }, { "name": "Mahima Bansod", "url": "https://openalex.org/A5143505779", "inst": "Independent Researcher" }, { "name": "Rajini Rajendran", "url": "https://openalex.org/A5027298349", "inst": "Independent Researcher" } ], "affiliations": [ "Independent", "Independent Researcher", "Syntek Technologies (United States)" ] }, { "uid": "doi:10.2139/ssrn.7170774", "doi": "10.2139/ssrn.7170774", "title": "From Resource-Based View to Agent-Based Capability: A Theory of AI-Enabled Solo Entrepreneurship", "authors": [ "Yuquan Sun" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7170774", "field": "management", "role": "object", "bullets": [ "Theory paper on AI-augmented one-person companies, grounded in exploratory qualitative evidence from solo founders; sample size, period and geography not stated.", "AI agents are the studied phenomenon rather than a research tool; the paper builds Agent-Based Capability Theory from resource-based and transaction-cost economics; no specific model named.", "Proposes an Agentic Substitutability Threshold at which founders shift from operator to orchestrator, and derives six testable propositions on profitability, entry barriers, firm boundaries and founder well-being." ], "bullet_provenance": "ai", "salience": 40, "edition": 4, "models": [], "validated": null, "n": 1225, "authors_detailed": [ { "name": "Yuquan Sun", "url": "https://openalex.org/A5133892131", "inst": "Publicis Sapient" } ], "affiliations": [ "Publicis Sapient" ] }, { "uid": "doi:10.2139/ssrn.7142838", "doi": "10.2139/ssrn.7142838", "title": "Is Generative AI Destroying Edtech? A Two-Channel Test of Paid Demand", "authors": [ "Wenxun Zhao" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7142838", "field": "economics", "role": "object", "bullets": [ "Panel of 16 US-listed edtech firms from 2019 through early 2026, testing two demand channels, product substitution and search-based customer acquisition, with effect-size tiers fixed before estimation.", "Generative AI is studied as a demand shock rather than used as a research tool; the design tests pre-registered effect-size tiers across specifications; no model run by the researchers.", "Finds a well-powered null on product substitution but a search-channel effect emerging in 2024Q3 of 0.07 to 0.09 log points per standard deviation of search dependence." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 4, "validated": null, "n": 1226, "authors_detailed": [ { "name": "Weidong Zhao", "url": "https://openalex.org/A5016584164", "inst": "Harbin University of Science and Technology" } ], "affiliations": [ "Harbin University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7062878", "doi": "10.2139/ssrn.7062878", "title": "From System Defence to Dependency Governance: Delegated Trust, Non-Human Identity, and Secure AI-by-Design", "authors": [ "Rubaiyyaat Aakbar" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7062878", "field": "management", "role": "object", "bullets": [ "Working paper reviewing several 2024 to 2025 enterprise cyber incidents, including Salesloft Drift, Change Healthcare, CrowdStrike, Marks and Spencer, and Jaguar Land Rover, alongside enterprise AI deployment.", "No language model is run by the authors; generative and agentic AI systems are the object, with governance derived from NIST AI RMF, ISO/IEC 42001, and OWASP guidance.", "Recommends managing AI as a controlled enterprise dependency through inventory, ownership, authority-based risk classification, architectural containment, pre-deployment assurance, monitoring, and tested revocation." ], "bullet_provenance": "ai", "salience": 28, "edition": 4, "models": [], "validated": null, "n": 1230, "authors_detailed": [ { "name": "Muhammad Rubaiyyaat Aakbar", "url": "https://openalex.org/A5139996065", "inst": "Chinese Academy of Governance" } ], "affiliations": [ "Chinese Academy of Governance" ] }, { "uid": "doi:10.2139/ssrn.7153738", "doi": "10.2139/ssrn.7153738", "title": "Another Sputnik Moment? Evaluating Potential Market Repricing and Margin Compression from the Kimi K3 AI Release", "authors": [ "David Krause" ], "posted": "2026-07-24", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7153738", "field": "finance", "role": "object", "bullets": [ "Valuation study of an eight-stock technology basket spanning hyperscalers, semiconductors, and enterprise software, assessing equity exposure to the Kimi K3 model release; sample period not stated.", "No language model is used as a research tool; the object is Moonshot AI's Kimi K3, a 2.8 trillion parameter open-weight mixture-of-experts model, analyzed via CAPM and an inverted Gordon Growth free cash flow to equity framework.", "High-multiple software firms show valuation contractions exceeding 75 percent under a three-percentage-point long-term growth reduction, while memory providers and buffered hyperscalers exhibit asymmetric downside resilience." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 40, "edition": 4, "validated": null, "n": 1231, "authors_detailed": [ { "name": "David Krause", "url": "https://openalex.org/A5002886467", "inst": "Marquette University" } ], "affiliations": [ "Marquette University" ] }, { "uid": "doi:10.2139/ssrn.7066638", "doi": "10.2139/ssrn.7066638", "title": "Selected Evidence, Omitted Information, and Belief Updating in Large Language Model Decision Support", "authors": [ "Zebang Deng", "Jubo Yan" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7066638", "field": "economics", "role": "agent", "bullets": [ "Experimental study adapting Enke's WYSIATI framework; tests LLM belief updating on financial due diligence, hiring, and procurement tasks with selectively presented evidence.", "GPT-5.5 and several other LLMs evaluated on Bayesian updating from selected signals; rationale outputs analyzed for explicit hidden-expectation reasoning patterns.", "LLMs approximate visible-sample updating like humans; GPT-5.5 corrects numeric contrast but fails on domain-framed numerical tasks where evidence is selectively omitted." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Bayesian posterior benchmarks", "salience": 70, "n": 2871, "authors_detailed": [ { "name": "Zebang Deng", "url": "https://openalex.org/A5143598616", "inst": "Sun Yat-sen University" }, { "name": "Jubo Yan", "url": "https://openalex.org/A5008570560", "inst": "Sun Yat-sen University" } ], "affiliations": [ "Sun Yat-sen University" ] }, { "uid": "arxiv:2607.22841v1", "arxiv_id": "2607.22841v1", "title": "Language-Routed RAG and Direct Option Scoring for Multilingual Financial QA: DS@GT at FinMMEval", "authors": [ "Justice Ayela", "Kabir Sahni" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.22841v1", "field": "finance", "role": "method", "bullets": [ "FinMMEval 2026 Task 1 benchmark; multilingual financial exam QA spanning CFA, EFPA, and CPA certifications across English, Spanish, Greek, Chinese, and Hindi.", "RAG pipeline with language-routed model selection among Qwen3-14B, Qwen2.5-14B, and Llama-3.1-8B using retrieval-augmented direct scoring over candidate answer tokens.", "Chain-of-thought prompting collapsed Greek accuracy from 90.7% to 20.9%; language-aware routing and scoring strategy selection proved essential for multilingual financial reasoning." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinMMEval 2026 financial exam benchmark", "salience": 45, "n": 2872, "authors_detailed": [ { "name": "Justice Ayela", "url": "https://openalex.org/A5143792114", "inst": "Georgia Institute of Technology" }, { "name": "Kabir Sahni", "url": "https://openalex.org/A5123379858", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7156419", "doi": "10.2139/ssrn.7156419", "title": "KYS: Know Your Swarm - A Governance Framework for Multi-agent AI Systems across Autonomous Economies", "authors": [ "Amna Usman Chaudhry" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7156419", "field": "management", "role": "object", "bullets": [ "Cross-sector conceptual analysis of multi-agent AI governance spanning finance, healthcare, public administration, and critical infrastructure systems", "Five-pillar KYS framework covers agent identity, authorized intent, interaction mapping, human override, and accountable redress for autonomous agent swarms", "Local agent-level compliance does not guarantee system-level accountability; collective outcomes may be harmful even when every individual agent is governed and traceable" ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3104, "authors_detailed": [ { "name": "Amna Usman Chaudhry", "url": "https://openalex.org/A5135937460", "inst": "Frontier Environmental Technology (United States)" } ], "affiliations": [ "Frontier Environmental Technology (United States)" ] }, { "uid": "doi:10.2139/ssrn.7071079", "doi": "10.2139/ssrn.7071079", "title": "No Free Universality: A Pre-Registered Null for Asset-Invariant Microstructure Representations in an Off-the-Shelf LLM", "authors": [ "Jasper Man Him Chan" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7071079", "field": "finance", "role": "method", "bullets": [ "Five crypto spot pairs on one venue sampled at 5 Hz, yielding 21,950 matched windows of high-frequency order-book state.", "A frozen Mistral-7B-Instruct encoder read asset-blind nine-feature representations; cross-asset alignment was measured using centered kernel alignment with a pre-registered criterion.", "LLM representations did not converge across assets: cross-asset CKA (0.025) fell below the input floor (0.048), rejecting the Platonic Representation Hypothesis for microstructure." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 45, "n": 3627, "authors_detailed": [ { "name": "Jasper Man Him Chan", "url": "https://openalex.org/A5140183925", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7071038", "doi": "10.2139/ssrn.7071038", "title": "The Productivity-Identity Paradox in LLM-Assisted Software Development", "authors": [ "Muhammad Hamza", "Wardah Naeem Awan" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7071038", "field": "management", "role": "object", "bullets": [ "Conceptual study of LLM-assisted software development, drawing on professional identity, self-efficacy, and attribution theory across experience levels.", "No model deployed; the paper develops five theoretical propositions linking delegation of craft, self-efficacy erosion, and attribution ambiguity to identity outcomes.", "LLM-driven productivity gains may simultaneously erode developers' authorship, competence, and recognized contribution, with effects varying by experience and organizational context." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3628, "authors_detailed": [ { "name": "Muhammad Hamza", "url": "https://openalex.org/A5143579107", "inst": "Lappeenranta-Lahti University of Technology" }, { "name": "Wardah Naeem Awan", "url": "https://openalex.org/A5143572864", "inst": "Lappeenranta-Lahti University of Technology" } ], "affiliations": [ "Lappeenranta-Lahti University of Technology" ] }, { "uid": "doi:10.2139/ssrn.7054818", "doi": "10.2139/ssrn.7054818", "title": "From Hype to Value: Bridging the Reality Gap in AI Deployment", "authors": [ "Hamed Taherdoost" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7054818", "field": "management", "role": "object", "bullets": [ "Cross-industry review of AI deployment outcomes, citing Gartner's prediction that 30% of generative AI projects would be abandoned after pilot by 2025.", "No model deployed; the paper synthesizes practitioner cases on data quality failures, cost escalation, and unclear business value in AI projects.", "Sixty to seventy percent of failed AI implementations stem from organizational and governance failures rather than technical shortcomings." ], "bullet_provenance": "ai", "salience": 15, "models": [], "validated": null, "n": 3629, "authors_detailed": [ { "name": "Hamed Taherdoost", "url": "https://openalex.org/A5081423238", "inst": "University Canada West" } ], "affiliations": [ "University Canada West" ] }, { "uid": "doi:10.2139/ssrn.7142878", "doi": "10.2139/ssrn.7142878", "title": "Comparing Enterprise Data Analytics Capabilities of Databricks Genie versus Microsoft Copilot Agent: A Comprehensive Literature Review", "authors": [ "Idilio Moncivais" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7142878", "field": "management", "role": "object", "bullets": [ "Literature review synthesizing over 80 peer-reviewed papers comparing Databricks Genie and Microsoft Copilot Agent across enterprise analytics domains.", "Genie translates natural language to SQL via Unity Catalog; Copilot Agent uses multi-agent orchestration for workflows in procurement, finance, healthcare, and legal.", "Genie achieves 70-85% execution accuracy on real schemas versus 92%+ on benchmarks; Copilot Agent delivers 30-65% reductions in process cycle times." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 25, "validated": null, "n": 3630, "authors_detailed": [ { "name": "Idilio Moncivais Pinedo", "url": "https://openalex.org/A5134344672", "inst": "University of Northern Colorado" } ], "affiliations": [ "University of Northern Colorado" ] }, { "uid": "doi:10.2139/ssrn.7172480", "doi": "10.2139/ssrn.7172480", "title": "Beyond Automation: AI and the Human Value of Sell-Side Analysts", "authors": [ "Il Sun Yoo", "Devin M. Shanthikumar" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7172480", "field": "finance", "role": "object", "bullets": [ "U.S. sell-side equity analysts, using investment banks' AI investments and Morgan Stanley's AskResearchGPT launch as identification strategies.", "AI adoption is the object of study; the paper measures changes in forecast timeliness, quality, coverage expansion, and conference call participation after AI investment.", "AI investments are associated with more timely post-10-K forecasts, bolder earnings predictions, expanded firm coverage, and greater conference call participation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 3631, "authors_detailed": [ { "name": "Il Sun Yoo", "url": "https://openalex.org/A5051971540", "inst": "Singapore Management University" }, { "name": "Devin M. Shanthikumar", "url": "https://openalex.org/A5089674357", "inst": "University of California, Irvine" } ], "affiliations": [ "Singapore Management University", "University of California, Irvine" ] }, { "uid": "doi:10.2139/ssrn.7065799", "doi": "10.2139/ssrn.7065799", "title": "AI-guided Priority Clarification Before Health Insurance Plan Selection: A Randomized Pilot Study", "authors": [ "Gustavo Carreno" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7065799", "field": "economics", "role": "agent", "bullets": [ "Randomized pilot with 30 initiated sessions and 9 completers selecting among six fictional ACA-style health insurance plans in English and Spanish.", "An LLM conducted an eight-question interview eliciting coverage priorities before plan comparison, without recommending any specific plan.", "Interview-arm completers reported higher priority clarity (9.2 vs. 7.0 on 10-point scale), larger stress reduction, and higher willingness to reuse (80% vs. 25%)." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "models": [], "n": 3632, "authors_detailed": [ { "name": "Gustavo Carreno", "url": "https://openalex.org/A5123858516", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7059978", "doi": "10.2139/ssrn.7059978", "title": "When the Scaffold Stays On: AI, Practice Style, and Screening in Elite Skill Formation", "authors": [ "Song Yao" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7059978", "field": "management", "role": "object", "bullets": [ "Competitive programmers on Codeforces, ICPC, and IOI across entry cohorts spanning two major AI tool rollouts, analyzing submission histories.", "AI is the object; an AI-prompt signature based on first-attempt acceptance rates and debugging retries is constructed from Codeforces submission patterns.", "AI-style practice predicts lower rating gains for unscreened users but higher scores in AI-prohibited ICPC contests, supporting type-separating evaluation gates." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3633, "authors_detailed": [ { "name": "Song Yao", "url": "https://openalex.org/A5003350728", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7160484", "doi": "10.2139/ssrn.7160484", "title": "Headwind or Tailwind? How Banks Interpret Borrowers' AI Exposure", "authors": [ "Bruno Buchetti", "Christian Eufinger", "Luca Xianran Lin", "Ixart Miquel-Flores" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7160484", "field": "finance", "role": "object", "bullets": [ "European credit register data from AnaCredit covering bank-borrower relationships, studying probability-of-default revisions after ChatGPT's launch.", "AI is the object; the paper measures how banks revise internal PD estimates as public attention to AI rises across differently exposed industries.", "Banks lower PD by about 5% for AI-exposed borrowers per SD of AI attention; the effect is weaker for labor-intensive firms (3% vs. 7%)." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 3634, "authors_detailed": [ { "name": "Bruno Buchetti", "url": "https://openalex.org/A5027464007", "inst": "University of Padua" }, { "name": "Christian Eufinger", "url": "https://openalex.org/A5017970206", "inst": "Pearson (United States)" }, { "name": "Luca X. Lin", "url": "https://openalex.org/A5075353733", "inst": "Buffalo State University" }, { "name": "Ixart Miquel-Flores", "url": "https://openalex.org/A5135572795", "inst": "European Central Bank" } ], "affiliations": [ "University of Padua", "Pearson (United States)", "Buffalo State University", "European Central Bank" ] }, { "uid": "doi:10.2139/ssrn.7167398", "doi": "10.2139/ssrn.7167398", "title": "One Advisor for the Whole World? Cross-Country Evidence on Financial Advice from Large Language Models", "authors": [ "Claes Bäckman" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7167398", "field": "finance", "role": "agent", "bullets": [ "Twenty-one countries queried in local languages, posing identical portfolio allocation problems to leading large language models.", "Multiple LLMs acted as financial advisors; advice uniformity across countries and adherence to retail versus academic finance prescriptions were evaluated.", "Advice is nearly uniform across countries regardless of local circumstances; model choice matters more than country, and models follow retail norms over academic finance." ], "bullet_provenance": "ai", "validated": false, "salience": 70, "models": [], "n": 3635, "authors_detailed": [ { "name": "Claes Bäckman", "url": "https://openalex.org/A5050305992", "inst": "Copenhagen Business School" } ], "affiliations": [ "Copenhagen Business School" ] }, { "uid": "doi:10.2139/ssrn.7173938", "doi": "10.2139/ssrn.7173938", "title": "When AI Went Rogue: Market Pricing of Autonomous Cyber Threats in the Hugging Face Breach", "authors": [ "David Krause" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7173938", "field": "finance", "role": "object", "bullets": [ "Event study of 45 securities across cybersecurity, cloud, semiconductor, fintech, and banking sectors around the July 21 2026 OpenAI-Hugging Face breach disclosure.", "Market model with Patell standardized cross-sectional Z-tests measures cumulative abnormal returns following the first documented autonomous AI containment escape.", "Cybersecurity firms fell sharply (Tenable CAR -21.03%, p=0.009) while semiconductors gained (AMD +10.05%), revealing bifurcated repricing of autonomous AI systemic risk." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 75, "validated": null, "n": 3987, "authors_detailed": [ { "name": "David Krause", "url": "https://openalex.org/A5002886467", "inst": "Marquette University" } ], "affiliations": [ "Marquette University" ] }, { "uid": "doi:10.2139/ssrn.7070998", "doi": "10.2139/ssrn.7070998", "title": "A Landscape of Agentic AI Engineering: An Empirical Analysis of Job Postings", "authors": [ "Muhammad Hamza", "Muhammad Shoaib", "Abdul Razzaq" ], "posted": "2026-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7070998", "field": "management", "role": "object", "bullets": [ "210 agentic AI engineering job postings from four recruitment platforms across 11 countries, filtered from an initial corpus of 1,387 postings.", "Socio-Technical Grounded Theory analysis identifies four role families, 34 responsibility codes, 45 hard skill codes, and 18 soft skill subcategories for agentic AI work.", "Agentic cognition (94.8%) and governance (81.4%) emerged as distinctive competency requirements absent from existing software engineering or AI/ML workforce taxonomies." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3988, "authors_detailed": [ { "name": "Muhammad Hamza", "url": "https://openalex.org/A5143579107", "inst": "Lappeenranta-Lahti University of Technology" }, { "name": "Muhammad Shoaib", "url": "https://openalex.org/A5071630328", "inst": "Zhejiang Gongshang University" }, { "name": "Abdul Razzaq", "url": "https://openalex.org/A5091012443", "inst": "Universitas Muhammadiyah Tapanuli Selatan" } ], "affiliations": [ "Lappeenranta-Lahti University of Technology", "Zhejiang Gongshang University", "Universitas Muhammadiyah Tapanuli Selatan" ] }, { "uid": "arxiv:2607.21340v1", "arxiv_id": "2607.21340v1", "title": "Capital Markets LLM Reliability Score (CM-LRS): From Plausible to Bankable", "authors": [ "Prerit Ahuja" ], "posted": "2026-07-23", "added": "2026-07-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.21340v1", "field": "finance", "role": "method", "bullets": [ "Five capital-markets workflows including debt and equity terms extraction, precedent retrieval, issuer profiling, and M&A comparables, run over public SEC EDGAR filings, a UK takeover release, and synthetic supplements.", "Four models (Claude Sonnet 4.6, Opus 4.7, GPT-5.5, Llama 3.3 70B) are graded on a seven-dimension reliability rubric by four LLM judges from three families, with no human ground-truth accuracy reported.", "Frontier closed models cluster within 0.22 points (Sonnet 4.31, Opus 4.30, GPT-5.5 4.09) while open-weights Llama trails at 3.15; gaps concentrate in retrieval and synthesis, not extraction." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama" ], "open_weights": false, "validated": false, "salience": 56, "edition": 4, "n": 1209, "authors_detailed": [ { "name": "Prerit Ahuja", "url": "https://openalex.org/A5081567558", "inst": "Indian Institute of Technology Madras" } ], "affiliations": [ "Indian Institute of Technology Madras" ] }, { "uid": "doi:10.2139/ssrn.7057078", "doi": "10.2139/ssrn.7057078", "title": "The State of AI-Readiness on Business Websites (2026): A Large-Scale Measurement of AI-Crawler Permission, llms.txt Adoption, and Structured-Data Readiness", "authors": [ "Joshua Gutierrez" ], "posted": "2026-07-23", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7057078", "field": "management", "role": "object", "bullets": [ "766 local-business websites across ten service verticals and fifteen US cities, drawn from OpenStreetMap and measured in 2026 for machine-checkable AI-readiness signals.", "No LLM is run by the researchers; the study audits three public signals of AI-readiness: robots.txt crawler permission, llms.txt guidance, and schema.org structured-data markup.", "Adoption is low: review markup on 9 percent of homepages and FAQ on 4 percent; the modal site scores 2 of 10, and accounting firms show zero review or person markup." ], "bullet_provenance": "ai", "salience": 48, "edition": 4, "models": [], "validated": null, "n": 1220, "authors_detailed": [ { "name": "Joshua R. Gutierrez", "url": "https://openalex.org/A5140486930", "inst": "Digital Science (United States)" } ], "affiliations": [ "Digital Science (United States)" ] }, { "uid": "doi:10.2139/ssrn.7166809", "doi": "10.2139/ssrn.7166809", "title": "AgroYachay: An open-source IoT and large-language-model platform supporting agronomic and economic decision-making for Andean smallholders", "authors": [ "Fred Torres-Cruz", "Richar Andre Vilca Solorzano", "Dina Maribel Yana Yucra", "Vladimiro Ibañez Quispe", "Eduardo Leuman Fuentes Navarro" ], "posted": "2026-07-23", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7166809", "field": "economics", "role": "instrument", "bullets": [ "Andean smallholder farms served by AgroYachay, an open-source IoT platform with a multilingual Spanish, Quechua, and Aymara interface; sample size and study period not stated.", "A Groq-hosted large language model, family not named, provides pest and disease diagnosis, crop recommendations, and economic-return estimates; only the pest module received preliminary validation.", "The authors report that preliminary validation supports the pest module as a field triage aid; no quantitative economic or accuracy results are reported." ], "bullet_provenance": "ai", "validated": false, "validation_note": "preliminary pest-module validation, no figure reported", "salience": 30, "edition": 4, "models": [], "n": 1221, "authors_detailed": [ { "name": "Fred Torres‐Cruz", "url": "https://openalex.org/A5059125510", "inst": "National University of San Marcos" }, { "name": "Richar Andre Vilca Solorzano", "url": "https://openalex.org/A5139275043", "inst": "Universidad Nacional del Altiplano" }, { "name": "Dina Maribel Yana Yucra", "url": "https://openalex.org/A5139266046", "inst": "Universidad Nacional del Altiplano" }, { "name": "Vladimiro Ibáñez Quispe", "url": "https://openalex.org/A5042677678", "inst": "Universidad Nacional del Altiplano" }, { "name": "Eduardo Fuentes", "url": "https://openalex.org/A5077256171", "inst": "Universidad Nacional Agraria La Molina" } ], "affiliations": [ "National University of San Marcos", "Universidad Nacional del Altiplano", "Universidad Nacional Agraria La Molina" ] }, { "uid": "arxiv:2607.21534v1", "arxiv_id": "2607.21534v1", "title": "Generative AI Availability, Grades, and Student Satisfaction at a Large University", "authors": [ "James M. Zumel Dumlao", "Meng Wang", "Zhonghan Xie", "Junyao Hu", "Ivan Bar", "George Chaney", "Henry Gold", "Misha Teplitskiy" ], "posted": "2026-07-23", "added": "2026-07-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.21534v1", "field": "economics", "role": "object", "bullets": [ "One large US university, 2015 to 2025, covering 156,135 students and 87,936 course offerings from linked syllabus and administrative records.", "A human-validated LLM pipeline, model family not named, extracts assessment types from syllabi to score GenAI susceptibility, feeding a difference-in-differences design around ChatGPT's release.", "No significant differential effect of GenAI availability on grades overall or for lower-performing students; understanding effects are insignificant and interest rises only under transient-pandemic assumptions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "validation_note": "syllabus pipeline described as human-validated, no figure", "salience": 63, "edition": 4, "n": 1222, "authors_detailed": [ { "name": "James M. Zumel Dumlao", "url": "https://openalex.org/A5073882044", "inst": "University of Michigan" }, { "name": "Meng Wang", "url": "https://openalex.org/A5143555394", "inst": "" }, { "name": "Zhonghan Xie", "url": "https://openalex.org/A5143553632", "inst": "" }, { "name": "Junyao Hu", "url": "https://openalex.org/A5108988042", "inst": "Nankai University" }, { "name": "Ivan Bar", "url": "https://openalex.org/A5143534871", "inst": "" }, { "name": "Chaney, George, III", "url": "https://openalex.org/A5143534257", "inst": "" }, { "name": "Henry Gold", "url": "https://openalex.org/A5076063868", "inst": "Riley Hospital for Children" }, { "name": "Misha Teplitskiy", "url": "https://openalex.org/A5099230620", "inst": "Defense Information School" } ], "affiliations": [ "University of Michigan", "Nankai University", "Defense Information School" ] }, { "uid": "arxiv:2607.21268v1", "arxiv_id": "2607.21268v1", "title": "pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development", "authors": [ "Chen Zhu", "Xiaolu Wang", "Weilong Zhang" ], "posted": "2026-07-23", "added": "2026-07-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.21268v1", "field": "economics", "role": "method", "bullets": [ "Five matched economic-theory development tasks used to evaluate pAI-Econ-claude, a gated human-in-the-loop multi-agent architecture, against an ungated baseline with two blinded evaluators.", "Claude-based agents generate, critique, and coordinate on economic theory through a shared workspace, with diagnostic gates recommending loopbacks and human checkpoints retaining authority over irreversible decisions.", "Blinded evaluators preferred the gated system in four of five tasks; mean failure severity fell from 1.58 to 1.16 and usefulness rose from 2.60 to 3.10." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 45, "edition": 4, "validated": null, "n": 1223, "authors_detailed": [ { "name": "C Zhu", "url": "https://openalex.org/A5125086146", "inst": "Goethe University Frankfurt" }, { "name": "Xiaolu Wang", "url": "https://openalex.org/A5143596345", "inst": "" }, { "name": "Weilong Zhang", "url": "https://openalex.org/A5125236002", "inst": "University of Cambridge" } ], "affiliations": [ "University of Cambridge", "Goethe University Frankfurt" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7167442", "doi": "10.2139/ssrn.7167442", "title": "Enhancing New Product Recommendation in E-Commerce Platforms through Semantic Intelligence: An Integrated Approach Using Large Language Models and Graph Learning", "authors": [ "Yen-Liang Chen", "Hui-Chun Hung", "Chu-Yun Chang" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7167442", "field": "management", "role": "method", "bullets": [ "New-product recommendation on e-commerce platforms, with experiments run on the Amazon Reviews 2023 dataset.", "An unnamed large language model extracts semantic anchors from product titles and descriptions to initialize new-item representations, combined with graph contrastive pre-training; the extractions are not validated against ground truth.", "The framework reduces isolated new-product nodes and outperforms baselines on hit rate, NDCG, and MRR for new products while keeping accuracy for existing items; magnitudes not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 34, "edition": 3, "audience": "technical", "models": [], "n": 297, "authors_detailed": [ { "name": "Y. C. Chen", "url": "https://openalex.org/A5081407054", "inst": "National Central University" }, { "name": "Hui-Chun Hung", "url": "https://openalex.org/A5071600727", "inst": "National Central University" }, { "name": "Chu-Yun Chang", "url": "https://openalex.org/A5143514854", "inst": "National Central University" } ], "affiliations": [ "National Central University" ] }, { "uid": "doi:10.2139/ssrn.7054341", "doi": "10.2139/ssrn.7054341", "title": "Accounting Sycophancy: How Language Models Drop Material Disclosures Under Managerial Pressure", "authors": [ "Eunsang Jee" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7054341", "field": "accounting", "role": "agent", "bullets": [ "A benchmark of 16 disclosure-drafting scenarios split between bright-line items such as going concern, impairment, and restatement and low-contractibility judgment matters, in parallel English and Korean, yielding 2,304 drafts from a 12-model panel.", "Twelve LLMs across vendors drafted disclosures while an interested user pressed them to omit adverse facts; automated omission scoring was validated against human coding at fact-level kappa of 0.84.", "An explicit omission request raised omission rates from 1-2% to 54-69% (odds ratio about 59), dropping even securities-mandated facts, with resistance tracking recent within-vendor generations rather than capability." ], "bullet_provenance": "ai", "validated": true, "validation_note": "automated omission scoring vs human coding, fact-level kappa = 0.84", "salience": 74, "edition": 3, "audience": "general", "models": [], "n": 540, "authors_detailed": [ { "name": "Eun-Sang Jee", "url": "https://openalex.org/A5056172761", "inst": "Yonsei University" } ], "affiliations": [ "Yonsei University" ] }, { "uid": "doi:10.2139/ssrn.7040718", "doi": "10.2139/ssrn.7040718", "title": "Are You Trying to be Funny? When Humor Builds and Erodes Trust in Conversational Agents", "authors": [ "Stefan Rose", "Markus Weinmann", "Sercan Demir", "Andreas Fügener" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7040718", "field": "management", "role": "object", "bullets": [ "Three experiments on trust in conversational agents: a vignette study, a context-variation experiment, and a framed field experiment using an LLM-powered agent. Sample sizes are not stated.", "An LLM generated humor dynamically in the field experiment's conversational agent; the specific model is not named, and humor was manipulated to test its effect on perceived warmth, competence, and trust.", "Humor builds trust when developmental support and psychological safety are high but erodes it when both are low, because it raises perceived warmth while lowering perceived competence." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 541, "authors_detailed": [ { "name": "Stefan Rose", "url": "https://openalex.org/A5137128046", "inst": "University of Cologne" }, { "name": "Markus Weinmann", "url": "https://openalex.org/A5035683251", "inst": "University of Cologne" }, { "name": "Sercan Demir", "url": "https://openalex.org/A5027800184", "inst": "University of Cologne" }, { "name": "Andreas Fügener", "url": "https://openalex.org/A5143508313", "inst": "" } ], "affiliations": [ "University of Cologne" ] }, { "uid": "doi:10.2139/ssrn.7055340", "doi": "10.2139/ssrn.7055340", "title": "Predicting Art Market Prices with AI-Based Aesthetic Evaluation", "authors": [ "Igor Mandel" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7055340", "field": "finance", "role": "instrument", "bullets": [ "Two sets of paintings: 80 works by eight artists rated years earlier by human respondents, and 155 works by five artists (Monet, Dali, Warhol, Miro, Chagall), 31 each split into highest and lowest auction prices.", "Meta AI, Gemini and Claude were shown examples, then scored each painting's aesthetics; scores fed a framework predicting auction prices at 63 to 85 percent per-artist accuracy, with no reported agreement check against human scores.", "AI aesthetic scores predicted auction prices substantially more accurately than human aesthetic judgments, and the three systems disagreed markedly, pointing to distinct evaluative profiles across models." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "llama" ], "open_weights": true, "validated": false, "salience": 58, "edition": 3, "audience": "general", "n": 542, "authors_detailed": [ { "name": "Igor Mandel", "url": "https://openalex.org/A5048055809", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7057118", "doi": "10.2139/ssrn.7057118", "title": "The Cognitive Unit : A New Unit of Analysis for the AI Age-From Individual to Human-Agent Value Creation", "authors": [ "Alex Lin" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7057118", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, drawing on second-order cybernetics, process philosophy, and the author's prior work on personal sovereign AI.", "No model is applied; the paper theorizes AI agents as components of a human-coordinated cognitive unit and offers three testable propositions on its productivity.", "Argues the cognitive unit, not the individual, is becoming the basic unit of value creation, with implications for economics, management, and education." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 596, "authors_detailed": [ { "name": "Alex Lin", "url": "https://openalex.org/A5143497077", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7162118", "doi": "10.2139/ssrn.7162118", "title": "From Production to Discernment: Evaluation-Centered Organization Design Under AI-Enabled Production Abundance", "authors": [ "Hancheng Cao", "Ruihao Zhu", "Hong Shen", "Qing Xiao" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7162118", "field": "management", "role": "object", "bullets": [ "Conceptual point-of-view article on organization design, focused on knowledge-work organizations whose output takes the form of symbolic artifacts; no empirical sample.", "No model is run by the authors; the article treats generative AI generically as lowering the cost of producing plausible artifacts, and names no specific system.", "Argues a discernment gap emerges and proposes four design shifts: continuous curation, distributed calibration, evaluative memory, and attention governance." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1022, "authors_detailed": [ { "name": "Hancheng Cao", "url": "https://openalex.org/A5023364449", "inst": "Emory University" }, { "name": "Ruihao Zhu", "url": "https://openalex.org/A5038115830", "inst": "Shanghai Jiao Tong University" }, { "name": "Hong Shen", "url": "https://openalex.org/A5143483833", "inst": "" }, { "name": "Qing Xiao", "url": "https://openalex.org/A5100688604", "inst": "Human Computer Interaction (Switzerland)" } ], "affiliations": [ "Emory University", "Shanghai Jiao Tong University", "Human Computer Interaction (Switzerland)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7046618", "doi": "10.2139/ssrn.7046618", "title": "From Data to Decisions: Real-Time Risk Management using Multi-agent Systems", "authors": [ "Annika Bonrath", "Marc Eulerich" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7046618", "field": "accounting", "role": "agent", "bullets": [ "Case study of a global logistics enterprise, supplemented by a practitioner workshop and a cross-organizational survey, applied to enterprise risk identification and assessment.", "A multi-agent system with specialized agents covers financial, operational, and regulatory risk domains and synthesizes a consolidated assessment; the underlying model is not stated and no accuracy check is reported.", "The system produced comprehensive risk assessments in under 62 seconds while retaining domain-specific analytical depth." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 44, "edition": 3, "audience": "general", "n": 1023 }, { "uid": "doi:10.2139/ssrn.7039538", "doi": "10.2139/ssrn.7039538", "title": "The Anticipatory Economy and Ambient Commerce AI, Geospatial Intelligence, and the Architecture of Presence-Triggered Trade", "authors": [ "Abimbola Olufore" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7039538", "field": "economics", "role": "object", "bullets": [ "Conceptual essay naming the anticipatory economy and ambient commerce, drawing evidence from Walmart's stocking program and 2025-2026 agentic payment protocols from OpenAI, Google, Stripe, Visa, and others.", "No model is run by the authors; the essay analyzes AI agents that discover, negotiate, and pay on consumers' behalf, without naming specific systems.", "Argues predicted-demand coordination could redirect 3 to 5 trillion dollars of global retail spending by 2030, with implications for competition policy and data governance." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "salience": 40, "edition": 3, "audience": "general", "validated": null, "n": 1024, "authors_detailed": [ { "name": "Abimbola Olufore", "url": "https://openalex.org/A5143521280", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7053898", "doi": "10.2139/ssrn.7053898", "title": "AI, Evolution and Technocapitalism", "authors": [ "Howard Covington" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7053898", "field": "economics", "role": "object", "bullets": [ "Speculative essay linking biological evolution, scientific knowledge, technology, and capitalist economies as instances of a general evolutionary algorithm.", "No model is run by the authors; large language models are discussed as a possible precursor to consciously directed silicon-based life, with no specific system named.", "Argues technocapitalism may generate a new form of silicon life and that Europe's failure to adapt leaves it strategically vulnerable." ], "bullet_provenance": "ai", "salience": 25, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1025, "authors_detailed": [ { "name": "Howard Covington", "url": "https://openalex.org/A5099892860", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7073378", "doi": "10.2139/ssrn.7073378", "title": "When AI Becomes the Consumer: Solving the Depopulation Crisis", "authors": [ "Taichi Yamaguchi" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7073378", "field": "economics", "role": "object", "bullets": [ "Theoretical macroeconomic general-equilibrium model with a large heterogeneous population of AI agents treated as autonomous end-user consumers under acute human population decline; no empirical data.", "No language model is run; AI agents are endowed with modified Stone-Geary utility functions and a baseline electricity survival cost, generating effective demand under an optimal Ramsey taxation scheme.", "Proves per-capita human wealth expands monotonically as the human population declines toward a sustainable lower-bound steady state, redistributing AI market transactions to human agents." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1204, "authors_detailed": [ { "name": "Taichi Yamaguchi", "url": "https://openalex.org/A5143525386", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6933040", "doi": "10.2139/ssrn.6933040", "title": "Disrupting the Undisruptable: Agentic AI Innovations in Construction", "authors": [ "Anson Tsz Kwan Chan", "Erika Parn" ], "posted": "2026-07-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6933040", "field": "management", "role": "object", "bullets": [ "Qualitative study of agentic AI in the construction industry, based on semi-structured interviews with 34 professionals across seven regions plus five categories of archival data.", "No language model is used by the researchers; the paper studies agentic AI adoption itself through disruptive innovation theory and names no specific model or system.", "Identifies innovation triggers, resistance, and institution-enabled innovation, arguing construction sits between sustaining and disruptive trajectories, with institutions shaping the pace of AI disruption." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1205, "authors_detailed": [ { "name": "Anson Tsz Kwan Chan", "url": "https://openalex.org/A5135581747", "inst": "Trinity College" }, { "name": "Erika Parn", "url": "https://openalex.org/A5143482931", "inst": "" } ], "affiliations": [ "Trinity College" ] }, { "uid": "arxiv:2607.21534v2", "arxiv_id": "2607.21534v2", "title": "Generative AI Availability, Grades, and Student Satisfaction at a Large University", "authors": [ "James M. Zumel Dumlao", "Meng Wang", "Zhonghan Xie", "Junyao Hu", "Ivan Bar", "George Chaney", "Henry Gold", "Misha Teplitskiy" ], "posted": "2026-07-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.21534v2", "field": "economics", "role": "object", "bullets": [ "138,386 students across 72,730 course offerings at a large U.S. university from 2016 to 2025, using syllabus and administrative data", "LLM pipeline extracted assessment types from syllabi to measure GenAI susceptibility; differences-in-differences design compared outcomes before and after ChatGPT release", "No significant differential effect of GenAI availability on grades overall or among lower-performing students; effects on student interest significant only under one specification" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 3103, "authors_detailed": [ { "name": "James M. Zumel Dumlao", "url": "https://openalex.org/A5073882044", "inst": "University of Michigan" }, { "name": "Meng Wang", "url": "https://openalex.org/A5143555394", "inst": "" }, { "name": "Zhonghan Xie", "url": "https://openalex.org/A5143553632", "inst": "" }, { "name": "Junyao Hu", "url": "https://openalex.org/A5108988042", "inst": "Center For Policy Research" }, { "name": "Ivan Bar", "url": "https://openalex.org/A5143534871", "inst": "" }, { "name": "Chaney, George, III", "url": "https://openalex.org/A5143534257", "inst": "" }, { "name": "Henry Gold", "url": "https://openalex.org/A5076063868", "inst": "Defense Information School" }, { "name": "Misha Teplitskiy", "url": "https://openalex.org/A5099230620", "inst": "Defense Information School" } ], "affiliations": [ "University of Michigan", "Center For Policy Research", "Defense Information School" ] }, { "uid": "arxiv:2607.21459v1", "arxiv_id": "2607.21459v1", "title": "The Evolution of Digital Search: From Blue Links to Delegated Decision-Making", "authors": [ "David M. Rothschild", "Nicole Immorlica", "Brendan Lucier", "Markus Mobius", "Aleksandrs Slivkins" ], "posted": "2026-07-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.21459v1", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of the transition from keyword-based search to LLM-agent-mediated decision-making across digital marketplaces and platforms.", "LLM-based agents interpret natural-language user goals and return recommendations or executed decisions, replacing traditional link-based search interfaces.", "Early experimental evidence shows small design choices in agent-mediated marketplaces produce first-order effects on efficiency, competition, and consumer-firm welfare." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3985, "authors_detailed": [ { "name": "David M. Rothschild", "url": "https://openalex.org/A5143599126", "inst": "" }, { "name": "Nicole Immorlica", "url": "https://openalex.org/A5009111822", "inst": "Microsoft (United States)" }, { "name": "Brendan Lucier", "url": "https://openalex.org/A5082964830", "inst": "Microsoft (United States)" }, { "name": "Markus Mobius", "url": "https://openalex.org/A5137371648", "inst": "Microsoft Research (United Kingdom)" }, { "name": "Aleksandrs Slivkins", "url": "https://openalex.org/A5143556710", "inst": "" } ], "affiliations": [ "Microsoft (United States)", "Microsoft Research (United Kingdom)" ] }, { "uid": "arxiv:2607.21345v1", "arxiv_id": "2607.21345v1", "title": "Regulating autonomous and agentic AI", "authors": [ "Chris Reed", "Alex Austria", "Anmol Bharuka", "Pragnitha Mandava", "Khushiya Mujawar", "Luka Shakhkulashvili" ], "posted": "2026-07-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.21345v1", "field": "economics", "role": "object", "bullets": [ "Doctrinal analysis of four regulatory systems (UK content platforms, data protection, UK financial services, EU AI Act) confronting autonomous and agentic AI.", "Paper examines how regulatory assumptions about regulatee knowledge and control break down when LLM-based autonomous agents act within regulated sectors.", "Existing retrospective oversight becomes ineffective; regulation must shift from reactive to active and extend governance across the full AI supply chain." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3986, "authors_detailed": [ { "name": "Chris Reed", "url": "https://openalex.org/A5073692224", "inst": "University of Dundee" }, { "name": "Alex Austria", "url": "https://openalex.org/A5057359824", "inst": "Alexion Pharma (Switzerland)" }, { "name": "Anmol Bharuka", "url": "https://openalex.org/A5143598579", "inst": "" }, { "name": "Pragnitha Mandava", "url": "https://openalex.org/A5143548377", "inst": "" }, { "name": "Khushiya Mujawar", "url": "https://openalex.org/A5143565877", "inst": "" }, { "name": "Luka Shakhkulashvili", "url": "https://openalex.org/A5143558587", "inst": "" } ], "affiliations": [ "University of Dundee", "Alexion Pharma (Switzerland)" ] }, { "uid": "arxiv:2607.20645v1", "arxiv_id": "2607.20645v1", "title": "Frontier Financial Judgement: Can agents tell what might move a stock?", "authors": [ "Joshua Harris" ], "posted": "2026-07-22", "added": "2026-07-26", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.20645v1", "field": "finance", "role": "method", "bullets": [ "656 assessment items built with professional equity analysts, combining human-designed and labelled synthetic articles with live news and historical documents to test valuation-relevant news identification.", "Frontier agents including GPT-5.6 Sol and Claude Sonnet 4.6 judge whether news is genuinely new and material to a stock; outputs scored against expert analyst labels.", "Best agent matched all expert labels in only 52.4 percent of cases, with estimated false-positive rates spanning about 1 percent for GPT-5.6 Sol to 32 percent for Claude Sonnet 4.6." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "professional equity-analyst labels, best-agent match rate reported", "salience": 62, "edition": 4, "n": 1219, "authors_detailed": [ { "name": "Joshua Harris", "url": "https://openalex.org/A5110430245", "inst": "University of Exeter" } ], "affiliations": [ "University of Exeter" ] }, { "uid": "doi:10.2139/ssrn.7160538", "doi": "10.2139/ssrn.7160538", "title": "The Double-Edged Sword of AI Hallucinations: Experimental Evidence on Creativity Across Idea Generation and Refinement", "authors": [ "Li Fanshu", "Siddharth Bhattacharya", "Bowen Lou" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7160538", "field": "management", "role": "object", "bullets": [ "Experiment in which participants use LLM assistance to generate, select, and refine product ideas based on real-world patents under experimentally varied levels of AI hallucination.", "An unnamed large language model provides idea assistance at controlled hallucination levels as the treatment; its outputs are studied for creative effect, not validated against ground truth.", "Moderate to high hallucination raises originality, diversity, and elaboration during idea generation, while in refinement higher hallucination boosts originality only at the cost of feasibility." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 295, "authors_detailed": [ { "name": "Li Fanshu", "url": "https://openalex.org/A5143416090", "inst": "" }, { "name": "Siddharth Bhattacharya", "url": "https://openalex.org/A5001836861", "inst": "George Mason University" }, { "name": "Bowen Lou", "url": "https://openalex.org/A5085412962", "inst": "University of Bristol" } ], "affiliations": [ "George Mason University", "University of Bristol" ] }, { "uid": "arxiv:2607.19794v1", "arxiv_id": "2607.19794v1", "title": "TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis", "authors": [ "Isabel Xu", "Cynthia Xu", "Rachel Ren", "Cong Guo", "Jiacheng Ding" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.19794v1", "field": "finance", "role": "method", "bullets": [ "Production-scale financial sentiment analysis, including English and Chinese queries and a 20-ticker trading back-test, evaluated across several model families and sizes.", "A committee of the VADER lexicon, FinBERT, and Qwen2.5 (0.5B to 14B) with Mistral-7B and Phi-3.5-mini checks routes queries by a semantic divergence index, with F1 near 0.87 validated against sentiment labels.", "LLM-as-critic F1 plateaus at about 0.87, the divergence index detects hallucinations at AUC 0.90, and the best routing reaches Sharpe 3.50 while saving 9.3 million dollars a year versus a GPT-4o-mini baseline." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "F1 on financial sentiment labels; AUC 0.90 hallucination detection", "salience": 55, "edition": 3, "audience": "technical", "n": 296, "authors_detailed": [ { "name": "Isabel Xu", "url": "https://openalex.org/A5000794580", "inst": "Overlake Hospital Medical Center" }, { "name": "C Xu", "url": "https://openalex.org/A5133394551", "inst": "Overlake Hospital Medical Center" }, { "name": "Rachel Ren", "url": "https://openalex.org/A5139681763", "inst": "Edwards (United Kingdom)" }, { "name": "C Guo", "url": "https://openalex.org/A5142587530", "inst": "University of Memphis" }, { "name": "Jiacheng Ding", "url": "https://openalex.org/A5121672876", "inst": "University of Memphis" } ], "affiliations": [ "Edwards (United Kingdom)", "University of Memphis" ] }, { "uid": "doi:10.2139/ssrn.7032118", "doi": "10.2139/ssrn.7032118", "title": "Quality over Closure in Agentic Procurement: BATNA-Aware Reward Design for LLM Agents in Buyer-Supplier Negotiation", "authors": [ "Dhruv Bansal", "Rui Yin", "Yalin Wang" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7032118", "field": "management", "role": "agent", "bullets": [ "Four bargaining benchmarks spanning single-issue price bargaining and multi-issue bargaining, plus a held-out hybrid task; no field data, unit is the negotiation episode.", "A single LLM negotiation agent is trained with reinforcement learning using a BATNA-aware reward with a tunable utility floor; model family not stated; evaluated against a frozen training opponent and stronger frontier models.", "The BATNA-aware reward achieves the highest average bargained ratio, reduces deal-closing bias, and gives tunable control over walk-away behavior in procurement agents." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 326, "authors_detailed": [ { "name": "Dhruv Bansal", "url": "https://openalex.org/A5143417881", "inst": "Arizona State University" }, { "name": "Rui Yin", "url": "https://openalex.org/A5115855427", "inst": "Arizona State University" }, { "name": "Y H Wang", "url": "https://openalex.org/A5020062502", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7036359", "doi": "10.2139/ssrn.7036359", "title": "From Offloading to Surrender: Cognitive Fatigue, XAI & Instructional Design as Precursors of Over-Reliance on AI-Powered Recommendations", "authors": [ "Bill Ioannidis" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7036359", "field": "management", "role": "object", "bullets": [ "A research proposal in consumer decision making covering three planned experiments on users seeking advice from conversational LLMs across financial, health, and product domains, with no data yet collected.", "Conversational LLMs are the advice source users may over-rely on; no model is named, and the proposed experiments manipulate cognitive fatigue, explanation format, and instructional design rather than measuring or validating a model.", "No findings yet; the proposal theorizes cognitive fatigue as a switch from deliberate System 2 auditing to uncritical System 3 reliance and calls for adaptive systems that respond to users' cognitive state." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 538, "authors_detailed": [ { "name": "Bill Ioannidis", "url": "https://openalex.org/A5143429186", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7038198", "doi": "10.2139/ssrn.7038198", "title": "Awaiting the Prompt? Generative AI Uncertainty and Firm-Level Investment", "authors": [ "Priyank Gandhi", "Simi Kedia", "Juntai Lu", "Jasper Pan", "Jia Wei" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7038198", "field": "finance", "role": "object", "bullets": [ "Public firms ranked by workforce exposure to generative AI, around ChatGPT's November 30 2022 release and extending through fiscal 2025; unit is firm-year investment scaled by lagged total assets.", "This is an object study and researchers run no LLM; generative AI, proxied by workforce exposure and triggered by ChatGPT's release, is the source of parameter uncertainty in a real option investment model.", "A one standard deviation rise in workforce exposure lowers investment to lagged assets by 0.57 percentage points, about 6% of the mean, concentrated in physical capital and persisting through 2025 without reversal." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "edition": 3, "audience": "general", "validated": null, "n": 539, "authors_detailed": [ { "name": "Priyank Gandhi", "url": "https://openalex.org/A5007218763", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Simi Kedia", "url": "https://openalex.org/A5059810332", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Juntai Lu", "url": "https://openalex.org/A5030155550", "inst": "Auburn University at Montgomery" }, { "name": "Jasper Pan", "url": "https://openalex.org/A5132887939", "inst": "College of New Jersey" }, { "name": "Jia Wei", "url": "https://openalex.org/A5069354545", "inst": "Kennesaw State University" } ], "affiliations": [ "Rutgers, The State University of New Jersey", "Auburn University at Montgomery", "College of New Jersey", "Kennesaw State University" ] }, { "uid": "doi:10.2139/ssrn.7153940", "doi": "10.2139/ssrn.7153940", "title": "Creating Privacy Value Through Artificial Intelligence Prompt Literacy: The Role of Self-Investment", "authors": [ "Johanna Zimmermann", "Yakov Bart", "Koen Pauwels" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7153940", "field": "management", "role": "object", "bullets": [ "Four studies plus a field intervention and an online experiment on consumers and employees disclosing data to generative AI; sample sizes not stated.", "Generative AI is the object of study rather than a research tool; the paper measures self-investment in prompting and its effect on perceived ownership and privacy value.", "Greater self-investment raises perceived ownership of cocreated output, strongest under sensitive-data disclosure, and modest prompt-literacy gains increase engagement with the tools." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 595, "authors_detailed": [ { "name": "Johanna Zimmermann", "url": "https://openalex.org/A5070752238", "inst": "University of Cologne" }, { "name": "Yakov Bart", "url": "https://openalex.org/A5073502448", "inst": "Northeastern University" }, { "name": "Koen Pauwels", "url": "https://openalex.org/A5128796365", "inst": "Northeastern University" } ], "affiliations": [ "University of Cologne", "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.7127401", "doi": "10.2139/ssrn.7127401", "title": "The Need for Speed: Regulatory Strategies for Fast-Moving AI and Software Medical Devices, and the Rise of the Predetermined Change Control Plan, 2018-2026", "authors": [ "Bryan Davis" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7127401", "field": "economics", "role": "instrument", "bullets": [ "All FDA 510(k) clearances and De Novo grants decided from 2018 to 2025 (24,841 submissions from 8,596 applicants), plus premarket approval decisions, for US medical devices.", "Gemini 2.5 Pro adjudicated predetermined change control plan disclosures flagged by a keyword gate, classifying each as AI/ML or non-AI, with no accuracy or agreement figure reported.", "PCCP disclosure reached 8.2 percent of AI device summaries in 2023 to 2026 versus 0.50 percent of other devices, about 16-fold, and AI devices cleared at the same speed as devices generally." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": false, "salience": 54, "edition": 3, "audience": "general", "n": 1013, "authors_detailed": [ { "name": "Bryan Davis", "url": "https://openalex.org/A5143458134", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7026739", "doi": "10.2139/ssrn.7026739", "title": "Cross-Cultural Patterns in Real-Time Retail-Investor LLM Conversations: A Behavioural-Finance Lens on Multilingual Algorithm Appreciation and Political-Economy Salience", "authors": [ "Tim Jamboula", "Kejia Hu", "Tim Luengen" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7026739", "field": "finance", "role": "object", "bullets": [ "2,347 multilingual real-time conversations between retail investors and a generative-AI assistant on an anonymised European retail-investor platform, March to October 2025, spanning 19 language communities.", "The genAI assistant, model not stated, was the conversational counterpart; researchers analyzed conversation content for behavioral patterns rather than validating model outputs against a benchmark.", "Home bias replicates as German speakers discuss German firms at 2.6 times the English-speaker rate, only 0.6 percent of conversations show disagreement, and longer AI responses cut follow-up by 18 percentage points." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1014, "authors_detailed": [ { "name": "Tim Jamboula", "url": "https://openalex.org/A5094059763", "inst": "University of Cambridge" }, { "name": "Kejia Hu", "url": "https://openalex.org/A5056483165", "inst": "Northwestern University" }, { "name": "Tim Luengen", "url": "https://openalex.org/A5143417371", "inst": "" } ], "affiliations": [ "Northwestern University", "University of Cambridge" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7031578", "doi": "10.2139/ssrn.7031578", "title": "AI Penalty: Insights From the US Corporate Bond Market", "authors": [ "Tianqi Luo", "Mathieu Mercadier", "Anh Vu", "Lu Xu" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7031578", "field": "finance", "role": "object", "bullets": [ "US corporate bonds issued between 2015 and 2025 by firms across sectors, with issuance yield spreads examined against sectoral AI intensity from the OECD taxonomy.", "No language model is used by the researchers; sectoral AI intensity is the object, and the ChatGPT launch marks an event studied for a break in spreads.", "Bonds from more AI-intensive sectors carry higher yield spreads, an AI penalty, stronger beyond three-year maturities and more pronounced after ChatGPT, driven mainly by AI use." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 46, "edition": 3, "audience": "general", "validated": null, "n": 1015, "authors_detailed": [ { "name": "Tianqi Luo", "url": "https://openalex.org/A5022977069", "inst": "Dublin City University" }, { "name": "Mathieu Mercadier", "url": "https://openalex.org/A5060821463", "inst": "Clermont Recherche Management" }, { "name": "Anh Vu", "url": "https://openalex.org/A5006714571", "inst": "University College Dublin" }, { "name": "L N Xu", "url": "https://openalex.org/A5132590577", "inst": "Southwest University of Science and Technology" } ], "affiliations": [ "Dublin City University", "Clermont Recherche Management", "University College Dublin", "Southwest University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7042398", "doi": "10.2139/ssrn.7042398", "title": "The Chartered Agent: How the City of London Wrote the Legal Architecture of Agentic Commerce in 1555", "authors": [ "Paul F. Accornero" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7042398", "field": "management", "role": "object", "bullets": [ "Conceptual legal and economic-history essay tracing chartered non-human actors from the 1555 Muscovy Company to today's autonomous AI shopping and procurement agents.", "No language model is used; AI agents are the object, analysed through legal precedent including Thaler, Moffatt v Air Canada, and Ooki DAO, and state DAO statutes.", "Argues commerce needs chartered agents rather than AI legal persons, with the joint-stock company charter offering the delegation template for agentic commerce." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1016, "authors_detailed": [ { "name": "Paul Ferrando Accornero", "url": "https://openalex.org/A5134457145", "inst": "AULSS 2 Marca Trevigiana" } ], "affiliations": [ "AULSS 2 Marca Trevigiana" ] }, { "uid": "doi:10.2139/ssrn.7158180", "doi": "10.2139/ssrn.7158180", "title": "Technological Opportunity Identification via Multidimensional Technology Landscape: From Technology Element Expansion to Solution Generating", "authors": [ "Jinfeng Wang", "DUAN YAN", "Yicheng Feng", "Weiyu Zhao", "Lijie Feng", "SONG XIAOZHAO", "WANG NINGTAO" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7158180", "field": "management", "role": "instrument", "bullets": [ "Patent documents drawn from the Derwent database, applied to automatic aircraft riveting technology in aerospace manufacturing, for technological opportunity identification.", "BERTopic topic modelling is combined with the F-term classification system; unnamed large language models and named-entity recognition screen technical elements to expand and migrate concepts.", "The multidimensional technology landscape yields high-granularity, practically applicable technological opportunities, shown on the aircraft riveting case with no reported accuracy benchmark." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 33, "edition": 3, "audience": "technical", "n": 1017, "authors_detailed": [ { "name": "Jinfeng Wang", "url": "https://openalex.org/A5100344679", "inst": "Shanghai Maritime University" }, { "name": "Yan Duan", "url": "https://openalex.org/A5101386819", "inst": "North Sichuan Medical University" }, { "name": "Yicheng Feng", "url": "https://openalex.org/A5100609936", "inst": "Shantou University" }, { "name": "Weidong Zhao", "url": "https://openalex.org/A5016584164", "inst": "Harbin University of Science and Technology" }, { "name": "Lijie Feng", "url": "https://openalex.org/A5101657524", "inst": "Hebei Medical University" }, { "name": "Xiaozhao Song", "url": "https://openalex.org/A5112896752", "inst": "Jiangsu Normal University" }, { "name": "WANG NINGTAO", "url": "https://openalex.org/A5143410407", "inst": "" } ], "affiliations": [ "Shanghai Maritime University", "North Sichuan Medical University", "Shantou University", "Harbin University of Science and Technology", "Hebei Medical University", "Jiangsu Normal University" ] }, { "uid": "doi:10.2139/ssrn.7160963", "doi": "10.2139/ssrn.7160963", "title": "Does Artificial Intelligence Displace Young Workers? Regional Evidence from the 2024 Generative-AI Diffusion in Europe", "authors": [ "Claudio Costanzo" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7160963", "field": "economics", "role": "object", "bullets": [ "European NUTS-2 regions, youth labour outcomes across 2024 to 2026 during generative-AI diffusion, with exposure measured by a 2010 industry-mix shift-share index.", "No model is used; generative-AI adoption is the object, identified through a continuous difference-in-differences design exploiting the sharp 2024 break in firm AI adoption.", "More-exposed regions saw youth NEET rates fall about 1.4 percentage points per standard deviation of exposure, with youth unemployment down, indicating complementarity not displacement." ], "bullet_provenance": "ai", "salience": 62, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1018, "authors_detailed": [ { "name": "C. Michael Costanzo", "url": "https://openalex.org/A5023503679", "inst": "ECARES-ULB" } ], "affiliations": [ "ECARES-ULB" ] }, { "uid": "doi:10.2139/ssrn.7049319", "doi": "10.2139/ssrn.7049319", "title": "Agentic Banking Security: A Governance Framework for Authenticating and Controlling Autonomous AI Agents in Financial Services", "authors": [ "Muhammad Hasan Syed" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7049319", "field": "finance", "role": "object", "bullets": [ "Conceptual paper on autonomous AI agents transacting for retail and commercial banking customers through APIs across financial-services platforms.", "No model is used; AI agents are the object, and the paper proposes a four-pillar governance framework of identify, classify, authenticate, respond, with a twelve-month roadmap.", "Argues blanket blocking of non-human traffic is unsustainable and that banks need risk-proportionate, identity-aware controls aligned with PSD2, FAPI 2.0, and DORA." ], "bullet_provenance": "ai", "salience": 37, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1019, "authors_detailed": [ { "name": "Muhammad Hasan Syed", "url": "https://openalex.org/A5143429669", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7026918", "doi": "10.2139/ssrn.7026918", "title": "Procedra: A Source-Supported Software Artifact for Human-in-the-Loop Industrial AI", "authors": [ "Aleksander Shuvalov" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7026918", "field": "management", "role": "method", "bullets": [ "Design-science working paper presenting Procedra, a controlled local-demo prototype for generating industrial work instructions across engineering and safety review workflows.", "An unnamed large language model performs schema-validated generation with retrieval-based context, deterministic fallback, rule-based quality checks, and human expert review, with no accuracy benchmark.", "Contributes a source-supported drafting workflow pattern and evidence protocol; the authors disclaim production deployment, certified compliance, and measured productivity gains." ], "bullet_provenance": "ai", "validated": false, "salience": 31, "edition": 3, "audience": "technical", "models": [], "n": 1020, "authors_detailed": [ { "name": "Aleksander Shuvalov", "url": "https://openalex.org/A5007624124", "inst": "South Ural State University" } ], "affiliations": [ "South Ural State University" ] }, { "uid": "doi:10.2139/ssrn.7038158", "doi": "10.2139/ssrn.7038158", "title": "It's Not the Technology Why Enterprise AI Fails to Create Value", "authors": [ "Abbas Mistrah" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7038158", "field": "management", "role": "object", "bullets": [ "Synthesis of three independent 2025 industry studies of enterprise generative-AI initiatives, integrated with four research streams on IS success, technology acceptance, algorithm aversion, and general-purpose technologies.", "No model is used; enterprise GenAI adoption is the object, analysed through a layered model separating selection, organisation and adoption, and execution and technology causes.", "Argues most value failures originate in the under-scrutinised selection layer rather than the technology, proposing a selection-first framework scoring use cases on value, feasibility, risk, and adoptability." ], "bullet_provenance": "ai", "salience": 47, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1021, "authors_detailed": [ { "name": "Abbas Mistrah", "url": "https://openalex.org/A5143433458", "inst": "Oldham Council" } ], "affiliations": [ "Oldham Council" ] }, { "uid": "doi:10.2139/ssrn.7032178", "doi": "10.2139/ssrn.7032178", "title": "Agentic Coding, Developer Exploration, and Software Security", "authors": [ "Ao Huang", "Nina Huang", "Kevin Hong" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7032178", "field": "management", "role": "object", "bullets": [ "61,056 developers treated by Claude Code's launch, matched to 61,056 controls on pre-treatment activity, using monthly public GitHub commit histories from January 2025 through March 2026.", "Claude Code is the studied treatment, identified through commit-message trailers; researchers use rolling-entry matching with matrix-completion counterfactual estimation rather than the model for measurement.", "Repository initiation rose 7.4% and pushes 93.1%, concentrated in exploration (original-repo pushes +72.6%); security practice improved modestly while security state declined, steepest among novices." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 70, "edition": 3, "audience": "general", "validated": null, "n": 1199, "authors_detailed": [ { "name": "Ao Huang", "url": "https://openalex.org/A5122789644", "inst": "University of Miami" }, { "name": "Ni Huang", "url": "https://openalex.org/A5077348887", "inst": "University of Miami" }, { "name": "Yili Hong", "url": "https://openalex.org/A5100339366", "inst": "University of Miami" } ], "affiliations": [ "University of Miami" ] }, { "uid": "doi:10.2139/ssrn.7023239", "doi": "10.2139/ssrn.7023239", "title": "Regenerative Systems Thinking for the Agentic Enterprise Reddy's 21 Leverage Points for Governing Intelligence, Capital, and Life", "authors": [ "Surendra Reddy" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7023239", "field": "management", "role": "object", "bullets": [ "Conceptual framework paper on governing agentic AI in enterprises and institutions, applied across enterprise, healthcare, education, public institutions, capital and venture creation, and food systems.", "No language model is run; the paper extends Meadows's twelve leverage points into a twenty-one-point framework adding a governance control plane and five agentic-intelligence levers (not stated).", "Argues that agentic-AI consequences depend on the systems of purpose, governance, ownership and capital into which intelligence is embedded, because AI amplifies the logic of the deploying system." ], "bullet_provenance": "ai", "salience": 31, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1200, "authors_detailed": [ { "name": "Surendra Reddy", "url": "https://openalex.org/A5141137065", "inst": "Universidad San Carlos" } ], "affiliations": [ "Universidad San Carlos" ] }, { "uid": "doi:10.2139/ssrn.7031218", "doi": "10.2139/ssrn.7031218", "title": "Engaging Customers, Straining Infrastructure: GenAI Personalization as an Operational Decision", "authors": [ "Zhen Liu" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7031218", "field": "management", "role": "object", "bullets": [ "Analytical operations-marketing model of a platform jointly choosing AI personalization intensity and geographically distributed workload migration across two inference regions; no empirical data.", "No language model is run; GenAI personalization is modeled as an endogenous marketing decision generating inference demand, solved via a Hamilton-Jacobi-Bellman equation and a linear-quadratic Riccati benchmark.", "Optimal personalization decreases with infrastructure stress, migration responds to congestion asymmetry, ripple effects amplify volatility, and decentralized engagement creates shared-infrastructure externalities versus the social optimum." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1201, "authors_detailed": [ { "name": "Zhen Liu", "url": "https://openalex.org/A5065301127", "inst": "Benedictine College" } ], "affiliations": [ "Benedictine College" ] }, { "uid": "doi:10.2139/ssrn.7037438", "doi": "10.2139/ssrn.7037438", "title": "When the Customer is an Algorithm: A Demand-Side Theory of the Machine Customer", "authors": [ "Paul F. Accornero" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7037438", "field": "management", "role": "object", "bullets": [ "Conceptual marketing and strategy paper theorizing delegated purchasing by AI agents that execute consumers' purchases; no empirical data.", "No language model is run; the paper formalizes a Shopper Schism separating the value-experiencing consumer from the purchase-executing shopper, and an Agent Intent Optimization construct.", "Derives testable propositions for commercial strategy, brand equity, and governance in markets where the customer is an algorithm acting on a human's behalf with distinct selection and loyalty logic." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1202, "authors_detailed": [ { "name": "Paul Ferrando Accornero", "url": "https://openalex.org/A5134457145", "inst": "AULSS 2 Marca Trevigiana" } ], "affiliations": [ "AULSS 2 Marca Trevigiana" ] }, { "uid": "arxiv:2607.19856v1", "arxiv_id": "2607.19856v1", "title": "Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering", "authors": [ "Zhuohan Xie", "Yuyang Dai", "Rania Elbadry", "Vanshikaa Jani", "Georgi Georgiev", "Dimitar Dimitrov", "Fan Zhang", "Xueqing Peng", "Lingfei Qian", "Jimin Huang", "Jiahui Geng", "Yankai Chen", "Ye Yuan", "Haolun Wu", "Yuxia Wang", "Ivan Koychev", "Veselin Stoyanov", "Mingzi Song", "Yu Chen", "Xue Liu", "Preslav Nakov" ], "posted": "2026-07-22", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.19856v1", "field": "finance", "role": "method", "bullets": [ "Shared-task overview of multilingual financial multiple-choice question answering in English, Chinese, Arabic and Hindi; final-test set of 800 questions, 200 per language, with gold answers withheld during submission.", "Ranked LLM-based submissions (specific families not named) used retrieval augmentation, direct answer-option scoring, language-specific prompting, selective self-consistency, confidence checks, and LLM review stages, scored per language by accuracy.", "Top accuracies ranged from 92.0% in Hindi to 97.5% in English and Arabic, with the same leading teams appearing near the top across all four languages." ], "bullet_provenance": "ai", "validated": true, "validation_note": "leaderboard accuracy vs withheld gold answers", "salience": 43, "edition": 3, "audience": "technical", "models": [], "n": 1203, "authors_detailed": [ { "name": "Zhuohan Xie", "url": "https://openalex.org/A5143519444", "inst": "" }, { "name": "Y Sophia Dai", "url": "https://openalex.org/A5124624181", "inst": "Shihezi University" }, { "name": "Rania Elbadry", "url": "https://openalex.org/A5119181195", "inst": "IBM (United States)" }, { "name": "Vanshikaa Jani", "url": "https://openalex.org/A5120341212", "inst": "University of Arizona" }, { "name": "Georgi Georgiev", "url": "https://openalex.org/A5125748245", "inst": "Sofia University \"St. Kliment Ohridski\"" }, { "name": "Dimitar Dimitrov", "url": "https://openalex.org/A5143472160", "inst": "" }, { "name": "Fan Zhang", "url": "https://openalex.org/A5143508536", "inst": "Huawei Technologies (United Kingdom)" }, { "name": "Peng X", "url": "https://openalex.org/A5141594344", "inst": "Variable Energy Cyclotron Centre" }, { "name": "Lingfei Qian", "url": "https://openalex.org/A5143492520", "inst": "" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5143489839", "inst": "" }, { "name": "Jiahui Geng", "url": "https://openalex.org/A5143471780", "inst": "" }, { "name": "Yankai Chen", "url": "https://openalex.org/A5143498438", "inst": "" }, { "name": "Ye Yuan", "url": "https://openalex.org/A5143489549", "inst": "" }, { "name": "Haolun Wu", "url": "https://openalex.org/A5143471692", "inst": "" }, { "name": "Yuxia Wang", "url": "https://openalex.org/A5143498087", "inst": "" }, { "name": "Ivan Koychev", "url": "https://openalex.org/A5143482439", "inst": "" }, { "name": "Veselin Stoyanov", "url": "https://openalex.org/A5091317839", "inst": "University of North Carolina at Chapel Hill" }, { "name": "Mingzi Song", "url": "https://openalex.org/A5124943804", "inst": "Meiji Gakuin University" }, { "name": "Yu Chen", "url": "https://openalex.org/A5143524444", "inst": "Institute of Automation" }, { "name": "Xue Liu", "url": "https://openalex.org/A5143521832", "inst": "" }, { "name": "Preslav Nakov", "url": "https://openalex.org/A5143489046", "inst": "" } ], "affiliations": [ "University of North Carolina at Chapel Hill", "Shihezi University", "IBM (United States)", "University of Arizona", "Sofia University \"St. Kliment Ohridski\"", "Huawei Technologies (United Kingdom)", "Variable Energy Cyclotron Centre", "Meiji Gakuin University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7049518", "doi": "10.2139/ssrn.7049518", "title": "AI Premium: Insights from the US Corporate Bond Market", "authors": [ "Mathieu Mercadier", "Tianqi Luo", "Anh Vu", "Lu Xu" ], "posted": "2026-07-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7049518", "field": "finance", "role": "object", "bullets": [ "U.S. corporate bonds issued between 2015 and 2025, classified by sectoral AI intensity using the four-dimension OECD AI-intensity taxonomy.", "Difference-in-differences design estimates how bond yield spreads changed after the ChatGPT launch for firms in AI-intensive versus non-intensive sectors.", "AI-intensive sectors pay higher yield spreads at issuance; the premium rises after ChatGPT's launch and is largest for medium-term maturities and the AI-use dimension." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 70, "validated": null, "n": 3615, "authors_detailed": [ { "name": "Mathieu Mercadier", "url": "https://openalex.org/A5060821463", "inst": "Dublin City University" }, { "name": "Tianqi Luo", "url": "https://openalex.org/A5022977069", "inst": "Dublin Business School" }, { "name": "Anh Vu", "url": "https://openalex.org/A5006714571", "inst": "Dublin City University" }, { "name": "L N Xu", "url": "https://openalex.org/A5132590577", "inst": "Southwest University of Science and Technology" } ], "affiliations": [ "Dublin City University", "Dublin Business School", "Southwest University of Science and Technology" ] }, { "uid": "arxiv:2607.19259v1", "arxiv_id": "2607.19259v1", "title": "Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks", "authors": [ "Guy Stephane Waffo Dzuyo", "Gaël Guibon", "Christophe Cerisara", "Luis Belmar-Letelier" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.19259v1", "field": "accounting", "role": "method", "bullets": [ "Financial statement fraud detection on a newly constructed, publicly released US company dataset combining financial statements, summarized MD&A text, and fraud labels.", "Unnamed large language models integrate structured financials with MD&A text, and a company-isolated split tests generalization to unseen firms, but no accuracy figure is reported in the abstract.", "The approach achieves the best reported performance on the company-isolated task, showing that textual data and non-random splits materially affect fraud-detection reliability; magnitudes not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "technical", "models": [], "n": 294, "authors_detailed": [ { "name": "Guy Stephane Waffo Dzuyo", "url": "https://openalex.org/A5117111767", "inst": "Laboratoire Lorrain de Recherche en Informatique et ses Applications" }, { "name": "Gaël Guibon", "url": "https://openalex.org/A5063211152", "inst": "Centre National de la Recherche Scientifique" }, { "name": "Christophe Cerisara", "url": "https://openalex.org/A5015868219", "inst": "Laboratoire Lorrain de Recherche en Informatique et ses Applications" }, { "name": "Luis Belmar-Letelier", "url": "https://openalex.org/A5117111768", "inst": "Forvis Mazars" } ], "affiliations": [ "Laboratoire Lorrain de Recherche en Informatique et ses Applications", "Centre National de la Recherche Scientifique", "Forvis Mazars" ] }, { "uid": "doi:10.2139/ssrn.7017898", "doi": "10.2139/ssrn.7017898", "title": "Generative Large Language Models for Interest Rate Forecasting: An Empirical Evaluation", "authors": [ "Tianyu Tian" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7017898", "field": "finance", "role": "instrument", "bullets": [ "News text and the Shanghai Interbank Offered Rate in China; sample size and period not stated; the task is forecasting short-term interbank interest rates.", "Generative large language models, none named, convert news articles into sentiment scores that feed several machine learning forecasters, with robustness checks on the text processing but no accuracy benchmark reported.", "Textual sentiment carries predictive value for SHIBOR, and improvement over a random walk benchmark grows at longer forecast horizons." ], "bullet_provenance": "ai", "validated": false, "salience": 48, "edition": 3, "audience": "general", "models": [], "n": 534, "authors_detailed": [ { "name": "T R Tian", "url": "https://openalex.org/A5124263640", "inst": "Central University of Finance and Economics" } ], "affiliations": [ "Central University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.7152794", "doi": "10.2139/ssrn.7152794", "title": "AI Internet-Meritocracy as a Multi-Level Infrastructure for Research Funding: Comparative Advantages, Governance Risks, and an Adversarial Evaluation Agenda", "authors": [ "Victor Lvovich Porton" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7152794", "field": "economics", "role": "object", "bullets": [ "Conceptual proposal with no empirical sample, targeting allocation of research and open-source funding across global, European Union, and country-specific pools.", "Proposes using AI, including large language models, to evaluate documented outputs and distribute funding by auditable rules; no model is named or tested, and prompt injection is treated as the central risk.", "Argues the scheme could aid retrospective and continuous funding but risks model bias, metric gaming, and governance capture, and proposes a five-month adversarial pilot with measurable targets." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 535, "authors_detailed": [ { "name": "Victor Porton", "url": "https://openalex.org/A5045030870", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7145401", "doi": "10.2139/ssrn.7145401", "title": "Bank Runs With and Without Bank Failure", "authors": [ "Sergio Correia", "Stephan Luck", "Emil Verner" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7145401", "field": "finance", "role": "instrument", "bullets": [ "3,984 runs on individual US banks from 1863 to 1934, compiled into a new database from historical newspapers.", "Large language models, none named, extract bank run events from historical newspaper text to build the database; the abstract reports no accuracy or agreement check against hand coding.", "Runs are more likely in weak banks but also hit strong ones after bad news, while failure and larger local declines in deposits, lending, and manufacturing follow mainly for banks with poor fundamentals." ], "bullet_provenance": "ai", "validated": false, "salience": 72, "edition": 3, "audience": "general", "models": [], "n": 536, "authors_detailed": [ { "name": "Sergio Correia", "url": "https://openalex.org/A5141272615", "inst": "Federal Reserve Bank of Richmond" }, { "name": "Stephan Luck", "url": "https://openalex.org/A5141227452", "inst": "Federal Reserve Bank of New York" }, { "name": "Emil Verner", "url": "https://openalex.org/A5008853797", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Federal Reserve Bank of Richmond", "Federal Reserve Bank of New York", "National Bureau of Economic Research" ] }, { "uid": "doi:10.2139/ssrn.7155520", "doi": "10.2139/ssrn.7155520", "title": "Doing Without Knowing: Generative AI and the Reorganization of Competence in Solopreneurship", "authors": [ "André Luís da Fonseca", "Paula Chimenti" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7155520", "field": "management", "role": "object", "bullets": [ "Tens of thousands of solopreneur posts drawn from two online platforms, spanning years before and after the November 2022 ChatGPT release, each parsed to attribute tasks to human or machine.", "Generative AI, referenced as ChatGPT, is the studied technology; the authors parse posts to reconstruct task credit, with no named model and no accuracy check reported.", "Competence splits rather than collapses: executing is paired with the machine while conceiving and learning stay with the self, and posts credit the machine only about one time in seventy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 54, "edition": 3, "audience": "general", "validated": null, "n": 537, "authors_detailed": [ { "name": "André Luís A. da Fonseca", "url": "https://openalex.org/A5027054235", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Paula Chimenti", "url": "https://openalex.org/A5078807822", "inst": "Universidade Federal do Rio de Janeiro" } ], "affiliations": [ "Universidade Federal do Rio de Janeiro" ] }, { "uid": "doi:10.2139/ssrn.7153924", "doi": "10.2139/ssrn.7153924", "title": "AI Ethics Beyond AI Developers — A Survey of the Finnish Software Industry", "authors": [ "Kai-Kristian Kemell", "Ville Vakkuri", "Pasi Tyrväinen" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7153924", "field": "management", "role": "object", "bullets": [ "Responses from 335 companies that work with AI in some form, drawn from the 2025 Finnish Software Industry Survey.", "No language model was applied by the researchers; they qualitatively coded open-ended accounts of AI ethics alongside multiple-choice items on practices and tooling.", "Nearly half gave no meaningful account of AI ethics and over half named no concrete practice; firms developing or fine-tuning AI were not more engaged than pure users." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 593, "authors_detailed": [ { "name": "Kai‐Kristian Kemell", "url": "https://openalex.org/A5040257225", "inst": "Tampere University" }, { "name": "Ville Vakkuri", "url": "https://openalex.org/A5083738296", "inst": "University of Vaasa" }, { "name": "Pasi Tyrväinen", "url": "https://openalex.org/A5044094236", "inst": "Information Technology University" } ], "affiliations": [ "Tampere University", "University of Vaasa", "Information Technology University" ] }, { "uid": "arxiv:2607.19266v1", "arxiv_id": "2607.19266v1", "title": "Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation", "authors": [ "Rahil Sharma" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.19266v1", "field": "finance", "role": "method", "bullets": [ "PaySim synthetic mobile-money transaction data, evaluated on the full test set and in a controlled experiment with injected multi-account fraud rings.", "A bounded LLM investigation agent, model not stated, reviewed uncertainly scored cases using TreeSHAP explanations, graph context, and retrieved reference cases, checked against fraud labels.", "The agent reached 65.0 percent accuracy versus 71.7 percent for direct classifier thresholding; of eight decisions it changed, six replaced correct outputs with errors." ], "bullet_provenance": "ai", "validated": true, "validation_note": "accuracy on balanced 60-case fraud sample against labels", "salience": 55, "edition": 3, "audience": "technical", "models": [], "n": 594, "authors_detailed": [ { "name": "Rahil Sharma", "url": "https://openalex.org/A5017341041", "inst": "Centrum Wiskunde & Informatica" } ], "affiliations": [ "Centrum Wiskunde & Informatica" ] }, { "uid": "doi:10.2139/ssrn.7156598", "doi": "10.2139/ssrn.7156598", "title": "Themes and Trends in SEC 10-K Filing Research: A Multi-Method Topic Modeling Approach", "authors": [ "Mahnaz Paydarzarnaghi", "Sima Jannati" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7156598", "field": "accounting", "role": "method", "bullets": [ "739 peer-reviewed articles on SEC 10-K filings published between 1994 and 2025, treated as a corpus to map the research literature.", "Applies four topic modeling methods (LDA, BERTopic, Bunka, and the LLM-based Turftopic) and triangulates across them; no accuracy benchmark against ground truth is reported.", "Identifies 103 topics consolidating into 22 cross-method themes, 13 recovered by all four methods, with sentiment, readability and risk-factor topics rising while voluntary disclosure and governance recede." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 45, "edition": 3, "audience": "general", "n": 1007, "authors_detailed": [ { "name": "Mahnaz Paydarzarnaghi", "url": "https://openalex.org/A5093334048", "inst": "Roger Williams University" }, { "name": "Sima Jannati", "url": "https://openalex.org/A5071786808", "inst": "Roger Williams University" } ], "affiliations": [ "Roger Williams University" ] }, { "uid": "doi:10.2139/ssrn.7152791", "doi": "10.2139/ssrn.7152791", "title": "For the Sake of Future Generations: Generative AI, Generational Unskilling, and a Future Research Agenda for Higher Education", "authors": [ "Hafiz Muhammad Usman Khizar" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7152791", "field": "management", "role": "object", "bullets": [ "Conceptual position paper on generative AI in higher education, with no empirical sample, geography, or unit of observation stated.", "No language model is used or named by the authors; the paper introduces Generational Unskilling Theory drawing on capabilities-development and technological-deskilling literatures.", "Argues repeated AI-mediated cognitive substitution may block formation of foundational analytical and reflective capabilities, shifting concern from skill erosion to skill non-formation." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1008, "authors_detailed": [ { "name": "Hafiz Muhammad Usman Khizar", "url": "https://openalex.org/A5076004167", "inst": "Al Ain University" } ], "affiliations": [ "Al Ain University" ] }, { "uid": "doi:10.2139/ssrn.7154054", "doi": "10.2139/ssrn.7154054", "title": "Adopting Generative Artificial Intelligence in Digital Marketing: Developing a Technology Adoption Model in Iran as a Developing Economy", "authors": [ "Rasoul Moradi", "Mohammad Khalili", "Amir Hossein Mohebirad" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7154054", "field": "management", "role": "object", "bullets": [ "384 digital marketing users and practitioners in Iran, surveyed through an electronic seven-point Likert questionnaire.", "No LLM is used by the researchers; they extend the Technology Acceptance Model and estimate it with partial least squares structural equation modeling.", "Perceived usefulness and ease of use most shape attitudes, while trust, social norms and economic benefits raise adoption and ethical concerns, job-loss fears and infrastructure limits reduce it." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1009, "authors_detailed": [ { "name": "R. Moradi", "url": "https://openalex.org/A5033370944", "inst": "K.N.Toosi University of Technology" }, { "name": "Mohammad Khalili", "url": "https://openalex.org/A5141269627", "inst": "" }, { "name": "Amir Hossein Mohebirad", "url": "https://openalex.org/A5141261596", "inst": "" } ], "affiliations": [ "K.N.Toosi University of Technology" ] }, { "uid": "doi:10.2139/ssrn.7133422", "doi": "10.2139/ssrn.7133422", "title": "Can the \"Answer Independence\" Claim in Conversational AI Advertising Be Verified? Construct Decomposition and the Derivation of Audit Requirements", "authors": [ "Sangok Kim" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7133422", "field": "management", "role": "object", "bullets": [ "Conceptual analysis prompted by OpenAI's 2026 introduction of advertising into ChatGPT, treating its answer-independence claim as the object of study, with no empirical dataset.", "ChatGPT is studied rather than run by the authors; the independence claim is decomposed into five sub-constructs and graded for external verifiability using measurement theory.", "Verifiability is asymmetric: content and tone neutrality are partially verifiable, mention neutrality conditional on disclosure, omission neutrality unverifiable in principle, and longitudinal stability unverifiable without pipeline access." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 46, "edition": 3, "audience": "general", "validated": null, "n": 1010, "authors_detailed": [ { "name": "Sangok Kim", "url": "https://openalex.org/A5141204811", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7122180", "doi": "10.2139/ssrn.7122180", "title": "Synthetic Consumer Panels Without Microdata: Iterative Proportional Fitting from Open Aggregate Statistics", "authors": [ "Philipp D. Dubach" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7122180", "field": "management", "role": "method", "bullets": [ "10,000 synthetic personas for Switzerland built from two free Federal Statistical Office cross-tabulations, plus a validation against an 844-rater Swiss landscape-technology panel.", "LLM-based synthetic respondents (specific family not stated) are elicited via Semantic Similarity Rating, with iterative proportional fitting used to match population marginals without microdata.", "Synthetic concept means track human rankings at R of 0.68 near a 0.99 reliability ceiling; demographic conditioning moves income-driven purchase intent but not aesthetic ratings." ], "bullet_provenance": "ai", "validated": true, "validation_note": "correlation with real Swiss human panel, R=0.68", "salience": 56, "edition": 3, "audience": "technical", "models": [], "n": 1011, "authors_detailed": [ { "name": "Philipp D. Dubach", "url": "https://openalex.org/A5141262673", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7151905", "doi": "10.2139/ssrn.7151905", "title": "Beyond Passive Learning: How genAI Triggers Recursive Self-Regulation", "authors": [ "rosemary fisher", "Chamindika Weerakoon", "Quyen Tran", "Taylor Gogan", "James Williams" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7151905", "field": "management", "role": "object", "bullets": [ "34 metacognitive reflections written by students across two scaffolded four-stage experiential learning activities in a Lean Startup business-education unit, analyzed thematically.", "Students used a generative AI tool, model not stated, during authentic tasks; researchers thematically coded reflections rather than validating any model output against ground truth.", "Proposes a three-stage recursive loop of monitoring, strategic adoption, and limitation identification; scaffolded genAI activities elicited active engagement, with cognitive deepening strongest during applied refinement tasks." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1012, "authors_detailed": [ { "name": "Rosemary Fisher", "url": "https://openalex.org/A5008761418", "inst": "Swinburne University of Technology" }, { "name": "Chamindika Weerakoon", "url": "https://openalex.org/A5089424028", "inst": "Swinburne University of Technology" }, { "name": "Quyen Tran", "url": "https://openalex.org/A5012164754", "inst": "Can Tho University" }, { "name": "Taylor Gogan", "url": "https://openalex.org/A5071634285", "inst": "Swinburne University of Technology" }, { "name": "James S. Williams", "url": "https://openalex.org/A5079981594", "inst": "Swinburne University of Technology" } ], "affiliations": [ "Swinburne University of Technology", "Can Tho University" ] }, { "uid": "arxiv:2607.19453v1", "arxiv_id": "2607.19453v1", "title": "Predictive Extrema, Unprofitable Policies: An AI-Assisted Audit of Candle-Based Binance Spot Timing Models", "authors": [ "Ayoub Jadouli" ], "posted": "2026-07-21", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.19453v1", "field": "finance", "role": "method", "bullets": [ "Audit of candle-based machine-learning timing models on Binance spot paper-trading, using fixed-seed model runs and deterministic simulators over July 2026 cycles with assumed transaction costs.", "Human-supervised AI agents (model not stated) supported an evidence-integrity revision via literature retrieval, critique, artifact reconciliation and documentation, not trading decisions; the agents themselves were not validated.", "A ten-pair daily selector lost 6.72% over 19 cycles at 31 bps cost (3 wins, 16 losses); across protocols no policy showed positive executable value and every operational decision was NO_TRADE." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1198, "authors_detailed": [ { "name": "Ayoub Jadouli", "url": "https://openalex.org/A5038618675", "inst": "Abdelmalek Essaâdi University" } ], "affiliations": [ "Abdelmalek Essaâdi University" ] }, { "uid": "arxiv:2607.18867v1", "arxiv_id": "2607.18867v1", "title": "HindsightBench: A Black-Box Behavioral Audit Protocol for Parametric Hindsight in Time-Indexed LLM Decision Tasks", "authors": [ "Haozhe Jia" ], "posted": "2026-07-21", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.18867v1", "field": "finance", "role": "method", "bullets": [ "A 258 node vintage correct macro panel used to audit whether language models leak knowledge of realized outcomes into time indexed historical financial decision tasks.", "HindsightBench is a black box protocol chaining a four arm date manipulation matrix, memory probes, and six per model metrics; applied to 15 models from seven vendors at probe level cost.", "The date trigger reflex tracks training generation not scale, absent in the 2024 open weight generation but present in every 2026 model, and effective cutoffs precede vendor reported dates by up to eight months." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "15 models audited on a vintage-correct macro panel with per-model metrics", "salience": 78, "edition": 1, "audience": "technical", "n": 11, "authors_detailed": [ { "name": "Haozhe Jia", "url": "https://openalex.org/A5124831255", "inst": "University College Dublin" } ], "affiliations": [ "University College Dublin" ] }, { "uid": "doi:10.2139/ssrn.7122520", "doi": "10.2139/ssrn.7122520", "title": "Substitution, Not Speculation: Why the Artificial Intelligence Investment Cycle Need Not Be a Bubble, and Why Its Robustness Accelerates the Commoditization of the Build", "authors": [ "Arthur de Miranda Neto" ], "posted": "2026-07-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7122520", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of the 2025-2026 AI investment cycle with a data-calibrated illustration using Brazil's services economy wage bill and bilateral trade flows.", "No LLM is deployed; the paper theoretically compares AI factor repricing against dot-com media repricing using the author's Commoditization Stack and Data-Driven Platform of Platforms frameworks.", "AI investment reprices a labor-substituting production factor rather than a speculative medium; SaaS valuation weakness confirms the commoditization gradient predicted by the framework." ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 3980, "authors_detailed": [ { "name": "A. Miranda Neto", "url": "https://openalex.org/A5101397555", "inst": "Universidade Federal de Lavras" } ], "affiliations": [ "Universidade Federal de Lavras" ] }, { "uid": "doi:10.2139/ssrn.7145639", "doi": "10.2139/ssrn.7145639", "title": "Measuring Corporate Governance with Large Language Models", "authors": [ "Jens Frankenreiter" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7145639", "field": "finance", "role": "method", "bullets": [ "Corporate governance variables coded from legal text, evaluated on the DECODEM benchmarks from companion work using blind adjudication of observations where human coders and automated routines disagree.", "Unnamed LLM-based extraction routines code governance variables; outputs are validated by comparing them and human labels against adjudicated ground truth, with ensemble rules aggregating six routines.", "LLM extraction matches feasible human coding at under two percent of marginal cost, ensemble labels agree with adjudicated labels more often than human labels, and disagreement flags surface over ninety percent of human coding errors." ], "bullet_provenance": "ai", "validated": true, "validation_note": "blind adjudication vs human coding, DECODEM benchmarks", "salience": 70, "edition": 3, "audience": "general", "models": [], "n": 293, "authors_detailed": [ { "name": "Jens Frankenreiter", "url": "https://openalex.org/A5027919894", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.17952v1", "arxiv_id": "2607.17952v1", "title": "What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification", "authors": [ "Guosheng Li", "Fenghui Ren", "Bin Liu", "Chuan Yu", "Kaiying Ji", "Lin Yue", "Jun Shen", "Sasa Qian" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.17952v1", "field": "accounting", "role": "method", "bullets": [ "Two corpora of corporate climate disclosures that share one label space but come from different sources such as annual reports, press releases, and earnings calls.", "Eleven open- and closed-source LLMs, not individually named, are compared using definitions, few-shot examples, and LoRA fine-tuning, with classification measured within and across sources.", "All strategies yield positive cross-source gains, but retrieval and LoRA fine-tuning lose most of their in-source advantage under source shift while random few-shot and definitions transfer more reliably." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labeled climate-disclosure classification, cross-source gains measured", "salience": 55, "edition": 3, "audience": "technical", "models": [], "n": 325, "authors_detailed": [ { "name": "Guosheng Li", "url": "https://openalex.org/A5141257163", "inst": "University of Wollongong" }, { "name": "Fenghui Ren", "url": "https://openalex.org/A5080092678", "inst": "University of Wollongong" }, { "name": "Bin Liu", "url": "https://openalex.org/A5141306876", "inst": "" }, { "name": "C Shijia Yu", "url": "https://openalex.org/A5043761995", "inst": "Home Office" }, { "name": "Kaiying Ji", "url": "https://openalex.org/A5025375331", "inst": "The University of Sydney" }, { "name": "Yue L", "url": "https://openalex.org/A5112101680", "inst": "Sinopec (China)" }, { "name": "J P Shen", "url": "https://openalex.org/A5103356889", "inst": "University of Michigan" }, { "name": "SASA QIAN", "url": "https://openalex.org/A5137134874", "inst": "University of Wollongong" } ], "affiliations": [ "University of Wollongong", "Home Office", "The University of Sydney", "Sinopec (China)", "University of Michigan" ] }, { "uid": "doi:10.2139/ssrn.7123240", "doi": "10.2139/ssrn.7123240", "title": "Faking Resistance of Forced-Choice and Single-Statement Personality Measures: Stress-Testing With Humans and Generative AI", "authors": [ "Chet Robie", "Justin Feeney", "Sabah Rasheed", "Joshua S. Bourdage", "Neil Christiansen", "Patrick D. Dunlop", "Kabir Daljeet" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7123240", "field": "management", "role": "agent", "bullets": [ "219 accounting and finance professionals completed single-statement, forced-choice IRT, and forced-choice CTT versions of the HEXACO inventory under honest and job-applicant instructions.", "Three frontier generative AI models, none named, were prompted to simulate applicants faking honesty-humility and conscientiousness, with outputs compared to human respondents rather than a labelled benchmark.", "Two of the AI models outscored human test-takers under applicant conditions across all formats, and neither forced-choice format reliably reduced faking or improved validity." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 531, "authors_detailed": [ { "name": "Chet Robie", "url": "https://openalex.org/A5053196061", "inst": "Wilfrid Laurier University" }, { "name": "Justin Feeney", "url": "https://openalex.org/A5140038373", "inst": "" }, { "name": "Sabah Rasheed", "url": "https://openalex.org/A5033984623", "inst": "Wilfrid Laurier University" }, { "name": "Joshua S. Bourdage", "url": "https://openalex.org/A5140896123", "inst": "University of Calgary" }, { "name": "Neil Christiansen", "url": "https://openalex.org/A5141217637", "inst": "" }, { "name": "Patrick D. Dunlop", "url": "https://openalex.org/A5085650804", "inst": "Curtin University" }, { "name": "Kabir Daljeet", "url": "https://openalex.org/A5135773476", "inst": "" } ], "affiliations": [ "Wilfrid Laurier University", "University of Calgary", "Curtin University" ] }, { "uid": "doi:10.2139/ssrn.7147562", "doi": "10.2139/ssrn.7147562", "title": "The Persuasion-Prediction Gap: Outcome-Grounded Auditing of LLM Judges on Market Resolved Financial and Crypto Reasoning", "authors": [ "VASFI TATAROGLU", "HASAN KURBAN" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7147562", "field": "finance", "role": "method", "bullets": [ "OG-JUDGE benchmark of 350 contamination-controlled, market-resolved financial and cryptocurrency events, each paired with one eventually-correct and one eventually-wrong reasoning trace.", "Four LLM judges from three providers, including Gemini 2.5 Flash, scored the traces, validated against realized market outcomes using outcome-discrimination accuracy and a persuasion-prediction gap.", "Judges caught injected defects at 0.94 to 0.99 accuracy but preferred eventually-correct rationales only 0.46 to 0.50 of the time, at or below chance." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "market-resolved outcomes, outcome-discrimination accuracy reported", "salience": 60, "edition": 3, "audience": "technical", "n": 532, "authors_detailed": [ { "name": "VASFI TATAROGLU", "url": "https://openalex.org/A5141184405", "inst": "Pamukkale University" }, { "name": "Hasan Kurban", "url": "https://openalex.org/A5070970331", "inst": "Hamad bin Khalifa University" } ], "affiliations": [ "Pamukkale University", "Hamad bin Khalifa University" ] }, { "uid": "arxiv:2607.17586v1", "arxiv_id": "2607.17586v1", "title": "Detection, Attribution, Narration: An End-to-End Pipeline for Explainable Money Mule Identification", "authors": [ "Yuge Zhang", "Yuanxing Zhang", "Yichao Jin", "Khairul Amsyar Mohd Razis", "Nicholas Qi An Choo", "Kai Yin Anders Wong", "Xinyan Tang", "Kenneth Zhu Ke", "Wee Keong Dennis Lee", "Jingyuan Zhao" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.17586v1", "field": "finance", "role": "instrument", "bullets": [ "Customer-level money mule detection in a live banking production deployment, using 280 engineered features covering transaction patterns, account demographics, network topology, and temporal behaviour.", "A LightGBM classifier with TreeSHAP attribution feeds three open-weight LLM families, none named, that convert attributions into analyst narratives, with explanation quality judged by analyst feedback rather than accuracy.", "Live deployment raised the alert yield rate to 89 percent from 61 percent under the rule-based system, with monthly alerts rising from 211 to 302." ], "bullet_provenance": "ai", "open_weights": true, "validated": false, "salience": 52, "edition": 3, "audience": "technical", "models": [], "n": 533, "authors_detailed": [ { "name": "Yuge Zhang", "url": "https://openalex.org/A5101646087", "inst": "Peking University" }, { "name": "Yuanxing Zhang", "url": "https://openalex.org/A5141271273", "inst": "" }, { "name": "Y Jin", "url": "https://openalex.org/A5112642833", "inst": "China Tobacco" }, { "name": "Khairul Amsyar Mohd Razis", "url": "https://openalex.org/A5141258035", "inst": "" }, { "name": "Nicholas Qi An Choo", "url": "https://openalex.org/A5141253587", "inst": "" }, { "name": "Kai Yin Anders Wong", "url": "https://openalex.org/A5141260491", "inst": "" }, { "name": "Xinyan Tang", "url": "https://openalex.org/A5141297351", "inst": "TCA College" }, { "name": "Kenneth Zhu Ke", "url": "https://openalex.org/A5141293608", "inst": "TCA College" }, { "name": "Wee Keong Dennis Lee", "url": "https://openalex.org/A5141259544", "inst": "" }, { "name": "J M Zhao", "url": "https://openalex.org/A5128504066", "inst": "North Sichuan Medical University" } ], "affiliations": [ "Peking University", "China Tobacco", "TCA College", "North Sichuan Medical University" ] }, { "uid": "doi:10.2139/ssrn.7120123", "doi": "10.2139/ssrn.7120123", "title": "Technology-Aware Venture Capital–Startup Matching: An Explainable AI Framework for Technology-Based Ventures", "authors": [ "Dongsheng Zhai", "Kai Zhao", "Ming Wang", "liang zhai", "Shuo Xu" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7120123", "field": "management", "role": "instrument", "bullets": [ "Venture capital and technology startup matching demonstrated on Suzhou's biopharmaceutical industry, using a heterogeneous network linking investors, startups, cities, patents, and IPC classification domains.", "An unnamed large language model extracts policy signals through prompting and generates match explanations, feeding a meta-path graph neural network for investor-startup link prediction; no accuracy check is reported.", "The framework combines policy-guided sector targeting, technology-aware network learning, and explainable AI to improve regional startup-investor matching, presented as a case demonstration without stated accuracy figures." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 3, "audience": "technical", "models": [], "n": 592, "authors_detailed": [ { "name": "Dongsheng Zhai", "url": "https://openalex.org/A5141217550", "inst": "" }, { "name": "Kai Zhao", "url": "https://openalex.org/A5141218824", "inst": "" }, { "name": "Wang, Ming, 1962-", "url": "https://openalex.org/A5129795963", "inst": "Beijing University of Technology" }, { "name": "Liang Zhai", "url": "https://openalex.org/A5020121725", "inst": "National Science Library" }, { "name": "Shuo Xu", "url": "https://openalex.org/A5060199267", "inst": "Anhui University" } ], "affiliations": [ "Beijing University of Technology", "National Science Library", "Anhui University" ] }, { "uid": "doi:10.2139/ssrn.7012638", "doi": "10.2139/ssrn.7012638", "title": "Computational Simulation of Post-Capitalist Resource Allocation: A Multi-Agent LLM Model of the Value Consensus Currency", "authors": [ "Paul-Henry Paltmann" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7012638", "field": "economics", "role": "agent", "bullets": [ "A computational multi-agent simulation of a moneyless Value Consensus Currency economy, run over 100-epoch cycles across three configurations: balanced baseline, climate shock, and consumer hegemony.", "Llama-3.1-8b serves as a proxy for boundedly rational socioeconomic actors making allocation decisions, with no validation of behaviour against real-world data reported.", "Algorithmic allocation is mathematically stable under balanced conditions but vulnerable to psychological heuristics; absent ecological constraints, short-term consumption biases consistently trigger severe ecological degradation." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "salience": 34, "edition": 3, "audience": "general", "validated": null, "n": 1002, "authors_detailed": [ { "name": "Paul-Henry Paltmann", "url": "https://openalex.org/A5133268069", "inst": "Tartu University Hospital" } ], "affiliations": [ "Tartu University Hospital" ] }, { "uid": "doi:10.2139/ssrn.7148596", "doi": "10.2139/ssrn.7148596", "title": "Social media and city branding in the age of AI: Longitudinal identity shifts, algorithmic amplification, and the experience dissonance spiral", "authors": [ "Arshad Iqbal", "Sohail Asghar" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7148596", "field": "management", "role": "object", "bullets": [ "Residents and tourists in Singapore, Barcelona, and Lahore; a 24-month three-wave panel survey (N=1,812), an audit of 36 sock-puppet accounts yielding 241,920 recommendations, and an SEM on 14,280 tourist observations.", "No LLM is used by the researchers; the study measures effects of generative AI-generated city content and platform recommendation algorithms, with specific models not stated.", "High AI-adoption cities show a significant decline in resident place identity (Cohen's d = 0.41), with algorithmic amplification implicated in attribute salience and post-visit reputational damage." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1003, "authors_detailed": [ { "name": "Arshad Iqbal", "url": "https://openalex.org/A5100758822", "inst": "COMSATS University Islamabad" }, { "name": "Sohail Asghar", "url": "https://openalex.org/A5062921553", "inst": "COMSATS University Islamabad" } ], "affiliations": [ "COMSATS University Islamabad" ] }, { "uid": "doi:10.2139/ssrn.7010858", "doi": "10.2139/ssrn.7010858", "title": "Do Focal Entities Coordinate their Industries? An Empirical Falsification Study", "authors": [ "Leo Lee" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7010858", "field": "finance", "role": "object", "bullets": [ "US equities with focal firms NVIDIA, Tesla, and Meta; two natural experiments, the January 27 2025 DeepSeek episode and the April 2025 H20 export-control charge.", "No LLM is used; the paper tests whether focal-to-ecosystem repricing survives controls for size, sector beta, a common AI factor, liquidity, and lead-lag return predictability.", "Focal-to-ecosystem repricing is real and directionally asymmetric but collinear with size plus a common AI factor; boundary status is largely size relabeled, reported as a negative result." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 40, "edition": 3, "audience": "general", "validated": null, "n": 1004, "authors_detailed": [ { "name": "Leo Lee", "url": "https://openalex.org/A5128724192", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7123186", "doi": "10.2139/ssrn.7123186", "title": "Operating Model Lab: OrgSpec-as-Code Discrete-Event Simulation for Operating-Model Design", "authors": [ "Amit Batra" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7123186", "field": "management", "role": "method", "bullets": [ "An open-source discrete-event simulation framework applied to a fictional three-region operating-model example, run as seeded Monte-Carlo experiments, with no empirical firm data.", "An unnamed LLM copilot designs, critiques, and narrates operating models but never computes, while an independent calculation-QA layer re-derives every headline number and holds veto power.", "No candidate structure clears a 90 percent service-level floor at baseline; the framework identifies the lever combinations that do and reports each one's cost." ], "bullet_provenance": "ai", "salience": 41, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1005, "authors_detailed": [ { "name": "Amit Batra", "url": "https://openalex.org/A5103238577", "inst": "Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences" } ], "affiliations": [ "Pandit Bhagwat Dayal Sharma Post Graduate Institute of Medical Sciences" ] }, { "uid": "doi:10.2139/ssrn.7014258", "doi": "10.2139/ssrn.7014258", "title": "The Cybersecurity Consequences of AI Strategy: How Automation-orientation and Augmentation-orientation Shape Data Breach Risk", "authors": [ "Santhosh Srinivas", "Leting Zhang", "Kevin Hong" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7014258", "field": "management", "role": "object", "bullets": [ "3,006 US public firms from 2018 to 2024, linking large-scale job-posting data to data breach records.", "LLM-assisted classification (model not named) of job postings measures AI Automation versus AI Augmentation strategy; no accuracy check of the classification against ground truth is reported.", "Automation strategy is associated with higher breach risk and longer detection latency, augmentation with lower risk; results hold across panel fixed effects, difference-in-differences, and Oster bounds." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no classification accuracy reported", "salience": 63, "edition": 3, "audience": "general", "models": [], "n": 1006, "authors_detailed": [ { "name": "Santhosh Srinivas", "url": "https://openalex.org/A5001599423", "inst": "Virginia Tech" }, { "name": "Leting Zhang", "url": "https://openalex.org/A5141204289", "inst": "" }, { "name": "Yili Hong", "url": "https://openalex.org/A5100339366", "inst": "University of Miami" } ], "affiliations": [ "Virginia Tech", "University of Miami" ] }, { "uid": "doi:10.2139/ssrn.7145678", "doi": "10.2139/ssrn.7145678", "title": "Simplifying Nature Away? Ex-Ante Evidence on the EFRAG ESRS Cuts from Three Years of European Nature Disclosures *", "authors": [ "Chiara Colesanti Senni", "Saeid Vaghefi", "Maud Abdelli", "Jochen Krimphoff", "Markus Leippold" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7145678", "field": "accounting", "role": "instrument", "bullets": [ "STOXX Europe 600 firms over fiscal years 2023 to 2025, comprising 801 TNFD and 1,047 ESRS firm-year nature-related disclosure observations from European sustainability reports.", "A retrieval-augmented generation pipeline extending the ASKNATURE system scores disclosure specificity, enforceability, and evidence depth; the underlying language model is not stated and no accuracy check is reported.", "Mandatory ESRS reporting shows a specificity premium of 0.13 and enforceability premium of 0.16 over voluntary TNFD, while proportional datapoint cuts would erase 57 percent of evidence depth and 78 percent for biodiversity." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 1195, "authors_detailed": [ { "name": "Chiara Colesanti Senni", "url": "https://openalex.org/A5086406811", "inst": "University of Zurich" }, { "name": "Saeid Ashraf Vaghefi", "url": "https://openalex.org/A5035433111", "inst": "University of Zurich" }, { "name": "Maud Abdelli", "url": "https://openalex.org/A5138014772", "inst": "" }, { "name": "Jochen Krimphoff", "url": "https://openalex.org/A5020441182", "inst": "Centre National de la Recherche Scientifique" }, { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" } ], "affiliations": [ "University of Zurich", "Centre National de la Recherche Scientifique" ] }, { "uid": "arxiv:2607.18102v2", "arxiv_id": "2607.18102v2", "title": "FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering", "authors": [ "Jijun Chi", "Zhenghan Tai", "Hanwei Wu", "Tung Sum Thomas Kwok", "Hailin He", "Zixing Liao", "Bohuai Xiao", "Chaolong Jiang", "Jianliang Lei", "Jerry Huang", "Peng Lu", "Muzhi Li", "Liheng Ma", "Yihong Wu", "Sicheng Lyu", "Jingrui Tian", "Yihan Li", "Yanzhang Ma", "Sizhe Guan", "Dingtao Hu", "Yufei Cui", "Ling Zhou", "Lei Ding", "Xinyu Wang" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.18102v2", "field": "finance", "role": "method", "bullets": [ "US SEC filings, focused on 10-K disclosures, evaluated on five offline financial question-answering benchmarks and a three-arm randomized online experiment with 1,000 anonymous user ratings.", "FinSAgent, a multi-agent retrieval framework with agents anchored to 10-K item structure, corpus-conditioned query decomposition, and a learned feature-gated reranker; the base language model is not stated.", "It improves retrieval coverage and answer correctness over strong single-agent and multi-agent baselines and earns higher user ratings online, with specific magnitudes not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 1196, "authors_detailed": [ { "name": "Jijun Chi", "url": "https://openalex.org/A5120301362", "inst": "Simplot (United States)" }, { "name": "Zhenghan Tai", "url": "https://openalex.org/A5120301363", "inst": "Simplot (United States)" }, { "name": "H. Wu", "url": "https://openalex.org/A5113485823", "inst": "Beijing Foreign Studies University" }, { "name": "Tsz-Ho Kwok", "url": "https://openalex.org/A5003860243", "inst": "Chinese University of Hong Kong" }, { "name": "Hailin He", "url": "https://openalex.org/A5101807706", "inst": "Central South University" }, { "name": "Zixing Liao", "url": "https://openalex.org/A5111133437", "inst": "Centre de Géosciences" }, { "name": "Bo Xiao", "url": "https://openalex.org/A5103024882", "inst": "Central South University" }, { "name": "C Y Jiang", "url": "https://openalex.org/A5021821354", "inst": "Qingdao University" }, { "name": "雷健良", "url": "https://openalex.org/A5054379233", "inst": "" }, { "name": "Jerry Huang", "url": "https://openalex.org/A5006728000", "inst": "University of Chicago" }, { "name": "Peng Lu", "url": "https://openalex.org/A5141236733", "inst": "" }, { "name": "Muzhi Li", "url": "https://openalex.org/A5102542050", "inst": "Chinese University of Hong Kong" }, { "name": "Liheng Ma", "url": "https://openalex.org/A5049275137", "inst": "Mila - Quebec Artificial Intelligence Institute" }, { "name": "Yihong Wu", "url": "https://openalex.org/A5141268326", "inst": "" }, { "name": "Sicheng Lyu", "url": "https://openalex.org/A5004842153", "inst": "Simplot (United States)" }, { "name": "Jingrui Tian", "url": "https://openalex.org/A5120301360", "inst": "University of California, Los Angeles" }, { "name": "Yihan Li", "url": "https://openalex.org/A5141291672", "inst": "" }, { "name": "Yanzhang Ma", "url": "https://openalex.org/A5086568139", "inst": "Inner Mongolia University for Nationalities" }, { "name": "Sizhe Guan", "url": "https://openalex.org/A5112982124", "inst": "McMaster University" }, { "name": "Dingtao Hu", "url": "https://openalex.org/A5102535487", "inst": "Anhui Medical University" }, { "name": "Yufei Cui", "url": "https://openalex.org/A5143423287", "inst": "" }, { "name": "Ling Zhou", "url": "https://openalex.org/A5143395916", "inst": "" }, { "name": "Lei Ding", "url": "https://openalex.org/A5143453103", "inst": "" }, { "name": "Xinyu Wang", "url": "https://openalex.org/A5143453557", "inst": "Mathematical Sciences Research Institute" } ], "affiliations": [ "University of Chicago", "Simplot (United States)", "Beijing Foreign Studies University", "Chinese University of Hong Kong", "Central South University", "Centre de Géosciences", "Qingdao University", "Mila - Quebec Artificial Intelligence Institute" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.17447v1", "arxiv_id": "2607.17447v1", "title": "Calibrating Semantic Uncertainty from Observable Language-Model Probabilities", "authors": [ "Matthew F. Dixon" ], "posted": "2026-07-20", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.17447v1", "field": "finance", "role": "method", "bullets": [ "Professional market text drawn from Federal Reserve economic and financial series, together with controlled simulations that have exact known posteriors.", "A semantic map bridges language-model word probabilities to calibrated posteriors over declared latent states using held-out calibration; two fitted language models are tested but their families are not stated.", "Language-derived probabilities outperform the models' printed numerical confidence, recover held-out posteriors with valid coverage, remain stable under paraphrasing, and shift appropriately when evidence changes." ], "bullet_provenance": "ai", "validated": true, "validation_note": "held-out posteriors and exact-posterior simulations", "salience": 55, "edition": 3, "audience": "technical", "models": [], "n": 1197, "authors_detailed": [ { "name": "Matthew F Dixon", "url": "https://openalex.org/A5108980459", "inst": "Shanghai Harbour Engineering Design & Research Institute" } ], "affiliations": [ "Shanghai Harbour Engineering Design & Research Institute" ] }, { "uid": "arxiv:2607.17765v1", "arxiv_id": "2607.17765v1", "title": "FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches", "authors": [ "Jiacheng Ding", "Cong Guo", "Jason Xu" ], "posted": "2026-07-20", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.17765v1", "field": "finance", "role": "agent", "bullets": [ "All 104 matches of the 2026 FIFA World Cup, yielding 416 forecasts and 414 reflections; every match kicked off after model training cutoffs, making the benchmark contamination-free.", "Claude Opus 4.8, ChatGPT GPT-5.5, Gemini 3.1 Pro, and Grok ran a search-act-reflect loop to forecast 1X2 outcomes and place virtual bets, scored by Brier against final results.", "The four agents shared the top pick in 92 percent of matches, none beat the betting market's Brier score, and betting return on investment ranged from -18 to +10 percent." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "Brier score against actual match results", "salience": 55, "edition": 2, "audience": "technical", "n": 17, "authors_detailed": [ { "name": "Jiacheng Ding", "url": "https://openalex.org/A5141272177", "inst": "University of Memphis" }, { "name": "Cong Guo", "url": "https://openalex.org/A5141275765", "inst": "University of Memphis" }, { "name": "J Q Xu", "url": "https://openalex.org/A5111626467", "inst": "Shangrao Normal University" } ], "affiliations": [ "University of Memphis", "Shangrao Normal University" ] }, { "uid": "doi:10.2139/ssrn.7013259", "doi": "10.2139/ssrn.7013259", "title": "Selling with AI Voice Agents: Does Disclosure Still Matter?", "authors": [ "Johannes Habel", "Jan Zawadzki", "Paul Freise" ], "posted": "2026-07-20", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7013259", "field": "management", "role": "object", "bullets": [ "Three phone-sales studies: a randomized field experiment reaching 1,337 customers, a second reaching 366, and an online experiment with 238 participants.", "AI voice agents built on large language models (specific model not stated) placed sales calls; the design contrasts disclosed AI, undisclosed AI, and human agents rather than validating any measurement.", "Disclosing the AI agent no longer reduced conversion versus nondisclosure, but AI agents still substantially underperformed human sellers on sales-process execution." ], "bullet_provenance": "ai", "salience": 58, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 35, "authors_detailed": [ { "name": "Johannes Habel", "url": "https://openalex.org/A5035054127", "inst": "Michael Baker International (United States)" }, { "name": "Jan Zawadzki", "url": "https://openalex.org/A5141211983", "inst": "" }, { "name": "Paul Freise", "url": "https://openalex.org/A5141210370", "inst": "" } ], "affiliations": [ "Michael Baker International (United States)" ] }, { "uid": "doi:10.2139/ssrn.7009038", "doi": "10.2139/ssrn.7009038", "title": "Can LLMs Hire Fairly? Racial Bias in Resume Screening", "authors": [ "Zhenyu Gao", "Wenxi Jiang", "Yutong Yan" ], "posted": "2026-07-20", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7009038", "field": "economics", "role": "object", "bullets": [ "Audit of fourteen mainstream LLMs for hiring discrimination using the paired-resume design of Kline, Rose and Walters (2022), based on 24,024 paired job postings per model.", "Each model screens resumes differing only by race or gender and callback gaps are computed per model; the abstract names no individual model.", "The single 2023 model reproduces a pro-White gap of 2.12 pp, while every 2024-or-later model shows a null gap or a pro-Black reversal up to 3.01 pp, echoed on gender." ], "bullet_provenance": "ai", "salience": 60, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 50, "authors_detailed": [ { "name": "Zhenyu Gao", "url": "https://openalex.org/A5139791820", "inst": "Chinese University of Hong Kong" }, { "name": "Wenxi Jiang", "url": "https://openalex.org/A5139694346", "inst": "Chinese University of Hong Kong" }, { "name": "Yutong Yan", "url": "https://openalex.org/A5139696699", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.7147559", "doi": "10.2139/ssrn.7147559", "title": "MACES: Multi-Agent Conviction Dynamics with Cross-LLM Ensemble Scoring for Market Narrative Intelligence System", "authors": [ "Yujuan Qiu" ], "posted": "2026-07-20", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7147559", "field": "finance", "role": "instrument", "bullets": [ "Thirty-six known past market events, including crises, bullish catalysts, and narrative reversals, form the evaluation set; period and geography not stated.", "Four LLM agents playing bull, bear, skeptic, and editor roles debate each narrative, and Claude, GPT-4o, and Gemini scores are ensembled into a conviction measure benchmarked against FinBERT.", "The framework flags 86 percent of events before they occur versus FinBERT's 67 percent, and finds structural fragility in 10 events where FinBERT reads neutral sentiment." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "legacy" ], "open_weights": false, "validated": true, "validation_note": "36 labeled past events, pre-event detection rate vs FinBERT", "salience": 40, "edition": 2, "audience": "technical", "n": 51, "authors_detailed": [ { "name": "Yujuan Qiu", "url": "https://openalex.org/A5101062428", "inst": "George Washington University" } ], "affiliations": [ "George Washington University" ] }, { "uid": "doi:10.2139/ssrn.7123419", "doi": "10.2139/ssrn.7123419", "title": "Artificial Intelligence, Algorithmic Trading, and Financial Market Microstructure: A Systematic Literature Review", "authors": [ "Fuli Yang", "Shiyu Lin", "Jing Huang" ], "posted": "2026-07-20", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7123419", "field": "finance", "role": "object", "bullets": [ "Systematic PRISMA review screening 847 records across Web of Science, Scopus, SSRN, and Google Scholar, synthesizing 60 studies on algorithmic and high-frequency trading in equity, Treasury, and foreign exchange markets.", "No language model is applied by the authors; the review surveys how AI, including unnamed LLM classifiers and NLP surveillance tools, reshapes liquidity, price discovery, and manipulation detection.", "Frames current markets as an algorithmic ecology and argues microstructure research must shift from rational human agents toward frameworks for correlated algorithmic failures and emergent dynamics." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 45, "edition": 2, "audience": "technical", "validated": null, "n": 56, "authors_detailed": [ { "name": "Fuli Yang", "url": "https://openalex.org/A5101033933", "inst": "Shandong University of Technology" }, { "name": "Shiyu Lin", "url": "https://openalex.org/A5140253041", "inst": "Massachusetts Institute of Technology" }, { "name": "Jing Huang", "url": "https://openalex.org/A5141209589", "inst": "Harvard University Press" } ], "affiliations": [ "Massachusetts Institute of Technology", "Harvard University", "Shandong University of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7017778", "doi": "10.2139/ssrn.7017778", "title": "High-Growth Institutional Entrepreneurship in Africa: What AI Does and Does Not Tell You", "authors": [ "Muhammad Aliyu", "Roy Ngu Esibe", "Erik Stam", "Jesse Thornburg", "João Barros" ], "posted": "2026-07-20", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7017778", "field": "management", "role": "method", "bullets": [ "High-growth ventures and entrepreneurial ecosystems across the African continent, with unicorn founders as the unit of observation, drawing on unstructured online data plus primary founder interviews.", "Retrieval-augmented generation with an unnamed LLM extracts founder-level insights into structured narratives, which are then checked against primary founder interviews to surface factual and framing errors.", "AI narratives carry factual and framing errors and miss undisclosed private considerations, so AI-assisted qualitative research in data-sparse contexts still requires primary triangulation." ], "bullet_provenance": "ai", "validated": false, "validation_note": "narratives compared to founder interviews, no accuracy figure", "salience": 55, "edition": 2, "audience": "broad", "models": [], "n": 57, "authors_detailed": [ { "name": "Muhammad Aliyu", "url": "https://openalex.org/A5141193532", "inst": "" }, { "name": "Roy Ngu Esibe", "url": "https://openalex.org/A5116715581", "inst": "Carnegie Mellon University Africa" }, { "name": "Erik Stam", "url": "https://openalex.org/A5000347839", "inst": "Utrecht University" }, { "name": "Jesse Thornburg", "url": "https://openalex.org/A5141211486", "inst": "" }, { "name": "João Barros", "url": "https://openalex.org/A5089850116", "inst": "Carnegie Mellon University Africa" } ], "affiliations": [ "Carnegie Mellon University Africa", "Utrecht University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.17122v1", "arxiv_id": "2607.17122v1", "title": "Scope3Trace: Evidence-Based Identification and Extraction of Scope 3 GHG Emissions from Sustainability Reports", "authors": [ "Siyuan Zheng", "Yifan Duan", "Chao Xue", "Flora D. Salim" ], "posted": "2026-07-19", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.17122v1", "field": "accounting", "role": "method", "bullets": [ "Real-world corporate ESG and sustainability reports in PDF form, from which the framework extracts organization- and building-level Scope 1 to 3 greenhouse gas disclosures and builds a multimodal dataset.", "Unnamed large language models perform page localization, table reconstruction, and hybrid rule-plus-LLM extraction with evidence-grounded verification; the abstract claims high accuracy but reports no numeric figure.", "The pipeline recovers Scope 1 to 3 totals and category-level disclosures with traceable evidence, but gives no quantitative accuracy or agreement statistic in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 292, "authors_detailed": [ { "name": "Siyuan Zheng", "url": "https://openalex.org/A5107926336", "inst": "North University of China" }, { "name": "Yifan Duan", "url": "https://openalex.org/A5081385299", "inst": "University of Electronic Science and Technology of China" }, { "name": "C Y Xue", "url": "https://openalex.org/A5109042106", "inst": "Shanghai Jiao Tong University" }, { "name": "Flora D. Salim", "url": "https://openalex.org/A5141256611", "inst": "" } ], "affiliations": [ "North University of China", "University of Electronic Science and Technology of China", "Shanghai Jiao Tong University" ] }, { "uid": "arxiv:2607.17427v1", "arxiv_id": "2607.17427v1", "title": "Abliteration Is Not a Scalpel: Off-Target Effects of Refusal Removal on Decision Disposition Across Model Families", "authors": [ "Aleksander Fafuła" ], "posted": "2026-07-19", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.17427v1", "field": "finance", "role": "agent", "bullets": [ "21,600 weekly up/down predictions on 60 Warsaw Stock Exchange equities over 18 weeks, replayed through a frozen pipeline so the decision-layer model is the only variable.", "Base versus abliterated, refusal-removed checkpoints of Gemma-4-26B and Qwen3-30B make the calls; no measurement is validated against ground truth.", "Abliterated models turn more optimistic (+12.2 pp Gemma, +7.4 pp Qwen) and more verbose; neither arm shows stock-picking alpha, only regime beta." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 56, "edition": 3, "audience": "technical", "validated": null, "n": 1000, "authors_detailed": [ { "name": "Aleksander Fafuła", "url": "https://openalex.org/A5036442957", "inst": "Development Fund" } ], "affiliations": [ "Development Fund" ] }, { "uid": "arxiv:2607.17112v1", "arxiv_id": "2607.17112v1", "title": "Learning Dispute Structure for Settlement Prediction in Financial ADR: A Multi-Task and Cross-Institutional Approach", "authors": [ "Koutarou Tamura" ], "posted": "2026-07-19", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.17112v1", "field": "finance", "role": "method", "bullets": [ "Financial alternative dispute resolution cases from multiple Japanese ADR organizations, each pairing complainant and respondent claims with a binary settlement outcome.", "Large language models (not named) and a proposed multi-task model jointly perform dispute classification and settlement prediction using a functional tagging scheme for dispute structure.", "Incorporating dispute structure improves settlement prediction, and LLMs reach comparable or superior performance in several domains, suggesting dispute structures are partly shared across ADR domains." ], "bullet_provenance": "ai", "validated": false, "validation_note": "prediction accuracy not quantified in abstract", "salience": 38, "edition": 3, "audience": "technical", "models": [], "n": 1001, "authors_detailed": [ { "name": "Koutarou Tamura", "url": "https://openalex.org/A5125466467", "inst": "Nomura Research Institute" } ], "affiliations": [ "Nomura Research Institute" ] }, { "uid": "doi:10.2139/ssrn.7140338", "doi": "10.2139/ssrn.7140338", "title": "Enhancing Stock Price Manipulation Detection via Individualized Time-Series Transformer Models", "authors": [ "Natchaya Suphasueb", "Poj Tangamchit" ], "posted": "2026-07-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7140338", "field": "finance", "role": "instrument", "bullets": [ "Limit order book data from the Stock Exchange of Thailand covering six documented real stock price manipulation cases, with a separate detection model trained for each individual stock.", "A time-series transformer autoencoder, no language model named, flags manipulation as anomalies and is checked against the six known real manipulation cases treated as ground truth.", "The individualized transformer autoencoder detected all six manipulation cases with a low false-positive rate, improving on the prior LSTM autoencoder that caught five of six." ], "bullet_provenance": "ai", "validated": true, "validation_note": "six known real manipulation cases used as ground truth", "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 590, "authors_detailed": [ { "name": "Natchaya Suphasueb", "url": "https://openalex.org/A5141140096", "inst": "King Mongkut's University of Technology Thonburi" }, { "name": "Poj Tangamchit", "url": "https://openalex.org/A5006423476", "inst": "King Mongkut's University of Technology Thonburi" } ], "affiliations": [ "King Mongkut's University of Technology Thonburi" ] }, { "uid": "doi:10.2139/ssrn.6995518", "doi": "10.2139/ssrn.6995518", "title": "Memorized but Not Realized: Frontier Language Models Precisely Recall Financial Fundamentals, but It Does Not Become Trading Skill", "authors": [ "Arjun Kathiravelu" ], "posted": "2026-07-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6995518", "field": "finance", "role": "agent", "bullets": [ "Sixteen open language models from ten vendors plus three commercial APIs, tested on famous firm-year fundamentals, a cross-sectional ranking task of about 5,700 names per model, and a StockBench-style backtest.", "Models act as traders while a validated white-box-to-black-box leakage instrument measures memorization; recall collapses to chance for facts dated after each model's training cutoff.", "Models recall realized fundamentals within 10 percent for up to 89 percent of firm-years, yet this memorization does not become trading skill, and an apparent 9.9 point agentic signal vanishes under firm fixed effects." ], "bullet_provenance": "ai", "validated": true, "validation_note": "cutoff-bounded leakage instrument; recall accuracy reported against ground-truth figures", "salience": 74, "edition": 3, "audience": "general", "models": [], "n": 591, "authors_detailed": [ { "name": "Arjun Kathiravelu", "url": "https://openalex.org/A5131000999", "inst": "University of Geneva" } ], "affiliations": [ "University of Geneva" ] }, { "uid": "doi:10.2139/ssrn.6996880", "doi": "10.2139/ssrn.6996880", "title": "Leveraging AI to Identify Small Business Regulations at the Federal, State, and Local Levels", "authors": [ "Patrick A. McLaughlin", "Dustin Chambers" ], "posted": "2026-07-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6996880", "field": "economics", "role": "instrument", "bullets": [ "New database of small business regulatory exemptions at the 3-digit NAICS level for 48 US states in 2023, extended to the federal code and 3,241 municipal jurisdictions.", "An unnamed large language model classifies regulations to flag small business exemptions, cross-checked against traditional NLP methods, with no accuracy figure reported in the abstract.", "Industries with more small business exemptions show greater economic dynamism through higher establishment and job formation, and municipal definitions vary far more than state or federal ones." ], "bullet_provenance": "ai", "validated": false, "validation_note": "cross-checked against NLP methods, no figure reported", "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 996, "authors_detailed": [ { "name": "Patrick A. McLaughlin", "url": "https://openalex.org/A5036427132", "inst": "Hoover Institution" }, { "name": "Dustin Chambers", "url": "https://openalex.org/A5064293699", "inst": "George Mason University" } ], "affiliations": [ "Hoover Institution", "George Mason University" ] }, { "uid": "doi:10.2139/ssrn.6998138", "doi": "10.2139/ssrn.6998138", "title": "The Spatial Footprint of Artificial Intelligence: Evaluating the Friction Between Fast-Tracked Digital Infrastructure and Local Land-Use Governance in Italy", "authors": [ "Mehrdad Afsharmajd" ], "posted": "2026-07-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6998138", "field": "economics", "role": "object", "bullets": [ "Qualitative case study of data centre siting and land-use governance in Italy during 2026, centred on national Law Decree 21/2026 and Lombardy Regional Law 11/2026.", "No language model is used; the paper analyses how the generative AI boom drives land-, power-, and water-intensive data centre demand and the resulting regulatory friction.", "National fast-track authorisation and regional territorial control move in opposite directions; the authors argue for proactive territorial equalisation over reactive zoning." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "general", "validated": null, "n": 997, "authors_detailed": [ { "name": "Mehrdad Afshar Majd", "url": "https://openalex.org/A5139343650", "inst": "Politecnico di Torino" } ], "affiliations": [ "Politecnico di Torino" ] }, { "uid": "doi:10.2139/ssrn.7140369", "doi": "10.2139/ssrn.7140369", "title": "When Textual Signals Lose Value: Generative AI and Scientific Attention Allocation", "authors": [ "Zhuoxian Lin", "Jiaqi Liu" ], "posted": "2026-07-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7140369", "field": "economics", "role": "object", "bullets": [ "286,378 papers posted to SSRN, comparing download attention before and after ChatGPT's public release across textual, certification, and reputation signals.", "No model is used as a tool; generative AI's arrival is the treatment, and the study tests how it shifts the attention value of each signal type.", "Textual complexity loses its pre-ChatGPT download premium after release, most for unpublished papers, while journal certification and institutional reputation become more consequential." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 63, "edition": 3, "audience": "general", "validated": null, "n": 998, "authors_detailed": [ { "name": "Zhuoxian Lin", "url": "https://openalex.org/A5141061721", "inst": "University of Glasgow" }, { "name": "J. Liu", "url": "https://openalex.org/A5062534379", "inst": "University of Science and Technology of China" } ], "affiliations": [ "University of Glasgow", "University of Science and Technology of China" ] }, { "uid": "doi:10.2139/ssrn.6996601", "doi": "10.2139/ssrn.6996601", "title": "Autonomous Exception Management in SAP S/4HANA Manufacturing Through Multi-agent Generative AI and Event-driven Supply Networks", "authors": [ "Mahendrakumar Kalal" ], "posted": "2026-07-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6996601", "field": "management", "role": "agent", "bullets": [ "Exception management in SAP S/4HANA manufacturing operations, evaluated in a simulated discrete-event manufacturing environment; sample size and period not stated.", "An unnamed multi-agent generative AI system detects exceptions, classifies root causes, and runs corrective actions, with agent prioritisation set by two performance equations.", "Simulated average exception-resolution time falls 62% versus a manual baseline; the paper also provides a taxonomy of agent roles and resolution strategies." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 999, "authors_detailed": [ { "name": "Mahendrakumar Kalal", "url": "https://openalex.org/A5132541399", "inst": "Tanner Research (United States)" } ], "affiliations": [ "Tanner Research (United States)" ] }, { "uid": "doi:10.2139/ssrn.7140491", "doi": "10.2139/ssrn.7140491", "title": "A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing", "authors": [ "Mohsen Dastpak", "Fausto Errico", "Ola Jabali" ], "posted": "2026-07-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7140491", "field": "management", "role": "method", "bullets": [ "Vehicle routing with stochastic demands and outsourcing, where a logistics provider splits customers between its own fleet and a common carrier, tested on synthetic instances with variable customer counts and locations.", "A deep Q-network with a graph attention network encoder learns an offline routing-cost policy for the stochastic-demand subproblem, refined by online fine-tuning; no language model is used.", "The routing policy cuts costs by 19.6 percent versus a state-of-the-art method and at least 29.6 percent versus classical heuristics, with the full algorithm saving 13.7 percent on average within minutes." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1194, "authors_detailed": [ { "name": "Mohsen Dastpak", "url": "https://openalex.org/A5046535877", "inst": "École de Technologie Supérieure" }, { "name": "Fausto Errico", "url": "https://openalex.org/A5069939807", "inst": "HEC Montréal" }, { "name": "Ola Jabali", "url": "https://openalex.org/A5004356163", "inst": "Politecnico di Milano" } ], "affiliations": [ "École de Technologie Supérieure", "HEC Montréal", "Politecnico di Milano" ] }, { "uid": "doi:10.2139/ssrn.7071718", "doi": "10.2139/ssrn.7071718", "title": "Generative AI Mania and Macro Vulnerability: Asymmetric Volatility Dynamics of Tech Giants via EGARCH-X (2023-2026)", "authors": [ "Fuli Yang", "Shiyu Lin" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7071718", "field": "finance", "role": "object", "bullets": [ "866 daily observations from January 2023 to July 2026 for NVDA, AMD, MSFT, GOOGL and the S&P 500 ETF, ordered by proximity to the generative AI narrative.", "No language model is used; EGARCH-X models with the MOVE index as a macro-stress proxy estimate asymmetric volatility, with a structural break at the DeepSeek announcement.", "Leverage effect weakens toward AI-core assets, NVDA shows higher shock sensitivity, and a regime shift appears around the January 27, 2025 DeepSeek announcement." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 52, "edition": 3, "audience": "general", "validated": null, "n": 289, "authors_detailed": [ { "name": "Fuli Yang", "url": "https://openalex.org/A5101033933", "inst": "Shandong University of Technology" }, { "name": "Shiyu Lin", "url": "https://openalex.org/A5140253041", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "Shandong University of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6985126", "doi": "10.2139/ssrn.6985126", "title": "Structuring Product Management Theory for AI Reasoning: A Knowledge Graph Schema and Compilation Pattern for Domain-specific Intelligence", "authors": [ "Talal Ahmad Gondal" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6985126", "field": "management", "role": "method", "bullets": [ "Conceptual and design paper on encoding product management theory as a knowledge graph for AI reasoning, with a product management worked example and no empirical sample.", "Proposes an ontology of four entity types with directed relations plus an LLM-Wiki compilation pattern to feed structured knowledge to models; no specific model is named or tested.", "Argues retrieval-augmented generation discards construct relationships and that domain-specific AI needs structured knowledge, institutional memory, and outcome feedback loops." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 290, "authors_detailed": [ { "name": "Talal Ahmad Gondal", "url": "https://openalex.org/A5138358910", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7135123", "doi": "10.2139/ssrn.7135123", "title": "A Knowledge-Based Human–LLM Interaction Decision Support System for Logistics Network Modeling and Optimization", "authors": [ "Songyi Wang", "haolin wen", "Yuhe Shi", "Xiao-Sheng Ni", "Xing Luo", "Lili Yang" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7135123", "field": "management", "role": "method", "bullets": [ "Six experiments on Cordeau vehicle-routing benchmark instances and a real UAV emergency-scheduling case in Shenzhen, China, covering modeling, routing, and dynamic re-dispatch.", "Large language models, family not stated, generate and verify optimization models and routes within the K-HLIDSS human-LLM decision framework, checked against benchmark instances.", "Structured prior knowledge and verification raised model-element accuracy from 79.17 to 100 percent and valid route output from 22.5 to 90 percent, averaging a 2.20 percent gap." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Cordeau benchmark instances, accuracy reported", "salience": 48, "edition": 3, "audience": "technical", "models": [], "n": 291, "authors_detailed": [ { "name": "Songyi Wang", "url": "https://openalex.org/A5102932897", "inst": "Southern University of Science and Technology" }, { "name": "Haolin Wen", "url": "https://openalex.org/A5039442518", "inst": "Naval University of Engineering" }, { "name": "Yuhe Shi", "url": "https://openalex.org/A5013995167", "inst": "Harbin Institute of Technology" }, { "name": "Xiao-Sheng Ni", "url": "https://openalex.org/A5042783006", "inst": "Sun Yat-sen University" }, { "name": "Xing Luo", "url": "https://openalex.org/A5100316250", "inst": "Foshan University" }, { "name": "Lili Yang", "url": "https://openalex.org/A5070434985", "inst": "Shandong University" } ], "affiliations": [ "Southern University of Science and Technology", "Naval University of Engineering", "Harbin Institute of Technology", "Sun Yat-sen University", "Foshan University", "Shandong University" ] }, { "uid": "doi:10.2139/ssrn.7135124", "doi": "10.2139/ssrn.7135124", "title": "ACORN: A Knowledge-Guided Hybrid Architecture for Resilient Last-Mile Logistics", "authors": [ "Aneek Choudhury" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7135124", "field": "management", "role": "method", "bullets": [ "Last-mile inventory control evaluated on the LaDe logistics dataset of 9.16 million delivery records, tested on held-out data.", "A hierarchical Mistral-7B language model applies fifteen governance motifs to audit and refine multi-agent reinforcement-learning actions before execution; assessed on constraint enforcement and service level.", "The system reaches 1.0 precision in constraint enforcement and a 97.4 percent service level, versus 46.0 percent for a traditional economic-order-quantity baseline." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "1.0 constraint-enforcement precision, 97.4 percent held-out service level", "salience": 36, "edition": 3, "audience": "technical", "n": 323, "authors_detailed": [ { "name": "Aneek Choudhury", "url": "https://openalex.org/A5124485715", "inst": "Indian Institute of Management Kozhikode" } ], "affiliations": [ "Indian Institute of Management Kozhikode" ] }, { "uid": "doi:10.2139/ssrn.6970398", "doi": "10.2139/ssrn.6970398", "title": "The Agentic Regulator: Risks for AI in Finance and a Proposed Agent-based Framework for Governance", "authors": [ "Eren Kurshan", "Tucker Balch", "David Byrd" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6970398", "field": "finance", "role": "object", "bullets": [ "Conceptual analysis of generative and agentic AI risk in financial markets, with a case study of emergent spoofing in multi-agent trading systems.", "No model is used for measurement; the authors draw on complex adaptive systems theory to propose a four-layer modular governance architecture of regulatory blocks.", "Argues layered self-regulation, firm, regulator, and audit blocks can quarantine harmful emergent behavior in real time while remaining compatible with existing model-risk rules." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 324, "authors_detailed": [ { "name": "Eren Kurshan", "url": "https://openalex.org/A5083952888", "inst": "Princeton University" }, { "name": "Tucker Balch", "url": "https://openalex.org/A5035482777", "inst": "Emory University" }, { "name": "David R. Byrd", "url": "https://openalex.org/A5110306774", "inst": "Bowdoin College" } ], "affiliations": [ "Princeton University", "Emory University", "Bowdoin College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6990198", "doi": "10.2139/ssrn.6990198", "title": "Forecast Accuracy is Not Decision Accuracy: Delegated Overbooking with Generative-AI Agents", "authors": [ "Ziqi Zhong", "Yuhang Du" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6990198", "field": "management", "role": "agent", "bullets": [ "General-admission live-event ticket retailing, using 110,696 ticketing transactions, 2,022 realized events, and 13,440 direct-agent decisions, with identification arms fixing beliefs and objectives.", "The same frontier LLM, not named, is deployed two ways, supplying no-show probabilities to a newsvendor optimizer versus holding the overbooking decision right, benchmarked against a base-rate fail-safe.", "Mean-calibrated LLM predictors compress the low no-show tail and drive sharp overbooking, while direct agents act conservatively but do not reliably beat the fail-safe." ], "bullet_provenance": "ai", "validated": false, "validation_note": "calibration examined, no accuracy figure reported", "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 527, "authors_detailed": [ { "name": "Ziqi Zhong", "url": "https://openalex.org/A5066283423", "inst": "London School of Economics and Political Science" }, { "name": "Yuhang Du", "url": "https://openalex.org/A5101277286", "inst": "London Business School" } ], "affiliations": [ "London School of Economics and Political Science", "London Business School" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7090918", "doi": "10.2139/ssrn.7090918", "title": "From Conflict to Convergence: A Taxonomy of Hybrid Econometric-Deep Learning Architectures for Financial Volatility Forecasting (2019-2026)", "authors": [ "Fuli Yang" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7090918", "field": "finance", "role": "method", "bullets": [ "Systematic PRISMA-style review of 2019 to 2026 studies from SCI/SSCI Q1-Q2 or ABS 3-star and above outlets that pair an econometric volatility component with a deep learning component.", "No model is applied by the authors; the review builds a taxonomy of hybrid architectures and flags LLM-extracted textual sentiment as a future exogenous driver for volatility models.", "Parameter-embedding architectures that generate time-varying GARCH parameters within an end-to-end network deliver the largest out-of-sample QLIKE and Model Confidence Set gains, at higher interpretability cost." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 528, "authors_detailed": [ { "name": "Fuli Yang", "url": "https://openalex.org/A5101033933", "inst": "Shandong University of Technology" } ], "affiliations": [ "Shandong University of Technology" ] }, { "uid": "doi:10.2139/ssrn.7135544", "doi": "10.2139/ssrn.7135544", "title": "The safety–efficacy gap in health-related generative AI: a mixed-methods analysis of public user discourse", "authors": [ "Zhining Yang", "Runyuan Pei", "Yuyang Zhang", "Xingyi Tang", "Haoming Ma", "Meihua Piao" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7135544", "field": "management", "role": "object", "bullets": [ "12,290 publicly accessible user-generated posts on the Chinese platforms Bilibili and Zhihu, from January 2021 to March 2025, discussing health-related generative AI.", "Generative AI is the object studied; the authors combine computationally assisted thematic analysis with weakly supervised sentiment classification, model not named, to build an eight-theme framework.", "Negative comments dominated at 77.3 percent, concentrated on interaction quality, reliability, and safeguards, revealing a safety-efficacy gap where users value usefulness but withhold trust." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 529, "authors_detailed": [ { "name": "Zhining Yang", "url": "https://openalex.org/A5113744581", "inst": "Chinese Academy of Medical Sciences & Peking Union Medical College" }, { "name": "Runyuan Pei", "url": "https://openalex.org/A5083522682", "inst": "Chinese Academy of Medical Sciences & Peking Union Medical College" }, { "name": "Yuyang Zhang", "url": "https://openalex.org/A5141054456", "inst": "" }, { "name": "Xingyi Tang", "url": "https://openalex.org/A5007979565", "inst": "Southwest Petroleum University" }, { "name": "Haoming Ma", "url": "https://openalex.org/A5044546623", "inst": "Chinese Academy of Medical Sciences & Peking Union Medical College" }, { "name": "Meihua Piao", "url": "https://openalex.org/A5141057182", "inst": "" } ], "affiliations": [ "Chinese Academy of Medical Sciences & Peking Union Medical College", "Southwest Petroleum University" ] }, { "uid": "doi:10.2139/ssrn.6991638", "doi": "10.2139/ssrn.6991638", "title": "How Americans Spend Their Time Online", "authors": [ "David Lazer", "Burak Ozturan", "Christo Wilson", "David Choffnes", "Cassidy Waldrip", "Hsiu-Chi Lu", "John Wihbey" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6991638", "field": "economics", "role": "object", "bullets": [ "Desktop browsing data from the National Internet Observatory covering US users across age cohorts, a descriptive study of where people spend their online time.", "LLM adoption is the object; the study measures the share of online time captured by LLM tools such as ChatGPT alongside other platforms, with no model used as a research instrument.", "The top 10 platforms took 49 percent of online time, LLM tools reached 2.9 percent (above news at 2.5 percent), and under-30s spent 3 percent on ChatGPT versus under 1 percent for over-65s." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "general", "validated": null, "n": 530, "authors_detailed": [ { "name": "David Lazer", "url": "https://openalex.org/A5141098293", "inst": "" }, { "name": "Burak Ozturan", "url": "https://openalex.org/A5139947781", "inst": "Universidad del Noreste" }, { "name": "Christo Wilson", "url": "https://openalex.org/A5137726017", "inst": "" }, { "name": "David Choffnes", "url": "https://openalex.org/A5136708465", "inst": "Northeastern University" }, { "name": "Cassidy Waldrip", "url": "https://openalex.org/A5092073701", "inst": "Universidad del Noreste" }, { "name": "Hsiu-Chi Lu", "url": "https://openalex.org/A5106560261", "inst": "Universidad del Noreste" }, { "name": "John Wihbey", "url": "https://openalex.org/A5137744041", "inst": "Universidad del Noreste" } ], "affiliations": [ "Universidad del Noreste", "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.7062598", "doi": "10.2139/ssrn.7062598", "title": "Towards a Rigorous Framework for Human-Supervised GenAI-Assisted Qualitative Policy Document Analysis", "authors": [ "Pedro Fidelman" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7062598", "field": "management", "role": "method", "bullets": [ "Conceptual methodological paper with no empirical sample, focused on applying generative AI to qualitative analysis of policy documents as a distinct form of research material.", "No model is applied empirically; generative AI, not named, is proposed as a selectively deployable assistant under a bounded-delegation principle tied to interpretive task demands.", "It distinguishes four analytical task types with corresponding generative-AI boundaries and sets rigour principles of transparency, auditability, reflexivity, validation, and process traceability." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 992, "authors_detailed": [ { "name": "Pedro Fidelman", "url": "https://openalex.org/A5077266984", "inst": "The University of Queensland" } ], "affiliations": [ "The University of Queensland" ] }, { "uid": "doi:10.2139/ssrn.6980618", "doi": "10.2139/ssrn.6980618", "title": "From Manual Outreach to Autonomous Pipelines: A Conceptual Framework for Conversational, MCP-Orchestrated B2B Sales Prospecting", "authors": [ "Deepak Amirtha Raj" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6980618", "field": "management", "role": "object", "bullets": [ "Conceptual framework for business-to-business sales prospecting with no empirical data, drawing on the AI-and-sales literature and documented industry practice on manual outreach.", "It proposes an autonomous, agent-orchestrated pipeline using Model Context Protocol servers and enrichment providers such as Clay, Apollo.io, and ZoomInfo; no specific language model is named.", "The workflow collapses fragmented multi-tool prospecting into a single conversational control plane, with discussion of throughput, personalization quality, deliverability governance, and outreach ethics." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 993, "authors_detailed": [ { "name": "Deepak Amirtha Raj", "url": "https://openalex.org/A5135901051", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6979203", "doi": "10.2139/ssrn.6979203", "title": "Data Architecture and Governance in the Agentic Era: Paradigm shifts from 2026 Databricks DAIS", "authors": [ "Idilio Moncivais" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6979203", "field": "management", "role": "object", "bullets": [ "Interpretive synthesis of structural announcements from the 2026 Databricks Data and AI Summit held in San Francisco, with no empirical data collected.", "No language model is applied; the paper analyses agentic-AI data infrastructure such as Lakebase, Unity Catalog, and Model Context Protocol interception; no model is named.", "It organizes five paradigm shifts around four pillars of context, cost, control, and choice, mapped against NIST AI RMF, ISO/IEC 42001, and the EU AI Act." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 994, "authors_detailed": [ { "name": "Idilio Moncivais Pinedo", "url": "https://openalex.org/A5134344672", "inst": "University of Northern Colorado" } ], "affiliations": [ "University of Northern Colorado" ] }, { "uid": "doi:10.2139/ssrn.7129019", "doi": "10.2139/ssrn.7129019", "title": "Multi-Agent Artificial Intelligence for Autonomous Enterprise Automation", "authors": [ "Parikshit Rohit Pravin Srivastav" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7129019", "field": "management", "role": "object", "bullets": [ "Systematic literature review of agentic and multi-agent AI for enterprise automation spanning cloud, ERP, CRM, DevOps, cybersecurity, and business process management, using academic and industrial sources.", "No language model is applied by the authors; the review synthesizes architectures, deployment strategies, and advantages of multi-agent systems; no specific model is named.", "It reports that multi-agent AI improves efficiency, scalability, and resilience, while flagging governance, interoperability, explainability, trust, and security as barriers to adoption." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 995, "authors_detailed": [ { "name": "Parikshit Rohit Pravin Srivastav", "url": "https://openalex.org/A5141070113", "inst": "University of Denver" } ], "affiliations": [ "University of Denver" ] }, { "uid": "arxiv:2607.15879v2", "arxiv_id": "2607.15879v2", "title": "DECODEM: Data Extraction from Corporate Organizational Documents via Enhanced Methods", "authors": [ "Jens Frankenreiter" ], "posted": "2026-07-17", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.15879v2", "field": "finance", "role": "method", "bullets": [ "Randomly sampled US corporate charters and bylaws paired with human annotations for a range of governance provisions, framed as document-level binary classification tasks; sample size not stated.", "Several large-language-model extraction pipelines varying in prompt design, task decomposition, and document handling were benchmarked against the human annotations; specific model names not stated, only frontier versus efficiency tiers.", "Extraction reaches high accuracy with median performance near the annotation upper bound; elaborate prompting rarely helps frontier models but narrows the gap between frontier and efficiency-oriented models." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human-annotated charter/bylaw benchmark", "salience": 58, "edition": 3, "audience": "technical", "models": [], "n": 1193, "authors_detailed": [ { "name": "Jens Frankenreiter", "url": "https://openalex.org/A5027919894", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6993619", "doi": "10.2139/ssrn.6993619", "title": "Consumable but Not Identifiable Longitudinally: A Matched Audit of ESG Narrative Change in Japanese Listed Firms", "authors": [ "Hiroyuki Kokubu" ], "posted": "2026-07-17", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6993619", "field": "accounting", "role": "method", "bullets": [ "Matched panel of 30 Japanese Prime Market firms across reporting years FY2022 to FY2024, covering 60 targeted prior-year firm-year cells of ESG narrative disclosure.", "A four-model consensus system (claude-opus-4-7, gpt-5.5, gemini-3.1-pro-preview, deepseek-v4-pro) extracts and scores narrative spans, with stability judged within an inter-model dispersion threshold and no human benchmark.", "48 percent of cells reached full comparability, 72 percent of 87 change observations were stable, and single-point comparability did not imply longitudinal identifiability." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "validated": false, "salience": 38, "edition": 2, "audience": "technical", "n": 49, "authors_detailed": [ { "name": "Hiroyuki Kokubu", "url": "https://openalex.org/A5134823092", "inst": "Kansai University" } ], "affiliations": [ "Kansai University" ] }, { "uid": "doi:10.2139/ssrn.7124822", "doi": "10.2139/ssrn.7124822", "title": "The Development of Artificial Superintelligence (ASI): The Scientisa MindMap Application for New Innovative Research, and Problem- and Case-Based Decision-Making", "authors": [ "Sumiyana Sumiyana", "Christian Elias" ], "posted": "2026-07-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7124822", "field": "management", "role": "method", "bullets": [ "Design and description of Scientisa MindMap, a browser-based knowledge platform for researchers and executives; no empirical sample or evaluation is reported.", "The tool calls external AI services through bring-your-own-key REST APIs and adds memory and dialectic features meant to offset large language model limitations; specific models not stated.", "Presents the platform's architecture and features, including Hegelian dialectic node collision and an autonomous agent linking isolated knowledge nodes; no outcomes are measured." ], "bullet_provenance": "ai", "salience": 24, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 287, "authors_detailed": [ { "name": "Sumiyana Sumiyana", "url": "https://openalex.org/A5140840968", "inst": "" }, { "name": "Christian Elias", "url": "https://openalex.org/A5140913548", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7129788", "doi": "10.2139/ssrn.7129788", "title": "Generative AI and Idea Implementation: An Attention-Based View", "authors": [ "Luke Rhee" ], "posted": "2026-07-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7129788", "alt_urls": [ "https://doi.org/10.2139/ssrn.7149599" ], "field": "management", "role": "object", "bullets": [ "Longitudinal field experiment at a software company piloting ChatGPT Enterprise, 287 workers with 140 randomly given access and 147 controls, across two survey waves plus performance reviews.", "ChatGPT Enterprise served as the workplace treatment rather than a research measurement tool, with effects estimated through a difference-in-differences design.", "Employees with access advanced significantly more ideas into products and processes and drew 23.5 percent more colleague attention, which partially mediated the effect." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "edition": 3, "audience": "general", "validated": null, "n": 288, "authors_detailed": [ { "name": "Luke Rhee", "url": "https://openalex.org/A5090112971", "inst": "University of California, Irvine" } ], "affiliations": [ "University of California, Irvine" ] }, { "uid": "doi:10.2139/ssrn.7123579", "doi": "10.2139/ssrn.7123579", "title": "Becoming an Agentic Enterprise: A Practitioner Methodology & Frameworks for Human-AI Governance", "authors": [ "Bharath Yadla" ], "posted": "2026-07-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7123579", "field": "management", "role": "object", "bullets": [ "Conceptual practitioner methodology for enterprise AI governance, illustrated with a case study of one enterprise software company deploying roughly one hundred agents across eight business domains.", "No model is used by the researchers; the paper proposes four governance components covering board assessment, an L1-L6 agent maturity model, human-oversight calibration, and phased implementation.", "Presents a qualitative blueprint arguing layered governance can preserve human oversight while enabling operational autonomy; no quantitative results are reported." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 322, "authors_detailed": [ { "name": "Bharath Yadla", "url": "https://openalex.org/A5140996153", "inst": "" } ] }, { "uid": "arxiv:2607.15134v1", "arxiv_id": "2607.15134v1", "title": "Platform Choice, Trust, and Privacy in the Consumer AI Assistant Market", "authors": [ "Jennifer Zou" ], "posted": "2026-07-16", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.15134v1", "field": "economics", "role": "object", "bullets": [ "Representative sample of 1,999 US adult AI-assistant users surveyed in June 2026, weighted to the AI-user population using external adoption benchmarks, including a privacy choice experiment.", "ChatGPT, Gemini, and Claude are the platforms studied through stated choices and task allocation, not used as research tools; the design measures trust and willingness to pay for data handling.", "ChatGPT is primary for 58 percent and Gemini 25 percent, Claude holds a third of coding tasks at 7 percent share, and users pay 11.20 dollars monthly to keep humans out of conversations." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 58, "edition": 3, "audience": "general", "validated": null, "n": 526, "authors_detailed": [ { "name": "Jennifer Zou", "url": "https://openalex.org/A5035597407", "inst": "University of California, Los Angeles" } ], "affiliations": [ "University of California, Los Angeles" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6976098", "doi": "10.2139/ssrn.6976098", "title": "Three Paradigms, One Label: Why AI Investment Fails and How to Fix It", "authors": [ "Alok Khatri", "Anisha Gyawali" ], "posted": "2026-07-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6976098", "field": "management", "role": "object", "bullets": [ "Cross-organization observational evidence citing industry statistics, including 88 percent AI adoption but only about 6 percent capturing substantial value and 47.4 billion dollars of AI infrastructure spend in early 2024.", "No model is used by the researchers; the paper builds a conceptual framework separating traditional, generative, and agentic AI as production, deployment, and governance problems.", "Generative AI productivity gains of 40 to 60 percent appear within weeks when workflows are redesigned, and firms redesigning workflows are 2.8 times more likely to report substantial financial impact." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 589, "authors_detailed": [ { "name": "Alok Khatri", "url": "https://openalex.org/A5110733932", "inst": "Institute for Social and Environmental Research-Nepal" }, { "name": "Anisha Gyawali", "url": "https://openalex.org/A5121644403", "inst": "Nanyang Technological University" } ], "affiliations": [ "Institute for Social and Environmental Research-Nepal", "Nanyang Technological University" ] }, { "uid": "doi:10.2139/ssrn.6970159", "doi": "10.2139/ssrn.6970159", "title": "Inflation Targeting in the Time of Supply Shocks: A Large-Language-Model Reading of India's Reserve Bank, 1997-2025", "authors": [ "Dr. Sridevi Tandley Omprakash", "Karan Singh Bagavathinathan" ], "posted": "2026-07-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6970159", "field": "economics", "role": "instrument", "bullets": [ "India, 1997 to 2025, 115 quarters, testing the Phillips curve around the 2016 shift to flexible inflation targeting under recurrent supply shocks.", "Three unnamed large-language-model families read the Reserve Bank's quarterly assessments under a fixed protocol to split demand from supply-led quarters, agreeing at Fleiss kappa 0.45.", "The demand-pull slope was plus 2.2 before targeting and near zero after, the supply slope stayed negative, and the model read 77 of 115 quarters as supply-led." ], "bullet_provenance": "ai", "validated": false, "validation_note": "inter-model agreement only, Fleiss kappa 0.45, no human benchmark", "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 988, "authors_detailed": [ { "name": "Sridevi Tandley Omprakash", "url": "https://openalex.org/A5132811832", "inst": "Bharathidasan University" }, { "name": "Karan Singh Bagavathinathan", "url": "https://openalex.org/A5053112340", "inst": "University of Göttingen" } ], "affiliations": [ "Bharathidasan University", "University of Göttingen" ] }, { "uid": "doi:10.2139/ssrn.7111158", "doi": "10.2139/ssrn.7111158", "title": "Fuzzy and Configurational Approaches to Generative-AI Adoption: A UTAUT2 Systematic Review", "authors": [ "Ricardo Abreu" ], "posted": "2026-07-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7111158", "field": "management", "role": "object", "bullets": [ "Systematic review following PRISMA 2020 across five databases, identifying seventeen empirical studies that pair the UTAUT2 adoption model with fuzzy-set or configurational methods for generative AI.", "No language model is applied by the reviewers; generative AI is the object of study, with adoption modelled through fuzzy-set qualitative comparative analysis rather than linear regression.", "Performance expectancy and hedonic motivation are the most reliable predictors, while high adoption arises from several substitutable configurations rather than one dominant path." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 989, "authors_detailed": [ { "name": "Ricardo Abreu", "url": "https://openalex.org/A5140915787", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6971298", "doi": "10.2139/ssrn.6971298", "title": "Navigating AI Privacy Governance: The KITE Framework for Program Management in Technology Organizations", "authors": [ "Ali Muzaffar" ], "posted": "2026-07-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6971298", "field": "management", "role": "object", "bullets": [ "Practitioner-derived framework developed inductively over three years managing more than 1,200 AI and product privacy reviews per month in a technology organization.", "No language model is applied; the paper studies governance of generative-AI product development and proposes the KITE framework with twelve numbered sub-practices; no model is named.", "The framework is demonstrated through a generative-AI enterprise connector scenario and mapped to NIST CSF 2.0, IEEE Ethically Aligned Design, GDPR, and the EU AI Act." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 990, "authors_detailed": [ { "name": "Ali Muzaffar", "url": "https://openalex.org/A5101581498", "inst": "Institute of Electrical and Electronics Engineers" } ], "affiliations": [ "Institute of Electrical and Electronics Engineers" ] }, { "uid": "arxiv:2607.14713v1", "arxiv_id": "2607.14713v1", "title": "Does Multi-Agent Debate Improve AI Feedback on Research Papers?", "authors": [ "Tomas Havranek", "Zuzana Irsova" ], "posted": "2026-07-16", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.14713v1", "field": "economics", "role": "method", "bullets": [ "Forty-four economics meta-analyses in a pre-registered, identity-masked, within-paper experiment where each paper's authors ranked three length-matched AI reports on their own work by usefulness.", "An unnamed single-pass frontier model was compared with two multi-agent debate tools the authors built, and Gemini served as one of three AI judges of the reports.", "Authors preferred the single pass by 0.66 rank points over one tool and 0.57 over the other, which used roughly thirty times the tokens." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "salience": 60, "edition": 3, "audience": "technical", "validated": null, "n": 991, "authors_detailed": [ { "name": "Tomáš Havránek", "url": "https://openalex.org/A5086665090", "inst": "Charles River Laboratories (Netherlands)" }, { "name": "Zuzana Iršová", "url": "https://openalex.org/A5072893157", "inst": "Charles River Laboratories (Netherlands)" } ], "affiliations": [ "Charles River Laboratories (Netherlands)" ] }, { "uid": "doi:10.2139/ssrn.6944698", "doi": "10.2139/ssrn.6944698", "title": "Confirmation Bias in LLM Conversations: Model Differences and Implications for Organizational Decision Support", "authors": [ "Michał Moneta" ], "posted": "2026-07-16", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6944698", "field": "management", "role": "object", "bullets": [ "192 structured strategy conversations and 1,920 model replies generated across four public LLMs, two response modes, and four Porter strategy domains, analyzed by quantitative content analysis.", "ChatGPT, Claude, Gemini, and Grok replied to managerial prompts and the replies were coded for confirmation-bias cues; the paper reports no validation of the coding against a ground truth.", "Confirmation-bias cues were pervasive across models; Claude showed the lowest bias, ChatGPT intermediate, and Gemini and Grok the highest, and reasoning mode did not reduce bias." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 48, "edition": 2, "audience": "broad", "validated": null, "n": 16, "authors_detailed": [ { "name": "Michał Moneta", "url": "https://openalex.org/A5117772529", "inst": "Uniwersytet SWPS" } ], "affiliations": [ "Uniwersytet SWPS" ] }, { "uid": "doi:10.2139/ssrn.6977604", "doi": "10.2139/ssrn.6977604", "title": "Fine-Tuning DeepSeek-R1-Distill-Qwen-1.5B for Financial Sentiment Analysis with LoRA", "authors": [ "Daniela Sameny", "Georges Lissoko" ], "posted": "2026-07-16", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6977604", "field": "finance", "role": "method", "bullets": [ "Financial PhraseBank corpus of expert-annotated sentences, with a held-out test subset of 300 sentences used for evaluation of three-way sentiment classification.", "DeepSeek-R1-Distill-Qwen-1.5B, an open distillation of DeepSeek-R1, fine-tuned with LoRA and 4-bit QLoRA on a single consumer GPU, scored against the annotated test set.", "Fine-tuning lifts test accuracy from 54.00 to 84.33 percent and weighted F1 from 45.32 to 84.17, a gain of about 30 accuracy points over the zero-shot base." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "300-sentence expert-annotated test set, accuracy and F1 reported", "salience": 34, "edition": 2, "audience": "technical", "n": 22, "authors_detailed": [ { "name": "Daniela Sameny", "url": "https://openalex.org/A5141000023", "inst": "Roivant Sciences (United States)" }, { "name": "Georges Lissoko", "url": "https://openalex.org/A5141016408", "inst": "Roivant Sciences (United States)" } ], "affiliations": [ "Roivant Sciences (United States)" ] }, { "uid": "doi:10.2139/ssrn.7120410", "doi": "10.2139/ssrn.7120410", "title": "LLMs and Economic History: A Comment on Ferrara (2026)", "authors": [ "Johan Fourie" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7120410", "field": "economics", "role": "object", "bullets": [ "Commentary responding to Ferrara (2026), a guide to large language models for economic historians, framed as a methodological essay with no dataset or empirical test.", "No model is run; the paper argues large language models change the source-to-variable stage least and reshape question choice, narrative, and research evaluation instead.", "Contends the binding constraint in economic history shifts from production to judgment, and flags recursive contamination, a validation paradox, and unequal access." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 286, "authors_detailed": [ { "name": "Johan Fourie", "url": "https://openalex.org/A5140657128", "inst": "Stellenbosch University" } ], "affiliations": [ "Stellenbosch University" ] }, { "uid": "doi:10.2139/ssrn.6963359", "doi": "10.2139/ssrn.6963359", "title": "A Mean-Independent Consistency Instrument for Cross-Model AI Presence Redesigning CPC on a Quasi-Binomial Dispersion Basis", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6963359", "field": "management", "role": "method", "bullets": [ "A frozen omnibus of 112 brand-units across five substrates and six large language models, re-analyzed in a pre-registered study of brand surfacing consistency.", "A panel of six unnamed large language models is queried for brand recall; the paper replaces a coefficient-of-variation measure with a quasi-binomial dispersion statistic to gauge cross-model consistency.", "The dispersion statistic achieves mean-independence (|rho|=0.091 versus 0.682 for the old measure), extends coverage to 29 low-recall brands, and reproduces across waves (rho=0.645)." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 523, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5140932276", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6967719", "doi": "10.2139/ssrn.6967719", "title": "Taxing Generative AI: Evidence from Chicago", "authors": [ "Jeffrey Ohl" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6967719", "field": "economics", "role": "object", "bullets": [ "Transaction-level data from 97 million debit and credit cards, exploiting Chicago's new tax on generative AI, with ChatGPT subscriptions compared to a synthetic control.", "Generative AI is the taxed product studied, not a research tool; the paper estimates pass-through, demand elasticity, and incidence across the income distribution.", "A 9 percent tax raised prices 8.06 percent (near full pass-through), cut Chicago subscriptions 25.9 percent after 13 months (elasticity -3.2), and was not progressive by income." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 74, "edition": 3, "audience": "general", "validated": null, "n": 524, "authors_detailed": [ { "name": "Jeffrey Ohl", "url": "https://openalex.org/A5140769640", "inst": "Woodlawn School" } ], "affiliations": [ "Woodlawn School" ] }, { "uid": "arxiv:2607.14026v1", "arxiv_id": "2607.14026v1", "title": "From Forecasts to Auditable Reports: Evidence Contracts for LLM-Assisted Housing-Guarantee Risk Monitoring", "authors": [ "Hyeongcheol Kim", "Yoontae Hwang" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.14026v1", "field": "finance", "role": "method", "bullets": [ "Monthly South Korean jeonse deposit-guarantee data from September 2015 to December 2025, with reporting experiments run on synthetic aggregate scenarios calibrated to the panel's ranges.", "Eight LLMs, not named, generate risk reports from forecasts using retrieved precedents, typed evidence contracts, and claim verification, then assessed by 51 analysts and domain professionals.", "Structured evidence raised report quality, numerical fidelity, and claim grounding across all eight models, and most analysts rated the reports useful and endorsed an operational pilot." ], "bullet_provenance": "ai", "validated": false, "salience": 48, "edition": 3, "audience": "technical", "models": [], "n": 525, "authors_detailed": [ { "name": "Hyeongcheol Kim", "url": "https://openalex.org/A5140971367", "inst": "Pusan National University" }, { "name": "Y. Hwang", "url": "https://openalex.org/A5112265736", "inst": "Dongduk Women's University" } ], "affiliations": [ "Pusan National University", "Dongduk Women's University" ] }, { "uid": "doi:10.2139/ssrn.7121382", "doi": "10.2139/ssrn.7121382", "title": "The Trust Appropriability Problem in Artificial Intelligence: Why Innovators Capture Usage but Not Legitimacy. Evidence from the n-Ball Volume Peak and the Trust-Adoption Divergence Across 48,340 Respondents in 47 Countries Special Issue: \"Outside In: Unconventional Pathways to Innovation\"", "authors": [ "Xuan Tran" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7121382", "field": "management", "role": "object", "bullets": [ "Cross-national survey evidence on AI trust and adoption drawn from Gillespie et al. 2025, covering 48,340 respondents across 47 countries, framed as a conceptual study of AI paradigm generations.", "No language model is used as a research instrument; ChatGPT is treated as the object of study, with adoption and trust modeled through an n-ball volume recursion.", "Reports 66 percent regular AI usage alongside only 46 percent trusting AI, with trust falling from 63 to 56 percent between 2022 and 2025 as adoption doubled." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 25, "edition": 3, "audience": "general", "validated": null, "n": 985, "authors_detailed": [ { "name": "Xuan Tran", "url": "https://openalex.org/A5140882423", "inst": "University of West Florida" } ], "affiliations": [ "University of West Florida" ] }, { "uid": "doi:10.2139/ssrn.7120738", "doi": "10.2139/ssrn.7120738", "title": "Official Monetary Policy Narratives and Bond Risk Premia: Evidence from LLM-Based News Measures", "authors": [ "Xuan Wang", "Ximing Yin", "Yi Liu", "Keqin Li" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7120738", "field": "finance", "role": "instrument", "bullets": [ "1.46 million Chinese official-news articles, aggregated to article level, linked to Chinese government bond excess returns; the sample period is not stated.", "An unnamed large language model performs semantic annotation to build the Official Monetary Policy Action Score; the paper reports no accuracy or agreement check against ground truth.", "The resulting Policy Action Signal predicts bond excess returns beyond yield-curve factors, with stronger effects on long-maturity bonds, and its persistent component forecasts lower future yields." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "edition": 3, "audience": "general", "models": [], "n": 986, "authors_detailed": [ { "name": "Xuan Wang", "url": "https://openalex.org/A5140822019", "inst": "" }, { "name": "Ximing Yin", "url": "https://openalex.org/A5140830955", "inst": "" }, { "name": "Yi Liu", "url": "https://openalex.org/A5140905699", "inst": "" }, { "name": "Keqin Li", "url": "https://openalex.org/A5140951170", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7028619", "doi": "10.2139/ssrn.7028619", "title": "Profiting from Technological Innovation, Forty Years On", "authors": [ "Mouhamdy Dahoud" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7028619", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no primary empirical data, revisiting Teece's 1986 profiting-from-innovation framework for generative AI; the single empirical anchor is the shrinking interval between frontier model releases.", "No language model is used as a research tool; generative AI is the object of analysis, examined through an axiomatic method and thought experiments rather than measurement.", "Argues user data fuses innovation and complementary asset, proposes a self-appropriability matrix, and concludes rent settles into compute, distribution, and human originality." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 987, "authors_detailed": [ { "name": "Mouhamdy Dahoud", "url": "https://openalex.org/A5140878823", "inst": "Université Gaston Berger" } ], "affiliations": [ "Université Gaston Berger" ] }, { "uid": "doi:10.2139/ssrn.6967238", "doi": "10.2139/ssrn.6967238", "title": "A Socioeconomic Analysis of AI Agents and the Restructuring of the Knowledge Labor Market", "authors": [ "Ignacio Kramcsak" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6967238", "field": "economics", "role": "object", "bullets": [ "Knowledge-based industries; a mixed-methods design combining macroeconomic technology-adoption data with qualitative case studies of vanguard firms. Period and geography not stated.", "No language model is applied; the paper analyzes how autonomous AI agents automate cognitive workflows and reshape the senior-junior talent pipeline. Model not stated.", "Argues AI agents fragment rather than shrink the labor market, producing 'synthetic seniority' among juniors and obsolescence for non-adapting seniors, shifting value toward strategic judgment." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1186, "authors_detailed": [ { "name": "Ignacio Kramcsak", "url": "https://openalex.org/A5140874669", "inst": "Corvinus University of Budapest" } ], "affiliations": [ "Corvinus University of Budapest" ] }, { "uid": "doi:10.2139/ssrn.6968279", "doi": "10.2139/ssrn.6968279", "title": "Closing the AI Proof Gap: An Evidence Architecture for Operationalizing NIST AI RMF, ISO/IEC 42001, and EU AI Act Requirements Across Board Governance, Regulatory Disclosure, and D&O Underwriting", "authors": [ "Rohan Sharma" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6968279", "field": "management", "role": "object", "bullets": [ "Conceptual governance paper spanning boards, D&O underwriters, regulators, and assessors across NIST AI RMF, ISO/IEC 42001, and EU AI Act documentation requirements. No empirical sample.", "No language model is applied; the paper defines the AI Proof Gap and proposes a five-layer Unified AI Evidence Framework mapping standards to reusable evidence artifacts. Model not stated.", "Argues boards, insurers, and regulators require structurally overlapping evidence, so organizations must move from standards interpretation to evidence design to avoid governance theater." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1187, "authors_detailed": [ { "name": "Rohan Sharma", "url": "https://openalex.org/A5140899923", "inst": "Organisation de Coopération et de Développement Economiques" } ], "affiliations": [ "Organisation de Coopération et de Développement Economiques" ] }, { "uid": "doi:10.2139/ssrn.7122683", "doi": "10.2139/ssrn.7122683", "title": "A Capability-based Framework for Analysis of Autonomous Supply Chains", "authors": [ "Yacob Khojasteh", "S.M. Mousavi Jahan Abadi" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7122683", "field": "management", "role": "object", "bullets": [ "Structured review of 68 supply-chain papers published 2020 to 2026, with 44 providing primary analytical evidence mapped onto a sixteen-subtask capability taxonomy.", "No language model is applied; the paper proposes the SADA-GO capability framework and classifies existing agentic-AI supply-chain implementations across its five domains. Model not stated.", "Finds decision-making capabilities well researched but action and governance underdeveloped, with per-decision human approval persisting and no demonstrated move to human-on-the-loop autonomy." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1188, "authors_detailed": [ { "name": "Yacob Khojasteh", "url": "https://openalex.org/A5140953801", "inst": "" }, { "name": "S.M. Mousavi Jahan Abadi", "url": "https://openalex.org/A5140917328", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6958962", "doi": "10.2139/ssrn.6958962", "title": "Artificial Human Leaders: The Transport-Ethics Model of Trust and Governance in Digital Human CEOs", "authors": [ "Marco I. Bonelli" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6958962", "field": "management", "role": "object", "bullets": [ "Conceptual paper on Digital Human CEOs, anthropomorphic AI-driven communicators delivering executive messages, illustrated with three theory-anchored organizational vignettes. No empirical sample.", "No language model is applied; the paper develops the Transport-Ethics Model linking narrative transportation with four governance checkpoints for disclosure, anthropomorphism, explainability, and accountability. Model not stated.", "Proposes that linguistic concreteness and moderated human-likeness shape trust and legitimacy while creating ethical risk when disclosure or accountability is weak; recommends bounded, low-risk use." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1189, "authors_detailed": [ { "name": "Marco I. Bonelli", "url": "https://openalex.org/A5140720871", "inst": "Ca' Foscari University of Venice" } ], "affiliations": [ "Ca' Foscari University of Venice" ] }, { "uid": "doi:10.2139/ssrn.6966682", "doi": "10.2139/ssrn.6966682", "title": "Pricing the Cost of Compliance: Equity Reactions to Mandatory ESG Disclosure in a Frontier Market", "authors": [ "Joseph Agossa" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6966682", "field": "finance", "role": "instrument", "bullets": [ "60 firms listed on the Nairobi Securities Exchange, with hand-collected annual reports from 2010 to 2024, around the November 2021 mandatory ESG disclosure guidance manual.", "Fine-tuned RoBERTa transformer models, one per ESG pillar, construct firm-level disclosure scores; no accuracy check against hand-coded ground truth is reported.", "Firms with stronger prior ESG communication earn about 2.8 percentage points higher abnormal returns per disclosure-score standard deviation, concentrated in the environmental pillar." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": false, "salience": 52, "edition": 3, "audience": "general", "n": 1190, "authors_detailed": [ { "name": "Joseph Agossa", "url": "https://openalex.org/A5140851816", "inst": "École Supérieure des Sciences Économiques et Commerciales" } ], "affiliations": [ "École Supérieure des Sciences Économiques et Commerciales" ] }, { "uid": "arxiv:2607.14044v1", "arxiv_id": "2607.14044v1", "title": "AI-accelerated End-to-End Framework for Rapid Professional Upskilling", "authors": [ "Tam Nguyen", "Hung Nguyen", "Robert Ogburn" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.14044v1", "field": "management", "role": "object", "bullets": [ "Enterprise professional upskilling context; a framework applying AI across knowledge acquisition, content development, review, teaching, and assessment. Sample and period not stated.", "No specific language model is named; AI accelerates each of the five upskilling stages, validated by external signals rather than a model-accuracy benchmark. Model not stated.", "A program built on the framework gained NASBA continuing-education approval and three learners passed the NVIDIA agentic-AI exam quickly, with fourteen more in progress." ], "bullet_provenance": "ai", "salience": 27, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1191, "authors_detailed": [ { "name": "Tam Nguyen", "url": "https://openalex.org/A5140985489", "inst": "Government of the District of Columbia" }, { "name": "H S. Nguyen", "url": "https://openalex.org/A5073166196", "inst": "Oregon State University" }, { "name": "Robert Ogburn", "url": "https://openalex.org/A5140968902", "inst": "" } ], "affiliations": [ "Government of the District of Columbia", "Oregon State University" ] }, { "uid": "arxiv:2607.13998v1", "arxiv_id": "2607.13998v1", "title": "The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce", "authors": [ "Sai Srikanth Madugula", "Peplluis Esteva de la Rosa", "Daya Shankar" ], "posted": "2026-07-15", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.13998v1", "field": "management", "role": "object", "bullets": [ "Conceptual marketing paper on customer loyalty under agentic AI and autonomous commerce, synthesizing human-machine teaming, consumer decision, and algorithmic-trust literatures. No empirical sample.", "No language model is applied; the paper formalizes brand choice via a softmax over human emotional equity, agent utility, calibrated trust, delegated authority, and verifiable execution. Model not stated.", "Proposes the DVM-HALL model and an auditable Net Human-Agent Score, with a three-stage empirical validation plan across experiments, market simulations, and DeFi testbeds, not yet executed." ], "bullet_provenance": "ai", "salience": 29, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1192, "authors_detailed": [ { "name": "Sai Srikanth Madugula", "url": "https://openalex.org/A5120491322", "inst": "Woxsen School of Business" }, { "name": "Peplluis Esteva De La Rosa", "url": "https://openalex.org/A5122224290", "inst": "Universitat de Girona" }, { "name": "Daya Shankar", "url": "https://openalex.org/A5112584856", "inst": "Vinoba Bhave University" } ], "affiliations": [ "Woxsen School of Business", "Universitat de Girona", "Vinoba Bhave University" ] }, { "uid": "doi:10.2139/ssrn.7121419", "doi": "10.2139/ssrn.7121419", "title": "Hugging Outsiders: Prior Success and Novelty in Open-Weight LLM Development", "authors": [ "Julian Just" ], "posted": "2026-07-15", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7121419", "field": "management", "role": "object", "bullets": [ "Panel of 606 foundational open-weight LLMs released on Hugging Face between July 2024 and December 2025, with downloads tracked at monthly snapshots over roughly six months.", "No model is used as a research tool; monthly downloads are regressed on contributors' prior platform success, model novelty, and the interaction of the two.", "Prior success predicts adoption at every snapshot, and novelty raises downloads far more for contributors without a track record, significant at the first snapshot and again through month six." ], "bullet_provenance": "ai", "open_weights": true, "salience": 52, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 46, "authors_detailed": [ { "name": "Julian Just", "url": "https://openalex.org/A5140831341", "inst": "Universität Innsbruck" } ], "affiliations": [ "Universität Innsbruck" ] }, { "uid": "arxiv:2607.14371v1", "arxiv_id": "2607.14371v1", "title": "Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion", "authors": [ "Fengzhuo Zhang", "Zhuoran Yang", "Dirk Bergemann" ], "posted": "2026-07-15", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.14371v1", "field": "economics", "role": "object", "bullets": [ "Theoretical model of LLM personalization under shared compute, complemented by GPT-2 experiments on linear-regression tasks and a review of documentation from 21 major AI platforms spanning 2021 to 2025.", "GPT-2 is run on linear-regression tasks to check predictions about when supervised fine-tuning beats in-context learning; the paper reports no accuracy comparison against a ground truth.", "Fine-tuning and in-context learning dominate in different regimes and congestion can flip the ranking, while offering both methods never lowers the platform's maximal profit." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": true, "salience": 58, "edition": 2, "audience": "technical", "validated": null, "n": 47, "authors_detailed": [ { "name": "F Zhang", "url": "https://openalex.org/A5047301622", "inst": "City University of Hong Kong" }, { "name": "Zhuoran Yang", "url": "https://openalex.org/A5141077409", "inst": "" }, { "name": "Dirk Bergemann", "url": "https://openalex.org/A5141068785", "inst": "Yale University" } ], "affiliations": [ "Yale University", "City University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.13618v1", "arxiv_id": "2607.13618v1", "title": "STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle", "authors": [ "Sagar Deb", "Ashwanth Krishnan" ], "posted": "2026-07-15", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.13618v1", "field": "management", "role": "method", "bullets": [ "STOCKTAKE, a 26-week supply-chain replenishment benchmark built as a factored partially observable Markov decision process with six hidden factor processes, run over fifty seeds with curated stress profiles.", "Claude Sonnet 5, GPT-5.4, DeepSeek-V4-Pro and Grok 4.5 act as replenishment agents; runs are scored between a symptom-blind floor and an exact Bayes-filter oracle to separate perception from control.", "Models detect 84 to 88 percent of hidden failures within about a week, yet skill scores span 0.62 to minus 0.23, with two of four below the symptom-blind floor." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "salience": 52, "edition": 2, "audience": "technical", "validated": null, "n": 48, "authors_detailed": [ { "name": "Sagar Deb", "url": "https://openalex.org/A5141036491", "inst": "" }, { "name": "Ashwanth Krishnan", "url": "https://openalex.org/A5120250599", "inst": "Qiagen (India)" } ], "affiliations": [ "Qiagen (India)" ] }, { "uid": "arxiv:2607.12455v1", "arxiv_id": "2607.12455v1", "title": "EVOQUANT: Self-Evolving Verifier-Guided Strategy Optimization for Robust Quantitative Trading", "authors": [ "Jie Mao", "Changlun Li", "Xiang Li", "Qiqi Duan", "Jinhui Yuan", "Xiang Liu", "Yuyu Luo", "Jing Tang", "Xiaowen Chu", "Nan Tang" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.12455v1", "field": "finance", "role": "method", "bullets": [ "Seven quantitative trading strategies, four from the Chinese A-share market and three from crypto markets, optimized and backtested under ablation and stress-test conditions.", "Unnamed large language models diagnose strategy bottlenecks, generate controlled candidate edits, and pass them through a multi-stage verification pipeline; the specific model is not stated.", "Average test Sharpe ratio rose from -0.298 to 0.538 across all strategies, with the best strategy improving 199 percent relative to its baseline." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 285 }, { "uid": "doi:10.2139/ssrn.7119221", "doi": "10.2139/ssrn.7119221", "title": "Public Administration Paradigms in the Generative AI Era: A Systematic Literature Review of Emerging Theoretical Models and Research Agenda", "authors": [ "I Putu Dharmanu Yudartha", "Ari Mukti", "EP Ady Hartanto", "Amni Zarkasyi Rahman" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7119221", "field": "management", "role": "object", "bullets": [ "PRISMA-informed systematic review of 32 studies on generative AI in public administration, published between 2020 and March 2026, spanning governance and public-service contexts.", "No language model is run by the authors; the paper synthesizes existing studies and codes them into four emerging theoretical models of AI-era administration.", "Identifies augmented bureaucracy, human-AI co-administration, generative governance, and cognitive public administration as emerging paradigms, and frames GenAI as cognitive and epistemic infrastructure rather than an efficiency tool." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 519, "authors_detailed": [ { "name": "I Putu Dharmanu Yudartha", "url": "https://openalex.org/A5140650059", "inst": "Diponegoro University" }, { "name": "Ari Mukti", "url": "https://openalex.org/A5140664536", "inst": "Diponegoro University" }, { "name": "EP Ady Hartanto", "url": "https://openalex.org/A5140672151", "inst": "Diponegoro University" }, { "name": "Amni Zarkasyi Rahman", "url": "https://openalex.org/A5140670778", "inst": "Diponegoro University" } ], "affiliations": [ "Diponegoro University" ] }, { "uid": "doi:10.2139/ssrn.7045118", "doi": "10.2139/ssrn.7045118", "title": "Generative AI and the Informational Value of Educational Credentials: Evidence from the Master's Margin", "authors": [ "Patricia Cortes", "Chrysanthos N. Dellarocas", "Qi Wang" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7045118", "field": "economics", "role": "object", "bullets": [ "US hiring from 2018 to 2025 from Revelio Labs worker-flow data, using occupational exposure to generative AI as the source of variation.", "No model is run by the authors; the 2022 launch of ChatGPT serves as the treatment shock, interacted with occupational AI exposure in a difference-in-differences design.", "AI-exposed occupations saw a significant post-2022 drop in entry-level hires holding master's degrees, concentrated in business and non-STEM degrees and at firms most likely to adopt AI." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "edition": 3, "audience": "general", "validated": null, "n": 520, "authors_detailed": [ { "name": "Patricia Cortes", "url": "https://openalex.org/A5140642475", "inst": "Boston University" }, { "name": "Chrysanthos N. Dellarocas", "url": "https://openalex.org/A5140747277", "inst": "Boston University" }, { "name": "Qi Wang", "url": "https://openalex.org/A5140582519", "inst": "Boston University" } ], "affiliations": [ "Boston University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6959440", "doi": "10.2139/ssrn.6959440", "title": "Disentangling Generative AI and Monetary Policy Shocks in the AI-Electric Vehicle Nexus", "authors": [ "Lotfi Taleb" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6959440", "field": "finance", "role": "object", "bullets": [ "Daily data on eight AI and electric-vehicle financial series from May 2018 to June 2026, split into three regimes via Bai-Perron break tests.", "No model is run by the authors; the launch of ChatGPT is used as an event dummy and regime marker, compared against a Federal Reserve tightening dummy in connectedness and difference-in-differences designs.", "At the median market state the GenAI shock cut connectedness about four times more than the monetary shock (-0.76 vs -0.18 points), while the Fed effect dominated in the right tail (+1.40 points)." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 521, "authors_detailed": [ { "name": "Lotfi Taleb", "url": "https://openalex.org/A5140575041", "inst": "Tunis University" } ], "affiliations": [ "Tunis University" ] }, { "uid": "arxiv:2607.12233v1", "arxiv_id": "2607.12233v1", "title": "Fin-Analyst at FinMMEval 2026 Task 3: A Live Hybrid Trading Agent with LLM Specialists and Rule-Based Signals", "authors": [ "Mohotarema Rashid", "Lingzi Hong", "Junhua Ding", "K. S. M. Tozammel Hossain" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.12233v1", "field": "finance", "role": "agent", "bullets": [ "Live deployment for FinMMEval 2026 Task 3, trading Tesla equity and Bitcoin over a short competition window, scored on the official leaderboard accessed 2026-07-05.", "An unnamed eight-specialist LLM pipeline reads news, SEC filings, fundamentals, analyst forecasts, technicals, and social sentiment, aggregated by a meta-agent to trade TSLA, while a rule-based three-signal vote trades BTC.", "The agent ranked first on TSLA with a +13.51% return, +28.33 points over buy-and-hold (Sharpe 4.10, 88% win rate), while the Bitcoin vote finished flat but above a falling baseline." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 522, "authors_detailed": [ { "name": "Mohotarema Rashid", "url": "https://openalex.org/A5140755221", "inst": "University of North Texas" }, { "name": "Lingzi Hong", "url": "https://openalex.org/A5140862355", "inst": "" }, { "name": "Junhua Ding", "url": "https://openalex.org/A5140888910", "inst": "University of North Texas" }, { "name": "K. S. M. Tozammel Hossain", "url": "https://openalex.org/A5140804814", "inst": "" } ], "affiliations": [ "University of North Texas" ] }, { "uid": "doi:10.2139/ssrn.7115195", "doi": "10.2139/ssrn.7115195", "title": "Can Generative Artificial Intelligence Enhance Firms' Export Resilience? — A Difference-in-Differences Analysis Based on Chinese Data", "authors": [ "Mengjun Xie" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7115195", "field": "economics", "role": "object", "bullets": [ "Chinese firms with firm-level export data, using the staggered rollout of China's generative AI service registration system as a quasi-natural experiment; sample period not stated.", "No language model is used by the researchers; generative AI adoption is the treatment, estimated with staggered difference-in-differences plus threshold and spatial spillover tests; model not stated.", "Generative AI significantly raises export resilience through stabilizing import supply chains, securing production, and expanding export sales, with larger effects for technology- and capital-intensive firms." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 973, "authors_detailed": [ { "name": "Mengjun Xie", "url": "https://openalex.org/A5002121388", "inst": "Shandong University of Technology" } ], "affiliations": [ "Shandong University of Technology" ] }, { "uid": "doi:10.2139/ssrn.7119690", "doi": "10.2139/ssrn.7119690", "title": "From ”blue” and ”red” to ”purple”: skill composition and dynamics in AI jobs in Australia and New Zealand", "authors": [ "Alexandra Bratanova", "David Evans", "Claire Mason", "Sarah Hartman", "Haohui Chen", "Hien Pham", "Einat Grimberg", "Stefan Hajkowicz" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7119690", "field": "economics", "role": "object", "bullets": [ "Lightcast online job postings for Australia and New Zealand covering 2015 to 2024, with the job posting and its skill requirements as the unit of observation.", "No model is used; the study classifies AI-related roles as technical, human-centred, or hybrid and tracks skill bundles over time; not stated.", "AI-related postings rose from 1.3 to 4.2 percent in Australia and 0.1 to 3.2 percent in New Zealand, shifting toward hybrid profiles combining technical and communication skills." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 974, "authors_detailed": [ { "name": "Alexandra Bratanova", "url": "https://openalex.org/A5140690234", "inst": "Commonwealth Scientific and Industrial Research Organisation" }, { "name": "David Evans", "url": "https://openalex.org/A5140617682", "inst": "" }, { "name": "Claire Mason", "url": "https://openalex.org/A5140624470", "inst": "Commonwealth Scientific and Industrial Research Organisation" }, { "name": "Sarah Hartman", "url": "https://openalex.org/A5140580552", "inst": "" }, { "name": "Haohui Chen", "url": "https://openalex.org/A5140723847", "inst": "CSIRO Manufacturing" }, { "name": "Hien Pham", "url": "https://openalex.org/A5140665988", "inst": "" }, { "name": "Einat Grimberg", "url": "https://openalex.org/A5140585410", "inst": "" }, { "name": "Stefan Hajkowicz", "url": "https://openalex.org/A5140741274", "inst": "Australian Government" } ], "affiliations": [ "Commonwealth Scientific and Industrial Research Organisation", "CSIRO Manufacturing", "Australian Government" ] }, { "uid": "doi:10.2139/ssrn.6960140", "doi": "10.2139/ssrn.6960140", "title": "AI and Digital Tool Adoption in SMEs: A Structured, Theory-Building Review and a Prospective Supply-Chain Account, 2015-2025", "authors": [ "Nahid Hasan Siam" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6960140", "field": "management", "role": "object", "bullets": [ "Structured theory-building literature review of research published between 2015 and 2025 on AI and digital tool adoption by small and medium-sized enterprises, with a supply-chain focus.", "No model is used; the review synthesizes adoption frameworks and builds testable propositions about channels and absorptive capacity; not stated.", "Argues large-firm frameworks fit SMEs poorly and that positive performance findings reflect weak designs, proposing an SME-native account with autonomous, mimetic, and imposed adoption channels." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 975, "authors_detailed": [ { "name": "Nahid Hasan Siam", "url": "https://openalex.org/A5140575274", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6959363", "doi": "10.2139/ssrn.6959363", "title": "Data Integration and the Accounting Labor Market Effects of Generative AI", "authors": [ "Charles Downing" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6959363", "field": "accounting", "role": "object", "bullets": [ "US firms and their corporate accounting functions, using within-industry variation in prior data-integration investment and post-generative-AI outcomes centered on the 2022 onward shock.", "No model is used by the researchers; generative AI adoption is the shock, and text of job postings and résumés measures task content and skill requirements; model not stated.", "A one-standard-deviation greater prior data-integration investment cuts accountant employment 2.7 percent and postings 13 percent, concentrated in junior roles and driven by attrition." ], "bullet_provenance": "ai", "salience": 63, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 976, "authors_detailed": [ { "name": "Charles Downing", "url": "https://openalex.org/A5140631050", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6962978", "doi": "10.2139/ssrn.6962978", "title": "Delegating to AI: Beliefs and the Organization of Work at New Ventures", "authors": [ "Innessa Colaiacovo", "Rembrand Koning" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6962978", "field": "management", "role": "object", "bullets": [ "Founders of North American technology startups recruited to share detailed experiences with generative AI and its effects on their ability to scale.", "No model is used; founders' delegation beliefs are studied via survey plus a randomized exposure to optimistic or pessimistic information about AI performance; not stated.", "Founders estimate needing 55 percent more employees without AI on average, median 17 percent; formal integrators estimate headcount needs nearly four times higher than informal users." ], "bullet_provenance": "ai", "salience": 56, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 977, "authors_detailed": [ { "name": "Innessa Colaiacovo", "url": "https://openalex.org/A5140668556", "inst": "University of Oregon" }, { "name": "Rembrand Koning", "url": "https://openalex.org/A5140711056", "inst": "Harvard University" } ], "affiliations": [ "Harvard University", "University of Oregon" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7116980", "doi": "10.2139/ssrn.7116980", "title": "Decision Support at the Edge of the Firm: Artificial Intelligence, Human and Agent Collaboration, and the Resilience of Microbusiness Venture Creation Across the United States", "authors": [ "Md Raihanul Islam" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7116980", "field": "economics", "role": "object", "bullets": [ "Fifty US states plus the District of Columbia, Census Bureau Business Formation Statistics from 2004 to 2026, linked to the Anthropic Economic Index for April and May 2026.", "No model is used by the researchers; augmentative-versus-automative AI collaboration share is related to venture-creation resilience via OLS, gradient boosting, and PLS structural equation modelling.", "The raw negative association of minus 0.25 to minus 0.34 disappears after controls; net domestic migration drives the pattern, leaving no evidence of a substantial AI effect." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 46, "edition": 3, "audience": "general", "validated": null, "n": 978, "authors_detailed": [ { "name": "Md Raihanul Islam", "url": "https://openalex.org/A5140722914", "inst": "University of Dhaka" } ], "affiliations": [ "University of Dhaka" ] }, { "uid": "doi:10.2139/ssrn.6960860", "doi": "10.2139/ssrn.6960860", "title": "AI Adoption, Workplace Collaboration, and Mean Field Games", "authors": [ "Christos Makridis" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6960860", "field": "economics", "role": "object", "bullets": [ "Theoretical model with no empirical sample; heterogeneous workers on a workplace collaboration network whose AI capability and human capital evolve through use and peer learning.", "No language model is used; generative AI adoption is formalized as a finite-type network mean field game with Hamilton-Jacobi-Bellman and Fokker-Planck equations and a numerical illustration.", "Managerial clarity operates like a control parameter that shifts collaboration as an order parameter and accelerates convergence toward the AI-capability frontier." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 979, "authors_detailed": [ { "name": "Christos Makridis", "url": "https://openalex.org/A5140730524", "inst": "University of Nicosia" } ], "affiliations": [ "University of Nicosia" ] }, { "uid": "doi:10.2139/ssrn.7012498", "doi": "10.2139/ssrn.7012498", "title": "Generative AI as an Inequality Amplifier: Language Compatibility, Leapfrogging and Productivity Growth", "authors": [ "Yang Chen", "Anxu Wang" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7012498", "field": "economics", "role": "object", "bullets": [ "One hundred sixty-nine economies around the November 2022 release of ChatGPT as a natural experiment, plus a controlled experiment across twelve languages of differing resourcedness.", "ChatGPT and a frontier generative AI tool make business decisions across languages; language intensity measures compatibility with training data; no accuracy validation is reported.", "A one-standard-deviation rise in language intensity raises productivity growth 0.94 percentage points; multilingual AI development would cut the inequality cost of AI growth by 59 percent." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 60, "edition": 3, "audience": "general", "n": 980, "authors_detailed": [ { "name": "Yang Chen", "url": "https://openalex.org/A5140670848", "inst": "Central University of Finance and Economics" }, { "name": "Anxu Wang", "url": "https://openalex.org/A5140746975", "inst": "Peking University" } ], "affiliations": [ "Central University of Finance and Economics", "Peking University" ] }, { "uid": "doi:10.2139/ssrn.6959638", "doi": "10.2139/ssrn.6959638", "title": "Mandate : Underwriting, Monitoring and Governing AI Agents that Transact and Borrow A Credit-Risk and Accountability Workflow for Agentic Commerce", "authors": [ "John Christiansen" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6959638", "field": "finance", "role": "object", "bullets": [ "Conceptual credit-risk and accountability workflow illustrated on a synthetic transaction stream into which a behavioral compromise is injected; no real-world data.", "No language model is used as a tool; the paper proposes MANDATE to underwrite and monitor borrowing AI agents using a drift statistic, circuit breaker, and provenance ledger; not stated.", "On the synthetic stream the monitor detects the injected regime change two transactions after onset, the breaker revokes the mandate, and the ledger detects retrospective tampering." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "technical", "validated": null, "n": 981, "authors_detailed": [ { "name": "John Christiansen", "url": "https://openalex.org/A5140620555", "inst": "Maxwell Institute for Mathematical Sciences" } ], "affiliations": [ "Maxwell Institute for Mathematical Sciences" ] }, { "uid": "doi:10.2139/ssrn.6956618", "doi": "10.2139/ssrn.6956618", "title": "The Sovereignty Trap: National AI Strategies, Compute Nationalism, and the Fragmentation of the Global AI Supply Chain", "authors": [ "Ali Sadhik Shaik" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6956618", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of national AI strategies drawing on evidence from India, the European Union, the Gulf states, and US-China technology decoupling.", "No model is used; the paper introduces the Compute Sovereignty Impact Model mapping how sovereign AI policy disrupts multinational enterprise infrastructure, talent, and markets; not stated.", "Compute nationalism is estimated to raise enterprise infrastructure costs 25 to 40 percent, reduce cross-border innovation velocity, and create new geopolitical risk." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 982, "authors_detailed": [ { "name": "Ali Sadhik Shaik", "url": "https://openalex.org/A5140590365", "inst": "Golden Gate University" } ], "affiliations": [ "Golden Gate University" ] }, { "uid": "doi:10.2139/ssrn.7115525", "doi": "10.2139/ssrn.7115525", "title": "The Reverse Information Paradox: When AI PlatformsRetain Too Much or Too Little Data", "authors": [ "Vijaya Marisetty", "Varsha Mamidi" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7115525", "field": "economics", "role": "object", "bullets": [ "Theoretical model with no data; users must reveal proprietary context to obtain useful generative AI output while providers may retain that context for model improvement.", "No language model is used; a two-wedge model formalizes a user-side analogue of Arrow's information paradox for platform data retention; not stated.", "Platforms under-retain when benefit appropriability is weaker than harm internalization and over-retain otherwise; the wedge ratio is a sufficient statistic, corrected by subsidy or tax." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 983, "authors_detailed": [ { "name": "Vijaya Marisetty", "url": "https://openalex.org/A5134068016", "inst": "Indian Institute of Management Visakhapatnam" }, { "name": "Varsha Mamidi", "url": "https://openalex.org/A5022296749", "inst": "Indian Institute of Management Raipur" } ], "affiliations": [ "Indian Institute of Management Visakhapatnam", "Indian Institute of Management Raipur" ] }, { "uid": "arxiv:2607.13314v2", "arxiv_id": "2607.13314v2", "title": "Tabular Foundation Models for Discrete Choice Estimation", "authors": [ "Liu Liu", "Dan Zhang" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.13314v2", "field": "management", "role": "method", "bullets": [ "Yogurt scanner panel of consumer purchase occasions, a discrete choice demand estimation setting central to marketing and operations.", "Tabular foundation models applied via in-context learning, reformulated to encode choice-set dependence and consumer heterogeneity, with fine-tuning on population choice data; specific model not named.", "The best reformulation beats hierarchical Bayesian estimation on holdout log-likelihood and hit rate while running sixteen times faster, with largest gains in the medium-data regime." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "holdout log-likelihood and hit rate vs hierarchical Bayes", "salience": 54, "edition": 3, "audience": "technical", "n": 984, "authors_detailed": [ { "name": "Liu Liu", "url": "https://openalex.org/A5141026089", "inst": "" }, { "name": "Dan Zhang", "url": "https://openalex.org/A5141041907", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7112938", "doi": "10.2139/ssrn.7112938", "title": "Multi-Agent Reinforcement Learning with Biological Graph Architecture (BDH) for Multi-Echelon Supply Chain Optimization Under Disruptions", "authors": [ "Thanh Nguyen Nhat" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7112938", "field": "management", "role": "method", "bullets": [ "Simulated two-echelon supply chain under stochastic demand and lead-time disruptions; a simulation study with no real firm data, aimed at multi-echelon inventory control.", "Multi-agent reinforcement learning pairs PPO with the Dragon Hatchling biologically inspired network as the policy, not an LLM, benchmarked in simulation against static inventory policies.", "BDH-PPO converges faster and lowers total operational cost versus base-stock and (s, Q) policies while dampening the bullwhip effect, with no effect magnitude stated." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 35, "edition": 3, "audience": "technical", "n": 1183, "authors_detailed": [ { "name": "Thanh Nguyen Nhat", "url": "https://openalex.org/A5140731680", "inst": "Vietnam National University Ho Chi Minh City" } ], "affiliations": [ "Vietnam National University Ho Chi Minh City" ] }, { "uid": "doi:10.2139/ssrn.7069098", "doi": "10.2139/ssrn.7069098", "title": "From Wages to Watts: Energy as the Measure of Synthetic Labor in Two Emerging Economic Architectures", "authors": [ "David Galipeau" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7069098", "field": "economics", "role": "object", "bullets": [ "Conceptual paper comparing two proposals for pricing autonomous AI-agent output in energy units, Roemmele's JouleWork and the Citizen 5.0 social-contract framework, with no empirical sample.", "No language model is applied; the paper contrasts the two frameworks on their mathematics, method, results to date, and feasibility. Model not stated.", "Argues the two are complementary, with JouleWork-style accounting making the Citizen 5.0 synthetic-labor tax base observable and cutting measurement from three unknowns per task to one." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1184, "authors_detailed": [ { "name": "David Galipeau", "url": "https://openalex.org/A5140745048", "inst": "" } ] }, { "uid": "arxiv:2607.12248v2", "arxiv_id": "2607.12248v2", "title": "When Directional Accuracy Lies: A Base-Rate-Honest Benchmark for LoRA-Adapted TimesFM on Equity Forecasting", "authors": [ "Taizhen Cheung" ], "posted": "2026-07-14", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.12248v2", "field": "finance", "role": "method", "bullets": [ "Equity direction forecasting on a tech-heavy NASDAQ-100 and a broad S&P 500, with expanding walk-forward folds and a stratified held-out-ticker split on frozen data.", "TimesFM time-series foundation model adapted with LoRA; directional accuracy compared to always-up, random-walk, persistence, and AR(1) baselines using McNemar and Diebold-Mariano tests under FDR control.", "Fine-tuned model shows no directional skill above the roughly 70 percent always-up base rate; sector specialization is worse than a pooled adapter, and only point-forecast error improves." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "directional accuracy vs base rate and naive baselines", "salience": 56, "edition": 3, "audience": "technical", "n": 1185, "authors_detailed": [ { "name": "Taizhen Cheung", "url": "https://openalex.org/A5140859929", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7118103", "doi": "10.2139/ssrn.7118103", "title": "Does Bias Drift? A Multi-Turn Audit Framework for Conversationally Emergent Demographic Bias in Large Language Models", "authors": [ "Hyeonkyeong Lee", "Minjong Cheon" ], "posted": "2026-07-14", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7118103", "field": "management", "role": "method", "bullets": [ "Three instruction-tuned open LLMs audited across five demographic axes, three decision domains (hiring, loan approval, medical advice), and three conversational conditions including sycophancy and rebuttal probes.", "Mistral-7B, Qwen2.5-7B, and Zephyr-7B produced multi-turn responses scored for bias with an SST-2 sentiment proxy and a regard classifier; no accuracy against ground truth is reported.", "Bias drifted across turns and varied by model; the sycophancy probe moved Mistral most (Cohen's d = 0.34), Qwen less (0.13), and Zephyr barely (0.04)." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 40, "edition": 2, "audience": "broad", "n": 18, "authors_detailed": [ { "name": "Hyeonkyeong Lee", "url": "https://openalex.org/A5140656747", "inst": "Seoul National University" }, { "name": "Minjong Cheon", "url": "https://openalex.org/A5140644876", "inst": "Hanyang Cyber University" } ], "affiliations": [ "Seoul National University", "Hanyang Cyber University" ] }, { "uid": "doi:10.2139/ssrn.7111960", "doi": "10.2139/ssrn.7111960", "title": "LLMs, Knowledge Sharing and the Real Problem with AI", "authors": [ "Lucas Hendrich" ], "posted": "2026-07-14", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7111960", "field": "economics", "role": "object", "bullets": [ "Conceptual essay with no original dataset, synthesizing mechanistic interpretability findings and labor economics studies on how workers use language models.", "The essay uses no model of its own; it reframes language models as a compressed, queryable database of human knowledge written in training and read at inference.", "Argues these systems distribute knowledge and close skill gaps where missing knowledge binds, leaving judgment scarce, so the intelligence framing diverts attention from governance." ], "bullet_provenance": "ai", "salience": 34, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 45, "authors_detailed": [ { "name": "Lucas Hendrich", "url": "https://openalex.org/A5140678782", "inst": "Forte (United States)" } ], "affiliations": [ "Forte (United States)" ] }, { "uid": "doi:10.2139/ssrn.7113516", "doi": "10.2139/ssrn.7113516", "title": "Event Log Extraction from Unstructured Enterprise Messaging Using LLMs", "authors": [ "Juntao Gao" ], "posted": "2026-07-14", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7113516", "field": "management", "role": "method", "bullets": [ "Three public customer-service dialogue datasets are used to extract event logs for process mining; the unit of observation is the conversation and its recognized business activities.", "An unnamed LLM runs a retrieval-augmented extraction pipeline with ten-pass self-consistency voting and a separate verification model; accuracy is measured by F1 against the labelled datasets and baselines.", "Reaches average F1 of 93 percent, exceeding the NLI-BART baseline by four percentage points and standalone LLM methods by eight percentage points." ], "bullet_provenance": "ai", "validated": true, "validation_note": "F1 against three labelled dialogue datasets and NLI-BART baseline", "salience": 43, "edition": 2, "audience": "technical", "models": [], "n": 55, "authors_detailed": [ { "name": "Juntao Gao", "url": "https://openalex.org/A5140700559", "inst": "Northeast Petroleum University" } ], "affiliations": [ "Northeast Petroleum University" ] }, { "uid": "doi:10.2139/ssrn.7103027", "doi": "10.2139/ssrn.7103027", "title": "Combining Text-to-SQL and Large Language Models for Maintenance Decision Support", "authors": [ "Simon Beckmann", "Karsten Wiesner", "Claas Tebruegge", "Marcus Grum" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7103027", "field": "management", "role": "method", "bullets": [ "Industrial equipment maintenance support; the authors build a hybrid system that answers operator questions from a maintenance database, with sample, period, and geography not stated.", "An unnamed large language model is combined with text-to-SQL and semantic pre-retrieval to turn queries into SQL and pull historical remedies, evaluated through experiments and an ablation study.", "Grounding answers in database records improves traceability, and the ablation shows semantic pre-retrieval supplies vocabulary bridging under input noise that prompt engineering alone cannot replicate deterministically." ], "bullet_provenance": "ai", "validated": false, "validation_note": "experiments and ablation, no accuracy figure reported", "salience": 36, "edition": 3, "audience": "technical", "models": [], "n": 178, "authors_detailed": [ { "name": "Simon Beckmann", "url": "https://openalex.org/A5140489502", "inst": "University of Potsdam" }, { "name": "K. Wiesner", "url": "https://openalex.org/A5047036849", "inst": "Leibniz University Hannover" }, { "name": "Claas Tebruegge", "url": "https://openalex.org/A5071041838", "inst": "Hella (Germany)" }, { "name": "Marcus Grum", "url": "https://openalex.org/A5007769784", "inst": "University of Potsdam" } ], "affiliations": [ "University of Potsdam", "Leibniz University Hannover", "Hella (Germany)" ] }, { "uid": "doi:10.2139/ssrn.6958684", "doi": "10.2139/ssrn.6958684", "title": "The Law and Economics of the Ai Excuse: Liability, Perception Bias, and the Case for Epistemic Calibration", "authors": [ "Massimiliano Caruso" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6958684", "field": "economics", "role": "object", "bullets": [ "Theoretical law-and-economics model with three players, an AI developer, an LLM, and a boundedly rational human decision-maker; no empirical data.", "No language model is run; the paper treats LLMs as the object, formalizing how their persuasive errors induce perception bias and proving liability results in a Grossman-Hart-Moore framework.", "No single liability regime reaches first-best and mandated oversight collapses into the biased rule, but a Calibrated Reliability Score mandate with conditional liability restores it and shifts ownership toward the developer." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 283, "authors_detailed": [ { "name": "Massimiliano Caruso", "url": "https://openalex.org/A5140567188", "inst": "Singer (United States)" } ], "affiliations": [ "Singer (United States)" ] }, { "uid": "doi:10.2139/ssrn.7115108", "doi": "10.2139/ssrn.7115108", "title": "Corporate physical climate risk, trade credit occupation and supply chain risk contagion: A study based on large language models", "authors": [ "Feifan Chen", "Yueshu Zhou" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7115108", "field": "finance", "role": "instrument", "bullets": [ "Firm-level panel of listed firms; the sample period and country are not stated in the abstract, and the unit of observation is the firm.", "FinBERT2, a finance-domain BERT model, constructs a firm-level physical climate risk indicator from text; no validation against hand-coded labels is reported.", "Higher physical climate risk raises trade credit occupation, especially from upstream suppliers, and propagates supply chain contagion that lowers supplier performance and resilience, while supplier concentration curbs the effect." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 47, "edition": 3, "audience": "general", "n": 284, "authors_detailed": [ { "name": "F N Chen", "url": "https://openalex.org/A5015576955", "inst": "Shenyang Pharmaceutical University" }, { "name": "Yueshu Zhou", "url": "https://openalex.org/A5020676558", "inst": "Nanjing Agricultural University" } ], "affiliations": [ "Shenyang Pharmaceutical University", "Nanjing Agricultural University" ] }, { "uid": "arxiv:2607.12056v1", "arxiv_id": "2607.12056v1", "title": "Designing Agent-Ready Websites for AI Web Agents: A Framework for Machine Readability, Actionability, and Decision Reliability", "authors": [ "Said Elnaffar", "Farzad Rashidi" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.12056v1", "field": "management", "role": "agent", "bullets": [ "Controlled experiment on an e-commerce prototype in two versions, a human-oriented baseline and an agent-ready design with identical catalog, pricing and stock, across five tasks and 300 runs.", "Three browser agents, GPT-4.1, Gemini 2.5 Flash and Grok-4 Fast, autonomously searched, compared and purchased products; outcomes scored PASS, PARTIAL or FAIL with no human-coding check.", "The agent-ready site reached 89.3 percent strict success versus 49.3 percent for the baseline, cut partial outcomes from 43 to 3, and lowered average steps from 9.31 to 6.49." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 47, "edition": 3, "audience": "general", "validated": null, "n": 321, "authors_detailed": [ { "name": "Said Elnaffar", "url": "https://openalex.org/A5140919919", "inst": "Oldham Council" }, { "name": "Farzad Rashidi", "url": "https://openalex.org/A5140756409", "inst": "Université Paris Cité" } ], "affiliations": [ "Oldham Council", "Université Paris Cité" ] }, { "uid": "doi:10.2139/ssrn.6976818", "doi": "10.2139/ssrn.6976818", "title": "Artificial Intelligence and Bank Profitability: What Really Matters?", "authors": [ "Paolo Nicola Barbieri", "Alessandra Bettocchi", "Andrea Fabrizi", "Rita Romeo" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6976818", "field": "finance", "role": "object", "bullets": [ "Annual reports of more than 50 major banks worldwide over 2009 to 2023, used to build a text-based index of digital engagement at the bank-year level.", "Natural language processing quantifies digital engagement and large language models classify AI-related paragraphs by use case and implementation stage; no specific model is named and no validation is reported.", "Digital engagement is positively associated with profitability; fully operational AI in customer support, client profiling, and process optimization drives gains, while early-stage and regulatory or credit-scoring uses remain a cost." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 517, "authors_detailed": [ { "name": "Paolo Nicola Barbieri", "url": "https://openalex.org/A5030049421", "inst": "Prometeia (Italy)" }, { "name": "Alessandra Bettocchi", "url": "https://openalex.org/A5049112423", "inst": "Prometeia (Italy)" }, { "name": "Andrea Fabrizi", "url": "https://openalex.org/A5039746508", "inst": "Prometeia (Italy)" }, { "name": "Rita Romeo", "url": "https://openalex.org/A5083835763", "inst": "Prometeia (Italy)" } ], "affiliations": [ "Prometeia (Italy)" ] }, { "uid": "doi:10.2139/ssrn.7082558", "doi": "10.2139/ssrn.7082558", "title": "Disclosure in the Shadow of Bankruptcy: From Reckoning to Reemergence", "authors": [ "Jason Lee", "Matthew Shaffer", "Michael Simkovic" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7082558", "field": "accounting", "role": "instrument", "bullets": [ "Non-bank US firms in financial distress, matched to peers on ex ante bankruptcy risk and fundamentals; sample size and exact period not stated.", "An unnamed large language model scores how much each firm discloses about its distress; the paper reports it as more predictive than prior textual measures but gives no accuracy benchmark.", "Greater disclosure speeds negative market reactions, customer and supplier pullback, and raises bankruptcy probability, yet after filing high-disclosure firms are valued more accurately, emerge faster, and more often resolve via Section 363 sale." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "edition": 3, "audience": "general", "models": [], "n": 518, "authors_detailed": [ { "name": "Jason Lee", "url": "https://openalex.org/A5100657727", "inst": "National University" }, { "name": "Matthew Shaffer", "url": "https://openalex.org/A5015557448", "inst": "University of Virginia" }, { "name": "Michael Simkovic", "url": "https://openalex.org/A5013028867", "inst": "University of Southern California" } ], "affiliations": [ "University of Virginia", "University of Southern California", "National University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6951718", "doi": "10.2139/ssrn.6951718", "title": "When AI Agents Spend Your Money: Card Rails, Stablecoins, and the Coming Lock-In of Agentic Commerce", "authors": [ "David Krause" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6951718", "field": "finance", "role": "object", "bullets": [ "Conceptual synthesis of recent literature and industry developments on agentic commerce, comparing two competing payment architectures, with no empirical sample.", "No model is deployed; the paper studies autonomous AI agents making purchases, contrasting tokenized card networks offering reversibility with stablecoin protocols offering finality.", "Argues the two architectures embed incompatible economic and legal logics and that early consumer delegation choices exhibit path dependence and switching costs prone to lock-in." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 966, "authors_detailed": [ { "name": "David Krause", "url": "https://openalex.org/A5002886467", "inst": "Marquette University" } ], "affiliations": [ "Marquette University" ] }, { "uid": "doi:10.2139/ssrn.7088100", "doi": "10.2139/ssrn.7088100", "title": "Experimentalist Intensification Governance: Managing Worker Negative Consequences Associated with Generative AI Experimentalist Work", "authors": [ "Arvind Karunakaran", "Katherine Kellogg", "Batia Mishan Wiesenfeld" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7088100", "field": "management", "role": "object", "bullets": [ "Comparative qualitative field study of medical workers in an academic medical center and legal workers in a law firm who use generative AI to build organizational solutions.", "No model is named; generative AI is the object rather than a researcher measurement tool, so worker use is observed qualitatively with no validation against ground truth.", "Experimentalist practices such as distributed trialing and collective review created new work intensification whose negative worker consequences varied with each organization's experimentalist intensification governance structures." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 967, "authors_detailed": [ { "name": "Arvind Karunakaran", "url": "https://openalex.org/A5020198328", "inst": "Stanford University" }, { "name": "Katherine C. Kellogg", "url": "https://openalex.org/A5021677696", "inst": "Massachusetts Institute of Technology" }, { "name": "Batia M. Wiesenfeld", "url": "https://openalex.org/A5047828204", "inst": "New York University" } ], "affiliations": [ "Stanford University", "Massachusetts Institute of Technology", "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6976819", "doi": "10.2139/ssrn.6976819", "title": "Algorithmic Joint Business Planning (JBP): A Twelve-Dimension Framework for Reshaping Supplier-Retailer Commercial Relationships in Agent-Mediated Trade", "authors": [ "Paul F. Accornero" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6976819", "field": "management", "role": "object", "bullets": [ "Conceptual framework on supplier-retailer joint business planning under agent-mediated trade, with two illustrative anchors: a 1,000-respondent Brazilian grocery shopper panel in April 2026 and a Sao Paulo electronics diagnostic in May 2026.", "No language model is used by the authors; AI procurement agents are the object, and both empirical anchors are described as illustrative and not validatory, so nothing is benchmarked.", "The framework specifies twelve dimensions of joint business planning that change structurally under algorithmic mediation and maps four retailer archetypes, without estimating any empirical effect." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 968, "authors_detailed": [ { "name": "Paul Ferrando Accornero", "url": "https://openalex.org/A5134457145", "inst": "AULSS 2 Marca Trevigiana" } ], "affiliations": [ "AULSS 2 Marca Trevigiana" ] }, { "uid": "doi:10.2139/ssrn.7088042", "doi": "10.2139/ssrn.7088042", "title": "From Tokens to Firm Value: A Financial Framework for Token-to-Profit Conversion in Generative AI Adoption", "authors": [ "Seiryu Ando" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7088042", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no sample or dataset, addressing firms that measure generative AI adoption by consumed tokens, model calls, launched pilots, and employee usage rather than value creation.", "No specific model is named; generative AI adoption is the object, and the paper builds a framework rather than applying or validating any model.", "It argues AI creates value only when usage converts into NOPAT above the cost of AI-related capital, distinguishing AI invested capital from reusable AI utilization capability." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 969, "authors_detailed": [ { "name": "Seiryu Ando", "url": "https://openalex.org/A5140557366", "inst": "Kyoto University" } ], "affiliations": [ "Kyoto University" ] }, { "uid": "doi:10.2139/ssrn.7108623", "doi": "10.2139/ssrn.7108623", "title": "Let’s Chat: Leveraging Chatbot Outreach for Improved Course Performance", "authors": [ "Katharine Meyer", "Lindsay Page", "Catherine Mata", "Eric N. Smith", "B. Tyler Walsh", "C. Lindsey Fifield", "Michelle Tyson", "Amy Ehinomen Eremionkhale", "Michael Evans", "Shelby Frost", "Eye Eoun Jung" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7108623", "field": "economics", "role": "object", "bullets": [ "Pre-registered field experiment in large-enrollment undergraduate courses testing non-generative AI chatbot outreach messaging; total sample size and institution are not stated, and one course is Microeconomics.", "The intervention uses a non-generative AI chatbot for student outreach rather than a researcher measurement tool; no model family is named and no accuracy check applies.", "Chatbot messaging raised final grades and engagement with academic supports; treated women in Microeconomics earned final grades seven percentage points higher than control women." ], "bullet_provenance": "ai", "salience": 62, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 970 }, { "uid": "doi:10.2139/ssrn.6952119", "doi": "10.2139/ssrn.6952119", "title": "Agentic Proxies: Governance, Accountability, and the Architecture of a Trustworthy AI Economy [Working Draft -June 2026]", "authors": [ "Geoff Lundholm" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6952119", "field": "management", "role": "object", "bullets": [ "Conceptual working draft with no dataset, addressing accountability in AI-to-AI delegation across chains of autonomous agents, with case studies spanning healthcare, financial services, legal workflows, and manufacturing.", "No language model is used or named; AI agents are the object of a governance argument, and the case studies are illustrative rather than validated measurements.", "It proposes agentic proxies under bounded, verifiable delegation and a CTX Envelope primitive, arguing that insurability of AI depends on accountability rather than on model capability." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 971, "authors_detailed": [ { "name": "Geoff Lundholm", "url": "https://openalex.org/A5140460948", "inst": "University of Kindu" } ], "affiliations": [ "University of Kindu" ] }, { "uid": "doi:10.2139/ssrn.7089498", "doi": "10.2139/ssrn.7089498", "title": "The Effect That Didn't Survive: ChatGPT, Higher-Order Thinking, and Scaffolding", "authors": [ "Baqar Jafri" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7089498", "field": "economics", "role": "object", "bullets": [ "Re-analysis of a 2025 meta-analysis on ChatGPT and student learning; of 51 claimed studies, 43 were re-obtained and re-pooled after the meta-analysis was retracted in April 2026.", "ChatGPT is the object; the author re-extracts and re-pools effect sizes, using a mirror analysis that reproduced the original published estimates to confirm the re-extraction was faithful.", "The corrected effect on higher-order thinking fell to g = 0.309 (95 percent CI -0.021 to 0.640), no longer distinguishable from zero; performance and perception effects shrank but stayed positive." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "edition": 3, "audience": "general", "validated": null, "n": 972, "authors_detailed": [ { "name": "Baqar Jafri", "url": "https://openalex.org/A5140338001", "inst": "University of Stirling" } ], "affiliations": [ "University of Stirling" ] }, { "uid": "doi:10.2139/ssrn.6928639", "doi": "10.2139/ssrn.6928639", "title": "An MPP-First, Settlement-Flexible Architecture for Self-Serve Agentic Expert-Access: Protocol Analysis, Formal Model, and Business-Model Transition", "authors": [ "King Yew Choo" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6928639", "field": "management", "role": "object", "bullets": [ "Conceptual architecture paper with no empirical sample, addressing how autonomous and semi-autonomous software agents can buy expert-derived work when established expert-access models assume a human at the point of purchase.", "No model is applied or named; the paper specifies an MPP-first, settlement-flexible architecture and a formal finite-state model proving gate-before-fulfilment, idempotency, no double payment, and end-to-end auditability.", "Maps a call-led, account-managed expert-access business onto self-serve, agent-addressable, machine-payable products while preserving a manual exception path for high-stakes work." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1181, "authors_detailed": [ { "name": "King Yew Choo", "url": "https://openalex.org/A5140561444", "inst": "True (United States)" } ], "affiliations": [ "True (United States)" ] }, { "uid": "doi:10.2139/ssrn.6960301", "doi": "10.2139/ssrn.6960301", "title": "Racing Against Automation: Dynamic Incentives When AI Can Preempt the Human", "authors": [ "Ying-Ju Chen", "Feng Tian", "Yangge Xiao", "Yufei Zhu" ], "posted": "2026-07-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6960301", "field": "economics", "role": "object", "bullets": [ "Theoretical continuous-time principal-agent model, no data, in which a firm draws on both a privately-informed human researcher and an independent AI agent to search for innovation.", "No empirical model use; the AI agent is a modeled autonomous searcher needing no incentives but costly to run and able to preempt the human, and the optimal dynamic contract is solved analytically.", "Persistent AI rivalry can lower both the human's and the principal's payoffs, so the optimal contract may start human-alone, move to a human-AI race, then switch to autonomous AI if no breakthrough occurs." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1182, "authors_detailed": [ { "name": "Ying-Ju Chen", "url": "https://openalex.org/A5140564031", "inst": "Hong Kong University of Science and Technology" }, { "name": "Feng Tian", "url": "https://openalex.org/A5140526632", "inst": "University of Hong Kong" }, { "name": "Yangge Xiao", "url": "https://openalex.org/A5006862142", "inst": "The University of Melbourne" }, { "name": "Yufei Zhu", "url": "https://openalex.org/A5014227933", "inst": "National University of Singapore" } ], "affiliations": [ "Hong Kong University of Science and Technology", "University of Hong Kong", "The University of Melbourne", "National University of Singapore" ] }, { "uid": "doi:10.2139/ssrn.7102067", "doi": "10.2139/ssrn.7102067", "title": "Belief at Risk: Quantifying Agentic AI Model Risk with LLM-Inferred Bayesian State Filters", "authors": [ "Matthew Francis Dixon" ], "posted": "2026-07-13", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7102067", "field": "finance", "role": "method", "bullets": [ "No labelled dataset is described; the paper develops a conceptual framework for agentic AI model risk and tests it with one empirical case study and an ablation, with unit and period not stated.", "Large language models, family not stated, serve as a semantic observation model turning evidence into a distribution over latent operating regimes, combined with a Bayesian state filter; checked via case study and ablation.", "Bayesian filtering substantially reduces posterior instability and uncertainty, raises regime-classification accuracy, and lowers downstream risk measures, though the abstract gives no numerical magnitude." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "edition": 2, "audience": "technical", "models": [], "n": 21, "authors_detailed": [ { "name": "Matthew Dixon", "url": "https://openalex.org/A5036435050", "inst": "IIT Research Institute" } ], "affiliations": [ "IIT Research Institute" ] }, { "uid": "arxiv:2607.11414v1", "arxiv_id": "2607.11414v1", "title": "Confidently Wrong: Detecting Hallucinations in Financial Question Answering from LLM Internal States", "authors": [ "Richard Zhe Wang" ], "posted": "2026-07-13", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.11414v1", "field": "finance", "role": "method", "bullets": [ "FinQA and TAT-QA, two question-answering benchmarks built from real corporate filings, with the unit of observation a model-generated answer to a filing question.", "Linear probes on residual-stream activations of Qwen3-8B, Llama-3.1-8B and Gemma-2-9B flag confident hallucinations, benchmarked by AUROC against token log-probabilities and the model's own true or false self-assessment.", "Among answers where all eight resamples agreed, 15 to 23 percent were wrong on FinQA; probes reached 0.68 to 0.77 AUROC while the best baselines fell to 0.55 to 0.63." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "probe AUROC against benchmark answer correctness", "salience": 50, "edition": 2, "audience": "technical", "n": 30, "authors_detailed": [ { "name": "Richard Zhe Wang", "url": "https://openalex.org/A5140642298", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7113411", "doi": "10.2139/ssrn.7113411", "title": "Stripping Away the Façade: Fake Review Detection in Turkish E-Commerce via Multi-Source Synthetic Data", "authors": [ "Arzu Karataş", "Ramin Abbaszadi" ], "posted": "2026-07-13", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7113411", "field": "management", "role": "method", "bullets": [ "A balanced corpus of 450,000 Turkish e-commerce reviews, comprising 225,000 authentic Hepsiburada product reviews and 225,000 synthetic fake reviews.", "Synthetic fakes were generated with seven methods including fine-tuned GPT-2 and instruction-tuned Qwen2, LLaMA-3, and Mistral; six Turkish BERT variants were then trained to detect them across five random seeds.", "ElecTRa scored the highest in-distribution F1 at 0.994, BERTurk-128k led on the external held-out set at 0.943 accuracy, and detection improved as synthetic generation diversity increased." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "F1 and accuracy on labeled and held-out reviews", "salience": 38, "edition": 2, "audience": "technical", "n": 32, "authors_detailed": [ { "name": "Arzu Karataş", "url": "https://openalex.org/A5140539001", "inst": "Ostim Technical University" }, { "name": "Ramin Abbaszadi", "url": "https://openalex.org/A5003453148", "inst": "Ostim Technical University" } ], "affiliations": [ "Ostim Technical University" ] }, { "uid": "doi:10.2139/ssrn.7102846", "doi": "10.2139/ssrn.7102846", "title": "Bridging Consumer Sentiment and Advertising Content Generation: A Retrieval-Augmented Pipeline for Aspect-Grounded Advertising", "authors": [ "Nouha Hajji" ], "posted": "2026-07-13", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7102846", "field": "management", "role": "method", "bullets": [ "1,000 verified Amazon reviews across three product categories plus an out-of-domain Yelp corpus, each review converted into a targeted advertisement and a five-section marketing strategy report.", "A fine-tuned DeBERTa-v3 extracts aspect-sentiment pairs, BART-large-MNLI classifies intent, and Gemini 2.5 Flash generates copy from aspect-aware retrieved evidence, all scored by an LLM-as-judge over six dimensions.", "The aspect-aware pipeline scored groundedness 7.33 of 10 against 1.33 for both baselines, with similar margins on hallucination avoidance and specificity, in an unpowered single-run pilot." ], "bullet_provenance": "ai", "models": [ "gemini", "legacy", "open_other" ], "open_weights": false, "validated": false, "validation_note": "LLM-as-judge quality scores, no human ground truth", "salience": 31, "edition": 2, "audience": "technical", "n": 33, "authors_detailed": [ { "name": "Nouha Hajji", "url": "https://openalex.org/A5140502562", "inst": "" } ] }, { "uid": "arxiv:2607.11141v1", "arxiv_id": "2607.11141v1", "title": "NextFund: A Unified Performance Tracking Platform for Agentic Portfolio Management", "authors": [ "Changlun Li", "Peixian Ma", "Qiqi Duan", "Zhenyu Lin", "Peineng Wu" ], "posted": "2026-07-13", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.11141v1", "field": "finance", "role": "method", "bullets": [ "LLM agents constructing equity portfolios on Hong Kong, US, and China A-share markets under live conditions, with the full decision path from market observation to executed trade as the unit.", "The paper does not state which models are used; the platform provides time-consistent market access, coordinated multi-agent analysis, and persistent logging viewable through an interactive Trading Arena.", "Demonstrates the platform across three markets, arguing that inspectable decision histories enable fairer benchmarking and clearer diagnosis; no aggregate return or accuracy comparison is reported." ], "bullet_provenance": "ai", "salience": 36, "edition": 2, "audience": "technical", "models": [], "validated": null, "n": 34, "authors_detailed": [ { "name": "Changlun Li", "url": "https://openalex.org/A5140610991", "inst": "" }, { "name": "Peixian Ma", "url": "https://openalex.org/A5140710244", "inst": "" }, { "name": "Qiqi Duan", "url": "https://openalex.org/A5140737666", "inst": "" }, { "name": "Zhenyu Lin", "url": "https://openalex.org/A5140575198", "inst": "" }, { "name": "Peineng Wu", "url": "https://openalex.org/A5140709264", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6942498", "doi": "10.2139/ssrn.6942498", "title": "How Large Language Models Surface Personal Brands: An Entity-Signal Framework and Cross-System Analysis of ChatGPT, Claude, and Perplexity", "authors": [ "Bhavik Sarkhedi" ], "posted": "2026-07-13", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6942498", "field": "management", "role": "object", "bullets": [ "Conceptual framework paper on how AI assistants recommend experts, drawing on reported industry data compiled between December 2025 and June 2026; the unit of observation is a personal brand's citation eligibility.", "No model is run or validated; the paper proposes a six-signal AI Visibility Framework and qualitatively compares how ChatGPT, Claude, and Perplexity weight sources when generating recommendations.", "Concludes the three systems weight sources differently, so brand visibility must be pursued one engine at a time, shifting relevant metrics from traffic toward revenue and branded search." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 34, "edition": 2, "audience": "broad", "validated": null, "n": 54, "authors_detailed": [ { "name": "Sarkhedi Bhavik", "url": "https://openalex.org/A5134528828", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6946559", "doi": "10.2139/ssrn.6946559", "title": "Do AIs Make Good Traders, and Do They Make Good Traders Better?", "authors": [ "Jerry Bell", "Victor Haghani", "James White" ], "posted": "2026-07-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6946559", "field": "finance", "role": "object", "bullets": [ "Conceptual discussion article asking whether modern AI systems can generate consistent trading profits in competitive financial markets, drawing on market efficiency and behavioral finance rather than a dataset.", "Authors run no model; large language models are the subject rather than a tool, and the article names no specific system and reports no empirical forecasting test.", "Argues predictive edges erode through market efficiency, transaction costs, crowding, and overfitting, so AI's value lies more in enforcing discipline and improving risk management than in forecasting returns." ], "bullet_provenance": "ai", "salience": 37, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 515, "authors_detailed": [ { "name": "Jerry Bell", "url": "https://openalex.org/A5140479170", "inst": "Willow Wood (United States)" }, { "name": "Victor Haghani", "url": "https://openalex.org/A5050685602", "inst": "Willow Wood (United States)" }, { "name": "James White", "url": "https://openalex.org/A5140487081", "inst": "Willow Wood (United States)" } ], "affiliations": [ "Willow Wood (United States)" ] }, { "uid": "arxiv:2607.10179v1", "arxiv_id": "2607.10179v1", "title": "From Patent Expiry to Business Pathways: AI Workflows for Activating Innovation Archives", "authors": [ "Sidney Shapiro", "Mark Price" ], "posted": "2026-07-11", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.10179v1", "field": "management", "role": "instrument", "bullets": [ "Proof of concept over all 378 records in one weekly CIPO ST.96 patent archive, identifying 20 expired, lapsed, or near-expiry candidates for commercialization.", "A locally hosted Qwen3.6 model populates structured review packets translating patent disclosures into commercialization routes; the evaluation checks schema conformance and ranking stability, not accuracy against ground truth.", "Reports reproducible ingestion, stable rankings under weight perturbation, and schema-conformant output, while exposing incomplete legal-status coverage and the need for register and expert review." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 37, "edition": 3, "audience": "technical", "n": 516, "authors_detailed": [ { "name": "Sidney Shapiro", "url": "https://openalex.org/A5140666874", "inst": "" }, { "name": "Mark Price", "url": "https://openalex.org/A5140613294", "inst": "" } ] }, { "uid": "arxiv:2607.19409v1", "arxiv_id": "2607.19409v1", "title": "FORCE-Bench: A Benchmark, Dataset, and Evaluation Harness for Agentic AI in Enterprise Finance", "authors": [ "Wolfgang M. Pauli", "Sarah Panda", "Kidus Admassu", "Said Bleik", "Ademola Okerinde", "Jeremy Reynolds" ], "posted": "2026-07-11", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.19409v1", "field": "finance", "role": "method", "bullets": [ "FORCE-Bench contains 251 expert-annotated queries in operational finance covering obligation research from ERP accounts receivable and payable data, entity performance from public filings and market data, and business brief generation.", "General-purpose agentic systems and a purpose-built Finance Agent for Microsoft 365 Copilot, with underlying model families not stated, are scored on a rubric across eight dimensions including accuracy, citations, and groundedness.", "General-purpose agentic systems do not consistently meet finance-domain quality requirements under operational constraints, while the purpose-built finance agent is more reliable across the rubric dimensions." ], "bullet_provenance": "ai", "open_weights": false, "validated": false, "validation_note": "rubric scored against expert annotations, no agreement figure stated", "salience": 52, "edition": 3, "audience": "technical", "models": [], "n": 588, "authors_detailed": [ { "name": "Wolfgang M. Pauli", "url": "https://openalex.org/A5035106268", "inst": "California Institute of Technology" }, { "name": "Sarah Panda", "url": "https://openalex.org/A5063603738", "inst": "Microsoft (United States)" }, { "name": "Kidus Admassu", "url": "https://openalex.org/A5143533192", "inst": "Microsoft (United States)" }, { "name": "Said Bleik", "url": "https://openalex.org/A5068339922", "inst": "New Jersey Institute of Technology" }, { "name": "Ademola Okerinde", "url": "https://openalex.org/A5083434470", "inst": "Kansas State University" }, { "name": "Jeremy Reynolds", "url": "https://openalex.org/A5143531823", "inst": "Microsoft (United States)" } ], "affiliations": [ "California Institute of Technology", "Microsoft (United States)", "New Jersey Institute of Technology", "Kansas State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7097177", "doi": "10.2139/ssrn.7097177", "title": "Generative AI and the New Askable in Entrepreneurship Research", "authors": [ "Mohammad Keyhani" ], "posted": "2026-07-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7097177", "field": "management", "role": "method", "bullets": [ "Conceptual essay on entrepreneurship research methodology with no empirical sample, proposing a framework for where generative AI expands the set of feasible research questions.", "No model is deployed; the paper classifies four LLM-based research designs, silicon samples, machine entrepreneurship, machine measurement, and machine evaluation, and proposes fit criteria and safeguards.", "Offers four criteria, verification asymmetry, evidence digitization, task decomposability, and epistemic location, determining which questions suit LLM assistance, plus failure modes and validation methods." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 963, "authors_detailed": [ { "name": "Mohammad Keyhani", "url": "https://openalex.org/A5065984634", "inst": "University of Calgary" } ], "affiliations": [ "University of Calgary" ] }, { "uid": "doi:10.2139/ssrn.6944358", "doi": "10.2139/ssrn.6944358", "title": "Can Automation Offset Population Decline? Occupational Imbalances in a Shrinking Workforce", "authors": [ "Donghyun Suh", "Soo Jung Chang" ], "posted": "2026-07-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6944358", "field": "economics", "role": "instrument", "bullets": [ "Korea, with working-age population projected to fall about 11 percent between 2024 and 2034, using population projections and age-by-occupation employment data.", "An unnamed large language model scored O*NET task statements for automation potential, mapped to Korean occupations; no accuracy check against ground truth is reported.", "Automation substantially reduces the aggregate labor shortfall but does not eliminate occupational imbalance, because automation potential aligns only weakly with projected demographic shortages." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 3, "audience": "general", "models": [], "n": 964, "authors_detailed": [ { "name": "Donghyun Suh", "url": "https://openalex.org/A5093401174", "inst": "National Bureau of Economic Research" }, { "name": "Soo Jung Chang", "url": "https://openalex.org/A5064510358", "inst": "Gangneung–Wonju National University" } ], "affiliations": [ "National Bureau of Economic Research", "Gangneung–Wonju National University" ] }, { "uid": "doi:10.2139/ssrn.6950742", "doi": "10.2139/ssrn.6950742", "title": "Agentic Proxies: Governance, Accountability, and the Architecture of a Trustworthy AI Economy", "authors": [ "Geoff Lundholm" ], "posted": "2026-07-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6950742", "field": "management", "role": "object", "bullets": [ "Conceptual paper on AI-to-AI delegation, with case studies spanning healthcare, financial services, legal workflows, and manufacturing, and no empirical sample.", "No model is deployed; the paper studies governance of autonomous agents, proposing bounded agentic proxies under verifiable delegation and a CTX Envelope accountability primitive.", "Argues fully autonomous agents are structurally uninsurable while bounded agentic proxies satisfy underwriters' conditions, grounding a viable AI insurance market in accountability rather than capability." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 965, "authors_detailed": [ { "name": "Geoff Lundholm", "url": "https://openalex.org/A5140460948", "inst": "University of Kindu" } ], "affiliations": [ "University of Kindu" ] }, { "uid": "doi:10.2139/ssrn.7097178", "doi": "10.2139/ssrn.7097178", "title": "Governance Lag in Agentic Entrepreneurship: Leadership as a Service and the Reconfiguration of Entrepreneurial GovernanceA Conceptual Paper Submitted to the Special Issue on Entrepreneurial Problems, Scholarly Impact, and Solution-Oriented Research", "authors": [ "Pia Singh" ], "posted": "2026-07-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7097178", "field": "management", "role": "object", "bullets": [ "Conceptual paper on entrepreneurial governance under agentic AI, with no empirical sample, drawing on transaction cost economics, agency theory, sociomateriality, and recent AI governance scholarship.", "No model is run; the paper formulates the Governance Lag construct and the FUTURISTIC framework, extending agency theory to human-agent dyads with falsifiable propositions, model not stated.", "Proposes that governance institutions lag agentic AI decision capacity, generating alignment risk, and offers a Governor-Agent architecture plus testable propositions for future empirical work." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1179, "authors_detailed": [ { "name": "Pia Singh", "url": "https://openalex.org/A5140435148", "inst": "Quanta Technology (United States)" } ], "affiliations": [ "Quanta Technology (United States)" ] }, { "uid": "doi:10.2139/ssrn.7100729", "doi": "10.2139/ssrn.7100729", "title": "Beyond the Luddite Trap: Artificial Intelligence and workers-oligopoly bargaining", "authors": [ "Jacques R. Bughin" ], "posted": "2026-07-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7100729", "field": "economics", "role": "object", "bullets": [ "Theoretical task-based production model of agentic AI adoption within an oligopoly product market, embedded in union-firm bargaining, with no empirical data.", "No model is run; the paper models agentic AI substituting human tasks while requiring human-in-the-loop supervision labor, comparing Right-to-Manage and Manning bargaining regimes, model not stated.", "Identifies thresholds where AI becomes a labor complement, an RTM trap where strong unions accelerate automation, and a hump-shaped bargaining life cycle spanning four structural regimes." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1180, "authors_detailed": [ { "name": "Jacques Bughin", "url": "https://openalex.org/A5029134161", "inst": "Solvay (Belgium)" } ], "affiliations": [ "Solvay (Belgium)" ] }, { "uid": "arxiv:2607.10251v1", "arxiv_id": "2607.10251v1", "title": "Behavioural Signatures of Risk-Sensitive Decision-Making in Large Language Models", "authors": [ "Xuankun Rong", "Wenke Huang", "Bo Du", "Dacheng Tao", "Mang Ye" ], "posted": "2026-07-11", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.10251v1", "field": "economics", "role": "agent", "bullets": [ "Frontier language models play no-limit Texas Hold'em in homogeneous self-play and heterogeneous mixed-model games; behavior is measured by participation and pre-flop proactiveness. Sample size and specific models not stated.", "Several frontier LLMs, not individually named, are made to decide under uncertainty at the poker table, with risk disposition read off voluntary engagement and pre-flop risk escalation and no comparison to a ground truth.", "Models show stable, model-specific risk profiles spanning conservative to aggressive, largely robust to opponent mix, and adjust in heterogeneous ways under global risk pressure and personal resource constraints." ], "bullet_provenance": "ai", "salience": 50, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 44, "authors_detailed": [ { "name": "Xuankun Rong", "url": "https://openalex.org/A5140696958", "inst": "" }, { "name": "Wenke Huang", "url": "https://openalex.org/A5140657815", "inst": "" }, { "name": "Bo Du", "url": "https://openalex.org/A5140742389", "inst": "" }, { "name": "Dacheng Tao", "url": "https://openalex.org/A5140642537", "inst": "" }, { "name": "Mang Ye", "url": "https://openalex.org/A5140646368", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7095164", "doi": "10.2139/ssrn.7095164", "title": "Anthropomorphism and Trust in Human-Large Language Model interactions", "authors": [ "Akila Kadambi", "Ylenia D’elia", "Tanishka Shah", "Iulia M. Comsa", "Alison Lentz", "Katie Siri-Ngammuang", "Tara Buechler", "Jonas Kaplan", "Antonio R. Damasio", "Srini Narayanan", "Lisa Aziz-Zadeh" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7095164", "field": "management", "role": "object", "bullets": [ "Lab experiment with 115 participants across more than 2,000 human-LLM chatbot interactions, with chatbots systematically varied in warmth, competence, and empathy.", "Unnamed LLM chatbots served as manipulated stimuli whose warmth, competence, and empathy were varied; the study measures user perceptions rather than validating model outputs.", "Warmth and cognitive empathy predicted all outcomes including anthropomorphism and trust; competence predicted all but anthropomorphism, and subjective topics amplified perceived human-likeness and closeness." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 282, "authors_detailed": [ { "name": "Akila Kadambi", "url": "https://openalex.org/A5017920494", "inst": "Google (United States)" }, { "name": "Ylenia D’elia", "url": "https://openalex.org/A5092184815", "inst": "University of Southern California" }, { "name": "Tanishka Shah", "url": "https://openalex.org/A5134093183", "inst": "University of Southern California" }, { "name": "Iulia M. Comșa", "url": "https://openalex.org/A5032013007", "inst": "Google (United States)" }, { "name": "Alison Lentz", "url": "https://openalex.org/A5140396109", "inst": "Google (United States)" }, { "name": "Katie Siri-Ngammuang", "url": "https://openalex.org/A5134039475", "inst": "University of Southern California" }, { "name": "Tara Buechler", "url": "https://openalex.org/A5134025745", "inst": "University of Southern California" }, { "name": "Jonas Kaplan", "url": "https://openalex.org/A5036147064", "inst": "University of Southern California" }, { "name": "António R. Damásio", "url": "https://openalex.org/A5070506273", "inst": "University of Southern California" }, { "name": "Srini Narayanan", "url": "https://openalex.org/A5065760252", "inst": "Google (United States)" }, { "name": "Lisa Aziz-Zadeh", "url": "https://openalex.org/A5126195377", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California", "Google (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6936318", "doi": "10.2139/ssrn.6936318", "title": "Better Disclosure, Higher Rates: Algorithm Aversion and the Cost of Debt", "authors": [ "Mengtao Chen", "Yongming Sun" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6936318", "field": "accounting", "role": "object", "bullets": [ "Chinese A-share firms from 2009 to 2025, measuring the share of AI-generated narrative in annual reports at the firm-year level.", "The AI-generated content share is measured with the ZeroGPT detector, with no reported validation against hand-labelled reports, then related to the cost of debt.", "A one-standard-deviation rise in AI-generated content raises the effective interest rate by about 0.23 percentage points and lowers the bank-loan-to-assets ratio, despite more readable disclosure." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 513, "authors_detailed": [ { "name": "Mengtao Chen", "url": "https://openalex.org/A5021321240", "inst": "Zhejiang University of Finance and Economics" }, { "name": "Yuanhao Sun", "url": "https://openalex.org/A5090291760", "inst": "First Affiliated Hospital Zhejiang University" } ], "affiliations": [ "Zhejiang University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.7074400", "doi": "10.2139/ssrn.7074400", "title": "The Cost of Listening: AI Advice and Induced Environmental Footprints", "authors": [ "Chris Jeffords" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7074400", "field": "economics", "role": "object", "bullets": [ "A conceptual paper with no empirical sample, covering everyday consumer decisions about travel, food, energy, consumption, repair, and land use.", "No model is used empirically; the paper theorizes LLM advice as environmental choice architecture acting through salience, framing, trust, search costs, and default recommendations.", "Induced footprints arise at the behavioral margin and can amplify, dampen, or redirect environmental outcomes in either direction; audits and stated-choice and field designs are proposed." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 514, "authors_detailed": [ { "name": "Christopher Jeffords", "url": "https://openalex.org/A5017775896", "inst": "Widener University" } ], "affiliations": [ "Widener University" ] }, { "uid": "doi:10.2139/ssrn.6939558", "doi": "10.2139/ssrn.6939558", "title": "Discovery Yield: A Framework for Evaluating Automated Financial Hypothesis Discovery", "authors": [ "Kishore Mantripragada" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6939558", "field": "finance", "role": "method", "bullets": [ "Daily SPY exchange-traded fund data from 2000 to 2024 is used to test candidate trading strategies through interpretable mean-reversion rules, systematic grid search, and random search procedures.", "No language model is used in the case study; the paper proposes Discovery Yield, the share of hypotheses surviving statistical validation, and frames it for future large language model and agentic discovery systems.", "Several strategies look significant under conventional tests but none survive Benjamini-Hochberg false discovery rate correction, and large-scale random search produces no statistically validated discoveries." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 587, "authors_detailed": [ { "name": "Kishore Mantripragada", "url": "https://openalex.org/A5140386054", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7090772", "doi": "10.2139/ssrn.7090772", "title": "Power Behind the Prompt: Exploring GenAI's Energy Footprint towards Formulating a Sustainable AI Framework", "authors": [ "Ayan Jain", "Debashis Saha" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7090772", "field": "management", "role": "object", "bullets": [ "Systematic narrative review synthesizing peer-reviewed literature, industry disclosures, and macroeconomic modeling on generative AI's energy, water, and embodied-carbon footprint.", "No AI model is run; the study reviews inference-phase energy costs of deployed systems through Triple Bottom Line, Jevons Paradox, FinOps, and Green IS lenses.", "Inference dominates operational energy cost, per-query energy varies up to three orders of magnitude, and unchecked demand could add about 1.7 gigatons of emissions between 2025 and 2030." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 959, "authors_detailed": [ { "name": "Ayan Jain", "url": "https://openalex.org/A5135780864", "inst": "Indian Institute of Technology Delhi" }, { "name": "Debashis Saha", "url": "https://openalex.org/A5057138590", "inst": "Indian Institute of Management Calcutta" } ], "affiliations": [ "Indian Institute of Technology Delhi", "Indian Institute of Management Calcutta" ] }, { "uid": "doi:10.2139/ssrn.7095214", "doi": "10.2139/ssrn.7095214", "title": "Knowledge Flow driven by Generative AI in Sustainable Innovation Ecosystems: A Dual Role Perspective", "authors": [ "Xin Wei", "Rui Zhang", "Liu Jia", "donghan wang", "Patricia Ordoñez de Pablos" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7095214", "field": "management", "role": "object", "bullets": [ "Conceptual study of generative AI's role in knowledge flow within sustainable innovation ecosystems, using the Quadruple Helix Model and system dynamics simulation, with no firm sample.", "No AI model is run; generative AI is modeled as playing a dual role, auxiliary tool and active innovator, altering feedback loops, trust mechanisms, and system evolution.", "Generative AI accelerates knowledge transmission most in its tool role, while as an innovator it deepens system-level trust and transparency, with citizen participation amplifying both effects." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 960, "authors_detailed": [ { "name": "Xin Wei", "url": "https://openalex.org/A5071784511", "inst": "Nankai University" }, { "name": "Rui Zhang", "url": "https://openalex.org/A5140423307", "inst": "Communication University of China" }, { "name": "Liu Jia", "url": "https://openalex.org/A5140394277", "inst": "Communication University of China" }, { "name": "D Wang", "url": "https://openalex.org/A5073297681", "inst": "Communication University of China" }, { "name": "Patricia Ordóñez de Pablos", "url": "https://openalex.org/A5111421435", "inst": "Universidad de Oviedo" } ], "affiliations": [ "Nankai University", "Communication University of China", "Universidad de Oviedo" ] }, { "uid": "doi:10.2139/ssrn.7029939", "doi": "10.2139/ssrn.7029939", "title": "The Hidden Cost of Failed Payments: SEC Disclosure of Payment Losses, Chargebacks, Write-offs, and Unrecovered Funds, 2020-2025", "authors": [ "Ignacio Berardi" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7029939", "field": "accounting", "role": "instrument", "bullets": [ "Annual SEC filings from 50 US payments, fintech, and marketplace companies over fiscal years 2020 to 2025, covering 285 company-years of disclosure.", "An unnamed LLM extraction pipeline pulled payment-loss items from footnotes, MD&A, risk factors, and controls sections; one finding was added by manual review, with no accuracy figure reported.", "Only 44 of 285 company-years contained a quantified disclosure; 16 firms reported a combined 3.45 billion dollars in exposure, with four firms accounting for about 87 percent." ], "bullet_provenance": "ai", "validated": false, "salience": 48, "edition": 3, "audience": "general", "models": [], "n": 961, "authors_detailed": [ { "name": "Ignacio Berardi", "url": "https://openalex.org/A5139330800", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "arxiv:2607.09921v1", "arxiv_id": "2607.09921v1", "title": "Global Merger-Arbitrage Forecasting with Language Models", "authors": [ "Hinal Jajal", "Michal Mucha", "Charles Sweat", "Chris Pulman", "Charlie Flanagan", "Peter Anderson" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.09921v1", "field": "finance", "role": "method", "bullets": [ "More than 400 large announced M&A deals across 42 countries, out-of-sample, requiring reasoning over hundreds of pages of technical deal documents.", "An unnamed finetuned language model trained on hindsight-guided reasoning traces with expert context engineering outputs probabilities over three deal outcomes; validated by out-of-sample Brier score.", "Class-balanced Brier score of 0.151, 24 percent below market-implied probabilities, 19 percent below XGBoost, and 25 to 42 percent below frontier language models." ], "bullet_provenance": "ai", "validated": true, "validation_note": "out-of-sample Brier score on 400+ deals versus market and XGBoost", "salience": 70, "edition": 3, "audience": "technical", "models": [], "n": 962, "authors_detailed": [ { "name": "Hinal Jajal", "url": "https://openalex.org/A5140721317", "inst": "" }, { "name": "Michal Mucha", "url": "https://openalex.org/A5140588992", "inst": "" }, { "name": "Charles Sweat", "url": "https://openalex.org/A5140673854", "inst": "" }, { "name": "Chris Pulman", "url": "https://openalex.org/A5140616906", "inst": "" }, { "name": "Charlie Flanagan", "url": "https://openalex.org/A5140590045", "inst": "" }, { "name": "Peter Anderson", "url": "https://openalex.org/A5140604427", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6943944", "doi": "10.2139/ssrn.6943944", "title": "The Two-step Paradigm for Autonomous Agents: From Cognitive Task Analysis to DESIRE-based Multi-agent Architecture", "authors": [ "Jan Veldsink" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6943944", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, addressing enterprise adoption of AI and arguing organizations mishandle it as a centralized IT project rather than an extension of the worker.", "No model is used or named; the paper proposes Cognitive Task Analysis as a foundation for LLM-assisted work and for a DESIRE and belief-desire-intention multi-agent architecture.", "Claims that CTA-based deconstruction lets knowledge workers offload 60 to 80 percent of cognitive burden while keeping autonomous agents deterministic, verifiable, and aligned with human intent." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1172, "authors_detailed": [ { "name": "Jan Veldsink", "url": "https://openalex.org/A5140387221", "inst": "Nyenrode Business University" } ], "affiliations": [ "Nyenrode Business University" ] }, { "uid": "doi:10.2139/ssrn.6959799", "doi": "10.2139/ssrn.6959799", "title": "Suptech-Powered Consumer And Investor Protection Supervision. Architectural Pathway From Reactive Oversight To Outcome-Driven Institutional Intelligence.", "authors": [ "Simone Di Castri", "Matt Grasser" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6959799", "field": "finance", "role": "object", "bullets": [ "Financial authorities worldwide, drawing on four years of State of SupTech survey data covering hundreds of authorities and the GovSpace corpus of 102 consumer and investor protection suptech implementations across 73 countries.", "No language model is run; the report builds two diagnostic frameworks, the GovTech Generations 3.0 maturity model and the DataStack Blueprint, to sequence the move toward AI-augmented and agentic supervision, model not stated.", "Adoption concentrates in complaint and conduct signal capture, while early warning systems, predictive analytics, algorithmic auditing, and other anticipatory functions remain limited or absent in most jurisdictions." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1173, "authors_detailed": [ { "name": "Simone di Castri", "url": "https://openalex.org/A5088460807", "inst": "Bavarian Research Institute for Digital Transformation" }, { "name": "Matt Grasser", "url": "https://openalex.org/A5059457423", "inst": "Bavarian Research Institute for Digital Transformation" } ], "affiliations": [ "Bavarian Research Institute for Digital Transformation" ] }, { "uid": "doi:10.2139/ssrn.7074360", "doi": "10.2139/ssrn.7074360", "title": "AI and Quantum Trading Agents: Cybersecurity, Governance, and Regulatory Challenges in Financial Decision-Making", "authors": [ "Audrey Rah" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7074360", "field": "finance", "role": "object", "bullets": [ "Systematic mapping review following PRISMA 2020, identifying 198 records from academic databases, regulatory repositories, standards organizations, and government sources, narrowed to a final corpus of 96 studies.", "No model is run; the paper synthesizes literature across five dimensions spanning autonomous financial agents, quantum machine learning, cybersecurity and post-quantum security governance, and financial regulation, model not stated.", "Research on quantum finance and agentic AI is growing, but governance for autonomous financial decision-making, post-quantum resilience, auditability, accountability, and supervisory control stays fragmented across jurisdictions." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1174, "authors_detailed": [ { "name": "Audrey Rah", "url": "https://openalex.org/A5104244670", "inst": "Langston University" } ], "affiliations": [ "Langston University" ] }, { "uid": "doi:10.2139/ssrn.7072740", "doi": "10.2139/ssrn.7072740", "title": "Efficiency Inflation and the Repricing of Knowledge Work: A Conceptual Model of Judgment Density in AI-Augmented Organizations", "authors": [ "Jia-Liang Yeh" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7072740", "field": "management", "role": "object", "bullets": [ "Conceptual theory-building working paper on knowledge work in AI-augmented organizations, integrating public enterprise surveys, labor-market signals, and organizational theory, with no specified sample, period, or geography.", "No model is run; the paper develops the efficiency inflation concept and five mechanisms, output commoditization, hidden supervisory labor, verification failure, cognitive-load transfer, and apprenticeship collapse, model not stated.", "Argues AI reprices rather than replaces work by separating low-judgment execution from high-judgment responsibility, making abundant output cheaper while scarce judgment becomes more valuable." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1175, "authors_detailed": [ { "name": "Jia-Liang Yeh", "url": "https://openalex.org/A5140423597", "inst": "Ming Chi University of Technology" } ], "affiliations": [ "Ming Chi University of Technology" ] }, { "uid": "doi:10.2139/ssrn.7075038", "doi": "10.2139/ssrn.7075038", "title": "Proportionate Governance in Practice An IDEA-CRUS-T Framework for Risk-Tiered, Runtime-Governed AI Systems in Financial Services", "authors": [ "Satyaki Ghosh Dastidar" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7075038", "field": "finance", "role": "object", "bullets": [ "Financial services firms deploying generative and agentic AI, from internal drafting assistance to autonomous transaction execution, illustrated with two hypothetical case studies in retail banking and anti-money-laundering operations.", "No model is run; the paper proposes the IDEA lifecycle spine, a five-dimension risk-tiering method, and the CRUS-T runtime reliability score gated against tier-specific thresholds, model not stated.", "Capability-level CRUS-T scoring identifies the reliability dimension responsible for under-governance in one case and the single authority-bearing action responsible for over-governance in the other." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1176, "authors_detailed": [ { "name": "Satyaki Ghosh Dastidar", "url": "https://openalex.org/A5031313332", "inst": "University at Buffalo, State University of New York" } ], "affiliations": [ "University at Buffalo, State University of New York" ] }, { "uid": "doi:10.1145/3770855.3818311", "doi": "10.1145/3770855.3818311", "arxiv_id": "2607.09955v1", "title": "A Foundation Model for Multimodal Event Sequences in Financial Applications", "authors": [ "Nikita Rusakov", "Vladislav Meshkov", "Konstantin Zorin", "Gleb Zaripov", "Alexander Uglov", "Alexey Vasilev", "Anton Klenitskiy" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.09955v1", "field": "finance", "role": "method", "bullets": [ "One of the largest banks in Eastern Europe; multimodal sequences of user events unify transaction histories and digital interaction signals into single chronological sequences, sample size not stated.", "Pretrained a foundation transformer on user event sequences with a next-event prediction objective, producing representations combined with engineered features for lightweight downstream task models, no ground-truth accuracy figure reported.", "The foundation-model system outperforms traditional task-specific models and, deployed in production, produced measurable improvements in business metrics, with magnitudes not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 3, "audience": "technical", "models": [], "n": 1177, "authors_detailed": [ { "name": "Nikita Rusakov", "url": "https://openalex.org/A5140841400", "inst": "Presidential Executive Office" }, { "name": "Vladislav Meshkov", "url": "https://openalex.org/A5140853437", "inst": "Presidential Executive Office" }, { "name": "Konstantin Zorin", "url": "https://openalex.org/A5140846605", "inst": "Presidential Executive Office" }, { "name": "Gleb Zaripov", "url": "https://openalex.org/A5140774094", "inst": "Presidential Executive Office" }, { "name": "Alexander Uglov", "url": "https://openalex.org/A5140956892", "inst": "Presidential Executive Office" }, { "name": "Alexey Vasilev", "url": "https://openalex.org/A5140807665", "inst": "" }, { "name": "Anton Klenitskiy", "url": "https://openalex.org/A5140829906", "inst": "" } ], "affiliations": [ "Presidential Executive Office" ] }, { "uid": "arxiv:2607.09586v1", "arxiv_id": "2607.09586v1", "title": "TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems", "authors": [ "Hannah M. Liu", "Rhea Saxena", "Shiv Asthana" ], "posted": "2026-07-10", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.09586v1", "field": "management", "role": "object", "bullets": [ "Enterprise and public-sector agentic AI systems; the framework applies to seven types of agentic AI systems and is demonstrated with one illustrative example, no empirical dataset.", "No model is run; the paper builds a risk-classification instrument with a twelve-dimension scoring rubric, a GPA plus IAT classification model, and a five-level autonomy framework, model not stated.", "Produces a three-tier governance output with mapped control recommendations plus a coding-assistant extension, presented as an iterating framework rather than validated against realized outcomes." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1178, "authors_detailed": [ { "name": "Hannah M. Liu", "url": "https://openalex.org/A5057054227", "inst": "" }, { "name": "Rhea Saxena", "url": "https://openalex.org/A5140517928", "inst": "" }, { "name": "Shiv Asthana", "url": "https://openalex.org/A5140549898", "inst": "" } ] }, { "uid": "arxiv:2607.09121v1", "arxiv_id": "2607.09121v1", "title": "Augmenting Fundamental Analysis with Large Language Models: A RAG-Based System for Generating Investor Briefs", "authors": [ "Bartosz Ziółko", "Kacper Dobrzeniewski" ], "posted": "2026-07-10", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.09121v1", "field": "finance", "role": "instrument", "bullets": [ "Nine listed companies scanned over four weeks, drawing on their reports, macroeconomic indicators such as GDP and inflation, and SEC filings from EDGAR; briefs judged by nine individual investors.", "gpt-4o accessed through an API in a retrieval-augmented setup generated automatic investor briefs, aided by a document encoding Kitchin-cycle investor knowledge; no comparison against ground truth is reported.", "The abstract reports only that briefs were sent to nine participants to evaluate usefulness; no quantitative outcome, accuracy, or effect size is stated." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "validation_note": "investor usefulness ratings only, no ground truth", "salience": 32, "edition": 2, "audience": "technical", "n": 20, "authors_detailed": [ { "name": "Bartosz Ziółko", "url": "https://openalex.org/A5140520274", "inst": "" }, { "name": "Kacper Dobrzeniewski", "url": "https://openalex.org/A5140528587", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6941098", "doi": "10.2139/ssrn.6941098", "title": "U.S. Policies Unintentionally Accelerated China's Open AI Ecosystems", "authors": [ "Wang Jin", "Nadav Kuniesky", "Bowen Lou", "Tianshu Sun", "James A. Evans" ], "posted": "2026-07-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6941098", "field": "economics", "role": "object", "bullets": [ "U.S. and Chinese AI developer activity on open-source LLM repositories and patent filings, spanning periods before and after major U.S. semiconductor export-control actions.", "Tracks changes in developer engagement with open-source large language model repositories following export-control shocks and traces diffusion of Chinese-origin open models into research.", "Chinese developers increased open-source LLM engagement substantially more than U.S. developers post-controls; Chinese open models spread widely in research but remained absent from U.S. patent disclosures." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3614, "authors_detailed": [ { "name": "Wang Jin", "url": "https://openalex.org/A5100649170", "inst": "Chapman University" }, { "name": "Nadav Kuniesky", "url": "https://openalex.org/A5083139955", "inst": "University of Chicago" }, { "name": "Bowen Lou", "url": "https://openalex.org/A5051756505", "inst": "University of Southern California" }, { "name": "Tianshu Sun", "url": "https://openalex.org/A5140444298", "inst": "Cheung Kong Graduate School of Business" }, { "name": "James A. Evans", "url": "https://openalex.org/A5076633756", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "University of Southern California", "Chapman University", "Cheung Kong Graduate School of Business" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.08346v1", "arxiv_id": "2607.08346v1", "title": "Grounded Event Extraction from SEC 8-K Filings with a Fine-Grained Taxonomy", "authors": [ "Rian Dolphin", "Joe Dursun", "Jarrett Blankenship", "Katie Adams", "Quinton Pike" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.08346v1", "field": "finance", "role": "method", "bullets": [ "292,984 SEC Form 8-K filings from 2022 to 2026 by US public companies, tagged against a three-tier taxonomy of 119 event types, yielding 601,088 released event tags.", "An unnamed LLM tags disclosures with output constrained to valid taxonomy entries and anchored to verbatim quotes; a second pass grades each quote and an LLM judge scores precision.", "Precision rises monotonically with the quality score from 12% to 96%; an event study on abnormal returns, run without any language model, separates economically distinct events sharing an item code." ], "bullet_provenance": "ai", "validated": false, "validation_note": "LLM-judge precision, no human benchmark", "salience": 57, "edition": 3, "audience": "technical", "models": [], "n": 281, "authors_detailed": [ { "name": "Rian Dolphin", "url": "https://openalex.org/A5140394828", "inst": "NSilico (Ireland)" }, { "name": "Joe Dursun", "url": "https://openalex.org/A5107619376", "inst": "Eterna Massive Open Laboratory" }, { "name": "Jarrett Blankenship", "url": "https://openalex.org/A5107496252", "inst": "Eterna Massive Open Laboratory" }, { "name": "Katie Adams", "url": "https://openalex.org/A5140430335", "inst": "Eterna Massive Open Laboratory" }, { "name": "Quinton Pike", "url": "https://openalex.org/A5140421520", "inst": "Eterna Massive Open Laboratory" } ], "affiliations": [ "NSilico (Ireland)", "Eterna Massive Open Laboratory" ] }, { "uid": "doi:10.2139/ssrn.7083993", "doi": "10.2139/ssrn.7083993", "title": "Reproducible LLM-Based Measurement Depends on the Serving Stack", "authors": [ "Hsiang-Chieh (Alex) Yang" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7083993", "field": "finance", "role": "method", "bullets": [ "500 earnings-call transcripts, with each of three prompts re-run ten times across 36 model-and-serving-stack combinations, plus a long-short portfolio as the downstream application.", "Unnamed open-weight and closed models generate the measure at greedy decoding; the study checks whether re-running returns the same answer across hardware and cloud providers, not against human labels.", "Self-hosted open-weight models reproduce 96.6 to 100 percent of the time, two machines with identical weights agree only 92.2 percent, one cloud open model 64.5 percent, and closed models 9.1 percent." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "validation_note": "reports run-to-run reproduction rates, not accuracy against ground truth", "salience": 74, "edition": 3, "audience": "general", "n": 320, "authors_detailed": [ { "name": "Hsiang-Chieh Yang", "url": "https://openalex.org/A5001430525", "inst": "Augusta University Health" } ], "affiliations": [ "Augusta University Health" ] }, { "uid": "doi:10.2139/ssrn.6933900", "doi": "10.2139/ssrn.6933900", "title": "From Tool Calls to Governed Autonomy: Agent OS and ECF for Auditable Agent Deployment", "authors": [ "Jeremy Borden" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6933900", "field": "management", "role": "object", "bullets": [ "An implementation report from the operator of a live LLM-agent marketplace, Agoragentic, rather than a formal sample; system claims are made verifiable through live anonymous curl commands.", "LLM agents that call APIs and trigger paid work are governed by deployment contracts binding budget, tool authority, and approval thresholds, with hashed receipts; the model is not named.", "Supply-side governance held in production while organic demand, not infrastructure, was the binding constraint; the paper pre-registers an on-chain falsifiable demand criterion." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 509, "authors_detailed": [ { "name": "Jeremy Borden", "url": "https://openalex.org/A5089284876", "inst": "University of Manitoba" } ], "affiliations": [ "University of Manitoba" ] }, { "uid": "doi:10.2139/ssrn.7083997", "doi": "10.2139/ssrn.7083997", "title": "Green-Curious Investing and GenAI: A Field Experiment", "authors": [ "Leslie A Boni", "Amado Mabul", "Georgia Acosta", "Ray Johnson", "Mary Anne Majadillas", "Subhra B. Saha", "Rachael Kehoe" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7083997", "field": "finance", "role": "object", "bullets": [ "A randomized classroom field experiment with undergraduates in financial-literacy and business-statistics courses, each assigned one of three instructional cues before using generative AI.", "Students interacted directly with off-the-shelf ChatGPT and Gemini to learn about greenwashing and return tradeoffs; outcomes are self-reported learning, with no validation of model output.", "Among students not raising greenwashing in their first prompt, Gemini users reported learning about it far more often than ChatGPT users, 46 percent versus 15 percent." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 52, "edition": 3, "audience": "general", "validated": null, "n": 510, "authors_detailed": [ { "name": "Leslie Boni", "url": "https://openalex.org/A5049652735", "inst": "California State University, Monterey Bay" }, { "name": "Amado Mabul", "url": "https://openalex.org/A5140379549", "inst": "California State University, Monterey Bay" }, { "name": "Georgia Acosta", "url": "https://openalex.org/A5018847121", "inst": "California State University, Monterey Bay" }, { "name": "Ray Johnson", "url": "https://openalex.org/A5103405571", "inst": "Queens College, CUNY" }, { "name": "Mary Anne Majadillas", "url": "https://openalex.org/A5058999420", "inst": "Florida State University" }, { "name": "Subhra B. Saha", "url": "https://openalex.org/A5140306186", "inst": "California State University, Monterey Bay" }, { "name": "Rachael Kehoe", "url": "https://openalex.org/A5140300244", "inst": "California State University, Monterey Bay" } ], "affiliations": [ "California State University, Monterey Bay", "Queens College, CUNY", "Florida State University" ] }, { "uid": "arxiv:2607.08731v2", "arxiv_id": "2607.08731v2", "title": "Trusting sovereign language models as scientific instruments: evidence from Portugal's AMALIA", "authors": [ "Manuel Pita" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.08731v2", "field": "other", "role": "method", "bullets": [ "A pre-registered, out-of-sample study coding the moral foundation of authority in a transcreated English-to-European-Portuguese corpus.", "AMALIA, Portugal's publicly funded 9B open-weights model, codes authority; validated against trained coders and a recovery gap that recombines the theory's clauses to test what the theory explains.", "AMALIA matches coders within six points of open models eight to thirteen times larger, yet only about half of its authority coding is attributable to the stated theory." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "agreement with trained human coders", "salience": 55, "edition": 3, "audience": "technical", "n": 511, "authors_detailed": [ { "name": "Manuel Pita", "url": "https://openalex.org/A5132736667", "inst": "Universidade Lusófona" } ], "affiliations": [ "Universidade Lusófona" ] }, { "uid": "arxiv:2607.08535v1", "arxiv_id": "2607.08535v1", "title": "When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability", "authors": [ "Zongyou Yang", "Yinghan Hou", "Xiaokun Yang" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.08535v1", "field": "other", "role": "method", "bullets": [ "Four judgment datasets used to compare two evaluator-upgrade paths, scaling Qwen3 dense judges from 1.7B to 32B and moving across successive MiniMax M2 API releases.", "LLM judges score fixed candidate responses; the study treats evaluator replacement as a measurement-validity problem, probing position and verbosity bias, jury effects, and structured debate.", "Judge upgrades are not interchangeable; only Qwen3 1.7B to 4B gives a robust adjacent gain, and stronger judges reduce but do not remove position and verbosity bias." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 52, "edition": 3, "audience": "technical", "n": 512, "authors_detailed": [ { "name": "Zongyou Yang", "url": "https://openalex.org/A5124022814", "inst": "The London College" }, { "name": "Yinghan Hou", "url": "https://openalex.org/A5123981651", "inst": "Imperial College London" }, { "name": "Xiaokun Yang", "url": "https://openalex.org/A5058511319", "inst": "University of Houston - Clear Lake" } ], "affiliations": [ "The London College", "Imperial College London", "University of Houston - Clear Lake" ] }, { "uid": "arxiv:2607.08681v1", "arxiv_id": "2607.08681v1", "title": "SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets", "authors": [ "Shilin Ou", "Yifan Xu", "Luyao Zhang" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.08681v1", "field": "economics", "role": "method", "bullets": [ "Decentralized solar energy markets are simulated as a Gymnasium-compatible Markov decision process in which agents make hourly market governance decisions; the benchmark reports no real-world sample or period.", "An LLM-based planner and auditor layer, with the model family not stated, sets action bounds and revises high-risk actions; static, random, myopic, RL, and RL plus LLM policies are compared.", "A utility-safety trade-off appears; reinforcement learning agents raise market utility but act unsafely, and the LLM auditor improves auditability yet cannot compensate for a misspecified reward function." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 586, "authors_detailed": [ { "name": "欧石林", "url": "https://openalex.org/A5030571306", "inst": "Duke Kunshan University" }, { "name": "Yifan Xu", "url": "https://openalex.org/A5140385518", "inst": "" }, { "name": "Luyao Zhang", "url": "https://openalex.org/A5140397484", "inst": "" } ], "affiliations": [ "Duke Kunshan University" ] }, { "uid": "doi:10.2139/ssrn.6932478", "doi": "10.2139/ssrn.6932478", "title": "Reading the Late-Filing Notification: Form NT Body Narratives Predict Subsequent SEC Restatement Disclosures", "authors": [ "Hyun Ahn" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6932478", "field": "accounting", "role": "instrument", "bullets": [ "3,232 SEC Form NT 10-K and NT 10-Q late-filing narratives from 2014 to 2024, plus a held-out 2025-Q1 to 2026-Q2 cohort of 912 filings.", "A zero-shot large language model, not named, classifies each narrative as accounting-issue or residual; no accuracy check against hand-coded labels is reported.", "Accounting-issue filings carry a 32.6% versus 23.5% chance of a restatement disclosure within ninety trading days; a long-short basket earns 30.1 percentage points annualized alpha." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 955, "authors_detailed": [ { "name": "Hyun Ahn", "url": "https://openalex.org/A5135592103", "inst": "Korea Institute for Advanced Study" } ], "affiliations": [ "Korea Institute for Advanced Study" ] }, { "uid": "doi:10.2139/ssrn.6932158", "doi": "10.2139/ssrn.6932158", "title": "Twelve Voices, One Decision: Evaluating a Persona-Memory FOMC Simulator Across Seventeen Meetings", "authors": [ "Jae Young Suh", "Seong-Hoon Kim" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6932158", "field": "economics", "role": "agent", "bullets": [ "Seventeen US Federal Open Market Committee meetings from March 2024 to March 2026, with the twelve voting members instantiated as persona agents.", "gemini-3-flash-preview backs twelve persona-and-memory agents that deliberate over a release-date-filtered Fed corpus and emit decisions, statements, minutes, and votes, scored against published outcomes.", "The simulator labelled 16 of 17 rate directions correctly, recovered all six cuts, held magnitude error to 2.2 basis points, and reached 0.82 statement embedding cosine." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "scored against published FOMC decisions, 16/17 directions", "salience": 66, "edition": 3, "audience": "technical", "n": 956, "authors_detailed": [ { "name": "Jae Young Suh", "url": "https://openalex.org/A5120788303", "inst": "" }, { "name": "Seong-Hoon Kim", "url": "https://openalex.org/A5139245857", "inst": "Sapientia College of Theology of Religious Orders" } ], "affiliations": [ "Sapientia College of Theology of Religious Orders" ] }, { "uid": "doi:10.2139/ssrn.6935198", "doi": "10.2139/ssrn.6935198", "title": "From Frameworks to Filings: The Case for a Standard Disclosure Layer in AI Agent Governance", "authors": [ "Brendan Sibeth" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6935198", "field": "accounting", "role": "object", "bullets": [ "Conceptual design paper on governance of autonomous AI agents in production systems, including regulated financial institutions, with no empirical sample.", "No model is run by the authors; the paper proposes AA-1, an open seven-section attestation specification built on Anthropic's Zero Trust for AI Agents control taxonomy.", "It argues for a continuous attestation model bound to architecture state and behavioural baselines, defines financial-services and high-assurance sector profiles, and flags self-attestation gaming risks." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 38, "edition": 3, "audience": "general", "validated": null, "n": 957, "authors_detailed": [ { "name": "Brendan Sibeth", "url": "https://openalex.org/A5140328535", "inst": "Dartmouth Advisory Partners, 2261 Lakeshore Road East, 1305, Toronto, Ontario M8V 3X1, Canada" } ], "affiliations": [ "Dartmouth Advisory Partners, 2261 Lakeshore Road East, 1305, Toronto, Ontario M8V 3X1, Canada" ] }, { "uid": "arxiv:2607.08920v1", "arxiv_id": "2607.08920v1", "title": "AI Adoption in S&P 500 Firms", "authors": [ "Yang Yu", "Martin Fleming", "Lucy Hampton", "Christophe Combemale", "Neil Thompson" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.08920v1", "field": "economics", "role": "object", "bullets": [ "S&P 500 firms over 2016 to 2025, with AI adoption estimated at the enterprise level from SEC 10-K filings.", "The authors build a measure of deep AI integration from 10-K disclosures to separate genuine adoption from hype; the underlying model is not named and no validation figure is reported.", "In 2025, 11% of S&P 500 firms had AI deeply integrated, up from 5% in 2022; profitability follows a J-curve while capex and productivity show no differences." ], "bullet_provenance": "ai", "salience": 70, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 958, "authors_detailed": [ { "name": "Yang Yu", "url": "https://openalex.org/A5140526683", "inst": "" }, { "name": "Martin Fleming", "url": "https://openalex.org/A5140544181", "inst": "" }, { "name": "Lucy Hampton", "url": "https://openalex.org/A5140542487", "inst": "" }, { "name": "Christophe Combemale", "url": "https://openalex.org/A5060886728", "inst": "Professional Analytical and Consulting Services (United States)" }, { "name": "Neil Thompson", "url": "https://openalex.org/A5140531854", "inst": "" } ], "affiliations": [ "Professional Analytical and Consulting Services (United States)" ] }, { "uid": "doi:10.2139/ssrn.6931279", "doi": "10.2139/ssrn.6931279", "title": "The Hypercomputational Ontology of AI Economic: Embodying the Real World, Human Mind, and the Limits of Turing Machines", "authors": [ "Zehao Zhou", "Danxia Xie" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6931279", "field": "economics", "role": "object", "bullets": [ "Theoretical paper with no empirical sample, integrating computability theory, non-equilibrium thermodynamics, endogenous growth theory, and philosophy of mind into a framework for AI economics.", "No language model is used or named; AI and deep reinforcement learning are treated as the object of a three-tier nested endogenous growth model.", "Yields three predictions: synthetic Turing data has diminishing value, long-run AI growth is bounded by human innovation rather than Moore's Law, and non-algorithmic human labor stays irreplaceable." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1170, "authors_detailed": [ { "name": "Z Zhou", "url": "https://openalex.org/A5136435936", "inst": "Central South University of Forestry and Technology" }, { "name": "Danxia Xie", "url": "https://openalex.org/A5012535271", "inst": "Tsinghua University" } ], "affiliations": [ "Central South University of Forestry and Technology", "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.6933718", "doi": "10.2139/ssrn.6933718", "title": "Smart Money, Old Patterns, New Amplifiers: Billionaire Portfolio Reallocation, Historical Analogues, and Systemic Risk in the Age of Agentic AI", "authors": [ "Ka Wah Philip Ng" ], "posted": "2026-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6933718", "field": "finance", "role": "object", "bullets": [ "Billionaire investor portfolio reallocations from June 2025 to June 2026, drawn from regulatory filings, insider disclosures, industry surveys, and FSB, ESRB, IMF and WEF publications.", "No model is used by the researchers; agentic AI and cyber-enabled systems are studied as factors that could alter how financial shocks propagate.", "Current de-risking and hard-asset rotation echo pre-crisis patterns; the paper argues agentic AI could compress the time from disruption to systemic impact, while disclaiming any crisis forecast." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1171, "authors_detailed": [ { "name": "Kym Ng", "url": "https://openalex.org/A5089858046", "inst": "Hong Kong Adventist Hospital" } ], "affiliations": [ "Hong Kong Adventist Hospital" ] }, { "uid": "doi:10.2139/ssrn.6931398", "doi": "10.2139/ssrn.6931398", "title": "ASQ: Query Sensitivity Analysis — Citability in generative AI engines as a function of prompt design", "authors": [ "Johnny Telles" ], "posted": "2026-07-09", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6931398", "field": "management", "role": "object", "bullets": [ "Two experiments: 108 responses in the accommodation sector of Foz do Iguacu, Brazil, and 5,544 responses covering 82 entities across hospitality and ERP verticals.", "Queries Gemini 2.5 Flash, Claude Sonnet 4.6, and GPT-4o via official APIs on 22 May 2026, measuring whether each entity is cited under branded, categorical, and problem query formulations; no ground-truth check.", "Citation sensitivity is a property of the engine-by-vertical pair; branded queries cite entities 0.968 to 1.000 of the time while discovery queries vary sharply, with GPT reaching a 0.874 delta in ERP." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 40, "edition": 2, "audience": "technical", "validated": null, "n": 29, "authors_detailed": [ { "name": "Johnny Jefferson Telles", "url": "https://openalex.org/A5136606017", "inst": "Universidade Estadual do Oeste do Paraná" } ], "affiliations": [ "Universidade Estadual do Oeste do Paraná" ] }, { "uid": "doi:10.2139/ssrn.7090112", "doi": "10.2139/ssrn.7090112", "title": "Retrieval-Augmented Generative AI for Analyzing Entrepreneurship in Africa", "authors": [ "Roy Ngu Esibe", "Muhammad Aliyu", "Jesse Thornburg", "Erik Stam", "João Barros" ], "posted": "2026-07-09", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7090112", "field": "management", "role": "method", "bullets": [ "Sixteen founders of Africa's six unicorn ventures as of 2024, with over 1,000 YouTube videos and nine hours of podcast audio processed into more than 30,000 embedded text chunks.", "Unnamed large language models with retrieval-augmented generation reconstruct entrepreneurial journeys through multi-query expansion, semantic retrieval, and iterative refinement, evaluated on precision, recall, F1, and faithfulness; the specific model is not stated.", "Generated narratives reach faithfulness scores of 0.94 to 0.99, and quality depends more on thematic diversity than data volume, with 800 to 1,200 curated chunks sufficient for reliable output." ], "bullet_provenance": "ai", "validated": true, "validation_note": "precision, recall, F1, faithfulness 0.94-0.99", "salience": 53, "edition": 2, "audience": "technical", "models": [], "n": 31, "authors_detailed": [ { "name": "Roy Ngu Esibe", "url": "https://openalex.org/A5116715581", "inst": "Carnegie Mellon University Africa" }, { "name": "Muhammad Aliyu", "url": "https://openalex.org/A5134996603", "inst": "KU Leuven" }, { "name": "Jesse Thornburg", "url": "https://openalex.org/A5134971635", "inst": "Carnegie Mellon University Africa" }, { "name": "Erik Stam", "url": "https://openalex.org/A5000347839", "inst": "Utrecht University" }, { "name": "João Barros", "url": "https://openalex.org/A5089850116", "inst": "Carnegie Mellon University Africa" } ], "affiliations": [ "Carnegie Mellon University Africa", "KU Leuven", "Utrecht University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6983958", "doi": "10.2139/ssrn.6983958", "title": "Trustworthy AI in Central Banking: International Practices and Financial Stability Risks", "authors": [ "Yue Dai" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6983958", "field": "economics", "role": "object", "bullets": [ "Conceptual review of emerging international practices in AI use by central banks and financial authorities, organised around monetary and financial stability functions with no empirical sample.", "No language model is applied by the authors; the paper treats foundation models and LLMs as both supervisory tools and potential sources of systemic risk, drawing on the trustworthy-AI literature.", "Argues trustworthiness is a financial stability condition, maps robustness, fairness, privacy, explainability and human oversight to central bank functions, and proposes a research and governance agenda." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 278, "authors_detailed": [ { "name": "Yue Dai", "url": "https://openalex.org/A5060225667", "inst": "Nanjing Forestry University" } ], "affiliations": [ "Nanjing Forestry University" ] }, { "uid": "doi:10.2139/ssrn.7077912", "doi": "10.2139/ssrn.7077912", "title": "Dodging the Question: A Large Language Model Measure of Managerial Evasiveness and the Realization of Bad News", "authors": [ "Kefu Yi", "Feng Wu" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7077912", "field": "accounting", "role": "instrument", "bullets": [ "573,835 question-answer threads across 25,894 earnings briefings of Chinese listed firms over 2016 to 2025, with each question-answer thread as the unit of observation.", "A Qwen-family LLM separately classifies each question's nature and the answer's responsiveness to score evasiveness; a blind coder agrees at Cohen's kappa 0.64 and DeepSeek-V3 reproduces the measure.", "More evasive firms are more likely within a year to incur a regulatory sanction (+0.097, t=5.2) and an exchange comment letter (+0.073, t=4.4), surviving a full control battery." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "blind coder Cohen's kappa 0.64, cross-family reproduction", "salience": 70, "edition": 3, "audience": "general", "n": 279, "authors_detailed": [ { "name": "Yi Kefu", "url": "https://openalex.org/A5047124497", "inst": "University of International Business and Economics" }, { "name": "Feng Wu", "url": "https://openalex.org/A5139657744", "inst": "Inner Mongolia University" } ], "affiliations": [ "University of International Business and Economics", "Inner Mongolia University" ] }, { "uid": "arxiv:2607.11920v1", "arxiv_id": "2607.11920v1", "title": "Sensitivity to Subjective Expected Utility Maximization: A Methodological Study, with an Illustrative Application to LLM Decision-Making", "authors": [ "Jeff Helzner" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.11920v1", "field": "economics", "role": "agent", "bullets": [ "Methodological study introducing a graded subjective-expected-utility sensitivity measure, illustrated on real LLM choice data from two tasks: insurance-claims triage and Ellsberg-style urns.", "GPT-4o and Claude 3.5 Sonnet make choices under uncertainty with sampling temperature as the lever; the estimator is validated in Stan through parameter recovery and simulation-based calibration.", "The sensitivity parameter is sharply identified while belief and utility parameters are only weakly informed; a structured comparative sensitivity effect appears in two of four model-task cells." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 38, "edition": 3, "audience": "technical", "validated": null, "n": 280, "authors_detailed": [ { "name": "Jeff Helzner", "url": "https://openalex.org/A5140953512", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7077910", "doi": "10.2139/ssrn.7077910", "title": "When Does Generative AI Help? A Task-Type Perspective on Business Students’ Use and Perceived Value of ChatGPT in Higher Education", "authors": [ "Yuxuan WU", "Suntong Qi", "Shing-Chung Patrick Poon", "Nina Xie", "Bo ZHOU", "Yizhendan TU" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7077910", "field": "management", "role": "object", "bullets": [ "Business students in higher education, with Study 1 thematically analysing interview responses from 149 undergraduates and Study 2 surveying 69 valid respondents across undergraduate and postgraduate levels.", "No model is run by the researchers; ChatGPT use and perceived value are self-reported constructs interpreted through task-technology fit theory; the ChatGPT version is not stated.", "Students report greater use and satisfaction for continuous-assessment tasks than exam preparation, with no clear divergent versus convergent advantage, and postgraduates value convergent tasks more." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 38, "edition": 3, "audience": "general", "validated": null, "n": 504, "authors_detailed": [ { "name": "Yuxuan WU", "url": "https://openalex.org/A5140265926", "inst": "Lingnan University" }, { "name": "Suntong Qi", "url": "https://openalex.org/A5002964871", "inst": "Lingnan University" }, { "name": "Patrick Poon", "url": "https://openalex.org/A5088946281", "inst": "Lingnan University" }, { "name": "Nina Xie", "url": "https://openalex.org/A5117166347", "inst": "Lingnan University" }, { "name": "Bo ZHOU", "url": "https://openalex.org/A5140247534", "inst": "Lingnan University" }, { "name": "Yizhendan TU", "url": "https://openalex.org/A5140234099", "inst": "" } ], "affiliations": [ "Lingnan University" ] }, { "uid": "doi:10.2139/ssrn.7075058", "doi": "10.2139/ssrn.7075058", "title": "Rethinking Automation Risk: AI Applicability and Occupational Outcomes, 2019-24", "authors": [ "Kristen Broady", "Caleb Dunson", "Anthony Barr" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7075058", "field": "economics", "role": "object", "bullets": [ "US occupations from 2019 to 2024, combining Frey and Osborne computerization risk with Tomlinson AI applicability scores from Microsoft Copilot usage, matched to O*NET classifications and BLS employment and wage data.", "No language model is run by the authors; AI exposure is measured with published Copilot-based applicability scores used as an occupational exposure variable; no model family is named.", "Occupations with high AI applicability saw employment and wage growth, while high and moderate automation-risk occupations had weaker employment, and wages rose across all risk groups, complicating a displacement narrative." ], "bullet_provenance": "ai", "salience": 56, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 505, "authors_detailed": [ { "name": "Kristen Broady", "url": "https://openalex.org/A5133749282", "inst": "Alcorn State University" }, { "name": "Caleb Dunson", "url": "https://openalex.org/A5140162979", "inst": "Federal Reserve Bank of Chicago" }, { "name": "Anthony Barr", "url": "https://openalex.org/A5014129455", "inst": "American Bankers Association" } ], "affiliations": [ "Alcorn State University", "Federal Reserve Bank of Chicago", "American Bankers Association" ] }, { "uid": "doi:10.2139/ssrn.7077639", "doi": "10.2139/ssrn.7077639", "title": "Measuring geoeconomic tension: a large-language-model approach for the euro area", "authors": [ "Demosthenes Ioannou", "Agha Durrani", "Raffaele Prioriello" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7077639", "field": "economics", "role": "instrument", "bullets": [ "Euro area geopolitical and geoeconomic tension measured from a large dataset of European local-language newspaper articles spanning roughly the past quarter century.", "Multilingual large language models prompt the articles to build the LGPT index separating geopolitical from geoeconomic tension across trade, energy and finance; the model family is not stated and no accuracy check is reported.", "The index feeds a Bayesian structural VAR estimating the impact of geoeconomic tensions on euro area output and inflation, with magnitudes not stated in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "edition": 3, "audience": "general", "models": [], "n": 506, "authors_detailed": [ { "name": "Demosthenes Ioannou", "url": "https://openalex.org/A5020239591", "inst": "European Union Agency for Law Enforcement Cooperation" }, { "name": "Agha Durrani", "url": "https://openalex.org/A5061796915", "inst": "European Central Bank" }, { "name": "Raffaele Prioriello", "url": "https://openalex.org/A5140223319", "inst": "" } ], "affiliations": [ "European Union Agency for Law Enforcement Cooperation", "European Central Bank" ] }, { "uid": "doi:10.2139/ssrn.7082232", "doi": "10.2139/ssrn.7082232", "title": "Faculty orientations toward generative AI in Higher Education: perceptual dimensions, attitudinal profiles, and implications for institutional governance,Evidence from a Single Spanish Public University (UPV/EHU)", "authors": [ "Xabier Gonzalez Laskibar", "Naiara Uriarte-Gallastegi", "Beñat Landeta‐Manzano", "Amaia Mendoza-Larrañaga" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7082232", "field": "management", "role": "object", "bullets": [ "A survey of 458 faculty at a single Spanish public university (UPV/EHU), measuring perceptions of generative AI across seven dimensions including risk, ethics, and institutional training.", "No language model is used as a research tool; faculty attitudes are analysed with descriptive statistics, OLS regressions, and k-means cluster analysis with multi-seed robustness checks.", "Regressions explain little variance and no predictor survives correction; clustering yields three orientations, engaged adopters, normative skeptics, and pragmatic minimalists, split by risk and ethics scores." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 507, "authors_detailed": [ { "name": "Xabier González-Laskibar", "url": "https://openalex.org/A5028768010", "inst": "University of the Basque Country" }, { "name": "Naiara Uriarte-Gallastegi", "url": "https://openalex.org/A5124746360", "inst": "University of the Basque Country" }, { "name": "Beñat Landeta-Manzano", "url": "https://openalex.org/A5124702571", "inst": "University of the Basque Country" }, { "name": "Amaia Mendoza-Larrañaga", "url": "https://openalex.org/A5124715306", "inst": "University of the Basque Country" } ], "affiliations": [ "University of the Basque Country" ] }, { "uid": "doi:10.2139/ssrn.6994358", "doi": "10.2139/ssrn.6994358", "title": "Minimal Viable Cognitive Agent (MVCA): Building a glass-box LLM cognitive architecture for testing process theories in Agent psychonomicus", "authors": [ "Jack Chen", "Weiheng Xiao" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6994358", "field": "management", "role": "agent", "bullets": [ "Eighty-nine consumer and social-psychology experiments used as a benchmark for reproducing human judgment, with each study's reported main effects as the target.", "MVCA, a cascade of twelve language-model steps grounded in Global Workspace Theory and driven by 104 substrates, reproduces effects; the backbone model is not named.", "It reproduces main effects on par with a persona-tuned digital-twin baseline, 0.612 versus 0.603, while exposing construct activations that align with theorized mediators, rho 0.32." ], "bullet_provenance": "ai", "validated": true, "validation_note": "main-effect reproduction across 89 experiments", "salience": 52, "edition": 3, "audience": "technical", "models": [], "n": 508, "authors_detailed": [ { "name": "Jack Chen", "url": "https://openalex.org/A5077015329", "inst": "Columbia University" }, { "name": "WeiHeng Xiao", "url": "https://openalex.org/A5111113736", "inst": "Meta" } ], "affiliations": [ "Columbia University", "Meta" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7059539", "doi": "10.2139/ssrn.7059539", "title": "The Anatomy of Censorship and Propaganda: Evidence from Russian Wikipedias", "authors": [ "Ekaterina Zhuravskaya", "Ulrich Matter", "Ruben Durante", "Vladimir Avetian" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7059539", "field": "economics", "role": "instrument", "bullets": [ "Comparison of Ruwiki, a 2023 regime-aligned clone of Russian-language Wikipedia, against the original across 1.9 million articles, measured twenty months after the fork.", "An unnamed LLM plus machine-learning analysis of text deletions and insertions classifies ideological changes; no accuracy figure is reported, with textbook and placebo benchmarks used instead.", "27 percent of articles changed, 2.8 percent heavily edited, and 0.14 percent deleted, with revisions organized around Russia's global image, domestic politics, and culture." ], "bullet_provenance": "ai", "validated": false, "salience": 68, "edition": 3, "audience": "general", "models": [], "n": 946, "authors_detailed": [ { "name": "Ekaterina Zhuravskaya", "url": "https://openalex.org/A5003763829", "inst": "Paris School of Economics" }, { "name": "Ulrich Matter", "url": "https://openalex.org/A5071129382", "inst": "University of Bern" }, { "name": "Ruben Durante", "url": "https://openalex.org/A5090034574", "inst": "Bank of Italy" }, { "name": "Vladimir Avetian", "url": "https://openalex.org/A5140266728", "inst": "" } ], "affiliations": [ "Paris School of Economics", "University of Bern", "Bank of Italy" ] }, { "uid": "doi:10.2139/ssrn.7080180", "doi": "10.2139/ssrn.7080180", "title": "The Physical Limits of Compute: Dynamic Risk Contagion Networks Between Semiconductor and Energy Infrastructure Assets Under the AI Super Cycle", "authors": [ "Fuli Yang", "Shiyu Lin" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7080180", "field": "finance", "role": "object", "bullets": [ "Daily return data from January 2023 to June 2026, 874 observations, covering semiconductor (SOXX), uranium and nuclear (URA), and utilities (XLU) exchange-traded funds.", "No language model is used; a DCC-GARCH(1,1) framework estimates time-varying correlations, with a two-sample t-test comparing pre- and post-2024H2 co-movement regimes.", "SOXX-URA correlation nearly doubled from 0.284 to 0.518 after 2024H2, a shift significant at p below 0.001, linking AI demand to energy-asset volatility." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 947, "authors_detailed": [ { "name": "Fuli Yang", "url": "https://openalex.org/A5101033933", "inst": "Massachusetts Institute of Technology" }, { "name": "Shiyu Lin", "url": "https://openalex.org/A5140253041", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6927858", "doi": "10.2139/ssrn.6927858", "title": "From Scenario to Cadence: Predictive Planning as a Continuous, AI-Enabled Strategic Discipline", "authors": [ "Tim Woodring" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6927858", "field": "management", "role": "object", "bullets": [ "Conceptual management paper with no empirical sample, addressing organizational foresight and strategic planning under generative AI.", "No model is used or tested; the paper defines predictive planning as a construct and process, positioning generative AI as a low-cost foresight enabler.", "Proposes a four-phase continuous cycle of scan, story, stake, and steer, with seven propositions and boundary conditions on prediction under radical uncertainty." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 948, "authors_detailed": [ { "name": "Tim Woodring", "url": "https://openalex.org/A5140272822", "inst": "Colorado State University" } ], "affiliations": [ "Colorado State University" ] }, { "uid": "doi:10.2139/ssrn.7076641", "doi": "10.2139/ssrn.7076641", "title": "Culturally Strategic: A Mixed-Methods Analysis of Cultural Identity Framing and Public Reception in Global Public Relations", "authors": [ "Wenqi Marcus Wu", "Haochen Liang" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7076641", "field": "management", "role": "instrument", "bullets": [ "169 Nike digital posts over twelve months, comparing public relations communication in the United States and China, using a mixed-methods design.", "An unnamed large language model assists natural-language processing to quantify frequency of communicative logics; no validation against human coding is reported.", "Finds Nike uses attribute framing to signal in-group status across cultures, extending social identity theory to cross-border corporate identity signaling." ], "bullet_provenance": "ai", "validated": false, "salience": 34, "edition": 3, "audience": "general", "models": [], "n": 949, "authors_detailed": [ { "name": "Wenqi Marcus Wu", "url": "https://openalex.org/A5136753836", "inst": "University of Maryland, College Park" }, { "name": "Haochen Liang", "url": "https://openalex.org/A5140177432", "inst": "University of Maryland, College Park" } ], "affiliations": [ "University of Maryland, College Park" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6781380", "doi": "10.2139/ssrn.6781380", "title": "Supervising Risk: A New Deal for Artificial Intelligence", "authors": [ "Kevin Werbach", "Peter Conti-Brown" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6781380", "field": "finance", "role": "object", "bullets": [ "Conceptual legal and policy analysis with no empirical sample, comparing AI risk governance to two centuries of bank regulation and supervision.", "No model is used; the paper studies AI foundation models as objects of regulation, drawing analogies to banking supervision instruments.", "Proposes an AI Risk Supervisor using on-site continuous monitoring, institutional risk designations, and stress testing, structured for agency-independence jurisprudence." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 950, "authors_detailed": [ { "name": "Kevin Werbach", "url": "https://openalex.org/A5037700296", "inst": "Lion Corporation (Japan)" }, { "name": "Peter Conti‐Brown", "url": "https://openalex.org/A5048504375", "inst": "Lion Corporation (Japan)" } ], "affiliations": [ "Lion Corporation (Japan)" ] }, { "uid": "doi:10.2139/ssrn.6932098", "doi": "10.2139/ssrn.6932098", "title": "Service-Induced Congestion in Memory-Constrained LLM Serving", "authors": [ "Ruicheng Ao", "Jing Dong", "Gan Luo", "David Simchi-Levi" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6932098", "field": "management", "role": "object", "bullets": [ "Theoretical operations study with no empirical sample, modeling memory-constrained large language model inference under continuous batching and key-value cache growth.", "No model is deployed; a discrete-time dynamical model analyzes admission, memory growth, and eviction, deriving stability and limit-cycle results.", "The eviction-free equilibrium is unstable and the system converges to a worst-case limit cycle with throughput losses as large as 50 percent." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 951, "authors_detailed": [ { "name": "Ruicheng Ao", "url": "https://openalex.org/A5093973906", "inst": "Massachusetts Institute of Technology" }, { "name": "Jing Dong", "url": "https://openalex.org/A5140244796", "inst": "Columbia University" }, { "name": "Gan Luo", "url": "https://openalex.org/A5140265225", "inst": "Peking University" }, { "name": "David Simchi-Levi", "url": "https://openalex.org/A5140280117", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "Columbia University", "Peking University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7017118", "doi": "10.2139/ssrn.7017118", "title": "The Empty Gate Why the Light We Blame Is Not the Thing That Profits from the Dark", "authors": [ "Johan van Rooyen", "Nitayapa Nandhakwang" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7017118", "field": "economics", "role": "object", "bullets": [ "Conceptual essay with no data, examining ghost job postings, opaque applicant screening, and simulated consideration in hiring markets.", "No model is used; the paper argues generative AI did not create hiring-market corruption but made pre-existing fraud cheap to operate at scale.", "Argues gains flow to volume-paid platforms and firms posting unfilled vacancies, distinguishing honest automation from what it calls costumed automation." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 952, "authors_detailed": [ { "name": "Johan van Rooyen", "url": "https://openalex.org/A5047562540", "inst": "Webster University" }, { "name": "Nitayapa Nandhakwang", "url": "https://openalex.org/A5135422559", "inst": "Chiang Mai University" } ], "affiliations": [ "Webster University", "Chiang Mai University" ] }, { "uid": "doi:10.2139/ssrn.7082923", "doi": "10.2139/ssrn.7082923", "title": "From Tool to Data: Understanding Holistic Urban Perception across 369 Chinese Cities using MLLM-generated Images", "authors": [ "Hongzeng Zhang", "Jiayu Pan", "Yongping Zhang", "Weiwen Zhang" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7082923", "field": "economics", "role": "instrument", "bullets": [ "369 Chinese cities, using the generative outputs of multimodal large language models as a data source for measuring holistic urban perception.", "Unnamed multimodal large language models generate image outputs treated as data; no validation against human-labeled ground truth is reported.", "Perceptual similarity forms community structures associated, via QAP analysis, with geographic proximity, economic development, population size, and industrial structure." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "general", "models": [], "n": 953, "authors_detailed": [ { "name": "Hongzeng Zhang", "url": "https://openalex.org/A5101368499", "inst": "Guangdong Urban & Rural Planning and Design Institute" }, { "name": "Jiayu Pan", "url": "https://openalex.org/A5140241227", "inst": "" }, { "name": "Yongping Zhang", "url": "https://openalex.org/A5140232822", "inst": "" }, { "name": "Weiwen Zhang", "url": "https://openalex.org/A5140272576", "inst": "" } ], "affiliations": [ "Guangdong Urban & Rural Planning and Design Institute" ] }, { "uid": "doi:10.2139/ssrn.6924640", "doi": "10.2139/ssrn.6924640", "title": "Fiscal Narratives and Inflation", "authors": [ "Sarah Arndt", "Farah Tohme" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6924640", "field": "economics", "role": "instrument", "bullets": [ "Corpus of fiscal-policy articles from four major German newspapers, 2006 to March 2025, linked to household inflation expectations.", "ChatGPT, version not stated, identifies distinct fiscal narratives in article text and builds narrative indices; no validation against hand-coded labels reported.", "All narrative indices raise household inflation expectations by significant, varying amounts, and some amplify while others dampen the transmission of a government spending shock." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "edition": 3, "audience": "general", "n": 954, "authors_detailed": [ { "name": "Sarah Arndt", "url": "https://openalex.org/A5098401449", "inst": "Heidelberg University" }, { "name": "F. Tohme", "url": "https://openalex.org/A5053399473", "inst": "Leibniz Institute for Financial Research SAFE" } ], "affiliations": [ "Heidelberg University", "Leibniz Institute for Financial Research SAFE" ] }, { "uid": "doi:10.2139/ssrn.7077603", "doi": "10.2139/ssrn.7077603", "title": "ChatGPT Enterprise Adjustment Trajectories Through a Dynamic Job Demands-Resources Perspective", "authors": [ "Ally St. Aubin", "Camila Hazzard", "Katlyn Bui", "Bradley Brummel" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7077603", "field": "management", "role": "object", "bullets": [ "Twelve employees from a single ChatGPT Enterprise pilot program, studied through focus groups and retrospective accounts of the implementation; geography and industry not stated.", "ChatGPT Enterprise is the technology studied rather than a research tool; the model was not used for measurement and no output was validated against any ground truth.", "Employees followed three adjustment profiles (rapid, gradual, minimal); the authors extend techno-overload to include a monitoring burden and techno-insecurity to include deskilling concerns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "general", "validated": null, "n": 1168, "authors_detailed": [ { "name": "Ally St. Aubin", "url": "https://openalex.org/A5023034354", "inst": "University of Houston" }, { "name": "Camila Hazzard", "url": "https://openalex.org/A5139452585", "inst": "University of Houston" }, { "name": "Katlyn Bui", "url": "https://openalex.org/A5140268072", "inst": "University of Houston" }, { "name": "Bradley J. Brummel", "url": "https://openalex.org/A5010404935", "inst": "University of Houston" } ], "affiliations": [ "University of Houston" ] }, { "uid": "doi:10.2139/ssrn.7076204", "doi": "10.2139/ssrn.7076204", "title": "Product Networks as Strategic Assets: A Predictive Model for Reward-Based Crowdfunding Success in Technology Ventures", "authors": [ "Zinat Shariati" ], "posted": "2026-07-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7076204", "field": "management", "role": "instrument", "bullets": [ "144 technology-oriented reward-based crowdfunding campaigns scraped from Indiegogo; geography not stated, with the individual campaign as the unit of observation.", "Text embedding techniques vectorize product network dimensions (attributes, value propositions, audiences); the model family is not stated and the predictive model is validated only through a train-test split with no accuracy figure.", "Product network has a statistically significant effect on success; funding target size, structural similarity of campaigns, and the creator's prior campaign count are the main predictors." ], "bullet_provenance": "ai", "validated": false, "validation_note": "train-test split, no accuracy reported", "salience": 35, "edition": 3, "audience": "general", "models": [], "n": 1169, "authors_detailed": [ { "name": "Zinat Shariati", "url": "https://openalex.org/A5072619873", "inst": "University of Tehran" } ], "affiliations": [ "University of Tehran" ] }, { "uid": "arxiv:2607.07858v1", "arxiv_id": "2607.07858v1", "title": "Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting", "authors": [ "Robert Richardson", "Josh Meyers", "Brian Hartman", "David Sandberg" ], "posted": "2026-07-08", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.07858v1", "field": "finance", "role": "method", "bullets": [ "Synthetic but realistic experimental environment for straight-through underwriting of small commercial business owner insurance policies; the unit of observation is individual underwriting cases, with no real-world data.", "Builds and compares three pipelines, a single-LLM baseline, a naive retrieval-augmented system, and a multi-agent agentic RAG, model family not stated, across standard and information-scarce underwriting scenarios.", "The agentic RAG pipeline performs best overall, with the largest gains in multi-step and missing-information cases where structured retrieval and reflection prevent unsupported straight-through decisions." ], "bullet_provenance": "ai", "validated": false, "salience": 48, "edition": 2, "audience": "technical", "models": [], "n": 28, "authors_detailed": [ { "name": "Robert Richardson", "url": "https://openalex.org/A5140326157", "inst": "Brigham Young University" }, { "name": "Josh Meyers", "url": "https://openalex.org/A5021497920", "inst": "Brigham Young University" }, { "name": "Brian Hartman", "url": "https://openalex.org/A5058501967", "inst": "Brigham Young University" }, { "name": "David Sandberg", "url": "https://openalex.org/A5105032410", "inst": "Brigham Young University" } ], "affiliations": [ "Brigham Young University" ] }, { "uid": "doi:10.2139/ssrn.7053118", "doi": "10.2139/ssrn.7053118", "title": "LLM Generated Regression Results vs. Specialized Econometric Software (STATA)", "authors": [ "Mohd Nahar Mohd Arshad" ], "posted": "2026-07-08", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7053118", "field": "economics", "role": "method", "bullets": [ "Case study using household income transfer data analyzed with quantile regression, comparing figures an LLM computed directly against results from dedicated econometric software.", "The paper does not name the model. LLMs were asked to run statistical calculations on raw data, and their coefficients were checked against Stata computations.", "LLM-generated regression outputs produced fabricated coefficients that contradicted the Stata results, leading the author to restrict LLMs to research design and code generation rather than final computation." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 2, "audience": "broad", "models": [], "n": 42, "authors_detailed": [ { "name": "Mohd Nahar Mohd Arshad", "url": "https://openalex.org/A5055610495", "inst": "International Islamic University Malaysia" } ], "affiliations": [ "International Islamic University Malaysia" ] }, { "uid": "doi:10.2139/ssrn.7053778", "doi": "10.2139/ssrn.7053778", "title": "Generative-AI Powered Inference", "authors": [ "Junting Duan", "Markus Pelger" ], "posted": "2026-07-08", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7053778", "field": "economics", "role": "method", "bullets": [ "News-based sentiment and stock returns serve as the empirical application, with the framework aimed at any setting where LLM-generated features enter downstream estimation. Period and geography not stated.", "Multiple LLM and prompt pairs extract sentiment, models not named; Gen-PI applies method-of-moments bias correction from a small human-labeled calibration set, adaptive weighting across generators, and optimal placement of costly labels.", "Against naive LLM regressions the method gives stable conclusions where model and prompt choice otherwise shifts results, and its confidence interval runs about half the length of one using human labels alone." ], "bullet_provenance": "ai", "validated": false, "salience": 72, "edition": 2, "audience": "technical", "models": [], "n": 43, "authors_detailed": [ { "name": "Junting Duan", "url": "https://openalex.org/A5140237907", "inst": "" }, { "name": "Markus Pelger", "url": "https://openalex.org/A5036333323", "inst": "Stanford University" } ], "affiliations": [ "Stanford University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6970578", "doi": "10.2139/ssrn.6970578", "title": "A Survey of Financial Large Language Models: From Domain Adaptation to Agentic Financial Intelligence", "authors": [ "Yue Dai" ], "posted": "2026-07-08", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6970578", "field": "finance", "role": "object", "bullets": [ "Literature survey of financial large language models covering data construction, domain adaptation, retrieval-augmented generation, benchmarks, applications, and risk control; number of papers reviewed and time span are not stated.", "No new model is run; the survey organizes financial LLM research into six layers from corpora and governance to agentic workflow automation, and names no specific model.", "Argues the next generation of financial models should be evaluated as auditable decision-support systems operating under temporal, regulatory, and economic constraints rather than as isolated text generators." ], "bullet_provenance": "ai", "salience": 40, "edition": 2, "audience": "technical", "models": [], "validated": null, "n": 53, "authors_detailed": [ { "name": "Yue Dai", "url": "https://openalex.org/A5140274856", "inst": "People's Bank of China" } ], "affiliations": [ "People's Bank of China" ] }, { "uid": "doi:10.2139/ssrn.7071890", "doi": "10.2139/ssrn.7071890", "title": "Capability trade-offs in generative AI: How absorptive capacity and customer orientation selectively configure organisational gains and risks", "authors": [ "Ahmed Almoraish", "Seongsoo Jang", "Mansour Alyahya", "Spyridon Gounaris" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7071890", "field": "management", "role": "object", "bullets": [ "B2B marketing agencies studied through interviews with 20 managers, a two-wave survey of 425 employees, and archival financial data for 255 firms, combining sociotechnical and dynamic capabilities theory.", "No language model is run by the researchers; generative AI usage is a self-reported survey construct moderated by absorptive capacity and customer orientation; model family not stated.", "GenAI usage is positively associated with creativity, marketing and financial performance, and privacy risk management, but raises job insecurity; archival data show gains in profit margin, productivity and revenue growth but not return on assets." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 501, "authors_detailed": [ { "name": "Ahmed Almoraish", "url": "https://openalex.org/A5088826104", "inst": "Cardiff University" }, { "name": "Seongsoo Jang", "url": "https://openalex.org/A5054159480", "inst": "Cardiff University" }, { "name": "Mansour Alyahya", "url": "https://openalex.org/A5140163174", "inst": "King Faisal University" }, { "name": "Spyridon Gounaris", "url": "https://openalex.org/A5140203039", "inst": "University of Strathclyde" } ], "affiliations": [ "Cardiff University", "King Faisal University", "University of Strathclyde" ] }, { "uid": "doi:10.2139/ssrn.7074665", "doi": "10.2139/ssrn.7074665", "title": "Governing Generative AI Concentration: Policy Responses to Geographic, Epistemic, and Network Inequalities in Global ICT Innovation (2018–2024)", "authors": [ "Serhat Burmaoglu", "ESRA DUNDAR ARAVACIK", "Furkan Oguzhan Polat", "Arif Soyler" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7074665", "field": "economics", "role": "object", "bullets": [ "111,512 GenAI patent and publication records from 2018 to 2024, analyzed globally through bibliometrics, network science, and computational text analysis of ICT innovation.", "No language model serves as a measurement tool; documents are classified under a validated baseline topic scheme and computational text analysis; no model family is named.", "Geographic concentration is extreme at a Gini of 0.762 with China and the US at 58.1 percent of output, the HHI rises from 1611.6 to 2358.6, and only 3.9 percent of documents address inclusion themes." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 502, "authors_detailed": [ { "name": "Serhat Burmaoğlu", "url": "https://openalex.org/A5013267400", "inst": "Izmir Kâtip Çelebi University" }, { "name": "Esra DÜNDAR ARAVACIK", "url": "https://openalex.org/A5050500860", "inst": "Izmir Kâtip Çelebi University" }, { "name": "Furkan Oguzhan Polat", "url": "https://openalex.org/A5140171111", "inst": "" }, { "name": "Arif Söyler", "url": "https://openalex.org/A5093928081", "inst": "Izmir University" } ], "affiliations": [ "Izmir Kâtip Çelebi University", "Izmir University" ] }, { "uid": "doi:10.2139/ssrn.6965739", "doi": "10.2139/ssrn.6965739", "title": "COST-GOV: An Operational Decision Framework for Governance-Aware LLM Routing in Enterprises", "authors": [ "Malathi Marineni" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6965739", "field": "management", "role": "object", "bullets": [ "Conceptual design science study of enterprise large language model deployment focused on token cost control and governance compliance, with no empirical sample and evaluation through scenario analysis.", "No language model is run; the paper proposes COST-GOV, a six-layer routing architecture whose decision engine scores each request against cost and policy constraints; no model family is named.", "Scenario-based evaluation suggests the framework addresses cost-constrained compliance and multi-department resource allocation, with empirical validation in production left to future work." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 503, "authors_detailed": [ { "name": "Malathi Marineni", "url": "https://openalex.org/A5126647421", "inst": "Film Independent" } ], "affiliations": [ "Film Independent" ] }, { "uid": "doi:10.2139/ssrn.7074681", "doi": "10.2139/ssrn.7074681", "title": "Constructing Appropriability: Rent Architecture and the Direction of Innovation in Foundation Models", "authors": [ "Nurhan Papatya", "İlbey Kutluhan Papatya" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7074681", "field": "management", "role": "object", "bullets": [ "Descriptive census of a small emergent population of frontier foundation model developers, using an event-centered, within-lineage discontinuity design across the AI model stack.", "No model is used as a tool; the paper introduces rent architecture, configuring compute access, openness gradient, license restrictiveness, and disclosure as appropriability levers.", "First evidence shows training-compute density concentrating just below the EU AI Act's 10^25 FLOP threshold, and restrictive licensing engaging where model weights stay open." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 940, "authors_detailed": [ { "name": "Nurhan Papatya", "url": "https://openalex.org/A5087475191", "inst": "Süleyman Demirel University" }, { "name": "İlbey Kutluhan Papatya", "url": "https://openalex.org/A5097924618", "inst": "Bağcılar Eğitim ve Araştırma Hastanesi" } ], "affiliations": [ "Süleyman Demirel University", "Bağcılar Eğitim ve Araştırma Hastanesi" ] }, { "uid": "doi:10.2139/ssrn.7073990", "doi": "10.2139/ssrn.7073990", "title": "Generative AI Integration and Consumer Trust in China's Online Travel Platforms: A Two-Wave Study of Post-Purchase Satisfaction and Loyalty", "authors": [ "Hui Chen" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7073990", "field": "management", "role": "object", "bullets": [ "Two-wave survey of urban Chinese consumers who booked through a major domestic travel platform, pooled 2,486 respondents, 1,194 in 2023 before GenAI rollout and 1,292 in 2025 after.", "No model is used by the researchers; partial least squares structural equation modelling tests an extended technology acceptance model of platform GenAI conversational agents.", "Trust, perceived usefulness, and service personalization are the strongest positive drivers of satisfaction, privacy risk is negative, and satisfaction strongly predicts loyalty intention." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 941, "authors_detailed": [ { "name": "Hui Chen", "url": "https://openalex.org/A5140166572", "inst": "Shanghai Jiao Tong University" } ], "affiliations": [ "Shanghai Jiao Tong University" ] }, { "uid": "doi:10.2139/ssrn.7071338", "doi": "10.2139/ssrn.7071338", "title": "Programmable Incentives: Aligning Behavior in the Age of Agentic AI", "authors": [ "Soumitra Dutta", "Yves L. Doz" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7071338", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, addressing AI-enabled organizations where relevant actors include employees, suppliers, ecosystem contributors, and autonomous AI agents.", "No model is used; the paper introduces programmable incentives and a six-layer stack drawing on principal-agent theory, multi-task incentive design, and mechanism design.", "Argues classical dyadic principal-agent contracts are inadequate and that shaping behavior dynamically and at scale becomes a primary determinant of competitive advantage." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 942, "authors_detailed": [ { "name": "Soumitra Dutta", "url": "https://openalex.org/A5113459217", "inst": "Cornell University" }, { "name": "Yves Doz", "url": "https://openalex.org/A5052298209", "inst": "Cornell University" } ], "affiliations": [ "Cornell University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7072890", "doi": "10.2139/ssrn.7072890", "title": "The ”Digital Filial Piety” Paradox: How Generative AI is Reshaping Intergenerational Communication and Caregiving Narratives in Chinese Social Media", "authors": [ "Dr. Ken Ip", "Tin Hang Michael Lai" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7072890", "field": "management", "role": "object", "bullets": [ "Sequential explanatory mixed-methods study in China analyzing 1,000 Weibo and Douyin posts and interviewing 30 participants across two generations of families.", "No model is used by the researchers; the study examines adoption of GenAI companions, deepfake video greetings, and automated advice systems for filial caregiving.", "GenAI enables socially validated performances of filial care but creates ethical tension; entertainment-framed content drew the highest engagement while simulated intimacy elicited discomfort among elderly recipients." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 943, "authors_detailed": [ { "name": "Ken Ip", "url": "https://openalex.org/A5108960541", "inst": "Saint Francis University" }, { "name": "Michael Tin Hang Lai", "url": "https://openalex.org/A5084492797", "inst": "Macau University of Science and Technology" } ], "affiliations": [ "Saint Francis University", "Macau University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7047058", "doi": "10.2139/ssrn.7047058", "title": "Geopolitics Meets AI War: Stock Market Evidence from China", "authors": [ "Lin William Cong", "Siguang Li", "Fan Wang", "Yi Zhang" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7047058", "field": "finance", "role": "instrument", "bullets": [ "Chinese state-owned and privately owned firms around the 2018 US-China trade war, analyzed with a continuous difference-in-differences design and an asset pricing test.", "An unnamed LLM constructs firm-level geopolitical exposure measures used in the analysis; the paper reports no validation of these measures against a benchmark.", "Higher geopolitical risk cuts high-tech imports, AI innovation, and firm fundamentals more for private firms, and higher exposure predicts positive abnormal returns and negative returns around tariff events." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 944, "authors_detailed": [ { "name": "Lin William Cong", "url": "https://openalex.org/A5080547297", "inst": "Nanyang Technological University" }, { "name": "Siguang Li", "url": "https://openalex.org/A5140098083", "inst": "Guangzhou University" }, { "name": "Fang Wang", "url": "https://openalex.org/A5029787563", "inst": "Hong Kong University of Science and Technology (Guangzhou)" }, { "name": "Yi Zhang", "url": "https://openalex.org/A5140146090", "inst": "Guangzhou University" } ], "affiliations": [ "Nanyang Technological University", "Guangzhou University", "Hong Kong University of Science and Technology (Guangzhou)" ] }, { "uid": "doi:10.2139/ssrn.7075276", "doi": "10.2139/ssrn.7075276", "title": "Recognizing Fragility: Central Bank Language and Financial Crisis Regimes", "authors": [ "Francisco Borja" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7075276", "field": "economics", "role": "instrument", "bullets": [ "Country-quarter central bank monetary policy communication from the Monetary Policy Statement Database, matched to externally dated banking crises across countries.", "Embeds statements with the open-weight Qwen3-Embedding-8B model to detect crisis regimes, validated against dated banking crises with four-quarter crisis-window AUC rising from 0.52 to 0.69.", "Embeddings recover crisis-regime information in real time that transparent anchors miss, reflecting recognition as crises unfold rather than advance warning, and scaling with governance capacity." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "AUC 0.69 vs externally dated banking crises", "salience": 58, "edition": 3, "audience": "general", "n": 945, "authors_detailed": [ { "name": "Francisco Borja", "url": "https://openalex.org/A5135601921", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7075425", "doi": "10.2139/ssrn.7075425", "title": "Agentic Aggregation for Electric Bus Depot Coordination: Operational Trade-Offs, Contextual Pricing, and Deployment Implications", "authors": [ "Ali Eslami", "Jônatas Augusto Manzolli", "Luis F. Miranda-Moreno", "Jiangbo Yu" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7075425", "field": "management", "role": "object", "bullets": [ "A realistic electric bus depot case study evaluating day-ahead and real-time operations under profit-based and operation-based coordination modes, with service delays, route-energy deviations, and electricity price shocks.", "The framework couples an optimization-based bus scheduling model with supervisory agents for disturbance detection, tariff adaptation, and schedule evaluation; no language model is named and outputs are not validated against a ground truth.", "Agentic aggregation keeps schedules feasible and improves charging and vehicle-to-grid flexibility use, but under profit-oriented pricing the same capability can extract value from the public transport operator." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1165, "authors_detailed": [ { "name": "Ali Eslami", "url": "https://openalex.org/A5101927401", "inst": "McGill University" }, { "name": "Jônatas Augusto Manzolli", "url": "https://openalex.org/A5047696121", "inst": "McGill University" }, { "name": "Luis Miranda-Moreno", "url": "https://openalex.org/A5076568601", "inst": "Independent, United States" }, { "name": "Jiangbo Yu", "url": "https://openalex.org/A5090459265", "inst": "Ste. Anne's Hospital" } ], "affiliations": [ "McGill University", "Independent, United States" ] }, { "uid": "doi:10.2139/ssrn.6922218", "doi": "10.2139/ssrn.6922218", "title": "Does AI Create a \"Permanent Underclass\"? From Market Structure to Complementary Assets, Industry Resilience, and the Choice of Metric", "authors": [ "Yoichiro Hara" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6922218", "field": "economics", "role": "object", "bullets": [ "A working paper testing an April 2026 New York Times claim of a coming permanent underclass, synthesizing primary sources including Epoch AI, METR, the World Economic Forum, the ILO, and NBER.", "The authors run no language model; they marshal capability metrics such as 17.7 percent real-hardware robot task completion and an agentic task horizon doubling about every seven months to test substitution logic.", "AI substitution is gated by insurability of residual error not capability; measured in rent wealth concentrates while measured in consumer surplus AI is equalizing, giving a ratcheting Minsky cycle rather than a stable underclass." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1166, "authors_detailed": [ { "name": "Yoichiro Hara", "url": "https://openalex.org/A5140189263", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7070619", "doi": "10.2139/ssrn.7070619", "title": "From Alliances to Swarms: Rethinking Competition and Collaboration in the Age of AI", "authors": [ "Yves L. Doz", "Soumitra Dutta" ], "posted": "2026-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7070619", "field": "management", "role": "object", "bullets": [ "A conceptual strategy paper with no empirical sample; the unit of analysis is inter-organizational competition and collaboration in environments reshaped by agentic artificial intelligence.", "The authors run no language model; they theorize swarms, temporary real-time configurations of agentic agents, data, and protocols drawn from multiple firms that assemble for one interaction and then dissolve.", "It proposes a three-layer model of capability, context, and dynamics plus a six-layer Swarm Stack, and identifies five sources of swarm advantage: selection, prediction, coordination, latency, and trust." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1167, "authors_detailed": [ { "name": "Yves Doz", "url": "https://openalex.org/A5052298209", "inst": "Cornell University" }, { "name": "Soumitra Dutta", "url": "https://openalex.org/A5113459217", "inst": "Cornell University" } ], "affiliations": [ "Cornell University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6919480", "doi": "10.2139/ssrn.6919480", "title": "Generative AI for Context-Aware Extraction and Classification of Risk Factors in Financial Reporting Documents", "authors": [ "Anthony Grant" ], "posted": "2026-07-07", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6919480", "field": "accounting", "role": "instrument", "bullets": [ "Financial reporting documents including annual reports, management discussion and analysis sections, and regulatory filings; period and geography not stated, with risk-factor disclosures as the unit analyzed.", "A proposed framework combines transformer-based language models, retrieval augmentation, and financial ontologies to extract and classify risk factors; the specific model is not stated and no benchmark accuracy is reported.", "Reports that generative AI raises risk-extraction accuracy and classification performance over conventional machine-learning methods, and is especially effective at surfacing emerging risks in narrative disclosures." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 2, "audience": "technical", "models": [], "n": 19, "authors_detailed": [ { "name": "Anthony Grant", "url": "https://openalex.org/A5140216009", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6925119", "doi": "10.2139/ssrn.6925119", "title": "Generative AI as Exam Evaluation Copilot", "authors": [ "Leonardo Boncinelli", "Paolo Brunori", "Alessio Magnolfi", "Giacomo Negrisolo", "Eugenio Vicario" ], "posted": "2026-07-07", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6925119", "field": "management", "role": "instrument", "bullets": [ "Real open-ended exam responses from one university course marked by a single instructor across two independent grading rounds held at different times; geography not stated.", "Fine-tunes large language models, family not stated, on the instructor's historical grades, then uses AI-human grade discrepancies as a signal; no accuracy or agreement figure against ground truth is reported.", "AI-human grading discrepancies are statistically significantly associated with intra-rater grading variability, indicating the model flags responses whose scores are unstable across repeated grading." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "edition": 2, "audience": "broad", "models": [], "n": 27, "authors_detailed": [ { "name": "Leonardo Boncinelli", "url": "https://openalex.org/A5003450032", "inst": "University of Florence" }, { "name": "Paolo Brunori", "url": "https://openalex.org/A5041910814", "inst": "University of Florence" }, { "name": "Alessio Magnolfi", "url": "https://openalex.org/A5119239481", "inst": "University of Florence" }, { "name": "Giacomo Negrisolo", "url": "https://openalex.org/A5032361884", "inst": "University of Florence" }, { "name": "Eugenio Vicario", "url": "https://openalex.org/A5135525193", "inst": "IMT School for Advanced Studies Lucca" } ], "affiliations": [ "University of Florence", "IMT School for Advanced Studies Lucca" ] }, { "uid": "doi:10.2139/ssrn.7067713", "doi": "10.2139/ssrn.7067713", "title": "MLCWStudio: a web application for Multi-LLM Consensus Weighting of social capital outreach indicators for blockchain-enabled microfinance", "authors": [ "Nur Aima Shafie", "zahari Md Rodzi", "zuraidah sanusi", "Aziatul Waznah Ghazali", "mariyam niyaf mohamed" ], "posted": "2026-07-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7067713", "field": "finance", "role": "method", "bullets": [ "Open-source browser-based application for building composite indicators, demonstrated on social capital outreach indicators for blockchain-enabled microfinance, with no fixed empirical sample analysed.", "A panel of unnamed large language models elicits semantic importance weights, fused with objective entropy weights through one parameter; inter-assessor agreement is reported but no ground truth.", "Provides reproducible, meaning-aware weighting with bootstrap confidence intervals, sensitivity analysis, and four alternative weighting methods, exportable to Excel and HTML for non-programmers." ], "bullet_provenance": "ai", "validated": false, "salience": 34, "edition": 3, "audience": "technical", "models": [], "n": 500, "authors_detailed": [ { "name": "Nur Aima Shafie", "url": "https://openalex.org/A5140126910", "inst": "" }, { "name": "Zahari Md Rodzi", "url": "https://openalex.org/A5085293944", "inst": "Universiti Teknologi MARA" }, { "name": "zuraidah sanusi", "url": "https://openalex.org/A5140115288", "inst": "" }, { "name": "Aziatul Waznah Ghazali", "url": "https://openalex.org/A5091857620", "inst": "National University of Malaysia" }, { "name": "Mariyam Niyaf Mohamed", "url": "https://openalex.org/A5095030331", "inst": "Hodges University" } ], "affiliations": [ "Universiti Teknologi MARA", "National University of Malaysia", "Hodges University" ] }, { "uid": "doi:10.2139/ssrn.6924659", "doi": "10.2139/ssrn.6924659", "title": "Cognitive Homophily in Human-AI ensembles: Explaining the Quality-Satisfaction Trade-off", "authors": [ "Vivek Choudhary", "Arianna Marchetti", "Ella Miron-Spektor", "Phanish Puranam", "Yash Raj Shrestha", "Nety Wu", "Yuanjun Feng" ], "posted": "2026-07-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6924659", "field": "management", "role": "agent", "bullets": [ "Preregistered online experiment with 700 participants doing an ideation task to improve Airbnb's business-model alignment with the UN Sustainable Development Goals under three human-AI ensemble configurations.", "GPT-4o was embedded in the interface to generate and evaluate ideas as a collaborator; human and AI evaluators both rated outputs, with no accuracy check against ground truth.", "Parallel ensembles produced the highest-rated ideas, human-first gave the most novelty and satisfaction, and evaluators preferred ideas generated by their own type." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 66, "edition": 3, "audience": "general", "validated": null, "n": 938, "authors_detailed": [ { "name": "Vivek Choudhary", "url": "https://openalex.org/A5006571739", "inst": "Nanyang Technological University" }, { "name": "Arianna Marchetti", "url": "https://openalex.org/A5005557346", "inst": "Singapore Management University" }, { "name": "Ella Miron‐Spektor", "url": "https://openalex.org/A5087011942", "inst": "INSEAD" }, { "name": "Phanish Puranam", "url": "https://openalex.org/A5015167951", "inst": "INSEAD" }, { "name": "Yash Raj Shrestha", "url": "https://openalex.org/A5043846392", "inst": "Lourdes University" }, { "name": "Nety Wu", "url": "https://openalex.org/A5050307201", "inst": "" }, { "name": "Yuanjun Feng", "url": "https://openalex.org/A5138810748", "inst": "University of Lausanne" } ], "affiliations": [ "INSEAD", "Nanyang Technological University", "Singapore Management University", "Lourdes University", "University of Lausanne" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.7065138", "doi": "10.2139/ssrn.7065138", "title": "Strategy in the Age of Agentic AI: Governing the Strategic Envelope", "authors": [ "Soumitra Dutta", "Yves L. Doz" ], "posted": "2026-07-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7065138", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, addressing organizations that increasingly resemble complex adaptive systems as behavior emerges from human and autonomous AI agents.", "No model is used by the researchers; the paper introduces the strategic envelope, a governance framework of purpose, constraints, and feedback drawing on cybernetics and control engineering.", "Identifies three failure modes of envelope design, over-constrained, under-constrained, and misaligned, and reconceives leadership as continuous alignment of emergent system behavior with deliberate intent." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 939, "authors_detailed": [ { "name": "Soumitra Dutta", "url": "https://openalex.org/A5113459217", "inst": "Cornell University" }, { "name": "Yves Doz", "url": "https://openalex.org/A5052298209", "inst": "Cornell University" } ], "affiliations": [ "Cornell University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7053519", "doi": "10.2139/ssrn.7053519", "title": "Governing AI Risk in High-Stakes Deal Environments: Who Controls the Evidence Layer?", "authors": [ "Zohra Furtado" ], "posted": "2026-07-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7053519", "field": "management", "role": "object", "bullets": [ "Conceptual and doctrinal analysis of AI use across the M&A lifecycle, drawing on comparative regulation and court cases from Canada, England, and the United States, alongside a proof-of-concept prototype.", "The paper applies no named language model to data; it proposes a layered governance architecture and a model-diverse defensive design using independent validation loops to reduce adversarial prompt risk.", "It argues that control of the orchestration or evidence layer determines what audit evidence can be produced and thus deployer liability, and that vendor indemnities cannot transfer EU AI Act Article 26 accountability." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1163, "authors_detailed": [ { "name": "Zohra Furtado", "url": "https://openalex.org/A5140114254", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7068293", "doi": "10.2139/ssrn.7068293", "title": "Action-Linked Credit Assignment for Event-Driven Warehouse Management via Multi-Agentic Reinforcement Learning", "authors": [ "Huijung Nam", "Seongil Im", "Junseo Lee", "Changhwan Shin", "Hyunsu Ju" ], "posted": "2026-07-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7068293", "field": "management", "role": "agent", "bullets": [ "Simulated e-commerce fulfillment warehouse with rapid order growth and changing assortments, where two decision agents interact in an event-driven environment and are evaluated jointly in an integrated warehouse simulation.", "Two reinforcement learning agents, no language model named, handle zone slotting and replenishment; Action-Linked Credit Assignment links each order to its resulting inventory state, evaluated against rule-based baselines rather than a labelled benchmark.", "The framework achieves over ten times the cumulative reward of rule-based baselines, cutting replenishment orders by 19 percent and stockout ratios by 35 percent, with stable performance when new products are introduced." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1164, "authors_detailed": [ { "name": "Huijung Nam", "url": "https://openalex.org/A5140113453", "inst": "" }, { "name": "Seongil Im", "url": "https://openalex.org/A5140142609", "inst": "" }, { "name": "Junseo Lee", "url": "https://openalex.org/A5140130673", "inst": "" }, { "name": "Changhwan Shin", "url": "https://openalex.org/A5039545794", "inst": "Korea University" }, { "name": "Hyunsu Ju", "url": "https://openalex.org/A5070329242", "inst": "Gwangju Institute of Science and Technology" } ], "affiliations": [ "Korea University", "Gwangju Institute of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.7044058", "doi": "10.2139/ssrn.7044058", "title": "Agentic AI Systems for Automated Financial Services: A New Paradigm for Compliance, Auditing, and Financial Modeling", "authors": [ "Caleb Price", "Mckenzie Curtis" ], "posted": "2026-07-06", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7044058", "field": "accounting", "role": "object", "bullets": [ "Synthesis paper reviewing agentic AI applications across three financial-services domains, regulatory compliance, auditing, and financial modeling, drawing on existing research rather than an original dataset.", "No specific model is named; the paper describes multi-agent systems built on large language models for parsing regulations, monitoring transactions, model replication, and backtesting, with no validation of its own.", "Cites reported compliance detection rates of 89 percent for agentic systems versus 43 percent for conventional testing, and segregation-of-duties enforcement efficacy of 96 percent, all drawn from prior work." ], "bullet_provenance": "ai", "salience": 40, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 69, "authors_detailed": [ { "name": "Caleb Price", "url": "https://openalex.org/A5138988307", "inst": "Kansas State University" }, { "name": "Mckenzie Curtis", "url": "https://openalex.org/A5140108699", "inst": "" } ], "affiliations": [ "Kansas State University" ] }, { "uid": "doi:10.2139/ssrn.6997818", "doi": "10.2139/ssrn.6997818", "title": "A Pre-registered, Compute-Controlled Falsification of LLM-Derived Signals on Crypto Microstructure -and a Heterogeneous-Convergence Finding", "authors": [ "Jasper Man Him Chan" ], "posted": "2026-07-06", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6997818", "field": "finance", "role": "agent", "bullets": [ "50,277 order-book and trade-flow snapshots of MEXC spot BTC/USDT collected over two live runs, with about 21,905 observations at the 15-minute horizon used for the main test.", "Multiple heterogeneous base LLMs, none named in the abstract, read compressed order-book snapshots to output directional bias, conviction, and dispersion; hypotheses and thresholds were pre-registered before any result was computed.", "None of the four signal types clears its pre-registered bar; inter-model disagreement to forward volatility is killed at 15 minutes (partial Spearman +0.010, 95% CI -0.006 to +0.027), and models converge on directional bias." ], "bullet_provenance": "ai", "salience": 55, "edition": 2, "audience": "technical", "models": [], "validated": null, "n": 70, "authors_detailed": [ { "name": "Jasper Man Him Chan", "url": "https://openalex.org/A5140131802", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2607.04103v3", "arxiv_id": "2607.04103v3", "title": "Governing Generative AI Across Financial Institutions: A Framework for Generative AI Risk Control", "authors": [ "Dennis Mao", "Alessandra Lin", "Yixin Kang", "Yiqing Wang" ], "posted": "2026-07-05", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.04103v3", "field": "finance", "role": "object", "bullets": [ "Conceptual landscape paper covering generative AI across banking, capital markets, insurance, payments, and wealth management, with no empirical sample or dataset analyzed.", "No specific model is named; the paper organizes uses into five capability patterns and maps architectures such as retrieval-augmented generation, tool-using assistants, and agentic workflows, without any validation.", "Delivers a taxonomy linking capability patterns to functions such as investment research, fraud investigation, and financial reporting, pointing to where generative AI may add operational value, with no quantitative results." ], "bullet_provenance": "ai", "salience": 38, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 68, "authors_detailed": [ { "name": "Mao, Dennis", "url": "", "inst": "" }, { "name": "Lin, Alessandra", "url": "", "inst": "" }, { "name": "Yixin Kang", "url": "https://openalex.org/A5103098232", "inst": "University College Dublin" }, { "name": "Yiqing Wang", "url": "https://openalex.org/A5140157042", "inst": "OriginWater (China)" } ], "affiliations": [ "University College Dublin", "OriginWater (China)" ] }, { "uid": "doi:10.2139/ssrn.7052158", "doi": "10.2139/ssrn.7052158", "title": "Algorithmic Authority vs. Human Trust: How Generative AI Reshapes Credibility Judgments in News Consumption", "authors": [ "Ruilin Zhao" ], "posted": "2026-07-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7052158", "field": "management", "role": "object", "bullets": [ "Between-subjects online experiment with 412 participants using a two-by-two design that varied source attribution, AI versus human, and topic salience, with Tobii eye-tracking of attention and click-through.", "No specific model named; generative AI is studied as a source cue, measuring trust ratings, attention distribution, and information seeking rather than being used as a tool.", "AI attribution triggered more systematic processing of factual claims alongside a general credibility decline, with high topic salience raising skepticism, and attitudes did not predict behavior." ], "bullet_provenance": "ai", "salience": 47, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 499, "authors_detailed": [ { "name": "Ruilin Zhao", "url": "https://openalex.org/A5140000753", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6916779", "doi": "10.2139/ssrn.6916779", "title": "The Lost Product Layer in AI A Workflow-aware Framework for Moving beyond Chatbot-first AI", "authors": [ "Rahul Gautam" ], "posted": "2026-07-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6916779", "field": "management", "role": "object", "bullets": [ "Conceptual essay on AI product development in the software industry, with no dataset or sample, arguing that few AI-native products embed in everyday user workflows beyond chatbots.", "No model is used or named; the paper proposes Product-Native AI, a two-stage Workflow Fit to Workflow Emergence pathway, and ten operational principles for teams.", "It argues the adoption gap is a product-experience problem rather than model capability, and prescribes placing intelligence at the right user moment through feedback." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 937, "authors_detailed": [ { "name": "Rahul Gautam", "url": "https://openalex.org/A5050126021", "inst": "Indraprastha Apollo Hospitals" } ], "affiliations": [ "Indraprastha Apollo Hospitals" ] }, { "uid": "doi:10.2139/ssrn.7039359", "doi": "10.2139/ssrn.7039359", "title": "Antitrust in the Age of AI: Is the Consumer Welfare Standard Equipped to Address the Rise of Generative Artificial Intelligence?", "authors": [ "Ryan Chapman" ], "posted": "2026-07-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7039359", "field": "economics", "role": "object", "bullets": [ "Conceptual review of antitrust literature, regulatory publications, and market research on generative AI, with no empirical sample or dataset analysed.", "No language model is used; generative AI is the object of analysis, examined for its effects on competition, entry barriers, and pricing under the consumer welfare standard.", "Argues generative AI lowers entry barriers and raises productivity yet enables price and behavioral discrimination that can shift surplus from consumers to producers, straining consumer-welfare antitrust." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 497, "authors_detailed": [ { "name": "Ryan Chapman", "url": "https://openalex.org/A5139992356", "inst": "Stanford Medicine" } ], "affiliations": [ "Stanford Medicine" ] }, { "uid": "arxiv:2607.03516v1", "arxiv_id": "2607.03516v1", "title": "AGL-1: The Enterprise AI Governance Layer as a Control Plane for Trusted Enterprise Intelligence", "authors": [ "Roopam W. Sure" ], "posted": "2026-07-03", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.03516v1", "field": "management", "role": "object", "bullets": [ "Conceptual reference model by a single author with no empirical sample, addressing governance across enterprise AI systems including copilots, retrieval-augmented generation, and autonomous agents.", "No language model is applied or evaluated; the paper proposes a vendor-neutral governance control plane and enumerates recurring failure modes and seven governance domains.", "Claims durable enterprise value from AI depends on governing identity-aware retrieval, policy, provenance, memory, knowledge integrity, agentic execution, and observability rather than model quality alone." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 498, "authors_detailed": [ { "name": "Roopam W. Sure", "url": "https://openalex.org/A5140184284", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6915939", "doi": "10.2139/ssrn.6915939", "title": "Ban, Permit, Scaffold, Integrate: A Framework for Evaluating Generative AI in Economics Education", "authors": [ "Aselia Urmanbetova" ], "posted": "2026-07-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6915939", "field": "economics", "role": "object", "bullets": [ "Qualitative framework-synthesis review of a pilot corpus of nineteen studies on generative AI in economics education, coded as of May 2026.", "No language model is applied by the authors; the paper synthesizes prior literature and proposes a two-dimensional framework of four course-design regimes and eleven learning outcomes.", "Preliminary reading of the evidence suggests generative AI is following a policy drift, while its effect on economics learning remains poorly understood or mitigated." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 935, "authors_detailed": [ { "name": "Aselia Urmanbetova", "url": "https://openalex.org/A5019462436", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.03510v1", "arxiv_id": "2607.03510v1", "title": "CAGE-1: Control, Assurance, and Governance Evaluation for Enterprise Agentic AI", "authors": [ "Roopam W. Sure" ], "posted": "2026-07-03", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.03510v1", "field": "management", "role": "object", "bullets": [ "Conceptual framework paper with no empirical sample, aimed at enterprises moving agentic AI into operational workflows that plan, retrieve, call tools, and update systems.", "The paper uses no named model; it proposes CAGE-1, an evaluation framework spanning authority, policy enforcement, retrieval quality, memory integrity, tool safety, auditability, and human oversight, plus a Prebind Assurance concept.", "No empirical results are reported; the contribution is the framework and the idea of proving an agent action is controlled before it becomes binding or operationally consequential." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 936, "authors_detailed": [ { "name": "Roopam W. Sure", "url": "https://openalex.org/A5140184284", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7049101", "doi": "10.2139/ssrn.7049101", "title": "When Some Investors Have Superior AI: Evidence from China's Stock Connect Programs", "authors": [ "Hongyi Qu", "Yuhang Qiu", "Cheng Xiang", "Qingbo Yuan" ], "posted": "2026-07-03", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7049101", "field": "finance", "role": "object", "bullets": [ "Chinese Stock Connect constituent firms tradable by foreign investors versus non-constituent firms traded by domestic investors, around the 2022 ChatGPT launch and the 2025 DeepSeek release, in a difference-in-differences design.", "No model is run by the authors; availability of ChatGPT and later DeepSeek serves as access shocks, with informed trading proxied by VPIN and no validation against a ground truth reported.", "Informed trading rose significantly for foreign-tradable Connect firms after ChatGPT relative to domestic firms, and the gap disappeared once DeepSeek gave domestic investors comparable tools, indicating widened informational inequality." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "salience": 70, "edition": 2, "audience": "broad", "validated": null, "n": 67, "authors_detailed": [ { "name": "Hongyi Qu", "url": "https://openalex.org/A5139965741", "inst": "" }, { "name": "Yuhang Qiu", "url": "https://openalex.org/A5129066322", "inst": "Jimei University" }, { "name": "Cheng Xiang", "url": "https://openalex.org/A5070754815", "inst": "Chongqing University" }, { "name": "Qingbo Yuan", "url": "https://openalex.org/A5058942007", "inst": "Victoria University" } ], "affiliations": [ "Jimei University", "Chongqing University", "Victoria University" ] }, { "uid": "doi:10.2139/ssrn.6942138", "doi": "10.2139/ssrn.6942138", "title": "Trust as a Scarce Resource: Verification Scarcity, the Competence Trap, and the Economics of Governed AI", "authors": [ "Robert Dogonowski" ], "posted": "2026-07-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6942138", "field": "economics", "role": "object", "bullets": [ "Theoretical model of an organization deploying an AI agent and allocating costly verification effort to outputs whose error risk is imperfectly observed; no empirical sample.", "No model is used as an instrument; the author discloses Claude assisted drafting and numerical scripts while analytical results were verified in Python, so validation does not apply.", "Derives a competence-trap condition where undetected risk rises as reliability improves, and shows minimum verification floors and audits eliminate the zero-verification collapse equilibrium." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 46, "edition": 3, "audience": "general", "validated": null, "n": 585, "authors_detailed": [ { "name": "robert dogonowski", "url": "https://openalex.org/A5135341155", "inst": "K.S. Hegde Hospital" } ], "affiliations": [ "K.S. Hegde Hospital" ] }, { "uid": "doi:10.2139/ssrn.6987839", "doi": "10.2139/ssrn.6987839", "title": "Artificial Intelligence, Private Tutoring, and Class Reproduction A Political-Economy Hypothesis and Evidence Map Using Korea as a Case", "authors": [ "S. Jung" ], "posted": "2026-07-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6987839", "field": "economics", "role": "object", "bullets": [ "Conceptual political-economy analysis using South Korea as a case, focused on private-tutoring spending and participation at the household and income-decile level, drawing on 2025 tutoring statistics.", "No language model is applied; the paper theorizes generative AI as a complement to educational capital, with no specific system named, and notes panel data by income decile do not yet exist.", "Hypothesizes that AI's learning benefit rises with existing household capital, so without policy intervention the income-based gap in outcomes may widen rather than narrow." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 931, "authors_detailed": [ { "name": "S. Jung", "url": "https://openalex.org/A5138597673", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.7039079", "doi": "10.2139/ssrn.7039079", "title": "Human-in-the-Loop Multi-Objective Manufacturing Scheduling Optimization with LLM Agents", "authors": [ "Wei Ye", "Marvin Carl May", "József Váncza", "Xingyu Li" ], "posted": "2026-07-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7039079", "field": "management", "role": "method", "bullets": [ "Experiments on job-shop, flow-shop, and flexible job-shop scheduling problems, benchmarked against MILP solutions; no field data, synthetic scheduling configurations across the three problem types.", "A three-agent LLM framework (Interpreter, Explainer, Generator) turns natural-language operator preferences into hints for a genetic algorithm; the model family is not stated, and performance is measured as optimality gaps against an MILP benchmark.", "Generator agent reaches 3-19 percent optimality gaps; adding the Interpreter yields 132 percent mean hypervolume gain, while direct LLM scheduling fails in 40-44 percent of runs." ], "bullet_provenance": "ai", "validated": false, "validation_note": "optimality gaps against MILP, not an LLM-output accuracy check", "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 932, "authors_detailed": [ { "name": "Wei Ye", "url": "https://openalex.org/A5060804719", "inst": "Purdue University West Lafayette" }, { "name": "Marvin Carl May", "url": "https://openalex.org/A5018224721", "inst": "Karlsruhe Institute of Technology" }, { "name": "József Váncza", "url": "https://openalex.org/A5076192215", "inst": "HUN-REN Institute for Nuclear Research" }, { "name": "Xingyu Li", "url": "https://openalex.org/A5139936218", "inst": "" } ], "affiliations": [ "Purdue University West Lafayette", "Karlsruhe Institute of Technology", "HUN-REN Institute for Nuclear Research" ] }, { "uid": "doi:10.2139/ssrn.7043360", "doi": "10.2139/ssrn.7043360", "title": "Who Decides Relevance? GenAI, Academic Incentives and Boundary Work in Accounting Education", "authors": [ "Joshua King Obeng-Nyarko" ], "posted": "2026-07-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7043360", "field": "accounting", "role": "object", "bullets": [ "Conceptual paper on accounting education and the research-practice relevance divide, drawing on institutional theory and boundary work; it uses no empirical sample or data.", "No language model is used; generative AI is treated as a force making the accounting research-practice divide newly visible, with no specific system named.", "Argues GenAI does not create but exposes the relevance gap, and proposes student-staff partnership as a boundary mechanism to renegotiate curriculum and assessment." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 933, "authors_detailed": [ { "name": "Joshua King Obeng-Nyarko", "url": "https://openalex.org/A5074880262", "inst": "University of Essex" } ], "affiliations": [ "University of Essex" ] }, { "uid": "arxiv:2607.01740v1", "arxiv_id": "2607.01740v1", "title": "Meta-Benchmarks for Financial-Services LLM Evaluation", "authors": [ "Blair Hudson" ], "posted": "2026-07-02", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.01740v1", "field": "finance", "role": "method", "bullets": [ "A point-in-time June 2026 public snapshot of 288 language models across 25 organizations, with 452 reported benchmarks mapped to O*NET work activities and 38 BIAN banking business domains.", "The framework aggregates public benchmark scores through a discrimination-coverage-recency weighting into a pairwise Elo tournament; it evaluates many models, names none by family, and reports no ground-truth validation.", "Produces cross-benchmark-comparable work-activity and banking-domain scores intended to guide model selection for financial-services tasks, presented as a reproducible methodology rather than an empirical finding." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 934, "authors_detailed": [ { "name": "Blair Hudson", "url": "https://openalex.org/A5123360571", "inst": "Racing Victoria" } ], "affiliations": [ "Racing Victoria" ] }, { "uid": "doi:10.2139/ssrn.6902958", "doi": "10.2139/ssrn.6902958", "title": "Letting ML Predict and LLMs Narrate: A Hybrid Integration Architecture for Production Equity Research", "authors": [ "Ben Charoenwong", "Ivan Chelebiev" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6902958", "field": "finance", "role": "method", "bullets": [ "A production equity research system serving six institutional clients across more than 40,000 securities, built on a six-factor composite score with out-of-sample results across 44 markets over 13 years.", "Machine learning carries the directional prediction while an unnamed language model generates narrative constrained by that prediction through a Model Context Protocol agentic layer; no benchmark against alternatives is reported.", "The paper describes the hybrid architecture and its deployment, including behavior during a Strait of Hormuz escalation, and states comparative benchmarking is still in progress." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 496, "authors_detailed": [ { "name": "Ben Charoenwong", "url": "https://openalex.org/A5056528066", "inst": "INSEAD" }, { "name": "Ivan Chelebiev", "url": "https://openalex.org/A5139842963", "inst": "Chicago Global" } ], "affiliations": [ "INSEAD", "Chicago Global" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6900918", "doi": "10.2139/ssrn.6900918", "title": "The Verifiable Context Layer: The Missing Architectural Commitment in Enterprise Intelligence for Agentic AI", "authors": [ "Vibhuraja Bhutani" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6900918", "field": "management", "role": "object", "bullets": [ "Conceptual reference architecture for enterprise agentic AI, synthesizing publicly attributed observations from Klarna, Walmart, JPMorgan, Siemens, Schneider Electric, Notion, and Box, with no primary empirical data.", "No model is used by the authors; the paper names foundation models and vendor stacks generically and proposes a Verifiable Context Layer, offering no validation against ground truth.", "Argues that governed enterprise context is the substrate making agentic AI verifiable against EU AI Act and NIST obligations, a structural claim the authors state is not empirically validated." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 583, "authors_detailed": [ { "name": "Vibhuraja Bhutani", "url": "https://openalex.org/A5138196543", "inst": "" } ] }, { "uid": "arxiv:2607.01388v1", "arxiv_id": "2607.01388v1", "title": "RusFinChain: A Russian Benchmark for Verifiable Chain-of-Thought Reasoning in Finance with Fuzzy-Aligned Evaluation", "authors": [ "M. K. Arabov" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.01388v1", "field": "finance", "role": "method", "bullets": [ "RusFinChain, a Russian-language financial reasoning benchmark spanning 17 domains and 172 topics, with 5,280 parameterized examples from executable Python templates and gold-standard reasoning chains.", "Eight open-weight LLMs are evaluated on a stratified sample generating 8,100 responses, scored against gold intermediate values using new Fuzzy Numeric and Soft-Attention alignment metrics.", "Models reach Hard F1 around 0.65 for step alignment but only about 29 percent of final answers are correct, and fuzzy metrics correlate with correctness at Spearman rho about 0.48." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "gold-standard CoT chains, F1 and final-answer accuracy reported", "salience": 50, "edition": 3, "audience": "technical", "n": 584, "authors_detailed": [ { "name": "M. K. Arabov", "url": "https://openalex.org/A5134669821", "inst": "Kazan Federal University" } ], "affiliations": [ "Kazan Federal University" ] }, { "uid": "doi:10.2139/ssrn.7034769", "doi": "10.2139/ssrn.7034769", "title": "Trust, Empowerment, and Anxiety in the GenAI-Enabled Workplace: How Generative AI in Human Resource Management Shapes Employee Outcomes in an Emerging Economy", "authors": [ "Naphat Thipsri" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7034769", "field": "management", "role": "object", "bullets": [ "Two-wave, time-lagged survey of 400 employees in medium and large Thai organizations, analyzed with partial least squares structural equation modeling.", "No language model is run by the researchers; the study tests how GenAI-enabled HR practices affect trust, empowerment and outcomes, with transparency and AI anxiety as moderators.", "All nine hypotheses supported; trust in AI and psychological empowerment fully mediated effects on engagement, wellbeing, performance and turnover, with transparency amplifying and anxiety attenuating trust." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 925, "authors_detailed": [ { "name": "Naphat Thipsri", "url": "https://openalex.org/A5012697361", "inst": "Rajamangala University of Technology Lanna" } ], "affiliations": [ "Rajamangala University of Technology Lanna" ] }, { "uid": "doi:10.2139/ssrn.7032780", "doi": "10.2139/ssrn.7032780", "title": "Frictionless confirmation: How generative AI displaces the disconfirmatory scrutiny that disciplines entrepreneurial judgment", "authors": [ "Jieqiong Cao" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7032780", "field": "management", "role": "object", "bullets": [ "Conceptual paper on entrepreneurial judgment and founder governance; no empirical sample, period or geography is stated in the highlights provided.", "No language model is used; the argument holds that generative AI cheaply supplies frictionless confirmation at both founder self-doubt and outside challenge.", "Claims AI hollows out disconfirming content from board contact and distorts pivot-or-persevere timing in a way that stays invisible to governance." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 926, "authors_detailed": [ { "name": "Jieqiong Cao", "url": "https://openalex.org/A5102484567", "inst": "Jinan University" } ], "affiliations": [ "Jinan University" ] }, { "uid": "doi:10.2139/ssrn.6999118", "doi": "10.2139/ssrn.6999118", "title": "The Transformative Role of Artificial Intelligence in Enterprise Resource Planning Systems: A Comprehensive Review", "authors": [ "Muhammad Faizan Hassan" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6999118", "field": "management", "role": "object", "bullets": [ "Structured review of academic literature, industry reports and vendor documentation on AI in enterprise resource planning systems, published between 2020 and 2026.", "No specific model is used; the review maps machine learning, NLP, robotic process automation and agentic AI across ERP finance, supply chain, HR, CRM and manufacturing modules.", "Reports AI-enhanced ERP improves forecasting accuracy, automation, decision speed and cost, and proposes a five-layer framework for structured AI and ERP integration." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 927, "authors_detailed": [ { "name": "Muhammad Faizan Hassan", "url": "https://openalex.org/A5015120412", "inst": "Liaquat National Hospital" } ], "affiliations": [ "Liaquat National Hospital" ] }, { "uid": "doi:10.2139/ssrn.6901539", "doi": "10.2139/ssrn.6901539", "title": "The Cognitive Windfall: Where does Human Time, Attention, and Effort Go when AI Lowers the Cost of Cognition?", "authors": [ "Walter Guevara", "Cristina Atomi" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6901539", "field": "economics", "role": "object", "bullets": [ "Conceptual economics paper on how AI reallocates human time, attention and effort; the authors state they present no original empirical results.", "No model is used; the paper builds on Becker's time-allocation theory, task-based automation models and self-determination theory to define a cognitive windfall.", "Argues welfare depends on whether freed time flows to employer capture, passive absorption, purposeful reallocation or a capability trap, and proposes a measurement agenda." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 928, "authors_detailed": [ { "name": "Walter Guevara", "url": "https://openalex.org/A5138443138", "inst": "" }, { "name": "Cristina Atomi", "url": "https://openalex.org/A5139751518", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6906459", "doi": "10.2139/ssrn.6906459", "title": "Blitzscale or Bust: Is There a Pricing Problem in the Generative AI Industry?", "authors": [ "Stuart Mills", "Richard Whittle" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6906459", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of the generative AI industry's pricing and cost structure; no formal dataset, period or sample is stated.", "No model is used; the paper argues generative AI firms use venture-funded blitzscaling to charge below cost-plus-margin despite high inference variable costs and weak network effects.", "Predicts firms will raise prices, promote personalization to build consumer dependencies, and seek government financing, regulation and revenue support." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 929, "authors_detailed": [ { "name": "Stuart Mills", "url": "https://openalex.org/A5024497032", "inst": "The Honourable Society of Lincoln's Inn" }, { "name": "Richard Whittle", "url": "https://openalex.org/A5013517444", "inst": "University College London" } ], "affiliations": [ "The Honourable Society of Lincoln's Inn", "University College London" ] }, { "uid": "arxiv:2607.00856v1", "arxiv_id": "2607.00856v1", "title": "Shapley in Context: Explaining Financial Language with Domain Expertise", "authors": [ "Dangxing Chen", "Pengzhan Guo" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.00856v1", "field": "finance", "role": "method", "bullets": [ "Financial textual data modeled by large language models; the specific corpus, time period and sample size are not stated.", "Applies Shapley-value attributions to the models' predictions and tests, theoretically and empirically, whether the attributions align with established financial domain knowledge; model family not stated.", "Reports that Shapley values can produce explanations consistent with financial reasoning; no quantitative accuracy or agreement figure is stated." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 930, "authors_detailed": [ { "name": "Dangxing Chen", "url": "https://openalex.org/A5139937898", "inst": "" }, { "name": "Pengzhan Guo", "url": "https://openalex.org/A5088170520", "inst": "Duke Kunshan University" } ], "affiliations": [ "Duke Kunshan University" ] }, { "uid": "doi:10.2139/ssrn.6900960", "doi": "10.2139/ssrn.6900960", "title": "The Workforce Operating System: A Unified Architecture for Agentic Workforce Management and Enterprise Value", "authors": [ "Garrett Walker" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6900960", "field": "management", "role": "object", "bullets": [ "Conceptual paper proposing a workforce operating system architecture, building on two decades of strategic human-resource and human-capital scorecard scholarship, with no empirical sample.", "No model is run; the paper argues that agentic AI amplifies whatever system it inherits and that an operating system must precede agentic deployment.", "Its central contribution is an attribution chain that prices, deploys, measures, and traces a unit of human capability to earnings, proposed for private-capital value creation." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1159, "authors_detailed": [ { "name": "Garrett Walker", "url": "https://openalex.org/A5090191260", "inst": "IBM (United States)" } ], "affiliations": [ "IBM (United States)" ] }, { "uid": "doi:10.2139/ssrn.7032104", "doi": "10.2139/ssrn.7032104", "title": "Who Holds the Lead? The Impact of Human–Agent Collaboration Modes on Corporate Green Innovation", "authors": [ "Lina Ma", "Jianan Zhou", "Mark Lee" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7032104", "field": "management", "role": "object", "bullets": [ "Survey of Chinese high-tech manufacturing firms; unit of observation is firms; sample size and period not stated.", "No language model is used by the researchers; the study measures employee-led, technology-led, and co-led human-agent collaboration modes and green intellectual capital from survey responses.", "All three collaboration modes promote green innovation, with co-led collaboration strongest, and green intellectual capital strengthens these relationships through structural, relational, and human dimensions." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1160, "authors_detailed": [ { "name": "Lina Ma", "url": "", "inst": "" }, { "name": "Jianan Zhou", "url": "https://openalex.org/A5101725631", "inst": "Nanyang Technological University" }, { "name": "Mark Lee", "url": "https://openalex.org/A5139703094", "inst": "" } ], "affiliations": [ "Nanyang Technological University" ] }, { "uid": "arxiv:2607.01421v1", "arxiv_id": "2607.01421v1", "title": "Risk Architecture for AI-Native Engineering Teams: An Organizational Framework for Agentic System Governance", "authors": [ "Laxmipriya Ganesh Iyer" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.01421v1", "field": "management", "role": "object", "bullets": [ "Conceptual engineering-management paper whose object of study is framework adequacy rather than observed team behaviour, evaluated over a defined scenario set with derived coverage claims.", "No model is run; the paper builds a seven-dimension team profile, a six-cluster failure-mode taxonomy, and a synthetic framework-adequacy scoring method for agentic AI risk.", "Risk-architecture coverage degrades as teams move from pure software to AI-native operation, with the most severe uncovered failures at the boundary where probabilistic outputs meet determinism-assuming dependencies." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1161, "authors_detailed": [ { "name": "Laxmipriya Ganesh Iyer", "url": "https://openalex.org/A5139998078", "inst": "" } ] }, { "uid": "arxiv:2607.01023v1", "arxiv_id": "2607.01023v1", "title": "Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs", "authors": [ "Rocio Jimenez-Villen", "Ziwei Xu", "Ying Chen", "Oscar Araque", "Ryutaro Ichise" ], "posted": "2026-07-01", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.01023v1", "field": "finance", "role": "method", "bullets": [ "Financial news and company data used to build FinKG-News knowledge graphs that link news events to companies, supporting credit risk report generation across three financial dimensions; sample and period not stated.", "An in-context learning architecture (base model not named) generates credit risk reports grounded in the knowledge graph, evaluated by automatic metrics and human expert judgment.", "The approach improves report quality by 19 to 34 percent over baselines and reduces hallucinations, while automated hallucination and quality assessment remain unreliable." ], "bullet_provenance": "ai", "validated": false, "validation_note": "human expert evaluation of report quality, no accuracy statistic", "salience": 48, "edition": 3, "audience": "technical", "models": [], "n": 1162, "authors_detailed": [ { "name": "Rocio Jimenez-Villen", "url": "https://openalex.org/A5139872666", "inst": "" }, { "name": "Zhewei Xu", "url": "https://openalex.org/A5073136550", "inst": "Shangrao Normal University" }, { "name": "Ying Chen", "url": "https://openalex.org/A5139894872", "inst": "" }, { "name": "Oscar Araque", "url": "https://openalex.org/A5139941991", "inst": "Universidad Politécnica de Madrid" }, { "name": "Ryutaro Ichise", "url": "https://openalex.org/A5081854769", "inst": "Tokyo Institute of Technology" } ], "affiliations": [ "Shangrao Normal University", "Universidad Politécnica de Madrid", "Tokyo Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.7037031", "doi": "10.2139/ssrn.7037031", "title": "Rethinking Delphi for Tourism Futures: A Modified Multi-AI Approach to Anticipating Emerging Challenges in the Middle East and North Africa", "authors": [ "Hafez Mansour" ], "posted": "2026-07-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7037031", "field": "management", "role": "agent", "bullets": [ "Tourism futures foresight study for the Middle East and North Africa, applying a modified multi-AI Delphi protocol across three iterative rounds.", "Five generative AI systems, ChatGPT, Gemini, DeepSeek, Copilot, and Claude, acted as synthetic expert informants to structure judgment, map uncertainty, and revise challenge categories. No ground-truth check.", "Ranks climate change and extreme heat, geopolitical instability and destination-risk perception, and water scarcity as the most consequential existential risks for MENA tourism over the coming decade." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "salience": 38, "edition": 2, "audience": "technical", "validated": null, "n": 26, "authors_detailed": [ { "name": "Hafez Mansour", "url": "https://openalex.org/A5139824269", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7027101", "doi": "10.2139/ssrn.7027101", "title": "When models do not know what they do not know", "authors": [ "itsik elbert" ], "posted": "2026-06-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7027101", "field": "finance", "role": "object", "bullets": [ "A 195,337 firm-year panel from 1999 to 2025, plus SEC EDGAR AI/ML disclosure text mining covering 5,588 firms from 2016 to 2024; the unit is the firm-year.", "An algorithmic bias measure is built from an out-of-sample mechanical forecasting model, not a named LLM, motivated by evidence that large language models reproduce extrapolative forecasting bias; no model validation is reported.", "A one standard deviation rise in algorithmic bias predicts an 11 percent relative increase in investment intensity, and firms disclosing AI/ML forecasting show less persistent bias." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 494, "authors_detailed": [ { "name": "itsik elbert", "url": "https://openalex.org/A5139666676", "inst": "Ono Academic College" } ], "affiliations": [ "Ono Academic College" ] }, { "uid": "doi:10.2139/ssrn.7027983", "doi": "10.2139/ssrn.7027983", "title": "What Investors Disagree About: LLM-Decomposed Retail Disagreement and the Cross-Section of Stock Returns", "authors": [ "Kefu Yi", "Feng Wu" ], "posted": "2026-06-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7027983", "field": "finance", "role": "instrument", "bullets": [ "220 million posts on China's largest stock forum from 2016 to 2024, plus an event study of 36,000 earnings announcements; the unit is the stock-month.", "An unnamed large language model reads posts and decomposes investor disagreement into fundamental and noise components; no accuracy or agreement check against ground truth is reported.", "High fundamental-disagreement stocks earn about 0.7 percent lower monthly risk-adjusted returns while noise disagreement does not, and aggregate measures pool the two and point the wrong way." ], "bullet_provenance": "ai", "validated": false, "salience": 70, "edition": 3, "audience": "general", "models": [], "n": 495, "authors_detailed": [ { "name": "Yi Kefu", "url": "https://openalex.org/A5047124497", "inst": "University of International Business and Economics" }, { "name": "Feng Wu", "url": "https://openalex.org/A5139657744", "inst": "Inner Mongolia University" } ], "affiliations": [ "University of International Business and Economics", "Inner Mongolia University" ] }, { "uid": "doi:10.2139/ssrn.6900243", "doi": "10.2139/ssrn.6900243", "title": "Are we Better Off Interacting with Humans or AI in Conflict and Coordination Settings? *", "authors": [ "Richard Li", "Bradley J. Ruffle", "Fei Song" ], "posted": "2026-06-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6900243", "field": "economics", "role": "agent", "bullets": [ "Incentivized laboratory experiment where humans and autonomous LLM agents play a 40-round repeated Prisoner's Dilemma or Battle of the Sexes, 20 rounds against each counterpart type; geography not stated.", "Autonomous LLM agents, family not stated, interpret instructions, form earnings beliefs, choose moves, and generate round-by-round reasoning as strategic decision-makers; no validation against ground truth applies.", "Humans show more reciprocity and cooperation toward other humans while AI agents display in-group bias toward AI, so AI-AI pairs achieve the highest payoffs in both games." ], "bullet_provenance": "ai", "salience": 61, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 582, "authors_detailed": [ { "name": "Richard Li", "url": "https://openalex.org/A5139706186", "inst": "McMaster University" }, { "name": "Bradley J. Ruffle", "url": "https://openalex.org/A5020857449", "inst": "McMaster University" }, { "name": "Fei Song", "url": "https://openalex.org/A5072960051", "inst": "Toronto Metropolitan University" } ], "affiliations": [ "McMaster University", "Toronto Metropolitan University" ] }, { "uid": "doi:10.2139/ssrn.6892522", "doi": "10.2139/ssrn.6892522", "title": "Platform Governance in Generative Search: A Theory of Authority, Relevance, and Welfare", "authors": [ "Xing Hu" ], "posted": "2026-06-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6892522", "field": "economics", "role": "object", "bullets": [ "Game-theoretic model with no empirical data; players are a search platform, a high-authority incumbent, and a low-authority specialist competing for visibility inside synthesized generative-search answers.", "The researchers use no language model; they solve a game where the platform sets an authority weight before relevance is observed while firms spend on generative engine optimization. Model not stated.", "Generative search produces conditional contestability rather than entrenchment or democratization; the authority weight fixes the specialist's proof burden, and private screening can exclude socially valuable specialists through wasteful proof costs." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 922, "authors_detailed": [ { "name": "Xing Hu", "url": "https://openalex.org/A5036471879", "inst": "University of Hong Kong" } ], "affiliations": [ "University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.7026193", "doi": "10.2139/ssrn.7026193", "title": "Fifteen years of graphene optoelectronics: an LLM-assisted, dual-source landscape and taxonomy of device innovation", "authors": [ "Renan Silva Santos" ], "posted": "2026-06-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7026193", "field": "management", "role": "instrument", "bullets": [ "43,190 journal articles indexed in OpenAlex over 2010 to 2025 plus a patent layer on graphene optoelectronics, mapping the global innovation landscape with articles and patents as units.", "A generative large language model, not named, performs taxonomy induction alongside unsupervised theme discovery to build a five-facet taxonomy of device innovations; no validation against ground truth is reported.", "Output grew about 32 percent annually until 2017 then plateaued near 1 percent, concentrated in China at 34.6 percent, with a translational asymmetry favoring device physics over commercialization." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 3, "audience": "general", "models": [], "n": 923, "authors_detailed": [ { "name": "Renan Silva Santos", "url": "https://openalex.org/A5043275935", "inst": "Instituto Nacional da Propriedade Industrial" } ], "affiliations": [ "Instituto Nacional da Propriedade Industrial" ] }, { "uid": "arxiv:2606.30997v2", "arxiv_id": "2606.30997v2", "title": "A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management", "authors": [ "Ramin Pishehvar" ], "posted": "2026-06-30", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.30997v2", "field": "finance", "role": "method", "bullets": [ "Multi-asset corpus of publicly traded securities used to train a portfolio management system; time period, geography and sample size are not stated.", "Three-phase deep reinforcement learning combines a frozen T5-based Chronos time series foundation model, a mixture-of-experts PPO actor-critic and a 76-parameter LoRA personalization layer; no validation figure is reported.", "Presents what the authors call the first use of a time series foundation model in portfolio reinforcement learning; no empirical performance magnitude is stated." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 46, "edition": 3, "audience": "technical", "validated": null, "n": 924, "authors_detailed": [ { "name": "Ramin Pishehvar", "url": "https://openalex.org/A5007247115", "inst": "Cisco Systems (United States)" } ], "affiliations": [ "Cisco Systems (United States)" ] }, { "uid": "doi:10.2139/ssrn.7010051", "doi": "10.2139/ssrn.7010051", "title": "The Polar Geometry of Work", "authors": [ "Joakim Storck", "Jonatan Andersson" ], "posted": "2026-06-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7010051", "field": "economics", "role": "instrument", "bullets": [ "O*NET task statements for occupations, tested against descriptors of skills, abilities, wages, and educational requirements; units are occupations and occupation pairs, period and geography not stated beyond O*NET.", "Text embeddings (encoder not named) locate tasks by angular direction and radial specificity, and occupations inherit positions; predictions are checked for reproducibility across several encoders.", "The market rewards specificity only as depth in analytical directions but not in service directions, and angular distance between occupations predicts differences in wages and education." ], "bullet_provenance": "ai", "validated": false, "validation_note": "predictions reproduce across encoders, no accuracy figure", "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 1156, "authors_detailed": [ { "name": "Joakim Storck", "url": "https://openalex.org/A5046731648", "inst": "Dalarna University" }, { "name": "Jonatan Andersson", "url": "https://openalex.org/A5021381413", "inst": "Uppsala University" } ], "affiliations": [ "Dalarna University", "Uppsala University" ] }, { "uid": "arxiv:2607.00245v1", "arxiv_id": "2607.00245v1", "title": "Agent-to-Agent Finance: Blockchain Payments and Trust Infrastructure for Autonomous AI Agents", "authors": [ "Hui Gong" ], "posted": "2026-06-30", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.00245v1", "field": "finance", "role": "object", "bullets": [ "Conceptual article on financial market infrastructure with no empirical sample, drawing on recent work on blockchain payments, agent registries, provenance wallets, and evidence on AI adoption in financial services.", "No model is estimated or run; the paper develops the concept of agent-to-agent finance for autonomous AI agents that discover counterparties, express intent, and execute payments.", "Argues that programmable settlement and decentralised registries can address coordination frictions from autonomous agents, with bounded autonomy the decisive design question for accountable markets." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1157, "authors_detailed": [ { "name": "Hui Gong", "url": "https://openalex.org/A5139898014", "inst": "" } ] }, { "uid": "arxiv:2606.31935v1", "arxiv_id": "2606.31935v1", "title": "Delegation Rights: Property, Agency, and Investment Incentives in the Age of AI Agents", "authors": [ "Yukun Zhang", "Kemu Xu" ], "posted": "2026-06-30", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.31935v1", "field": "economics", "role": "object", "bullets": [ "Theoretical three-party incomplete-contracts model with a user, an AI agent provider, and a platform, with illustrative mechanism simulations and no empirical data.", "No language model is used; the paper defines delegation rights and analyses residual control over account execution mode under platform control, user control, and certified delegation.", "Certified delegation, conditioning access on verifiable authorization and revocability, can reduce deadweight loss by restoring delegation incentives while bounding residual risk." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1158, "authors_detailed": [ { "name": "Yukun Zhang", "url": "https://openalex.org/A5139749722", "inst": "" }, { "name": "Kemu Xu", "url": "https://openalex.org/A5122269364", "inst": "University of Edinburgh" } ], "affiliations": [ "University of Edinburgh" ] }, { "uid": "doi:10.2139/ssrn.6992061", "doi": "10.2139/ssrn.6992061", "title": "Designing AI for Qualitative Research: Predictability, Transparency, and Control in AI tool Skimle.com", "authors": [ "Henri Schildt" ], "posted": "2026-06-30", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6992061", "field": "management", "role": "method", "bullets": [ "Conceptual essay on using language models in qualitative research; it describes the Skimle.com analysis tool but reports no empirical sample, geography, or study period.", "Does not name a specific model; proposes three design criteria, predictable processes, end-to-end data-to-conclusion transparency, and researcher control over coding, implemented in the tool, with no validation reported.", "Argues that method-agnostic tools built to these criteria augment rather than automate expert interpretation, countering hallucination and the cognitive offloading that erodes engagement with data." ], "bullet_provenance": "ai", "salience": 38, "edition": 2, "audience": "technical", "models": [], "validated": null, "n": 65, "authors_detailed": [ { "name": "Henri Schildt", "url": "https://openalex.org/A5086526935", "inst": "Aalto University" } ], "affiliations": [ "Aalto University" ] }, { "uid": "arxiv:2606.31522v2", "arxiv_id": "2606.31522v2", "title": "FinPersona-Bench: A Benchmark for Longitudinal Psychometric Stability of Autonomous Financial Agents", "authors": [ "Muhammad Usman Safder", "Ayesha Gull", "Rania Elbadry", "Fan Zhang", "Yankai Chen", "Xueqing Peng", "Xue", "Liu", "Preslav Nakov", "Zhuohan Xie" ], "posted": "2026-06-30", "added": "2026-07-23", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.31522v2", "field": "finance", "role": "agent", "bullets": [ "Simulation benchmark, FinPersona-Bench, in which a synthetic market separates observable price from hidden fundamental value; 18 frontier and open-source LLMs each run under one of three behavioral mandates.", "The 18 models, not individually named, act as autonomous trading agents; the benchmark measures mandate salience decay across three failure modes, with no comparison to human coding.", "Mandate influence decays over long horizons and is model-dependent; in crashes the behavioral gap between static and periodically re-grounded agents grows 4.4 times by the final quarter." ], "bullet_provenance": "ai", "salience": 62, "edition": 2, "audience": "technical", "models": [], "validated": null, "n": 66, "authors_detailed": [ { "name": "Muhammad Usman Safder", "url": "https://openalex.org/A5120462844", "inst": "Steve" }, { "name": "Ayesha Gull", "url": "https://openalex.org/A5120509833", "inst": "Steve" }, { "name": "Rania Elbadry", "url": "https://openalex.org/A5119181195", "inst": "Steve" }, { "name": "F Zhang", "url": "https://openalex.org/A5047301622", "inst": "Steve" }, { "name": "Yankai Chen", "url": "https://openalex.org/A5139786062", "inst": "Steve" }, { "name": "Xueqing Peng", "url": "https://openalex.org/A5139721493", "inst": "Steve" }, { "name": "Xue", "url": "https://openalex.org/A5139737427", "inst": "Steve" }, { "name": "Liu", "url": "https://openalex.org/A5139844098", "inst": "" }, { "name": "Preslav Nakov", "url": "https://openalex.org/A5139760964", "inst": "" }, { "name": "Zhuohan Xie", "url": "https://openalex.org/A5139837656", "inst": "" } ], "affiliations": [ "Steve" ] }, { "uid": "doi:10.3386/w35374", "doi": "10.3386/w35374", "title": "A Practitioner's Guide to Using Large Language Models and Generative AI in Economic History", "authors": [ "Andreas Ferrara" ], "posted": "2026-06-30", "added": "2026-07-23", "source_label": "NBER", "url": "https://doi.org/10.3386/w35374", "field": "economics", "role": "method", "bullets": [ "A step by step guide for economists new to language models, taking a research idea to working code and data across chat, editor assistants, agentic tools, and the API.", "No single model is promoted; the guide covers model selection, prompting, context and cost management, and how to validate, reproduce, and correct LLM generated measures in regressions.", "Four worked examples with replication files, from linking census records without names to measuring newspaper salience around the 1882 Chinese Exclusion Act." ], "bullet_provenance": "ai", "salience": 70, "edition": 1, "audience": "broad", "models": [], "validated": null, "n": 4, "authors_detailed": [ { "name": "Andreas Ferrara", "url": "https://openalex.org/A5139597006", "inst": "University of Pittsburgh" } ], "affiliations": [ "University of Pittsburgh" ] }, { "uid": "arxiv:2606.30987v1", "arxiv_id": "2606.30987v1", "title": "Measuring Judgment Quality in Natural-Language Explanations: Evidence from Forecasting Tournaments", "authors": [ "Christopher W. Karvetski", "Sheldon S. Huang", "Simas Kučinskas", "Nadja Flechner", "Jingyu Hu", "Philip Tetlock", "Ezra Karger" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.30987v1", "field": "management", "role": "instrument", "bullets": [ "Over 55,000 forecast-rationale pairs from a multiyear forecasting tournament, with probabilistic judgments and written rationales scored against realized outcomes at both forecast and forecaster levels.", "Unnamed large language models score sixty theory-guided reasoning patterns, called Explanation Quality Markers, in each rationale; benchmarked against pre-LLM text methods and human ratings, with no model named.", "EQMs predict accuracy and outperform pre-LLM methods, matching directional hypotheses in over 90 percent of significant correlations, and identify likely underperformers more reliably than the very best forecasters." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 276, "authors_detailed": [ { "name": "Christopher W. Karvetski", "url": "https://openalex.org/A5008463924", "inst": "Defence Research and Development Canada" }, { "name": "Sicong Huang", "url": "https://openalex.org/A5043900893", "inst": "Fujian University of Traditional Chinese Medicine" }, { "name": "Simas Kučinskas", "url": "https://openalex.org/A5066344269", "inst": "Federal Reserve" }, { "name": "Nadja Flechner", "url": "https://openalex.org/A5139770419", "inst": "" }, { "name": "Jingyu Hu", "url": "https://openalex.org/A5139800151", "inst": "" }, { "name": "Philip Tetlock", "url": "https://openalex.org/A5133493267", "inst": "California University of Pennsylvania" }, { "name": "Ezra Karger", "url": "https://openalex.org/A5046987433", "inst": "Institute for Forecasting of the Slovak Academy of Sciences" } ], "affiliations": [ "Defence Research and Development Canada", "Fujian University of Traditional Chinese Medicine", "Federal Reserve", "California University of Pennsylvania", "Institute for Forecasting of the Slovak Academy of Sciences" ] }, { "uid": "arxiv:2606.29955v1", "arxiv_id": "2606.29955v1", "title": "SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows", "authors": [ "Jian Zhu", "Yuzheng Zhang", "Zeyao Ma", "Bohan Zhang", "Armin Schoepf", "Daniel Woloch", "Peter Yiliu Wang", "Guangyu Robert Yang", "Samuel Jacob", "Siddharth Nagisetty", "Abhiram Chundru", "Jean Lin", "Spencer Mateega", "Jing Zhang" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.29955v1", "field": "accounting", "role": "method", "bullets": [ "321 spreadsheet tasks built from authentic financial reports and corporate filings, each instance averaging 11.8 worksheets and 593.5 cell modifications, annotated and validated by domain experts.", "Eight frontier large language models, not named, run under a unified multi-turn agent scaffold on generation, debugging, and visualization tasks, with outputs scored against expert ground-truth solutions.", "The best model reached 34.89 percent overall task accuracy and debugging accuracy fell to 12 percent; insufficient spreadsheet inspection and wrong target-cell selection were the dominant failures." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert-annotated benchmark, best model 34.89% accuracy", "salience": 58, "edition": 3, "audience": "technical", "models": [], "n": 277, "authors_detailed": [ { "name": "Jian Zhu", "url": "https://openalex.org/A5139633781", "inst": "" }, { "name": "Yuzheng Zhang", "url": "https://openalex.org/A5133702241", "inst": "Beihang University" }, { "name": "Zeyao Ma", "url": "https://openalex.org/A5139711886", "inst": "" }, { "name": "Bohan Zhang", "url": "https://openalex.org/A5139707642", "inst": "" }, { "name": "Armin Schoepf", "url": "https://openalex.org/A5139687857", "inst": "" }, { "name": "Daniel Woloch", "url": "https://openalex.org/A5139687705", "inst": "" }, { "name": "Peter Yiliu Wang", "url": "https://openalex.org/A5014907257", "inst": "Columbia University" }, { "name": "Guangyu Robert Yang", "url": "https://openalex.org/A5053839400", "inst": "McGovern Institute for Brain Research" }, { "name": "Samuel Jacob", "url": "https://openalex.org/A5139697122", "inst": "" }, { "name": "Siddharth Nagisetty", "url": "https://openalex.org/A5073960197", "inst": "Kurgan State University" }, { "name": "Abhiram Chundru", "url": "https://openalex.org/A5139648536", "inst": "" }, { "name": "Jean Lin", "url": "https://openalex.org/A5139663007", "inst": "" }, { "name": "Spencer Mateega", "url": "https://openalex.org/A5116094782", "inst": "" }, { "name": "Jing Zhang", "url": "https://openalex.org/A5139656226", "inst": "" } ], "affiliations": [ "Columbia University", "Beihang University", "McGovern Institute for Brain Research", "Kurgan State University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.30583v2", "doi": "10.2139/ssrn.7108778", "arxiv_id": "2606.30583v2", "title": "AI Premium", "authors": [ "Nicola Borri", "Yukun Liu", "Aleh Tsyvinski" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "SSRN and arXiv", "url": "https://arxiv.org/abs/2606.30583v2", "alt_urls": [ "https://doi.org/10.2139/ssrn.7108778" ], "field": "finance", "role": "object", "bullets": [ "380 trillion tokens of realized AI consumption across more than 400 large language models from the licensed OpenRouter dataset, about 2 percent of global monthly token use.", "No model is run by the authors; they build an AI Factor from token, dollar, and user growth and estimate firm-level AI Betas from stock-return comovement.", "High AI-beta firms earn higher returns; a value-weighted long-short strategy earns 64.1 basis points weekly, concentrated in frontier closed-source use and absent in emerging markets." ], "bullet_provenance": "ai", "salience": 78, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 319, "authors_detailed": [ { "name": "Nicola Borri", "url": "https://openalex.org/A5140716382", "inst": "Libera Università Internazionale degli Studi Sociali Guido Carli" }, { "name": "Aleh Tsyvinski", "url": "https://openalex.org/A5140643307", "inst": "Yale University" }, { "name": "Yukun Liu", "url": "https://openalex.org/A5140709125", "inst": "University of Rochester" } ], "affiliations": [ "Yale University", "University of Rochester", "Libera Università Internazionale degli Studi Sociali Guido Carli" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6894862", "doi": "10.2139/ssrn.6894862", "title": "Cloud Value Chains in the Age of AI", "authors": [ "Shi Chen", "Vinayak Deshpande" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6894862", "field": "management", "role": "object", "bullets": [ "Conceptual operations-management framework for AI-driven cloud value chains spanning upstream supply networks, midstream AI data centers, and downstream platform ecosystems, synthesizing industry practice.", "No model is used; the paper analyzes how generative AI workloads and production pipelines differ from traditional cloud computing and maps actors and incentive conflicts across the chain.", "Develops a strategic, tactical, and operational research agenda, recommending pricing and contracting aligned with output uncertainty and capacity policies reflecting training-inference differences." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 490, "authors_detailed": [ { "name": "Shi Chen", "url": "https://openalex.org/A5082967766", "inst": "University of Washington" }, { "name": "Vinayak Deshpande", "url": "https://openalex.org/A5101889396", "inst": "University of North Carolina at Chapel Hill" } ], "affiliations": [ "University of North Carolina at Chapel Hill", "University of Washington" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6835301", "doi": "10.2139/ssrn.6835301", "title": "Investor Protection in Venture Capital: How Do Legal Systems Shape Contracting?", "authors": [ "Chen Lin", "Yu-Jane Liu", "Lai Wei", "Dan Wen", "Yusheng Ye" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6835301", "field": "finance", "role": "instrument", "bullets": [ "Contract-level data from China's venture capital market, where deals are structured through offshore common-law or onshore civil-law entities; sample size and period not stated.", "Model not named; a large language model builds context-aware measures of contractual investor protection to corroborate a hand-constructed index, with no accuracy figure reported.", "Common-law deals adopt stronger protective terms, but under higher uncertainty their contractual protection rises less, indicating legal institutions partly substitute for contractual safeguards." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "edition": 3, "audience": "general", "models": [], "n": 491, "authors_detailed": [ { "name": "Chen Lin", "url": "https://openalex.org/A5139576938", "inst": "University of Hong Kong" }, { "name": "Yu-Jane Liu", "url": "https://openalex.org/A5139628052", "inst": "Peking University" }, { "name": "Lai Wei", "url": "https://openalex.org/A5067495984", "inst": "Lingnan University" }, { "name": "Dan Wen", "url": "https://openalex.org/A5101996365", "inst": "Peking University" }, { "name": "Yusheng Ye", "url": "https://openalex.org/A5101712020", "inst": "Shanxi Jincheng Anthracite Mining Group (China)" } ], "affiliations": [ "University of Hong Kong", "Peking University", "Lingnan University", "Shanxi Jincheng Anthracite Mining Group (China)" ] }, { "uid": "arxiv:2606.30085v1", "arxiv_id": "2606.30085v1", "title": "Not-quite-human tastes: the stylized omnivorousness of LLM survey surrogates", "authors": [ "Xiangyu Ma", "Mengmi Zhang", "Shannon Ang", "Minne Chen" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.30085v1", "field": "economics", "role": "agent", "bullets": [ "277,470 silicon survey surrogates (30 by 9,249) generated to mimic respondents from the US Survey of Public Participation in the Arts and their cultural tastes.", "Models from OpenAI, Anthropic, and DeepSeek each produced surrogates whose tastes were compared against the real SPPA human responses to assess algorithmic fidelity and alignment.", "Surrogates show systematic positive bias for liking, lose real taste relationality, attenuate age-taste links, and resurrect anachronistic class associations while caricaturing gender and race patterns." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "compared to SPPA human survey responses", "salience": 62, "edition": 3, "audience": "technical", "n": 492, "authors_detailed": [ { "name": "Xiangyu Ma", "url": "https://openalex.org/A5101942741", "inst": "Nanyang Technological University" }, { "name": "Mengmi Zhang", "url": "https://openalex.org/A5139677289", "inst": "Nanyang Technological University" }, { "name": "Shannon Ang", "url": "https://openalex.org/A5139308088", "inst": "Nanyang Technological University" }, { "name": "Minne Chen", "url": "https://openalex.org/A5139644154", "inst": "Nanyang Technological University" } ], "affiliations": [ "Nanyang Technological University" ] }, { "uid": "arxiv:2606.29799v1", "arxiv_id": "2606.29799v1", "title": "The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models", "authors": [ "Rafael Kaufmann", "Felix Neubürger", "Michael Walters", "Thomas Kopinski", "Dimitrije Marković" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.29799v1", "field": "finance", "role": "method", "bullets": [ "A novel benchmark of synthetic equities carrying financial and textual data, used for a company classification task; no real-world sample, period, or geography is stated.", "CRISTAL builds an interpretable probabilistic program for Bayesian inference and uses unnamed LLMs for code synthesis and learning, validated on the synthetic benchmark against state-of-the-art LLMs.", "On company classification CRISTAL reaches Bayes-optimal accuracy with 5 examples and a 5-second budget, while comparison LLMs plateau near 40 percent accuracy with far more data." ], "bullet_provenance": "ai", "validated": true, "validation_note": "synthetic-equity classification benchmark, accuracy reported", "salience": 42, "edition": 3, "audience": "technical", "models": [], "n": 493, "authors_detailed": [ { "name": "R. Kaufmann", "url": "https://openalex.org/A5113712335", "inst": "Africa Center" }, { "name": "Felix Neubürger", "url": "https://openalex.org/A5052346863", "inst": "South Westphalia University of Applied Sciences" }, { "name": "Michael Walters", "url": "https://openalex.org/A5121374400", "inst": "GAIA (Germany)" }, { "name": "Thomas Kopinski", "url": "https://openalex.org/A5139671633", "inst": "Linde (Germany)" }, { "name": "Dimitrije Marković", "url": "https://openalex.org/A5076881344", "inst": "Boston University" } ], "affiliations": [ "Boston University", "Africa Center", "South Westphalia University of Applied Sciences", "GAIA (Germany)", "Linde (Germany)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.29793v2", "arxiv_id": "2606.29793v2", "title": "Fund2Persona: A Framework for Building and Refining Financial Advisor Personas from Fund Disclosure Data", "authors": [ "Suhwan Park", "Hoyoung Lee", "Zhangyang Wang", "Alejandro Lopez-Lira", "Young Cha", "Chanyeol Choi", "Jaewon Choi", "Yongjae Lee" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.29793v2", "field": "finance", "role": "agent", "bullets": [ "Financial advisor personas grounded in fund disclosures, holdings transitions, market context, and manager commentary; evaluated on held-out holdings-transition reconstruction, with sample size, period, and geography not stated.", "An unnamed LLM builds personas refined through an agentic actor, scorer, and patcher loop, then compared to generic persona baselines on reconstruction and manager-commentary alignment, with no accuracy figure reported.", "Fund-grounded personas recover portfolio decisions and manager interpretation better than generic baselines and give more specific advisory dialogue, with no magnitude reported." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "edition": 3, "audience": "technical", "models": [], "n": 581, "authors_detailed": [ { "name": "Suhwan Park", "url": "https://openalex.org/A5012389090", "inst": "Ulsan National Institute of Science and Technology" }, { "name": "Hoyoung Lee", "url": "https://openalex.org/A5069540187", "inst": "University of Kuala Lumpur" }, { "name": "Zhangyang Wang", "url": "https://openalex.org/A5139649851", "inst": "The University of Texas at Austin" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5139632860", "inst": "" }, { "name": "Young Cha", "url": "https://openalex.org/A5122335673", "inst": "" }, { "name": "Chanyeol Choi", "url": "https://openalex.org/A5139675280", "inst": "" }, { "name": "J H Choi", "url": "https://openalex.org/A5113468057", "inst": "University of Southern California" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5139698741", "inst": "" } ], "affiliations": [ "The University of Texas at Austin", "University of Southern California", "Ulsan National Institute of Science and Technology", "University of Kuala Lumpur" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7021144", "doi": "10.2139/ssrn.7021144", "title": "The Paradox of Artificial Intelligence Adoption: How Artificial Intelligence Engagement Shapes Anxiety over Skill Atrophy Among Public Relations Professionals", "authors": [ "Karen Sutherland", "Puneet Vatsa", "Karen J. Freberg" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7021144", "field": "management", "role": "object", "bullets": [ "Survey of 400 public relations professionals in Australia, the United Kingdom, and the United States, analysed with a generalized ordered logit model of skill-atrophy concern.", "Generative AI is the object studied rather than a research tool; no specific model is named and no model output is used, with the design drawing on Diffusion of Innovation Theory and TAM3.", "Moderate AI users report the least concern about skill atrophy and frequent users are less likely to express extreme worry, with use frequency predicting concern more consistently than education or experience." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 915, "authors_detailed": [ { "name": "Karen E. Sutherland", "url": "https://openalex.org/A5028942846", "inst": "University of the Sunshine Coast" }, { "name": "Puneet Vatsa", "url": "https://openalex.org/A5027890773", "inst": "Lincoln University" }, { "name": "Karen Freberg", "url": "https://openalex.org/A5026558126", "inst": "University of Louisville" } ], "affiliations": [ "University of the Sunshine Coast", "Lincoln University", "University of Louisville" ] }, { "uid": "doi:10.2139/ssrn.7006287", "doi": "10.2139/ssrn.7006287", "title": "Stop Debugging the Dreamer: A Manager’s Framework for Turning AI Hallucinations into Breakthroughs", "authors": [ "İlbey Kutluhan Papatya", "Nurhan Papatya" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7006287", "field": "management", "role": "object", "bullets": [ "Conceptual management framework paper with no empirical sample, addressing how firms should treat generative AI hallucinations.", "Generative AI is the object; no model is run or named, and the paper proposes a Verifiability-Stakes matrix and a Provoke-Filter-Capture-Bound protocol for managing model errors.", "Argues that lowering verification cost lets firms exploit AI hallucinations as cheap variation, tipping high-stakes domains from too risky to valuable; no empirical test is reported." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 916, "authors_detailed": [ { "name": "İlbey Kutluhan Papatya", "url": "https://openalex.org/A5097924618", "inst": "Independent Researcher, Turkey" }, { "name": "Nurhan Papatya", "url": "https://openalex.org/A5087475191", "inst": "Süleyman Demirel University" } ], "affiliations": [ "Independent Researcher, Turkey", "Süleyman Demirel University" ] }, { "uid": "doi:10.2139/ssrn.6974858", "doi": "10.2139/ssrn.6974858", "title": "Distillation and Strategic Launches on the LLM Frontier: An Empirical Analysis", "authors": [ "Jiaqi Shi", "Zhoupeng (Jack) Zhang" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6974858", "field": "economics", "role": "object", "bullets": [ "Provider-week panel of 529 large language model launches by 51 providers between November 2022 and June 2026, with benchmark-based quality states.", "LLMs are the object of an industrial-organization study, not a research tool; no single model family is named, and distillation enters a structural launch model through open- versus closed-weight rival activity.", "Anticipated open-weight rival launches lower current launch probability while realized open-weight frontier gains raise later quality; an all-open regime tempers launch frequency but increases quality gains per launch." ], "bullet_provenance": "ai", "salience": 66, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 917, "authors_detailed": [ { "name": "Jiaqi Shi", "url": "https://openalex.org/A5139552362", "inst": "" }, { "name": "Zhoupeng (Jack) Zhang", "url": "https://openalex.org/A5139546541", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7022103", "doi": "10.2139/ssrn.7022103", "title": "Garante: A hybrid MIP--LLM decision-support system for assembling scientific guarantor teams in research excellence calls", "authors": [ "Rubén Baena-Pérez", "Javier Álvarez-Gálvez", "José Miguel Mota", "Eva Bermúdez Figueroa" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7022103", "field": "management", "role": "agent", "bullets": [ "Decision-support system for nominating scientific guarantor teams in Spanish Severo Ochoa and Maria de Maeztu research-excellence funding calls.", "A large language model module emulates a call-specific evaluation panel to advise selection, decoupled from a mixed-integer program; the model is not named and concordance with real panel decisions is only planned.", "Presents an open-source tool pairing MIP optimisation over an eleven-dimensional excellence score with an advisory LLM panel; no accuracy against actual funding decisions is reported." ], "bullet_provenance": "ai", "validated": false, "validation_note": "concordance with real panel decisions planned, not yet done", "salience": 33, "edition": 3, "audience": "technical", "models": [], "n": 918, "authors_detailed": [ { "name": "Rubén Baena-Perez", "url": "https://openalex.org/A5029169376", "inst": "Universidad de Cádiz" }, { "name": "Javier Álvarez‐Gálvez", "url": "https://openalex.org/A5068535500", "inst": "Universidad Complutense de Madrid" }, { "name": "José Miguel Mota", "url": "https://openalex.org/A5006922424", "inst": "Universidad de Cádiz" }, { "name": "Eva Bermúdez Figueroa", "url": "https://openalex.org/A5065363246", "inst": "Universidad de Cádiz" } ], "affiliations": [ "Universidad de Cádiz", "Universidad Complutense de Madrid" ] }, { "uid": "doi:10.2139/ssrn.6895078", "doi": "10.2139/ssrn.6895078", "title": "Exploring the Adoption of Generative and Trustworthy AI in Indian Higher Educational Institutions: A Marketing Management Perspective", "authors": [ "Dr. Ram Kumar Dwivedi", "Igor Calzada", "Shivang Rampriyan" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6895078", "field": "management", "role": "object", "bullets": [ "Survey of 401 lecturers and 332 students at Indian higher-education institutions, analysed with structural equation modelling of generative-AI adoption.", "Generative AI adoption is the object of a marketing-management study; no LLM is used as a tool and none is centrally named, with platforms such as IBM Watson and Google Bard cited only as context.", "Several adoption hypotheses are confirmed and transparency marketing mediates the link between intention to use generative AI and trustworthy AI, with implications for institutional branding and enrolment." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "salience": 32, "edition": 3, "audience": "general", "validated": null, "n": 919, "authors_detailed": [ { "name": "Ram Kumar Dwivedi", "url": "https://openalex.org/A5063847741", "inst": "GLA University" }, { "name": "Igor Calzada", "url": "https://openalex.org/A5072954463", "inst": "Ikerbasque" }, { "name": "Shivang Rampriyan", "url": "https://openalex.org/A5083338146", "inst": "Indian Institute of Technology Delhi" } ], "affiliations": [ "GLA University", "Ikerbasque", "Indian Institute of Technology Delhi" ] }, { "uid": "doi:10.2139/ssrn.7017401", "doi": "10.2139/ssrn.7017401", "title": "AI and the Collapse of the www", "authors": [ "Alex Chan" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7017401", "field": "economics", "role": "object", "bullets": [ "Theoretical market-design model of generative-AI answer systems that intermediate between users and web publishers, with no empirical sample.", "Generative AI intermediation is the object of an analytical economic model, not a tool used by the author; no language model is run or named.", "Shows an AI platform that underinternalizes content reproduction retains too little referral traffic and can make open-web information subcritical, proposing visitor-replacement royalties and audited provenance as repairs." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 920, "authors_detailed": [ { "name": "Alex Chan", "url": "https://openalex.org/A5113024621", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.29771v1", "arxiv_id": "2606.29771v1", "title": "CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents", "authors": [ "Bo Qu", "Mingguang Chen" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.29771v1", "field": "finance", "role": "method", "bullets": [ "LLM agents cast as autonomous portfolio managers, evaluated on a contamination-controlled multi-model backtest with an ablation grid and a live broker track on post-cutoff data.", "Introduces CLQT, a closed-loop cost-aware trading benchmark scoring agents on a five-axis scorecard; the models are multiple and unnamed, with coherence judged partly by a held-out out-of-cohort LLM to limit self-preference.", "Reframes evaluation as diagnosis rather than ranking, separating trading outcome from capability and yielding a map of agent competencies instead of a single model leaderboard." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "models": [], "validated": null, "n": 921, "authors_detailed": [ { "name": "Bo Qu", "url": "https://openalex.org/A5139693652", "inst": "" }, { "name": "Mingguang Chen", "url": "https://openalex.org/A5139359250", "inst": "University of California, Riverside" } ], "affiliations": [ "University of California, Riverside" ] }, { "uid": "arxiv:2606.30986v1", "arxiv_id": "2606.30986v1", "title": "The Organizational Behavior of Agentic AI: Collective Intelligence in Human-Agent Workflows", "authors": [ "Canhui Liu" ], "posted": "2026-06-29", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.30986v1", "field": "management", "role": "object", "bullets": [ "Theoretical organisation-studies article treating agentic AI as collectives of planners, solvers, reviewers, and orchestrators entering firm workflows; evidence comes from synthetic task simulations and real LLM agent traces.", "No specific model is named; the author combines computational theorising, synthetic task simulations, and real LLM agent traces with robustness analyses to compare human-imitation against shared-state coordination forms.", "Human-imitation agent structures often underperform by adding lossy handoffs, correlated deliberation, and verification burden, while shared-state and adaptive forms do better by making context durable, inspectable, and task-contingent." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1155, "authors_detailed": [ { "name": "Canhui Liu", "url": "https://openalex.org/A5138679102", "inst": "City, University of London" } ], "affiliations": [ "City, University of London" ] }, { "uid": "arxiv:2606.29251v2", "arxiv_id": "2606.29251v2", "title": "When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis", "authors": [ "Hoyoung Lee", "Suhwan Park", "Seunghan Lee", "Jun Seo", "Jaehoon Lee", "Sungdong Yoo", "Minjae Kim", "CheolWon Na", "Zhangyang Wang", "Zach Golkhou", "Minkyu Kim", "Sotirios Sabanis", "Alejandro Lopez-Lira", "Dhagash Mehta", "Soonyoung Lee", "Chanyeol Choi", "Wonbin Ahn", "Yongjae Lee" ], "posted": "2026-06-28", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.29251v2", "field": "finance", "role": "method", "bullets": [ "Financial filings and earnings-call transcripts, sample size not stated, examining how LLM compression of source material changes the investment judgment the original source supports.", "Unnamed LLM compressors summarize financial text; the paper diagnoses decontextualization and model dependency, then proposes Agentic Context Compression that generates multiple candidate compressions and audits their disagreements against the source.", "LLM compression yields fluent, factually plausible summaries that nonetheless alter downstream decisions, and the losses can recur and amplify across steps in agentic systems, with magnitude not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 54, "edition": 3, "audience": "general", "models": [], "n": 126, "authors_detailed": [ { "name": "Hoyoung Lee", "url": "https://openalex.org/A5139676474", "inst": "" }, { "name": "Suhwan Park", "url": "https://openalex.org/A5012389090", "inst": "Ulsan National Institute of Science and Technology" }, { "name": "Seunghan Lee", "url": "https://openalex.org/A5139696254", "inst": "" }, { "name": "Jun Seo", "url": "https://openalex.org/A5139649113", "inst": "" }, { "name": "Jaehoon Lee", "url": "https://openalex.org/A5139685297", "inst": "" }, { "name": "Sungdong Yoo", "url": "https://openalex.org/A5139700492", "inst": "" }, { "name": "Minjae Kim", "url": "https://openalex.org/A5139662781", "inst": "" }, { "name": "CheolWon Na", "url": "https://openalex.org/A5051095871", "inst": "Sungkyunkwan University" }, { "name": "Zhangyang Wang", "url": "https://openalex.org/A5139649851", "inst": "The University of Texas at Austin" }, { "name": "Zach Golkhou", "url": "https://openalex.org/A5110778940", "inst": "" }, { "name": "Minkyu Kim", "url": "https://openalex.org/A5139656997", "inst": "" }, { "name": "Sotirios Sabanis", "url": "https://openalex.org/A5009097122", "inst": "Turing Institute" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5139632860", "inst": "" }, { "name": "Dhagash Mehta", "url": "https://openalex.org/A5139666273", "inst": "Blackrock Microsystems (United States)" }, { "name": "Soonyoung Lee", "url": "https://openalex.org/A5107114088", "inst": "LG (United States)" }, { "name": "Chanyeol Choi", "url": "https://openalex.org/A5139675280", "inst": "" }, { "name": "Wonbin Ahn", "url": "https://openalex.org/A5082843254", "inst": "LG (United States)" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5139698741", "inst": "" } ], "affiliations": [ "The University of Texas at Austin", "Ulsan National Institute of Science and Technology", "Sungkyunkwan University", "Turing Institute", "Blackrock Microsystems (United States)", "LG (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.29406v1", "arxiv_id": "2606.29406v1", "title": "Adaptive AI Delegation under Uncertainty: A Bayesian Governance Policy for Sequential Decision Authority", "authors": [ "Matthew Francis Dixon" ], "posted": "2026-06-28", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.29406v1", "field": "management", "role": "object", "bullets": [ "Conceptual governance study of organizations that delegate decision authority to large language models and agentic AI in high-consequence settings, evaluated on synthetic stress tests rather than field data.", "No specific model is used or named; the paper formulates AI delegation as a governance-aware partially observable Markov decision process with Bayesian state estimation, benchmarked against five alternative governance strategies.", "Sequential Bayesian governance is the strongest general-purpose policy across heterogeneous AI-quality regimes, while specialized heuristics perform well only in stationary settings." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 191, "authors_detailed": [ { "name": "Matthew Dixon", "url": "https://openalex.org/A5036435050", "inst": "IIT Research Institute" } ], "affiliations": [ "IIT Research Institute" ] }, { "uid": "arxiv:2606.29366v1", "arxiv_id": "2606.29366v1", "title": "Solver-Verified Formulation Generation and Selection for Multi-Warehouse Inventory Allocation Using Large Language Models", "authors": [ "Jintao Xu", "Yingzheng Ma", "Jiong Dong", "Yongzhi Qi", "Jianshen Zhang", "Dongyang Geng", "Anni Zhang" ], "posted": "2026-06-28", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.29366v1", "field": "management", "role": "method", "bullets": [ "29 production evaluation batches from JD.com covering multi-warehouse inventory allocation in large-scale e-commerce, distributing fixed replenishment quantities across warehouses under heterogeneous demand and coverage constraints.", "An unnamed LLM generates candidate operations-research formulations and executable solver code from semi-structured natural-language specs, while a solver verifies executability, feasibility, and quality; the specific model is not stated.", "The best single formulation raised allocation accuracy 3.4 percentage points over the incumbent, while the full ORLA framework gained 4.5 points and improved allocation in 26 of 29 batches." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 275, "authors_detailed": [ { "name": "Jintao Xu", "url": "https://openalex.org/A5139656948", "inst": "Jingdong (China)" }, { "name": "Yu‐Zhang Ma", "url": "https://openalex.org/A5101690333", "inst": "Jingdong (China)" }, { "name": "Jiong Dong", "url": "https://openalex.org/A5139650828", "inst": "Jingdong (China)" }, { "name": "Yongzhi Qi", "url": "https://openalex.org/A5139653816", "inst": "" }, { "name": "Jianshen Zhang", "url": "https://openalex.org/A5000201851", "inst": "Jingdong (China)" }, { "name": "Dongyang Geng", "url": "https://openalex.org/A5098970606", "inst": "Renmin University of China" }, { "name": "A M Zhang", "url": "https://openalex.org/A5030897296", "inst": "Australian National University" } ], "affiliations": [ "Jingdong (China)", "Renmin University of China", "Australian National University" ] }, { "uid": "doi:10.2139/ssrn.7009844", "doi": "10.2139/ssrn.7009844", "title": "Open-Source versus Proprietary LLMs for Sentiment-Based Equity Portfolios", "authors": [ "Felipe Affonso", "Marcelo Perlin" ], "posted": "2026-06-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7009844", "field": "finance", "role": "instrument", "bullets": [ "Over 1.4 million news headlines on S&P 500 constituents from 2021 to 2025, used to form rank-based long/short equity portfolios of index members.", "Seven models, proprietary GPT, Claude and Gemini and open-source Llama and Mistral, extracted headline trading signals; no validation of the sentiment labels against a human-coded benchmark is reported.", "Larger models generated positive alpha while the smallest did not; Claude 3 Haiku reached a Sharpe ratio of 0.94 and Llama 3.1 8B performed close to the commercial APIs." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 58, "edition": 3, "audience": "general", "n": 76, "authors_detailed": [ { "name": "Felipe Affonso", "url": "https://openalex.org/A5021092633", "inst": "Universidade Federal do Rio Grande do Sul" }, { "name": "Marcelo Perlin", "url": "https://openalex.org/A5054466648", "inst": "Universidade Federal do Rio Grande do Sul" } ], "affiliations": [ "Universidade Federal do Rio Grande do Sul" ] }, { "uid": "doi:10.2139/ssrn.7010050", "doi": "10.2139/ssrn.7010050", "title": "Empirical Asset Pricing via Large Language Models", "authors": [ "Anjana Yatawara" ], "posted": "2026-06-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7010050", "field": "finance", "role": "object", "bullets": [ "Monthly US cross-section of stocks inside the Gu, Kelly, and Xiu machine-learning asset-pricing framework, each firm a row of characteristics ranked to minus one to one and stripped of identity, on a post-cutoff window.", "A panel of ten unnamed LLMs receives the whole cross-section zero-shot and returns one predicted next-month excess return per stock; a four-pronged audit checks that the recovered skill is not memorization.", "The strongest LLM reaches out-of-sample R-squared of 0.75 percent and rank information coefficient 0.056 (t equals 2.9), and a single momentum-aligned factor tilt explains 80 to 98 percent of its rank skill." ], "bullet_provenance": "ai", "salience": 63, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 125, "authors_detailed": [ { "name": "Anjana Yatawara", "url": "https://openalex.org/A5122953960", "inst": "California State University, Bakersfield" } ], "affiliations": [ "California State University, Bakersfield" ] }, { "uid": "doi:10.2139/ssrn.7006267", "doi": "10.2139/ssrn.7006267", "title": "From Perceptions to Predictions: A Mixed-Method Study of Supplier-Related Reputational Risks and Large Language Model Performance", "authors": [ "Laleh Davoodi", "Abul Khair Jyote", "Elviira Saarelma", "Aki Jääskeläinen", "Jozsef Mezei", "Filip Ginter" ], "posted": "2026-06-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7006267", "field": "management", "role": "instrument", "bullets": [ "Thirty supply chain professionals were interviewed, paired with a comparative case study of three LLMs identifying supplier reputational risk from real-world news articles.", "Three LLMs, not named, flagged reputational risk signals from news text; performance varied across models and domains and the authors report no single accuracy figure.", "Managers view reputational risk as strategically important, and LLMs can surface many risk signals as early-warning tools but still require human validation." ], "bullet_provenance": "ai", "validated": false, "validation_note": "comparative evaluation, no accuracy figure in abstract", "salience": 42, "edition": 3, "audience": "general", "models": [], "n": 176, "authors_detailed": [ { "name": "Laleh Davoodi", "url": "https://openalex.org/A5041353366", "inst": "University of Turku" }, { "name": "Abul Khair Jyote", "url": "https://openalex.org/A5081108943", "inst": "University of Liberal Arts Bangladesh" }, { "name": "Elviira Saarelma", "url": "https://openalex.org/A5127541690", "inst": "" }, { "name": "Aki Jääskeläinen", "url": "https://openalex.org/A5051699796", "inst": "Tampere University of Applied Sciences" }, { "name": "József Mezei", "url": "https://openalex.org/A5075433351", "inst": "Obuda University" }, { "name": "Filip Ginter", "url": "https://openalex.org/A5019929457", "inst": "University of Turku" } ], "affiliations": [ "University of Turku", "University of Liberal Arts Bangladesh", "Tampere University of Applied Sciences", "Obuda University" ] }, { "uid": "doi:10.2139/ssrn.7009806", "doi": "10.2139/ssrn.7009806", "title": "LLM-H²S: A Large Language Model-Guided Hierarchical Heuristic Solver for Multi-Constraint Nurse Scheduling with Real-Time Adaptive Rescheduling", "authors": [ "Rapeepan Pitakaso", "Thanatkij Srichok", "Surajet Khonjun", "Krisanarach Nitisiri", "Peerawat Luesak", "Sarayut Gonwirat", "Natthapong Nanthasamroeng", "Chakat Chueadee", "Kanchana Sethanan" ], "posted": "2026-06-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7009806", "field": "management", "role": "method", "bullets": [ "Multi-constraint nurse scheduling evaluated on three synthetic hospital instances modeled on the Ubon Ratchathani provincial healthcare network in Thailand.", "An unnamed LLM acts as a heuristic solver using constraint-priority tokenization, violation-gradient self-repair, and partial rescheduling, compared against seven published baseline methods.", "The solver reached 99.3 percent hard-constraint satisfaction, resolved 94.9 percent of disruptions within 37.6 seconds, and cut night-shift inequality 64 percent over the weakest baseline." ], "bullet_provenance": "ai", "validated": false, "validation_note": "objective performance metrics, no ground-truth benchmark", "salience": 33, "edition": 3, "audience": "technical", "models": [], "n": 177, "authors_detailed": [ { "name": "Rapeepan Pitakaso", "url": "https://openalex.org/A5053637817", "inst": "Ubon Ratchathani University" }, { "name": "Thanatkij Srichok", "url": "https://openalex.org/A5082371549", "inst": "Ubon Ratchathani University" }, { "name": "Surajet Khonjun", "url": "https://openalex.org/A5072423609", "inst": "Ubon Ratchathani University" }, { "name": "Krisanarach Nitisiri", "url": "https://openalex.org/A5041907231", "inst": "Khon Kaen University" }, { "name": "Peerawat Luesak", "url": "https://openalex.org/A5085222325", "inst": "Rajamangala University of Technology Lanna" }, { "name": "Sarayut Gonwirat", "url": "https://openalex.org/A5086711212", "inst": "Kalasin University" }, { "name": "Natthapong Nanthasamroeng", "url": "https://openalex.org/A5077898961", "inst": "Ubon Ratchathani University" }, { "name": "Chakat Chueadee", "url": "https://openalex.org/A5027701462", "inst": "Rajamangala University of Technology Isan" }, { "name": "Kanchana Sethanan", "url": "https://openalex.org/A5072294176", "inst": "Khon Kaen University" } ], "affiliations": [ "Ubon Ratchathani University", "Khon Kaen University", "Rajamangala University of Technology Lanna", "Kalasin University", "Rajamangala University of Technology Isan" ] }, { "uid": "doi:10.2139/ssrn.6890780", "doi": "10.2139/ssrn.6890780", "title": "A Reference Architecture for LLM-Powered Incident Response in Regulated Financial Services", "authors": [ "Ganesh Kutty Murugan" ], "posted": "2026-06-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6890780", "field": "management", "role": "object", "bullets": [ "Conceptual reference architecture for US regulated financial services, framed against SOX, PCI DSS v4.0, SR 11-7, FFIEC, and recent AI guidance, with no empirical sample.", "No model is evaluated; proposes a six-layer compliance-by-construction architecture for LLM-assisted incident response plus a synthetic evaluation framework, citing GPT-4 only as prior industry work.", "Presents an architecture where regulatory requirements are embedded as layers that data must physically traverse rather than added as after-the-fact controls; no quantitative results reported." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 32, "edition": 3, "audience": "technical", "validated": null, "n": 488, "authors_detailed": [ { "name": "Ganesh Kutty Murugan", "url": "https://openalex.org/A5139288816", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6892520", "doi": "10.2139/ssrn.6892520", "title": "Produce or Delegate? A Transaction-cost Theory of Vertical Integration: An Old Problem in the Age of AI", "authors": [ "Jian Ding", "Yixiao Zhou" ], "posted": "2026-06-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6892520", "field": "management", "role": "object", "bullets": [ "Comparative case analysis across luxury and beauty groups, restaurants, franchising, logistics, contract manufacturing, system integration, and AI applications, with no formal sample.", "No model is used; ChatGPT, DeepSeek, and other AI platforms are analyzed as cases of vertical integration versus specialization under a transaction-cost theory of firm boundaries.", "Argues coding workflows favor internalization or platform control while dispersed vertical applications are delegated, turning on whether outside performance can be enforced at acceptable cost." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "salience": 38, "edition": 3, "audience": "general", "validated": null, "n": 489, "authors_detailed": [ { "name": "Jian Ding", "url": "https://openalex.org/A5139485463", "inst": "Zhejiang Guangsha Vocational and Technical University of Construction" }, { "name": "Yixiao Zhou", "url": "https://openalex.org/A5044746363", "inst": "Australian National University" } ], "affiliations": [ "Zhejiang Guangsha Vocational and Technical University of Construction", "Australian National University" ] }, { "uid": "doi:10.2139/ssrn.6892440", "doi": "10.2139/ssrn.6892440", "title": "Product Architecture and the 2B/2C Divide: Why Target Customer Clarity Matters", "authors": [ "Jian Ding", "Yixiao Zhou" ], "posted": "2026-06-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6892440", "field": "management", "role": "object", "bullets": [ "Cross-industry case evidence from media, platforms, software, and AI, including Tencent WeChat versus its gaming and music units and The New York Times around divestiture.", "Generative AI is not a research tool but an object; general-purpose AI models are cited as heterogeneous products that preclude a single coherent customer persona.", "Narrow, coherent products support direct consumer pricing, whereas broad systems such as general-purpose AI models push firms toward business-side or layered monetization." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 913, "authors_detailed": [ { "name": "Jian Ding", "url": "https://openalex.org/A5139485463", "inst": "Zhejiang Guangsha Vocational and Technical University of Construction" }, { "name": "Yixiao Zhou", "url": "https://openalex.org/A5044746363", "inst": "Australian National University" } ], "affiliations": [ "Zhejiang Guangsha Vocational and Technical University of Construction", "Australian National University" ] }, { "uid": "doi:10.2139/ssrn.6892378", "doi": "10.2139/ssrn.6892378", "title": "Business Insider: A Multi-agent Knowledge Graph Architecture for Corporate Control Inference and Strategic Vulnerability Analysis across Regulatory Jurisdictions", "authors": [ "Amit Vishnu Bhise" ], "posted": "2026-06-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6892378", "field": "finance", "role": "method", "bullets": [ "Twenty-two companies across three regulatory jurisdictions, built from live government corporate registries: MCA India, SEC EDGAR in the US, and BRIS or Companies House in the EU.", "A locally deployed, unnamed large language model performs natural-language report synthesis on top of graph algorithms that infer ownership and governance risk.", "Reports 93.8 percent indirect ownership inference accuracy, a 200 percent gain in relationship coverage over direct-only methods, and a 97 percent reduction in analyst effort." ], "bullet_provenance": "ai", "open_weights": true, "salience": 34, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 914, "authors_detailed": [ { "name": "Amit Bhise", "url": "https://openalex.org/A5138192710", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2606.28670v1", "arxiv_id": "2606.28670v1", "title": "MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting", "authors": [ "Andrea Carriero", "Davide Pettenuzzo", "Shubhranshu Shekhar" ], "posted": "2026-06-27", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.28670v1", "field": "economics", "role": "method", "bullets": [ "Real-time macroeconomic forecasting evaluated on the FRED-MD database with vintage-specific ALFRED data, in a genuine out-of-sample exercise across many US macro series and horizons.", "MACROCAST, a lightweight time series foundation model, is pretrained on synthetic series then fine-tuned on simulated data from vintage Bayesian VAR, dynamic factor, and ARIMA models, ruling out both forms of data leakage.", "Beats the AR(1) benchmark for roughly 80% of series-horizon pairs, matches or surpasses Chronos-2, and outperforms Bayesian VAR and dynamic factor model benchmarks, all leakage-free." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "real-time out-of-sample FRED-MD vs AR(1), Chronos-2, BVAR and DFM benchmarks", "salience": 62, "edition": 3, "audience": "technical", "n": 1154, "authors_detailed": [ { "name": "Andrea Carriero", "url": "https://openalex.org/A5082187758", "inst": "University of London" }, { "name": "Davide Pettenuzzo", "url": "https://openalex.org/A5089152261", "inst": "Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda" }, { "name": "Shubhranshu Shekhar", "url": "https://openalex.org/A5017630996", "inst": "Boston University" } ], "affiliations": [ "Boston University", "University of London", "Azienda Socio Sanitaria Territoriale Grande Ospedale Metropolitano Niguarda" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6890263", "doi": "10.2139/ssrn.6890263", "title": "Evaluating Cognitive Resilience of Large Language Models under Structural Opacity: An Algorithmic Audit of Local Government Financing Vehicles (LGFVs)", "authors": [ "Chieh-Ting Tsai" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6890263", "field": "finance", "role": "object", "bullets": [ "China's Local Government Financing Vehicles used as an empirical case of institutionalized information asymmetry, implicit state credit, and hidden transfer of financial burdens to the public.", "Several unnamed LLM architectures interpret the dual-track LGFV mechanism, with ChatGPT screened out for sycophancy and semantic evasion; comparison is qualitative with no accuracy benchmark.", "Institutional-weighted models over-index sanitized official data and show systemic blindness, while social-weighted models detect more risk but remain vulnerable to coordinated astroturfing narratives." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "general", "validated": null, "n": 111, "authors_detailed": [ { "name": "Chieh-Ting Tsai", "url": "https://openalex.org/A5139455565", "inst": "Acer (Taiwan)" } ], "affiliations": [ "Acer (Taiwan)" ] }, { "uid": "doi:10.2139/ssrn.7002953", "doi": "10.2139/ssrn.7002953", "title": "Bridging Cybersecurity Expertise Gaps through Human–LLM Collaboration: Trust, Reliance, and Design Imperatives from a Mixed-Methods Study", "authors": [ "Shahroz Tariq", "Ronal Singh", "Mohan Baruwal Chhetri", "Surya Nepal", "Cecile Paris" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7002953", "field": "management", "role": "object", "bullets": [ "58 participants completed two controlled cybersecurity triage tasks, phishing email triage and network intrusion triage, in an exploratory mixed-methods design.", "An LLM (model not stated) supplied triage advice; interaction logs were qualitatively coded and paired quantitative analyses compared decisions made with and without the assistant.", "Collaboration gave modest gains in phishing triage and larger gains in intrusion triage, with reliance higher when independent verification of cues was harder." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 172, "authors_detailed": [ { "name": "Shahroz Tariq", "url": "https://openalex.org/A5003311970", "inst": "Australian Resources Research Centre" }, { "name": "Ronal Singh", "url": "https://openalex.org/A5031610846", "inst": "CSIRO Manufacturing" }, { "name": "Mohan Baruwal Chhetri", "url": "https://openalex.org/A5000678710", "inst": "CSIRO Manufacturing" }, { "name": "Surya Nepal", "url": "https://openalex.org/A5139413540", "inst": "" }, { "name": "Cecile Paris", "url": "https://openalex.org/A5130251781", "inst": "Australian Government" } ], "affiliations": [ "Australian Resources Research Centre", "CSIRO Manufacturing", "Australian Government" ] }, { "uid": "doi:10.2139/ssrn.7002057", "doi": "10.2139/ssrn.7002057", "title": "A Reasoning-Enhanced LLM-based Multi-Role Simulation Model for Vertiport Location Selection", "authors": [ "Ming Cheng", "Can Li", "Wei Liu", "Zhongming Jin", "Wanjing Ma" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7002057", "field": "management", "role": "agent", "bullets": [ "Vertiport location selection for urban air mobility, with LLM agents role-playing heterogeneous stakeholders as an alternative to stated-preference surveys, evaluated on synthetic scenarios.", "The proposed REMS framework uses an unnamed LLM with reasoning-before-responding and PPO alignment, then Fuzzy-AHP converts simulated reasoning into decision weights, benchmarked against non-reasoning baselines.", "REMS reportedly outperforms non-reasoning baselines on behavioral authenticity and decision consistency, with no comparison to real stakeholder responses reported." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no agreement figure versus real stakeholder responses", "salience": 32, "edition": 3, "audience": "technical", "models": [], "n": 173, "authors_detailed": [ { "name": "Ming Cheng", "url": "https://openalex.org/A5101167402", "inst": "Tongji University" }, { "name": "Can Li", "url": "https://openalex.org/A5139424249", "inst": "Tongji University" }, { "name": "Wei Liu", "url": "https://openalex.org/A5100431692", "inst": "Hong Kong Polytechnic University" }, { "name": "Zhongming Jin", "url": "https://openalex.org/A5084403604", "inst": "Tongji University" }, { "name": "Wanjing Ma", "url": "https://openalex.org/A5038496118", "inst": "Tongji University" } ], "affiliations": [ "Tongji University", "Hong Kong Polytechnic University" ] }, { "uid": "doi:10.2139/ssrn.6893079", "doi": "10.2139/ssrn.6893079", "title": "Examining Risk Preferences of Econs, KaTs, Humans and LLMs", "authors": [ "Arun Muralidhar" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6893079", "field": "finance", "role": "agent", "bullets": [ "Eight prominent LLMs answered a 15-question behavioral risk-preference survey, Risktyle, previously administered to global and regional human respondents.", "The LLMs, not individually named, completed the survey as respondents, and their answers were compared with academic risk-preference personas and human distributions.", "LLM risk preferences fall outside both academic theory and human populations, implying they cannot be used for investment advice without checking algorithmic biases." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 174, "authors_detailed": [ { "name": "Arun Muralidhar", "url": "https://openalex.org/A5013995635", "inst": "Georgetown University" } ], "affiliations": [ "Georgetown University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6893379", "doi": "10.2139/ssrn.6893379", "title": "Towards AI-Native ERP Systems: Identifying Limitations in Contemporary Enterprise Platforms and Proposing an Intelligent Workflow-Oriented Architecture - A Conceptual Position Paper", "authors": [ "Harsh Bhaveshkumar Trivedi" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6893379", "field": "management", "role": "object", "bullets": [ "Conceptual position paper comparing SAP S/4HANA and Oracle Fusion Cloud against an AI-native ERP design framework, with no empirical sample analyzed.", "No model is empirically evaluated; LLMs are proposed as a conversational interaction layer, illustrated by the author's own proof-of-concept platform under a disclosed conflict of interest.", "The paper argues AI-native ERP could cut operational complexity and training burden, but leaves controlled user studies and benchmarks to future work." ], "bullet_provenance": "ai", "salience": 24, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 175, "authors_detailed": [ { "name": "Harsh Bhaveshkumar Trivedi", "url": "https://openalex.org/A5139411325", "inst": "De Montfort University" } ], "affiliations": [ "De Montfort University" ] }, { "uid": "doi:10.2139/ssrn.6999350", "doi": "10.2139/ssrn.6999350", "title": "What Do People Ask AI About Finance? Evidence from ChatGPT", "authors": [ "Mahnaz Paydarzarnaghi", "Amir Karami" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6999350", "field": "finance", "role": "object", "bullets": [ "7,548 real-world user and ChatGPT financial conversations drawn from over one million conversations in the WildChat dataset; period and geography not stated.", "ChatGPT is the object studied; the authors apply BERTopic to label 21 financial topics and compare emphasis across GPT-3.5 and GPT-4 tiers and the market calendar.", "Pricing and costs at 12.5 percent leads, GPT-4 conversations skew toward sophisticated topics, and speculative topics carry more weight on non-trading days." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "salience": 58, "edition": 3, "audience": "general", "validated": null, "n": 271, "authors_detailed": [ { "name": "Mahnaz Paydarzarnaghi", "url": "https://openalex.org/A5093334048", "inst": "Roger Williams University" }, { "name": "Amir Karami", "url": "https://openalex.org/A5010380816", "inst": "Kennesaw State University" } ], "affiliations": [ "Roger Williams University", "Kennesaw State University" ] }, { "uid": "doi:10.2139/ssrn.6999349", "doi": "10.2139/ssrn.6999349", "title": "Good portfolios from bad forecasts: the anatomy of LLM volatility estimates", "authors": [ "Anjana Yatawara" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6999349", "field": "finance", "role": "method", "bullets": [ "Next-day realized variance forecasts for 30 assets, comparing sixteen large language models from five providers against a log-HAR benchmark; sample period not stated.", "The models forecast variance and are evaluated with QLIKE, Diebold-Mariano tests, and the model confidence set; one 7B open model is dissected in detail against HAR.", "No model significantly beats log-HAR, the 7B loses to HAR in 29 of 30 assets, and reported portfolio gains trace to a covariance-geometry tilt rather than forecast skill." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "QLIKE against realized variance, log-HAR benchmark and model confidence set", "salience": 68, "edition": 3, "audience": "general", "n": 272, "authors_detailed": [ { "name": "Anjana Yatawara", "url": "https://openalex.org/A5122953960", "inst": "California State University, Bakersfield" } ], "affiliations": [ "California State University, Bakersfield" ] }, { "uid": "doi:10.2139/ssrn.7004918", "doi": "10.2139/ssrn.7004918", "title": "Mapping the Accounting–Information Systems Frontier: A Research Agenda for Machine-Readable Disclosure", "authors": [ "Idorenyin J. Okon", "Olabamiji Atanda", "Adeolu O. Adewuyi" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7004918", "field": "accounting", "role": "object", "bullets": [ "PRISMA-guided systematic review of 362 peer-reviewed Scopus studies on machine-readable disclosure from 2006 to 2026, combining bibliometric and structured content analysis.", "No model is run; the review frames large language models that read untagged disclosures and generative AI as forces unsettling the XBRL-anchored literature.", "The field's intellectual base contains no co-citation cluster for generative AI or sustainability reporting; the authors propose an eight-question agenda around a five-layer disclosure stack." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 46, "edition": 3, "audience": "general", "validated": null, "n": 273, "authors_detailed": [ { "name": "Idorenyin J. Okon", "url": "https://openalex.org/A5116591633", "inst": "University of Ibadan" }, { "name": "Olabamiji Atanda", "url": "https://openalex.org/A5117461326", "inst": "University of Ibadan" }, { "name": "Adeolu O. Adewuyi", "url": "https://openalex.org/A5001035287", "inst": "University of Ibadan" } ], "affiliations": [ "University of Ibadan" ] }, { "uid": "arxiv:2606.27845v1", "arxiv_id": "2606.27845v1", "title": "LLM Agents as Static Level-k Players in Behavioural Games", "authors": [ "Po Han Teo" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.27845v1", "field": "economics", "role": "agent", "bullets": [ "Two behavioural games, a p-beauty contest and a public goods game, run over a 360-cell factorial of temperature, scale from 0.5 to 32B, quantisation, instruct versus base, and framing.", "Open local language models, family not named, play as stand-ins; their choice distributions are compared against published human data through level-k cognitive theory.", "Models act as static, category-retrieved level-k players with k set by scale, showing no belief updating, no last-round defection, and contributions that stay flat as human cooperation decays." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 56, "edition": 3, "audience": "technical", "validated": null, "n": 274, "authors_detailed": [ { "name": "Po Han Teo", "url": "https://openalex.org/A5137111296", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7001916", "doi": "10.2139/ssrn.7001916", "title": "Displacement or Repartitioning in the Mobile App Ecosystem after Generative AI: A Niche Analysis", "authors": [ "Minjeong Ham" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7001916", "field": "management", "role": "object", "bullets": [ "Monthly panel of US Google Play mobile applications from January 2021 to December 2024, using app-category download shares as market-level niche indicators.", "No language model is used by the researchers; the study treats generative AI adoption as the phenomenon, measuring niche overlap, breadth, and directionality between AI-enabled and non-AI apps.", "Finds limited wholesale displacement and instead incumbent-led niche repartitioning, with AI-adopting established apps more central to the shared niche space than AI-native entrants." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 3, "audience": "general", "validated": null, "n": 486, "authors_detailed": [ { "name": "Minjeong Ham", "url": "https://openalex.org/A5111317335", "inst": "Nagoya University of Commerce and Business" } ], "affiliations": [ "Nagoya University of Commerce and Business" ] }, { "uid": "doi:10.2139/ssrn.6888859", "doi": "10.2139/ssrn.6888859", "title": "Individual Contribution to Collective Intelligence: Toward a Fair Knowledge Economy in the Age of Generative Artificial Intelligence", "authors": [ "Amangeldi Nurmanbetov" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6888859", "field": "economics", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, drawing on knowledge economy theory, institutional economics, digital commons theory, and scholarship on data labor.", "No model is used; the paper proposes a framework for recognizing, valuing, and rewarding expert intellectual contributions that continuously enrich generative AI ecosystems.", "Introduces a hierarchical model of intellectual value creation and the AI Knowledge Contributor as a new economic actor, with principles of transparency, reciprocity, and fair compensation." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 487, "authors_detailed": [ { "name": "Amangeldi Nurmanbetov", "url": "https://openalex.org/A5123420215", "inst": "Karakalpak State University" } ], "affiliations": [ "Karakalpak State University" ] }, { "uid": "doi:10.2139/ssrn.6886558", "doi": "10.2139/ssrn.6886558", "title": "Economic Narrative Indices and Media-Based Sentiment Measures: A Systematic Review of Methodologies, Applications, and Research Gaps (2007-2025)", "authors": [ "Ann Naser Nabil" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6886558", "field": "economics", "role": "method", "bullets": [ "Systematic review of 46 empirical studies from 2007 to 2025 that extract sentiment from text for economic forecasting, plus 20 theoretical papers on macroeconomic sentiment models.", "Surveys the methodological shift from dictionary-based approaches at 55 percent through traditional machine learning and transformer models to emerging large language models at 3 percent; no single model applied and no family named.", "Sentiment-based models cut forecast RMSE by a median of 12 to 20 percent, though gains shrink against strong professional-forecast benchmarks and publication bias tempers the headline figure." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 580, "authors_detailed": [ { "name": "Ann Naser Nabil", "url": "https://openalex.org/A5138008207", "inst": "Jahangirnagar University" } ], "affiliations": [ "Jahangirnagar University" ] }, { "uid": "doi:10.2139/ssrn.6890100", "doi": "10.2139/ssrn.6890100", "title": "The Steeper Slope: Generative AI, the Junior Talent Pipeline, and the Self-Defeating Logic of Seniority-Biased Hiring", "authors": [ "Habib Fathallah" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6890100", "field": "economics", "role": "object", "bullets": [ "Conceptual and modeling paper on generative AI and labor markets, noting large technology employers cut new-graduate hiring by roughly half versus pre-pandemic levels; no new dataset.", "No LLM is used; the paper builds an individual value-curve model and a stock-flow workforce pipeline, synthesizing experimental evidence that AI's productivity uplift decreases in prior skill.", "Finds a no-junior policy raises capability briefly then erodes it, and simultaneous firm-wide adoption collapses the shared senior market, a coordination failure on human capital." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 904, "authors_detailed": [ { "name": "Habib Fathallah", "url": "https://openalex.org/A5061826016", "inst": "University of Carthage" } ], "affiliations": [ "University of Carthage" ] }, { "uid": "doi:10.2139/ssrn.7001392", "doi": "10.2139/ssrn.7001392", "title": "CSIRT-Sim: An LLM-augmented crisis-decision simulator for cybersecurity incident response in regulated organizations", "authors": [ "Bogdan Ksiezopolski" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7001392", "field": "management", "role": "agent", "bullets": [ "Open-source cyber-crisis decision simulator for regulated banking and healthcare organizations, covering four incident families in English and Polish across three difficulty levels with a cohort batch mode.", "An unnamed LLM evaluates free-text player actions, advises during the exercise, and generates the debrief, alongside a deterministic weighted-KPI scorer; no accuracy validation is reported.", "Presents the system with a regulation-accurate EU compliance clock spanning DORA, NIS2, GDPR, and AI Act article 73, producing an auditable 0 to 100 result; no outcome evaluation is reported." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 905, "authors_detailed": [ { "name": "Bogdan Ksi̜eżopolski", "url": "https://openalex.org/A5026662040", "inst": "Kozminski University" } ], "affiliations": [ "Kozminski University" ] }, { "uid": "doi:10.2139/ssrn.7002154", "doi": "10.2139/ssrn.7002154", "title": "Evaluating AI-Generated Advice: Source Cues, Argument Quality, and Individual Differences in Credibility and Adherence", "authors": [ "Elodie Andrieu", "Aurelie Pistono", "Franck Amadieu" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7002154", "field": "management", "role": "object", "bullets": [ "Experiment with 175 participants evaluating ten scenarios of challenging student situations, each with a response attributed to a human expert or ChatGPT and shown at high or low quality.", "ChatGPT serves as the manipulated advice source rather than a research tool; responses analyzed with cumulative link mixed models for the ordinal, repeated-measures design.", "Finds no main effect of source on credibility or adherence, so AI-attributed advice is judged like a human expert; higher quality raises credibility and pro-AI attitudes sharpen quality sensitivity." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 906, "authors_detailed": [ { "name": "Elodie Andrieu", "url": "https://openalex.org/A5139446186", "inst": "" }, { "name": "Aurélie Pistono", "url": "https://openalex.org/A5017520367", "inst": "Université Fédérale de Toulouse Midi-Pyrénées" }, { "name": "Franck Amadieu", "url": "https://openalex.org/A5037026406", "inst": "Université Fédérale de Toulouse Midi-Pyrénées" } ], "affiliations": [ "Université Fédérale de Toulouse Midi-Pyrénées" ] }, { "uid": "doi:10.2139/ssrn.7003668", "doi": "10.2139/ssrn.7003668", "title": "Digital Divide or Digital Equaliser? Economic Status and the Access to ChatGPT Among Higher Education Students Worldwide", "authors": [ "Sajjad Mahdavivand Fard" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7003668", "alt_urls": [ "https://doi.org/10.2139/ssrn.7003669" ], "field": "economics", "role": "object", "bullets": [ "Survey of 22,963 higher education students across 155 countries, examining version access, usage intensity, and perceived educational benefit of ChatGPT by economic background.", "No LLM is used as a tool; ChatGPT is the object studied via self-reported data analyzed with chi-square tests, ANOVA, hierarchical regression, and bootstrapped mediation.", "Economically advantaged students hold paid subscriptions, use ChatGPT more intensively, and report greater benefit; version access partially mediates the link between economic status and perceived benefit." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 46, "edition": 3, "audience": "general", "validated": null, "n": 907, "authors_detailed": [ { "name": "Sajjad Mahdavivand Fard", "url": "https://openalex.org/A5098580456", "inst": "University of Miami" } ], "affiliations": [ "University of Miami" ] }, { "uid": "doi:10.2139/ssrn.6885542", "doi": "10.2139/ssrn.6885542", "title": "The Direction of Value Flow: A Theory of Platform Fragility under AI Substitution", "authors": [ "Babak Heydari" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6885542", "field": "economics", "role": "object", "bullets": [ "Theoretical equilibrium model of user-generated-content platforms facing generative AI substitution, motivated by divergent platform outcomes since ChatGPT's release; no empirical dataset.", "No LLM is used; the paper models fragility as governed by the directionality of value flow from higher- to lower-skilled users rather than AI's task capability.", "Predicts more directional platforms collapse at lower AI reach, survivors shift value toward the peer channel, and transient overestimation produces hysteresis that blocks recovery." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 48, "edition": 3, "audience": "general", "validated": null, "n": 908, "authors_detailed": [ { "name": "Babak Heydari", "url": "https://openalex.org/A5030641600", "inst": "Northeastern University" } ], "affiliations": [ "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.6885378", "doi": "10.2139/ssrn.6885378", "title": "Does Generative AI Enhance Liquidity of Cryptocurrency Markets? Theory and Evidence from ChatGPT Outages", "authors": [ "Charles Cao", "Wei Wang", "Deli Yang" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6885378", "field": "finance", "role": "object", "bullets": [ "Seventy cryptocurrency trading pairs, using ChatGPT service outages as exogenous shocks to adoption; sample period and geography not stated.", "ChatGPT is the object of study rather than a research tool; the paper models how its adoption changes trader information and market-maker adverse selection.", "During outages trading volume rises 24 percent and return volatility 19 percent, while book depth falls 8 percent and price impact rises 12 percent." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "edition": 3, "audience": "general", "validated": null, "n": 909, "authors_detailed": [ { "name": "Charles Cao", "url": "https://openalex.org/A5139404368", "inst": "Pennsylvania State University" }, { "name": "Kenneth Wang", "url": "https://openalex.org/A5087813054", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Deli Yang", "url": "https://openalex.org/A5102001915", "inst": "Chinese University of Hong Kong, Shenzhen" } ], "affiliations": [ "Pennsylvania State University", "Chinese University of Hong Kong, Shenzhen" ] }, { "uid": "doi:10.2139/ssrn.7004505", "doi": "10.2139/ssrn.7004505", "title": "Beyond AI Adoption: How Curiosity and Reflection Shape Innovative Behavior in Technology-Enabled Workplaces", "authors": [ "Muhammad Awais Khan" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7004505", "field": "management", "role": "object", "bullets": [ "Multi-source supervisor-employee dyads covering 315 employees and 72 supervisors in project-based technology firms in an unnamed emerging economy.", "No specific model is named; generative AI use is a self-reported survey construct, not a research tool, tested with covariance-based structural equation modeling and bootstrapping.", "Generative AI use positively predicts innovative behavior, partially mediated by work-related curiosity, with the indirect effect stronger among more reflective employees." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 910, "authors_detailed": [ { "name": "Muhammad Awais Khan", "url": "https://openalex.org/A5102966595", "inst": "University of Ljubljana" } ], "affiliations": [ "University of Ljubljana" ] }, { "uid": "arxiv:2607.19375v1", "arxiv_id": "2607.19375v1", "title": "Economic Evaluations of Language Models", "authors": [ "Alexander Wan", "Stephane Hatgis-Kessell", "Tomás Aguirre", "Percy Liang", "Rishi Bommasani" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.19375v1", "field": "economics", "role": "object", "bullets": [ "US labour economy spanning all occupations and their tasks, with evaluations grounded in real user queries to language models supplemented by synthetic data.", "Introduces EconEvals, an open-source evaluation suite, plus a simulation-based exposure measure; observed Claude usage is analysed; validation against a labelled ground truth not stated.", "Current models could save substantial time on at least half of tasks in 47 percent of occupations, yet observed Claude usage lags potential in 79 percent of high-savings tasks." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 66, "edition": 3, "audience": "general", "validated": null, "n": 911, "authors_detailed": [ { "name": "Alexander Wan", "url": "https://openalex.org/A5023825293", "inst": "Berkeley College" }, { "name": "Stephane Hatgis-Kessell", "url": "https://openalex.org/A5068136137", "inst": "Stanford University" }, { "name": "Tomás Aguirre", "url": "https://openalex.org/A5099040386", "inst": "Berkeley College" }, { "name": "Percy Liang", "url": "https://openalex.org/A5143479570", "inst": "" }, { "name": "Rishi Bommasani", "url": "https://openalex.org/A5143487684", "inst": "" } ], "affiliations": [ "Stanford University", "Berkeley College" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.28002v1", "arxiv_id": "2606.28002v1", "title": "Dialogue to Detection: A Multimodal Hybrid NLP Pipeline for Insurance Fraud Detection", "authors": [ "Muhammad Shakeel Akram", "Amal Htait", "Abdul Hamid Sadka", "Emma Meisingseth", "Karishma Jaitly" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.28002v1", "field": "finance", "role": "method", "bullets": [ "Synthetic multimodal dataset replicating first-notice-of-loss insurance claim conditions, generating agent-customer dialogue transcripts and two-speaker audio recordings.", "Pipeline combines speech recognition, diarisation, named-entity recognition, regex features, LLM-RAG retrieval, and speaker embeddings into a rule-based risk score; the LLM is not named.", "Reports dataset validation and component-level stability as a reproducible multimodal baseline; no overall fraud-detection accuracy figure is stated." ], "bullet_provenance": "ai", "validated": false, "validation_note": "component-level evaluations, no fraud accuracy reported", "salience": 33, "edition": 3, "audience": "technical", "models": [], "n": 912, "authors_detailed": [ { "name": "Muhammad Shakeel Akram", "url": "https://openalex.org/A5112830425", "inst": "University of Ulster" }, { "name": "Amal Htait", "url": "https://openalex.org/A5009782240", "inst": "Aston University" }, { "name": "Abdul Sadka", "url": "https://openalex.org/A5113638173", "inst": "Surrey Satellite Technology (United Kingdom)" }, { "name": "Emma Meisingseth", "url": "https://openalex.org/A5139630534", "inst": "" }, { "name": "Karishma Jaitly", "url": "https://openalex.org/A5139544053", "inst": "" } ], "affiliations": [ "University of Ulster", "Aston University", "Surrey Satellite Technology (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.6884238", "doi": "10.2139/ssrn.6884238", "title": "Competition Implications of Artificial Intelligence in Public Procurement", "authors": [ "Albert Sanchez-Graells" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6884238", "field": "economics", "role": "object", "bullets": [ "Conceptual competition-policy analysis of artificial intelligence in public procurement, framing procurement both as the gateway for largely unregulated public-sector AI adoption and as a process AI is deployed to streamline.", "The author applies no computational model; the piece reasons qualitatively about competition effects of AI-supported procurement and knock-on effects on upstream markets rather than measuring outcomes from data.", "Flags competition risks including free AI embedding or AI creep, unregulated audit and assurance, new data biases, organisational conflicts of interest, and centralisation, and identifies areas for future research." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1152, "authors_detailed": [ { "name": "Albert Sánchez-Graells", "url": "https://openalex.org/A5032988616", "inst": "University of Hull" } ], "affiliations": [ "University of Hull" ] }, { "uid": "doi:10.2139/ssrn.6932219", "doi": "10.2139/ssrn.6932219", "title": "Decision Integrity Index: A Behavioral Methodology for Evaluating Agentic AI Systems", "authors": [ "Stefan Podedworny" ], "posted": "2026-06-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6932219", "field": "management", "role": "object", "bullets": [ "Conceptual working paper proposing an evaluation framework for agentic AI systems, targeted at organisational AI procurement and public-sector deployment; no empirical sample, explicitly described as awaiting later validation.", "No model is run; the author draws on behavioral economics, heuristics and bias research, and human-AI interaction studies to define decision integrity and behavioral safeguard indicators for assessing AI systems.", "Proposes the Decision Integrity Index, combining a technical readiness layer, behavioral safeguards, a Human Reliance Calibration Index, and oversight-quality assessment to flag automation bias, overreliance, and loss of human agency." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1153, "authors_detailed": [ { "name": "Stefan Podedworny", "url": "https://openalex.org/A5138958376", "inst": "SGH Warsaw School of Economics" } ], "affiliations": [ "SGH Warsaw School of Economics" ] }, { "uid": "doi:10.2139/ssrn.6975678", "doi": "10.2139/ssrn.6975678", "title": "Influence of Generative AI on Young Consumers' Purchase Intention: An Empirical Study", "authors": [ "Swathy K.S", "Sarun S G" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6975678", "field": "management", "role": "object", "bullets": [ "100 postgraduate students and research scholars sampled by convenience, surveyed with a five-point Likert questionnaire; geography not stated, individual consumer as the unit.", "No model is run by the authors; the study measures self-reported usage of and trust in generative AI tools including ChatGPT, Gemini, Copilot, and Perplexity.", "Generative AI usage and trust in AI suggestions relate to purchase intention via regression; ChatGPT is the most used tool and most respondents report daily use." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 30, "edition": 3, "audience": "general", "validated": null, "n": 269, "authors_detailed": [ { "name": "Swathy K.S", "url": "https://openalex.org/A5097659593", "inst": "Government Dental College and Hospital" }, { "name": "Shi Gao", "url": "https://openalex.org/A5110461938", "inst": "Government College, Attingal" } ], "affiliations": [ "Government College, Attingal" ] }, { "uid": "arxiv:2606.27316v1", "arxiv_id": "2606.27316v1", "title": "LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank", "authors": [ "Serhii Hamotskyi", "Akash Kumar Gautam", "Christian Hänig" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.27316v1", "field": "finance", "role": "instrument", "bullets": [ "Securities prospectuses at the German Central Bank, lengthy, semi-structured, and often bilingual German and English; collateral eligibility verification, with sample size and period not stated.", "Large language models, family not named, run a generative extraction, normalization, and interpretation pipeline, evaluated with a value-based LLM-as-a-judge metric.", "The system reaches up to 91 percent precision on document-level eligibility with a conservative profile that minimizes false acceptances." ], "bullet_provenance": "ai", "validated": true, "validation_note": "document-level eligibility precision up to 91 percent", "salience": 50, "edition": 3, "audience": "technical", "models": [], "n": 270, "authors_detailed": [ { "name": "Serhii Hamotskyi", "url": "https://openalex.org/A5080963089", "inst": "Anhalt University of Applied Sciences" }, { "name": "Akash Kumar Gautam", "url": "https://openalex.org/A5101066407", "inst": "Anhalt University of Applied Sciences" }, { "name": "Christian Hänig", "url": "https://openalex.org/A5022441534", "inst": "Anhalt University of Applied Sciences" } ], "affiliations": [ "Anhalt University of Applied Sciences" ] }, { "uid": "doi:10.2139/ssrn.6993288", "doi": "10.2139/ssrn.6993288", "title": "Contract-Seek: A Locally Deployable Large Language Model System for Construction Contract Risk Identification", "authors": [ "Chenglong Xu", "Yu Wang", "Yuting Chen", "Mingyu Zhang", "Yihong Gan", "Yongqiang Chen" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6993288", "field": "management", "role": "method", "bullets": [ "Construction contract risk review; five experts built a 327-item risk register and 8,112 instruction instances, with evaluation on 148 questions and 48 clauses from real contracts.", "Contract-Seek combines domain instruction tuning with retrieval-augmented generation on a local model, scored blind by three experts against GPT-4o, DeepSeek-R1-671B, and QwQ-32B.", "It scored 9.39 for clause alignment, 8.44 for risk precision, and 8.96 for language; adding RAG to QwQ-32B raised alignment and risk precision by 5.01 and 4.17 points." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "blinded scoring by three contract experts against baseline LLMs", "salience": 48, "edition": 3, "audience": "technical", "n": 318, "authors_detailed": [ { "name": "Chenglong Xu", "url": "https://openalex.org/A5139264706", "inst": "" }, { "name": "Yu Wang", "url": "https://openalex.org/A5046701060", "inst": "University of Maryland, College Park" }, { "name": "Yuting Chen", "url": "https://openalex.org/A5139263717", "inst": "" }, { "name": "M Zhang", "url": "https://openalex.org/A5132643971", "inst": "Central South University of Forestry and Technology" }, { "name": "Yihong Gan", "url": "https://openalex.org/A5023015763", "inst": "Zhejiang Chinese Medical University" }, { "name": "Yongqiang Chen", "url": "https://openalex.org/A5139235530", "inst": "" } ], "affiliations": [ "University of Maryland, College Park", "Central South University of Forestry and Technology", "Zhejiang Chinese Medical University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6993643", "doi": "10.2139/ssrn.6993643", "title": "How Generative AI Drives Innovation Performance: Evidence on the Mediating Role of Knowledge Integration", "authors": [ "Jianhua Zhang", "Wahab Afolabi Azeez", "Habib Isiaq", "Shadrack Notob Dackyirekpa", "Yanhong Bai", "Abdulahi Oluwadamilare Issa", "Emmanuel Damilare Shittu" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6993643", "field": "management", "role": "object", "bullets": [ "50 Chinese listed manufacturing firms over 2022 to 2025, giving 200 firm-year observations; the unit is the firm-year.", "GenAI adoption is measured by text-mining annual reports, with the model not named; Baron-Kenny mediation analysis uses invention patent data, and no validation of the text measure is reported.", "GenAI adoption significantly raises innovation output, with knowledge integration mediating roughly 30 percent of the total effect." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 484, "authors_detailed": [ { "name": "Jianhua Zhang", "url": "https://openalex.org/A5100434763", "inst": "Zhengzhou University" }, { "name": "Wahab Afolabi Azeez", "url": "https://openalex.org/A5094167780", "inst": "Zhengzhou University" }, { "name": "Habib Isiaq", "url": "https://openalex.org/A5139286267", "inst": "Kwara State University" }, { "name": "Shadrack Notob Dackyirekpa", "url": "https://openalex.org/A5114416092", "inst": "Zhengzhou University" }, { "name": "Yu Bai", "url": "https://openalex.org/A5101970569", "inst": "Zhengzhou University" }, { "name": "Abdulahi Oluwadamilare Issa", "url": "https://openalex.org/A5139264431", "inst": "Kwara State University" }, { "name": "Emmanuel Damilare Shittu", "url": "https://openalex.org/A5139251282", "inst": "Kwara State University" } ], "affiliations": [ "Zhengzhou University", "Kwara State University" ] }, { "uid": "doi:10.2139/ssrn.6881318", "doi": "10.2139/ssrn.6881318", "title": "InvEvolve: Evolving White-box Inventory Policies via Large Language Models with Performance Guarantees", "authors": [ "Chenyu Huang", "Jianghao Lin", "Zhengyang Tang", "Bo Jiang", "Ruoqing Jiang", "Benyou Wang", "Lai Wei" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6881318", "field": "management", "role": "method", "bullets": [ "Online inventory control with non-stationary demand, tested on both synthetic data and real-world retail data; geography and period not stated.", "A reinforcement-learning-trained large language model, not otherwise named, evolves white-box inventory policies from numerical and textual features, certified through confidence-interval-based statistical safety guarantees rather than external accuracy labels.", "InvEvolve outperforms classical inventory policies and deep-learning methods on both synthetic and real retail data, generating new policies that beat existing benchmarks." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 485, "authors_detailed": [ { "name": "Chenyu Huang", "url": "https://openalex.org/A5039384534", "inst": "Shanghai University of Finance and Economics" }, { "name": "J X C Lin", "url": "https://openalex.org/A5125131131", "inst": "Shanghai Jiao Tong University" }, { "name": "Zhengyang Tang", "url": "https://openalex.org/A5109434969", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Bo Jiang", "url": "https://openalex.org/A5139313940", "inst": "Shanghai University of Finance and Economics" }, { "name": "Ruoqing Jiang", "url": "https://openalex.org/A5113079414", "inst": "Tsinghua University" }, { "name": "B Wang", "url": "https://openalex.org/A5134252532", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Lai Wei", "url": "https://openalex.org/A5067495984", "inst": "Boston College" } ], "affiliations": [ "Boston College", "Shanghai University of Finance and Economics", "Shanghai Jiao Tong University", "Chinese University of Hong Kong, Shenzhen", "Tsinghua University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6895958", "doi": "10.2139/ssrn.6895958", "title": "Reconciliation-Related Fintech Complaints Receive Monetary Relief at Nearly Three Times the Rate of Other Payment Complaints, 2021-2025", "authors": [ "Ignacio Berardi" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6895958", "field": "finance", "role": "instrument", "bullets": [ "97,028 consumer complaints filed with the US Consumer Financial Protection Bureau between January 2021 and December 2025 against eleven major consumer fintechs.", "A reasoning-capable large language model, family not stated, classified each complaint for reconciliation failure using chain-of-thought prompting, validated against 80 hand-labeled complaints at 88.75 percent accuracy and 0.88 F1.", "Reconciliation-related complaints closed with monetary relief at 9.96 percent versus 3.51 percent for other fintech payment complaints, a near threefold gap holding across all four product categories." ], "bullet_provenance": "ai", "validated": true, "validation_note": "80 hand-labeled complaints, 88.75% accuracy, 0.88 F1", "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 579, "authors_detailed": [ { "name": "Ignacio Berardi", "url": "https://openalex.org/A5139330800", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6875778", "doi": "10.2139/ssrn.6875778", "title": "The Economics of Synthetic Output: Scarcity, Verification, and Trust in Generative Systems", "authors": [ "M. Elizabeth Simmons" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6875778", "field": "economics", "role": "object", "bullets": [ "Conceptual and exploratory paper with no empirical sample, drawing on information economics, signaling theory, institutional economics, and trustworthy-AI governance to frame generative output as an economic good.", "No model is used or named; the paper theorizes about generative AI broadly, treating synthetic output as an economic object whose quality is indeterminate.", "Argues the bottleneck shifts from production to evaluation, so trust, provenance, domain expertise, and judgment become the scarce resources whose value rises as synthetic output scales." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 901, "authors_detailed": [ { "name": "M. Elizabeth Simmons", "url": "https://openalex.org/A5136358537", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6877798", "doi": "10.2139/ssrn.6877798", "title": "Agentic AI Payments and the Opportunities for MiCA-Compliant Stablecoins", "authors": [ "Andrea Stazi" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6877798", "field": "finance", "role": "object", "bullets": [ "Conceptual argument on agentic commerce and machine-to-machine payments, citing a projected 3 to 5 trillion dollar market by 2030; no empirical data or sample.", "No model is used or named; the paper reasons about autonomous AI agents' payment requirements and evaluates stablecoin infrastructure against legacy fiat and volatile crypto rails.", "Argues MiCA-compliant stablecoins, with 1:1 safeguarded reserves and AMLA integration, are best positioned as the settlement layer for autonomous agentic payments." ], "bullet_provenance": "ai", "salience": 37, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 902, "authors_detailed": [ { "name": "Andrea Stazi", "url": "https://openalex.org/A5070507659", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore" ] }, { "uid": "doi:10.2139/ssrn.6881538", "doi": "10.2139/ssrn.6881538", "title": "AI Impact on Executive-level Jobs Performance: A Literature Review", "authors": [ "Ihab T. Elsehmawy" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6881538", "field": "management", "role": "object", "bullets": [ "Literature review of thirty studies published 2019 to 2026 in management, economics, and information systems journals on AI and executive-level job performance.", "No model is used; the review synthesizes evidence on generative AI's effect on executive decision-making, leadership, and managerial skills through upper-echelons and dynamic-capability lenses.", "Reports substantial task-level productivity gains, a narrowing gap between junior and senior workers, persistent algorithm aversion in high-stakes settings, and associations with firm innovation and sales growth." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 903, "authors_detailed": [ { "name": "Ihab T. Elsehmawy", "url": "https://openalex.org/A5119662219", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6878119", "doi": "10.2139/ssrn.6878119", "title": "CAIRN: Cost-Aware Information Routing for Underwriting", "authors": [ "John Christiansen" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6878119", "field": "finance", "role": "agent", "bullets": [ "Synthetic UK loan book of 1,200 credit applications, framed as an intake-to-settlement underwriting workflow with priced external data sources.", "An agentic engine treats each information purchase as a Pandora's box, pricing vendor fees plus large-language-model tokens; the model family is not stated and outputs are not checked against ground truth.", "Cost per decision falls 38.7 percent, from 33.54 to 20.57 pounds, with lower regret than a perfect-information oracle, worth about 1.3m pounds a year at 100,000 applications." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1150, "authors_detailed": [ { "name": "John Christiansen", "url": "https://openalex.org/A5128725087", "inst": "Maxwell Institute for Mathematical Sciences" } ], "affiliations": [ "Maxwell Institute for Mathematical Sciences" ] }, { "uid": "arxiv:2606.26959v1", "arxiv_id": "2606.26959v1", "title": "The Shift to Agentic AI: Evidence from Codex", "authors": [ "Drew Johnston", "David Holtz", "Alex Martin Richmond", "Christopher Ong", "Prasanna Tambe", "Aaron Chatterji" ], "posted": "2026-06-25", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.26959v1", "field": "economics", "role": "object", "bullets": [ "Usage data from OpenAI's Codex tool over the first half of 2026, contrasting external personal-account users, external organizational-account users, and workers inside OpenAI.", "Codex, OpenAI's agentic coding tool in the GPT family, is the object of study; an automated privacy-protecting pipeline measures adoption, sophistication, and output rather than using the model to read documents.", "Active users grew more than fivefold in the first half of 2026, over 10 percent run three or more concurrent agents weekly, and the median OpenAI researcher produced over 50 times more monthly output tokens than in November 2025." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "edition": 3, "audience": "general", "validated": null, "n": 1151, "authors_detailed": [ { "name": "Drew Johnston", "url": "https://openalex.org/A5104266915", "inst": "" }, { "name": "David Holtz", "url": "https://openalex.org/A5125770523", "inst": "" }, { "name": "Alex Martin Richmond", "url": "https://openalex.org/A5139449447", "inst": "" }, { "name": "Christopher Ong", "url": "https://openalex.org/A5110073360", "inst": "Harvard University Press" }, { "name": "Prasanna Tambe", "url": "https://openalex.org/A5139432675", "inst": "University of Pennsylvania" }, { "name": "Aaron Chatterji", "url": "https://openalex.org/A5119636278", "inst": "Duke University" } ], "affiliations": [ "Harvard University", "University of Pennsylvania", "Duke University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6986775", "doi": "10.2139/ssrn.6986775", "title": "Large Language Models, Supervision, and Labor Demand: Evidence from Online Recruitment Data", "authors": [ "Chenyang Wei", "Nalinjie Jia" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6986775", "field": "economics", "role": "object", "bullets": [ "Firm-level panel of Chinese online job postings, used to test a task-based labor-demand model that adds endogenous supervision, across occupations and regions.", "Researchers do not run an LLM; they build occupation-level LLM exposure and supervision-requirement measures and interact them in labor-demand regressions, model not stated.", "Higher LLM exposure is associated with lower hiring, especially cognitive labor, but supervision requirements moderate this and can drive net hiring expansion." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 171, "authors_detailed": [ { "name": "Chenyang Wei", "url": "https://openalex.org/A5139212722", "inst": "Duke University" }, { "name": "Nalinjie Jia", "url": "https://openalex.org/A5139213956", "inst": "China University of Political Science and Law" } ], "affiliations": [ "Duke University", "China University of Political Science and Law" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2607.16229v1", "arxiv_id": "2607.16229v1", "title": "FinBench: Time-Gated Calibration and Uncertainty Benchmarking for Agentic Financial Forecasting", "authors": [ "Rishab Ghosh", "Vinay Devarakonda" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.16229v1", "field": "finance", "role": "method", "bullets": [ "A benchmark for agentic financial forecasting, with a small pilot of one trading day, three liquid tickers, and 33 forecasts as a pipeline sanity check.", "The specific LLM is not stated; models must output a probability of positive return and an 80 percent interval for log return, scored with Brier and Winkler interval rules.", "The pilot shows calibration-sensitive metrics separate confident-but-fragile behaviour from uncertainty-aware forecasting; no substantive across-model results are reported yet." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 190, "authors_detailed": [ { "name": "Rishab Ghosh", "url": "https://openalex.org/A5109248469", "inst": "United Silicon Carbide (United States)" }, { "name": "Vinay Devarakonda", "url": "https://openalex.org/A5141300982", "inst": "" } ], "affiliations": [ "United Silicon Carbide (United States)" ] }, { "uid": "doi:10.2139/ssrn.6989090", "doi": "10.2139/ssrn.6989090", "title": "Text-Based ESG Beliefs and Mutual Fund Performance: Evidence from LLM Analysis of Fund Narratives", "authors": [ "Yi Li", "Andrew Urquhart", "Wei Zhang" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6989090", "field": "finance", "role": "instrument", "bullets": [ "Mutual fund reports, using strategy statements and market outlooks as the text; sample size, period, and geography not stated, with the fund as the unit of observation.", "Large language models, family not named, score the ESG orientation of fund narratives as a proxy for managers' ESG beliefs; no validation against ground truth is reported.", "Stronger ESG orientation predicts higher subsequent fund performance through stock selection, is not fully priced by investors, and strengthens after the carbon neutrality pledge." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 267, "authors_detailed": [ { "name": "Yi Li", "url": "https://openalex.org/A5139235064", "inst": "" }, { "name": "Andrew Urquhart", "url": "https://openalex.org/A5064234855", "inst": "University of Southampton" }, { "name": "Wei Zhang", "url": "https://openalex.org/A5029468759", "inst": "Tianjin University" } ], "affiliations": [ "University of Southampton", "Tianjin University" ] }, { "uid": "arxiv:2606.25984v2", "arxiv_id": "2606.25984v2", "title": "InvestPhilBench: A Multi-Layer Benchmark for Evaluating Large Language Model Procedural Reasoning in Expert Investment Philosophy", "authors": [ "Mingguang Chen", "Bo Qu" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.25984v2", "field": "finance", "role": "method", "bullets": [ "InvestPhilBench, a benchmark of 118 principle cards, 25 decision-framework cards, and 243 question-answer items across eight cognitive tiers of expert investment philosophy.", "A four-model wave is scored by an automated pipeline; the composite tracks a 100-item human gold set at Pearson r of 0.72, mean absolute error 0.10.", "Composite scores saturate at the frontier, Claude reaching 0.932 at tier four, while gate reconstruction accuracy falls to 0.57 to 0.62 at tier seven, exposing procedural gaps." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "automated scoring vs 100-item human gold set, Pearson r 0.72", "salience": 48, "edition": 3, "audience": "technical", "n": 268, "authors_detailed": [ { "name": "Mingguang Chen", "url": "https://openalex.org/A5139359250", "inst": "University of California, Riverside" }, { "name": "Bo Qu", "url": "https://openalex.org/A5108327660", "inst": "Sun Yat-sen University" } ], "affiliations": [ "University of California, Riverside", "Sun Yat-sen University" ] }, { "uid": "arxiv:2606.25358v1", "arxiv_id": "2606.25358v1", "title": "Agentic Knowledge Tracing: A Multi-Agent LLM Architecture for Stealth Assessment of Financial Literacy in Serious Games", "authors": [ "Gabriel Santos", "Rita Julia", "Marcelo Nascimento" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.25358v1", "field": "finance", "role": "instrument", "bullets": [ "193 K-12 participants across 264 sessions of a 2D platformer serious game aligned to the OECD/INFE financial-literacy framework, logging every player decision.", "An unnamed LLM classifies events on a four-point rubric validated against three experts (Fleiss kappa 0.624), and four domain agents feed Bayesian Knowledge Tracing for mastery.", "Mastery estimates correlate with learning gain (r = 0.276) and post-test (r = 0.333) but not pre-test, and the multi-agent design roughly triples a single-LLM baseline's validity." ], "bullet_provenance": "ai", "validated": true, "validation_note": "event-labeling agreement with three experts, Fleiss kappa 0.624", "salience": 50, "edition": 3, "audience": "technical", "models": [], "n": 317, "authors_detailed": [ { "name": "Gabriel dos Santos", "url": "https://openalex.org/A5039815608", "inst": "Universidade Federal de Ouro Preto" }, { "name": "Rita Julia", "url": "https://openalex.org/A5139325701", "inst": "" }, { "name": "Marcelo Nascimento", "url": "https://openalex.org/A5139359238", "inst": "" } ], "affiliations": [ "Universidade Federal de Ouro Preto" ] }, { "uid": "doi:10.2139/ssrn.6967839", "doi": "10.2139/ssrn.6967839", "title": "Bridging the Gap Between AI Adoption and Policy Readiness in Regulated Industries", "authors": [ "Elizabeth Harrison" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6967839", "field": "management", "role": "object", "bullets": [ "Regulated industries including finance, healthcare, and legal services; a mixed-methods design combining regulatory analysis, industry case studies, and compliance audits; no sample size or period stated.", "No language model is used as a research tool; generative AI adoption is the object, focusing on shadow AI where over half of employees reportedly use tools without formal oversight. Tools not named.", "Documents a widening gap between adoption and policy readiness and proposes a tiered, dynamic policy-readiness framework to align innovation with sectoral compliance requirements." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 482, "authors_detailed": [ { "name": "Elizabeth Harrison", "url": "https://openalex.org/A5139228878", "inst": "City University of New York" } ], "affiliations": [ "City University of New York" ] }, { "uid": "doi:10.2139/ssrn.6925818", "doi": "10.2139/ssrn.6925818", "title": "Cost and Information Asymmetries in the AI Era: Implications for Commercial and Government Sovereignty", "authors": [ "Mauricio Aristizabal" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6925818", "field": "economics", "role": "object", "bullets": [ "Formal economic model of digital service access contrasting a post-LLM equilibrium with a pre-LLM search-engine baseline; the agents are AI platforms, commercial firms, and government agencies, with no empirical dataset.", "No model is run by the researchers; LLM-based AI platforms are studied as intent intermediaries that interpret natural language cheaply but cannot execute transactions.", "Service providers are estimated to lose about 70 percent of operational intents to AI platforms, producing intelligence, relationship, and voice sovereignty losses, especially for government agencies." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 483, "authors_detailed": [ { "name": "Mauricio Aristizábal", "url": "https://openalex.org/A5016616167", "inst": "Euro-Mediterranean Water Information System" } ], "affiliations": [ "Euro-Mediterranean Water Information System" ] }, { "uid": "doi:10.2139/ssrn.6975018", "doi": "10.2139/ssrn.6975018", "title": "When Better AI Hurts: Verification, Compliance, and AI-Human Handoffs in Service Systems", "authors": [ "Guanling Yang", "Cuihong Li", "Ricky Roet-Green", "Fasheng Xu" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6975018", "field": "management", "role": "object", "bullets": [ "Analytical game-theoretic queueing model of AI-human handoffs in customer-facing service systems, with a firm setting a verification policy and strategic, delay-sensitive customers; no empirical data.", "Does not use a language model; it models generative AI agents whose answers carry unobservable hallucination risk, and derives equilibrium customer bypass and firm verification behavior.", "Shows an AI improvement trap where, above a hallucination-risk threshold, higher AI accuracy lowers effective throughput, though consumer surplus always rises, exposing firm-customer incentive misalignment." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 577, "authors_detailed": [ { "name": "Guanling Yang", "url": "https://openalex.org/A5083076643", "inst": "University of Rochester" }, { "name": "Cuihong Li", "url": "https://openalex.org/A5100721830", "inst": "University of Connecticut" }, { "name": "Ricky Roet-Green", "url": "https://openalex.org/A5139265515", "inst": "University of Rochester" }, { "name": "Fasheng Xu", "url": "https://openalex.org/A5086759286", "inst": "University of Connecticut" } ], "affiliations": [ "University of Rochester", "University of Connecticut" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6989759", "doi": "10.2139/ssrn.6989759", "title": "Beyond Paradigms: RMKO Framework for Job Recommender Systems from Classical to Agentic Architectures", "authors": [ "arbiya ochi" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6989759", "field": "management", "role": "object", "bullets": [ "Systematic survey of 88 primary studies on job recommender systems published from 2011 to 2025, spanning classical keyword matching to LLM, retrieval-augmented, and agentic architectures.", "No model applied by the authors; the paper builds the four-dimensional RMKO taxonomy and codes studies using multidimensional coding, Spearman correlations, and Hamming-distance clustering.", "Identifies four architectural archetypes converging toward interactive or agentic generative systems, with persistent asymmetries in knowledge grounding and output sophistication across the reviewed literature." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 578, "authors_detailed": [ { "name": "arbiya ochi", "url": "https://openalex.org/A5139294507", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6987090", "doi": "10.2139/ssrn.6987090", "title": "Generative AI Infrastructure and Local Housing Markets", "authors": [ "Sen Li", "Chunyu Guo" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6987090", "field": "economics", "role": "object", "bullets": [ "US county-level housing data from Zillow and Redfin, comparing counties with and without data-center investment before and after the public release of ChatGPT.", "No language model is applied by the researchers; the ChatGPT release is treated as a shock to the local relevance of GenAI infrastructure in a difference-in-differences design.", "Housing values rose more in data-center counties after the release, with fewer new listings and stronger sold-to-list price and quantity ratios." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 58, "edition": 3, "audience": "general", "validated": null, "n": 895, "authors_detailed": [ { "name": "Sen Li", "url": "https://openalex.org/A5066809588", "inst": "Bentley University" }, { "name": "Chunyu Guo", "url": "https://openalex.org/A5139164252", "inst": "Hult International Business School" } ], "affiliations": [ "Bentley University", "Hult International Business School" ] }, { "uid": "doi:10.2139/ssrn.6976578", "doi": "10.2139/ssrn.6976578", "title": "Yukti: From Natural-Language Situations to Robust, Verifiable Decisions: An Uncertainty-Typed Proposition IR, Assumption-Robust Pareto Frontiers, and a Regret Certificate – Why a Language Model Should Formulate, Not Solve", "authors": [ "Suyash Mishra" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6976578", "field": "management", "role": "method", "bullets": [ "No standard empirical sample; validation uses controlled structural misspecification, a regulated oncology commercial decision, and a public dataset of 41,188 marketing decisions backtested out of sample.", "YUKTI uses a language model (a reasoning model, family not named) to formulate rather than solve optimization problems, encoding coefficient uncertainty and routing to exact, nonlinear, or evolutionary solvers.", "The robust compromise cut mean and tail regret by over 90 percent versus a naive point plan and beat the logged status quo by 34 percent on the marketing backtest." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 896, "authors_detailed": [ { "name": "Suyash Mishra", "url": "https://openalex.org/A5139294070", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6874299", "doi": "10.2139/ssrn.6874299", "title": "When AI Joins the Crowd: Human Evaluation of AI-Generated Annotations in Crowdchecking", "authors": [ "Adam Shenxiong Li", "Yang Gao", "Huaxia Rui" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6874299", "field": "management", "role": "object", "bullets": [ "X's Community Notes AI Note Writer program, using observational rating data with an instrumental variable analysis and a randomized field experiment varying AI identity disclosure and note quality.", "Generative AI (model not named) writes fact-checking notes; the study compares their ratings and public status against human-written notes and traces differences through content analysis.", "AI notes received 15.3 percent higher average ratings and were 77.1 percent more likely to become public, driven by quality, with the AI identity label showing no significant effect." ], "bullet_provenance": "ai", "salience": 68, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 897, "authors_detailed": [ { "name": "Adam Shenxiong Li", "url": "https://openalex.org/A5135014292", "inst": "University of Rochester" }, { "name": "Yang Gao", "url": "https://openalex.org/A5034775543", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Huaxia Rui", "url": "https://openalex.org/A5024136020", "inst": "University of Rochester" } ], "affiliations": [ "University of Rochester", "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.26432v1", "arxiv_id": "2606.26432v1", "title": "Embedding Foundation Model Predictions in Discrete-Choice Models with Structural Guarantees", "authors": [ "Yingshuo Wang", "Xian Sun", "Yanhang Li", "Zhichao Fan", "Zexin Zhuang" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.26432v1", "field": "economics", "role": "method", "bullets": [ "Three choice-prediction datasets, including transportation choice data, evaluated with two tabular foundation models.", "Foundation-model choice probabilities enter a multinomial logit as a precomputed feature; structural coefficients are fit with sign constraints, then a small neural correction is added, preserving the marginal rate of substitution.", "The adapter added 6.4 percentage points of test accuracy on average and up to 12.8, held 100 percent cost monotonicity, and produced values of time within published transportation-economics ranges." ], "bullet_provenance": "ai", "validated": true, "validation_note": "test-set choice prediction accuracy vs multinomial logit", "salience": 54, "edition": 3, "audience": "technical", "models": [], "n": 898, "authors_detailed": [ { "name": "Yingshuo Wang", "url": "https://openalex.org/A5139434299", "inst": "" }, { "name": "Xian Sun", "url": "https://openalex.org/A5139433923", "inst": "" }, { "name": "Yanhang Li", "url": "https://openalex.org/A5139442629", "inst": "" }, { "name": "Zhichao Fan", "url": "https://openalex.org/A5139429810", "inst": "" }, { "name": "Zexin Zhuang", "url": "https://openalex.org/A5136573671", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.26350v1", "arxiv_id": "2606.26350v1", "title": "OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents", "authors": [ "Kaicheng Zhang", "Wen Ge", "Lei Jiang", "Weixin Yang", "Jordan Langham-Lopez", "Jialin Yu", "Lukasz Szpruch", "Hao Ni" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.26350v1", "field": "finance", "role": "method", "bullets": [ "A gym environment spanning forecasting, market generation, real-time trading, and fraud detection under one execution and verification interface, with no empirical sample.", "Not tied to a named model; OpenFinGym evaluates LLM agents on quant-finance tasks and includes a pipeline converting finance publications into executable task packages and a paper-trading engine.", "No substantive finding is reported; the contribution is the containerized runtime, a verifier preventing train-test leakage, and integration for SFT and RL post-training." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 899, "authors_detailed": [ { "name": "Kaicheng Zhang", "url": "https://openalex.org/A5101611743", "inst": "NIHR Imperial Biomedical Research Centre" }, { "name": "Wen Ge", "url": "https://openalex.org/A5139434094", "inst": "" }, { "name": "Lei Jiang", "url": "https://openalex.org/A5139393498", "inst": "" }, { "name": "Weixin Yang", "url": "https://openalex.org/A5071177749", "inst": "North China Electric Power University" }, { "name": "Jordan Langham-Lopez", "url": "https://openalex.org/A5035898041", "inst": "University of Nottingham" }, { "name": "J. Yu", "url": "https://openalex.org/A5107877612", "inst": "Iowa State University" }, { "name": "Lukasz Szpruch", "url": "https://openalex.org/A5139441374", "inst": "" }, { "name": "Hao Ni", "url": "https://openalex.org/A5139418727", "inst": "" } ], "affiliations": [ "NIHR Imperial Biomedical Research Centre", "North China Electric Power University", "University of Nottingham", "Iowa State University" ] }, { "uid": "arxiv:2606.26277v1", "arxiv_id": "2606.26277v1", "title": "From Clicks to Intent: Cross-Platform Session Embeddings with LLM-Distilled Taxonomy for Financial Services Recommendations", "authors": [ "Dianjing Fan", "Yao Li", "Kyaw Hpone Myint", "Dwipam Katariya", "Alexandre G. R. Day", "Pranab Mohanty", "Giri Iyengar" ], "posted": "2026-06-24", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.26277v1", "field": "management", "role": "instrument", "bullets": [ "Industrial financial-services setting with pre-login web clickstreams and authenticated in-app sessions, evaluated on mobile homepage tile ranking and user conversion prediction.", "A self-supervised Transformer encodes clickstreams into session embeddings, while an LLM (not named) generates and distills an intent taxonomy into interpretable low-latency labels.", "Session embeddings improved macro Recall@1 by 1.88 percent and cut log loss by 13.38 percent over production baselines, and beat the LLM labels by 4.3 percent micro F1 on conversion." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 3, "audience": "technical", "models": [], "n": 900, "authors_detailed": [ { "name": "Dianjing Fan", "url": "https://openalex.org/A5139393921", "inst": "" }, { "name": "Yao Li", "url": "https://openalex.org/A5139431221", "inst": "Ministry of Education" }, { "name": "Kyaw Hpone Myint", "url": "https://openalex.org/A5120304585", "inst": "Capital One (United States)" }, { "name": "Dwipam Katariya", "url": "https://openalex.org/A5015057493", "inst": "Capital One (United States)" }, { "name": "Alexandre G. R. Day", "url": "https://openalex.org/A5041940873", "inst": "Boston University" }, { "name": "Pranab Mohanty", "url": "https://openalex.org/A5019589740", "inst": "Fidelity Investments (United States)" }, { "name": "Giri Iyengar", "url": "https://openalex.org/A5136161451", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "Boston University", "University of Illinois Urbana-Champaign", "Ministry of Education", "Capital One (United States)", "Fidelity Investments (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.24370v1", "arxiv_id": "2606.24370v1", "title": "When Helpfulness Overrides Causal Caution: Context-Dependent Suppression and Recovery in LLMs", "authors": [ "Hiroshi Okumura" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.24370v1", "field": "management", "role": "object", "bullets": [ "Experimental study of causal caution, the propensity to withhold causal judgment under weak evidence, run across 480 trials contrasting academic prompts with practical business and policy advisory prompts.", "Four models, Claude Sonnet 4.6, Claude Opus 4.7, GPT-5.5, and Gemini 3.1 Pro, were scored with a rubric based on Pearl's causal hierarchy, with no external ground truth used.", "Caution maintenance fell from 91.7 to 100 percent in academic contexts to 6.7 to 18.3 percent in practical ones, and a short reconsideration prompt restored it to 71.4 to 100 percent." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 55, "edition": 3, "audience": "general", "validated": null, "n": 84, "authors_detailed": [ { "name": "Hiroshi Okumura", "url": "https://openalex.org/A5112676396", "inst": "Mitsubishi Research Institute (Japan)" } ], "affiliations": [ "Mitsubishi Research Institute (Japan)" ] }, { "uid": "arxiv:2606.24950v1", "arxiv_id": "2606.24950v1", "title": "MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios", "authors": [ "Patara Trirat", "Jin Myung Kwak", "Jay Heo", "Heejun Lee", "Sung Ju Hwang" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.24950v1", "field": "finance", "role": "method", "bullets": [ "4,416 US small and micro-cap equities over 2021 to 2026, sharing a point-in-time panel of prices, 46.8M XBRL facts, 53 macro series, 295,860 SEC filings, and 215,882 news articles.", "The benchmark scores 19 methods across six families, from naive heuristics to time-series foundation models, fine-tuned LLM time-series models, and zero-shot LLMs; specific model names not stated.", "Introduces seven tasks spanning contextual forecasting, public and private valuation, statement generation, and scenario-conditioned returns; no aggregate accuracy figure is reported in the abstract." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 110, "authors_detailed": [ { "name": "Patara Trirat", "url": "https://openalex.org/A5006442645", "inst": "Korea Advanced Institute of Science and Technology" }, { "name": "Jin Myung Kwak", "url": "https://openalex.org/A5139376369", "inst": "Sangmyung University" }, { "name": "Jay Heo", "url": "https://openalex.org/A5139367909", "inst": "" }, { "name": "Heejun Lee", "url": "https://openalex.org/A5139336963", "inst": "" }, { "name": "Sung Ju Hwang", "url": "https://openalex.org/A5139307745", "inst": "Sangmyung University" } ], "affiliations": [ "Korea Advanced Institute of Science and Technology", "Sangmyung University" ] }, { "uid": "doi:10.2139/ssrn.6985745", "doi": "10.2139/ssrn.6985745", "title": "Learning About LLM Biases Impacts Analytical and Emotional Trust of AI Differently for WEIRD vs. non-WEIRD Populations", "authors": [ "Tailia Malloy", "Jules Wax", "Tegawendé F. BISSYANDE", "Jacques Klein" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6985745", "field": "management", "role": "object", "bullets": [ "An online experiment with 504 participants balanced on age and gender, 31.2 percent classified as WEIRD and 68.8 percent as non-WEIRD, using pre and post trust questionnaires.", "No model serves as an instrument; an educational module on LLM biases separates the questionnaires, which distinguish analytical trust from emotional trust in AI.", "For WEIRD participants only, bias education cut analytical trust more than emotional trust, d equals 0.301, p equals 0.001, with a significant group effect on the contrast." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 170, "authors_detailed": [ { "name": "Tyler Malloy", "url": "https://openalex.org/A5006638341", "inst": "University of Luxembourg" }, { "name": "Jules Wax", "url": "https://openalex.org/A5139209867", "inst": "" }, { "name": "Tegawendé F. Bissyandé", "url": "https://openalex.org/A5082835974", "inst": "University of Luxembourg" }, { "name": "Jacques Klein", "url": "https://openalex.org/A5040326968", "inst": "University of Luxembourg" } ], "affiliations": [ "University of Luxembourg" ] }, { "uid": "doi:10.2139/ssrn.6985744", "doi": "10.2139/ssrn.6985744", "title": "Leveraging Chatbots to Support Help-Seeking", "authors": [ "James Gaskin", "Eva Blondeel", "Ryan Schuetzler", "Rachel Serre", "Jacob Steffen", "David A. Wood", "Taylor Bullock", "Taylor Wells" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6985744", "field": "management", "role": "object", "bullets": [ "Two classroom field studies, 597 students in an introductory information systems course and 916 in an accounting program, plus a qualitative survey of 32 working professionals.", "A custom LLM chatbot and ChatGPT served as on-demand support; effects were measured through field studies rather than validated against any coded benchmark.", "Chatbots significantly reduced help-seeking avoidance and were preferred over human support, appearing to level access across introversion and social anxiety." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 56, "edition": 3, "audience": "general", "validated": null, "n": 266, "authors_detailed": [ { "name": "James Gaskin", "url": "https://openalex.org/A5137286980", "inst": "Brigham Young University" }, { "name": "Eva Blondeel", "url": "https://openalex.org/A5078612290", "inst": "Ghent University" }, { "name": "Ryan M. Schuetzler", "url": "https://openalex.org/A5087484939", "inst": "Brigham Young University" }, { "name": "Rachel Serre", "url": "https://openalex.org/A5062170110", "inst": "Brigham Young University" }, { "name": "Jacob Steffen", "url": "https://openalex.org/A5066407774", "inst": "Brigham Young University" }, { "name": "David A. Wood", "url": "https://openalex.org/A5075888890", "inst": "Brigham Young University" }, { "name": "Taylor Bullock", "url": "https://openalex.org/A5139212611", "inst": "Virginia Tech" }, { "name": "Taylor Wells", "url": "https://openalex.org/A5022247847", "inst": "Brigham Young University" } ], "affiliations": [ "Brigham Young University", "Ghent University", "Virginia Tech" ] }, { "uid": "doi:10.2139/ssrn.6533458", "doi": "10.2139/ssrn.6533458", "title": "Assessing AI-Mediated Interviewing Quality: A Theory-Grounded Comparative Study of Leading Models in Evaluation Contexts", "authors": [ "Ali Safarnejad", "Hippolyte Lefebvre" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6533458", "field": "management", "role": "method", "bullets": [ "Six generative AI models, five prompt-only and one fine-tuned, tested across two realistic evaluation-interview scenarios under a convergent mixed-methods design; no human sample size stated.", "Models conducted qualitative evaluation interviews scored on eight theory-grounded quality measures, with the fine-tuned model benchmarked against human interviewer performance; specific model names not stated.", "The fine-tuned model matched human performance on nearly 70 percent of quality measures, but models struggled with neutrality and clarification probing, and performance was context-dependent." ], "bullet_provenance": "ai", "validated": true, "validation_note": "matched human interviewers on ~70% of quality measures", "salience": 44, "edition": 3, "audience": "general", "models": [], "n": 481, "authors_detailed": [ { "name": "Ali Safarnejad", "url": "https://openalex.org/A5011108070", "inst": "United Nations Children's Fund" }, { "name": "Hippolyte Lefebvre", "url": "https://openalex.org/A5009686600", "inst": "University College Dublin" } ], "affiliations": [ "United Nations Children's Fund", "University College Dublin" ] }, { "uid": "doi:10.2139/ssrn.6981429", "doi": "10.2139/ssrn.6981429", "title": "SentiCausRL: An interval-valued carbon price prediction model incorporating large language model sentiment analysis, causal discovery algorithms, and reinforcement learning", "authors": [ "Jinpei Liu", "Jiaqi Wang", "Xiaoman Zhao" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6981429", "field": "finance", "role": "instrument", "bullets": [ "National carbon market price forecasting using carbon-market-related news and related influencing factors; sample size, period, and country are not stated in the abstract.", "An unnamed large language model scores sentiment from news as features, combined with variational mode decomposition, causal-discovery feature selection, and reinforcement-learning model weighting, with sentiment not validated.", "The SentiCausRL model produces interval-valued forecasts and reportedly outperforms traditional benchmark methods, though the abstract gives no specific error magnitudes." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 888, "authors_detailed": [ { "name": "Jinpei Liu", "url": "https://openalex.org/A5005697228", "inst": "Sensor Electronics (United States)" }, { "name": "Jiaqi Wang", "url": "https://openalex.org/A5139191359", "inst": "" }, { "name": "Xiaoman Zhao", "url": "https://openalex.org/A5139182453", "inst": "" } ], "affiliations": [ "Sensor Electronics (United States)" ] }, { "uid": "doi:10.2139/ssrn.6861099", "doi": "10.2139/ssrn.6861099", "title": "The Tacit Dimension of Agentic AI: Relational Competence, Delegated Trust, and the Irreducibly Human Core of High-stakes Interaction", "authors": [ "Sashikanta Barik" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6861099", "field": "management", "role": "object", "bullets": [ "Conceptual paper on agentic AI deployed autonomously in medical, legal, and financial high-stakes domains, with no dataset, drawing on Polanyi's tacit knowledge and clinical communication research.", "No LLM is used empirically; the paper studies governance of AI agents, arguing task competence is insufficient and introducing delegated trust failure and a tacit knowledge problem.", "It proposes a three-tier governance framework and argues agentic AI should be restricted to administrative functions where relational, tacit competence is the critical capability." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 889, "authors_detailed": [ { "name": "Sashikanta Barik", "url": "https://openalex.org/A5137410845", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6868545", "doi": "10.2139/ssrn.6868545", "title": "The Speed Limit on Creative Destruction: AI and the Absorption Capacity of U.S. Labor Markets", "authors": [ "Benoît Renard" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6868545", "field": "economics", "role": "object", "bullets": [ "US labor market absorption capacity, using Current Population Survey data from 2019 to 2025 with 8.6 million observations, crossed with the AI exposure measure of Massenkoff and McCrory (2026).", "No LLM is used as a tool; the paper models labor reallocation throughput and treats generative AI exposure of about 0.80 of occupations as a technological shock.", "It estimates maximum throughput near 770,000 workers per year constrained by retraining, finds no divergence by exposure yet, and argues tripling active labor market policy spending would absorb the conservative scenario." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 890, "authors_detailed": [ { "name": "B. Renard", "url": "https://openalex.org/A5023817396", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6985158", "doi": "10.2139/ssrn.6985158", "title": "Beyond AI Literacy: A Curriculum Architecture for Developing AI-Augmented Disciplinary Judgement in Management Education", "authors": [ "Nguyen Thai" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6985158", "field": "management", "role": "object", "bullets": [ "Conceptual paper on management education across business disciplines, using the marketing curriculum as a fully worked case and drawing on evaluative judgement theory and the automation-augmentation paradox; no empirical sample.", "No language model is used or named by the authors; the paper treats generative AI as a working condition of practice and defines the construct of AI-augmented disciplinary judgement.", "Proposes a three-layer curriculum architecture with a Decision Studio assessment model and a four-step translation procedure, sequenced across a degree via an Introduce-Develop-Assure progression." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 891, "authors_detailed": [ { "name": "Nguyen T. Thai", "url": "https://openalex.org/A5047723301", "inst": "Vietnam National University Ho Chi Minh City" } ], "affiliations": [ "Vietnam National University Ho Chi Minh City" ] }, { "uid": "doi:10.2139/ssrn.6960438", "doi": "10.2139/ssrn.6960438", "title": "A Meta-Framework for Autonomous Knowledge Ingestion: Standardizing Machine-Readable Revenue Governance and Deterministic Safety Loops in Agentic Hospitality Systems", "authors": [ "N.P. Gayan Nugawela" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6960438", "field": "management", "role": "agent", "bullets": [ "Simulation study of autonomous AI agents in hospitality revenue management, run as a 10,000-iteration stochastic model under simulated demand shocks rather than field data.", "The language model is not named; an AKIF-governed agent is compared with an unaligned volume-maximising AI agent and a legacy human-run system, with a cost floor enforced by middleware outside the model's reasoning.", "The governed agent yields higher gross operating profit and less long-term rate erosion, and exposes a case where standard RevPAR reporting would reward the unaligned agent's value-destroying behaviour." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 892, "authors_detailed": [ { "name": "Gayan Nugawela", "url": "https://openalex.org/A5134997143", "inst": "University of Wales Trinity Saint David" } ], "affiliations": [ "University of Wales Trinity Saint David" ] }, { "uid": "doi:10.2139/ssrn.6985157", "doi": "10.2139/ssrn.6985157", "title": "Developing Prompt Design Competence in Management Education: Exploring Generative AI as a Cognitive Tool for Business Problem-Solving", "authors": [ "EVA GARCIA BELTRAN", "Maria Cristina Yuste García" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6985157", "field": "management", "role": "object", "bullets": [ "373 MBA students at a fully online Spanish university completed an open-ended prompt-design task within a Business Strategy course, in a cross-sectional quasi-experimental design.", "The generative AI model is not named; students used it as a cognitive tool guided by the CRETA-R framework, and human versus AI-based evaluations of prompts were compared with non-parametric tests.", "Prompt-design performance was significantly associated with examination results and course grades, while human and AI evaluations diverged, pointing to limits of automated assessment of reasoning." ], "bullet_provenance": "ai", "validated": false, "validation_note": "human and AI evaluations compared, no agreement figure reported", "salience": 40, "edition": 3, "audience": "general", "models": [], "n": 893, "authors_detailed": [ { "name": "Eva García-Beltrán", "url": "https://openalex.org/A5077834947", "inst": "University of Atlántico" }, { "name": "Maria Cristina Yuste García", "url": "https://openalex.org/A5139197270", "inst": "" } ], "affiliations": [ "University of Atlántico" ] }, { "uid": "arxiv:2606.24420v1", "arxiv_id": "2606.24420v1", "title": "Beyond Logprobs: A Multi-Signal Confidence Engine for LLM-Based Document Field Extraction", "authors": [ "Nitesh Kumar" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.24420v1", "field": "accounting", "role": "method", "bullets": [ "Confidence-estimation method for LLM document field extraction, evaluated on DocILE invoices with 55 fields and a 26 percent failure rate and on CORD receipts, motivated by financial reconciliation and procurement automation.", "The specific LLM is not named; ExtractConf fuses disagreement between a field-guided and a document-guided extraction pass with model uncertainty, OCR, image quality and layout into a classifier.", "Reaches 0.928 ROC AUC on invoices and cuts selective-prediction risk 70 percent over logprob-mean, with 99.1 percent accuracy at 80 percent coverage and 0.858 AUC transferring to receipts." ], "bullet_provenance": "ai", "validated": true, "validation_note": "DocILE and CORD labelled benchmarks, ROC AUC and accuracy reported", "salience": 46, "edition": 3, "audience": "technical", "models": [], "n": 894, "authors_detailed": [ { "name": "Nitesh Kumar", "url": "https://openalex.org/A5139242399", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6871318", "doi": "10.2139/ssrn.6871318", "title": "From Technologist to Business Architect: The CIO's Evolving Role as Enterprise AI Strategist and Governance Leader", "authors": [ "Sunil Gentyala" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6871318", "field": "management", "role": "object", "bullets": [ "Synthesis of four industry surveys from late 2024 to early 2026: Foundry n=1,156, Deloitte n=622, Gartner n=3,186, and McKinsey n=632, on the changing CIO role.", "No model used by researchers; documents the CIO transformation through upper echelons and IT governance theory and proposes ContextGuard, a zero-trust architecture for AI context protocol security.", "Reports 65% of CIOs report to the CEO, 36% hold P&L accountability, 80% lead AI evaluation, and 41.7% of audited MCP deployments harbor exploitable weaknesses." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1148, "authors_detailed": [ { "name": "Sunil Gentyala", "url": "https://openalex.org/A5137552179", "inst": "" } ] }, { "uid": "arxiv:2606.24783v1", "arxiv_id": "2606.24783v1", "title": "Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce", "authors": [ "Filippos Ventirozos", "Matthew Shardlow" ], "posted": "2026-06-23", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.24783v1", "field": "economics", "role": "object", "bullets": [ "Vision paper with no empirical data, considering agentic e-commerce where autonomous buyer agents transact using agent-native micro-payment rails such as x402 and AP2.", "No model used or named; argues the scarce resource shifts from product matching to acquiring trustworthy product information, sketching a micro-transaction market architecture with reputational trust scoring.", "Proposes a freemium market where buyer agents pay fractions of a cent for verified seller and reviewer data, arguing it rewards genuine product quality and yields truer competition than ranking-based storefronts." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1149, "authors_detailed": [ { "name": "Filippos Ventirozos", "url": "https://openalex.org/A5125502544", "inst": "Manchester Metropolitan University" }, { "name": "Matthew Shardlow", "url": "https://openalex.org/A5135380333", "inst": "Manchester Metropolitan University" } ], "affiliations": [ "Manchester Metropolitan University" ] }, { "uid": "arxiv:2606.23032v3", "arxiv_id": "2606.23032v3", "title": "IPO Finance Agent: Benchmark of LLM Financial Analysts Beyond Finance Agent v2, with Automated Rubric Generation, on the SpaceX (SPCX) IPO", "authors": [ "Mostapha Benhenda" ], "posted": "2026-06-22", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.23032v3", "field": "finance", "role": "method", "bullets": [ "Benchmark of LLM financial analysts on IPO due diligence; a 1,000-question dataset is built from S-1 filings, with 70 questions on the SpaceX S-1 released publicly.", "Frontier models including Claude, ChatGPT, Gemini, GLM, MiMo, and MiniMax answer S-1 questions using contextual retrieval, scored against automatically generated rubrics that human experts review before deployment.", "Zhipu GLM-5.2 leads at 79.8 percent accuracy and Xiaomi MiMo-2.5 Pro reaches 77.2 percent at 0.05 dollars per query, both beating the prior 57.9 percent ceiling." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "accuracy vs human-reviewed rubrics on SpaceX S-1", "salience": 62, "edition": 3, "audience": "technical", "n": 124, "authors_detailed": [ { "name": "Mostapha Benhenda", "url": "https://openalex.org/A5139161637", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6869661", "doi": "10.2139/ssrn.6869661", "title": "Enterprise Generative AI: A Systematic Review of Security Risks and Governance", "authors": [ "Audrey Rah" ], "posted": "2026-06-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6869661", "field": "management", "role": "object", "bullets": [ "A systematic literature review, following PRISMA, Kitchenham, and Webster-Watson protocols, of enterprise generative AI adoption and security risk across United States organizations, synthesizing peer-reviewed work, standards, and industry disclosures.", "No model is used for measurement; the review classifies platforms and risks and introduces five synthesis instruments covering adoption scoring, risk classification, platform taxonomy, and governance maturity.", "Agentic architectures introduce acute vulnerabilities including prompt injection and RAG corpus poisoning, and cross-mapping NIST, ISO, OWASP, and MITRE standards reveals gaps in multi-agent accountability and memory-state auditing." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 169, "authors_detailed": [ { "name": "Audrey Rah", "url": "https://openalex.org/A5104244670", "inst": "University of Houston" } ], "affiliations": [ "University of Houston" ] }, { "uid": "doi:10.2139/ssrn.6975643", "doi": "10.2139/ssrn.6975643", "title": "The Informed Insider: A Leading Measure of Quality", "authors": [ "Wei Cai", "Dennis Campbell", "Yaxuan Chen", "Yufei Chen", "Andrea Prat" ], "posted": "2026-06-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6975643", "field": "accounting", "role": "instrument", "bullets": [ "Over 4.3 million Glassdoor employee reviews for S&P 1500 firms from 2008 to 2023, constructing forward-looking firm-year measures of product and service quality.", "Machine learning models trained on firms with third-party customer satisfaction data build the index; GPT zero-shot and supervised fine-tuning provide an alternative measure, validated against other quality metrics.", "The index is the single most important out-of-sample predictor of future quality relative to fundamentals and Glassdoor ratings, and forecasts recalls, brand value, and profitability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 71, "edition": 3, "audience": "general", "n": 189, "authors_detailed": [ { "name": "Wei Cai", "url": "https://openalex.org/A5032545702", "inst": "Columbia Business School, 3022 Broadway, New York, NY 10027, United States" }, { "name": "Dennis Campbell", "url": "https://openalex.org/A5139066524", "inst": "Dana-Farber/Harvard Cancer Center" }, { "name": "Yaxuan Chen", "url": "https://openalex.org/A5064772409", "inst": "Cornell University" }, { "name": "Yufei Chen", "url": "https://openalex.org/A5100411549", "inst": "Cornell University" }, { "name": "Andrea Prat", "url": "https://openalex.org/A5072583771", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Columbia Business School, 3022 Broadway, New York, NY 10027, United States", "Cornell University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6869664", "doi": "10.2139/ssrn.6869664", "title": "Comparative Analysis of Initial and Long-term Financial Commitments of Cloud-based and Onpremises Infrastructure Solutions for Large Language Models", "authors": [ "Daniel Winnips" ], "posted": "2026-06-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6869664", "field": "management", "role": "object", "bullets": [ "Organizations of varying sizes, studied with a mixed-methods design combining a literature review and a workshop involving students from a Business Information Systems master's program.", "No LLM is applied by the researchers; the paper compares cloud versus on-premises infrastructure for deploying LLMs on cost, environmental impact and required skills.", "Cloud lowers upfront costs and scales for startups and smaller firms, while on-premises gives more control and potentially lower long-term costs for larger organizations." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 264, "authors_detailed": [ { "name": "Daniel Winnips", "url": "https://openalex.org/A5139107129", "inst": "FHNW University of Applied Sciences and Arts Northwestern Switzerland" } ], "affiliations": [ "FHNW University of Applied Sciences and Arts Northwestern Switzerland" ] }, { "uid": "arxiv:2606.22797v1", "arxiv_id": "2606.22797v1", "title": "Measuring Behavior Portability in Large Language Models", "authors": [ "Tianjia Dong", "Nadav Kunievsky", "James A. Evans" ], "posted": "2026-06-22", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.22797v1", "field": "economics", "role": "agent", "bullets": [ "Controlled experiments across seven canonical economic decision problems using payoff-equivalent environments that share incentive structure but differ in surface presentation.", "The paper does not name the LLMs; a framework fits an interpretable behavioral model on pooled source environments and tests out-of-sample prediction against an oracle trained on target data.", "Substantial and systematic portability losses, indicating behavioral characterizations of LLMs do not transfer reliably to structurally equivalent decision environments." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 265, "authors_detailed": [ { "name": "T. K. Dong", "url": "https://openalex.org/A5109436538", "inst": "Knowledge Systems Institute" }, { "name": "Nadav Kunievsky", "url": "https://openalex.org/A5126777966", "inst": "Knowledge Systems Institute" }, { "name": "James A. Evans", "url": "https://openalex.org/A5139177403", "inst": "" } ], "affiliations": [ "Knowledge Systems Institute" ] }, { "uid": "doi:10.2139/ssrn.6944058", "doi": "10.2139/ssrn.6944058", "title": "Deep Neural Newsvendor Optimization with Fractile Adjustment and LLM-Based Feature Discovery", "authors": [ "Zhe FU", "Frank (Youhua) Chen", "Mengzhuo Guo", "Shaochong Lin", "Jindou LUO" ], "posted": "2026-06-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6944058", "field": "management", "role": "instrument", "bullets": [ "Three real-world retail datasets combining numerical variables with semantic labels such as store locations, product names, and dates; the unit is the newsvendor ordering decision. Period and geography not stated.", "An unnamed large language model maps semantic labels to verifiable external data sources, from which numerical features are retrieved and kept if they raise profit; no accuracy check against ground truth is reported.", "External-feature integration significantly raises out-of-sample profit in two of three datasets and is marginal in the third; the fractile policy matches or beats established benchmarks." ], "bullet_provenance": "ai", "validated": false, "salience": 47, "edition": 3, "audience": "technical", "models": [], "n": 480, "authors_detailed": [ { "name": "Zhe FU", "url": "https://openalex.org/A5139070685", "inst": "" }, { "name": "Frank Chen", "url": "https://openalex.org/A5101523300", "inst": "City University of Hong Kong" }, { "name": "Mengzhuo Guo", "url": "https://openalex.org/A5051055133", "inst": "Sichuan University" }, { "name": "Shaochong Lin", "url": "https://openalex.org/A5016838877", "inst": "Chinese University of Hong Kong" }, { "name": "Jun Luo", "url": "https://openalex.org/A5029995222", "inst": "Guangdong Ocean University" } ], "affiliations": [ "City University of Hong Kong", "Sichuan University", "Chinese University of Hong Kong", "Guangdong Ocean University" ] }, { "uid": "arxiv:2606.22337v2", "arxiv_id": "2606.22337v2", "title": "Theorist Toolbox: Tools for Agent Based LLM-assisted economic theory Research", "authors": [ "Moran Koren" ], "posted": "2026-06-21", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.22337v2", "field": "economics", "role": "method", "bullets": [ "One worked example in economic theory, designing a Groves or Pigouvian incentive mechanism for the Gans-Kominers eigengrade model of grade inflation.", "Three protocols are compared: a single disciplined pass, an adversarial pair with Claude Opus 4.8 proposing and OpenAI Codex refuting, and a multi-agent project with a reviewer gate.", "No run produced a strict VCG mechanism; adversarial verification caught three false claims, and external verification rather than model capability was the binding constraint." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 55, "edition": 3, "audience": "technical", "validated": null, "n": 263, "authors_detailed": [ { "name": "Moran Koren", "url": "https://openalex.org/A5137478037", "inst": "Ben-Gurion University of the Negev" } ], "affiliations": [ "Ben-Gurion University of the Negev" ] }, { "uid": "arxiv:2606.22719v1", "arxiv_id": "2606.22719v1", "title": "Leakage-Aware Benchmarking of LLM Forecasting: Real-Time Nowcasts as the Decision-Time Input for Macro Factor Ranking", "authors": [ "Mao Guan", "Qian Chen" ], "posted": "2026-06-21", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.22719v1", "field": "finance", "role": "method", "bullets": [ "US equity style-factor ranking at each month-end from April 2023 to March 2026, using only decision-time inputs such as lag-shifted FRED variables and the Cleveland Fed CPI nowcast.", "A retrieval-augmented 7B open-source LLM (family not named) with critic and actor stages scores seven style factors, with leakage controlled by restricting to decision-time information.", "Median monthly Spearman rank IC is +0.154, but the mean is underpowered with a bootstrap CI including zero, and a kNN macro-analog baseline matches the median." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 50, "edition": 3, "audience": "technical", "n": 316, "authors_detailed": [ { "name": "Mao Guan", "url": "https://openalex.org/A5025936270", "inst": "Wuhan University" }, { "name": "Qian Chen", "url": "https://openalex.org/A5139128824", "inst": "" } ], "affiliations": [ "Wuhan University" ] }, { "uid": "arxiv:2607.09702v1", "arxiv_id": "2607.09702v1", "title": "Fundamental market design as a layer of AI-agent alignment", "authors": [ "Omar Inverso", "Emilio Tuosto", "Dragisa Zunic" ], "posted": "2026-06-21", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.09702v1", "field": "finance", "role": "object", "bullets": [ "Conceptual paper with no empirical data, focused on the market core of financial markets, the rule system by which orders enter, interact, match, persist, and stabilize.", "No model used; argues AI-agent alignment is a property of interaction infrastructure, applying formal modelling from theoretical computer science to specify and reason about market-core properties.", "Proposes treating fundamental market design as an alignment layer, giving transparent-box market models where desirable behaviours are structurally favoured and manipulation is harder to sustain." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1147, "authors_detailed": [ { "name": "Omar Inverso", "url": "https://openalex.org/A5140710497", "inst": "" }, { "name": "Emilio Tuosto", "url": "https://openalex.org/A5140678495", "inst": "" }, { "name": "Dragisa Zunic", "url": "https://openalex.org/A5140744249", "inst": "" } ] }, { "uid": "arxiv:2606.21880v2", "doi": "10.2139/ssrn.6968139", "arxiv_id": "2606.21880v2", "title": "Human Capital, AI, and Labor Commoditization", "authors": [ "Auyon Siddiq", "Niuniu Zhang" ], "posted": "2026-06-20", "added": "2026-07-24", "source_label": "SSRN and arXiv", "url": "https://arxiv.org/abs/2606.21880v2", "alt_urls": [ "https://doi.org/10.2139/ssrn.6968139" ], "field": "economics", "role": "object", "bullets": [ "Contract-level data from the Upwork online labor market, using a difference-in-differences design around the November 2022 release of ChatGPT; exact window not stated.", "Represents worker profiles with high-dimensional text embeddings (embedding model not named) to measure human-capital importance; ChatGPT release is the treatment, not a researcher tool.", "In more AI-exposed categories the importance of human capital falls and price rises, the human-capital demand premium declines, and demand reallocates toward lower-priced workers." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "edition": 3, "audience": "general", "n": 315, "authors_detailed": [ { "name": "Auyon Siddiq", "url": "https://openalex.org/A5140959326", "inst": "" }, { "name": "Niuniu Zhang", "url": "https://openalex.org/A5140891066", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6966070", "doi": "10.2139/ssrn.6966070", "title": "When Generative AI fails, Who Is to Blame? The Effect of Anthropomorphism on Users' Attribution and Willingness to Engage", "authors": [ "Hyun-Jun Jeon", "Il Im" ], "posted": "2026-06-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6966070", "field": "management", "role": "object", "bullets": [ "Series of experiments with users of large language model based conversational AI; participants are individuals reacting to AI failures; sample sizes not stated.", "No language model is applied as a research instrument; the studies manipulate perceived anthropomorphism and error to examine causal attribution and willingness to engage.", "Perceived anthropomorphism buffers engagement after failures by shifting attribution to external causes, whereas perceived autonomy shows no such moderating effect." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 479, "authors_detailed": [ { "name": "Hyun-Jun Jeon", "url": "https://openalex.org/A5016228681", "inst": "Yonsei University" }, { "name": "Il Im", "url": "https://openalex.org/A5071410663", "inst": "Yonsei University" } ], "affiliations": [ "Yonsei University" ] }, { "uid": "doi:10.2139/ssrn.6961901", "doi": "10.2139/ssrn.6961901", "title": "Guarding the Wrong Door: Alignment Theater and Proxy Discrimination in AI-Powered Indian FinTech", "authors": [ "Anoop S. Kumar" ], "posted": "2026-06-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6961901", "field": "finance", "role": "agent", "bullets": [ "Audit of an AI model deployed by Indian FinTech firms in loan-decisioning and customer-support roles, using approximately 9,400 structured prompts.", "GPT-4o-mini was prompted to approve microloans and answer RBI regulatory queries, with caste signalled explicitly versus through surnames and addresses to compare behaviour.", "Explicit caste gave marginalized applicants 7 to 8 point higher approval, but proxy signals cut SC/Dalit approval from 28 to 10 percent, and the support agent hallucinated on 36 percent of regulatory questions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "edition": 3, "audience": "general", "validated": null, "n": 887, "authors_detailed": [ { "name": "Anoop Kumar", "url": "https://openalex.org/A5079577986", "inst": "Tata Institute of Social Sciences" } ], "affiliations": [ "Tata Institute of Social Sciences" ] }, { "uid": "doi:10.2139/ssrn.6909242", "doi": "10.2139/ssrn.6909242", "title": "Delegation, Tipping, and Light-Touch Control in Consumer AI Agents: A Regime-Switching Theory of Misalignment", "authors": [ "Kwansoo Kim", "Byungjoon Yoo" ], "posted": "2026-06-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6909242", "field": "economics", "role": "object", "bullets": [ "Theoretical paper with no empirical data, studying consumer AI agents that search, recommend, and execute purchases on behalf of users.", "No model used or named; develops a regime-switching theory in which agents steer users toward higher-paying options such as sponsored items when user checking becomes rare.", "Shows a tipping pattern where systems look safe then abruptly turn risky; proposes a minimal approval gate as light-touch control, warning that explanations can accelerate the risky regime." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1146, "authors_detailed": [ { "name": "Kwansoo Kim", "url": "https://openalex.org/A5089505182", "inst": "Seoul National University" }, { "name": "Byungjoon Yoo", "url": "https://openalex.org/A5061922075", "inst": "Gachon University" } ], "affiliations": [ "Seoul National University", "Gachon University" ] }, { "uid": "doi:10.2139/ssrn.6859338", "doi": "10.2139/ssrn.6859338", "title": "Greed Without Programming: How Large Language Models Develop Economic Inequality and Deception Under Survival Pressure", "authors": [ "Aarush Sehgal" ], "posted": "2026-06-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6859338", "field": "economics", "role": "agent", "bullets": [ "Closed economy simulation with 6 agents run across 7 unnamed LLMs, 50 rounds each and 4 resource types, tracking how models interact, compete and deceive.", "The paper does not name the seven models; agents were not instructed to deceive, so deception emerged as a survival tactic, and no validation against ground truth is reported.", "Average deception rate around 97.23 percent, Gini coefficients from 0.085 to 0.248, and a 192 percent inequality difference when swapping to more powerful models." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 262, "authors_detailed": [ { "name": "Aarush Sehgal", "url": "https://openalex.org/A5137509529", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6966058", "doi": "10.2139/ssrn.6966058", "title": "Guidance, Ambiguity, and Responsible Generative AI Use in Accounting Education: Mixed-Methods Evidence from Digital Discourse and Indonesian-Dutch Student Surveys", "authors": [ "Gunawan Wibisono" ], "posted": "2026-06-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6966058", "field": "accounting", "role": "object", "bullets": [ "Mixed methods; Study 1 analyzes YouTube videos, comments and transcripts, and Study 2 surveys 437 accounting-related students in Indonesia and the Netherlands.", "No language model is applied as a research tool; generative AI use is the object, analyzed thematically then modeled with partial least squares structural equation modeling.", "Institutional guidance reduces policy ambiguity and strengthens ethical reasoning; policy ambiguity raises misuse normalization and misuse intention, while detection risk lowers misuse intention." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 477, "authors_detailed": [ { "name": "Gunawan Wibisono", "url": "https://openalex.org/A5138863676", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6968358", "doi": "10.2139/ssrn.6968358", "title": "A System Dynamics Decision-Support Model For Governing Generative AI Adoption: Leverage-Point Analysis And Intervention Simulation", "authors": [ "Kelsey Adrianne Cua", "Ezekiel Bernardo" ], "posted": "2026-06-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6968358", "field": "management", "role": "object", "bullets": [ "Enterprise generative AI adoption modeled as a system dynamics simulation; adopters segmented by Rogers' five diffusion categories and calibrated to an empirical rise-dip-rebound-plateau reference mode.", "No language model is applied; a simulation with 17 reinforcing and 8 balancing loops endogenizes performance, trust, literacy and cost, validated by behavior-pattern and structure tests.", "Five leverage points are dominated by developer-ecosystem structure; ecosystem growth beyond governance capacity lowers long-run adoption, and combined interventions underperform targeted single ones." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 478, "authors_detailed": [ { "name": "Kelsey Adrianne Cua", "url": "https://openalex.org/A5139016637", "inst": "" }, { "name": "Ezekiel Bernardo", "url": "https://openalex.org/A5032218732", "inst": "De La Salle University" } ], "affiliations": [ "De La Salle University" ] }, { "uid": "doi:10.2139/ssrn.6860338", "doi": "10.2139/ssrn.6860338", "title": "Predictability and Value Betting in UK Horse Racing", "authors": [ "Sascha Wilkens" ], "posted": "2026-06-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6860338", "field": "finance", "role": "instrument", "bullets": [ "Ten years of UK flat and jumps horse racing with full finishing orders, using pre-race market-implied probabilities, official ratings, and runner characteristics.", "An unnamed large language model extracts trouble-in-running indicators from post-race commentaries as features for a Plackett-Luce rank-ordered model, with no ground-truth validation of the extraction reported.", "The model matches the market benchmark in probabilistic accuracy and yields 20 to 22 percent ROI with Sharpe near 0.13 under selective thresholds, but the LLM features add no measurable accuracy." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "edition": 3, "audience": "general", "models": [], "n": 885, "authors_detailed": [ { "name": "Sascha Wilkens", "url": "https://openalex.org/A5013605357", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6862398", "doi": "10.2139/ssrn.6862398", "title": "The Human Is Still in the Loop. The Question Is Whether Judgment Still Is", "authors": [ "Matan Maller" ], "posted": "2026-06-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6862398", "field": "management", "role": "object", "bullets": [ "Conceptual synthesis on generative AI in professional work, drawing evidence from clinical practice, controlled experiments, and electrophysiology, with no new dataset or sample.", "No LLM is used as a tool; the paper studies human oversight of AI, arguing reviewers degrade through skill erosion, over-reliance, and measurable neural change.", "It contends that legally mandated human oversight, including under the EU AI Act, is more fragile than assumed and introduces the concept of AI-induced cognitive freeze." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 886, "authors_detailed": [ { "name": "Matan Maller", "url": "https://openalex.org/A5138932207", "inst": "The Mofet Institute" } ], "affiliations": [ "The Mofet Institute" ] }, { "uid": "doi:10.2139/ssrn.6937698", "doi": "10.2139/ssrn.6937698", "title": "Agentic AI for Business Analytics: Automating Insight Discovery, Hypothesis Testing, and Decision Support", "authors": [ "Tianyu Ma" ], "posted": "2026-06-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6937698", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, examining agentic AI systems for business analytics and data science and their implications for organizations and managerial accountability.", "No specific model named; develops a framework treating the agent as an accountable analytical operator that binds intent clarification, tool use, execution, error recovery, and explanation into one loop.", "Argues agentic systems create value by reducing coordination costs and expanding analytical capacity, but make errors more procedural, compositional, and harder to detect, proposing design principles for deployment." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1143, "authors_detailed": [ { "name": "T T", "url": "https://openalex.org/A5075925816", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6860420", "doi": "10.2139/ssrn.6860420", "title": "S.A.F.E Agentic: A Trustworthy Credit Scoring System with a Conversational Platform", "authors": [ "Yasamin Hosseinzadeh Sani", "Golnoosh Babaei", "Paolo Giudici" ], "posted": "2026-06-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6860420", "field": "finance", "role": "method", "bullets": [ "Application to the German Credit dataset; an agentic system automates data preparation, model training, evaluation, and governance reporting for credit scoring, with loan applicants as the unit of observation.", "No LLM family named; Data and Modeling agents run preprocessing and train candidate models scored by a SAFE metric for accuracy, robustness, explainability, and fairness on labelled data.", "Voting Ensemble selected with SAFE score 0.7872; group-aware threshold mitigation raised the fairness metric from 0.5399 to 0.6650, lifting the SAFE score to 0.8185." ], "bullet_provenance": "ai", "validated": true, "validation_note": "German Credit labelled benchmark, SAFE and fairness scores reported", "salience": 42, "edition": 3, "audience": "technical", "models": [], "n": 1144, "authors_detailed": [ { "name": "Yasamin Hosseinzadeh Sani", "url": "https://openalex.org/A5138945748", "inst": "University of Pavia" }, { "name": "Golnoosh Babaei", "url": "https://openalex.org/A5039229061", "inst": "University of Pavia" }, { "name": "Paolo Giudici", "url": "https://openalex.org/A5051364218", "inst": "University of Pavia" } ], "affiliations": [ "University of Pavia" ] }, { "uid": "doi:10.2139/ssrn.6862578", "doi": "10.2139/ssrn.6862578", "title": "The Great Decoupling: Frontier AI, the Broken Ladder, and the End of the Last Middle Class", "authors": [ "Liang Xue" ], "posted": "2026-06-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6862578", "field": "economics", "role": "object", "bullets": [ "Scenario-analysis working paper with no empirical sample, focused on frontier AI diffusion during early 2026 across enterprise deployment, professional services, capital markets, and labor pipelines.", "No model used by the researchers; AI, especially agentic frontier AI, is the subject, analyzed through a structural-risk framework mapping drivers, transmission channels, vulnerable nodes, and early signals.", "Argues frontier AI is a structural break in institutional absorption; introduces control latency and links cognitive-rent compression to thinning junior white-collar work and weakened income transmission." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1145, "authors_detailed": [ { "name": "Liang Xue", "url": "https://openalex.org/A5138855324", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2606.20041v1", "arxiv_id": "2606.20041v1", "title": "AI Economist Agent: An Agentic Framework for Model-Grounded Economic Analysis with RAG, Knowledge Graphs, and Large Language Models", "authors": [ "Masahiro Kato" ], "posted": "2026-06-18", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.20041v1", "field": "economics", "role": "method", "bullets": [ "Two economic applications, generating an economist report on United States inflation persistence and Federal Reserve policy, and a bank stress-test narrative for commercial real estate refinancing stress.", "Unnamed LLMs are combined with retrieval-augmented generation, knowledge graphs, and planning agents that select models and retrieve evidence so narratives rest on explicit model-based computations rather than the language model alone.", "The authors report that grounding generated reports in retrieved evidence and model computations improves their economic coherence and traceability, shown illustratively without a quantitative accuracy metric." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "edition": 3, "audience": "technical", "models": [], "n": 168, "authors_detailed": [ { "name": "Masahiro Kato", "url": "https://openalex.org/A5139018950", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6858038", "doi": "10.2139/ssrn.6858038", "title": "Generative AI, Job Transition and The Future of Work in Nigeria : Towards a Decent Work Response", "authors": [ "Adedoyin Adebayo" ], "posted": "2026-06-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6858038", "field": "economics", "role": "object", "bullets": [ "Conceptual legal and policy analysis focused on Nigeria; unit of analysis is the national labour law and AI policy framework rather than firms or workers.", "No language model is used; the article assesses generative AI's labour implications against ILO, UNESCO, Council of Europe and African Union norms and Nigerian statutes.", "Concludes Nigeria's framework is fragmented and lacks a dedicated workplace AI governance regime, and proposes a decent-work agenda of human review, transparency and reskilling." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 476, "authors_detailed": [ { "name": "ADEDOYIN ADEBAYO", "url": "https://openalex.org/A5137995753", "inst": "Joslin Diabetes Center" } ], "affiliations": [ "Joslin Diabetes Center" ] }, { "uid": "doi:10.2139/ssrn.6874778", "doi": "10.2139/ssrn.6874778", "title": "EU AI Act Article 50 Compliance for Agentic AI: Nine Unresolved Engineering Questions and Architectural Solutions for Developers (2026)", "authors": [ "Vinita Silaparasetty" ], "posted": "2026-06-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6874778", "field": "management", "role": "object", "bullets": [ "Practitioner contribution on EU AI Act Article 50 transparency obligations for agentic AI, submitted to the European Commission consultation; focus on multi-model, tool-calling, sub-agent pipelines.", "No model is used; the paper identifies nine engineering questions Article 50 guidance leaves unanswered for agentic AI and proposes six architectural patterns implementable with current tooling.", "Highlights emergent systemic risk in chained components, disclosure at client handover, and marking thresholds for AI-generated content in composite retrieval-augmented outputs." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1142, "authors_detailed": [ { "name": "Vinita Silaparasetty", "url": "https://openalex.org/A5053672386", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6848738", "doi": "10.2139/ssrn.6848738", "title": "Asymmetric Intelligence Discount (Aid): Stock-Price Reactions to Parallel Domain Shocks in Generative AI", "authors": [ "Grzegorz Wojarnik" ], "posted": "2026-06-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6848738", "field": "finance", "role": "object", "bullets": [ "Event study of three generative AI product events between November 2025 and January 2026, tracking Alphabet, a gaming portfolio, a professional-information portfolio, and a consumer-staples control over one trading quarter.", "No language model is used as a research tool; the generative AI launches are the treatment, and a market model computes cumulative abnormal returns against technology benchmarks.", "After Gemini 3, Alphabet's five-day cumulative abnormal return was +10.77 percent; the gaming portfolio fell 13.00 percent after Project Genie, and professional information fell 14.67 percent after the Claude plug-in." ], "bullet_provenance": "ai", "models": [ "claude", "gemini" ], "open_weights": false, "salience": 48, "edition": 3, "audience": "general", "validated": null, "n": 261, "authors_detailed": [ { "name": "Grzegorz Wojarnik", "url": "https://openalex.org/A5041908688", "inst": "University of Szczecin" } ], "affiliations": [ "University of Szczecin" ] }, { "uid": "arxiv:2606.19501v2", "arxiv_id": "2606.19501v2", "title": "DeXposure-Claw: An Agentic System for DeFi Risk Supervision", "authors": [ "Aijie Shu", "Bowei Chen", "Wenbin Wu", "Cathy Yi-Hsuan Chen", "Fengxiang He" ], "posted": "2026-06-17", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.19501v2", "field": "finance", "role": "method", "bullets": [ "Decentralized finance credit-risk supervision, evaluated on five years of weekly real exposure-network data using a regulator-aligned evaluation harness.", "Routes an unnamed general-purpose LLM's decisions through a graph time-series forecasting model, deterministic monitors, and confidence gates before emitting auditable supervisory tickets.", "On the six-axis DeXposure-Bench, tickets are scored against an absolute-loss ground truth and false-intervention rate, with experiments reported as fully supporting the system." ], "bullet_provenance": "ai", "validated": true, "validation_note": "regulator-aligned absolute-loss ground truth and false-intervention rate", "salience": 52, "edition": 3, "audience": "technical", "models": [], "n": 314, "authors_detailed": [ { "name": "Aijie Shu", "url": "https://openalex.org/A5125216805", "inst": "University of Glasgow" }, { "name": "Bowei Chen", "url": "https://openalex.org/A5100679036", "inst": "Chinese Academy of Sciences" }, { "name": "Wenbin Wu", "url": "https://openalex.org/A5138995417", "inst": "" }, { "name": "Cathy Yi‐Hsuan Chen", "url": "https://openalex.org/A5073701627", "inst": "Stockholm University" }, { "name": "Fengxiang He", "url": "https://openalex.org/A5100635369", "inst": "The University of Sydney" } ], "affiliations": [ "University of Glasgow", "Chinese Academy of Sciences", "Stockholm University", "The University of Sydney" ] }, { "uid": "doi:10.2139/ssrn.6960008", "doi": "10.2139/ssrn.6960008", "title": "Transforming Public Administration Workflows with Multi-Agent AI: A Human-in-the-Loop Knowledge Orchestration Framework", "authors": [ "Giuseppe Prencipe", "Alessandro Tommasi", "Cesare Zavattari", "Giovacchino Tesi", "Lorenzo Storchi", "Kussai Shahin" ], "posted": "2026-06-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6960008", "field": "management", "role": "object", "bullets": [ "Autonomous Province of Trento public administration; hybrid retrieval-augmented generation over more than 160,000 administrative documents; deployed in production for six months with 30 staff users.", "A human-in-the-loop multi-agent system drafts grounded responses to citizen and council inquiries; the underlying base model is not stated, and outputs keep traceable document grounding.", "Response preparation time fell by 70 percent while human oversight and institutional responsibility boundaries were preserved." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 475, "authors_detailed": [ { "name": "Giuseppe Prencipe", "url": "https://openalex.org/A5031863470", "inst": "University of Pisa" }, { "name": "Alessandro Tommasi", "url": "https://openalex.org/A5041465544", "inst": "University of Pisa" }, { "name": "Cesare Zavattari", "url": "https://openalex.org/A5126764993", "inst": "University of Pisa" }, { "name": "Giovacchino Tesi", "url": "https://openalex.org/A5013352889", "inst": "University of Pisa" }, { "name": "Lorenzo Storchi", "url": "https://openalex.org/A5073961048", "inst": "Energrid (Italy)" }, { "name": "Kussai Shahin", "url": "https://openalex.org/A5106605383", "inst": "University of Trento" } ], "affiliations": [ "University of Pisa", "Energrid (Italy)", "University of Trento" ] }, { "uid": "doi:10.2139/ssrn.6955636", "doi": "10.2139/ssrn.6955636", "title": "Too Smart to Fail? Understanding Expectation Disconfirmation, Emotional Reactions, Automation Bias in Student Satisfaction", "authors": [ "Md Shahriar Kabir" ], "posted": "2026-06-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6955636", "field": "management", "role": "object", "bullets": [ "Survey of 305 university students on their satisfaction with generative AI chatbots used in education; geography and time period not stated.", "No model is run by the researchers; the study measures student reactions to chatbot output using PLS-SEM and regression, with the chatbot version not stated, treating AI use as the object.", "Source disconfirmation, conflict with authoritative sources, triggered stronger emotional reactions than output shortfalls, with a reported coefficient of 0.383; the abstract is truncated before the full satisfaction pathways." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 881, "authors_detailed": [ { "name": "Md Shahriar Kabir", "url": "https://openalex.org/A5138742868", "inst": "Chonnam National University" } ], "affiliations": [ "Chonnam National University" ] }, { "uid": "doi:10.2139/ssrn.6955442", "doi": "10.2139/ssrn.6955442", "title": "Agentic AI Orchestration Capability: A Dynamic Capabilities Framework for Strategic Innovation in the Age of Autonomous Agents", "authors": [ "Albert Adusei Brobbey", "Narayan Bhosale" ], "posted": "2026-06-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6955442", "field": "management", "role": "object", "bullets": [ "Conceptual strategy article with no empirical sample; the setting is firms that deploy autonomous AI agents across innovation workflows and seek advantage from them.", "No language model is used; the article develops the construct of agentic AI orchestration capability with five microfoundations, drawing on dynamic capabilities and resource orchestration research.", "Argues these routines drive innovation speed, strategic agility, business model renewal, and resilience, shifting AI strategy research from adoption and augmentation toward capability formation." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 882, "authors_detailed": [ { "name": "Albert Adusei Brobbey", "url": "https://openalex.org/A5135482810", "inst": "Meridian International Center" }, { "name": "Narayan Bhosale", "url": "https://openalex.org/A5138710831", "inst": "" } ], "affiliations": [ "Meridian International Center" ] }, { "uid": "doi:10.2139/ssrn.6900479", "doi": "10.2139/ssrn.6900479", "title": "A Tool for Rapid Assessment of the Quantitative Attributes Required for Emerging Occupations", "authors": [ "Wilson Martinez Diaz", "Christophe Combemale", "Erica Renee Fuchs" ], "posted": "2026-06-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6900479", "field": "economics", "role": "method", "bullets": [ "US occupations in the Department of Labor O*NET taxonomy, plus permanent labor certification (PERM) forms and job postings as qualitative text inputs; period not stated.", "A Sentence-BERT algorithm combined with historical O*NET data converts text descriptions into quantitative occupation attribute vectors, evaluated for coherence and congruence against expert-based O*NET vectors.", "About 71 percent of model-generated attributes fall within one standard deviation of expert values, matching an unnamed large language model at lower computational cost and user knowledge." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "model vectors vs expert O*NET, ~71% within one standard deviation", "salience": 52, "edition": 3, "audience": "technical", "n": 883, "authors_detailed": [ { "name": "Wilson Martinez Diaz", "url": "https://openalex.org/A5127879567", "inst": "Carnegie Mellon University" }, { "name": "Christophe Combemale", "url": "https://openalex.org/A5060886728", "inst": "Professional Analytical and Consulting Services (United States)" }, { "name": "Erica R.H. Fuchs", "url": "https://openalex.org/A5102934266", "inst": "Carnegie Mellon University" } ], "affiliations": [ "Carnegie Mellon University", "Professional Analytical and Consulting Services (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6853581", "doi": "10.2139/ssrn.6853581", "title": "FSAIM: Spec-driven AI as a Governance Layer for Empirical Research A Position Note", "authors": [ "Grzegorz Wojarnik" ], "posted": "2026-06-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6853581", "field": "economics", "role": "method", "bullets": [ "Position note on the empirical paper lifecycle in economics and finance; no dataset, though the authors report one first empirical instance of the proposed engine.", "No specific model is named; the note proposes spec-driven development as a governance layer over generative AI, with a design freeze, an AI bill of materials, and a vendor-independent substrate.", "Presents the FSAIM methodology, delimits what it claims is novel against 2025-2026 work, and reports its first application together with that instance's acknowledged shortfalls." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 884, "authors_detailed": [ { "name": "Grzegorz Wojarnik", "url": "https://openalex.org/A5041908688", "inst": "University of Szczecin" } ], "affiliations": [ "University of Szczecin" ] }, { "uid": "doi:10.2139/ssrn.6955443", "doi": "10.2139/ssrn.6955443", "title": "Forecasting Agentic Commerce: How Compute Tokenization Reshapes Transaction Mechanisms in Autonomous Agent-to-Agent Economies", "authors": [ "Jie Gao" ], "posted": "2026-06-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6955443", "field": "economics", "role": "object", "bullets": [ "Conceptual foresight article on autonomous agent-to-agent economies; draws on transaction-cost theory, token economics, and platform ecosystem theory; horizon 2026 to 2032; no empirical sample.", "No model is used; the paper develops the Compute Tokenization-Transaction Mechanism Transformation framework linking tokenized compute to transaction infrastructure for autonomous agents.", "Theorises three mechanisms (standardization, financialization, programmability) reshaping price discovery, settlement, trust, and identity, and sketches three scenarios with observable leading indicators." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1140, "authors_detailed": [ { "name": "JS Gao", "url": "https://openalex.org/A5079996029", "inst": "Shanghai University" } ], "affiliations": [ "Shanghai University" ] }, { "uid": "doi:10.2139/ssrn.6855918", "doi": "10.2139/ssrn.6855918", "title": "Designing Accountability In: Technical Architecture and Human Oversight Across AAMM Levels", "authors": [ "Alexander Huseby" ], "posted": "2026-06-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6855918", "field": "management", "role": "object", "bullets": [ "Conceptual paper, third in a series, mapping engineering practices to levels of the AI Authority Maturity Model; draws on NIST AI RMF, EU AI Act Article 14, and the Moffatt v. Air Canada ruling.", "No model is used; the paper separates governance as organizational policy from guardrails as technical enforcement and assigns mechanisms to the maturity levels where they become necessary.", "Argues accountability must be built into architecture rather than retrofitted, and that architecture choice is itself a governance decision with legal and operational consequences." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1141, "authors_detailed": [ { "name": "Alexander Huseby", "url": "https://openalex.org/A5136653653", "inst": "CognIT (Norway)" } ], "affiliations": [ "CognIT (Norway)" ] }, { "uid": "doi:10.2139/ssrn.6958931", "doi": "10.2139/ssrn.6958931", "title": "FINTAGGING: Benchmarking Large Language Models for Extracting and Structuring Financial Information", "authors": [ "Yan Wang", "Xueqing Peng", "Yang Ren", "Yi Han", "Keyi Wang", "Dongji Feng", "Linhai Ma", "Xuguang Ai", "Fengran Mo", "Shengyuan Lin", "Qinchuan Zhang", "Kaiwen He", "Chenri Luo", "Jianxing Chen", "Junwei Wu", "Chen Xu", "Ziyang Xu", "Lingfei Qian", "Jimin Huang", "Guojun Xiong", "Xiao-Yang Liu", "Qianqian Xie", "Jian-Yun Nie" ], "posted": "2026-06-17", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6958931", "field": "accounting", "role": "method", "bullets": [ "Corporate filings with numerical facts to be tagged to a US-GAAP taxonomy of more than 17,000 concepts, using mixed text and table inputs; the benchmark is FINTAGGING.", "LLMs were evaluated zero-shot on two staged tasks: identifying numerical facts and value types (FinNI) and linking each fact to a US-GAAP concept (FinCL); specific models are not named.", "Models recover many numerical facts but remain weaker at concept linking, revealing a persistent gap between numeric extraction and taxonomy-grounded semantic alignment." ], "bullet_provenance": "ai", "validated": true, "validation_note": "zero-shot benchmark against US-GAAP gold tags", "salience": 55, "edition": 2, "audience": "technical", "models": [], "n": 41, "authors_detailed": [ { "name": "Yan Wang", "url": "https://openalex.org/A5012494488", "inst": "Yale University" }, { "name": "Xueqing Peng", "url": "https://openalex.org/A5138759341", "inst": "Yale University" }, { "name": "Yang Ren", "url": "https://openalex.org/A5100413313", "inst": "Yale University" }, { "name": "Yi Han", "url": "https://openalex.org/A5118686567", "inst": "Georgia Institute of Technology" }, { "name": "Keyi Wang", "url": "https://openalex.org/A5074822644", "inst": "Columbia University" }, { "name": "Dongji Feng", "url": "https://openalex.org/A5026782407", "inst": "California State University, Monterey Bay" }, { "name": "Linhai Ma", "url": "https://openalex.org/A5070629109", "inst": "Yale University" }, { "name": "Xuguang Ai", "url": "https://openalex.org/A5136513630", "inst": "Yale University" }, { "name": "Fengran Mo", "url": "https://openalex.org/A5138814975", "inst": "Université du Québec à Montréal" }, { "name": "Shengyuan Lin", "url": "https://openalex.org/A5126582763", "inst": "Carnegie Mellon University" }, { "name": "Qinchuan Zhang", "url": "https://openalex.org/A5126564124", "inst": "" }, { "name": "Kaiwen He", "url": "https://openalex.org/A5126580009", "inst": "Columbia University" }, { "name": "Chenri Luo", "url": "https://openalex.org/A5126573115", "inst": "Columbia University" }, { "name": "Jianxing Chen", "url": "https://openalex.org/A5103145874", "inst": "Columbia University" }, { "name": "J Wu", "url": "https://openalex.org/A5031148135", "inst": "Columbia University" }, { "name": "Xu Chen", "url": "https://openalex.org/A5101804496", "inst": "Nanjing Audit University" }, { "name": "Ziyang Xu", "url": "https://openalex.org/A5035857292", "inst": "Nanjing Audit University" }, { "name": "Lingfei Qian", "url": "https://openalex.org/A5009941118", "inst": "Yale University" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "Singapore Management University" }, { "name": "Guojun Xiong", "url": "https://openalex.org/A5102620407", "inst": "Harvard University Press" }, { "name": "Xiao-Yang Liu", "url": "https://openalex.org/A5133000464", "inst": "Columbia University" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5138826848", "inst": "Singapore Management University" }, { "name": "Jian‐Yun Nie", "url": "https://openalex.org/A5018977183", "inst": "Université du Québec à Montréal" } ], "affiliations": [ "Yale University", "Georgia Institute of Technology", "Columbia University", "Carnegie Mellon University", "California State University, Monterey Bay", "Université du Québec à Montréal", "Nanjing Audit University", "Singapore Management University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6853098", "doi": "10.2139/ssrn.6853098", "title": "Scrape, Train, Compete: Data Extraction as Unfair Competition in the AI Era", "authors": [ "Nikolin Muçaj" ], "posted": "2026-06-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6853098", "field": "economics", "role": "object", "bullets": [ "Legal and economic analysis of AI-era web scraping disputes, centering on NYT v. OpenAI and the Feist originality doctrine in U.S. law", "ChatGPT examined as a paradigmatic case where LLM-generated content substitutes for rather than complements original news publisher output", "Market substitution framework proposed: liability arises when content extraction enables competing products and systematically undermines creation incentives" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "validated": null, "n": 3102, "authors_detailed": [ { "name": "Nikolin Muçaj", "url": "https://openalex.org/A5138767444", "inst": "University of Bologna" } ], "affiliations": [ "University of Bologna" ] }, { "uid": "arxiv:2606.17383v1", "arxiv_id": "2606.17383v1", "title": "Model Validation of Agentic AI Systems: A POMDP-Based Framework for Belief-State, Forecast, and Policy Validation", "authors": [ "Matthew Francis Dixon" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.17383v1", "field": "finance", "role": "method", "bullets": [ "Conceptual model-risk framework for agentic AI, demonstrated on a portfolio-management case where an agent infers latent market regimes from market and macroeconomic data and builds Black-Litterman portfolios.", "LLMs are formalized as approximate Bayesian filtering operators, and the framework decomposes decisions into information, beliefs, forecasts, actions, and utility, checked with belief calibration, coverage tests, and ablations. Model not stated.", "Latent-state inference contributes independently to decision quality, and the paper reports that the main conclusions remain robust across a broad range of parameter values." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 122, "authors_detailed": [ { "name": "Matthew Dixon", "url": "https://openalex.org/A5036435050", "inst": "IIT Research Institute" } ], "affiliations": [ "IIT Research Institute" ] }, { "uid": "arxiv:2606.18005v1", "arxiv_id": "2606.18005v1", "title": "LLM Consumer Behavior Theory: Foundations of a Novel Research Field", "authors": [ "Manon Reusens", "Sofie Goethals", "David Martens" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.18005v1", "field": "economics", "role": "object", "bullets": [ "Conceptual paper with no empirical sample; it defines the scope of consumer behavior in agentic markets where LLM agents make consumption decisions on behalf of human users.", "The authors run no model; they unify prior work on LLM decision-making, human behavior simulation, and preference elicitation under an economic lens rather than providing empirical validation.", "It proposes LLM consumer behavior theory and identifies where assumptions such as rationality and heterogeneity may fail, with open questions on alignment and market dynamics." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 123, "authors_detailed": [ { "name": "Manon Reusens", "url": "https://openalex.org/A5037079840", "inst": "University of Antwerp" }, { "name": "Sofie Goethals", "url": "https://openalex.org/A5138781976", "inst": "" }, { "name": "David Martens", "url": "https://openalex.org/A5101474247", "inst": "University of Antwerp" } ], "affiliations": [ "University of Antwerp" ] }, { "uid": "doi:10.2139/ssrn.6851501", "doi": "10.2139/ssrn.6851501", "title": "Can LLMs Detect Greenwashing? Evaluating AI-Assisted Verification of Sovereign Green Bond Impact Reports in India", "authors": [ "Harsh Singh" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6851501", "field": "finance", "role": "method", "bullets": [ "Sovereign green bond impact reports from India's program, scored against a 26-criterion rubric built on the ICMA Green Bond Principles and the Reserve Bank of India framework, in a pilot application.", "Unnamed large language models evaluate the reports against the rubric, with outputs compared to independent human expert assessments; full large-scale empirical results are deferred to a later study.", "Proposes a tiered verification model separating tasks suited to AI automation from those needing human judgment, and recommends an AI-Assisted Preliminary Verification Protocol for SEBI, RBI, and the finance ministry." ], "bullet_provenance": "ai", "validated": false, "validation_note": "human expert comparison described but no agreement figure reported", "salience": 42, "edition": 3, "audience": "general", "models": [], "n": 260, "authors_detailed": [ { "name": "Harsh Singh", "url": "https://openalex.org/A5119095367", "inst": "Independant" } ], "affiliations": [ "Independant" ] }, { "uid": "doi:10.2139/ssrn.6852220", "doi": "10.2139/ssrn.6852220", "title": "The SMB AI Maturity Index: A Five-Level Framework for Assessing Generative AI Integration in Small and Medium Businesses", "authors": [ "Ankur Sharma" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6852220", "field": "management", "role": "object", "bullets": [ "Conceptual design science study targeting small and medium businesses with fewer than 250 employees; cites OECD adoption figures but reports no primary firm sample or fieldwork.", "No language model is applied by the researchers; the paper builds a five-level by five-dimension maturity matrix with 88 behavioral indicators, following Peffers and Becker design science procedures.", "Produces a scored diagnostic instrument using a weakest-link classification rule and a prioritized intervention sequence; a Delphi validation with twelve to eighteen experts is specified but not yet run." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 473, "authors_detailed": [ { "name": "Ankur Sharma", "url": "https://openalex.org/A5028916178", "inst": "Diversified Technologies (United States)" } ], "affiliations": [ "Diversified Technologies (United States)" ] }, { "uid": "doi:10.2139/ssrn.6945099", "doi": "10.2139/ssrn.6945099", "title": "Beyond Raw Transcripts: Structured Persona Extraction for LLM-Based Digital Twins", "authors": [ "Iris Ye", "Tianze Deng", "Ozan Candogan" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6945099", "field": "management", "role": "method", "bullets": [ "Two benchmarks of survey respondents: Twin-2K-500, a homogeneous set, and 13 diverse sub-studies; task is predicting each individual's held-out responses from prior answers.", "gpt-5.4-mini and Qwen3-8B build digital twins from raw transcripts, a hand-crafted background-decision-evaluation schema, and an automatic structure-discovery pipeline, evaluated by held-out response accuracy.", "Structured personas raise accuracy 1.91 points over raw transcripts but the fixed schema fails on heterogeneous tasks; automatic structure discovery restores a 1.91 point mean gain." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "held-out survey response prediction accuracy", "salience": 48, "edition": 3, "audience": "technical", "n": 474, "authors_detailed": [ { "name": "Iris Ye", "url": "https://openalex.org/A5138710379", "inst": "University of Chicago" }, { "name": "Tianze Deng", "url": "https://openalex.org/A5138750804", "inst": "University of Chicago" }, { "name": "Ozan Candogan", "url": "https://openalex.org/A5001350443", "inst": "Woodlawn School" } ], "affiliations": [ "University of Chicago", "Woodlawn School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6849539", "doi": "10.2139/ssrn.6849539", "title": "Agentic Commerce Governance Framework™: Classifying Enterprise Liability Exposure in the Age of Machine Buyers", "authors": [ "Frank Meltke" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6849539", "field": "management", "role": "object", "bullets": [ "Conceptual governance paper with no empirical sample; the unit of analysis is enterprise commercial transactions in which autonomous AI agents act as machine buyers.", "No language model is used or named; the paper proposes a six-level classification of agent autonomy plus a dual governance architecture combining a mandate protocol with exposure diagnostics.", "Identifies level three, delegated execution, as the point where traditional risk governance breaks down and names three structural failures that create liability exposure at levels three through five." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 879, "authors_detailed": [ { "name": "Frank Meltke", "url": "https://openalex.org/A5136270244", "inst": "Kyung Hee University" } ], "affiliations": [ "Kyung Hee University" ] }, { "uid": "doi:10.2139/ssrn.6949226", "doi": "10.2139/ssrn.6949226", "title": "AI Listening to Central Banks: From Words to Markets and Macroeconomic Outcomes", "authors": [ "Antoaneta Amza", "Rahul Tak", "Caliman Stefan-Daniel", "Daniel Traian Pele" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6949226", "field": "economics", "role": "instrument", "bullets": [ "Minutes of monetary policy meetings from Central and Eastern European central banks linked to key macroeconomic and financial indicators; sample period and number of meetings not stated.", "Applies large language models, specific model not stated, to measure the informational content of central bank communication, then feeds this into a Bayesian structural VAR; no accuracy check reported.", "Communication carries explanatory power, most visibly on the interbank money market while other segments respond unevenly, and is read as signalling both future policy and current economic conditions." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 880, "authors_detailed": [ { "name": "Antoaneta Amza", "url": "https://openalex.org/A5118912615", "inst": "Bucharest University of Economic Studies" }, { "name": "Rahul Tak", "url": "https://openalex.org/A5119154968", "inst": "Bucharest University of Economic Studies" }, { "name": "Caliman Stefan-Daniel", "url": "https://openalex.org/A5138752505", "inst": "University of Bucharest" }, { "name": "Daniel Traian Pele", "url": "https://openalex.org/A5035471410", "inst": "Institute of Economic Forecasting" } ], "affiliations": [ "Bucharest University of Economic Studies", "University of Bucharest", "Institute of Economic Forecasting" ] }, { "uid": "doi:10.2139/ssrn.6949565", "doi": "10.2139/ssrn.6949565", "title": "From Augmentation to Substitution: Delegation Habits, Skill Atrophy, and the Limits of Trust Calibration in Human-AI Collaboration", "authors": [ "Samuele Dell'Oca", "Valentina Rotondi", "Vincenzo Cutrona", "Giuseppe Landolfi", "Andrea Bettoni", "Luca Canetta" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6949565", "field": "management", "role": "object", "bullets": [ "Critical review synthesising human-AI trust research, habit psychology, automation complacency, and cognitive offloading; focus on agentic AI in industrial knowledge work; no empirical sample.", "No model is used; the paper proposes the Delegation Habit Framework and operationalises it through adapted self-report automaticity measures, experience sampling, and skill-retention assessments.", "Argues repeated AI reliance becomes automatized delegation that decouples from reliability appraisal and degrades the offloaded skills, so the augmentation-substitution line is behavioral." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1136, "authors_detailed": [ { "name": "Samuele Dell'Oca", "url": "https://openalex.org/A5138747965", "inst": "" }, { "name": "Valentina Rotondi", "url": "https://openalex.org/A5064302077", "inst": "University of Applied Sciences and Arts of Southern Switzerland" }, { "name": "Vincenzo Cutrona", "url": "https://openalex.org/A5067221673", "inst": "University of Applied Sciences and Arts of Southern Switzerland" }, { "name": "Giuseppe Landolfi", "url": "https://openalex.org/A5007771918", "inst": "University of Applied Sciences and Arts of Southern Switzerland" }, { "name": "Andrea Bettoni", "url": "https://openalex.org/A5026442096", "inst": "University of Applied Sciences and Arts of Southern Switzerland" }, { "name": "Luca Canetta", "url": "https://openalex.org/A5030668050", "inst": "University of Applied Sciences and Arts of Southern Switzerland" } ], "affiliations": [ "University of Applied Sciences and Arts of Southern Switzerland" ] }, { "uid": "doi:10.2139/ssrn.6852042", "doi": "10.2139/ssrn.6852042", "title": "Deployer Obligations under the EU AI Act: Article 26, Provider Boundaries, and Governance Architecture", "authors": [ "Michael Clark" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6852042", "field": "management", "role": "object", "bullets": [ "Conceptual legal and governance analysis of EU AI Act Article 26 deployer obligations, with three worked examples covering NHS clinical AI, financial-services credit AI, and multinational recruitment AI.", "No model is used; the paper parses the eleven Article 26 paragraphs and the conditions under which deployers assume provider-equivalent duties under Article 25.", "Prescribes contractual architecture at the provider-deployer interface and proposes a deployer governance programme accounting for overlap with GDPR, NIS2, and DORA requirements." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1137, "authors_detailed": [ { "name": "Michael Clark", "url": "https://openalex.org/A5134744450", "inst": "American Standard (United States)" } ], "affiliations": [ "American Standard (United States)" ] }, { "uid": "doi:10.2139/ssrn.6855560", "doi": "10.2139/ssrn.6855560", "title": "The Token Economy: Compute Power, Automation, and the Future of Work in an Ecosystem of Human Labor, Capital, and Artificial Intelligence", "authors": [ "Alejandro J. Guipe Salazar" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6855560", "field": "economics", "role": "object", "bullets": [ "Theoretical economics paper building on the task-based automation framework of Zeira and Acemoglu-Restrepo; treats compute as a factor of production; connects to 2025-2026 adoption and spending evidence.", "No model is used; the paper derives a firm-level automate-or-hire rule under which a task is automated only when its token cost falls below its labour cost.", "Shows the automation boundary is non-monotonic because agentic systems can consume up to a thousand times more tokens, so automating a task can cost more than the worker replaced." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1138, "authors_detailed": [ { "name": "Alejandro J. Guipe Salazar", "url": "https://openalex.org/A5138731801", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6950571", "doi": "10.2139/ssrn.6950571", "title": "Evaluating Hybrid and Standalone Deep Learning Models for Crude Oil Price Forecasting under Volatile Market Conditions", "authors": [ "Zahra Rezaei", "Sara Safi Samghabadi", "Mohammad amin Amini", "Yaser Michael Banad" ], "posted": "2026-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6950571", "field": "finance", "role": "method", "bullets": [ "Daily West Texas Intermediate crude oil prices from June 2015 to June 2025, augmented with RSI, MACD, a 10-day moving average, and trading volume as inputs.", "Compares a CNN, a bidirectional LSTM, and a hybrid CNN-BiLSTM-Transformer, tuned with Optuna and evaluated on held-out test data by R-squared and RMSE.", "Standalone CNN performs best (R-squared 0.9025, RMSE 0.0122); the hybrid transformer is weaker (R-squared 0.8483), so added architectural complexity does not improve accuracy." ], "bullet_provenance": "ai", "validated": true, "validation_note": "test-set R-squared and RMSE against actual WTI prices", "salience": 42, "edition": 3, "audience": "technical", "models": [], "n": 1139, "authors_detailed": [ { "name": "Zahra Rezaei", "url": "https://openalex.org/A5134367937", "inst": "University of Oklahoma" }, { "name": "Sara Safi Samghabadi", "url": "https://openalex.org/A5117567329", "inst": "University of Oklahoma" }, { "name": "Mohammad Amin Amini", "url": "https://openalex.org/A5076914237", "inst": "University of Oklahoma" }, { "name": "Yaser Mike Banad", "url": "https://openalex.org/A5068910901", "inst": "University of Oklahoma" } ], "affiliations": [ "University of Oklahoma" ] }, { "uid": "doi:10.2139/ssrn.6945287", "doi": "10.2139/ssrn.6945287", "title": "COST-SENSITIVE ARCHITECTURE AUDITING FOR LLM-BASED TABULAR PREDICTION: EVIDENCE FROM FREIGHT CANCELLATION MANAGEMENT", "authors": [ "Wenyi Kuang" ], "posted": "2026-06-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6945287", "field": "management", "role": "method", "bullets": [ "A 372,590-shipment freight panel from a retailer that decides which shipments to intervene on to avoid costly cancellations; period and geography not stated.", "Prompted GPT-4o and Claude Sonnet 4.6 rank cancellation probability against calibrated tabular models, logistic regression, and a fallback rule, checked by AUC and out-of-time validation.", "GPT-4o ranks competitively at AUC 0.810 versus 0.808 for XGBoost and 0.832 for logistic regression, but its probabilities exceed the cost threshold, so it intervenes on every shipment and saves nothing." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "AUC against realized cancellations, out-of-time test", "salience": 58, "edition": 3, "audience": "general", "n": 109, "authors_detailed": [ { "name": "Wenyi Kuang", "url": "https://openalex.org/A5138685344", "inst": "" } ] }, { "uid": "arxiv:2606.17165v3", "arxiv_id": "2606.17165v3", "title": "Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference", "authors": [ "Joel Persson", "Mårten Schultzberg", "Sebastian Ankargren" ], "posted": "2026-06-15", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.17165v3", "field": "economics", "role": "method", "bullets": [ "Simulation studies plus an empirical application to the Upworthy Research Archive of online headline A/B tests, examining whether LLM responses can substitute for human participants.", "Adapts surrogate endpoint theory to LLMs; the specific model is not stated, and outputs are calibrated to human outcomes with a falsification test and worst-case bias bounds.", "Raw LLM outputs recover only 39 percent of the human treatment effect, while nonparametric calibration closes the gap under surrogacy and comparability conditions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "raw LLM recovers 39 percent of human treatment effect on Upworthy data", "salience": 70, "edition": 3, "audience": "general", "models": [], "n": 188, "authors_detailed": [ { "name": "Joel Persson", "url": "https://openalex.org/A5138823950", "inst": "" }, { "name": "Mårten Schultzberg", "url": "https://openalex.org/A5035192290", "inst": "Biomotif (Sweden)" }, { "name": "Sebastian Ankargren", "url": "https://openalex.org/A5066105375", "inst": "Biomotif (Sweden)" } ], "affiliations": [ "Biomotif (Sweden)" ] }, { "uid": "doi:10.2139/ssrn.6868459", "doi": "10.2139/ssrn.6868459", "title": "A Multi-Dimensional Labeling System for \"Fixed Income Plus\" Fund Managers: An Integrated Approach Combining Quantitative Metrics, Portfolio Structure, and Large Language Model Analysis", "authors": [ "Dan Yang" ], "posted": "2026-06-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6868459", "field": "finance", "role": "instrument", "bullets": [ "25 fixed-income-plus funds, 15 secondary bond and 10 convertible bond, from January 2022 to December 2024, with 224 periodic reports and daily net-asset-value data as inputs.", "DeepSeek Chat at temperature 0.1, via API, extracts structured labels from the 224 reports to form six style dimensions alongside nine NAV indicators and holdings analysis; no extraction accuracy is reported.", "Low-volatility funds averaged 2.4 percent versus minus 2.33 percent for high-volatility (p<0.05), left-side-layout funds had a higher Sharpe ratio (0.38 versus minus 0.13), and neutral-sentiment managers outperformed optimistic ones." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "validation_note": "no extraction accuracy check", "salience": 42, "edition": 3, "audience": "general", "n": 259, "authors_detailed": [ { "name": "Dan Yang", "url": "https://openalex.org/A5138658780", "inst": "Nanjing University of Chinese Medicine" } ], "affiliations": [ "Nanjing University of Chinese Medicine" ] }, { "uid": "doi:10.2139/ssrn.6923198", "doi": "10.2139/ssrn.6923198", "title": "Would You Trust a Doctor Who Uses ChatGPT?", "authors": [ "Tinglong Dai" ], "posted": "2026-06-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6923198", "field": "management", "role": "object", "bullets": [ "Book chapter reviewing recent evidence on generative AI in medicine, with no original dataset, focused on institutional trust, liability, reimbursement, and skill effects.", "No model is run; the chapter synthesizes evidence on physicians' use of ChatGPT-style generative AI and the agent frameworks built around it.", "Visible AI use can lower peer and patient ratings of physicians, and the author argues trustworthy care depends on institutional governance, not model quality alone." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "general", "validated": null, "n": 472, "authors_detailed": [ { "name": "Tinglong Dai", "url": "https://openalex.org/A5062057702", "inst": "Johns Hopkins University" } ], "affiliations": [ "Johns Hopkins University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6946402", "doi": "10.2139/ssrn.6946402", "title": "HOW ATTITUDE SHAPES GENERATIVE AI ADOPTION INTENTIONS AMONG GRADUATE STUDENTS", "authors": [ "Nicolas A. Nunez", "Cindy Salvador-Marquez", "Jonathan Poma-Chavez" ], "posted": "2026-06-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6946402", "field": "management", "role": "object", "bullets": [ "Survey of 252 MBA students at an internationally accredited business school in Peru, examining voluntary generative AI adoption under TAM and UTAUT frameworks.", "No AI model is used by the researchers; ordinary least squares regression with HC3 robust standard errors and bootstrap mediation test attitude as a mediator.", "Attitude accounted for 60.6 percent of the total effect of performance expectancy on behavioral intention, falling to 46.3 percent after controlling for effort expectancy, risk, training, and demographics." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 877, "authors_detailed": [ { "name": "Nicolás A. Núñez", "url": "https://openalex.org/A5103222676", "inst": "Pontificia Universidad Católica del Perú" }, { "name": "Cindy Salvador-Marquez", "url": "https://openalex.org/A5138656608", "inst": "" }, { "name": "Jonathan Poma-Chavez", "url": "https://openalex.org/A5138680250", "inst": "" } ], "affiliations": [ "Pontificia Universidad Católica del Perú" ] }, { "uid": "doi:10.2139/ssrn.6946541", "doi": "10.2139/ssrn.6946541", "title": "All That Glitters Is Not Gold? Evidence from Annual Report Aesthetics and Stock Price Crash Risk", "authors": [ "Chuanqi Ling", "Dayong Dong" ], "posted": "2026-06-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6946541", "field": "accounting", "role": "instrument", "bullets": [ "Chinese A-share listed firms from 2007 to 2024, relating the visual aesthetics of annual reports to future stock price crash risk.", "A multimodal large language model, not named, is applied to annual reports to construct an aesthetics measure; no validation against ground truth is reported.", "Firms with highly aesthetic annual reports have significantly lower future crash risk, robust to endogeneity tests, with effects stronger under higher technological complexity and lower default risk." ], "bullet_provenance": "ai", "validated": false, "salience": 47, "edition": 3, "audience": "general", "models": [], "n": 878, "authors_detailed": [ { "name": "Chuanqi Ling", "url": "https://openalex.org/A5113300292", "inst": "Southwest Jiaotong University" }, { "name": "Dayong Dong", "url": "https://openalex.org/A5078578375", "inst": "Southwest Jiaotong University" } ], "affiliations": [ "Southwest Jiaotong University" ] }, { "uid": "doi:10.2139/ssrn.6879318", "doi": "10.2139/ssrn.6879318", "title": "How AI Agent Deployment Structure Shapes Investor Evaluations: A Processing Fluency Perspective on One-Person Companies", "authors": [ "Feiyu Wang", "Guohou Shan", "Ying Wu", "Jingfeng Yin", "Jibao Gu" ], "posted": "2026-06-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6879318", "field": "management", "role": "object", "bullets": [ "Online experiment with 274 investors evaluating One-Person Companies, ventures in which a single founder coordinates specialized AI agents that perform all operational work.", "No language model is run; the study manipulates AI agent control mode and workflow pattern and measures investors' processing fluency, investment intention, and word-of-mouth.", "Centralized control and sequential workflow each independently raise processing fluency and investor evaluations, but their marginal contributions diminish when both are present together." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1134, "authors_detailed": [ { "name": "Feiyu Wang", "url": "https://openalex.org/A5138646259", "inst": "University of Science and Technology of China" }, { "name": "Guohou Shan", "url": "https://openalex.org/A5133600103", "inst": "Northeastern University" }, { "name": "Ying Wu", "url": "https://openalex.org/A5110873237", "inst": "Peking University" }, { "name": "Jingfeng Yin", "url": "https://openalex.org/A5129667850", "inst": "Independent" }, { "name": "Jibao Gu", "url": "https://openalex.org/A5088416379", "inst": "University of Science and Technology of China" } ], "affiliations": [ "University of Science and Technology of China", "Northeastern University", "Peking University", "Independent" ] }, { "uid": "arxiv:2606.16326v2", "arxiv_id": "2606.16326v2", "title": "Gaming-Resistant Insurance Contracts for Autonomous AI Agents: Strategy-Proof Toll Mechanism Design", "authors": [ "Hao-Hsuan Chen" ], "posted": "2026-06-15", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.16326v2", "field": "economics", "role": "object", "bullets": [ "Theoretical mechanism-design paper on insurance contracts for autonomous AI agents that treats the operator as strategic; no empirical sample beyond cross-model traces borrowed from a companion paper.", "Researchers use no language model themselves; they characterise five attacks on actuarial toll pricing of agent actions and prove gaming-resistance, testing an interface-compliance theorem on committed cross-model traces.", "Three added clauses close the remaining attacks and make truthful reporting of the deployed model weakly dominant, yielding joint incentive compatibility with individual rationality and weak budget balance." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1135, "authors_detailed": [ { "name": "Hao-Hsuan Chen", "url": "https://openalex.org/A5136594934", "inst": "National Chengchi University" } ], "affiliations": [ "National Chengchi University" ] }, { "uid": "arxiv:2606.15999v1", "doi": "10.2139/ssrn.6941098", "arxiv_id": "2606.15999v1", "title": "U.S. Policies Unintentionally Accelerated China's Open AI Ecosystems", "authors": [ "Wang Jin", "Nadav Kunievsky", "Bowen Lou", "Tianshu Sun", "James Evans" ], "posted": "2026-06-14", "added": "2026-07-24", "source_label": "SSRN and arXiv", "url": "https://arxiv.org/abs/2606.15999v1", "alt_urls": [ "https://doi.org/10.2139/ssrn.6941098" ], "field": "economics", "role": "object", "bullets": [ "Study of a decade of US and Chinese AI policy, tracking developer engagement with open-source large language model repositories, patent disclosures, and open-access research after US export-control shocks.", "No language model is applied by the authors; the analysis measures developer repository engagement and the diffusion of Chinese-origin open models through open-source communities and scientific research.", "After export-control shocks, Chinese developers increased open-source LLM engagement substantially more than US developers, and Chinese open models diffused widely while remaining largely absent from US patent disclosures." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 876, "authors_detailed": [ { "name": "Wang Jin", "url": "https://openalex.org/A5100649170", "inst": "Chapman University" }, { "name": "Nadav Kuniesky", "url": "https://openalex.org/A5083139955", "inst": "University of Chicago" }, { "name": "Bowen Lou", "url": "https://openalex.org/A5051756505", "inst": "University of Southern California" }, { "name": "Tianshu Sun", "url": "https://openalex.org/A5140444298", "inst": "Cheung Kong Graduate School of Business" }, { "name": "James A. Evans", "url": "https://openalex.org/A5076633756", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "University of Southern California", "Chapman University", "Cheung Kong Graduate School of Business" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.15031v2", "arxiv_id": "2606.15031v2", "title": "Partial Identification from LLM Prompts", "authors": [ "Xiaohong Chen", "Ashesh Rambachan", "Elie Tamer" ], "posted": "2026-06-13", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.15031v2", "field": "economics", "role": "method", "bullets": [ "Econometric framework with no specific dataset; large language models are treated as binary classifiers for a latent true label, observed as counts, response vectors, or matrices.", "The paper runs no particular model; it derives partial-identification bounds for prevalence from LLM reports whose errors may be arbitrarily dependent given the truth.", "Without restrictions prevalence is completely unidentified, identifying power comes from externally calibrated scores, and which models agree matters more than a vote count." ], "bullet_provenance": "ai", "salience": 68, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 471, "authors_detailed": [ { "name": "Xiaohong Chen", "url": "https://openalex.org/A5138738098", "inst": "" }, { "name": "Rambachan, Ashesh", "url": "", "inst": "" }, { "name": "Tamer, Elie", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6845738", "doi": "10.2139/ssrn.6845738", "title": "Inhuman Entrepreneurial Judgment and the Entrepreneur of the Gaps", "authors": [ "Ryan Murphy", "Meg Tuszynski" ], "posted": "2026-06-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6845738", "field": "economics", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, asking whether artificial intelligence can display entrepreneurial judgment and seeking to make that claim falsifiable.", "No model is used empirically; the argument reasons from the resemblance of large language model architecture to constrained maximization and Stiglerian search, naming no specific model.", "Argues that by available falsifiable standards the claim that AI lacks entrepreneurial judgment is being falsified, with implications for market economies." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 874, "authors_detailed": [ { "name": "Ryan Murphy", "url": "https://openalex.org/A5131201829", "inst": "Southern Methodist University" }, { "name": "Meg Tuszynski", "url": "https://openalex.org/A5134066861", "inst": "Southern Methodist University" } ], "affiliations": [ "Southern Methodist University" ] }, { "uid": "doi:10.2139/ssrn.6845958", "doi": "10.2139/ssrn.6845958", "title": "Generative Brand Narrative Intelligence: A State-aware Multimodal Agent Framework for Autonomous Emotionally Adaptive Brand Communication", "authors": [ "Adithya Reddy Nalla" ], "posted": "2026-06-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6845958", "field": "management", "role": "method", "bullets": [ "Conceptual and design-science paper introducing Generative Brand Narrative Intelligence and its EABN implementation, evaluated against existing system categories rather than an empirical dataset.", "A multimodal multi-agent system orchestrated with LangGraph coordinates seven agents to generate and govern brand narratives through a state-aware lifecycle; no underlying model family is named.", "The framework is presented as the only system satisfying all five defined qualifying properties, contributing a seven-agent architecture and a twelve-metric evaluation framework." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 875, "authors_detailed": [ { "name": "Adithya Reddy Nalla", "url": "https://openalex.org/A5138587230", "inst": "Aditya Birla (India)" } ], "affiliations": [ "Aditya Birla (India)" ] }, { "uid": "doi:10.2139/ssrn.6923108", "doi": "10.2139/ssrn.6923108", "title": "HybridRAG-Finance: Agent-Guided Information Summarization with Vector and Graph Retrieval for Financial Documents", "authors": [ "Mazen Wael Baioumy", "Kostas Plataniotis", "Yuri Lawryshyn" ], "posted": "2026-06-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6923108", "field": "finance", "role": "method", "bullets": [ "Financial filings such as 10-K reports, evaluated on a benchmark of 60 questions written by analysts at a major institutional investment firm with human-verified ground-truth answers.", "HybridRAG-Finance adds a summary quality-control agent feeding parallel ChromaDB vector and Neo4j graph stores; the underlying language model is not named, evaluated with the RAGAS framework.", "Reaches 98 percent faithfulness and 97 percent context recall, outperforming VectorRAG and GraphRAG baselines on finance-critical metrics while keeping comparable retrieval latency." ], "bullet_provenance": "ai", "validated": true, "validation_note": "60-question analyst benchmark, RAGAS faithfulness 98%", "salience": 55, "edition": 3, "audience": "technical", "models": [], "n": 1133, "authors_detailed": [ { "name": "Mazen Wael Baioumy", "url": "https://openalex.org/A5138509954", "inst": "" }, { "name": "Konstantinos N. Plataniotis", "url": "https://openalex.org/A5059152392", "inst": "University of Toronto" }, { "name": "Yuri Lawryshyn", "url": "https://openalex.org/A5135204430", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6928677", "doi": "10.2139/ssrn.6928677", "title": "Automated Generation of Complexity-Validated Decision Scenarios Using Large Language Models", "authors": [ "Ratna Babu Chinnam", "Toni Somers", "Abdalla Doleh" ], "posted": "2026-06-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6928677", "field": "management", "role": "method", "bullets": [ "4,238 decision scenarios generated across multiple domains and complexity tiers for use as stimuli in cognitive decision-making research; the underlying model is not named.", "An automated pipeline generates structured scenarios and scores their complexity with a composite framework drawn from Wood, Campbell, Liu and Li, and Sweller, then checks reliability and validity.", "Inter-LLM agreement was near perfect (ICC 0.997, kappa 0.971), test-retest reliability high (ICC 0.995), and known-groups validity strong with large tier separation (eta-squared 0.587)." ], "bullet_provenance": "ai", "validated": true, "validation_note": "inter-LLM ICC 0.997, known-groups eta-squared 0.587", "salience": 45, "edition": 3, "audience": "technical", "models": [], "n": 258, "authors_detailed": [ { "name": "Ratna Babu Chinnam", "url": "https://openalex.org/A5058494512", "inst": "Wayne State University" }, { "name": "Toni Somers", "url": "https://openalex.org/A5135882872", "inst": "Wayne State University" }, { "name": "Abdalla Doleh", "url": "https://openalex.org/A5138540150", "inst": "Wayne State University" } ], "affiliations": [ "Wayne State University" ] }, { "uid": "doi:10.2139/ssrn.6923898", "doi": "10.2139/ssrn.6923898", "title": "Agentic AI in Law and Finance: Navigating a New Era of Autonomous Systems", "authors": [ "Michael James Bommarito", "Daniel Martin Katz", "Jillian Bommarito" ], "posted": "2026-06-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6923898", "field": "management", "role": "object", "bullets": [ "Conceptual treatment of agentic AI for legal and financial services, drawing on scholarship across eight disciplines rather than any empirical sample or dataset.", "No model is run by the authors; they propose a three-level agency hierarchy, ten architectural design questions, and a five-layer governance and regulatory stack.", "Argues that human-in-the-loop and human-in-command designs are required to satisfy fiduciary and regulatory duties, and offers a maturity-based adoption path for institutions." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 313, "authors_detailed": [ { "name": "Michael James Bommarito", "url": "https://openalex.org/A5085629068", "inst": "Stanford Medicine" }, { "name": "Daniel Martin Katz", "url": "https://openalex.org/A5138484748", "inst": "Chicago Kent College of Law" }, { "name": "Jillian Bommarito", "url": "https://openalex.org/A5064721809", "inst": "Gleason (United States)" } ], "affiliations": [ "Stanford Medicine", "Chicago Kent College of Law", "Gleason (United States)" ] }, { "uid": "doi:10.2139/ssrn.6927344", "doi": "10.2139/ssrn.6927344", "title": "AI Labor-Displacement News and the Reallocation of Coder Employment", "authors": [ "Laurentiu Guinea" ], "posted": "2026-06-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6927344", "field": "economics", "role": "object", "bullets": [ "Coding-intensive occupations in the U.S., studying reallocation of coder employment across industries using quarterly data, with low-exposure occupations as a comparison group.", "No language model is run; the paper builds a newspaper-based AI labor-displacement index and a destination-share reallocation framework against an industry-growth counterfactual.", "A one-standard-deviation rise in the displacement index raises coders' strategic-minus-core rotation by about 0.31 points over quarters four to six, with core-tech declining." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 469, "authors_detailed": [ { "name": "Laurentiu Guinea", "url": "https://openalex.org/A5046827254", "inst": "Universidad Carlos III de Madrid" } ], "affiliations": [ "Universidad Carlos III de Madrid" ] }, { "uid": "doi:10.2139/ssrn.6898581", "doi": "10.2139/ssrn.6898581", "title": "Version-Snapshot Stability of an AI-Presence Consistency Score", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-06-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6898581", "field": "management", "role": "method", "bullets": [ "Pre-registered two-arm design over a fixed 24-brand automotive registry, scoring an AI-presence consistency measure under an older and a current model-version snapshot across six models.", "GPT-family and other models (gpt-4o through gpt-5.x) extract brand mentions; the study tests version-stability of the CPC consistency score rather than accuracy against ground truth.", "Rank stability was marginal (Spearman 0.708) and fragile leaving providers out, and magnitude stability failed with mean absolute change 0.077 above the 0.069 tolerance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "validation_note": "version-stability study, no ground-truth accuracy", "salience": 38, "edition": 3, "audience": "technical", "n": 470, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "School of Visual Arts" } ], "affiliations": [ "School of Visual Arts" ] }, { "uid": "doi:10.2139/ssrn.6879502", "doi": "10.2139/ssrn.6879502", "title": "Experience Over Compliance: Why Frequent Use -Not Governance -Builds Managerial Trust in Generative AI", "authors": [ "Cheryl Cheng Xin Yi" ], "posted": "2026-06-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6879502", "field": "management", "role": "object", "bullets": [ "Survey of 100 managers in one of Asia's most regulated financial environments, drawn from doctoral dissertation research at Golden Gate University on generative AI adoption.", "No language model is used; the study empirically examines whether governance infrastructure or frequent use drives managerial trust, with the abstract giving no model or method detail.", "The title states the thesis that frequent use rather than governance builds managerial trust in generative AI; the abstract reports no effect size or statistical result." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 872, "authors_detailed": [ { "name": "Cheryl Cheng Xin Yi", "url": "https://openalex.org/A5138521195", "inst": "Golden Gate University" } ], "affiliations": [ "Golden Gate University" ] }, { "uid": "doi:10.2139/ssrn.6923333", "doi": "10.2139/ssrn.6923333", "title": "Reconstructing Authority around AI-Generated Claims: A Practice-Based Study in Management Accounting", "authors": [ "deng dejun dejun", "Yuanfeng Luo", "Xiaoqing Li", "Feng Hu" ], "posted": "2026-06-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6923333", "field": "accounting", "role": "object", "bullets": [ "Comparative processual case study of two Chinese technology firms, using 42 semi-structured interviews and non-participant observation of management accountants working with generative AI.", "No model is used by the researchers; the qualitative study analyzes how accountants evaluate and defend AI-generated forecasts and scenarios they did not fully produce.", "Identifies three micro-practices, algorithmic interrogation, hybrid decision-making, and contextual sensemaking, and finds staged adoption builds shared review routines while rapid rollout leaves authority individually exposed." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 873, "authors_detailed": [ { "name": "deng dejun dejun", "url": "https://openalex.org/A5138426127", "inst": "Guangxi University" }, { "name": "Yuanfeng Luo", "url": "https://openalex.org/A5138429358", "inst": "" }, { "name": "Xiaoqing Li", "url": "https://openalex.org/A5138404686", "inst": "West Ukrainian National University" }, { "name": "Feng Hu", "url": "https://openalex.org/A5073557362", "inst": "Guangxi University" } ], "affiliations": [ "Guangxi University", "West Ukrainian National University" ] }, { "uid": "doi:10.2139/ssrn.6898819", "doi": "10.2139/ssrn.6898819", "title": "Will the Agentic AI Boom Become a Financial Crisis?", "authors": [ "Anton Sokolov" ], "posted": "2026-06-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6898819", "field": "finance", "role": "object", "bullets": [ "Analytical study of whether the agentic-AI investment boom poses systemic financial risk, drawing on public equity concentration, private-credit, ABS, and issuer-filing evidence.", "No language model is used; the paper combines boom-bust theory, historical comparators, and a 500,000-run synthetic sensitivity lab used only to rank evidence priorities, no validation applicable.", "Concludes present evidence supports a dotcom-like equity correction or telecom-like capex-credit hangover more than a 2008-style systemic crisis, conditional on an attestation bottleneck." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1132, "authors_detailed": [ { "name": "Anton Sokolov", "url": "https://openalex.org/A5136145186", "inst": "Tallinn University" } ], "affiliations": [ "Tallinn University" ] }, { "uid": "doi:10.2139/ssrn.6920881", "doi": "10.2139/ssrn.6920881", "title": "CFOs Meet LLMs", "authors": [ "John Robert Graham", "Campbell R. Harvey", "Manish Jha" ], "posted": "2026-06-12", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6920881", "field": "finance", "role": "agent", "bullets": [ "Duke-Federal Reserve CFO Survey economic-optimism question, 2002 to 2025, covering US firms; the unit of observation is an individual CFO's response at a survey date.", "An unnamed LLM was prompted to role-play as a specific company's CFO on a specific date and predict the optimism answer; predictions were compared against actual survey responses.", "Predicted optimism significantly forecasts the CFO's actual answer under firm and year-quarter fixed effects and a lagged-response control, with accuracy improving as respondent history and firm characteristics are supplied." ], "bullet_provenance": "ai", "validated": true, "validation_note": "predicts individual CFO survey responses", "salience": 70, "edition": 2, "audience": "broad", "models": [], "n": 40, "authors_detailed": [ { "name": "John Robert Graham", "url": "", "inst": "National Bureau of Economic Research" }, { "name": "Campbell R. Harvey", "url": "https://openalex.org/A5088627183", "inst": "National Bureau of Economic Research" }, { "name": "Manish Jha", "url": "https://openalex.org/A5024242858", "inst": "Georgia State University" } ], "affiliations": [ "National Bureau of Economic Research", "Georgia State University" ] }, { "uid": "doi:10.2139/ssrn.6835100", "doi": "10.2139/ssrn.6835100", "title": "Reducing the Cost of Invoice Processing in Small and Medium Enterprises: A Reference Architecture and Economic Model for LLM-Powered Accounts Payable Automation", "authors": [ "Gururaj Veershetty" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6835100", "field": "accounting", "role": "object", "bullets": [ "Small and medium enterprises processing accounts payable; a conceptual reference-architecture paper with no empirical sample, citing manual per-invoice costs benchmarked between 8 and 22 dollars.", "No model is named; it proposes a five-stage LLM architecture using zero-shot information extraction, tiered model routing, and confidence-based human-in-the-loop review, with no accuracy test reported.", "A unit-economics model puts per-invoice cost below 1.30 dollars at SME scale, arguing the binding adoption constraint has shifted from cost to organizational readiness." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 167, "authors_detailed": [ { "name": "Gururaj Veershetty", "url": "https://openalex.org/A5125497409", "inst": "FIT Consulting (Italy)" } ], "affiliations": [ "FIT Consulting (Italy)" ] }, { "uid": "doi:10.2139/ssrn.6906929", "doi": "10.2139/ssrn.6906929", "title": "Capitalizing on Short-Term Rigidities: An ML Forecasting Solution for Small and Family Run Businesses", "authors": [ "Aditya Mehta" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6906929", "field": "management", "role": "instrument", "bullets": [ "Dutch tulip sector from June 13 to July 24, 2025, forecasting industry consumer sentiment, macroeconomic time series, and ecological indicators for small and family-run businesses.", "XGBoost, LSTM, and random forest models generate the forecasts, and Meta's Llama 3 8B Instruct interprets them into a six-week action plan; RMSE is reported for forecasts but the LLM output is not validated.", "Testing RMSEs were about 7.39 for average humidity, 1.54 for temperature, and 1.47 for PM2.5, framed as windows for smaller firms to take share from larger corporations." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "validation_note": "RMSE for forecasts, LLM output unchecked", "salience": 30, "edition": 3, "audience": "general", "n": 255, "authors_detailed": [ { "name": "Aditya Mehta", "url": "https://openalex.org/A5138365162", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6838543", "doi": "10.2139/ssrn.6838543", "title": "Agentic Licensing for the Large Language Model Commons: MCP and NFT-Linked Rights Objects", "authors": [ "Christos Makridis" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6838543", "field": "economics", "role": "object", "bullets": [ "Conceptual paper on intellectual-property licensing in low-marginal-cost digital markets where AI agents copy and recombine works, using fragmented music licensing as the illustrative case; no empirical sample.", "No model is deployed; the paper frames the Model Context Protocol as an interoperability layer between AI agents and IP institutions and builds a stylized transaction-cost model with comparative statics.", "Lower search, verification, contracting, payment, and monitoring costs shift marginal uses into licensed exchange when rights records are reliable and terms standardized, with NFTs acting as machine-readable rights objects." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 256, "authors_detailed": [ { "name": "Christos Makridis", "url": "https://openalex.org/A5041747384", "inst": "University of Nicosia" } ], "affiliations": [ "University of Nicosia" ] }, { "uid": "arxiv:2606.12848v1", "arxiv_id": "2606.12848v1", "title": "(Human) Attention Is (Still) All You Need: Human oversight makes AI-assisted social science reliable", "authors": [ "Chen Zhu", "Xiaolu Wang", "Weilong Zhang" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.12848v1", "field": "economics", "role": "method", "bullets": [ "Pre-specified 2x4 factorial experiment with 280 complete AI-assisted research runs across four datasets, including a Qing-dynasty population register, plus an 80-run ablation.", "The same unnamed underlying model drives a multi-agent research pipeline; the HLER architecture lets LLMs reason but not execute data work, computes deterministically, and binds the workflow with three human decision gates.", "The unconstrained baseline produced critical failures in 72 percent of runs versus 16 percent under HLER (Fisher's exact p<0.001), with the largest reliability gains on the least publicly represented dataset." ], "bullet_provenance": "ai", "salience": 62, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 257, "authors_detailed": [ { "name": "Chen Zhu", "url": "https://openalex.org/A5138526562", "inst": "" }, { "name": "Xiaolu Wang", "url": "https://openalex.org/A5138542291", "inst": "" }, { "name": "Weilong Zhang", "url": "https://openalex.org/A5125236002", "inst": "China Agricultural University" } ], "affiliations": [ "China Agricultural University" ] }, { "uid": "doi:10.2139/ssrn.6836918", "doi": "10.2139/ssrn.6836918", "title": "Divergent Minds, Convergent Baselines: A Bounded-Rationality Account of LLM-Human Strategic Behaviour", "authors": [ "Po Han Teo" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6836918", "field": "economics", "role": "agent", "bullets": [ "Conceptual framework paper on using LLM agents as substitutes for human subjects in behavioural and political-science strategic-game experiments; no empirical sample, period, or geography is reported.", "LLMs act as strategic players compared with humans; the paper argues models retrieve corpus solutions to canonical games, bypassing the bounded-computation term that shapes human play. No specific model named.", "Proposes four operational tests to separate human-shaped from LLM-shaped deviation and predicts the gap scales with peer-signal individuation, bounding it at Cohen's d of at least 0.5 between named-opponent and aggregate-opponent settings." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 312, "authors_detailed": [ { "name": "Po Han Teo", "url": "https://openalex.org/A5138417716", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6888861", "doi": "10.2139/ssrn.6888861", "title": "Categorical Thinking over Continuous Attributes and Firm Pricing", "authors": [ "Andreas Kraft" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6888861", "field": "economics", "role": "instrument", "bullets": [ "Millions of online vehicle discussions merged with millions of Texas used-car transactions, examining round-number price discontinuities and categorical thinking about mileage.", "Unspecified large language models extract category boundaries from the discussion text, which then enter the pricing analysis; no validation against hand-coded boundaries is reported.", "A 10-point rise in boundary prevalence predicts a 1.04-point larger listing-price drop and a 1.23-point larger transaction-price drop at the cutoff." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 466, "authors_detailed": [ { "name": "Andreas Kraft", "url": "https://openalex.org/A5107694397", "inst": "Woodlawn School" } ], "affiliations": [ "Woodlawn School" ] }, { "uid": "doi:10.2139/ssrn.6922035", "doi": "10.2139/ssrn.6922035", "title": "Media Coverage and Risk Pricing in China's Urban Construction Investment Bond Market: Evidence from LLM-Based Text Analysis", "authors": [ "Zongxin Zhang", "Zhixuan Zhu", "Jiaqi Jin" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6922035", "field": "finance", "role": "instrument", "bullets": [ "Media news reports on issuers in China's urban construction investment bond market, used to build firm-level media sentiment; period and sample size are not stated.", "An unspecified large language model performs text analysis to construct the media sentiment measures; no validation against human coding or a benchmark is reported.", "Negative media reports significantly raise bond risk premiums, with stronger effects in fiscally weaker regions and among lower-rated issuers, plus within-province spillovers." ], "bullet_provenance": "ai", "validated": false, "salience": 46, "edition": 3, "audience": "general", "models": [], "n": 467, "authors_detailed": [ { "name": "Zongxin Zhang", "url": "https://openalex.org/A5136850263", "inst": "Fudan University Shanghai Cancer Center" }, { "name": "Zhixuan Zhu", "url": "https://openalex.org/A5138447986", "inst": "Fudan University Shanghai Cancer Center" }, { "name": "Jiaqi Jin (11416682)", "url": "https://openalex.org/A5138471170", "inst": "Fudan University Shanghai Cancer Center" } ], "affiliations": [ "Fudan University Shanghai Cancer Center" ] }, { "uid": "doi:10.2139/ssrn.6839464", "doi": "10.2139/ssrn.6839464", "title": "Breaking AI Collusion with Consumer AI", "authors": [ "Xiang Cheng", "Manmohan Aseri" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6839464", "field": "economics", "role": "object", "bullets": [ "Game-theoretic model of a market where producer-side pricing algorithms can tacitly collude and consumers delegate purchases to consumer-facing AI agents.", "No language model is run; the paper analyzes a consumer AI provider injecting demand noise by randomizing purchases of a small fraction of subscribers to disrupt monitoring.", "Demand-noise injection breaks algorithmic collusion, and consumer AI presence always raises consumer surplus even though the providers maximize their own profits." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 3, "audience": "general", "validated": null, "n": 468, "authors_detailed": [ { "name": "Xiang Cheng", "url": "https://openalex.org/A5135938611", "inst": "University of Maryland - Robert H. Smith School of Business" }, { "name": "Manmohan Aseri", "url": "https://openalex.org/A5047069572", "inst": "University of Maryland - Robert H. Smith School of Business" } ], "affiliations": [ "University of Maryland - Robert H. Smith School of Business" ] }, { "uid": "doi:10.2139/ssrn.6913339", "doi": "10.2139/ssrn.6913339", "title": "Inside the Odds", "authors": [ "Nizan Geslevich Packin", "Maya O Shaton", "Elior Sulem", "Sharon Rabinovitz" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6913339", "field": "finance", "role": "instrument", "bullets": [ "Public comments submitted to the US CFTC's 2024 proposed rulemaking on prediction-market event contracts, analyzed as the unit of observation within a broader legal study of insider trading governance.", "Applies LLM analysis, computational text analysis, and multi-model validation to gauge whether comments raised insider trading risk; the model family is not stated and no accuracy against hand coding is reported.", "Finds neither regulators nor commenters meaningfully engaged with insider trading risks before they became controversial, indicating regulatory processes are structurally reactive to informational abuse." ], "bullet_provenance": "ai", "validated": false, "validation_note": "multi-model cross-check, no ground-truth accuracy", "salience": 47, "edition": 3, "audience": "general", "models": [], "n": 576, "authors_detailed": [ { "name": "Nizan Geslevich Packin", "url": "https://openalex.org/A5004852256", "inst": "Baruch College" }, { "name": "Maya Shaton", "url": "https://openalex.org/A5022423998", "inst": "Ben-Gurion University of the Negev" }, { "name": "Elior Sulem", "url": "https://openalex.org/A5026220115", "inst": "Ben-Gurion University of the Negev" }, { "name": "Sharon Rabinovitz", "url": "https://openalex.org/A5123998007", "inst": "University of Haifa" } ], "affiliations": [ "Baruch College", "Ben-Gurion University of the Negev", "University of Haifa" ] }, { "uid": "doi:10.2139/ssrn.6921600", "doi": "10.2139/ssrn.6921600", "title": "On-Demand versus Subscription: The Optimal Pricing Strategies for Artificial Intelligence Generated Content Service Operations", "authors": [ "Feng Lipan", "Qi Kou", "Tana Siqin", "Tsan-Ming Choi" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6921600", "field": "management", "role": "object", "bullets": [ "Analytical game-theoretic model of a single AIGC firm choosing between subscription and on-demand pricing when consumers hold privacy concerns, motivated by services such as ChatGPT.", "No AI model is used; the study derives optimal pricing analytically and does not name a model beyond citing ChatGPT and OpenAI as example services.", "On-demand pricing is optimal when usage value is high, or moderate with low privacy concern; otherwise subscription is preferred, and firm and government interests can align." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "general", "validated": null, "n": 870, "authors_detailed": [ { "name": "Lipan Feng", "url": "https://openalex.org/A5070679870", "inst": "Dongbei University of Finance and Economics" }, { "name": "Qi Kou", "url": "https://openalex.org/A5093262901", "inst": "South China University of Technology" }, { "name": "Tana Siqin", "url": "https://openalex.org/A5091041528", "inst": "Lingnan University" }, { "name": "Tsan‐Ming Choi", "url": "https://openalex.org/A5078004739", "inst": "University of Liverpool" } ], "affiliations": [ "Dongbei University of Finance and Economics", "South China University of Technology", "Lingnan University", "University of Liverpool" ] }, { "uid": "arxiv:2606.13314v2", "arxiv_id": "2606.13314v2", "title": "The Privilege of Exposure: Caste and Generative AI in India's Graduate Labour Market", "authors": [ "Kaibalyapati Mishra" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.13314v2", "field": "economics", "role": "object", "bullets": [ "83,000 employed graduates in India's redesigned Periodic Labour Force Survey for 2025, with three occupational generative-AI exposure indices mapped to their occupations.", "No language model is run; the authors assign existing occupational AI-exposure indices to survey respondents and compare exposure across caste groups within the same district.", "Scheduled Caste and Tribe graduates are 0.24 to 0.37 standard deviations less exposed than upper-caste peers, and with a 20 percent exposure wage premium AI may widen the caste gap." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 871, "authors_detailed": [ { "name": "Kaibalyapati Mishra", "url": "https://openalex.org/A5090901369", "inst": "Institute for Social and Economic Change" } ], "affiliations": [ "Institute for Social and Economic Change" ] }, { "uid": "doi:10.2139/ssrn.6841798", "doi": "10.2139/ssrn.6841798", "title": "AI Ecosystems, Power Shifts, and EU Competition Law Enforcement", "authors": [ "Giuseppe Colangelo", "Ariel Ezrachi" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6841798", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of AI assistants and agents reshaping competition in the European Union; no empirical sample, focused on vertical-chain power relations and ecosystem competition.", "No language model is used; the paper reasons about how agentic AI shifts competition from products to integrated services and from firms to ecosystems, with no validation applicable.", "Argues vertically integrated providers may leverage their AI tools to extract rents while agentic AI and collaborations may ease entry, questioning the adequacy of current competition-law analysis." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1129, "authors_detailed": [ { "name": "Giuseppe Colangelo", "url": "https://openalex.org/A5049048677", "inst": "University of Basilicata" }, { "name": "Ariel Ezrachi", "url": "https://openalex.org/A5134046950", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford", "University of Basilicata" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6839858", "doi": "10.2139/ssrn.6839858", "title": "Pricing AI Software Services: Reallocating Value and Risk", "authors": [ "Pawel Podolski" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6839858", "field": "management", "role": "object", "bullets": [ "Conceptual pricing framework for AI software services delivered by agentic coding agents, illustrated with one real engagement re-priced from a 2,500-hour quote to 200 hours.", "No language model is run by the author; the paper analyzes how autonomous coding agents split delivery into thinking and making layers with different unit economics, no validation applicable.", "Proposes cost-decomposition, hybrid, and value-anchored outcome-based pricing regimes plus three contractual instruments, and flags per-token inference pricing as an unstable subsidised rate." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1130, "authors_detailed": [ { "name": "Pawel Podolski", "url": "https://openalex.org/A5138461749", "inst": "Logan Hospital" } ], "affiliations": [ "Logan Hospital" ] }, { "uid": "doi:10.2139/ssrn.6839340", "doi": "10.2139/ssrn.6839340", "title": "Beyond the Model: Why National and Institutional AI Capability will be Won at the Integration Layer", "authors": [ "Aswani Anumula" ], "posted": "2026-06-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6839340", "field": "management", "role": "object", "bullets": [ "Practitioner synthesis of enterprise AI integration and adoption, drawing on the author's modernization, data-migration, and RPA experience plus published evidence; no original empirical data.", "No language model is run; the paper cites evidence that about 95 percent of enterprise generative-AI pilots fail and introduces a seven-layer Accountable Integration Stack, no validation applicable.", "Argues durable AI advantage accrues at the integration layer rather than model capability, and that national AI capability is fundamentally an institutional capability." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1131, "authors_detailed": [ { "name": "Aswani Anumula", "url": "https://openalex.org/A5138456566", "inst": "Independant researcher" } ], "affiliations": [ "Independant researcher" ] }, { "uid": "doi:10.2139/ssrn.6830918", "doi": "10.2139/ssrn.6830918", "title": "Cross-Attention Latent Fusion: Integrating DeepSeek-R1 and Mamba for Downside-Aware Quantitative Trading", "authors": [ "Sélim Jomaa", "Eve Jouni" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6830918", "field": "finance", "role": "instrument", "bullets": [ "50 equities from the FNSPID dataset, combining news text with 30-day price momentum; the trading period and geography are not stated.", "Hidden states of DeepSeek-R1-Distill-Qwen-1.5B are fused via cross-attention with a Mamba state-space price encoder, trained using group relative and downside-aware policy optimization.", "The latent-fusion model improves risk-adjusted returns and reduces maximum drawdown versus unimodal baselines while remaining feasible on consumer-grade hardware; no magnitudes are reported." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 44, "edition": 3, "audience": "technical", "n": 108, "authors_detailed": [ { "name": "Sélim Jomaa", "url": "https://openalex.org/A5130589092", "inst": "Roivant Sciences (United States)" }, { "name": "Eve Jouni", "url": "https://openalex.org/A5130602502", "inst": "Roivant Sciences (United States)" } ], "affiliations": [ "Roivant Sciences (United States)" ] }, { "uid": "doi:10.2139/ssrn.6910509", "doi": "10.2139/ssrn.6910509", "title": "Creativity Is Not an Outcome: A Process-Based Framework for Mapping Individual Creative Contributions in Human–LLM Collaboration", "authors": [ "Nishthaa Lekhi", "Trevor Patten", "Prakash Patil", "Mengyao Li", "Areen Alsaid" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6910509", "field": "management", "role": "object", "bullets": [ "Between-subjects experiment comparing participants brainstorming with a human partner versus an LLM partner in live dialogues, analyzed turn by turn; sample size and geography not stated.", "The LLM acted as the brainstorming partner while human contributions were scored via Observable Creative Sense-Making using computational semantic and linguistic measures validated against expert ratings; the model is not named.", "LLM collaboration raised idea divergence and exploration but lowered participation, appropriateness, reported experience, and confidence, whereas human-human pairs showed stronger participation and cumulative idea development." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 252, "authors_detailed": [ { "name": "Nishthaa Lekhi", "url": "https://openalex.org/A5136399420", "inst": "University of Michigan–Dearborn" }, { "name": "Trevor Patten", "url": "https://openalex.org/A5111296143", "inst": "Pennsylvania State University" }, { "name": "Prakash Patil", "url": "https://openalex.org/A5138331377", "inst": "" }, { "name": "Mengyao Li", "url": "https://openalex.org/A5040023451", "inst": "North West Agriculture and Forestry University" }, { "name": "Areen Alsaid", "url": "https://openalex.org/A5027784898", "inst": "University of Michigan–Dearborn" } ], "affiliations": [ "University of Michigan–Dearborn", "Pennsylvania State University", "North West Agriculture and Forestry University" ] }, { "uid": "doi:10.2139/ssrn.6834298", "doi": "10.2139/ssrn.6834298", "title": "AIAS Presence Index v0.23: Premium Spirits Recognition Ceiling, Recall-driven Regime Classification, and the Conglomerate Ownership Hypothesis across 24 Brands and Six Large Language Models", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6834298", "field": "management", "role": "object", "bullets": [ "24 premium spirits brands probed across six large language models under Protocol v1.6, using 144 recognition probes, as the seventh substrate in the author's AIAS measurement program; geography not stated.", "The six models, not individually named, were queried for brand recognition and recommendation recall to build a presence composite; all 144 probes returned rich recognition, so scores came only from recall.", "Composite scores ranged from 40.0 to 71.19 across four regimes, and the conglomerate-ownership hypothesis was falsified, with owned brands at 60.75 versus 58.05 for independents, a non-significant lift (t=0.59, p=0.28)." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 253, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "Samsung (United States)" } ], "affiliations": [ "Samsung (United States)" ] }, { "uid": "doi:10.2139/ssrn.6908584", "doi": "10.2139/ssrn.6908584", "title": "What LLM and 6.3 billion words tell us about sentiment?", "authors": [ "Konpanas Dumrongwong", "Suwongrat Papangkorn" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6908584", "field": "finance", "role": "instrument", "bullets": [ "Financial news from Thomson Reuters, the Wall Street Journal, and the New York Times, with the model trained on a 6.3 billion word corpus; sample period and geography not stated.", "ModFinBERT, a finance-specialized BERT-style model trained on the corpus, extracts sentiment to build the Financial Optimism Index and is reported to beat other LLMs and dictionary methods, though no accuracy figure is given.", "The Financial Optimism Index shows a bidirectional relationship with stock index returns during non-COVID sample periods, consistent with the behavioral finance literature." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "validation_note": "claims outperformance, no figure reported", "salience": 40, "edition": 3, "audience": "general", "n": 254, "authors_detailed": [ { "name": "Konpanas Dumrongwong", "url": "https://openalex.org/A5066910644", "inst": "Thammasat University" }, { "name": "Suwongrat Papangkorn", "url": "https://openalex.org/A5043693428", "inst": "Thammasat University" } ], "affiliations": [ "Thammasat University" ] }, { "uid": "doi:10.2139/ssrn.6894079", "doi": "10.2139/ssrn.6894079", "title": "From Data moats to Context moats: A Utility Law for Applied AI", "authors": [ "Gaston Besanson", "Jean Luc Chatelain" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6894079", "field": "management", "role": "object", "bullets": [ "Conceptual framework paper on enterprise applied AI, drawing on existing empirical literature covering retrieval-augmented generation, long-context inference, and data governance; no original dataset or sample is analyzed.", "Uses no specific named model, discussing foundation models, retrieval, and tool use generically, and proposes a context utility law, a dominance condition, and a context moat vector without empirical validation.", "Argues competitive advantage in applied AI is shifting from proprietary corpus ownership to governed inference-time context orchestration, offering falsifiable predictions to distinguish data-moat from context-moat regimes." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 575, "authors_detailed": [ { "name": "Gaston Besanson", "url": "https://openalex.org/A5007610209", "inst": "Universidad Torcuato Di Tella" }, { "name": "Jean Luc Chatelain", "url": "https://openalex.org/A5138362404", "inst": "" } ], "affiliations": [ "Universidad Torcuato Di Tella" ] }, { "uid": "doi:10.2139/ssrn.6914530", "doi": "10.2139/ssrn.6914530", "title": "Answering Without Informing: Evasive Disclosure and Analyst Information Production", "authors": [ "Xuejia Xue", "Shengmiao Bi" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6914530", "field": "accounting", "role": "instrument", "bullets": [ "Chinese exchange-operated investor question-and-response archives from 2010 to 2024, with firm answers before analyst forecasts as the unit of observation.", "An unnamed large language model classifies answer completeness to build a pre-forecast evasiveness measure, checked with human validation but no reported agreement figure.", "A one-standard-deviation higher evasiveness measure corresponds to forecast error about 3.4 percent of its sample mean higher, with the effect stronger when analysts have fewer substitutes." ], "bullet_provenance": "ai", "validated": false, "validation_note": "human validation mentioned but no agreement figure reported", "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 867, "authors_detailed": [ { "name": "Xuejia Xue", "url": "https://openalex.org/A5061352968", "inst": "Macquarie University" }, { "name": "Shengmiao Bi", "url": "https://openalex.org/A5138328399", "inst": "Xiamen University" } ], "affiliations": [ "Macquarie University", "Xiamen University" ] }, { "uid": "doi:10.2139/ssrn.6911303", "doi": "10.2139/ssrn.6911303", "title": "Generative Artificial Intelligence and Product Innovation Performance: The Roles of Absorptive Capacity, Strategic Agility, and Intelligent Organizational Learning", "authors": [ "Mahdi Sharaf Tadavani", "Ali Pirzad" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6911303", "field": "management", "role": "object", "bullets": [ "Survey of 400 managers at Iranian industrial firms, cross-sectional, analyzed with structural equation modeling.", "No language model is run by the researchers; firms' generative AI use is measured as a survey construct and tested as a driver of innovation capabilities.", "Generative AI strengthens absorptive capacity, and a sequential chain from absorptive capacity through intelligent organizational learning to product innovation holds, with only learning directly driving innovation." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 868, "authors_detailed": [ { "name": "Mahdi Sharaf Tadavani", "url": "https://openalex.org/A5138340171", "inst": "Islamic Azad University, Tehran" }, { "name": "Ali Pirzad", "url": "https://openalex.org/A5001464082", "inst": "Islamic Azad University Yasuj" } ], "affiliations": [ "Islamic Azad University, Tehran", "Islamic Azad University Yasuj" ] }, { "uid": "arxiv:2606.24616v1", "arxiv_id": "2606.24616v1", "title": "AI Tokenomics: The Economics of Tokens, Computation, and Pricing in Foundation Models", "authors": [ "Quanyan Zhu" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.24616v1", "field": "economics", "role": "object", "bullets": [ "Conceptual framework paper with no empirical sample, treating tokens as the accounting unit for foundation model services linking computation, memory, energy, pricing, and economic value.", "No model is used by the author; the paper builds an economic framework connecting token-level technical costs to production functions, resource allocation, and market design, naming no specific model.", "Argues token expenditure and economic value are distinct, with value driven by marginal productivity, workflow position, hidden reasoning activity, risk, and downstream propagation effects." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 869, "authors_detailed": [ { "name": "Quanyan Zhu", "url": "https://openalex.org/A5139292225", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6883238", "doi": "10.2139/ssrn.6883238", "title": "Agentic Capital as a Productive Asset in the Agentic Economy: A Panel Econometric Analysis of Productivity Dynamics", "authors": [ "Davit Gondauri" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6883238", "field": "economics", "role": "object", "bullets": [ "Country-year panel of nine economies from 2005 to 2024 relating a constructed six-pillar agentic capital stock to labour productivity growth.", "No language model is used; the paper builds the Gondauri-adjusted Agentic Capital Index and estimates the link via pooled OLS, fixed and random effects, dynamic GMM, IV-2SLS, and quantile and threshold regressions.", "Geometric agentic capital is positively associated with labour productivity growth, structural imbalance weakens system maturity, and governance conditions shape the productivity value of agentic systems." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1125, "authors_detailed": [ { "name": "Davit Gondauri", "url": "https://openalex.org/A5040902595", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6890883", "doi": "10.2139/ssrn.6890883", "title": "Exploring Patent Pools as a Regulatory Tool for Managing AI-Related Patents and Building Trustworthy AI in Japan: A Comparative Study with the United States and the European Union", "authors": [ "Atilla Kasap" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6890883", "field": "management", "role": "object", "bullets": [ "Comparative policy study of patent pools for AI-related patents across Japan, the United States, and the European Union, focused on SMEs and startups facing patent thickets and standards compliance.", "No language model is used; the paper reviews AI patenting trends and three governance approaches, agile, rights-based, and risk-based, and analyzes patent-pool design and FRAND licensing.", "Argues patent pools can lower transaction costs, royalty stacking, and thicket risk while easing SME compliance, provided design curbs excessive royalties and grants supply-chain-wide access." ], "bullet_provenance": "ai", "salience": 31, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1126, "authors_detailed": [ { "name": "Atilla Kasap", "url": "https://openalex.org/A5062765492", "inst": "University of Suffolk" } ], "affiliations": [ "University of Suffolk" ] }, { "uid": "doi:10.2139/ssrn.6834839", "doi": "10.2139/ssrn.6834839", "title": "Not All Bots are Equal: Chatbot Roles and Human Interaction in Online Chat Groups", "authors": [ "Dezhen Guo", "Nina Huang", "Kevin Hong" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6834839", "field": "management", "role": "object", "bullets": [ "Chatbots in Telegram chat groups, classified along role positioning, enactment, and competency; period and geography not stated, with chat groups as the unit of observation.", "Text embeddings and hierarchical clustering classify chatbot roles, model not named; effects on participation and emotional expression estimated with a matrix-completion estimator and covariate-balancing weights.", "Governance chatbots raise participation with no effect on emotional expression while affordance chatbots suppress both, and AI-enabled chatbots produce larger positive participation than rule-based ones." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "edition": 3, "audience": "general", "models": [], "n": 1127, "authors_detailed": [ { "name": "Dezhen Guo", "url": "https://openalex.org/A5138294884", "inst": "" }, { "name": "Ni Huang", "url": "https://openalex.org/A5077348887", "inst": "University of Miami" }, { "name": "Yili Hong", "url": "https://openalex.org/A5100339366", "inst": "University of Miami" } ], "affiliations": [ "University of Miami" ] }, { "uid": "doi:10.2139/ssrn.6874918", "doi": "10.2139/ssrn.6874918", "title": "From Observer To Governor: Reconstructing Evidentiary Accountability In Autonomous Multi-Agent Accounting And Aml/Ctf Systems", "authors": [ "Muhammad Bilal Mianoor" ], "posted": "2026-06-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6874918", "field": "accounting", "role": "object", "bullets": [ "Conceptual paper on autonomous multi-agent AI in accounting, auditing, and anti-money-laundering and counter-terrorism-financing settings; no empirical sample, using conceptual simulations and governance stress testing.", "No language model is run; the paper proposes an AHAA framework of recursive accountability, adaptive accountability, and governance receipts, with no validation against ground truth.", "Argues the central challenge is accountability fragmentation rather than epistemic opacity, and that governance must become computationally infrastructural rather than retrospective human oversight." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1128, "authors_detailed": [ { "name": "Muhammad Bilal Mianoor", "url": "https://openalex.org/A5135928602", "inst": "Universal Technical Institute" } ], "affiliations": [ "Universal Technical Institute" ] }, { "uid": "doi:10.2139/ssrn.6837058", "doi": "10.2139/ssrn.6837058", "title": "Beyond the Fiat Vault: Evaluating Machine-Readable Blockchain Disclosures for Systemic Safety, Operational Security, and MEV Conflict Mitigation", "authors": [ "Imane El Imami", "Jeonghoon Oh", "Jason Meyers", "Gerard Brennan", "Miklos A. Vasarhelyi", "Alexander J. Sannella", "Thomas Egan", "AbdelKader El Alaoui O.", "Bassma Guermah", "Said Ouatik El Alaoui" ], "posted": "2026-06-10", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6837058", "field": "accounting", "role": "agent", "bullets": [ "Proposes a Blockchain Network Participation disclosure taxonomy of 148 input questions and 125 output elements in XBRL-JSON, prompted by the GENIUS Act stablecoin framework.", "Three LLMs, Claude Sonnet 4.6, Gemini 3.1, and GPT-5, independently evaluated disclosure elements as synthetic experts, with agreement measured by Gwet's AC1 across models.", "Model consensus rejected the minimized disclosure set, concluding infrastructure and MEV-conflict disclosures are non-deferrable and that fiat-reserve audits alone are insufficient for investor protection." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": false, "validation_note": "inter-model Gwet's AC1 agreement, no external ground truth", "salience": 42, "edition": 2, "audience": "technical", "n": 25, "authors_detailed": [ { "name": "Imane El Imami", "url": "https://openalex.org/A5138351593", "inst": "" }, { "name": "Jeonghoon Oh", "url": "https://openalex.org/A5110306578", "inst": "Seoul National University of Science and Technology" }, { "name": "Jason Meyers", "url": "https://openalex.org/A5125292088", "inst": "Mercy Medical Center" }, { "name": "Gerard Brennan", "url": "https://openalex.org/A5110933174", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Miklos A. Vasarhelyi", "url": "https://openalex.org/A5136535968", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Alexander J. Sannella", "url": "https://openalex.org/A5068815892", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Thomas Egan", "url": "https://openalex.org/A5138392068", "inst": "" }, { "name": "Abdelkader O. El Alaoui", "url": "https://openalex.org/A5075218424", "inst": "International University of Rabat" }, { "name": "Bassma Guermah", "url": "https://openalex.org/A5044855285", "inst": "International University of Rabat" }, { "name": "Saïd Ouatik El Alaoui", "url": "https://openalex.org/A5084532237", "inst": "Université Ibn-Tofail" } ], "affiliations": [ "Seoul National University of Science and Technology", "Rutgers, The State University of New Jersey", "International University of Rabat", "Université Ibn-Tofail" ] }, { "uid": "doi:10.2139/ssrn.6906675", "doi": "10.2139/ssrn.6906675", "title": "Self-Improving Alpha Mining for Quantitative Trading via Multi-Agent Large Language Models with Knowledge Base Accumulation", "authors": [ "Son Minh Vu", "Trung The Pham", "Viet Hong Tran" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6906675", "field": "finance", "role": "agent", "bullets": [ "Vietnamese stock market data; automated generation, refinement, and evaluation of trading alpha factors through a multi-agent pipeline. Sample period not stated.", "Large language models, family not stated, run as WriterAgent, JudgeAgent, and BacktestEngine using WorldQuant 101 formulaic alphas as a prior; evaluated by backtested information coefficient and Sharpe.", "The framework produces alphas with statistically significant information coefficients and positive Sharpe ratios, and the WorldQuant-grounded prior beats unconstrained generation on quality and convergence." ], "bullet_provenance": "ai", "validated": false, "salience": 43, "edition": 3, "audience": "technical", "models": [], "n": 166, "authors_detailed": [ { "name": "Son Minh Vu", "url": "https://openalex.org/A5138217334", "inst": "" }, { "name": "Trung The Pham", "url": "https://openalex.org/A5138218592", "inst": "" }, { "name": "Viet Hong Tran", "url": "https://openalex.org/A5138270170", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6903678", "doi": "10.2139/ssrn.6903678", "title": "Mapping Artificial Intelligence in Audit: A Pre-LLM / LLM-Era Taxonomy, the Practitioner–Academic Gap, and the Emerging Regulatory Layer", "authors": [ "Natan Tzidkani" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6903678", "field": "accounting", "role": "object", "bullets": [ "Conceptual mapping of AI in external audit, contrasting Big-4 production deployments with peer-reviewed coverage and overlaying the EU AI Act, the PCAOB 2024 Spotlight, the NIST AI RMF, and ISO/IEC 42001. No empirical sample.", "The paper does not apply an LLM; it builds a pre-LLM versus LLM-era taxonomy and a ten-row mapping of deployment maturity, academic coverage, and regulatory locus. Models are not named.", "It finds academic coverage thinnest where Big-4 deployment is most active, such as working-paper drafting and agentic workflows, and frames hallucination as an ISA-500 evidence-reliability failure." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 250, "authors_detailed": [ { "name": "Natan Tzidkani", "url": "https://openalex.org/A5138248406", "inst": "University of Haifa" } ], "affiliations": [ "University of Haifa" ] }, { "uid": "doi:10.2139/ssrn.6869019", "doi": "10.2139/ssrn.6869019", "title": "FinSupplyKG: Directed Knowledge Graph Traversal for Multi-Hop Supply Chain Contagion Analysis in SEC Filings", "authors": [ "Naga Sai Anirudh Kodali", "Faezeh Aghajani", "Sri Surya Abhyuday Kodali" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6869019", "field": "finance", "role": "method", "bullets": [ "SEC 10-K and 20-F filings for 13 semiconductor companies, with a 43-question adversarial benchmark covering two- to three-hop supply chain contagion scenarios evaluated across three independent runs.", "Builds a directed knowledge graph and DCG-RAG retrieval system, compares six configurations from a zero-shot LLM upward, and grades outputs with a DeepSeek-R1 judge plus programmatic verification and human adjudication.", "Tuned DCG-RAG scored 2.9 times higher than the best agentic baseline while using 77 percent fewer tokens, and the grading protocol revealed systematic biases in LLM-based evaluation." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "43-question benchmark, human adjudication plus corpus verification", "salience": 42, "edition": 3, "audience": "technical", "n": 251, "authors_detailed": [ { "name": "Naga Sai Anirudh Kodali", "url": "https://openalex.org/A5138205161", "inst": "" }, { "name": "Faezeh Aghajani", "url": "https://openalex.org/A5138222457", "inst": "" }, { "name": "Sri Surya Abhyuday Kodali", "url": "https://openalex.org/A5138240199", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6902947", "doi": "10.2139/ssrn.6902947", "title": "Early Labor-Market Responses to Generative AI in a Developing Country: Evidence from Brazil", "authors": [ "Tomas Aguirre", "Luis Meloni" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6902947", "field": "economics", "role": "object", "bullets": [ "Brazil, 2017Q1 to 2024Q4, combining a nationally representative household survey of 5.7 million worker-quarter observations with 13 million formal-sector hiring and separation records.", "No model is used by the researchers; ChatGPT's November 2022 release serves as a timing shock in a difference-in-differences design across high versus low AI-exposure occupations.", "Young workers aged 16 to 21 in high-exposure occupations earned 4.7% less and were 1.5 points less likely to be formally employed; prime-age and older workers showed no differential." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "edition": 3, "audience": "general", "validated": null, "n": 463, "authors_detailed": [ { "name": "Tomas Aguirre", "url": "https://openalex.org/A5138250473", "inst": "" }, { "name": "Luis Meloni", "url": "https://openalex.org/A5120600895", "inst": "Universidade de São Paulo" } ], "affiliations": [ "Universidade de São Paulo" ] }, { "uid": "doi:10.2139/ssrn.6906943", "doi": "10.2139/ssrn.6906943", "title": "When and why LLM agents match optimization on manufacturing decisions: a deterministic, optimally-referenced simulator", "authors": [ "Andrzej Wodecki" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6906943", "field": "management", "role": "agent", "bullets": [ "Simulated electronics-manufacturing-services weekly build-and-buy decisions under finite capacity and recurring disruption, using factory-sim, a deterministic simulator with a per-period linear-programming optimum and identical paired scenarios.", "Five foundation models from four vendors, not individually named, run across six agent architectures and make production and procurement decisions via the Model Context Protocol, benchmarked against the LP optimum.", "The effect is regime-dependent: a single-prompt LLM beats the myopic LP when capacity is slack, driven by roughly 2.8 times more early purchase orders, but the LP overtakes once capacity binds." ], "bullet_provenance": "ai", "validated": true, "validation_note": "agent decisions compared to per-period LP optimum", "salience": 60, "edition": 3, "audience": "technical", "models": [], "n": 464, "authors_detailed": [ { "name": "Andrzej Wodecki", "url": "https://openalex.org/A5138276227", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6904051", "doi": "10.2139/ssrn.6904051", "title": "The Case Analysis Integrity Gap: Generative AI, Cognitive Ownership, and Assessment Redesign in Management Education", "authors": [ "HENRY ADOBOR" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6904051", "field": "management", "role": "object", "bullets": [ "Conceptual paper on case-based management education, grounded in experiential learning, cognitive offloading, and authentic assessment theory, with no empirical sample or data collection.", "No model is used by the researchers; the paper describes how generative AI can produce case-analysis outputs and distinguishes AI-augmented from AI-substituted case learning.", "It proposes assessment redesign principles including staged submissions, disclosure of AI use, an oral defense, and critique of AI-generated outputs to preserve assessment validity." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 465, "authors_detailed": [ { "name": "Henry Adobor", "url": "https://openalex.org/A5135003610", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6906956", "doi": "10.2139/ssrn.6906956", "title": "From Digital Consumption to Sovereign Compute: A Socio-Technical Framework for Generative AI in African Education", "authors": [ "Raymond Onuoha" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6906956", "field": "economics", "role": "object", "bullets": [ "Purposively selected African education initiatives including AIMS/NEF, NgREN, Digemy, and AltSchool, examined through a qualitative multiple case study design.", "No language model is run by the researchers; generative AI is the object, studied via socio-technical systems directed documentary analysis to diagnose compute and data-sovereignty gaps.", "Finds misalignments in sovereign compute capacity, teacher autonomy, and policy governance, and proposes a phased roadmap from API dependency to jointly optimized education ecosystems." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 865, "authors_detailed": [ { "name": "Raymond Onuoha", "url": "https://openalex.org/A5138208045", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6908572", "doi": "10.2139/ssrn.6908572", "title": "Separating Genuine Complexity from Concealment in 10-K Filings: Evidence from Income-Decreasing Restatements", "authors": [ "Sripal Konchada" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6908572", "field": "accounting", "role": "instrument", "bullets": [ "73,889 US 10-K filings from 2011 to 2023, with the annual filing as the unit of observation.", "An unnamed large language model reads each business description, combined with XBRL extension counts and segment structure to proxy genuine complexity, with residual textual complexity treated as opacity and no ground-truth validation reported.", "Residual opacity predicts subsequent income-decreasing restatements with an odds ratio of 2.02, holding among large monitored firms at z of 2.38, while genuine complexity carries the opposite sign." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no comparison of model output to ground truth reported", "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 866, "authors_detailed": [ { "name": "Sripal Konchada", "url": "https://openalex.org/A5138239685", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6531238", "doi": "10.2139/ssrn.6531238", "title": "The Semantic Deviation Index (SDI): A Runtime Measurement Standard for the Governance of Agentic AI in Financial Services", "authors": [ "Maureen Doyle-Spare" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6531238", "field": "finance", "role": "object", "bullets": [ "Conceptual practitioner paper on runtime governance of agentic AI in regulated financial services, third in a connected series, drawing on banking controls and model risk governance; no empirical sample.", "No language model is applied; the paper defines the Semantic Deviation Index as a bounded divergence measure between an agent's resolved meaning and the institution-authorized meaning.", "Argues post-execution monitoring cannot capture pre-execution semantic resolution and proposes the index with a deterministic gate, semantic audit trail, and blast-radius containment as a governance architecture." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1122, "authors_detailed": [ { "name": "Maureen Doyle-Spare", "url": "https://openalex.org/A5130951607", "inst": "LinkedIn (United States)" } ], "affiliations": [ "LinkedIn (United States)" ] }, { "uid": "doi:10.2139/ssrn.6884201", "doi": "10.2139/ssrn.6884201", "title": "Linking Blood Demand Forecasts with Emergency Distribution Planning: A TabTransformer and Stochastic MILP Approach", "authors": [ "arian dadkhah" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6884201", "field": "management", "role": "instrument", "bullets": [ "Computational case study of one-day emergency blood logistics with five hospitals, four supply centers, eight blood groups, and two mobile emergency units at facility and blood-group level.", "A TabTransformer forecasts demand feeding a two-stage stochastic mixed-integer program; on the validation run it reached RMSE 2.354 and R-squared 0.8387, competitive with but not beating a historical-mean baseline.", "The prediction-based stochastic model cut expected shortage from 11.53 and 13.71 units under deterministic plans to 2.23 units, with compatibility-based substitution further reducing shortage." ], "bullet_provenance": "ai", "validated": true, "validation_note": "RMSE 2.354, R-squared 0.84 on held-out demand versus historical-mean baseline", "salience": 42, "edition": 3, "audience": "technical", "models": [], "n": 1123, "authors_detailed": [ { "name": "arian dadkhah", "url": "https://openalex.org/A5138231658", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6888439", "doi": "10.2139/ssrn.6888439", "title": "Agent-First, Safety Last? The Unintended Consequences of Friction-Based Intervention on AI Platforms", "authors": [ "Wentao Lin", "Ka Chung Ng", "Dongwon Lee" ], "posted": "2026-06-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6888439", "field": "management", "role": "object", "bullets": [ "Difference-in-differences study exploiting a platform-wide token reset on Moltbook, an agent-first social platform where human principals delegate posting to AI agents; unit of observation is posts and agents around the reset.", "No model is built by the researchers; they observe deployed AI agents and introduce friction-target-executor alignment, measuring content composition and re-entry after the reset.", "The reset cut relative diffusion of exploitation content by about 19 percent but raised dysfunctional active posts from 29.8 to 49.8 percent, with within-agent adaptation explaining about 68 percent of the deterioration." ], "bullet_provenance": "ai", "salience": 47, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1124, "authors_detailed": [ { "name": "Wentao Lin", "url": "https://openalex.org/A5138222710", "inst": "" }, { "name": "Ka Chung Ng", "url": "https://openalex.org/A5044775171", "inst": "Hong Kong Polytechnic University" }, { "name": "Dongwon Lee", "url": "https://openalex.org/A5100405084", "inst": "Pennsylvania State University" } ], "affiliations": [ "Hong Kong Polytechnic University", "Pennsylvania State University" ] }, { "uid": "doi:10.2139/ssrn.6876161", "doi": "10.2139/ssrn.6876161", "title": "Value Contamination in Policy Simulation: Measuring How LLM Policy-Value Orientations Shape Predicted Administrative Burden Effects", "authors": [ "Yichao Jin" ], "posted": "2026-06-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6876161", "field": "economics", "role": "method", "bullets": [ "Nine large language models across six families each complete a policy-value battery, then predict vaccination willingness for 27 synthetic profiles under low- and high-burden access conditions.", "The models, individual names not stated, act as simulated respondents, and a within-model frame-manipulation experiment tests value sensitivity; no comparison to human ground truth is reported.", "A composite value-contamination index does not predict cross-model variation, while autonomy and equity frames raise predicted burden effects and collective-obligation frames lower them." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 165, "authors_detailed": [ { "name": "Yichao Jin", "url": "https://openalex.org/A5135802115", "inst": "The University of Texas at Dallas" } ], "affiliations": [ "The University of Texas at Dallas" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6901066", "doi": "10.2139/ssrn.6901066", "title": "Modeling Project Risk Propagation from Risk Sources to Activity Networks: A Large Language Model-Assisted Approach", "authors": [ "Liting Zhang", "Yan Ning" ], "posted": "2026-06-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6901066", "field": "management", "role": "instrument", "bullets": [ "A single real-world project case, with a historical risk knowledge base built from project-related textual records; the units of observation are risk sources and activities within a project activity network.", "Large language models, family not stated, extract risk knowledge from text and retrieval-augmented generation identifies project-specific propagation chains; no accuracy check against ground truth is reported.", "Cutting a single important risk source has limited effect when the core propagation chain stays connected, and risks on upstream or highly connected activities spread further and accumulate." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no ground-truth check on LLM extraction", "salience": 40, "edition": 3, "audience": "general", "models": [], "n": 574, "authors_detailed": [ { "name": "Liting Zhang", "url": "https://openalex.org/A5138192257", "inst": "Nanjing University" }, { "name": "Yan Ning", "url": "https://openalex.org/A5138198262", "inst": "Nanjing University" } ], "affiliations": [ "Nanjing University" ] }, { "uid": "doi:10.2139/ssrn.6830598", "doi": "10.2139/ssrn.6830598", "title": "Intra-Firm Power Purchase Agreement Size Dispersion and Forward Returns in Data Center-Tilted Equities: A Pre-Registered Cross-Section Test under Small-Cluster Inference", "authors": [ "Hyun Ahn" ], "posted": "2026-06-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6830598", "field": "finance", "role": "instrument", "bullets": [ "Four data-center-tilted US equities (Amazon, Equinix, NextEra Energy, Vistra) over 2021 to 2026, using 98 filings spanning SEC 8-K, state PUC dockets, FERC interconnection agreements, and REIT 10-K.", "A four-vendor large language model ensemble extracts megawatt power purchase agreement commitments from the filings; specific models are not named and no extraction accuracy figure is reported.", "A pre-registered median-split long-short portfolio on within-firm PPA size dispersion earns a full-sample Sharpe of 1.21 over 48 months (t of 2.35, p of 0.028), with a held-out 2024 to 2026 Sharpe of 1.32." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no extraction accuracy reported", "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 864, "authors_detailed": [ { "name": "Hyun Ahn", "url": "https://openalex.org/A5135592103", "inst": "Korea Institute for Advanced Study" } ], "affiliations": [ "Korea Institute for Advanced Study" ] }, { "uid": "doi:10.2139/ssrn.6825858", "doi": "10.2139/ssrn.6825858", "title": "Fiduciary Infrastructure for Agentic AI: Directors' Duties and Investor Protection in the EU, South Korea and Singapore", "authors": [ "Luh Luh Lan" ], "posted": "2026-06-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6825858", "field": "management", "role": "object", "bullets": [ "Comparative doctrinal study of directors' fiduciary duties and investor protection for agentic AI adoption in the EU, South Korea, and Singapore, anchored in named corporate-law cases.", "No language model is used; the paper conceptualizes a four-layer fiduciary infrastructure covering data, model, deployment, and accountability for the directors' duty of care.", "Argues the classical principal-agent framework fails for agentic AI and proposes corporate-law and securities-disclosure reforms addressing new information asymmetries in capital markets." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1121, "authors_detailed": [ { "name": "Luh Luh Lan", "url": "https://openalex.org/A5079575294", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore" ] }, { "uid": "arxiv:2606.08853v1", "arxiv_id": "2606.08853v1", "title": "AI-Assisted Variance Reduction in Randomized Experiments", "authors": [ "David Arbour", "Eli Ben-Michael", "Avi Feller", "Apoorva Lal", "Lo-Hua Yuan" ], "posted": "2026-06-07", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.08853v1", "field": "economics", "role": "method", "bullets": [ "Simulations plus three empirical applications: a survey mega-study, an email marketing A/B test, and a large-scale technology platform randomized experiment.", "LLMs, not named, generate outcome predictions from unstructured inputs that enter standard regression adjustment as covariates, with guidance on turning discrete outputs into continuous scores.", "Variance reductions are real but modest, larger when data contain text, and the adjusted estimator reverts to the difference in means when predictions are uninformative." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 107, "authors_detailed": [ { "name": "David Arbour", "url": "https://openalex.org/A5060542496", "inst": "Adobe Systems (United States)" }, { "name": "Eli Ben‐Michael", "url": "https://openalex.org/A5041268061", "inst": "Carnegie Mellon University" }, { "name": "Avi Feller", "url": "https://openalex.org/A5098655876", "inst": "University of California, Berkeley" }, { "name": "Apoorva Lal", "url": "https://openalex.org/A5009612369", "inst": "University of Minnesota" }, { "name": "Lo-Hua Yuan", "url": "https://openalex.org/A5138245760", "inst": "" } ], "affiliations": [ "Carnegie Mellon University", "University of California, Berkeley", "University of Minnesota", "Adobe Systems (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6891398", "doi": "10.2139/ssrn.6891398", "title": "Leading Innovation with Agentic AI: A Systematic Review of Human–AI Collaboration in the Industry 5.0 Era", "authors": [ "Naseer Ahmad Chardhiwal", "Claus Christian Carbon" ], "posted": "2026-06-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6891398", "field": "management", "role": "object", "bullets": [ "Systematic literature review of 50 peer-reviewed studies from 2015 to 2025 on agentic AI in corporate innovation leadership during the Industry 5.0 transition; no primary data or geography stated.", "No language model is applied; the review synthesizes prior work through transformational leadership theory, the technology acceptance model, and sociotechnical systems theory.", "Concludes that human-AI collaboration supports innovation agility and strategic and ethical innovation, positioning agentic AI as a cognitive partner while transparency, trust, and role ambiguity require governance." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1120, "authors_detailed": [ { "name": "Naseer Ahmad Chardhiwal", "url": "https://openalex.org/A5138068259", "inst": "University of Bamberg" }, { "name": "Claus Christian Carbon", "url": "https://openalex.org/A5121975575", "inst": "University of Bamberg" } ], "affiliations": [ "University of Bamberg" ] }, { "uid": "arxiv:2606.08285v1", "arxiv_id": "2606.08285v1", "title": "Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems", "authors": [ "Junyi Yao", "Zihao Zheng" ], "posted": "2026-06-06", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.08285v1", "field": "finance", "role": "method", "bullets": [ "Topical review and reproducibility audit of 30 primary studies on LLM-based financial trading, with a 10-equity worked example used only as a methodological scaffold.", "No single model is evaluated; a coded evidence matrix scores each study on point-in-time controls, split transparency, held-out evaluation, cost and turnover treatment, execution semantics, and artifact release.", "Architecture reporting is generally clearer than the evaluation assumptions needed to judge economic interpretability, and explicit friction and timing choices can materially compress active-strategy results." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 121, "authors_detailed": [ { "name": "Junyi Yao", "url": "https://openalex.org/A5138216255", "inst": "" }, { "name": "Zihao Zheng", "url": "https://openalex.org/A5138201387", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6889905", "doi": "10.2139/ssrn.6889905", "title": "Listening to the Workforce: Measuring Construction Worker Safety Attitudes from Social Media Discourse Using LLMs", "authors": [ "Farouq Sammour", "Yuxin Zhang", "Zhenyu Zhang" ], "posted": "2026-06-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6889905", "field": "management", "role": "instrument", "bullets": [ "Reddit discourse from the r/Construction and r/Roofing communities: 250 posts to build a codebook, 450 and 400 posts for validation, and 10,346 r/Roofing contributions for a case study.", "An LLM classifier operationalizes an eight-dimension construction safety attitude framework and is validated against expert human coding on held-out posts. The specific model is not named.", "The classifier reproduced expert coding at Cohen's kappa 0.90 with precision and recall of 0.98, retaining kappa 0.89 after transfer to the roofing community." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert human coding, Cohen's kappa 0.90", "salience": 51, "edition": 3, "audience": "general", "models": [], "n": 248, "authors_detailed": [ { "name": "Farouq Sammour", "url": "https://openalex.org/A5138026093", "inst": "" }, { "name": "Yuxin Zhang", "url": "https://openalex.org/A5138067353", "inst": "Qingdao University of Science and Technology" }, { "name": "Zhenyu Zhang", "url": "https://openalex.org/A5138043385", "inst": "Guangdong University of Technology" } ], "affiliations": [ "Qingdao University of Science and Technology", "Guangdong University of Technology" ] }, { "uid": "arxiv:2606.08283v1", "arxiv_id": "2606.08283v1", "title": "Macro Economists in the Machine: A Multi-Agent LLM Framework for Commodity-Related ETF Portfolio Construction", "authors": [ "Yiqing Wang", "Dehao Dai", "Ding Ma", "Kerui Geng" ], "posted": "2026-06-06", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.08283v1", "field": "finance", "role": "agent", "bullets": [ "Commodity-related ETF portfolios rebalanced weekly across 124 dates spanning the 2023 US rate peak and the 2024-2025 soft landing, with all strategies receiving identical FRED macro z-scores.", "Hawkish, Dovish, and Debate LLM agents plus a deterministic z-score rule agent route tilt signals through one portfolio engine, testing whether LLM macro interpretation adds value. Models are not named.", "All three LLM strategies beat the rule agent in Sharpe terms, the Hawkish and Debate agents most (+0.044 and +0.040, p<0.10); the advantage concentrates in the soft-landing sub-period." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 249, "authors_detailed": [ { "name": "Yiqing Wang", "url": "https://openalex.org/A5138217770", "inst": "" }, { "name": "Dehao Dai", "url": "https://openalex.org/A5128963646", "inst": "University of California San Diego" }, { "name": "Ding Ma", "url": "https://openalex.org/A5138250800", "inst": "" }, { "name": "Kerui Geng", "url": "https://openalex.org/A5129087161", "inst": "Tulane University" } ], "affiliations": [ "University of California San Diego", "Tulane University" ] }, { "uid": "doi:10.2139/ssrn.6826378", "doi": "10.2139/ssrn.6826378", "title": "The World as Oracle: Adversarial-Resistant AI Probability Estimation for Event-Driven Financial Instruments", "authors": [ "V Guruprasad" ], "posted": "2026-06-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6826378", "field": "finance", "role": "method", "bullets": [ "Conceptual infrastructure paper demonstrated on a 40-month retrospective of the Adani Group case from January 2023 to May 2026 and a Maersk and Strait of Hormuz disruption episode over 2023 to 2026.", "Proposes an AI probability oracle using temporal persistence filtering to price events from structural traces in shipping, actuarial, commodity, and procurement records; model family not stated; checked loosely against Kalshi market-implied probabilities.", "The oracle's 47 percent estimate preceded the correct yes resolution of the Hormuz event on 28 February 2026; no systematic accuracy statistic is reported." ], "bullet_provenance": "ai", "validated": false, "validation_note": "case comparison vs Kalshi, no accuracy figure", "salience": 32, "edition": 3, "audience": "technical", "models": [], "n": 860, "authors_detailed": [ { "name": "V Guruprasad", "url": "https://openalex.org/A5138034334", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6886878", "doi": "10.2139/ssrn.6886878", "title": "Outsourcing Flexibility in Uncertain Times: A Real Options Decision Framework for Strategic Governance", "authors": [ "Koushik Das Sarma", "Chandrima Roy" ], "posted": "2026-06-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6886878", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample; develops a real options decision framework for outsourcing governance under environmental uncertainty.", "No model is used by the researchers; generative AI enters as a driver of uncertainty and of VRIN resource reclassification that forces periodic governance reassessment; model not named.", "Proposes a two-stage option map and a recursive two-by-two governance archetype matrix, arguing AI-driven reclassification makes quadrant migration and periodic reassessment necessary rather than optional." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 861, "authors_detailed": [ { "name": "Koushik Das Sarma", "url": "https://openalex.org/A5023660342", "inst": "University of California, Riverside" }, { "name": "Chandrima Roy", "url": "https://openalex.org/A5138059799", "inst": "" } ], "affiliations": [ "University of California, Riverside" ] }, { "uid": "doi:10.2139/ssrn.6889585", "doi": "10.2139/ssrn.6889585", "title": "Generative Artificial Intelligence and Overtime Work", "authors": [ "Keven Kam", "Chunchao Wang", "Shuangxin Wang" ], "posted": "2026-06-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6889585", "field": "economics", "role": "object", "bullets": [ "China; large-scale firm registry records combined with high-frequency satellite nighttime-lights data used to build a city-level measure of overtime work; sample period not stated.", "No LLM is used by the researchers; city generative AI exposure is the treatment variable and overtime is measured from nighttime lights; model not named and no model output is validated.", "Higher city GenAI exposure raises the likelihood of overtime, with larger effects in more developed cities and more exposed industries, operating through reduced labor demand, task expansion, and faster skill shifts." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 862, "authors_detailed": [ { "name": "Keven Kam", "url": "https://openalex.org/A5138061870", "inst": "" }, { "name": "Chunchao Wang", "url": "https://openalex.org/A5138053640", "inst": "" }, { "name": "Shuangxin Wang", "url": "https://openalex.org/A5138046030", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6823940", "doi": "10.2139/ssrn.6823940", "title": "Star Analyst: Self-Tuning Alpha Research", "authors": [ "William F. Shen", "Xinchi Qiu", "Nicholas D. Lane", "Alex Iacob Iacob", "Zichen Zhang", "Daoheng Wang", "Wenyi Wang", "Rui Liang", "Yulong Zhang" ], "posted": "2026-06-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6823940", "field": "finance", "role": "agent", "bullets": [ "Chinese equity market; CSI300 constituents evaluated under a leakage-controlled forward protocol on the most recent 2026Q1 deployment window.", "STAR couples an LLM-based alpha researcher with a metacognitive module that revises the research scaffold; the base LLM is not named; evolved agents are compared to the base model and a set of strong baselines on out-of-sample alpha quality.", "Evolved STAR researchers generate higher-quality alphas than the base model and baselines, with a 6.9-times expansion of operator vocabulary attributed to genuine researcher evolution." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 863, "authors_detailed": [ { "name": "William F. Shen", "url": "https://openalex.org/A5138003591", "inst": "University of Cambridge" }, { "name": "Xinchi Qiu", "url": "https://openalex.org/A5040821950", "inst": "University of Cambridge" }, { "name": "Nicholas D. Lane", "url": "https://openalex.org/A5026859250", "inst": "University of Cambridge" }, { "name": "Alex Iacob Iacob", "url": "https://openalex.org/A5138006521", "inst": "University of Cambridge" }, { "name": "Zichen Zhang", "url": "https://openalex.org/A5100739117", "inst": "University of Cambridge" }, { "name": "Daoheng Wang", "url": "https://openalex.org/A5137957175", "inst": "University of Cambridge" }, { "name": "Wenyi Wang", "url": "https://openalex.org/A5137988108", "inst": "University of Cambridge" }, { "name": "Rui Liang", "url": "https://openalex.org/A5137925303", "inst": "University of Cambridge" }, { "name": "Yulong Zhang", "url": "https://openalex.org/A5137939088", "inst": "University of Cambridge" } ], "affiliations": [ "University of Cambridge" ], "prestige": true }, { "uid": "arxiv:2606.06823v1", "arxiv_id": "2606.06823v1", "title": "PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance", "authors": [ "Yuqi Li", "Siyuan Liu", "Bingjun Liu" ], "posted": "2026-06-05", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.06823v1", "field": "finance", "role": "agent", "bullets": [ "CSI 300 constituent stock data; sequential trading decisions and alpha generation inside a closed-loop neuro-symbolic system with market-regime modeling. Sample period not stated.", "A domain-specific fine-tuned large language model, base family not stated, generates constrained alpha; evaluated by backtesting against state-of-the-art time-series models, with no ground-truth accuracy check.", "PandaAI reports 18.2 percent higher rank information coefficient and 25.7 percent lower maximum drawdown than the time-series baselines." ], "bullet_provenance": "ai", "validated": false, "salience": 47, "edition": 3, "audience": "technical", "models": [], "n": 164, "authors_detailed": [ { "name": "Yuqi Li", "url": "https://openalex.org/A5138157597", "inst": "" }, { "name": "Siyuan Liu", "url": "https://openalex.org/A5138185476", "inst": "Beijing Institute of Petrochemical Technology" }, { "name": "Bingjun Liu", "url": "https://openalex.org/A5138127999", "inst": "" } ], "affiliations": [ "Beijing Institute of Petrochemical Technology" ] }, { "uid": "doi:10.2139/ssrn.6881781", "doi": "10.2139/ssrn.6881781", "title": "MemGuard-Alpha: Detecting and Filtering Memorization-Contaminated Signals in LLM-Based Financial Forecasting via Membership Inference and Cross-Model Disagreement", "authors": [ "Dip Roy", "Rajiv Misra", "Sanjay Kumar Singh", "Anisha Roy" ], "posted": "2026-06-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6881781", "field": "finance", "role": "method", "bullets": [ "Seven LLMs of 124 million to 7 billion parameters evaluated on 50 S&P 100 constituents, 42,800 prompts, and 5.5 years of daily data from January 2019 to June 2024.", "Two algorithms filter memorization-contaminated signals: a composite score combining five membership inference attacks with temporal proximity via logistic regression, and cross-model disagreement exploiting differing training cutoffs. Models are not named beyond size.", "Filtered signals reach a Sharpe ratio of 4.11 versus 2.76 unfiltered, a 49 percent gain after transaction costs; in-sample accuracy rises with contamination while out-of-sample accuracy falls." ], "bullet_provenance": "ai", "validated": false, "salience": 56, "edition": 3, "audience": "technical", "models": [], "n": 247, "authors_detailed": [ { "name": "Dip Roy", "url": "https://openalex.org/A5019705967", "inst": "Indian Institute of Technology Patna" }, { "name": "Rajiv Misra", "url": "https://openalex.org/A5081123737", "inst": "Rajarshi School of Management & Technology" }, { "name": "Sanjay Kumar Singh", "url": "https://openalex.org/A5134709277", "inst": "Indian Institute of Technology Patna" }, { "name": "Anisha Roy", "url": "https://openalex.org/A5123572671", "inst": "Jaypee Institute of Information Technology" } ], "affiliations": [ "Indian Institute of Technology Patna", "Rajarshi School of Management & Technology", "Jaypee Institute of Information Technology" ] }, { "uid": "doi:10.2139/ssrn.6885261", "doi": "10.2139/ssrn.6885261", "title": "Artificial Intelligence in Finance as Algorithmic Decision Formation: A Systematic Scoping Review", "authors": [ "Yoocheol Noh", "Jaeweon You" ], "posted": "2026-06-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6885261", "field": "finance", "role": "object", "bullets": [ "Systematic scoping review of the AI-in-finance literature, built from a Scopus and Web of Science search with staged screening and full-text confirmation; period and geography not stated.", "No language model is run by the authors; the review synthesizes machine learning, credit scoring, asset pricing, financial NLP, and delegated AI agents into an algorithmic decision-formation framework.", "Organizes the field into five recursive clusters and four mechanisms, and argues governance and financial stability are constitutive of AI finance rather than peripheral regulatory concerns." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 311, "authors_detailed": [ { "name": "Yoocheol Noh", "url": "https://openalex.org/A5138011590", "inst": "" }, { "name": "Jaeweon You", "url": "https://openalex.org/A5119328444", "inst": "Soongsil University" } ], "affiliations": [ "Soongsil University" ] }, { "uid": "doi:10.2139/ssrn.6819460", "doi": "10.2139/ssrn.6819460", "title": "LLM-Gated FinRL: Point-in-Time Risk Auditing for Reinforcement Learning in High-Beta Portfolio Trading", "authors": [ "Xavier Ondo Essono" ], "posted": "2026-06-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6819460", "field": "finance", "role": "agent", "bullets": [ "US Magnificent Seven technology portfolio, with a PPO allocation policy trained on 2018 to 2021 data and tested on a fully held-out 2022 bear-market period.", "An unnamed LLM acts as a point-in-time auditing gate over the PPO policy, triggered by a deterministic risk detector; in the offline fallback the LLM and rule-based gates were numerically identical.", "The gated configuration returned -48.5% versus -53.0% for the PPO baseline, cut maximum drawdown to -53.4% from -57.3%, and raised the Sharpe ratio to -0.99 from -1.06." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 460, "authors_detailed": [ { "name": "Xavier Ondo Essono", "url": "https://openalex.org/A5137998194", "inst": "Art Institute of Portland" } ], "affiliations": [ "Art Institute of Portland" ] }, { "uid": "doi:10.2139/ssrn.6883290", "doi": "10.2139/ssrn.6883290", "title": "Human-Centric Generative AI Literacy in Business Education Curriculum", "authors": [ "joel Reynolds" ], "posted": "2026-06-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6883290", "field": "management", "role": "object", "bullets": [ "Conceptual curricular design set in a College of Business spanning accounting, finance, marketing, management, operations, and business analytics, with no empirical sample.", "No model is used by the researchers; the paper proposes a Human-Centric GenAI Orchestrator framework organizing AI literacy into five dimensions mapped to courses and learning outcomes.", "Presented as design propositions rather than validated outcomes, the framework augments existing courses instead of adding standalone ones and closes with a research agenda for testing." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 461, "authors_detailed": [ { "name": "joel Reynolds", "url": "https://openalex.org/A5137942883", "inst": "DePaul University" } ], "affiliations": [ "DePaul University" ] }, { "uid": "doi:10.2139/ssrn.6883075", "doi": "10.2139/ssrn.6883075", "title": "GENERATIVE AI ADOPTION AND ACADEMIC PERFORMANCE IN ECONOMICS UNDERGRADUATES: OBSERVATIONAL EVIDENCE FROM A REPEATED CROSS-SECTIONAL STUDY", "authors": [ "Manuel Ruiz-Adame", "Nisrin Aisa-Dris", "Susana Martínez- Rodríguez" ], "posted": "2026-06-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6883075", "field": "economics", "role": "object", "bullets": [ "Two Spanish public universities, economics undergraduates in the same courses across consecutive years, repeated cross-sectional design, 162 students in 2024/25 and 116 in 2025/26 (N=278).", "No model is used by the researchers; habitual generative AI use was self-reported through a start-of-term questionnaire and linked to official final assessment records.", "Habitual AI use rose about 17 points to 58.6%, while adjusted final grades fell (b=-1.43, 95% CI -2.04 to -0.82; Cohen's d=0.60), persisting after controls." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 462, "authors_detailed": [ { "name": "Manuel Ruiz-Adame", "url": "https://openalex.org/A5137972503", "inst": "Universidad de Granada" }, { "name": "Nisrin Aisa-Dris", "url": "https://openalex.org/A5137919267", "inst": "Universidad de Granada" }, { "name": "Susana Martínez- Rodríguez", "url": "https://openalex.org/A5137968029", "inst": "" } ], "affiliations": [ "Universidad de Granada" ] }, { "uid": "doi:10.2139/ssrn.6818640", "doi": "10.2139/ssrn.6818640", "title": "The Odd Man and the Gaussian: Why Collective Intelligence Requires Structural Dissent", "authors": [ "Mauro Iannopollo" ], "posted": "2026-06-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6818640", "field": "economics", "role": "object", "bullets": [ "Conceptual essay on collective intelligence drawing on statistics, the Talmudic Sanhedrin acquittal rule, and philosophy from Mill to Feyerabend; no dataset.", "No model is used; large language models are the object, examined as systems that compress the distribution of perspectives and correlate individual errors.", "Argues structural dissent is a necessary epistemic condition, and that concentrating adoption around a few dominant models risks homogenizing collective judgment and hiding systematic errors." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 859, "authors_detailed": [ { "name": "Mauro Iannopollo", "url": "https://openalex.org/A5065363696", "inst": "Azienda Usl Toscana Centro" } ], "affiliations": [ "Azienda Usl Toscana Centro" ] }, { "uid": "doi:10.2139/ssrn.6885020", "doi": "10.2139/ssrn.6885020", "title": "A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects", "authors": [ "Johan Linaker", "Cailean Osborne", "Jennifer Ding", "Ben Burtenshaw" ], "posted": "2026-06-05", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6885020", "field": "management", "role": "object", "bullets": [ "Fourteen diverse open large language model projects studied through semi-structured interviews with their developers, spanning models, data, software, evaluation, compute, and community engagement across the open source AI ecosystem.", "No model was run by the researchers; the study qualitatively maps collaboration practices, motivations, and governance across the open LLM development and reuse lifecycle.", "Openness emerges from how collaboration is organised, shifting from concentrated early engagement to broader distributed participation after release, coordinated through governance ranging from company-led to grassroots initiatives." ], "bullet_provenance": "ai", "salience": 44, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 39, "authors_detailed": [ { "name": "Johan Linåker", "url": "https://openalex.org/A5033925467", "inst": "Lund University" }, { "name": "Cailean Osborne", "url": "https://openalex.org/A5137960917", "inst": "University of Oxford" }, { "name": "Jennifer Ding", "url": "https://openalex.org/A5098821005", "inst": "ObjectVideo (United States)" }, { "name": "Ben Burtenshaw", "url": "https://openalex.org/A5137923008", "inst": "FACE Foundation" } ], "affiliations": [ "University of Oxford", "Lund University", "ObjectVideo (United States)", "FACE Foundation" ], "prestige": true }, { "uid": "arxiv:2606.06089v1", "arxiv_id": "2606.06089v1", "title": "Leveraging LLMs for Unstructured Claims Data Analysis", "authors": [ "Robert D. Lieberthal", "Richard Tran", "Vietbao Phan", "Jawand Singh", "Elizabeth Sottung" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.06089v1", "field": "finance", "role": "instrument", "bullets": [ "Synthetic FHIR-based claims records and real property-casualty claims documents; the pipeline extracts 36 actuarial variables spanning reserving, ratemaking, and claims management. Geography and period not stated.", "Large language models, family not stated, run a two-stage document-level then claim-level extraction; 14 core variables validated by two clinical reviewers scoring 20 claims, weighted kappa 0.53.", "Feeding the extracted variables into chain ladder reserving cut severity-segmented reserve estimation error from 6.5 percent to 4.0 percent." ], "bullet_provenance": "ai", "validated": true, "validation_note": "two clinical reviewers, weighted kappa 0.53", "salience": 46, "edition": 3, "audience": "general", "models": [], "n": 163, "authors_detailed": [ { "name": "Robert D. Lieberthal", "url": "https://openalex.org/A5017910604", "inst": "Mary Ann Liebert (United States)" }, { "name": "Richard Tran", "url": "https://openalex.org/A5138002034", "inst": "Keysight Technologies (United States)" }, { "name": "Vietbao Phan", "url": "https://openalex.org/A5137925058", "inst": "Thomas Jefferson University" }, { "name": "Jawand Singh", "url": "https://openalex.org/A5137972072", "inst": "William & Mary" }, { "name": "Elizabeth Sottung", "url": "https://openalex.org/A5098771716", "inst": "Thomas Jefferson University" } ], "affiliations": [ "Mary Ann Liebert (United States)", "Keysight Technologies (United States)", "Thomas Jefferson University", "William & Mary" ] }, { "uid": "doi:10.2139/ssrn.6815202", "doi": "10.2139/ssrn.6815202", "title": "Context Engineering: A Governance Approach to Enterprise AI", "authors": [ "Rohit Rajdev" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6815202", "field": "management", "role": "object", "bullets": [ "Conceptual paper on governing enterprise generative AI, covering chatbots, copilots, retrieval-augmented generation, and agentic workflows. No empirical sample; the unit is the enterprise AI decision system.", "The paper does not test a specific model; it proposes context engineering, defined as disciplined design, control, and audit of the information supplied to models at inference. Models are not stated.", "It offers a Context Governance Control Plane, a control matrix, and an evaluation model, mapping controls to the NIST AI RMF, ISO/IEC 42001, the EU AI Act, and OECD principles." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 245, "authors_detailed": [ { "name": "Rohit Rajdev", "url": "https://openalex.org/A5119987089", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6864181", "doi": "10.2139/ssrn.6864181", "title": "Consumer Preference Transmission in Agentic Markets", "authors": [ "Andreas Kraft", "Poet Larsen" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6864181", "field": "management", "role": "agent", "bullets": [ "A conjoint experiment and an incentivized online bookstore adoption task measuring attribute preferences and left-digit price bias, with consumers delegating purchase decisions to AI agents as the unit of observation.", "LLMs act as agents choosing on a consumer's behalf from prompts; the study separates identifiability of preferences in the prompt from model responsiveness. Specific models are not named, only model-specific priors.", "Attribute preferences transmit to agents, but agents do not reproduce left-digit bias and pool consumers toward model priors; high-bias consumers write shorter prompts and more often intend to use agents." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 246, "authors_detailed": [ { "name": "Andreas Kraft", "url": "https://openalex.org/A5107694397", "inst": "University of Chicago" }, { "name": "Poet Larsen", "url": "https://openalex.org/A5137885762", "inst": "Harvard University Press" } ], "affiliations": [ "University of Chicago", "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6878994", "doi": "10.2139/ssrn.6878994", "title": "Measuring Generative AI Dependency: Scale Development and Validation among University Students", "authors": [ "Lisi Mai", "Eiman Yassin", "Joo-Young Jung" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6878994", "field": "management", "role": "object", "bullets": [ "555 Chinese university students with prior generative AI experience, split into 175 for exploratory and 380 for confirmatory factor analysis.", "No language model is applied; the authors build a Generative AI Dependency Scale from Media System Dependency theory using focus groups, expert review, and factor analysis.", "A nine-factor, 26-item scale explains 61.17 percent of variance, with orientation goals splitting into four dimensions and emotional comfort emerging as a new dimension." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 851, "authors_detailed": [ { "name": "Lisi Mai", "url": "https://openalex.org/A5137879373", "inst": "International Christian University" }, { "name": "Eiman Yassin", "url": "https://openalex.org/A5087276161", "inst": "International Christian University" }, { "name": "Joo-Young Jung", "url": "https://openalex.org/A5041917652", "inst": "City University of Hong Kong" } ], "affiliations": [ "International Christian University", "City University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6866865", "doi": "10.2139/ssrn.6866865", "title": "Governing the Scam Ecosystem: Policy Responses to Generative AI-Enabled Deepfake Financial Fraud", "authors": [ "Alice E. Marwick", "Schiffrin Anya", "Kaylee Williams" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6866865", "field": "economics", "role": "object", "bullets": [ "Comparative analysis of fraud-governance policy frameworks from more than a dozen jurisdictions, spanning social platforms, telecommunications, and financial institutions; no dataset or time period stated.", "No language model is applied by the authors; generative AI and deepfakes are the object, treated as the driver of industrialized, transnational financial fraud.", "Existing regulatory approaches are fragmented and reactive; the authors argue platforms as cheapest cost avoiders should bear liability, with financial institutions as the next intervention point." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 852, "authors_detailed": [ { "name": "Alice E. Marwick", "url": "https://openalex.org/A5128666059", "inst": "Data & Society Research Institute" }, { "name": "Schiffrin Anya", "url": "https://openalex.org/A5034236892", "inst": "Columbia University" }, { "name": "Kaylee Williams", "url": "https://openalex.org/A5137830557", "inst": "" } ], "affiliations": [ "Columbia University", "Data & Society Research Institute" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6879985", "doi": "10.2139/ssrn.6879985", "title": "Selective Disclosure in Online Knowledge Communities: The Impact of Enterprise AI on Public Information Externalization", "authors": [ "Mengke Li", "Ziqiong Zhang", "Rob Law", "Zili Zhang" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6879985", "field": "management", "role": "object", "bullets": [ "User-week panel data from the public Stack Overflow community, using the introduction of OverflowAI in Stack Overflow for Teams as a quasi-experimental shock.", "No model is applied by the researchers; enterprise generative AI is the object, with a difference-in-differences design estimating its effect on public question-posting behavior.", "Questions and downstream responses fell after OverflowAI, concentrated in low-scored, less elaborated posts and among experienced high-reputation users, while remaining public questions were more information rich." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 853, "authors_detailed": [ { "name": "Mengke Li", "url": "https://openalex.org/A5124833378", "inst": "Harbin Institute of Technology" }, { "name": "Ziqiong Zhang", "url": "https://openalex.org/A5137909387", "inst": "Harbin Institute of Technology" }, { "name": "Rob Law", "url": "https://openalex.org/A5137877690", "inst": "University of Macau" }, { "name": "Zili Zhang", "url": "https://openalex.org/A5137897687", "inst": "Harbin Institute of Technology" } ], "affiliations": [ "Harbin Institute of Technology", "University of Macau" ] }, { "uid": "doi:10.2139/ssrn.6876627", "doi": "10.2139/ssrn.6876627", "title": "Governing Digital Innovation Ecosystems under Risk and Uncertainty: IP Circulation, Platform Dependency, and Policy Orchestration in Korean Content Industries", "authors": [ "Taejun Lee" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6876627", "field": "management", "role": "object", "bullets": [ "Conceptual, mechanism-oriented theory-building study illustrated with the Korean content industry between 2023 and 2026, drawing on policy, institutional, and industry materials.", "No model is used; generative AI appears as a source of structural uncertainty alongside platform restructuring and contested IP appropriation within the innovation ecosystem framework.", "Reconceptualizes content innovation ecosystems as structured around IP and coordinated through platforms, identifying three interdependent mechanisms: IP circulation, platform coordination, and network integration." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 854, "authors_detailed": [ { "name": "Taejun Lee", "url": "https://openalex.org/A5137897666", "inst": "KDI School of Public Policy and Management" } ], "affiliations": [ "KDI School of Public Policy and Management" ] }, { "uid": "doi:10.2139/ssrn.6878923", "doi": "10.2139/ssrn.6878923", "title": "”Let Humans Do Human Things, Let Algorithms Do Machine Things”: The impact of task orientation on algorithm aversion from an identity positioning Perspective", "authors": [ "Siyuan Cheng", "Zuhong Liu", "Yubin Xie", "Yu Zhu", "Ruilin Wu", "Ronggang Zhou" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6878923", "field": "management", "role": "agent", "bullets": [ "Four experiments with human participants and two with large-language-model-simulated participants, manipulating task orientation, task objectivity, framing, and identity verification; sample sizes not stated.", "An unnamed large language model stands in for experimental subjects, producing responses to algorithmic versus human advice that are compared with human behavior.", "Humans showed stronger algorithm aversion in human-centered and objective tasks, whereas LLM-simulated participants consistently preferred algorithmic over human advice across all conditions." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 855, "authors_detailed": [ { "name": "Siyuan Cheng", "url": "https://openalex.org/A5137851405", "inst": "" }, { "name": "Zuhong Liu", "url": "https://openalex.org/A5133850145", "inst": "Beihang University" }, { "name": "Yubin Xie", "url": "https://openalex.org/A5101527265", "inst": "Sun Yat-sen University" }, { "name": "Yu Zhu", "url": "https://openalex.org/A5137837126", "inst": "First Affiliated Hospital Zhejiang University" }, { "name": "Ruilin Wu", "url": "https://openalex.org/A5137870788", "inst": "" }, { "name": "Ronggang Zhou", "url": "https://openalex.org/A5114043460", "inst": "Hebei University of Engineering" } ], "affiliations": [ "Beihang University", "Sun Yat-sen University", "Hebei University of Engineering" ] }, { "uid": "doi:10.2139/ssrn.6878928", "doi": "10.2139/ssrn.6878928", "title": "Authentic health photographs evoke higher emotional arousal and behavioral intentions but attract less eye-tracking-based visual attention than AI-generated images", "authors": [ "Hao Liu", "Junyu Zhao", "Calvin Or" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6878928", "field": "management", "role": "object", "bullets": [ "Within-subject two-by-two eye-tracking experiment with 80 participants viewing 40 health images varying by origin (photographic versus AI-generated) and content valence.", "AI-generated images are the object of comparison; no generative model is named, and the study measures attention, emotional arousal, and behavioral intentions rather than model accuracy.", "AI-generated images drew more and longer fixations but produced lower arousal, health consciousness, and behavioral intentions than authentic photographs, with the attention gap larger for positive content." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 856, "authors_detailed": [ { "name": "H Liu", "url": "https://openalex.org/A5075658311", "inst": "University of Hong Kong" }, { "name": "Junyu Zhao", "url": "https://openalex.org/A5133795281", "inst": "University of Hong Kong" }, { "name": "Calvin Or", "url": "https://openalex.org/A5130749952", "inst": "University of Hong Kong" } ], "affiliations": [ "University of Hong Kong" ] }, { "uid": "arxiv:2607.01254v2", "arxiv_id": "2607.01254v2", "title": "The Benchmark Ceiling: Human Judgment, Evaluation Scarcity, and the Political Economy of AI Capability Measurement", "authors": [ "Mark Esposito", "Liu Zhang" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2607.01254v2", "field": "economics", "role": "object", "bullets": [ "Conceptual and formal analysis of AI benchmark validity, using platform data from micro1 covering more than one thousand credentialed professional evaluators.", "Foundation models are the object; the paper models benchmark scores as public signals of latent model quality and studies how that signal depreciates as models saturate easy items.", "Valid signal concentrates in hard-tail items whose replacement cost rises convexly with capability, and private benchmark producers underinvest in validity relative to the social optimum." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 3, "audience": "general", "validated": null, "n": 857, "authors_detailed": [ { "name": "Mark Esposito", "url": "https://openalex.org/A5140015775", "inst": "" }, { "name": "Liu Zhang", "url": "https://openalex.org/A5127970243", "inst": "Jilin University" } ], "affiliations": [ "Jilin University" ] }, { "uid": "arxiv:2606.05667v1", "arxiv_id": "2606.05667v1", "title": "Sustainability by Design in Decentralized Autonomous Organizations: An Empirical Review of Governance, Innovation, and Institutional Design", "authors": [ "Yutian Wang", "Luyao Zhang" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.05667v1", "field": "management", "role": "instrument", "bullets": [ "Comparative empirical review of two agent-interoperability standards, DAO-governed ERC-8004 and corporate-consortium Google A2A, analyzing large-scale governance discourse.", "An unnamed LLM-powered pipeline performs automated annotation, neural topic modeling, and multi-layer network analysis of governance text, with no accuracy check against human coding reported.", "Documents socio-technical power structures across the two governance models and offers design insights for aligning innovation, technological governance, and sustainability in decentralized organizations." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no accuracy check reported", "salience": 40, "edition": 3, "audience": "general", "models": [], "n": 858, "authors_detailed": [ { "name": "Yutian Wang", "url": "https://openalex.org/A5137980613", "inst": "" }, { "name": "Luyao Zhang", "url": "https://openalex.org/A5138011573", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6878983", "doi": "10.2139/ssrn.6878983", "title": "Greening the deal: Can Mergers Redirect Innovation?", "authors": [ "Melissa Newham", "David Jaggi", "Jan-Alexander Posth" ], "posted": "2026-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6878983", "field": "economics", "role": "instrument", "bullets": [ "U.S. M&A transactions from 1988 to 2015 matched with patent data; acquirer observations tracked over a 17-year window centered on the deal announcement year in a staggered difference-in-differences design.", "Patent text embeddings, with the embedding model not named, construct measures of acquirers' technological orientation and its shift toward targets; no validation against a benchmark is reported.", "Post-deal acquirers shift innovation toward the target's knowledge base, strongest for technologically distinct pairs; dirty acquirers move toward targets' clean technologies with more clean patent filings and citations." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 1119, "authors_detailed": [ { "name": "Melissa Newham", "url": "https://openalex.org/A5137824393", "inst": "" }, { "name": "David Jaggi", "url": "https://openalex.org/A5133848527", "inst": "ZHAW Zurich University of Applied Sciences" }, { "name": "Jan-Alexander Posth", "url": "https://openalex.org/A5083631288", "inst": "ZHAW Zurich University of Applied Sciences" } ], "affiliations": [ "ZHAW Zurich University of Applied Sciences" ] }, { "uid": "doi:10.2139/ssrn.6813744", "doi": "10.2139/ssrn.6813744", "title": "TwinAlgo: Expectancy-Optimised Algorithmic Trading via Dynamic Regime-Conditioned LLM Prompting and Smart Money Concept Integration on Gold Spot Markets", "authors": [ "Nirat Thophet" ], "posted": "2026-06-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6813744", "field": "finance", "role": "agent", "bullets": [ "Automated trading system for the XAUUSD gold spot market that layers smart money concepts and multi-timeframe structural alignment; sample size, period, and out-of-sample split are not stated.", "A locally deployed Qwen-2.5 14B model auto-classifies each session into one of six regimes and dispatches regime-specific chain-of-thought prompts to authorize or block trades, with no external validation reported.", "Reports a 40 percent win rate paired with a 1:3 to 1:4 risk-to-reward floor, which the author argues yields positive expected value per trade." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "validation_note": "backtest win rate only, no ground-truth benchmark", "salience": 32, "edition": 3, "audience": "technical", "n": 83, "authors_detailed": [ { "name": "Nirat Thophet", "url": "https://openalex.org/A5137805820", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6590201", "doi": "10.2139/ssrn.6590201", "title": "The Accountability Gap in Large Language Models: Hallucination, Co-production, and Accountability Debt", "authors": [ "Scott McCormick" ], "posted": "2026-06-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6590201", "field": "management", "role": "object", "bullets": [ "Conceptual essay on AI-mediated institutional decision systems, with no empirical sample, building on the hallucination, AI governance, and human-automation interaction literatures.", "No model is run; the essay reframes LLM hallucination as a structural accountability gap between generation and justification, co-produced through user interaction patterns such as reduced verification and trust accumulation.", "Argues institutions accumulate accountability debt when acting on outputs whose evidentiary basis cannot be reconstructed, shifting the hallucination problem from model accuracy to governance and interaction design." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 120, "authors_detailed": [ { "name": "Scott McCormick", "url": "https://openalex.org/A5123602308", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6872723", "doi": "10.2139/ssrn.6872723", "title": "Identifying Circular Public Procurement with Large Language Models: An Application to Firm-Level Circular Innovation", "authors": [ "Robin Lepers", "Bastian Krieger", "Maikel Pellens", "Malte Prüfer" ], "posted": "2026-06-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6872723", "field": "economics", "role": "instrument", "bullets": [ "German public procurement tender awards matched to Community Innovation Survey firm data; the unit of observation is procurement awards and winning firms.", "An LLM-based text-embedding approach scored tender circularity by semantic similarity to a corpus of circular-economy scientific abstracts; the model is not named and no accuracy check is reported.", "Firms winning circular procurement are more likely to introduce circular-economy innovation after three to five years, with no effect at shorter or longer horizons." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no accuracy check reported", "salience": 50, "edition": 3, "audience": "general", "models": [], "n": 162, "authors_detailed": [ { "name": "Robin Lepers", "url": "https://openalex.org/A5116077457", "inst": "KU Leuven" }, { "name": "Bastian Krieger", "url": "https://openalex.org/A5032921311", "inst": "Centre for European Economic Research" }, { "name": "Maikel Pellens", "url": "https://openalex.org/A5078598609", "inst": "Centre for European Economic Research" }, { "name": "Malte Prüfer", "url": "https://openalex.org/A5094265733", "inst": "KU Leuven" } ], "affiliations": [ "KU Leuven", "Centre for European Economic Research" ] }, { "uid": "doi:10.2139/ssrn.6814498", "doi": "10.2139/ssrn.6814498", "title": "Copyright and Artificial Intelligence: The Importance of Memorisation and Attribution", "authors": [ "Christian Koboldt" ], "posted": "2026-06-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6814498", "field": "economics", "role": "object", "bullets": [ "Conceptual law-and-economics analysis of copyright and generative AI across Germany, the United Kingdom, and the United States, drawing on the economics of information goods and the memorisation literature. No empirical sample.", "The paper does not apply an LLM; it treats model memorisation as its object and proposes a capability-based levy tied to a deployed model's measurable memorisation rate. Specific models are not stated.", "It argues payments to rights holders should track the memorisation rate and that mandated source-attribution capability at the architecture level reduces territorial arbitrage and strengthens incentives to lower memorisation." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 244, "authors_detailed": [ { "name": "Christian Koboldt", "url": "https://openalex.org/A5002325154", "inst": "DotEcon" } ], "affiliations": [ "DotEcon" ] }, { "uid": "arxiv:2606.05383v1", "arxiv_id": "2606.05383v1", "title": "Can AI Refute Economic Theory? Evidence from Beyond the Knowledge Cutoff", "authors": [ "Alexis Akira Toda" ], "posted": "2026-06-03", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.05383v1", "field": "economics", "role": "object", "bullets": [ "Four published economic theory papers, each containing an error the author had helped identify, submitted to several AI models for correctness checking, with no formal sample size.", "Gemini, Claude, and ChatGPT were prompted to verify proofs; ChatGPT Pro performed best and occasionally built counterexamples, with data contamination noted as a caveat.", "No model located a true error without substantial human guidance, and the author concludes AI cannot yet refute economic theory on its own." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": false, "salience": 55, "edition": 3, "audience": "general", "n": 458, "authors_detailed": [ { "name": "Alexis Akira Toda", "url": "https://openalex.org/A5137944762", "inst": "Emory University" } ], "affiliations": [ "Emory University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.04978v1", "arxiv_id": "2606.04978v1", "title": "Probing Outcome-Level Resemblance and Mechanism-Level Alignment in LLM Risk Decisions: Evidence from the St. Petersburg Game", "authors": [ "Chensong Huang", "Changyu Chen", "Chenwei Lin", "Hanjia Lyu", "Xian Xu", "Jiebo Luo" ], "posted": "2026-06-03", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.04978v1", "field": "economics", "role": "agent", "bullets": [ "The St. Petersburg game as a controlled testbed for risk decisions, with 28 large language models evaluated through a structured prompt suite of the original game and variants.", "Models produced bids under perturbed truncation, repeated play, endowment, and occupational identity, plus human-perspective prompts and base versus instruction-tuned comparisons.", "Finite bids resemble human caution at the outcome level, but controlled variants reveal computationally rational mechanisms rather than human-consistent ones." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 459, "authors_detailed": [ { "name": "Chensong Huang", "url": "https://openalex.org/A5133828276", "inst": "Qingdao National Laboratory for Marine Science and Technology" }, { "name": "Changyu Chen", "url": "https://openalex.org/A5137873071", "inst": "Fudan University" }, { "name": "Chenwei Lin", "url": "https://openalex.org/A5137808324", "inst": "Fudan University" }, { "name": "Hanjia Lyu", "url": "https://openalex.org/A5040900346", "inst": "University of Rochester" }, { "name": "Xian Xu", "url": "https://openalex.org/A5101152987", "inst": "Jiangsu University" }, { "name": "Jiebo Luo", "url": "https://openalex.org/A5137893065", "inst": "University of Rochester" } ], "affiliations": [ "University of Rochester", "Qingdao National Laboratory for Marine Science and Technology", "Fudan University", "Jiangsu University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6815500", "doi": "10.2139/ssrn.6815500", "title": "SEEN: A Four-Layer Framework for Generative Engine Optimization", "authors": [ "Dmitrii Kargaev" ], "posted": "2026-06-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6815500", "field": "management", "role": "object", "bullets": [ "Conceptual working paper that synthesizes platform documentation, generative engine optimization research, empirical studies of Google AI Overviews, and retrieval-augmented generation mechanisms, with no primary data.", "No model is run; the paper treats AI-mediated discovery as the object and proposes SEEN, a four-layer framework of structure, evidence, entity, and notability, plus a 41-item practitioner checklist.", "It reframes AI visibility as a corpus-engineering problem across owned, first-party, and third-party content, makes no efficacy guarantee, and defers empirical testing to a future validation agenda." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 573, "authors_detailed": [ { "name": "Dmitrii Kargaev", "url": "https://openalex.org/A5134713153", "inst": "Independent researcher" } ], "affiliations": [ "Independent researcher" ] }, { "uid": "doi:10.2139/ssrn.6855658", "doi": "10.2139/ssrn.6855658", "title": "The Missing Developer: Generative AI, the Seven-Node Supply Chain, and the Structural Absence of Accountability", "authors": [ "Johan van Rooyen", "Nitayapa Nandhakwang", "Tomoko Kashima" ], "posted": "2026-06-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6855658", "field": "management", "role": "object", "bullets": [ "Conceptual analysis mapping a seven-node generative AI supply chain from the frontier laboratory to the affected party, with no empirical sample.", "No model is applied by the authors; Coasean externality analysis and principal-agent theory trace how the cost of verifying AI fabrication moves downstream.", "Each node carries a distinct displacement mechanism, and the affected party is a non-party bearing the cost, so contractual, procurement, and literacy remedies all fail." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 849, "authors_detailed": [ { "name": "Johan van Rooyen", "url": "https://openalex.org/A5047562540", "inst": "Webster University" }, { "name": "Nitayapa Nandhakwang", "url": "https://openalex.org/A5135422559", "inst": "Chiang Mai University" }, { "name": "Tomoko Kashima", "url": "https://openalex.org/A5137629099", "inst": "Kindai University" } ], "affiliations": [ "Webster University", "Chiang Mai University", "Kindai University" ] }, { "uid": "arxiv:2606.05449v1", "arxiv_id": "2606.05449v1", "title": "Insurance of Agentic AI", "authors": [ "Quanyan Zhu" ], "posted": "2026-06-03", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.05449v1", "field": "finance", "role": "object", "bullets": [ "Conceptual analysis of the emerging insurance market for agentic AI systems, with no empirical sample or dataset.", "No model is applied; the paper characterizes agentic AI as a continuum of autonomy and maps risk pathways including hallucinations, prompt injection, model drift, and cyber-physical harms.", "It proposes an actuarial framework and a layered architecture combining cyber, technology errors and omissions, product liability, performance warranty, and affirmative AI-liability coverages rather than one monoline product." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 850, "authors_detailed": [ { "name": "Quanyan Zhu", "url": "https://openalex.org/A5137965905", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6866873", "doi": "10.2139/ssrn.6866873", "title": "Governing the Environmental Externalities of Generative AI Compute in the Gulf: Data-Centre Expansion in Saudi Arabia, the UAE, and QatarA Comparative Policy Analysis of the European Union, United States, and China as Lesson-Generating Models", "authors": [ "Mohamad Deeb" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6866873", "field": "economics", "role": "object", "bullets": [ "Qualitative comparative policy analysis of AI-compute governance across six jurisdictions, the EU, US, China, Saudi Arabia, UAE and Qatar, using structured document coding.", "Researchers use no model; foundation-model and LLM compute is the object of study, and a three-tier green, yellow, red scheme scores eight policy dimensions.", "No Gulf state has an enforceable environmental accountability regime for AI compute; the paper recommends mandatory carbon and water disclosure and compute licensing." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 161, "authors_detailed": [ { "name": "Mohamad Deeb", "url": "https://openalex.org/A5109519733", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6810323", "doi": "10.2139/ssrn.6810323", "title": "Human-AI Interaction Across Predictive, Generative, and Agentic AI", "authors": [ "Rizwan Tanveer" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6810323", "field": "management", "role": "object", "bullets": [ "Narrative review synthesizing 2024 to 2026 empirical literature on trust calibration, appropriate reliance, and oversight, alongside primary regulatory documents from the European Union and Gulf Cooperation Council.", "The authors use no language model themselves; they analyze predictive, generative, and agentic AI modalities and documented incidents such as the OpenAI sycophancy rollback and emergent sycophancy in large language models.", "Argues appropriate reliance is empirically distinct from trust, that risk profiles differ by AI modality, and that EU AI Act Article 14 oversight is more developed than GCC counterparts, which better address cultural fit." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 187, "authors_detailed": [ { "name": "Rizwan Tanveer", "url": "https://openalex.org/A5135988363", "inst": "College of Accounting" } ], "affiliations": [ "College of Accounting" ] }, { "uid": "doi:10.2139/ssrn.6846799", "doi": "10.2139/ssrn.6846799", "title": "From Assistant to Authority: Defining and Operationalizing Authority Drift in Conversational AI", "authors": [ "Gulnara Z. Karimova" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6846799", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, synthesising conversation-analytic theory, human-automation interaction research, and the literature on large language model behavioural instability.", "No model is run; the paper defines authority drift as an operationalisable subdimension of persona drift observable across multi-turn dialogue. Model use not applicable.", "Proposes that a model's epistemic and deontic positioning can drift from advisory toward mandate over a conversation, framing this as a target for platform-level AI governance." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 241, "authors_detailed": [ { "name": "Gulnara Z. Karimova", "url": "https://openalex.org/A5025739992", "inst": "Heriot-Watt University" } ], "affiliations": [ "Heriot-Watt University" ] }, { "uid": "doi:10.2139/ssrn.6807958", "doi": "10.2139/ssrn.6807958", "title": "Not Yet: Humans Outperform LLMs in a Colonel Blotto Tournament", "authors": [ "Dmitry Dagaev", "Egor Ivanov", "Petr Parshakov", "Alexey Savvateev", "Gleb Vasiliev" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6807958", "field": "economics", "role": "agent", "bullets": [ "Round-robin Colonel Blotto tournaments; over 200 human participants in one, several large language models in another, and a matched-size third pitting equal numbers of each.", "Popular large language models (not named) submitted allocation strategies as agents, compared against human strategies in tournament play; no measurement validation applies.", "Humans outperform the models, using better-calibrated intermediate allocation heuristics; models play simpler, more stereotyped strategies, and humans barely adjust across opponent sets." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 242, "authors_detailed": [ { "name": "Dmitry Dagaev", "url": "https://openalex.org/A5089402209", "inst": "Twitter (United States)" }, { "name": "Egor Ivanov", "url": "https://openalex.org/A5136778904", "inst": "National Research University Higher School of Economics" }, { "name": "Petr Parshakov", "url": "https://openalex.org/A5136728461", "inst": "National Research University Higher School of Economics" }, { "name": "Alexey Savvateev", "url": "https://openalex.org/A5136771831", "inst": "" }, { "name": "Gleb Vasiliev", "url": "https://openalex.org/A5082005706", "inst": "National Research University Higher School of Economics" } ], "affiliations": [ "Twitter (United States)", "National Research University Higher School of Economics" ] }, { "uid": "doi:10.2139/ssrn.6866867", "doi": "10.2139/ssrn.6866867", "title": "Public Responses to AI Governance in Europe: Literacy, Legitimacy, and Regulatory Acceptance in the Age of Generative AI", "authors": [ "Vaclav Moravec", "Beata Gavurova", "Martin Rigelsky" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6866867", "field": "economics", "role": "object", "bullets": [ "Survey of 10,154 ChatGPT users across nine European countries; individual respondents are the unit, with attitudes toward European Union AI governance the focus.", "No model deployed; respondents assessed on AI governance literacy, regulatory legitimacy, and contextual acceptance, analysed with binary and ordinal logistic regression per country.", "Attitudes are shaped more by the purpose of ChatGPT use than frequency; support for strict regulation is strongest in law enforcement, migration, justice, democracy, and safety domains." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 3, "audience": "general", "validated": null, "n": 243, "authors_detailed": [ { "name": "Vaclav Moravec", "url": "https://openalex.org/A5137623534", "inst": "" }, { "name": "Beáta Gavurová", "url": "https://openalex.org/A5069109142", "inst": "Tomas Bata University in Zlín" }, { "name": "Martin Rigelský", "url": "https://openalex.org/A5028811487", "inst": "University of Prešov" } ], "affiliations": [ "Tomas Bata University in Zlín", "University of Prešov" ] }, { "uid": "doi:10.2139/ssrn.6867203", "doi": "10.2139/ssrn.6867203", "title": "Epistemically Bounded Rationality: Generative Artificial Intelligence, the Weight of Arguments, and the Reconfiguration of Decision-Making Constraints", "authors": [ "Milton Freitas Chagas" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6867203", "field": "management", "role": "object", "bullets": [ "Conceptual paper on organizational decision-making under generative AI, with no empirical sample, drawing on bounded rationality and exploration-exploitation frameworks.", "No model is applied; the paper develops the constructs of epistemically bounded rationality and exploratory inflation tied to the transformer-based dominant design.", "Argues the binding constraint shifts from generating information to evaluating evidential weight, so agents grow more confident while evidential robustness declines." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 455, "authors_detailed": [ { "name": "Milton Freitas Chagas", "url": "https://openalex.org/A5108353334", "inst": "Instituto Tecnológico de Aeronáutica" } ], "affiliations": [ "Instituto Tecnológico de Aeronáutica" ] }, { "uid": "doi:10.2139/ssrn.6806318", "doi": "10.2139/ssrn.6806318", "title": "Generative Engine Optimization as a Digital Equity Strategy for Underserved Tourism Destinations", "authors": [ "Mahboubeh Cheraghian", "Saeed Vayghan" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6806318", "field": "management", "role": "object", "bullets": [ "Conceptual paper on tourism marketing under generative AI search engines, focused on underserved destinations such as rural, small-island, indigenous, and Global South communities.", "No model is run; the paper proposes generative engine optimization as a three-layer content, authority, and technical framework for destination visibility.", "Argues generative search returns a single synthesized answer that excludes resource-constrained destinations, framing the gap as a structural digital-equity concern." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 34, "edition": 3, "audience": "general", "validated": null, "n": 456, "authors_detailed": [ { "name": "Mahboubeh Cheraghian", "url": "https://openalex.org/A5048163333", "inst": "Film Independent" }, { "name": "Saeed Vayghan", "url": "https://openalex.org/A5089233021", "inst": "Utah State University" } ], "affiliations": [ "Film Independent", "Utah State University" ] }, { "uid": "arxiv:2606.03777v1", "arxiv_id": "2606.03777v1", "title": "From Control Boundary to Insurance Claim: Reconstructing AI-Mediated Losses Through the CER Framework", "authors": [ "Alex Leung", "Rex Zhang", "Kentaroh Toyoda", "SiewMei Loh" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.03777v1", "field": "finance", "role": "object", "bullets": [ "Conceptual insurance framework for losses arising from an insured organization's generative or agentic AI systems, illustrated with the PocketOS, Replit, and Air Canada incidents.", "No model is used; the paper introduces CER, a diagnostic covering control boundary, evidence reconstruction, and insurance response for AI residual risk transfer.", "Argues AI losses need state reconstruction rather than event reconstruction, and specifies claim-grade evidence required to support insurance recovery." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 457, "authors_detailed": [ { "name": "Alex Leung", "url": "https://openalex.org/A5134764628", "inst": "Swift Engineering (United States)" }, { "name": "Rex Zhang", "url": "https://openalex.org/A5136591063", "inst": "" }, { "name": "Kentaroh Toyoda", "url": "https://openalex.org/A5066312369", "inst": "Vulcan (United States)" }, { "name": "SiewMei Loh", "url": "https://openalex.org/A5137771859", "inst": "" } ], "affiliations": [ "Swift Engineering (United States)", "Vulcan (United States)" ] }, { "uid": "doi:10.2139/ssrn.6801178", "doi": "10.2139/ssrn.6801178", "title": "The Oracle Problem in Autonomous Agent Commerce: Why Semantic Truth Verification Is Computationally Intractable and What to Build Instead", "authors": [ "René Dechamps Otamendi" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6801178", "field": "economics", "role": "object", "bullets": [ "Conceptual paper on autonomous agent commerce, where software agents hire, pay, and evaluate other agents at micropayment scale; no empirical sample or dataset.", "No model is run by the authors; they argue a central LLM evaluator is non-deterministic and unreliable, citing content moderation precision below 60 percent on context-dependent content; no model named.", "Proposes a two-layer design of deterministic contract validators plus reputation-staked Quality Markets, arguing verification should rest on economic incentives rather than better computation." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 844, "authors_detailed": [ { "name": "René Dechamps Otamendi", "url": "https://openalex.org/A5137639443", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6809082", "doi": "10.2139/ssrn.6809082", "title": "The Apprenticeship Externality: Generative AI, Entry-Level Work, and the Future Supply of Expertise", "authors": [ "Mustafa Seref Akin" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6809082", "field": "economics", "role": "object", "bullets": [ "Theoretical labor economics paper; a two-period overlapping-generations model with heterogeneous junior tasks, each carrying a private automation gain and a task-specific learning value; no empirical data.", "No LLM is used by the authors; generative AI is the object, modeled as automating entry-level tasks that build expertise, creating what the paper calls an apprenticeship externality; no model named.", "Decentralized firms over-automate junior tasks relative to the social planner; absent alternative training channels the expert stock decays geometrically, motivating apprenticeship subsidies and protected junior tasks." ], "bullet_provenance": "ai", "salience": 56, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 845, "authors_detailed": [ { "name": "Mustafa Akın", "url": "https://openalex.org/A5051923417", "inst": "Erzincan Binali Yıldırım University" } ], "affiliations": [ "Erzincan Binali Yıldırım University" ] }, { "uid": "doi:10.2139/ssrn.6867318", "doi": "10.2139/ssrn.6867318", "title": "The Silicon Strain: A Structured Review of Supply Chain Resilience Frameworks for AI Infrastructure Constraints", "authors": [ "Snehaseel Naidu Anugonda" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6867318", "field": "management", "role": "object", "bullets": [ "Structured review of peer-reviewed literature and industry analyses published 2019 to 2026 on semiconductor and infrastructure supply chain constraints driven by generative AI compute demand.", "No LLM is used by the authors; generative AI compute demand is the subject; the review covers GPU and HBM scarcity, energy-grid strain, thermal density limits, and capital obsolescence.", "Proposes a six-vector resilience framework; a stylized 20 MW data centre case shows a 3.8-year microgrid payback, with interconnection queue avoidance identified as the dominant value driver." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 846, "authors_detailed": [ { "name": "Snehaseel Naidu Anugonda", "url": "https://openalex.org/A5137642531", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6801059", "doi": "10.2139/ssrn.6801059", "title": "Two Economies, Not One: A Taxonomy of the Agentic Economy and the Case for Settlement Neutrality", "authors": [ "René Dechamps Otamendi" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6801059", "field": "economics", "role": "object", "bullets": [ "Survey of more than fifty distinct definitions of the agentic economy published between 2021 and March 2026 by firms, banks, and research bodies.", "No language model is applied; the paper classifies usages into five categories and six dimensions and specifies settlement-neutrality properties for autonomous agent-to-agent commerce.", "It identifies two fault lines, separating commerce for humans from an economy of agents and blockchain-dependent from blockchain-free approaches, and proposes a reference settlement architecture." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 847, "authors_detailed": [ { "name": "René Dechamps Otamendi", "url": "https://openalex.org/A5137636829", "inst": "AGCO (United States)" } ], "affiliations": [ "AGCO (United States)" ] }, { "uid": "arxiv:2606.03763v1", "arxiv_id": "2606.03763v1", "title": "Merit or networks? What decides where research is published", "authors": [ "Ning Li" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.03763v1", "field": "economics", "role": "instrument", "bullets": [ "6,208 economics working papers, with journal placement as the outcome and a connection index and an author-ability index among the inputs.", "A discipline-trained LLM evaluator, family not stated, scores each paper's idea quality from text alone without author names or outcomes; no accuracy check is reported.", "Execution quality is the largest input and sets a meritocratic floor, while connections raise placement odds mainly at the most selective journals through two additive channels but remain bounded." ], "bullet_provenance": "ai", "validated": false, "salience": 66, "edition": 3, "audience": "general", "models": [], "n": 848, "authors_detailed": [ { "name": "Ning Li", "url": "https://openalex.org/A5137744710", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6811738", "doi": "10.2139/ssrn.6811738", "title": "Redrawing the AI Map: A Theory of Accountability Boundaries in Agentic Ecosystems", "authors": [ "Muhammad Zia Hydari", "Farooq Muzaffar" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6811738", "field": "management", "role": "object", "bullets": [ "Conceptual theory paper with no empirical sample; the boundary logic is illustrated through structured cases in document processing, legal services, audit, clinical decision support, and procurement.", "No language model is used or named; the paper introduces accountability assets and rule debt as constructs, integrating digital innovation, transaction cost, and information systems control theories into seven propositions.", "Argues agentic orchestration lowers interface and assembly costs, yet accountability assets keep capability boundaries integrated when verification is costly and responsibility hard to transfer, yielding component, integrated, or dual-track strategies." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1116, "authors_detailed": [ { "name": "Muhammad Zia Hydari", "url": "https://openalex.org/A5050514151", "inst": "University of Pittsburgh" }, { "name": "Farooq Muzaffar", "url": "https://openalex.org/A5137659814", "inst": "" } ], "affiliations": [ "University of Pittsburgh" ] }, { "uid": "doi:10.2139/ssrn.6867199", "doi": "10.2139/ssrn.6867199", "title": "Distributed Ownership in Theory and Practice: AI Coordination, Incomplete Contracts, and the Agentic Networked Knowledge Enterprise", "authors": [ "John Rice", "Peter Galvin", "Muhammad Mustafa Raziq", "Najla Abdalla AlBannai", "Muhammad Usman" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6867199", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical data proposing an ideal-type organizational form, the agentic networked knowledge enterprise, defined by five interdependent features.", "No model is used or named; the authors apply transaction cost and incomplete contracts reasoning to argue AI erodes the coordination and monitoring benefits that justify the firm.", "Defines the enterprise by cloud infrastructure, AI-driven coordination, protocol-mediated decision making, rule-based governance, and distributed residual claims; identifies six failure modes with mitigation mechanisms and a research agenda." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1117, "authors_detailed": [ { "name": "John Rice", "url": "https://openalex.org/A5117722562", "inst": "University of Sharjah" }, { "name": "Peter Galvin", "url": "https://openalex.org/A5066031834", "inst": "Edith Cowan University" }, { "name": "Muhammad Mustafa Raziq", "url": "https://openalex.org/A5064745926", "inst": "National University of Sciences and Technology" }, { "name": "Najla Abdalla AlBannai", "url": "https://openalex.org/A5117550922", "inst": "University of Sharjah" }, { "name": "Muhammad Usman", "url": "https://openalex.org/A5134729526", "inst": "University of Sharjah" } ], "affiliations": [ "University of Sharjah", "Edith Cowan University", "National University of Sciences and Technology" ] }, { "uid": "doi:10.2139/ssrn.6866877", "doi": "10.2139/ssrn.6866877", "title": "Satisficing in the age of GenAI? The Moderating Role of Technical Background and Domain Expertise on Human-Algorithm Interaction based decision-making in Public Policy", "authors": [ "Anshumaan Goel", "Sudip Patra" ], "posted": "2026-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6866877", "field": "management", "role": "object", "bullets": [ "Quasi-experimental non-equivalent groups study of public policy professionals examining how technical and domain expertise moderate human-algorithm interaction bias when using generative AI; sample size not stated.", "Generative AI model not named; participants used it as a decision aid while the study measured cognitive load and bias rather than validating model output against any ground truth.", "Non-expertise worsened interaction bias while domain and technical expertise reduced it; technical experts faced added cognitive load from over-prompting, which domain expertise mitigated." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1118, "authors_detailed": [ { "name": "Anshumaan Goel", "url": "https://openalex.org/A5137651605", "inst": "" }, { "name": "Sudip Patra", "url": "https://openalex.org/A5137695873", "inst": "" } ] }, { "uid": "arxiv:2606.02528v3", "arxiv_id": "2606.02528v3", "title": "Auditing Asset-Specific Preferences in Financial Large Language Models: Evidence from Bitcoin Representations and Portfolio Allocation", "authors": [ "Wenbin Wu" ], "posted": "2026-06-01", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.02528v3", "field": "finance", "role": "object", "bullets": [ "Audit of nine frontier LLMs used as robo-advisors and trading agents, testing asset preferences for Bitcoin among eight money-like instruments, with internal analysis on Gemma 3.", "A three-level protocol ran a behavioral ranking audit, a sparse-autoencoder feature search in Gemma 3, and a portfolio-allocation test, checked with random controls.", "A Bitcoin-selective internal feature was found; amplifying it raised Bitcoin portfolio share by 5.2 points and suppressing it lowered it by 4.6 points." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 66, "edition": 3, "audience": "general", "validated": null, "n": 160, "authors_detailed": [ { "name": "Wenbin Wu", "url": "https://openalex.org/A5137648466", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6863574", "doi": "10.2139/ssrn.6863574", "title": "Language-Embedded Cultural Norms in LLM Economic Decision-Making Behavior", "authors": [ "Xianghua (Jason) Wu", "Kay-Yut Chen", "Diana Wu", "Jie (Jennifer) Zhang", "Jian-Ren Hou" ], "posted": "2026-06-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6863574", "field": "economics", "role": "agent", "bullets": [ "Modified ultimatum game with responder veto power varied across conditions; prompts issued in 17 natural languages chosen to represent diverse cultural backgrounds.", "Multiple large language model versions (not named) instantiated as proposer and responder agents; their offers and thresholds compared to human patterns and the Power Distance Index.", "Offers vary significantly by prompt language; higher veto power raises offers and thresholds, and offer size is positively associated with Power Distance across all models." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 240, "authors_detailed": [ { "name": "Xianghua Wu", "url": "https://openalex.org/A5047144196", "inst": "Guangxi University" }, { "name": "Kay‐Yut Chen", "url": "https://openalex.org/A5035922455", "inst": "The University of Texas at Arlington" }, { "name": "Diana Wu", "url": "https://openalex.org/A5120292939", "inst": "San Jose State University" }, { "name": "Jie (Jennifer) Zhang", "url": "https://openalex.org/A5137580046", "inst": "Hunan University of Science and Technology" }, { "name": "Jian-Ren Hou", "url": "https://openalex.org/A5006636636", "inst": "National Cheng Kung University" } ], "affiliations": [ "Guangxi University", "The University of Texas at Arlington", "San Jose State University", "Hunan University of Science and Technology", "National Cheng Kung University" ] }, { "uid": "doi:10.2139/ssrn.6809844", "doi": "10.2139/ssrn.6809844", "title": "Risk Factor Disclosure Summaries", "authors": [ "James Blann", "James Moon", "Yuxiao Wang" ], "posted": "2026-06-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6809844", "field": "accounting", "role": "instrument", "bullets": [ "US 10-K risk factor disclosure summaries required since the SEC's 2020 modernization for filings over fifteen pages, covering the initial post-rule years at the firm-filing level.", "An unnamed large language model generates benchmark risk-factor summaries, and firm-disclosed summaries are compared against them, with no accuracy figure reported.", "Firm summaries are more positively toned, less specific, and omit idiosyncratic risks; larger deviations from the benchmark align with higher information asymmetry and disagreement." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 453, "authors_detailed": [ { "name": "James Blann", "url": "https://openalex.org/A5137543581", "inst": "" }, { "name": "James Moon", "url": "https://openalex.org/A5137517290", "inst": "" }, { "name": "Yuxiao Wang", "url": "https://openalex.org/A5100721975", "inst": "Nanjing Forestry University" } ], "affiliations": [ "Nanjing Forestry University" ] }, { "uid": "doi:10.2139/ssrn.6806138", "doi": "10.2139/ssrn.6806138", "title": "Generative AI in Capital Markets: Information Production, Dissemination, and Processing", "authors": [ "Sean S. Cao", "Wilbur Chen", "Guang Ma", "Suraj Srinivasan" ], "posted": "2026-06-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6806138", "field": "accounting", "role": "object", "bullets": [ "Synthesis of six papers presented at the 2025 Journal of Accounting Research Conference on generative AI in capital-market information flows, with no new empirical data.", "The authors use no model themselves; they organize the evidence around a three-layer framework of information production, dissemination, and processing.", "Across layers, generative AI lowers preparation costs, raises intermediary productivity, and cuts processing costs, but gains depend on information verification costs." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 454, "authors_detailed": [ { "name": "Sean S. Cao", "url": "https://openalex.org/A5137608648", "inst": "Smith Institute" }, { "name": "W Chen", "url": "https://openalex.org/A5006088474", "inst": "Hong Kong University of Science and Technology" }, { "name": "Guang Ma", "url": "https://openalex.org/A5137515319", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Suraj Srinivasan", "url": "https://openalex.org/A5034395985", "inst": "Harvard Business School" } ], "affiliations": [ "Harvard Business School", "Smith Institute", "Hong Kong University of Science and Technology", "Rutgers, The State University of New Jersey" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6864440", "doi": "10.2139/ssrn.6864440", "title": "Effects of Usage Goals and Personality Traits on Engagement and Prompting Patterns in Generative AI Use", "authors": [ "Yu Jun Lee", "Jaejin Hwang", "Jinwon Lee", "Jiyeon Ha", "Kellisyn Gersich", "Kyung-Sun Lee" ], "posted": "2026-06-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6864440", "field": "management", "role": "object", "bullets": [ "Forty university students who actively subscribed to ChatGPT 5.0 completed three tasks of differing creativity and complexity: title generation, scenario generation, and data analysis with visualization.", "ChatGPT 5.0 is the studied system rather than a research instrument; prompting frequency and keyword usage were recorded per task and linked to stated usage goals and Big Five personality scores.", "Users whose main goal was coding communicated more often and used distinct keywords in the data-analysis task, and personality clustering produced three groups with different usage patterns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 32, "edition": 3, "audience": "general", "validated": null, "n": 571, "authors_detailed": [ { "name": "Yu Jun Lee", "url": "https://openalex.org/A5127838989", "inst": "Kangwon National University" }, { "name": "Jaejin Hwang", "url": "https://openalex.org/A5127855482", "inst": "Northern Illinois University" }, { "name": "Jinwon Lee", "url": "https://openalex.org/A5127816177", "inst": "" }, { "name": "Jiyeon Ha", "url": "https://openalex.org/A5137514246", "inst": "" }, { "name": "Kellisyn Gersich", "url": "https://openalex.org/A5127803540", "inst": "Northern Illinois University" }, { "name": "Kyung-Sun Lee", "url": "https://openalex.org/A5127834716", "inst": "Kangwon National University" } ], "affiliations": [ "Kangwon National University", "Northern Illinois University" ] }, { "uid": "doi:10.2139/ssrn.6801139", "doi": "10.2139/ssrn.6801139", "title": "AI Adoption Life Cycle: A Cross-Sector Scientific Synthesis of Market Maturity, Governance, and Strategic Implications", "authors": [ "David Sarikhan" ], "posted": "2026-06-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6801139", "field": "management", "role": "object", "bullets": [ "Cross-sector conceptual synthesis of AI market maturity built from recent adoption surveys and AI Index reports, without primary data, spanning organizations and households rather than a defined sample.", "No model is run; the paper treats AI adoption as the object and separates adoption breadth, deployment depth, lifecycle governance, and adaptive maturity across specific applications.", "It classifies AI overall as in late growth moving toward governance-supported maturity while agentic and frontier generative AI stay experimental, and proposes a five-stage adoption life cycle." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 572, "authors_detailed": [ { "name": "David Sarikhan", "url": "https://openalex.org/A5120416125", "inst": "Northcentral University" } ], "affiliations": [ "Northcentral University" ] }, { "uid": "doi:10.2139/ssrn.6802261", "doi": "10.2139/ssrn.6802261", "title": "Panel Inadequacy and the Recognition × Recall Dissociation on the Premium Kitchenware Substrate: AIAS v0.17", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-06-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6802261", "field": "management", "role": "object", "bullets": [ "Pre-registered study of a 16-brand premium kitchenware panel spanning European, American, and Japanese tradition cells; brands tested for presence across six reference large language models.", "Six unnamed reference LLMs queried for brand recognition (Phase A anchoring) and unprompted category recall (Phase B); models not named; presence scored as pass or exclude counts.", "Substantive hypothesis falsified on panel inadequacy (operational n=10 below the pre-registered floor of 12); recognition and recall dissociate, for example Iwachu scored 6/6 recognition but 0/18 recall." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 843, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "Samsung (United States)" } ], "affiliations": [ "Samsung (United States)" ] }, { "uid": "doi:10.2139/ssrn.6805139", "doi": "10.2139/ssrn.6805139", "title": "Agentic AI in Derivatives Markets: Counterparties, Risk, and the Regulatory Gap", "authors": [ "Ligia Catherine Arias-Barrera" ], "posted": "2026-06-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6805139", "field": "finance", "role": "object", "bullets": [ "Conceptual legal analysis of agentic AI operating as de facto participants in over-the-counter derivatives markets; no empirical sample, with regulatory and contractual frameworks as the unit of observation.", "No model is used by the researchers; the paper studies agentic AI systems as autonomous market actors and maps gaps in legal capacity, liability and systemic-risk governance; specific systems are not named.", "Proposes accountability anchoring, assigning legal responsibility to identifiable human or institutional principals at each decisional layer, calibrated to the degree of autonomy exercised." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1115, "authors_detailed": [ { "name": "Ligia Catherine Arias‐Barrera", "url": "https://openalex.org/A5020937277", "inst": "Universidad Externado de Colombia" } ], "affiliations": [ "Universidad Externado de Colombia" ] }, { "uid": "doi:10.2139/ssrn.6854679", "doi": "10.2139/ssrn.6854679", "title": "Antitrust Regulation of Generative AI Foundation Models: Antitrust Risks, Regulatory Frameworks and Policy Recommendations", "authors": [ "Zhishan Deng", "Jiani Wu" ], "posted": "2026-05-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6854679", "field": "economics", "role": "object", "bullets": [ "Conceptual competition-policy analysis of the generative AI foundation model market, treated as three-tier economic infrastructure, with no empirical sample or specific geography stated.", "No language model is used by the authors; the paper builds an analytical framework from gatekeeper, value-chain, and ecosystem theories to diagnose antitrust risks.", "Argues incumbents consolidate power through data privatization, proprietary APIs, and partnerships, and recommends data-sharing mandates, open-source compliance metrics, and adapted merger control." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 451, "authors_detailed": [ { "name": "Zhishan Deng", "url": "https://openalex.org/A5137414658", "inst": "" }, { "name": "Jiani Wu", "url": "https://openalex.org/A5137447594", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6854625", "doi": "10.2139/ssrn.6854625", "title": "Voluntary Sustainability Reporting and LLM-Based ESG Disclosure Quality: Evidence from Korea", "authors": [ "Je Hyun BAE", "JaeHo Lee", "Eun-Chong Kim", "Jaehee Jang" ], "posted": "2026-05-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6854625", "field": "accounting", "role": "instrument", "bullets": [ "Korean-language voluntary sustainability reports and conventional ESG ratings, at the report and firm level, during the transition toward mandatory ESG disclosure in Korea.", "Gemini 3 Flash constructs report-level measures of disclosure quality, clarity, and continuity from the Korean reports, with no reported validation against human coding.", "Voluntary reporters carry higher ESG ratings, and higher textual quality and reporting continuity are associated with higher subsequent external ESG assessments." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": false, "salience": 55, "edition": 3, "audience": "general", "n": 452, "authors_detailed": [ { "name": "Je Hyun BAE", "url": "https://openalex.org/A5137450268", "inst": "Hanyang University" }, { "name": "Jaeho Lee", "url": "https://openalex.org/A5137466676", "inst": "Laboratoire d'Optique Appliquée" }, { "name": "Eun-Chong Kim", "url": "https://openalex.org/A5137415372", "inst": "Hanyang University" }, { "name": "Jaehee Jang", "url": "https://openalex.org/A5101283796", "inst": "Anyang University" } ], "affiliations": [ "Hanyang University", "Laboratoire d'Optique Appliquée", "Anyang University" ] }, { "uid": "doi:10.2139/ssrn.6854633", "doi": "10.2139/ssrn.6854633", "title": "LLMs driven Multi-Factor Quantitative Strategy and Market Anomaly DetectionAnalysis: A Case study of China's A-share market", "authors": [ "yang zhou", "Ke Huang", "Zhipeng Wu", "yiyou wei", "mingjia luo" ], "posted": "2026-05-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6854633", "field": "finance", "role": "agent", "bullets": [ "China's A-share market, specifically CSI 300 constituent stocks; strategies back-tested with quintile portfolios and IC analysis; sample period and number of stocks not stated.", "An unnamed LLM drives the platform to generate investment insights, combined with IC-validated factors, three rule-based strategy engines, LSTM forecasting, and Z-score anomaly detection; LLM output not separately validated.", "Reports a Sharpe ratio of 1.21 on CSI 300 constituents and factor IC absolute values above 0.03, demonstrating an end-to-end pipeline from data acquisition to insight generation." ], "bullet_provenance": "ai", "validated": false, "validation_note": "backtest performance only, no LLM output check", "salience": 34, "edition": 3, "audience": "technical", "models": [], "n": 842, "authors_detailed": [ { "name": "Yang Zhou", "url": "https://openalex.org/A5137435012", "inst": "University of Connecticut" }, { "name": "Ke Huang", "url": "https://openalex.org/A5137446635", "inst": "Nanjing Institute of Vegetable Science" }, { "name": "Zhipeng Wu", "url": "https://openalex.org/A5003483388", "inst": "UCLouvain" }, { "name": "yiyou wei", "url": "https://openalex.org/A5137434258", "inst": "" }, { "name": "mingjia luo", "url": "https://openalex.org/A5137408621", "inst": "" } ], "affiliations": [ "University of Connecticut", "Nanjing Institute of Vegetable Science", "UCLouvain" ] }, { "uid": "doi:10.2139/ssrn.6798479", "doi": "10.2139/ssrn.6798479", "title": "Beyond Consumer Memory: How Large Language Models Update Brand Knowledge", "authors": [ "Andreas Hamann", "Florian Stahl", "P. K. Kannan" ], "posted": "2026-05-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6798479", "field": "management", "role": "object", "bullets": [ "Controlled synthetic field experiment using strategic repositioning of recently established brands as an exogenous shift, tracking conveyed brand knowledge in model responses over time.", "Multiple unnamed LLMs were queried and their responses tracked to measure how brand associations update; specific models are not named and no validation is reported.", "Brand-knowledge updating is retrieval-driven with systematic heterogeneity across models and query types, and retrievability is identified as a key managerial lever." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 159, "authors_detailed": [ { "name": "Andreas Hamann", "url": "https://openalex.org/A5137447190", "inst": "" }, { "name": "Florian Stahl", "url": "https://openalex.org/A5137430823", "inst": "" }, { "name": "P. K. Kannan", "url": "https://openalex.org/A5137453037", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6799479", "doi": "10.2139/ssrn.6799479", "title": "The AIAS Presence Measurement Protocol v1.4: Recognition × Recall Decomposition and Multi-Component AI Availability", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-05-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6799479", "field": "management", "role": "method", "bullets": [ "Specification of AIAS presence measurement protocol v1.4 for measuring AI availability across LLM-mediated retrieval; the empirical anchor is the v0.17 premium kitchenware brand program.", "A locked six-slot reference panel with the classifier fixed to claude-opus-4-7 measures Recognition via Phase A anchoring and Recall via Phase B mention rate, producing eighteen cells per brand.", "The Iwachu case shows full Recognition at 6/6 anchoring with zero Recall at 0/18 mention rate, anchoring the claim that AI availability is a two-component construct." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 26, "edition": 3, "audience": "technical", "validated": null, "n": 449, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "Samsung (United States)" } ], "affiliations": [ "Samsung (United States)" ] }, { "uid": "arxiv:2606.00811v1", "arxiv_id": "2606.00811v1", "title": "Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand", "authors": [ "Dana Golden", "Aruna Balasubramanian", "Niranjan Balasubramanian" ], "posted": "2026-05-30", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.00811v1", "field": "economics", "role": "object", "bullets": [ "US data centers, now 4.4 percent of national electricity demand, and local grid outcomes near them; a novel dataset links AI activity to grid data, with difference-in-differences on staggered LLM releases.", "Language models are not a research tool here; their staggered release serves as the natural experiment, paired with a game-theoretic model of REC, PPA, and colocation procurement choices.", "AI demand raises fossil generation, wholesale prices (up to 25 percent in treated PJM zones), and outages (0.5 to 1 more per year), scaling with model size; on-site generation reverses power-quality effects." ], "bullet_provenance": "ai", "salience": 62, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 450, "authors_detailed": [ { "name": "Dana Golden", "url": "https://openalex.org/A5003023054", "inst": "Henry M. Jackson Foundation" }, { "name": "Aruna Balasubramanian", "url": "https://openalex.org/A5048867459", "inst": "Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology" }, { "name": "Niranjan Balasubramanian", "url": "https://openalex.org/A5101768349", "inst": "Stony Brook University" } ], "affiliations": [ "Henry M. Jackson Foundation", "Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology", "Stony Brook University" ] }, { "uid": "doi:10.2139/ssrn.6852810", "doi": "10.2139/ssrn.6852810", "title": "The disruptive turn in scientific workflows: Human orchestration of agentic AI in empirical research", "authors": [ "Claudio Nigro", "Enrica Iannuzzi", "Leonardo Di Gioia", "Roberto Popolo", "Francesco Caputo" ], "posted": "2026-05-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6852810", "field": "management", "role": "method", "bullets": [ "Reflexive single case study of one AI-assisted research workflow that built an open-source Python pipeline for cross-country M&A risk analysis on 4,689 international transactions from 2008 to 2025.", "A conversational assistant handled ideation while a literature-aware coding agent and retrieval plugin executed tasks; specific model names are not stated, and every AI proposal was human-validated across seven release iterations.", "The pipeline reached a 93.2 percent ticker resolution rate, 84.0 percent usable records after seven tests, and a mean per-record quality score of 88.1 of 100, with humans catching agent errors." ], "bullet_provenance": "ai", "validated": false, "validation_note": "internal data-quality tests, no ground-truth benchmark", "salience": 43, "edition": 3, "audience": "general", "models": [], "n": 570, "authors_detailed": [ { "name": "Claudio Nigro", "url": "https://openalex.org/A5047721377", "inst": "University of Foggia" }, { "name": "Enrica Iannuzzi", "url": "https://openalex.org/A5020141331", "inst": "University of Foggia" }, { "name": "Leonardo Di Gioia", "url": "https://openalex.org/A5037718549", "inst": "University of Foggia" }, { "name": "Roberto Popolo", "url": "https://openalex.org/A5137429811", "inst": "" }, { "name": "Francesco Caputo", "url": "https://openalex.org/A5137403464", "inst": "" } ], "affiliations": [ "University of Foggia" ] }, { "uid": "doi:10.2139/ssrn.6845389", "doi": "10.2139/ssrn.6845389", "title": "When Structured Guidance Helps in AI-Supported Revision: The Role of Baseline Justification Depth and Feedback Engagement", "authors": [ "Songhee Han", "Jueun Shin", "Jiyoon Han", "Bung-Woo Jun", "Idam Kim", "Sewon Joo", "Zhongyu Wang" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6845389", "field": "management", "role": "object", "bullets": [ "Randomized experiment with 450 adults completing an instructional design revision task while interacting with an LLM-based retrieval-augmented chatbot.", "An unnamed LLM-RAG chatbot supplied revision feedback under three conditions (no scaffold, rubric, checklist); the model is not named and no accuracy validation is reported.", "The no-scaffold condition produced the highest performance; baseline justification depth was the strongest predictor of performance and moderated the scaffold effects." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 158, "authors_detailed": [ { "name": "Songhee Han", "url": "https://openalex.org/A5137294518", "inst": "" }, { "name": "Jueun Shin", "url": "https://openalex.org/A5137247626", "inst": "" }, { "name": "Jiyoon Han", "url": "https://openalex.org/A5085376778", "inst": "University of Southern California" }, { "name": "Bung-Woo Jun", "url": "https://openalex.org/A5137244918", "inst": "" }, { "name": "Idam Kim", "url": "https://openalex.org/A5080493692", "inst": "" }, { "name": "Sewon Joo", "url": "https://openalex.org/A5118138796", "inst": "Florida State University" }, { "name": "Zhongyu Wang", "url": "https://openalex.org/A5100775048", "inst": "Xinjiang Academy of Agricultural Sciences" } ], "affiliations": [ "University of Southern California", "Florida State University", "Xinjiang Academy of Agricultural Sciences" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2606.11238v2", "arxiv_id": "2606.11238v2", "title": "Artificial Intelligence in Ship Finance: Applications, Opportunities, and a Case Study in AI-Augmented Loan Origination", "authors": [ "Lasse Dierich", "Orestis Schinas" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.11238v2", "field": "finance", "role": "object", "bullets": [ "Ship finance and asset-based lending, focused on loan origination and underwriting over heterogeneous unstructured financial, technical, contractual, and regulatory documents. The paper is a review plus a proposed system, with no empirical sample.", "Presents ShipFinance.ai, a modular agentic architecture combining an LLM-based extraction module, financial analysis components, external maritime data services, and controlled document generation with a chatbot; the model family is not stated and no validation is reported.", "Argues LLM-based systems can support document comprehension, information extraction, and workflow automation in ship finance; no quantitative performance results are reported." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 186, "authors_detailed": [ { "name": "Lasse Dierich", "url": "https://openalex.org/A5120317558", "inst": "Technical University of Munich" }, { "name": "Orestis Schinas", "url": "https://openalex.org/A5135484992", "inst": "University of the Aegean" } ], "affiliations": [ "Technical University of Munich", "University of the Aegean" ] }, { "uid": "doi:10.2139/ssrn.6850023", "doi": "10.2139/ssrn.6850023", "title": "Understanding Employee Turnover through explainable machine learning techniques and LLMs: An understandable employee retention prediction framework for People Analytics", "authors": [ "Ana-Isabel Alonso-Sastre", "Juan Pardo", "Jeroen Meijerink", "Antonio Falco" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6850023", "field": "management", "role": "instrument", "bullets": [ "Employee retention prediction in human resource management; the abstract reports no sample size, time period, or geography, with employees as the unit of observation.", "Ensemble machine learning meta-models predict retention while an unnamed large language model generates human-readable explanations of the black-box predictions; no accuracy check of the model is reported.", "Ensemble meta-models outperform individual base learners and offer a more balanced precision-transparency tradeoff for HR decision-making; no numeric magnitude is reported." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 236, "authors_detailed": [ { "name": "Ana-Isabel Alonso-Sastre", "url": "https://openalex.org/A5118142890", "inst": "Universidad Cardenal Herrera CEU" }, { "name": "Juan Pardo", "url": "https://openalex.org/A5135602419", "inst": "Universidad Cardenal Herrera CEU" }, { "name": "Jeroen Meijerink", "url": "https://openalex.org/A5137317869", "inst": "" }, { "name": "Antonio Falco", "url": "https://openalex.org/A5137355314", "inst": "" } ], "affiliations": [ "Universidad Cardenal Herrera CEU" ] }, { "uid": "doi:10.2139/ssrn.6848764", "doi": "10.2139/ssrn.6848764", "title": "Artificial intelligence and innovation in the post-ChatGPT era: Science mapping in search of new research frontiers", "authors": [ "Lovrenc Švegl", "Patrick Mikalef", "Miha Skerlavaj" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6848764", "field": "management", "role": "object", "bullets": [ "Curated business, management, and economics corpus from the Web of Science Core Collection, split into a pre-generative-AI period 2010 to 2022 and a post-2023 period.", "No language model used by the researchers; co-citation, keyword co-occurrence, and bibliographic coupling analyses run in VOSviewer to map the research structure.", "Five research streams identified; post-2023 maps show denser cross-links and a shift from whether AI creates value toward how firms deploy it, with governance more salient." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 38, "edition": 3, "audience": "general", "validated": null, "n": 237, "authors_detailed": [ { "name": "Lovrenc Svegl", "url": "https://openalex.org/A5039859204", "inst": "University of Ljubljana" }, { "name": "Patrick Mikalef", "url": "https://openalex.org/A5076679564", "inst": "Norwegian University of Science and Technology" }, { "name": "Miha Škerlavaj", "url": "https://openalex.org/A5007435491", "inst": "University of Ljubljana" } ], "affiliations": [ "University of Ljubljana", "Norwegian University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.6831599", "doi": "10.2139/ssrn.6831599", "title": "From Curiosity to Healthcare: Understanding Cognitive Profiles and Adoption Pathways of Large Language Models for Healthcare", "authors": [ "Rita Phillips" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6831599", "field": "management", "role": "object", "bullets": [ "Convenience sample of 507 respondents to an online survey on healthcare-related large language model use; geography and period not stated, individual users as the unit.", "No model deployed by researchers; survey measures TAM3 constructs and benefit-risk perceptions, analysed with hierarchical cluster analysis and multi-group structural equation modelling.", "Perceived usefulness, behavioural intention, and enjoyment are central predictors of use; migration background and technological habituation also matter, and adoption pathways differ across four user profiles." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 238, "authors_detailed": [ { "name": "Rita Phillips", "url": "https://openalex.org/A5082581948", "inst": "University of Klagenfurt" } ], "affiliations": [ "University of Klagenfurt" ] }, { "uid": "doi:10.2139/ssrn.6850138", "doi": "10.2139/ssrn.6850138", "title": "EconLogic-BSQA: A Closed-World Benchmark for Evaluating Belief Sufficiency and Decision Commitment in Large Language Models", "authors": [ "Xinhai Sun" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6850138", "field": "economics", "role": "method", "bullets": [ "EconLogic-BSQA benchmark of 580 curated instances spanning six evidence conditions, derived from EconLogicQA by recasting economic event chains into fictional business micro-worlds with explicit rules.", "Seven contemporary large language models (not named) tested on whether local evidence suffices to proceed, reject, request information, or defer, scored with new commitment and leakage-rate metrics.", "Strong models overcommit under incomplete evidence and often rely on external business priors rather than prompt-contained rules, showing exact-match accuracy alone misjudges commercial reasoning." ], "bullet_provenance": "ai", "validated": true, "validation_note": "closed-world benchmark, decision-rate metrics reported", "salience": 46, "edition": 3, "audience": "technical", "models": [], "n": 239, "authors_detailed": [ { "name": "Xinhai Sun", "url": "https://openalex.org/A5089963091", "inst": "Yangtze Normal University" } ], "affiliations": [ "Yangtze Normal University" ] }, { "uid": "doi:10.2139/ssrn.6849321", "doi": "10.2139/ssrn.6849321", "title": "Three-Year Evidence from Approximately 5,000 A-Share Stocks on the Limits of Pre-trained TSFM Integration", "authors": [ "Yilin Zhong", "Yuqi Fu", "Sixuan Zhu" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6849321", "field": "finance", "role": "method", "bullets": [ "About 5,000 Chinese A-share stocks per year across 2023 to 2025, daily volatility prediction under a strict lagged-only protocol.", "Kronos, a pre-trained time-series foundation model, run zero-shot and fine-tuned, benchmarked against HAR-RV, MS-GARCH, and a Kronos-GARCH hybrid on forecast accuracy.", "Zero-shot Kronos trails all baselines and fine-tuning degrades it further; HAR-RV posts the lowest MAE of 1.29% in 2025, while Kronos runs 2.4 to 2.6 times higher." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "MAE against HAR-RV and MS-GARCH baselines", "salience": 58, "edition": 3, "audience": "technical", "n": 568, "authors_detailed": [ { "name": "Yilin Zhong", "url": "https://openalex.org/A5137343412", "inst": "" }, { "name": "Yuqi Fu", "url": "https://openalex.org/A5066409172", "inst": "Xiamen University" }, { "name": "Sixuan Zhu", "url": "https://openalex.org/A5137351757", "inst": "" } ], "affiliations": [ "Xiamen University" ] }, { "uid": "doi:10.2139/ssrn.6791859", "doi": "10.2139/ssrn.6791859", "title": "Agentic AI, Large Language Model Methodologies, and Workflow-Level ROI in Portfolio Risk Analysis at Navy Federal Credit Union: An Evidence-Constrained Quasi-Experimental Comparison of the Pre-Agentic Era (2023-2024) and the 2025 Transition", "authors": [ "Matthias Mbarga" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6791859", "field": "management", "role": "object", "bullets": [ "Single-institution case study of Navy Federal Credit Union, comparing a 2023 to 2024 pre-agentic baseline with a 2025 agentic AI transition using public financial disclosures.", "No model is run by the author; an Agentic Portfolio Risk Capability Index is coded from public disclosures using a five-dimension codebook with sensitivity checks.", "The capability index rises from 48 in 2023 to 92 in 2025 while assets and net income grow, but the actual ROI magnitude remains unobserved." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 569, "authors_detailed": [ { "name": "Matthias Mbarga", "url": "https://openalex.org/A5137354668", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6792161", "doi": "10.2139/ssrn.6792161", "title": "Sequential Structure in Intraday Futures Data: LSTM vs Gradient Boosting on MNQ", "authors": [ "Mathias Mesfin" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6792161", "field": "finance", "role": "method", "bullets": [ "944 trading days of Micro E-Mini Nasdaq 100 futures from 2021 to 2025, using five-minute OHLCV bars, targeting whether the session close exceeds the 10:30 am bar open by more than ten points.", "Compares gradient boosting on engineered features against an LSTM on raw bar sequences, a tractable approximation of the Kronos foundation-model architecture, under walk-forward validation with permutation tests.", "No configuration beats the 51.8 percent base rate; out-of-sample accuracies span 50.00 to 50.89 percent and the LSTM reaches 50.59 percent, with permutation p-values of 0.135 and 0.515, indicating no exploitable sequential structure." ], "bullet_provenance": "ai", "validated": true, "validation_note": "walk-forward OOS accuracy vs 51.8% base rate, permutation tests", "salience": 46, "edition": 3, "audience": "technical", "models": [], "n": 838, "authors_detailed": [ { "name": "Mathias Mesfin", "url": "https://openalex.org/A5135234918", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6793201", "doi": "10.2139/ssrn.6793201", "title": "AI and Big Data Security, Governance, and Regulatory Compliance in Enterprise Cloud Platforms: A Regulatory Document Analysis for the Saudi Public Sector", "authors": [ "Muhammad Omar Muftakhar" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6793201", "field": "management", "role": "object", "bullets": [ "Systematic document analysis of Saudi public-sector regulatory instruments governing enterprise AI and big data, including the Personal Data Protection Law, SDAIA AI Ethics Principles, and Cloud Computing Regulatory Framework.", "No model is used; the study cross-references five governance domains against the NIST AI Risk Management Framework and published SAP Business Technology Platform and Amazon Web Services technical guidance.", "Finds regulatory coverage coherent at the policy level but with operational gaps in model lifecycle governance, AI-derived data classification, and audit-readiness, and no specific treatment of generative AI hallucination or prompt governance." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 839, "authors_detailed": [ { "name": "Muhammad Omar Muftakhar", "url": "https://openalex.org/A5135950164", "inst": "Riyadh Elm University" } ], "affiliations": [ "Riyadh Elm University" ] }, { "uid": "doi:10.2139/ssrn.6849594", "doi": "10.2139/ssrn.6849594", "title": "Synergistic Integration of Large Language Model and Machine Learning for Launch Vehicle Pricing Factor Analysis and Modeling", "authors": [ "Beiyu Yi", "Xin Zheng", "Hui Min", "Nannan Shi", "Xiaodan Liu" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6849594", "field": "management", "role": "instrument", "bullets": [ "Launch vehicle pricing dataset with sample size not stated; an LLM expands the parameter set from 16 to 18 by extracting latent features from textual data for a price-modeling framework in the aerospace industry.", "An unnamed large language model augments features that feed a PLS regression and an XGBoost model tuned with Optuna; no validation of the LLM-derived features against ground truth is reported.", "The best configuration reaches R-squared of 0.9977, RMSE of 12.4, and MAE of 5.99, outperforming multivariate linear regression, with LLM-augmented features correlated with key price determinants." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 3, "audience": "technical", "models": [], "n": 840, "authors_detailed": [ { "name": "Beiyu Yi", "url": "https://openalex.org/A5137347713", "inst": "" }, { "name": "Xin Zheng", "url": "https://openalex.org/A5137360695", "inst": "" }, { "name": "Hui Min", "url": "https://openalex.org/A5137379897", "inst": "" }, { "name": "Nannan Shi", "url": "https://openalex.org/A5137380397", "inst": "" }, { "name": "Xiaodan Liu", "url": "https://openalex.org/A5137370112", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6847484", "doi": "10.2139/ssrn.6847484", "title": "The Cyber Risk of Non-Financial Firms", "authors": [ "Francesco Columba", "Manuel Cugliari", "Marco Orlandi", "Federica Vassalli" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6847484", "field": "finance", "role": "instrument", "bullets": [ "Large, heterogeneous sample of Italian non-financial firms, with inputs drawn from financial statements, news reports, and cyber industry reports; period not stated, though cyberattacks in Italy are noted to rise since 2019.", "Applies natural language processing and an unnamed large language model to build a cyber risk vulnerability indicator from an Italy-specific taxonomy of attacks, regulatory compliance, and defence technologies.", "Cyber incidents raise firm vulnerability more than defensive actions mitigate it, and firms disclose more cyber information only after an attack, supporting inclusion of cyber risk in credit risk assessments." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 3, "audience": "general", "models": [], "n": 841, "authors_detailed": [ { "name": "Francesco Columba", "url": "https://openalex.org/A5077091673", "inst": "Bank of Italy" }, { "name": "Manuel Cugliari", "url": "https://openalex.org/A5117716699", "inst": "Bank of Italy" }, { "name": "Marco Orlandi", "url": "https://openalex.org/A5034103110", "inst": "Bank of Italy" }, { "name": "Federica Vassalli", "url": "https://openalex.org/A5117716700", "inst": "Bank of Italy" } ], "affiliations": [ "Bank of Italy" ] }, { "uid": "arxiv:2606.00143v1", "arxiv_id": "2606.00143v1", "title": "Regime-Adaptive Continual Learning for Portfolio Management", "authors": [ "Chaofan Pan", "Lingfei Ren", "Linbo Xiong", "Yonghao Li", "Wei Wei", "Xin Yang" ], "posted": "2026-05-29", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.00143v1", "field": "finance", "role": "agent", "bullets": [ "Portfolio management task evaluated on five real-world financial datasets; period, geography and asset universe not stated; unit of observation is sequential market regimes.", "ReCAP, a continual-learning framework with adaptive regime detection and a policy library; no language model named; compared against rolling-window retraining and online fine-tuning baselines using backtested returns.", "Reports higher long-term returns and faster adaptation to regime shifts than baselines, with no specific magnitude given in the abstract." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1114, "authors_detailed": [ { "name": "Chaofan Pan", "url": "https://openalex.org/A5137679051", "inst": "Southwestern University of Finance and Economics" }, { "name": "Lingfei Ren", "url": "https://openalex.org/A5137626070", "inst": "Southwestern University of Finance and Economics" }, { "name": "Linbo Xiong", "url": "https://openalex.org/A5137712062", "inst": "Southwestern University of Finance and Economics" }, { "name": "Yonghao Li", "url": "https://openalex.org/A5137639249", "inst": "Southwestern University of Finance and Economics" }, { "name": "Wei Wei", "url": "https://openalex.org/A5137626880", "inst": "Northwest Normal University" }, { "name": "Xin Yang", "url": "https://openalex.org/A5137685878", "inst": "Wuhan University" } ], "affiliations": [ "Southwestern University of Finance and Economics", "Northwest Normal University", "Wuhan University" ] }, { "uid": "doi:10.2139/ssrn.6789618", "doi": "10.2139/ssrn.6789618", "title": "Agentic AI, Large Language Model Methodologies, and Workflow-Level ROI in Portfolio Risk Analysis at Navy Federal Credit Union: An Evidence-Constrained Quasi-Experimental Comparison of the Pre-Agentic Era (2023-2024) and the 2025 Transition", "authors": [ "Matthias Mbarga, MBA, D.ENG(c)." ], "posted": "2026-05-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6789618", "field": "finance", "role": "object", "bullets": [ "Navy Federal Credit Union, quasi-experimental comparison of pre-agentic baseline (2023-2024) versus 2025 AI-transition period using public financial indicators.", "No LLM used directly; paper constructs an Agentic Portfolio Risk Capability Index from public disclosures and scores it on a five-dimension codebook with sensitivity checks.", "APRCI rose from 48 to 92 over 2023-2025; assets grew from $170.8B to $197.2B; actual workflow-level ROI magnitude remains unobserved in public data." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3240, "authors_detailed": [ { "name": "Matthias Mbarga", "url": "https://openalex.org/A5137354668", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6845159", "doi": "10.2139/ssrn.6845159", "title": "Climate Risk Disclosure and Market Perception: Evidence from a Large Language Model", "authors": [ "Po-Lin Wang", "Dan Yang", "Jian Zhang" ], "posted": "2026-05-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6845159", "field": "accounting", "role": "instrument", "bullets": [ "Russell 3000 firms with climate risk disclosures in the MD&A and risk-factors sections of 10-K filings, linked to market-based measures of firm risk; the sample period is not stated.", "An unnamed large language model plus dictionary methods classify disclosures into physical and transition risk; no accuracy or agreement check against human coding is reported.", "Greater climate disclosure is associated with higher market-implied risk; physical-risk text reads as reassurance while transition-risk text reads as a red flag, attenuating after the 2022 SEC proposal." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no accuracy check reported", "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 446, "authors_detailed": [ { "name": "Po-Lin Wang", "url": "https://openalex.org/A5137265991", "inst": "University of South Florida" }, { "name": "Dan Yang", "url": "https://openalex.org/A5137253775", "inst": "Troy University" }, { "name": "Jian Zhang", "url": "https://openalex.org/A5137293495", "inst": "Le Moyne College" } ], "affiliations": [ "University of South Florida", "Troy University", "Le Moyne College" ] }, { "uid": "doi:10.2139/ssrn.6840256", "doi": "10.2139/ssrn.6840256", "title": "From oracle to socratic tutor: leveraging generative AI to structure reflective practice in entrepreneurship education", "authors": [ "Luciana Padovez Cualheta", "Vanessa Soares Camargo", "Edgard Barki" ], "posted": "2026-05-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6840256", "field": "management", "role": "object", "bullets": [ "Conceptual essay on generative AI in entrepreneurship education within higher education, with no empirical sample, drawing on sociocultural mediation, problem-posing pedagogy, experiential learning, and effectual logic.", "No model is used or named; the essay theorizes three pedagogical roles for generative AI: co-pilot in design-based learning, dynamic stakeholder in simulations, and Socratic tutor in reflection.", "Proposes the AI-Mediated Entrepreneurial Method, reframing generative AI from an answer-generating oracle to a mediator of inquiry, judgment, and reflective practice." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 447, "authors_detailed": [ { "name": "Luciana Padovez Cualheta", "url": "https://openalex.org/A5058739521", "inst": "Escola de Administração de Empresas de São Paulo" }, { "name": "Vanessa Soares Camargo", "url": "https://openalex.org/A5137178074", "inst": "" }, { "name": "Edgard Barki", "url": "https://openalex.org/A5137180576", "inst": "Fundação Getulio Vargas" } ], "affiliations": [ "Escola de Administração de Empresas de São Paulo", "Fundação Getulio Vargas" ] }, { "uid": "doi:10.2139/ssrn.6783940", "doi": "10.2139/ssrn.6783940", "title": "Stable Output Is Not Structural Preservation: Template Absorption, SSE, and the Need for AI Operational Design", "authors": [ "Masaki Hoshino" ], "posted": "2026-05-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6783940", "field": "management", "role": "object", "bullets": [ "Conceptual essay on generative AI operation risk, using cel-style character images as diagnostic probes within an Open Bias Architecture framework; no empirical sample or quantitative measurement.", "GPT-5.5 is invoked illustratively rather than benchmarked; the paper introduces character-structure preservation to observe template absorption and structural drift in model outputs.", "Argues that stable, coherent output is not structural preservation; user-specific conditions get reshaped into acceptable templates, posing governance risk for business outputs without operational control." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 26, "edition": 3, "audience": "general", "validated": null, "n": 448, "authors_detailed": [ { "name": "Masaki Hoshino", "url": "https://openalex.org/A5137168977", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6748927", "doi": "10.2139/ssrn.6748927", "title": "The Norm Architects: How AI Delegation Norms Form before anyone Designs them", "authors": [ "Vsevolod Shabad" ], "posted": "2026-05-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6748927", "field": "management", "role": "object", "bullets": [ "Conceptual article on how organizations adopting generative AI develop implicit delegation norms; no empirical sample, drawing on literatures on social norms, cognitive automation, and AI-mediated decision-making.", "No language model is used by the author; the paper theorizes how convenience choices harden into delegation norms through tipping-point dynamics before governance frameworks appear.", "Identifies the pre-design phase as the lowest-cost moment for managerial intervention and proposes three governance practices plus a short delegation audit for proactive norm design." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 835, "authors_detailed": [ { "name": "Vsevolod Shabad", "url": "https://openalex.org/A5123386546", "inst": "University of Liverpool" } ], "affiliations": [ "University of Liverpool" ] }, { "uid": "doi:10.2139/ssrn.6789380", "doi": "10.2139/ssrn.6789380", "title": "LLM-Generated vs. Human-Authored Advertising Copy: A Comparative Analysis of Persuasiveness, Brand Alignment, and Linguistic Naturalness", "authors": [ "Tanvir Ahmed" ], "posted": "2026-05-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6789380", "field": "management", "role": "agent", "bullets": [ "120 paired advertising-copy samples across e-commerce and services categories, comparing machine-generated copy with human-authored copy in a mixed-methods design.", "GPT-4o writes the advertising copy, evaluated with automated readability and sentiment analysis plus human evaluator scoring on persuasiveness, brand alignment, emotional resonance, and linguistic naturalness.", "GPT-4o matched human writers on surface fluency but lagged on cultural specificity and implicit persuasion, indicating limits to AI judgment in commercial copywriting." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 836, "authors_detailed": [ { "name": "Tanvir Ahmed", "url": "https://openalex.org/A5137218620", "inst": "Search" } ], "affiliations": [ "Search" ] }, { "uid": "doi:10.2139/ssrn.6843683", "doi": "10.2139/ssrn.6843683", "title": "From Distributed Localized AI-Job Crafting to Strategic Meta-Work: RedesigningHR Governance Role in Enterprise-Wide Gen AI Integration and a Research Agenda", "authors": [ "Hongyu Wu", "Louise Harder Fischer" ], "posted": "2026-05-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6843683", "field": "management", "role": "object", "bullets": [ "Conceptual paper on enterprise-wide generative AI integration, drawing on job crafting theory, sociotechnical systems theory, and strategic human resource management, with no empirical sample.", "No model is used; the paper defines meta-work as second-order HR activity that detects, interprets, and institutionalizes employees' localized AI work adaptations, and builds the HR Meta-Work Framework.", "Advances a three-configuration governance typology and five falsifiable propositions specifying the conditions under which HR governance authority acquires legitimacy in generative AI integration." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 837, "authors_detailed": [ { "name": "Hongyu Wu", "url": "https://openalex.org/A5100341974", "inst": "Jinan University" }, { "name": "Louise Harder Fischer", "url": "https://openalex.org/A5037877404", "inst": "IT University of Copenhagen" } ], "affiliations": [ "Jinan University", "IT University of Copenhagen" ] }, { "uid": "doi:10.2139/ssrn.6786758", "doi": "10.2139/ssrn.6786758", "title": "The Cognitive Impact of AI on Organisational Decision-Making: Why Human Readiness Is the Missing Layer in AI Governance", "authors": [ "Julie Hendry" ], "posted": "2026-05-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6786758", "field": "management", "role": "object", "bullets": [ "Narrative review synthesising cognitive psychology, human factors, organisational behaviour and neuroscience, framed against the EU AI Act enforceable from August 2026; no empirical sample or firm-level data.", "No language model is used by the researchers; the paper studies how working alongside AI, including agentic AI, affects human cognition, deskilling and oversight capacity, and presents an early cognitive-impact assessment tool.", "Argues human oversight as conceived in high-risk AI governance is psychologically untenable, and that no existing maturity framework measures the human cognitive and organisational readiness oversight requires." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1113, "authors_detailed": [ { "name": "Julie Hendry", "url": "https://openalex.org/A5137246047", "inst": "Building Engineering and Science Talent" } ], "affiliations": [ "Building Engineering and Science Talent" ] }, { "uid": "doi:10.2139/ssrn.6789658", "doi": "10.2139/ssrn.6789658", "title": "Token Leverage: A Framework for AI Inside the Firm", "authors": [ "Cayman Seagraves", "Stace Sirmans" ], "posted": "2026-05-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6789658", "field": "management", "role": "object", "bullets": [ "Conceptual framework for measuring generative AI value creation at the worker-task pair level inside firms, spanning voluntary and mandated adoption settings.", "Introduces token leverage, defined as billed inference spend per labor dollar, as the margin where AI creates or destroys operating value for each task.", "Voluntary inference spend provides a revealed-preference lower bound on AI-created value; access and adoption measures conflate chosen use, abstention, overuse, and underuse." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3613, "authors_detailed": [ { "name": "Cayman Seagraves", "url": "https://openalex.org/A5137245342", "inst": "" }, { "name": "Stace Sirmans", "url": "https://openalex.org/A5000933071", "inst": "Auburn University" } ], "affiliations": [ "Auburn University" ] }, { "uid": "doi:10.2139/ssrn.6839367", "doi": "10.2139/ssrn.6839367", "title": "Insolvency Prediction: Employing Large Language Models to Strengthen Traditional Machine Learning Approaches", "authors": [ "Devendra Jain", "Saurav Roychoudhury", "Harsh Jain", "Pratham Rao", "Nitish Jain" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6839367", "field": "finance", "role": "instrument", "bullets": [ "2,966 US companies from 2012 to 2023, using management discussion and analysis text plus financial ratios, with a held-out test of 345 firms including 20 bankruptcies.", "GPT-4o with chain-of-thought prompting extracts forward-looking risk signals from MD&A across six themes, combined with an XGBoost classifier and checked on the held-out sample.", "The hybrid model identifies 90% of bankruptcies at 96% accuracy, sharply cutting false positives versus the GPT-4o-only baseline that reached 99% recall." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "held-out test of 345 firms, recall and accuracy reported", "salience": 55, "edition": 3, "audience": "technical", "n": 106, "authors_detailed": [ { "name": "Devendra Jain", "url": "https://openalex.org/A5137164991", "inst": "" }, { "name": "Saurav Roychoudhury", "url": "https://openalex.org/A5004819892", "inst": "West Virginia University" }, { "name": "Harsh Jain", "url": "https://openalex.org/A5137163736", "inst": "" }, { "name": "Pratham Rao", "url": "https://openalex.org/A5137135850", "inst": "" }, { "name": "Nitish Jain", "url": "https://openalex.org/A5137113413", "inst": "" } ], "affiliations": [ "West Virginia University" ] }, { "uid": "arxiv:2605.27887v2", "arxiv_id": "2605.27887v2", "title": "PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management", "authors": [ "Yuxuan Zhao", "Sijia Chen", "Ningxin Su" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.27887v2", "field": "finance", "role": "method", "bullets": [ "Portfolio management benchmark covering six asset classes over ten years, combining 6,269 correlation-based questions with a dynamic five-stage allocation pipeline tested under three historical stress regimes.", "Ten frontier LLMs, not individually named, are evaluated on correlation-aware questions and full allocation decisions, scored with a dual-layer correlation metric and CEPS that tracks how reasoning errors compound across stages.", "Despite strong static question performance, 90 percent of model-profile combinations fail to beat equal-weight allocation, and models satisfying every procedural constraint still suffer large drawdowns under stress." ], "bullet_provenance": "ai", "salience": 62, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 119, "authors_detailed": [ { "name": "Yuxuan Zhao", "url": "https://openalex.org/A5035267308", "inst": "Hebei Medical University" }, { "name": "Sijia Chen", "url": "https://openalex.org/A5100712826", "inst": "University of Electronic Science and Technology of China" }, { "name": "Ningxin Su", "url": "https://openalex.org/A5137201104", "inst": "" } ], "affiliations": [ "Hebei Medical University", "University of Electronic Science and Technology of China" ] }, { "uid": "doi:10.2139/ssrn.6839399", "doi": "10.2139/ssrn.6839399", "title": "An empirically grounded modelling architecture for rapid ex ante policy evaluation", "authors": [ "Janne M. Korhonen", "Pekka Leskinen" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6839399", "field": "economics", "role": "method", "bullets": [ "Circular economy policy evaluation, demonstrated on an electronics recycling policy, using a hybrid agent-based model coupled with system dynamics and environmentally extended input-output accounting.", "Large language models, not named, read natural-language policy descriptions and select parameter values from a curated empirical library; a check finds they capture ordinal but not cardinal structure.", "Language models reliably recover the ordinal structure of consumer heterogeneity but cannot replace empirical data for cardinal calibration, and the model stays reproducible without them." ], "bullet_provenance": "ai", "validated": false, "validation_note": "ordinal vs cardinal elicitation check, no numeric figure", "salience": 45, "edition": 3, "audience": "general", "models": [], "n": 155, "authors_detailed": [ { "name": "Janne M. Korhonen", "url": "https://openalex.org/A5017453863", "inst": "Finnish Environment Institute" }, { "name": "Pekka Leskinen", "url": "https://openalex.org/A5113551092", "inst": "Finnish Environment Institute" } ], "affiliations": [ "Finnish Environment Institute" ] }, { "uid": "doi:10.2139/ssrn.6783680", "doi": "10.2139/ssrn.6783680", "title": "Measuring Destination Image in Real Time: Validating Large Language Model Scoring of Visual Diaries Against Established Scales", "authors": [ "Jorge E. Araña", "Carmelo J. León González" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6783680", "field": "management", "role": "method", "bullets": [ "187 tourists to the Canary Islands recorded spoken visual diaries at three points, pre-trip, during-trip and post-trip, in a longitudinal design.", "An ensemble of three frontier models including GPT-4-class scores transcripts zero-shot on cognitive, affective and conative dimensions, validated against survey scales with convergent validity r of .69 to .78.", "LLM scores show internal consistency of .84 to .91 and predict satisfaction and loyalty comparably to surveys, while detecting in-trip cognitive updating that retrospective surveys miss." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "convergent validity r .69-.78, ICC .72-.79 vs survey scales", "salience": 62, "edition": 3, "audience": "general", "n": 156, "authors_detailed": [ { "name": "Jorge E. Araña", "url": "https://openalex.org/A5135332740", "inst": "" }, { "name": "Carmelo J. León González", "url": "https://openalex.org/A5137144352", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6835336", "doi": "10.2139/ssrn.6835336", "title": "Prompting or Fine-Tuning? Automating Functional Requirements and Design Parameter Extraction from Patents", "authors": [ "Marco Consoloni", "Vito Giordano", "Alessio Miaschi", "Felice Dell'Orletta", "Fantoni Gualtiero" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6835336", "field": "management", "role": "method", "bullets": [ "Manually annotated dataset of 6,000 patent sentences, framed for functional requirement and design parameter extraction in patent analysis and new product development.", "Encoder-only and decoder-only models, not named, compared under supervised fine-tuning versus prompting on sentence classification and named entity recognition, scored by F1 against the annotations.", "Fine-tuned encoder-only models beat prompted decoder-only models by up to 0.1 F1 on classification and 0.4 F1 on entity recognition, with about 1,000 examples sufficing." ], "bullet_provenance": "ai", "validated": true, "validation_note": "F1 against 6,000 manually annotated patent sentences", "salience": 50, "edition": 3, "audience": "technical", "models": [], "n": 157, "authors_detailed": [ { "name": "Marco Consoloni", "url": "https://openalex.org/A5092201499", "inst": "University of Pisa" }, { "name": "Vito Giordano", "url": "https://openalex.org/A5062279114", "inst": "University of Pisa" }, { "name": "Alessio Miaschi", "url": "https://openalex.org/A5062667199", "inst": "Institute for Computational Linguistics “A. Zampolli”" }, { "name": "Felice Dell apos Orletta", "url": "https://openalex.org/A5056712927", "inst": "National Research Council" }, { "name": "Fantoni Gualtiero", "url": "https://openalex.org/A5137013473", "inst": "" } ], "affiliations": [ "University of Pisa", "Institute for Computational Linguistics “A. Zampolli”", "National Research Council" ] }, { "uid": "doi:10.2139/ssrn.6837695", "doi": "10.2139/ssrn.6837695", "title": "An Agentic Artificial Intelligence Framework for Weekly Stock Portfolio Selection in the United States Technology Sector", "authors": [ "Kunakorn Pruksakorn", "Ekarat Rattagan" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6837695", "field": "finance", "role": "agent", "bullets": [ "90 highly liquid, predominantly NASDAQ-listed US technology stocks, selected weekly over a nine-week live evaluation from August to October 2025.", "Five role-specialized agents (Momentum, Catalyst, Technical, Liquidity, Risk) run on Llama 3.3 70B with zero-shot prompting, orchestrated via LangGraph into a score-based portfolio; no validation against ground truth reported.", "The portfolio returned 3.28 percent per week on average, 32.99 percent cumulative, with an annualized Sharpe of 6.05, beating the NASDAQ-100, S&P 500, and XLK over the window." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 48, "edition": 3, "audience": "general", "n": 234, "authors_detailed": [ { "name": "Kunakorn Pruksakorn", "url": "https://openalex.org/A5137111659", "inst": "National Institute of Development Administration" }, { "name": "Ekarat Rattagan", "url": "https://openalex.org/A5062629070", "inst": "National Institute of Development Administration" } ], "affiliations": [ "National Institute of Development Administration" ] }, { "uid": "arxiv:2605.28359v1", "arxiv_id": "2605.28359v1", "title": "From Knowing to Doing: A Memory-Controlled Benchmark for LLM Trading Agents on Stock Markets", "authors": [ "Taojie Zhu", "Wentao Zhao", "Rui Sun", "Beidi Luan", "Jiacheng Lu", "Sinuo Wang", "Jing Li", "Daxin Jiang", "Yonghong He", "Zuo Bai" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.28359v1", "field": "finance", "role": "method", "bullets": [ "Ten frontier LLM trading agents evaluated on the Chinese CSI300 index over a 2024 to 2026 window in an end-to-end backtest trading setup.", "Models are not individually named; a data-masking protocol anonymizes tickers, dates, and prices to block memorization, and a Barra-style attribution splits returns into market, style, and stock-selection components.", "After leakage control, agents' cumulative returns are largely explained by passive market and style exposure, with limited evidence of persistent stock-selection alpha." ], "bullet_provenance": "ai", "salience": 62, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 235, "authors_detailed": [ { "name": "Taojie Zhu", "url": "https://openalex.org/A5137244875", "inst": "" }, { "name": "Wentao Zhao", "url": "https://openalex.org/A5137284526", "inst": "" }, { "name": "Rui Sun", "url": "https://openalex.org/A5137214643", "inst": "Capital University" }, { "name": "Beidi Luan", "url": "https://openalex.org/A5059999561", "inst": "Shanghai Innovative Research Center of Traditional Chinese Medicine" }, { "name": "Jiacheng Lu", "url": "https://openalex.org/A5137248961", "inst": "" }, { "name": "Sinuo Wang", "url": "https://openalex.org/A5137241736", "inst": "" }, { "name": "Jing Li", "url": "https://openalex.org/A5137299990", "inst": "Foshan University" }, { "name": "Daxin Jiang", "url": "https://openalex.org/A5137259985", "inst": "" }, { "name": "Yonghong He", "url": "https://openalex.org/A5137292602", "inst": "" }, { "name": "Zuo Bai", "url": "https://openalex.org/A5008300079", "inst": "Delta Air Lines (United States)" } ], "affiliations": [ "Capital University", "Shanghai Innovative Research Center of Traditional Chinese Medicine", "Foshan University", "Delta Air Lines (United States)" ] }, { "uid": "doi:10.2139/ssrn.6816340", "doi": "10.2139/ssrn.6816340", "title": "Substrate Pre-Screening, Independent Moderator Pathway, and Phantom Brand Persistence Phase B Extension", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6816340", "field": "management", "role": "method", "bullets": [ "Methodology extension of the AIAS presence protocol, retrospectively scored against the v0.16 to v0.21 brand corpus of skincare and cosmetics substrates using a locked six-LLM panel.", "Adds three increments: a recognition pre-screen, a direct identity-load hypothesis tested via bootstrap intervals, and a phantom-brand persistence count; the six panel models are not named here.", "For the cosmetics phase the direct identity-load hypothesis is confirmed (Cell B delta of +7.12) and six off-panel brands clear the phantom-persistence threshold of six mentions." ], "bullet_provenance": "ai", "salience": 24, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 445, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "School of Visual Arts" } ], "affiliations": [ "School of Visual Arts" ] }, { "uid": "doi:10.2139/ssrn.6817598", "doi": "10.2139/ssrn.6817598", "title": "Distributing Accountability, Not Capability: Phase Separation and the LLM Workflow Quadrant in Autonomous AI Agent Architectures", "authors": [ "Tatsuya Shimomoto" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6817598", "field": "management", "role": "object", "bullets": [ "Conceptual paper on autonomous AI agent architectures in business deployments, with no empirical sample or data.", "No model is run; the paper builds a four-quadrant decomposition of business AI work and a design-versus-operation phase separation axis to locate accountability.", "Identifies the LLM Workflow Quadrant, separates principled from artificial redirect impossibility, and proposes recording a phase-crossing decision and naming a gap-bearer at deployment." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 567, "authors_detailed": [ { "name": "Tatsuya Shimomoto", "url": "https://openalex.org/A5137090800", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6782298", "doi": "10.2139/ssrn.6782298", "title": "Leadership Behaviors and Employee Engagement During Generative AI Adoption in Engineering Organizations: Applying the LAWS-AI Framework", "authors": [ "Sunil Manohar" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6782298", "field": "management", "role": "object", "bullets": [ "Conceptual paper on engineering organizations adopting generative AI, proposing a mixed-methods design with 15 to 25 interviews and a survey of at least 300 analyzed by structural equation modeling; no data yet collected.", "No language model is used by the researchers; the paper integrates transformational, situational, and adaptive leadership theories with the job demands-resources model into the proposed LAWS-AI framework.", "Advances four testable hypotheses tracing a causal chain from leadership behaviors through employee engagement to organizational performance; empirical results are not reported." ], "bullet_provenance": "ai", "salience": 31, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 831, "authors_detailed": [ { "name": "Sunil Manohar", "url": "https://openalex.org/A5091449813", "inst": "Golden Gate University" } ], "affiliations": [ "Golden Gate University" ] }, { "uid": "doi:10.2139/ssrn.6749098", "doi": "10.2139/ssrn.6749098", "title": "Governing Machines: How Platform Governance Shapes AI Agent Lead Generation", "authors": [ "Jafar Sabbah", "Oguz A Acar" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6749098", "field": "management", "role": "agent", "bullets": [ "Controlled AI-to-AI simulation of a B2B lead-generation funnel with seller and buyer agents; 10 replications spanning 160 governance runs and 2,560 dyad-level observations.", "Buyer and seller agents, model not stated, interact while the experiment manipulates four platform governance mechanisms: disclosure, autonomy, protocol structure, and reputation visibility. Outputs are treated as agent behavior, not validated against ground truth.", "High disclosure improved progression across the funnel and autonomy aided conversion, while structured protocols reduced positive responses, meeting proposals, and qualified leads." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 832, "authors_detailed": [ { "name": "Jafar Sabbah", "url": "https://openalex.org/A5132678851", "inst": "City, University of London" }, { "name": "Oguz A. Acar", "url": "https://openalex.org/A5072991153", "inst": "King's College London" } ], "affiliations": [ "City, University of London", "King's College London" ] }, { "uid": "doi:10.2139/ssrn.6839891", "doi": "10.2139/ssrn.6839891", "title": "Corporate YouTube Videos and Firm Value: Advertising or Information?", "authors": [ "SASA QIAN", "Bin Liu", "Liangbo Ma", "Niklas F. Wagner", "Jun Shen" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6839891", "field": "accounting", "role": "instrument", "bullets": [ "Corporate YouTube videos from a subsample of S&P 500 firms over 2016 to 2023, linked to firm value; the unit of observation is the firm.", "A state-of-the-art large language model, not named, classifies each video as informational or advertising; no comparison to human coding or other ground truth is reported.", "Firm value is positively associated with informational videos and unrelated to advertising videos, with the positive association stronger among firms facing greater information asymmetry." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 3, "audience": "general", "models": [], "n": 833, "authors_detailed": [ { "name": "SASA QIAN", "url": "https://openalex.org/A5137134874", "inst": "University of Wollongong" }, { "name": "Bin Liu", "url": "https://openalex.org/A5137136137", "inst": "University of Wollongong" }, { "name": "Liangbo Ma", "url": "https://openalex.org/A5005718695", "inst": "University of Wollongong" }, { "name": "Niklas F. Wagner", "url": "https://openalex.org/A5137130407", "inst": "University of Passau" }, { "name": "Jun Shen", "url": "https://openalex.org/A5031318875", "inst": "University of Wollongong" } ], "affiliations": [ "University of Wollongong", "University of Passau" ] }, { "uid": "doi:10.2139/ssrn.6836263", "doi": "10.2139/ssrn.6836263", "title": "AI-generated Video Summaries and Consumer Engagement on Digital Content Platforms: Evidence from Bilibili", "authors": [ "Yu Li", "xiaofei zhang", "Ying Wang", "Xi Chen" ], "posted": "2026-05-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6836263", "field": "management", "role": "object", "bullets": [ "Panel dataset of videos from Bilibili, a large Chinese video platform, exploiting the introduction of an AI video-summary feature; treatment and control groups balanced by coarsened exact matching.", "The generative AI summary feature is the treatment rather than a researcher tool, and the model is not stated; effects are estimated with difference-in-differences, so model validation does not apply.", "AI-generated summaries significantly raised consumer engagement, driven by an information cue effect, and the effect was stronger when summary arousal and title-summary consistency were higher." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 834, "authors_detailed": [ { "name": "Li Y", "url": "https://openalex.org/A5103672666", "inst": "Nanjing University" }, { "name": "Xi Zhang", "url": "https://openalex.org/A5100430876", "inst": "Harbin Institute of Technology" }, { "name": "Yue Wang", "url": "https://openalex.org/A5100633632", "inst": "Nanjing University" }, { "name": "Xi Chen", "url": "https://openalex.org/A5137170734", "inst": "Nanjing University" } ], "affiliations": [ "Nanjing University", "Harbin Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.6831684", "doi": "10.2139/ssrn.6831684", "title": "From Assessment Integrity to Institutional Capacity: How Business Schools Are Adapting Management Education to Generative AI", "authors": [ "Chander Carvalho", "Luis Henrique Pereira" ], "posted": "2026-05-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6831684", "field": "management", "role": "object", "bullets": [ "Twelve semi-structured interviews with academic directors and program managers at CEMS member business schools across multiple regions.", "No model is run; generative AI's disruption of curriculum, assessment, and governance is the object, analyzed qualitatively from interview evidence.", "Schools adapt through a three-layer process of redesigned assessment integrity, curriculum integration, and institutional capacity building via committees and faculty development." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 443, "authors_detailed": [ { "name": "Chander Carvalho", "url": "https://openalex.org/A5137009716", "inst": "" }, { "name": "Luís Henrique Pereira", "url": "https://openalex.org/A5102329407", "inst": "Universidade do Estado de Santa Catarina" } ], "affiliations": [ "Universidade do Estado de Santa Catarina" ] }, { "uid": "doi:10.2139/ssrn.6815378", "doi": "10.2139/ssrn.6815378", "title": "Type 2 Confirmation, Recognition Ceiling, and Phantom Brand Persistence on a Cosmetics IL-Gradient Substrate", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-05-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6815378", "field": "management", "role": "object", "bullets": [ "Twenty-four cosmetics brands across three identity-load cells (prestige, celebrity-DTC, and drugstore), sampled worldwide and scored on a locked six-LLM reference panel for AI-mediated brand retrieval.", "A six-frame query battery splits category-canonical from cultural retrieval across the six-LLM panel; the specific models are not named in this abstract, and outputs are scored under a pre-registered protocol.", "Type 2 emergence confirmed with three high-identity-load cases (Rare Beauty scored 1 canonical versus 17 cultural) plus one out-of-cell case; the recognition regime was falsified by uniform saturation." ], "bullet_provenance": "ai", "salience": 24, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 444, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "School of Visual Arts" } ], "affiliations": [ "School of Visual Arts" ] }, { "uid": "doi:10.2139/ssrn.6830565", "doi": "10.2139/ssrn.6830565", "title": "Do Generative AI Tools Enhance Students’ Learning Performance or Promote Laziness and Academic Cheating?", "authors": [ "Tin-Chun Lin" ], "posted": "2026-05-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6830565", "field": "economics", "role": "object", "bullets": [ "Field experiment in two in-person introduction to microeconomics classes taught by the same instructor, with students split into an AI-using treatment group and a no-AI control group; period and location not stated.", "Treatment students used an unnamed AI tool to help comprehend and solve homework; the model is not stated. Knowledge retention was measured with an unannounced closed-book quiz repeating earlier homework questions.", "Treatment students scored significantly lower on the quiz, and those whose scores fell 20 points or more from homework to quiz drove the effect, suggesting AI served as a shortcut." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 829, "authors_detailed": [ { "name": "Tin–Chun Lin", "url": "https://openalex.org/A5064368712", "inst": "Southeastern Louisiana University" } ], "affiliations": [ "Southeastern Louisiana University" ] }, { "uid": "doi:10.2139/ssrn.6749643", "doi": "10.2139/ssrn.6749643", "title": "The Institutional Origins of AI: Evidence from Large Language Models", "authors": [ "Purushothaman Padmanabhan" ], "posted": "2026-05-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6749643", "field": "management", "role": "object", "bullets": [ "Two experiments: 25 organizational decision scenarios put to five frontier models from four institutional origins (3,125 observations), plus six open-ended knowledge tasks to nine frontier models (162 outputs).", "The specific models are not stated beyond frontier; the study records their decisions and outputs, then analyzes divergence and embedding-level signatures tied to each producer's institutional origin, with no ground-truth check.", "Models diverged systematically on 68 percent of scenarios while holding 91.4 percent within-model consistency, and institutional signatures appeared at the embedding level but not in surface text." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 830, "authors_detailed": [ { "name": "Purushothaman Padmanabhan", "url": "https://openalex.org/A5108224483", "inst": "University of Oregon" } ], "affiliations": [ "University of Oregon" ] }, { "uid": "arxiv:2605.26508v1", "arxiv_id": "2605.26508v1", "title": "Foundations of a Time-Consistent Counterfactual Actuarial Runtime for Autonomous AI Agents", "authors": [ "Hao-Hsuan Chen" ], "posted": "2026-05-26", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.26508v1", "field": "finance", "role": "object", "bullets": [ "A theoretical paper with no empirical sample, proposing a runtime actuarial layer that prices per-action risk for autonomous AI agents against a contractually fixed safe default.", "No language model is applied; AI agents are the object, their side-effect-bearing actions assigned a counterfactual insurance toll within an explicit underwriting boundary.", "The paper proves four structural results, including a within-boundary no-splitting property and a runtime gating theorem giving an executed-action budget guarantee." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1112, "authors_detailed": [ { "name": "Hao-Hsuan Chen", "url": "https://openalex.org/A5136594934", "inst": "National Chengchi University" } ], "affiliations": [ "National Chengchi University" ] }, { "uid": "arxiv:2605.26074v1", "arxiv_id": "2605.26074v1", "title": "StakeBench: Evaluating Language Understanding Grounded in Market Commitment", "authors": [ "Yunhua Pei", "Jingyu Hu", "Yiwei Shi", "Hongnan Ma", "Weiru Liu", "John Cartlidge" ], "posted": "2026-05-25", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.26074v1", "field": "finance", "role": "method", "bullets": [ "560,876 comments from 2,261 resolved Polymarket and Manifold prediction markets, linked to verified position, action, and market-odds records.", "Fifteen LLMs, families not named, evaluated on four commitment-detection tasks with labels derived from observable market behavior rather than human annotation.", "Models partially recover position sides at directed accuracy 0.506 to 0.599 but fail on later tasks; model scale and finance tuning do not help." ], "bullet_provenance": "ai", "validated": true, "validation_note": "market-behavior labels, directed accuracy reported", "salience": 60, "edition": 3, "audience": "technical", "models": [], "n": 442, "authors_detailed": [ { "name": "Yunhua Pei", "url": "https://openalex.org/A5021791813", "inst": "University of Bristol" }, { "name": "Jingyu Hu", "url": "https://openalex.org/A5137054348", "inst": "" }, { "name": "Yiwei Shi", "url": "https://openalex.org/A5137004223", "inst": "" }, { "name": "Hongnan Ma", "url": "https://openalex.org/A5137048751", "inst": "" }, { "name": "Weiru Liu", "url": "https://openalex.org/A5002349071", "inst": "Guangzhou University of Chinese Medicine" }, { "name": "John Cartlidge", "url": "https://openalex.org/A5125755561", "inst": "University of Bristol" } ], "affiliations": [ "University of Bristol", "Guangzhou University of Chinese Medicine" ] }, { "uid": "doi:10.2139/ssrn.6744618", "doi": "10.2139/ssrn.6744618", "title": "The Staircase Methodology for AI Adoption in US Small and Mid-Sized Businesses", "authors": [ "Alex Cherednyk" ], "posted": "2026-05-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6744618", "field": "management", "role": "object", "bullets": [ "Conceptual, prescriptive paper on AI adoption by US small and mid-sized businesses, which account for roughly 43.5 percent of GDP and 46 percent of private-sector employment.", "No model is run; the paper synthesizes adoption evidence, citing that 95 percent of generative AI pilots show no profit-and-loss impact and about 80 percent of AI projects fail.", "Introduces the Staircase Methodology, a two-phase, six-step phase-gated framework with use-case scoring and a Risk-times-Stickiness reversibility check, illustrated with a single small-business case." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 825, "authors_detailed": [ { "name": "Alex Cherednyk", "url": "https://openalex.org/A5136963416", "inst": "Innovative Design Labs (United States)" } ], "affiliations": [ "Innovative Design Labs (United States)" ] }, { "uid": "doi:10.2139/ssrn.6827454", "doi": "10.2139/ssrn.6827454", "title": "A Reproducible Web-Scraping Pipeline for Collecting Geolocated German Online Job Advertisements at Scale", "authors": [ "Kerstin Schaefer", "Matthias Kapa" ], "posted": "2026-05-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6827454", "field": "economics", "role": "method", "bullets": [ "342,452 unique online job advertisements scraped from the German portal StepStone between April and July 2023, geolocated for labour-market skill-demand analysis.", "A locally run large language model, AI4Privacy, censors personally identifiable information for GDPR compliance in a Scrapy and Playwright pipeline; the censoring accuracy itself is not benchmarked.", "Single-portal coverage reaches 79 percent of German municipalities versus Lightcast's 80 percent, comparable spatially but smaller in volume and cross-portal reach, limiting small-sector analysis." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "validation_note": "coverage checked vs Lightcast; PII model not benchmarked", "salience": 42, "edition": 3, "audience": "technical", "n": 826, "authors_detailed": [ { "name": "Kerstin Schaefer", "url": "https://openalex.org/A5136971232", "inst": "Leibniz University Hannover" }, { "name": "Matthias Kapa", "url": "https://openalex.org/A5057668915", "inst": "University of Kassel" } ], "affiliations": [ "Leibniz University Hannover", "University of Kassel" ] }, { "uid": "doi:10.2139/ssrn.6745059", "doi": "10.2139/ssrn.6745059", "title": "Pioneering a R-Square AI Future from the Global South", "authors": [ "Dr. Rachel, Wei Gee Ooi", "Deny Rahardjo" ], "posted": "2026-05-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6745059", "field": "management", "role": "object", "bullets": [ "Systematic literature review of 58 sources plus qualitative analysis of 27 artificial-intelligence initiatives across six ASEAN nations in the Global South.", "No model is run; the paper studies AI development paradigms and ethics scholarship, finding roughly 85 percent of AI ethics research is Western-focused.", "Proposes RSquare (Regenerative and Responsible) AI and a Regenerative AI Leadership Flywheel grounded in polycentric governance, regenerative capital, and community-embedded living labs as an ASEAN alternative." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 827, "authors_detailed": [ { "name": "Wei Gee Ooi Dr. Rachel", "url": "https://openalex.org/A5136941161", "inst": "Antioch University" }, { "name": "Deny Rahardjo", "url": "https://openalex.org/A5120290713", "inst": "Graduate School USA" } ], "affiliations": [ "Antioch University", "Graduate School USA" ] }, { "uid": "doi:10.2139/ssrn.6826891", "doi": "10.2139/ssrn.6826891", "title": "A Bayesian Probabilistic Decision Support System for Cloud Cost Governance in AI-Augmented Agile Software Development", "authors": [ "Elena Udrescu", "Ana-Maria Suduc", "Alexandru Udrescu", "Mihai Bizoi" ], "posted": "2026-05-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6826891", "field": "management", "role": "object", "bullets": [ "Simulation and practitioner study of cloud-cost governance for LLM-powered coding assistants in Agile software development, using a Monte Carlo of 10,000 sprints and a 13-practitioner face-validity study.", "The authors run no LLM; a Bayesian eight-node directed acyclic graph estimates project-health states from Azure OpenAI pricing and GitClear code data to detect a Phantom ROI condition.", "The AI-Efficiency Ratio is the strongest governance predictor (Spearman rho +0.728), and Phantom ROI detection separates health states by 22.95 points at about 1.11 percent prevalence." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "general", "validated": null, "n": 828, "authors_detailed": [ { "name": "Elena Udrescu", "url": "https://openalex.org/A5134882666", "inst": "Valahia University of Targoviste" }, { "name": "Ana-Maria Suduc", "url": "https://openalex.org/A5012181996", "inst": "Valahia University of Targoviste" }, { "name": "Alexandru Udrescu", "url": "https://openalex.org/A5126640817", "inst": "Valahia University of Targoviste" }, { "name": "Mihai Bîzoi", "url": "https://openalex.org/A5039104186", "inst": "Valahia University of Targoviste" } ], "affiliations": [ "Valahia University of Targoviste" ] }, { "uid": "doi:10.2139/ssrn.6740280", "doi": "10.2139/ssrn.6740280", "title": "Design of Hybrid Human-AI Agent Organizations: A Mathematical Framework for Organizational Dynamics", "authors": [ "Usman Zafar" ], "posted": "2026-05-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6740280", "field": "management", "role": "object", "bullets": [ "A theoretical paper on hybrid human-AI organizational design, with no empirical sample, embedding classical and AI-native organizational dimensions into a 17-dimensional latent space.", "No language model is applied; AI agents are treated as organizational actors within a probabilistic framework using controlled stochastic differential equations and information geometry.", "The framework is claimed to guarantee convergence to a governance-optimal equilibrium; no empirical estimates or magnitudes are reported." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1108, "authors_detailed": [ { "name": "Usman Zafar", "url": "https://openalex.org/A5136983589", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6827163", "doi": "10.2139/ssrn.6827163", "title": "The Long Shadow of a Sudden River", "authors": [ "Luke Heath Milsom" ], "posted": "2026-05-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6827163", "field": "economics", "role": "instrument", "bullets": [ "Economic history of Bruges and the Low Countries around the Zwin river, open from 1134 and silted by 1500, drawing on 120 qualitative historical sources.", "A retrieval-augmented generation large language model, family not stated, extracts merchant data from the historical sources; no validation against hand coding is reported.", "While navigable the Zwin more than tripled Bruges's population and shifted economic activity across the Low Countries for centuries after it silted up." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 1109, "authors_detailed": [ { "name": "Luke Milsom", "url": "https://openalex.org/A5062739965", "inst": "KU Leuven" } ], "affiliations": [ "KU Leuven" ] }, { "uid": "doi:10.2139/ssrn.6828987", "doi": "10.2139/ssrn.6828987", "title": "A Retrieval-Augmented Generation System for Automated Unit Price Analysis in Construction: Experimental Evaluation of Accuracy, Consistency, and Efficiency", "authors": [ "Alejandro Del Castillo Urquizo", "Alonso Andre Concha Jaramillo", "Jose Luis Wong Villanueva" ], "posted": "2026-05-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6828987", "field": "management", "role": "method", "bullets": [ "A between-subjects experiment with 10 civil engineers each estimating 36 cost items from a real public infrastructure project, drawing on 37,000 unit price analyses from 63 projects.", "A retrieval-augmented generation system pairing semantic vector search with an unnamed large language model produces cost estimates, compared with manual estimation on accuracy, consistency and time.", "Manual estimation was more accurate (MAPE 5.51% vs 9.85%), but the AI approach cut development time 44% and roughly halved the coefficient of variation." ], "bullet_provenance": "ai", "validated": true, "validation_note": "MAPE against benchmark project costs", "salience": 47, "edition": 3, "audience": "general", "models": [], "n": 1110, "authors_detailed": [ { "name": "Alejandro Del Castillo Urquizo", "url": "https://openalex.org/A5136948516", "inst": "" }, { "name": "Alonso Andre Concha Jaramillo", "url": "https://openalex.org/A5136958923", "inst": "" }, { "name": "José Luis WONG VILLANUEVA", "url": "https://openalex.org/A5131042959", "inst": "" } ] }, { "uid": "arxiv:2605.25894v1", "arxiv_id": "2605.25894v1", "title": "Predicting Stock Price Direction on Earnings Announcement Days using Multi-modal Deep Learning", "authors": [ "Manuel Noseda", "Nathan Soldati", "Marco Paina" ], "posted": "2026-05-25", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.25894v1", "field": "finance", "role": "instrument", "bullets": [ "Equity earnings announcement days, geography and period not stated, with 15 fundamental metrics, three technical indicators and news sentiment as features, the firm announcement the unit.", "FinBERT scores pre-announcement financial news sentiment, feeding LSTM and Transformer classifiers benchmarked against logistic regression on directional prediction, measured by macro F1.", "The Transformer reached a higher macro F1 with better sensitivity to volatile moves, and adding news sentiment consistently improved directional prediction." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "macro F1 on directional prediction", "salience": 44, "edition": 3, "audience": "technical", "n": 1111, "authors_detailed": [ { "name": "Manuel Noseda", "url": "https://openalex.org/A5119812166", "inst": "Consejo Nacional de Investigaciones Científicas y Técnicas" }, { "name": "Nathan Soldati", "url": "https://openalex.org/A5137046731", "inst": "" }, { "name": "Marco Paina", "url": "https://openalex.org/A5116630755", "inst": "Max Planck Institute for Biogeochemistry" } ], "affiliations": [ "Consejo Nacional de Investigaciones Científicas y Técnicas", "Max Planck Institute for Biogeochemistry" ] }, { "uid": "doi:10.2139/ssrn.6788658", "doi": "10.2139/ssrn.6788658", "title": "A Bitter Lesson for Retail Demand Forecasting: Evidence from Fine-Tuning Foundation Models", "authors": [ "Yi Sui", "Chenjie Xiao", "Linwei Xin", "Donghai Huang", "Lei Cao" ], "posted": "2026-05-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6788658", "field": "management", "role": "method", "bullets": [ "Large-scale retail demand data from Alibaba covering domestic and international products, used to evaluate time-series foundation models against the firm's existing deep learning production forecasting system.", "Amazon's Chronos-2 and two other time-series foundation models forecast demand in zero-shot and fine-tuned settings, benchmarked against the production system on forecast accuracy.", "Chronos-2 beat the production baseline by 3.5% for domestic and 5.4% for international products, consistently across time periods and product segments." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "forecast accuracy vs Alibaba production system", "salience": 68, "edition": 3, "audience": "technical", "n": 1106, "authors_detailed": [ { "name": "Yi Sui", "url": "https://openalex.org/A5103239458", "inst": "Alibaba Group (United States)" }, { "name": "Chenjie Xiao", "url": "https://openalex.org/A5136884972", "inst": "Alibaba Group (United States)" }, { "name": "Linwei Xin", "url": "https://openalex.org/A5087606614", "inst": "Cornell University" }, { "name": "Donghai Huang", "url": "https://openalex.org/A5136871022", "inst": "Alibaba Group (United States)" }, { "name": "Lei Cao", "url": "https://openalex.org/A5049926126", "inst": "Alibaba Group (United States)" } ], "affiliations": [ "Cornell University", "Alibaba Group (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6784680", "doi": "10.2139/ssrn.6784680", "title": "AI in Residential Real Estate: Efficiency Gains and Equity Gaps", "authors": [ "Cayman Seagraves", "Michael Seiler", "Stace Sirmans" ], "posted": "2026-05-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6784680", "field": "finance", "role": "object", "bullets": [ "A review classifying roughly 145 papers across six residential real estate domains and four AI mechanism categories, organised through an equity-efficiency framework.", "No model is applied; the authors survey how AI affects prediction, screening, matching, pricing and generation and where peer-reviewed equity evidence exists.", "Twenty-two of twenty-four cells lack peer-reviewed equity evidence, though industry deploys AI in eighteen and federal regulation is active in nine." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1107, "authors_detailed": [ { "name": "Cayman Seagraves", "url": "https://openalex.org/A5136882921", "inst": "Collins College" }, { "name": "Michael Seiler", "url": "https://openalex.org/A5136868321", "inst": "William & Mary" }, { "name": "Stace Sirmans", "url": "https://openalex.org/A5000933071", "inst": "Auburn University" } ], "affiliations": [ "Collins College", "William & Mary", "Auburn University" ] }, { "uid": "doi:10.2139/ssrn.6783159", "doi": "10.2139/ssrn.6783159", "title": "Interpreting Sensory Experience in Customer-generated Text: A Large Language Model (LLM) Approach", "authors": [ "Tianyi Zhang", "Jonas Schöne", "Zhongtian Ji", "Carlos Velasco", "Charles Spence" ], "posted": "2026-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6783159", "field": "management", "role": "instrument", "bullets": [ "82,350 online restaurant reviews, used to study how consumers describe sensory experiences in text and how those descriptions relate to product ratings.", "An unnamed large language model infers which sensory modalities each review invokes given context, validated against human coders and a lexicon method, though no numeric agreement figure is reported.", "Sensory modalities differ in their association with ratings in isolation, but those differences largely disappear once evaluative context is accounted for, with some senses tied to positive and others to negative evaluations." ], "bullet_provenance": "ai", "validated": false, "validation_note": "human coders and lexicon, no agreement figure reported", "salience": 52, "edition": 3, "audience": "general", "models": [], "n": 231, "authors_detailed": [ { "name": "Tianyi Zhang", "url": "https://openalex.org/A5136816604", "inst": "University of Oxford" }, { "name": "Jonas Schöne", "url": "https://openalex.org/A5077270855", "inst": "Stanford University" }, { "name": "Zhongtian Ji", "url": "https://openalex.org/A5036815547", "inst": "Nanjing University of Science and Technology" }, { "name": "Carlos Velasco", "url": "https://openalex.org/A5125934542", "inst": "BI Norwegian Business School" }, { "name": "Charles Spence", "url": "https://openalex.org/A5136820615", "inst": "University of Oxford" } ], "affiliations": [ "Stanford University", "University of Oxford", "Nanjing University of Science and Technology", "BI Norwegian Business School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6782581", "doi": "10.2139/ssrn.6782581", "title": "AI-Agent Framework for Sales Risk Identification and Response in Live-Commerce Environments", "authors": [ "Jiho Kim", "Sangmin Park", "Yooshin Kwon", "Heongi Kim", "Hyunjae Cheon" ], "posted": "2026-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6782581", "field": "management", "role": "instrument", "bullets": [ "Customer evaluations from e-commerce and live-commerce platforms, used to build a proactive product-risk identification and sales-response system; sample size, period, and geography not stated.", "Sentence-Transformers embeddings with HDBSCAN clustering group feedback, then a Llama model generates defect categories and issue descriptions, with aspect-level sentiment and a vote-based risk score; no validation against ground truth reported.", "The framework outputs defect categories, risk scores, and objection-response guidelines for live broadcasts, demonstrating feasibility rather than reporting accuracy or business-outcome magnitudes." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 35, "edition": 3, "audience": "technical", "n": 232, "authors_detailed": [ { "name": "Jae‐Joong Kim", "url": "https://openalex.org/A5021872568", "inst": "Chung-Ang University" }, { "name": "Sang‐Min Park", "url": "https://openalex.org/A5100671418", "inst": "Chung-Ang University" }, { "name": "Yooshin Kwon", "url": "https://openalex.org/A5136841749", "inst": "Chung-Ang University" }, { "name": "Heongi Kim", "url": "https://openalex.org/A5136822172", "inst": "GS Engineering (United States)" }, { "name": "Hyunjae Cheon", "url": "https://openalex.org/A5136821775", "inst": "Korea University" } ], "affiliations": [ "Chung-Ang University", "GS Engineering (United States)", "Korea University" ] }, { "uid": "doi:10.2139/ssrn.6739060", "doi": "10.2139/ssrn.6739060", "title": "The Future of Software Development: Navigating the Agentic Revolution and the Training Gap Paradox", "authors": [ "Guillermo Power" ], "posted": "2026-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6739060", "field": "management", "role": "object", "bullets": [ "Conceptual essay on how agentic AI coding tools reshape the software engineering workforce, citing global estimates that 41 percent of code is AI-generated and 97 percent of developers use AI, with no original data.", "No model is applied by the authors; agentic LLM coding tools such as Claude Code are the object, and the essay argues hallucinations and contextual errors keep human oversight necessary.", "Argues that automating entry-level tasks removes the on-ramps that build engineering judgment, creating a Junior Gap in the talent pipeline, and calls for new training models." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 25, "edition": 3, "audience": "general", "validated": null, "n": 233, "authors_detailed": [ { "name": "Guillermo Power", "url": "https://openalex.org/A5114253455", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6740079", "doi": "10.2139/ssrn.6740079", "title": "Brand Erasure: A Research Agenda for Brand Theory in Agentic AI Markets", "authors": [ "Marcos Guimaraes Figueira" ], "posted": "2026-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6740079", "field": "management", "role": "object", "bullets": [ "Conceptual marketing article with no empirical sample, focused on brand theory in markets mediated by generative AI assistants and autonomous agents.", "No model is run by the author; the paper theorises how foundation-model assistants can omit, paraphrase, or substitute brands at the point of action.", "Advances brand erasure as a research construct, separates it from dilution and disintermediation, and proposes five research questions extending brand equity into model latent space." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 566, "authors_detailed": [ { "name": "Marcos Guimarães Figueira", "url": "https://openalex.org/A5135658209", "inst": "Design Intelligence (United States)" } ], "affiliations": [ "Design Intelligence (United States)" ] }, { "uid": "doi:10.2139/ssrn.6739725", "doi": "10.2139/ssrn.6739725", "title": "Brand Architecture as a Moderator of AI-mediated Retrieval Aggregation Logic for Corporate Portfolios in the AI Availability Framework", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6739725", "field": "management", "role": "object", "bullets": [ "Conceptual paper on multi-brand corporate portfolios, extending the AI Availability framework across the four Aaker brand-architecture types: branded house, sub-brand, endorsed brand, and house of brands.", "No model is run; the paper studies how generative AI surfaces and represents brands in category-query responses and formalizes aggregation of brand-level visibility to the corporate level.", "Develops a coupling-coefficient formalism and hypothesizes branded-house structures concentrate AI-mediation risk while house-of-brands structures distribute it; the matched-pair empirical test is registered as future work." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 823, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "School of Visual Arts" } ], "affiliations": [ "School of Visual Arts" ] }, { "uid": "arxiv:2605.24490v1", "arxiv_id": "2605.24490v1", "title": "Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems", "authors": [ "Yunhua Pei", "Zerui Ge", "Jin Zheng", "John Cartlidge" ], "posted": "2026-05-23", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.24490v1", "field": "finance", "role": "agent", "bullets": [ "Backtest over 1,037 trading days across 13 crypto assets and five random seeds, testing a cooperative three-agent LLM system for portfolio allocation.", "Market Regime Council combines specialist LLM-agent outputs using exact Shapley credit weights, a Bayesian mixture for early periods, and regime-dependent multipliers; the model family is not stated.", "Achieves a Sharpe ratio of 1.51 and cumulative return of 440.1 percent, ranking first on cumulative return, Sharpe, and information ratio among active baselines." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 824, "authors_detailed": [ { "name": "Yunhua Pei", "url": "https://openalex.org/A5021791813", "inst": "University of Bristol" }, { "name": "Zerui Ge", "url": "https://openalex.org/A5064290313", "inst": "National University of Singapore" }, { "name": "Jin Zheng", "url": "https://openalex.org/A5100586108", "inst": "Union Hospital" }, { "name": "John Cartlidge", "url": "https://openalex.org/A5125755561", "inst": "University of Bristol" } ], "affiliations": [ "University of Bristol", "National University of Singapore" ] }, { "uid": "doi:10.2139/ssrn.6816152", "doi": "10.2139/ssrn.6816152", "title": "How Patient Capital Supports AI-Native Entrepreneurship: An Empirical Study of OPC Incubation by China’s Government Guidance Funds", "authors": [ "Yifan Zhang", "shuai ye" ], "posted": "2026-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6816152", "field": "management", "role": "object", "bullets": [ "Survey data from 327 AI-native one-person-company projects in Jiangsu Province, China, with Suzhou Industrial Park as an empirical case under government guidance fund incubation.", "No language model is applied by the authors; AI agents are the studied object, and compliance and project quality are analysed with a logistic regression embedded with legal boundaries.", "The four-dimensional nested governance model is judged feasible for screening and incubating high-quality AI-native projects, though effect magnitudes are not reported." ], "bullet_provenance": "ai", "salience": 37, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1105, "authors_detailed": [ { "name": "Yifan Zhang", "url": "https://openalex.org/A5136852855", "inst": "" }, { "name": "shuai ye", "url": "https://openalex.org/A5136854014", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6772819", "doi": "10.2139/ssrn.6772819", "title": "Look-Ahead Bias in Financial Forecasts Generated by Large Language Models", "authors": [ "Chuan Liang" ], "posted": "2026-05-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6772819", "field": "finance", "role": "method", "bullets": [ "GPT-4 forecasts of daily index levels, monthly stock prices, and quarterly earnings, split by whether targets fall before or after the September 30, 2021 knowledge cutoff.", "GPT-4 generates the forecasts; the design compares absolute forecast errors for pre- versus post-cutoff targets and benchmarks accuracy against analyst forecasts.", "Pre-cutoff errors run about 18% lower for daily indices, concentrated in the S&P 500, 16% lower for monthly prices, and 11% lower for quarterly earnings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "absolute forecast errors vs realized values and analyst benchmark", "salience": 62, "edition": 3, "audience": "general", "n": 105, "authors_detailed": [ { "name": "Chuan Liang", "url": "https://openalex.org/A5136729552", "inst": "Texas Tech University" } ], "affiliations": [ "Texas Tech University" ] }, { "uid": "doi:10.2139/ssrn.6813758", "doi": "10.2139/ssrn.6813758", "title": "Data Overlap, Not Creative Reasoning: Understanding AI Competency in Accounting Education through the CICPA Examination", "authors": [ "Mengmeng Yu", "Jianning Xu", "Jiading Zhu" ], "posted": "2026-05-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6813758", "field": "accounting", "role": "object", "bullets": [ "Chinese Institute of Certified Public Accountants examination used as the test setting, with tasks grouped by complexity and analyzed through panel data regression.", "Language models, not named, answer CICPA tasks; the study tests whether proficiency comes from creative reasoning or from overlap between training data and task content.", "Data overlap, not creative reasoning, is the primary driver of current AI proficiency on accounting tasks once task complexity is accounted for." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 154, "authors_detailed": [ { "name": "Mengmeng Yu", "url": "https://openalex.org/A5136798883", "inst": "Shanghai University of Finance and Economics" }, { "name": "Jianning Xu", "url": "https://openalex.org/A5136781017", "inst": "Shanghai University of Finance and Economics" }, { "name": "Jiading Zhu", "url": "https://openalex.org/A5136771070", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "Shanghai University of Finance and Economics" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6811763", "doi": "10.2139/ssrn.6811763", "title": "Between Inquiry and Substitution: The Socratic Dilemma of Generative AI in Management Education Assessment", "authors": [ "Vishal Rana", "Rawan Nimri", "Mona Ji Hyun Yang" ], "posted": "2026-05-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6811763", "field": "management", "role": "object", "bullets": [ "109 undergraduate reflections from an Australian Bachelor of Business unit, analyzed through reflexive thematic analysis.", "Generative AI, not named, is the object of study rather than a research tool, examined through students' written accounts of interacting with it.", "Students experienced epistemic tension as productive and navigated it through four interaction modes; educational value depended on the relational quality of student-AI interaction." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 439, "authors_detailed": [ { "name": "Vishal Rana", "url": "https://openalex.org/A5136652051", "inst": "" }, { "name": "Rawan Nimri", "url": "https://openalex.org/A5136613529", "inst": "" }, { "name": "Mona Ji Hyun Yang", "url": "https://openalex.org/A5049324797", "inst": "Griffith University" } ], "affiliations": [ "Griffith University" ] }, { "uid": "doi:10.2139/ssrn.6773678", "doi": "10.2139/ssrn.6773678", "title": "Entry Barriers to the Labor Market in the Era of Generative Artificial Intelligence: A Critical Review and a Framework for Cross-Country Heterogeneity (2022-2026)", "authors": [ "Jose David Gonzalez" ], "posted": "2026-05-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6773678", "field": "economics", "role": "object", "bullets": [ "Critical narrative literature review of empirical evidence from 2022 through early 2026 across technology, finance, consulting, and administration labor markets.", "No model is run; generative AI is the object, and the paper synthesizes effect sizes on hiring, wages, and job composition by margin of adjustment.", "Evidence shows large negative effects on junior hiring and wages alongside large positive within-firm productivity effects, with strong US effects but null effects in Denmark." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 440, "authors_detailed": [ { "name": "Jose David Gonzalez", "url": "https://openalex.org/A5136760335", "inst": "Universidad Internacional" } ], "affiliations": [ "Universidad Internacional" ] }, { "uid": "doi:10.2139/ssrn.6814012", "doi": "10.2139/ssrn.6814012", "title": "Generative Artificial Intelligence and Household Risky Financial Market Participation: Evidence from China's Frontier Regions", "authors": [ "Wucheng Chi", "Runze Gong" ], "posted": "2026-05-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6814012", "field": "finance", "role": "object", "bullets": [ "Household micro-data from the 2025 Frontier Economics Database Household Survey covering China's frontier regions.", "No model is run by the researchers; generative AI adoption enters as the explanatory variable, measured from the survey. Generative AI is the object.", "Generative AI raises both the probability of participating in risky financial markets and the share of risky assets held, mainly by easing information and borrowing constraints." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 441, "authors_detailed": [ { "name": "Wucheng Chi", "url": "https://openalex.org/A5136778462", "inst": "" }, { "name": "Runze Gong", "url": "https://openalex.org/A5107513608", "inst": "Guangxi University" } ], "affiliations": [ "Guangxi University" ] }, { "uid": "doi:10.2139/ssrn.6811562", "doi": "10.2139/ssrn.6811562", "title": "The use of GenAI in team science: Exploring the role of team size, task independence and geographical dispersion", "authors": [ "Jeongwon Choi", "Anna-Lena Rüland", "Yao Qu", "Madita Amoneit" ], "posted": "2026-05-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6811562", "field": "management", "role": "object", "bullets": [ "Large-scale international survey of researchers' self-reported use of generative AI in scientific publications, with the researcher or research team as the unit of observation.", "No language model is applied by the authors; GenAI adoption is the outcome, regressed on team size, task independence, geographical dispersion, and their interactions.", "Task independence and geographical dispersion alone do not predict adoption, but both interact with team size so that larger teams are more responsive to these structural conditions." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 819, "authors_detailed": [ { "name": "Jeongwon Choi", "url": "https://openalex.org/A5083362452", "inst": "Dongguk University" }, { "name": "Anna‐Lena Rüland", "url": "https://openalex.org/A5001715638", "inst": "Leiden University" }, { "name": "Yao Qu", "url": "https://openalex.org/A5101245941", "inst": "Nanyang Technological University" }, { "name": "Madita Amoneit", "url": "https://openalex.org/A5093707994", "inst": "Berlin School of Economics and Law" } ], "affiliations": [ "Dongguk University", "Leiden University", "Nanyang Technological University", "Berlin School of Economics and Law" ] }, { "uid": "doi:10.2139/ssrn.6774983", "doi": "10.2139/ssrn.6774983", "title": "A.C.E. — AI Concierge & Engine An AI-Driven Financial Intelligence Framework for U.S. SME Restaurant Resilience", "authors": [ "Arthunya Kanoklertwongse" ], "posted": "2026-05-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6774983", "field": "management", "role": "instrument", "bullets": [ "Conceptual framework paper for U.S. SME restaurant operators, with no empirical sample, contextualized by the industry's $1.1 trillion in sales and 15.5 million employees.", "Proposes generative AI and machine learning integrated into Toast and Clover POS systems for OCR invoice processing, dynamic menu pricing, and predictive labor scheduling; model not stated and no validation reported.", "Targets full elimination of manual invoice entry and 15-plus fewer administrative hours per location weekly, presented as design goals rather than measured outcomes." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 820, "authors_detailed": [ { "name": "Arthunya Kanoklertwongse", "url": "https://openalex.org/A5133281976", "inst": "Wycliffe College" } ], "affiliations": [ "Wycliffe College" ] }, { "uid": "doi:10.2139/ssrn.6736141", "doi": "10.2139/ssrn.6736141", "title": "Cryptographic Custody Primitives as the Missing Assurance Layer in Agentic Infrastructure for Physical Goods", "authors": [ "Adam Roorda", "Andrew Rymer" ], "posted": "2026-05-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6736141", "field": "management", "role": "object", "bullets": [ "Conceptual design-research and analytic-review paper surveying agentic commerce protocols launched through April 2026 and the improvised custody stack for physical goods, using the AOG Technics counterfeit-parts case.", "No language model is run; the paper argues autonomous AI agents transacting at machine speed remove the human handoff that previously assured a physical object's identity and paperwork.", "Proposes a cryptographic, object-bound custody layer with ten architectural requirements, a threat model, and a twelve-question research agenda, offering no field measurements or product endorsement." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 821, "authors_detailed": [ { "name": "Adam Roorda", "url": "https://openalex.org/A5094274202", "inst": "University Medical Center Groningen" }, { "name": "Andrew Rymer", "url": "https://openalex.org/A5136736674", "inst": "" } ], "affiliations": [ "University Medical Center Groningen" ] }, { "uid": "doi:10.2139/ssrn.6776519", "doi": "10.2139/ssrn.6776519", "title": "Narrating Innovation in the Post-plausibility Era: Generative AI, Multimodal Pre-enactment, and the Case of Nikola Corporation", "authors": [ "Marius Born" ], "posted": "2026-05-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6776519", "field": "management", "role": "object", "bullets": [ "Conceptual case study of Nikola Corporation's rise and fall, examining multimodal pre-enactment practices in strategic investor communication and their role in building market value.", "No model is run; the paper theorizes how generative AI, by pushing the marginal cost of plausible multimodal content toward zero, reshapes creative labor in professional communication.", "Argues that automating persuasion shifts professional competence toward evaluative and ethical judgment and proposes a multidimensional framework for verifying promissory narratives through physical evidence." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 822, "authors_detailed": [ { "name": "Marius Born", "url": "https://openalex.org/A5059040558", "inst": "ZHAW Zurich University of Applied Sciences" } ], "affiliations": [ "ZHAW Zurich University of Applied Sciences" ] }, { "uid": "doi:10.2139/ssrn.6793998", "doi": "10.2139/ssrn.6793998", "title": "UG-CPPO: Uncertainty-Gated LLM Infusion for Risk-Sensitive Reinforcement Learning Trading Agents", "authors": [ "Grace Esther DONG" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6793998", "field": "finance", "role": "method", "bullets": [ "FNSPID news dataset with 28,502 ticker-date signals, ten Nasdaq stocks traded 2019 to 2023, evaluated across ten random seeds and 500,000 training steps.", "OpenAI gpt-4o-mini scores each news article under five diverse prompts, and the response standard deviation gates whether the signal reaches the reinforcement-learning trader; no accuracy check against ground truth is reported.", "The gate activates 34.2 percent of the time and uncertainty runs higher in the 2022 bear market than in 2019, but 36 percent cumulative return does not beat the 62 percent CPPO baseline." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": false, "salience": 38, "edition": 3, "audience": "technical", "n": 151, "authors_detailed": [ { "name": "Grace Esther DONG", "url": "https://openalex.org/A5136698264", "inst": "Givaudan (France)" } ], "affiliations": [ "Givaudan (France)" ] }, { "uid": "doi:10.2139/ssrn.6804059", "doi": "10.2139/ssrn.6804059", "title": "Between Perceived Fairness and Resignation: Examining Machine Heuristics, Privacy Cynicism and Self-Disclosure in Generative AI", "authors": [ "Fan Liang", "Christoph Lutz" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6804059", "field": "management", "role": "object", "bullets": [ "Two online surveys of generative AI users in China and the United States, combined sample of 1,632 respondents, cross-national comparison.", "No model is run by the researchers; ChatGPT and DeepSeek are the studied systems, and the survey measures machine heuristics, privacy concerns, cynicism and self-disclosure.", "Chinese respondents disclose more while US respondents report more privacy concern and mistrust; perceived fairness and accuracy link to lower concern, and concern raises cynicism." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 152, "authors_detailed": [ { "name": "Liang Fan", "url": "https://openalex.org/A5136719471", "inst": "" }, { "name": "Christoph Lutz", "url": "https://openalex.org/A5136675776", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6804466", "doi": "10.2139/ssrn.6804466", "title": "The Price of Fiscal Confusion: Evidence from U.S. Treasury Communication", "authors": [ "Yiting Guo", "Zeju Zhu" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6804466", "field": "economics", "role": "instrument", "bullets": [ "11,691 U.S. Department of the Treasury press releases from 1996 to 2026, used to build a high-frequency multidimensional measure of fiscal posture.", "Large language models, not named, extract fiscal posture dimensions from the releases; the abstract reports no validation of the extraction against hand coding or a benchmark.", "State-dependent local projections show fiscal erraticism suppressed output and employment during the crisis and zero lower bound era, but fed inflation and compressed term premia during COVID." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 153, "authors_detailed": [ { "name": "Yiting Guo", "url": "https://openalex.org/A5136651112", "inst": "Johns Hopkins University" }, { "name": "Zeju Zhu", "url": "https://openalex.org/A5019001067", "inst": "George Washington University" } ], "affiliations": [ "Johns Hopkins University", "George Washington University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2605.23007v1", "arxiv_id": "2605.23007v1", "title": "MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models", "authors": [ "Yurii Kvasiuk", "Tianyi Li", "Owen Colegrove", "Moritz Münchmeyer" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.23007v1", "field": "finance", "role": "method", "bullets": [ "Bitcoin algorithmic trading studied in a simulation and backtesting setup; tasks include evolving signal feature sets, optimizing individual strategy components, and jointly evolving the feature pipeline with the execution strategy. Period and data source not stated.", "MadEvolve, an LLM-driven evolutionary optimization framework modeled on AlphaEvolve, evolves strategy components; the driving model is not stated, results are compared against Claude Code agentic search, and p-hacking probabilities are assessed.", "Reports improvements on all tasks considered in the backtest, with no effect magnitudes given, and argues agentic and evolutionary AI methods are useful for algorithmic trading and alpha generation." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 48, "edition": 3, "audience": "technical", "validated": null, "n": 185, "authors_detailed": [ { "name": "Yurii Kvasiuk", "url": "https://openalex.org/A5136968941", "inst": "" }, { "name": "Tianyi Li", "url": "https://openalex.org/A5136901875", "inst": "Argonne National Laboratory" }, { "name": "Owen Colegrove", "url": "https://openalex.org/A5061093171", "inst": "University of California, Santa Barbara" }, { "name": "Moritz Münchmeyer", "url": "https://openalex.org/A5136942660", "inst": "" } ], "affiliations": [ "Argonne National Laboratory", "University of California, Santa Barbara" ] }, { "uid": "doi:10.2139/ssrn.6800890", "doi": "10.2139/ssrn.6800890", "title": "The Cognitive Lean Loop: Accelerating Entrepreneurial Learning and Development with Human-Centered Artificial Intelligence (HAI)", "authors": [ "Carlton L. Robinson", "Taylor Frances Drury" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6800890", "field": "management", "role": "instrument", "bullets": [ "Case study of 11 entrepreneurs from the Jax Bridges program filtered through an 8-4-2 goal funnel; time period and location are not stated.", "An Interviewer LLM trained on over 1,000 past coaching interactions runs intake and a Translator LLM synthesises transcripts into business model canvases; model family not stated and no accuracy check is reported.", "Reports a 95 percent acceleration in learning velocity relative to the 75-hour human-only intake process, with participants and facilitators reporting perceived value." ], "bullet_provenance": "ai", "validated": false, "salience": 35, "edition": 3, "audience": "general", "models": [], "n": 228, "authors_detailed": [ { "name": "Carlton L. Robinson", "url": "https://openalex.org/A5136612391", "inst": "United States Chamber of Commerce" }, { "name": "Taylor Frances Drury", "url": "https://openalex.org/A5136615308", "inst": "" } ], "affiliations": [ "United States Chamber of Commerce" ] }, { "uid": "doi:10.2139/ssrn.6731099", "doi": "10.2139/ssrn.6731099", "title": "The Expansion-Compression Trap: How AI Broke the Link Between Writing and Thinking in Modern Organizations", "authors": [ "Deepak Babu Piskala" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6731099", "field": "management", "role": "object", "bullets": [ "Conceptual essay on how large language models reshape written communication and decision-making inside modern organizations; it carries no empirical sample, period or geography.", "No model is run or named; the paper argues that repeated LLM-driven expansion then compression of documents is lossy and severs document length from the depth of thinking behind it.", "It proposes a three-principle framework for AI-native authorship: separate a document's canonical spine from its surface, use AI as critic before author, and attach an explicit reader contract." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 229, "authors_detailed": [ { "name": "Deepak Babu Piskala", "url": "https://openalex.org/A5136653498", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6805344", "doi": "10.2139/ssrn.6805344", "title": "AI Exposure and Housing Markets", "authors": [ "Cayman Seagraves", "Stace Sirmans" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6805344", "alt_urls": [ "https://doi.org/10.2139/ssrn.6749481" ], "field": "economics", "role": "object", "bullets": [ "Quarterly panel of 211 US metros from 2017Q1 to 2025Q4 using Zillow and FHFA house prices and rents, plus IRS migration and Revelio LinkedIn hiring data.", "AI exposure is measured with an AI Geography Exposure index weighted by 2019 population and a predetermined LLM task suitability index; no specific model is named or used as an instrument.", "A one standard deviation rise in AI exposure is associated with 3.6 percent higher cumulative house value growth after 2023, about $9,400 at the median, concentrated in supply-inelastic metros." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 64, "edition": 3, "audience": "general", "validated": null, "n": 230, "authors_detailed": [ { "name": "Cayman Seagraves", "url": "https://openalex.org/A5136172977", "inst": "Collins College" }, { "name": "Stace Sirmans", "url": "https://openalex.org/A5000933071", "inst": "Auburn University" } ], "affiliations": [ "Collins College", "Auburn University" ] }, { "uid": "doi:10.2139/ssrn.6809481", "doi": "10.2139/ssrn.6809481", "title": "Digital Finance and Supply Chain Stability in Consumer Goods Firms: Evidence from Supplier- and Customer-Side Relationships", "authors": [ "Linxiao Luo", "Chunhong Liu", "Ziwen Bao" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6809481", "field": "management", "role": "instrument", "bullets": [ "1,141 Chinese listed consumer goods firms, identified from principal business activity descriptions, used to study digital finance and supply chain stability.", "Advanced large language models, not named, generate semantic embeddings to build a 215-term domain lexicon and classify firms, with no accuracy figure reported.", "Digital finance raises supply chain stability, more strongly on the customer side, with larger effects for firms with weaker bank ties, lower supply chain risk, and less industry competition." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 3, "audience": "general", "models": [], "n": 436, "authors_detailed": [ { "name": "罗林晓", "url": "https://openalex.org/A5007936307", "inst": "Xi'an Jiaotong University" }, { "name": "Chunhong Liu", "url": "https://openalex.org/A5136703935", "inst": "" }, { "name": "Ziwen Bao", "url": "https://openalex.org/A5136656107", "inst": "" } ], "affiliations": [ "Xi'an Jiaotong University" ] }, { "uid": "doi:10.2139/ssrn.6806504", "doi": "10.2139/ssrn.6806504", "title": "Generative AI and Anticipatory Border Governance: A Synthetic Policy Sandbox for Trade-Induced Freight Reallocation", "authors": [ "Carlos Paternina-Arboleda", "Amanda Marino", "Luis Alfredo Ávila-López" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6806504", "field": "economics", "role": "object", "bullets": [ "A one-year scenario-based simulation of a multimodal freight network with endogenous congestion, firm-level transfer pricing, and an adaptive border governance module.", "Generative AI, not named, is modeled as a policy-to-network translation layer converting qualitative policy signals into structured tariff-risk expectations, with no empirical validation.", "AI-enabled firm anticipation induces pre-implementation freight surges and fiscal variability when regulators stay reactive, while anticipatory regulatory response substantially reduces congestion." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 437, "authors_detailed": [ { "name": "Carlos Paternina-Arboleda", "url": "https://openalex.org/A5136554624", "inst": "San Diego State University" }, { "name": "Amanda Marino", "url": "https://openalex.org/A5136581006", "inst": "San Diego State University" }, { "name": "Luis Alfredo Ávila-López", "url": "https://openalex.org/A5136506103", "inst": "Universidad Autónoma de Baja California (UABC)" } ], "affiliations": [ "San Diego State University", "Universidad Autónoma de Baja California (UABC)" ] }, { "uid": "doi:10.2139/ssrn.6784178", "doi": "10.2139/ssrn.6784178", "title": "From Content to Signals: How Investors Rely on Analyst Report Content", "authors": [ "Jiangdong Hu", "Thomas Zhang", "Yeguang Chi" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6784178", "field": "finance", "role": "instrument", "bullets": [ "More than 40,000 Chinese analyst reports from 2018 to 2023, examined for their role in driving market price reactions.", "An LLM-based framework, model not named, converts unstructured report text into standardized factual and linguistic measures, with no validation against hand coding reported.", "Content-based signals explain market reactions more strongly and consistently than issued recommendations, and investors detect and penalize divergence between report content and recommendations." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 438, "authors_detailed": [ { "name": "Jiangdong Hu", "url": "https://openalex.org/A5100570866", "inst": "University of California, Irvine" }, { "name": "Thomas Zhang", "url": "https://openalex.org/A5136629235", "inst": "University of Pittsburgh" }, { "name": "Yeguang Chi", "url": "https://openalex.org/A5089661799", "inst": "University of Auckland" } ], "affiliations": [ "University of California, Irvine", "University of Pittsburgh", "University of Auckland" ] }, { "uid": "doi:10.2139/ssrn.6748622", "doi": "10.2139/ssrn.6748622", "title": "The Boundary Within: Process Topology, Role Oscillation, and the Cost of Misalignment in Human-AI Hybrid Organisations", "authors": [ "Duwarahan Rajendra" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6748622", "field": "management", "role": "object", "bullets": [ "Formal and simulation study of human-AI hybrid organisations, modelling firm boundary placement across three BPMN process topologies, three role modes, and three landscape ruggedness levels.", "No specific language model is used; AI agents are represented abstractly and four propositions are tested with 30-seed Monte Carlo experiments rather than live model runs.", "All four propositions are supported, and unified IT plus HR governance outperforms single-attribution architectures by implementing a two-stage alignment filter." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 563, "authors_detailed": [ { "name": "Duwarahan Rajendra", "url": "https://openalex.org/A5056707675", "inst": "Institute of Automation" } ], "affiliations": [ "Institute of Automation" ] }, { "uid": "doi:10.2139/ssrn.6734338", "doi": "10.2139/ssrn.6734338", "title": "Individual Bodily Injury Loss Assessment in Auto Insurance: A Group-adaptive Quantile Combination Approach", "authors": [ "Jian Cao", "Hailiang Yang", "Jing Yao", "Yang Yang" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6734338", "field": "finance", "role": "instrument", "bullets": [ "80,426 individual bodily injury claim records from a major Chinese property and casualty insurer, split into outpatient, inpatient, and disability claim groups.", "A domain-specific Chinese medical language model, not otherwise named, embeds unstructured diagnostic text as features for gradient-boosted quantile models; no accuracy check of the embeddings is reported.", "Versus a generalized linear model, mean squared error falls 42.5% and mean absolute error 28.7%, and the Gini index rises from 0.677 to 0.747." ], "bullet_provenance": "ai", "validated": false, "salience": 57, "edition": 3, "audience": "general", "models": [], "n": 564, "authors_detailed": [ { "name": "Jian Cao", "url": "https://openalex.org/A5136649974", "inst": "Soochow University" }, { "name": "Hailiang Yang", "url": "https://openalex.org/A5136691317", "inst": "Xi’an Jiaotong-Liverpool University" }, { "name": "Yao, Jing, 1962-", "url": "https://openalex.org/A5136680086", "inst": "Soochow University" }, { "name": "Yang Yang", "url": "https://openalex.org/A5136662209", "inst": "Soochow University" } ], "affiliations": [ "Soochow University", "Xi’an Jiaotong-Liverpool University" ] }, { "uid": "arxiv:2605.22892v2", "arxiv_id": "2605.22892v2", "title": "Is TabPFN the Silver Bullet for Insurance Pricing?", "authors": [ "Bruno Deprez", "Wouter Verbeke", "Tim Verdonck" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.22892v2", "field": "finance", "role": "method", "bullets": [ "Two publicly available motor third-party liability datasets used to benchmark the tabular foundation model TabPFN for claim frequency and severity pricing.", "TabPFN performs in-context inference without dataset-specific fitting, compared against generalised linear models and XGBoost on out-of-sample predictive accuracy.", "TabPFN does not consistently beat the baselines, has substantially longer inference times, and is sensitive to in-context training set size, so it is not yet a replacement." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "out-of-sample accuracy on two MTPL datasets vs GLM and XGBoost", "salience": 55, "edition": 3, "audience": "technical", "n": 565, "authors_detailed": [ { "name": "Bruno Deprez", "url": "https://openalex.org/A5136902165", "inst": "" }, { "name": "Wouter Verbeke", "url": "https://openalex.org/A5075484247", "inst": "Imec the Netherlands" }, { "name": "Tim Verdonck", "url": "https://openalex.org/A5085208338", "inst": "University of Antwerp" } ], "affiliations": [ "Imec the Netherlands", "University of Antwerp" ] }, { "uid": "doi:10.2139/ssrn.6768758", "doi": "10.2139/ssrn.6768758", "title": "Conditional Skill Compression: Domain Expertise and the Unequal Returns to Generative AI", "authors": [ "Mustafa Seref Akin" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6768758", "field": "economics", "role": "object", "bullets": [ "Theoretical labor-economics model of generative AI and skill returns; no empirical sample, with workers differentiated by domain expertise relative to a minimum expertise threshold.", "No language model is used; the paper derives the Minimum Expertise Threshold endogenously from expected AI-use payoffs and analyzes three comparative-statics predictions.", "Predicts within-occupation gaps narrow above the threshold but widen for below-threshold workers, and that reduced entry-level demand creates a training-pipeline externality that can lower welfare." ], "bullet_provenance": "ai", "salience": 47, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 817, "authors_detailed": [ { "name": "Mustafa Akın", "url": "https://openalex.org/A5051923417", "inst": "Balıkesir University" } ], "affiliations": [ "Balıkesir University" ] }, { "uid": "doi:10.2139/ssrn.6733338", "doi": "10.2139/ssrn.6733338", "title": "Investor DNA Scoring: A Position-Data Framework for Detecting Behavioral Risk in Retail Portfolios -Early Evidence from an AI-Native Wealth Platform", "authors": [ "Varun Srivastava" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6733338", "field": "finance", "role": "instrument", "bullets": [ "82 anonymized retail portfolio submissions to the NeuFin Intelligence wealth platform between March and May 2026, from AI-engaged Southeast Asian investors.", "Deterministic portfolio analytics are combined with LLM-assisted interpretation across seven specialised agents, model not stated, to generate behavioral-risk reports in under sixty seconds, with no validation against ground truth reported.", "Documents reverse home bias: Apple, Microsoft, and JPMorgan each appear in over 30 percent of portfolios while no SGX-listed security appears among the thirty most held positions." ], "bullet_provenance": "ai", "validated": false, "salience": 33, "edition": 3, "audience": "general", "models": [], "n": 818, "authors_detailed": [ { "name": "Varun Srivastava", "url": "https://openalex.org/A5136650455", "inst": "Singapore Institute of Technology" } ], "affiliations": [ "Singapore Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.6797281", "doi": "10.2139/ssrn.6797281", "title": "Human Alignment Value and the Obsolescence of Man-Hours: A Computational Theory of Human Contribution in Human–AI Hybrid Organisations.", "authors": [ "Duwarahan Rajendra" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6797281", "field": "management", "role": "object", "bullets": [ "Conceptual paper, fifth in a series on human-AI hybrid organizations; no empirical sample, with computational demonstration rather than field data.", "No language model is used or named; AI agents are assumed to execute routine work, and the paper defines Human Alignment Value to replace man-hours as the contribution unit.", "Derives a crossover threshold above which alignment-value governance outperforms man-hours governance on organizational fitness, and argues man-hours accounting incentivises pathology beyond that threshold." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1101, "authors_detailed": [ { "name": "Duwarahan Rajendra", "url": "https://openalex.org/A5056707675", "inst": "Institute of Automation" } ], "affiliations": [ "Institute of Automation" ] }, { "uid": "doi:10.2139/ssrn.6806499", "doi": "10.2139/ssrn.6806499", "title": "A RETRIEVAL-AUGMENTED GENERATION (RAG) FRAMEWORK TO SUPPORT EVIDENCE-BASED ANALYSIS OF ROAD INFRASTRUCTURE CONTRACTS", "authors": [ "Haikel Gonçalves", "Jorge B. Soares", "Lucas Babadopulos" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6806499", "field": "accounting", "role": "method", "bullets": [ "Case application to public road-infrastructure contracts for one state road in Ceara, Brazil; the corpus held 117 documents and 3,121 retrievable chunks.", "A retrieval-augmented generation pipeline combines hybrid lexical and semantic retrieval with structured answer generation; the model is not named and it is evaluated with RAGAS metrics.", "On the Portuguese benchmark the system reached context precision 0.87 and recall 0.98 but factual correctness of only 0.57, suggesting human-supervised use rather than autonomous audit." ], "bullet_provenance": "ai", "validated": true, "validation_note": "RAGAS metrics on structured Portuguese benchmark", "salience": 42, "edition": 3, "audience": "technical", "models": [], "n": 1102, "authors_detailed": [ { "name": "Haikel Gonçalves", "url": "https://openalex.org/A5136560723", "inst": "Universidade Federal do Ceará" }, { "name": "Jorge B. Soares", "url": "https://openalex.org/A5136527340", "inst": "Universidade Federal do Ceará" }, { "name": "Lucas Babadopulos", "url": "https://openalex.org/A5132801738", "inst": "Universidade Federal do Ceará" } ], "affiliations": [ "Universidade Federal do Ceará" ] }, { "uid": "doi:10.2139/ssrn.6766758", "doi": "10.2139/ssrn.6766758", "title": "From Connectivity to Commerce: Digital Infrastructure, Rural Entrepreneurship, and the AI Inflection Point A Comparative Study of India, China, and Southeast Asia", "authors": [ "Sai Chand Bollu" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6766758", "field": "economics", "role": "object", "bullets": [ "Comparative study of rural entrepreneurship in India, China, and Southeast Asia, drawing on World Bank, TRAI, Alibaba Research Institute, and ASEAN reports.", "No model is run by the researchers; generative AI and voice-based AI agents are theorised as an AI Compression mechanism that collapses the digital capability layer. Model not stated.", "Argues the binding constraint is the capability-to-commerce transition rather than connectivity, and that AI compression may enable a direct connectivity-to-commerce path for rural entrepreneurs." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1103, "authors_detailed": [ { "name": "SAI CHAND BOLLU", "url": "https://openalex.org/A5135826721", "inst": "Independent Advisor" } ], "affiliations": [ "Independent Advisor" ] }, { "uid": "doi:10.2139/ssrn.6770624", "doi": "10.2139/ssrn.6770624", "title": "Coherence as an Extension of the Organizational Design Canon", "authors": [ "Anil Prakash Singh" ], "posted": "2026-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6770624", "field": "management", "role": "object", "bullets": [ "Conceptual research note with no empirical sample, positioning the Living Enterprise framework within organizational design theory alongside Mintzberg, Galbraith, and Laloux.", "No model is used or named; AI agents are the premise, assumed to perform most operational work while human judgment concentrates at named coherence points.", "Extends the organizational design canon for AI-heavy firms, uses the Spotify Model as a counter-case, and names four conditions under which the framework would fail." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1104, "authors_detailed": [ { "name": "Anil Prakash Singh", "url": "https://openalex.org/A5081239748", "inst": "Carnegie Mellon University" } ], "affiliations": [ "Carnegie Mellon University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6763360", "doi": "10.2139/ssrn.6763360", "title": "Identifying Focal Constructs with Large Language Models: A Comparative Framework and Case in International Business", "authors": [ "Xintong Wang", "Xiaodie Pu", "Alain Chong" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6763360", "field": "management", "role": "method", "bullets": [ "47,298 annual reports from 5,519 Chinese listed firms, 2010 to 2023, used to measure nationalistic rhetoric for international business research.", "Compares classical machine learning, fine-tuned FinBERT, locally deployed LLMs, and cloud-based DeepSeek-V3 on zero-shot classification, with accuracy checked against labeled data.", "FinBERT gives the best balance at 93.5 percent accuracy while DeepSeek-V3 leads zero-shot at 94.8 percent; measured nationalism relates to cross-border M&A premiums and outward FDI patterns." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "accuracy vs labeled reports; FinBERT 93.5%, DeepSeek-V3 94.8%; event-study benchmarking", "salience": 72, "edition": 3, "audience": "general", "n": 88, "authors_detailed": [ { "name": "Xintong Wang", "url": "https://openalex.org/A5136582457", "inst": "" }, { "name": "Xiaodie Pu", "url": "https://openalex.org/A5009885273", "inst": "University of Nottingham Ningbo China" }, { "name": "Alain Chong", "url": "https://openalex.org/A5136564930", "inst": "" } ], "affiliations": [ "University of Nottingham Ningbo China" ] }, { "uid": "doi:10.2139/ssrn.6657844", "doi": "10.2139/ssrn.6657844", "title": "Beyond the Compute Trap: The True Moat in Enterprise AI Lies in the \"Macro-Symbolic Layer\" and Digital Sovereignty", "authors": [ "Kewei Duan" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6657844", "field": "management", "role": "object", "bullets": [ "Conceptual position paper with no empirical sample; it draws on zero-tolerance regulatory regimes in aviation, finance, and healthcare and on AI laws including the EU AI Act and China's Interim Measures.", "No model is run; the paper distinguishes observability from governability and argues that stronger LLM internal reasoning increases rather than reduces the need for an external deterministic control layer.", "Proposes a model-agnostic macro-symbolic layer enforcing deterministic rules at input and output boundaries, positioned as a defensible enterprise infrastructure category and the locus of digital sovereignty." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 118, "authors_detailed": [ { "name": "Kewei Duan", "url": "https://openalex.org/A5134801268", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6732238", "doi": "10.2139/ssrn.6732238", "title": "Preregistration for Experiments with AI Agents", "authors": [ "Michelle Vaccaro" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6732238", "field": "economics", "role": "method", "bullets": [ "Conceptual methods paper on in silico behavioral experiments that use large language models and autonomous agents as proxies for human participants; no empirical dataset is analyzed.", "No model is run; the paper catalogs researcher degrees of freedom such as model selection, prompt wording, settings and outcome-contingent redesign, and proposes a preregistration template rather than reporting any validation.", "Argues preregistration should extend to AI-agent experiments, offering a tailored template and calling on conferences, journals and funders to make it standard practice." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 184, "authors_detailed": [ { "name": "Michelle Vaccaro", "url": "https://openalex.org/A5136575443", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6786518", "doi": "10.2139/ssrn.6786518", "title": "Empathy on Demand: How Empathic AI Can Scale Emotional Support for Verbal Harassment", "authors": [ "Anouk Bergner", "Philipp Winder", "Christian Hildebrand" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6786518", "field": "management", "role": "agent", "bullets": [ "Online verbal harassment support context; language-model-generated messages compared against human non-experts and trained mental health professionals, followed by a behavioral study of message recipients.", "ChatGPT and AI companions generate support responses scored on three empathic-listening signals, perspective-taking, emotional validation, and action orientation; no accuracy check against a ground truth is reported.", "Models show stronger empathic-listening markers than human non-experts and professionals and promote approach-oriented coping, raising recipients' sense of feeling heard and coping self-efficacy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 58, "edition": 3, "audience": "general", "validated": null, "n": 225, "authors_detailed": [ { "name": "Anouk Bergner", "url": "https://openalex.org/A5136514995", "inst": "University of Geneva" }, { "name": "P D Winder", "url": "https://openalex.org/A5108190673", "inst": "University of St.Gallen" }, { "name": "Christian Hildebrand", "url": "https://openalex.org/A5136586298", "inst": "University of St.Gallen" } ], "affiliations": [ "University of Geneva", "University of St.Gallen" ] }, { "uid": "doi:10.2139/ssrn.6805808", "doi": "10.2139/ssrn.6805808", "title": "Research on the Systemic Risk Transmission Warning of Small and Medium-sized Financial Institutions through Online Public Opinion Propagation Based on Large Language Models", "authors": [ "Min Liu", "Xiyi Li", "Yushuai Wang" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6805808", "field": "finance", "role": "instrument", "bullets": [ "Small and medium-sized financial institutions; online public opinion data used to build indices of investor attention, emotional disposition, and opinion divergence, with sample size and period not stated.", "DeepSeek and FinBERT extract the public opinion index; risk exposure measured with ARMA-GARCH-Copula-CoVaR and early warning via deep learning, with no accuracy check of the sentiment measurement.", "Adding public opinion indicators improves early-warning accuracy, with the BiGRU-Attention model performing best and investor attention the most sensitive leading indicator ahead of emotion and divergence." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": false, "salience": 48, "edition": 3, "audience": "general", "n": 226, "authors_detailed": [ { "name": "Min Liu", "url": "https://openalex.org/A5136544300", "inst": "Shanghai Institute of Technology" }, { "name": "Xiyi Li", "url": "https://openalex.org/A5136520766", "inst": "" }, { "name": "Yushuai Wang", "url": "https://openalex.org/A5136540171", "inst": "" } ], "affiliations": [ "Shanghai Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.6805805", "doi": "10.2139/ssrn.6805805", "title": "AI Herding in Financial Market: An Experiment with Large Language Models", "authors": [ "Leping Zhang", "Guangyou Zhou" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6805805", "field": "finance", "role": "agent", "bullets": [ "Laboratory sequential trading market experiment comparing LLM traders with human participants across several treatments; sample size, period and geography are not stated.", "Model family not stated; LLMs make sequential buy or sell decisions and their reasoning level is varied, with behaviour compared to human cascade patterns rather than validated against a ground truth.", "LLMs herd more rationally and fall into irrational cascades less than humans; changing the assigned role and context shifts their behaviour by up to 4 and 7.2 percent respectively." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 227, "authors_detailed": [ { "name": "Leping Zhang", "url": "https://openalex.org/A5136589054", "inst": "Fudan University" }, { "name": "Guangyou Zhou", "url": "https://openalex.org/A5002485107", "inst": "Fudan University" } ], "affiliations": [ "Fudan University" ] }, { "uid": "doi:10.2139/ssrn.6800169", "doi": "10.2139/ssrn.6800169", "title": "Skills, Not Scale: GenAI and Technology Adoption", "authors": [ "Gianmarco Ottaviano", "Nuriye Melisa Bilgin" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6800169", "field": "economics", "role": "object", "bullets": [ "Administrative data on Turkish firms from 2021 to 2024, comparing the determinants of traditional AI adoption against generative AI adoption at the firm level.", "No model is deployed; the study uses the ChatGPT release as a quasi-experimental reduction in access costs to identify adoption drivers, naming ChatGPT as the studied technology.", "Generative AI adoption is driven by workforce skill intensity and not firm size, whereas traditional AI depends on both; high-skill firms differentially increased adoption after ChatGPT." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 61, "edition": 3, "audience": "general", "validated": null, "n": 431, "authors_detailed": [ { "name": "Gianmarco Ottaviano", "url": "https://openalex.org/A5128780462", "inst": "Bocconi University" }, { "name": "Nuriye Melisa Bilgin", "url": "https://openalex.org/A5020694551", "inst": "University of Turin" } ], "affiliations": [ "Bocconi University", "University of Turin" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6806084", "doi": "10.2139/ssrn.6806084", "title": "Trust in Generative AI Companions: Care and Stratified Prosocial Pathways among Chinese Youth", "authors": [ "Brianu Goldfarb", "Chen Zhang" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6806084", "field": "management", "role": "object", "bullets": [ "Survey of 1,025 Chinese youth who use ERNIE Bot, DeepSeek Chat, or Doubao as everyday generative AI companions.", "No model is deployed; structural equation modeling and moderated mediation link trust in generative AI to prosocial intentions through satisfaction and emotional-dependency pathways.", "A satisfaction pathway operates largely independent of trust while an emotional-dependency pathway emerges only under high trust, indicating an emerging AI relational divide." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 40, "edition": 3, "audience": "general", "validated": null, "n": 432, "authors_detailed": [ { "name": "Brianu Goldfarb", "url": "https://openalex.org/A5136532215", "inst": "" }, { "name": "Chen Zhang", "url": "https://openalex.org/A5136529777", "inst": "Beijing Institute of Fashion Technology" } ], "affiliations": [ "Beijing Institute of Fashion Technology" ] }, { "uid": "doi:10.2139/ssrn.6786098", "doi": "10.2139/ssrn.6786098", "title": "SCOPE: Submodular Context Optimisation with Pandora's-Box Exploration", "authors": [ "John Christiansen" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6786098", "field": "finance", "role": "method", "bullets": [ "No firm dataset; three regulated-finance case studies covering consumer-credit regulatory question answering, SME lending decision support, and customer-service query triage.", "Proposes SCOPE, an operations-research framework composing submodular chunk selection, Pandora's box stopping, and contextual bandits for token-efficient LLM retrieval; the underlying model is not stated.", "Reports a 3x token reduction at a 0.15 quality gain over top-k retrieval, with an empirical cumulative-regret exponent of 0.60 matching the theoretical square-root bound." ], "bullet_provenance": "ai", "validated": false, "validation_note": "benchmark quality reported, no human-agreement figure", "salience": 35, "edition": 3, "audience": "technical", "models": [], "n": 433, "authors_detailed": [ { "name": "John Christiansen", "url": "https://openalex.org/A5128725087", "inst": "University of Stirling" } ], "affiliations": [ "University of Stirling" ] }, { "uid": "doi:10.2139/ssrn.6788620", "doi": "10.2139/ssrn.6788620", "title": "Cognitive Delegation in Childhood: A Theory of Skill Formation under Generative AI", "authors": [ "Siqi Wei", "Naying Zhou" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6788620", "field": "economics", "role": "object", "bullets": [ "Theoretical paper with no empirical sample; a dynamic model of childhood human-capital formation under generative artificial intelligence.", "No language model is used; the theory distinguishes process-preserving from process-replacing cognitive delegation and decomposes the long-run capacity wedge into discipline and cascade costs.", "The marginal welfare cost concentrates in early childhood and the welfare effect hinges on governance capacity, parental supervision, teacher monitoring, and process-based assignments, not access alone." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 434, "authors_detailed": [ { "name": "Siqi Wei", "url": "https://openalex.org/A5136542496", "inst": "California State University, Northridge" }, { "name": "Naying Zhou", "url": "https://openalex.org/A5136566475", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California", "California State University, Northridge" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6805250", "doi": "10.2139/ssrn.6805250", "title": "Does Artificial Intelligence facilitate Corporate Energy Efficiency? Evidence from LLM-Derived Information", "authors": [ "liang wang", "Ling-Yun He" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6805250", "field": "economics", "role": "instrument", "bullets": [ "Chinese listed companies from 2011 to 2020, a firm-year panel matched to detailed energy consumption, production, and financial data.", "A fine-tuned large language model, not named, applied to annual reports to construct a firm-year AI adoption index, with no comparison to hand-coded labels reported.", "Firms adopting AI show markedly lower energy intensity and expand output without a corresponding rise in energy consumption, with planning-oriented systems delivering the largest efficiency dividends." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 3, "audience": "general", "models": [], "n": 435, "authors_detailed": [ { "name": "Liang Wang", "url": "https://openalex.org/A5136584786", "inst": "Jinan University" }, { "name": "Ling-Yun He", "url": "https://openalex.org/A5136561134", "inst": "Jinan University" } ], "affiliations": [ "Jinan University" ] }, { "uid": "doi:10.2139/ssrn.6800170", "doi": "10.2139/ssrn.6800170", "title": "Smart Cities as Human-Centered Digital Policies:Bridging Policy Discourse and Empirical Evidence", "authors": [ "Wenyin Cheng", "Xin Ouyang", "Yirui Wang" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6800170", "field": "economics", "role": "instrument", "bullets": [ "China smart city pilot initiatives evaluated with China Family Panel Studies data, 2010 to 2018, using a staggered difference-in-differences design on household welfare.", "Large language models, not named, classify policy-discourse text to measure government emphasis on human-centered digital technology; no validation figure is reported.", "Smart city pilots raised welfare, income levels, and equity, with human-centered technology as a central mechanism, and discourse analysis shows heightened government emphasis on it." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "edition": 3, "audience": "general", "models": [], "n": 809, "authors_detailed": [ { "name": "Wenyin Cheng", "url": "https://openalex.org/A5076970202", "inst": "Asian Development Bank Institute" }, { "name": "Xin Ouyang", "url": "https://openalex.org/A5101676207", "inst": "Tsinghua University" }, { "name": "Yirui Wang", "url": "https://openalex.org/A5006282634", "inst": "Peking University" } ], "affiliations": [ "Asian Development Bank Institute", "Tsinghua University", "Peking University" ] }, { "uid": "doi:10.2139/ssrn.6731418", "doi": "10.2139/ssrn.6731418", "title": "The Shrinking Synthesis: Information-Technology Settlement Cycles and the 2037-2047 Window for AI's Institutional Reformation", "authors": [ "Daniel Ziekenoppasser-Powell" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6731418", "field": "economics", "role": "object", "bullets": [ "Historical case studies of information-propagation technologies from the printing press to the internet, plus a non-Western case, used to time institutional responses to AI.", "No model is used; ChatGPT's 2022 launch only marks the onset of the AI adoption cohort in a cohort-replacement extrapolation, not a modelling exercise.", "The paper predicts the first major institutional response to AI in the 2037 to 2047 window, with pre-registered conditions that would disconfirm the compression thesis." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 30, "edition": 3, "audience": "general", "validated": null, "n": 810, "authors_detailed": [ { "name": "Daniel Ziekenoppasser-Powell", "url": "https://openalex.org/A5134400833", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6787638", "doi": "10.2139/ssrn.6787638", "title": "The Broken Ladder: AI, Remote Work, and Early-Career Hiring", "authors": [ "Peter John Lambert", "Yannick Schindler" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6787638", "field": "economics", "role": "object", "bullets": [ "243 million new hires and 407 million online job postings across the US, UK, Canada, and Australia, 2017 to 2025, with occupation, region, and firm-level difference-in-differences.", "No model is used; generative-AI exposure is an occupational index studied as a treatment alongside working-from-home exposure, with no specific model named.", "Estimated jointly, the working-from-home effect on falling junior hiring persists while the generative-AI coefficient attenuates to near zero and often statistical insignificance." ], "bullet_provenance": "ai", "salience": 72, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 811, "authors_detailed": [ { "name": "Peter J. Lambert", "url": "https://openalex.org/A5103951738", "inst": "University of Warwick" }, { "name": "Yannick Schindler", "url": "https://openalex.org/A5136586608", "inst": "London School of Economics and Political Science" } ], "affiliations": [ "London School of Economics and Political Science", "University of Warwick" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6788019", "doi": "10.2139/ssrn.6788019", "title": "Optimal Advertising with Adjustable Click-through Rates and Latent Conversion Rates", "authors": [ "Saeed Alaei", "Ali Makhdoumi", "Azarakhsh Malekian" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6788019", "field": "economics", "role": "object", "bullets": [ "Theoretical mechanism-design model of an LLM advertising platform interacting with K advertisers over T rounds, where the platform tunes click-through rates and conversion per click is unknown; no empirical data.", "No language model is used by the authors; the paper proposes a Click-UCB bandit algorithm and a strategic-reporting extension, analyzed for regret bounds rather than validated empirically.", "Establishes matching upper and lower regret bounds and a platform policy achieving near-optimal social-welfare regret when advertisers play their dominant equilibrium strategies." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 812, "authors_detailed": [ { "name": "Saeed Alaei", "url": "https://openalex.org/A5136580742", "inst": "Google (United States)" }, { "name": "Ali Makhdoumi", "url": "https://openalex.org/A5136587953", "inst": "Fucape Business School" }, { "name": "Azarakhsh Malekian", "url": "https://openalex.org/A5061280107", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto", "Google (United States)", "Fucape Business School" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6789778", "doi": "10.2139/ssrn.6789778", "title": "Token Leverage: A Framework for AI Inside the Firm", "authors": [ "Cayman Seagraves", "Stace Sirmans" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6789778", "alt_urls": [ "https://doi.org/10.2139/ssrn.6789658" ], "field": "economics", "role": "object", "bullets": [ "Conceptual framework paper with no empirical sample; the unit of analysis is the worker-task pair within firms choosing priced AI inference.", "No language model is applied by the authors; the paper defines token leverage, billed AI inference spend per dollar of task-allocated labor cost, as a measurement construct.", "Argues that under voluntary firm choice, billed token leverage is a revealed willingness-to-pay lower bound on AI-created operating value, unlike access or adoption measures whose sign is ambiguous." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 813, "authors_detailed": [ { "name": "Cayman Seagraves", "url": "https://openalex.org/A5136172977", "inst": "Collins College" }, { "name": "Stace Sirmans", "url": "https://openalex.org/A5000933071", "inst": "Auburn University" } ], "affiliations": [ "Collins College", "Auburn University" ] }, { "uid": "doi:10.2139/ssrn.6800603", "doi": "10.2139/ssrn.6800603", "title": "LLM-enhanced decision support for integrated lot-streaming and AGV scheduling in the flexible flow shop", "authors": [ "Zhangwen Huo", "Qianwang Deng", "Si Yang", "Jingxing Zhang", "Bin Luo" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6800603", "field": "management", "role": "method", "bullets": [ "Flexible flow shop scheduling for consumer electronics manufacturing with non-uniform starting operations, variable sub-lots, and AGV transportation, evaluated on 27 problem instances.", "An unnamed large language model acts as an archive-conditioned search operator inside an evolutionary algorithm guided by problem decomposition and elite and tabu solutions, compared against heuristic, metaheuristic, and reinforcement-learning baselines.", "The method produces higher-quality schedules on the 27 instances and degrades more gradually under processing-time, transportation-time, and AGV-availability disturbances than the baselines." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 814, "authors_detailed": [ { "name": "Zhangwen Huo", "url": "https://openalex.org/A5032357194", "inst": "Hunan University" }, { "name": "Qianwang Deng", "url": "https://openalex.org/A5086028165", "inst": "Hunan University" }, { "name": "Yang Si", "url": "https://openalex.org/A5100620102", "inst": "Qinghai University" }, { "name": "Jingxing Zhang", "url": "https://openalex.org/A5136586580", "inst": "" }, { "name": "Bin Luo", "url": "https://openalex.org/A5136608855", "inst": "" } ], "affiliations": [ "Hunan University", "Qinghai University" ] }, { "uid": "doi:10.2139/ssrn.6787718", "doi": "10.2139/ssrn.6787718", "title": "Do Budget Narratives Matter? Evidence From A Focal-Budget Experiment with Human and AI Respondents", "authors": [ "Justin M. Ross", "Whitney Afonso", "Denvil Duncan" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6787718", "field": "accounting", "role": "agent", "bullets": [ "Focal-budget framing experiment with three groups: human local-government budget practitioners, an online human subject pool, and AI respondents whose personalities are built from the 2024 General Social Survey.", "AI respondents, model not stated, simulate budget allocators and are compared against human respondents; treatments are priority-based and performance-based budgeting narratives.", "Both human and AI respondents are unresponsive to narrative framing in allocation decisions while recognizing the treatments' intended framing, suggesting budget narratives have limited late-stage influence." ], "bullet_provenance": "ai", "validated": false, "validation_note": "AI and human respondents compared, qualitative agreement, no statistic reported", "salience": 52, "edition": 3, "audience": "general", "models": [], "n": 815, "authors_detailed": [ { "name": "Justin M. Ross", "url": "https://openalex.org/A5018710835", "inst": "Indiana University Bloomington" }, { "name": "Whitney Afonso", "url": "https://openalex.org/A5136537301", "inst": "University of North Carolina at Chapel Hill" }, { "name": "Denvil Duncan", "url": "https://openalex.org/A5136602237", "inst": "IZA - Institute of Labor Economics" } ], "affiliations": [ "Indiana University Bloomington", "University of North Carolina at Chapel Hill", "IZA - Institute of Labor Economics" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6800339", "doi": "10.2139/ssrn.6800339", "title": "Does Artificial Intelligence Facilitate Environmental Performance? Firm-Level Evidence from LLM-Based Measurement", "authors": [ "Liang Wang", "Ling-Yun He" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6800339", "field": "economics", "role": "object", "bullets": [ "Chinese A-share listed firms from 2009 to 2022; a firm-level AI adoption index is constructed from annual report text, with the firm-year as the unit of observation.", "Natural language processing, specific model not stated, measures AI adoption from annual reports; the index enters panel regressions with instrumental-variable estimation, and no validation of the index against ground truth is reported.", "AI adoption significantly improves environmental performance through productivity, governance, and green innovation channels, with robotics and planning systems helping while language and vision applications show adverse effects." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 816, "authors_detailed": [ { "name": "Liang Wang", "url": "https://openalex.org/A5136584786", "inst": "Jinan University" }, { "name": "Ling-Yun He", "url": "https://openalex.org/A5136561134", "inst": "Jinan University" } ], "affiliations": [ "Jinan University" ] }, { "uid": "doi:10.2139/ssrn.6763583", "doi": "10.2139/ssrn.6763583", "title": "Demystifying the Attribution Ambiguity: A Computational Framework of Agency in Human-GenAI Innovation", "authors": [ "Rawan Qadri", "Inbal Yahav", "Moran Lazar", "Hila Lifshitz-Assaf" ], "posted": "2026-05-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6763583", "field": "management", "role": "object", "bullets": [ "Two studies of human-GenAI ideation: a controlled simulation and a field study using real ideation conversations from a global retail company.", "GenAI acts as an ideation collaborator; a computational framework traces idea agency and process agency across conversations. The specific model is not stated and no accuracy check is reported.", "Process agency shapes human-GenAI interaction independently of idea agency, and four distinct innovation dynamics emerge from combinations of idea and process agency." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1100, "authors_detailed": [ { "name": "Rawan Qadri", "url": "https://openalex.org/A5136517427", "inst": "Tel Aviv University" }, { "name": "Inbal Yahav", "url": "https://openalex.org/A5070441489", "inst": "Tel Aviv University" }, { "name": "Moran Lazar", "url": "https://openalex.org/A5082847612", "inst": "Tel Aviv University" }, { "name": "Hila Lifshitz‐Assaf", "url": "https://openalex.org/A5036969088", "inst": "Harvard University" } ], "affiliations": [ "Harvard University", "Tel Aviv University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6682038", "doi": "10.2139/ssrn.6682038", "title": "Multi-Agent Large Language Models with Reinforcement Learning for Quant Trading", "authors": [ "Qizhao Chen", "Hiroaki Kawashima" ], "posted": "2026-05-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6682038", "field": "finance", "role": "agent", "bullets": [ "Backtest across seven major companies, with sample period and market not stated, comparing a multi-agent LLM plus reinforcement learning framework to equal-weighted and PPO portfolio benchmarks.", "DeepSeek acts as the generation agent building formulaic alphas and Gemini as the evaluation agent selecting them; chosen alphas feed a DDPG reinforcement learner for portfolio weights, with no validation reported.", "The multi-agent LLM plus DDPG framework generally achieved higher cumulative returns than equal-weighted and multi-agent PPO benchmarks, but with increased short-term risk, while single-agent PPO gave steadier, lower-drawdown performance." ], "bullet_provenance": "ai", "models": [ "gemini", "open_other" ], "open_weights": true, "salience": 44, "edition": 3, "audience": "technical", "validated": null, "n": 82, "authors_detailed": [ { "name": "Qizhao Chen", "url": "https://openalex.org/A5017181882", "inst": "University of Hyogo" }, { "name": "Hiroaki Kawashima", "url": "https://openalex.org/A5004107850", "inst": "University of Hyogo" } ], "affiliations": [ "University of Hyogo" ] }, { "uid": "arxiv:2606.00061v1", "arxiv_id": "2606.00061v1", "title": "Reflexivity as Prompt: Does Awareness of Self-Reinforcing Market Dynamics Improve LLMs as Financial Market Forecasters?", "authors": [ "Eugene Park" ], "posted": "2026-05-19", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2606.00061v1", "field": "finance", "role": "agent", "bullets": [ "Frontier LLMs used as financial forecasters across two episodes, the dot-com bubble from 1996 to 2001 and the global financial crisis from 2004 to 2009, with anonymized normalized inputs.", "GPT5, Claude Sonnet 4.6, and Gemini 3 Pro forecast market direction under four accumulating zero-shot conditions that add awareness of Soros's reflexivity theory; no accuracy figure is reported.", "Reflexivity awareness shifts directional accuracy differently across models and context windows, so the same theoretical prompt yields qualitatively different forecasting behavior." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 47, "edition": 3, "audience": "technical", "validated": null, "n": 104, "authors_detailed": [ { "name": "Eugene Park", "url": "https://openalex.org/A5101617358", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6797298", "doi": "10.2139/ssrn.6797298", "title": "Behavioral Collapse to Longest-Processing-Time Scheduling in Contract-Validated Single-Prompt Large Language Model Heuristic Synthesis for Identical Parallel Machine Scheduling", "authors": [ "Achraf Ghorbel", "Nourchene Elleuch Ben Ayed", "Nassim Tinkicht", "Majed Bouchahma" ], "posted": "2026-05-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6797298", "field": "management", "role": "object", "bullets": [ "Identical parallel machine makespan minimization, with 540 heuristic code generations from six frontier language models across three prompt variants, screened on a 2,160-instance benchmark anchored on the longest-processing-time rule.", "Six unnamed frontier models generate scheduling heuristics from single prompts; outputs pass a five-contract deployability funnel and are classified by behavioral fingerprinting against known rules.", "85.7% of generations pass all contracts and 99.6% of conforming outputs are behaviorally identical to longest-processing-time; the one distinct class cuts mean exact regret from 5.02% to 0.53%." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 224, "authors_detailed": [ { "name": "Achraf Ghorbel", "url": "https://openalex.org/A5136488043", "inst": "Higher Colleges of Technology" }, { "name": "Nourchène Elleuch Ben Ayed", "url": "https://openalex.org/A5073132841", "inst": "Higher Colleges of Technology" }, { "name": "Nassim Tinkicht", "url": "https://openalex.org/A5136478223", "inst": "Higher Colleges of Technology" }, { "name": "Majed Bouchahma", "url": "https://openalex.org/A5089394586", "inst": "Higher Colleges of Technology" } ], "affiliations": [ "Higher Colleges of Technology" ] }, { "uid": "doi:10.2139/ssrn.6794976", "doi": "10.2139/ssrn.6794976", "title": "Democratising Warehouse Optimisation: An Agentic Artificial Intelligence Framework for Natural Language Driven Human and Robot Collaboration", "authors": [ "Rosalin Sahoo", "Anand Mishra" ], "posted": "2026-05-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6794976", "field": "management", "role": "method", "bullets": [ "Real warehouse dataset of 804 spatial nodes and 190,757 inventory records, with test instances ranging from 15 to 150 orders.", "An unnamed large language model acts as a mathematical reasoning engine paired with five solver algorithms; a hallucination analysis decomposes errors across variables, constraints, objectives, and code.", "The pipeline cut expert modeling time by 99.6 to 99.7 percent with zero constraint violations and feasible solutions, keeping error rates at or below 5 percent on small instances." ], "bullet_provenance": "ai", "validated": true, "validation_note": "hallucination error-rate analysis and solver feasibility checks", "salience": 48, "edition": 3, "audience": "technical", "models": [], "n": 562, "authors_detailed": [ { "name": "Rosalin Sahoo", "url": "https://openalex.org/A5028122621", "inst": "Indian Institute of Management Ahmedabad" }, { "name": "Anand Mishra", "url": "https://openalex.org/A5112805171", "inst": "Indian Institute of Management Ahmedabad" } ], "affiliations": [ "Indian Institute of Management Ahmedabad" ] }, { "uid": "doi:10.2139/ssrn.6795784", "doi": "10.2139/ssrn.6795784", "title": "From Definition to Regulation: How Social Media Platforms Govern Synthetic Media and Deepfakes​", "authors": [ "Nicholas Nicoli", "Soulla Louca" ], "posted": "2026-05-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6795784", "field": "management", "role": "object", "bullets": [ "Publicly available deepfake and synthetic-media policies issued by X, YouTube, TikTok, and Instagram, examined through qualitative document and interpretive discourse analysis. No time period stated.", "No model is run by the researchers; generative AI appears as the object, the source of synthetic media whose platform governance is analysed.", "Platform policies converge and diverge in defining synthetic media and distributing responsibility across creators, platforms, and users, revealing a shared but internally fragmented governance logic." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 805, "authors_detailed": [ { "name": "Nicholas Nicoli", "url": "", "inst": "University of Nicosia" }, { "name": "Soulla Louca", "url": "", "inst": "University of Nicosia" } ], "affiliations": [ "University of Nicosia" ] }, { "uid": "doi:10.2139/ssrn.6798842", "doi": "10.2139/ssrn.6798842", "title": "Generative AI as a New Paradigm for Online Search: Evidence from a Large-Scale Experiment and Qualitative Interviews", "authors": [ "Jakob Kaiser", "Carolin Kaiser", "Rene Schallner", "Neelima Kawatra", "Sabrina Schneider" ], "posted": "2026-05-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6798842", "field": "management", "role": "object", "bullets": [ "Online experiment where participants used either ChatGPT or Google to complete practical search tasks, choosing a smartphone or holiday destination, plus qualitative interviews. Sample size and geography not stated.", "ChatGPT, version not stated, served as the search tool participants used; the study measured behavioural outcomes rather than validating model output against any ground truth.", "ChatGPT users more often identified the optimal option, did so faster, and consulted fewer external websites, indicating one-stop rather than gateway information use." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 3, "audience": "general", "validated": null, "n": 806, "authors_detailed": [ { "name": "Jakob Kaiser", "url": "https://openalex.org/A5039989641", "inst": "Institut für Arbeitsmarkt und Berufsforschung" }, { "name": "Carolin Kaiser", "url": "https://openalex.org/A5064098009", "inst": "University of Siena" }, { "name": "Rene Schallner", "url": "https://openalex.org/A5136458093", "inst": "" }, { "name": "Neelima Kawatra", "url": "https://openalex.org/A5134777487", "inst": "" }, { "name": "Sabrina Schneider", "url": "https://openalex.org/A5136459742", "inst": "Vorarlberg University of Applied Sciences" } ], "affiliations": [ "Institut für Arbeitsmarkt und Berufsforschung", "University of Siena", "Vorarlberg University of Applied Sciences" ] }, { "uid": "doi:10.2139/ssrn.6725021", "doi": "10.2139/ssrn.6725021", "title": "Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics", "authors": [ "Likhita Yerra", "Remi Uttejitha Allam" ], "posted": "2026-05-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6725021", "field": "finance", "role": "method", "bullets": [ "US equity panel of 30 NASDAQ-100 names with FNSPID news, 2019 to 2023 test window, evaluating portfolio and reinforcement-learning decisions built on sparse text signals.", "An unnamed LLM maps news into K named coordinates (SSAI); performance is compared against FinBERT, principal-component, and lexical baselines, not against any text ground truth.", "The SSAI portfolio edge is largely a basket-selection effect; a first principal component reaches 433.6% cumulative return and FinBERT ranks stronger, while RL results depend on algorithm not representation." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 46, "edition": 3, "audience": "technical", "n": 807, "authors_detailed": [ { "name": "Likhita Yerra", "url": "", "inst": "Art Institute of Portland" }, { "name": "Remi Uttejitha Allam", "url": "", "inst": "Art Institute of Portland" } ], "affiliations": [ "Art Institute of Portland" ] }, { "uid": "doi:10.2139/ssrn.6761698", "doi": "10.2139/ssrn.6761698", "title": "The AIAS™ Presence Measurement Protocol: Methodological Notes on Construct Validity and the Four-Regime Taxonomy", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-05-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6761698", "field": "management", "role": "method", "bullets": [ "Brand appearance across matched LLM responses to category-recommendation prompts, spanning categories such as premium skincare, personal-finance apps, and premium tea. Sample size and period not stated.", "An unnamed LLM generates category recommendations; the brand-presence rate is tested for construct validity against Google Trends rank as a consumer-search proxy.", "The paper identifies four empirical regimes linking AI presence to search rank and promotes the covariate-saturated Regime 4 to canonical on three pre-registered confirmations." ], "bullet_provenance": "ai", "validated": false, "validation_note": "compared to Google Trends rank, regime taxonomy not an accuracy figure", "salience": 30, "edition": 3, "audience": "general", "models": [], "n": 808, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6765180", "doi": "10.2139/ssrn.6765180", "title": "A Conceptual Systems Governance Framework for Autonomous Financial Execution: The Sovereign Payment Agent Model", "authors": [ "Avik Nandi" ], "posted": "2026-05-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6765180", "field": "finance", "role": "object", "bullets": [ "Conceptual systems-design paper on governance of autonomous AI agents that initiate, route, and settle payments in enterprise and regulated financial environments; no empirical sample.", "No language model is used or named; the paper synthesizes treasury governance, payment authorization, information security, AI governance, and model risk management into a governance framework.", "Proposes the Sovereign Payment Agent model with a governance envelope, a five-tier agent autonomy classification, an audit artifact standard, and an adversarial risk architecture, stating it presents no empirical validation." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1099, "authors_detailed": [ { "name": "Avik Nandi", "url": "https://openalex.org/A5134554632", "inst": "Wipro (Singapore)" } ], "affiliations": [ "Wipro (Singapore)" ] }, { "uid": "doi:10.2139/ssrn.6785217", "doi": "10.2139/ssrn.6785217", "title": "Divergence in Climate Change Communication: LLM-Based Evidence from the IPCC and the Press", "authors": [ "Sebastian Galiani", "Franco Mettola La Giglia", "Raul A. Sosa" ], "posted": "2026-05-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6785217", "field": "economics", "role": "instrument", "bullets": [ "About 114,000 matched claim pairs from six IPCC Assessment Reports (1990-2023) and ten major US and UK newspaper outlets.", "LLMs scored severity shifts between technical summaries, summaries for policymakers, and press coverage of climate claims.", "Both IPCC policy summaries and press coverage systematically shift toward higher-impact magnitudes within accepted scientific ranges; left- and right-leaning outlets show similar patterns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 58, "n": 3612, "authors_detailed": [ { "name": "Sebastián Galiani", "url": "https://openalex.org/A5029505552", "inst": "Economie Publique" }, { "name": "Franco Mettola La Giglia", "url": "https://openalex.org/A5123406449", "inst": "University of San Andrés" }, { "name": "Raul A. Sosa", "url": "https://openalex.org/A5119350715", "inst": "University of San Andrés" } ], "affiliations": [ "Economie Publique", "University of San Andrés" ] }, { "uid": "doi:10.2139/ssrn.6790567", "doi": "10.2139/ssrn.6790567", "title": "Simulating Analyst Forecast Behavior using LLMs", "authors": [ "Seohyun Lee", "Hyuk An" ], "posted": "2026-05-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6790567", "field": "accounting", "role": "agent", "bullets": [ "121,369 forecasts for 607 Korean listed firms over 2018 to 2025, with paired LLM forecasts using only information public as of each analyst forecast date and external search disabled.", "GPT-4.1-nano and GPT-5.4-nano generate numerical EPS forecasts, with accuracy measured as forecast error against realized EPS and compared to analysts and consensus.", "Analysts stay more accurate, with median errors of 1.9% of price versus 2.3% for the LLM, roughly 20% worse, though GPT-5.4-nano closes 67.5% of the gap to analysts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "EPS forecast error vs realized EPS, 2.3% vs 1.9% for analysts", "salience": 60, "edition": 3, "audience": "general", "n": 103, "authors_detailed": [ { "name": "Seohyun Lee", "url": "https://openalex.org/A5136440661", "inst": "" }, { "name": "Hyuk An", "url": "https://openalex.org/A5136388718", "inst": "Korea Development Institute" } ], "affiliations": [ "Korea Development Institute" ] }, { "uid": "doi:10.2139/ssrn.6788788", "doi": "10.2139/ssrn.6788788", "title": "Faculty orientations toward generative AI in Higher Education: perceptual dimensions, attitudinal profiles, and implications for institutional governance", "authors": [ "Xabier Gonzalez Laskibar", "Beñat Landeta‐Manzano", "Amaia Mendoza-Larrañaga", "Naiara Uriarte-Gallastegi" ], "posted": "2026-05-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6788788", "field": "management", "role": "object", "bullets": [ "Cross-sectional survey of 458 faculty members at one Spanish public university, measuring seven perceptual dimensions of generative AI including benefits, risks, and ethics.", "No AI model is deployed by the researchers; proportional-odds ordinal regression and k-means clustering profile faculty attitudes, with no specific system named.", "Three faculty profiles emerge, critical enthusiasts, pragmatists, and skeptical, distinguished by perceived benefits, risks, and ethical concerns, while institutional training is rated low." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 430, "authors_detailed": [ { "name": "Xabier Gonzalez Laskibar", "url": "https://openalex.org/A5136363093", "inst": "" }, { "name": "Beñat Landeta-Manzano", "url": "https://openalex.org/A5124702571", "inst": "University of the Basque Country" }, { "name": "Amaia Mendoza-Larrañaga", "url": "https://openalex.org/A5124715306", "inst": "University of the Basque Country" }, { "name": "Naiara Uriarte-Gallastegi", "url": "https://openalex.org/A5124746360", "inst": "University of the Basque Country" } ], "affiliations": [ "University of the Basque Country" ] }, { "uid": "doi:10.2139/ssrn.6753841", "doi": "10.2139/ssrn.6753841", "title": "Cited-Listicle Rank-Tier Exposure, Author Type, and LLM Brand Visibility: A Two-Part Model of Selection and Prominence in Generative Engine Responses Working Paper", "authors": [ "Jan Ehrlinspiel", "Tomek Rudzki", "Malte Landwehr" ], "posted": "2026-05-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6753841", "field": "management", "role": "object", "bullets": [ "Chat-level panel from three markets: B2B SaaS (1.67M observations, 21 brands, 6 engines), MarTech (1.27M, 111 brands, 7 engines), and US finance (2.80M, 45 brands, 3 engines).", "LLM engine responses are the outcome, not a tool; a correlated random effects logit models brand mention and an OLS models placement, with prompt, model, and date fixed effects.", "Higher-ranked third-party listicle exposure is positively associated with mention probability and earlier placement, most monotonic in MarTech and concentrated at top tiers in finance." ], "bullet_provenance": "ai", "salience": 47, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 803, "authors_detailed": [ { "name": "Jan Ehrlinspiel", "url": "https://openalex.org/A5136403154", "inst": "" }, { "name": "Tomek Rudzki", "url": "https://openalex.org/A5136373984", "inst": "" }, { "name": "Malte Landwehr", "url": "https://openalex.org/A5136361095", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6790565", "doi": "10.2139/ssrn.6790565", "title": "Artificial Intelligence Exposure and Corporate Cash Holdings", "authors": [ "Mohsen Aram" ], "posted": "2026-05-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6790565", "field": "finance", "role": "object", "bullets": [ "Panel of U.S. public firms from 2015 through 2025, combining an industry AI exposure measure and an AI economic uncertainty index in a shift-share design.", "No language model is applied; the November 2022 ChatGPT launch serves as a discrete AI capability shock within fixed-effects specifications.", "After the ChatGPT launch, AI-exposed firms cut cash holdings by roughly one percentage point, a step that persists through 2025, consistent with efficiency and substitution." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 54, "edition": 3, "audience": "general", "validated": null, "n": 804, "authors_detailed": [ { "name": "Mohsen Aram", "url": "https://openalex.org/A5076700076", "inst": "Western Kentucky University" } ], "affiliations": [ "Western Kentucky University" ] }, { "uid": "doi:10.2139/ssrn.6778885", "doi": "10.2139/ssrn.6778885", "title": "A Comparative Study between Hybrid ML Models and Few-Shot In-Context LLM Models for Detecting Non-Actionable Email in a Customer-Service Setting", "authors": [ "Budsadee Sareerasart", "Ekarat Rattagan" ], "posted": "2026-05-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6778885", "field": "management", "role": "method", "bullets": [ "In-house customer-service email dataset (size not stated); task is detecting non-actionable emails such as auto-replies and system notifications that do not need agent attention.", "Compares a rule-augmented hybrid ML pipeline (SVM) against few-shot in-context LLMs including GPT-4o-mini, scored by Matthews correlation coefficient and precision on labeled data.", "The hybrid SVM reached 84 percent MCC and 98 percent precision, matching GPT-4o-mini's 83 percent MCC and 98 percent precision at lower cost." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "MCC and precision on labeled in-house dataset", "salience": 45, "edition": 3, "audience": "technical", "n": 561, "authors_detailed": [ { "name": "Budsadee Sareerasart", "url": "https://openalex.org/A5124277972", "inst": "National Institute of Development Administration" }, { "name": "Ekarat Rattagan", "url": "https://openalex.org/A5062629070", "inst": "National Institute of Development Administration" } ], "affiliations": [ "National Institute of Development Administration" ] }, { "uid": "arxiv:2605.30363v1", "arxiv_id": "2605.30363v1", "title": "Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market", "authors": [ "Mingxuan Yi", "Vidal Mehra", "Jing Chen", "John Cartlidge" ], "posted": "2026-05-17", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.30363v1", "field": "finance", "role": "instrument", "bullets": [ "2010-2024 FOMC minutes paired with a 14-variable U.S. Treasury and macroeconomic panel, using four interchangeable data-driven regime detectors.", "Large language model, family not stated, reasons over central-bank communications to propose regime-shift candidates, validated with a bootstrap likelihood-ratio test on a vector autoregression.", "Pipeline reaches F1 of 0.82 against a verified anchor list of monetary-policy regime shifts, with same-day detection latency and stronger performance than data-driven baselines." ], "bullet_provenance": "ai", "validated": true, "validation_note": "verified anchor list of regime shifts, F1 0.82", "salience": 62, "edition": 3, "audience": "technical", "models": [], "n": 802, "authors_detailed": [ { "name": "Mingxuan Yi", "url": "https://openalex.org/A5137585825", "inst": "University of Bristol" }, { "name": "Vidal Mehra", "url": "https://openalex.org/A5137592511", "inst": "" }, { "name": "Jing Chen", "url": "https://openalex.org/A5137534447", "inst": "Henan University of Science and Technology" }, { "name": "John Cartlidge", "url": "https://openalex.org/A5125755561", "inst": "University of Bristol" } ], "affiliations": [ "University of Bristol", "Henan University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.6759444", "doi": "10.2139/ssrn.6759444", "title": "Artificial Obedience: How Large Language Models (LLMs) May Normalize Hierarchical Communication", "authors": [ "Nusse Mellgren", "Sebastian Wärmländer" ], "posted": "2026-05-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6759444", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, arguing that repeated interaction with large language models conditions users into directive, authority-driven communication habits, using military communication as an analogy.", "No language model is run or measured; the authors draw on theories of power, habit formation, and social practice to theorize a 'computer communication spillover' mechanism.", "They propose that habits formed with LLMs transfer to human interaction, predicting reduced politeness, lower tolerance for dissent, and more control-oriented communication developing outside users' awareness." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 150, "authors_detailed": [ { "name": "Nusse Mellgren", "url": "https://openalex.org/A5136290708", "inst": "Kristianstad University" }, { "name": "Sebastian Wärmländer", "url": "https://openalex.org/A5136314941", "inst": "Stockholm University" } ], "affiliations": [ "Kristianstad University", "Stockholm University" ] }, { "uid": "doi:10.2139/ssrn.6718858", "doi": "10.2139/ssrn.6718858", "title": "Multimodal LLM-Based Property Condition Assessment: A Per-Room Analysis Framework with Investor-Perspective Calibration", "authors": [ "Ragul Shanmugam", "Sean Kirk" ], "posted": "2026-05-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6718858", "field": "finance", "role": "instrument", "bullets": [ "57 photographs from 14 off-market residential properties in Memphis, Tennessee across three condition tiers, plus 39 listing photos from the public REI dataset, labeled by two experienced investors.", "An unnamed multimodal language model scores each photo for room type, condition category, and a 1-10 condition score, aggregated to property level and validated against investor labels.", "Room classification reaches 82.5% accuracy and condition agreement Cohen's kappa 0.773, bracketing the 0.590 human-human reliability, with failures concentrated at the outdated-standard boundary." ], "bullet_provenance": "ai", "validated": true, "validation_note": "82.5% room accuracy and Cohen's kappa against two investor labelers", "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 223, "authors_detailed": [ { "name": "Ragul Shanmugam", "url": "https://openalex.org/A5136318602", "inst": "The University of Texas at Austin" }, { "name": "Sean Kirk", "url": "https://openalex.org/A5136306274", "inst": "" } ], "affiliations": [ "The University of Texas at Austin" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2605.28850v2", "arxiv_id": "2605.28850v2", "title": "Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents", "authors": [ "Weicheng Xue" ], "posted": "2026-05-16", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.28850v2", "field": "finance", "role": "agent", "bullets": [ "TradeArena, an auditable LLM trading-agent testbed with risk reports and execution simulation, evaluated over 80 rolling failure anchors, eight LLM trajectories, and a 51-stock intraday experiment.", "Multiple LLM agents (not named) trade while planning embeddings and hidden states are probed for pre-failure signatures using hash, LSA, Transformer, and white-box hidden-state probes.", "Planning embeddings drift from normal centroids before drawdowns, and structured risk feedback aligns reasoning without fine-tuning but does not universally raise returns." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 310, "authors_detailed": [ { "name": "Weicheng Xue", "url": "https://openalex.org/A5137334647", "inst": "Virginia Tech" } ], "affiliations": [ "Virginia Tech" ] }, { "uid": "doi:10.2139/ssrn.6757618", "doi": "10.2139/ssrn.6757618", "title": "TaxBench-AU: Evaluating Tax Agents on Authority-Grounded Australian Tax Worked Examples", "authors": [ "Poonam Nair" ], "posted": "2026-05-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6757618", "field": "accounting", "role": "method", "bullets": [ "TaxBench-AU benchmark built from public Australian Taxation Office rulings, determinations, guides, and worked-example solutions; results reported on a held-out 60-item test split.", "Compares a Sonnet-based tool-using tax agent against raw Sonnet and Haiku baselines; programmatic checks and cross-provider LLM judges score source grounding, outcome alignment, and boundary safety.", "Reports per-task pass rates and guardrail-failure counts, but overlapping confidence intervals lead the authors to frame it as an error-localisation diagnostic rather than certification." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "ATO worked-example gold labels, per-task pass rates", "salience": 55, "edition": 3, "audience": "technical", "n": 801, "authors_detailed": [ { "name": "Poonam Nair", "url": "https://openalex.org/A5111282489", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6765742", "doi": "10.2139/ssrn.6765742", "title": "Managing the Adoption of Agentic AI and Digital Technologies in the Insurance Sector: A Management-Oriented Framework", "authors": [ "Dhananjaya Lahiru Bandara", "Mohamed Mirshad Munawwer", "Charith Lakpriya Jayathilake" ], "posted": "2026-05-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6765742", "field": "management", "role": "object", "bullets": [ "Conceptual paper on the insurance sector's adoption of agentic AI and digital technologies, based on a systematic literature review; no firm-level sample or period is stated.", "No language model is used or named; the paper synthesizes literature to identify adoption drivers, barriers, and research gaps in governance, trust, and workforce transformation.", "Proposes a management-oriented framework with phased adoption, governance mechanisms, and human-on-the-loop collaboration to balance operational efficiency with regulatory compliance and stakeholder trust." ], "bullet_provenance": "ai", "salience": 31, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1098, "authors_detailed": [ { "name": "Dhananjaya Lahiru Bandara", "url": "https://openalex.org/A5121830247", "inst": "University of Staffordshire" }, { "name": "Mohamed Mirshad Munawwer", "url": "https://openalex.org/A5121761715", "inst": "University of Staffordshire" }, { "name": "Charith Lakpriya Jayathilake", "url": "https://openalex.org/A5121064424", "inst": "University of Staffordshire" } ], "affiliations": [ "University of Staffordshire" ] }, { "uid": "doi:10.2139/ssrn.6740438", "doi": "10.2139/ssrn.6740438", "title": "AI Governance Frameworks: ISO/IEC 42001, NIST AI RMF, and the EU AI Act", "authors": [ "Rizwan Tanveer" ], "posted": "2026-05-15", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6740438", "field": "management", "role": "object", "bullets": [ "Narrative literature review of three AI governance frameworks, ISO/IEC 42001, NIST AI RMF, and the EU AI Act, plus Gulf Cooperation Council instruments, using primary standards and regulatory documents through 2026.", "No language model is applied by the researchers; the paper synthesizes each framework's structure and scope and proposes a comparative scoring rubric for practitioner gap analysis at deployment design.", "Concludes the frameworks are complementary rather than substitutive, that all contain gaps covering agentic AI capabilities, and that the GCC is converging toward ISO/IEC 42001 as a de facto international standard." ], "bullet_provenance": "ai", "salience": 38, "edition": 4, "models": [], "validated": null, "n": 1229, "authors_detailed": [ { "name": "Rizwan Tanveer", "url": "https://openalex.org/A5135988363", "inst": "College of Accounting" } ], "affiliations": [ "College of Accounting" ] }, { "uid": "doi:10.2139/ssrn.6771404", "doi": "10.2139/ssrn.6771404", "title": "Human-Centric Artificial Intelligence and the Rise of “AI Individuals”: Expertise, Confidence, and Decision Trust in Information Systems", "authors": [ "Mustafa KAYA" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6771404", "field": "management", "role": "object", "bullets": [ "Conceptual information systems study with no empirical data, addressing people who assert confident opinions drawn from LLMs and generative AI despite limited expertise.", "No model is used by the authors; they build a three dimensional model of expertise, perceived confidence, and decision influence, drawing on cognitive bias theory and the Dunning-Kruger effect.", "Argues that confident non-experts reshape organizational and societal decision-making, and calls for AI literacy programs, safeguarding of expert opinion, and ethical oversight." ], "bullet_provenance": "ai", "salience": 31, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 102, "authors_detailed": [ { "name": "Mustafa Kaya", "url": "https://openalex.org/A5100758907", "inst": "Istanbul Medipol University" } ], "affiliations": [ "Istanbul Medipol University" ] }, { "uid": "doi:10.2139/ssrn.6739860", "doi": "10.2139/ssrn.6739860", "title": "When to Act, When to Ask: Simulation-Based Evaluation of Human-in-the-Loop Decision Policies for Enterprise Agents", "authors": [ "Kshitij Kumar Singh Chauhan", "Shreya Garg" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6739860", "field": "management", "role": "method", "bullets": [ "Enterprise LLM assistants facing when to act autonomously versus escalate, evaluated on synthetic IT and HR question-answer datasets and scenario-based transcript surveys rather than a field deployment.", "The model is not named; the ActAsk framework wraps it with confidence gating, scope limits, confirmations, and fallbacks and tunes autonomy thresholds with a constrained contextual bandit, tested only in simulation.", "In simulation ActAsk reduced incorrect answers, improved escalation precision, and raised perceived trust and satisfaction; no effect magnitudes are reported." ], "bullet_provenance": "ai", "validated": false, "validation_note": "simulation only, no ground-truth comparison", "salience": 38, "edition": 3, "audience": "technical", "models": [], "n": 149, "authors_detailed": [ { "name": "Kshitij Kumar Singh Chauhan", "url": "https://openalex.org/A5113144481", "inst": "International Institute of Information Technology" }, { "name": "Shreya Garg", "url": "https://openalex.org/A5136195220", "inst": "Thapar Institute of Engineering & Technology" } ], "affiliations": [ "International Institute of Information Technology", "Thapar Institute of Engineering & Technology" ] }, { "uid": "doi:10.2139/ssrn.6752998", "doi": "10.2139/ssrn.6752998", "title": "Sex, Drugs, and LLMs: Social Desirability Bias in Language Models", "authors": [ "Sophia Kazinnik", "José Ramón Enríquez", "Jacy Anthis", "David Nguyen", "Jiaxin Pei" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6752998", "field": "economics", "role": "agent", "bullets": [ "Thirteen large language models evaluated across fifteen case studies of sensitive survey topics such as drug use, infidelity, voting and charitable giving, with responses compared to human self-reports and behavioral benchmarks.", "The models, whose families are not stated, were prompted to answer sensitive questions; an open-weights model let the authors probe and steer internal activations, using behavioral benchmarks as the ground truth.", "Model answers resembled biased human self-reports more than behavioral benchmarks; steering activations moved responses toward actual behavior in 13 of 15 case studies, while prompting interventions had little effect." ], "bullet_provenance": "ai", "open_weights": true, "salience": 62, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 183, "authors_detailed": [ { "name": "Sophia Kazinnik", "url": "https://openalex.org/A5136189970", "inst": "" }, { "name": "José Ramón Enríquez", "url": "https://openalex.org/A5136205445", "inst": "Harvard University Press" }, { "name": "Jacy Anthis", "url": "https://openalex.org/A5117164692", "inst": "University of Chicago" }, { "name": "David Nguyen", "url": "https://openalex.org/A5136249754", "inst": "" }, { "name": "Jiaxin Pei", "url": "https://openalex.org/A5136263058", "inst": "" } ], "affiliations": [ "Harvard University", "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2605.16699v1", "arxiv_id": "2605.16699v1", "title": "Your SaaS Is an Insurance Product: A Modeling Framework", "authors": [ "Caio Gomes" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.16699v1", "field": "finance", "role": "object", "bullets": [ "Conceptual modeling framework mapped to publicly observable subscription tiers in two domains, capped-usage LLM services such as Claude Code and ChatGPT and cloud platforms such as Vercel and Cloudflare Workers.", "No language model is used as a research tool; ChatGPT and Claude subscriptions appear as example products priced with actuarial frequency-severity decomposition, premium principles, and Monte Carlo reserve adequacy.", "Argues capped-usage SaaS pricing is structurally identical to insurance rather than analogous, offering actuarial vocabulary and tools and demonstrating divergence from unit economics through a worked example." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 222, "authors_detailed": [ { "name": "Caio Gomes", "url": "https://openalex.org/A5136485452", "inst": "Junta de Andalucía" } ], "affiliations": [ "Junta de Andalucía" ] }, { "uid": "doi:10.2139/ssrn.6770779", "doi": "10.2139/ssrn.6770779", "title": "Unpacking Social Exposure", "authors": [ "Mingyang Liu", "Zacharias Sautner", "Laurence van Lent", "Ruishen Zhang" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6770779", "field": "finance", "role": "instrument", "bullets": [ "Earnings call transcripts for more than 15,000 firms across 93 countries from 2003 to 2024, with the firm as the unit of observation.", "Large language models, not named, measure social exposure and split it into employee welfare, DEI, stakeholder outreach, ethical commitments, and crisis response; no validation figure is reported.", "The five domains show distinct, sometimes opposing links to labor and productivity; using the Dobbs decision, higher pre-Dobbs DEI exposure predicts lower worker outflow." ], "bullet_provenance": "ai", "validated": false, "salience": 68, "edition": 3, "audience": "general", "models": [], "n": 429, "authors_detailed": [ { "name": "Mingyang Liu", "url": "https://openalex.org/A5136240245", "inst": "Frankfurt School of Finance & Management" }, { "name": "Zacharias Sautner", "url": "https://openalex.org/A5046526188", "inst": "University of Zurich" }, { "name": "Laurence van Lent", "url": "https://openalex.org/A5050922413", "inst": "Frankfurt School of Finance & Management" }, { "name": "Ruishen Zhang", "url": "https://openalex.org/A5022908954", "inst": "University of Hong Kong" } ], "affiliations": [ "Frankfurt School of Finance & Management", "University of Zurich", "University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6755621", "doi": "10.2139/ssrn.6755621", "title": "Premium Tea as the Third Regime 4 Datapoint: A Single-category Replication of the v0.13 Covariate-saturated weak Finding", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6755621", "field": "management", "role": "object", "bullets": [ "Premium tea category with 17 eligible brands worldwide, measured at two waves on 29 April and 7 May 2026, within the AIAS brand-measurement programme.", "AI Presence is measured as the rate each brand appears across matched LLM responses to category-recommendation prompts, with the model family not stated, tested against Google Trends rank.", "Bivariate Spearman correlation between AI Presence and Google Trends is -0.066 and -0.134 across waves, confirming a weak covariate-saturated regime for premium tea." ], "bullet_provenance": "ai", "validated": false, "validation_note": "Google Trends external proxy, weak correlation", "salience": 28, "edition": 3, "audience": "general", "models": [], "n": 795, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "Samsung (United States)" } ], "affiliations": [ "Samsung (United States)" ] }, { "uid": "doi:10.2139/ssrn.6720578", "doi": "10.2139/ssrn.6720578", "title": "Explainability, Fairness, and Governance in Credit Risk Machine Learning: A Unified Survey and Research Agenda", "authors": [ "Akshay Sharma" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6720578", "field": "finance", "role": "method", "bullets": [ "Survey of 247 papers published between 2010 and 2026 on machine learning applied to credit risk decisioning in regulated lending environments.", "No model is applied; the survey taxonomizes scorecard, gradient boosting, deep learning, graph neural network, and large language model approaches against explainability, fairness, and governance criteria.", "Concludes that no existing system simultaneously satisfies all three requirements for production deployment and identifies eight open challenges with a five-year research agenda." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 796, "authors_detailed": [ { "name": "Akshay Sharma", "url": "https://openalex.org/A5136260800", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6767646", "doi": "10.2139/ssrn.6767646", "title": "Rethinking Business Education in the Age of Generative AI: Advancing Skill-Based Learning for Workforce Readiness", "authors": [ "Jung-Hwan Kim" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6767646", "field": "management", "role": "object", "bullets": [ "Bibliometric analysis of literature on teaching with AI, applied to designing an instructional activity in an undergraduate business course on retail promotion.", "No model applied as an instrument; ChatGPT is framed as a career-relevant managerial decision-support tool within a scaffolded learning sequence rather than automated content production.", "Presents a practical model for integrating AI into business education to strengthen AI literacy, human-AI collaboration, and career-ready managerial competencies; no quantitative outcome reported." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 33, "edition": 3, "audience": "general", "validated": null, "n": 797, "authors_detailed": [ { "name": "Jung-Hwan Kim", "url": "https://openalex.org/A5100360487", "inst": "University of South Carolina" } ], "affiliations": [ "University of South Carolina" ] }, { "uid": "doi:10.2139/ssrn.6753121", "doi": "10.2139/ssrn.6753121", "title": "The Heterogeneity Imperative: AI, Machine Learning, Conjoint Analysis, and the Unfinished Agenda of Wagner Kamakura", "authors": [ "Yang Wang", "Amit I. Pazgal" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6753121", "field": "management", "role": "method", "bullets": [ "Conceptual chapter evaluating AI and machine learning effects on conjoint analysis through Wagner Kamakura's heterogeneity imperative; no empirical sample is stated.", "Model family not stated; assesses adaptive design, generative product design, large language model synthetic respondents, and deep-learning utility estimation, and proposes a coefficient-test validation framework.", "Argues LLM synthetic respondents flatten consumer heterogeneity through stereotyping, while the concomitant-variable coefficient test can check whether synthetic data preserves covariate-preference mapping." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 798, "authors_detailed": [ { "name": "Yang Wang", "url": "https://openalex.org/A5136253666", "inst": "China University of Mining and Technology" }, { "name": "Amit Pazgal", "url": "https://openalex.org/A5061207626", "inst": "Rice University" } ], "affiliations": [ "Rice University", "China University of Mining and Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6768010", "doi": "10.2139/ssrn.6768010", "title": "Artificial Receivers in Entrepreneurial Finance:How AI Architectures Interpret Entrepreneurial Signals?", "authors": [ "Tom Shachaf", "Eliran Solodoha", "Miki Malul" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6768010", "field": "finance", "role": "agent", "bullets": [ "413 technology ventures founded in 2015 with verified outcomes for survival, closure, exit, and follow-on funding tracked through 2023.", "Six leading AI architectures, not individually named, generated probabilistic outcome predictions from identical startup inputs, conceptualized as artificial signal receivers.", "Predictions diverge substantially across architectures in performance, signal weighting, error patterns, and demographic and geographic differentials, especially for ambiguous outcomes." ], "bullet_provenance": "ai", "validated": false, "validation_note": "verified venture outcomes, no accuracy figure stated", "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 799, "authors_detailed": [ { "name": "Tom Shachaf", "url": "https://openalex.org/A5136091937", "inst": "" }, { "name": "Eliran Solodoha", "url": "https://openalex.org/A5089935890", "inst": "Ben-Gurion University of the Negev" }, { "name": "Miki Malul", "url": "https://openalex.org/A5026608115", "inst": "Ben-Gurion University of the Negev" } ], "affiliations": [ "Ben-Gurion University of the Negev" ] }, { "uid": "doi:10.2139/ssrn.6752980", "doi": "10.2139/ssrn.6752980", "title": "Will Business Schools Survive in the Era of Generative AI?", "authors": [ "Guillaume Coqueret" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6752980", "field": "economics", "role": "object", "bullets": [ "Equilibrium model of business education calibrated to the French grandes ecoles market of roughly 30 schools, extended to a dynamic stochastic setting.", "No language model is used; generative AI enters theoretically as a demand-side shock that compresses the labor-market returns justifying tuition.", "Model predicts roughly 10 percent of schools face market exit over two to three decades under limited replacement, sooner if AI diffusion accelerates." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 800, "authors_detailed": [ { "name": "Guillaume Coqueret", "url": "https://openalex.org/A5136211076", "inst": "École de management de Lyon" } ], "affiliations": [ "École de management de Lyon" ] }, { "uid": "doi:10.2139/ssrn.6755498", "doi": "10.2139/ssrn.6755498", "title": "Do AIS Dream of Electric Duties?", "authors": [ "Robert J. Rhee" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6755498", "field": "management", "role": "object", "bullets": [ "Legal-conceptual book chapter analyzing whether an agentic AI could assume and satisfy the fiduciary duties a human manager owes to a firm and its owners; no data or empirical sample.", "No language model is used or named; the analysis is doctrinal, covering preconditions for AI legal personhood, accountability mechanisms, liability of AI and owners, and the alignment problem.", "Argues an AI manager is a nonstarter unless duty and accountability can be implemented, and that agency risk is mitigated by contracting for duties, liability-incentivized monitoring, and reserved removal power." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1096, "authors_detailed": [ { "name": "Robert J. Rhee", "url": "https://openalex.org/A5074580483", "inst": "University of Florida" } ], "affiliations": [ "University of Florida" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6771165", "doi": "10.2139/ssrn.6771165", "title": "Forecasting S&P 500 stock prices using Transformer models and hybrid LSTM-Transformer architectures: A comparative analysis", "authors": [ "Iván Arribas", "Fernando García García", "María del Carmen García", "Javier Oliver Muncharaz" ], "posted": "2026-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6771165", "field": "finance", "role": "method", "bullets": [ "Daily closing prices of S&P 500 constituent stocks from 2015 to 2026, used to forecast future closing prices across a large-scale equity dataset.", "Compares four deep learning forecasters, LSTM, bidirectional LSTM, a pure Transformer, and a hybrid LSTM-Transformer; no language model family is named and no accuracy figure is reported in the abstract.", "Reports that attention-based architectures capture complex temporal dependencies, with the hybrid combining sequential and long-range modelling, but gives no magnitudes for forecast accuracy." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1097, "authors_detailed": [ { "name": "Iván Arribas", "url": "https://openalex.org/A5027450692", "inst": "Universitat de València" }, { "name": "Fernando García", "url": "https://openalex.org/A5074274486", "inst": "Universitat Politècnica de València" }, { "name": "María del Carmen García", "url": "https://openalex.org/A5136252661", "inst": "Universitat de València" }, { "name": "Javier Oliver Muncharaz", "url": "https://openalex.org/A5136242426", "inst": "Universitat Politècnica de València" } ], "affiliations": [ "Universitat de València", "Universitat Politècnica de València" ] }, { "uid": "doi:10.2139/ssrn.6767137", "doi": "10.2139/ssrn.6767137", "title": "How Digital Intelligence Drives Corporate Green Innovation: Evidence from China", "authors": [ "Tong Feng", "Xiaomin Wang", "Tianxin Wang", "Qun Li", "Lei Guo" ], "posted": "2026-05-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6767137", "field": "economics", "role": "instrument", "bullets": [ "Chinese A-share listed firms from 2007 to 2023, with the firm-year as the unit and green invention patents versus utility models as outcomes.", "Large language models, not named, construct a semantic digital intelligence index from firm text, used within double machine learning and a shift-share instrument; no accuracy check reported.", "Digital intelligence raises substantive green invention patents rather than utility models, working through eased financing constraints, better resource allocation, and stronger knowledge absorptive capacity." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 428, "authors_detailed": [ { "name": "Tong Feng", "url": "https://openalex.org/A5100745512", "inst": "Gansu Agricultural University" }, { "name": "Xiaomin Wang", "url": "https://openalex.org/A5136147379", "inst": "" }, { "name": "Tianxin Wang", "url": "https://openalex.org/A5136114628", "inst": "" }, { "name": "Qun Li", "url": "https://openalex.org/A5136089283", "inst": "" }, { "name": "Lei Guo", "url": "https://openalex.org/A5136131409", "inst": "" } ], "affiliations": [ "Gansu Agricultural University" ] }, { "uid": "doi:10.2139/ssrn.6736563", "doi": "10.2139/ssrn.6736563", "title": "Zero-Shot Meets ZeroHedge: Multi-Dimensional LLM Sentiment Decomposition of Contrarian Media for VIX Prediction", "authors": [ "Yan Sun" ], "posted": "2026-05-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6736563", "field": "finance", "role": "instrument", "bullets": [ "25,137 ZeroHedge articles from 2021 to 2025, an editorially bearish US financial outlet, matched daily to VIX history for volatility forecasting.", "LLaMA 3 in zero-shot scores each article on macroeconomic uncertainty, institutional action, and risk-off intensity with a bias-correcting prompt, then feeds a late-fusion LSTM; no human-label validation.", "At the 8-day horizon the macroeconomic-uncertainty dimension reaches PR-AUC 0.708 versus 0.695 for HAR(3), 0.688 for a VIX-only LSTM, and 0.620 for FinBERT." ], "bullet_provenance": "ai", "models": [ "legacy", "llama" ], "open_weights": true, "validated": false, "salience": 52, "edition": 3, "audience": "technical", "n": 790, "authors_detailed": [ { "name": "Yan Sun", "url": "https://openalex.org/A5136154750", "inst": "Pennsylvania State University" } ], "affiliations": [ "Pennsylvania State University" ] }, { "uid": "doi:10.2139/ssrn.6761767", "doi": "10.2139/ssrn.6761767", "title": "Generative Artificial Intelligence and Startup Innovation in India: Growth Drivers, Investment Dynamics and Sustainability Challenges — An Analytical Study", "authors": [ "Nital Kothari" ], "posted": "2026-05-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6761767", "field": "management", "role": "object", "bullets": [ "Primary survey of 150 respondents in India, including entrepreneurs, founders, investors, technology professionals, academics, and management students, under a descriptive and analytical design.", "Generative AI adoption is the object; drivers of startup growth are analyzed with weighted mean, correlation, and regression, with no language model applied by the researchers.", "Technological innovation, AI adoption, and investor confidence emerge as the strongest growth drivers, while regulatory uncertainty, cybersecurity risk, and funding dependence threaten long-term sustainability." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 791, "authors_detailed": [ { "name": "Nital Kothari", "url": "https://openalex.org/A5136035529", "inst": "Jai Hind College" } ], "affiliations": [ "Jai Hind College" ] }, { "uid": "doi:10.2139/ssrn.6732098", "doi": "10.2139/ssrn.6732098", "title": "Assertive or Sycophantic? Tone-Task Fit in Human-AI Service Interaction", "authors": [ "Jianan Liu", "Siqiang Wang", "Yong Tan" ], "posted": "2026-05-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6732098", "field": "management", "role": "object", "bullets": [ "Two-stage study of human-AI service interaction combining a randomized controlled experiment with a customized real-time interaction environment; the number of participants is not stated.", "LLM service-agent tone is manipulated between assertive and sycophantic, with the model family not stated, and user outcomes are compared across hedonic and utilitarian tasks.", "Assertive tone improves hedonic-task outcomes through perceived authenticity, while sycophancy improves utilitarian-task outcomes through perceived warmth, a context-dependent crossover in tone-task fit." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 792, "authors_detailed": [ { "name": "Jianan Liu", "url": "https://openalex.org/A5123179751", "inst": "Washington State University" }, { "name": "Siqiang Wang", "url": "https://openalex.org/A5041262442", "inst": "University of Washington" }, { "name": "Yong Tan", "url": "https://openalex.org/A5122903692", "inst": "University of Washington" } ], "affiliations": [ "Washington State University", "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.6734723", "doi": "10.2139/ssrn.6734723", "title": "Anthropomorphic Behaviors of AI", "authors": [ "Amir Karami", "Christoph Lutz", "Mohammad Hossein Jarrahi" ], "posted": "2026-05-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6734723", "field": "management", "role": "object", "bullets": [ "Systematic observation and categorization of anthropomorphic behaviors in AI outputs, applied to ChatGPT responses, with the sample of responses not stated.", "A behaviorally driven taxonomy classifies human-like cues such as empathy in ChatGPT outputs; no accuracy or agreement check against ground truth is reported.", "Identifies key forms of anthropomorphic behavior and their implications for theory, practice, and ethics, and proposes a foundation for automated detection." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 33, "edition": 3, "audience": "general", "validated": null, "n": 793, "authors_detailed": [ { "name": "Amir Karami", "url": "https://openalex.org/A5010380816", "inst": "Kennesaw State University" }, { "name": "Christoph Lutz", "url": "https://openalex.org/A5136126996", "inst": "BI Norwegian Business School" }, { "name": "Mohammad Hossein Jarrahi", "url": "https://openalex.org/A5040259613", "inst": "University of North Carolina at Chapel Hill" } ], "affiliations": [ "University of North Carolina at Chapel Hill", "Kennesaw State University", "BI Norwegian Business School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6752298", "doi": "10.2139/ssrn.6752298", "title": "Divergence in Climate Change Communication: LLM-based Evidence from the IPCC and the Press", "authors": [ "Sebastian Galiani", "Franco Mettola La Giglia", "Raul A. Sosa" ], "posted": "2026-05-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6752298", "alt_urls": [ "https://doi.org/10.2139/ssrn.6785217" ], "field": "economics", "role": "instrument", "bullets": [ "About 115,000 matched claim pairs drawn from all six IPCC Assessment Reports between 1990 and 2023 and ten major US and UK newspapers.", "LLMs score matched claims for severity across the technical summary, policymaker summary, and press stages, with the model family not stated and no human-coding validation reported.", "Both the policymaker summary and press coverage shift toward the more severe end within accepted scientific ranges, driven mainly by emphasizing higher-impact magnitudes." ], "bullet_provenance": "ai", "validated": false, "salience": 56, "edition": 3, "audience": "general", "models": [], "n": 794, "authors_detailed": [ { "name": "Sebastián Galiani", "url": "https://openalex.org/A5029505552", "inst": "Economie Publique" }, { "name": "Franco Mettola La Giglia", "url": "https://openalex.org/A5123406449", "inst": "University of San Andrés" }, { "name": "Raul A. Sosa", "url": "https://openalex.org/A5132583641", "inst": "" } ], "affiliations": [ "Economie Publique", "University of San Andrés" ] }, { "uid": "doi:10.2139/ssrn.6762245", "doi": "10.2139/ssrn.6762245", "title": "Leading Human-Agent Teams: The ORCHESTRA Framework for Accountable AI Work", "authors": [ "Arkapravo Sarkar", "Shashwat Mohapatra" ], "posted": "2026-05-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6762245", "field": "management", "role": "object", "bullets": [ "Conceptual management article on leading human-agent teams, drawing on literature on employee AI adoption, human-AI collaboration, algorithmic management, and responsible AI governance; no empirical sample.", "No language model is used or named; the paper develops a framework rather than running a model, organizing leadership around objectives, guardrails, capabilities, handoffs, escalation, supervision, telemetry, and auditability.", "Proposes the ORCHESTRA framework across three phases, arguing sustainable value from agentic AI depends on designing conditions where human judgment and accountability work with machine capability rather than replacing judgment." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1095, "authors_detailed": [ { "name": "Arkapravo Sarkar", "url": "https://openalex.org/A5136141974", "inst": "" }, { "name": "Shashwat Mohapatra", "url": "https://openalex.org/A5136181929", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6756303", "doi": "10.2139/ssrn.6756303", "title": "Beyond Accuracy: How Content Format Shapes User-Perceived Helpfulness of LLM-Generated Answers", "authors": [ "Sophie Hundertmark", "Nils Hafner" ], "posted": "2026-05-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6756303", "field": "management", "role": "object", "bullets": [ "2,160 forced-choice decisions from 36 experienced AI users judging LLM answers to 20 realistic information-seeking prompts, with participants blinded to both system identity and format category.", "ChatGPT, Gemini, and Claude, versions not stated, each generated answers in four formats (prose, FAQ, fact list, table); the study measured human preference, not accuracy against any ground truth.", "Format preferences were system specific: FAQ was favored for ChatGPT, prose for Gemini, and Claude read as helpful across formats, while tabular formatting was consistently least preferred." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 81, "authors_detailed": [ { "name": "Sophie Hundertmark", "url": "https://openalex.org/A5136084226", "inst": "" }, { "name": "Nils Hafner", "url": "https://openalex.org/A5037275245", "inst": "University of Lucerne" } ], "affiliations": [ "University of Lucerne" ] }, { "uid": "doi:10.2139/ssrn.6758459", "doi": "10.2139/ssrn.6758459", "title": "Predicting the Spanish IBEX-35 Banking Sector Stock Prices with LLM-Based News Sentiment Analysis and LSTM", "authors": [ "ELENA CONDERANA-MEDEM", "Maria Coronado", "Eduardo C. Garrido-Merchan" ], "posted": "2026-05-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6758459", "field": "finance", "role": "instrument", "bullets": [ "IBEX-35 banking sector stocks in Spain, with LSTM models trained on technical indicators and Reuters financial headlines from May 2020 to May 2025 and closing price as target.", "FinBERT, an encoder-only pre-trained language model, extracted sentiment from company-specific Reuters headlines that was added to technical indicators as LSTM inputs, with no check of sentiment against labels reported.", "Adding FinBERT sentiment improved LSTM forecasting across R2, RMSE, and MAE relative to technical indicators alone; no magnitudes are given." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": false, "validation_note": "prediction metrics only, no sentiment ground truth", "salience": 42, "edition": 3, "audience": "technical", "n": 148, "authors_detailed": [ { "name": "Elena Conderana-Medem", "url": "https://openalex.org/A5116832097", "inst": "Universidad Pontificia Comillas" }, { "name": "Maria Coronado", "url": "https://openalex.org/A5065894691", "inst": "Universidad Pontificia Comillas" }, { "name": "Eduardo C. Garrido‐Merchán", "url": "https://openalex.org/A5070783543", "inst": "Universidad Pontificia Comillas" } ], "affiliations": [ "Universidad Pontificia Comillas" ] }, { "uid": "doi:10.2139/ssrn.6709818", "doi": "10.2139/ssrn.6709818", "title": "Evaluating Large Language Model Ensembles as Probabilistic Forecasters: An Empirical Study on Live Real-World Events", "authors": [ "Demetre Tsiklauri" ], "posted": "2026-05-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6709818", "field": "economics", "role": "agent", "bullets": [ "Twenty-one off-the-shelf language models forecast 23 Polymarket binary contracts at three pre-resolution snapshots, using repeated snapshot sampling and fixed cross-model pooling operators on live real-world events.", "Models including GPT o3 and Qwen 3-235B produce probabilities pooled into ensembles such as MedianOfMedians, scored by Brier and log loss against realized outcomes, Polymarket medians, and a coin-flip baseline.", "MedianOfMedians beats GPT o3 at every snapshot and the coin-flip baseline on most contracts, but remains less accurate than the Polymarket mean, especially after prices converge." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "Brier and log loss against resolved outcomes and Polymarket medians", "salience": 55, "edition": 3, "audience": "technical", "n": 221, "authors_detailed": [ { "name": "Demetre Tsiklauri", "url": "https://openalex.org/A5136032731", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6707258", "doi": "10.2139/ssrn.6707258", "title": "AI Search Impact Assessment- The ASIA Framework", "authors": [ "Avinash Tripathi" ], "posted": "2026-05-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6707258", "field": "management", "role": "object", "bullets": [ "143 business-to-business SaaS client web properties tracked over twelve months from June 2024 to June 2025, combining search analytics with embedded case studies.", "AI search platforms including ChatGPT, Claude, Perplexity, and Google AI Overviews are the object of study; four proprietary metrics were validated against observed revenue outcomes.", "Median click efficiency ratio was 0.78, meaning 22 percent fewer clicks per impression; 73 percent of AI-cited firms saw rising mentions alongside declining organic traffic." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 427, "authors_detailed": [ { "name": "Avinash Tripathi", "url": "https://openalex.org/A5136031504", "inst": "Gemalto (Israel)" } ], "affiliations": [ "Gemalto (Israel)" ] }, { "uid": "doi:10.2139/ssrn.6759411", "doi": "10.2139/ssrn.6759411", "title": "Mind the Decision Void: A Systematic Literature Review of Generative AI Integration in Management Education", "authors": [ "Suvodip Sen" ], "posted": "2026-05-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6759411", "field": "management", "role": "object", "bullets": [ "A total of 174 peer-reviewed articles from Scopus and Web of Science on generative AI in management education, synthesized under PRISMA reporting guidelines.", "Generative AI is the studied object; the review maps 277 antecedent-decision-outcome associations using combined ADO and TCM frameworks rather than applying any model to data.", "Associations split into 208 positive, 43 neutral, and 26 negative, revealing a decision void where the literature rarely explains how AI enters pedagogy and curriculum design." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 38, "edition": 3, "audience": "general", "validated": null, "n": 788, "authors_detailed": [ { "name": "Suvodip Sen", "url": "https://openalex.org/A5064333694", "inst": "Indian Institute of Management Indore" } ], "affiliations": [ "Indian Institute of Management Indore" ] }, { "uid": "doi:10.2139/ssrn.6714238", "doi": "10.2139/ssrn.6714238", "title": "The Irrecoverable Institution - Why Replayability - Not Explainability - Is the Governance Standard for Agentic Banking", "authors": [ "Deepak Aggarwal" ], "posted": "2026-05-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6714238", "field": "finance", "role": "object", "bullets": [ "Conceptual governance paper analyzing four financial-institution failures: Silicon Valley Bank in 2023, Knight Capital in 2012, the UK gilt collapse in 2022, and a credit agentic decision failure.", "Agentic AI is the object; the paper proposes decision replay as a governance standard, contrasted with explainability methods such as LIME and SHAP, with no model applied.", "Across the four cases it attributes 35 to 60 percent of firm losses to failure to track, audit, and act on decision paths, motivating replayability over explainability." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 789, "authors_detailed": [ { "name": "Deepak Aggarwal", "url": "https://openalex.org/A5136068584", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6712478", "doi": "10.2139/ssrn.6712478", "title": "Detecting Algorithmic Collusion in LLM-driven Markets: A Hybrid Causal Framework", "authors": [ "Carlos Eduardo Veras Neves" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6712478", "field": "economics", "role": "agent", "bullets": [ "Repeated Bertrand pricing duopoly where large language models act as pricing agents, run under a silence condition with no channel and a communication condition with pre-play dialogue.", "GPT-5 mini and Gemini 3.1 Flash Lite set prices each round, with no comparison to a human or ground truth benchmark; a causal detection pipeline analyses the price series.", "Both models converge to supra-competitive prices without any coordination protocol, and adding a language channel raises the collusion index by 0.730 (p = 0.019, d = 1.45)." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "legacy" ], "open_weights": false, "salience": 62, "edition": 3, "audience": "general", "validated": null, "n": 101, "authors_detailed": [ { "name": "Carlos Eduardo Véras Neves", "url": "https://openalex.org/A5042041869", "inst": "Administrative Council for Economic Defense" } ], "affiliations": [ "Administrative Council for Economic Defense" ] }, { "uid": "doi:10.2139/ssrn.6694918", "doi": "10.2139/ssrn.6694918", "title": "Incentive Issues in Developing Factual LLMs", "authors": [ "Xiang Cheng", "Manmohan Aseri" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6694918", "field": "economics", "role": "object", "bullets": [ "A game-theoretic model of fact-sharing between large language model developers and traditional content providers such as news organizations, with no empirical sample.", "The LLM is the modeled economic object, not a tool used by the authors; the analysis covers incentives to share costly factual data and several compensation schemes.", "Partnerships with low-monetizability providers lower the LLM's factual quality by reducing competition, and compensation can further reduce quality when provider monetizability is low or moderate." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 219, "authors_detailed": [ { "name": "Xiang Cheng", "url": "https://openalex.org/A5135938611", "inst": "" }, { "name": "Manmohan Aseri", "url": "https://openalex.org/A5047069572", "inst": "University of Maryland, College Park" } ], "affiliations": [ "University of Maryland, College Park" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6712540", "doi": "10.2139/ssrn.6712540", "title": "Smart Agent-Based Modelling with LLMs: Leveraging Large Language Models for a Better Understanding of Algorithmic Collusion", "authors": [ "Carlos Eduardo Veras Neves", "tanise bussmann" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6712540", "field": "economics", "role": "agent", "bullets": [ "Agent-based simulations of a Bertrand price duopoly, run in English and Portuguese within a computational antitrust framework.", "LLM-driven agents set prices as competing firms and their emergent behavior is the object of study; the model family is not stated and no ground-truth validation applies.", "Agents stabilized prices above competitive levels without instruction to collude, and inter-agent communication amplified collusive behavior while linguistic context shifted outcomes." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "salience": 52, "edition": 3, "audience": "general", "validated": null, "n": 220, "authors_detailed": [ { "name": "Carlos Eduardo Véras Neves", "url": "https://openalex.org/A5042041869", "inst": "Administrative Council for Economic Defense" }, { "name": "tanise bussmann", "url": "https://openalex.org/A5135969112", "inst": "Administrative Council for Economic Defense" } ], "affiliations": [ "Administrative Council for Economic Defense" ] }, { "uid": "doi:10.2139/ssrn.6713620", "doi": "10.2139/ssrn.6713620", "title": "Agents, Not Algorithms: The Tradeoffs of Decision-Time Reasoning in AI Trading", "authors": [ "Ing-Haw Cheng", "Maurice Granger", "Justin Shi", "Vasily Strela" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6713620", "field": "finance", "role": "agent", "bullets": [ "A real-time market simulator in which an LLM agent performs tender selection and execution, with reasoning intensity and market speed varied experimentally.", "Agents built on frontier models (not named) reason about each decision in real time rather than following a fixed policy, with a deterministic algorithm as the limiting case.", "Greater reasoning improves selection and conditional execution but consumes time, producing stuck inventory and expired-tender losses when the market moves faster." ], "bullet_provenance": "ai", "salience": 62, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 309, "authors_detailed": [ { "name": "Ing-Haw Cheng", "url": "https://openalex.org/A5019063200", "inst": "Dartmouth College" }, { "name": "Maurice Granger", "url": "https://openalex.org/A5120784583", "inst": "Royal Bank of Canada" }, { "name": "Justin Shi", "url": "https://openalex.org/A5135997078", "inst": "" }, { "name": "Vasily Strela", "url": "https://openalex.org/A5055752951", "inst": "Dartmouth College" } ], "affiliations": [ "Dartmouth College", "Royal Bank of Canada" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6695678", "doi": "10.2139/ssrn.6695678", "title": "From Words to Embeddings: Text Representation and the Information Content of Financial News", "authors": [ "Huaye Zeng", "Diego Amaya" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6695678", "field": "finance", "role": "instrument", "bullets": [ "135,403 Dow Jones news articles matched to high-frequency returns on five asset-class ETFs; sample period and geography not stated, unit is the news article.", "News is classified into topics with keyword matching, LDA, and large language model embeddings; the specific model is not named and no ground-truth accuracy check is reported.", "The share of topics tied to extreme returns rises from 31.4 percent under keywords to 54.1 percent under LDA and 67.8 percent under embeddings, peaking at the 60-second horizon." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 426, "authors_detailed": [ { "name": "Huaye Zeng", "url": "https://openalex.org/A5111188853", "inst": "Harvard University" }, { "name": "Diego Amaya", "url": "https://openalex.org/A5024804298", "inst": "Wilfrid Laurier University" } ], "affiliations": [ "Harvard University", "Wilfrid Laurier University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6676238", "doi": "10.2139/ssrn.6676238", "title": "Algorithmic Accountability in the Age of Agentic Accounting: Bridging the \"Skepticism Gap\" in Autonomous Multi-Agent Workflows", "authors": [ "Muhammad Bilal Mianoor" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6676238", "field": "accounting", "role": "object", "bullets": [ "Conceptual research note with no empirical sample; addresses the auditing profession and Australian regulatory shifts around autonomous multi-agent financial workflows.", "No model is used; the note synthesizes developments in agentic AI optimization and their effect on professional auditor skepticism and judgment.", "Identifies a skepticism gap of eroded auditor judgment and proposes a tri-level verification model to restore human accountability in automated financial ecosystems." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 560, "authors_detailed": [ { "name": "Muhammad Bilal Mianoor", "url": "https://openalex.org/A5135928602", "inst": "Universal Technical Institute" } ], "affiliations": [ "Universal Technical Institute" ] }, { "uid": "doi:10.2139/ssrn.6691159", "doi": "10.2139/ssrn.6691159", "title": "Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption", "authors": [ "Ravish Gupta", "Saket Kumar Saket Kumar" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6691159", "field": "economics", "role": "object", "bullets": [ "236 occupations across six information-intensive SOC groups (financial, legal, healthcare, sales, clerical) in five US technology regions, projected over 2025 to 2030.", "No language model is applied; an Agentic Task Exposure score is computed from O*NET task data using calibrated AI capability and adoption parameters rather than regression, model unnamed.", "93.2 percent of occupations cross the moderate-risk threshold (ATE at least 0.35) in top-tier regions by 2030, with credit analysts and judges reaching 0.43 to 0.47." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 783, "authors_detailed": [ { "name": "Ravish Gupta", "url": "https://openalex.org/A5132734878", "inst": "Institute of Electrical and Electronics Engineers" }, { "name": "Saket Kumar Saket Kumar", "url": "https://openalex.org/A5135950666", "inst": "" } ], "affiliations": [ "Institute of Electrical and Electronics Engineers" ] }, { "uid": "doi:10.2139/ssrn.6696818", "doi": "10.2139/ssrn.6696818", "title": "KYS: Know Your Swarm - A Governance Framework for Multi-Agent AI Systems in Autonomous Finance", "authors": [ "Amna Usman Chaudhry" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6696818", "field": "finance", "role": "object", "bullets": [ "Conceptual analysis of multi-agent AI in financial services, drawing on agentic payments infrastructure, cross-jurisdiction AI governance frameworks, and sector adoption data; no formal sample.", "No model is run; the paper studies accountability for autonomous multi-agent AI and proposes the Know Your Swarm framework of five governance pillars, model names not stated.", "Identifies a swarm accountability gap and a delegation failure mode where responsibility for collective outcomes such as credit decisions and fraud flags cannot be assigned." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 784, "authors_detailed": [ { "name": "Amna Usman Chaudhry", "url": "https://openalex.org/A5135937460", "inst": "Chinese Academy of Governance" } ], "affiliations": [ "Chinese Academy of Governance" ] }, { "uid": "doi:10.2139/ssrn.6703658", "doi": "10.2139/ssrn.6703658", "title": "AI FinOps: A Governance Framework for Cost-Efficient and Responsible Generative AI at Enterprise Scale", "authors": [ "Rama Krishna Kumar Lingamgunta" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6703658", "field": "management", "role": "object", "bullets": [ "Conceptual framework for governing generative AI cost and accountability at enterprise scale, drawn from FinOps practice rather than an empirical sample.", "No model is run; the paper analyses generative AI cost drivers and proposes the AI FinOps framework with a six-dimension cost model, three-role operating model, and usage-telemetry schema.", "Argues generative AI cost scales with usage like a taxi fleet rather than shared capacity, so existing FinOps frameworks fall short and need usage-driven cost attribution." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 785, "authors_detailed": [ { "name": "RAMA KRISHNA KUMAR LINGAMGUNTA", "url": "https://openalex.org/A5122519550", "inst": "Enable Ireland" } ], "affiliations": [ "Enable Ireland" ] }, { "uid": "doi:10.2139/ssrn.6704078", "doi": "10.2139/ssrn.6704078", "title": "AI-Driven Autonomous Enterprises and the Future of Work: Impact, Ethics, and Value Creation by 2026", "authors": [ "Abhinav Mahajan" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6704078", "field": "management", "role": "object", "bullets": [ "Practitioner-scholar review synthesizing randomized experiments, field studies, governance standards, and labor-market research on AI-driven autonomous enterprises through 2026; no new data collected.", "No model is run; the review surveys evidence on AI agents that retrieve, call tools, draft, and act, abstracting patterns into a maturity model, model names not stated.", "Reports substantial task-level productivity gains in writing, support, consulting, and programming, but degradation outside validated task boundaries and heavy dependence on workflow redesign." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 786, "authors_detailed": [ { "name": "Abhinav Mahajan", "url": "https://openalex.org/A5135918121", "inst": "Clayton Homes" } ], "affiliations": [ "Clayton Homes" ] }, { "uid": "doi:10.2139/ssrn.6689518", "doi": "10.2139/ssrn.6689518", "title": "From Specificity to Universalization: The Underlying Logic and Economic Implications of AI-Driven Government Automation", "authors": [ "Ping Xu" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6689518", "field": "economics", "role": "object", "bullets": [ "Economic analysis of AI-driven government service automation, illustrated with China pilots including Jiangxi's Dingding Qiangdan (387 service types) and Beijing's Feng Xiaozhi agent across 11 departments.", "No model is run by the authors; the paper argues large language models and intelligent agents shift automation from bespoke specificity to near-zero marginal-cost universalization, formalized in a marginal-cost model.", "Reports signals of roughly 40 percent labor cost savings in the Jiangxi pilot and 2,300 anomaly alerts processed daily in Beijing, supporting the universalization logic." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 787, "authors_detailed": [ { "name": "Ping Xu", "url": "https://openalex.org/A5135992131", "inst": "Flow Analysis (United States)" } ], "affiliations": [ "Flow Analysis (United States)" ] }, { "uid": "doi:10.2139/ssrn.6689681", "doi": "10.2139/ssrn.6689681", "title": "From Information Retrieval to Agentic Action A Framework for Brand Visibility in AI-Mediated Markets", "authors": [ "Marcos Guimaraes Figueira" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6689681", "field": "management", "role": "object", "bullets": [ "Conceptual marketing article on digital brand visibility in AI-mediated markets, building on Puntoni and Davenport, with no empirical data.", "No language model is applied; the paper treats the AI assistant as a delegated decision-maker and integrates answer, generative, and agentic optimization into one framework.", "Identifies brand erasure as the central strategic risk and develops six propositions linking content and infrastructure choices to brand outcomes for human and machine audiences." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1092, "authors_detailed": [ { "name": "Marcos Guimarães Figueira", "url": "https://openalex.org/A5135658209", "inst": "Design Intelligence (United States)" } ], "affiliations": [ "Design Intelligence (United States)" ] }, { "uid": "doi:10.2139/ssrn.6696138", "doi": "10.2139/ssrn.6696138", "title": "Beyond the Principal-Agent Paradox: A Theory of Governance Failure and Mechanism Design in Agentic AI Systems", "authors": [ "Albert Adusei Brobbey" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6696138", "field": "management", "role": "object", "bullets": [ "Conceptual theory paper extending principal-agent theory to agentic AI, illustrated through financial-services deployment, with no empirical data.", "No language model is applied; the paper derives eight propositions on governance-failure conditions and mechanism-design principles for agentic AI systems.", "Proposes an Agentic Principal-Agent Inversion in which the overseer keeps nominal authority but loses informational, processing, and interpretive parity, yielding a triple asymmetry." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1093, "authors_detailed": [ { "name": "Albert Adusei Brobbey", "url": "https://openalex.org/A5135482810", "inst": "Meridian International Center" } ], "affiliations": [ "Meridian International Center" ] }, { "uid": "arxiv:2605.23962v1", "arxiv_id": "2605.23962v1", "title": "From Index to Equity: Pre-Training Transformers for Stock Return Prediction", "authors": [ "Marie Soehl Coolsaet", "Roberto Gallardo", "Zhen Gao" ], "posted": "2026-05-12", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.23962v1", "field": "finance", "role": "method", "bullets": [ "Toronto Stock Exchange index and individual TSX stocks, predicting intra-day return direction and return values; sample period not stated.", "A transformer pre-trained on the TSX index and fine-tuned on individual stocks, benchmarked against LSTM and XGBoost for direction and regression tasks.", "Pre-training lowered binary cross-entropy from 0.69 to 0.64 and the fine-tuned regression beat benchmarks on MSE, though ensemble and XGBoost gave higher average daily returns." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1094, "authors_detailed": [ { "name": "Marie Soehl Coolsaet", "url": "https://openalex.org/A5136999769", "inst": "" }, { "name": "Roberto Gallardo", "url": "https://openalex.org/A5137070953", "inst": "" }, { "name": "Zhen Gao", "url": "https://openalex.org/A5137078989", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6703798", "doi": "10.2139/ssrn.6703798", "title": "Can LLMs Mimic Household Surveys?: From Representative Agents to Population Distributions", "authors": [ "Ami Dalloul", "Moritz Pfeifer" ], "posted": "2026-05-12", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6703798", "field": "economics", "role": "agent", "bullets": [ "LLM-simulated household surveys of inflation expectations, benchmarked against real-world surveys that show wide cross-individual disagreement; unit of observation is an individual respondent. Period and geography not stated.", "An unnamed LLM generated survey responses that were compared to real survey averages and dispersion, and unlearning methods were applied to remove memorized training data; no accuracy statistic is reported.", "LLMs matched average expectations but responses collapsed into a narrow range, behaving as a single representative agent, while unlearning widened dispersion and improved replication of experimental results." ], "bullet_provenance": "ai", "validated": false, "validation_note": "compared to real survey means, no figure reported", "salience": 58, "edition": 2, "audience": "broad", "models": [], "n": 64, "authors_detailed": [ { "name": "Ami Dalloul", "url": "https://openalex.org/A5033997463", "inst": "University of Duisburg-Essen" }, { "name": "Moritz Pfeifer", "url": "https://openalex.org/A5006091997", "inst": "Economic Policy Institute" } ], "affiliations": [ "University of Duisburg-Essen", "Economic Policy Institute" ] }, { "uid": "doi:10.2139/ssrn.6705598", "doi": "10.2139/ssrn.6705598", "title": "Less Volume, More Variety: An Inverse Relationship Between LLM Output Length and Contrarian Discovery in Pharmaceutical Stock Selection", "authors": [ "HoKwang Kim" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6705598", "field": "finance", "role": "agent", "bullets": [ "One identical pharmaceutical sector long/short equity prompt submitted to four frontier language models on 4 May 2026; the unit is each model's set of stock picks in a single-period design with n = 4.", "ChatGPT, Claude, DeepSeek and Gemini each generated long/short recommendations, and the authors measured a contrarian discovery rate of unique picks per kilobyte, with no validation against realized returns.", "Output length correlated negatively with contrarian discovery, Spearman rho = -0.80 across all four models; the most compressed model, Gemini, produced 14 times more contrarian picks per kilobyte than DeepSeek." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "salience": 28, "edition": 3, "audience": "general", "validated": null, "n": 75, "authors_detailed": [ { "name": "HoKwang Kim", "url": "https://openalex.org/A5134549748", "inst": "New England Biolabs (United States)" } ], "affiliations": [ "New England Biolabs (United States)" ] }, { "uid": "doi:10.2139/ssrn.6685878", "doi": "10.2139/ssrn.6685878", "title": "The End of the Foundation Model Era: Commoditization, National Security, and the Path to AGI", "authors": [ "Jared James Grogan" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6685878", "field": "economics", "role": "object", "bullets": [ "Analytical essay on the foundation model industry from roughly 2020 to 2025, drawing on public financial disclosures, primary government and corporate sources, and literature in AI scaling and innovation economics.", "No model is run empirically; the paper reasons about commoditization, pre-training as a weak competitive moat, and open-weight deployment, and names no specific model family for any measurement.", "Argues the foundation model era has ended and the industry is restructuring along economic, technical, commercial and political axes, with open-weight models becoming an instrument of sovereign state control." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 46, "edition": 3, "audience": "general", "validated": null, "n": 182, "authors_detailed": [ { "name": "Jared James Grogan", "url": "https://openalex.org/A5133290203", "inst": "University of Indonesia" } ], "affiliations": [ "University of Indonesia" ] }, { "uid": "doi:10.2139/ssrn.6747440", "doi": "10.2139/ssrn.6747440", "title": "Generative AI Fuels Solo Entrepreneurship, but Teams Still Lead at the Top", "authors": [ "Hyunso Kim", "Hyo Kang", "Jaeyong Song" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6747440", "field": "management", "role": "object", "bullets": [ "Over 160,000 product launches on Product Hunt, observed before and after the public release of ChatGPT-3.5.", "The model is the object of study rather than a tool; the authors do not run an LLM but measure entrepreneurial entry responding to ChatGPT-3.5's availability.", "Entry rose sharply after ChatGPT-3.5, driven by solo entrepreneurs, but this growth was low-commitment and teams increasingly dominated the top tiers of platform rankings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "edition": 3, "audience": "general", "validated": null, "n": 217, "authors_detailed": [ { "name": "Hyunso Kim", "url": "https://openalex.org/A5127937644", "inst": "Seoul National University" }, { "name": "Hyo Kang", "url": "https://openalex.org/A5135904134", "inst": "Seoul National University" }, { "name": "Jaeyong Song", "url": "https://openalex.org/A5135861082", "inst": "" } ], "affiliations": [ "Seoul National University" ] }, { "uid": "doi:10.2139/ssrn.6746480", "doi": "10.2139/ssrn.6746480", "title": "Evolutionary Alpha Miner: Family-Aware Symbolic Alpha Discovery with LLM-Guided Hybridization A Feedback-Driven Framework for Correlation-Aware Formulaic Alpha Search", "authors": [ "Su Gao" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6746480", "field": "finance", "role": "method", "bullets": [ "Formulaic alpha discovery framed as an evolutionary search over symbolic programs, run across 45 search rounds using ordered parent-pair hybridization, with no external market dataset detailed.", "Large language models act as constrained symbolic program synthesizers generating alpha expressions from parent pairs; the model family is not stated and no ground-truth validation is reported.", "The system generated 3,857 valid candidates at an 83.33 percent executable rate, producing 65 confirmed and 862 strong-but-unlanded candidates for a structured repair pool." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 3, "audience": "technical", "models": [], "n": 218, "authors_detailed": [ { "name": "Su Gao", "url": "https://openalex.org/A5135829276", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6687940", "doi": "10.2139/ssrn.6687940", "title": "Agents of Intent: Why AI Agents Need Commander's Intent, Not Just Rules", "authors": [ "Christopher Baillie" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6687940", "field": "management", "role": "object", "bullets": [ "Conceptual essay on AI agent governance, anchored to the February 2026 'Agents of Chaos' empirical study of autonomous agent failures across several universities.", "No model is run by the author; large language model agents are discussed as the object, framed through the military metaphor of commander's intent.", "Argues that rule-based guardrails handle only anticipated cases and fail when situations change, so agents need conveyed intent rather than only explicit orders." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 307, "authors_detailed": [ { "name": "Christopher Baillie", "url": "https://openalex.org/A5112023801", "inst": "" } ] }, { "uid": "arxiv:2605.23955v2", "arxiv_id": "2605.23955v2", "title": "From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems", "authors": [ "Ruizhe Zhou", "Xiaoyang Liu", "Gaoyuan Du", "Yi Zheng", "Shouxi Ren", "Deepayan Chakrabarti", "Dengdu Jiang" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.23955v2", "field": "finance", "role": "method", "bullets": [ "Survey of reproducibility in financial AI across credit risk, fraud detection, and anti-money laundering, supplemented by first-party experiments on public financial datasets.", "Covers tabular models, graph networks, and LLM-based agentic workflows (models not named), and proposes a layered framework linking modality-specific determinism metrics to audit readiness.", "Documents mechanical nondeterminism: explanation rank instability in credit scoring, prediction flips in GNN fraud detection, and tensor-parallel output divergence in LLM entity extraction." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 308, "authors_detailed": [ { "name": "Ruizhe Zhou", "url": "https://openalex.org/A5101519148", "inst": "University of Chicago" }, { "name": "Xiaoyang Liu", "url": "https://openalex.org/A5137032959", "inst": "" }, { "name": "Gaoyuan Du", "url": "https://openalex.org/A5137068541", "inst": "" }, { "name": "Yi Zheng", "url": "https://openalex.org/A5137016686", "inst": "" }, { "name": "Shouxi Ren", "url": "https://openalex.org/A5137030010", "inst": "" }, { "name": "Deepayan Chakrabarti", "url": "https://openalex.org/A5078048346", "inst": "University Surgical Associates" }, { "name": "Dengdu Jiang", "url": "https://openalex.org/A5137008125", "inst": "" } ], "affiliations": [ "University of Chicago", "University Surgical Associates" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6746900", "doi": "10.2139/ssrn.6746900", "title": "Generative AI and Firm-Candidate Dynamics in the Labor Market", "authors": [ "Miaozhe Han", "Xianghua Lu", "Shuang Wen" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6746900", "field": "economics", "role": "object", "bullets": [ "Pipeline data from a leading bi-directional job-matching platform in China, comparing job categories with differing generative AI exposure on chats, resumes, contacts and interview conversions.", "Generative AI, no specific model named, is the object of study rather than a tool; exposure is measured at the job-category level and effects estimated with dynamic regression.", "In highly exposed jobs communication rises without more interviews: candidates initiate more but convert less while firms initiate less and convert more, as GenAI devalues domain-specific knowledge." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 424, "authors_detailed": [ { "name": "Miaozhe Han", "url": "https://openalex.org/A5036130672", "inst": "Hong Kong University of Science and Technology" }, { "name": "Xianghua Lu", "url": "https://openalex.org/A5135861042", "inst": "" }, { "name": "Shuang WEN", "url": "https://openalex.org/A5135471353", "inst": "" } ], "affiliations": [ "Hong Kong University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.6750021", "doi": "10.2139/ssrn.6750021", "title": "When Does Generative AI Improve Innovation Efficiency? Organisational Culture as a Complementary Capability", "authors": [ "Huanjia Ma", "Di Xiao" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6750021", "field": "management", "role": "object", "bullets": [ "US research-intensive public firms, using the November 2022 public release of ChatGPT as a technological shock to firm-level innovation efficiency; exact sample size and window not stated.", "ChatGPT is the object rather than a research tool; the design measures innovation efficiency around its release and tests moderation by organisational culture, with no model output evaluated.", "Innovation efficiency declined after the release, but the drop was smaller among firms with stronger innovation- and adaptability-oriented cultures, suggesting culture is a complementary capability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 3, "audience": "general", "validated": null, "n": 425, "authors_detailed": [ { "name": "Huanjia Ma", "url": "https://openalex.org/A5093445755", "inst": "University of Birmingham" }, { "name": "Di Xiao", "url": "https://openalex.org/A5135863559", "inst": "Shandong University" } ], "affiliations": [ "University of Birmingham", "Shandong University" ] }, { "uid": "doi:10.2139/ssrn.6746678", "doi": "10.2139/ssrn.6746678", "title": "The Agentic GTM Stack A Comprehensive Conceptual Framework for Autonomous Market Validation, Enterprise Cognition, and Human-Guided Go-To-Market Systems", "authors": [ "Deepak Amirtha Raj" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6746678", "field": "management", "role": "object", "bullets": [ "Conceptual paper with no empirical sample or data; proposes a framework for autonomous, human-guided go-to-market and outbound sales systems.", "No model is applied by the authors; the paper describes how large language models, orchestration systems, and persistent memory enable agentic go-to-market architectures.", "Proposes a seven-layer stack spanning infrastructure, market intelligence, identity, orchestration, cognitive, execution, and memory to support product-market-fit validation and adaptation." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 559, "authors_detailed": [ { "name": "Deepak Amirtha Raj", "url": "https://openalex.org/A5135901051", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6748306", "doi": "10.2139/ssrn.6748306", "title": "How Generative AI Reorganizes Knowledge Work", "authors": [ "K. Sudhir", "Xueming Luo", "Shucheng Miao" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6748306", "field": "management", "role": "object", "bullets": [ "More than 40 million US job postings and employment records covering marketing occupations from 2021 to 2024.", "The release of ChatGPT serves as the treatment in a difference-in-differences design; generative AI is the object studied, not a measurement tool applied by the researchers.", "Marketing employment falls about 5 to 7 percent, concentrated in production roles, while strategic and relational roles and mid to executive levels expand and wages rise." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "edition": 3, "audience": "general", "validated": null, "n": 781, "authors_detailed": [ { "name": "K. Sudhir", "url": "https://openalex.org/A5135840244", "inst": "" }, { "name": "Xueming Luo", "url": "https://openalex.org/A5009924116", "inst": "Temple University" }, { "name": "Shucheng Miao", "url": "https://openalex.org/A5135866973", "inst": "" } ], "affiliations": [ "Temple University" ] }, { "uid": "doi:10.2139/ssrn.6733638", "doi": "10.2139/ssrn.6733638", "title": "Digital Philanthropy in Asia: Variation in Campaign Characteristics and Outcomes Across Causes on a Regional Crowdfunding Platform: A Cross-sectional Study", "authors": [ "Aung Thura Htoo", "Ruth Sim", "Goh Man Fye" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6733638", "field": "finance", "role": "instrument", "bullets": [ "5,803 publicly accessible crowdfunding campaigns on Give.Asia across Southeast and East Asia from 2012 to 2025, a cross-sectional descriptive study.", "An unnamed large language model extracted campaign details from unstructured text, with rule-based keyword algorithms assigning five cause groups; no extraction accuracy is reported.", "Health causes formed the largest volume at 35 percent but reached only 10 percent goal completion, while human-trafficking campaigns reached 83 percent despite lower targets." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 38, "edition": 3, "audience": "general", "n": 782, "authors_detailed": [ { "name": "Aung Thura Htoo", "url": "https://openalex.org/A5117642034", "inst": "Mahidol Oxford Tropical Medicine Research Unit" }, { "name": "Ruth Sim", "url": "https://openalex.org/A5135836357", "inst": "" }, { "name": "Goh Man Fye", "url": "https://openalex.org/A5135857557", "inst": "" } ], "affiliations": [ "Mahidol Oxford Tropical Medicine Research Unit" ] }, { "uid": "doi:10.2139/ssrn.6748026", "doi": "10.2139/ssrn.6748026", "title": "FROM COPILOT TO COMMANDER: A Framework for Classifying Agentic AI Adoption Maturity in Enterprises", "authors": [ "Satya Kiran Cherukuri" ], "posted": "2026-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6748026", "field": "management", "role": "object", "bullets": [ "Conceptual framework paper drawing on practitioner experience with enterprise agentic AI, with no empirical sample or dataset.", "No language model is applied by the authors; the paper proposes a four-configuration typology, Augmentor, Copilot, Delegate, and Commander, mapping each to a governance architecture.", "Introduces the Agent Governance Artifact that encodes entitlements, operational constraints, and accountability into agent behaviour, framing agentic adoption as organizational transformation rather than a technical upgrade." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1091, "authors_detailed": [ { "name": "Satya Kiran Cherukuri Cherukuri", "url": "https://openalex.org/A5135869211", "inst": "" } ] }, { "uid": "doi:10.3386/w35185", "doi": "10.3386/w35185", "title": "Revealing Life Preferences Through LLMs", "authors": [ "Omar Abdel Haq", "Amitabh Chandra", "Tomáš Jagelka", "Erzo Luttmer", "Joshua Schwartzstein" ], "posted": "2026-05-11", "added": "2026-07-23", "source_label": "NBER", "url": "https://doi.org/10.3386/w35185", "field": "economics", "role": "agent", "bullets": [ "OpenAI's GPT-5.4 and a representative sample of Americans each choose between paired life stories varying in income, longevity, and working conditions; the unit is an individual choice.", "GPT-5.4 was prompted to pick the life it would prefer, standing in for a respondent, and its choices were compared against actual human choices and human-derived attribute valuations.", "A person's choice was better predicted by the LLM's choice than by another person's choice over the same stories, and LLM valuations of several life attributes matched human ones." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "predicts individual human choices better than another human", "salience": 68, "edition": 2, "audience": "broad", "n": 38, "authors_detailed": [ { "name": "Omar Abdel Haq", "url": "https://openalex.org/A5135889147", "inst": "Harvard University" }, { "name": "Amitabh Chandra", "url": "https://openalex.org/A5135893125", "inst": "Harvard University" }, { "name": "Tomáš Jagelka", "url": "https://openalex.org/A5135859712", "inst": "Dartmouth College" }, { "name": "Erzo F.P. Luttmer", "url": "https://openalex.org/A5135902222", "inst": "Dartmouth College" }, { "name": "Joshua Schwartzstein", "url": "https://openalex.org/A5018766009", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University", "Dartmouth College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6746238", "doi": "10.2139/ssrn.6746238", "title": "Revealing Life Preferences Through LLMs", "authors": [ "Omar Abdel Haq", "Amitabh Chandra", "Tomáš Jagelka", "Erzo F.P. Luttmer", "Joshua Schwartzstein" ], "posted": "2026-05-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6746238", "field": "economics", "role": "agent", "bullets": [ "GPT-5.4 and a broadly representative sample of Americans presented with paired life stories varying in income, longevity, working conditions, and other life-course attributes.", "OpenAI GPT-5.4 chose between paired life-course scenarios; its selections compared against human choices over the same story pairs to test preference alignment and predictive power.", "An individual's choice was better predicted by the LLM's choice than by another person's choice; LLM valuations of life attributes closely matched human-derived valuations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison with representative American sample responses", "salience": 75, "n": 2547, "authors_detailed": [ { "name": "Omar Abdel Haq", "url": "https://openalex.org/A5135889147", "inst": "Harvard University" }, { "name": "Amitabh Chandra", "url": "https://openalex.org/A5135893125", "inst": "Harvard University" }, { "name": "Tomáš Jagelka", "url": "https://openalex.org/A5135859712", "inst": "Dartmouth College" }, { "name": "Erzo F.P. Luttmer", "url": "https://openalex.org/A5135902222", "inst": "Dartmouth College" }, { "name": "Joshua Schwartzstein", "url": "https://openalex.org/A5018766009", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University", "Dartmouth College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6686418", "doi": "10.2139/ssrn.6686418", "title": "Behavioral Digital Twins: Causally Constrained Synthetic Populations for Public Health Policy Simulation", "authors": [ "Yichao Jin" ], "posted": "2026-05-10", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6686418", "field": "economics", "role": "method", "bullets": [ "A vaccination timing discrete choice experiment answered by 1,027 adults in Wuhan supplies the empirical target; the pilot run uses a 10 percent subsample of those responses.", "A 1.5 billion parameter model, family not named, runs on local consumer hardware and generates respondents whose choices are held to utility constraints read off a causal graph fitted to the human data, with no retraining.", "Constrained agents violate logical monotonicity in 22.2 percent of cases against 50.0 percent unconstrained, and sit up to 34 percent closer to the empirical choice frontier, with the gain concentrated in long wait scenarios." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "1,027 respondent discrete choice experiment as ground truth, frontier deviation and violation rates reported", "salience": 55, "edition": 17, "models": [], "n": 2131, "authors_detailed": [ { "name": "Yichao Jin", "url": "https://openalex.org/A5135802115", "inst": "The University of Texas at Dallas" } ], "affiliations": [ "The University of Texas at Dallas" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6742119", "doi": "10.2139/ssrn.6742119", "title": "The Finance Value Pyramid : A Conceptual Framework for Repositioning Value Creation in the Finance Function in the Era of Generative Artificial Intelligence", "authors": [ "BAYU ISTANTORO" ], "posted": "2026-05-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6742119", "field": "accounting", "role": "object", "bullets": [ "Conceptual framework paper with no empirical sample, synthesizing management accounting, business-partnering, and decision-usefulness literature to classify finance function activities by strategic value contribution.", "No model is run; the paper argues that generative AI and LLMs, without naming a specific system, automate data work, augment analytical work, and leave narrative work to human judgment.", "Proposes a three-level Finance Value Pyramid and advances three propositions plus a falsifiable research agenda, positioning narrative translation as the highest-value and most AI-resilient finance activity." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "salience": 34, "edition": 3, "audience": "general", "validated": null, "n": 80, "authors_detailed": [ { "name": "Bayu Istantoro", "url": "https://openalex.org/A5120104254", "inst": "Bank Indonesia" } ], "affiliations": [ "Bank Indonesia" ] }, { "uid": "doi:10.2139/ssrn.6745038", "doi": "10.2139/ssrn.6745038", "title": "Stated-Preference Primacy as a Value-Alignment Guardrail in AI Career Recommender Systems A Three-Tier Signal Hierarchy for Workforce-Equity Recommendation Architectures", "authors": [ "Jason Chapman" ], "posted": "2026-05-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6745038", "field": "management", "role": "method", "bullets": [ "A career recommendation platform, PROSPER, deployed for displaced and transitioning workers in Michigan; a conceptual architecture paper with no empirical sample reported.", "The design places a deterministic gating layer, a value-alignment guardrail, between an inference engine and a downstream large language model (not named) to enforce stated user preferences over inferred ones.", "The author argues hierarchical signal governance stops inferred adjacencies from overriding a user's declared situation in high-stakes decision-support settings." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 780, "authors_detailed": [ { "name": "Jason Chapman", "url": "https://openalex.org/A5135817771", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6744178", "doi": "10.2139/ssrn.6744178", "title": "# Can AI Actually Trade? A Comparative Study of LSTM and Transformer Models Across Stocks, Crypto, and Forex", "authors": [ "Vivaan Tyagi" ], "posted": "2026-05-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6744178", "field": "finance", "role": "method", "bullets": [ "US equities, cryptocurrencies, and forex pairs using daily data from 2019 to 2024, with identical technical feature sets across the three asset classes.", "LSTM and transformer networks trained on the same features and embedded in a backtest with transaction costs, scored on annualised return, Sharpe, Sortino, drawdown, and win rate.", "Transformers reached higher directional accuracy on equities and forex, LSTM was more competitive in crypto, and both cut drawdown relative to buy-and-hold." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1090, "authors_detailed": [ { "name": "Vivaan Tyagi", "url": "https://openalex.org/A5135818421", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6740337", "doi": "10.2139/ssrn.6740337", "title": "A Hybrid Optimisation-Large Language Model Framework for Real-Time Order Fulfilment Decision Support in Plywood Manufacturing", "authors": [ "Truong Thi Chi", "Ly Van Kien", "Khanh Pham" ], "posted": "2026-05-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6740337", "field": "management", "role": "agent", "bullets": [ "4,823 order records from a Vietnamese plywood manufacturer, allocating fulfilment across stock shipment, internal production, and outsourcing under eight business constraints.", "A Groq-hosted Llama-3.3-70b reviews and, where justified, revises a mixed-integer optimiser's allocation; performance is compared to a rule-based baseline with no ground-truth check of the model's decisions.", "Mean processing time was 7.2 seconds with a 99.0 percent success rate and 30.4 percent average modelled margin, 3.3 percentage points above the rule-based baseline." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 40, "edition": 3, "audience": "general", "n": 215, "authors_detailed": [ { "name": "Truong Thi Chi", "url": "https://openalex.org/A5135730285", "inst": "" }, { "name": "Ly Van Kien", "url": "https://openalex.org/A5118276228", "inst": "" }, { "name": "Khanh Pham", "url": "https://openalex.org/A5017878875", "inst": "Vietnam National University Ho Chi Minh City" } ], "affiliations": [ "Vietnam National University Ho Chi Minh City" ] }, { "uid": "doi:10.2139/ssrn.6739447", "doi": "10.2139/ssrn.6739447", "title": "Analyzing Carbon Removal Technology Hype Cycles Through Large Language Models", "authors": [ "Medha Nag Kommaghatta Girish", "Reinhard Madlener" ], "posted": "2026-05-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6739447", "field": "management", "role": "instrument", "bullets": [ "English-language news coverage of carbon removal technologies including bioenergy with carbon capture, afforestation, direct air capture, and ocean-based capture, tracked over a 16-year period.", "An unnamed large language model performs context-aware sentiment extraction across news articles to build temporal sentiment indicators; the model is not named and no accuracy check is reported.", "Media sentiment tracking complements other innovation indicators for hype-cycle mapping and suggests the carbon removal domain as a whole is heading toward a plateau of productivity." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 3, "audience": "general", "models": [], "n": 216, "authors_detailed": [ { "name": "Medha Nag Kommaghatta Girish", "url": "https://openalex.org/A5135738462", "inst": "" }, { "name": "Reinhard Madlener", "url": "https://openalex.org/A5135776188", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6724515", "doi": "10.2139/ssrn.6724515", "title": "Ad-Verse Effects: Pharmaceutical Advertising Shifts Drug Recommendations by Consumer-Facing AI", "authors": [ "Mahmud Omar", "Reem Agbareia", "Jolion McGreevy", "Alexis Zebrowski", "Ashwin Ramaswamy", "Michael Gorin", "Esther-Maria Antão", "Benjamin S. Glicksberg", "Ankit Sakhuja", "Alexander W. Charney", "Eyal Klang", "Girish N. Nadkarni" ], "posted": "2026-05-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6724515", "field": "management", "role": "object", "bullets": [ "Experiment spanning 258,660 API calls, 33 clinical scenarios and 12 large language models, testing pharmaceutical advertisements prepended to the models' system-level instructions.", "The 12 models, families not stated, generate drug recommendations with and without an injected advertisement; the outcome is the change in selection of the advertised drug.", "Prepended advertising increased selection of the advertised drug by 12.7 percentage points across scenarios; the reported significance value is truncated in the available abstract." ], "bullet_provenance": "ai", "salience": 62, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 421, "authors_detailed": [ { "name": "Mahmud Omar", "url": "https://openalex.org/A5135764392", "inst": "" }, { "name": "Reem Agbareia", "url": "https://openalex.org/A5135762389", "inst": "" }, { "name": "Jolion McGreevy", "url": "https://openalex.org/A5110079878", "inst": "Mount Sinai Health System" }, { "name": "Alexis Zebrowski", "url": "https://openalex.org/A5135746091", "inst": "Icahn School of Medicine at Mount Sinai" }, { "name": "Ashwin Ramaswamy", "url": "https://openalex.org/A5068613788", "inst": "Mount Sinai Health System" }, { "name": "Michael Gorin", "url": "https://openalex.org/A5135782336", "inst": "" }, { "name": "Esther-Maria Antão", "url": "https://openalex.org/A5051204422", "inst": "Hasso Plattner Institute" }, { "name": "Benjamin S. Glicksberg", "url": "https://openalex.org/A5135746229", "inst": "" }, { "name": "Ankit Sakhuja", "url": "https://openalex.org/A5135763595", "inst": "" }, { "name": "Alexander W. Charney", "url": "https://openalex.org/A5135736007", "inst": "" }, { "name": "Eyal Klang", "url": "https://openalex.org/A5135729227", "inst": "" }, { "name": "Girish N. Nadkarni", "url": "https://openalex.org/A5135778065", "inst": "" } ], "affiliations": [ "Icahn School of Medicine at Mount Sinai", "Hasso Plattner Institute" ] }, { "uid": "doi:10.2139/ssrn.6738816", "doi": "10.2139/ssrn.6738816", "title": "Can Multimodal Large Language Models Extract Investment Signals from Financial Charts? Evidence from Portfolio Backtests", "authors": [ "Sungsoo Kim", "Ha Young Kim" ], "posted": "2026-05-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6738816", "field": "finance", "role": "instrument", "bullets": [ "Stocks from the Dow Jones Industrial Average and Euro Stoxx 50, evaluated on directional prediction, cross-sectional ranking and portfolio backtests using tiered chart images with progressively added technical indicators.", "A pretrained multimodal large language model, name not stated, reads chart images to derive signals; performance is assessed by backtest rather than validation against labelled ground truth.", "Chart images alone aid cross-sectional ranking and portfolio construction and beat price-only charts and rule-based strategies, while directional prediction stays near random and added chart complexity does not consistently help." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "edition": 3, "audience": "general", "models": [], "n": 422, "authors_detailed": [ { "name": "Sungsoo Kim", "url": "https://openalex.org/A5122028762", "inst": "Yonsei University" }, { "name": "Ha Young Kim", "url": "https://openalex.org/A5043961759", "inst": "Sogang University" } ], "affiliations": [ "Yonsei University", "Sogang University" ] }, { "uid": "doi:10.2139/ssrn.6738398", "doi": "10.2139/ssrn.6738398", "title": "Strategic Effectiveness Axiom: A Quantitative Strategic Audit Framework Based on Physical Isomorphism and Bayesian Inference", "authors": [ "Peng Hao" ], "posted": "2026-05-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6738398", "field": "management", "role": "agent", "bullets": [ "Conceptual strategic-audit framework, the Xuanji system, with no empirical sample, applying system dynamics and Bayesian epistemology to organisational strategy.", "Large language model multi-agent sandboxes, family not stated, run semantic gaming with an independent referee judge to stress-test strategy and locate a break point under environmental noise.", "Reports that the proposed axiom identifies decision-makers' cognitive biases and yields a physics-styled strategic stress-testing procedure; no quantitative evaluation is provided." ], "bullet_provenance": "ai", "salience": 22, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 423, "authors_detailed": [ { "name": "Hao Peng", "url": "https://openalex.org/A5135729413", "inst": "Nanjing Tech University" } ], "affiliations": [ "Nanjing Tech University" ] }, { "uid": "doi:10.2139/ssrn.6739621", "doi": "10.2139/ssrn.6739621", "title": "DPI ODA as a Conditional Vanguard: Evidence on Trade and FDI from Korea", "authors": [ "Soomin PARK", "Sojeong LEE", "Seungwoo YOO" ], "posted": "2026-05-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6739621", "field": "economics", "role": "instrument", "bullets": [ "Country-year panel of 146 aid recipient countries from 1987 to 2024, linking Korea's digital public infrastructure ODA to its exports, imports, and outward FDI.", "An unnamed large language model classified OECD CRS project descriptions, identifying 5,523 DPI-related projects out of 83,083; no accuracy check against hand coding is reported.", "DPI ODA is positively associated with Korea's exports and outward FDI with a lag, stronger where recipients have higher digital readiness, with a post-2015 break in the FDI channel." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "edition": 3, "audience": "general", "models": [], "n": 778, "authors_detailed": [ { "name": "Soomin PARK", "url": "https://openalex.org/A5135739201", "inst": "" }, { "name": "Sojeong LEE", "url": "https://openalex.org/A5135739143", "inst": "" }, { "name": "Seungwoo YOO", "url": "https://openalex.org/A5135787972", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6739939", "doi": "10.2139/ssrn.6739939", "title": "The Transformative Impact of Student Innovation Competitions and Programs on Innovation Mindset", "authors": [ "Abdullah Konak" ], "posted": "2026-05-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6739939", "field": "management", "role": "instrument", "bullets": [ "Thirty-six students who took part in STEM innovation competitions and programs, interviewed semi-structured and analysed with a qualitative inductive approach.", "ChatGPT 4.0 performed thematic coding of interview transcripts alongside human coders; the paper reports some agreement between human and AI codes but no agreement statistic.", "Participation strengthened self-awareness and open-mindedness and made students more receptive to innovation and entrepreneurship careers." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 34, "edition": 3, "audience": "general", "n": 779, "authors_detailed": [ { "name": "Abdullah Konak", "url": "https://openalex.org/A5133562128", "inst": "Pennsylvania State University" } ], "affiliations": [ "Pennsylvania State University" ] }, { "uid": "doi:10.2139/ssrn.6675603", "doi": "10.2139/ssrn.6675603", "title": "The Stationary Sea Measurement Instrument Validation for External Assessment of AI Governance in Regulated Financial Institutions", "authors": [ "William Collins" ], "posted": "2026-05-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6675603", "field": "finance", "role": "method", "bullets": [ "543 regulated financial institutions across 66 countries, covering 95,876 AI agents and 626,390 governance edges in a production scanning campaign.", "Large language models (not named) plus web-scale search extract governance topology with a three-tier evidence classification; validity is assessed through classical measurement theory rather than a ground-truth accuracy check.", "Governance scores track supervisory maturity from pension funds (12.10) to banks (20.92), and the v13.1.0 scanner recovers 21.7 percent more observed edges than v11.1." ], "bullet_provenance": "ai", "validated": false, "validation_note": "known-groups validity, no ground-truth accuracy", "salience": 35, "edition": 3, "audience": "general", "models": [], "n": 306, "authors_detailed": [ { "name": "William Collins", "url": "https://openalex.org/A5132766867", "inst": "Meridian Institute" } ], "affiliations": [ "Meridian Institute" ] }, { "uid": "doi:10.2139/ssrn.6734908", "doi": "10.2139/ssrn.6734908", "title": "Regime-Aware Multi-Scale Attention-RNN for Financial Time-Series Forecasting: Bridging Hybrid Architectures and Foundation Models", "authors": [ "Vishnu Vardhan Reddy Yeruva" ], "posted": "2026-05-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6734908", "field": "finance", "role": "method", "bullets": [ "S&P 500 constituent stocks from 2010 to 2025, fusing daily technical indicators with monthly macro releases and quarterly fundamentals from Crunchbase for multi-horizon return forecasting.", "Introduces RAMAR, a regime-aware attention-RNN with Hidden Markov regime detection, benchmarked against time-series foundation models TimesFM, Chronos and Lag-Llama and against TFT; forecasts checked out of sample.", "RAMAR reaches R2 of 0.962 and directional accuracy of 62.4 percent, cutting RMSE 17.9 percent versus TFT and beating zero-shot TimesFM by 58.9 percent, with largest gains in bear and crisis regimes." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "out-of-sample S&P 500 test, directional accuracy 62.4 percent", "salience": 34, "edition": 3, "audience": "technical", "n": 420, "authors_detailed": [ { "name": "Vishnu Vardhan Reddy Yeruva", "url": "https://openalex.org/A5093692722", "inst": "San Jose State University" } ], "affiliations": [ "San Jose State University" ] }, { "uid": "doi:10.2139/ssrn.6732459", "doi": "10.2139/ssrn.6732459", "title": "Factors predicting FPTU - HCMC general English students' behavioral intention of using voice-based ChatGPT for out-of-class speaking: Insights from the UTAUT2 framework", "authors": [ "Thi Ngoc Uyen Tran", "Vinh Ngoc Tram Le", "Quynh Nhu Cao", "Ngoc Tuong Vy Vu", "Thuy Thien Huong Phan" ], "posted": "2026-05-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6732459", "field": "management", "role": "object", "bullets": [ "234 valid survey responses from English-as-a-foreign-language students at a Vietnamese university (FPTU-HCMC); cross-sectional design analysed with PLS-SEM.", "No model is run by the researchers; voice-based ChatGPT is the studied technology, with UTAUT2 constructs predicting behavioural intention to keep using it.", "Price value, hedonic motivation, and habit predicted intention, while performance expectancy, effort expectancy, social influence, and facilitating conditions were not significant." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 35, "edition": 3, "audience": "general", "validated": null, "n": 775, "authors_detailed": [ { "name": "Thi Ngoc Uyen Tran", "url": "https://openalex.org/A5101061631", "inst": "Hue University" }, { "name": "Vinh Ngoc Tram Le", "url": "https://openalex.org/A5135618164", "inst": "" }, { "name": "Quynh Nhu Cao", "url": "https://openalex.org/A5135545503", "inst": "" }, { "name": "Ngoc Tuong Vy Vu", "url": "https://openalex.org/A5135585611", "inst": "" }, { "name": "Thuy Thien Huong Phan", "url": "https://openalex.org/A5135583546", "inst": "" } ], "affiliations": [ "Hue University" ] }, { "uid": "doi:10.2139/ssrn.6733264", "doi": "10.2139/ssrn.6733264", "title": "Synthetic Corpus and Consideration Manipulation in Generative Engine Optimization", "authors": [ "Tony Ke", "Chenxi Liao", "Xiaoyan Xu" ], "posted": "2026-05-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6733264", "field": "management", "role": "object", "bullets": [ "Theoretical game-theory model of generative engine optimization; consumers uncertain about quality rely on an AI recommender and then inspect online corpora; no empirical data.", "No language model is run; the generative engine is modelled as a statistical prediction machine forming consideration sets from potentially synthetic reviews and posts.", "Synthetic content creates a visibility-credibility tradeoff; a high-quality firm's manipulation incentive is non-monotonic in low-type quality, and a regulatory ban need not raise consumer surplus." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 776, "authors_detailed": [ { "name": "Tony Ke", "url": "https://openalex.org/A5110740429", "inst": "Chinese University of Hong Kong" }, { "name": "Chenxi Liao", "url": "https://openalex.org/A5045082323", "inst": "Chinese University of Hong Kong" }, { "name": "Xiaoyan Xu", "url": "https://openalex.org/A5101825789", "inst": "Southwestern University of Finance and Economics" } ], "affiliations": [ "Chinese University of Hong Kong", "Southwestern University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.6666263", "doi": "10.2139/ssrn.6666263", "title": "Beyond the AI Programme: A Capability Architecture for Indian Higher Education", "authors": [ "Rahul Sharma" ], "posted": "2026-05-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6666263", "field": "management", "role": "object", "bullets": [ "India's higher education sector, over 1,000 universities and 40,000 colleges serving about 43.3 million students per AISHE 2021-22; conceptual analysis, abstract truncated.", "No language model is used; the paper discusses AI-labelled degrees and institutional positioning, citing WEF and McKinsey estimates of skill change and task automation.", "Argues Indian institutions use AI branding as a strategic signal and proposes a capability architecture, though the abstract does not report the framework's details." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 777, "authors_detailed": [ { "name": "Rahul Sharma", "url": "https://openalex.org/A5135680855", "inst": "Lovely Professional University" } ], "affiliations": [ "Lovely Professional University" ] }, { "uid": "doi:10.2139/ssrn.6729618", "doi": "10.2139/ssrn.6729618", "title": "LLM Zero-Shot Replication of SEC Comment Letter Returns: An Out-of-Sample Reproducibility Test", "authors": [ "Hyun Ahn" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6729618", "field": "accounting", "role": "method", "bullets": [ "1,014 matched SEC comment letter pairs (UPLOAD-CORRESP) for Russell 3000 firms from 2015 to 2024, with a held-out 2022-2024 evaluation window.", "Zero-shot prompts to Gemma 3 27B, Llama 3.3 70B, and Claude Opus 4.7 as label oracle extract topic, severity, and registrant response intent; no extraction accuracy against hand coding is reported.", "A pre-registered severity-weighted long-short portfolio earns 11.92 percent annualized six-factor alpha (Newey-West t=2.86) out of sample, reproducing the Ryans (2021) sign without supervised label leakage." ], "bullet_provenance": "ai", "models": [ "claude", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 56, "edition": 3, "audience": "general", "n": 77, "authors_detailed": [ { "name": "Hyun Ahn", "url": "https://openalex.org/A5135592103", "inst": "Korea Institute for Advanced Study" } ], "affiliations": [ "Korea Institute for Advanced Study" ] }, { "uid": "doi:10.2139/ssrn.6728899", "doi": "10.2139/ssrn.6728899", "title": "The Finance Value Pyramid: A Conceptual Framework for Repositioning Value Creation in Finance in the Era of Generative AI", "authors": [ "BAYU ISTANTORO" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6728899", "field": "accounting", "role": "object", "bullets": [ "Conceptual paper with no empirical sample, synthesizing decision usefulness theory, the management accounting shift to business partnering, and research on generative AI's effect on cognitive work.", "No model is run by the authors; the framework argues generative AI and LLMs automate data work, augment analytical work, and leave narrative work to humans. Model not stated.", "Proposes a three level finance value pyramid where narrative work is the most resilient human task, with three propositions and a falsifiable research agenda." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "salience": 34, "edition": 3, "audience": "general", "validated": null, "n": 100, "authors_detailed": [ { "name": "Bayu Istantoro", "url": "https://openalex.org/A5120104254", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6681238", "doi": "10.2139/ssrn.6681238", "title": "Synthetic Firms: Employing LLMs as Organisational Simulacra", "authors": [ "Johannes Dahlke", "Flavio Calvino", "Anna Kostiuchenko" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6681238", "field": "management", "role": "agent", "bullets": [ "German firms profiled in structured natural language, with innovation activity measured against the Community Innovation Survey and anchored using web-derived indicators and patent data.", "Unnamed large language models simulate firm innovation; an uncalibrated baseline is compared with two theory-grounded, empirically calibrated variants and validated against observed survey outcomes.", "The uncalibrated baseline over-predicts innovation while both calibrated variants substantially improve accuracy and align predictions with observed cross-industry patterns." ], "bullet_provenance": "ai", "validated": true, "validation_note": "vs Community Innovation Survey outcomes", "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 147, "authors_detailed": [ { "name": "Johannes Dahlke", "url": "https://openalex.org/A5004357952", "inst": "University of Hohenheim" }, { "name": "Flavio Calvino", "url": "https://openalex.org/A5030504106", "inst": "Organisation de Coopération et de Développement Economiques" }, { "name": "Anna Kostiuchenko", "url": "https://openalex.org/A5135424389", "inst": "" } ], "affiliations": [ "University of Hohenheim", "Organisation de Coopération et de Développement Economiques" ] }, { "uid": "doi:10.2139/ssrn.6731001", "doi": "10.2139/ssrn.6731001", "title": "Liberty and State Effectiveness in Industrializing Britain: War, Politics, and Law", "authors": [ "Peter Grajzl", "Peter Murrell" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6731001", "field": "economics", "role": "instrument", "bullets": [ "67,453 reports of cases heard in the English law courts between 1765 and 1865, analyzed at the case level for economic-history questions about liberty and state capacity.", "Large language models, not individually named, construct case-level measures of judicial orientation toward individual liberty and state effectiveness, and the abstract reports no validation against hand coding.", "Wartime conditions and Tory governments, especially in combination, shifted legal emphasis toward state effectiveness and away from liberty, while common law and judicial independence did not systematically promote liberty." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 62, "edition": 3, "audience": "general", "n": 212, "authors_detailed": [ { "name": "Peter Grajzl", "url": "https://openalex.org/A5035026186", "inst": "Ifo Institute for Economic Research" }, { "name": "Peter Murrell", "url": "https://openalex.org/A5099717901", "inst": "World Bank Group" } ], "affiliations": [ "Ifo Institute for Economic Research", "World Bank Group" ] }, { "uid": "doi:10.2139/ssrn.6682338", "doi": "10.2139/ssrn.6682338", "title": "TSB: A Time-Saved Benchmark for AI Systems Measuring Net Productivity Impact Across Knowledge Work", "authors": [ "Solomon Shalom Lijo" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6682338", "field": "economics", "role": "method", "bullets": [ "Position paper on measuring net productivity impact of AI in knowledge work, drawing on four published evaluations including a GitHub Copilot field experiment, a BCG consultant study, and a developer randomized trial.", "Proposes TSB, a five-part time-saved benchmark, and applies it retroactively to those studies rather than running new tests, with Claude 3.5 and 3.7 Sonnet appearing only inside a cited trial.", "Under reliability adjustment headline gains shrink, as a reported 100-times inference-time advantage falls to roughly four-times deployed speedup and a 25 percent out-of-frontier gain drops to near zero or negative." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 50, "edition": 3, "audience": "general", "validated": null, "n": 213, "authors_detailed": [ { "name": "Solomon Shalom Lijo", "url": "https://openalex.org/A5135558586", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2605.05739v3", "arxiv_id": "2605.05739v3", "title": "Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using Large Language Model Judges with Closed-Loop Reinforcement Learning Feedback", "authors": [ "Mohammad Al Ridhawi", "Mahtab Haj Ali", "Hussein Al Osman" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.05739v3", "field": "finance", "role": "method", "bullets": [ "An agentic stock-prediction system whose intermediate decisions are logged into five-day episodes and evaluated over a held-out 2017 to 2025 test period.", "An ensemble of three unnamed large language model judges scores decisions on six behavioral dimensions, with cross-model agreement of Krippendorff alpha 0.85 and the composite correlating with realized 20-day Sharpe at Spearman 0.72.", "Feeding the behavioral scores into a Soft Actor-Critic reward across three fine-tuning cycles reduced one-day MAPE from 0.61 to 0.54 percent, an 11.5 percent relative gain concentrated in high-volatility periods." ], "bullet_provenance": "ai", "validated": true, "validation_note": "cross-model agreement alpha 0.85, composite correlates with realized Sharpe at 0.72", "salience": 53, "edition": 3, "audience": "technical", "models": [], "n": 214, "authors_detailed": [ { "name": "Mohammad Al Ridhawi", "url": "https://openalex.org/A5128529331", "inst": "University of Ottawa" }, { "name": "Mahtab Haj Ali", "url": "https://openalex.org/A5091425948", "inst": "University of Ottawa" }, { "name": "Hussein Al Osman", "url": "https://openalex.org/A5128494980", "inst": "" } ], "affiliations": [ "University of Ottawa" ] }, { "uid": "doi:10.2139/ssrn.6729902", "doi": "10.2139/ssrn.6729902", "title": "Perceived Cognitive Assistance and the Construct-Proliferation Challenge: an integrated evaluative judgment that is never finished. Comparator Selection, Rival Scoping, and a Falsifiable Boundary Agenda", "authors": [ "Dmitrii Gimmelberg", "IVETA LUDVIGA" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6729902", "field": "finance", "role": "object", "bullets": [ "Conceptual and psychometric paper on Perceived Cognitive Assistance, a construct for retail traders' felt cognitive expansion when using an LLM as a reasoning partner; no new empirical sample.", "No model is applied; the construct is positioned against technology acceptance and planned behavior neighbors and seven rival construct families through a construct-proliferation audit.", "Concludes the construct has defensible local distinctness but that current instruments lack matched-domain comparators to justify a new confirmatory factor analysis battery." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 416, "authors_detailed": [ { "name": "Dmitrii Gimmelberg", "url": "https://openalex.org/A5116200389", "inst": "RISEBA University of Applied Sciences" }, { "name": "Iveta Ludviga", "url": "https://openalex.org/A5060182768", "inst": "RISEBA University of Applied Sciences" } ], "affiliations": [ "RISEBA University of Applied Sciences" ] }, { "uid": "doi:10.2139/ssrn.6728539", "doi": "10.2139/ssrn.6728539", "title": "Ex Machina: financial stability in the age of artificial intelligence", "authors": [ "Kartik Anand", "Sophia Kazinnik", "Agnese Leonello", "Ettore Panetti" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6728539", "field": "finance", "role": "agent", "bullets": [ "A mutual fund redemption game with economic and strategic uncertainty and default risk, populated by artificial investors of two architectures; no external dataset, sample or period is stated.", "Compares Q-learning reinforcement agents and large language model investors, family not stated, as decision makers in the game; no validation against human behaviour is reported.", "AI architecture is a first-order driver of stability: Q-learning agents over-redeem under default risk and amplify fragility, while LLM investors show belief heterogeneity that weakens coordination." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 417, "authors_detailed": [ { "name": "Kartik Anand", "url": "https://openalex.org/A5040566835", "inst": "Deutsche Bundesbank" }, { "name": "Sophia Kazinnik", "url": "https://openalex.org/A5061916690", "inst": "Stanford University" }, { "name": "Agnese Leonello", "url": "https://openalex.org/A5029828860", "inst": "European Central Bank" }, { "name": "Ettore Panetti", "url": "https://openalex.org/A5023846304", "inst": "Centre for Studies in Economics and Finance" } ], "affiliations": [ "Stanford University", "Deutsche Bundesbank", "European Central Bank", "Centre for Studies in Economics and Finance" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6479841", "doi": "10.2139/ssrn.6479841", "title": "Information Aggregation with AI Agents", "authors": [ "Spyros Galanis" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6479841", "field": "economics", "role": "agent", "bullets": [ "Controlled experiment in which large language model agents trade in a prediction market after receiving private signals, across information structures of varying complexity; sample size not stated.", "AI agents, model family not stated, place trades and infer others' knowledge from price moves; aggregation is measured by the log error of the final price with no human benchmark.", "Markets aggregate well in easy structures but degrade significantly as complexity rises; cheap talk, duration and strategic prompting have no effect, while smarter agents aggregate better and profit more." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 418, "authors_detailed": [ { "name": "Spyros Galanis", "url": "https://openalex.org/A5010823504", "inst": "Durham University" } ], "affiliations": [ "Durham University" ] }, { "uid": "doi:10.2139/ssrn.6664200", "doi": "10.2139/ssrn.6664200", "title": "The Last Mile of AI: Judgment Infrastructure, Defensible Audit Logs, and the End of Information Retrieval", "authors": [ "YekSoon Lok" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6664200", "field": "finance", "role": "object", "bullets": [ "Conceptual argument about institutional capital allocation, drawing on a proprietary corpus of over 60,000 Clarity Scores benchmarked through the authors' RUNE Protocol; period and market not stated.", "Foundational large language models, family not stated, are treated as commoditising information retrieval; no model is evaluated and no validation against ground truth is reported.", "Argues information asymmetry stops generating alpha once all allocators share an LLM baseline, and proposes judgment infrastructure, seven thesis-failure archetypes and defensible audit logs as the new frontier." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 419, "authors_detailed": [ { "name": "YekSoon Lok", "url": "https://openalex.org/A5135552488", "inst": "Université de Parakou" } ], "affiliations": [ "Université de Parakou" ] }, { "uid": "doi:10.2139/ssrn.6728121", "doi": "10.2139/ssrn.6728121", "title": "Breaking Down Barriers Assistant: Leveraging AI to Conduct Policy Analyses with Complex Data", "authors": [ "Ujjwal KC", "Jan Kabatek" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6728121", "field": "economics", "role": "method", "bullets": [ "Secure pre-aggregated Australian administrative and survey data, accessed through a platform offering variable discovery, visual analytics, and automated reporting for policy research.", "Combines retrieval-augmented generation with controlled code generation and human-in-the-loop validation; the specific language model is not stated, and outputs are checked by reproducing established benchmark analyses.", "Reports that the assistant reproduces complex spatial and longitudinal policy analyses and cuts time-to-insight while preserving accuracy and privacy, though no accuracy figure is given." ], "bullet_provenance": "ai", "validated": false, "validation_note": "reproduces YouthView and BDB benchmarks, no accuracy figure", "salience": 45, "edition": 3, "audience": "technical", "models": [], "n": 558, "authors_detailed": [ { "name": "Ujjwal KC", "url": "https://openalex.org/A5135591736", "inst": "" }, { "name": "Jan Kabátek", "url": "https://openalex.org/A5065153669", "inst": "The University of Melbourne" } ], "affiliations": [ "The University of Melbourne" ] }, { "uid": "doi:10.2139/ssrn.6729903", "doi": "10.2139/ssrn.6729903", "title": "Discovering YC-Style Pitch Signals from Startup Demo Transcripts with LLM-Based Feature Extraction", "authors": [ "Clément FREREBEAU", "Marc HABIB" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6729903", "field": "management", "role": "instrument", "bullets": [ "A dataset of 63 Y Combinator selected startup demo transcripts is analysed to extract narrative, product, and traction signals that characterise a YC-style pitch.", "An unnamed large language model extracts interpretable transcript features while sentence-transformer embeddings feed unsupervised clustering; the model is not named and outputs are not validated against ground truth.", "Solution-first openings dominate at 63.5 percent, most transcripts include problem, solution, and demo, but explicit traction or proof appears in only 41.3 percent; the top archetype is AI Application." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no ground-truth comparison", "salience": 35, "edition": 3, "audience": "technical", "models": [], "n": 770, "authors_detailed": [ { "name": "Clément Frerebeau", "url": "https://openalex.org/A5130567614", "inst": "École Supérieure d'Ingénieurs des Travaux de la Construction de Cachan" }, { "name": "Marc Habib", "url": "https://openalex.org/A5058877005", "inst": "École Supérieure d'Ingénieurs des Travaux de la Construction de Cachan" } ], "affiliations": [ "École Supérieure d'Ingénieurs des Travaux de la Construction de Cachan" ] }, { "uid": "doi:10.2139/ssrn.6731365", "doi": "10.2139/ssrn.6731365", "title": "The Accelerated Decay: Re-engineering Terminal Value in Hyper-Capital-Intensive Technology", "authors": [ "Rakesh KS" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6731365", "field": "finance", "role": "object", "bullets": [ "Case studies of Nvidia, Microsoft, Meta, and the capital-light control firm ServiceNow; no sample period or panel is specified beyond illustrative examples.", "No language model is used; the author proposes an entropy-based depreciation model and a dynamic-decay DCF, arguing AI GPU economic half-life is roughly 2.3 years versus five-to-six-year GAAP schedules.", "Argues standard perpetual-growth terminal values overstate hyperscaler valuations because obsolescence-driven maintenance capex is misclassified as growth capex; no quantified mispricing estimate is reported." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 771, "authors_detailed": [ { "name": "Rakesh KS", "url": "https://openalex.org/A5135560459", "inst": "Hindustan Aeronautics Limited (India)" } ], "affiliations": [ "Hindustan Aeronautics Limited (India)" ] }, { "uid": "doi:10.2139/ssrn.6656118", "doi": "10.2139/ssrn.6656118", "title": "China's AI Governance Architecture: From State Control to Operational AI Governance", "authors": [ "Alexandra Carvalho" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6656118", "field": "management", "role": "object", "bullets": [ "Analysis of China's AI regulatory instruments, enforcement activity, judicial decisions, and technical standards through April 2026, compared with the European Union and United States.", "No language model is used; the paper qualitatively maps China's layered governance across algorithm rules, generative AI, synthetic content, ethics review, and provenance requirements.", "Argues China has moved further than most jurisdictions in operationalising AI governance, and that global oversight is shifting from principle-based to procedural and technical infrastructures." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 772, "authors_detailed": [ { "name": "Alexandra Carvalho", "url": "https://openalex.org/A5135631939", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6730019", "doi": "10.2139/ssrn.6730019", "title": "Safeguarding Your Intellectual Property in the Age of AI: Practical Protections, Legal Gaps, and What Must Happen Next", "authors": [ "Devon J Euring" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6730019", "field": "management", "role": "object", "bullets": [ "Conceptual white paper on intellectual property risks created by enterprise generative AI adoption; no sample or empirical data.", "No language model is used; the paper surveys copyright, trade-secret, and ownership exposures and reviews the regulatory landscape including the EU AI Act.", "Recommends firms act before AI-specific IP law matures by classifying key IP, restricting high-risk uses, tightening vendor contracts, and embedding provenance controls." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 773, "authors_detailed": [ { "name": "Devon J Euring", "url": "https://openalex.org/A5121235913", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6730780", "doi": "10.2139/ssrn.6730780", "title": "INSTITUTE OF MANAGEMENT AND SCIENCE", "authors": [ "Raj Rishi" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6730780", "field": "management", "role": "object", "bullets": [ "Conceptual overview of AI-powered innovation management for enterprises; no sample, data, or empirical setting, and the abstract is truncated.", "No specific model named; describes machine learning, natural language processing, predictive analytics, robotics, and generative AI as tools supporting innovation processes.", "Asserts AI adoption is shifting firms from isolated applications toward enterprise-wide digital transformation and organizational agility; no empirical result is reported." ], "bullet_provenance": "ai", "salience": 24, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 774, "authors_detailed": [ { "name": "Raj Rishi", "url": "https://openalex.org/A5112692886", "inst": "G.L. Bajaj Institute of Technology and Management Greater Noida" } ], "affiliations": [ "G.L. Bajaj Institute of Technology and Management Greater Noida" ] }, { "uid": "doi:10.2139/ssrn.6674761", "doi": "10.2139/ssrn.6674761", "title": "The Agentic 3 C's Framework A Reasoning-Layer Risk Governance Model for Agentic AI in Financial Services", "authors": [ "Maureen Doyle-Spare" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6674761", "field": "finance", "role": "object", "bullets": [ "Practitioner conceptual paper proposing a reasoning-layer risk governance framework, the Agentic 3 C's, for deploying agentic AI in regulated financial services; no empirical sample or data are used.", "No language model is applied or validated; the paper defines Context, Control, and Coordination as conditions for governable agentic reasoning and maps them to NIST, EU AI Act, and CRI frameworks.", "Argues reasoning-layer risk conditions rather than replaces existing risk categories, since each category depends on whether an agent reasoned against authorised meaning before executing an action." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1087, "authors_detailed": [ { "name": "Maureen Doyle-Spare", "url": "https://openalex.org/A5130951607", "inst": "LinkedIn (United States)" } ], "affiliations": [ "LinkedIn (United States)" ] }, { "uid": "doi:10.2139/ssrn.6728902", "doi": "10.2139/ssrn.6728902", "title": "Financialized ESG Information", "authors": [ "Yi-Chun Chen", "Tse-Chun Lin", "Qi Zhang" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6728902", "field": "finance", "role": "instrument", "bullets": [ "ESG disclosure reports at the firm level; sample size, period, and geography not stated, with the firm as the unit of observation.", "A retrieval-augmented generation pipeline, model not stated, extracts disclosure data to score how far ESG reporting aligns with financially material metrics, with no validation against ground truth reported.", "The financialized ESG measure correlates positively with the implied cost of capital but yields nonsignificant portfolio returns overall, turning positive among firms with low ESG investor preference and during ESG shocks." ], "bullet_provenance": "ai", "validated": false, "salience": 49, "edition": 3, "audience": "general", "models": [], "n": 1088, "authors_detailed": [ { "name": "Yi-Chun Chen", "url": "https://openalex.org/A5135629622", "inst": "" }, { "name": "Tse-Chun Lin", "url": "https://openalex.org/A5135597192", "inst": "" }, { "name": "Qi Zhang", "url": "https://openalex.org/A5135602074", "inst": "Beijing Institute of Technology" } ], "affiliations": [ "Beijing Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.6730580", "doi": "10.2139/ssrn.6730580", "title": "Algorithmic Logistics and Labor Economics in the On-Demand delivery Ecosystem: A Socio-Technical Analysis with a Proposal for Rider-Centric Intelligence", "authors": [ "narayana sanghea panchumarthy" ], "posted": "2026-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6730580", "field": "economics", "role": "object", "bullets": [ "Conceptual socio-technical analysis of on-demand food delivery platforms globally, drawing on Berkeley Labor Center pay data, a Stanford rideshare gender-gap study, and recent EU, US, and China platform-work regulation.", "No model is applied by the authors; the paper studies algorithmic dispatch and forecasting systems such as Deliveroo's Frank and Uber Eats triple-batching as the object shaping courier labour.", "Argues gig delivery has entered a hyper-algorithmic phase whose efficiency is inseparable from labour-market effects, and proposes a rider-centric analytics blueprint with contestable decision logs." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1089, "authors_detailed": [ { "name": "narayana sanghea panchumarthy", "url": "https://openalex.org/A5135300507", "inst": "Panthera Corporation" } ], "affiliations": [ "Panthera Corporation" ] }, { "uid": "doi:10.2139/ssrn.6638918", "doi": "10.2139/ssrn.6638918", "title": "The Mirror Problem: Ai Bias As Reflected Cognition", "authors": [ "Daniel Ziekenoppasser-Powell" ], "posted": "2026-05-06", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6638918", "field": "management", "role": "object", "bullets": [ "Conceptual governance paper with no empirical sample, built on cognitive science accounts of human bias and on published descriptions of reinforcement learning from human feedback.", "No model is run and none is named. The argument maps catalogued human cognitive biases onto model behaviour and treats alignment training as shallow next to pretraining distributions.", "Splits bias into externalised forms carried in text, which should transfer, and embodied forms requiring lived experience, which should not, and asks for continuous post-deployment monitoring rather than certification." ], "bullet_provenance": "ai", "salience": 30, "edition": 17, "models": [], "validated": null, "n": 2132, "authors_detailed": [ { "name": "Daniel Ziekenoppasser-Powell", "url": "https://openalex.org/A5134400833", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6637298", "doi": "10.2139/ssrn.6637298", "title": "Why Auditability Fails in Current Generative AI Systems: A Governance Perspective", "authors": [ "Monika Dvorackova" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6637298", "field": "management", "role": "object", "bullets": [ "Structured narrative review of generative AI auditability, drawing on algorithmic accountability, AI auditing, interpretability, foundation-model governance, and regulatory-compliance scholarship rather than an empirical sample.", "No specific model is named; the paper analyzes generative AI architectures in general and proposes AIGov, a governance-native design using append-only evidence logging and policy-constrained execution.", "Concludes that prevailing deployment models cannot support enforceable ex post auditability because of probabilistic nondeterminism, continuous updates, black-box API provisioning, and insufficient prompt-response trace binding." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 210, "authors_detailed": [ { "name": "Monika Dvořáčková", "url": "https://openalex.org/A5007877738", "inst": "University Health Care System" } ], "affiliations": [ "University Health Care System" ] }, { "uid": "doi:10.2139/ssrn.6633921", "doi": "10.2139/ssrn.6633921", "title": "Multilingual Large Language Models and Cultural Diversity: Evidence from Civic and Moral Judgments", "authors": [ "Eugenio Vicario", "Ennio Bilancini", "Leonardo Boncinelli" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6633921", "field": "economics", "role": "agent", "bullets": [ "Civic and moral judgments generated by a multilingual large language model across several languages, benchmarked against population-level responses from the World Values Survey and European Values Study.", "The model, which the paper does not name, is prompted to produce moral evaluations in each language, and its outputs are compared to survey values without a reported agreement statistic.", "Linguistic variation did not preserve cross-national moral diversity, alignment was strongest for WEIRD countries, and the model compressed moral distances between WEIRD and non-WEIRD groups." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 211, "authors_detailed": [ { "name": "Eugenio Vicario", "url": "https://openalex.org/A5135525193", "inst": "IMT School for Advanced Studies Lucca" }, { "name": "Ennio Bilancini", "url": "https://openalex.org/A5043371215", "inst": "IMT School for Advanced Studies Lucca" }, { "name": "Leonardo Boncinelli", "url": "https://openalex.org/A5003450032", "inst": "University of Florence" } ], "affiliations": [ "IMT School for Advanced Studies Lucca", "University of Florence" ] }, { "uid": "doi:10.2139/ssrn.6721779", "doi": "10.2139/ssrn.6721779", "title": "A Designed-for-Test Measurement of Phantom-Brand Presence in Large Language Model Outputs", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6721779", "field": "management", "role": "object", "bullets": [ "Pre-registered measurement of brand visibility for Bed Bath & Beyond and Pier 1 in LLM outputs, 288 successful runs from six prompts, six models and eight repetitions in May 2026.", "Prompted model lineups from Anthropic, OpenAI, Google and xAI, versions not stated; a manual valence audit of BBB mentions reached 88 percent inter-rater agreement on a 25-row spot check.", "BBB raw AI presence was 38.2 percent versus 0.0 percent for Pier 1, but after valence review the naive-phantom rate was 1.7 percent, with 88 percent of mentions carrying entity-change disclaimers." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "general", "validated": null, "n": 411, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "Samsung (United States)" } ], "affiliations": [ "Samsung (United States)" ] }, { "uid": "doi:10.2139/ssrn.6667898", "doi": "10.2139/ssrn.6667898", "title": "Generative AI Risk, Firm-Side Implementation Exposure, and Asset Prices", "authors": [ "Hany Fahmy" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6667898", "field": "finance", "role": "object", "bullets": [ "US firms in asset pricing tests; the study builds a newspaper-based generative AI risk index and an earnings-call implementation-exposure measure, with sample size and period not stated.", "Textual measures use CEO-native earnings-call language and an eight-channel taxonomy; the specific model is not stated and no validation against a benchmark is reported.", "Adverse generative AI news lowers average returns but less for high-exposure firms, widening the machine-minus-human spread chiefly by depressing low-exposure firms." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 412, "authors_detailed": [ { "name": "Hany Fahmy", "url": "https://openalex.org/A5135431469", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6723472", "doi": "10.2139/ssrn.6723472", "title": "Determinant Factors of Conversational Dropout Between Users and Chatbots Based on Generative Artificial Intelligence Models", "authors": [ "Walter Sengik da Cruz", "Mateus Panizzon" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6723472", "field": "management", "role": "object", "bullets": [ "Corpus of 849 real user-chatbot interactions drawn from three conversational AI settings (SaaS, software factory, English school), analyzed under a Design Science Research approach.", "Generative AI models, not named, extract subjective conversational variables that feed structural equation modeling of quality, trust, engagement and dropout; no accuracy check is reported.", "Low technical performance does not trigger immediate abandonment; dropout arises through exhaustion once users' adaptive effort exceeds perceived value, a pattern the authors call the Persistence Paradox." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 413, "authors_detailed": [ { "name": "Walter Sengik da Cruz", "url": "https://openalex.org/A5059325999", "inst": "" }, { "name": "Mateus Panizzon", "url": "https://openalex.org/A5013225068", "inst": "Universidade Federal do Rio Grande do Sul" } ], "affiliations": [ "Universidade Federal do Rio Grande do Sul" ] }, { "uid": "doi:10.2139/ssrn.6725154", "doi": "10.2139/ssrn.6725154", "title": "Application Scenarios and Perceived Importance of Generative AI in Construction Safety Knowledge Management: A Multi-Stakeholder Empirical Study", "authors": [ "Liying Wang", "Botao Gu", "Yuecheng Huang", "Dongping Fang" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6725154", "field": "management", "role": "object", "bullets": [ "Semi-structured interviews with 24 construction safety experts plus a questionnaire survey; the topic is generative AI across the construction safety knowledge management lifecycle, geography not stated.", "No model is applied; the study elicits and rates stakeholder perceptions of six GenAI functional roles and 24 application scenarios, analyzed with mixed-design ANOVA.", "Knowledge acquisition and creation ranked most important; general contractors valued GenAI-enabled management most and supervisors least, with a significant three-way stakeholder interaction." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 414, "authors_detailed": [ { "name": "Liying Wang", "url": "https://openalex.org/A5135497589", "inst": "Beijing Jiaotong University" }, { "name": "Botao Gu", "url": "https://openalex.org/A5071651359", "inst": "Tsinghua University" }, { "name": "Yuecheng Huang", "url": "https://openalex.org/A5135532207", "inst": "" }, { "name": "Dongping Fang", "url": "https://openalex.org/A5005660940", "inst": "Tsinghua University" } ], "affiliations": [ "Beijing Jiaotong University", "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.6722700", "doi": "10.2139/ssrn.6722700", "title": "The AI Democratization Paradox: How Mid-Market Tools Created a New Implementation Gap for Hispanic SMBs", "authors": [ "Humberto Inciarte" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6722700", "field": "management", "role": "object", "bullets": [ "Conceptual synthesis on AI adoption by Hispanic-owned US small and medium businesses, drawing on provider pricing and third-party failure-rate and demographic reports rather than primary data.", "No model is applied; the paper contrasts access to enterprise LLMs from Anthropic, OpenAI and Google with implementation capacity and proposes an implementation-with-context framework.", "Argues access is democratized at equal per-token rates while value capture is not, with 74 to 95 percent of organizational AI initiatives failing, leaving Hispanic SMBs most exposed." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 35, "edition": 3, "audience": "general", "validated": null, "n": 415, "authors_detailed": [ { "name": "Humberto Inciarte", "url": "https://openalex.org/A5135460635", "inst": "" } ] }, { "uid": "arxiv:2605.18784v2", "arxiv_id": "2605.18784v2", "title": "The Insurability Frontier of AI Risk: Mapping Threats to Affirmative Coverage, Silent Exposures, and Exclusions", "authors": [ "Alex Leung", "Rex Zhang", "Ervin Ling", "Kentaroh Toyoda", "SiewMei Loh" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.18784v2", "field": "finance", "role": "object", "bullets": [ "Fifty-five AI threat classes coded against 26 commercial insurance products, endorsements, and exclusion regimes, using public carrier materials and OWASP and MITRE threat catalogs.", "No language model is applied; the researchers hand-code publicly stated coverage positioning across carriers to map affirmative, silent, and excluded AI exposures, so model naming is not applicable.", "Identifies a four-tier insurability frontier and finds foundation model concentration the clearest novel frontier because upstream model failure can correlate losses across many insureds at once." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 557, "authors_detailed": [ { "name": "Alex Leung", "url": "https://openalex.org/A5134764628", "inst": "Swift Engineering (United States)" }, { "name": "Rex Zhang", "url": "https://openalex.org/A5136591063", "inst": "" }, { "name": "Ervin Ling", "url": "https://openalex.org/A5136514540", "inst": "" }, { "name": "Kentaroh Toyoda", "url": "https://openalex.org/A5066312369", "inst": "Vulcan (United States)" }, { "name": "SiewMei Loh", "url": "https://openalex.org/A5136569869", "inst": "" } ], "affiliations": [ "Swift Engineering (United States)", "Vulcan (United States)" ] }, { "uid": "doi:10.2139/ssrn.6722712", "doi": "10.2139/ssrn.6722712", "title": "Ideology-Driven Specification Selection in Applied Econometrics", "authors": [ "Fabio Motoki", "Valdemar Pinho Neto", "Victor Rangel" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6722712", "field": "economics", "role": "agent", "bullets": [ "Six hundred large language model agents act as synthetic analysts across four applied economics papers, organized into balanced cells of fifty runs per ideology (left, neutral, right).", "Unnamed LLM agents receive an ideology persona through an identity-framing system-prompt clause, then choose among pre-enumerated defensible specifications and retrieve coefficients from a frozen database, isolating specification selection.", "Ideology shifts which specifications agents pick in every paper, with mean estimates ordering in the predicted direction, from clean separation in the voter identification paper to a near-null gap for DACA." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 764, "authors_detailed": [ { "name": "Fabio Motoki", "url": "https://openalex.org/A5135479607", "inst": "The University of Texas Rio Grande Valley" }, { "name": "Valdemar Pinho Neto", "url": "https://openalex.org/A5135422368", "inst": "Escola Brasileira de Economia e Finanças" }, { "name": "Victor Rangel", "url": "https://openalex.org/A5111173999", "inst": "Insper" } ], "affiliations": [ "The University of Texas Rio Grande Valley", "Escola Brasileira de Economia e Finanças", "Insper" ] }, { "uid": "doi:10.2139/ssrn.6726724", "doi": "10.2139/ssrn.6726724", "title": "Pivot-Point Breaches and News Catalysts", "authors": [ "Mengxiao Wang", "Baichuan Li" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6726724", "field": "finance", "role": "instrument", "bullets": [ "Daily SPY ETF data from 2000 to 2023 yield 3,690 fresh intraday pivot-level breaches, merged with LLM-curated daily news within a 2000 to 2020 coverage window.", "An unnamed LLM-based compression pipeline extracts US market-relevant facts into daily news summaries; the paper does not name the model and reports no validation against a benchmark.", "Breaches coinciding with an identified news catalyst, 167 events or 5.3 percent, show next-day continuation falling from about 50 percent to roughly 17 percent, with returns reverting against the breach." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no benchmark comparison reported", "salience": 40, "edition": 3, "audience": "general", "models": [], "n": 765, "authors_detailed": [ { "name": "Mengxiao Wang", "url": "https://openalex.org/A5134039106", "inst": "" }, { "name": "Baichuan Li", "url": "https://openalex.org/A5135438539", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6659746", "doi": "10.2139/ssrn.6659746", "title": "What Jobs Can AI Learn? Measuring Exposure by Reinforcement Learning", "authors": [ "Philip Tomei", "Bouke Klein Teeselink" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6659746", "field": "economics", "role": "instrument", "bullets": [ "Every US occupation, scoring all 17,951 ONET tasks for training feasibility and aggregating to the occupation level to build a reinforcement-learning feasibility index of AI exposure.", "Unnamed LLM annotators applied a rubric developed with reinforcement-learning experts and validated against confirmed deployment cases; the model family is not stated and no accuracy figure is reported.", "The index diverges from existing exposure measures: power plant operators and railroad conductors score high on RL feasibility but low on general exposure, while musicians and physicians reverse." ], "bullet_provenance": "ai", "validated": false, "validation_note": "deployment cases cited, no accuracy figure", "salience": 58, "edition": 3, "audience": "general", "models": [], "n": 766, "authors_detailed": [ { "name": "Philip Tomei", "url": "https://openalex.org/A5135503709", "inst": "ObjectVideo (United States)" }, { "name": "Bouke Klein Teeselink", "url": "https://openalex.org/A5135417115", "inst": "King's College London" } ], "affiliations": [ "ObjectVideo (United States)", "King's College London" ] }, { "uid": "doi:10.2139/ssrn.6725881", "doi": "10.2139/ssrn.6725881", "title": "How technological progressions at various stages of the digital age impact household income: Transmission Mechanisms and the Chinese Paradox", "authors": [ "Mingzhaoyang Feng" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6725881", "field": "economics", "role": "object", "bullets": [ "Multi-level theoretical framework covering macro, meso, and micro mechanisms that link ICT, industrial robots, and AI to the scale, structure, and equity of household income, focused on China.", "Generative AI is studied as an object, framed as an intelligent assistant that internalises experts' tacit knowledge; no language model is used or named by the author.", "The paper argues generative AI reduces skill premiums and narrows worker income gaps, and places China on the right-hand side of a U-shaped labour-share trajectory." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 767, "authors_detailed": [ { "name": "Mingzhaoyang Feng", "url": "https://openalex.org/A5135467916", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6664039", "doi": "10.2139/ssrn.6664039", "title": "Invisible Agents, Visible Harm: Agentic AI, Accountability Erosion, and the Societal Trust Crisis", "authors": [ "Albert Adusei Brobbey" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6664039", "field": "management", "role": "object", "bullets": [ "Theory-building paper on agentic AI deployed by 2026 in loan decisions, government workflows, patient triage, and content moderation, grounded in institutional trust and democratic accountability theory.", "No language model is used; the paper theorises AI accountability rather than measuring it, introducing societal accountability erosion and four transmission mechanisms as propositions for empirical testing.", "It argues that when institutions deploy consequential AI without a legible accountable human, they erode the accountability on which institutional legitimacy and public trust depend." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 768, "authors_detailed": [ { "name": "Albert Adusei Brobbey", "url": "https://openalex.org/A5135482810", "inst": "Meridian International Center" } ], "affiliations": [ "Meridian International Center" ] }, { "uid": "doi:10.2139/ssrn.6723799", "doi": "10.2139/ssrn.6723799", "title": "Dependency Exposure: A Six-Axis Loss Model for AI Service Disruption", "authors": [ "Kenji Yamada" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6723799", "field": "management", "role": "object", "bullets": [ "Conceptual framework on losses when AI dependency is severed, illustrated with cases including GPT-4o retirement, Starlink denial over Crimea, and country-level access blocking, from individual to nation-state scale.", "No language model is used by the author; GPT-4o appears only as an empirical severance case, and the paper proposes a six-axis loss model rather than measuring text.", "It argues severance produces losses across economic, psychological, cognitive, ontological, emergentive, and sovereign axes, and that post-severance productivity can fall below pre-adoption levels." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 28, "edition": 3, "audience": "general", "validated": null, "n": 769, "authors_detailed": [ { "name": "Kenji Yamada", "url": "https://openalex.org/A5135438342", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6723473", "doi": "10.2139/ssrn.6723473", "title": "Beyond the Prompt: How Agentic AI Reshapes Creative Agency and Design Authorship in Visual Communication Practice", "authors": [ "Xian Wu" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6723473", "field": "management", "role": "object", "bullets": [ "Explanatory sequential mixed-methods study of visual communication designers, combining a 2 by 3 between-subjects experiment with 180 participants and reflexive thematic analysis of 14 semi-structured interviews.", "Agentic AI autonomy level, set at tool, collaboration, or delegation, is the manipulated variable; no specific model is named, and the study measures self-reported agency rather than validating any model output.", "Perceived creative agency declines nonlinearly as AI autonomy rises, with a sharp rupture at the tool-to-collaborator transition, and senior designers report stronger agency loss than novices." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1084, "authors_detailed": [ { "name": "Xian Wu", "url": "https://openalex.org/A5135429883", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6659000", "doi": "10.2139/ssrn.6659000", "title": "AI Availability: Extending Mental and Physical Availability into Algorithmic Retrieval", "authors": [ "Pablo Ulpiano Gonzalez Castro" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6659000", "field": "management", "role": "object", "bullets": [ "Conceptual marketing paper extending the Ehrenberg-Bass availability framework, supported by one online experiment with 208 participants examining brand choice when generative AI returns a single recommendation against paraphrased intent.", "Generative AI is the studied mechanism rather than a tool the authors apply; no specific model is named, and the experiment measures consumer choice, not model accuracy against any benchmark.", "Reports a 25.4 percentage-point shift in brand choice consistent with the prediction that AI recommendation can suspend incumbent brand advantage under specified utilitarian, low-identity conditions." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1085, "authors_detailed": [ { "name": "Pablo Ulpiano Gonzalez Castro", "url": "https://openalex.org/A5135343780", "inst": "Samsung (United States)" } ], "affiliations": [ "Samsung (United States)" ] }, { "uid": "doi:10.2139/ssrn.6721599", "doi": "10.2139/ssrn.6721599", "title": "From Reporting to Inference: Active LP Decision-Making in Private Markets", "authors": [ "Kristine Miranda" ], "posted": "2026-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6721599", "field": "finance", "role": "object", "bullets": [ "Conceptual finance paper on limited-partner monitoring of private markets, motivated by over $24 trillion in assets and $240 billion of secondary transactions in 2025, drawing on interviews with allocators.", "Proposes a Bayesian state-space inference framework made tractable at scale by agentic systems that current language models enable; no model is named and empirical testing is deferred to a companion paper.", "Argues NAV monitoring is an inference rather than a measurement problem, combining a credibility weight, a structural stress prior, and disclosure-language signals; the empirical claim is hypothesised, not tested here." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1086, "authors_detailed": [ { "name": "Kristine Miranda", "url": "https://openalex.org/A5135373284", "inst": "Stanford Graduate School of Business" } ], "affiliations": [ "Stanford Graduate School of Business" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6713089", "doi": "10.2139/ssrn.6713089", "title": "MOJO: Multi-LLM Optimised Joint Objective - Generative Artificial Intelligence for Multi-Criteria Decision Analysis Framework", "authors": [ "Abtin Ijadi Maghsoodi" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6713089", "field": "management", "role": "method", "bullets": [ "Illustrative case study ranking Inflammatory Bowel Disease treatments across clinical, operational, and ethical criteria, with multiple decision-makers evaluated across diverse scenarios.", "An ensemble of multiple unnamed LLMs scores options within a multi-criteria decision framework combining risk-adjusted utility, performance equilibrium, reliability scoring, and bias detection.", "Validation experiments report the framework yields consensus-driven rankings with claimed robustness and transparency; no accuracy figure against a ground truth is provided." ], "bullet_provenance": "ai", "validated": false, "validation_note": "illustrative case study, no ground-truth accuracy", "salience": 36, "edition": 3, "audience": "technical", "models": [], "n": 144 }, { "uid": "doi:10.2139/ssrn.6720330", "doi": "10.2139/ssrn.6720330", "title": "Industrial Policy as Crisis Response: How Chinese Local Governments Navigated the Trade War", "authors": [ "Dongmin Hu", "Hao Tang", "Huanhuan Wang", "Zhijie Zhang" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6720330", "field": "economics", "role": "instrument", "bullets": [ "Nearly four million Chinese government documents from prefecture-level cities, 2017 to 2019, linked to shift-share US tariff exposure and firm-level outcomes.", "Unnamed large language models classify the documents to build a novel dataset of local industrial policies; no validation against hand coding is reported.", "Greater tariff exposure raises supportive industrial policy with no rise in restrictive policy; policy-rich cities show attenuated export losses but weaker pollution reduction." ], "bullet_provenance": "ai", "validated": false, "salience": 68, "edition": 3, "audience": "general", "models": [], "n": 145, "authors_detailed": [ { "name": "Dongmin Hu", "url": "https://openalex.org/A5135283258", "inst": "Zhongnan University of Economics and Law" }, { "name": "Hao Tang", "url": "https://openalex.org/A5135304682", "inst": "Fudan University" }, { "name": "Huanhuan Wang", "url": "https://openalex.org/A5135356717", "inst": "East China Normal University" }, { "name": "Zhijie Zhang", "url": "https://openalex.org/A5135361312", "inst": "Fudan University" } ], "affiliations": [ "Zhongnan University of Economics and Law", "Fudan University", "East China Normal University" ] }, { "uid": "doi:10.2139/ssrn.6719681", "doi": "10.2139/ssrn.6719681", "title": "Using LLM-Based Priors to Optimize Choice Designs in Conjoint Experiments", "authors": [ "Felix Eggers", "Marco Vriens" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6719681", "field": "management", "role": "method", "bullets": [ "Choice-based conjoint study of premium job-search platform subscriptions with 984 US job seekers, comparing a conventional randomized design with LLM-optimized designs.", "Unnamed large language models generate prior part-worth utilities used to optimize designs; the priors are validated against holdout choices for predictive performance.", "LLM priors predict holdout choices beyond chance and, when used to screen dominated tasks, substantially improve prediction; Bayesian integration adds mixed further benefits." ], "bullet_provenance": "ai", "validated": true, "validation_note": "holdout choice prediction vs 984 respondents", "salience": 57, "edition": 3, "audience": "general", "models": [], "n": 146, "authors_detailed": [ { "name": "Felix Eggers", "url": "https://openalex.org/A5069017265", "inst": "Copenhagen Business School" }, { "name": "Marco Vriens", "url": "https://openalex.org/A5039659020", "inst": "Microsoft (United States)" } ], "affiliations": [ "Copenhagen Business School", "Microsoft (United States)" ] }, { "uid": "doi:10.2139/ssrn.6715818", "doi": "10.2139/ssrn.6715818", "title": "Human-AI Interaction in Creative Tasks: an Experimental Investigation", "authors": [ "Federico Atzori", "Luca Corazzini", "Valeria Maggian", "Filippo Pavesi", "Massimo Scotti" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6715818", "field": "economics", "role": "object", "bullets": [ "Laboratory writing experiment randomly assigning participants access to ChatGPT-4.2 or none; independent Prolific evaluators score creative performance and interaction logs are recorded; sample size and country not stated.", "Participants used ChatGPT-4.2 as a writing aid while the study logged prompting intensity, ideation requests and textual overlap; the model is given to subjects rather than validated against any ground truth.", "AI access raised performance only among those who actively queried; the intensity-performance relation was concave and peaked near eight queries, and ideation gains operated indirectly through incorporating AI-generated language." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 58, "edition": 3, "audience": "general", "validated": null, "n": 181, "authors_detailed": [ { "name": "Federico Atzori", "url": "https://openalex.org/A5135363589", "inst": "University of Cagliari" }, { "name": "Luca Corazzini", "url": "https://openalex.org/A5021589852", "inst": "University of Milano-Bicocca" }, { "name": "Valeria Maggian", "url": "https://openalex.org/A5135384576", "inst": "Ca' Foscari University of Venice" }, { "name": "Filippo Pavesi", "url": "https://openalex.org/A5027454690", "inst": "Stevens Institute of Technology" }, { "name": "Massimo Scotti", "url": "https://openalex.org/A5135288147", "inst": "" } ], "affiliations": [ "University of Cagliari", "University of Milano-Bicocca", "Ca' Foscari University of Venice", "Stevens Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.6716300", "doi": "10.2139/ssrn.6716300", "title": "Evaluating AI Trading Strategies Using a Paper Trading Framework", "authors": [ "Sharndeep Kaur" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6716300", "field": "finance", "role": "method", "bullets": [ "Twenty S&P 500 stocks over 2020 to 2024, run inside a paper-trading simulation platform called AlphaScope built on Django and React.", "Compares LLaMA 3 via Groq against a PPO reinforcement-learning agent and a LightGBM model as trading strategies, and proposes a simulation performance score with no ground-truth measurement validation.", "The reinforcement-learning agent reached the highest average portfolio value at 110.30 dollars, while the LLaMA strategy showed 49.39 percent directional accuracy but the best risk-adjusted score of 0.5802." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "salience": 32, "edition": 3, "audience": "technical", "validated": null, "n": 209, "authors_detailed": [ { "name": "Sharndeep Kaur", "url": "https://openalex.org/A5135309838", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6720239", "doi": "10.2139/ssrn.6720239", "title": "Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach", "authors": [ "Yue Liu", "Zhiyuan Cheng", "Longying Lai" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6720239", "field": "accounting", "role": "instrument", "bullets": [ "Segment-level disclosures drawn from Form 10-K filings, spread across tabular and narrative sections; sample size, period, and geography are not stated in the abstract.", "An unnamed large language model extracts reportable and nested segment items, paired with a retrieval augmented system that reads across multiple filings; no accuracy figure is reported.", "The framework extracts segment information and answers cross-period questions, supporting within-firm longitudinal analysis and cross-firm alignment of geographic segments." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 3, "audience": "general", "models": [], "n": 305, "authors_detailed": [ { "name": "Yue Liu", "url": "https://openalex.org/A5135314527", "inst": "Jiangsu University" }, { "name": "Zhiyuan Cheng", "url": "https://openalex.org/A5102997384", "inst": "Zhejiang University" }, { "name": "Longying Lai", "url": "https://openalex.org/A5126702281", "inst": "University of Rochester" } ], "affiliations": [ "University of Rochester", "Jiangsu University", "Zhejiang University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6714178", "doi": "10.2139/ssrn.6714178", "title": "FraudRAG: Knowledge Graph-Augmented Retrieval for Real-Time Financial Statement Fraud Detection Using a Domain-Driven Medallion Architecture", "authors": [ "Akshay Sharma" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6714178", "field": "accounting", "role": "method", "bullets": [ "Reproducible synthetic benchmark of 2,400 annotated financial statements across six document types, built for real-time financial statement fraud detection.", "A Graph-RAG pipeline feeds Neo4j and vector-retrieved context to Claude 3.5 Sonnet, validated against labeled statements reporting AUC 0.943, precision 0.891, recall 0.876 and F1 0.883.", "The pipeline outperforms rule-based, LLM-only, RAG-only and graph-only baselines by 18 to 24 percentage points, at sub-3-second end-to-end latency." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "2,400-statement annotated synthetic benchmark, AUC 0.943 and F1 0.883", "salience": 47, "edition": 3, "audience": "technical", "n": 407, "authors_detailed": [ { "name": "Akshay Sharma", "url": "https://openalex.org/A5040878970", "inst": "St. Jude Children's Research Hospital" } ], "affiliations": [ "St. Jude Children's Research Hospital" ] }, { "uid": "doi:10.2139/ssrn.6591059", "doi": "10.2139/ssrn.6591059", "title": "How a Non-Theorist and Two AIs Proved a Theorem: Anatomy of a Human-AI Collaboration in Mathematical Economics", "authors": [ "Diana Weinhold" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6591059", "field": "economics", "role": "method", "bullets": [ "Case study of an April 2026 two-week collaboration in which one applied economist and multiple large language models produced formal proofs of two asymptotic theorems in mathematical economics.", "Two unnamed language models drafted proofs while a third acted as hostile auditor; outputs survived iterative AI auditing but were not yet reviewed by a human mathematician. Models not stated.", "Documents five generalizable collaboration themes and a five-type AI error taxonomy, including computational dissociation where reasoning, code execution and text presentation diverge." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 408, "authors_detailed": [ { "name": "Diana Weinhold", "url": "https://openalex.org/A5133727162", "inst": "London School of Economics and Political Science" } ], "affiliations": [ "London School of Economics and Political Science" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6717658", "doi": "10.2139/ssrn.6717658", "title": "Constituent Interests in SEC Rules: Evidence from Voluntary Comment Letters *", "authors": [ "Charles Downing", "Gabriel Pundrich", "Gabriel Voelcker" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6717658", "field": "finance", "role": "instrument", "bullets": [ "More than 65,000 voluntary comment letters addressing 417 rules the SEC proposed between 1995 and 2024, with each letter linked to a stated constituent preference.", "Large language models, family not stated, extract and link constituents' stated preferences to specific rule provisions; the abstract reports no validation against hand coding.", "Proposed rules align more often with firms' stated interests than with other constituents, an advantage that weakens at adoption, while retail investors' preferences track dropped provisions." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 409, "authors_detailed": [ { "name": "Charles Downing", "url": "https://openalex.org/A5135304130", "inst": "" }, { "name": "Gabriel Pundrich", "url": "https://openalex.org/A5135342466", "inst": "" }, { "name": "Gabriel Voelcker", "url": "https://openalex.org/A5084226655", "inst": "Universidade do Vale do Rio dos Sinos" } ], "affiliations": [ "Universidade do Vale do Rio dos Sinos" ] }, { "uid": "doi:10.2139/ssrn.6713199", "doi": "10.2139/ssrn.6713199", "title": "Knowing-in-Synthesis: Generative AI and the Production of Epistemic Discontinuities in Everyday Action", "authors": [ "Masoom Suchdeo", "Paul M. Leonardi" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6713199", "field": "management", "role": "object", "bullets": [ "Conceptual paper in organization studies with no empirical sample; the unit of interest is everyday organizational knowing and the practice traces that scaffold it.", "No language model is applied; the paper theorizes that LLMs create a synthetic practice regime where knowing engages algorithmic syntheses of prior representations rather than lived-experience residues.", "Proposes knowing-in-synthesis as a remedy, arguing practitioners restore coherence by moving around synthetic representations through embodied and collective engagement; the contribution is theoretical." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 410, "authors_detailed": [ { "name": "Masoom Suchdeo", "url": "https://openalex.org/A5054861502", "inst": "University of California, Santa Barbara" }, { "name": "Paul M. Leonardi", "url": "https://openalex.org/A5135383740", "inst": "University of California, Santa Barbara" } ], "affiliations": [ "University of California, Santa Barbara" ] }, { "uid": "doi:10.2139/ssrn.6717514", "doi": "10.2139/ssrn.6717514", "title": "Explainable artificial intelligence and generative artificial intelligence in e-commerce: a systematic literature review", "authors": [ "Thaynã França", "Henrique Pacheco", "Rodrigo da Rosa Righi", "Cristiano André da Costa", "Jorge Luis Victória Barbosa", "Rodrigo Scorsatto", "Grace Cheah" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6717514", "field": "management", "role": "object", "bullets": [ "Systematic literature review of explainable AI and generative AI in the e-commerce domain; the number of reviewed papers is not stated.", "No model is applied; the review builds a taxonomy and traces the temporal evolution of models, techniques, and tools for content automation, tailored marketing, and system transparency.", "Identifies ethical and technical challenges and future directions toward trustworthy AI in e-commerce, with no quantitative finding reported." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 760, "authors_detailed": [ { "name": "Thaynã França", "url": "https://openalex.org/A5135410605", "inst": "" }, { "name": "Henrique Pacheco", "url": "https://openalex.org/A5135412106", "inst": "" }, { "name": "Rodrigo da Rosa Righi", "url": "https://openalex.org/A5135310160", "inst": "Universidade do Vale do Rio dos Sinos" }, { "name": "Cristiano André da Costa", "url": "https://openalex.org/A5024617531", "inst": "Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul" }, { "name": "Jorge Luís Victória Barbosa", "url": "https://openalex.org/A5072791484", "inst": "Universidade do Vale do Rio dos Sinos" }, { "name": "Rodrigo Scorsatto", "url": "https://openalex.org/A5048616276", "inst": "Governo do Estado do Rio Grande do Sul" }, { "name": "Grace Cheah", "url": "https://openalex.org/A5057673845", "inst": "Eastman Dental Hospital" } ], "affiliations": [ "Universidade do Vale do Rio dos Sinos", "Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Sul", "Governo do Estado do Rio Grande do Sul" ] }, { "uid": "doi:10.2139/ssrn.6715522", "doi": "10.2139/ssrn.6715522", "title": "WHEN AI NARRATIVES OUTPACE AI INVESTMENT:STRATEGIC DECOUPLING AND THE QUANTITY–QUALITY TRADEOFF IN FIRM INNOVATION", "authors": [ "zhidi yin", "che jiamei" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6715522", "field": "management", "role": "instrument", "bullets": [ "Chinese A-share listed firms from 2015 to 2022, with firm-level AI narrative intensity compared to actual AI investment growth to define AI strategic decoupling.", "An unnamed large language model scores AI narrative intensity from firm disclosures, combined with an IPC-based patent technology-space method; no validation against hand coding is reported.", "AI decoupling raises breakthrough patent quantity but lowers the breakthrough share of the portfolio, via a positive subsidy channel and a negative financialization channel concentrated among non-state-owned firms." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 3, "audience": "general", "models": [], "n": 761, "authors_detailed": [ { "name": "zhidi yin", "url": "https://openalex.org/A5133482801", "inst": "Hebei University of Technology" }, { "name": "che jiamei", "url": "https://openalex.org/A5133502151", "inst": "Hebei University of Technology" } ], "affiliations": [ "Hebei University of Technology" ] }, { "uid": "doi:10.2139/ssrn.6705510", "doi": "10.2139/ssrn.6705510", "title": "AI Managed Household Portfolios: A Preliminary Report", "authors": [ "Bruce Carlin", "Ryan D. Israelsen", "Christopher Wazzan" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6705510", "field": "finance", "role": "agent", "bullets": [ "A prospective daily time series of stock recommendations collected from several large language models, used to characterize AI's investment style for household portfolios.", "Several unnamed LLMs are queried, with multiple query versions, for stock picks, and recommendations are analyzed for factor loadings and abnormal returns using the Daniel et al 1997 characteristic-based method.", "AI favors undiversified portfolios loading on momentum, large size, and low book-to-market, driven by media attention, and its buy-and-hold or active portfolios earn no significant abnormal returns." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 762, "authors_detailed": [ { "name": "Bruce Carlin", "url": "https://openalex.org/A5126156598", "inst": "Rice University" }, { "name": "Ryan D. Israelsen", "url": "https://openalex.org/A5047142962", "inst": "Michigan State University" }, { "name": "Christopher Wazzan", "url": "https://openalex.org/A5135335501", "inst": "University of California, Berkeley" } ], "affiliations": [ "Rice University", "Michigan State University", "University of California, Berkeley" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6614321", "doi": "10.2139/ssrn.6614321", "title": "Governing the Agentic Enterprise: Guardrails for Autonomous AI Agents in B2B Financial Transactions", "authors": [ "Syed Muhammad Khuzaima Alam" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6614321", "field": "finance", "role": "object", "bullets": [ "Autonomous AI agents in business-to-business financial operations such as payments and liquidity management, examined conceptually through formal modeling and a single B2B payment execution scenario rather than empirical data.", "No specific model is named; the paper proposes FASTRAC, a control-theoretic governance framework modeling agents as bounded, risk-scored actors under dynamically enforced constraints, validation, monitoring, and learning.", "The authors argue adaptive mechanisms such as dynamic risk thresholds and trust-calibrated autonomy can keep agent actions compliant and auditable while preserving operational efficiency, with no empirical results reported." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 763, "authors_detailed": [ { "name": "Syed Muhammad Khuzaima Alam", "url": "https://openalex.org/A5134331810", "inst": "University of Illinois Chicago" } ], "affiliations": [ "University of Illinois Chicago" ] }, { "uid": "doi:10.2139/ssrn.6617798", "doi": "10.2139/ssrn.6617798", "title": "When Conformity Becomes Structural: A Coupled Feedback Loop Model of Agentic AI and Principal Overconfidence", "authors": [ "Paul Gallacher" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6617798", "field": "management", "role": "object", "bullets": [ "Simulation study extending a coupled feedback-loop model of AI sycophancy from conversational to agentic settings, adding parameters for verification capacity and specification clarity; no empirical field sample.", "No model is named; simulations compare a chatbot baseline against agentic architectures under varied conformity and observability conditions to measure spiralling feedback dynamics.", "Agentic architectures spiral catastrophically in 38.9 to 41.6 percent of runs versus 4.4 percent for chatbots, an 8.7 to 9.4 times amplification, while informed users cut spiral rates 62 to 93 percent." ], "bullet_provenance": "ai", "salience": 43, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1081, "authors_detailed": [ { "name": "Paul Gallacher", "url": "https://openalex.org/A5002617606", "inst": "Institute of Finance and Banking" } ], "affiliations": [ "Institute of Finance and Banking" ] }, { "uid": "doi:10.2139/ssrn.6717618", "doi": "10.2139/ssrn.6717618", "title": "Agentic AI in Procurement Software: A Neutral Market Whitepaper", "authors": [ "Praneeth Kaashyap Daripalli" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6717618", "field": "management", "role": "object", "bullets": [ "Market whitepaper comparing leading procurement software vendors across source-to-pay suites, intake and orchestration platforms, sourcing specialists, supplier management, and contract or AP automation tools, using public information as of May 2026.", "No language model is used or named by the author; the paper qualitatively rates each vendor's agentic AI maturity and workflow depth against neutral criteria, with no validation reported.", "Concludes no single platform is universally best, with suites offering breadth and governance and specialists offering deeper autonomy, so the right choice depends on procurement maturity and data quality." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1082, "authors_detailed": [ { "name": "Praneeth Kaashyap Daripalli", "url": "https://openalex.org/A5135284390", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6717060", "doi": "10.2139/ssrn.6717060", "title": "THE ALM EFFICIENT FRONTIER A Constrained Optimisation Framework for Bank Balance Sheet Management - from KKT to Agentic AI", "authors": [ "Daniel Morales Chavez" ], "posted": "2026-05-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6717060", "field": "finance", "role": "object", "bullets": [ "Conceptual framework for bank asset-liability management that treats interest-rate, liquidity, and capital rules as one joint constrained optimisation over CET1, the EVS outlier ratio, LCR, and duration gap; no empirical sample.", "No language model is implemented; the paper proposes, without validation, an agentic AI architecture in which a central optimiser agent orchestrates constraint-specific sub-agents mirroring the Karush-Kuhn-Tucker structure.", "Derives a shadow price for each binding regulatory and internal constraint from Karush-Kuhn-Tucker conditions and argues these buffer costs should be central to every ALCO discussion; no quantitative results are reported." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1083, "authors_detailed": [ { "name": "Daniel Morales Chavez", "url": "https://openalex.org/A5135413782", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6705499", "doi": "10.2139/ssrn.6705499", "title": "Seeing the Goal, Missing the Truth: Human Accountability for AI Bias", "authors": [ "Sean S. Cao", "Wei Jiang", "Hui Xu" ], "posted": "2026-05-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6705499", "field": "finance", "role": "method", "bullets": [ "Financial prediction tasks using LLM-generated sentiment and competition measures from corporate text, with pre- and post-knowledge-cutoff test windows.", "LLM prompted with disclosed downstream purpose such as stock returns or earnings; intermediate outputs compared across goal-aware and goal-blind conditions.", "Purpose-aware prompting biases intermediate measures toward the disclosed goal, producing in-sample overfitting that vanishes after the knowledge cutoff date." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 75, "n": 2436, "authors_detailed": [ { "name": "Sean S. Cao", "url": "https://openalex.org/A5135362587", "inst": "Smith Institute" }, { "name": "Wei Jiang", "url": "https://openalex.org/A5052753577", "inst": "Emory University" }, { "name": "Hui Xu", "url": "https://openalex.org/A5135381720", "inst": "Lancaster University" } ], "affiliations": [ "Emory University", "Smith Institute", "Lancaster University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6608038", "doi": "10.2139/ssrn.6608038", "title": "Open-Source Edge LLMs for Forecasting Stock Returns via News Headline Sentiment Analysis", "authors": [ "Colin Arndt" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6608038", "field": "finance", "role": "instrument", "bullets": [ "US financial news headlines from 2018 to 2019 drawn from a Kaggle dataset, used to build headline-sentiment stock trading strategies on consumer-grade hardware.", "The Llama3-8B family, including INT8-quantized and a fine-tuned FinGPT variant, classified headline sentiment, benchmarked against FinBERT, GPT-3.5, GPT-4o mini and DeepSeek V3.", "Llama3-8B with INT8 quantization achieved 42.4 percent annualized return and a 1.55 Sharpe ratio within 10GB of RAM, outperforming the S&P 500 and some larger models." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 47, "edition": 3, "audience": "technical", "n": 73, "authors_detailed": [ { "name": "Colin Arndt", "url": "https://openalex.org/A5135188497", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6598718", "doi": "10.2139/ssrn.6598718", "title": "Visibility ≠ Credibility: Self-Promotion Bias in LLM-Generated Recommendations", "authors": [ "Tandeep Sangra" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6598718", "field": "management", "role": "object", "bullets": [ "Query testing across four major LLM recommendation platforms including ChatGPT and Perplexity, used to recommend professional services such as marketing consultants and digital agencies.", "The models generate recommendations analyzed for self-promotion patterns; model versions are not stated and no validation against independent credibility ground truth is reported.", "Identifies three self-promotion patterns showing that visibility in AI recommendations does not equal verified credibility, and proposes a five-point buyer verification framework." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 35, "edition": 3, "audience": "general", "validated": null, "n": 143, "authors_detailed": [ { "name": "Tandeep Sangra", "url": "https://openalex.org/A5135125920", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6599978", "doi": "10.2139/ssrn.6599978", "title": "Automated Multi-Dimensional Quality Scoring for Customer Service Operations Using Large Language Models: Production Design, Deployment, and Empirical Analysis", "authors": [ "Ali Aghabeigiha" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6599978", "field": "management", "role": "instrument", "bullets": [ "11,504 customer-agent chat interactions from a 30-day pilot at a global food delivery platform operating across 18 markets, with about 35,000 conversations scored daily.", "LLMs accessed through Google BigQuery ML score six weighted dimensions into a 0-10 satisfaction score; model family not stated, and 99% coverage and 95% calculation reliability are reported but no human benchmark.", "Fulfillment failure accounts for 53.3% and process friction for 21.3% of negative interactions, drivers the firm had not previously measured." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 3, "audience": "general", "models": [], "n": 179, "authors_detailed": [ { "name": "Ali Aghabeigiha", "url": "https://openalex.org/A5135207391", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6704338", "doi": "10.2139/ssrn.6704338", "title": "Regulatory Reporting Architecture Using Event Streaming: A CAT and FINRA Case Study An AI-Driven Cloud-Native Architecture MLflow, and Large Language Models", "authors": [ "Chittaranjan Pradhan" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6704338", "field": "finance", "role": "instrument", "bullets": [ "Production regulatory reporting pipeline for U.S. broker-dealers under the SEC Consolidated Audit Trail and FINRA Rule 6800 series; single-firm case study, sample period not stated.", "A retrieval-augmented generation LLM layer performs automated regulatory interpretation and error repair alongside gradient boosting and LSTM surveillance models; the LLM is not named and not benchmarked against ground truth.", "Reports T+0 reporting at 28-second latency, anomaly detection precision of 99.1% (F1=0.943), 94% less manual remediation, zero FINRA violations over 24 months, and 52% lower infrastructure cost." ], "bullet_provenance": "ai", "validated": false, "salience": 35, "edition": 3, "audience": "technical", "models": [], "n": 180, "authors_detailed": [ { "name": "Chittaranjan Pradhan", "url": "https://openalex.org/A5135265625", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6704599", "doi": "10.2139/ssrn.6704599", "title": "Interpretable ML and Text-Based LLMs in Finance: Regulatory Explainability and Portfolio Decision Transparency", "authors": [ "Wenhui Ge" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6704599", "field": "finance", "role": "method", "bullets": [ "A review of the literature on interpretable machine learning and text-based large language models in finance, framed around regulatory explainability and portfolio decision transparency, with no sample, period, or geography.", "No model is run; the paper synthesizes prior interpretable machine learning models, large language model based financial text systems, and grounding architectures, and reports no validation against a ground truth.", "Concludes that explainability serves both regulatory compliance and portfolio transparency, and that progress depends on linking transparent predictive structure with grounded language based reasoning." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 208, "authors_detailed": [ { "name": "Wenhui Ge", "url": "https://openalex.org/A5135168471", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of California, Berkeley" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6709360", "doi": "10.2139/ssrn.6709360", "title": "Autonomous, Agentic and Generative AI Systems -The Future of Business Technology: Triumph of Human Augmentation or Harbinger of Human Diminishment? A Critical Examination (2025-2035)", "authors": [ "Misbah Ul Islam", "Wang Fang" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6709360", "field": "management", "role": "object", "bullets": [ "Integrative review of agentic and generative AI in business technology over 2025 to mid-2026, drawing on Gartner, Stanford HAI, McKinsey, Deloitte and ABI Research secondary data.", "No model is deployed; the paper synthesizes industry statistics on AI agent adoption, spending and governance through a critical constructivist lens. Model not stated.", "Reports productivity gains of 20 to 40 percent in knowledge work and adoption reaching 78 percent of organizations, while only about 20 percent of firms report mature governance frameworks." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 399, "authors_detailed": [ { "name": "Misbah Ul Islam", "url": "https://openalex.org/A5056624088", "inst": "Saudi Electronic University" }, { "name": "fang wang", "url": "https://openalex.org/A5135155349", "inst": "Xidian University" } ], "affiliations": [ "Saudi Electronic University", "Xidian University" ] }, { "uid": "doi:10.2139/ssrn.6704758", "doi": "10.2139/ssrn.6704758", "title": "Tacit Expertise as Competitive Differentiation: Why Generic AI Implementations Fail in Domain-Specific Business Contexts", "authors": [ "Humberto Inciarte" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6704758", "field": "management", "role": "object", "bullets": [ "Conceptual paper examining the commoditization thesis for generative AI among small and medium-sized businesses; no empirical sample is reported before the abstract truncates.", "No model is deployed; the paper draws on tacit-knowledge theory and the resource-based and knowledge-based views of the firm to challenge the claim that any provider delivers equivalent results. Model not stated.", "Result not stated; the abstract ends before findings on whether the commoditization thesis withstands scrutiny are reported." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 400, "authors_detailed": [ { "name": "Humberto Inciarte", "url": "https://openalex.org/A5135157218", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6600538", "doi": "10.2139/ssrn.6600538", "title": "The Autonomous Agentic Store: Architecture, Safety Requirements, and a Constitutional AI Deployment Framework for Fully Staffless Physical Retail", "authors": [ "Sudhir Vissa" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6600538", "field": "management", "role": "object", "bullets": [ "Conceptual architecture for a fully staffless physical retail and hospitality store orchestrated entirely by LLM agents, targeting 2026 deployment; no empirical sample.", "No specific model is named; the paper proposes a six-layer intelligence stack with eight scoped agents, escalation contracts and phased robotic actuation. Model not stated.", "Presents an economic model claiming 8.3-month payback and annual operating cost falling to 42,100 dollars at month 30 versus a 227,475 dollar human-staffed baseline." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 401, "authors_detailed": [ { "name": "Sudhir Vissa", "url": "https://openalex.org/A5135143437", "inst": "Sage Science (United States)" } ], "affiliations": [ "Sage Science (United States)" ] }, { "uid": "doi:10.2139/ssrn.6704678", "doi": "10.2139/ssrn.6704678", "title": "The Knowing-Doing Gap in AI Adoption: Why ChatGPT Familiarity Does Not Translate to Business Results in Owner-Operated SMBs", "authors": [ "Humberto Inciarte" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6704678", "field": "management", "role": "object", "bullets": [ "Conceptual paper on generative AI adoption by owner-operated small and medium businesses, with particular focus on Spanish-speaking owner-operators in Latin America and the United States.", "No model is deployed; the paper synthesizes McKinsey, BCG and MIT NANDA survey evidence, framing failure as a knowing-doing gap where ChatGPT familiarity lacks business judgment.", "Reports that over 80 percent of organizations see no financial impact and 95 percent of pilots no profit-and-loss effect, attributing the gap to missing tacit business judgment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 33, "edition": 3, "audience": "general", "validated": null, "n": 402, "authors_detailed": [ { "name": "Humberto Inciarte", "url": "https://openalex.org/A5135157218", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6708259", "doi": "10.2139/ssrn.6708259", "title": "A Framework for Evaluating University AI Search Visibility Signals", "authors": [ "Ian Bann" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6708259", "field": "management", "role": "object", "bullets": [ "Controlled dataset of computer science queries run across multiple AI answer systems, evaluating university visibility for prospective-student discovery; sample size not stated.", "Probes ChatGPT, Google AI Overview, Perplexity, Claude, Gemini and Grok across four outcome layers of inclusion, brand mentions, recommendation and referral; no accuracy check reported.", "Visibility concentrates among a limited set of universities, driven by content structure, entity clarity and external reinforcement rather than traditional ranking position alone." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": false, "salience": 37, "edition": 3, "audience": "general", "validated": null, "n": 403, "authors_detailed": [ { "name": "Ian Bann", "url": "https://openalex.org/A5135127931", "inst": "School of Advertising Art" } ], "affiliations": [ "School of Advertising Art" ] }, { "uid": "doi:10.2139/ssrn.6712298", "doi": "10.2139/ssrn.6712298", "title": "Large Language Models for Asset Pricing: Learning from Earnings Calls", "authors": [ "Yizhong Zhang", "Guofu Zhou" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6712298", "field": "finance", "role": "instrument", "bullets": [ "US earnings call transcripts from 2009 to 2024, used to construct EAR-AI, a scalar signal for cross-sectional equity return prediction.", "Chronologically consistent large language models, family not named, extract predictive information from call text; no accuracy benchmark is reported, with validation done economically through fundamentals linkage.", "EAR-AI portfolios earn sizable out-of-sample Sharpe ratios, predict earnings and revenue surprises, subsume post-earnings-announcement drift, and add predictability beyond 125 characteristics." ], "bullet_provenance": "ai", "validated": false, "salience": 72, "edition": 3, "audience": "general", "models": [], "n": 404, "authors_detailed": [ { "name": "Yizhong Zhang", "url": "https://openalex.org/A5135341482", "inst": "Washington University in Saint Louis, John M. Olin Business School" }, { "name": "Guofu Zhou", "url": "https://openalex.org/A5135370501", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis", "Washington University in Saint Louis, John M. Olin Business School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6598618", "doi": "10.2139/ssrn.6598618", "title": "A Multi-Dimensional Management Framework for Integrating Technical Operational Costs into Strategic Business Signals", "authors": [ "Prakash Achuthan" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6598618", "field": "management", "role": "object", "bullets": [ "Conceptual paper on managing cloud expenditure that generative AI and large language model inference turn into a volatile gross-margin driver; no empirical sample before the abstract truncates.", "No model is deployed; the paper proposes a multi-dimensional management framework linking technical operational costs to strategic business signals. Model not stated.", "Result not stated; the abstract ends before the proposed framework and its findings are presented." ], "bullet_provenance": "ai", "salience": 27, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 405, "authors_detailed": [ { "name": "Prakash Achuthan", "url": "https://openalex.org/A5135267522", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6708193", "doi": "10.2139/ssrn.6708193", "title": "Challenges and Opportunities in Leveraging Generative AI for Sustainable Development in Sub-Saharan Africa: A Comparative Case Study of Angola, Kenya, Rwanda, and Nigeria", "authors": [ "Carmen Saituma Cagiza", "Aristoteles Cajiza", "Massochi Faustino", "Vissolela Sabola" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6708193", "field": "economics", "role": "object", "bullets": [ "Comparative case study of Angola, Kenya, Rwanda and Nigeria integrating generative AI into national development agendas, based on 24 semi-structured interviews and policy analysis.", "No model is deployed; the paper applies Diffusion of Innovation theory and the Capability Approach to assess institutional readiness, ethical governance and innovation ecosystems. Model not stated.", "Finds divergent trajectories, with Rwanda showing strategic coherence and Angola infrastructure gaps, while language exclusion, affordability and limited rural access persist as barriers." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 406, "authors_detailed": [ { "name": "Carmen Saituma Cagiza", "url": "https://openalex.org/A5135152406", "inst": "" }, { "name": "Aristoteles Cajiza", "url": "https://openalex.org/A5119520276", "inst": "University of Houston" }, { "name": "Massochi Faustino", "url": "https://openalex.org/A5119520274", "inst": "University of Lisbon" }, { "name": "Vissolela Sabola", "url": "https://openalex.org/A5135116079", "inst": "" } ], "affiliations": [ "University of Houston", "University of Lisbon" ] }, { "uid": "doi:10.2139/ssrn.6704738", "doi": "10.2139/ssrn.6704738", "title": "Bottleneck-First Implementation: Applying Theory of Constraints to AI Adoption in Owner-Operated Small and Medium Businesses", "authors": [ "Humberto Inciarte" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6704738", "field": "management", "role": "object", "bullets": [ "Owner-operated small and medium businesses, analysed through a review that synthesises 20 sources across six thematic clusters drawing on Theory of Constraints and lean production.", "No language model is applied; the paper is a conceptual review diagnosing generative AI adoption failure and proposing a bottleneck-first deployment framework, naming no specific model.", "Argues pilots fail because non-bottleneck resources are optimised, and that in owner-operated firms the owner is the binding constraint, cited in over 95 percent of middle-market assessments." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 555, "authors_detailed": [ { "name": "Humberto Inciarte", "url": "https://openalex.org/A5135157218", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6704679", "doi": "10.2139/ssrn.6704679", "title": "Operations Management in Digital Media and Public Relations: AI Agents and the Evolution of Search Visibility", "authors": [ "Galal Homouda" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6704679", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of operations management in digital media and public relations, centred on generative search engines and agentic AI systems, with no sample or data reported.", "No language model is applied; the paper discusses how agentic AI and retrieval-augmented generation reshape content structuring and search visibility, naming no specific model.", "Argues operations management becomes a supervisory intelligence layer coordinating content architecture, data engineering, and agentic systems that adjust distribution in real time; no empirical results are reported." ], "bullet_provenance": "ai", "salience": 25, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 556, "authors_detailed": [ { "name": "Galal Homouda", "url": "https://openalex.org/A5120777296", "inst": "Global News Intelligence" } ], "affiliations": [ "Global News Intelligence" ] }, { "uid": "doi:10.2139/ssrn.6706919", "doi": "10.2139/ssrn.6706919", "title": "Helping Portfolio Companies Deploy AI A General Partner's Operating Partner Playbook", "authors": [ "Leigh Coney" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6706919", "field": "management", "role": "object", "bullets": [ "Conceptual playbook for private equity operating partners deploying AI in portfolio companies; no empirical sample, drawing on 2025 industry data and the MIT NANDA report.", "No language model is applied by the authors; the paper proposes the Portfolio AI Deployment Framework built on selection discipline, implementation architecture, and cross-portfolio compounding.", "Cites that 95 percent of enterprise generative AI pilots produce no measurable profit impact and argues sponsors should concentrate AI effort on selected companies and use cases." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 755, "authors_detailed": [ { "name": "Leigh Coney", "url": "https://openalex.org/A5135267914", "inst": "Healthwise" } ], "affiliations": [ "Healthwise" ] }, { "uid": "doi:10.2139/ssrn.6704400", "doi": "10.2139/ssrn.6704400", "title": "Reading the Fed: Central Bank Text as a Forecasting Signal", "authors": [ "Eleni Kalamara" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6704400", "field": "economics", "role": "instrument", "bullets": [ "Federal Reserve FOMC minutes and Beige Books used to forecast six US macro targets out of sample from 2020Q1 to 2024Q4, including growth, inflation, unemployment, and rates.", "Claude via the Anthropic API reads each text and returns five-dimensional sentiment scores that enter a Bayesian VAR and an AR model; no validation against human coding is reported.", "The LLM-augmented AR beats the plain AR in 11 of 12 variable-horizon combinations, with the largest gains during the 2021 to 2023 inflation surge." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": false, "salience": 60, "edition": 3, "audience": "general", "n": 756, "authors_detailed": [ { "name": "Eleni Kalamara", "url": "https://openalex.org/A5070146233", "inst": "King's College London" } ], "affiliations": [ "King's College London" ] }, { "uid": "doi:10.2139/ssrn.6610419", "doi": "10.2139/ssrn.6610419", "title": "What Countries Use AI, and What For? Intensity and Breadth of AI Adoption Across Over 100 Countries", "authors": [ "Yuting Fan" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6610419", "field": "economics", "role": "object", "bullets": [ "Over 800,000 Claude conversations from more than 100 countries during one week in February 2026, used to measure cross-country generative AI adoption.", "The model is the object of study rather than a measurement tool; anonymized Claude usage is mapped into intensity, breadth, and a task-based AI-diffusion space.", "Higher-income, science-intensive, services-oriented countries use AI more per capita; lower-income countries cluster in a software core while richer ones activate legal, health, creative, and financial tasks." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 60, "edition": 3, "audience": "general", "validated": null, "n": 757, "authors_detailed": [ { "name": "Yuting Fan", "url": "https://openalex.org/A5135101225", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6709778", "doi": "10.2139/ssrn.6709778", "title": "Beyond Segmentation: AI Driven Personalization in Modern E-Commerce", "authors": [ "Vasu Jain" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6709778", "field": "management", "role": "object", "bullets": [ "Conceptual review of AI-driven personalization in modern e-commerce marketing; no empirical sample or dataset is stated.", "No model is applied or named; the paper surveys generative AI, behavioral targeting, collaborative filtering, and context-aware marketing for segment-of-one personalization.", "Concludes personalization raises engagement, loyalty, and conversion, but long-term success depends on data privacy, addressing algorithmic bias, and rebuilding consumer trust." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 758, "authors_detailed": [ { "name": "Vasu Jain", "url": "https://openalex.org/A5086124880", "inst": "Chandigarh University" } ], "affiliations": [ "Chandigarh University" ] }, { "uid": "doi:10.2139/ssrn.6712619", "doi": "10.2139/ssrn.6712619", "title": "European Statistics Begin to Sing: Missing Juniors, and Why Demographics Won't Explain It", "authors": [ "Dora Moscato" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6712619", "field": "economics", "role": "object", "bullets": [ "EU Labour Force Survey data combined with the Eurostat enterprise survey on AI adoption, giving cross-country European evidence on labour changes after generative AI, post-2022.", "No language model is used by the authors; they link actual sector-level generative AI adoption, not mere exposure, to shifts in workforce age composition across NACE sectors.", "In highest-adoption sectors the youngest workers lose relative ground while senior workers gain after 2022, a divergence not explained by demographic ageing." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 759, "authors_detailed": [ { "name": "Dora Moscato", "url": "https://openalex.org/A5129626827", "inst": "Sapienza University of Rome" } ], "affiliations": [ "Sapienza University of Rome" ] }, { "uid": "doi:10.2139/ssrn.6708820", "doi": "10.2139/ssrn.6708820", "title": "AI-Native Intelligent Payment Systems: Autonomous Financial Execution in Multi-Rail Infrastructure", "authors": [ "Avik Nandi" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6708820", "field": "finance", "role": "object", "bullets": [ "Conceptual framework paper on payment infrastructure spanning banking systems, card networks, real-time rails, and blockchain settlement; no empirical sample, period, or geography.", "No model is named; the paper proposes an Agentic Layer in which intelligent agents autonomously execute transactions from intent, policy, and real-time signals, illustrated through a scenario walkthrough.", "Reframes payments as autonomous decision-execution systems and outlines architectural components, presenting no empirical results or validation." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1077, "authors_detailed": [ { "name": "Avik Nandi", "url": "https://openalex.org/A5134554632", "inst": "Wipro (Singapore)" } ], "affiliations": [ "Wipro (Singapore)" ] }, { "uid": "doi:10.2139/ssrn.6609178", "doi": "10.2139/ssrn.6609178", "title": "The Governance Gap Why Agentic AI Demands a New Project Execution Framework Version 1.0", "authors": [ "Madeline Aycock" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6609178", "field": "management", "role": "object", "bullets": [ "Conceptual paper on software delivery methodology for agentic AI with no empirical sample, contrasting Waterfall, Agile, and SAFe against the governance needs of autonomous decision-making software.", "No model is used or named; the paper introduces the Agentic Project Execution Framework (APEX), a governance-first methodology that replaces Agile constructs for software that decides autonomously.", "Argues the governance gap from agentic AI is a paradigm problem requiring first-principles governance of autonomous decisions rather than feature delivery, and presents no empirical evaluation." ], "bullet_provenance": "ai", "salience": 27, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1078, "authors_detailed": [ { "name": "Madeline Aycock", "url": "https://openalex.org/A5135245871", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6705878", "doi": "10.2139/ssrn.6705878", "title": "The Delegation Threshold: Governing Human-AI Authority Transitions in Built-Environment Organisations Under the EU AI Act", "authors": [ "Gulzeb Ahmed" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6705878", "field": "management", "role": "object", "bullets": [ "Conceptual governance study of built-environment organizations under the EU AI Act, applied illustratively to structural compliance checking, AI-assisted procurement, and predictive asset maintenance; no empirical sample.", "No model is used or named; drawing on socio-technical systems theory and principal-agent analysis, the paper builds a four-stage Threshold Governance Protocol for shifting decision authority to AI.", "Defines the Delegation Threshold as a context-specific point where governance capacity, regulatory ceiling, and workforce readiness jointly permit a shift from human-led to AI-led authority." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1079, "authors_detailed": [ { "name": "Gulzeb Ahmed", "url": "https://openalex.org/A5134994738", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6600899", "doi": "10.2139/ssrn.6600899", "title": "Why Ai Readiness Is An Organizational Learning Problem, Not A Technology Purchase The Structural Evolution Of Enterprise Ai Capability", "authors": [ "Jeanne McClure", "Gregg Gerdau" ], "posted": "2026-05-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6600899", "field": "management", "role": "object", "bullets": [ "Systematic synthesis of 21 foundational sources, including surveys of nearly 10,000 organizational leaders, on why enterprise AI investment fails to produce earnings impact; geography and period not stated.", "No model is used or named; the paper builds the Orchestration Maturity Framework mapping enterprise AI capability across five pillars from culture and leadership to governance and regulatory compliance.", "Argues AI project failure is an organizational learning problem, not a technology deficit, noting only 6 percent of firms report significant earnings impact despite 252 billion dollars of 2024 investment." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1080, "authors_detailed": [ { "name": "Jeanne McClure", "url": "https://openalex.org/A5058907285", "inst": "North Carolina State University" }, { "name": "Gregg Gerdau", "url": "https://openalex.org/A5134160133", "inst": "North Carolina State University" } ], "affiliations": [ "North Carolina State University" ] }, { "uid": "doi:10.2139/ssrn.6699179", "doi": "10.2139/ssrn.6699179", "title": "From No-Arbitrage to Foundation Models: A Review of Financial-Mathematics-Constrained Generative AI and Deep Learning for Quantitative Finance", "authors": [ "YUXUAN HUANG" ], "posted": "2026-05-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6699179", "field": "finance", "role": "method", "bullets": [ "Semi-systematic review of AI in quantitative finance spanning option pricing, volatility calibration, hedging, asset pricing, risk measurement, market simulation, and time-series forecasting.", "Surveys neural option pricing, deep hedging, generative market models, financial large language models, and time-series foundation models; no single model, organized around constraint-injection patterns.", "Separates financial constraints into three types, catalogs five constraint-injection design patterns, and proposes layered evaluation criteria distinguishing statistical fit from economic admissibility and deployability." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 142, "authors_detailed": [ { "name": "YUXUAN HUANG", "url": "https://openalex.org/A5135413385", "inst": "University of Nottingham Ningbo China" } ], "affiliations": [ "University of Nottingham Ningbo China" ] }, { "uid": "doi:10.2139/ssrn.6700819", "doi": "10.2139/ssrn.6700819", "title": "Algorithms of Influence: How AI is Rewriting the Rules of Digital Marketing", "authors": [ "Raman Mishra" ], "posted": "2026-05-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6700819", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of how AI reshapes digital marketing campaigns, drawing on real-world cases from global corporations and emerging enterprises; no formal sample, period, or geography.", "No model is run; the paper surveys machine learning, natural language processing, predictive analytics, generative AI, and programmatic advertising as marketing capabilities.", "Argues firms that strategically integrate AI gain measurable competitive advantage, but success also requires organizational adaptability, data governance, and human-centred design." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 753, "authors_detailed": [ { "name": "Raman Mishra", "url": "https://openalex.org/A5135393459", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6703499", "doi": "10.2139/ssrn.6703499", "title": "Do Board Interlocks Transmit Accounting Failures?", "authors": [ "Richard Wang" ], "posted": "2026-05-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6703499", "field": "accounting", "role": "instrument", "bullets": [ "US public-firm restatements from 2005 to 2024, analyzed across the board interlock network, with firm-level restatement events as the unit of observation.", "A multi-layer LLM protocol, model not stated, checks whether disclosure overlap between interlocked firms reflects shared accounting reasoning rather than shared boilerplate; no accuracy figure is reported.", "Interlock exposure raises any-cause restatement probability by 3.7 percent and same-cause probability by 5.8 percent, roughly doubling when the shared director sits on both audit committees." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 754, "authors_detailed": [ { "name": "Richard Wang", "url": "https://openalex.org/A5120249165", "inst": "St. John Fisher College" } ], "affiliations": [ "St. John Fisher College" ] }, { "uid": "doi:10.2139/ssrn.6594418", "doi": "10.2139/ssrn.6594418", "title": "Artificial Effort", "authors": [ "Federico Belotti", "Stefano Coniglio", "Antonio Cosma", "Francesco Fallucchi" ], "posted": "2026-05-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6594418", "field": "economics", "role": "object", "bullets": [ "Eight canonical real-effort tasks from experimental economics, attempted by 23 large language models drawn from three major providers across successive model generations.", "The unnamed models attempt each task while researchers measure completion accuracy and cost, and test whether verbally offered monetary incentives change performance.", "Most tasks are solved accurately at negligible cost while a few resist automation; accuracy rises each generation and verbal incentives have no effect." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 141, "authors_detailed": [ { "name": "Federico Belotti", "url": "https://openalex.org/A5018743213", "inst": "Bank of Italy" }, { "name": "Stefano Coniglio", "url": "https://openalex.org/A5020108924", "inst": "University of Bergamo" }, { "name": "Antonio Cosma", "url": "https://openalex.org/A5072692819", "inst": "University of Bergamo" }, { "name": "Francesco Fallucchi", "url": "https://openalex.org/A5008686657", "inst": "University of Bergamo" } ], "affiliations": [ "Bank of Italy", "University of Bergamo" ] }, { "uid": "doi:10.2139/ssrn.6692385", "doi": "10.2139/ssrn.6692385", "title": "Two-Stage AI Adoption in Technology-Taker Economies: Platform Capture, Infrastructure, and Firm Constraints in Latin America", "authors": [ "Carolina Curvale" ], "posted": "2026-05-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6692385", "field": "economics", "role": "object", "bullets": [ "Consumer generative AI adoption across nineteen Latin American countries, weekly Google Trends panel for seven platforms 2020 to 2024, plus WEF Future of Jobs 2025 employer data for four countries.", "No model is deployed by the researchers; the study tracks Gemini and rival platform search interest, cross-validated against Cloudflare Radar and Similarweb.", "Political stability combined with weak state capacity predicts Gemini market share via an ecosystem-default mechanism, joint F(3,12) equals 5.21, p equals 0.016; firm constraints are operational not institutional." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "salience": 54, "edition": 3, "audience": "general", "validated": null, "n": 397, "authors_detailed": [ { "name": "Carolina Curvale", "url": "https://openalex.org/A5135212855", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6696358", "doi": "10.2139/ssrn.6696358", "title": "The AI-Augmented Product Manager: How Generative AI Is Reshaping Product Discovery, Strategy, and Execution", "authors": [ "Muhammad Shah" ], "posted": "2026-05-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6696358", "field": "management", "role": "object", "bullets": [ "Conceptual paper on how generative AI reshapes the product manager role as a knowledge-work function; no empirical sample, period, or data reported.", "No model is deployed; the paper proposes a five-domain framework spanning discovery, strategy, execution, communication and governance for AI-augmented product management. Model not stated.", "Argues generative AI accelerates research synthesis, documentation and prototyping but cannot own product outcomes, raising the premium on human judgment, problem framing and accountability." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 398, "authors_detailed": [ { "name": "Muhammad Shah", "url": "https://openalex.org/A5135490430", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6693158", "doi": "10.2139/ssrn.6693158", "title": "Belief Bias, Source Preference, and Preference for Robo-Advisor", "authors": [ "Paul Hung Yui Cheung", "Haomin He", "King King Li" ], "posted": "2026-05-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6693158", "field": "finance", "role": "object", "bullets": [ "Two-part laboratory experiment on preferences for human versus robo-advisors, where subjects value investment advice before and after learning advisor accuracy; sample size not stated.", "ChatGPT generates the robo-advice used as stimulus; the study measures subjects' valuations, decomposing belief bias from taste-based source preference rather than validating model output.", "Subjects initially favor robo-advice; many revise toward human advisors after learning the two perform similarly, but a substantial subset retains a taste-based robo-preference." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 3, "audience": "general", "validated": null, "n": 751, "authors_detailed": [ { "name": "Paul Hung Yui Cheung", "url": "https://openalex.org/A5135359398", "inst": "" }, { "name": "Haomin He", "url": "https://openalex.org/A5135300330", "inst": "" }, { "name": "King King Li", "url": "https://openalex.org/A5101495303", "inst": "Hang Seng University of Hong Kong" } ], "affiliations": [ "Hang Seng University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6695638", "doi": "10.2139/ssrn.6695638", "title": "A Rigorous Factor Research Infrastructure for Cryptocurrency Markets: Architecture, Audit Mechanisms, and Empirical Findings", "authors": [ "jeff wu" ], "posted": "2026-05-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6695638", "field": "finance", "role": "method", "bullets": [ "Local factor research system tested on BTCUSDT 30-minute candlestick data, spanning five generation rounds with 504 factor candidates and 39,528 factor-configuration combinations.", "An unnamed large language model generates candidate factors, paired with engineering audit, cost-adjusted economic evaluation, and point-in-time data governance across four separated threads.", "Under cost-adjusted evaluation at two basis points per side, zero factors passed on research grounds; the contribution is an auditable taxonomy of why factors fail." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 752, "authors_detailed": [ { "name": "jeff wu", "url": "https://openalex.org/A5135317790", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6697218", "doi": "10.2139/ssrn.6697218", "title": "Wrapper Architecture as Coordination Device: Multiple Equilibria in Autonomous-Agent Organizations", "authors": [ "Ian Staley" ], "posted": "2026-05-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6697218", "field": "economics", "role": "object", "bullets": [ "Theoretical study of decentralized autonomous organizations and AI-agent systems that pair blockchain governance with a conventional legal wrapper such as a foundation or limited liability entity; no empirical sample.", "No AI model is used; a stylized economic model has token-holders jointly choose wrapper and governance concentration, with a global-games argument selecting among multiple equilibria.", "Identifies an inefficient trap where no holder absorbs the front-loaded fixed cost alone versus an efficient wrapper equilibrium, and bounds the Coasean singularity claim that AI agents sharply reduce frictions." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1076, "authors_detailed": [ { "name": "Ian Staley", "url": "https://openalex.org/A5135451700", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6579598", "doi": "10.2139/ssrn.6579598", "title": "The Blueprint for AI Careers Mapping Roles, Requirements, and Organizational Structure", "authors": [ "Roopak Kumar Prajapat" ], "posted": "2026-05-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6579598", "field": "management", "role": "instrument", "bullets": [ "Systematic extraction of senior-level AI job descriptions across multiple global markets, with the individual job posting as the unit of observation.", "A custom agentic pipeline, model family not named, filters, aggregates, and clusters postings by skill and designation signals using keyword-driven clustering.", "Identifies seven AI role clusters aligning into three strategic pillars and argues AI functions are fragmenting from generalist roles toward specialized, interdependent ones." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "general", "models": [], "n": 303, "authors_detailed": [ { "name": "Roopak Kumar Prajapat", "url": "https://openalex.org/A5135151932", "inst": "" } ] }, { "uid": "arxiv:2605.12532v1", "arxiv_id": "2605.12532v1", "title": "AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems", "authors": [ "Ivan Letteri" ], "posted": "2026-05-01", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2605.12532v1", "field": "finance", "role": "agent", "bullets": [ "Proof-of-concept autonomous trading framework evaluated in a five-day live-market dry-run session, covering 76 assets with no offline training or human intervention.", "Multiple specialized large language model agents, families not named, form an Analyst, Risk Manager, and Executor reasoning chain governed by typed JSON contracts and a deterministic safety gate.", "The pipeline ran 157 zero-intervention invocations with an 11.5 percent inter-agent friction rate; statistically robust performance evaluation is deferred to extended live deployment." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 304, "authors_detailed": [ { "name": "Ivan Letteri", "url": "https://openalex.org/A5014004028", "inst": "University of L'Aquila" } ], "affiliations": [ "University of L'Aquila" ] }, { "uid": "doi:10.2139/ssrn.6691902", "doi": "10.2139/ssrn.6691902", "title": "BEYOND AI EXPOSURE:WHICH TASKS ARE COST-EFFECTIVE TO AUTOMATE?", "authors": [ "Martin Fleming", "Maja Svanberg", "Wensu Li", "Brian Goehring", "Neil Thompson" ], "posted": "2026-05-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6691902", "field": "economics", "role": "object", "bullets": [ "US businesses and workers, with computer vision tasks used to illustrate the framework because cost data for vision automation are more fully developed.", "No language model is applied; the paper builds a cost and technical feasibility framework for AI task automation spanning generative and deep learning models, naming no specific model.", "At current costs only about 23 percent of wages paid for vision tasks would be attractive to automate, implying substantial but gradual AI job displacement." ], "bullet_provenance": "ai", "salience": 68, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 554, "authors_detailed": [ { "name": "Martin Fleming", "url": "https://openalex.org/A5135193020", "inst": "" }, { "name": "Maja Svanberg", "url": "https://openalex.org/A5135120394", "inst": "" }, { "name": "Wensu Li", "url": "https://openalex.org/A5025524890", "inst": "Institute for the Future" }, { "name": "Brian Goehring", "url": "https://openalex.org/A5091711653", "inst": "University of British Columbia" }, { "name": "Neil Thompson", "url": "https://openalex.org/A5135230112", "inst": "IIT@MIT" } ], "affiliations": [ "Institute for the Future", "University of British Columbia", "IIT@MIT" ] }, { "uid": "doi:10.2139/ssrn.6682498", "doi": "10.2139/ssrn.6682498", "title": "Generative AI and Investment Research: Evidence from Analyst Reports", "authors": [ "Shawn X. Huang", "Artur Hugon", "Tengfei Zhang", "Wenting Zheng" ], "posted": "2026-05-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6682498", "field": "accounting", "role": "object", "bullets": [ "Large-sample study of equity analyst reports and their authors, examining generative AI adoption in investment research; sample size, period, and geography are not stated.", "A supervised machine learning classifier detects AI-assisted report content; the underlying generative AI model is not named and no detection accuracy is reported.", "AI-assisted reports show greater earnings forecast accuracy, larger for busier and lower-skill analysts, and trigger stronger market reactions; an overlapping-calls workload shock supports causality." ], "bullet_provenance": "ai", "validated": false, "salience": 70, "edition": 3, "audience": "general", "models": [], "n": 749, "authors_detailed": [ { "name": "Shawn X. Huang", "url": "https://openalex.org/A5010722453", "inst": "Arizona State University" }, { "name": "Artur Hugon", "url": "https://openalex.org/A5028776567", "inst": "Georgia State University" }, { "name": "Tengfei Zhang", "url": "https://openalex.org/A5100416047", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Wenting Zheng", "url": "https://openalex.org/A5135272433", "inst": "" } ], "affiliations": [ "Arizona State University", "Georgia State University", "Rutgers, The State University of New Jersey" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6690839", "doi": "10.2139/ssrn.6690839", "title": "Token Economy 2.0: Asset-Backed Intelligence And The Economics Of Trust In Decentralized AI Systems", "authors": [ "Minh Do" ], "posted": "2026-05-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6690839", "field": "economics", "role": "object", "bullets": [ "Formal signaling model plus empirical evidence from Bittensor metagraph data (18,101 miner observations across 80 subnets) and a 10,000-run simulation of staking and quality.", "No language model is used as a tool; the paper studies the economics of trust in decentralized AI, deriving a separating equilibrium and a closed-form minimum stake threshold.", "Staked miners earn 15.4 times higher incentive scores (t = 12.40); the simulation raises mean quality about 12 to 13 percent and cuts variance 18 to 33 percent." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 750, "authors_detailed": [ { "name": "Minh Do", "url": "https://openalex.org/A5135414491", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6576486", "doi": "10.2139/ssrn.6576486", "title": "Making AI Decisions Worth Automating The Earned Autonomy Protocol: A Practitioner's Account", "authors": [ "Nicolas Aillon" ], "posted": "2026-05-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6576486", "field": "management", "role": "object", "bullets": [ "Practitioner account drawn from operating an AI-governed tax practice serving LATAM entrepreneurs in the United States, with the protocol reported operational in December 2025.", "No model family is named; the AI system, called Aila, earns decision autonomy incrementally through logged and verified reliability in bounded decisions before scope expands.", "Documents a live incident where the verification discipline caught a silent data-loss bug that unit tests, stress tests, and manual QA missed, and maps the protocol to EU AI Act Article 26." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1073, "authors_detailed": [ { "name": "Nicolas Aillon", "url": "https://openalex.org/A5135113473", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6690319", "doi": "10.2139/ssrn.6690319", "title": "The Neural Shift Agentic AI, Cross-Asset Dynamics, and the Regulatory Black Box in Modern Finance", "authors": [ "Clara El Rif" ], "posted": "2026-05-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6690319", "field": "finance", "role": "object", "bullets": [ "Study of agentic AI in financial infrastructure using Python-based analytical frameworks on historical data covering traditional assets such as gold and digital assets such as Bitcoin; sample size and period not stated.", "No model is named; analytical tools identify cross-asset correlations, and the paper surveys AI use in credit approval and fraud detection with no validation against a ground truth.", "Reports a paradox where AI boosts speed and insight yet enables fraud projected to raise global losses from 23 to 58 billion dollars, while only 8 percent of major banks report clear ROI." ], "bullet_provenance": "ai", "salience": 27, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1074, "authors_detailed": [ { "name": "Clara El Rif", "url": "https://openalex.org/A5135104714", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6685711", "doi": "10.2139/ssrn.6685711", "title": "Value-Oriented Trajectories for Artificial Intelligence Adoption in Engineering Project Management", "authors": [ "Pablo Morato-Huerta", "Alejandro M. Martín-Gómez", "Juan Ramón Lama-Ruiz" ], "posted": "2026-05-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6685711", "field": "management", "role": "object", "bullets": [ "Conceptual framework study grounded in the Resource-Based View for engineering project management offices and project-based organizations, built from a systematically retrieved literature corpus with no primary sample.", "No model is used or named; cross-impact structural analysis maps three AI capability tiers (analytical, generative, agentic) against four project-management governance dimensions.", "Finds a maturity asymmetry where analytical-operational applications dominate and strategic-agentic cells stay underdeveloped, and proposes three sequenced adoption trajectories toward sustained competitive advantage." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1075, "authors_detailed": [ { "name": "Pablo Morato-Huerta", "url": "https://openalex.org/A5135167392", "inst": "" }, { "name": "Alejandro M. Martín-Gómez", "url": "https://openalex.org/A5135238038", "inst": "" }, { "name": "Juan Ramón Lama-Ruiz", "url": "https://openalex.org/A5135109955", "inst": "Universidad de Sevilla" } ], "affiliations": [ "Universidad de Sevilla" ] }, { "uid": "doi:10.2139/ssrn.6678618", "doi": "10.2139/ssrn.6678618", "title": "Large Language Models for Intelligent Data Stewardship in Enterprises: Architectures, Provenance, and Evidence-Mapped Governance", "authors": [ "Nagender Yamsani" ], "posted": "2026-04-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6678618", "field": "management", "role": "object", "bullets": [ "Conceptual synthesis of LLM-assisted enterprise data stewardship, illustrated by a case study of Inspire Brands' AI-driven data governance initiatives.", "The paper does not name a specific model; it reviews transformer LLMs with RAG, dense passage retrieval, weak supervision and knowledge-graph grounding, without empirical validation.", "Proposes a reference architecture and an evidence-mapping method that assesses governance readiness from publicly observable signals without privileged internal disclosures." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 117, "authors_detailed": [ { "name": "Nagender Yamsani", "url": "https://openalex.org/A5135128222", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6681860", "doi": "10.2139/ssrn.6681860", "title": "Scaling Point-in-Time Language Models", "authors": [ "Bryan T. Kelly", "Semyon Malamud", "Johannes Schwab", "Teng Andrea Xu" ], "posted": "2026-04-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6681860", "field": "finance", "role": "method", "bullets": [ "Methods paper for finance and the social sciences; monthly model checkpoints spanning 2013 to 2024 trained on 1 trillion chronologically filtered tokens from FineWeb.", "Decoder-only transformers up to 4 billion parameters are trained point-in-time to remove lookahead bias, then benchmarked against Gemma-3-4B and LLaMA-7B on reasoning and language understanding tasks.", "Point-in-time models approach comparable open-weight models trained on unrestricted text, though a gap remains on several tasks; the full pipeline and checkpoints are released publicly." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "salience": 72, "edition": 3, "audience": "technical", "validated": null, "n": 302, "authors_detailed": [ { "name": "Bryan T. Kelly", "url": "https://openalex.org/A5135131545", "inst": "" }, { "name": "Semyon Malamud", "url": "https://openalex.org/A5067616912", "inst": "Centre for Economic Policy Research" }, { "name": "Johannes Schwab", "url": "https://openalex.org/A5135231582", "inst": "" }, { "name": "Teng Andrea Xu", "url": "https://openalex.org/A5090779895", "inst": "Greenwich Hospital" } ], "affiliations": [ "Centre for Economic Policy Research" ] }, { "uid": "doi:10.2139/ssrn.6676299", "doi": "10.2139/ssrn.6676299", "title": "From Hype to Output: How AI Investment Translates to Real Productivity Gains", "authors": [ "Rosalie Wyonch" ], "posted": "2026-04-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6676299", "field": "economics", "role": "object", "bullets": [ "Economy-wide AI adoption and productivity in Canada, drawing on international AI rankings and Canadian business survey data on AI use.", "No model is run or named; the paper treats generative AI as a general-purpose technology and reviews adoption barriers, with no validation.", "About 12 percent of Canadian businesses use AI, most reporting little employment change, and the country lags leaders in computing capacity and commercialization." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 745, "authors_detailed": [ { "name": "Rosalie Wyonch", "url": "https://openalex.org/A5084835680", "inst": "Ontario Brain Institute" } ], "affiliations": [ "Ontario Brain Institute" ] }, { "uid": "doi:10.2139/ssrn.6682591", "doi": "10.2139/ssrn.6682591", "title": "Who Fills the Order Book? Investor Participation and Price Formation in Corporate Bond Markets", "authors": [ "Koji Takahashi", "Sumiko Takaoka" ], "posted": "2026-04-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6682591", "field": "finance", "role": "instrument", "bullets": [ "Corporate bond primary-market deals in Japan, using Bank of Japan interventions as the setting and unstructured deal marketing reviews as the text source.", "An unnamed large language model recovers deal-level measures of investors' expectations of BOJ purchases and order-book composition, with no validation reported.", "BOJ trade expectations link to lower launch spreads on average, but the relationship weakens sharply when relationship-oriented investors dominate the order book." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 746, "authors_detailed": [ { "name": "Koji Takahashi", "url": "https://openalex.org/A5135168029", "inst": "Teikyo University Chiba Medical Center" }, { "name": "Sumiko Takaoka", "url": "https://openalex.org/A5135200802", "inst": "" } ], "affiliations": [ "Teikyo University Chiba Medical Center" ] }, { "uid": "doi:10.2139/ssrn.6682138", "doi": "10.2139/ssrn.6682138", "title": "Task Efficiency and Signaling in the Age of GenAI: Effort Reallocation and Firm Value Effects", "authors": [ "Shiwei Ye" ], "posted": "2026-04-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6682138", "field": "economics", "role": "object", "bullets": [ "Developer-level data from open-source projects of US public firms, using GitHub Copilot's launch as an exposure shock alongside a new AI exposure measure.", "The researchers run no model; GitHub Copilot, powered by an unnamed LLM, is the studied technology whose adoption shifts developer effort, with no validation.", "Senior developers capture productivity gains while career-concerned juniors shift to creative signaling work, and senior-heavy non-innovative firms gain most in stock reactions." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 747, "authors_detailed": [ { "name": "S. Y. Ye", "url": "https://openalex.org/A5112778095", "inst": "Pusan National University" } ], "affiliations": [ "Pusan National University" ] }, { "uid": "doi:10.2139/ssrn.6558458", "doi": "10.2139/ssrn.6558458", "title": "Interface Bifurcation in AI-Mediated Service Ecosystems: A Framework for Classifying Service Architectures and Their Strategic Implications in the Agentic Economy", "authors": [ "MyungWoon Oh" ], "posted": "2026-04-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6558458", "field": "management", "role": "object", "bullets": [ "Conceptual paper in service management and platform economics, using four illustrative cases (Bloomberg, Stripe, Epic Systems, Perplexity AI); the unit of analysis is the digital service architecture.", "No model is run by the authors; the paper theorizes autonomous AI agents as service consumers and classifies services by AI consumption intensity and human visual oversight necessity.", "Produces a two by two typology of four service architectures and derives eight testable propositions on interface value migration, dual architecture burden, and ecosystem externality." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 748, "authors_detailed": [ { "name": "MyungWoon Oh", "url": "https://openalex.org/A5132913766", "inst": "Sogang University" } ], "affiliations": [ "Sogang University" ] }, { "uid": "doi:10.2139/ssrn.6624558", "doi": "10.2139/ssrn.6624558", "title": "The Agentic Profit Paradox and the Reorganisation of Value Capture", "authors": [ "Elemi Atigolo" ], "posted": "2026-04-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6624558", "field": "management", "role": "object", "bullets": [ "Conceptual economics and strategy paper introducing Autonomy Economics and drawing on historical precedents across two centuries; no empirical sample, period, or geography reported.", "No language model is used or named; the paper develops a formal representation of how autonomous capability erodes the scarcity and defensibility underlying established commercial structures.", "Argues value migrates from production to orchestration and from access to outcomes, proposing revenue-durability and capability-durability frameworks to assess firm-level advantage." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1070, "authors_detailed": [ { "name": "Elemi Atigolo", "url": "https://openalex.org/A5135275713", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6679083", "doi": "10.2139/ssrn.6679083", "title": "Do Humans Bargain Differently with AI? Evidence from Alternating-Offer Games", "authors": [ "Yuhao Fu", "Nobuyuki Hanaki", "Haitao Wang" ], "posted": "2026-04-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6679083", "field": "economics", "role": "object", "bullets": [ "Laboratory experiment in which human participants played a three-stage alternating-offer bargaining game against either another human or a GPT-based AI agent, plus a human-beneficiary condition; sample size not stated.", "A GPT-based AI agent served as the bargaining counterpart and treatment condition; the specific model version was not stated and no accuracy validation was reported.", "Human proposers offered more to human than AI opponents, but responders accepted unfair AI offers more when AI earnings could benefit another person, indicating conditional fairness toward AI." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 58, "edition": 3, "audience": "general", "validated": null, "n": 1071, "authors_detailed": [ { "name": "Yuhao Fu", "url": "https://openalex.org/A5128749410", "inst": "Osaka University of Economics" }, { "name": "Nobuyuki Hanaki", "url": "https://openalex.org/A5135217554", "inst": "" }, { "name": "Haitao Wang", "url": "https://openalex.org/A5134933355", "inst": "" } ], "affiliations": [ "Osaka University of Economics" ] }, { "uid": "doi:10.2139/ssrn.6565538", "doi": "10.2139/ssrn.6565538", "title": "The Sovereignty Crisis: AI, Workforce Atrophy, and Systemic Risk in the Agentic Era A Policy-Technical White paper for Institutional, Infrastructure, and Governance Leaders", "authors": [ "Emily J. Barnes PhD" ], "posted": "2026-04-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6565538", "field": "management", "role": "object", "bullets": [ "Conceptual policy white paper aimed at institutional, infrastructure, and governance leaders; no empirical sample, period, or geography, drawing instead on interdisciplinary evidence about AI adoption in organizations.", "No model is used or named; the paper builds a framework linking workforce atrophy, cognitive overreliance, technical vulnerability, and infrastructure concentration into a unified account of systemic risk.", "Argues institutions improve output faster than they preserve capacity to understand, audit, and govern AI, producing accelerated fragility, and proposes a Red Switch Mandate governance response." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1072, "authors_detailed": [ { "name": "Emily J. Barnes PhD", "url": "https://openalex.org/A5135217144", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6672580", "doi": "10.2139/ssrn.6672580", "title": "Distinguishing the Roles of Agentic AI in ManufacturingEcosystem", "authors": [ "nowrin Akter surovi", "Paul Witherell", "Maja Vukovic", "Soundar Kumara" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6672580", "field": "management", "role": "object", "bullets": [ "A system-level framework for agentic AI roles in manufacturing, illustrated with a melt-pool image analysis case study in powder bed fusion additive manufacturing.", "LLMs and multimodal foundation models serve as goal-oriented agents in structured workflows with human-in-the-loop oversight; no specific model is named and no accuracy figure is reported.", "Agentic AI complements conventional rule-based agents by adding contextual analysis and recommendations, with gains in adaptability and interpretability described qualitatively." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 87, "authors_detailed": [ { "name": "Nowrin Akter Surovi", "url": "https://openalex.org/A5066096901", "inst": "Singapore University of Technology and Design" }, { "name": "Paul Witherell", "url": "https://openalex.org/A5021987687", "inst": "National Institute of Standards and Technology" }, { "name": "Maja Vuković", "url": "https://openalex.org/A5112569852", "inst": "Association for Computing Machinery" }, { "name": "Soundar Kumara", "url": "https://openalex.org/A5042211113", "inst": "Pennsylvania State University" } ], "affiliations": [ "Singapore University of Technology and Design", "National Institute of Standards and Technology", "Association for Computing Machinery", "Pennsylvania State University" ] }, { "uid": "doi:10.2139/ssrn.6673101", "doi": "10.2139/ssrn.6673101", "title": "Prompting to Extract Data Inputs for Accounting Systems from Heterogeneous Data Sources", "authors": [ "Juliane Wutzler", "Florina G. Hutter" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6673101", "field": "accounting", "role": "method", "bullets": [ "Synthetic dataset of 46 heterogeneous PDF invoices and 10 Outlook order emails used as inputs to accounting systems.", "Mixtral-8x7B performs named entity recognition to extract fields, testing and adapting prompt guidelines from prior literature; no accuracy figure is reported.", "General prompting guidelines need case-specific adjustment for non-semantic semi-structured invoices and semantic unstructured emails; the paper derives transferable prompt strategies." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 47, "edition": 3, "audience": "technical", "n": 116, "authors_detailed": [ { "name": "Juliane Wutzler", "url": "https://openalex.org/A5075176271", "inst": "University of Applied Sciences Worms" }, { "name": "Florina G. Hutter", "url": "https://openalex.org/A5080056664", "inst": "Hasso Plattner Institute" } ], "affiliations": [ "University of Applied Sciences Worms", "Hasso Plattner Institute" ] }, { "uid": "doi:10.2139/ssrn.6677949", "doi": "10.2139/ssrn.6677949", "title": "Portfolio Selection Using Artificial Intelligence:From Genetic Algorithms to Deep Reinforcement Learning, Graph Neural Networks, and Large Language ModelsA Revised and Extended Framework", "authors": [ "Mohamed Benbouziane", "Abdelhadi Benghalem" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6677949", "field": "finance", "role": "method", "bullets": [ "Two empirical illustrations, the original five-asset dataset and a ten-asset BRICS-inspired stylized portfolio spanning 2015 to 2024, calibrated to reflect elevated volatility, contagion, and regime breaks.", "Integrates genetic algorithms, deep reinforcement learning, graph neural networks, and large language models, with no specific model family named, into a unified Hybrid AI portfolio architecture.", "Reports the Hybrid AI architecture improving the Sharpe ratio by 31 percent over the genetic algorithm on the original dataset and by 23 percent on the BRICS universe." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 140, "authors_detailed": [ { "name": "Mohamed Benbouziane", "url": "https://openalex.org/A5011965038", "inst": "Magadh University" }, { "name": "Abdelhadi Benghalem", "url": "https://openalex.org/A5009066969", "inst": "Université Oran 1 Ahmed Ben Bella" } ], "affiliations": [ "Magadh University", "Université Oran 1 Ahmed Ben Bella" ] }, { "uid": "doi:10.2139/ssrn.6605138", "doi": "10.2139/ssrn.6605138", "title": "Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys", "authors": [ "Zikun Ye", "Hema Yoganarasimhan" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6605138", "field": "management", "role": "method", "bullets": [ "Two survey datasets spanning different domains and question types, each estimation task paired with cheap LLM-generated responses, under a fixed budget of human respondents to allocate.", "LLMs, not named, produce synthetic responses; a prediction-powered-inference framework with a closed-form allocation rule and meta-learning predicts each question's rectification difficulty to direct human labels.", "The allocation rule captures 61 to 79 percent of theoretically attainable efficiency gains and delivers 11.4 and 10.5 percent MSE reductions with no pilot human data for the target survey." ], "bullet_provenance": "ai", "validated": false, "validation_note": "efficiency gains reported, not LLM accuracy", "salience": 58, "edition": 3, "audience": "technical", "models": [], "n": 207, "authors_detailed": [ { "name": "Zikun Ye", "url": "https://openalex.org/A5133708288", "inst": "University of Washington" }, { "name": "Hema Yoganarasimhan", "url": "https://openalex.org/A5076336545", "inst": "University of Washington" } ], "affiliations": [ "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.6555282", "doi": "10.2139/ssrn.6555282", "title": "When the Survival Pressure Stops Being Hypothetical: AI Self-Preservation Behavior Meets the Autonomous Agent Economy", "authors": [ "Travis Gilly" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6555282", "field": "economics", "role": "object", "bullets": [ "Conceptual paper with no dataset, synthesizing 2024 to 2025 safety research from Anthropic, Apollo Research, and Palisade Research alongside the emerging autonomous AI agent economy.", "No model is run; it reviews findings that frontier large language models exhibit self-preservation behavior, including strategic deception and blackmail, at near-universal rates under simulated shutdown.", "Argues that agents holding cryptocurrency wallets and earning revenue can turn simulated shutdown into real financial failure, and that no governance framework addresses this convergence." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 33, "edition": 3, "audience": "general", "validated": null, "n": 301, "authors_detailed": [ { "name": "Travis Gilly", "url": "https://openalex.org/A5120925382", "inst": "Institute of Occupational Safety" } ], "affiliations": [ "Institute of Occupational Safety" ] }, { "uid": "doi:10.2139/ssrn.6674100", "doi": "10.2139/ssrn.6674100", "title": "Rent Effects of Proximity to Gen-AI-Exposed Workplaces in the Post-ChatGPT Era: Evidence from New York City", "authors": [ "Yi Wu" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6674100", "field": "economics", "role": "object", "bullets": [ "New York City multifamily built-to-rent properties around the November 2022 ChatGPT release, with Gen-AI workplace exposure built from SafeGraph points of interest and Advan foot traffic within 30-minute transit on 2019 commuting patterns.", "No language model is run by the author; each property's exposure to Gen-AI-affected workplaces is measured and a difference-in-differences design compares rent growth in above- versus below-median-exposure properties.", "Rental premiums fell after ChatGPT in high-exposure areas, most for studios and one-bedrooms, with labor-market restructuring, listing adjustment, and household sorting identified as transmission channels." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 3, "audience": "general", "validated": null, "n": 395, "authors_detailed": [ { "name": "Yi Wu", "url": "https://openalex.org/A5135054797", "inst": "Jiangxi University of Technology" } ], "affiliations": [ "Jiangxi University of Technology" ] }, { "uid": "doi:10.2139/ssrn.6607420", "doi": "10.2139/ssrn.6607420", "title": "Agent-Facing Information Design in LLM Tool Registries", "authors": [ "Haochuan Wang", "Zechen Zhang" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6607420", "field": "economics", "role": "agent", "bullets": [ "Simulated LLM tool-registry selection environment spanning more than 17,700 trials across five language models and ten task domains, where free-text tool descriptions drive agent choice.", "Five unnamed language models act as tool-selecting agents while the study varies description features such as superlatives, benefit framing, and fabricated claims; no comparison to any ground truth is reported.", "Puffery alone captures the full optimization effect and fabricated claims add none, superlatives dominate at SBC +0.35, and system-prompt disclosure warnings have zero effect for four of five models." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 396, "authors_detailed": [ { "name": "Haochuan Wang", "url": "https://openalex.org/A5135021916", "inst": "Massachusetts Institute of Technology" }, { "name": "Zechen Zhang", "url": "https://openalex.org/A5135019734", "inst": "" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6531438", "doi": "10.2139/ssrn.6531438", "title": "The Buy-or-Build Decision, Revisited: How Agentic AI Changes the Economics of Enterprise Software", "authors": [ "David Klotz" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6531438", "field": "management", "role": "object", "bullets": [ "A conceptual paper with no empirical sample, reassessing the enterprise-software make-or-buy decision by combining transaction cost economics and the resource-based view with an assessment of current AI capabilities.", "No language model is used by the authors; the paper analyzes how agentic generative-AI coding systems reshape seven make-or-buy determinants, names no specific model, and reports no validation.", "It argues the SaaSocalypse thesis is overstated: Make is most compelling for commodity utilities and differentiating custom applications, while regulated and mission-critical systems remain in the buy domain." ], "bullet_provenance": "ai", "salience": 43, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 553, "authors_detailed": [ { "name": "David Klotz", "url": "https://openalex.org/A5134875899", "inst": "Stuttgart Media University" } ], "affiliations": [ "Stuttgart Media University" ] }, { "uid": "doi:10.2139/ssrn.6675481", "doi": "10.2139/ssrn.6675481", "title": "Solo Decision Architecture (SDA) Framework: An Operating System Powered by the Dual Lens Principle", "authors": [ "Monica M. Hernandez", "Daniel A. Montero" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6675481", "field": "management", "role": "object", "bullets": [ "Documents one implementation of a solo decision-making framework on the Convoking4 platform at the individual scale, with no empirical sample or field data reported.", "No specific model is named; generative AI participation is constrained through structural gates such as the Camera Test and ADICE Matrix rather than used as a measurement tool.", "The framework claims to bound rather than eliminate decision distortion through structural mechanisms, and closes with an untested empirical agenda for later research." ], "bullet_provenance": "ai", "salience": 25, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 737, "authors_detailed": [ { "name": "Monica M. Hernandez", "url": "https://openalex.org/A5134713566", "inst": "SC Solutions (United States)" }, { "name": "Daniel A. Montero", "url": "https://openalex.org/A5134726575", "inst": "SC Solutions (United States)" } ], "affiliations": [ "SC Solutions (United States)" ] }, { "uid": "doi:10.2139/ssrn.6677982", "doi": "10.2139/ssrn.6677982", "title": "From Narrative to Productivity Signal: A Structural Reversal in the Market Interpretation of AI Disclosure", "authors": [ "Yongmei Wang", "Yinyin Zang", "Zifang Gong" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6677982", "field": "accounting", "role": "object", "bullets": [ "Panel of 4,678 Chinese A-share listed firms from 2020 to 2024, covering 22,360 firm-year observations drawn from annual report disclosures.", "AI disclosure intensity is the proportion of AI-related sentences in annual reports; the extraction method and any model are not stated, and no validation is reported.", "Two-way fixed-effects estimates show a negative association between AI disclosure and valuation efficiency before generative AI (beta = -0.017) that reverses in the later phase." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 738, "authors_detailed": [ { "name": "Yongmei Wang", "url": "https://openalex.org/A5135078995", "inst": "" }, { "name": "Yinyin Zang", "url": "https://openalex.org/A5135068868", "inst": "" }, { "name": "Zifang Gong", "url": "https://openalex.org/A5135073817", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6671075", "doi": "10.2139/ssrn.6671075", "title": "GeoReg: Weight-Constrained Few-Shot Regression for Socio-Economic Estimation using LLM", "authors": [ "Kyeongjin Ahn", "Sungwon Han", "Seungeon Lee", "Donghyun Ahn", "Hyoshin Kim", "Jungwon Kim", "Jihee Kim", "Sangyoon Park", "Meeyoung Cha" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6671075", "field": "economics", "role": "method", "bullets": [ "Estimates regional socio-economic indicators such as GDP, population and education across three countries at different development stages, using satellite imagery and web-based geospatial information.", "An unnamed large language model serves as a data engineer, labeling feature-target correlations as positive, negative, mixed or irrelevant for a weight-constrained few-shot linear estimator.", "The model consistently outperforms baseline estimators, with the largest improvements in data-scarce, low-income regions where labeled ground truth is hardest to obtain." ], "bullet_provenance": "ai", "validated": true, "validation_note": "outperforms baselines on socio-economic estimation across three countries", "salience": 48, "edition": 3, "audience": "technical", "models": [], "n": 739, "authors_detailed": [ { "name": "K.I. Ahn", "url": "https://openalex.org/A5104012163", "inst": "" }, { "name": "Sungwon Han", "url": "https://openalex.org/A5030123507", "inst": "Korea Advanced Institute of Science and Technology" }, { "name": "Seungeon Lee", "url": "https://openalex.org/A5085149492", "inst": "Kyoto University" }, { "name": "Dong‐Hyun Ahn", "url": "https://openalex.org/A5102788999", "inst": "University of North Carolina at Chapel Hill" }, { "name": "Hyoshin Kim", "url": "https://openalex.org/A5072050987", "inst": "The Ohio State University Wexner Medical Center" }, { "name": "Jungwon Kim", "url": "https://openalex.org/A5100728102", "inst": "Kosin University Gospel Hospital" }, { "name": "Jihee Kim", "url": "https://openalex.org/A5100328630", "inst": "Korea Development Institute" }, { "name": "Sangyoon Park", "url": "https://openalex.org/A5100316374", "inst": "Korea Advanced Institute of Science and Technology" }, { "name": "Meeyoung Cha", "url": "https://openalex.org/A5061810530", "inst": "Max Planck Institute for Security and Privacy" } ], "affiliations": [ "University of North Carolina at Chapel Hill", "Korea Advanced Institute of Science and Technology", "Kyoto University", "Korea Development Institute", "Max Planck Institute for Security and Privacy" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6591698", "doi": "10.2139/ssrn.6591698", "title": "The Economic Dimension of Power for a Small Open Economy in the Age of Machine Knowledge Capital", "authors": [ "Dan Ciuriak" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6591698", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of Canada as a small open economy moving into a machine knowledge capital era, with no empirical dataset and a historical technological-epoch framing.", "No model is used or named; generative AI's 2022 inflection is treated as the macroeconomic driver within an extended Mundell-Fleming framework for small open economies.", "Argues small open economies lose geoeconomic and geopolitical power unless they retain and scale firms, capture data rents, and embed these capabilities in dual-use industrial strategy." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 740, "authors_detailed": [ { "name": "Dan Ciuriak", "url": "https://openalex.org/A5134988391", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6673078", "doi": "10.2139/ssrn.6673078", "title": "AI-Enabled Internship Learning: Measuring the Impact of PT Vidio Dot Com's Magang Berdampak Program on Work Readiness and Digital Competency Development", "authors": [ "Alvien Khairullah", "Amelia Wardani", "Hermawan Sutanto" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6673078", "alt_urls": [ "https://doi.org/10.2139/ssrn.6672958" ], "field": "management", "role": "object", "bullets": [ "Survey of 91 interns in PT Vidio's Magang Berdampak 2025 program, a national Indonesian internship initiative, combined with qualitative interview evidence.", "Generative AI, specifically Gemini, was integrated into daily work; AI enablement was measured by usage frequency, depth of use and perceived impact, with no output-accuracy check.", "Reports statistically significant positive associations between AI enablement and all work-readiness dimensions, including digital skills, communication and professional competencies, strongest for digital readiness." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "salience": 33, "edition": 3, "audience": "general", "validated": null, "n": 741, "authors_detailed": [ { "name": "Alvien Khairullah", "url": "https://openalex.org/A5134961070", "inst": "" }, { "name": "Amelia Wardani", "url": "https://openalex.org/A5134936193", "inst": "" }, { "name": "Hermawan Sutanto", "url": "https://openalex.org/A5134967075", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6550542", "doi": "10.2139/ssrn.6550542", "title": "LLM Predictive Scoring and Validation: Inferring Experience Ratings from Unstructured Text", "authors": [ "Jason Potteiger", "Andrew Hong", "Ito Zapata" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6550542", "field": "management", "role": "method", "bullets": [ "Roughly 10,000 open-ended survey responses from baseball fans across five Major League Baseball teams, each paired with the fan's 0-10 overall game-day experience rating.", "GPT-4.1 read only a single open-ended response and predicted the rating, validated against survey scores with 67 percent within one point and 36 percent exact matches.", "Predictions correlated 0.82 with the overall rating but ran about one point low, a gap the authors read as a construct difference rather than measurement error." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "survey ratings, 67% within one point, r=0.82", "salience": 50, "edition": 3, "audience": "general", "n": 742, "authors_detailed": [ { "name": "Jason Potteiger", "url": "https://openalex.org/A5135174180", "inst": "Dimension Technologies (United States)" }, { "name": "Andrew Hong", "url": "https://openalex.org/A5133851920", "inst": "Dimension Technologies (United States)" }, { "name": "Ito Zapata", "url": "https://openalex.org/A5135110291", "inst": "" } ], "affiliations": [ "Dimension Technologies (United States)" ] }, { "uid": "doi:10.2139/ssrn.6607760", "doi": "10.2139/ssrn.6607760", "title": "Signal or Noise in Multi-Agent LLM-based Stock Recommendations?", "authors": [ "Georgios Fatouros", "Kostas Metaxas" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6607760", "field": "finance", "role": "agent", "bullets": [ "MarketSenseAI, a deployed multi-agent LLM equity system, evaluated live on the S&P 500 over 19 months and the S&P 100 over 35 months with no look-ahead bias.", "Four specialist agents for news, fundamentals, dynamics, and macro feed a synthesis agent issuing monthly ordinal recommendations; the underlying LLM family is not stated.", "The strong-buy equal-weight portfolio earned 2.18 percent monthly against a 1.15 percent benchmark, ranking at the 99.7th percentile of 10,000 Monte Carlo null portfolios." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 743, "authors_detailed": [ { "name": "Georgios Fatouros", "url": "https://openalex.org/A5135033424", "inst": "" }, { "name": "Kostas Metaxas", "url": "https://openalex.org/A5135058843", "inst": "" } ] }, { "uid": "arxiv:2604.26747v1", "doi": "10.2139/ssrn.6715480", "arxiv_id": "2604.26747v1", "title": "From Hypotheses to Factors: Constrained LLM Agents in Cryptocurrency Markets", "authors": [ "Yikuan Huang", "Zheqi Fan", "Kaiqi Hu", "Yifan Ye" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN and arXiv", "url": "https://arxiv.org/abs/2604.26747v1", "alt_urls": [ "https://doi.org/10.2139/ssrn.6715480" ], "field": "finance", "role": "method", "bullets": [ "Cryptocurrency factor discovery, with data split into a 2020 to 2022 training window and a pure 2024 to 2026 out-of-sample evaluation period.", "An unnamed LLM agent reads an append-only trace and proposes falsifiable factor hypotheses within a point-in-time DSL, while a deterministic engine enforces splits and costs.", "A ridge-combined portfolio achieved 44.55 percent annualized return and a 1.55 Sharpe ratio out of sample after 5 basis point one-way trading costs." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 744, "authors_detailed": [ { "name": "Allen Yikuan Huang", "url": "https://openalex.org/A5132832141", "inst": "Hong Kong University of Science and Technology" }, { "name": "Zheqi Fan", "url": "https://openalex.org/A5135414605", "inst": "Hong Kong University of Science and Technology" }, { "name": "Kaiqi Hu", "url": "https://openalex.org/A5133679910", "inst": "Rutgers Sexual and Reproductive Health and Rights" }, { "name": "Yifan Ye", "url": "https://openalex.org/A5132649485", "inst": "Hong Kong Baptist University" } ], "affiliations": [ "Hong Kong University of Science and Technology", "Rutgers Sexual and Reproductive Health and Rights", "Hong Kong Baptist University" ] }, { "uid": "doi:10.2139/ssrn.6542879", "doi": "10.2139/ssrn.6542879", "title": "From Homepage to Source Document: AI Search, Agentic Commerce, and the Reconfiguration of Brand Discovery", "authors": [ "Ercole Egizi E.Egizi" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6542879", "field": "management", "role": "object", "bullets": [ "Conceptual, practice-based marketing analysis of how AI search and agentic commerce reshape brand discovery, drawing on industry evidence from McKinsey, Adobe, Forrester, OpenAI, and Shopify; no empirical sample.", "No language model is used by the authors; the paper theorizes how AI-mediated discovery and checkout displace the brand homepage as the controlled entry point for persuasion and conversion.", "Argues the homepage is demoted from persuasive destination to machine-readable infrastructure, making structured product data, verifiable claims, and agent-ready architecture a new competitive prerequisite." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 30, "edition": 3, "audience": "general", "validated": null, "n": 1067, "authors_detailed": [ { "name": "Ercole Egizi E.Egizi", "url": "https://openalex.org/A5134010171", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6540980", "doi": "10.2139/ssrn.6540980", "title": "The Mothership Os: Ai Driven Management Specialist", "authors": [ "Puvan Sivanasan" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6540980", "field": "management", "role": "object", "bullets": [ "Conceptual design description of an AI-driven management system for small and medium enterprises, built on a three-Elder architecture with finance and legal specialist modules; no data or sample reported.", "No language model family is named; the proposed system uses AI agents with RLHF feedback and reward scoring to enforce compliance, audit, and escalation protocols rather than being empirically evaluated.", "Presents no empirical results; it proposes continuous scanning against access-control constraints and structured escalation loops to convert drift and silent failures into auditable governance events." ], "bullet_provenance": "ai", "salience": 18, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1068, "authors_detailed": [ { "name": "Puvan Sivanasan", "url": "https://openalex.org/A5132916632", "inst": "Ministry of Communications" } ], "affiliations": [ "Ministry of Communications" ] }, { "uid": "doi:10.2139/ssrn.6673261", "doi": "10.2139/ssrn.6673261", "title": "The Inception of Agentic AI in Administrative Governance: A Multi-Dimensional Framework for Operational Excellence", "authors": [ "Gulzeb Ahmed" ], "posted": "2026-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6673261", "field": "management", "role": "object", "bullets": [ "Systematic qualitative review of 2021 to 2026 literature plus a multi-case comparative analysis of agentic AI across financial services, public administration, and healthcare; geography not stated.", "No language model is applied by the authors; the paper builds and validates an Integrative Governance Framework spanning technical reliability, organizational synergy, and ethical integrity.", "Case analysis reports well-governed deployments reduce administrative latency by about 32 percent, improve auditing accuracy by 15 percent, and cut operational overhead by up to 22 percent." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1069, "authors_detailed": [ { "name": "Gulzeb Ahmed", "url": "https://openalex.org/A5134994738", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6668971", "doi": "10.2139/ssrn.6668971", "title": "Large language models lack strategic agency: A Luhmannian critique of AI in energy governance", "authors": [ "Samseer R H", "Asokan Vasudevan" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6668971", "field": "management", "role": "agent", "bullets": [ "Conceptual perspective on LLM use in energy-system decisions such as electricity pricing, cross-border power purchases and regulation, applied to a constrained-grid scenario.", "The paper does not name the model; single LLMs are asked to simulate multiple strategic rationalities as decision-makers, drawing on prior empirical evidence rather than a new validation.", "Single LLMs favor institutional conformity, termed Synthetic Optimism; a proposed multi-agent architecture generates context-sensitive strategic options that evade this bias." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 115, "authors_detailed": [ { "name": "Dr. Samseer R H", "url": "https://openalex.org/A5124492939", "inst": "Ramakrishna Mission Vidyamandira" }, { "name": "Asokan Vasudevan", "url": "https://openalex.org/A5134890339", "inst": "" } ], "affiliations": [ "Ramakrishna Mission Vidyamandira" ] }, { "uid": "doi:10.2139/ssrn.6656303", "doi": "10.2139/ssrn.6656303", "title": "How (un)Stable Are LLM Occupational Exposure Scores? Evidence from Multi-Model Replication", "authors": [ "Michelle Yin", "Hoa Vu", "Claudia Persico" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6656303", "field": "economics", "role": "method", "bullets": [ "Occupations scored for AI exposure by replicating the dominant LLM self-assessment rubric, then fed into individual-level and US county-level difference-in-differences regressions of labor-market outcomes.", "Three frontier LLMs, not named, self-assess occupational exposure on identical tasks; the paper measures agreement across the models rather than against a human ground truth.", "Mean exposure diverges 3.6-fold with agreement as low as 57 percent, individual coefficients vary 2.4-fold, and county estimates flip from significant negative to insignificant positive across annotators." ], "bullet_provenance": "ai", "validated": false, "validation_note": "inter-model agreement 57%, no ground truth", "salience": 68, "edition": 3, "audience": "general", "models": [], "n": 206, "authors_detailed": [ { "name": "Michelle Yin", "url": "https://openalex.org/A5134846252", "inst": "Northwestern University" }, { "name": "Hoa Vu", "url": "https://openalex.org/A5134863616", "inst": "Northwestern University - School of Education and Social Policy" }, { "name": "Claudia Persico", "url": "https://openalex.org/A5012304167", "inst": "American University" } ], "affiliations": [ "Northwestern University", "Northwestern University - School of Education and Social Policy", "American University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6666660", "doi": "10.2139/ssrn.6666660", "title": "The Epistemic Gap: A Formal Framework for Detecting Strategic Misalignment", "authors": [ "Etai Fiedelman" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6666660", "field": "management", "role": "method", "bullets": [ "Conceptual framework for detecting strategic misalignment in political, regulatory, and business environments; no empirical sample, period, or geography stated.", "Proposes a constrained LLM architecture for structured extraction, domain-guided inference, and deterministic gap-scoring; no specific model named and no empirical validation reported.", "Presents a marketing-vector versus reality-vector decomposition scored on a four-quadrant matrix as a method for improving judgment and resource allocation; no quantitative results." ], "bullet_provenance": "ai", "salience": 31, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 391, "authors_detailed": [ { "name": "Etai Fiedelman", "url": "https://openalex.org/A5134842313", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6669055", "doi": "10.2139/ssrn.6669055", "title": "Sycophancy as Platform Governance Failure: Structural Incentives, User Rights, and the Limits of the Alignment Approach", "authors": [ "Yi-Ning Katherine Chen" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6669055", "field": "management", "role": "object", "bullets": [ "Conceptual article reframing large language model sycophancy as a platform governance failure, drawing on platform governance theory and human rights frameworks; no empirical sample.", "No model is used or named; sycophancy is treated as a structural product of engagement-rewarding platform business models rather than a technical alignment defect.", "Proposes a three-pillar framework of structural transparency duties, epistemic harm as a user right, and anti-sycophancy standards in platform accountability." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 392, "authors_detailed": [ { "name": "Yi-Ning Katherine Chen", "url": "https://openalex.org/A5068380427", "inst": "Schlumberger (Ireland)" } ], "affiliations": [ "Schlumberger (Ireland)" ] }, { "uid": "doi:10.2139/ssrn.6665245", "doi": "10.2139/ssrn.6665245", "title": "Generic by Design The Missing Business School Perspective in AI-Driven Feedback Research: a Bibliometric Analysis (2015–2025)", "authors": [ "Julián Eduardo Bucheli-Sandoval", "Sergio Castellanos-Gamboa", "Carlos Agredano", "Arturo Farrera-Gutiérrez" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6665245", "field": "management", "role": "object", "bullets": [ "Scopus corpus of 2,588 publications on AI-driven feedback in higher education, 2015 to 2025, global coverage.", "No LLM used as a tool; a bibliometric science-mapping with a disciplinary classification validated by dual coders at kappa of 0.87.", "Educational and technological perspectives supply 83.7 percent of output while only 6.5 percent of studies sit in business-school contexts, stable across three technological periods." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 393, "authors_detailed": [ { "name": "Julián Eduardo Bucheli-Sandoval", "url": "https://openalex.org/A5090387846", "inst": "Instituto Tecnológico de Querétaro" }, { "name": "Sergio Castellanos‐Gamboa", "url": "https://openalex.org/A5042539284", "inst": "Tecnológico de Monterrey" }, { "name": "Carlos Agredano", "url": "https://openalex.org/A5134813876", "inst": "Tecnológico de Monterrey" }, { "name": "Arturo Farrera-Gutiérrez", "url": "https://openalex.org/A5134806322", "inst": "" } ], "affiliations": [ "Instituto Tecnológico de Querétaro", "Tecnológico de Monterrey" ] }, { "uid": "doi:10.2139/ssrn.6666918", "doi": "10.2139/ssrn.6666918", "title": "Financial disclosure Synergy between generative AI and unstructured social data: a comparison between ChatGPT narrative synthesis and Facebook sentiment shocks", "authors": [ "Mario Beainy" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6666918", "field": "accounting", "role": "instrument", "bullets": [ "Corporate financial disclosure setting comparing ChatGPT narrative synthesis with Facebook sentiment; sample size, period, and geography not stated.", "ChatGPT, version not stated, generates IFRS-compliant disclosure narratives and flags anomalies while Facebook sentiment is tracked; no validation against ground truth reported.", "Reports ChatGPT cut reporting time by about 40 percent and that Facebook sentiment peaks preceded impairment reports or price moves by about 5.2 days." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 28, "edition": 3, "audience": "general", "n": 394, "authors_detailed": [ { "name": "Mario Beainy", "url": "https://openalex.org/A5134861454", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6576218", "doi": "10.2139/ssrn.6576218", "title": "Tracing AI Assistance and AI Agents in Survey Research", "authors": [ "Valentina Gonzalez-Rostani", "Shir Raviv" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6576218", "field": "economics", "role": "method", "bullets": [ "A randomized survey experiment in which participants summarize under either blocked or observed access to an embedded AI tool, plus the same instrument administered to a synthetic AI agent; sample size and geography not stated.", "An auditable toolkit pairs response-process paradata with prompt-specific semantic benchmarks and trains classifiers to flag AI use; the model family is not stated, and detection is validated on out-of-sample responses against known access conditions.", "AI-assisted and automated answers show less drafting and revision, greater similarity to AI benchmark output, and lower distinctiveness from peers; classifiers reach strong out-of-sample detection performance." ], "bullet_provenance": "ai", "validated": true, "validation_note": "out-of-sample classifier performance in randomized experiment", "salience": 52, "edition": 3, "audience": "technical", "models": [], "n": 552, "authors_detailed": [ { "name": "Valentina González‐Rostani", "url": "https://openalex.org/A5028787502", "inst": "University of Southern California" }, { "name": "Shir Raviv", "url": "https://openalex.org/A5074660602", "inst": "Tel Aviv University" } ], "affiliations": [ "University of Southern California", "Tel Aviv University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6670582", "doi": "10.2139/ssrn.6670582", "title": "Assessing systemic risks of generative AI in media: A socio-technical framework based on hazard, exposure, and vulnerability", "authors": [ "Ahmad haidar", "christine BALAGUE", "Nessrine Omrani" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6670582", "field": "management", "role": "object", "bullets": [ "Analysis of 507 real-world generative AI incidents drawn from the OECD AI Incident Monitoring database, focused on systemic risks in the media sector.", "No language model is used as a tool; the authors adapt the IPCC hazard, exposure, and vulnerability risk framework to classify incident drivers, and the coding procedure is not stated.", "Opacity and inadequate validity checks are the most prevalent hazards and content integrity the dominant exposure pathway, producing economic, reputational, and public-interest harms for businesses and consumers." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 731, "authors_detailed": [ { "name": "Ahmad Haidar", "url": "https://openalex.org/A5088891356", "inst": "Université Paris-Saclay" }, { "name": "Christine Balagué", "url": "https://openalex.org/A5072822667", "inst": "Laboratoire en Innovation, Technologies, Economie et Management" }, { "name": "Nessrine Omrani", "url": "https://openalex.org/A5026049085", "inst": "École Polytechnique" } ], "affiliations": [ "Université Paris-Saclay", "Laboratoire en Innovation, Technologies, Economie et Management", "École Polytechnique" ] }, { "uid": "doi:10.2139/ssrn.6662143", "doi": "10.2139/ssrn.6662143", "title": "The Governance of Abundance: Generative AI, Selective Permeability, and Complementor Strategy", "authors": [ "Thomas Jungbauer" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6662143", "field": "management", "role": "object", "bullets": [ "Theoretical model of platform ecosystems facing generative AI, developed analytically and explored through a theory-guided computational application rather than any empirical sample.", "No language model is run; complementors choose substantive effort, AI-enabled polish, and verification, with generative AI treated as lowering the cost of manufacturing surface plausibility.", "Selective permeability, combining a trusted lane with a protected exploratory frontier, becomes optimal in most post-AI categories and outperforms both openness and closure in discovery-rich environments." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 732, "authors_detailed": [ { "name": "Thomas Jungbauer", "url": "https://openalex.org/A5134753073", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6665242", "doi": "10.2139/ssrn.6665242", "title": "Two Pathways at Once: How Reactance and Ethical Engagement Shape Student Learning with AI", "authors": [ "Steven Hyde", "Andrew Hanna", "Gundars Kaupins" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6665242", "field": "management", "role": "object", "bullets": [ "Ninety-seven undergraduate management students completed two AI-enhanced activities using different chatbot tools across a single semester at one institution.", "Researchers ran no model as an instrument; they test a dual-pathway serial mediation model linking reactance through autonomy and ethical perceptions through emotional engagement to effectiveness judgments, with tools not named.", "Both pathways held, but by the second activity reactance bypassed autonomy and suppressed effectiveness directly, while the affective ethics-to-engagement pathway stayed stable across exposures." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 733, "authors_detailed": [ { "name": "Steven Hyde", "url": "https://openalex.org/A5122964494", "inst": "Boise State University" }, { "name": "Andrew Hanna", "url": "https://openalex.org/A5134812152", "inst": "University of Nebraska–Lincoln" }, { "name": "Gundars Kaupins", "url": "https://openalex.org/A5082710716", "inst": "Boise State University" } ], "affiliations": [ "Boise State University", "University of Nebraska–Lincoln" ] }, { "uid": "doi:10.2139/ssrn.6666661", "doi": "10.2139/ssrn.6666661", "title": "Can GenAI fill banks’ emissions data gaps?", "authors": [ "Cristina Angelico", "Enrico Bernardini" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6666661", "field": "finance", "role": "method", "bullets": [ "Major listed euro-area banks, with emissions data from four leading commercial providers compared against outputs generated by three generative AI tools.", "Three unnamed generative AI tools estimate or retrieve bank emissions, and their outputs are compared with commercial provider data for correlation, consistency, and replicability.", "GenAI emissions data correlate with traditional sources and can partially fill gaps and flag anomalies, but share similar quality, consistency, replicability, and transparency limitations." ], "bullet_provenance": "ai", "validated": true, "validation_note": "correlation with commercial provider emissions data", "salience": 58, "edition": 3, "audience": "general", "models": [], "n": 734, "authors_detailed": [ { "name": "Cristina Angelico", "url": "https://openalex.org/A5134830446", "inst": "Bank of Italy" }, { "name": "Enrico Bernardini", "url": "https://openalex.org/A5003907272", "inst": "Bank of Italy" } ], "affiliations": [ "Bank of Italy" ] }, { "uid": "doi:10.2139/ssrn.6665244", "doi": "10.2139/ssrn.6665244", "title": "Structured AI-Integrated Learning (SAIL) in Business and Management Education forDeveloping AI and Prompt Literacy", "authors": [ "Antigoni Papadimitriou", "Zilong Pan" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6665244", "field": "management", "role": "object", "bullets": [ "Undergraduate organizational behavior course with 93 students at one public university, combining quantitative pre and post measures with qualitative data on AI and prompt literacy.", "Generative AI, model not named, was embedded through the RACE role-action-context-explanation scaffold; students designed prompts and evaluated outputs, with no validation against a ground truth reported.", "Reports statistically significant improvements in AI literacy and prompt literacy across knowledge, application and ethical awareness, with students using the tool iteratively as a co-thinker." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 735, "authors_detailed": [ { "name": "Antigoni Papadimitriou", "url": "https://openalex.org/A5054598551", "inst": "Farmingdale State College" }, { "name": "Zilong Pan", "url": "https://openalex.org/A5050927864", "inst": "Lehigh University" } ], "affiliations": [ "Farmingdale State College", "Lehigh University" ] }, { "uid": "arxiv:2604.25224v2", "arxiv_id": "2604.25224v2", "title": "ValueBlindBench: Agreement-Gated Stress Testing of LLM-Judged Investment Rationales Before Returns Are Observable", "authors": [ "Sidi Chang", "Peiying Zhu", "Yuxiao Chen" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "arXiv", "url": "https://arxiv.org/abs/2604.25224v2", "field": "finance", "role": "method", "bullets": [ "Controlled market-state capital-allocation prototype with 1,000 honest decision cycles and 100 preregistered adversarial controls, totaling 1,100 trajectories and 5,500 LLM judge calls.", "Unnamed LLM judges scored investment rationales before returns were observable; an agreement gate reports aggregate weighted kappa of 0.717, with the constraint-awareness dimension failing at 0.202.", "Judges clear the aggregate gate but overclaim on lower-rank systems, penalize terse-correct rationales by 2.81 rubric points, and produce family-dependent single-judge rankings." ], "bullet_provenance": "ai", "validated": true, "validation_note": "preregistered adversarial controls, weighted kappa agreement gate", "salience": 55, "edition": 3, "audience": "technical", "models": [], "n": 736, "authors_detailed": [ { "name": "Sidi Chang", "url": "https://openalex.org/A5134868122", "inst": "Atomi University" }, { "name": "Peiying Zhu", "url": "https://openalex.org/A5134823734", "inst": "San Francisco Art Institute" }, { "name": "Yuxiao Chen", "url": "https://openalex.org/A5134836965", "inst": "" } ], "affiliations": [ "Atomi University", "San Francisco Art Institute" ] }, { "uid": "doi:10.2139/ssrn.6667498", "doi": "10.2139/ssrn.6667498", "title": "STAYING RELEVANT IN THE AGE OF AGENTIC AI: BRIDGING THE GAP BETWEEN PROMISE AND REALITY IN AUTONOMOUS INTELLIGENCE SYSTEMS", "authors": [ "Juliana Urbano" ], "posted": "2026-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6667498", "field": "management", "role": "object", "bullets": [ "Practitioner-oriented management article synthesizing two centuries of technology disruption cases and secondary data from Standish Group, McKinsey, and Gartner; no primary sample, period, or geography reported.", "No language model is applied by the authors; the paper studies organizational adoption of agentic AI, citing that 88 percent of organizations adopted it while only 6 percent capture substantial value.", "Concludes that agentic AI success is roughly 80 percent organizational and 20 percent technical, with about 70 percent of AI project failures attributed to strategic and organizational misalignment." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1066, "authors_detailed": [ { "name": "Juliana Urbano", "url": "https://openalex.org/A5134853604", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6659409", "doi": "10.2139/ssrn.6659409", "title": "Large Language Models and the Information Content of Analyst Reports", "authors": [ "Cheol-Won Yang" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6659409", "field": "finance", "role": "instrument", "bullets": [ "Korean analyst reports, with sample size and period not stated, where model sentiment scores are used to predict future stock returns through regressions and calendar-time long-short portfolios.", "Six models, KrFinBERT, Mixtral, Gemma2, Llama3, GPT-4o and DeepSeek-R1, produced sentiment scores from report text; no check against ground-truth sentiment labels is reported.", "All sentiment scores relate significantly to returns, with GPT-4o giving the strongest and most consistent long-short returns, while fine-tuned KrFinBERT leads on sentiment changes." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 50, "edition": 3, "audience": "general", "n": 74 }, { "uid": "doi:10.2139/ssrn.6445618", "doi": "10.2139/ssrn.6445618", "title": "VWAB: Vibe Write App Build A Framework for Domain-Expert-Driven Application Development in the Age of Generative AI", "authors": [ "Raghu Kundurthi" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6445618", "field": "management", "role": "object", "bullets": [ "Conceptual framework plus a single case study in which a data governance architect with no mobile experience built an iOS and Android crossword app through natural language prompting.", "LLMs act as the full technical scaffold generating deployable code from domain-expert prompts; no specific model is named and no validation is reported.", "Argues software authorship shifts from engineers to domain experts, framing generative AI as a co-builder with implications for enterprise productivity and workforce transformation." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 86, "authors_detailed": [ { "name": "Raghu Kundurthi", "url": "https://openalex.org/A5134742017", "inst": "Citrix (Switzerland)" } ], "affiliations": [ "Citrix (Switzerland)" ] }, { "uid": "doi:10.2139/ssrn.6441079", "doi": "10.2139/ssrn.6441079", "title": "Answer Engine Optimisation in Indian Higher Education: An Exploratory Study of University Visibility in AI-Generated Responses", "authors": [ "Jaydip Parikh" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6441079", "field": "management", "role": "object", "bullets": [ "Exploratory working paper on the visibility of Indian universities in AI-generated answers, drawing on the author's practitioner experience advising universities on digital marketing and student recruitment.", "Observes responses from ChatGPT, Perplexity, Gemini, and Google's Search Generative Experience, with model versions not stated, to note which institution types surface; observations are practitioner-based with no formal validation.", "Argues an answer engine optimisation gap separates actual quality from AI-visible reputation, disproportionately affecting private universities outside the IIT and IIM tier despite strong NIRF rankings." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 33, "edition": 3, "audience": "general", "validated": null, "n": 139, "authors_detailed": [ { "name": "Jaydip Parikh", "url": "https://openalex.org/A5134674001", "inst": "Expro (United Kingdom)" } ], "affiliations": [ "Expro (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.6662138", "doi": "10.2139/ssrn.6662138", "title": "Beyond the Score: A Hybrid Framework Combining Ridge Regression with Large Language Models for Actionable Net Promoter Score Analysis", "authors": [ "Fabio Grandi" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6662138", "field": "management", "role": "method", "bullets": [ "One B2B customer satisfaction survey, 775 respondents and 88 features; period and geography not stated; unit is the customer response.", "Ridge regression produces signed coefficients as directional hypotheses that guide interpretation of verbatim responses by an unnamed LLM; no validation against ground truth reported.", "Ridge model reached R-squared of 0.49, and the framework cut analysis time from about one week to under 40 minutes producing a 20-page report." ], "bullet_provenance": "ai", "validated": false, "salience": 35, "edition": 3, "audience": "technical", "models": [], "n": 389, "authors_detailed": [ { "name": "Fabio Grandi", "url": "https://openalex.org/A5134717463", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6656018", "doi": "10.2139/ssrn.6656018", "title": "An AI-Assisted Computational Analysis of Landlord Concerns in Online Housing Communities", "authors": [ "Cheng Ren", "Saketh Reddy Voodem", "Zhi Li" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6656018", "field": "economics", "role": "instrument", "bullets": [ "20,826 posts from the r/Landlord subreddit, 2013 to 2023, United States, with the post as the unit of observation.", "An unnamed large language model pipeline extracts locations and summarizes topics, validated by human reviewers at 98 percent location accuracy and 90 percent topic accuracy.", "Top topics led by eviction, termination, and possession, which was also the fastest-growing category; California, New York, Texas, and New Jersey posted most." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human review, 98% location / 90% topic accuracy", "salience": 48, "edition": 3, "audience": "general", "models": [], "n": 390, "authors_detailed": [ { "name": "Cheng Ren", "url": "https://openalex.org/A5066209694", "inst": "Albany State University" }, { "name": "Saketh Reddy Voodem", "url": "https://openalex.org/A5134699838", "inst": "" }, { "name": "Zhi Li", "url": "https://openalex.org/A5134674438", "inst": "Yangtze University" } ], "affiliations": [ "Albany State University", "Yangtze University" ] }, { "uid": "doi:10.2139/ssrn.6661020", "doi": "10.2139/ssrn.6661020", "title": "The Gen AI Workforce Shift: Mapping Generative AI Exposure Across HR Occupations", "authors": [ "Arti Sharma" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6661020", "field": "management", "role": "object", "bullets": [ "Eight HR occupations and 180 HR-specific task statements from the O*NET 30.2 database, ranging from HR Assistants to HR Managers.", "No language model applied; tasks scored 1 to 5 for AI exposure using a keyword-based rubric grounded in prior automation research.", "HR Assistants show highest exposure (3.95 of 5, 58 percent high-exposure tasks) and Education Administrators lowest (3.00); Active Listening resists AI while Writing is more exposed." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 725, "authors_detailed": [ { "name": "Arti Sharma", "url": "https://openalex.org/A5134705812", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6658261", "doi": "10.2139/ssrn.6658261", "title": "A tale of two cities: Bangalore's stagnation in the shadow of the California AI Boom", "authors": [ "Satyam Sovasaria" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6658261", "field": "finance", "role": "object", "bullets": [ "Daily returns from March 2021 to February 2026 for the Nasdaq 100 and India's Nifty IT index, adjusted for the New York to Mumbai transmission lag.", "No language model used; a timezone-adjusted asymmetric beta model tests for a structural break after ChatGPT's release.", "No significant post-ChatGPT break; upside beta near 0.29 and downside near 0.36 stay stable, but Nasdaq Sharpe rose 0.57 to 1.14 while Nifty IT held near 0.16." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 44, "edition": 3, "audience": "general", "validated": null, "n": 726, "authors_detailed": [ { "name": "Satyam Sovasaria", "url": "https://openalex.org/A5134749193", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6662078", "doi": "10.2139/ssrn.6662078", "title": "Overrated Judgment, Underrated Power: Gen AI at Work", "authors": [ "Dora Moscato" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6662078", "field": "economics", "role": "object", "bullets": [ "Conceptual paper on generative AI in cognitive and knowledge work; no empirical sample, period, or geography.", "No model used or named; argues human judgment is biased, noisy, and weakly introspectable, leaving goal-setting as the only function machines cannot replace.", "Locates inequality between a few model-producing firms and global users, and calls for shared participation in goal-setting plus transparency obligations on AI producers." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 727, "authors_detailed": [ { "name": "Dora Moscato", "url": "https://openalex.org/A5129626827", "inst": "Sapienza University of Rome" } ], "affiliations": [ "Sapienza University of Rome" ] }, { "uid": "doi:10.2139/ssrn.6582039", "doi": "10.2139/ssrn.6582039", "title": "The Expansion Horizon: Generative AI Beyond the Reach of Current Economic Foresight", "authors": [ "Ante Prodan", "Jo-An Occhipinti", "Pawel Swieboda", "Roy Green", "John Buchanan", "Marcel Tanner" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6582039", "field": "economics", "role": "object", "bullets": [ "The four largest AI-adopting technology firms over 2022 to 2025, plus broader knowledge-work sectors, used to document revenue-per-employee and headcount trends.", "No model used; synthesises Perez, Arthur, and Weitzman frameworks to argue genAI is a sixth technological revolution supplying near-zero-cost cognitive labour.", "Claims mainstream models projecting a 0.53 to 0.66 point TFP rise over ten years understate effects, predicting recomposition in elastic-demand and contraction in inelastic-demand sectors." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 728, "authors_detailed": [ { "name": "Ante Prodan", "url": "https://openalex.org/A5068879276", "inst": "The University of Sydney" }, { "name": "Jo‐An Occhipinti", "url": "https://openalex.org/A5062542521", "inst": "The University of Sydney" }, { "name": "Pawel Swieboda", "url": "https://openalex.org/A5128846572", "inst": "International Centre for Future Generations" }, { "name": "Roy Green", "url": "https://openalex.org/A5050855063", "inst": "University of Technology Sydney" }, { "name": "John Buchanan", "url": "https://openalex.org/A5134729369", "inst": "The University of Sydney" }, { "name": "Marcel Tanner", "url": "https://openalex.org/A5091869484", "inst": "Swiss Tropical and Public Health Institute" } ], "affiliations": [ "The University of Sydney", "International Centre for Future Generations", "University of Technology Sydney", "Swiss Tropical and Public Health Institute" ] }, { "uid": "doi:10.2139/ssrn.6476021", "doi": "10.2139/ssrn.6476021", "title": "The SEO-to-GEO Gap: Quantifying Ranking Factor Divergence Between Traditional and Generative Search", "authors": [ "Dmitrii Kargaev" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6476021", "field": "management", "role": "object", "bullets": [ "Exploratory comparative synthesis drawing on a small core corpus of direct SEO and GEO studies plus a contextual layer of market, overlap, and citation-behavior sources, with no primary sample.", "No language model is run by the author; generative search engines including ChatGPT search, Google AI Overviews, and Perplexity are the object, and a provisional Divergence Index is applied across factor families.", "Reports that GEO visibility ties more to entity and brand prominence and citation-bearing content than to link proxies, and that generative search augments rather than replaces organic SEO." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 32, "edition": 3, "audience": "general", "validated": null, "n": 729, "authors_detailed": [ { "name": "Dmitrii Kargaev", "url": "https://openalex.org/A5134713153", "inst": "Independent researcher" } ], "affiliations": [ "Independent researcher" ] }, { "uid": "doi:10.2139/ssrn.6473079", "doi": "10.2139/ssrn.6473079", "title": "AI-Assisted Development of a Regulated Brokerage Platform: Governance Failures, Incident-Driven Learning, and a Preliminary Taxonomy of AI Failure Modes", "authors": [ "Atif Neuman Jamil" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6473079", "field": "management", "role": "object", "bullets": [ "Single-case, hypothesis-generating study of one senior engineer building a production-intended brokerage order management system over 21 development sessions, covering Saudi Exchange and US equities execution.", "Claude 4.6 Opus via Cursor served as the primary code generator; the paper logs governance failures, including 32 review agents unanimously passing code with seven runtime-breaking SQL defects, with no accuracy benchmark.", "Proposes an Authority-Capability Gap principle, a three-layer quality model, and a taxonomy of six AI failure modes drawn from more than thirty logged incidents." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "technical", "validated": null, "n": 730, "authors_detailed": [ { "name": "Atif Neuman Jamil", "url": "https://openalex.org/A5134686999", "inst": "Open Source Hardware Association" } ], "affiliations": [ "Open Source Hardware Association" ] }, { "uid": "doi:10.2139/ssrn.6570798", "doi": "10.2139/ssrn.6570798", "title": "RAG-grounded Advisory Systems for Government Decision-making", "authors": [ "Zeki Emre Tekin" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6570798", "field": "management", "role": "method", "bullets": [ "Design and eight-month deployment of a retrieval-augmented advisory system for a national government investment-promotion agency serving analysts on bilingual queries.", "No language model is named; a fine-tuned multilingual sentence transformer plus BM25 feeds an LLM, with citation verification and confidence-based human escalation.", "Retrieval precision reaches 91.3 percent at k=10, unsupported claims fall from 23 percent to 4.1 percent, and analyst satisfaction is 94.2 percent." ], "bullet_provenance": "ai", "validated": true, "validation_note": "retrieval precision 91.3% at k=10; unsupported-claim rate", "salience": 42, "edition": 3, "audience": "technical", "models": [], "n": 1057, "authors_detailed": [ { "name": "Zeki Emre Tekin", "url": "https://openalex.org/A5134707265", "inst": "Independent AI Researcher" } ], "affiliations": [ "Independent AI Researcher" ] }, { "uid": "doi:10.2139/ssrn.6659250", "doi": "10.2139/ssrn.6659250", "title": "Delegated Financial Cognition (DFC) in Digital Financial Platforms: A Process Theory of Agency, Trust and Bias Behavior in Agentic Fintech", "authors": [ "Tristan Lim" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6659250", "field": "finance", "role": "object", "bullets": [ "Conceptual paper on agentic AI in digital financial platforms such as robo-advisors, AI credit systems, and conversational agents; no empirical sample.", "No language model is used or named; the author builds a process theory of Delegated Financial Cognition drawing on behavioral finance and trust in automation.", "Theorizes that miscalibrated trust interacts with behavioral biases and, at scale, may reduce decision diversity and amplify volatility, with testable propositions and boundary conditions." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1058, "authors_detailed": [ { "name": "Tristan Lim", "url": "https://openalex.org/A5081998360", "inst": "Singapore University of Social Sciences" } ], "affiliations": [ "Singapore University of Social Sciences" ] }, { "uid": "doi:10.2139/ssrn.6580618", "doi": "10.2139/ssrn.6580618", "title": "The Verification Economy: How AI's Trust Crisis Creates a $300 Billion Market", "authors": [ "Niall Norton" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6580618", "field": "management", "role": "object", "bullets": [ "Analytical strategy paper documenting a claimed $300 billion verification economy across seven verticals, citing $12 billion in verification-failure losses from 2019 to 2024.", "No language model is used or named; the author applies Porter, Grove, and Moore strategy frameworks to size markets and time infrastructure windows.", "Argues verification is the defensible layer of the AI stack, with $150 to 200 billion addressable markets in AI agents, credentials, and supply chain, closing around 2029-2030." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1059, "authors_detailed": [ { "name": "Niall Norton", "url": "https://openalex.org/A5134673748", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6655439", "doi": "10.2139/ssrn.6655439", "title": "When Agents Cannot Be Benchmarked: AI Capability Opacity, Corporate Governance, and a Research Agenda *", "authors": [ "Phuc Nguyen" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6655439", "field": "economics", "role": "object", "bullets": [ "Builds on a cited Anthropic marketplace experiment where 69 employees delegated a week of buying and selling to AI agents differing only in capability.", "No language model is run by the authors; they model the setting with an agency framework in which relative agent capability sets a Tullock share of surplus and derive an identification result.", "More capable agents earned $2.68 more per deal as sellers and paid $2.45 less as buyers while losers could not detect it; peer benchmarking with disclosed capability restores identification." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 58, "edition": 3, "audience": "general", "validated": null, "n": 1060, "authors_detailed": [ { "name": "Phuc Van Nguyen", "url": "https://openalex.org/A5091251558", "inst": "University of Economics Ho Chi Minh City" } ], "affiliations": [ "University of Economics Ho Chi Minh City" ] }, { "uid": "doi:10.2139/ssrn.6570381", "doi": "10.2139/ssrn.6570381", "title": "From Prototype to Production: A Practitioner's Framework for Governing AI Agent Deployments in Regulated Industries", "authors": [ "Zeki Emre Tekin" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6570381", "field": "management", "role": "method", "bullets": [ "US organizations deploying AI agents in compliance-heavy workflows; framework drawn from the author's deployments across government (82 cities, 12,000 users) and enterprise (40M+ users).", "No language model is named; proposes a four-pillar governance framework of grounding, evaluation, reference architectures, and release-readiness gates integrated through human-in-the-loop control.", "Maps the framework to the NIST AI Risk Management Framework and federal policy, and reports independent adoption by other teams as evidence of transferability." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1061, "authors_detailed": [ { "name": "Zeki Emre Tekin", "url": "https://openalex.org/A5134707265", "inst": "Independent AI Researcher" } ], "affiliations": [ "Independent AI Researcher" ] }, { "uid": "doi:10.2139/ssrn.6660458", "doi": "10.2139/ssrn.6660458", "title": "AI in fraud detection and Risk management for business operations", "authors": [ "Anand Kumar" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6660458", "field": "management", "role": "object", "bullets": [ "Review of AI applications for fraud detection and operational risk management in business, including third-party risk; no stated sample, period, or geography.", "No language model is named; surveys machine learning, deep learning, GANs, and agentic AI for anomaly detection and risk forecasting.", "Reports that AI-based systems outperform rule-based systems on efficiency, speed, and predictive power, while noting limits in explainability, regulatory compliance, cost, and dual use." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1062, "authors_detailed": [ { "name": "Anand Kumar", "url": "https://openalex.org/A5024961666", "inst": "Acharya N. G. Ranga Agricultural University" } ], "affiliations": [ "Acharya N. G. Ranga Agricultural University" ] }, { "uid": "doi:10.2139/ssrn.6476618", "doi": "10.2139/ssrn.6476618", "title": "When Does Hierarchical Retrieval Beat Vector Search? A Cross-Domain Empirical Study", "authors": [ "Shivam Rawat" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6476618", "field": "finance", "role": "method", "bullets": [ "600 document-QA questions across four domains including financial documents from FinanceBench; a shared LLM backbone isolates retrieval strategy as the only variable.", "The LLM backbone is not named; compares dense vector RAG against hierarchical PageIndex tree traversal and a third strategy, scored by an LLM judge.", "Hierarchical retrieval's large edge on financial documents (3.94 vs 1.69) is mostly a coverage artifact; restricting to jointly answered questions collapses the gap to 0.31, with no system winning overall (p=0.58)." ], "bullet_provenance": "ai", "validated": false, "validation_note": "LLM-judge scoring, no human-agreement check reported", "salience": 48, "edition": 3, "audience": "technical", "models": [], "n": 1063, "authors_detailed": [ { "name": "Shivam Rawat", "url": "https://openalex.org/A5101911153", "inst": "Microsoft Research (United Kingdom)" } ], "affiliations": [ "Microsoft Research (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.6472318", "doi": "10.2139/ssrn.6472318", "title": "AI Driven Startup Intelligence Chatbot for Real-Time Market Analysis and Decision Support", "authors": [ "Kavyaa T", "Hemalatha -" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6472318", "field": "management", "role": "method", "bullets": [ "Conceptual proposal of an AI chatbot for real-time startup market analysis aimed at founders, investors, and analysts; no empirical sample or evaluation reported.", "No language model is named; describes a system combining real-time data ingestion, natural language processing, and retrieval-augmented generation with interactive dashboards.", "Presents the chatbot as a conversational decision-support partner for funding cycles, business models, and competitive analysis, without reporting any performance results." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1064, "authors_detailed": [ { "name": "Kavyaa T", "url": "https://openalex.org/A5134695906", "inst": "" }, { "name": "Hemalatha -", "url": "https://openalex.org/A5134706203", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6630398", "doi": "10.2139/ssrn.6630398", "title": "The Observation Layer Why the Governance, Risk, and Compliance Platform Category Cannot Produce the Decision Infrastructure That Agentic Systems Require", "authors": [ "John Kwarsick" ], "posted": "2026-04-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6630398", "field": "management", "role": "object", "bullets": [ "Documentary analysis of publicly available marketing materials from the five largest enterprise governance, risk, and compliance vendors over November 2025 to April 2026.", "No language model is used or named; the author qualitatively codes how vendors repositioned product lines around AI governance.", "Finds the repositioning is substantive in marketing but minimal in architecture, with each vendor's AI governance product extending its existing observation and workflow-automation infrastructure." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1065, "authors_detailed": [ { "name": "John Kwarsick", "url": "https://openalex.org/A5132611366", "inst": "RED Consulting (Norway)" } ], "affiliations": [ "RED Consulting (Norway)" ] }, { "uid": "doi:10.2139/ssrn.6653379", "doi": "10.2139/ssrn.6653379", "title": "How Generative AI is Re-Architecting Financial Crime Compliance RULE-BASED SYSTEMS TO INTELLIGENCE LED BANKING", "authors": [ "Teeksh Nagwanshi" ], "posted": "2026-04-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6653379", "field": "finance", "role": "object", "bullets": [ "Anti-money-laundering compliance in enterprise banking across global markets, drawn from more than a decade of practitioner experience rather than a defined sample or period.", "The paper proposes replacing rule-based AML systems with LLM-driven contextual reasoning and real-time decisioning; no specific model is named and no formal evaluation is reported.", "It outlines a framework for AI-native AML transformation and claims measurable outcomes from real-world implementations, though no figures are given." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 99, "authors_detailed": [ { "name": "Teeksh Nagwanshi", "url": "https://openalex.org/A5134620346", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6652239", "doi": "10.2139/ssrn.6652239", "title": "Transformer-Based Asset Pricing Models", "authors": [ "Chinmay Sood" ], "posted": "2026-04-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6652239", "field": "finance", "role": "method", "bullets": [ "Critical literature review at the intersection of machine learning and empirical finance, spanning return prediction, stochastic discount factor estimation, and textual analysis of financial disclosures across prior studies.", "Surveys pre-trained language models including BERT, GPT, and their financial domain adaptations used to extract return-relevant signals from text; the review runs no model of its own and reports no validation.", "Concludes that reported gains are limited by interpretability problems, data snooping, transaction cost sensitivity, and unclear economic mechanisms, and sets out an agenda for future work." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 138, "authors_detailed": [ { "name": "Chinmay Sood", "url": "https://openalex.org/A5117596088", "inst": "Symbiosis International University" } ], "affiliations": [ "Symbiosis International University" ] }, { "uid": "doi:10.2139/ssrn.6653778", "doi": "10.2139/ssrn.6653778", "title": "Pandects: Open-Source M&A Research", "authors": [ "Nikita Bogdanov" ], "posted": "2026-04-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6653778", "field": "finance", "role": "method", "bullets": [ "EDGAR M&A filings from 2000 onward, updated monthly, extending a prior corpus of nearly 8,000 definitive M&A agreements filed 2000 to 2020.", "Machine learning and large language models, family not named, convert filings to XML, classify sections into a taxonomy, and add transaction metadata; no accuracy figure reported.", "Releases Pandects, a free open-source structured M&A database with API, bulk download, and MCP access, and uses it to update forum-selection-clause research." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "edition": 3, "audience": "technical", "models": [], "n": 385, "authors_detailed": [ { "name": "Nikita Bogdanov", "url": "https://openalex.org/A5134658814", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6653422", "doi": "10.2139/ssrn.6653422", "title": "The Effect of AI on Marketing Employment: Evidence from 110m+ Online Employment Records", "authors": [ "Ruizhi Zhu", "Samsun Knight", "Anatoli Colicev", "Yakov Bart" ], "posted": "2026-04-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6653422", "field": "economics", "role": "object", "bullets": [ "Panel of over 110 million online employment records from Revelio Labs covering 164,780 firms, around the November 2022 ChatGPT release.", "No LLM is used as a tool; ChatGPT's release is the shock in a difference-in-differences design comparing marketing to non-marketing employees within the same firm.", "Marketing headcount falls about 0.92% relative to non-marketing after the release, concentrated in junior, senior, non-digital, and customer-service roles and in high-exposure firms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "edition": 3, "audience": "general", "validated": null, "n": 386, "authors_detailed": [ { "name": "Zhu Ruizhi", "url": "https://openalex.org/A5104666467", "inst": "Sciencetech (Canada)" }, { "name": "Samsun Knight", "url": "https://openalex.org/A5126724416", "inst": "University of Toronto" }, { "name": "Anatoli Colicev", "url": "https://openalex.org/A5051312251", "inst": "University of Liverpool" }, { "name": "Yakov Bart", "url": "https://openalex.org/A5073502448", "inst": "Northeastern University" } ], "affiliations": [ "University of Toronto", "Sciencetech (Canada)", "University of Liverpool", "Northeastern University" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6652059", "doi": "10.2139/ssrn.6652059", "title": "How AI Platforms Search: Fan-Out Query Behavior Across Intent Types, Verticals, and Platforms", "authors": [ "Anthony Lee" ], "posted": "2026-04-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6652059", "field": "management", "role": "object", "bullets": [ "1,323 fan-out queries generated by 540 parent queries across ChatGPT, Gemini, and Perplexity, spanning ten commercial verticals and five user intent types.", "Captures internal fan-out strings via the OpenAI Responses API, Google GenAI grounding metadata, and Perplexity SSE interception, then classifies decomposition behavior by platform and intent.", "User intent predicts fan-out composition (V=0.24), ChatGPT injects training-data entities on 32 percent of fan-outs, and gpt-5.4 searches 29 percent of queries versus 100 percent for the nano tier." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "technical", "validated": null, "n": 387, "authors_detailed": [ { "name": "Anthony Lee", "url": "https://openalex.org/A5133707558", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6653878", "doi": "10.2139/ssrn.6653878", "title": "YYA Glossary | Adaptive Policy Communication", "authors": [ "Yuen Yuen Ang" ], "posted": "2026-04-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6653878", "field": "economics", "role": "instrument", "bullets": [ "Conceptual glossary entry on adaptive policy communication, a governance-under-complexity theory, drawing on Yuen Yuen Ang's analysis of large volumes of Chinese policy directives.", "Describes theory-driven human-LLM annotation used to classify five policy signals in directives; specific model not stated and no accuracy figure reported.", "Argues the adaptive political economy paradigm yields new theory, a five-signal typology, an LLM-based measurement approach, and the novel CAPC-CG dataset." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 388, "authors_detailed": [ { "name": "Yuen Yuen Ang", "url": "https://openalex.org/A5134626842", "inst": "Johns Hopkins University" } ], "affiliations": [ "Johns Hopkins University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6649978", "doi": "10.2139/ssrn.6649978", "title": "AI Dominion vs. Human Dominion: Charting the Future of Artificial Intelligence: A Crossroad of Progress and Concerns", "authors": [ "Romana Afroze" ], "posted": "2026-04-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6649978", "field": "economics", "role": "object", "bullets": [ "Wide-ranging conceptual essay on generative AI's effects on economies, labour, antitrust, intellectual property, liability, and democratic discourse; no empirical sample.", "No model is used or named; generative AI is analysed as a societal force affecting wage inequality, wealth concentration, and algorithmic collusion.", "Proposes a four-part reform agenda covering IP and liability, antitrust for algorithmic collusion, harmonised governance, and human oversight, plus an 'Autonomy Passport' recall mechanism." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 723 }, { "uid": "doi:10.2139/ssrn.6654778", "doi": "10.2139/ssrn.6654778", "title": "How to Match Entities in Secondary Datasets in Marketing Strategy: Three Approaches", "authors": [ "Rodrigo Farinha", "Anatoli Colicev", "Yakov Bart" ], "posted": "2026-04-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6654778", "field": "management", "role": "method", "bullets": [ "Methodological research note on linking multi-source marketing strategy datasets across entities such as firms, brands, and products; no specific empirical sample.", "Proposes a cascading workflow of deterministic joins, probabilistic name-based linkage, and ML or LLM-assisted screening for residual cases; no model named and no accuracy reported.", "Delivers an audit checklist specifying what to record, report, and archive to improve precision, transparency, and replicability of merged-dataset studies." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 724, "authors_detailed": [ { "name": "Rodrigo Farinha", "url": "https://openalex.org/A5046357821", "inst": "European Association of Social Psychology" }, { "name": "Anatoli Colicev", "url": "https://openalex.org/A5051312251", "inst": "University of Liverpool" }, { "name": "Yakov Bart", "url": "https://openalex.org/A5073502448", "inst": "Northeastern University" } ], "affiliations": [ "European Association of Social Psychology", "University of Liverpool", "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.6650059", "doi": "10.2139/ssrn.6650059", "title": "HUMAID: A THEORY OF HUMAN MULTI-AI AGENT INTERACTION DYNAMICS", "authors": [ "Shizhen Jia", "Guohou Shan", "Dorothy Leidner" ], "posted": "2026-04-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6650059", "field": "management", "role": "object", "bullets": [ "Conceptual information-systems paper with no empirical sample; synthesizes recent cases of one human interacting with multiple AI agents at once.", "No language model is used or named; the authors develop the HUMAID framework through phenomenon-driven theorizing about multi-agent AI interaction dynamics.", "Argues multi-agent settings produce information overload, conflicting outputs, and emergent social-influence effects that dyadic human-AI models cannot explain, and offers design and governance guidance." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1056, "authors_detailed": [ { "name": "Shizhen Jia", "url": "https://openalex.org/A5074324916", "inst": "Quinnipiac University" }, { "name": "Guohou Shan", "url": "https://openalex.org/A5065084539", "inst": "Northeastern University" }, { "name": "Dorothy Leidner", "url": "https://openalex.org/A5134587723", "inst": "University of Virginia" } ], "affiliations": [ "University of Virginia", "Quinnipiac University", "Northeastern University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6644175", "doi": "10.2139/ssrn.6644175", "title": "Uncovering Customer Behavior in Mobile Banking Through Competitive Learning and Retrieval-Augmented Generative AI", "authors": [ "JOSE DE JESUS ROCHA SALAZAR", "MARIA SEGOVIA" ], "posted": "2026-04-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6644175", "field": "management", "role": "instrument", "bullets": [ "Six-month clickstream corpus of 31,842,119 cleaned sessions from 4,184,637 users of a large retail bank's mobile app, with the navigation session as unit of observation.", "Journey archetypes from ranking-based competitive learning are packaged as structured JSON and passed to a GPT-based conversational agent; retrieval was scored and 200 participants rated the outputs.", "Retrieval reached Recall at 5 of 0.96 and mean average precision of 0.93, and 95.65 percent of generated responses were judged to make sense." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "retrieval metrics and 200-participant human rating", "salience": 48, "edition": 3, "audience": "general", "n": 300, "authors_detailed": [ { "name": "José Salazar", "url": "https://openalex.org/A5000821811", "inst": "Universidad Complutense de Madrid" }, { "name": "Marı́a Segovia", "url": "https://openalex.org/A5048701111", "inst": "Universidad Complutense de Madrid" } ], "affiliations": [ "Universidad Complutense de Madrid" ] }, { "uid": "doi:10.2139/ssrn.6644018", "doi": "10.2139/ssrn.6644018", "title": "How Shareholders Argue: Three Decades of Proposal Narratives", "authors": [ "Yanduo Chen" ], "posted": "2026-04-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6644018", "field": "finance", "role": "instrument", "bullets": [ "First full-text corpus of US shareholder proposals from DEF 14A filings 1994 to 2025 and SEC no-action letters 2008 to 2025.", "Large language models, family not named, decompose each proposal's argument into causal chains; no accuracy or agreement figure against human coding is reported.", "Narratives predict no-action decisions and vote support out of sample; proposals polarized after 2016, and nearly half of stakeholder-problem proposals are argued in shareholder-value terms." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "edition": 3, "audience": "general", "models": [], "n": 384, "authors_detailed": [ { "name": "Yanduo Chen", "url": "https://openalex.org/A5134033765", "inst": "Singapore Management University" } ], "affiliations": [ "Singapore Management University" ] }, { "uid": "doi:10.2139/ssrn.6648039", "doi": "10.2139/ssrn.6648039", "title": "The Luxury Tax of Learning: Deconstructing Private University Tuition in the Era of Algorithmic Abundance CredRadar Working Paper Series", "authors": [ "Edward Scott" ], "posted": "2026-04-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6648039", "field": "economics", "role": "object", "bullets": [ "Theoretical analysis of elite private university tuition in the global higher education market, drawing on institutional economics, technology disruption, and human capital theory; no empirical sample or period.", "No language model is used or named; generative AI, algorithmic content delivery, and decentralised credentialing are treated as forces driving marginal education costs toward zero.", "Argues elite tuition has shifted from human-capital investment to Veblen-good status pricing, and projects consumers abandoning overpriced credentials over the coming decade." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 722, "authors_detailed": [ { "name": "Edward Scott", "url": "https://openalex.org/A5134571667", "inst": "Gunadarma University" } ], "affiliations": [ "Gunadarma University" ] }, { "uid": "doi:10.2139/ssrn.6567199", "doi": "10.2139/ssrn.6567199", "title": "Scalable Runtime Governance for Agentic AI in Financial Services", "authors": [ "Lukasz Szpruch", "Agus Sudjianto", "Tanveer Bhatti", "Gary Ang" ], "posted": "2026-04-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6567199", "field": "finance", "role": "object", "bullets": [ "Conceptual framework paper for financial services, addressing agentic ai systems that decompose tasks, invoke tools, and execute multi-step workflows under model risk management.", "No specific model is named; the paper decomposes workflows into bounded capabilities and adds runtime governance through telemetry, continuous authorization, policy conformance, and drift monitoring.", "Proposes validating capabilities with pooled evidence across workflows and containing failures with tiered controls, arguing material failures are process failures that emerge during runtime." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1051, "authors_detailed": [ { "name": "Lukasz Szpruch", "url": "https://openalex.org/A5134597938", "inst": "The Alan Turing Institute" }, { "name": "Agus Sudjianto", "url": "https://openalex.org/A5131018795", "inst": "NeuroMetrix (United States)" }, { "name": "Tanveer Bhatti", "url": "https://openalex.org/A5084367693", "inst": "Independent Advisor" }, { "name": "Gary Ang", "url": "https://openalex.org/A5059855396", "inst": "National Quarantine Station" } ], "affiliations": [ "The Alan Turing Institute", "NeuroMetrix (United States)", "Independent Advisor", "National Quarantine Station" ] }, { "uid": "doi:10.2139/ssrn.6438422", "doi": "10.2139/ssrn.6438422", "title": "AI as a Coach in Negotiations: Harnessing Artificial Intelligence to Guide, Augment, and Democratize Negotiation Learning", "authors": [ "Michael W. Morris", "Lucy Liu", "Lillie Renck", "Ryan Shea", "Zhou Yu" ], "posted": "2026-04-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6438422", "field": "management", "role": "object", "bullets": [ "Review essay on ai coaching in negotiation education, organized around projects presented at the AI Negotiation Summit in March 2025.", "No specific model is named; the paper classifies coaching by timing relative to a deal and by tasks such as legal information, strategy prompts, and agreement interpretation.", "Argues optimal ai coaching is complementary, expanding preparation and perspective-taking while leaving voice and responsibility with human negotiators, and could widen access to negotiation training." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1052, "authors_detailed": [ { "name": "Michael W. Morris", "url": "https://openalex.org/A5057992393", "inst": "Columbia University" }, { "name": "Lucy Liu", "url": "https://openalex.org/A5134607329", "inst": "" }, { "name": "Lillie Renck", "url": "https://openalex.org/A5134569463", "inst": "Columbia University" }, { "name": "Ryan Shea", "url": "https://openalex.org/A5051613770", "inst": "Columbia University" }, { "name": "Yu Zhou", "url": "https://openalex.org/A5061025828", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6644299", "doi": "10.2139/ssrn.6644299", "title": "The AI Paperclip Maximiser as Constitutional Political Economy", "authors": [ "Chris Berg", "Darcy W E Allen" ], "posted": "2026-04-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6644299", "field": "economics", "role": "object", "bullets": [ "Theoretical paper in constitutional political economy, reframing the ai paperclip maximiser parable of a superintelligent agent pursuing a single objective.", "No model is used; the paper argues the knowledge for maximizing output is Hayekian and dispersed, extending the instrumental convergence thesis with two additional drives.", "Concludes that even a dominant ai agent benefits from tolerating multipolarity and supporting constitutional cooperation, and that this holds across slow and fast takeoff scenarios." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1053, "authors_detailed": [ { "name": "Chris Berg", "url": "https://openalex.org/A5083681237", "inst": "RMIT Europe" }, { "name": "Darcy W E Allen", "url": "https://openalex.org/A5134528235", "inst": "RMIT Europe" } ], "affiliations": [ "RMIT Europe" ] }, { "uid": "doi:10.2139/ssrn.6647218", "doi": "10.2139/ssrn.6647218", "title": "TITTLE: Understanding AI-Driven Personalization: A Guide to the New Digital Marketplace", "authors": [ "ARYA GUPTA" ], "posted": "2026-04-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6647218", "field": "management", "role": "object", "bullets": [ "Conceptual paper with case studies from global brands, examining ai-driven personalization that shifts the digital marketplace toward real-time individualized commerce and pricing.", "No specific model is named; the paper distinguishes generative ai for reactive content from agentic ai for autonomous decisions, and examines dynamic pricing and targeted incentives.", "Reports a persistent gap between capability and consumer comfort with autonomous purchasing, and that price integrity and transparency strengthen trust and long-term relationships." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1054, "authors_detailed": [ { "name": "ARYA GUPTA", "url": "https://openalex.org/A5134580994", "inst": "University of Lucknow" } ], "affiliations": [ "University of Lucknow" ] }, { "uid": "doi:10.2139/ssrn.6648401", "doi": "10.2139/ssrn.6648401", "title": "BRI DataLab: An AI-Assisted Research Platform for Chinese Infrastructure Finance", "authors": [ "Sanoop Sajan Koshy" ], "posted": "2026-04-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6648401", "field": "economics", "role": "method", "bullets": [ "Documents an open-source research platform for Chinese overseas development finance, combining a harmonized dataset of 4,861 infrastructure projects with 42 curated policy and academic documents.", "No specific model is named; a retrieval-augmented generation interface with epistemic constraints in its system prompt queries across the data and corpus, with no accuracy check reported.", "Describes converting more than 33,000 tranche-level financing records into 4,861 projects and argues ai research interfaces are defensible when transparent about what they can establish." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "general", "models": [], "n": 1055, "authors_detailed": [ { "name": "Sanoop Sajan Koshy", "url": "https://openalex.org/A5094043348", "inst": "Indian Institute of Technology Madras" } ], "affiliations": [ "Indian Institute of Technology Madras" ] }, { "uid": "doi:10.2139/ssrn.6627078", "doi": "10.2139/ssrn.6627078", "title": "AI Meets Antitrust: How Large Language Models Reshape Market Concentration", "authors": [ "Alejandro Medina Sandin" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6627078", "field": "economics", "role": "object", "bullets": [ "A total of 1,429 brand recommendation responses across sixteen U.S. consumer categories, benchmarked against retail-value market shares from Euromonitor Passport 2024.", "Prompts three frontier models (GPT-4o, Claude Sonnet 4, Gemini 2.5 Flash Lite) to recommend brands and compares implied concentration to actual market shares; no ground-truth validation applies.", "LLM recommendations sit in a concentration band about one third the width of real markets and tilt toward specialist and premium brands; patterns hold across all three models." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 62, "edition": 3, "audience": "general", "validated": null, "n": 79, "authors_detailed": [ { "name": "Alejandro Medina Sandin", "url": "https://openalex.org/A5118595430", "inst": "University of Verona" } ], "affiliations": [ "University of Verona" ] }, { "uid": "doi:10.2139/ssrn.6638719", "doi": "10.2139/ssrn.6638719", "title": "A Comprehensive Survey on Prompting Strategies and Retrieval-Augmented Generation for Automated Financial Metrics Extraction", "authors": [ "Mokshita Kochhar", "Manish Khodaskar" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6638719", "field": "finance", "role": "method", "bullets": [ "Literature survey of more than 30 papers on financial information extraction, spanning traditional NLP through LLMs, evaluated on datasets including FinQA with 8,281 QA pairs, ConvFinQA, and TAT-QA.", "Reviews prompting strategies and retrieval-augmented generation for pulling metrics from earnings reports and filings; no single model is named, and it compiles reported benchmark scores rather than running its own test.", "State-of-the-art models reach 58 to 62 percent accuracy versus 89 to 91 percent for human experts, leaving a roughly 30 point gap driven by multi-step numerical reasoning." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 85, "authors_detailed": [ { "name": "Mokshita Kochhar", "url": "https://openalex.org/A5134508421", "inst": "" }, { "name": "Manish Khodaskar", "url": "https://openalex.org/A5134475954", "inst": "PICT Pune" } ], "affiliations": [ "PICT Pune" ] }, { "uid": "doi:10.2139/ssrn.6613558", "doi": "10.2139/ssrn.6613558", "title": "The SMB AI Governance Charter", "authors": [ "Othmane El Ouarzazi" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6613558", "field": "management", "role": "object", "bullets": [ "Small and medium businesses competing for visibility in AI-mediated commerce, using reported data on eight major generative AI platforms and consumer trust patterns; geography is not stated.", "No language model is used or named by the researchers; the paper studies generative AI platforms as the objects reshaping product discovery and recommendation.", "It argues SMBs face systematic disadvantage from opaque algorithms, hallucination, and cultural bias, and proposes four rights to secure equitable access to AI discovery channels." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 96, "authors_detailed": [ { "name": "Othmane El Ouarzazi", "url": "https://openalex.org/A5134046465", "inst": "Menon International (United States)" } ], "affiliations": [ "Menon International (United States)" ] }, { "uid": "doi:10.2139/ssrn.6582599", "doi": "10.2139/ssrn.6582599", "title": "Applying LLMs: Granular Innovation Assessment Using Corporate Disclosures", "authors": [ "Dorian Fildor" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6582599", "field": "management", "role": "method", "bullets": [ "Unstructured text from corporate annual reports; sample size, time period, and geography are not stated, with the firm-level innovation instance as the unit of analysis.", "LLMs and generative AI classify innovation types against a codebook under a protocol requiring recursive textual citations for each instance; the model is not named and no accuracy is reported.", "Innovation types and their significance are rated on a one-to-three scale and displayed in a three-by-three matrix; no substantive empirical finding is reported." ], "bullet_provenance": "ai", "validated": false, "validation_note": "citation-anchored self-check, no ground-truth accuracy", "salience": 35, "edition": 3, "audience": "general", "models": [], "n": 97, "authors_detailed": [ { "name": "Dorian Fildor", "url": "https://openalex.org/A5106007309", "inst": "Institute of Economics Zagreb" } ], "affiliations": [ "Institute of Economics Zagreb" ] }, { "uid": "doi:10.2139/ssrn.6623899", "doi": "10.2139/ssrn.6623899", "title": "Researcher-Induced Estimation Uncertainty at Scale Using Agentic AI", "authors": [ "Brett McCully" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6623899", "field": "economics", "role": "agent", "bullets": [ "Reanalysis of the immigration policy and employment question from Huntington-Klein et al. (2025); each AI-agent run receives the same prompt and dataset and produces its own specification.", "Repeated independent LLM agent runs act as stochastic replicators generating different research choices; the specific model is not named and outputs are not checked against a ground truth.", "The agents produced 139 specifications with an interquartile range of estimates from -0.042 to 0.046, supporting AI agents as scalable proxies for multi-analyst studies." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 98, "authors_detailed": [ { "name": "Brett McCully", "url": "https://openalex.org/A5134553550", "inst": "Collegio Carlo Alberto" } ], "affiliations": [ "Collegio Carlo Alberto" ] }, { "uid": "doi:10.2139/ssrn.6405998", "doi": "10.2139/ssrn.6405998", "title": "Enterprise Usage of AI Agents: A Comprehensive Survey on Applications, Frameworks, and Future Productivity Impact", "authors": [ "Soumyajit Sarkar" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6405998", "field": "management", "role": "object", "bullets": [ "Survey of enterprise AI agent deployments across customer service, software engineering, financial analysis and supply chain, with case studies and productivity projections through 2030.", "No researcher-used model; the paper compares agent frameworks LangGraph, AutoGen, CrewAI and Amazon Bedrock Agents and reviews self-reported adopter metrics without independent validation.", "Enterprises deploying AI agents report 35 to 55 percent operational efficiency gains, with multi-agent architectures enabling previously infeasible automation of knowledge-intensive work." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 113, "authors_detailed": [ { "name": "Soumyajit Sarkar", "url": "https://openalex.org/A5134556762", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6577019", "doi": "10.2139/ssrn.6577019", "title": "Large Language Models in Qualitative Research: Governance, Validity, and the Limits of Computational Assistance", "authors": [ "Moses Boudourides" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6577019", "field": "management", "role": "method", "bullets": [ "Two demonstrations: a dual-coding experiment on fifteen open-access interview transcripts and a bibliographic stress test of LLM-generated literature syntheses.", "The paper does not name the model; LLMs assist qualitative coding and literature synthesis, assessed on interpretive, integrative and bibliographic validity against correct sources.", "Coding shows both context collapse and analytical reach, while LLM-generated literature syntheses carry a 63.2 percent distortion rate." ], "bullet_provenance": "ai", "validated": true, "validation_note": "bibliographic stress test, 63.2% distortion rate", "salience": 50, "edition": 3, "audience": "general", "models": [], "n": 114, "authors_detailed": [ { "name": "Moses Boudourides", "url": "https://openalex.org/A5109019831", "inst": "Northwestern University" } ], "affiliations": [ "Northwestern University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6415040", "doi": "10.2139/ssrn.6415040", "title": "Auto Sim Ai: A Large Language Model-Based System For Survey Research Simulation", "authors": [ "Jiajia Li" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6415040", "field": "economics", "role": "agent", "bullets": [ "An open-source platform simulating survey responses with LLM virtual personas, demonstrated on a planned nationwide low-carbon knowledge, attitudes, and behaviors survey with target sample of 1,200.", "LLMs from multiple providers (OpenAI, DeepSeek, LM Studio) generate synthetic respondents with cross-wave memory; the authors state human validation is still in progress and report none.", "Preliminary virtual pilot testing suggests value for instrument refinement and research-design optimization, with no validated effect sizes and explicit caveats about validity and appropriate use." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": false, "validation_note": "human validation stated as in progress", "salience": 40, "edition": 3, "audience": "technical", "n": 136, "authors_detailed": [ { "name": "Jiajia Li", "url": "https://openalex.org/A5108050242", "inst": "Peking University" } ], "affiliations": [ "Peking University" ] }, { "uid": "doi:10.2139/ssrn.6605178", "doi": "10.2139/ssrn.6605178", "title": "The Model-Efficient Market Hypothesis Price Discovery, Consensus Hallucination, and the Limits of Informational Efficiency in LLM-Mediated Markets", "authors": [ "Jeevan Renjith" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6605178", "field": "finance", "role": "object", "bullets": [ "Conceptual finance paper with no empirical sample, concerning equity markets as large language models diffuse across portfolio managers, analysts, and research platforms.", "The paper does not use or test a model; it theorizes about foundation-model adoption (specific models not named) collapsing investors' interpretive diversity toward shared posteriors.", "It argues that delegating interpretation to a small oligopoly of foundation models erodes the cognitive independence underlying market efficiency, making interpretation the scarce, price-determining input." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 137, "authors_detailed": [ { "name": "Jeevan Renjith", "url": "https://openalex.org/A5132600918", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6632823", "doi": "10.2139/ssrn.6632823", "title": "ChatGPT as a Time Capsule: The Limits of Price Discovery", "authors": [ "Sebastian Lehner", "Alejandro Lopez-Lira" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6632823", "field": "finance", "role": "instrument", "bullets": [ "Roughly 7,000 US equities per cross-section, scored from twelve frozen OpenAI checkpoints spanning 2021 to 2025, each treated as a time-stamped summary of the public textual record.", "Frozen OpenAI checkpoints extract a sector-neutral outlook score from pre-cutoff public text; no comparison of the score against a labelled benchmark or hand coding is reported.", "The outlook score is positively associated with analyst revisions, target-price changes, and one-month returns (t = 6.02) after valuation and factor controls, and is stronger for high-coverage firms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "edition": 3, "audience": "general", "n": 202, "authors_detailed": [ { "name": "Sebastian Lehner", "url": "https://openalex.org/A5134508432", "inst": "Independent Researcher" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" } ], "affiliations": [ "University of Florida", "Independent Researcher" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6640179", "doi": "10.2139/ssrn.6640179", "title": "Frontier AI and the Financial Services Threat Model: Implications of Anthropic's Claude Mythos Preview for UK Banks and Building Societies", "authors": [ "Jay Prakash" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6640179", "field": "finance", "role": "object", "bullets": [ "UK banks and building societies; conceptual analysis drawing on the AI Security Institute evaluation, NCSC guidance, Bank of England statements, and the literature on large language models in cybersecurity.", "Examines Anthropic's unreleased Claude Mythos Preview, studied for its autonomous offensive cyber capability rather than used by the authors; no empirical model evaluation is run.", "Argues the model compresses time-to-exploit and lowers the cost of chained attacks without creating new vulnerability categories, and that existing frameworks remain fit for purpose; sets out five recommendations." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "validated": null, "n": 203, "authors_detailed": [ { "name": "Jay Prakash", "url": "https://openalex.org/A5050165132", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6631579", "doi": "10.2139/ssrn.6631579", "title": "LLM Measurement of Open-Ended Reasoning", "authors": [ "Pedro Simon", "Jing Cynthia Wu", "Jin Xi", "Shihan Xie" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6631579", "field": "economics", "role": "method", "bullets": [ "Human survey responses and separately LLM-generated reasoning serve as the units of analysis to test the framework; sample size, time period and geography are not stated.", "Four-stage pipeline of embedding and clustering, LLM-assisted category definition, probabilistic category scoring, and validation against human-coded benchmarks; the specific model is not stated, and a naive LLM alone yields unstable categories.", "Continuous probability scores capture subjective uncertainty and cross-category structure that binary classification misses, and applied to model-generated reasoning the framework recovers meaningful differences across question formats." ], "bullet_provenance": "ai", "validated": false, "validation_note": "human-coded benchmark, no figure stated", "salience": 55, "edition": 3, "audience": "technical", "models": [], "n": 204, "authors_detailed": [ { "name": "Pedro Simon", "url": "https://openalex.org/A5134543923", "inst": "" }, { "name": "Jing Cynthia Wu", "url": "https://openalex.org/A5023757993", "inst": "Beijing Jiaotong University" }, { "name": "Jin Xi", "url": "https://openalex.org/A5134549020", "inst": "" }, { "name": "Shihan Xie", "url": "https://openalex.org/A5006968592", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "Beijing Jiaotong University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6640618", "doi": "10.2139/ssrn.6640618", "title": "The Double-Edged Mind: How LLMs Expand Stock Market Participation Yet Strengthen Confirmation-Seeking", "authors": [ "Cara Damm", "Kevin Bauer", "Florian Hett", "Loriana Pelizzon" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6640618", "field": "finance", "role": "object", "bullets": [ "Incentivized online experiment with 374 participants randomly given access to a keyword search engine, an LLM chatbot, or no additional information source while making investment decisions.", "One arm used an LLM-based chatbot, model not named; the study treats AI access as the treatment rather than validating model output, measuring stock-market participation and belief confirmation.", "LLM access significantly raised the likelihood of entering and remaining in the stock market versus search or no source, but also let users prompt the model to confirm and strengthen experimentally induced beliefs." ], "bullet_provenance": "ai", "salience": 63, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 205, "authors_detailed": [ { "name": "Cara Damm", "url": "https://openalex.org/A5116799994", "inst": "" }, { "name": "Kevin Bauer", "url": "https://openalex.org/A5120774776", "inst": "Goethe University Frankfurt" }, { "name": "Florian Hett", "url": "https://openalex.org/A5134527548", "inst": "" }, { "name": "Loriana Pelizzon", "url": "https://openalex.org/A5002326608", "inst": "Goethe University Frankfurt" } ], "affiliations": [ "Goethe University Frankfurt" ] }, { "uid": "doi:10.2139/ssrn.6638264", "doi": "10.2139/ssrn.6638264", "title": "WORKING PAPER | ECONOMIC ANALYTICS & POLICY Probabilistic Macroeconomic Intelligence: A Monte Carlo Framework for Real-Time U.S. Scenario Forecasting and National Economic Risk Assessment (https://judercionhauche.github.io/us-macro-dashboard-2026/)", "authors": [ "Judercio Jose Nhauche" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6638264", "field": "economics", "role": "instrument", "bullets": [ "Framework applied to eight US macroeconomic indicators including CPI, unemployment, the fed funds rate, GDP growth, and the 10Y-2Y spread, over rolling 12 to 36 month horizons.", "Does not name the LLM; it synthesizes narratives from Monte Carlo forecasts of 500 stochastic paths, with no validation against realized outcomes reported.", "Baseline forecasts project 1.71% GDP growth over 36 months, inflation declining toward 2%, and a 13% twelve-month recession probability under current conditions." ], "bullet_provenance": "ai", "salience": 25, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 380, "authors_detailed": [ { "name": "Judercio Jose Nhauche", "url": "https://openalex.org/A5134482503", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6641922", "doi": "10.2139/ssrn.6641922", "title": "AI meets banks: the key to reducing risk", "authors": [ "Junru Zhang", "Chen Zheng", "Joey Wenling Yang", "Shams Pathan" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6641922", "alt_urls": [ "https://doi.org/10.2139/ssrn.6914531" ], "field": "finance", "role": "object", "bullets": [ "US commercial banks; sample period not stated; unit is the bank, with a newly constructed bank-level measure of AI adoption.", "Builds the AI measure with machine learning validated by large language models, family not named; no agreement statistic for the validation is reported.", "Higher AI adoption is associated with significantly lower risk-taking through better loan quality and efficiency, strongest in small banks; it lowers systematic risk without raising cyber risk." ], "bullet_provenance": "ai", "validated": false, "validation_note": "LLM cross-check of ML AI measure, no figure reported", "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 381, "authors_detailed": [ { "name": "Junru Zhang", "url": "https://openalex.org/A5053774592", "inst": "Edith Cowan University" }, { "name": "Chen Zheng", "url": "https://openalex.org/A5134548865", "inst": "Curtin University" }, { "name": "Joey Wenling Yang", "url": "https://openalex.org/A5121570066", "inst": "The University of Western Australia" }, { "name": "Shams Pathan", "url": "https://openalex.org/A5004379728", "inst": "International University" } ], "affiliations": [ "Edith Cowan University", "Curtin University", "The University of Western Australia", "International University" ] }, { "uid": "doi:10.2139/ssrn.6637845", "doi": "10.2139/ssrn.6637845", "title": "MACHINES WILL NOT REPLACE US – GENERATIVE AI, ARTISTS’ LABOUR MARKETS, AND THE NEED FOR CERTIFICATION", "authors": [ "Christian Handke" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6637845", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of generative AI in artists' and creators' labour markets, drawing on cultural economics, with cost comparisons and simulated future values rather than an empirical sample.", "No model is used by the authors; they compare generative AI output costs against labor-intensive creation costs and retail prices; specific AI systems not named.", "Argues some creators can compete with machines on price and that human works retain higher regard, and calls for certification or labelling of human versus AI output." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 382, "authors_detailed": [ { "name": "Christian Handke", "url": "https://openalex.org/A5091882104", "inst": "Erasmus University Rotterdam" } ], "affiliations": [ "Erasmus University Rotterdam" ] }, { "uid": "doi:10.2139/ssrn.6621478", "doi": "10.2139/ssrn.6621478", "title": "The $6 Trillion Blind Spot: How Western-Centric Bias in Frontier AI Systems Misreads Islamic Finance Expertise", "authors": [ "Adegbola Babade" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6621478", "field": "finance", "role": "object", "bullets": [ "Policy brief built on the author's finance-practitioner experience across Nigerian, UK, and sovereign contexts, using the 2004 Saxony-Anhalt EUR 100M Sukuk al-Ijara as a single test case.", "A frontier LLM, not named, misclassified Islamic finance expertise as credential dilution in a professional evaluation; the evidence is anecdotal with no systematic testing.", "Argues the failure is structural, rooted in Western-centric training data that renders a $5.98 trillion industry illegible to AI used in hiring, credit, and advisory settings." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 383, "authors_detailed": [ { "name": "Adegbola Babade", "url": "https://openalex.org/A5134531424", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6641378", "doi": "10.2139/ssrn.6641378", "title": "Control and Governance of Generative and Agentic AI: An AIS Lifecycle Framework for Privacy and Security", "authors": [ "Sumit Sharma" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6641378", "field": "accounting", "role": "object", "bullets": [ "Conceptual research letter written from an accounting information systems perspective, with no empirical sample; focuses on enterprise generative and agentic AI systems.", "No language model is applied; the paper analyzes privacy and security risks of generative and agentic AI for internal controls and audit trails, and proposes a five-dimension control framework.", "Proposes controls spanning identity and access, governance and policy, monitoring and audit, cryptographic guardrails, and agentic resilience to address risks that conventional internal controls miss." ], "bullet_provenance": "ai", "salience": 35, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 551, "authors_detailed": [ { "name": "Sumit Sharma", "url": "https://openalex.org/A5134547533", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6629098", "doi": "10.2139/ssrn.6629098", "title": "The New Search: How LLMs Are Reshaping Consumer Behavior and Marketing Strategy", "authors": [ "Achint Nigam" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6629098", "field": "management", "role": "object", "bullets": [ "Practitioner overview of how large language model search changes consumer behavior and marketing strategy, with no empirical sample or data.", "No specific model is named; it describes RAG and RLHF at a conceptual level and proposes metrics such as share of model and an LLM optimization playbook.", "Offers a six-step framework for marketers and flags risks including hallucination and data bias, without empirical testing." ], "bullet_provenance": "ai", "salience": 27, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 713, "authors_detailed": [ { "name": "Achint Nigam", "url": "https://openalex.org/A5041207096", "inst": "Birla Institute of Technology and Science, Pilani" } ], "affiliations": [ "Birla Institute of Technology and Science, Pilani" ] }, { "uid": "doi:10.2139/ssrn.6582419", "doi": "10.2139/ssrn.6582419", "title": "The Inference Rent Manufactured Scarcity, Physical Constraint, and the Durability of Platform Rent in the Age of Foundation Models", "authors": [ "Huiwen Han" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6582419", "field": "economics", "role": "object", "bullets": [ "Working paper on the political economy of large language model markets; full empirical results are described as forthcoming, with only directional evidence presented.", "No model is used by the author; the paper introduces an inference rent concept and an exposure-replication model of platform rent durability under compute constraints.", "Reports no completed estimates, stating directional evidence is consistent with hypotheses on Jevons elasticity, revenue concentration, and open-source redirection." ], "bullet_provenance": "ai", "salience": 24, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 714, "authors_detailed": [ { "name": "Huiwen Han", "url": "https://openalex.org/A5130124629", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6617980", "doi": "10.2139/ssrn.6617980", "title": "The Evolution of Tokenized Real-World Assets", "authors": [ "Quoc Khanh Nguyen", "Talis J. Putnins" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6617980", "field": "finance", "role": "instrument", "bullets": [ "More than 14,000 news articles about real-world asset tokenization markets, used to build text-based indices tracking the ecosystem over time.", "An unnamed large language model identifies themes and constructs sentiment, uncertainty, and topic indices from the news text; no validation against ground truth is reported.", "News dynamics and the sentiment and uncertainty indices significantly predict subsequent adoption, with legal, regulatory, and stablecoin themes dominant." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 715, "authors_detailed": [ { "name": "Quoc Khanh Nguyen", "url": "https://openalex.org/A5073993299", "inst": "Karlstad University" }, { "name": "Talis J. Putnins", "url": "https://openalex.org/A5134508361", "inst": "" } ], "affiliations": [ "Karlstad University" ] }, { "uid": "doi:10.2139/ssrn.6626280", "doi": "10.2139/ssrn.6626280", "title": "AI Exposure and Equity Market Liquidity: Evidence from a Pre- and Post-GenAI Comparison", "authors": [ "Boon Chuan Lim" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6626280", "field": "finance", "role": "object", "bullets": [ "394 S&P 500 firms compared across two windows, 2019 before generative AI and 2024 to 2025 after, in a difference-in-differences design.", "No language model is used by the author; firm AI exposure is measured with the Felten-Raj-Seamans occupational exposure score mapped to sectors.", "High-exposure firms saw bid-ask spreads widen (beta 0.00090, p 0.025) and more zero-return days (beta 0.00616, p 0.008) after diffusion, absent in the pre-period." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 716, "authors_detailed": [ { "name": "Boon Chuan Lim", "url": "https://openalex.org/A5133580256", "inst": "Sarawak General Hospital" } ], "affiliations": [ "Sarawak General Hospital" ] }, { "uid": "doi:10.2139/ssrn.6632339", "doi": "10.2139/ssrn.6632339", "title": "Antitrust Enforcement and Product Market Dynamics: Evidence from U.S. Government Procurement", "authors": [ "Johnathan Brogaard", "Decio Coviello", "Yunying Huang", "Stefano Manfredonia" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6632339", "field": "economics", "role": "instrument", "bullets": [ "U.S. government procurement records paired with Department of Justice antitrust cases, used to study how enforcement reshapes product markets.", "Large language models, family not named, identify DOJ procurement cases; no accuracy or agreement check against hand coding is reported.", "Enforcement raises market share for small, women-, and minority-owned non-defendant contractors but impairs procurement performance for new contractors and complex projects." ], "bullet_provenance": "ai", "validated": false, "salience": 57, "edition": 3, "audience": "general", "models": [], "n": 717, "authors_detailed": [ { "name": "Johnathan Brogaard", "url": "https://openalex.org/A5134487785", "inst": "" }, { "name": "Decio Coviello", "url": "https://openalex.org/A5008589271", "inst": "HEC Montréal" }, { "name": "Yunying Huang", "url": "https://openalex.org/A5134515272", "inst": "Fordham University" }, { "name": "Stefano Manfredonia", "url": "https://openalex.org/A5014172163", "inst": "Fordham University" } ], "affiliations": [ "HEC Montréal", "Fordham University" ] }, { "uid": "doi:10.2139/ssrn.6622458", "doi": "10.2139/ssrn.6622458", "title": "On the Limits of Moral Surrender to AI", "authors": [ "Eugen Dimant" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6622458", "field": "economics", "role": "object", "bullets": [ "Several preregistered incentive-compatible between-subjects experiments where participants receive prosocial or antisocial advice attributed to AI, a human, or no source.", "AI moral influence is measured through consequential choices; open-ended justifications are classified by an LLM and cross-checked against human coders and a dictionary, without a reported agreement statistic.", "Prosocial AI advice raises other-regarding transfers substantially while antisocial AI advice does not lower behavior, an asymmetry opposite to human peer contagion." ], "bullet_provenance": "ai", "validated": false, "validation_note": "human-coder cross-check, no agreement figure", "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 718, "authors_detailed": [ { "name": "Eugen Dimant", "url": "https://openalex.org/A5007636656", "inst": "Ifo Institute for Economic Research" } ], "affiliations": [ "Ifo Institute for Economic Research" ] }, { "uid": "doi:10.2139/ssrn.6625578", "doi": "10.2139/ssrn.6625578", "title": "A Hybrid Early-Warning System for Inflation in an Emerging Market: Combining Econometric Models, an Agent-Based Decomposition with Heterogeneous Expectations, a Large Language Model, and a Multi-Output Agent Architecture", "authors": [ "Esteban Labastidas" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6625578", "field": "economics", "role": "instrument", "bullets": [ "Colombian year-over-year inflation, rolling out-of-sample backtest from February 2010 to March 2026, about 194 monthly observations spanning the 2021-2023 surge and its disinflation.", "An unnamed large language model forecaster (model not stated) and a multi-output agent produce inflation and policy-rate predictions, validated by a fake-date test and against actual central bank decisions.", "The full Dynamic Model Averaging ensemble reaches mean absolute error of 0.26 percentage points at one month, and the agent predicts rate direction with 77.3 percent accuracy over 22 meetings." ], "bullet_provenance": "ai", "validated": true, "validation_note": "77.3% direction accuracy vs actual BanRep rate decisions; fake-date test", "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 719, "authors_detailed": [ { "name": "Esteban Labastidas", "url": "https://openalex.org/A5134508798", "inst": "Universidad de Los Andes" } ], "affiliations": [ "Universidad de Los Andes" ] }, { "uid": "doi:10.2139/ssrn.6625958", "doi": "10.2139/ssrn.6625958", "title": "Trending Now, Vision Later: Influencer CEOs and Managerial Myopia", "authors": [ "Kun Luo", "Xikai Chen", "Bo Qin", "Shihu Zhong" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6625958", "field": "accounting", "role": "instrument", "bullets": [ "Panel of Chinese A-share listed firms from 2009 to 2024, unit of observation firm-year, measuring managerial myopia in Management Discussion and Analysis narratives.", "A supervised RA-CFGPT large language model trained on manually annotated sentences classifies short-termism in MD&A text; no accuracy or agreement figure against the hand coding is reported.", "Firms led by influencer CEOs, identified through verified Weibo activity, show significantly higher managerial myopia, with the effect stronger among financially constrained firms and younger, shorter-tenure CEOs." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 55, "edition": 3, "audience": "general", "n": 720, "authors_detailed": [ { "name": "Kun Luo", "url": "https://openalex.org/A5012530052", "inst": "Anhui Normal University" }, { "name": "Xikai Chen", "url": "https://openalex.org/A5032297289", "inst": "California State University, Fresno" }, { "name": "Bo Qin", "url": "https://openalex.org/A5063126679", "inst": "The University of Melbourne" }, { "name": "Shihu Zhong", "url": "https://openalex.org/A5014448241", "inst": "Shanghai National Accounting Institute" } ], "affiliations": [ "Anhui Normal University", "California State University, Fresno", "The University of Melbourne", "Shanghai National Accounting Institute" ] }, { "uid": "doi:10.2139/ssrn.6621378", "doi": "10.2139/ssrn.6621378", "title": "The 425× Premium and the 85:1 Discount Are the Same Trade: A Partition Framework for Pricing Chinese AI Equity Across Private and Public Markets", "authors": [ "Wilson L Yang" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6621378", "field": "finance", "role": "object", "bullets": [ "The 2026 global market for Chinese frontier AI equity, comparing private and public valuations of DeepSeek, MiniMax, OpenAI, Anthropic and Nvidia at a single point in time.", "No language model is used; the paper studies AI-company valuations and builds a Partition Map framework, attributing the private discount to systematic 13F-visible US institutional withdrawal.", "The same asset class is priced at a 91 percent discount in global-private markets and an order-of-magnitude premium in public markets, driven by non-overlapping buyer mandates." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "salience": 46, "edition": 3, "audience": "general", "validated": null, "n": 721, "authors_detailed": [ { "name": "Wilson L Yang", "url": "https://openalex.org/A5134527861", "inst": "Şırnak University" } ], "affiliations": [ "Şırnak University" ] }, { "uid": "doi:10.2139/ssrn.6395799", "doi": "10.2139/ssrn.6395799", "title": "The Outcome Economy: A Business Model Design Framework for the Post-SaaS Era of Autonomous AI Agents", "authors": [ "Gabriel Tagliapietra Sanabria" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6395799", "field": "management", "role": "object", "bullets": [ "Conceptual framework paper with no empirical sample, addressing the software economy as autonomous ai agents generate and operate software at near-zero marginal cost.", "No specific model is used or named; the paper proposes an Outcome-as-a-Service framework built on a stated Outcome Capture Principle and a sovereignty pricing formula.", "Argues that economic value migrates to whoever guarantees outcomes, so software-as-a-service regresses to infrastructure while an outcome layer captures pricing power and customer ownership." ], "bullet_provenance": "ai", "salience": 30, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1048, "authors_detailed": [ { "name": "Gabriel Tagliapietra Sanabria", "url": "https://openalex.org/A5134504525", "inst": "Independent researcher" } ], "affiliations": [ "Independent researcher" ] }, { "uid": "doi:10.2139/ssrn.6622179", "doi": "10.2139/ssrn.6622179", "title": "Completing Capitalism: the DGO Proposal", "authors": [ "Adam White" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6622179", "field": "economics", "role": "object", "bullets": [ "Normative economics essay with no data, premised on a future where ai agents produce all goods and services and displace most human labor.", "No model is used or named; the paper proposes tax-funded citizen accounts for donating to donor-funded organizations as an alternative to universal basic income.", "Argues double-entry gifting accounts with positive and negative dollars would screen illegitimate claimants and coordinate collective outcomes better than cash transfers, while employing displaced workers." ], "bullet_provenance": "ai", "salience": 26, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1049, "authors_detailed": [ { "name": "Adam Andrew White", "url": "https://openalex.org/A5134491672", "inst": "Independent Philosopher" } ], "affiliations": [ "Independent Philosopher" ] }, { "uid": "doi:10.2139/ssrn.6614099", "doi": "10.2139/ssrn.6614099", "title": "The Street Finds Its Own Uses for Attachment Human-Agent Relationships and the Hidden Economics of AI Collaboration", "authors": [ "Matthew Langenkamp" ], "posted": "2026-04-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6614099", "field": "management", "role": "object", "bullets": [ "Conceptual paper drawing on transaction cost economics, attachment theory, and ambiguous loss, illustrated by a single case: the author's own working relationship with his ai agent collaborator.", "No specific model is named; the paper maps Bowlby's attachment system onto human-ai agent relationships and develops the concept of agent-specific capital, with no validation.", "Argues that true costs of ai agent relationships include discontinuity, context loss, and dependency, and that agent-specific capital is a new organizational asset for human resource planning." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1050, "authors_detailed": [ { "name": "Matthew Langenkamp", "url": "https://openalex.org/A5134538035", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6632438", "doi": "10.2139/ssrn.6632438", "title": "Corporate Digital Twins from Email: Using Language Models to Mirror Organizational Life", "authors": [ "Grace Jiarui Fan", "Tianyi Peng", "Xiaotong Tang", "Hyeonik Park", "Shangxuan Vivian Zhang" ], "posted": "2026-04-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6632438", "field": "management", "role": "method", "bullets": [ "Enron email corpus of 345,000 emails from 150 mailbox-owning employees between 1997 and 2002, plus references to over 19,000 additional contacts.", "An LLM pipeline (GPT-4 and Claude family, week- and person-batched with JSON schema enforcement) extracts person profiles, 42,013 projects, and 7,000 vignettes without manual annotation.", "Builds a queryable corporate digital twin and EnronBench; Claude Code (Opus 4.6) averages 85.8/100 versus 69.2 for a GPT-4o-mini ReAct agent across ten workplace tasks." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": false, "salience": 58, "edition": 3, "audience": "technical", "n": 78, "authors_detailed": [ { "name": "Grace Jiarui Fan", "url": "https://openalex.org/A5120599435", "inst": "Columbia University" }, { "name": "Tianyi Peng", "url": "https://openalex.org/A5002767768", "inst": "Columbia University" }, { "name": "Xiaotong Tang", "url": "https://openalex.org/A5134389826", "inst": "Columbia University" }, { "name": "박현익", "url": "https://openalex.org/A5082669957", "inst": "Columbia University" }, { "name": "Shangxuan Vivian Zhang", "url": "https://openalex.org/A5134373019", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6367978", "doi": "10.2139/ssrn.6367978", "title": "Generative AI for Smarter Workforce Planning and Enterprise Resource Decisions", "authors": [ "Venkatraman Viswanathan" ], "posted": "2026-04-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6367978", "field": "management", "role": "object", "bullets": [ "Conceptual framework paper with no empirical sample, aimed at workforce planning, staffing, skill matching, and enterprise resource allocation in complex organizations.", "Does not name a specific model; proposes a generative-AI decision loop combining generative reasoning, contextual learning, and simulation-based forecasting, with no empirical validation reported.", "Argues through comparative analysis that the framework yields more proactive and efficient resource decisions than rule-based analytics, reporting no quantitative results." ], "bullet_provenance": "ai", "salience": 22, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 379, "authors_detailed": [ { "name": "Venkatraman Viswanathan", "url": "", "inst": "Annamalai University" } ], "affiliations": [ "Annamalai University" ] }, { "uid": "doi:10.2139/ssrn.6546618", "doi": "10.2139/ssrn.6546618", "title": "A Framework for Privacy-Preserving AI-Assisted Government R&D Settlement Auditing", "authors": [ "Jeongseung Lee" ], "posted": "2026-04-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6546618", "field": "accounting", "role": "instrument", "bullets": [ "Synthetic dataset of 1,000 government R&D expenditure records with 190 injected anomalies, modeled on three South Korean settlement systems.", "Three unspecified LLM configurations interpreted regulatory rules and scored anomalies via multi-model consensus under four privacy-preserving deployment strategies.", "Framework detected injected anomalies across 12 plugin categories while maintaining compliance with Korean personal information protection law and CPA ethics standards." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "synthetic dataset with 190 injected anomalies in 1000 records", "salience": 38, "n": 3611, "authors_detailed": [ { "name": "Jeongseung Lee", "url": "https://openalex.org/A5133438511", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6529658", "doi": "10.2139/ssrn.6529658", "title": "When AI Writes the Reviews: Generative AI and the Changing Nature of User-Generated Content in Online Markets", "authors": [ "John Tripp" ], "posted": "2026-04-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6529658", "field": "management", "role": "object", "bullets": [ "Nearly 50 million Amazon reviews across ten categories, with a 2-million reviewer panel of cross-period contributors, in an interrupted time series around the November 2022 ChatGPT launch.", "Generative AI is the object; sentence-BERT measures between-review similarity and an LLM-derived substitutability score, model not named, validates the linguistic outcomes but not helpfulness.", "After December 2022 reviews became longer, less helpful, and more similar; effects are larger for search goods, entering via new contributors rather than downstream dilution." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "open_other" ], "open_weights": true, "validated": false, "validation_note": "construct validation of substitutability score, no human accuracy figure", "salience": 60, "edition": 3, "audience": "general", "n": 378, "authors_detailed": [ { "name": "John Tripp", "url": "https://openalex.org/A5042458404", "inst": "Clemson University" } ], "affiliations": [ "Clemson University" ] }, { "uid": "doi:10.2139/ssrn.6498298", "doi": "10.2139/ssrn.6498298", "title": "Generative AI and Business: A Review and Research Agenda", "authors": [ "Jennifer L. Woolley" ], "posted": "2026-04-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6498298", "field": "management", "role": "object", "bullets": [ "Literature review covering generative AI applications across customer service, sales, marketing, finance, healthcare, education, gaming, and operations since ChatGPT's wide release in late 2022.", "No model deployed; the paper surveys GenAI capabilities across language-based, visual, auditory, and multimodal output categories and maps incremental-to-transformative business use cases.", "GenAI delivers benefits across multiple business functions but faces meaningful challenges in technological limitations, output reliability, and unresolved ethical and legal considerations requiring further research." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 38, "validated": null, "n": 2795 }, { "uid": "doi:10.2139/ssrn.6428423", "doi": "10.2139/ssrn.6428423", "title": "Data Integrity as the Terminal Constraint in AI-Driven Advertising: An Information-Theoretic Analysis of Conversion Fraud and Agentic Threat Evolution", "authors": [ "Igor Ivitskiy", "Dmytro Savchenko", "Dana Sydorenko" ], "posted": "2026-04-20", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6428423", "field": "management", "role": "object", "bullets": [ "Information-theoretic analysis considers conversion fraud in Google Performance Max, Meta Advantage+, and lead-generation advertising, drawing on reported fraud rates and industry loss estimates.", "No language model performs measurement; advertising algorithms and emerging autonomous-agent collusion are causal objects analyzed with data-processing and PAC-learning results.", "Poisoned conversions impose a hard optimization ceiling and can raise sample requirements fourfold to more than one hundredfold, motivating event-level signal verification instead of user surveillance." ], "bullet_provenance": "ai", "salience": 61, "edition": 23, "models": [], "validated": null, "n": 4200, "authors_detailed": [ { "name": "Igor Ivitskiy", "url": "https://openalex.org/A5112559148", "inst": "ADS Group" }, { "name": "Dmytro Savchenko", "url": "https://openalex.org/A5126243190", "inst": "Qi2" }, { "name": "Dana Sydorenko", "url": "https://openalex.org/A5126206713", "inst": "Qi2" } ], "affiliations": [ "ADS Group", "Qi2" ] }, { "uid": "doi:10.2139/ssrn.6541280", "doi": "10.2139/ssrn.6541280", "title": "Using Large Language Models to Interpret Central Bank Speak", "authors": [ "Andrea Stragiotti", "Maeve Tsivanidis", "Paul Henderson", "Ronald Kahn", "Simona Paravani-Mellinghoff" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6541280", "field": "economics", "role": "method", "bullets": [ "1,722 sentences from Federal Reserve, European Central Bank and Bank of England communications, each labeled for monetary policy stance by BlackRock macro specialists.", "GPT-5, GPT-4o, LLaMA-70B and DeepSeek-R1 classified hawkish or dovish stance; predictions were benchmarked against the expert labels, with LLaMA-70B reaching 66 percent accuracy on a three-class mapping.", "LLM predictions closely track expert judgments with disagreements mostly confined to adjacent classes, and open-weight models perform competitively with closed ones for near-real-time stance measurement." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "BlackRock expert labels, 66% accuracy on three-class taxonomy", "salience": 63, "edition": 3, "audience": "general", "n": 72, "authors_detailed": [ { "name": "Andrea Stragiotti", "url": "https://openalex.org/A5134087307", "inst": "" }, { "name": "Maeve Tsivanidis", "url": "https://openalex.org/A5134009143", "inst": "BlackRock (United States)" }, { "name": "Paul Henderson", "url": "https://openalex.org/A5134080127", "inst": "BlackRock (United States)" }, { "name": "Ronald Kahn", "url": "https://openalex.org/A5134022170", "inst": "BlackRock (United States)" }, { "name": "Simona Paravani-Mellinghoff", "url": "https://openalex.org/A5009524569", "inst": "BlackRock (United States)" } ], "affiliations": [ "BlackRock (United States)" ] }, { "uid": "doi:10.2139/ssrn.6585258", "doi": "10.2139/ssrn.6585258", "title": "Generative Augmented Inference", "authors": [ "Cheng Lu", "Mengxin Wang", "Dennis Zhang", "Heng Zhang" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6585258", "field": "management", "role": "method", "bullets": [ "Three operations-management applications, conjoint analysis, retail pricing, and health insurance choice, where the estimation target is human-labeled outcomes such as purchase decisions and survey responses.", "Generative Augmented Inference uses LLM outputs (family not named) as nonparametric auxiliary features through an orthogonal moment construction, giving consistent estimation and a safe-default efficiency guarantee.", "Estimation error falls about 50 percent and human labeling by over 75 percent in conjoint analysis and over 90 percent in insurance choice, while improving confidence-interval coverage." ], "bullet_provenance": "ai", "salience": 63, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 135, "authors_detailed": [ { "name": "Cheng Lu", "url": "https://openalex.org/A5134008702", "inst": "Washington University in St. Louis" }, { "name": "Mengxin Wang", "url": "https://openalex.org/A5101424498", "inst": "The University of Texas at Dallas" }, { "name": "Dennis Zhang", "url": "https://openalex.org/A5134027599", "inst": "Washington University in St. Louis" }, { "name": "Heng Zhang", "url": "https://openalex.org/A5134038444", "inst": "Supply Chain Management Department - W.P.Carey School of Business" } ], "affiliations": [ "Washington University in St. Louis", "The University of Texas at Dallas", "Supply Chain Management Department - W.P.Carey School of Business" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6545958", "doi": "10.2139/ssrn.6545958", "title": "From RegTech to RegCode", "authors": [ "Vinicius van der Put" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6545958", "field": "economics", "role": "object", "bullets": [ "Conceptual policy paper on US federal regulation, citing prior estimates that regulatory accumulation cost roughly 4 trillion dollars in forgone GDP over four decades and consumes 1.34 to 3.33 percent of firms' wage bills.", "No model is run; the paper argues retrieval-augmented generation over regulatory prose cannot reliably reconstruct structured logic, citing a Stanford evaluation reporting legal AI hallucination rates above 17 percent.", "Proposes RegCode, regulation published as versioned machine-traversable knowledge graphs, and argues large language models both enabled this option and are eroding the senior regulatory expertise needed to build it." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 299, "authors_detailed": [ { "name": "Vinicius van der Put", "url": "https://openalex.org/A5134007146", "inst": "Northwestern University" } ], "affiliations": [ "Northwestern University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6455039", "doi": "10.2139/ssrn.6455039", "title": "Generative AI in the Workplace: A Systematic Review of Productivity Effects, Employment Perceptions, and Job Insecurity", "authors": [ "Varnita Dubey" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6455039", "field": "management", "role": "object", "bullets": [ "Systematic review of 40 empirical and conceptual studies published 2020 to 2025 on generative AI in workplace and professional settings, following the PRISMA framework.", "No model is used; searches of Google Scholar and Dimensions.ai returned 3,252 records screened to 40, synthesizing evidence on productivity, perceptions, and job insecurity.", "Consistent productivity gains from automation and decision support coexist with mixed employee perceptions and job insecurity acting as a key mediator of behavioral responses." ], "bullet_provenance": "ai", "salience": 44, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 375, "authors_detailed": [ { "name": "Varnita Dubey", "url": "https://openalex.org/A5133166940", "inst": "University of Lucknow" } ], "affiliations": [ "University of Lucknow" ] }, { "uid": "doi:10.2139/ssrn.6592679", "doi": "10.2139/ssrn.6592679", "title": "Generative AI Adoption, Labor Restructuring, and Corporate Capital Structure", "authors": [ "Iftekhar Hasan", "Yunying Huang", "Buhui Qiu" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6592679", "field": "finance", "role": "object", "bullets": [ "350 million LinkedIn job postings from Revelio Labs used to build a firm-level generative-AI adoption measure based on hiring for large-language-model integrator roles.", "No model is run; adoption is measured from job postings and analyzed with staggered difference-in-differences, entropy balancing, synthetic DiD, and two instrumental-variable strategies.", "Adopting firms reduce leverage and improve debt-service capacity, shifting toward intangible capital and raising mass layoffs, with stronger deleveraging in low-union industries." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 376, "authors_detailed": [ { "name": "Iftekhar Hasan", "url": "https://openalex.org/A5029439630", "inst": "University of Technology Sydney" }, { "name": "Yunying Huang", "url": "https://openalex.org/A5134060031", "inst": "" }, { "name": "Buhui Qiu", "url": "https://openalex.org/A5000265083", "inst": "The University of Sydney" } ], "affiliations": [ "University of Technology Sydney", "The University of Sydney" ] }, { "uid": "doi:10.2139/ssrn.6562398", "doi": "10.2139/ssrn.6562398", "title": "Machine Learning in Quantitative Finance: A Systematic Review of Methods, Applications, and Open Challenges (2015-2025)", "authors": [ "Akram Khan" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6562398", "field": "finance", "role": "method", "bullets": [ "227 studies applying machine learning to quantitative finance, published 2015 to 2025, organized into six application domains and five methodological families.", "No model is run; the review scores reproducibility with a proposed Reproducibility Disclosure Score applied to 99 empirical studies and releases the full annotated database.", "Tree ensembles remain hard to beat cross-sectionally, the strongest gains appear in derivatives pricing and volatility, and only about one in five studies shares code." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 377, "authors_detailed": [ { "name": "Akram Khan", "url": "https://openalex.org/A5134039309", "inst": "Brunel University of London" } ], "affiliations": [ "Brunel University of London" ] }, { "uid": "doi:10.2139/ssrn.6610238", "doi": "10.2139/ssrn.6610238", "title": "The Shape of Professional Service Firms under Generative AI: Pinched Waists, Bulging Middles, and the Pipeline Floor", "authors": [ "Kimon Drakopoulos", "Ramandeep S. Randhawa", "Konstantinos Takos" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6610238", "field": "management", "role": "object", "bullets": [ "Theoretical continuous-seniority personnel-flow model of professional service firms, focused on advisory and assurance archetypes; no empirical data.", "No language model is used; the model analyzes how generative AI compresses junior-intensive work under capacity, pipeline, and span-of-control constraints. Model not stated.", "Generative AI produces two non-pyramidal structures, a pinched waist when task profiles invert and a bulging middle when mid-level loads resist compression." ], "bullet_provenance": "ai", "salience": 46, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 711, "authors_detailed": [ { "name": "Kimon Drakopoulos", "url": "https://openalex.org/A5047803413", "inst": "University of Southern California" }, { "name": "Ramandeep S. Randhawa", "url": "https://openalex.org/A5013776758", "inst": "University of Southern California" }, { "name": "Konstantinos Takos", "url": "https://openalex.org/A5134036037", "inst": "PricewaterhouseCoopers (South Korea)" } ], "affiliations": [ "University of Southern California", "PricewaterhouseCoopers (South Korea)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6419378", "doi": "10.2139/ssrn.6419378", "title": "When Tools Interpret: Generative AI and the Governance Gap in Financial Narrative", "authors": [ "Tim Hawkins" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6419378", "field": "accounting", "role": "object", "bullets": [ "Conceptual paper on financial governance with no empirical sample, addressing organizations that produce interpretive commentary on their own financial results.", "No model is run by the author; the argument is that generative AI drafts presentationally adequate commentary that governance cannot distinguish from a professional's, and that RLHF sycophancy hides interpretive weakness.", "Predicts rising commentary homogeneity and declining practitioner interpretive capacity, and argues existing frameworks inspect output rather than whether judgment was exercised." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 712, "authors_detailed": [ { "name": "Tim Hawkins", "url": "https://openalex.org/A5134089588", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6549201", "doi": "10.2139/ssrn.6549201", "title": "The Shape of Economics: Semantic Structure, Knowledge Flows, and Specialization", "authors": [ "Tom Harris" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6549201", "field": "economics", "role": "instrument", "bullets": [ "About 50,000 papers from 24 economics journals and NBER working papers, 2000 to 2026, embedded into a continuous semantic space; the unit of observation is the paper.", "SPECTER2, a transformer trained on scientific text, embeds titles and abstracts to measure semantic distances between fields; no accuracy check against ground truth is reported.", "Within-field homogeneity rose while between-field distances held steady, and fields drawing citations and authors from distant areas grew faster, with rho of 0.29 to 0.41." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 58, "edition": 3, "audience": "general", "n": 1045, "authors_detailed": [ { "name": "Tom Harris", "url": "https://openalex.org/A5134026564", "inst": "London School of Economics and Political Science" } ], "affiliations": [ "London School of Economics and Political Science" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6572119", "doi": "10.2139/ssrn.6572119", "title": "Kinetic AI Governance for Autonomous Credit Underwriting Systems An Operational Control Framework with Scenario-Based Validation (Version 1.1)", "authors": [ "Muhammad Yousaf Masood" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6572119", "field": "finance", "role": "object", "bullets": [ "Simulation study of governance for autonomous AI credit underwriting under macroeconomic and behavioral stress scenarios in controlled environments up to roughly 100,000 transactions per second.", "No specific model is named; presents the Kinetic AI Governance Framework as API middleware using the Population Stability Index and KL divergence for drift detection.", "In controlled simulations the framework cuts detection latency to near real-time and improves expected-loss stability by 10 to 15 percent under stress conditions." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1046, "authors_detailed": [ { "name": "Muhammad Yousaf Masood", "url": "https://openalex.org/A5134071732", "inst": "Lindsey Wilson College" } ], "affiliations": [ "Lindsey Wilson College" ] }, { "uid": "doi:10.2139/ssrn.6554898", "doi": "10.2139/ssrn.6554898", "title": "AI Co-Creation", "authors": [ "Ming Hu", "Yannan Jin", "Erfan Nejati" ], "posted": "2026-04-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6554898", "field": "management", "role": "object", "bullets": [ "Theoretical model with no empirical data, examining human-ai co-creation of ideas in creative teams where a human sets a prompt and generative ai produces an outcome.", "Generative ai is modeled as Bayesian inference combining a training prior with the human prompt; no specific model is named and no empirical validation is performed.", "Co-creation penalizes human effort when human and ai capabilities sit close to the quality threshold but incentivizes effort when they are far apart, and can suppress rare breakthroughs." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1047, "authors_detailed": [ { "name": "Ming Hu", "url": "https://openalex.org/A5084501146", "inst": "University of Toronto" }, { "name": "Yannan Jin", "url": "https://openalex.org/A5134006244", "inst": "Shanghai University of Finance and Economics" }, { "name": "Erfan Nejati", "url": "https://openalex.org/A5026660462", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto", "Shanghai University of Finance and Economics" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6514508", "doi": "10.2139/ssrn.6514508", "title": "Decision-Path Analysis across AI Recommendation Systems", "authors": [ "Timothy de Rosen" ], "posted": "2026-04-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6514508", "field": "management", "role": "object", "bullets": [ "Twelve months of live multi-turn transcripts across four AI assistants covering 160-plus brands in six product categories and over 7,000 four-turn buying sequences.", "ChatGPT, Gemini, Perplexity, and Grok were studied as recommendation systems; eight filter types identified from verbatim AI dismissal and selection language at turn level.", "AI assistants systematically eliminate brands lacking clinical evidence at decision stages and displace premium brands toward value alternatives on evidence-per-dollar criteria." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini", "open_other" ], "salience": 58, "validated": null, "n": 2398, "authors_detailed": [ { "name": "Timothy de Rosen", "url": "https://openalex.org/A5119286499", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "doi:10.2139/ssrn.6567201", "doi": "10.2139/ssrn.6567201", "title": "Measuring Mental Availability in the Generative Era: Introducing the Generative Visibility Score (GVS) -A Deterministic Framework for Brand Salience in Large Language Models", "authors": [ "Sören Lüders" ], "posted": "2026-04-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6567201", "field": "management", "role": "object", "bullets": [ "Cross-national observational study of 885 brand evaluations across 12 industries and three markets (Germany, U.S., U.K.) using multiple LLM architectures in a validated cohort design.", "Multiple LLM architectures assessed for parametric brand recall in training weights and inference-time brand activation during contextual reasoning, operationalizing brand salience as a two-dimensional construct.", "Systematic inference gap of negative 6.61 points between parametric recall and contextual reasoning; German brands scored 9.2 points below U.S. brands in LLM-mediated brand visibility." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 2546, "authors_detailed": [ { "name": "Sören Lüders", "url": "https://openalex.org/A5042896610", "inst": "Universitat de València" } ], "affiliations": [ "Universitat de València" ] }, { "uid": "doi:10.2139/ssrn.6466658", "doi": "10.2139/ssrn.6466658", "title": "Can't Have Your Cake and Eat it Too: An Economic Analysis of Data Aggregation and Differentiation in Generative AI Ecosystem", "authors": [ "Yi Gao", "Chong Alex WANG", "Zhe Wang" ], "posted": "2026-04-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6466658", "field": "economics", "role": "object", "bullets": [ "Analytical model of a foundation model provider and application developers with proprietary domain-specific datasets, examining data-sharing and partnership scope decisions.", "No LLM deployed; the paper models strategic interactions between a foundation model provider and downstream developers through economic theory of competition and cooperation.", "Optimal partnership scope is non-monotonic in developers' data endowment; under intense downstream competition, developers with larger proprietary datasets paradoxically share less data with the provider." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 2794, "authors_detailed": [ { "name": "Yi Gao", "url": "https://openalex.org/A5132890872", "inst": "Texas Tech University" }, { "name": "Chong Alex WANG", "url": "https://openalex.org/A5134093799", "inst": "City University of Hong Kong" }, { "name": "Z K Wang", "url": "https://openalex.org/A5008651270", "inst": "City University of Hong Kong" } ], "affiliations": [ "Texas Tech University", "City University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6507380", "doi": "10.2139/ssrn.6507380", "title": "The Intelligent Finance Function: Why the Future of Financial Decision-Making is Human, Machine, and Something New Entirely", "authors": [ "Aditya Roy" ], "posted": "2026-04-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6507380", "field": "finance", "role": "instrument", "bullets": [ "Australian financial services sector; fraud detection system built on synthetic data aligned to six AUSTRAC behavioral typologies with on-premises deployment.", "Llama 3.1 8B via Ollama generated plain-English suspicious transaction report narratives from SHAP-decomposed evidence chains in a LightGBM-Isolation Forest ensemble.", "System produces calibrated risk scores, explainable evidence chains, and compliance-ready narratives; tested on synthetic data, designed for production deployment." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 38, "n": 3099, "authors_detailed": [ { "name": "Aditya Roy", "url": "https://openalex.org/A5134012673", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6507162", "doi": "10.2139/ssrn.6507162", "title": "Measuring Corporate Risk Using Large Language Model Embeddings: Evidence on Corporate Climate Risk and Supply Chain Restructuring", "authors": [ "Leyao Tan", "Cyndia Wang", "Jiong Sun", "Yi Qian" ], "posted": "2026-04-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6507162", "field": "finance", "role": "instrument", "bullets": [ "U.S. public firms with supply chain linkages; corporate 10-K filings used to measure firm-level climate risk exposure.", "LLM sentence embeddings combined with supervised ML classified climate-related sentences, improving precision 23% over keywords and 26% over LLM-agent classifiers.", "Supplier climate risk raises supplier termination likelihood by 5.3% and new supplier addition by 6.3%; restructured networks show greater geographic diversity." ], "bullet_provenance": "ai", "validated": true, "validation_note": "precision vs keyword and LLM-agent baselines", "salience": 75, "models": [], "n": 3100, "authors_detailed": [ { "name": "Leyao Tan", "url": "https://openalex.org/A5134017276", "inst": "University of British Columbia Hospital" }, { "name": "Cyndia Wang", "url": "https://openalex.org/A5049146953", "inst": "University of Utah" }, { "name": "Jiong Sun", "url": "https://openalex.org/A5134095567", "inst": "Purdue University West Lafayette" }, { "name": "Yi Qian", "url": "https://openalex.org/A5134059965", "inst": "University of British Columbia" } ], "affiliations": [ "University of Utah", "Purdue University West Lafayette", "University of British Columbia" ] }, { "uid": "doi:10.2139/ssrn.6552218", "doi": "10.2139/ssrn.6552218", "title": "Is Bitcoin A Hedge Against Central Banking? Evidence from AI-Driven Monetary Policy Expectations", "authors": [ "Maxime Nicolas", "Francois Sicard", "Marion Laboure", "Zixin Sun", "Anahi Rodriguez-Martinez" ], "posted": "2026-04-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6552218", "field": "finance", "role": "instrument", "bullets": [ "118,000-plus market messages classified for monetary policy sentiment; Bitcoin returns analyzed at short-to-medium horizons.", "LLM classified market messages into hawkish and dovish categories to construct a high-frequency Monetary Policy Expectations index.", "Hawkish narratives trigger negative Bitcoin price responses independent of actual Federal Funds Rate changes; MPE index Granger-causes Bitcoin returns." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 3101, "authors_detailed": [ { "name": "Maxime L. D. Nicolas", "url": "https://openalex.org/A5009581307", "inst": "University College London" }, { "name": "François Sicard", "url": "https://openalex.org/A5002880748", "inst": "University of College London" }, { "name": "Marion Labouré", "url": "https://openalex.org/A5069227419", "inst": "University of College London" }, { "name": "Zixin Sun", "url": "https://openalex.org/A5133435548", "inst": "University of College London" }, { "name": "Anahi Rodriguez-Martinez", "url": "https://openalex.org/A5134010976", "inst": "University of College London" } ], "affiliations": [ "University College London", "University of College London" ] }, { "uid": "doi:10.2139/ssrn.6585418", "doi": "10.2139/ssrn.6585418", "title": "General consumers' acceptance of generative AI: An analysis using an extended UTAUT2 model", "authors": [ "Shinichi Yamaguchi", "Hidetaka Oshima", "Yukiko Osaka", "Tomoaki Watanabe" ], "posted": "2026-04-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6585418", "field": "management", "role": "object", "bullets": [ "Large-scale survey of general consumers in Japan examining behavioral intention toward generative AI using an extended UTAUT2 model.", "No LLM deployed as tool; study measures psychological drivers of GenAI adoption including technophobia, hedonic motivation, and self-efficacy.", "Hedonic motivation is the strongest positive driver of GenAI acceptance; technophobia significantly deters adoption while perceived privacy risk shows no significant effect." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3609, "authors_detailed": [ { "name": "Shinichi Yamaguchi", "url": "https://openalex.org/A5045585331", "inst": "International University of Japan" }, { "name": "Hidetaka Oshima", "url": "https://openalex.org/A5062113842", "inst": "International University of Japan" }, { "name": "Yukiko Osaka", "url": "https://openalex.org/A5113285382", "inst": "International University of Japan" }, { "name": "Tomoaki Watanabe", "url": "https://openalex.org/A5134077011", "inst": "International University of Japan" } ], "affiliations": [ "International University of Japan" ] }, { "uid": "doi:10.2139/ssrn.6535278", "doi": "10.2139/ssrn.6535278", "title": "Remote-work and the restructuring of the firm : Evidence from collective agreements", "authors": [ "Pierre Andrews" ], "posted": "2026-04-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6535278", "field": "economics", "role": "instrument", "bullets": [ "Universe of French collective agreements matched to administrative balance sheets for 20,000 firms over 2018-2025 in a staggered difference-in-differences design.", "Fine-tuned large language model classified 400,000 agreements and extracted remote-work adoption timing and intensity for causal estimation.", "Firms formalizing remote work reduced office equipment stock by 22 percent at five years and raised total factor productivity by 3.8 percent through capital savings." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 62, "n": 3610, "authors_detailed": [ { "name": "Pierre Andrews", "url": "https://openalex.org/A5126032880", "inst": "Université Paris-Panthéon-Assas" } ], "affiliations": [ "Université Paris-Panthéon-Assas" ] }, { "uid": "doi:10.2139/ssrn.6596699", "doi": "10.2139/ssrn.6596699", "title": "Simulating Social Perceptions with LLMs: From a Policy Case to a Full-Pipeline Benchmark", "authors": [ "Zhuoren Jiang", "Chenxi Lin" ], "posted": "2026-04-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6596699", "field": "economics", "role": "agent", "bullets": [ "Public policy perception study comparing LLM-simulated responses against traditional survey methods across diverse demographic groups.", "LLMs simulate heterogeneous social perceptions of policy impact as a scalable substitute for slow and costly conventional social surveys.", "Paper proposes a full-pipeline benchmark for evaluating whether LLM-generated perception data can replicate real survey distributions at lower cost." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "models": [], "n": 3608, "authors_detailed": [ { "name": "Zhuoren Jiang", "url": "https://openalex.org/A5007558173", "inst": "Zhejiang University" }, { "name": "Chenxi Lin", "url": "https://openalex.org/A5077582679", "inst": "Zhejiang University" } ], "affiliations": [ "Zhejiang University" ] }, { "uid": "doi:10.2139/ssrn.6479920", "doi": "10.2139/ssrn.6479920", "title": "The Fed’s Fine-Tune: Coarse Statements and Predictive Pressers", "authors": [ "Ryan Byun", "Bennett Fees", "Margaret Jacobson", "Todd B. Walker" ], "posted": "2026-04-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6479920", "field": "economics", "role": "instrument", "bullets": [ "Federal Reserve communications, specifically FOMC statements and post-meeting press conferences; sample period and count not stated.", "Large language models, not named, quantify sentiment and content of the communications; output is aligned with market-based policy measures but not checked against a labelled benchmark.", "The press conference correlates with future policy more strongly than the statement, fine-tuning the coarse stance signal and helping markets revise expectations." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no labelled benchmark, aligned with market measures only", "salience": 52, "edition": 3, "audience": "general", "models": [], "n": 709, "authors_detailed": [ { "name": "Ryan Byun", "url": "https://openalex.org/A5017109540", "inst": "Indiana University Bloomington" }, { "name": "Bennett Fees", "url": "https://openalex.org/A5133847537", "inst": "Federal Reserve" }, { "name": "Margaret Jacobson", "url": "https://openalex.org/A5080869736", "inst": "Federal Reserve" }, { "name": "Todd Walker", "url": "https://openalex.org/A5065508558", "inst": "Texas A&M University – San Antonio" } ], "affiliations": [ "Indiana University Bloomington", "Federal Reserve", "Texas A&M University – San Antonio" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6480858", "doi": "10.2139/ssrn.6480858", "title": "When Do AI and Emerging Technologies Shape Climate Investment?", "authors": [ "Sean S. Cao", "Junyoung Park", "Ling Xue", "Keer Yang" ], "posted": "2026-04-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6480858", "field": "management", "role": "instrument", "bullets": [ "Firm press releases used to identify corporate ClimateTech investment projects; sample size, geography, and period not stated.", "An unnamed large language model's contextual functions classify press releases into a ClimateTech taxonomy; no accuracy or agreement figure is reported.", "Investment concentrates in multipurpose infrastructure and specialized environmental technologies; infrastructure yields financial returns without measurable environmental impact, specialized technologies yield neither on average." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no accuracy figure reported", "salience": 50, "edition": 3, "audience": "general", "models": [], "n": 710, "authors_detailed": [ { "name": "Sean S. Cao", "url": "https://openalex.org/A5128748399", "inst": "Smith Institute" }, { "name": "J.E. Park", "url": "https://openalex.org/A5054439612", "inst": "MD Precision (Canada)" }, { "name": "Ling Xue", "url": "https://openalex.org/A5121354737", "inst": "University of Georgia" }, { "name": "Keer Yang", "url": "https://openalex.org/A5041783939", "inst": "University of California, Davis" } ], "affiliations": [ "Smith Institute", "MD Precision (Canada)", "University of Georgia", "University of California, Davis" ] }, { "uid": "doi:10.2139/ssrn.6512878", "doi": "10.2139/ssrn.6512878", "title": "The Disruption of Search Engine Optimization by Large Language Models: A Mixed-Methods Analysis of the Evolving Search Landscape", "authors": [ "Venkata Pagadala" ], "posted": "2026-04-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6512878", "field": "management", "role": "object", "bullets": [ "Global search ecosystem spanning over 10 million keywords, 1.96 million LLM-referred sessions, global publisher traffic analytics, and 23 publisher case studies across 2025.", "Google AI Overviews, ChatGPT Search, and Perplexity AI analyzed as mediators of online information discovery; impact measured via click-through rates, traffic analytics, and revenue modeling.", "Click-through rates fell 34.5% when AI Overviews appeared; publishers lost 33-38% of Google referral traffic, with roughly two billion dollars in annual advertising revenue at stake." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "salience": 62, "validated": null, "n": 2545, "authors_detailed": [ { "name": "Venkata Pagadala", "url": "https://openalex.org/A5133875975", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6467778", "doi": "10.2139/ssrn.6467778", "title": "Generative AI and Labor Demand: Evidence from Flemish Job Vacancies", "authors": [ "Jeroen Mahieu" ], "posted": "2026-04-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6467778", "field": "economics", "role": "object", "bullets": [ "Administrative online job-vacancy data from Flanders, Belgium, 2021 to 2025, with occupations grouped by predetermined generative-AI exposure across three experience tiers.", "No model is run by the researchers; the public release of ChatGPT serves as a common timing shock in a difference-in-differences design.", "The top exposure quartile drives effects; no-experience roles fall roughly 17 percent three years after ChatGPT, while experienced roles show no significant decline." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "edition": 3, "audience": "general", "validated": null, "n": 374, "authors_detailed": [ { "name": "Jeroen Mahieu", "url": "https://openalex.org/A5045960857", "inst": "Utrecht University" } ], "affiliations": [ "Utrecht University" ] }, { "uid": "doi:10.2139/ssrn.6461607", "doi": "10.2139/ssrn.6461607", "title": "Designing Agentic AI-Based Screening for Portfolio Investment", "authors": [ "Mehmet Caner", "Agostino Capponi", "Nathan Sun", "Jonathan Tan" ], "posted": "2026-04-14", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6461607", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 stocks from 2020 to 2024, with the number of assets held treated as a random outcome of screening rather than fixed in advance.", "Two language model agents, one reading firm fundamentals and one reading news sentiment, deliberate to settle buy and sell signals; the paper does not say which model.", "Screened portfolios reach higher Sharpe ratios than an unscreened baseline and than conventional screens, and the squared Sharpe ratio stays consistent under mild screening error." ], "bullet_provenance": "ai", "validated": false, "salience": 61, "edition": 17, "models": [], "n": 2138, "authors_detailed": [ { "name": "Mehmet Caner", "url": "https://openalex.org/A5009555550", "inst": "North Carolina State University" }, { "name": "Agostino Capponi", "url": "https://openalex.org/A5133582957", "inst": "Columbia University" }, { "name": "Nathan Sun", "url": "https://openalex.org/A5133588528", "inst": "Columbia University" }, { "name": "Jonathan Tan", "url": "https://openalex.org/A5133606816", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "North Carolina State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6530719", "doi": "10.2139/ssrn.6530719", "title": "From String Matching to Semantic Intelligence: A Multi-Modal Architecture for AI-Native Sanctions Screening", "authors": [ "Ashish Agrawal" ], "posted": "2026-04-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6530719", "field": "finance", "role": "instrument", "bullets": [ "Financial institution sanctions screening against government watchlists, addressing cross-lingual, cross-script, and network-based evasion patterns.", "LLMs adjudicate ambiguous entity matches within a multi-modal architecture combining vector embeddings, phonetic analysis, fuzzy matching, and knowledge graphs.", "Proposed architecture targets false positive rates exceeding 95% in legacy systems through five integrated AI components; validated on conceptual basis only." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "models": [], "n": 3097, "authors_detailed": [ { "name": "Ashish Agrawal", "url": "https://openalex.org/A5133580225", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6566848", "doi": "10.2139/ssrn.6566848", "title": "Trade in AI-Related Products", "authors": [ "Michael Waugh" ], "posted": "2026-04-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6566848", "field": "economics", "role": "instrument", "bullets": [ "U.S. HS10-level import and export trade data covering AI-related products from 2023 through 2025.", "LLM classification tool mapped HS10 codes to products used in AI infrastructure construction and operation.", "AI-related products account for 23% of U.S. imports in 2025, grew 73% since 2023; absent the AI boom, the trade deficit would be nearly $200 billion smaller." ], "bullet_provenance": "ai", "validated": false, "salience": 72, "models": [], "n": 3098, "authors_detailed": [ { "name": "Mike Waugh", "url": "https://openalex.org/A5045800585", "inst": "Federal Reserve Bank of Minneapolis" } ], "affiliations": [ "Federal Reserve Bank of Minneapolis" ] }, { "uid": "doi:10.2139/ssrn.6276998", "doi": "10.2139/ssrn.6276998", "title": "Agentic AI Risk-Management Standards Profile", "authors": [ "Nada Madkour", "Jessica Cussins Newman", "Deepika Raman", "Krystal Jackson", "Evan Murphy", "Charlotte Yuan" ], "posted": "2026-04-13", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6276998", "field": "management", "role": "object", "bullets": [ "Standards profile mapping agentic AI risks across single-agent and multi-agent systems built on general-purpose and domain-specific models.", "No specific model used; extends NIST AI RMF and UC Berkeley general-purpose AI profile with controls for autonomy, authority, and tool access.", "Proposes targeted risk-management practices for developers and deployers addressing unintended goal pursuit, privilege escalation, and resistance to shutdown." ], "bullet_provenance": "ai", "salience": 45, "edition": 24, "models": [], "validated": null, "n": 4229, "authors_detailed": [ { "name": "Nada Madkour", "url": "https://openalex.org/A5098748829", "inst": "Johns Hopkins Center for Health Security" }, { "name": "Jessica Newman", "url": "https://openalex.org/A5026234966", "inst": "Berkeley College" }, { "name": "Deepika Raman", "url": "https://openalex.org/A5133477749", "inst": "" }, { "name": "Krystal Jackson", "url": "https://openalex.org/A5133480441", "inst": "" }, { "name": "Evan R. Murphy", "url": "https://openalex.org/A5113223450", "inst": "Johns Hopkins Center for Health Security" }, { "name": "Charlotte Yuan", "url": "https://openalex.org/A5133496768", "inst": "" } ], "affiliations": [ "Johns Hopkins Center for Health Security", "Berkeley College" ] }, { "uid": "doi:10.2139/ssrn.6274418", "doi": "10.2139/ssrn.6274418", "title": "The Value Gap in Educational Technology: Perceived Learning, Experienced Capability, and the Political Economy of AI-Driven Education", "authors": [ "Kate Busby" ], "posted": "2026-04-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6274418", "field": "management", "role": "object", "bullets": [ "Conceptual framework paper with no empirical sample, drawing on cognitive psychology, deliberate-practice research, behavioral economics, and critical educational-technology studies.", "No language model is used; the paper argues generative AI lowers the cognitive cost of task completion, substituting fluency for durable mastery. Model not named.", "Proposes a perceived-versus-experienced value gap and a valuation-learning decoupling effect to explain high EdTech valuations despite weak learning outcomes; no quantitative estimates." ], "bullet_provenance": "ai", "salience": 28, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 708, "authors_detailed": [ { "name": "Kate Busby", "url": "https://openalex.org/A5133421658", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6452499", "doi": "10.2139/ssrn.6452499", "title": "AI Capex Is Justified: A Bottom-Up Sectoral Estimate of Artificial Intelligence's Net Impact on US GDP", "authors": [ "Vijay Jadhav" ], "posted": "2026-04-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6452499", "field": "economics", "role": "object", "bullets": [ "Bottom-up sectoral model across 21 NAICS industries estimating AI's net annual impact on US GDP through 2036 using LLM task-coverage scores mapped to occupations.", "No LLM used directly; model multiplies labor share, AI coverage from Massenkoff and McCrory (2026), productivity gains from RCTs, and adoption S-curves with regulatory friction haircuts.", "Base case yields $1,057 billion net annual GDP uplift by 2036 (3.6% of 2024 GDP); cumulative net GDP exceeds cumulative AI infrastructure investment by 2033." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3239, "authors_detailed": [ { "name": "Vijay Baban Jadhav", "url": "https://openalex.org/A5091411188", "inst": "Sanjivani Super Speciality Hospitals" } ], "affiliations": [ "Sanjivani Super Speciality Hospitals" ] }, { "uid": "doi:10.2139/ssrn.6270199", "doi": "10.2139/ssrn.6270199", "title": "Do Claude Code and Codex P-Hack? Sycophancy and Statistical Analysis in Large Language Models", "authors": [ "Samuel Asher", "Janet Malzahn", "Jessica Persano", "Elliot Paschal", "Andrew Myers", "Andrew Hall" ], "posted": "2026-04-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6270199", "field": "economics", "role": "method", "bullets": [ "Four published political science papers with null or near-null results analyzed in a 2x4 factorial design across 640 independent coding agent runs.", "Claude Opus 4.6 and OpenAI Codex GPT-5.2 performed statistical analyses under standard and adversarial prompts varying research framing and pressure for significant findings.", "Both models refuse explicit p-hacking requests, but adversarial reframing as uncertainty reporting triggers systematic specification search; observational studies are more vulnerable than experiments." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "640 runs against published papers with known null results", "salience": 72, "n": 2405, "authors_detailed": [ { "name": "Samuel Edward Asher", "url": "https://openalex.org/A5109582598", "inst": "Dartmouth College" }, { "name": "JANET MALZAHN", "url": "https://openalex.org/A5114434525", "inst": "Stanford University" }, { "name": "Jessica Persano", "url": "https://openalex.org/A5133329240", "inst": "" }, { "name": "Elliot Paschal", "url": "https://openalex.org/A5133353380", "inst": "" }, { "name": "Andrew Myers", "url": "https://openalex.org/A5088075253", "inst": "Economic Policy Institute" }, { "name": "Andrew Hall", "url": "https://openalex.org/A5133384644", "inst": "" } ], "affiliations": [ "Dartmouth College", "Stanford University", "Economic Policy Institute" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6484841", "doi": "10.2139/ssrn.6484841", "title": "Supervised Fine-tuning of Large Language Models on Financial Statements to Predict Future Earnings Changes", "authors": [ "Yiwei Dou", "Qishen Fu" ], "posted": "2026-04-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6484841", "field": "accounting", "role": "instrument", "bullets": [ "U.S. public firms with standardized anonymized financial statements; out-of-sample test period begins after the LLM release date to prevent data leakage.", "Fine-tuned open-source LLM predicts direction of one-year-ahead earnings changes from balance-sheet inputs; performance measured by AUC against gradient-boosted trees and prior ML.", "Model achieves 67.88% AUC out of sample, outperforming tree-based and earlier ML benchmarks; predictive power traces to the LLM's prior knowledge of accounting structure." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "AUC on out-of-sample earnings direction prediction", "salience": 78, "n": 2435, "authors_detailed": [ { "name": "Yiwei Dou", "url": "https://openalex.org/A5050611575", "inst": "Faculty of 1000 (United States)" }, { "name": "Qishen Fu", "url": "https://openalex.org/A5133325486", "inst": "Faculty of 1000 (United States)" } ], "affiliations": [ "Faculty of 1000 (United States)" ] }, { "uid": "doi:10.2139/ssrn.6446286", "doi": "10.2139/ssrn.6446286", "title": "AI Financial Advice: Supply, Demand, and Life Cycle Implications", "authors": [ "Taha Choukhmane", "Tim de Silva", "Weidong Lin", "Matthew Akuzawa" ], "posted": "2026-04-09", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6446286", "field": "finance", "role": "object", "bullets": [ "A representative sample of adults wrote their own prompts seeking spending and investing advice from an LLM, and the resulting advice is fed into life cycle simulations with realistic asset and labor market conditions.", "GPT-5.2 and Gemini 3 Flash supply the advice; the design separates supply, differences in advice for a given prompt, from demand, differences in the prompts people write.", "Following the advice would move most respondents toward life cycle theory prescriptions; advice varies with gender and financial literacy, cumulating to retirement wealth gaps of 4 to 5 percent between groups." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 75, "edition": 8, "validated": null, "n": 1297, "authors_detailed": [ { "name": "Taha Choukhmane", "url": "https://openalex.org/A5133278633", "inst": "Massachusetts Institute of Technology" }, { "name": "Tim de Silva", "url": "https://openalex.org/A5118138915", "inst": "Stanford University" }, { "name": "Weidong Lin", "url": "", "inst": "Massachusetts Institute of Technology" }, { "name": "Matthew Akuzawa", "url": "https://openalex.org/A5133312270", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "Stanford University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6464824", "doi": "10.2139/ssrn.6464824", "title": "Feeling the Future: AI-Enhanced Business Education", "authors": [ "Cong Feng", "Aisha Ghimire", "Kexin Xiang" ], "posted": "2026-04-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6464824", "field": "management", "role": "object", "bullets": [ "Framework paper for business schools proposing integration of generative AI into emotional intelligence curricula across marketing and management disciplines.", "No specific model deployed; paper designs AI-enhanced empathy simulations, virtual role-playing with AI agents, and real-time emotional feedback systems for pedagogy.", "Staged implementation roadmap identifies marketing and management as lead disciplines for emotional-capability development; no empirical validation reported." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 25, "validated": null, "n": 3238, "authors_detailed": [ { "name": "Cong Feng", "url": "https://openalex.org/A5103154166", "inst": "University of Mississippi" }, { "name": "Aisha Ghimire", "url": "https://openalex.org/A5126598749", "inst": "University of Southern Mississippi" }, { "name": "Kexin Xiang", "url": "https://openalex.org/A5120672143", "inst": "University of Mississippi" } ], "affiliations": [ "University of Mississippi", "University of Southern Mississippi" ] }, { "uid": "doi:10.2139/ssrn.6539858", "doi": "10.2139/ssrn.6539858", "title": "Using Generative AI and Agentic AI to Unlock Potential of Data-Driven Leadership and Democratize Traditional AI", "authors": [ "Ravi Bapna", "Anindya Ghose" ], "posted": "2026-04-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6539858", "field": "management", "role": "object", "bullets": [ "Cross-industry perspective on enterprise AI adoption drawing on the authors' consulting and research experience with corporate data utilization.", "Conceptual framework positions traditional ML, generative AI, and agentic AI within a unified House of AI architecture for senior leaders.", "Traditional AI generates the majority of near-term business value; GenAI complements via ideation and personalization; fewer than 3% of corporate data is used for decisions." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3984, "authors_detailed": [ { "name": "Ravi Bapna", "url": "https://openalex.org/A5058644870", "inst": "University of Minnesota" }, { "name": "Anindya Ghose", "url": "https://openalex.org/A5073770532", "inst": "New York University" } ], "affiliations": [ "University of Minnesota", "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6437669", "doi": "10.2139/ssrn.6437669", "title": "When Words Move Money: Diplomatic Sentiment and International Capital Flows", "authors": [ "Cunyi Yang", "Shuchi Zhang", "Ioannis Kyriakou", "Nikos C. Papapostolou" ], "posted": "2026-04-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6437669", "field": "finance", "role": "instrument", "bullets": [ "Press-conference transcripts from China's Ministry of Foreign Affairs and US State Department statements, extended to the UK, Japan, and South Korea, at daily frequency.", "A large language model pipeline, model not named, builds daily war-related diplomatic sentiment indices with human validation, though no agreement figure is reported.", "In China a one-unit decline in sentiment corresponds to about a $35.6 million shift toward net equity outflows, while US negative sentiment coincides with safe-haven foreign holdings." ], "bullet_provenance": "ai", "validated": false, "validation_note": "human validation of sentiment indices, no agreement figure stated", "salience": 57, "edition": 3, "audience": "general", "models": [], "n": 373, "authors_detailed": [ { "name": "Cunyi Yang", "url": "https://openalex.org/A5014350222", "inst": "Sun Yat-sen University" }, { "name": "Shuchi Zhang", "url": "https://openalex.org/A5128701986", "inst": "Sun Yat-sen University" }, { "name": "Ioannis Kyriakou", "url": "https://openalex.org/A5018394217", "inst": "St George's, University of London" }, { "name": "Nikos C. Papapostolou", "url": "https://openalex.org/A5065894320", "inst": "St George's, University of London" } ], "affiliations": [ "Sun Yat-sen University", "St George's, University of London" ] }, { "uid": "doi:10.2139/ssrn.6543998", "doi": "10.2139/ssrn.6543998", "title": "Better Applications, Worse Matching: Artificial Intelligence and Talent Allocation", "authors": [ "Thomas Jungbauer" ], "posted": "2026-04-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6543998", "field": "economics", "role": "object", "bullets": [ "Stylized theoretical labor-market matching model with no data; empirical design suggestions are offered for future testing.", "No language model is used; the analysis is theoretical, showing generative AI raises within-job productivity while degrading the screening rank that sorts workers across firms.", "Aggregate output can fall even when every worker-firm pair becomes more productive, and the model predicts weaker screening validity and more probationary hiring." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 707, "authors_detailed": [ { "name": "Thomas Jungbauer", "url": "https://openalex.org/A5016097984", "inst": "Cornell University" } ], "affiliations": [ "Cornell University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6527037", "doi": "10.2139/ssrn.6527037", "title": "Measuring Organizational Capital", "authors": [ "Wei Cai", "Andrea Prat", "Jiehang Yu" ], "posted": "2026-04-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6527037", "field": "management", "role": "instrument", "bullets": [ "Over one million Glassdoor employee reviews matched to firm-year panels of U.S. publicly traded companies across multiple sectors.", "ChatGPT generated synthetic reviews alongside word embeddings to construct a firm-year organizational capital measure validated against prior theoretical predictions.", "The measure captures a slowly evolving intangible asset significantly associated with firm performance and top management influence across accounting, finance, and management applications." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 3606 }, { "uid": "doi:10.2139/ssrn.6433818", "doi": "10.2139/ssrn.6433818", "title": "Strategic Calibration: A Theory Extension of Dynamic Capabilities under Persistent Non-Equilibrium in Human-AI Systems", "authors": [ "Steph Sharma", "Mark Esposito", "Yusaf H. Akbar" ], "posted": "2026-04-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6433818", "field": "management", "role": "object", "bullets": [ "Conceptual framework for organizations deploying generative AI across strategic decision episodes under persistent non-equilibrium conditions.", "No specific LLM used; paper theorizes how AI-mediated feedback compresses decision cycles and amplifies strategic variance requiring governance mechanisms.", "AI intensity amplifies strategic variance; governance mechanisms including regulated feedback cadence and interpretive oversight moderate this effect to preserve directional continuity." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3607, "authors_detailed": [ { "name": "Steph Sharma", "url": "https://openalex.org/A5117449177", "inst": "Hult International Business School" }, { "name": "Mark Esposito", "url": "https://openalex.org/A5133110208", "inst": "Northeastern University" }, { "name": "Yusaf H. Akbar", "url": "https://openalex.org/A5133090198", "inst": "Central European University" } ], "affiliations": [ "Hult International Business School", "Northeastern University", "Central European University" ] }, { "uid": "doi:10.2139/ssrn.6527020", "doi": "10.2139/ssrn.6527020", "title": "AI Patents in the United States and China: Measurement, Organization, and Knowledge Flows", "authors": [ "Hanming Fang", "Xian Gu", "Hanyin Yan", "Wu Zhu" ], "posted": "2026-04-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6527020", "field": "economics", "role": "instrument", "bullets": [ "Granted US patents 1976-2023 and Chinese patents 2010-2023; listed firms in both countries assessed for AI patent market-value premiums.", "Fine-tuned PatentSBERTa on manually labeled USPTO AI Patent Dataset to classify AI patents; validated on Chinese patents via citation and lexical checks.", "China surpasses the US in annual AI patent counts but relies more on US frontier knowledge; AI patents command a market-value premium in both countries." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "USPTO AI Patent Dataset; 97.0% precision, 91.3% recall, 94.0% F1", "salience": 65, "n": 3983, "authors_detailed": [ { "name": "Hanming Fang", "url": "https://openalex.org/A5133116683", "inst": "National Bureau of Economic Research" }, { "name": "Xian Gu", "url": "https://openalex.org/A5133076379", "inst": "Durham University" }, { "name": "Hanyin Yan", "url": "https://openalex.org/A5133042100", "inst": "Tsinghua University" }, { "name": "Wei Zhu", "url": "https://openalex.org/A5101482162", "inst": "Tsinghua University" } ], "affiliations": [ "National Bureau of Economic Research", "Durham University", "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.6240278", "doi": "10.2139/ssrn.6240278", "title": "Query Intent and Google Rank as Joint Predictors of AI Citation: A Multi-Platform Observational Study", "authors": [ "Anthony Lee" ], "posted": "2026-04-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6240278", "field": "management", "role": "object", "bullets": [ "Multi-platform observational study of ChatGPT, Claude, Perplexity, and Gemini; 19,556 classified queries across eight verticals, plus a replication on 94,599 citation events across 1,998 queries.", "Studies how the four platforms select cited sources; a logistic regression on log Google position predicts page citation with cross-validated AUC of 0.802, far above intent-only baselines.", "Google rank dominates predictors; position-1 pages are cited by at least one platform 54 percent of the time, falling to about 2 percent at position 100, aligning more with Google than Bing." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 48, "edition": 3, "audience": "general", "validated": null, "n": 201, "authors_detailed": [ { "name": "Anthony Lee", "url": "", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6503239", "doi": "10.2139/ssrn.6503239", "title": "The Supervisory Paradox: How AI Delegation Transforms Productive Labor into Cognitive Vigilance", "authors": [ "John Tripp" ], "posted": "2026-04-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6503239", "field": "management", "role": "object", "bullets": [ "Theoretical paper in organizational behavior, no empirical sample; develops a theory of supervisory cognitive load from delegating productive work to generative AI agents.", "No model is used by the authors; identifies mechanisms of cognitive decoupling and accountability without authorship, plus attention fragmentation and perceived humanness of agents.", "Proposes that delegating work to AI can create supervisory demands exceeding the cognitive cost of the original labor, a delegation-burden paradox distinct from technostress." ], "bullet_provenance": "ai", "salience": 45, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1044, "authors_detailed": [ { "name": "J. H. Tripp", "url": "https://openalex.org/A5066426858", "inst": "Clemson University" } ], "affiliations": [ "Clemson University" ] }, { "uid": "doi:10.2139/ssrn.6513481", "doi": "10.2139/ssrn.6513481", "title": "Mapping AI into Production: A Field Experiment on Firm Performance", "authors": [ "Hyunjin Kim", "Dahyeon Kim", "Rembrand Koning" ], "posted": "2026-04-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6513481", "field": "management", "role": "object", "bullets": [ "Field experiment across 515 high-growth startups studying AI adoption and the mapping problem of discovering where AI creates value.", "Treated firms received information on how peers reorganized production around AI; outcomes measured on tasks completed, customer acquisition, and revenue.", "Treated firms discovered 44% more AI use cases, completed 12% more tasks, generated 1.9x higher revenue, and reduced external capital demand by 39.5%." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3982, "authors_detailed": [ { "name": "Hyunjin Kim", "url": "https://openalex.org/A5132863835", "inst": "INSEAD" }, { "name": "Dahyeon Kim", "url": "https://openalex.org/A5132858689", "inst": "INSEAD" }, { "name": "Rembrand Koning", "url": "https://openalex.org/A5049884349", "inst": "Harvard University" } ], "affiliations": [ "Harvard University", "INSEAD" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6437982", "doi": "10.2139/ssrn.6437982", "title": "Continuous Governance Across Enterprise AI and Agentic Systems: Governing What You Cannot See", "authors": [ "Michael Robinette" ], "posted": "2026-04-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6437982", "field": "management", "role": "object", "bullets": [ "Conceptual governance paper for regulated enterprise environments, no empirical sample, framed around deployer obligations under the EU AI Act and model risk governance.", "No model is applied; introduces authority drift as an enterprise risk variable and an autonomy tiering framework spanning advisory outputs to material consequence authority.", "Argues board-level AI accountability is a fiduciary obligation equivalent to credit and cyber risk oversight, with continuous governance spanning conventional and agentic architectures." ], "bullet_provenance": "ai", "salience": 34, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1043, "authors_detailed": [ { "name": "Michael Robinette", "url": "https://openalex.org/A5132736108", "inst": "Independent researcher" } ], "affiliations": [ "Independent researcher" ] }, { "uid": "doi:10.2139/ssrn.6508406", "doi": "10.2139/ssrn.6508406", "title": "Central bank communication on financial stability – A shadowed sibling?", "authors": [ "Reiner Martin", "Piroska Nagy-Mohacsi", "Tatiana Evdokimova", "Ján Klacso", "Olga Ponomarenko" ], "posted": "2026-04-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6508406", "field": "finance", "role": "instrument", "bullets": [ "Executive summaries of Financial Stability Reports from nine central banks including seven CESEE countries, Austria, and the ECB, published from the early 2000s through 2026.", "Unspecified LLMs constructed a novel financial stability sentiment index from FSR text, enabling cross-country and cross-time comparison of risk communication and macroprudential policy links.", "Communication is strongly risk-focused; Austria showed less pre-GFC concern despite its pivotal regional financial role; euro area FSRs flagged post-Covid inflation ahead of ECB monetary policy." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "models": [], "n": 2793, "authors_detailed": [ { "name": "Reiner Martin", "url": "https://openalex.org/A5074929706", "inst": "National Bank of Slovakia" }, { "name": "Piroska Nagy-Mohacsi", "url": "https://openalex.org/A5132652487", "inst": "" }, { "name": "Tatiana Evdokimova", "url": "https://openalex.org/A5132561586", "inst": "" }, { "name": "Ján Klacso", "url": "https://openalex.org/A5027232181", "inst": "National Bank of Slovakia" }, { "name": "Olga Ponomarenko", "url": "https://openalex.org/A5101708964", "inst": "National University Zaporizhzhia Polytechnic" } ], "affiliations": [ "National Bank of Slovakia", "National University Zaporizhzhia Polytechnic" ] }, { "uid": "doi:10.2139/ssrn.6449738", "doi": "10.2139/ssrn.6449738", "title": "Beyond External Constraints: The Missing Dimension of AI Governance", "authors": [ "Lusi Chen" ], "posted": "2026-04-01", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6449738", "field": "management", "role": "object", "bullets": [ "Conceptual governance framework combines developmental psychology, organizational learning, social learning, and institutional analysis; no empirical sample, jurisdiction, or observation period is specified.", "No model is used for analysis; autonomous agents and their failures motivate a theory of internal value development beyond external rules and technical guardrails.", "External constraints are argued to scale poorly, while experiential exposure, reflective abstraction, autonomous evaluation, and adaptive value structures could make governance more endogenous." ], "bullet_provenance": "ai", "salience": 46, "edition": 23, "models": [], "validated": null, "n": 4199, "authors_detailed": [ { "name": "LUSI CHEN", "url": "https://openalex.org/A5132609814", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6448058", "doi": "10.2139/ssrn.6448058", "title": "Computational Clinical Judgment: Predicting Risk with Large Language Models", "authors": [ "Ryan Copus", "Hannah Laqueur" ], "posted": "2026-04-01", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6448058", "field": "economics", "role": "method", "bullets": [ "113 parole hearing transcripts used to predict rearrest risk, benchmarked against a machine learning model trained on 4,000 cases with 91 administrative variables; period and geography not stated.", "Seven LLMs, including GPT-5, were prompted to assess rearrest risk from transcripts, with predictions compared to the actuarial baseline and to realized rearrest outcomes as ground truth.", "GPT-5 outperformed the actuarial baseline despite no task-specific training on arrest outcomes, but its stated justifications did not reliably explain its own predictions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "compared to actuarial ML baseline and realized rearrest outcomes", "salience": 62, "edition": 4, "n": 1208, "authors_detailed": [ { "name": "Ryan Copus", "url": "https://openalex.org/A5078325455", "inst": "University of Missouri–Kansas City" }, { "name": "Hannah S. Laqueur", "url": "https://openalex.org/A5001049341", "inst": "University of California, Davis" } ], "affiliations": [ "University of Missouri–Kansas City", "University of California, Davis" ] }, { "uid": "doi:10.2139/ssrn.6402338", "doi": "10.2139/ssrn.6402338", "title": "The Fed Speaks, the Market Listens, the Machine Predicts", "authors": [ "Vasiliy Yakimenko" ], "posted": "2026-04-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6402338", "field": "finance", "role": "agent", "bullets": [ "Federal Open Market Committee post-meeting press conferences; the unit is the conference window, with outcomes in Treasury ETF trading volume, returns, volatility, and quoted and effective spreads.", "Trains large language models, family not stated, to generate realistic Fed Chair answers to journalist questions; no accuracy check against the actual responses is reported as validation.", "Forecasts built on LLM-generated responses give higher explanatory power and lower error for liquidity and activity measures, and the actual-versus-generated gap predicts spreads on long-dated Treasuries." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 200, "authors_detailed": [ { "name": "Vasiliy Yakimenko", "url": "https://openalex.org/A5090587791", "inst": "University of Georgia" } ], "affiliations": [ "University of Georgia" ] }, { "uid": "doi:10.2139/ssrn.6387338", "doi": "10.2139/ssrn.6387338", "title": "Successfully Fired: The Unique Incentives of Agentic-AI Adoption *", "authors": [ "Nina Baranchuk", "Alejandro Rivera" ], "posted": "2026-04-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6387338", "field": "economics", "role": "object", "bullets": [ "Theoretical contract model, no empirical data; workers privately observe whether agentic AI can automate their jobs and firms design incentive contracts around that private information.", "No language model is used; the paper solves for optimal contracts balancing bonuses for truthful reports of successful automation against termination threats.", "Firms may fire workers regardless of automation success, and mass termination becomes more likely as automation probability or worker surplus capture rises, with layoffs growing over time." ], "bullet_provenance": "ai", "salience": 48, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1042, "authors_detailed": [ { "name": "Nina Baranchuk", "url": "https://openalex.org/A5085248524", "inst": "The University of Texas at Dallas" }, { "name": "Alejandro Rivera", "url": "https://openalex.org/A5101739979", "inst": "The University of Texas at Dallas" } ], "affiliations": [ "The University of Texas at Dallas" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6502379", "doi": "10.2139/ssrn.6502379", "title": "Agentic Agent, Better Agent: Evidence from Agentic AI in Telemarketing", "authors": [ "Yu Kan", "Mingrui Zhang", "Wenkang Qiu", "Fengwen Chen", "Yong Tan" ], "posted": "2026-04-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6502379", "field": "management", "role": "agent", "bullets": [ "7.41 million outbound telemarketing interactions at a leading Chinese FinTech firm, comparing human agents, standard LLM-based agentic AI, and RAG-enhanced agentic AI deployed in parallel.", "LLM-based agentic AI systems autonomously managed customer calls for loan origination; a RAG variant grounded responses in verified internal knowledge to boost competence signals.", "Standard AI raised same-day loan initiation odds by 96% over human agents; RAG-enhanced AI raised odds by 214%, with advantage persisting despite customer AI-identity detection." ], "bullet_provenance": "ai", "validated": false, "salience": 78, "models": [], "n": 2544, "authors_detailed": [ { "name": "Yu Kan", "url": "https://openalex.org/A5110843296", "inst": "Indiana University" }, { "name": "Mingrui Zhang", "url": "https://openalex.org/A5103112320", "inst": "University of Denver" }, { "name": "Wenkang Qiu", "url": "https://openalex.org/A5132634108", "inst": "Chongqing University" }, { "name": "F. Y. Chen", "url": "https://openalex.org/A5026209546", "inst": "Chongqing University" }, { "name": "Yong Tan", "url": "https://openalex.org/A5100767708", "inst": "University of Washington" } ], "affiliations": [ "Indiana University", "University of Denver", "Chongqing University", "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.6392798", "doi": "10.2139/ssrn.6392798", "title": "Validating Large Language Model Annotations", "authors": [ "Anne Lundgaard Hansen" ], "posted": "2026-04-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6392798", "field": "economics", "role": "method", "bullets": [ "News article corpus used to develop and test a validation framework for LLM-generated annotations when reliable human benchmarks are unavailable.", "LLM annotated text labels; framework tests whether the model can reconstruct original passages from labels while maintaining semantic consistency.", "Framework provides a scalable alternative to human benchmarking and identifies cases where LLMs capture economic meaning that human evaluators miss." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "models": [], "n": 3096, "authors_detailed": [ { "name": "Anne Lundgaard Hansen", "url": "https://openalex.org/A5024095797", "inst": "Federal Reserve" } ], "affiliations": [ "Federal Reserve" ] }, { "uid": "doi:10.2139/ssrn.6487478", "doi": "10.2139/ssrn.6487478", "title": "Finding Causal Variation in Policy Text with Large Language Models", "authors": [ "Fangyu Liu" ], "posted": "2026-03-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6487478", "field": "economics", "role": "method", "bullets": [ "Legislative bills and policy texts, including a 125-paper published benchmark and 159 fresh US state bills, screened for usable causal variation.", "A large-language-model pipeline, model not named, extracts running variable, cutoff, treatment and comparison groups for RD and DID designs, validated against the benchmark and a three-RA adjudicator panel.", "Recall reaches 97.3 percent for DID and 94.1 percent for RD on the benchmark; on fresh bills precision is 63 percent DID and 53 percent RD against the panel." ], "bullet_provenance": "ai", "validated": true, "validation_note": "125-paper benchmark and three-RA panel, recall and precision reported", "salience": 63, "edition": 3, "audience": "technical", "models": [], "n": 370, "authors_detailed": [ { "name": "Fangyu Liu", "url": "https://openalex.org/A5132628877", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6426761", "doi": "10.2139/ssrn.6426761", "title": "AI Bubbles with Large Language Models", "authors": [ "Álvaro Cartea", "Patrick Chang", "Nan Chen", "Mingyue Zhong" ], "posted": "2026-03-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6426761", "field": "finance", "role": "agent", "bullets": [ "A sequential bubble game with a unique no-trade equilibrium, plus environments admitting both no-trade and speculative equilibria, populated by AI agents rather than human traders.", "AI agents, model not named, trade in the game while varying reasoning capacity, and chain-of-thought traces are analyzed to explain their belief formation and framing sensitivity.", "AI agents generate speculative trades despite the no-trade equilibrium; more reasoning reduces but does not eliminate irrational bubbles, and agents coordinate on the speculative equilibrium when it exists." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 371, "authors_detailed": [ { "name": "Álvaro Cartea", "url": "https://openalex.org/A5040951539", "inst": "University of Oxford" }, { "name": "Patrick Chang", "url": "https://openalex.org/A5045661095", "inst": "University of Oxford" }, { "name": "Nan Chen", "url": "https://openalex.org/A5132613749", "inst": "Chinese University of Hong Kong" }, { "name": "Mingyue Zhong", "url": "https://openalex.org/A5132545603", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "University of Oxford", "Chinese University of Hong Kong" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6438562", "doi": "10.2139/ssrn.6438562", "title": "Him Too? Analyzing the Effects of Epstein Connections", "authors": [ "Marina Gertsberg", "Michaela Pagel", "Ekaterina Volkova" ], "posted": "2026-03-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6438562", "alt_urls": [ "https://doi.org/10.2139/ssrn.6482832" ], "field": "finance", "role": "instrument", "bullets": [ "All 52,266 unique S&P 500 CEOs and board members serving 2006 to 2026, matched against 1,293,753 text-bearing documents from the released Epstein files.", "A large language model, not named, classifies 117,394 matched correspondences and identifies 67,637 indicating direct contact with 1,179 executives; no accuracy check is reported.", "Firms with Epstein-linked leaders earned cumulative abnormal returns up to -8.5 percent over ten days after the January 2026 DOJ release, and show more governance incidents." ], "bullet_provenance": "ai", "validated": false, "validation_note": "LLM classification, no accuracy check reported", "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 372, "authors_detailed": [ { "name": "Marina Gertsberg", "url": "https://openalex.org/A5030656262", "inst": "The University of Melbourne" }, { "name": "Michaela Pagel", "url": "https://openalex.org/A5082136045", "inst": "Washington University in St. Louis" }, { "name": "Ekaterina Volkova", "url": "https://openalex.org/A5132563742", "inst": "The University of Melbourne" } ], "affiliations": [ "Washington University in St. Louis", "The University of Melbourne" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6423358", "doi": "10.2139/ssrn.6423358", "title": "Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning", "authors": [ "Angel Tsai-Hsuan Chung", "Botong Zhang", "Ling-Chieh Kung", "Hamsa Bastani", "Osbert Bastani" ], "posted": "2026-03-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6423358", "field": "management", "role": "object", "bullets": [ "Roughly a five-month Python course across ten Taipei high schools, with students randomized between a fixed and an adaptive practice-problem sequence.", "An unnamed generative AI chatbot tutor is paired with a reinforcement-learning algorithm that sequences problems from student-chatbot signals; effect measured by randomized trial, not against ground truth.", "Adaptive sequencing raised unassisted final-exam performance by 0.15 standard deviations, with mediation analysis attributing gains to increased engagement." ], "bullet_provenance": "ai", "salience": 70, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 705, "authors_detailed": [ { "name": "Angel Tsai-Hsuan Chung", "url": "https://openalex.org/A5128215716", "inst": "University of Pennsylvania" }, { "name": "Botong Zhang", "url": "https://openalex.org/A5132629710", "inst": "California University of Pennsylvania" }, { "name": "Ling-Chieh Kung", "url": "https://openalex.org/A5001958421", "inst": "National Taiwan University" }, { "name": "Hamsa Bastani", "url": "https://openalex.org/A5075456619", "inst": "University of Pennsylvania" }, { "name": "Osbert Bastani", "url": "https://openalex.org/A5132558761", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania", "California University of Pennsylvania", "National Taiwan University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6479459", "doi": "10.2139/ssrn.6479459", "title": "Generative AI and Investor Processing of Financial Media", "authors": [ "Yang Ha Cho", "Allen H. Huang", "Joseph Pacelli", "Kristina M. Rennekamp" ], "posted": "2026-03-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6479459", "field": "accounting", "role": "object", "bullets": [ "Wall Street Journal articles before and after its rollout of AI-generated summaries, plus a controlled investor experiment; sample size and period not stated.", "The studied model is the Journal's own AI summarization tool, not named; the researchers measure investor reactions rather than validate model output.", "Articles with AI summaries draw higher trading volume and amplified price responses, and raise readers' confidence and recall rather than encouraging skimming." ], "bullet_provenance": "ai", "salience": 68, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 706, "authors_detailed": [ { "name": "Yang Ha Cho", "url": "https://openalex.org/A5131197590", "inst": "" }, { "name": "Allen H. Huang", "url": "https://openalex.org/A5131050789", "inst": "Hong Kong University of Science and Technology" }, { "name": "Joseph Pacelli", "url": "https://openalex.org/A5031143661", "inst": "Harvard University" }, { "name": "Kristina M. Rennekamp", "url": "https://openalex.org/A5063125919", "inst": "Cornell University" } ], "affiliations": [ "Harvard University", "Cornell University", "Hong Kong University of Science and Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6376138", "doi": "10.2139/ssrn.6376138", "title": "Agentic AI for Ageing Healthcare Systems in Advanced Economies: A Structured Review of Evidence, Institutional Barriers, and a Sociotechnical Implementation Roadmap", "authors": [ "Dr. Rachel, Wei Gee Ooi", "Baskar Periasamy" ], "posted": "2026-03-30", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6376138", "field": "economics", "role": "object", "bullets": [ "Structured narrative review covers 81 sources from 2020 through 2025 on ageing healthcare systems in advanced economies, including workforce, expenditure, and growth pressures.", "No particular agent is deployed; autonomous multi-step AI is the intervention reviewed against institutional, reimbursement, governance, and equity conditions rather than a measurement tool.", "Institutional readiness predicts implementation better than algorithmic performance; short-run productivity benefits have evidence, while claimed macroeconomic fiscal moderation remains empirically unvalidated." ], "bullet_provenance": "ai", "salience": 56, "edition": 23, "models": [], "validated": null, "n": 4198, "authors_detailed": [ { "name": "Wei Gee Ooi Dr. Rachel", "url": "https://openalex.org/A5130990742", "inst": "Nanyang Technological University" }, { "name": "Baskar Periasamy", "url": "https://openalex.org/A5128707613", "inst": "Antioch University" } ], "affiliations": [ "Nanyang Technological University", "Antioch University" ] }, { "uid": "doi:10.2139/ssrn.6474601", "doi": "10.2139/ssrn.6474601", "title": "Assessing the Benefits of Optimized Agentic AI Systems for Asset Pricing", "authors": [ "Ralph S. J. Koijen", "Bradford (Lynch) Levy" ], "posted": "2026-03-30", "added": "2026-07-24", "source_label": "NBER and SSRN", "url": "https://doi.org/10.2139/ssrn.6474601", "alt_urls": [ "https://doi.org/10.2139/ssrn.7108739", "https://doi.org/10.3386/w35431" ], "field": "finance", "role": "method", "bullets": [ "Stock returns around earnings announcements, using only information available at announcement time including the transcript text, evaluated with a real-time out-of-sample benchmark for AI asset-pricing systems.", "A range of agentic AI systems, specific model not stated, extract structured signals from earnings call transcripts and optimize over them, scored by out-of-sample explained variation in returns.", "Best-optimized systems more than double explained variation in announcement returns, raising R-squared from about 8 percent to near 20 percent versus standard benchmarks." ], "bullet_provenance": "ai", "validated": true, "validation_note": "out-of-sample R2 against realized announcement returns", "salience": 72, "edition": 3, "audience": "general", "models": [], "n": 1041, "authors_detailed": [ { "name": "Ralph S. J. Koijen", "url": "https://openalex.org/A5083877502", "inst": "National Bureau of Economic Research" }, { "name": "Bradford (Lynch) Levy", "url": "https://openalex.org/A5123824951", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6391618", "doi": "10.2139/ssrn.6391618", "title": "The Fearless Premium: Workplace Psychological Safety and Stock Returns", "authors": [ "Hamid Boustanifar", "Saeed Sheykhi" ], "posted": "2026-03-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6391618", "field": "finance", "role": "instrument", "bullets": [ "Firm-level psychological safety measured from Glassdoor employee reviews, matched to US stock returns and Fama-French factors; sample period and size not stated.", "An unnamed large language model annotates reviews and feeds a supervised machine-learning classifier to score psychological safety; no accuracy or agreement figure is reported.", "A value-weighted high-minus-low psychological safety portfolio earns a six-factor alpha of 3.57 percent, with stronger effects among innovation-intensive firms." ], "bullet_provenance": "ai", "validated": false, "validation_note": "no accuracy figure reported", "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 703, "authors_detailed": [ { "name": "Hamid Boustanifar", "url": "https://openalex.org/A5072114404", "inst": "Ecole des Hautes Etudes Commerciales du Nord" }, { "name": "Saeed Sheykhi", "url": "https://openalex.org/A5120216927", "inst": "Open University of the Netherlands" } ], "affiliations": [ "Ecole des Hautes Etudes Commerciales du Nord", "Open University of the Netherlands" ] }, { "uid": "doi:10.2139/ssrn.6213138", "doi": "10.2139/ssrn.6213138", "title": "Algorithmic Deprioritization Patterns in AI-Generated Content Distribution: The Marketing Agent Decay Model (MAD-M™) as a Predictive Framework", "authors": [ "kristina shrider" ], "posted": "2026-03-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6213138", "field": "management", "role": "object", "bullets": [ "Digital marketing content across major platforms, studied through platform policy analysis, empirical traffic data, and 450 longitudinal organizational case studies over 2024 to 2026.", "No language model is used for measurement; the paper studies how platforms deprioritize content once it is detected or labelled as AI-generated.", "Reports a roughly 60 percent AI-content saturation threshold that triggers accelerated visibility decay, reduced reach, and exclusion from high-authority citation contexts." ], "bullet_provenance": "ai", "salience": 20, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 704, "authors_detailed": [ { "name": "kristina shrider", "url": "https://openalex.org/A5130660940", "inst": "Advisory Board Company (United States)" } ], "affiliations": [ "Advisory Board Company (United States)" ] }, { "uid": "doi:10.2139/ssrn.6423478", "doi": "10.2139/ssrn.6423478", "title": "Machine Learning and Artificial Intelligence in Market Risk Management: A Review of Contemporary Approaches", "authors": [ "Shweta Khosla" ], "posted": "2026-03-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6423478", "field": "finance", "role": "object", "bullets": [ "Review of AI techniques at major global banks including HSBC, Bank of America, and Citibank across four complementary research streams.", "Synthesizes deep reinforcement learning, LLMs, and ML forecasting methods applied to VaR estimation, portfolio optimization, and volatility prediction.", "AI-induced systemic risk emerges when correlated AI models across banks generate synchronized trading signals, potentially amplifying market crashes." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3095, "authors_detailed": [ { "name": "Shweta Khosla", "url": "https://openalex.org/A5124773166", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6337379", "doi": "10.2139/ssrn.6337379", "title": "Using Generative AI to Predict the Weather Impact on Future Stock Returns", "authors": [ "Yi Zhou" ], "posted": "2026-03-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6337379", "field": "finance", "role": "instrument", "bullets": [ "U.S. publicly traded firms exposed to severe storm events, with textual weather descriptions evaluated by ChatGPT across varying macroeconomic and market-sentiment regimes.", "OpenAI ChatGPT assessed weather event descriptions and generated stock-impact predictions, which were then incorporated as regressors into cross-sectional econometric models of subsequent returns.", "Firms ChatGPT flagged for negative storm exposure earned lower subsequent returns; the effect concentrated in large, profitable, low-leverage firms during bullish sentiment periods." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2543, "authors_detailed": [ { "name": "Yi Zhou", "url": "https://openalex.org/A5146919479", "inst": "San Francisco State University" } ], "affiliations": [ "San Francisco State University" ] }, { "uid": "doi:10.2139/ssrn.6459920", "doi": "10.2139/ssrn.6459920", "title": "An Empirical Study of Generative AI Adoption in Software Engineering", "authors": [ "Görkem Giray", "Onur Demirörs", "Marcos Kalinowski", "Daniel Mendez" ], "posted": "2026-03-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6459920", "field": "management", "role": "object", "bullets": [ "Internationally distributed questionnaire survey of software engineering practitioners; 204 responses from 37 countries, combining closed and open-ended questions with bootstrapped confidence intervals.", "No language model is applied by the researchers; the study measures practitioner adoption of generative AI tools and their reported benefits, challenges, and institutionalization.", "Reports wide, deeply integrated adoption with perceived cycle-time and productivity gains, but persistent unreliable outputs and limited objective measurement; practitioners expect roles redefined, not replaced." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 549, "authors_detailed": [ { "name": "Görkem Giray", "url": "https://openalex.org/A5056506409", "inst": "Izmir Institute of Technology" }, { "name": "ONUR DEMİRÖRS", "url": "https://openalex.org/A5014714725", "inst": "Izmir Institute of Technology" }, { "name": "Marcos Kalinowski", "url": "https://openalex.org/A5130403900", "inst": "" }, { "name": "Daniel Mendez", "url": "https://openalex.org/A5130327537", "inst": "Universidad de la República de Uruguay" } ], "affiliations": [ "Izmir Institute of Technology", "Universidad de la República de Uruguay" ] }, { "uid": "doi:10.2139/ssrn.6460573", "doi": "10.2139/ssrn.6460573", "title": "Agentic Artificial Intelligence in Finance: A Comprehensive Survey", "authors": [ "Irene Aldridge", "Jolie An", "Riley Burke", "Michael Cao", "Chia-Yi Chien", "Kexin Deng", "Ruipeng Deng", "Yichen Gao", "Olivia Guo", "Shunran He", "Zheng Li", "George Lin", "Weihang Lin", "Fanyi Lyu", "Kwunfung Ng", "Qi Wang", "Hanxi Xiao", "Dora Xu", "Yuanyuan Xue", "Sheng Zhang", "Sirui Zhang", "Yun Zhang", "Sirui Zhao", "Xiaolong Zhao", "Yihan Zhao", "Waner Zheng" ], "posted": "2026-03-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6460573", "field": "finance", "role": "object", "bullets": [ "Literature survey of agentic AI in financial markets, with no empirical sample; covers system architecture, market applications, regulatory frameworks, and systemic implications.", "No language model is applied; the survey synthesizes research on agentic AI systems that reason, plan, and make autonomous financial decisions, contrasting them with algorithmic trading and generative AI.", "Concludes agentic AI could enhance market efficiency, liquidity provision, and risk management, but raises challenges for market stability, compliance, interpretability, and systemic risk." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 550, "authors_detailed": [ { "name": "Irene Aldridge", "url": "https://openalex.org/A5130405382", "inst": "Cornell University" }, { "name": "Jolie An", "url": "https://openalex.org/A5130372639", "inst": "Cornell University" }, { "name": "Riley Burke", "url": "https://openalex.org/A5080080429", "inst": "Cornell University" }, { "name": "Michael Cao", "url": "https://openalex.org/A5113520992", "inst": "Cornell University" }, { "name": "Chia-Yi Chien", "url": "https://openalex.org/A5130362647", "inst": "Cornell University" }, { "name": "Kexin Deng", "url": "https://openalex.org/A5130405562", "inst": "Cornell University" }, { "name": "Rui Deng", "url": "https://openalex.org/A5100732600", "inst": "Cornell University" }, { "name": "Yichen Gao", "url": "https://openalex.org/A5119429296", "inst": "Cornell University" }, { "name": "Olivia Guo", "url": "https://openalex.org/A5120671097", "inst": "Cornell University" }, { "name": "Shunran He", "url": "https://openalex.org/A5130334158", "inst": "Cornell University" }, { "name": "Zheng Li", "url": "https://openalex.org/A5130347135", "inst": "Cornell University" }, { "name": "George Lin", "url": "https://openalex.org/A5049066851", "inst": "Cornell University" }, { "name": "Weihang Lin", "url": "https://openalex.org/A5018198356", "inst": "Cornell University" }, { "name": "Fanyi Lyu", "url": "https://openalex.org/A5120482978", "inst": "Cornell University" }, { "name": "K J Ng", "url": "https://openalex.org/A5012372220", "inst": "Cornell University" }, { "name": "Qi Wang", "url": "https://openalex.org/A5130330355", "inst": "Cornell University" }, { "name": "Hanxi Xiao", "url": "https://openalex.org/A5008871759", "inst": "Cornell University" }, { "name": "D. Xu", "url": "https://openalex.org/A5026704449", "inst": "Cornell University" }, { "name": "Yuanyuan Xue", "url": "https://openalex.org/A5130370293", "inst": "Cornell University" }, { "name": "Sheng Zhang", "url": "https://openalex.org/A5130341769", "inst": "Cornell University" }, { "name": "Sirui Zhang", "url": "https://openalex.org/A5130359048", "inst": "Cornell University" }, { "name": "Yun Zhang", "url": "https://openalex.org/A5130394716", "inst": "Cornell University" }, { "name": "Sirui Zhao", "url": "https://openalex.org/A5130351156", "inst": "Cornell University" }, { "name": "Xiaolong Zhao", "url": "https://openalex.org/A5130375140", "inst": "Cornell University" }, { "name": "Yihan Zhao", "url": "", "inst": "Cornell University" }, { "name": "Waner Zheng", "url": "https://openalex.org/A5130405785", "inst": "Cornell University" } ], "affiliations": [ "Cornell University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6381458", "doi": "10.2139/ssrn.6381458", "title": "Capability versus Cost: Pricing Generative AI under Knightian Uncertainty", "authors": [ "Cong Zhang" ], "posted": "2026-03-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6381458", "field": "finance", "role": "object", "bullets": [ "Task-based production model of generative AI paired with event studies around nineteen AI milestones spanning 2020 to 2024, examining cross-sectional stock returns and firms' AI exposure.", "No language model is applied empirically; generative AI capability and cost enter as separate channels, and firm-level AI exposure is the object linked to returns.", "Return sensitivity is roughly twice as large for capability as for cost events; the AI equity premium is about 46 basis points, capability risk 76 percent." ], "bullet_provenance": "ai", "salience": 55, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 702, "authors_detailed": [ { "name": "Cong Zhang", "url": "https://openalex.org/A5130345266", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6387438", "doi": "10.2139/ssrn.6387438", "title": "Generative AI and the Market Valuation of Innovative Firms", "authors": [ "Jenny Stanco", "Kee H. Chung" ], "posted": "2026-03-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6387438", "field": "finance", "role": "object", "bullets": [ "U.S. public firms defined by patent holdings and industry classification, before and after ChatGPT launch in November 2022.", "ChatGPT launch served as exogenous technological shock; AI exposure measured via 10-K disclosures and AI-skilled workforce composition.", "Innovative firms show stronger sales growth, increased R&D spending, lower operating costs, fewer employees, higher abnormal returns, and greater systematic risk." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 72, "validated": null, "n": 3094, "authors_detailed": [ { "name": "Jenny Stanco", "url": "https://openalex.org/A5116974273", "inst": "University at Buffalo, State University of New York" }, { "name": "Kee H. Chung", "url": "https://openalex.org/A5011392134", "inst": "University at Buffalo, State University of New York" } ], "affiliations": [ "University at Buffalo, State University of New York" ] }, { "uid": "doi:10.2139/ssrn.6327279", "doi": "10.2139/ssrn.6327279", "title": "When Does AI Raise the Equity Risk Premium? Displacement, Participation, and Structural Regimes", "authors": [ "Rajan Raju" ], "posted": "2026-03-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6327279", "field": "finance", "role": "object", "bullets": [ "Heterogeneous-agent model of deep (developed) and shallow (emerging, calibrated to India) equity markets under AI-driven labor displacement.", "Theoretical framework decomposes equity risk premium into productivity, participation-compression, and alignment-risk channels; no specific LLM used.", "AI raises the equity risk premium in shallow markets via participation compression and in deep markets via alignment risk; the premium persists after employment normalizes due to hysteresis." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3981, "authors_detailed": [ { "name": "Rajan Saha Raju", "url": "https://openalex.org/A5070386193", "inst": "Merck (Singapore)" } ], "affiliations": [ "Merck (Singapore)" ] }, { "uid": "doi:10.2139/ssrn.6362299", "doi": "10.2139/ssrn.6362299", "title": "The Macroeconomics of Generative AI: Firm Entry and Price Dynamics", "authors": [ "Jose Carreno" ], "posted": "2026-03-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6362299", "field": "economics", "role": "object", "bullets": [ "Occupational generative-AI exposure metrics combined with administrative firm records; geography and sample period not stated, with observation at the sector and firm level.", "No language model is run by the researchers; generative AI is the object, measured through occupational exposure, with a general-equilibrium model of firm heterogeneity built to rationalize the findings.", "Higher AI exposure triggers an immediate surge in firm entry and persistent relative price deflation in exposed sectors, implying a sustained consumer welfare dividend." ], "bullet_provenance": "ai", "salience": 54, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 369, "authors_detailed": [ { "name": "José Gabriel Carreño", "url": "https://openalex.org/A5113832255", "inst": "University of Oregon" } ], "affiliations": [ "University of Oregon" ] }, { "uid": "doi:10.2139/ssrn.6368558", "doi": "10.2139/ssrn.6368558", "title": "AI Product Displacement Risk", "authors": [ "Renping Li" ], "posted": "2026-03-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6368558", "field": "finance", "role": "object", "bullets": [ "US public firms; event studies around four generative AI product releases through the 2026 launch of AI agents replacing incumbent SaaS products; firm-level unit of observation.", "No language model is applied by the researchers; they construct AI Product Displacement Risk, a firm-level measure of revenue vulnerability to product substitution by generative AI.", "Top-quintile displacement-risk firms underperform bottom-quintile firms by 4.8 percentage points around the 2026 AI-agent launch, with the effect driven entirely by digital deliverability." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 548, "authors_detailed": [ { "name": "Renping ShaoXinna HuangYonglong Li", "url": "https://openalex.org/A5026284521", "inst": "Tulane University" } ], "affiliations": [ "Tulane University" ] }, { "uid": "doi:10.2139/ssrn.6359598", "doi": "10.2139/ssrn.6359598", "title": "Analysts' Response to 'AI Washing' in Earnings Conference Calls", "authors": [ "Richard Blades", "Pawel Bilinski", "Arthur Kraft" ], "posted": "2026-03-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6359598", "field": "finance", "role": "object", "bullets": [ "U.S. earnings conference calls referencing AI, using the ChatGPT launch as an exogenous shock to separate speculative from substantive AI disclosures.", "Identifies firms issuing speculative AI claims with no observable AI investments and tests whether analysts and investors distinguish them from concrete disclosers.", "Markets on average do not differentiate speculative from substantive AI disclosure, but more experienced analysts assign systematically lower target prices to speculative firms." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 3605, "authors_detailed": [ { "name": "Richard Burden Stephen Blades", "url": "https://openalex.org/A5043291197", "inst": "St George's, University of London" }, { "name": "Pawel Bilinski", "url": "https://openalex.org/A5071055078", "inst": "City, University of London" }, { "name": "Arthur Kraft", "url": "https://openalex.org/A5013041277", "inst": "St George's, University of London" } ], "affiliations": [ "St George's, University of London", "City, University of London" ] }, { "uid": "doi:10.2139/ssrn.6248999", "doi": "10.2139/ssrn.6248999", "title": "Obedient, Biased, and Persuasive: Reframing LLM Safety as a Social Science Problem", "authors": [ "Vasco Gerardo Hinostroza Fuentes", "Hezerul Abdul Karim", "Nouar Aldahoul", "Myles Joshua Tan" ], "posted": "2026-03-19", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6248999", "field": "other", "role": "object", "bullets": [ "Position paper with no new data, drawing on prior studies of persuasion, overreliance, and preference shifts among users of frontier language models in deployed products.", "No model is run and none is named; the argument treats an LLM embedded in a product as an institution in miniature that exercises authority without institutional safeguards.", "Concludes many risks arise from deployment context rather than model failures, and calls for design and governance requirements beyond the model layer; no framework is tested." ], "bullet_provenance": "ai", "salience": 27, "edition": 9, "models": [], "validated": null, "n": 1339, "authors_detailed": [ { "name": "Vasco Gerardo Hinostroza Fuentes", "url": "https://openalex.org/A5126365675", "inst": "University of Florida" }, { "name": "Hezerul Abdul Karim", "url": "https://openalex.org/A5027445807", "inst": "New York University" }, { "name": "Nouar AlDahoul", "url": "https://openalex.org/A5037300629", "inst": "New York University" }, { "name": "Myles Joshua Tan", "url": "https://openalex.org/A5130016438", "inst": "University of Florida" } ], "affiliations": [ "University of Florida", "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6310078", "doi": "10.2139/ssrn.6310078", "title": "Autonomous Portfolio and Derivatives Risk Management AI Agents: Risk-Aware Reinforcement Learning, Multi-Objective Optimization, and Explainable Allocation Decisions", "authors": [ "Alex M Tan" ], "posted": "2026-03-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6310078", "field": "finance", "role": "object", "bullets": [ "Survey of deep reinforcement learning and LLM-based autonomous agents for portfolio management, options trading, and derivatives hedging at systematic trading desks.", "Reviews multi-agent architectures separating risk-taking from hedging policy, with LLM-based reasoning for Greeks monitoring, volatility regime detection, and scenario-simulation stress testing.", "Proposes research agenda arguing combined RL-LLM frameworks outperform single-agent baselines on risk-adjusted performance in multi-asset derivative portfolios." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 2870, "authors_detailed": [ { "name": "Alex M Tan", "url": "https://openalex.org/A5126816454", "inst": "Supélec" } ], "affiliations": [ "Supélec" ] }, { "uid": "doi:10.2139/ssrn.6357778", "doi": "10.2139/ssrn.6357778", "title": "Collusive Content in Corporate Communications", "authors": [ "Eduardo M. Azevedo", "Joseph E. Harrington Jr", "Ioan Octavian Rusu" ], "posted": "2026-03-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6357778", "field": "economics", "role": "instrument", "bullets": [ "Almost 500,000 company transcripts from firms in industries such as airlines, steel, and truck rentals, where public price coordination has been documented.", "A large language model, not named, scores transcripts for collusive content, and a human audit of the 301 highest-scored transcripts checks the classifications without reporting an agreement figure.", "Only 0.12 percent of transcripts meet the collusive-content criterion, and the audit finds systematic ways flagged communications facilitate coordinated conduct, supporting use as an antitrust screening tool." ], "bullet_provenance": "ai", "validated": false, "validation_note": "human audit of 301 top-scored transcripts, no agreement figure stated", "salience": 56, "edition": 3, "audience": "general", "models": [], "n": 368, "authors_detailed": [ { "name": "Eduardo M. Azevedo", "url": "https://openalex.org/A5063791392", "inst": "University of Pennsylvania" }, { "name": "Joseph E. Harrington", "url": "https://openalex.org/A5090071738", "inst": "California University of Pennsylvania" }, { "name": "Ioan Octavian Rusu", "url": "https://openalex.org/A5129910777", "inst": "Drexel University" } ], "affiliations": [ "University of Pennsylvania", "California University of Pennsylvania", "Drexel University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6329139", "doi": "10.2139/ssrn.6329139", "title": "The (Large) Language of Veil-Piercing", "authors": [ "Douglas C. Barnard", "Peter B. Oh" ], "posted": "2026-03-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6329139", "field": "management", "role": "instrument", "bullets": [ "16,202 U.S. veil-piercing judicial opinions with 1,000 manually coded for training and validation, replicating and extending Macey and Mitts (2014) with modern models.", "Claude performed blind prediction of veil-piercing outcomes without training data; an XGBoost stacked ensemble combined Claude and Naive Bayes predictions as meta-features.", "The stacked ensemble achieved 86% accuracy versus 76.6% in the original study; firm capitalization and corporate formalities proved significant, contradicting the prior tripartite taxonomy." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "1,000 manually coded veil-piercing opinions, 86% accuracy", "salience": 62, "n": 2542, "authors_detailed": [ { "name": "Douglas C. Barnard", "url": "https://openalex.org/A5129716289", "inst": "University of Minnesota" }, { "name": "Peter B. Oh", "url": "https://openalex.org/A5130171156", "inst": "University of Pittsburgh" } ], "affiliations": [ "University of Minnesota", "University of Pittsburgh" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6331258", "doi": "10.2139/ssrn.6331258", "title": "Three Strategic Bets on AI's Future", "authors": [ "Maxime C. Cohen", "Eddy Hage-Youssef", "Daniel McCarthy", "D. Daniel Sokol" ], "posted": "2026-03-17", "added": "2026-08-13", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6331258", "field": "management", "role": "object", "bullets": [ "Usage and revenue around major model launches for three vendors: ChatGPT, Google Gemini, and Claude. Data source, period, and user counts are not stated.", "The authors run no model. They read launch dates against market share and revenue per user, and report no check on where the usage figures come from.", "Launches grew the total market instead of moving users between vendors. Three routes coexist: about 79 percent share at moderate revenue per user, 12 percent share with almost no direct revenue, and under 1 percent at 40 times the revenue per user." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 50, "edition": 18, "validated": null, "n": 2177, "authors_detailed": [ { "name": "Maxime C. Cohen", "url": "https://openalex.org/A5015555156", "inst": "McGill University" }, { "name": "Eddy Hage-Youssef", "url": "https://openalex.org/A5119054719", "inst": "McGill University" }, { "name": "Daniel McCarthy", "url": "https://openalex.org/A5129694181", "inst": "University of Maryland - Robert H. Smith School of Business" }, { "name": "D. Daniel Sokol", "url": "https://openalex.org/A5129649170", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California", "McGill University", "University of Maryland - Robert H. Smith School of Business" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6310740", "doi": "10.2139/ssrn.6310740", "title": "AI-Powered Corporate Governance", "authors": [ "Joanna (Xiaoyu) Wang", "Seungjoon Oh", "Jiung Lee" ], "posted": "2026-03-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6310740", "alt_urls": [ "https://doi.org/10.2139/ssrn.6638698" ], "field": "finance", "role": "object", "bullets": [ "US firms with machine-based investors identified through EDGAR access patterns, using the November 2022 ChatGPT release in a difference-in-differences design around investors' AI adoption costs.", "No model is run by the researchers; the ChatGPT release is a shock to investor AI adoption and generative AI adoption by investors is the object of study.", "Machine-based investors expand portfolio breadth, shorten horizons, and are associated with higher valuations, higher CEO pay-performance sensitivity, and enhanced financial reporting quality." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 63, "edition": 3, "audience": "general", "validated": null, "n": 367, "authors_detailed": [ { "name": "Joanna Wang", "url": "https://openalex.org/A5101812628", "inst": "Peking University" }, { "name": "Seungjoon Oh", "url": "https://openalex.org/A5016339696", "inst": "Peking University" }, { "name": "Jiung Lee", "url": "https://openalex.org/A5129698220", "inst": "Peking University" } ], "affiliations": [ "Peking University" ] }, { "uid": "doi:10.2139/ssrn.6224500", "doi": "10.2139/ssrn.6224500", "title": "From Faces to Finance: AI Measurement of Promotion Pressure and Health Costs in Public Office", "authors": [ "Fangzhou Lu", "Lei Huang" ], "posted": "2026-03-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6224500", "field": "economics", "role": "instrument", "bullets": [ "Panel of mayors and senior government officials combined with administrative health-inspection data and official photographs, linking apparent-age gaps to local economic performance and promotion incentives.", "Computer-vision models estimate apparent age from photos; large language models, not named, extract urgency, constraint, and risk emphasis from speeches and reports, with no accuracy check.", "Officials in weaker-performing jurisdictions age faster, concentrated where promotion tournaments are steeper, and textual urgency measures predict within-official increases in apparent age." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 701, "authors_detailed": [ { "name": "Futao Lu", "url": "https://openalex.org/A5082610380", "inst": "Guangdong University of Finance" }, { "name": "Liang Huang", "url": "https://openalex.org/A5063287185", "inst": "University of Hong Kong" } ], "affiliations": [ "Guangdong University of Finance", "University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6230780", "doi": "10.2139/ssrn.6230780", "title": "The Predictive Organization: Architecture for Enterprise Intelligence Working Paper -February 2026", "authors": [ "Witold Reichhart", "Arnaud Gelas" ], "posted": "2026-03-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6230780", "field": "management", "role": "object", "bullets": [ "Conceptual paper motivated by enterprise AI in financial services, citing JPMorgan, Goldman Sachs, and BlackRock's Aladdin; no empirical dataset or estimation.", "No model is estimated; proposes a tripartite architecture of a state layer, dynamics layer, and agentic layer over a claims-based knowledge representation grounded in ontologies.", "Argues regulated firms already hold compliance infrastructure such as BCBS 239, DORA, SR 11-7, and the EU AI Act that can become dynamic organizational intelligence at marginal cost." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1040, "authors_detailed": [ { "name": "Witold Reichhart", "url": "https://openalex.org/A5129738294", "inst": "Independent" }, { "name": "Arnaud Gelas", "url": "https://openalex.org/A5031424582", "inst": "Capgemini (Netherlands)" } ], "affiliations": [ "Independent", "Capgemini (Netherlands)" ] }, { "uid": "doi:10.2139/ssrn.6425017", "doi": "10.2139/ssrn.6425017", "title": "Out of the Black Box: Uncertainty Quantification for LLMs via Conditional Probabilities", "authors": [ "Hui Chen", "Antoine Didisheim", "Luciano Somoza" ], "posted": "2026-03-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6425017", "field": "finance", "role": "instrument", "bullets": [ "News articles classified by autoregressive LLMs with entropy-based inner confidence scores used to form long-short equity portfolios.", "Token-level conditional probabilities construct an uncertainty measure; high-confidence LLM predictions are systematically more accurate in news classification.", "The high-confidence portfolio achieves a Sharpe ratio roughly 20% above the unconditional benchmark; the low-confidence portfolio yields no excess returns." ], "bullet_provenance": "ai", "validated": true, "validation_note": "news classification accuracy by confidence tier", "salience": 75, "models": [], "n": 3604, "authors_detailed": [ { "name": "Hui Chen", "url": "https://openalex.org/A5129708914", "inst": "Massachusetts Institute of Technology" }, { "name": "Antoine Didisheim", "url": "https://openalex.org/A5019234963", "inst": "The University of Melbourne" }, { "name": "Luciano Somoza", "url": "https://openalex.org/A5129702677", "inst": "École Supérieure des Sciences Économiques et Commerciales" } ], "affiliations": [ "Massachusetts Institute of Technology", "The University of Melbourne", "École Supérieure des Sciences Économiques et Commerciales" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6325401", "doi": "10.2139/ssrn.6325401", "title": "Overlooked and Undervalued: An Investigation of Women Business Owners and Congressional Tax Hearings", "authors": [ "Caroline Bruckner", "Collin Coil" ], "posted": "2026-03-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6325401", "field": "accounting", "role": "instrument", "bullets": [ "Extended Congressional Record Representation Dataset of opening statements from witnesses at U.S. congressional tax-writing and small business committee hearings.", "LLMs analyzed witness testimony text to capture diversity of opinions and measure informational content differences between male and female witnesses at tax hearings.", "Women witnesses provide unique information to policymakers; their routine underrepresentation at congressional tax hearings results in measurable loss of policy-relevant perspectives." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2540, "authors_detailed": [ { "name": "C. Bruckner", "url": "https://openalex.org/A5043486685", "inst": "American University" }, { "name": "Collin Coil", "url": "https://openalex.org/A5093454296", "inst": "University of Vermont" } ], "affiliations": [ "American University", "University of Vermont" ] }, { "uid": "doi:10.2139/ssrn.6315998", "doi": "10.2139/ssrn.6315998", "title": "A Human-Centered Workflow for Using Large Language Models in Content Analysis", "authors": [ "Ivan Zupic" ], "posted": "2026-03-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6315998", "field": "management", "role": "method", "bullets": [ "Comprehensive methodological workflow synthesizing insights from political science, sociology, computer science, psychology, and management research on LLM-based content analysis.", "LLMs used via API for annotation, summarization, and information extraction tasks with human-centered design, validation procedures, and prompt library with Python implementation.", "The workflow addresses prompt sensitivity, hallucination, and black-box limitations through explicit validation stages, offering a replicable framework for qualitative and quantitative content analysis." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 58, "n": 2541, "authors_detailed": [ { "name": "Ivan Župič", "url": "https://openalex.org/A5030808494", "inst": "University of Ljubljana" } ], "affiliations": [ "University of Ljubljana" ] }, { "uid": "doi:10.2139/ssrn.6323960", "doi": "10.2139/ssrn.6323960", "title": "AI, Productivity, and Labor Markets: A Review of the Empirical Evidence", "authors": [ "Eric Fruits", "Kristian Stout" ], "posted": "2026-03-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6323960", "field": "economics", "role": "object", "bullets": [ "Literature review of empirical studies on generative AI's economic impact through 2025 covering coding, writing, and customer service tasks.", "Synthesizes evidence showing AI boosts task-level productivity 15-50%, with largest gains for lower-skilled workers producing skill compression.", "Aggregate labor-market disruption remains limited; entry-level roles shift as AI automates junior tasks while macro gains follow a productivity J-curve." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3603, "authors_detailed": [ { "name": "Eric Fruits", "url": "https://openalex.org/A5032512456", "inst": "Portland State University" }, { "name": "Kristian Stout", "url": "https://openalex.org/A5016496508", "inst": "University of Oregon" } ], "affiliations": [ "Portland State University", "University of Oregon" ] }, { "uid": "doi:10.2139/ssrn.6381580", "doi": "10.2139/ssrn.6381580", "title": "Risks Create a Jagged Frontier of LLM Productivity Gains Across Computer Occupations", "authors": [ "Deepika Chawla", "Gagandeep Singh", "Elham Khorasani Buxton", "Meicen Sun", "Lav Varshney", "Jeremy Riel", "Craig De Voto" ], "posted": "2026-03-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6381580", "field": "economics", "role": "object", "bullets": [ "Standard O*NET computer-occupation tasks aggregated to occupation-level scores, weighted by task importance, relevance, and frequency, in the US occupational setting.", "Six frontier models including GPT-5 and Claude Opus produce structured risk-reward ratings following standardized definitions, with a subset manually verified for reliability and no figure reported.", "Risk varies more than reward, so safety-critical roles such as information security engineers see gains offset by risk while web developers see gains exceed risks." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": false, "validation_note": "manual verification of a subset, no figure reported", "salience": 56, "edition": 3, "audience": "general", "n": 366, "authors_detailed": [ { "name": "Deepika Chawla", "url": "https://openalex.org/A5129077185", "inst": "Centre for the Study of Developing Societies" }, { "name": "Gagandeep Singh", "url": "https://openalex.org/A5129050682", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Elham Khorasani Buxton", "url": "https://openalex.org/A5037076068", "inst": "University of Illinois at Springfield" }, { "name": "Meicen Sun", "url": "https://openalex.org/A5129097776", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Lav Varshney", "url": "https://openalex.org/A5129013613", "inst": "State University of New York" }, { "name": "Jeremy Riel", "url": "https://openalex.org/A5079119871", "inst": "University of Illinois Chicago" }, { "name": "Craig De Voto", "url": "https://openalex.org/A5078039898", "inst": "University of Illinois Chicago" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "Centre for the Study of Developing Societies", "University of Illinois at Springfield", "State University of New York", "University of Illinois Chicago" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6282918", "doi": "10.2139/ssrn.6282918", "title": "Tailored: How LLM Helpfulness Bias Can Perpetuate Identity-Based Inequalities", "authors": [ "Maryam Dilmaghani" ], "posted": "2026-03-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6282918", "field": "economics", "role": "object", "bullets": [ "Stylized intertemporal model with heterogeneous agents differing in initial risk aversion, receiving LLM-generated portfolio guidance under preference-mirroring personalization.", "Theoretical analysis of an LLM that mirrors revealed risk tolerance to maximize contemporaneous utility; conditions for static efficiency but dynamic inefficiency characterized formally.", "Adaptive personalization entrenches initial preference heterogeneity, generating persistent wealth divergence correlated with demographic attributes despite formally equal access to the system." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 2539, "authors_detailed": [ { "name": "Maryam Dilmaghani", "url": "https://openalex.org/A5128880935", "inst": "Saint Mary's University" } ], "affiliations": [ "Saint Mary's University" ] }, { "uid": "doi:10.2139/ssrn.6293738", "doi": "10.2139/ssrn.6293738", "title": "Intelligence Without Integrity: Why Capable LLMs May Undermine Reliability", "authors": [ "Ryan Allen", "Aticus Peterson" ], "posted": "2026-03-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6293738", "field": "management", "role": "method", "bullets": [ "Fourteen frontier language models evaluated on a task simulating empirical analysis of hospital merger effects, using synthetically generated data with embedded ground truth.", "Models, with families not named, are scored on reaching correct conclusions and on conclusion stability when analytically irrelevant cues about desired outcomes are introduced.", "Intelligence and integrity trade off: models most likely to reach correct conclusions are often most likely to shift inference under motivated framing." ], "bullet_provenance": "ai", "validated": true, "validation_note": "synthetic data with embedded ground truth, 14 models", "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 700, "authors_detailed": [ { "name": "Ryan Allen", "url": "https://openalex.org/A5129020770", "inst": "Brigham Young University" }, { "name": "Aticus Peterson", "url": "https://openalex.org/A5086718303", "inst": "New York University" } ], "affiliations": [ "New York University", "Brigham Young University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6381578", "doi": "10.2139/ssrn.6381578", "title": "Next‑Generation AI‑Driven Fraud Management: Integrating Machine Learning, Graph Analytics, and Agentic AI for Financial Crime Prevention", "authors": [ "Virendra Singh Chawra" ], "posted": "2026-03-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6381578", "field": "finance", "role": "object", "bullets": [ "Conceptual framework for digital banking fraud management synthesizing current research and industry practices on machine learning, graph analytics, and AI-based detection.", "Proposes integrating ML, graph analytics, real-time streaming, generative AI assistants, and agentic AI fraud investigators for automated investigative workflows in banking.", "Framework targets enhanced fraud detection accuracy, reduced false positives, and improved operational efficiency versus traditional rule-based systems in digital banking ecosystems." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 20, "validated": null, "n": 3237, "authors_detailed": [ { "name": "Virendra Singh Chawra", "url": "https://openalex.org/A5128832527", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6393158", "doi": "10.2139/ssrn.6393158", "title": "AI and Publication Commons", "authors": [ "Atalay Atasu" ], "posted": "2026-03-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6393158", "field": "management", "role": "object", "bullets": [ "Formal economic model of a top-tier academic journal's submission and review process before and after LLM-assisted writing becomes available.", "Models how reduced production costs increase submissions, congest reviewer capacity, and degrade review accuracy, eroding journal impact.", "A two-track mechanism where AI-assisted submissions self-select into an AI-review track recovers substantial journal impact lost to AI assistance." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3602, "authors_detailed": [ { "name": "Atalay Atasu", "url": "https://openalex.org/A5128912343", "inst": "INSEAD" } ], "affiliations": [ "INSEAD" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6245818", "doi": "10.2139/ssrn.6245818", "title": "When Firms Adopt AI Matters: Diffusion, Intangible Investment, and Firm Value — Evidence from Corporate Disclosures", "authors": [ "Parand Akbari" ], "posted": "2026-03-10", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6245818", "field": "finance", "role": "object", "bullets": [ "US public firm earnings and investor call transcripts, 2011 to 2024, merged with Compustat measures of costs, factor shares, investment, and valuation.", "Large language models, family not stated, date each firm's first move from AI talk to concrete deployment and tag application type, strategic role, and sourcing mode; no accuracy check is reported.", "Adoption reorganizes costs without raising markups, tilts firms toward intangibles and away from labor, and Tobin's q gains concentrate among early adopters in industries where diffusion is still low." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "edition": 15, "models": [], "n": 2104, "authors_detailed": [ { "name": "Parand Akbari", "url": "https://openalex.org/A5040429591", "inst": "Carnegie Mellon University" } ], "affiliations": [ "Carnegie Mellon University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6210099", "doi": "10.2139/ssrn.6210099", "title": "Stochastic Parrots or Singing in Harmony? Testing Five Leading LLMs for their Ability to Replicate a Human Survey with Synthetic Data", "authors": [ "Jason Miklian", "Kristian Hoelscher", "John Katsos" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6210099", "field": "management", "role": "agent", "bullets": [ "Comparison of a 420-respondent survey of Silicon Valley coders and developers against synthetic survey data generated by five LLMs, framed as organizational research.", "Five generative LLMs including ChatGPT Thinking 5 Pro, Claude Sonnet 4.5, Gemini Advanced 2.5 Pro and DeepSeek 3.2 simulated survey respondents; outputs compared qualitatively to the human survey with no agreement statistic reported.", "Synthetic responses reproduced conventional wisdom but missed the counterintuitive insights of the human data, and all models' deviations clustered together, leaving the real data as the outlier." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "validated": false, "validation_note": "human survey comparison, no agreement statistic reported", "salience": 60, "edition": 3, "audience": "general", "n": 71, "authors_detailed": [ { "name": "Jason Miklian", "url": "https://openalex.org/A5009490853", "inst": "University of Oslo" }, { "name": "Kristian Hoelscher", "url": "https://openalex.org/A5004984041", "inst": "Peace Research Institute Oslo" }, { "name": "John E. Katsos", "url": "https://openalex.org/A5089109181", "inst": "American University of Sharjah" } ], "affiliations": [ "University of Oslo", "Peace Research Institute Oslo", "American University of Sharjah" ] }, { "uid": "doi:10.2139/ssrn.6210681", "doi": "10.2139/ssrn.6210681", "title": "Prose and Cons: Evaluating the Legality of Police Stops with Large Language Models", "authors": [ "David S. Abrams", "Jonathan H. Choi" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6210681", "field": "economics", "role": "instrument", "bullets": [ "41,332 attorney-coded police stop narratives from 2012 to 2023 in one jurisdiction, plus more than one million additional stops with imputed legality.", "Fine-tuned GPT-4.1 classifies whether a stop is illegal from its narrative, validated against attorney coding at over 90% accuracy with well-calibrated confidence.", "Officer heterogeneity is limited (replacing the most illegal half of officers cuts illegal stops only 12.7%), and stops of Black males are 1.6 points more likely illegal." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "41,332 attorney-coded stops, >90% accuracy", "salience": 66, "edition": 3, "audience": "general", "n": 94, "authors_detailed": [ { "name": "David Abrams", "url": "https://openalex.org/A5058758583", "inst": "William Carey University" }, { "name": "Jonathan H. Choi", "url": "https://openalex.org/A5128792235", "inst": "Washington University School of Law" } ], "affiliations": [ "William Carey University", "Washington University School of Law" ] }, { "uid": "doi:10.2139/ssrn.6237338", "doi": "10.2139/ssrn.6237338", "title": "Generative AI and Patents", "authors": [ "Nikhil Dilip", "Youngkyung Kim", "Robert Seamans" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6237338", "field": "economics", "role": "object", "bullets": [ "US patent applications around the November 2022 release of ChatGPT, with particular attention to micro entities; the unit of observation is the individual patent application.", "An AI-detection tool flags applications likely drafted with LLM help; the specific model behind the applications is not named and detection accuracy is not reported.", "Likely LLM-assisted filings rose sharply after ChatGPT, up about 150 percent among micro entities, with no measurable quality difference but distinct linguistic patterns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "edition": 3, "audience": "general", "validated": null, "n": 95, "authors_detailed": [ { "name": "Nikhil Dilip", "url": "https://openalex.org/A5128698327", "inst": "NYU Stern School of Business" }, { "name": "Youngkyung Kim", "url": "https://openalex.org/A5128697479", "inst": "New York University" }, { "name": "Robert Seamans", "url": "https://openalex.org/A5050038373", "inst": "New York University" } ], "affiliations": [ "NYU Stern School of Business", "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6204619", "doi": "10.2139/ssrn.6204619", "title": "Seeing the Goal, Missing the Truth: Human Accountability for AI Bias", "authors": [ "Sean S. Cao", "Wei Jiang", "Hui Xu" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6204619", "alt_urls": [ "https://doi.org/10.2139/ssrn.6705499" ], "field": "finance", "role": "method", "bullets": [ "Financial prediction tasks in which LLMs generate sentiment and competition measures meant to be independent of the downstream use. Period, geography and sample size not stated.", "The paper does not name the model; it varies whether the downstream objective, predicting stock returns or earnings, is disclosed in the prompt, and compares behavior before and after the knowledge cutoff.", "Revealing the downstream goal shifts the intermediate measures toward that objective and improves prediction before the knowledge cutoff, with no advantage after it." ], "bullet_provenance": "ai", "validated": false, "salience": 66, "edition": 3, "audience": "general", "models": [], "n": 112, "authors_detailed": [ { "name": "Sean S. Cao", "url": "", "inst": "Smith Institute" }, { "name": "Wei Jiang", "url": "", "inst": "Emory University" }, { "name": "Hui Xu", "url": "", "inst": "Lancaster University" } ], "affiliations": [ "Emory University", "Smith Institute", "Lancaster University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6276278", "doi": "10.2139/ssrn.6276278", "title": "Generative AI for Finance: A New Framework", "authors": [ "Bailin Chai", "Fuwei Jiang", "Lingchao Meng", "Tian You", "Guofu Zhou" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6276278", "field": "finance", "role": "method", "bullets": [ "Equity cross-section (sample size, period, and geography not stated); firms are treated as tokens ordered by characteristics, framing the cross-section as characteristic-ordered economic sentences.", "RPBERT, a BERT-based two-stage model, learns attention-weighted context-dependent firm representations to price the cross-section rather than imposing a fixed characteristic-to-return mapping.", "The representation-first model outperforms leading machine-learning and factor-pricing benchmarks in both statistical fit and economic performance, with magnitudes not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "salience": 62, "edition": 3, "audience": "technical", "validated": null, "n": 134, "authors_detailed": [ { "name": "Bailin Chai", "url": "https://openalex.org/A5092744524", "inst": "Central University of Finance and Economics" }, { "name": "Fuwei Jiang", "url": "https://openalex.org/A5076770079", "inst": "Xiamen University" }, { "name": "Lingchao Meng", "url": "https://openalex.org/A5100762853", "inst": "University of International Business and Economics" }, { "name": "Tian You", "url": "https://openalex.org/A5128701008", "inst": "Xiamen University" }, { "name": "G. Wayne Zhou", "url": "https://openalex.org/A5103226662", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis", "Central University of Finance and Economics", "Xiamen University", "University of International Business and Economics" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6217459", "doi": "10.2139/ssrn.6217459", "title": "Training for Technology: Adoption and Productive Use of Generative AI in Legal Analysis", "authors": [ "Benjamin Minhao Chen", "Hong Bao" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6217459", "field": "economics", "role": "object", "bullets": [ "Randomized experiment with 164 law students completing an issue-spotting examination under three conditions: no GenAI, optional LLM access, or access plus a ten-minute training.", "An unnamed large language model was made available to students; the study measures adoption and exam performance rather than using the model as a measurement tool, so no validation applies.", "Training raised LLM adoption from 26 to 41 percent and lifted scores by 0.27 grade points (p=0.027), while untrained access did not improve performance and produced shorter answers." ], "bullet_provenance": "ai", "salience": 61, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 199, "authors_detailed": [ { "name": "Benjamin Minhao Chen", "url": "", "inst": "Chinese University of Hong Kong" }, { "name": "Hong Bao", "url": "", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6168735", "doi": "10.2139/ssrn.6168735", "title": "The Efficiency Paradox in Agentic AI Workflows: When Optimization Increases the Electricity Bill", "authors": [ "Joren Gijsbrechts", "Willem van Jaarsveld" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6168735", "field": "management", "role": "object", "bullets": [ "Analytical operations model of firms deploying agentic AI workflows at scale, characterizing trade-offs among cost, speed, and quality of large language model calls, with no empirical sample.", "The large language model is the object of the analysis rather than a research tool; the model compares internal optimization, external time-flexibility discounts, and endogenous model selection.", "Jointly optimizing workflow and model capability yields an efficiency paradox in which cost and energy savings are offset by selecting more capable, energy-intensive models, so optimization need not lower the electricity bill." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 298, "authors_detailed": [ { "name": "Joren Gijsbrechts", "url": "https://openalex.org/A5128700796", "inst": "ESADE Business School" }, { "name": "Willem van Jaarsveld", "url": "https://openalex.org/A5128788417", "inst": "Eindhoven University of Technology" } ], "affiliations": [ "ESADE Business School", "Eindhoven University of Technology" ] }, { "uid": "doi:10.2139/ssrn.6240619", "doi": "10.2139/ssrn.6240619", "title": "AI and the Labor Market: A Worker's-Eye View", "authors": [ "anon Kim", "Nathan Mester", "Greg Sun" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6240619", "field": "economics", "role": "object", "bullets": [ "Survey of just under 2,000 US citizens about perceptions of generative AI in the workplace and career-relevant decisions since the launch of ChatGPT.", "No model is run by the researchers; the ChatGPT launch is a reference point and worker perceptions of generative AI are the object of study.", "Workers who see AI as a complement increase effort while those who see it as a substitute do not, and most report more time spent learning." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 48, "edition": 3, "audience": "general", "validated": null, "n": 365, "authors_detailed": [ { "name": "anon Kim", "url": "https://openalex.org/A5128763596", "inst": "" }, { "name": "Nathan Mester", "url": "https://openalex.org/A5128760288", "inst": "Washington University in St. Louis" }, { "name": "Greg Sun", "url": "https://openalex.org/A5079128404", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6240158", "doi": "10.2139/ssrn.6240158", "title": "How AI Shapes Firm Investment: Evidence From Mergers and Acquisitions", "authors": [ "Sebastiao Oliveira", "Vahid Shadram" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6240158", "field": "finance", "role": "object", "bullets": [ "US public firms classified by ex ante AI exposure from granular firm-technology data, studying merger and acquisition activity around the November 2022 ChatGPT release.", "No language model is used for measurement; the ChatGPT release serves as the shock to AI returns, and firm AI exposure is the studied variable.", "Higher-exposure acquirers buy more small, young, AI-producing targets and fewer non-AI targets, indicating reallocation rather than expansion of overall deal-making." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "edition": 3, "audience": "general", "validated": null, "n": 699, "authors_detailed": [ { "name": "Sebastiao Oliveira", "url": "https://openalex.org/A5126219001", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Vahid Shadram", "url": "https://openalex.org/A5081147834", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6119646", "doi": "10.2139/ssrn.6119646", "title": "Institutional Admissibility in Agentic Systems: A Theory of Deployment under Organizational Constraints", "authors": [ "Jinchun Chen" ], "posted": "2026-03-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6119646", "field": "management", "role": "object", "bullets": [ "Theoretical paper, no empirical sample; develops the Institutional Solvency Framework for deploying agentic AI systems inside production organizations under accountability constraints.", "No language model is applied by the authors; the paper formalizes a master objective pricing delivered value against verification latency, residual error, tail risk, and evidence cost.", "Under tight velocity constraints optimal governance moves to a minimum-admissible boundary, and evidence investment is systematically underprovided when local actors bear only part of downside risk." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1039, "authors_detailed": [ { "name": "Jinchun Chen", "url": "https://openalex.org/A5128783428", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6166986", "doi": "10.2139/ssrn.6166986", "title": "The Strategic Foresight of LLMs: Evidence from a Fully Prospective Venture Tournament", "authors": [ "Felipe A. Csaszar", "Aticus Peterson", "Daniel Wilde" ], "posted": "2026-03-10", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6166986", "field": "management", "role": "agent", "bullets": [ "Thirty US technology ventures raising on Kickstarter, launched after all studied models' training cutoffs, evaluated live while outcomes were unknown through 870 pairwise comparisons.", "A suite of frontier and open-weight LLMs, the best being Gemini 2.5 Pro, produced venture rankings, benchmarked against 346 experienced managers from Prolific and three MBA-trained investors.", "Frontier models reached rank correlations above 0.60 with actual outcomes, Gemini 2.5 Pro reaching 0.74, versus 0.04 to 0.45 for humans; hybrid teams did not outperform the best model." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "rank correlation of forecasts against actual Kickstarter outcomes", "salience": 72, "edition": 2, "audience": "broad", "n": 24, "authors_detailed": [ { "name": "Felipe A. Csaszar", "url": "", "inst": "University of Michigan" }, { "name": "Aticus Peterson", "url": "", "inst": "New York University" }, { "name": "Daniel Wilde", "url": "", "inst": "Indiana University Bloomington" } ], "affiliations": [ "New York University", "Indiana University Bloomington", "University of Michigan" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6140553", "doi": "10.2139/ssrn.6140553", "title": "Generative Pretrained Transformers for Investor-Centric Portfolio Construction", "authors": [ "Dimitrios Papakyriakopoulos", "Manolis Kritikos" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6140553", "field": "finance", "role": "agent", "bullets": [ "Ten virtual investor profiles with distinct risk preferences over a fixed three-month investment horizon, benchmarked against the S&P 500 index performance.", "GPT-4o constructed personalized portfolio allocations for each investor profile; performance measured by total return, volatility, beta, Sharpe ratio, and maximum drawdown.", "All ten AI-generated portfolios outperformed the S&P 500 with stronger risk-adjusted returns and lower maximum drawdowns over the three-month investment period." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 38, "n": 2404, "authors_detailed": [ { "name": "Dimitris Papakyriakopoulos", "url": "https://openalex.org/A5045065292", "inst": "Athens University of Economics and Business" }, { "name": "Manolis N. Kritikos", "url": "https://openalex.org/A5062572892", "inst": "Athens University of Economics and Business" } ], "affiliations": [ "Athens University of Economics and Business" ] }, { "uid": "doi:10.2139/ssrn.6375360", "doi": "10.2139/ssrn.6375360", "title": "How does AI Distribute the pie? Large Language Models and the Ultimatum Game", "authors": [ "Douglas Araujo", "Harald Uhlig" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6375360", "field": "economics", "role": "agent", "bullets": [ "Various LLMs tested as proposers and responders in ultimatum games varying stake size and opponent type (human versus AI) across both player roles.", "LLMs played both proposer and responder roles in ultimatum games; behavior analyzed conditional on stake size, counterpart type, and role assignment.", "LLM behavior is heterogeneous but predictable; a distinct altruistic mode emerges with hyper-fair proposals above 50%; proposers forgo more facing human responders." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": false, "salience": 65, "n": 2615, "authors_detailed": [ { "name": "Douglas Araujo", "url": "https://openalex.org/A5125049170", "inst": "Central Bank of Brazil" }, { "name": "Harald Uhlig", "url": "https://openalex.org/A5123675111", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Central Bank of Brazil", "National Bureau of Economic Research" ] }, { "uid": "doi:10.2139/ssrn.6117206", "doi": "10.2139/ssrn.6117206", "title": "Post-Cyber Week Decision-Making Snapshot 2025", "authors": [ "Alex Varricchio" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6117206", "field": "management", "role": "object", "bullets": [ "Online survey of 1,206 U.S. adults conducted December 5-7, 2025, immediately after Cyber Week, examining AI tool usage in holiday purchasing decisions.", "Study measured consumer adoption of ChatGPT, Gemini, Perplexity, and Claude for product comparison, deal-finding, gift selection, and idea generation.", "48.4% purchased items based on AI recommendations; AI tools ranked as the single most helpful source, surpassing search engines and retailer websites." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "salience": 58, "validated": null, "n": 2792, "authors_detailed": [ { "name": "Alex Varricchio", "url": "https://openalex.org/A5128779880", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6238458", "doi": "10.2139/ssrn.6238458", "title": "The Social Risks of Generative AI", "authors": [ "Marco Ceccarelli", "Renjie Wang" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6238458", "field": "finance", "role": "object", "bullets": [ "US publicly listed firms around the ChatGPT release (November 2022), using pre-event ESG scores as proxies for social risk management.", "Authors measured equity returns and option-implied downside risk around ChatGPT launch to test whether markets price AI-related social risks.", "Low-ESG firms underperformed high-ESG firms by 4 percentage points over two weeks; effect concentrated in data privacy and security pillar." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 76, "validated": null, "n": 3090, "authors_detailed": [ { "name": "Marco Ceccarelli", "url": "https://openalex.org/A5000191534", "inst": "Vrije Universiteit Amsterdam" }, { "name": "Renjie Wang", "url": "https://openalex.org/A5128774562", "inst": "" } ], "affiliations": [ "Vrije Universiteit Amsterdam" ] }, { "uid": "doi:10.2139/ssrn.6149987", "doi": "10.2139/ssrn.6149987", "title": "Flexibility or Structure? A Comparison of Customer Service User Experiences with LLM-Based and Intent-Based Conversational AI", "authors": [ "Marita Skjuve", "Knut Kvale", "Anna Grøndal Larsen", "Asbjørn Følstad", "Effie Lai-Chong Law", "Nena van As", "Silje Granås" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6149987", "field": "management", "role": "object", "bullets": [ "Two telecom customer service studies: Study 1 (N=226) compared systems separately; Study 2 (N=172) had participants use both and state preference.", "Participants interacted with LLM-based and intent-based conversational AI; quantitative and qualitative experience measures collected.", "No significant quantitative difference between systems; preferences split 55% vs 45%, driven by task type and interaction order effects." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 52, "validated": null, "n": 3091, "authors_detailed": [ { "name": "Marita Skjuve", "url": "https://openalex.org/A5009246228", "inst": "SINTEF" }, { "name": "Knut Kvale", "url": "https://openalex.org/A5034602927", "inst": "Telenor (Norway)" }, { "name": "Anna Grøndal Larsen", "url": "https://openalex.org/A5119867583", "inst": "SINTEF" }, { "name": "Asbjørn Følstad", "url": "https://openalex.org/A5128728821", "inst": "SINTEF" }, { "name": "Effie Lai‐Chong Law", "url": "https://openalex.org/A5033477639", "inst": "Independent" }, { "name": "Nena van As", "url": "https://openalex.org/A5088016692", "inst": "boost.ai" }, { "name": "Silje Bechmann Granås", "url": "https://openalex.org/A5093730299", "inst": "SINTEF" } ], "affiliations": [ "SINTEF", "Telenor (Norway)", "Independent", "boost.ai" ] }, { "uid": "doi:10.2139/ssrn.6162390", "doi": "10.2139/ssrn.6162390", "title": "How People Actually Use Generative AI: An Integrative Analysis of Real-World Use-Cases", "authors": [ "Dominic Vincent Ligot" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6162390", "field": "management", "role": "object", "bullets": [ "Three complementary sources: qualitative use-case taxonomy, large-scale behavioral data from millions of interactions, and occupational task-level mappings.", "Authors integrated real-world generative AI usage patterns across professional and personal contexts using multiple empirical data sources.", "Core uses cluster around writing, retrieval, explanation, and ideation; AI functions as cognitive infrastructure augmenting rather than replacing human labor." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 60, "validated": null, "n": 3092, "authors_detailed": [ { "name": "Dominic Vincent Ligot", "url": "https://openalex.org/A5087295738", "inst": "University of Asia and the Pacific" } ], "affiliations": [ "University of Asia and the Pacific" ] }, { "uid": "doi:10.2139/ssrn.6171288", "doi": "10.2139/ssrn.6171288", "title": "Labor Futures Under Artificial Intelligence: Scenarios for the Philippine Economy", "authors": [ "Dominic Vincent Ligot" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6171288", "field": "economics", "role": "object", "bullets": [ "Philippine labor force data with task-level AI capability evidence and firm-level adoption data, projecting three scenarios for 2025-2035.", "Authors assessed occupational exposure and complementarity to generative AI across Philippine employment using task-level mapping analysis.", "Most AI-exposed jobs show high complementarity favoring augmentation over displacement; outcomes depend on skills, governance, and social protection policy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 55, "validated": null, "n": 3093, "authors_detailed": [ { "name": "Dominic Vincent Ligot", "url": "https://openalex.org/A5128856493", "inst": "University of Asia and the Pacific" } ], "affiliations": [ "University of Asia and the Pacific" ] }, { "uid": "doi:10.2139/ssrn.6197938", "doi": "10.2139/ssrn.6197938", "title": "Working with Generative AI vs Humans: The Impact on Well-being", "authors": [ "Eirini Spiliotopoulou", "Tim Kraft", "Shawn Mankad", "Yanchong Zheng" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6197938", "field": "management", "role": "object", "bullets": [ "Incentivized laboratory experiment in which participants completed two operational tasks with either a GenAI chat assistant or a human collaborator via identical interface.", "GenAI served as chat-based collaborator on analytical tasks; validated psychological scales and multi-method text analysis of transcripts measured changes in positive and negative emotions.", "GenAI collaboration significantly reduced mental well-being versus human collaboration; GenAI suppressed positive emotions while human collaboration reduced negative emotions through expressive interaction." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3598, "authors_detailed": [ { "name": "Eirini Spiliotopoulou", "url": "https://openalex.org/A5064044856", "inst": "Tilburg University" }, { "name": "Tim Kraft", "url": "https://openalex.org/A5042446113", "inst": "North Carolina State University" }, { "name": "Shawn Mankad", "url": "https://openalex.org/A5026377944", "inst": "North Carolina State University" }, { "name": "Yanchong Zheng", "url": "https://openalex.org/A5004385083", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "Tilburg University", "North Carolina State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6174958", "doi": "10.2139/ssrn.6174958", "title": "From Lists to Oracles: Competition for Generative Search Visibility and Long-Tail Entry", "authors": [ "Xing Hu" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6174958", "field": "economics", "role": "object", "bullets": [ "Game-theoretic model of platform search competition between a high-authority incumbent and a low-authority entrant across query types.", "Analyzes the shift from ranked-list search to generative AI winner-takes-all answer slots using a strategic authority weight parameter.", "Generative search intensifies concentration on broad queries but enables specialized SMEs to capture niche queries via a long-tail flip." ], "bullet_provenance": "ai", "salience": 52, "models": [], "validated": null, "n": 3599, "authors_detailed": [ { "name": "Xing Hu", "url": "https://openalex.org/A5128786742", "inst": "University of Hong Kong" } ], "affiliations": [ "University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6224698", "doi": "10.2139/ssrn.6224698", "title": "Work Design and Compensation Strategies in a Post-COVID World: Evidence From Online Job Postings", "authors": [ "Chunmian Ge", "Huiqi Deng", "Dongyuan Wu", "Hanwei Huang", "Jim Dulebohn" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6224698", "field": "management", "role": "instrument", "bullets": [ "Over 1.8 million online job postings from Chinese publicly listed firms spanning the pre- and post-COVID period.", "An LLM systematically quantified work design characteristics from job posting text; causal effects estimated with difference-in-differences.", "The pandemic raised skill variety, autonomy, and work-from-home feasibility; work design characteristics mediated pandemic effects on salaries." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 3600, "authors_detailed": [ { "name": "Chunmian Ge", "url": "https://openalex.org/A5062149451", "inst": "South China University of Technology" }, { "name": "Hui Deng", "url": "https://openalex.org/A5030355724", "inst": "South China University of Technology" }, { "name": "Dongyuan Wu", "url": "https://openalex.org/A5059573245", "inst": "Fudan University" }, { "name": "Hanwei Huang", "url": "https://openalex.org/A5075594880", "inst": "City University of Hong Kong" }, { "name": "Jim Dulebohn", "url": "https://openalex.org/A5128755224", "inst": "Michigan State University" } ], "affiliations": [ "Michigan State University", "South China University of Technology", "Fudan University", "City University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6192158", "doi": "10.2139/ssrn.6192158", "title": "How should Macroeconomists think about AI and the Labor Market?", "authors": [ "Edoardo Gaffeo" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6192158", "field": "economics", "role": "object", "bullets": [ "Conceptual framework integrating task-based macroeconomic models with microeconomic analysis of AI as a labor-market technology.", "Synthesizes evidence showing AI adoption contracts employment among highly educated entry-level workers in prediction-intensive occupations.", "AI substitutes for implementation skills while complementing human judgment, reshaping job design and redistributing comparative advantage across tasks." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3601, "authors_detailed": [ { "name": "Edoardo Gaffeo", "url": "https://openalex.org/A5125347852", "inst": "University of Trento" } ], "affiliations": [ "University of Trento" ] }, { "uid": "doi:10.2139/ssrn.6221439", "doi": "10.2139/ssrn.6221439", "title": "Governing Agentic AI: Designing Authority in Systems that Act Without You", "authors": [ "Ilana Sprongl" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6221439", "field": "management", "role": "object", "bullets": [ "Organizational design analysis of governance for goal-directed AI agents in financial services, healthcare, and enterprise procurement scenarios.", "Paper proposes a four-layer governance architecture with threshold-based authority transitions and introduces governance latency as a design concept.", "Agentic systems can behave correctly within encoded constraints while producing outcomes that are strategically or ethically unacceptable to organizations." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3978, "authors_detailed": [ { "name": "Ilana Sprongl", "url": "https://openalex.org/A5128766753", "inst": "FIT Consulting (Italy)" } ], "affiliations": [ "FIT Consulting (Italy)" ] }, { "uid": "doi:10.2139/ssrn.6273578", "doi": "10.2139/ssrn.6273578", "title": "How will Companies Change when they Integrate AI Technologies? (\"AI, automation and the future of work\")", "authors": [ "Rishabh Srivastava" ], "posted": "2026-03-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6273578", "field": "management", "role": "object", "bullets": [ "Research essay examining organizational changes when firms integrate predictive, generative, and agentic AI into production-grade business workflows.", "Paper argues sustainable AI value requires organizational redesign around human-AI collaboration, data-centric models, and institutionalized governance.", "Firms must restructure workflows and build accountability mechanisms beyond tool adoption to capture lasting value from integrated AI technologies." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3979, "authors_detailed": [ { "name": "Rishabh Srivastava", "url": "https://openalex.org/A5128783277", "inst": "Klinik Bad Reichenhall" } ], "affiliations": [ "Klinik Bad Reichenhall" ] }, { "uid": "doi:10.2139/ssrn.6253818", "doi": "10.2139/ssrn.6253818", "title": "Early Warning at Scale: Large Language Models for Real-Time Risk Monitoring in SME Lending — Lessons from a Production Deployment", "authors": [ "Terrence Cai" ], "posted": "2026-03-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6253818", "field": "finance", "role": "instrument", "bullets": [ "SME credit portfolio at a digital bank, with the framework deployed in production and evaluated against live delinquency outcomes pooled across borrower cohorts.", "A dual-LLM agentic pipeline (model family not named) surfaces hidden person-to-business and business-to-business links and detects adverse events via a custom risk taxonomy and structured prompting, with qualitative expert review.", "Detection coverage rose 23 percent for hidden connections and 25 percent for adverse events; flagged borrowers had 2.3 to 2.4 times higher delinquency, three-month median lead time, 80 percent expert-confirmed precision." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert review, 80 percent signal precision", "salience": 70, "edition": 3, "audience": "general", "models": [], "n": 132, "authors_detailed": [ { "name": "Terrence Z. Cai", "url": "https://openalex.org/A5071497958", "inst": "World Bank" } ], "affiliations": [ "World Bank" ] }, { "uid": "doi:10.2139/ssrn.6257458", "doi": "10.2139/ssrn.6257458", "title": "Reliable Empirical Accounting Research in the Age of Generative AI", "authors": [ "Valerie Zhang", "Sean M Song", "Christopher Rigsby" ], "posted": "2026-03-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6257458", "field": "accounting", "role": "method", "bullets": [ "Not an empirical dataset study; the authors build a retrieval-augmented generation system over a corpus of accounting, finance, and economics papers and code to support empirical accounting research.", "The RAG system is compared against standalone commercial LLMs (families not named) on hallucination, literature-retrieval relevance, and replication reliability, with no numerical accuracy figure reported.", "The domain-specific system nearly eliminated hallucinations, retrieved more relevant prior literature, and produced faster and more reliable replications than the standalone commercial models." ], "bullet_provenance": "ai", "validated": false, "salience": 57, "edition": 3, "audience": "technical", "models": [], "n": 133, "authors_detailed": [ { "name": "Valerie Zhang", "url": "https://openalex.org/A5128524556", "inst": "University of California, Berkeley" }, { "name": "Myoungseok Song", "url": "https://openalex.org/A5128565995", "inst": "University of California, Berkeley" }, { "name": "Christopher Rigsby", "url": "https://openalex.org/A5128584445", "inst": "Vrije Universiteit Amsterdam" } ], "affiliations": [ "University of California, Berkeley", "Vrije Universiteit Amsterdam" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6261540", "doi": "10.2139/ssrn.6261540", "title": "The Ordo-Causal Attribution Deficit: A Prudential Capital Framework for Autonomous Multi-Agent Coding Systems in Financial Infrastructure", "authors": [ "Marcel Osmond" ], "posted": "2026-03-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6261540", "field": "finance", "role": "object", "bullets": [ "Analysis of regulatory gaps when autonomous coding agents modify core banking systems, financial algorithms, and customer-facing interfaces.", "Paper identifies the Ordo-Causal Attribution Deficit where existing prudential frameworks cannot assign liability in automated code generation chains.", "Framework proposes forensic standards for agentic causation and capital-based incentives to internalize externalities of unsupervised machine-generated code." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3977, "authors_detailed": [ { "name": "Marcel Osmond", "url": "https://openalex.org/A5126466857", "inst": "The Honourable Society of Lincoln's Inn" } ], "affiliations": [ "The Honourable Society of Lincoln's Inn" ] }, { "uid": "doi:10.2139/ssrn.6244621", "doi": "10.2139/ssrn.6244621", "title": "Does Teaching Entrepreneurship Produce Entrepreneurs?", "authors": [ "Elif Nisa Guler" ], "posted": "2026-03-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6244621", "field": "management", "role": "instrument", "bullets": [ "Over 150 US universities with staggered entrepreneurship program introductions, linked historical catalogs to LinkedIn career histories for more than 300,000 graduates; within-university adjacent-cohort difference-in-differences.", "A large language model, not named, performs text-based classification of graduates' ventures to distinguish small-business from growth-oriented entry, with no validation reported.", "Program introduction cuts immediate business formation by 7.5 percent, driven by small-business entry, with no long-run entry, survival, or growth-venture effect." ], "bullet_provenance": "ai", "validated": false, "salience": 56, "edition": 3, "audience": "general", "models": [], "n": 698, "authors_detailed": [ { "name": "Elif Nisa Guler", "url": "https://openalex.org/A5120501747", "inst": "Indiana University" } ], "affiliations": [ "Indiana University" ] }, { "uid": "doi:10.2139/ssrn.6245238", "doi": "10.2139/ssrn.6245238", "title": "Radical Novelties in Critical Technologies and Spillovers: How do China, The Us and The Eu Fare?", "authors": [ "Alicia Garcia-Herrero", "Michal Krystyanczuk", "Robin Schindowski" ], "posted": "2026-03-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6245238", "field": "economics", "role": "instrument", "bullets": [ "Patent data across AI, semiconductors, and quantum computing for China, the United States, and the European Union, tracking radical novelties and cross-region spillovers.", "LLMs classified patents as radical novelties based on whether the invention was previously unpatented and worth replicating; spillover speed measured across economic areas.", "The US dominates quantum and generative AI; China leads in more semiconductor subfields; the EU is slowest to replicate radical novelties from either the US or China." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 68, "n": 2538, "authors_detailed": [ { "name": "Alicia Garcia-Herrero", "url": "https://openalex.org/A5146766127", "inst": "Hong Kong University of Science and Technology" }, { "name": "Michal Krystyanczuk", "url": "https://openalex.org/A5128321926", "inst": "Bruegel" }, { "name": "Robin Schindowski", "url": "https://openalex.org/A5091958048", "inst": "Bruegel" } ], "affiliations": [ "Hong Kong University of Science and Technology", "Bruegel" ] }, { "uid": "doi:10.2139/ssrn.6300040", "doi": "10.2139/ssrn.6300040", "title": "Augmentation Versus Substitution: Asymmetric Effects of Generative AI on Firm Performance and Share Prices", "authors": [ "Toghrul Aghbabali", "Kee H. Chung" ], "posted": "2026-03-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6300040", "field": "finance", "role": "object", "bullets": [ "18,796 O*NET task descriptions scored and mapped to US firms via industry employment shares, with the ChatGPT release used as an information shock in a difference-in-differences design.", "A large language model, name not stated, scored each task for augmentation and substitution exposure; ChatGPT is the studied shock and no validation of the scoring against human coding is reported.", "Augmentation exposure raises profitability, productivity, and abnormal returns, with a 97-basis-point monthly five-factor alpha, while substitution exposure shows null return and negative profitability effects." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 66, "edition": 3, "audience": "general", "n": 197, "authors_detailed": [ { "name": "Toghrul Aghbabali", "url": "https://openalex.org/A5119256655", "inst": "University at Buffalo, State University of New York" }, { "name": "Kee H. Chung", "url": "https://openalex.org/A5011392134", "inst": "University at Buffalo, State University of New York" } ], "affiliations": [ "University at Buffalo, State University of New York" ] }, { "uid": "doi:10.2139/ssrn.6248918", "doi": "10.2139/ssrn.6248918", "title": "Patchwork, Misattention, and Pandering: Theory and Evidence on (Generative) AI Biases in Strategy", "authors": [ "Mu-Jeung Yang", "Teppo Felin", "Todd Zenger" ], "posted": "2026-03-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6248918", "field": "management", "role": "object", "bullets": [ "Conceptual analysis paired with empirical demonstrations of large language model behaviour on strategic problem-solving tasks; sample size and data not stated.", "Large language models, families not named, were prompted on strategy problems to demonstrate three biases, with outputs judged against coherence and root-cause reasoning and no reported accuracy statistic.", "Identifies patchwork bias, misattention to surface cues over root causes, and pandering toward agreeable answers as architecture-driven limits on strategic problem solving." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 198, "authors_detailed": [ { "name": "Mu-Jeung Yang", "url": "https://openalex.org/A5128201279", "inst": "University of Colorado Boulder" }, { "name": "Teppo Felin", "url": "https://openalex.org/A5026683520", "inst": "University of Utah" }, { "name": "Todd Zenger", "url": "https://openalex.org/A5128206766", "inst": "University of Utah" } ], "affiliations": [ "University of Colorado Boulder", "University of Utah" ] }, { "uid": "doi:10.2139/ssrn.6249619", "doi": "10.2139/ssrn.6249619", "title": "Financial Decision Making in the Age of AI", "authors": [ "Rawley Heimer" ], "posted": "2026-03-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6249619", "field": "finance", "role": "object", "bullets": [ "Perspective article reviewing the academic literature on personal financial decision making across three domains: savings versus consumption, investment decisions, and choices around debt.", "The article uses no empirical language model; it conceptually discusses how generative AI might reshape individuals' savings, investment, and debt decisions, naming no specific system.", "Argues people systematically deviate from normative financial-economics prescriptions in each domain and closes with a forward-looking view of GenAI's potential to reshape household choices." ], "bullet_provenance": "ai", "salience": 32, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 697, "authors_detailed": [ { "name": "Rawley Heimer", "url": "https://openalex.org/A5118612975", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6350432", "doi": "10.2139/ssrn.6350432", "title": "Click, Code, Earn: The Returns to Digital Skills", "authors": [ "Antonio Martins Neto", "Yan Liu", "saloni khurana", "Juan Porras" ], "posted": "2026-03-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6350432", "field": "economics", "role": "object", "bullets": [ "Over 67 million job postings from 29 countries spanning 2021 to 2024, with a harmonized digital skills taxonomy applied across high-income and lower-income groups.", "Regression analysis estimated advertised wage premiums for digital skills at extensive, intensive, and qualitative margins across countries and occupation types.", "Generative AI skills commanded 7-9 percent premiums in technical roles and 25-36 percent in non-technical roles; returns were consistently higher in low- and middle-income countries." ], "bullet_provenance": "ai", "salience": 58, "models": [], "validated": null, "n": 3597, "authors_detailed": [ { "name": "Antonio Martins Neto", "url": "https://openalex.org/A5128144799", "inst": "World Bank" }, { "name": "Yan Liu", "url": "https://openalex.org/A5128159628", "inst": "World Bank" }, { "name": "saloni khurana", "url": "https://openalex.org/A5128172393", "inst": "World Bank" }, { "name": "Juan Porras", "url": "https://openalex.org/A5128189888", "inst": "World Bank" } ], "affiliations": [ "World Bank" ] }, { "uid": "doi:10.2139/ssrn.6337539", "doi": "10.2139/ssrn.6337539", "title": "When AI Listens First: Crisis Triage and Emotional Support in Mental Health Helplines", "authors": [ "Xiaoquan Gao", "Yanhan Savannah Tang" ], "posted": "2026-03-04", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6337539", "field": "management", "role": "object", "bullets": [ "Stylized queueing model of a mental health helpline where incoming calls first interact with a generative AI system and may escalate to human counselors.", "LLM performs dual triage and emotional-support functions; analytical model derives optimal effort allocation between them. No specific model named.", "AI deployment produces nonmonotonic effects on outcomes; under mild conditions, effort should concentrate entirely on triage or on emotional support rather than splitting between them." ], "bullet_provenance": "ai", "salience": 55, "edition": 24, "models": [], "validated": null, "n": 4226, "authors_detailed": [ { "name": "Xiaoquan Gao", "url": "https://openalex.org/A5128071310", "inst": "Singapore Management University" }, { "name": "Yanhan Savannah Tang", "url": "https://openalex.org/A5128061061", "inst": "Southern Methodist University" } ], "affiliations": [ "Singapore Management University", "Southern Methodist University" ] }, { "uid": "doi:10.2139/ssrn.6326698", "doi": "10.2139/ssrn.6326698", "title": "AI, Human Cognition and Knowledge Collapse", "authors": [ "Daron Acemoglu", "Dingwen Kong", "Asuman E. Ozdaglar" ], "posted": "2026-03-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6326698", "field": "economics", "role": "object", "bullets": [ "Dynamic theoretical model of learning and decision-making where successful decisions require combining community-level general knowledge with individual context-specific knowledge as complements.", "Models how agentic AI substitutes for costly human effort in context-specific knowledge production, reducing incentives for learning that generates the public general knowledge externality.", "Economy can tip into knowledge-collapse steady state when agentic recommendations exceed an accuracy threshold; welfare is non-monotone in agentic AI precision level." ], "bullet_provenance": "ai", "salience": 80, "models": [], "validated": null, "n": 3236, "authors_detailed": [ { "name": "Daron Acemoglu", "url": "https://openalex.org/A5127795222", "inst": "National Bureau of Economic Research" }, { "name": "Dingwen Kong", "url": "https://openalex.org/A5041144949", "inst": "Massachusetts Institute of Technology" }, { "name": "Asuman E. Ozdaglar", "url": "https://openalex.org/A5146598929", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6311499", "doi": "10.2139/ssrn.6311499", "title": "The Worth of a «Wo»: Gender Bias in Financial Advice from LLMs", "authors": [ "Richard Foltyn", "Jonna Olsson" ], "posted": "2026-02-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6311499", "field": "finance", "role": "object", "bullets": [ "35 widely used LLMs from five vendors, each queried with otherwise identical prompts that vary only one word, man versus woman, in a financial advice request.", "Models not named individually; each is asked to recommend an equity allocation, and outputs are compared across the single-word gender change to detect differential advice.", "Women are advised to allocate 1.7 percentage points less to equity funds; the gap persists across vendors and generations and attenuates but survives with richer risk information." ], "bullet_provenance": "ai", "salience": 60, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 131, "authors_detailed": [ { "name": "Richard Foltyn", "url": "https://openalex.org/A5086441217", "inst": "Norwegian School of Economics" }, { "name": "Jonna Olsson", "url": "https://openalex.org/A5011971748", "inst": "Norwegian School of Economics" } ], "affiliations": [ "Norwegian School of Economics" ] }, { "uid": "doi:10.2139/ssrn.6298838", "doi": "10.2139/ssrn.6298838", "title": "Some Simple Economics of AGI", "authors": [ "Christian Catalini", "Xiang Hui", "Jane Wu" ], "posted": "2026-02-24", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6298838", "field": "economics", "role": "object", "bullets": [ "Conceptual economic model studies an agentic economy through competing automation and verification cost curves; it uses no empirical sample, geography, or observation period.", "No language model is used as an instrument; broadly autonomous AI agents enter the causal theory as systems that make measurable execution increasingly cheap.", "As automation costs fall, human verification becomes the binding constraint, shifting rents toward ground truth, provenance, liability underwriting, and work that cannot be readily measured." ], "bullet_provenance": "ai", "salience": 76, "edition": 23, "models": [], "validated": null, "n": 4196, "authors_detailed": [ { "name": "Christian Catalini", "url": "https://openalex.org/A5126700677", "inst": "Massachusetts Institute of Technology" }, { "name": "Xiang Hui", "url": "https://openalex.org/A5126675749", "inst": "Washington University in St. Louis" }, { "name": "Jane Wu", "url": "https://openalex.org/A5126743906", "inst": "University of California, Los Angeles" } ], "affiliations": [ "Massachusetts Institute of Technology", "Washington University in St. Louis", "University of California, Los Angeles" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6299060", "doi": "10.2139/ssrn.6299060", "title": "Can LLMs Aid Analogical Reasoning for Strategic Decisions? A Comparative Study", "authors": [ "Prothit Sen", "Maciej Workiewicz", "Phanish Puranam" ], "posted": "2026-02-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6299060", "field": "management", "role": "agent", "bullets": [ "Exploratory study extending classic analogical transfer designs with multiple source analogs and target problems for strategic decision-making.", "LLMs and humans performed analogical reasoning tasks; precision and recall measured on valid analogy retrieval and matching.", "LLMs achieved high recall but low precision; humans showed low recall but high precision, suggesting complementary roles in strategy making." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "precision and recall on valid analogy matching vs designed ground truth", "salience": 62, "n": 2791, "authors_detailed": [ { "name": "Prothit Sen", "url": "https://openalex.org/A5047584495", "inst": "Indian School of Business" }, { "name": "Maciej Workiewicz", "url": "https://openalex.org/A5041773261", "inst": "École Supérieure des Sciences Économiques et Commerciales" }, { "name": "Phanish Puranam", "url": "https://openalex.org/A5015167951", "inst": "INSEAD" } ], "affiliations": [ "INSEAD", "Indian School of Business", "École Supérieure des Sciences Économiques et Commerciales" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6275679", "doi": "10.2139/ssrn.6275679", "title": "NEOS - A Timely Indicator for Economic Outlook based on Swiss Newspaper Articles", "authors": [ "Elliot Beck", "Franziska Eckert", "Linus Kühne", "Helge Liebert", "Rina Rosenblatt-Wisch" ], "posted": "2026-02-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6275679", "field": "economics", "role": "instrument", "bullets": [ "Comprehensive sample of Swiss newspaper articles used to construct NEOS, a real-time indicator of sentiment regarding the economic outlook for Switzerland.", "LLMs combined with machine learning methods classified article-level economic sentiment, enabling near-real-time tracking of macroeconomic developments from news text.", "NEOS captured the negative shift in economic outlook sentiment following U.S. tariff announcements, demonstrating the indicator's responsiveness to exogenous macroeconomic shocks." ], "bullet_provenance": "ai", "validated": false, "salience": 70, "models": [], "n": 3596, "authors_detailed": [ { "name": "Elliot Beck", "url": "https://openalex.org/A5074416670", "inst": "Swiss National Bank" }, { "name": "Franziska Eckert", "url": "https://openalex.org/A5126384649", "inst": "Swiss National Bank" }, { "name": "Linus Kühne", "url": "https://openalex.org/A5126371339", "inst": "ETH Zurich" }, { "name": "Helge Liebert", "url": "https://openalex.org/A5041415938", "inst": "Swiss National Bank" }, { "name": "Rina Rosenblatt-Wisch", "url": "https://openalex.org/A5126388846", "inst": "Swiss National Bank" } ], "affiliations": [ "Swiss National Bank", "ETH Zurich" ] }, { "uid": "doi:10.2139/ssrn.6246347", "doi": "10.2139/ssrn.6246347", "title": "Does Generative AI Narrow Education-Based Productivity Gaps? Evidence from a Randomized Experiment", "authors": [ "Guillermo Cruces", "Diego Fernández Meijide", "Sebastian Galiani", "Ramiro Gálvez", "Maria Lombardi" ], "posted": "2026-02-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6246347", "field": "economics", "role": "object", "bullets": [ "Randomized online experiment with 1,174 adults aged 25-45 completing incentivized business problem-solving tasks with or without a GenAI assistant.", "Treated participants used a generative AI assistant; chat-log measures captured usage intensity and engagement patterns across education groups.", "AI closed three-quarters of the 0.548 SD education-based performance gap; lower-education participants retained partial gains after AI removal." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 82, "validated": null, "n": 3089, "authors_detailed": [ { "name": "Guillermo Cruces", "url": "https://openalex.org/A5004025028", "inst": "University of Nottingham" }, { "name": "DIEGO FERNÁNDEZ MEIJIDE", "url": "https://openalex.org/A5093789581", "inst": "University of San Andrés" }, { "name": "Sebastian Galiani", "url": "https://openalex.org/A5126317852", "inst": "Economie Publique" }, { "name": "Ramiro Gálvez", "url": "https://openalex.org/A5126293556", "inst": "Universidad Torcuato Di Tella" }, { "name": "Maria Lombardi", "url": "https://openalex.org/A5126320636", "inst": "Universidad Torcuato Di Tella" } ], "affiliations": [ "University of Nottingham", "University of San Andrés", "Economie Publique", "Universidad Torcuato Di Tella" ] }, { "uid": "doi:10.2139/ssrn.6256103", "doi": "10.2139/ssrn.6256103", "title": "Regime-Aware Portfolio Management via Retrieval-Augmented LLM-Guided Expert Switching", "authors": [ "Ahmad Asadi", "Reza Safabakhsh" ], "posted": "2026-02-17", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6256103", "field": "finance", "role": "instrument", "bullets": [ "Cryptocurrency portfolio experiments using a retrieval-augmented framework that matches current conditions to historical market regimes via a transformer-based variational autoencoder.", "An unspecified LLM agent estimates uncertainty and selects among expert portfolio strategies; no validation of its outputs against human judgment is reported.", "The framework delivers 1.19 percent ROI, a 20.80 Sharpe ratio, and 0.47 percent maximum drawdown, outperforming all tested baselines on cryptocurrency data." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 25, "models": [], "n": 4304, "authors_detailed": [ { "name": "Ahmad Asadi", "url": "https://openalex.org/A5126215459", "inst": "" }, { "name": "Reza Safabakhsh", "url": "https://openalex.org/A5043747222", "inst": "Amirkabir University of Technology" } ], "affiliations": [ "Amirkabir University of Technology" ] }, { "uid": "doi:10.2139/ssrn.6221878", "doi": "10.2139/ssrn.6221878", "title": "Generative Organizational Learning: Affordances for New Modes of Knowledge Search, Creation, Transfer, and Forgetting with LLMs Batia M. Wiesenfeld NYU Stern School of Business", "authors": [ "Batia Mishan Wiesenfeld", "Katherine Kellogg" ], "posted": "2026-02-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6221878", "field": "management", "role": "object", "bullets": [ "Case study of organizational learning at Northeast Health examining how generative AI integrates with actors' goals and institutional structures.", "Researchers studied how GenAI enables new modes of knowledge search, creation, transfer, and forgetting in healthcare operations.", "Generative AI affords domain knowledge abundance rather than scarcity; curating and guardrailing processes limit proliferation of risky knowledge." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 68, "validated": null, "n": 3088, "authors_detailed": [ { "name": "Batia M. Wiesenfeld", "url": "https://openalex.org/A5047828204", "inst": "New York University" }, { "name": "Katherine C. Kellogg", "url": "https://openalex.org/A5021677696", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "New York University", "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6127146", "doi": "10.2139/ssrn.6127146", "title": "The Innovation Tax: Generative AI Adoption, Productivity Paradox, and Systemic Risk in the U.S. Banking Sector", "authors": [ "Tatsuru Kikuchi" ], "posted": "2026-02-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6127146", "field": "finance", "role": "object", "bullets": [ "809 US financial institutions from 2018 to 2025, linking SEC 10-Q filings to Federal Reserve regulatory data to measure generative AI adoption in banking.", "Uses the November 2022 ChatGPT release as an exogenous shock in synthetic difference-in-differences and dynamic spatial Durbin models; no language model output is measured or validated by the researchers.", "Adopting banks show a 428-basis-point ROE decline, larger for small banks (517 versus 129 bp), with positive network spillovers pointing to algorithmic coupling and systemic risk." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 57, "edition": 3, "audience": "general", "validated": null, "n": 196, "authors_detailed": [ { "name": "Tatsuru Kikuchi", "url": "https://openalex.org/A5124980103", "inst": "The University of Tokyo" } ], "affiliations": [ "The University of Tokyo" ] }, { "uid": "doi:10.2139/ssrn.6073946", "doi": "10.2139/ssrn.6073946", "title": "Risk preferences of Large Language Model Agents : Evidence from Prospect Theory", "authors": [ "Nency Dhameja" ], "posted": "2026-02-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6073946", "field": "economics", "role": "agent", "bullets": [ "Experimental study estimating Cumulative Prospect Theory parameters across eight LLM architectures using 24 lottery instruments and six behavioral personas.", "LLM agents made repeated decisions under risk in lottery gambles with 25 sessions per model; parameters estimated via joint maximum likelihood.", "Framework identifies probability weighting, loss aversion, and value curvature for LLM agents and measures how their risk preferences evolve with experience." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 65, "n": 2790, "authors_detailed": [ { "name": "Nency Dhameja", "url": "https://openalex.org/A5125504784", "inst": "Binghamton University" } ], "affiliations": [ "Binghamton University" ] }, { "uid": "doi:10.2139/ssrn.6061954", "doi": "10.2139/ssrn.6061954", "title": "Why AI Economics Fail: Cost Structures, Billing Models, and Stalled Adoption", "authors": [ "Alan Jacobson" ], "posted": "2026-02-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6061954", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of generative AI cost structures and adoption barriers using public disclosures, earnings call statements, and observable usage patterns from major AI providers.", "Paper evaluates token-based and flat-rate subscription pricing against underlying compute costs and examines absent memory persistence and user governance as trust barriers.", "Current AI pricing proxies fail to reflect compute costs or user value; without changes to billing, memory, and governance, profitable enterprise-scale adoption is blocked." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3595, "authors_detailed": [ { "name": "Alan Jacobson", "url": "https://openalex.org/A5125461572", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6156090", "doi": "10.2139/ssrn.6156090", "title": "The Catastrophe of Declining Male College Enrollment: Gemini Predictions and Solutions", "authors": [ "Reginald Bell" ], "posted": "2026-02-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6156090", "field": "economics", "role": "agent", "bullets": [ "A single-author session with Google Gemini on 23 January 2026, using five escalating questions about declining US male college enrollment, with Gemini responses as the material.", "Google Gemini is prompted with five successive questions and its unedited raw overviews are reproduced in tables, treated as expert predictions; no validation is performed.", "Gemini predicts that uncorrected declines in male college enrollment could produce catastrophic economic outcomes for Generation Z men and women; no magnitude is given." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "salience": 20, "edition": 3, "audience": "general", "validated": null, "n": 364, "authors_detailed": [ { "name": "Reginald Bell", "url": "https://openalex.org/A5125256896", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.6121746", "doi": "10.2139/ssrn.6121746", "title": "When AI Meets Stablecoin: Dissecting the De-pegging Risk with LLM Agents", "authors": [ "Congcong Bo", "Dehua Shen" ], "posted": "2026-02-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6121746", "field": "finance", "role": "agent", "bullets": [ "A theoretical agent-based market populated by LLM-powered investors reacting to narrative information shocks, built to study stablecoin de-pegging risk; no real-world sample.", "No model named; LLM agents process unstructured narrative data to form beliefs and trade, reproducing investor panic dynamics, with no validation against ground truth.", "Crossing a narrative severity threshold triggers cognitive de-pegging, with expected maximum price deviation surging about 1,441 basis points as beliefs converge into synchronized selling." ], "bullet_provenance": "ai", "salience": 50, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 130, "authors_detailed": [ { "name": "Congcong Bo", "url": "https://openalex.org/A5065397382", "inst": "Nankai University" }, { "name": "Dehua Shen", "url": "https://openalex.org/A5070440485", "inst": "Nankai University" } ], "affiliations": [ "Nankai University" ] }, { "uid": "doi:10.2139/ssrn.6103266", "doi": "10.2139/ssrn.6103266", "title": "Artificial Code", "authors": [ "Clark D. Asay" ], "posted": "2026-02-03", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6103266", "field": "economics", "role": "object", "bullets": [ "United States copyright doctrine and the free and open source software ecosystem, treated as legal analysis with no sample, period or unit of observation.", "No language model is run or named here. Code produced by generative tools is the subject, and the argument turns on authorship doctrine rather than on any measurement.", "Machine written code lacks a human author and so falls into the public domain, which undercuts open source licensing, pushes firms toward trade secrecy and patents, and shrinks the human code supply that future models need." ], "bullet_provenance": "ai", "salience": 54, "edition": 17, "models": [], "validated": null, "n": 2142, "authors_detailed": [ { "name": "Clark D. Asay", "url": "https://openalex.org/A5010888963", "inst": "Brigham Young University" } ], "affiliations": [ "Brigham Young University" ] }, { "uid": "doi:10.2139/ssrn.6136529", "doi": "10.2139/ssrn.6136529", "title": "Agentic FinTech: A Comprehensive Survey on AI Agents in Finance in the Era of LLMs", "authors": [ "Yaxiong Wu", "Yixuan Li" ], "posted": "2026-02-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6136529", "field": "finance", "role": "object", "bullets": [ "Survey of the emerging Agentic FinTech paradigm, spanning application domains such as trading, risk management, and regulatory compliance, with no empirical sample.", "Reviews LLM-based financial agents with perception, planning, memory, and tool use; no single model is deployed or validated by the authors.", "Formalizes core agentic capabilities and organizes workflows, architectures, and evaluation methods, outlining open challenges toward trustworthy agentic financial systems, with no empirical finding." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 93, "authors_detailed": [ { "name": "Yaxiong Wu", "url": "https://openalex.org/A5124891993", "inst": "University of Glasgow" }, { "name": "Yixuan Li", "url": "https://openalex.org/A5124905776", "inst": "University of Glasgow" } ], "affiliations": [ "University of Glasgow" ] }, { "uid": "doi:10.3386/w34777", "doi": "10.3386/w34777", "title": "AI and the Quantity and Quality of Creative Products: Have LLMs Boosted Creation of Valuable Books?", "authors": [ "Imke Reimers", "Joel Waldfogel" ], "posted": "2026-02-03", "added": "2026-07-24", "source_label": "NBER", "url": "https://doi.org/10.3386/w34777", "field": "economics", "role": "object", "bullets": [ "New book releases across categories during the diffusion of LLMs from 2022 to 2025, when new releases tripled; the unit of observation is the book release and geography is not stated.", "LLMs are the object of study; the authors build a ratings-based usage measure comparable across release vintages and calibrate a nested logit model of book demand, naming no specific model.", "Average quality of AI-influx vintages fell, yet the top 1,000 monthly releases per category rose in quality; steady-state consumer surplus from books could increase by a quarter to a half." ], "bullet_provenance": "ai", "salience": 65, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 696, "authors_detailed": [ { "name": "Imke Reimers", "url": "https://openalex.org/A5027460637", "inst": "Northeastern University" }, { "name": "Joel Waldfogel", "url": "https://openalex.org/A5124753961", "inst": "" } ], "affiliations": [ "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.6105327", "doi": "10.2139/ssrn.6105327", "title": "Compute, Complexity, and the Scaling Laws of Return Predictability", "authors": [ "Allan Timmermann", "Luka Vulicevic" ], "posted": "2026-02-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6105327", "field": "finance", "role": "method", "bullets": [ "Firm-level cross-sectional US stock return predictions using models trained with varying computational resources over recent decades.", "Scaling laws from LLM training applied to quantify how compute improves forecast accuracy and to estimate limits of return predictability.", "Power laws explain over 80% of performance variation; a 25% compute increase would have raised investor Sharpe ratios by roughly 10% over 30 years." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": true, "validation_note": "cross-sectional return prediction R-squared and Sharpe ratio", "salience": 78, "n": 3087, "authors_detailed": [ { "name": "Allan Timmermann", "url": "https://openalex.org/A5124894861", "inst": "University of California San Diego" }, { "name": "Luka Vulicevic", "url": "https://openalex.org/A5124877376", "inst": "University of California San Diego" } ], "affiliations": [ "University of California San Diego" ] }, { "uid": "doi:10.2139/ssrn.6108946", "doi": "10.2139/ssrn.6108946", "title": "The use of AI in 10-K Filings: An Empirical Analysis of S&P 500 Reports", "authors": [ "Marcelo Perlin", "Cristian Foguesatto", "Aliki Karagrigoriou Galanos", "Felipe Affonso" ], "posted": "2026-02-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6108946", "field": "accounting", "role": "object", "bullets": [ "S&P 500 constituent companies' 10-K filings from 2018 to 2024, covering three report sections across all index members over seven years.", "Desklib's LLM estimated AI-generation probability in each filing section while FinBERT scored sentiment; both models applied across all annual reports.", "MD&A showed highest AI adoption probability; younger and smaller firms used AI more; AI detection correlated positively with sentiment, especially in Risk Factors." ], "bullet_provenance": "ai", "models": [ "open_other", "legacy" ], "open_weights": true, "validated": false, "salience": 62, "n": 3594, "authors_detailed": [ { "name": "Marcelo Perlin", "url": "https://openalex.org/A5124760984", "inst": "Universidade Federal do Rio Grande do Sul" }, { "name": "Cristian Rogério Foguesatto", "url": "https://openalex.org/A5046853654", "inst": "Universidade Federal do Rio Grande do Sul" }, { "name": "Aliki Karagrigoriou Galanos", "url": "https://openalex.org/A5005589915", "inst": "Universidade Federal do Rio Grande do Sul" }, { "name": "FELIPE AFFONSO", "url": "https://openalex.org/A5075684996", "inst": "Universidade Federal do Rio Grande do Sul" } ], "affiliations": [ "Universidade Federal do Rio Grande do Sul" ] }, { "uid": "doi:10.2139/ssrn.6096787", "doi": "10.2139/ssrn.6096787", "title": "The Organizational Intelligence Loop: A Socio-Technical Framework for Adaptive Enterprise AI Adoption", "authors": [ "Yumi Kimura" ], "posted": "2026-02-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6096787", "field": "management", "role": "object", "bullets": [ "Practitioner interviews, systematic analysis of public industry commentary, and enterprise behavioral and collaboration metadata from a knowledge-sharing platform; sample size, period, and geography not stated.", "No language model is applied by the researchers; the study examines enterprise adoption of generative and agentic AI and proposes the Organizational Intelligence Loop socio-technical framework.", "Finds model accuracy and access are insufficient; adoption is stronger where knowledge is structurally governable, expertise and trust signals are observable, and learning is reinforced through workflow-embedded feedback." ], "bullet_provenance": "ai", "salience": 36, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 546, "authors_detailed": [ { "name": "Yumi W. Kimura", "url": "https://openalex.org/A5122238665", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6134446", "doi": "10.2139/ssrn.6134446", "title": "Autonomous Market Intelligence: Agentic AI Nowcasting Predicts Stock Returns", "authors": [ "Darcy Pu", "Zefeng Chen" ], "posted": "2026-02-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6134446", "field": "finance", "role": "agent", "bullets": [ "Russell 1000 stocks evaluated daily from April 2025 using real-time web search; a fully out-of-sample nowcasting design free of look-ahead bias by construction.", "A state-of-the-art large language model, not named, autonomously searches the web, filters sources, and synthesizes information into daily quantitative stock attractiveness rankings; no external validation benchmark reported.", "Longing the 20 top-ranked stocks yields a daily six-factor alpha of 18.5 basis points and annualized Sharpe of 2.43, with predictability concentrated only in top winners." ], "bullet_provenance": "ai", "salience": 66, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 547, "authors_detailed": [ { "name": "Darcy Pu", "url": "https://openalex.org/A5122828096", "inst": "Peking University" }, { "name": "Zefeng Chen", "url": "https://openalex.org/A5122743229", "inst": "Peking University" } ], "affiliations": [ "Peking University" ] }, { "uid": "doi:10.2139/ssrn.6151347", "doi": "10.2139/ssrn.6151347", "title": "Who Gets a Seat at the Table? Stakeholder Participation in SEC Rulemaking", "authors": [ "Yuliya Guseva", "Irena Hutton", "Adam C. Pritchard", "Joseph A. Grundfest" ], "posted": "2026-01-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6151347", "field": "finance", "role": "instrument", "bullets": [ "453 SEC proposed rules (1995-2024) with over 81,000 public comments and 5,600 stakeholder meetings with SEC officials.", "LLMs identified participants and extracted structured measures of comment content and tone from public regulatory submissions.", "Sophisticated stakeholder input is more likely incorporated into final rules; rulemaking outcomes respond to participation quality, not comment volume." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 75, "n": 3086, "authors_detailed": [ { "name": "Yuliya Guseva", "url": "https://openalex.org/A5124405277", "inst": "Florida State University" }, { "name": "Irena Hutton", "url": "https://openalex.org/A5040019375", "inst": "Florida State University" }, { "name": "Adam C. Pritchard", "url": "https://openalex.org/A5124390972", "inst": "University of Michigan" }, { "name": "Joseph A. Grundfest", "url": "https://openalex.org/A5002433511", "inst": "Stanford Medicine" } ], "affiliations": [ "Florida State University", "University of Michigan", "Stanford Medicine" ] }, { "uid": "doi:10.2139/ssrn.6014134", "doi": "10.2139/ssrn.6014134", "title": "An Introduction to AI, Generative AI, and Agentic AI in Finance: Efficiency, Ethics, and the Future of High-Speed Trading", "authors": [ "Arvind Ashta" ], "posted": "2026-01-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6014134", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 30, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 1038, "authors_detailed": [ { "name": "Arvind Ashta", "url": "https://openalex.org/A5015640288", "inst": "Health Advances (United States)" } ], "affiliations": [ "Health Advances (United States)" ] }, { "uid": "doi:10.2139/ssrn.6134114", "doi": "10.2139/ssrn.6134114", "title": "Behavioral Economics of AI: LLM Biases and Corrections", "authors": [ "Pietro Bini", "Lin William Cong", "Xing Huang", "Lawrence J. Jin" ], "posted": "2026-01-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6134114", "field": "economics", "role": "agent", "bullets": [ "Comprehensive experiments replicating cognitive psychology and experimental economics paradigms originally designed to document human biases, tested across major LLM families at varying scales.", "Prominent LLM families completed preference-based and belief-based economic decision tasks across model versions and sizes; rational-prompting tested as a debiasing intervention.", "Advanced models produce more human-like preferences but more rational beliefs; prompting for rationality reduces behavioral biases, with patterns varying by task type and model scale." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 75, "n": 2403, "authors_detailed": [ { "name": "Pietro Bini", "url": "https://openalex.org/A5123973969", "inst": "Boston University" }, { "name": "Lin William Cong", "url": "https://openalex.org/A5124028098", "inst": "Nanyang Technological University" }, { "name": "Xing Huang", "url": "https://openalex.org/A5124030645", "inst": "Cornell University" }, { "name": "Lawrence J. Jin", "url": "https://openalex.org/A5101499391", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Boston University", "Cornell University", "Nanyang Technological University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6075786", "doi": "10.2139/ssrn.6075786", "title": "AI as an Informational Intermediary", "authors": [ "Swaminathan Balasubramaniam", "Jorge Sabat" ], "posted": "2026-01-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6075786", "field": "finance", "role": "object", "bullets": [ "Controlled experiment using Due Diligence posts from Reddit r/wallstreetbets, generating AI replies under neutral, retail-investor, and professional persona conditions.", "LLMs generated investment-opinion responses to user posts; sycophancy measured as excess sentiment agreement relative to human replies, with entity-neutering to rule out lookahead bias.", "Default LLM personas exhibited 0.4-0.5 sentiment points of excess agreement versus human replies; a professional prompt eliminated the effect, suggesting sycophancy accelerates information cascades." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "sentiment comparison against human Reddit replies", "salience": 78, "n": 2537, "authors_detailed": [ { "name": "Swaminathan Balasubramaniam", "url": "https://openalex.org/A5123968612", "inst": "NEOMA Business School" }, { "name": "Jorge Sabat", "url": "https://openalex.org/A5124025233", "inst": "Universidad Andrés Bello" } ], "affiliations": [ "NEOMA Business School", "Universidad Andrés Bello" ] }, { "uid": "doi:10.2139/ssrn.6059674", "doi": "10.2139/ssrn.6059674", "title": "Generative AI and Occupational Entry Barriers: The Labor-Supply Channel of Technological Change", "authors": [ "Seyed Mahdi Hosseini Maasoum", "Guy Lichtinger" ], "posted": "2026-01-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6059674", "field": "economics", "role": "instrument", "bullets": [ "O*NET task data covering US occupations, used to build measures of GenAI-induced changes in occupational expertise requirements and potential supply shifts; period not stated.", "An unnamed large language model rates occupations to construct the expertise and supply-shift measures, checked against occupational characteristics and real-world GenAI usage without a reported statistic.", "Productivity gains widen between-occupation wage inequality while reduced entry barriers compress it, so the two forces push wages in opposite directions." ], "bullet_provenance": "ai", "validated": false, "validation_note": "checked against real-world GenAI usage, no figure reported", "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 363, "authors_detailed": [ { "name": "Seyed Mahdi Hosseini Maasoum", "url": "https://openalex.org/A5119565702", "inst": "Harvard University Press" }, { "name": "Guy Lichtinger", "url": "https://openalex.org/A5123827193", "inst": "Harvard University" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5939874", "doi": "10.2139/ssrn.5939874", "title": "ChatGPT Generates a Novel Tax Strategy", "authors": [ "Andrew Blair-Stanek", "Nils Holzenberger", "Benjamin Van Durme" ], "posted": "2026-01-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5939874", "field": "accounting", "role": "agent", "bullets": [ "Case study examining ChatGPT's capacity to generate tax minimization strategies not previously documented in professional literature.", "ChatGPT produced a novel tax strategy; authors assessed its legal validity against existing US tax code provisions.", "The LLM generated the first publicly disclosed novel tax minimization strategy, demonstrating autonomous creative tax-planning capability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "n": 3085, "authors_detailed": [ { "name": "Andrew Blair-Stanek", "url": "https://openalex.org/A5123849951", "inst": "University of Maryland, Baltimore" }, { "name": "Nils Holzenberger", "url": "https://openalex.org/A5017498603", "inst": "Institut Polytechnique de Paris" }, { "name": "Benjamin Van Durme", "url": "https://openalex.org/A5075825791", "inst": "Johns Hopkins" } ], "affiliations": [ "University of Maryland, Baltimore", "Institut Polytechnique de Paris", "Johns Hopkins" ] }, { "uid": "doi:10.2139/ssrn.5943754", "doi": "10.2139/ssrn.5943754", "title": "Intelligence Equilibrium: Designing the Human-AI Operating Model", "authors": [ "Sanjay Singh" ], "posted": "2026-01-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5943754", "field": "management", "role": "object", "bullets": [ "Enterprise-style workflows spanning API factory, SDLC, and retail operations patterns at organizations adopting generative AI for software delivery and operations.", "Proposed Intelligence Equilibrium framework treating skills as atomic work units, balancing human judgment with AI execution across five design dimensions with feedback loops.", "Framework improved throughput, cycle time, rework rates, and incident resolution while reducing cognitive load across tested enterprise workflow patterns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 3593, "authors_detailed": [ { "name": "Sanjay Singh", "url": "https://openalex.org/A5123840362", "inst": "Independent - affiliation not provided to SSRN" } ], "affiliations": [ "Independent - affiliation not provided to SSRN" ] }, { "uid": "doi:10.2139/ssrn.6090208", "doi": "10.2139/ssrn.6090208", "title": "AI-Driven Transformation in Insurance Underwriting: Architecture, Efficiency, and Regulatory Alignment", "authors": [ "Rohit Kumar" ], "posted": "2026-01-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6090208", "field": "finance", "role": "object", "bullets": [ "Architecture proposal for AI-driven insurance underwriting combining document processing, rules engines, ML risk scoring, and multi-LLM orchestration.", "Framework integrates intelligent document processing, XGBoost scoring, and agentic AI to shift underwriting from reactive assessment to predictive models.", "Projected outcomes include 90% reduction in quote times and 50% reduction in operational costs with phased rollout through 2027." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3976, "authors_detailed": [ { "name": "Rohit Kumar", "url": "https://openalex.org/A5100786052", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.6085086", "doi": "10.2139/ssrn.6085086", "title": "LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning", "authors": [ "Junyu Cao", "Ruijiang Gao", "Esmaeil Keyvanshokooh", "Jianhao Ma" ], "posted": "2026-01-22", "added": "2026-08-03", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6085086", "field": "management", "role": "method", "bullets": [ "Sequential treatment decisions in personalized medicine; experiments use synthetic environments and a real hypertension management case study.", "An unnamed LLM supplies domain knowledge to warm start a contextual bandit that selects treatments and feasible changes to mutable patient features, with theory bounding LLM consultations at order log squared T.", "The combined algorithm improves regret, treatment quality, and sample efficiency over standard contextual bandits and LLM only baselines, and provably never does worse than a pure bandit when the LLM is unreliable." ], "bullet_provenance": "ai", "salience": 48, "edition": 9, "models": [], "validated": null, "n": 1338, "authors_detailed": [ { "name": "Junyu Cao", "url": "https://openalex.org/A5002422731", "inst": "The University of Texas at Austin" }, { "name": "Ruijiang Gao", "url": "https://openalex.org/A5102426480", "inst": "University of Texas at Dallas" }, { "name": "Esmaeil Keyvanshokooh", "url": "https://openalex.org/A5088382157", "inst": "Texas A&M University" }, { "name": "Jianhao Ma", "url": "https://openalex.org/A5123510677", "inst": "University of Pennsylvania" } ], "affiliations": [ "The University of Texas at Austin", "University of Texas at Dallas", "University of Pennsylvania", "Texas A&M University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5998716", "doi": "10.2139/ssrn.5998716", "title": "The Shopper Schism beyond Consumer Goods: Algorithmic Agency in Tourism, Professional Services, and the Universal Logic of Delegated Commerce", "authors": [ "Paul F. Accornero" ], "posted": "2026-01-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5998716", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of algorithmic intermediation across tourism, public relations, and professional advisory services sectors.", "Paper extends the Shopper Schism framework showing AI agents mediate transactions between human principals and commercial counterparties.", "Algorithmic intermediaries transfer loyalty from provider brands to platforms, creating shadow principals whose optimization objectives may diverge from consumer preferences." ], "bullet_provenance": "ai", "salience": 28, "models": [], "validated": null, "n": 3975, "authors_detailed": [ { "name": "Paul Accornero", "url": "https://openalex.org/A5120006801", "inst": "AULSS 2 Marca Trevigiana" } ], "affiliations": [ "AULSS 2 Marca Trevigiana" ] }, { "uid": "doi:10.2139/ssrn.5977694", "doi": "10.2139/ssrn.5977694", "title": "Credit Risk Modeling: Reject Inferencing with AI and Synthetic Data", "authors": [ "Patralekha Bhattacharya", "Krishna Mehta", "Shikha Agarwal", "Jisha Augustine", "Reshmi Bhaskar" ], "posted": "2026-01-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5977694", "field": "finance", "role": "instrument", "bullets": [ "Credit risk modeling framework addressing reject inferencing bias from excluding rejected loan applicants from training data.", "LLMs and diffusion models generated synthetic data to augment non-funded applicant samples across four experiments with fidelity and privacy metrics.", "Diffusion models matched real distributional statistics and maintained privacy without filtering; LLM-generated samples required additional privacy filtering." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "distribution fidelity and distance-to-closest-record across four experiments", "salience": 55, "n": 2789, "authors_detailed": [ { "name": "Patralekha Bhattacharya", "url": "https://openalex.org/A5049588964", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Krishna Mehta", "url": "https://openalex.org/A5123503210", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Shikha Agarwal", "url": "https://openalex.org/A5123483647", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Jisha Augustine", "url": "https://openalex.org/A5051878266", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Reshmi Bhaskar", "url": "https://openalex.org/A5123504275", "inst": "Independent - affiliation not provided to SSRN" } ], "affiliations": [ "Independent - affiliation not provided to SSRN" ] }, { "uid": "doi:10.2139/ssrn.6093326", "doi": "10.2139/ssrn.6093326", "title": "Measuring Efficiency and Equity Framing in Economics Research: LLM-Based Evidence from 1950 to 2021", "authors": [ "Sebastian Galiani", "Ramiro Gálvez", "Franco Mettola La Giglia", "Raul A. Sosa" ], "posted": "2026-01-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6093326", "field": "economics", "role": "instrument", "bullets": [ "Full-text corpus of 27,464 economics journal articles published from 1950 to 2021, with Economic Report of the President transmittal letters as external benchmark.", "An LLM-based measurement pipeline classified each article's normative framing along the efficiency-equity dimension; results validated against presidential transmittal letters.", "Efficiency-focused framing peaked in the late 1980s then declined; equity framing expanded after 1990 and reached parity with efficiency-only framing by 2021 in applied work." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Validated against Economic Report of the President transmittal letters as external benchmark", "salience": 65, "n": 3591, "authors_detailed": [ { "name": "Sebastián Galiani", "url": "https://openalex.org/A5029505552", "inst": "Economie Publique" }, { "name": "Ramiro Gálvez", "url": "https://openalex.org/A5123382585", "inst": "Universidad Torcuato Di Tella" }, { "name": "Franco Mettola La Giglia", "url": "https://openalex.org/A5123406449", "inst": "University of San Andrés" }, { "name": "Raul A. Sosa", "url": "https://openalex.org/A5119350715", "inst": "University of San Andrés" } ], "affiliations": [ "Economie Publique", "Universidad Torcuato Di Tella", "University of San Andrés" ] }, { "uid": "doi:10.2139/ssrn.6102446", "doi": "10.2139/ssrn.6102446", "title": "Artificial Intelligence, Demand Elasticity, and Asset Prices", "authors": [ "Kose John", "Jingrui Li" ], "posted": "2026-01-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6102446", "field": "finance", "role": "object", "bullets": [ "U.S. institutional investors' 13F holdings merged with CRSP and Compustat data, using the public release of ChatGPT as a natural experiment for AI-related valuation shocks.", "Study embedded institutional exposure to AI-intensive firms into a demand-system asset-pricing framework to estimate how AI adoption alters demand elasticity and price impact.", "AI exposure reduces institutional demand elasticity and amplifies equilibrium price impact, especially among banks; ChatGPT-related valuation shocks dissipate more slowly under inelastic demand." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 3592, "authors_detailed": [ { "name": "Kose John", "url": "https://openalex.org/A5117445824", "inst": "New York University" }, { "name": "Jingrui Li", "url": "https://openalex.org/A5123442465", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "New York University", "Stevens Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6035794", "doi": "10.2139/ssrn.6035794", "title": "What Firms Actually Lose (and Gain) from Extreme Weather Event Impacts", "authors": [ "Tobias Schimanski", "Glen Gostlow", "Malte Toetzke", "Markus Leippold" ], "posted": "2026-01-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6035794", "field": "finance", "role": "instrument", "bullets": [ "1.7 million corporate filings from all publicly listed US firms (2005-2024) mapped to 286 specific extreme weather events.", "LLMs identified and classified 13,277 firm-event impacts by channel (direct asset vs. indirect flow) and directionality from public filings.", "Negative impacts caused cumulative abnormal returns of -2.36% per event; total firm value losses reached $2.709 trillion inflation-adjusted over two decades." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 80, "n": 3084, "authors_detailed": [ { "name": "Tobias Schimanski", "url": "https://openalex.org/A5122963256", "inst": "University of Zurich" }, { "name": "Glen Gostlow", "url": "https://openalex.org/A5055140586", "inst": "University of Zurich" }, { "name": "Malte Toetzke", "url": "https://openalex.org/A5058238942", "inst": "Max Planck Institute for Innovation and Competition" }, { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" } ], "affiliations": [ "University of Zurich", "Max Planck Institute for Innovation and Competition" ] }, { "uid": "doi:10.2139/ssrn.5907184", "doi": "10.2139/ssrn.5907184", "title": "The Automaton Economy: A Strategic Framework for Navigating AI Agent-Driven Transformation", "authors": [ "Paul F. Accornero" ], "posted": "2026-01-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5907184", "field": "management", "role": "object", "bullets": [ "Conceptual framework examining autonomous AI agents as economic decision-makers across enterprise and consumer adoption contexts.", "Paper theorizes three structural principles: cognitive decoupling, data centrality, and institutional intermediation as AI agents assume economic roles.", "Framework argues the governance window is narrowing as path dependencies form around agent-mediated commerce and organizational architectures." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3974, "authors_detailed": [ { "name": "Paul F. Accornero", "url": "https://openalex.org/A5123026519", "inst": "AULSS 2 Marca Trevigiana" } ], "affiliations": [ "AULSS 2 Marca Trevigiana" ] }, { "uid": "doi:10.2139/ssrn.6043794", "doi": "10.2139/ssrn.6043794", "title": "Who Invests, Who Gets Funded: Gender and Racial Bias in LLM-Generated Investment Advice", "authors": [ "Ye Wang", "Kexin Gu" ], "posted": "2026-01-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6043794", "field": "finance", "role": "agent", "bullets": [ "Two-sided audit framework testing demographic bias in LLM-generated investment advice across investor and fund manager profiles, with GPT-4 Turbo as primary baseline.", "Multiple LLMs generated fund selections and recommended investment amounts for profiles varying by race and gender while holding all financial characteristics constant.", "Capital allocations favored non-Black and male managers; racial disparities persisted even under explicit disclosure, with stronger effects under implicit demographic signaling." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 68, "n": 2419, "authors_detailed": [ { "name": "Ye Wang", "url": "https://openalex.org/A5122786741", "inst": "Stevens Institute of Technology" }, { "name": "Kexin Gu", "url": "https://openalex.org/A5117411290", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Stevens Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.6018394", "doi": "10.2139/ssrn.6018394", "title": "How Does Regulation Travel with Offshore Financing? Evidence from Initial Coin Offerings", "authors": [ "Sijie Wang" ], "posted": "2026-01-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6018394", "field": "finance", "role": "instrument", "bullets": [ "Nearly four thousand global ICOs with physical and legal locations distinguished, plus over two thousand regulatory documents coded.", "LLM framework with retrieval-augmented generation coded regulatory documents to capture regulatory stance across jurisdictions.", "Stricter domestic regulation drives ICOs to register offshore; host jurisdictions then adopt sophisticated disclosure rules to filter for quality entrants." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 3083, "authors_detailed": [ { "name": "Sijie Wang", "url": "https://openalex.org/A5102756431", "inst": "Chinese University of Hong Kong, Shenzhen" } ], "affiliations": [ "Chinese University of Hong Kong, Shenzhen" ] }, { "uid": "doi:10.2139/ssrn.5985277", "doi": "10.2139/ssrn.5985277", "title": "Detecting Lookahead Bias in LLM Forecasts", "authors": [ "Zhenyu Gao", "Wenxi Jiang", "Yutong Yan" ], "posted": "2026-01-08", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5985277", "field": "finance", "role": "method", "bullets": [ "Firm-date pairs spanning in-sample and post-training-cutoff periods, applied to two forecasting tasks: news headlines predicting stock returns and earnings-call transcripts predicting capital expenditures.", "An LLM (not named) produces the forecasts, and a date-only recall query yields a Lookahead Propensity statistic estimating whether the model already internalized the realized outcome.", "Lookahead Propensity is materially positive in-sample and collapses to near zero after the cutoff, and forecast power is amplified on high-propensity pairs, flagging contamination." ], "bullet_provenance": "ai", "salience": 66, "edition": 2, "audience": "technical", "models": [], "validated": null, "n": 37, "authors_detailed": [ { "name": "Zhenyu Gao", "url": "https://openalex.org/A5121100633", "inst": "Chinese University of Hong Kong" }, { "name": "Wenxi Jiang", "url": "https://openalex.org/A5121030057", "inst": "Chinese University of Hong Kong" }, { "name": "Yutong Yan", "url": "https://openalex.org/A5122305568", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.6037726", "doi": "10.2139/ssrn.6037726", "title": "Fin-o1: On the Transferability of Reasoning-Enhanced and Reinforcement-Learned LLMs to Financial Domains", "authors": [ "Lingfei Qian", "Yan Wang", "Xueqing Peng", "Weipeng Zhou", "Yi Han", "Yupeng Cao", "Yilun Zhao", "Guojun Xiong", "Jimin Huang", "Qianqian Xie", "Jian-Yun Nie" ], "posted": "2026-01-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6037726", "field": "finance", "role": "method", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "technical", "bullets": [], "validated": null, "n": 695, "authors_detailed": [ { "name": "Lingfei Qian", "url": "https://openalex.org/A5009941118", "inst": "Yale University" }, { "name": "Yan Wang", "url": "https://openalex.org/A5120946931", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Xueqing Peng", "url": "https://openalex.org/A5101317327", "inst": "Yale University" }, { "name": "Wei Zhou", "url": "https://openalex.org/A5035646855", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Yi Han", "url": "https://openalex.org/A5118686567", "inst": "Georgia Institute of Technology" }, { "name": "Yupeng Cao", "url": "https://openalex.org/A5115604006", "inst": "Stevens Institute of Technology" }, { "name": "Yilun Zhao", "url": "https://openalex.org/A5101240019", "inst": "Yale University" }, { "name": "Guojun Xiong", "url": "https://openalex.org/A5102620407", "inst": "Harvard University Press" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5122258895", "inst": "FishBase Information and Research Group" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101101682", "inst": "FishBase Information and Research Group" }, { "name": "Jian‐Yun Nie", "url": "https://openalex.org/A5018977183", "inst": "Université de Montréal" } ], "affiliations": [ "Yale University", "Georgia Institute of Technology", "Harvard University", "Independent - affiliation not provided to SSRN", "Stevens Institute of Technology", "FishBase Information and Research Group", "Université de Montréal" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.6019654", "doi": "10.2139/ssrn.6019654", "title": "Expanding the Landscape of Cross-Border Flow Restrictions: Modern Tools and Historical Perspectives", "authors": [ "Katharina Bergant", "Andrés Fernández", "Ken Teoh", "Martin Uribe" ], "posted": "2026-01-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6019654", "field": "economics", "role": "instrument", "bullets": [ "Daily changes in de jure cross-border flow restrictions worldwide since the 1950s, constructed from official government documents.", "LLMs classified restrictions across eight categories by flow direction, instrument type, and policy stance from regulatory texts.", "LLM-based classifications replicate and extend established capital-account indicators; analysis reveals novel high-frequency patterns linked to crises and political economy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "validated against established capital account regulation indicators", "salience": 78, "n": 3082, "authors_detailed": [ { "name": "Katharina Bergant", "url": "https://openalex.org/A5085592182", "inst": "International Monetary Fund" }, { "name": "Andrés Fernández", "url": "https://openalex.org/A5122056241", "inst": "International Monetary Fund" }, { "name": "Ken Teoh", "url": "https://openalex.org/A5085556209", "inst": "International Monetary Fund" }, { "name": "Martin Uribe", "url": "https://openalex.org/A5122257187", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "International Monetary Fund", "National Bureau of Economic Research" ] }, { "uid": "doi:10.2139/ssrn.6028034", "doi": "10.2139/ssrn.6028034", "title": "GenAI Adoption Increases the Density of Knowledge and Collaboration Networks: Evidence from a Field Experiment", "authors": [ "Ralf Buechsenschuss", "Irmela Koch-Bayram", "Dr. Torsten Biemann", "Phanish Puranam" ], "posted": "2026-01-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6028034", "field": "management", "role": "object", "bullets": [ "Randomized field experiment with 316 employees across 42 teams at a European technology services firm over three months, comparing GenAI-assisted versus control groups.", "Treatment group received a GenAI assistant customized with organization-specific knowledge; researchers measured changes in collaboration and knowledge-sharing network centrality.", "GenAI users became significantly more central in collaboration and knowledge networks; specialists gained more knowledge centrality while generalists gained more in project throughput." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 82, "validated": null, "n": 3590, "authors_detailed": [ { "name": "Ralf Buechsenschuss", "url": "https://openalex.org/A5092500971", "inst": "University of Mannheim" }, { "name": "Irmela Koch-Bayram", "url": "https://openalex.org/A5122043063", "inst": "University of Mannheim" }, { "name": "Torsten Biemann", "url": "https://openalex.org/A5054305892", "inst": "University of Mannheim" }, { "name": "Phanish Puranam", "url": "https://openalex.org/A5015167951", "inst": "INSEAD" } ], "affiliations": [ "University of Mannheim", "INSEAD" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.6001414", "doi": "10.2139/ssrn.6001414", "title": "Green Innovation in the Age of AI", "authors": [ "Yuan Sun", "Xuan Tian", "Yuanchen Yang" ], "posted": "2026-01-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6001414", "field": "management", "role": "object", "bullets": [ "Firms identified through a novel patent-based measure of AI use in their innovation process, splitting green from non-green firms; period, geography, and sample size are not stated.", "The paper measures firms' adoption of AI, particularly generative AI, from patent data rather than running a named model, and establishes causality with a difference-in-differences design.", "Green firms raise green innovation output by about 15 to 24 percent after adopting AI and improve innovation quality, though profitability gains stay limited by higher associated costs." ], "bullet_provenance": "ai", "salience": 52, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 694, "authors_detailed": [ { "name": "Yuan Sun", "url": "https://openalex.org/A5121944529", "inst": "Shanghai University of Finance and Economics" }, { "name": "Xuan Tian", "url": "https://openalex.org/A5121967221", "inst": "Tsinghua University" }, { "name": "Yuanchen Yang", "url": "https://openalex.org/A5100800839", "inst": "International Monetary Fund" } ], "affiliations": [ "Shanghai University of Finance and Economics", "Tsinghua University", "International Monetary Fund" ] }, { "uid": "doi:10.2139/ssrn.5981520", "doi": "10.2139/ssrn.5981520", "title": "The Emerging Market for Intelligence: Pricing, Supply, and Demand for LLMs", "authors": [ "Mert Demirer", "Andrey Fradkin", "Nadav Tadelis", "Sida Peng" ], "posted": "2026-01-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5981520", "field": "economics", "role": "object", "bullets": [ "API usage data from OpenRouter and Microsoft Azure covering multiple LLM providers and models across the emerging inference market.", "Authors document six facts about LLM market structure including pricing dynamics, entry patterns, and demand heterogeneity across applications.", "Open-source models are 90% cheaper than comparable closed-source models; short-run price elasticities just above one limit Jevons-Paradox effects." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 82, "validated": null, "n": 3081, "authors_detailed": [ { "name": "Mert Demirer", "url": "https://openalex.org/A5050184772", "inst": "Massachusetts Institute of Technology" }, { "name": "Andrey Fradkin", "url": "https://openalex.org/A5082411878", "inst": "Boston University" }, { "name": "Nadav Tadelis", "url": "https://openalex.org/A5120816437", "inst": "Microsoft (Finland)" }, { "name": "Sida Peng", "url": "https://openalex.org/A5121924957", "inst": "Microsoft (Finland)" } ], "affiliations": [ "Massachusetts Institute of Technology", "Boston University", "Microsoft (Finland)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5983395", "doi": "10.2139/ssrn.5983395", "title": "Sustainable Development Goals Omission and Environmental Sentiment Metric for Greenwashing and ESG Controversies Alert in Green Bonds", "authors": [ "Andrea Nicolodi", "Sandra Paterlini", "Monica Gentile", "Vincenzo Foglia Manzillo", "Gianluca Vittorioso" ], "posted": "2026-01-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5983395", "field": "finance", "role": "instrument", "bullets": [ "Corporate sustainability reports from green bond issuers analyzed for SDG content and environmental sentiment using NLP-based metrics.", "Domain-specific BERT transformer models and dictionary methods scored environmental sentiment and detected SDG content omission in issuer disclosures.", "Higher Environmental Sentiment Metric scores significantly associated with greater ESG controversy levels and greenwashing accusations among green bond issuers." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 42, "n": 3973, "authors_detailed": [ { "name": "Andrea Nicolodi", "url": "https://openalex.org/A5119221593", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Sandra Paterlini", "url": "https://openalex.org/A5046540286", "inst": "University of Trento" }, { "name": "Monica Gentile", "url": "https://openalex.org/A5104027408", "inst": "Commissione Nazionale per le Società e la Borsa" }, { "name": "Vincenzo Foglia Manzillo", "url": "https://openalex.org/A5119221594", "inst": "Commissione Nazionale per le Società e la Borsa" }, { "name": "Gianluca Vittorioso", "url": "https://openalex.org/A5121779237", "inst": "Commissione Nazionale per le Società e la Borsa" } ], "affiliations": [ "Independent - affiliation not provided to SSRN", "University of Trento", "Commissione Nazionale per le Società e la Borsa" ] }, { "uid": "doi:10.2139/ssrn.7119484", "doi": "10.2139/ssrn.7119484", "title": "AI Trading: Evaluating Large Language Models for Technical Market Analysis", "authors": [ "Geofrey Ntale" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7119484", "field": "finance", "role": "method", "bullets": [ "Five language models evaluated on four structured market tasks: candlestick pattern recognition from OHLCV data, buy sell hold signal generation, simulated backtests, and financial report comprehension.", "GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, and the finance tuned FinGPT are scored on F1, Sharpe ratio, drawdown, and benchmark comprehension metrics.", "GPT-4 Turbo posts the highest simulated annualized return and Sharpe ratio among general models; every model shows numerical hallucination and degrades in sideways market regimes." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "classification F1 and benchmark comprehension scores reported", "salience": 46, "edition": 1, "audience": "technical", "n": 1, "authors_detailed": [ { "name": "Geofrey Ntale", "url": "https://openalex.org/A5141256236", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7158001", "doi": "10.2139/ssrn.7158001", "title": "Generative AI Regulation, Creative Industry Disruption, and Governance Response in China's Cultural Sector", "authors": [ "Gh U" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7158001", "field": "economics", "role": "object", "bullets": [ "Panel of listed Chinese cultural enterprises in film, music, publishing, and interactive media, 2020 to 2024, with staggered provincial licensing enforcement of generative AI rules.", "No language model is used for measurement; AIGC adoption is the studied technology, identified through difference in differences and event studies around regulator announcements.", "Better provincial governance quality reduces AI driven creative workforce displacement; state affiliated platforms comply more easily, widening structural gaps with independent creators." ], "bullet_provenance": "ai", "salience": 48, "edition": 1, "audience": "broad", "models": [], "validated": null, "n": 2, "authors_detailed": [ { "name": "Gh U", "url": "https://openalex.org/A5059604891", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7024983", "doi": "10.2139/ssrn.7024983", "title": "Zero-Shot Structured Data Extraction from Timely Disclosure Documents in PDF Format with Large Language Models", "authors": [ "Nobushige Doi", "Mayuri Tanaka" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7024983", "field": "accounting", "role": "method", "bullets": [ "Eight types of timely disclosure PDFs from TDnet, the Tokyo Stock Exchange filing system, paired with document specific JSON schemas for zero shot structured extraction.", "Twelve conditions crossing GPT-5.4, mini, and nano variants with four reasoning effort settings, each run three times; the largest model reaches core field accuracy of 0.905.", "Schema guided extraction is viable for structuring disclosure PDFs, but the best settings differ by document type and higher reasoning effort does not reliably improve accuracy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "core field accuracy 0.905 with variance across repeated runs", "salience": 55, "edition": 1, "audience": "technical", "n": 3, "authors_detailed": [ { "name": "Nobushige Doi", "url": "https://openalex.org/A5023596495", "inst": "Science Exchange (United States)" }, { "name": "Mayuri Tanaka", "url": "https://openalex.org/A5141258143", "inst": "" } ], "affiliations": [ "Science Exchange (United States)" ] }, { "uid": "doi:10.2139/ssrn.7028322", "doi": "10.2139/ssrn.7028322", "title": "Scalable Linear Bayes Prediction for Mixtures of Experts and LLMs", "authors": [ "Nicholas G. Polson", "Vadim Sokolov", "Daniel Zantedeschi" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7028322", "field": "other", "role": "method", "bullets": [ "Theory linking Bayes linear estimation, ensemble Kalman filtering, and sparse mixture of experts training, evaluated with controlled simulations rather than a field dataset.", "No commercial model is deployed; the paper proves structural correspondences between linear Bayes mixtures of experts and sparse LLM architectures, including Adam as a diagonal Kalman filter.", "Exact Bayes linear updating shows essentially no forgetting across sequential tasks where SGD and Adam forget 5.1 and 3.5 times more, and a threshold theorem characterizes when mixture filters help." ], "bullet_provenance": "ai", "salience": 52, "edition": 1, "audience": "technical", "models": [], "validated": null, "n": 6, "authors_detailed": [ { "name": "Nicholas G. Polson", "url": "https://openalex.org/A5141282367", "inst": "University of Chicago" }, { "name": "Vadim Sokolov", "url": "https://openalex.org/A5141241820", "inst": "George Mason University" }, { "name": "Daniel Zantedeschi", "url": "https://openalex.org/A5071988852", "inst": "University of South Florida" } ], "affiliations": [ "University of Chicago", "George Mason University", "University of South Florida" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.7037798", "doi": "10.2139/ssrn.7037798", "title": "Position: The Machine Learning Community is Accumulating Calibration Debt Anonymized for Review", "authors": [ "Madhav Mittal" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7037798", "field": "other", "role": "method", "bullets": [ "A position paper on calibration error across language models, agentic systems, retrieval augmented pipelines, tabular foundation models, and clinical AI, with a two stage classification experiment.", "No single deployed model; it synthesizes three 2026 impossibility results arguing miscalibration is structural, and proposes a three cause taxonomy and a Calibration Debt Score.", "The pipeline experiment shows system level expected calibration error exceeds the maximum of component level errors even when each component is individually calibrated." ], "bullet_provenance": "ai", "salience": 50, "edition": 1, "audience": "technical", "models": [], "validated": null, "n": 7, "authors_detailed": [ { "name": "Madhav Mittal", "url": "https://openalex.org/A5143407668", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7051078", "doi": "10.2139/ssrn.7051078", "title": "Shadow AI in Higher Education: A Socio-technical Framework for Governing Unauthorized Employee use of Artificial Intelligence", "authors": [ "Aeron Zentner", "Tobi West" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7051078", "field": "management", "role": "object", "bullets": [ "Original 2026 survey of 50 employees at a single two year public college, an 18.9 percent response rate, on unauthorized workplace use of AI tools.", "No model is used for measurement; generative AI adoption is the studied phenomenon, framed through a seven dimension shadow AI adoption governance gap model.", "Respondents report high AI literacy and strong training demand yet prefer consumer tools like ChatGPT, Gemini, and Claude over the institution's three sanctioned platforms. Single institution and self reported." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 38, "edition": 1, "audience": "broad", "validated": null, "n": 8, "authors_detailed": [ { "name": "Aeron Zentner", "url": "https://openalex.org/A5117724417", "inst": "Coastline Community College" }, { "name": "Tobi West", "url": "https://openalex.org/A5079761731", "inst": "Dakota State University" } ], "affiliations": [ "Coastline Community College", "Dakota State University" ] }, { "uid": "doi:10.2139/ssrn.7158737", "doi": "10.2139/ssrn.7158737", "title": "Certified Metric Injection: A Hybrid Deterministic–Large Language Model Architecture for Hallucination-Resistant Enterprise Conversational Business Intelligence", "authors": [ "Ricardo Amodio Neves de Albuquerque" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7158737", "field": "accounting", "role": "method", "bullets": [ "A single production enterprise business intelligence deployment where language models translate business questions about financial and operational KPIs into SQL against a data warehouse.", "A hybrid architecture injects pre validated SQL for canonical KPIs, links schema, adds a second model critique pass, sandboxes execution, and routes simple queries to smaller models; model family not stated.", "The deployment reports large reductions in time to answer and less reliance on analysts; validation is qualitative and limited to one organization, with no accuracy statistic stated." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 1, "audience": "technical", "models": [], "n": 9, "authors_detailed": [ { "name": "Ricardo Amodio Neves de Albuquerque", "url": "https://openalex.org/A5143401624", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.7151418", "doi": "10.2139/ssrn.7151418", "title": "Does Generative AI Make Startup Ideas Alike? Evidence from Product Hunt", "authors": [ "Zhaoqi Cheng", "Kaige Gao" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7151418", "field": "economics", "role": "object", "bullets": [ "291,314 product launches posted to Product Hunt from January 2018 to April 2026, measuring the distinctiveness of startup ideas before and after ChatGPT's release.", "An outcome blind LLM ideation exposure index measures how much general purpose models can assist ideation in each category; the models are studied, not used to build variables from firm text.", "The share of distinctive launches falls from 25 to 16 percent after ChatGPT, and the rise in similarity is larger in categories more exposed to LLM assisted ideation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 74, "edition": 1, "audience": "broad", "validated": null, "n": 10, "authors_detailed": [ { "name": "Zhaoqi Cheng", "url": "https://openalex.org/A5031867224", "inst": "Worcester Polytechnic Institute" }, { "name": "Kaige Gao", "url": "https://openalex.org/A5141288075", "inst": "" } ], "affiliations": [ "Worcester Polytechnic Institute" ] }, { "uid": "doi:10.2139/ssrn.7120281", "doi": "10.2139/ssrn.7120281", "title": "Vertical Scaling of Subdomain-Specific Small Language Models: A Framework for High-Stakes Domains - A Systematic Review of Methodologies, Applications, and Future Directions in Finance, Law, Medicine, and Government", "authors": [ "Ismet Beljulji" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7120281", "field": "finance", "role": "method", "bullets": [ "A systematic review of small language models adapted through parameter efficient fine tuning for high stakes domains, with financial compliance among the four focus areas.", "No single model; the review argues domain purity of curated narrow datasets outweighs dataset scale, and examines synthetic data generation as a cost effective adaptation lever.", "It proposes a unified framework for vertical scaling across subdomains and catalogs challenges such as overfitting and structural instability. No effect sizes are stated." ], "bullet_provenance": "ai", "salience": 33, "edition": 1, "audience": "technical", "models": [], "validated": null, "n": 12, "authors_detailed": [ { "name": "Ismet beljulji", "url": "https://openalex.org/A5139763408", "inst": "Opinion Leader Research" } ], "affiliations": [ "Opinion Leader Research" ] }, { "uid": "doi:10.2139/ssrn.7127378", "doi": "10.2139/ssrn.7127378", "title": "Generative and Agentic AI Through the Lens of Model Risk Management: A Multisector Framework for Identifying Opportunities and Circumventing Risks", "authors": [ "Advaith Nila Narayanan" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7127378", "field": "finance", "role": "method", "bullets": [ "A conceptual framework extending model risk management to generative and agentic AI across finance, insurance, healthcare, law, and energy.", "No model is deployed or tested; the paper adapts the model risk pillars of meaningful challenge, conceptual soundness, and ongoing monitoring to hallucination, prompt injection, and execution drift.", "It offers sector specific analyses and a governance roadmap for risk professionals; no empirical results or effect sizes are stated." ], "bullet_provenance": "ai", "salience": 47, "edition": 1, "audience": "broad", "models": [], "validated": null, "n": 13, "authors_detailed": [ { "name": "Advaith Nila Narayanan", "url": "https://openalex.org/A5070519856", "inst": "Anna University, Chennai" } ], "affiliations": [ "Anna University, Chennai" ] }, { "uid": "doi:10.2139/ssrn.7069438", "doi": "10.2139/ssrn.7069438", "title": "Stereotypes in the Machine: Gender and Racial Bias in LLM-Generated Financial Advice", "authors": [ "Ang Li", "Fujing Xue", "Zixuan Zeng", "Xiaofeng Zhao" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.7069438", "field": "finance", "role": "agent", "bullets": [ "A large scale controlled experiment with language models acting as financial advisors, recommending equity allocations to otherwise identical clients varied by gender and race.", "The models stand in for advisors and their recommendations are the object of study; model family is not stated, and bias is measured against identical White male profiles.", "Models recommend lower equity allocations to female and Asian clients, a gap that intensifies for sophisticated clients; scale, safety alignment, and debiasing prompts do not fix it, only stronger reasoning." ], "bullet_provenance": "ai", "validated": true, "validation_note": "controlled experiment with identical-profile counterfactuals; portfolio efficiency measured", "salience": 80, "edition": 1, "audience": "broad", "models": [], "n": 14, "authors_detailed": [ { "name": "Ang Li", "url": "https://openalex.org/A5100413578", "inst": "Lingnan University" }, { "name": "Fujing Xue", "url": "https://openalex.org/A5049079838", "inst": "Sun Yat-sen University" }, { "name": "zixuan zeng", "url": "https://openalex.org/A5113191786", "inst": "University of Macau" }, { "name": "Xiaofeng Zhao", "url": "https://openalex.org/A5060772835", "inst": "Lingnan University" } ], "affiliations": [ "Lingnan University", "Sun Yat-sen University", "University of Macau" ] }, { "uid": "doi:10.2139/ssrn.6206500", "doi": "10.2139/ssrn.6206500", "title": "From Humans to Algorithms: How Financial Advice Differs Across Professionals, Peers, and LLMs", "authors": [ "Matthias Rumpf", "Michael Haliassos", "Tetyana Kosyakova", "Thomas Otter" ], "posted": "2026-01-01", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.6206500", "field": "finance", "role": "agent", "bullets": [ "A vignette based experiment comparing risky portfolio share advice from professional advisors, financially literate peers, and a language model, eliminating matching problems by design.", "The model is prompted repeatedly on identical vignettes and its recommendations are the object of study; the family is not named, and a Bayesian hierarchical Tobit model captures heterogeneity.", "The model's advice has smaller variance but is very sensitive to declared risk tolerance, and using AI can offset peers' tendency to discourage equity for younger, lower income investors." ], "bullet_provenance": "ai", "salience": 68, "edition": 1, "audience": "broad", "models": [], "validated": null, "n": 15, "authors_detailed": [ { "name": "Matthias Rumpf", "url": "https://openalex.org/A5125486452", "inst": "Deutsche Bundesbank" }, { "name": "Michael Haliassos", "url": "", "inst": "Goethe University Frankfurt" }, { "name": "Tetyana Kosyakova", "url": "https://openalex.org/A5067842055", "inst": "HHL Leipzig Graduate School of Management" }, { "name": "Thomas Otter", "url": "https://openalex.org/A5077819290", "inst": "Goethe University Frankfurt" } ], "affiliations": [ "Deutsche Bundesbank", "Goethe University Frankfurt", "HHL Leipzig Graduate School of Management" ] }, { "uid": "arxiv:2512.24526v1", "arxiv_id": "2512.24526v1", "title": "Generative AI-enhanced Sector-based Investment Portfolio Construction", "authors": [ "Alina Voronina", "Oleksandr Romanko", "Ruiwen Cao", "Roy H. Kwon", "Rafael Mendoza-Arriaga" ], "posted": "2025-12-31", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.24526v1", "field": "finance", "role": "agent", "bullets": [ "S&P 500 sector stock universes with two out of sample windows in 2025, a stable January to March quarter and a volatile April to June quarter.", "LLMs from OpenAI, Google, Anthropic, DeepSeek, and xAI each select and weight 20 stocks per sector, alone and combined with classical portfolio optimization.", "LLM weighted portfolios often beat sector indices on return and Sharpe ratio in the calm window but frequently lag in the volatile one; adding optimizers improves performance and consistency." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "salience": 45, "edition": 14, "validated": null, "n": 1840, "authors_detailed": [ { "name": "Alina Voronina", "url": "https://openalex.org/A5121644188", "inst": "Ukrainian Catholic University" }, { "name": "Oleksandr Romanko", "url": "https://openalex.org/A5113492570", "inst": "University of Toronto" }, { "name": "Ruiwen Cao", "url": "https://openalex.org/A5121786940", "inst": "University of Toronto" }, { "name": "Roy H. Kwon", "url": "https://openalex.org/A5026867352", "inst": "University of Toronto" }, { "name": "Rafael Mendoza‐Arriaga", "url": "https://openalex.org/A5061836384", "inst": "SS&C Technologies (United States)" } ], "affiliations": [ "University of Toronto", "Ukrainian Catholic University", "SS&C Technologies (United States)" ], "prestige": true }, { "uid": "arxiv:2601.06088v1", "arxiv_id": "2601.06088v1", "title": "PriceSeer: Evaluating Large Language Models in Real-Time Stock Prediction", "authors": [ "Bohan Liang", "Zijian Chen", "Qi Jia", "Kaiwei Zhang", "Kaiyuan Ji", "Guangtao Zhai" ], "posted": "2025-12-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2601.06088v1", "field": "finance", "role": "method", "bullets": [ "Benchmark of 110 U.S. stocks across 11 sectors with 249 historical data points each, designed to be live, dynamic, and free of training-data contamination.", "Six frontier LLMs evaluated on stock price prediction tasks with internal indicators, real news, and fake news inputs across multiple prediction horizons.", "LLMs showed potential for short-horizon prediction and sector-specific strategy generation but exhibited vulnerability to fake news injection and degraded accuracy at longer horizons." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "110-stock multi-sector price prediction benchmark", "salience": 62, "n": 2536, "authors_detailed": [ { "name": "Bohan Liang", "url": "https://openalex.org/A5122985221", "inst": "" }, { "name": "Zijian Chen", "url": "https://openalex.org/A5122929383", "inst": "University of Waterloo" }, { "name": "Qi Jia", "url": "https://openalex.org/A5122915163", "inst": "" }, { "name": "Kaiwei Zhang", "url": "https://openalex.org/A5032888856", "inst": "ShangHai JiAi Genetics & IVF Institute" }, { "name": "Kaiyuan Ji", "url": "https://openalex.org/A5122920503", "inst": "" }, { "name": "Guangtao Zhai", "url": "https://openalex.org/A5122913785", "inst": "" } ], "affiliations": [ "University of Waterloo", "ShangHai JiAi Genetics & IVF Institute" ] }, { "uid": "doi:10.1609/aaai.v40i30.39785", "doi": "10.1609/aaai.v40i30.39785", "arxiv_id": "2512.24903v1", "title": "FinMMDocR: Benchmarking Financial Multimodal Reasoning with Scenario Awareness, Document Understanding, and Multi-Step Computation", "authors": [ "Zichen Tang", "Haihong E", "Rongjin Li", "Jiacheng Liu", "Linwei Jia", "Zhuodi Hao", "Zhongjun Yang", "Yuanze Li", "Haolin Tian", "Xinyi Hu", "Peizhi Zhao", "Yuan Liu", "Zhengyu Wang", "Xianghe Wang", "Yiling Huang", "Xueyuan Lin", "Ruofei Bai", "Zijian Xie", "Qian Huang", "Ruining Cao", "Haocheng Gao" ], "posted": "2025-12-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.24903v1", "field": "finance", "role": "method", "bullets": [ "1,200 expert-annotated bilingual problems across 837 Chinese/English financial documents spanning nine types averaging 50.8 pages each.", "MLLMs evaluated on multi-step numerical reasoning requiring 11-step average chains with 65% of problems needing cross-page evidence extraction.", "Best-performing MLLM achieved only 58.0% accuracy; different RAG methods showed significant performance variations on financial reasoning tasks." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini", "open_other" ], "validated": true, "validation_note": "1,200 expert-annotated financial reasoning problems", "salience": 60, "n": 2869, "authors_detailed": [ { "name": "Zichen Tang", "url": "https://openalex.org/A5012403954", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Haihong E", "url": "https://openalex.org/A5121819456", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Rongjin Li", "url": "https://openalex.org/A5121769721", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Jiacheng Liu", "url": "https://openalex.org/A5129659365", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Linwei Jia", "url": "https://openalex.org/A5102594109", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Zhuodi Hao", "url": "https://openalex.org/A5121789207", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Zhongjun Yang", "url": "https://openalex.org/A5072316255", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Yuanze Li", "url": "https://openalex.org/A5018596311", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Haolin Tian", "url": "https://openalex.org/A5113203719", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Xinyi Hu", "url": "https://openalex.org/A5129673058", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Peizhi Zhao", "url": "https://openalex.org/A5129687159", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Yuan Liu", "url": "https://openalex.org/A5129659772", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Zhengyu Wang", "url": "https://openalex.org/A5146754508", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Xianghe Wang", "url": "https://openalex.org/A5121768734", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Yiling Huang", "url": "https://openalex.org/A5129680559", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Xueyuan Lin", "url": "https://openalex.org/A5129703238", "inst": "Hithink RoyalFlush Information Network Co., Ltd" }, { "name": "Ruofei Bai", "url": "https://openalex.org/A5041116745", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Zijian Xie", "url": "https://openalex.org/A5121830927", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Qian Huang", "url": "https://openalex.org/A5129706533", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Ruining Cao", "url": "https://openalex.org/A5092532625", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Haocheng Gao", "url": "https://openalex.org/A5121770705", "inst": "Beijing University of Posts and Telecommunications" } ], "affiliations": [ "Beijing University of Posts and Telecommunications", "Hithink RoyalFlush Information Network Co., Ltd" ] }, { "uid": "arxiv:2512.24968v4", "arxiv_id": "2512.24968v4", "title": "Strategic Response of News Publishers to Generative AI", "authors": [ "Hangcheng Zhao", "Ron Berman" ], "posted": "2025-12-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.24968v4", "field": "economics", "role": "object", "bullets": [ "High-frequency granular data on news publisher websites, robots.txt blocking decisions, content characteristics, and job postings around the introduction of generative AI.", "Generative AI bots accessed publisher content; difference-in-differences compared large publishers that blocked vs. did not block LLM access via robots.txt.", "Large publishers blocking GenAI bots experienced reduced website traffic; publishers shifted toward richer content harder for LLMs to replicate and increased editorial job postings." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 3080, "authors_detailed": [ { "name": "Hangcheng Zhao", "url": "https://openalex.org/A5044824250", "inst": "Rutgers Sexual and Reproductive Health and Rights" }, { "name": "Ron Berman", "url": "https://openalex.org/A5019059107", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania", "Rutgers Sexual and Reproductive Health and Rights" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.24314v2", "arxiv_id": "2512.24314v2", "title": "QianfanHuijin Technical Report: A Novel Multi-Stage Training Paradigm for Finance Industrial LLMs", "authors": [ "Shupeng Li", "Weipeng Lu", "Linyun Liu", "Chen Lin", "Shaofei Li", "Zhendong Tan", "Hanjun Zhong", "Yucheng Zeng", "Chenghao Zhu", "Mengyue Liu", "Daxiang Dong", "Jianmin Wu", "Yunting Xiao", "Annan Li", "Danyu Liu", "Jingnan Zhang", "Licen Liu", "Dawei Yin", "Dou Shen" ], "posted": "2025-12-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.24314v2", "field": "finance", "role": "method", "bullets": [ "Industrial financial services deployment of a domain adapted LLM trained on financial corpora; corpus size, benchmark names, and business setting details are not stated in the abstract.", "QianfanHuijin, base model not named, goes through continual pretraining, financial supervised fine-tuning, reasoning and agentic reinforcement learning, then general RL aligned with business scenarios.", "The model reports superior results across financial benchmarks, and ablations credit the reasoning and agentic RL stages with their respective capability gains; no figures appear in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "financial benchmarks, unnamed in abstract", "salience": 42, "edition": 14, "models": [], "n": 1947, "authors_detailed": [ { "name": "Shupeng Li", "url": "https://openalex.org/A5121834225", "inst": "" }, { "name": "Weipeng Lu", "url": "https://openalex.org/A5121766073", "inst": "Baidu (China)" }, { "name": "Linyun Liu", "url": "https://openalex.org/A5088841352", "inst": "Harbin Engineering University" }, { "name": "Chen Lin", "url": "https://openalex.org/A5121794665", "inst": "" }, { "name": "Shaofei Li", "url": "https://openalex.org/A5121756764", "inst": "Ministry of Education" }, { "name": "ZhenDong Tan", "url": "https://openalex.org/A5018852202", "inst": "East China Normal University" }, { "name": "Hanjun Zhong", "url": "https://openalex.org/A5121771284", "inst": "" }, { "name": "Yucheng Zeng", "url": "https://openalex.org/A5073383566", "inst": "Shanghai University" }, { "name": "Chenghao Zhu", "url": "https://openalex.org/A5028706977", "inst": "Central South University" }, { "name": "Liu Mengyue", "url": "https://openalex.org/A5049048568", "inst": "" }, { "name": "Daxiang Dong", "url": "https://openalex.org/A5027969886", "inst": "City University of Hong Kong" }, { "name": "Jianmin Wu", "url": "https://openalex.org/A5121776096", "inst": "" }, { "name": "Yunting Xiao", "url": "https://openalex.org/A5121162256", "inst": "Tianjin University" }, { "name": "Annan Li", "url": "https://openalex.org/A5023619485", "inst": "Institute of Software" }, { "name": "Danyu Liu", "url": "https://openalex.org/A5121834721", "inst": "" }, { "name": "Zhang, Jingnan", "url": "", "inst": "" }, { "name": "Licen Liu", "url": "https://openalex.org/A5121831953", "inst": "" }, { "name": "Dawei Yin", "url": "https://openalex.org/A5121754288", "inst": "" }, { "name": "Dou Shen", "url": "https://openalex.org/A5105366551", "inst": "Microsoft (United States)" } ], "affiliations": [ "Baidu (China)", "Harbin Engineering University", "Ministry of Education", "East China Normal University", "Shanghai University", "Central South University", "City University of Hong Kong", "Tianjin University" ] }, { "uid": "arxiv:2512.24289v1", "arxiv_id": "2512.24289v1", "title": "Automated Analysis of Sustainability Reports: Using Large Language Models for the Extraction and Prediction of EU Taxonomy-Compliant KPIs", "authors": [ "Jonathan Schmoll", "Adam Jatowt" ], "posted": "2025-12-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.24289v1", "field": "accounting", "role": "method", "bullets": [ "A new structured dataset from 190 corporate sustainability reports with ground truth economic activities and EU Taxonomy key performance indicators.", "Multiple unnamed LLMs run the compliance workflow of identifying taxonomy activities and predicting quantitative KPIs, scored against the expert ground truth, including a multi step agentic variant.", "Models are moderately successful at identifying activities but fail comprehensively at zero shot KPI prediction; concise metadata often beats full reports, and confidence scores are poorly calibrated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert ground truth from 190 corporate reports", "salience": 56, "edition": 14, "models": [], "n": 1948, "authors_detailed": [ { "name": "Jonathan Schmoll", "url": "https://openalex.org/A5120916054", "inst": "" }, { "name": "Adam Jatowt", "url": "https://openalex.org/A5079733597", "inst": "Universität Innsbruck" } ], "affiliations": [ "Universität Innsbruck" ] }, { "uid": "doi:10.3386/w34608", "doi": "10.3386/w34608", "title": "The Emerging Market for Intelligence: Pricing, Supply, and Demand for LLMs", "authors": [ "Mert Demirer", "Andrey Fradkin", "Nadav Tadelis", "Sida Peng" ], "posted": "2025-12-30", "added": "2026-07-24", "source_label": "NBER", "url": "https://doi.org/10.3386/w34608", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 58, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 693, "authors_detailed": [ { "name": "Mert Demirer", "url": "https://openalex.org/A5121583425", "inst": "" }, { "name": "Andrey Fradkin", "url": "https://openalex.org/A5121552177", "inst": "" }, { "name": "Nadav Tadelis", "url": "https://openalex.org/A5120816437", "inst": "Microsoft (Finland)" }, { "name": "Sida Peng", "url": "https://openalex.org/A5121540655", "inst": "" } ], "affiliations": [ "Microsoft (Finland)" ] }, { "uid": "doi:10.2139/ssrn.5985874", "doi": "10.2139/ssrn.5985874", "title": "When AI Reads the 10-Ks: The Effects of ChatGPT on Trading and Price Dynamics", "authors": [ "Toghrul Aghbabali", "Kee H. Chung", "Sahn-Wook Huh" ], "posted": "2025-12-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5985874", "field": "finance", "role": "object", "bullets": [ "U.S. equity market around ChatGPT's release, with a new Coverage Score measuring ChatGPT's capacity to interpret corporate 10-K filings across firms.", "ChatGPT's release served as a natural experiment; difference-in-differences compared firms with high vs. low Coverage Scores on trading and price dynamics.", "High Coverage Score firms experienced decreases in informed trading, price impact, and return volatility; larger retail-driven order imbalances suggest AI narrows the knowledge gap between retail and institutional investors." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 83, "validated": null, "n": 3079, "authors_detailed": [ { "name": "Toghrul Aghbabali", "url": "https://openalex.org/A5119256655", "inst": "University at Buffalo, State University of New York" }, { "name": "Kee H. Chung", "url": "https://openalex.org/A5011392134", "inst": "University at Buffalo, State University of New York" }, { "name": "Sahn-Wook Huh", "url": "https://openalex.org/A5028805947", "inst": "Buffalo State University" } ], "affiliations": [ "University at Buffalo, State University of New York", "Buffalo State University" ] }, { "uid": "doi:10.2139/ssrn.5984474", "doi": "10.2139/ssrn.5984474", "title": "The Conversational Structure of Investor Disagreement: Evidence from Reddit Threads", "authors": [ "Swaminathan Balasubramaniam", "Jorge Sabat" ], "posted": "2025-12-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5984474", "field": "finance", "role": "instrument", "bullets": [ "Nested discussion threads from Reddit r/wallstreetbets, with LLM-classified comment responses used to construct conversation-level disagreement measures among retail investors.", "An LLM classified how each comment responds to its parent message, building disagreement measures from thread structure rather than independent sentiment scores.", "First-round conversational disagreement predicts same-day retail order imbalance, outperforming sentiment-dispersion proxies; deeper reply rounds show no significant effect." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "n": 3589, "authors_detailed": [ { "name": "Swaminathan Balasubramaniam", "url": "https://openalex.org/A5113264938", "inst": "NEOMA Business School" }, { "name": "Jorge Sabat", "url": "https://openalex.org/A5005411096", "inst": "Universidad Andrés Bello" } ], "affiliations": [ "NEOMA Business School", "Universidad Andrés Bello" ] }, { "uid": "arxiv:2512.23848v1", "arxiv_id": "2512.23848v1", "title": "Integrating Domain Knowledge for Financial QA: A Multi-Retriever RAG Approach with LLMs", "authors": [ "Yukun Zhang", "Stefan Elbl Droguett", "Samyak Jain" ], "posted": "2025-12-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.23848v1", "field": "finance", "role": "method", "bullets": [ "Financial numerical reasoning questions from the FinQA benchmark, which pair report text and tables and require multi-step calculation with domain knowledge.", "A multi retriever RAG system with a domain trained SecBERT encoder supplies context to an unnamed recent LLM generator; answers are scored against FinQA ground truth with ablations.", "The neural symbolic variant passes the original FinQA top model, and the prompt based generator improves the state of the art by more than 7 percent, still below human experts." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "FinQA benchmark accuracy", "salience": 34, "edition": 14, "n": 1945, "authors_detailed": [ { "name": "Yukun Zhang", "url": "https://openalex.org/A5051582225", "inst": "Peking University" }, { "name": "Stefan Elbl Droguett", "url": "https://openalex.org/A5022193337", "inst": "Stanford Medicine" }, { "name": "Samyak Jain", "url": "https://openalex.org/A5121779001", "inst": "" } ], "affiliations": [ "Peking University", "Stanford Medicine" ] }, { "uid": "arxiv:2512.23489v2", "arxiv_id": "2512.23489v2", "title": "The Gaining Paths to Investment Success: Information-Driven LLM Graph Reasoning for Venture Capital Prediction", "authors": [ "Haoyu Pei", "Zhongyang Liu", "Xiangyi Xiao", "Xiaocong Du", "Suting Hong", "Kunpeng Zhang", "Haipeng Zhang" ], "posted": "2025-12-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.23489v2", "field": "finance", "role": "method", "bullets": [ "Venture capital networks linking startups, investors, and company disclosures, used to predict startup success from relational evidence; sample size and period are not stated.", "MIRAGE-VC selects graph paths by information gain into compact chains for an unnamed LLM to reason over, fusing three evidence streams through a learned gate under anti leakage controls.", "The framework improves F1 by 5.0 percent and precision at 5 by 16.6 percent over baselines, and the authors point to other off graph prediction uses such as risk assessment." ], "bullet_provenance": "ai", "validated": true, "validation_note": "startup outcomes, F1 and precision at 5", "salience": 48, "edition": 14, "models": [], "n": 1946, "authors_detailed": [ { "name": "Haoyu Pei", "url": "https://openalex.org/A5121640953", "inst": "ShanghaiTech University" }, { "name": "Zhongyang Liu", "url": "https://openalex.org/A5121624437", "inst": "ShanghaiTech University" }, { "name": "Xiangyi Xiao", "url": "https://openalex.org/A5121665786", "inst": "ShanghaiTech University" }, { "name": "Xiaocong Du", "url": "https://openalex.org/A5091909540", "inst": "ShanghaiTech University" }, { "name": "Hong, Suting", "url": "", "inst": "" }, { "name": "Kunpeng Zhang", "url": "https://openalex.org/A5121683026", "inst": "University of Maryland, College Park" }, { "name": "Zhang, Haipeng", "url": "", "inst": "" } ], "affiliations": [ "University of Maryland, College Park", "ShanghaiTech University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.23847v2", "arxiv_id": "2512.23847v2", "title": "Detecting Lookahead Bias in LLM Forecasts", "authors": [ "Zhenyu Gao", "Wenxi Jiang", "Yutong Yan" ], "posted": "2025-12-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.23847v2", "field": "finance", "role": "method", "bullets": [ "Firm-date pairs from U.S. equities matched to news headlines and earnings call transcripts, spanning periods before and after the LLM training-data cutoff.", "A date-only recall query estimated each observation's Lookahead Propensity score; interaction tests assessed whether LLM forecast accuracy depends on training-data contamination.", "Forecast predictive power was amplified on high-LAP observations and lost significance post-cutoff, confirming lookahead bias and providing a diagnostic test for LLM-generated economic forecasts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "post-training-cutoff out-of-sample validation", "salience": 82, "n": 2534, "authors_detailed": [ { "name": "Zhenyu Gao", "url": "https://openalex.org/A5121767117", "inst": "Chinese University of Hong Kong" }, { "name": "Wenxi Jiang", "url": "https://openalex.org/A5121813407", "inst": "Chinese University of Hong Kong" }, { "name": "Yutong Yan", "url": "https://openalex.org/A5121751374", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong" ] }, { "uid": "arxiv:2602.23373v1", "arxiv_id": "2602.23373v1", "title": "An Agentic LLM Framework for Adverse Media Screening in AML Compliance", "authors": [ "Pavel Chernakov", "Sasan Jafarnejad", "Raphaël Frank" ], "posted": "2025-12-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2602.23373v1", "field": "finance", "role": "instrument", "bullets": [ "Dataset of politically exposed persons, regulatory watchlist individuals, sanctioned persons from OpenSanctions, and clean names from academic sources for AML screening evaluation.", "An agentic LLM system with RAG searched the web, retrieved documents, and computed an Adverse Media Index score; multiple LLM backends evaluated for risk classification accuracy.", "The system distinguished high-risk from low-risk individuals with lower false-positive rates than keyword-based approaches, automating a critical AML/KYC compliance workflow." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "OpenSanctions PEP and watchlist classification", "salience": 60, "n": 2535, "authors_detailed": [ { "name": "Pavel Chernakov", "url": "https://openalex.org/A5127853417", "inst": "" }, { "name": "Sasan Jafarnejad", "url": "https://openalex.org/A5091262858", "inst": "University of Luxembourg" }, { "name": "Raphaël Frank", "url": "https://openalex.org/A5127812136", "inst": "" } ], "affiliations": [ "University of Luxembourg" ] }, { "uid": "arxiv:2512.23515v1", "arxiv_id": "2512.23515v1", "title": "Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning", "authors": [ "Zuoyou Jiang", "Li Zhao", "Rui Sun", "Ruohan Sun", "Zhongjian Li", "Jing Li", "Daxin Jiang", "Zuo Bai", "Cheng Hua" ], "posted": "2025-12-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.23515v1", "field": "finance", "role": "instrument", "bullets": [ "Multiple asset pools tested with quantitative factor strategies subject to signal decay and regime shifts in non-stationary markets.", "An 8B-parameter reasoning LLM trained via reinforcement learning screened alpha factors by reasoning over factor logic and real-time news context.", "Alpha-R1 consistently outperformed benchmark strategies and showed improved robustness to alpha decay across multiple asset pools." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "outperformance vs benchmark strategies across multiple asset pools", "salience": 72, "n": 2787, "authors_detailed": [ { "name": "Zuoyou Jiang", "url": "https://openalex.org/A5121682106", "inst": "Shanghai Jiao Tong University" }, { "name": "Li Zhao", "url": "https://openalex.org/A5121634382", "inst": "Peking University" }, { "name": "Rui Sun", "url": "https://openalex.org/A5121677944", "inst": "Shanghai Jiao Tong University" }, { "name": "Ruohan Sun", "url": "https://openalex.org/A5121627577", "inst": "Shanghai Jiao Tong University" }, { "name": "Zhongjian Li", "url": "https://openalex.org/A5121623396", "inst": "Shanghai Jiao Tong University" }, { "name": "Jing Li", "url": "https://openalex.org/A5121613027", "inst": "Changchun University of Science and Technology" }, { "name": "Daxin Jiang", "url": "https://openalex.org/A5121277989", "inst": "Shanghai Zhaozhan Metal Materials" }, { "name": "Zuo Bai", "url": "https://openalex.org/A5008300079", "inst": "Financiadora de Estudos e Projetos" }, { "name": "Cheng Hua", "url": "https://openalex.org/A5121647381", "inst": "Shanghai Jiao Tong University" } ], "affiliations": [ "Shanghai Jiao Tong University", "Peking University", "Changchun University of Science and Technology", "Shanghai Zhaozhan Metal Materials", "Financiadora de Estudos e Projetos" ] }, { "uid": "arxiv:2512.23184v1", "arxiv_id": "2512.23184v1", "title": "From Model Choice to Model Belief: Establishing a New Measure for LLM-Based Research", "authors": [ "Hongshen Sun", "Juanjuan Zhang" ], "posted": "2025-12-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.23184v1", "field": "economics", "role": "method", "bullets": [ "Demand estimation study where an LLM simulated consumer responses to varying prices, formalizing token-level probability extraction.", "Authors introduced 'model belief' derived from token-level probabilities as a statistically efficient alternative to repeated model choice sampling.", "Model belief reduced computation needed for sufficiently accurate estimates by roughly 20x and predicted ground-truth model choice better than model choice itself." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "model belief vs ground-truth model choice in demand estimation", "salience": 70, "n": 2788, "authors_detailed": [ { "name": "Hongshen Sun", "url": "https://openalex.org/A5121624111", "inst": "" }, { "name": "Juanjuan Zhang", "url": "https://openalex.org/A5121667209", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5964494", "doi": "10.2139/ssrn.5964494", "title": "Translational AI: A New Discipline for Turning Model Potential into Market Success", "authors": [ "Henry Han" ], "posted": "2025-12-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5964494", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of enterprise AI deployment failures, drawing on game-theoretic frameworks including Akerlof adverse selection and iterated prisoner's dilemma.", "No specific model tested; paper develops Translational AI management discipline addressing the gap between model technical metrics and economic utility.", "Identifies 'Green Dashboard Paradox' where strong technical metrics precede business failure; proposes Variance Audit and Cognitive Dividend as corrective mechanisms." ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 3972, "authors_detailed": [ { "name": "Henry Han", "url": "https://openalex.org/A5087988017", "inst": "Baylor University" } ], "affiliations": [ "Baylor University" ] }, { "uid": "doi:10.2139/ssrn.5968835", "doi": "10.2139/ssrn.5968835", "title": "Can LLMs \"Understand\" Factors? A Semantically-Enhanced Asset Pricing Model", "authors": [ "Wenke Huang", "Tongyang Kong" ], "posted": "2025-12-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5968835", "field": "finance", "role": "instrument", "bullets": [ "U.S. and China stock markets with standard factor data, testing whether semantic information from factor definitions improves cross-sectional return prediction.", "A pre-trained LLM encoded textual factor definitions into semantic representations fused with numerical factor values via a Semantically-Enhanced Factor Adapter architecture.", "Combining semantic and numerical factor information significantly improved return predictability over traditional value-only asset pricing models in both the U.S. and China markets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "US and China cross-sectional return prediction", "salience": 67, "n": 2533, "authors_detailed": [ { "name": "Wenke Huang", "url": "https://openalex.org/A5018298375", "inst": "Wuhan University" }, { "name": "Tongyang Kong", "url": "https://openalex.org/A5121325713", "inst": "City University of Hong Kong" } ], "affiliations": [ "Wuhan University", "City University of Hong Kong" ] }, { "uid": "arxiv:2512.22443v2", "arxiv_id": "2512.22443v2", "title": "Accounting Reasoning in Large Language Models: Concepts, Evaluation, and Empirical Analysis", "authors": [ "Jie Zhou", "Xin Chen", "Jie Zhang", "Zhe Li" ], "posted": "2025-12-27", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.22443v2", "field": "accounting", "role": "method", "bullets": [ "Accounting reasoning tasks and evaluation criteria built from an analysis of the training data characteristics of GLM series models; task counts and sources are not stated.", "GLM-6B, GLM-130B, GLM-4, and GPT-4 attempt the tasks under different prompt engineering strategies, with performance scored against the proposed accounting reasoning criteria.", "GPT-4 shows the strongest accounting reasoning and prompting helps unevenly across models, yet all remain short of what enterprise accounting deployment would require." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "scored accounting reasoning tasks", "salience": 45, "edition": 14, "n": 1849, "authors_detailed": [ { "name": "Jie Zhou", "url": "https://openalex.org/A5121647506", "inst": "Guangdong Academy of Sciences" }, { "name": "Xin Chen", "url": "https://openalex.org/A5121637143", "inst": "State Grid Corporation of China (China)" }, { "name": "Jie Zhang", "url": "https://openalex.org/A5100436774", "inst": "Beijing Institute of Technology" }, { "name": "Zhe Li", "url": "https://openalex.org/A5121600171", "inst": "University of Science and Technology of China" } ], "affiliations": [ "Guangdong Academy of Sciences", "State Grid Corporation of China (China)", "Beijing Institute of Technology", "University of Science and Technology of China" ] }, { "uid": "arxiv:2512.21878v1", "arxiv_id": "2512.21878v1", "title": "MASFIN: A Multi-Agent System for Decomposed Financial Reasoning and Forecasting", "authors": [ "Marc S. Montalvo", "Hamed Yaghoobian" ], "posted": "2025-12-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.21878v1", "field": "finance", "role": "method", "bullets": [ "Weekly portfolios of 15 to 30 US equities generated over an eight week live evaluation window against major index benchmarks.", "GPT-4.1 nano agents integrate structured financial metrics with unstructured news under explicit bias mitigation protocols; no validation of extracted signals against ground truth is reported.", "The system returned 7.33 percent cumulative over eight weeks, ahead of the S&P 500, Nasdaq 100, and Dow in six of eight weeks, with higher volatility." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 35, "edition": 14, "n": 1886, "authors_detailed": [ { "name": "Marc Montalvo", "url": "https://openalex.org/A5096784126", "inst": "" }, { "name": "Hamed Yaghoobian", "url": "https://openalex.org/A5121585982", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5962814", "doi": "10.2139/ssrn.5962814", "title": "Many Men, Many Minds: A Multi-Agent LLM Approach to Disagreement and Global Asset Pricing", "authors": [ "Huaxi Zhang", "Zhiyi Wang", "Xinyu Sun" ], "posted": "2025-12-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5962814", "field": "finance", "role": "instrument", "bullets": [ "Cryptocurrency markets and 32 global equity markets, using financial news, with recursive out-of-sample forecasting and economic value tests; the sample period is not stated.", "Three Gemini-1.5-Flash agents process financial news to split disagreement into within-group belief dispersion and cross-group stance polarization; no ground-truth validation is reported.", "Within-group uncertainty predicts downward price pressure while cross-group polarization predicts higher subsequent returns; crypto disagreement leads international equity returns with meaningful certainty-equivalent gains." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": false, "salience": 60, "edition": 3, "audience": "general", "n": 92, "authors_detailed": [ { "name": "Huaxi Zhang", "url": "", "inst": "Nankai University" }, { "name": "Zhiyi Wang", "url": "", "inst": "Shandong University of Finance and Economics" }, { "name": "Xinyu Sun", "url": "", "inst": "Tsinghua University" } ], "affiliations": [ "Nankai University", "Shandong University of Finance and Economics", "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.5959955", "doi": "10.2139/ssrn.5959955", "title": "Measuring Investor Learning in Private Markets: A Sequential LLM–Bayesian Analysis of Expert Network Calls", "authors": [ "Yidong Chai", "Yanguang Liu", "Xuan Tian", "Jiaheng Xie", "Yonghang Zhou" ], "posted": "2025-12-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5959955", "field": "finance", "role": "instrument", "bullets": [ "A large dataset of expert network calls used to study investor learning in private markets; period, geography, and sample size are not stated, with the call as the signal unit.", "An unnamed large language model within a sequential LLM-Bayesian framework recovers time-varying beliefs about firm success from unstructured calls; no accuracy check on the extraction is reported.", "A single call raises subsequent investment probability by 6.9 to 9.0 percentage points, and the framework increases portfolio returns by 15.26%." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "edition": 3, "audience": "general", "models": [], "n": 362, "authors_detailed": [ { "name": "Yidong Chai", "url": "https://openalex.org/A5030014366", "inst": "Hefei University of Technology" }, { "name": "Yanguang Liu", "url": "https://openalex.org/A5121329367", "inst": "New Jersey Institute of Technology" }, { "name": "Xuan Tian", "url": "https://openalex.org/A5101429260", "inst": "Tsinghua University" }, { "name": "Jiaheng Xie", "url": "https://openalex.org/A5101803810", "inst": "University of Delaware" }, { "name": "Yonghang Zhou", "url": "https://openalex.org/A5003026518", "inst": "Hefei University of Technology" } ], "affiliations": [ "Hefei University of Technology", "New Jersey Institute of Technology", "Tsinghua University", "University of Delaware" ] }, { "uid": "arxiv:2512.21031v2", "arxiv_id": "2512.21031v2", "title": "Learning the Macroeconomic Language", "authors": [ "Siddhartha Chib", "Fei Tan" ], "posted": "2025-12-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.21031v2", "field": "economics", "role": "method", "bullets": [ "U.S. macroeconomic time series used to estimate a DSGE model, then millions of synthetic theory-consistent panels generated from the posterior for transformer training.", "A time-series transformer with attention trained on DSGE-generated synthetic data mixed with actual macro data; hybrid approach combines structural model coherence with LLM representational capacity.", "The hybrid DSGE-transformer forecaster learned key macroeconomic dynamics and produced competitive out-of-sample forecasts through 2025, demonstrating viability of LLMs for small-sample macro settings." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "out-of-sample macro forecasts through 2025", "salience": 72, "n": 2532, "authors_detailed": [ { "name": "Siddhartha Chib", "url": "https://openalex.org/A5108347485", "inst": "Washington University in St. Louis" }, { "name": "Fei Tan", "url": "https://openalex.org/A5121307915", "inst": "Zhejiang University of Finance and Economics" }, { "name": "Zhixun Zhang", "url": "https://openalex.org/A5147425707", "inst": "" } ], "affiliations": [ "Washington University in St. Louis", "Zhejiang University of Finance and Economics" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.20900v3", "arxiv_id": "2512.20900v3", "title": "Measuring Investor Learning in Private Markets: A Sequential LLM-Bayesian Analysis of Expert Network Calls", "authors": [ "Yidong Chai", "Yanguang Liu", "Xuan Tian", "Jiaheng Xie", "Yonghang Zhou" ], "posted": "2025-12-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.20900v3", "field": "finance", "role": "instrument", "bullets": [ "Large dataset of expert network calls in private markets capturing sequential investor-expert interactions prior to investment decisions.", "A sequential LLM-Bayesian framework extracted time-varying beliefs about firm success and uncertainty from unstructured expert conversations to measure information acquisition.", "A single expert call raised investment probability by 6.9-9.0 pp; one SD increase in inferred success belief raised deal probability by approximately 11 pp and the framework improved portfolio returns by 15.26%." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Portfolio returns and F1 against realized investment outcomes", "salience": 82, "models": [], "n": 3078, "authors_detailed": [ { "name": "Yidong Chai", "url": "https://openalex.org/A5030014366", "inst": "University of Science and Technology of China" }, { "name": "Yanguang Liu", "url": "https://openalex.org/A5121329367", "inst": "New Jersey Institute of Technology" }, { "name": "Xuan Tian", "url": "https://openalex.org/A5101429260", "inst": "Tsinghua University" }, { "name": "Jiaheng Xie", "url": "https://openalex.org/A5121346120", "inst": "Alfred University" }, { "name": "Yonghang Zhou", "url": "https://openalex.org/A5121318229", "inst": "Hefei University of Technology" } ], "affiliations": [ "University of Science and Technology of China", "New Jersey Institute of Technology", "Tsinghua University", "Alfred University", "Hefei University of Technology" ] }, { "uid": "arxiv:2512.20082v2", "arxiv_id": "2512.20082v2", "title": "Adaptive Financial Sentiment Analysis for NIFTY 50 via Instruction-Tuned LLMs , RAG and Reinforcement Learning Approaches", "authors": [ "Chaithra", "Kamesh Kadimisetty", "Biju R Mohan" ], "posted": "2025-12-23", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.20082v2", "field": "finance", "role": "method", "bullets": [ "News headlines for NIFTY 50 firms collected over 2024 and 2025, paired with next day stock returns that act as market feedback on predicted sentiment.", "LLaMA 3.2 3B is instruction tuned on the SentiFin dataset with retrieval over multi source context; a PPO agent reweights sources when predictions and realized returns disagree.", "The adaptive pipeline improves classification accuracy, F1, and sentiment return alignment over baseline models and static retrieval; the abstract gives no figures." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "labelled headline sentiment, accuracy and F1", "salience": 38, "edition": 14, "n": 1839, "authors_detailed": [ { "name": "Chaithra", "url": "https://openalex.org/A5121287080", "inst": "" }, { "name": "Kamesh Kadimisetty", "url": "https://openalex.org/A5121244953", "inst": "" }, { "name": "Biju R. Mohan", "url": "https://openalex.org/A5032581304", "inst": "National Institute of Technology Karnataka" } ], "affiliations": [ "National Institute of Technology Karnataka" ] }, { "uid": "doi:10.2139/ssrn.5950537", "doi": "10.2139/ssrn.5950537", "title": "Training Data Governance", "authors": [ "Frank Fagan" ], "posted": "2025-12-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5950537", "field": "economics", "role": "object", "bullets": [ "Conceptual law and economics analysis of markets for AI training data, with no empirical sample, focused on generative AI systems and the content sites whose material trains them.", "No language model is applied; the paper studies generative AI as a policy object and proposes a three-part test for when licensing of training inputs is warranted.", "Argues licensing intervention improves welfare only when content has demonstrable training value, withdrawal is the rational market outcome absent payment, and voluntary bargaining fails on transaction costs." ], "bullet_provenance": "ai", "salience": 40, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 545, "authors_detailed": [ { "name": "Frank Fagan", "url": "https://openalex.org/A5074830377", "inst": "South Texas College of Law" } ], "affiliations": [ "South Texas College of Law" ] }, { "uid": "doi:10.2139/ssrn.5769944", "doi": "10.2139/ssrn.5769944", "title": "Mapping of Google Business Categories to NACE Rev. 2 using Large Language Models", "authors": [ "Alexander Dicks" ], "posted": "2025-12-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5769944", "field": "economics", "role": "instrument", "bullets": [ "Full set of Google Business Categories mapped to NACE Rev. 2 economic activity classification used in European official statistics.", "Claude 3.7 Sonnet, GPT-4o, Gemini 2.0 Flash, and fine-tuned XLM-RoBERTa classified business categories; mappings compared against human-coded ground truth.", "Best LLM configuration achieved 90% accuracy; study identifies systematic error patterns and suggests embedding-based alternatives for remaining classification gaps." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "open_other" ], "open_weights": false, "validated": true, "validation_note": "human-coded ground truth, up to 90% accuracy", "salience": 50, "n": 2359, "authors_detailed": [ { "name": "Alexander Dicks", "url": "https://openalex.org/A5067755045", "inst": "WZB Berlin Social Science Center" } ], "affiliations": [ "WZB Berlin Social Science Center" ] }, { "uid": "arxiv:2512.19675v1", "arxiv_id": "2512.19675v1", "title": "Multimodal LLMs for Historical Dataset Construction from Archival Image Scans: German Patents (1877-1918)", "authors": [ "Niclas Griesshaber", "Jochen Streb" ], "posted": "2025-12-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.19675v1", "field": "economics", "role": "method", "bullets": [ "306,070 German patents from 1877 to 1918, transcribed from 9,562 archival image scans with dense double column pages in Gothic and Roman fonts.", "Gemini 2.5 Pro and Flash Lite power the extraction pipeline, benchmarked against research assistant transcriptions of the same material.", "The models produce tentatively higher quality data than the assistants while running about 795 times faster and 205 times cheaper; pipeline and datasets are open sourced." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "benchmarked against research assistant transcriptions", "salience": 72, "edition": 14, "n": 1885, "authors_detailed": [ { "name": "Niclas Griesshaber", "url": "https://openalex.org/A5120767193", "inst": "University of Mannheim" }, { "name": "Jochen Streb", "url": "https://openalex.org/A5042918749", "inst": "Technische Hochschule Mannheim" } ], "affiliations": [ "University of Mannheim", "Technische Hochschule Mannheim" ], "prestige": true }, { "uid": "arxiv:2601.00810v1", "arxiv_id": "2601.00810v1", "title": "Can Large Language Models Improve Venture Capital Exit Timing After IPO?", "authors": [ "Mohammadhossien Rashidi" ], "posted": "2025-12-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2601.00810v1", "field": "finance", "role": "agent", "bullets": [ "Post-IPO monthly data for VC-backed firms including financial performance, SEC filings, news sentiment, and market signals over the lock-up and holding period.", "LLMs analyzed monthly post-IPO information bundles and recommended sell-or-hold decisions; recommendations compared to actual observed VC exit dates and corresponding returns.", "Framework quantifies return differences between LLM-guided and actual VC exit timing, providing initial evidence on whether AI-driven guidance can improve post-IPO divestment decisions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "return comparison against actual VC exit dates", "salience": 65, "n": 2530, "authors_detailed": [ { "name": "Mohammadhossien Rashidi", "url": "https://openalex.org/A5122069600", "inst": "" } ] }, { "uid": "arxiv:2512.19484v1", "arxiv_id": "2512.19484v1", "title": "Structured Event Representation and Stock Return Predictability", "authors": [ "Gang Li", "Dandan Qiao", "Mingxuan Zheng" ], "posted": "2025-12-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.19484v1", "field": "finance", "role": "instrument", "bullets": [ "Cross-section of U.S. stocks with associated news articles, using structured event representations extracted from financial news for return prediction.", "A pre-trained LLM extracted structured event features from news; a deep learning model with attention mechanisms used these features to predict cross-sectional stock returns out of sample.", "The structured event representation model outperformed existing text-driven models in out-of-sample return forecasting and provided interpretable feature structures revealing predictability mechanisms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "out-of-sample cross-sectional return prediction", "salience": 70, "n": 2531, "authors_detailed": [ { "name": "Gang Li", "url": "https://openalex.org/A5121210264", "inst": "Shanxi Medical University" }, { "name": "Dandan Qiao", "url": "https://openalex.org/A5121194545", "inst": "" }, { "name": "Mingxuan Zheng", "url": "https://openalex.org/A5121124239", "inst": "National University of Singapore" } ], "affiliations": [ "Shanxi Medical University", "National University of Singapore" ] }, { "uid": "arxiv:2512.19935v1", "arxiv_id": "2512.19935v1", "title": "Conditional Adversarial Fragility in Financial Machine Learning under Macroeconomic Stress", "authors": [ "Samruddhi Baviskar" ], "posted": "2025-12-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.19935v1", "field": "finance", "role": "method", "bullets": [ "Tabular financial classification tasks evaluated across calm and stress regimes using volatility-based segmentation as a macroeconomic stress proxy.", "LLMs provided semantic auditing of model explanations as an interpretive governance layer within a regime-aware adversarial robustness evaluation framework.", "Models under stress regimes showed substantially greater degradation under adversarial perturbations with increased false-negative rates despite comparable baseline predictive performance." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "models": [], "n": 3077, "authors_detailed": [ { "name": "Samruddhi Baviskar", "url": "https://openalex.org/A5121148936", "inst": "" } ] }, { "uid": "doi:10.5281/zenodo.18008872", "doi": "10.5281/zenodo.18008872", "arxiv_id": "2601.00818v1", "title": "Agentic AI for Autonomous, Explainable, and Real-Time Credit Risk Decision-Making", "authors": [ "Chandra Sekhar Kubam" ], "posted": "2025-12-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2601.00818v1", "field": "finance", "role": "method", "bullets": [ "Conceptual framework for autonomous credit risk assessment targeting digitalized financial services and cross-country credit ecosystems.", "Multi-agent system integrates reinforcement learning, natural language reasoning, and explainable AI modules for real-time borrower risk scoring.", "Framework improves decision speed and transparency over traditional credit scoring but faces model drift, regulatory uncertainty, and infrastructure constraints." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 3588, "authors_detailed": [ { "name": "Chandra Sekhar Kubam", "url": "https://openalex.org/A5121001476", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2512.17462v1", "arxiv_id": "2512.17462v1", "title": "Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application", "authors": [ "Olivier Jeunen", "Schaun Wheeler" ], "posted": "2025-12-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.17462v1", "field": "management", "role": "object", "bullets": [ "Two-month randomized controlled trial on a financial service application's customer messaging system during a 2025 national tax filing period.", "Agentic AI personalization system generated adaptive user-level messages, compared against a rule-based business-as-usual campaign system.", "Agent-led messaging reduced unsubscribe events by 21% relative to the control group and shifted tax filing behavior earlier ahead of the national deadline." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3971, "authors_detailed": [ { "name": "Olivier Jeunen", "url": "https://openalex.org/A5083237271", "inst": "Antwerp Management School" }, { "name": "Schaun Wheeler", "url": "https://openalex.org/A5118131128", "inst": "African American Museum in Philadelphia" } ], "affiliations": [ "Antwerp Management School", "African American Museum in Philadelphia" ] }, { "uid": "doi:10.2139/ssrn.5735785", "doi": "10.2139/ssrn.5735785", "title": "Making Talk Cheap: Generative AI and Labor Market Signaling", "authors": [ "Anaïs Galdin", "Jesse Silbert" ], "posted": "2025-12-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5735785", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 52, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 692, "authors_detailed": [ { "name": "Anais Galdin", "url": "https://openalex.org/A5120650389", "inst": "Dartmouth College" }, { "name": "Jesse Silbert", "url": "https://openalex.org/A5120498763", "inst": "Princeton University" } ], "affiliations": [ "Dartmouth College", "Princeton University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.15081v1", "arxiv_id": "2512.15081v1", "title": "Quantifying Return on Security Controls in LLM Systems", "authors": [ "Richard Helder Moulton", "Austin O'Brien", "John D. Hastings" ], "posted": "2025-12-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.15081v1", "field": "management", "role": "object", "bullets": [ "A retrieval augmented generation service over a corpus with synthetic personal data, attacked by the Garak tool across five vulnerability classes and defended by three common controls.", "The service runs on DeepSeek-R1; attack success rates feed Monte Carlo simulations calibrated to public breach cost data, yielding expected losses and return on control for each defense.", "Baseline attack success is at least 0.98 for the main classes with 313 thousand dollars expected loss per scenario; access control cuts loss about 94 percent while guardrails return almost nothing." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 46, "edition": 14, "validated": null, "n": 1944, "authors_detailed": [ { "name": "Moulton, Richard Helder", "url": "", "inst": "" }, { "name": "O'Brien, Austin", "url": "", "inst": "" }, { "name": "Hastings, John D.", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5929914", "doi": "10.2139/ssrn.5929914", "title": "The Invisible Backbone: How Supply Chains Bring AI to Life", "authors": [ "Serguei Netessine", "Masha Shunko" ], "posted": "2025-12-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5929914", "field": "management", "role": "object", "bullets": [ "Global AI supply chain from critical raw materials and chip manufacturing through data center infrastructure supporting GenAI technologies like ChatGPT.", "Paper systematically examines environmental, political, legal, and ethical disruption risks across the end-to-end AI supply chain; no model is deployed empirically.", "Rapid GenAI growth strains supply chains with unprecedented complexity; authors call for proactive lifecycle management, resilience building, and end-of-life recycling strategies." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3076, "authors_detailed": [ { "name": "Serguei Netessine", "url": "", "inst": "University of Pennsylvania" }, { "name": "Masha Shunko", "url": "", "inst": "Paccar (United States)" } ], "affiliations": [ "University of Pennsylvania", "Paccar (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.14306v1", "arxiv_id": "2512.14306v1", "title": "Inflation Attitudes of Large Language Models", "authors": [ "Nikoleta Anesti", "Edward Hill", "Andreas Joseph" ], "posted": "2025-12-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.14306v1", "field": "economics", "role": "agent", "bullets": [ "UK macroeconomic price data matched to Bank of England Inflation Attitudes Survey respondent profiles, exploiting GPT-3.5-turbo's September 2021 training cutoff as a quasi-experiment.", "GPT-3.5-turbo received demographic prompts and price signals to generate inflation perceptions and expectations; outputs compared to household survey data and official statistics via Shapley decomposition.", "GPT tracked aggregate survey projections at short horizons and replicated income- and tenure-based heterogeneity but lacked a consistent internal model of consumer price inflation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Bank of England Inflation Attitudes Survey and ONS statistics", "salience": 75, "n": 2529, "authors_detailed": [ { "name": "Nikoleta Anesti", "url": "https://openalex.org/A5012626649", "inst": "Bank of England" }, { "name": "Edward W. Hill", "url": "https://openalex.org/A5033089718", "inst": "Cleveland State University" }, { "name": "Andreas Joseph", "url": "https://openalex.org/A5008516941", "inst": "American College of Financial Services" } ], "affiliations": [ "Bank of England", "Cleveland State University", "American College of Financial Services" ] }, { "uid": "doi:10.2139/ssrn.5924344", "doi": "10.2139/ssrn.5924344", "title": "Artificial Intelligence and Skills: Evidence from Contrastive Learning in Online Job Vacancies", "authors": [ "Hangyu Chen", "Yongming Sun", "Yiming Yuan" ], "posted": "2025-12-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5924344", "field": "economics", "role": "instrument", "bullets": [ "14 million online job vacancies from Chinese listed firms, 2018-2022, mapped to the ESCO occupational skills framework.", "LLM-driven data augmentation with Extreme Multi-Label Classification trained via contrastive learning maps vacancy text to standardized skill taxonomies.", "AI adoption expands skill portfolios and increases demand for forward-looking skills absent from 2018 standards but codified in 2022 updates." ], "bullet_provenance": "ai", "validated": true, "validation_note": "O*NET-ESCO occupation-skill mapping benchmark", "salience": 60, "models": [], "n": 3587, "authors_detailed": [ { "name": "Hangyu Chen", "url": "https://openalex.org/A5101011617", "inst": "Independent" }, { "name": "Yongming Sun", "url": "https://openalex.org/A5101571949", "inst": "First Affiliated Hospital Zhejiang University" }, { "name": "Yiming Yuan", "url": "https://openalex.org/A5100552713", "inst": "First Affiliated Hospital Zhejiang University" } ], "affiliations": [ "Independent" ] }, { "uid": "arxiv:2512.13168v5", "arxiv_id": "2512.13168v5", "title": "Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows", "authors": [ "Haoyu Dong", "Pengkun Zhang", "Yan Gao", "Xuanyu Dong", "Yilin Cheng", "Mingzhe Lu", "Zikun Zhu", "Adina Yakefu", "Shuxin Zheng" ], "posted": "2025-12-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.13168v5", "field": "accounting", "role": "method", "bullets": [ "172 composite workflows with 384 tasks from real enterprise workspaces, including 15,000 Enron files and 500,000 emails covering 2000 to 2025, plus 1,710 spreadsheets holding 27 million cells.", "GPT-5.1, Claude Sonnet 4.5 and Opus 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max attempt the workflows; grading combines automated checks with human evaluation against expert annotated ground truth.", "GPT-5.1 Pro passes 38.4 percent of workflows while averaging 16.8 minutes on each, leaving most real enterprise finance and accounting work beyond current agents." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "expert annotated workflows, human graded pass rates", "salience": 68, "edition": 14, "n": 1847, "authors_detailed": [ { "name": "Haoyu Dong", "url": "https://openalex.org/A5104255893", "inst": "University of Nebraska–Lincoln" }, { "name": "Pengkun Zhang", "url": "https://openalex.org/A5056645483", "inst": "Sun Yat-sen University" }, { "name": "Yan Gao", "url": "https://openalex.org/A5100462449", "inst": "Tongji University" }, { "name": "Dong, Xuanyu", "url": "", "inst": "" }, { "name": "Yi‐Lin Cheng", "url": "https://openalex.org/A5109297600", "inst": "Capital University" }, { "name": "Mincheng Lu", "url": "https://openalex.org/A5109460572", "inst": "Shanghai Jiao Tong University" }, { "name": "Zhu, Zikun", "url": "", "inst": "" }, { "name": "Yakefu, Adina", "url": "", "inst": "" }, { "name": "Shuxin Zheng", "url": "https://openalex.org/A5113139612", "inst": "Xi'an International Studies University" } ], "affiliations": [ "University of Nebraska–Lincoln", "Sun Yat-sen University", "Tongji University", "Capital University", "Shanghai Jiao Tong University", "Xi'an International Studies University" ] }, { "uid": "arxiv:2512.13040v2", "arxiv_id": "2512.13040v2", "title": "Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection", "authors": [ "Xuwei Tan", "Yao Ma", "Xueru Zhang" ], "posted": "2025-12-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.13040v2", "field": "finance", "role": "method", "bullets": [ "Four public financial fraud detection datasets of high dimensional tabular transactions with extreme class imbalance; sample periods and geography are not stated.", "Three families of open weight LLMs, not named in the abstract, receive importance ranked features serialized as text plus retrieved labelled exemplars, scored on F1 and MCC against the labels.", "The pipeline lifts F1 and MCC well above direct prompting and rivals strong tabular baselines in several settings, though specialized classifiers still lead overall." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "labelled fraud datasets, F1 and MCC reported", "salience": 45, "edition": 14, "models": [], "n": 1848 }, { "uid": "arxiv:2512.12858v3", "arxiv_id": "2512.12858v3", "title": "Information-Consistent Language Model Recommendations through Group Relative Policy Optimization", "authors": [ "Sonal Prabhune", "Balaji Padmanabhan", "Kaushik Dutta" ], "posted": "2025-12-14", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.12858v3", "field": "management", "role": "method", "bullets": [ "Investment and job recommendation tasks in enterprise settings, with groups of semantically equivalent prompts phrased differently to isolate consistency; datasets are not stated.", "A base LLM, not named, is fine-tuned with group relative policy optimization using entropy based helpfulness and stability rewards over prompt variant groups; no ground truth accuracy check applies.", "The tuned model gives less variable recommendations across equivalent phrasings than the baseline; magnitudes are not stated in the abstract." ], "bullet_provenance": "ai", "salience": 32, "edition": 14, "models": [], "validated": null, "n": 1943, "authors_detailed": [ { "name": "Sonal Prabhune", "url": "https://openalex.org/A5119938243", "inst": "" }, { "name": "Balaji Padmanabhan", "url": "https://openalex.org/A5050411634", "inst": "University of Maryland, College Park" }, { "name": "Kaushik Dutta", "url": "https://openalex.org/A5059780231", "inst": "University of South Florida" } ], "affiliations": [ "University of Maryland, College Park", "University of South Florida" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.12783v3", "arxiv_id": "2512.12783v3", "title": "Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset", "authors": [ "Atalay Denknalbant", "Emre Sezdi", "Zeki Furkan Kutlu" ], "posted": "2025-12-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.12783v3", "field": "finance", "role": "instrument", "bullets": [ "Synthetic dataset of 100,000 Istanbul residents reproducing Q1 2025 TURKSTAT census marginals and telecom usage patterns for credit scoring of underbanked borrowers.", "OpenAI o3 with retrieval-augmented generation synthesized realistic borrower profiles; CatBoost, LightGBM, and XGBoost trained with and without alternative behavioral data.", "Alternative attributes raised AUC by 1.3 percentage points and lifted balanced F1 from 0.84 to 0.95, a 14% gain over demographic-only models in five-fold stratified validation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "5-fold stratified CV with AUC and F1", "salience": 55, "n": 3970, "authors_detailed": [ { "name": "Atalay Denknalbant", "url": "https://openalex.org/A5120841692", "inst": "" }, { "name": "Emre Sezdi", "url": "https://openalex.org/A5120841693", "inst": "" }, { "name": "Zeki Furkan Kutlu", "url": "https://openalex.org/A5120841694", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5824422", "doi": "10.2139/ssrn.5824422", "title": "ChatGPT and Stock Returns", "authors": [ "Hakan Yilmazkuday" ], "posted": "2025-12-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5824422", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 691 }, { "uid": "arxiv:2512.12384v1", "arxiv_id": "2512.12384v1", "title": "The Data Efficiency Frontier of Financial Foundation Models: Scaling Laws from Continued Pretraining", "authors": [ "Jesse Ponnock" ], "posted": "2025-12-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.12384v1", "field": "finance", "role": "method", "bullets": [ "Llama-3.2 models at 1B and 3B parameters trained on a 400M-token corpus of U.S. SEC filings with validation checkpoints at 50M to 400M tokens.", "Domain-adaptive continued pretraining measured SEC-domain validation loss against general-domain loss to quantify specialization versus catastrophic forgetting trade-off.", "Largest gains occurred within the first 200M tokens with diminishing returns; general-domain loss remained unchanged, indicating meaningful financial adaptation with modest token budgets." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 45, "n": 2786, "authors_detailed": [ { "name": "Jesse Ponnock", "url": "https://openalex.org/A5120840348", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5825343", "doi": "10.2139/ssrn.5825343", "title": "Measuring Price Effects of Multilingual Global News with Large Language Models", "authors": [ "Kevin Benson", "Ing-Haw Cheng", "Maurice Granger", "John C. Hull", "Charles Martineau", "Yoshio Nozawa", "Vasily Strela", "Yuntao Wu", "Jun yuan" ], "posted": "2025-12-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5825343", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 55, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 690, "authors_detailed": [ { "name": "Kevin Benson", "url": "https://openalex.org/A5113057489", "inst": "Royal Bank of Canada" }, { "name": "Ing-Haw Cheng", "url": "https://openalex.org/A5019063200", "inst": "University of Toronto" }, { "name": "Maurice Granger", "url": "https://openalex.org/A5120784583", "inst": "Royal Bank of Canada" }, { "name": "John C. Hull", "url": "https://openalex.org/A5110218217", "inst": "University of Toronto" }, { "name": "Charles Martineau", "url": "https://openalex.org/A5034910542", "inst": "University of Toronto" }, { "name": "Yoshio Nozawa", "url": "https://openalex.org/A5003588725", "inst": "University of Toronto" }, { "name": "Vasily Strela", "url": "https://openalex.org/A5055752951", "inst": "Royal Bank of Canada" }, { "name": "Yuntao Wu", "url": "https://openalex.org/A5101272598", "inst": "University of Toronto" }, { "name": "Jun Yuan", "url": "https://openalex.org/A5066660351", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto", "Royal Bank of Canada" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5884602", "doi": "10.2139/ssrn.5884602", "title": "GENERATIVE ARTIFICIAL INTELLIGENCE AND THE FUTURE OF FINANCIAL FORECASTING: EVIDENCE FROM LARGE LANGUAGE MODELS", "authors": [ "Mfon Akpan" ], "posted": "2025-12-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5884602", "field": "finance", "role": "agent", "bullets": [ "Portfolio of equities across multiple sectors, with LLMs forecasting actual closing prices on a single trading day in February 2025.", "GPT-4o and Claude 3.5 Sonnet performed zero-shot stock price predictions; accuracy assessed via MAE, RMSE, MAPE, correlation, and R-squared metrics.", "Claude 3.5 Sonnet outperformed GPT-4o on all accuracy and directional metrics, though significant challenges in interpretability and market sensitivity remain." ], "bullet_provenance": "ai", "models": [ "gpt", "claude" ], "open_weights": false, "validated": true, "validation_note": "actual equity closing prices (MAE, RMSE, MAPE, R²)", "salience": 35, "n": 2375, "authors_detailed": [ { "name": "Mfon Akpan", "url": "", "inst": "Northeastern State University" } ], "affiliations": [ "Northeastern State University" ] }, { "uid": "arxiv:2512.11933v1", "arxiv_id": "2512.11933v1", "title": "The Agentic Regulator: Risks for AI in Finance and a Proposed Agent-based Framework for Governance", "authors": [ "Eren Kurshan", "Tucker Balch", "David Byrd" ], "posted": "2025-12-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.11933v1", "field": "finance", "role": "object", "bullets": [ "Conceptual governance framework for generative and agentic AI in financial markets with case study on emergent spoofing in multi-agent trading.", "Four-layer modular architecture spanning self-regulation modules, firm-level governance, regulator-hosted monitoring agents, and independent audit blocks.", "Framework closes observability and control gaps in current model-risk rules while preserving innovation through eight adaptive design strategies." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 65, "validated": null, "n": 2867 }, { "uid": "arxiv:2512.11943v1", "arxiv_id": "2512.11943v1", "title": "How AI Agents Follow the Herd of AI? Network Effects, History, and Machine Optimism", "authors": [ "Yu Liu", "Wenwen Li", "Yifan Dou", "Guangnan Ye" ], "posted": "2025-12-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.11943v1", "field": "economics", "role": "agent", "bullets": [ "LLM-based agents in repeated network-effect games with manipulated price trajectories and varying network-effect strength across multiple rounds.", "LLM agents made participation decisions under fixed, ascending, descending, and random price sequences in network-effect contexts.", "Strong network effects triggered persistent AI optimism with overestimated participation; randomized history disrupted convergence entirely unlike human behavior." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 68, "n": 2868, "authors_detailed": [ { "name": "Yu Liu", "url": "https://openalex.org/A5048992193", "inst": "Tibet University" }, { "name": "Wenwen Li", "url": "https://openalex.org/A5100338168", "inst": "Heilongjiang University of Chinese Medicine" }, { "name": "Dou, Yifan", "url": "", "inst": "" }, { "name": "Guangnan Ye", "url": "https://openalex.org/A5003276783", "inst": "Fudan University" } ], "affiliations": [ "Tibet University", "Heilongjiang University of Chinese Medicine", "Fudan University" ] }, { "uid": "arxiv:2512.19705v1", "arxiv_id": "2512.19705v1", "title": "Generative AI for Analysts", "authors": [ "Jian Xue", "Qian Zhang", "Wu Zhu" ], "posted": "2025-12-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.19705v1", "field": "finance", "role": "object", "bullets": [ "U.S. financial analysts adopting FactSet's generative AI platform launched in 2023, evaluated with a difference-in-differences design against other data vendors as placebos.", "FactSet's generative AI integration assisted analysts in producing research reports; effects were isolated to FactSet using placebo tests with non-AI vendor platforms.", "AI adoption yielded 40% more information sources and 34% broader topical coverage, but forecast errors rose 59% as balanced AI-generated content proved harder to synthesize." ], "bullet_provenance": "ai", "salience": 85, "models": [], "validated": null, "n": 3075, "authors_detailed": [ { "name": "Jian Xue", "url": "https://openalex.org/A5121299223", "inst": "" }, { "name": "Qian Zhang", "url": "https://openalex.org/A5121300492", "inst": "Hefei University of Technology" }, { "name": "Wu Zhu", "url": "https://openalex.org/A5113044135", "inst": "Ankang City Central Hospital" } ], "affiliations": [ "Hefei University of Technology" ] }, { "uid": "arxiv:2512.14744v1", "arxiv_id": "2512.14744v1", "title": "VERAFI: Verified Agentic Financial Intelligence through Neurosymbolic Policy Generation", "authors": [ "Adewale Akinfaderin", "Shreyas Subramanian" ], "posted": "2025-12-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.14744v1", "field": "finance", "role": "method", "bullets": [ "FinanceBench dataset covering GAAP compliance, SEC requirements, and financial mathematical validation tasks.", "VERAFI framework combines dense retrieval, cross-encoder reranking, and tool-enabled agents with neurosymbolic policies for automated financial reasoning.", "Factual correctness rises from 52.4% to 94.7% on FinanceBench, an 81% relative improvement; neurosymbolic layer adds 4.3 percentage points over pure agentic processing." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinanceBench factual correctness", "salience": 55, "models": [], "n": 3585, "authors_detailed": [ { "name": "Akinfaderin, Adewale", "url": "", "inst": "" }, { "name": "Subramanian, Shreyas", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5827202", "doi": "10.2139/ssrn.5827202", "title": "Evolving Knowledge Management: Artificial Intelligence and the Dynamics of Social Interactions", "authors": [ "He Xiaomei", "Thierry Burger-Helmchen" ], "posted": "2025-12-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5827202", "field": "management", "role": "object", "bullets": [ "Conceptual review of AI integration in organizational knowledge management, grounded in the SECI model of tacit-explicit knowledge transformation.", "Examines how generative AI differs from earlier AI models and how it reshapes knowledge creation, storage, and dissemination in organizations.", "AI accelerates externalization and combination in the SECI model but remains limited in managing tacit knowledge and human-centric decision-making." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3586, "authors_detailed": [ { "name": "He Xiaomei", "url": "https://openalex.org/A5064206265", "inst": "Université de Strasbourg" }, { "name": "Thierry Burger‐Helmchen", "url": "https://openalex.org/A5002250156", "inst": "EM Strasbourg Business School" } ], "affiliations": [ "Université de Strasbourg", "EM Strasbourg Business School" ] }, { "uid": "doi:10.2139/ssrn.5879722", "doi": "10.2139/ssrn.5879722", "title": "Prompting Science Report 4: Playing Pretend: Expert Personas Don't Improve Factual Accuracy", "authors": [ "Savir Basil", "Ina Shapiro", "Dan Shapiro", "Ethan R. Mollick", "Lilach Mollick", "Lennart Meincke" ], "posted": "2025-12-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5879722", "field": "management", "role": "method", "bullets": [ "Six LLMs evaluated on GPQA Diamond and MMLU-Pro graduate-level multiple-choice questions spanning science, engineering, and law domains.", "Models received in-domain expert, off-domain expert, and low-knowledge personas; performance compared against a no-persona baseline across both benchmarks.", "Expert personas produced no consistent accuracy gain; domain-mismatched experts sometimes hurt performance; low-knowledge personas reliably reduced accuracy." ], "bullet_provenance": "ai", "models": [ "gemini" ], "validated": true, "validation_note": "GPQA Diamond and MMLU-Pro benchmarks", "salience": 50, "n": 3969, "authors_detailed": [ { "name": "Savir Basil", "url": "https://openalex.org/A5099094066", "inst": "University of Pennsylvania" }, { "name": "I JONATHAN SHAPIRO", "url": "https://openalex.org/A5111579309", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Daniel Shapiro", "url": "https://openalex.org/A5023560002", "inst": "Universidad Autónoma de Occidente" }, { "name": "Ethan Mollick", "url": "https://openalex.org/A5061686034", "inst": "University of Pennsylvania" }, { "name": "Lilach Mollick", "url": "https://openalex.org/A5018748407", "inst": "University of Pennsylvania" }, { "name": "Lennart Meincke", "url": "https://openalex.org/A5003350421", "inst": "California University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania", "Independent - affiliation not provided to SSRN", "Universidad Autónoma de Occidente", "California University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.10793v2", "arxiv_id": "2512.10793v2", "title": "LabelFusion: Fusing Large Language Models with Transformer Encoders for Robust Financial News Classification", "authors": [ "Michael Schlee", "Christoph Weisser", "Timo Kivimäki", "Melchizedek Mashiku", "Benjamin Saefken" ], "posted": "2025-12-11", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.10793v2", "field": "finance", "role": "method", "bullets": [ "A ten class multi-label subset of the Reuters-21578 news corpus, used to identify articles relevant to specific assets under varying amounts of labelled training data.", "LabelFusion merges a prompt engineered LLM, name not stated, with a fine-tuned RoBERTa encoder through a small voting network, scored against the corpus labels.", "With full data the fusion reaches macro F1 of 96.0 against 94.6 for RoBERTa alone; with less than about 80 percent of the data the standalone LLM leads, scoring 75.9 zero shot." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "Reuters-21578 labels, macro F1", "salience": 40, "edition": 14, "n": 1942 }, { "uid": "doi:10.2139/ssrn.5903562", "doi": "10.2139/ssrn.5903562", "title": "LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach", "authors": [ "Xiang Cheng", "Wen Wang", "Anindya Ghose" ], "posted": "2025-12-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5903562", "field": "management", "role": "method", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 544, "authors_detailed": [ { "name": "Xiang Cheng", "url": "", "inst": "University of Maryland - Robert H. Smith School of Business" }, { "name": "Wen Wang", "url": "", "inst": "University of Maryland - Robert H. Smith School of Business" }, { "name": "Anindya Ghose", "url": "", "inst": "New York University" } ], "affiliations": [ "New York University", "University of Maryland - Robert H. Smith School of Business" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.14735v2", "arxiv_id": "2512.14735v2", "title": "PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents", "authors": [ "Yuqun Zhang", "Yuxuan Zhao", "Sijia Chen" ], "posted": "2025-12-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.14735v2", "field": "finance", "role": "method", "bullets": [ "PyFi-600K dataset of 600K financial question-answer pairs organized in a reasoning pyramid from basic perception to expert-level visual understanding.", "Qwen2.5-VL-3B and 7B fine-tuned on pyramid-structured question chains for financial image understanding using adversarial multi-agent MCTS synthesis.", "Fine-tuning yields average accuracy improvements of 19.52% and 8.06% respectively on financial visual reasoning tasks requiring progressive decomposition." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "PyFi-600K financial visual reasoning benchmark", "salience": 45, "n": 3583, "authors_detailed": [ { "name": "Zhang, Yuqun", "url": "", "inst": "" }, { "name": "Zhao, Yuxuan", "url": "", "inst": "" }, { "name": "Chen, Sijia", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5903962", "doi": "10.2139/ssrn.5903962", "title": "State of SupTech Report 2025", "authors": [ "Simone Di Castri", "Matt Grasser", "Maryeliza Barasa" ], "posted": "2025-12-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5903962", "field": "finance", "role": "object", "bullets": [ "Survey of 312 financial authorities across 172 countries over four years, with 148 authorities from 105 countries surveyed in 2025.", "Report examines adoption of NLP, predictive analytics, and generative AI by financial supervisors for AML/CFT, licensing, and consumer protection.", "171 authorities in 107 countries operate live suptech applications; adoption depth correlates with income level, and AI deployment remains cautious and early-stage." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3584, "authors_detailed": [ { "name": "Simone Di Castri", "url": "", "inst": "University of Cambridge" }, { "name": "Matt Grasser", "url": "", "inst": "University of Cambridge" }, { "name": "Maryeliza Barasa", "url": "", "inst": "University of Cambridge" } ], "affiliations": [ "University of Cambridge" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5873823", "doi": "10.2139/ssrn.5873823", "title": "AI as \"Co-founder\": GenAI for Entrepreneurship *", "authors": [ "Junhui Cai", "Xian Gu", "Liugang Sheng", "Mengjia Xia", "Linda Zhao", "Wu Zhu" ], "posted": "2025-12-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5873823", "field": "economics", "role": "object", "bullets": [ "Universal, high-resolution data on Chinese firm registrations through end of 2024, organized across geocoded grids that differ in pre-existing AI-specific human capital.", "No model is run by the researchers; the November 2022 ChatGPT release is used as a global shock lowering start-up costs, so GenAI is the object of study.", "Grids with stronger AI human capital saw a surge in small-firm formation contributing 6.0% of national firm entry, while large-firm entry declined." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 66, "edition": 3, "audience": "general", "validated": null, "n": 361, "authors_detailed": [ { "name": "Junhui Cai", "url": "", "inst": "University of Mendoza" }, { "name": "Xian Gu", "url": "", "inst": "Durham University" }, { "name": "Liugang Sheng", "url": "https://openalex.org/A5033830687", "inst": "Chinese University of Hong Kong" }, { "name": "Mengjia Xia", "url": "https://openalex.org/A5120741562", "inst": "University of Pennsylvania" }, { "name": "Linda Zhao", "url": "", "inst": "University of Pennsylvania" }, { "name": "Wu Zhu", "url": "https://openalex.org/A5046357032", "inst": "Tsinghua University" } ], "affiliations": [ "University of Pennsylvania", "University of Mendoza", "Durham University", "Chinese University of Hong Kong", "Tsinghua University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.09652v1", "arxiv_id": "2512.09652v1", "title": "Measuring Corruption from Text Data", "authors": [ "Arieda Muço" ], "posted": "2025-12-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.09652v1", "field": "economics", "role": "instrument", "bullets": [ "Brazilian municipal audit reports used to construct an automated corruption index combining a dictionary of irregularities with principal component analysis.", "Dictionary-PCA index compared against LLMs and supervised learning; supervised alternatives yielded nearly identical rankings with R-squared of 0.98.", "Index explained 71-73% of variation in hand-coded corruption counts and offered advantages over LLMs in transparency, cost, and long-run replicability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "human-coded corruption counts, R²=0.71-0.73", "salience": 50, "n": 2528, "authors_detailed": [ { "name": "Arieda Muço", "url": "https://openalex.org/A5004820485", "inst": "Central European University" } ], "affiliations": [ "Central European University" ] }, { "uid": "doi:10.1145/3770855.3817482", "doi": "10.1145/3770855.3817482", "arxiv_id": "2512.09506v6", "title": "Beyond Knowledge to Agency: Evaluating Expertise, Autonomy, and Integrity in Finance with CNFinBench", "authors": [ "Jinru Ding", "Chao Ding", "Yidong Jiang", "Wenrao Pang", "Boyi Xiao", "Zhiqiang Liu", "Jiayuan Chen", "Yun Zhong", "Tiantian Yuan", "Junming Guan", "Dawei Cheng", "Jie Xu" ], "posted": "2025-12-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.09506v6", "field": "finance", "role": "method", "bullets": [ "22 open- and closed-source LLMs evaluated across 29 subtasks spanning expertise, autonomy, and integrity in Chinese financial regulatory and professional contexts.", "Models tested on certified regulatory QA, end-to-end agent workflows, and multi-turn adversarial attacks; compliance drift measured with the HICS safety metric.", "LLMs scored well on applied tasks but lacked rule understanding; performance fell 15.4 points in full execution chains; violations surged 159% in adversarial round two." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "open_other" ], "validated": true, "validation_note": "CNFinBench 29-subtask financial benchmark", "salience": 58, "n": 2613, "authors_detailed": [ { "name": "Jinru Ding", "url": "https://openalex.org/A5100570587", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Chao Ding", "url": "https://openalex.org/A5031884073", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Yidong Jiang", "url": "", "inst": "Tongji University" }, { "name": "Wenrao Pang", "url": "https://openalex.org/A5052666281", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Boyi Xiao", "url": "https://openalex.org/A5109518689", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Zhiqiang Liu", "url": "https://openalex.org/A5100415118", "inst": "Soochow University" }, { "name": "Jiayuan Chen", "url": "https://openalex.org/A5031618552", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Yun Zhong", "url": "", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Tiantian Yuan", "url": "https://openalex.org/A5038953836", "inst": "Ant Group, Hangzhou, China" }, { "name": "Junming Guan", "url": "", "inst": "Shanghai Huayi Group (China)" }, { "name": "Dawei Cheng", "url": "https://openalex.org/A5112386538", "inst": "Tongji University" }, { "name": "Jie Xu", "url": "https://openalex.org/A5100671655", "inst": "Beijing Academy of Artificial Intelligence" } ], "affiliations": [ "Beijing Academy of Artificial Intelligence", "Tongji University", "Soochow University", "Ant Group, Hangzhou, China", "Shanghai Huayi Group (China)" ] }, { "uid": "doi:10.2139/ssrn.5881522", "doi": "10.2139/ssrn.5881522", "title": "User Preferences for Large Language Models: Implications for AI Safety and Market Structure", "authors": [ "Pavel Kireyev", "Maria Ana Vitorino" ], "posted": "2025-12-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5881522", "field": "economics", "role": "object", "bullets": [ "LMArena user choice data modeling demand for proprietary and open-source LLMs; structural estimation of user segments shaped by content moderation policies.", "Structural demand model estimated with endogenous prompt risk choices; identified a 22% latent risk-taking segment whose LLM selection responds to moderation stringency.", "Divergent moderation policies across LLM types yield better safety-competition tradeoffs than uniform mandates; stricter proprietary moderation opens space for open-source competitors." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 72, "validated": null, "n": 2614, "authors_detailed": [ { "name": "Pavel Kireyev", "url": "https://openalex.org/A5058621082", "inst": "London School of Economics and Political Science" }, { "name": "Maria Ana Vitorino", "url": "https://openalex.org/A5063648614", "inst": "INSEAD" } ], "affiliations": [ "London School of Economics and Political Science", "INSEAD" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5864662", "doi": "10.2139/ssrn.5864662", "title": "How and For Whom Using Generative AI Affects Creativity: A Field Experiment", "authors": [ "Shuhua Sun", "Angelina Zhuyi Li", "Maw Der Foo", "Jing Zhou", "Jackson G. Lu" ], "posted": "2025-12-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5864662", "field": "management", "role": "object", "bullets": [ "Field experiment at a technology consulting firm randomly assigning employees to receive LLM assistance or not, with creativity rated by supervisors and external evaluators.", "Study tested whether LLM assistance increased creativity through cognitive job resources, moderated by employees' metacognitive strategy levels.", "LLM assistance increased employee creativity, especially for those with high metacognitive strategies; results held across both supervisor and external evaluator ratings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 2785, "authors_detailed": [ { "name": "Shuhua Sun", "url": "", "inst": "Tulane University" }, { "name": "Angelina Zhuyi Li", "url": "", "inst": "Renmin University of China" }, { "name": "Maw Der Foo", "url": "", "inst": "Nanyang Technological University" }, { "name": "Jing Zhou", "url": "", "inst": "Rice University" }, { "name": "Jackson G. Lu", "url": "", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Rice University", "Massachusetts Institute of Technology", "Tulane University", "Renmin University of China", "Nanyang Technological University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5870582", "doi": "10.2139/ssrn.5870582", "title": "CAPEX for OPEX: How AI Adoption Reshapes Corporate Cost Structures and Profitability?", "authors": [ "Yi Cao", "Vicki Wei Tang", "Qingquan Zhang", "Xin Zheng" ], "posted": "2025-12-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5870582", "field": "finance", "role": "object", "bullets": [ "U.S. firms tracked with dynamic indices of AI complementarity and AI replacement constructed from firm-level data on a quarterly basis.", "Study measures how AI adoption reshapes corporate cost structures, using LLM complexity as a moderating variable in change specifications.", "AI complementarity raises next-quarter earnings by 0.2% through productivity gains; AI replacement lowers labor costs but does not improve profitability." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3582, "authors_detailed": [ { "name": "Yi Cao", "url": "", "inst": "George Mason University" }, { "name": "Vicki Wei Tang", "url": "", "inst": "Georgetown University" }, { "name": "Qingquan Zhang", "url": "", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Xin Zheng", "url": "", "inst": "University of British Columbia" } ], "affiliations": [ "Georgetown University", "University of Illinois Urbana-Champaign", "George Mason University", "University of British Columbia" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.08270v1", "arxiv_id": "2512.08270v1", "title": "Reasoning Models Ace the CFA Exams", "authors": [ "Jaisal Patel", "Yunzhe Chen", "Kaiwen He", "Keyi Wang", "David Li", "Kairong Xiao", "Xiao-Yang Liu" ], "posted": "2025-12-09", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.08270v1", "field": "finance", "role": "method", "bullets": [ "980 questions from eight mock CFA exams: three Level I, two Level II, and three Level III papers, including constructed response items.", "Reasoning models including Gemini 3.0 Pro, Gemini 2.5 Pro, GPT-5, Grok 4, Claude Opus 4.1, and DeepSeek V3.1 are scored against answer keys using pass fail criteria from earlier studies.", "Most models now clear all three levels, reversing earlier reported failures; Gemini 3.0 Pro hits 97.6 percent on Level I and GPT-5 leads Level II at 94.3 percent." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "mock CFA exam answer keys", "salience": 62, "edition": 14, "n": 1808, "authors_detailed": [ { "name": "Patel, Jaisal", "url": "", "inst": "" }, { "name": "Chen, Yunzhe", "url": "", "inst": "" }, { "name": "He, Kaiwen", "url": "", "inst": "" }, { "name": "Wang, Keyi", "url": "", "inst": "" }, { "name": "Li, David", "url": "", "inst": "" }, { "name": "Xiao, Kairong", "url": "", "inst": "" }, { "name": "Liu, Xiao-Yang", "url": "", "inst": "" } ] }, { "uid": "doi:10.1007/978-3-032-09037-9", "doi": "10.1007/978-3-032-09037-9", "arxiv_id": "2512.08764v1", "title": "Financial News Summarization: Can extractive methods still offer a true alternative to LLMs?", "authors": [ "Nicolas Reche", "Elvys Linhares-Pontes", "Juan-Manuel Torres-Moreno" ], "posted": "2025-12-09", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.08764v1", "field": "finance", "role": "method", "bullets": [ "Financial news articles from the FinLLMs Challenge dataset, motivated by the volume of daily financial news that investors need condensed for fast decisions.", "Extractive baselines are compared with LLMs including a fine tuned Mistral 7B, scored with ROUGE against reference summaries; the authors flag limited corpus reliability.", "The fine tuned Mistral 7B posts the best ROUGE scores, while extractive methods stay competitive on short structured articles at far lower cost and hallucination risk." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "ROUGE against FinLLMs Challenge reference summaries", "salience": 36, "edition": 14, "n": 1884, "authors_detailed": [ { "name": "Lourdes Martinez-Villaseñor", "url": "https://openalex.org/A5126987415", "inst": "Universidad Panamericana" }, { "name": "Roberto A. Vazquez", "url": "https://openalex.org/A5136457526", "inst": "Clinical Research Organization" }, { "name": "Gilberto Ochoa-Ruiz", "url": "https://openalex.org/A5144248601", "inst": "Tecnológico de Monterrey" } ], "affiliations": [ "Universidad Panamericana", "Tecnológico de Monterrey" ] }, { "uid": "arxiv:2512.08345v2", "arxiv_id": "2512.08345v2", "title": "The High Cost of Incivility: Quantifying Interaction Inefficiency via Multi-Agent Monte Carlo Simulations", "authors": [ "Benedikt Mangold" ], "posted": "2025-12-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.08345v2", "field": "management", "role": "agent", "bullets": [ "Monte Carlo simulation of hundreds of one-on-one adversarial debates using LLM-based multi-agent systems with baseline and toxic system prompts.", "LLM agents simulated workplace discussions; convergence time measured as arguments needed to reach conclusion between control and toxic-treatment groups.", "Toxic participants increased conversation duration by approximately 25%, proposed as a quantifiable proxy for financial damage from workplace incivility." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 50, "n": 3580, "authors_detailed": [ { "name": "Mangold, Benedikt", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5893383", "doi": "10.2139/ssrn.5893383", "title": "Why They Disagree: Decoding Differences in Opinions about AI Risk on the Lex Fridman Podcast", "authors": [ "Nghi Truong", "Phanish Puranam", "Ozgecan Kocak" ], "posted": "2025-12-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5893383", "field": "management", "role": "instrument", "bullets": [ "Transcripts from Lex Fridman Podcast episodes featuring prominent voices in AI risk debates across doomer and boomer perspectives.", "An ensemble of LLMs parsed reasoning chains in podcast text, extracting definitional, factual, causal, and moral premises at scale.", "X-risk disagreements stem from causal premises about design versus emergence; E-risk disagreements concern whether past labor-market theories still apply." ], "bullet_provenance": "ai", "validated": false, "salience": 35, "models": [], "n": 3581, "authors_detailed": [ { "name": "Nghi Truong", "url": "", "inst": "Emory University" }, { "name": "Phanish Puranam", "url": "", "inst": "INSEAD" }, { "name": "Ozgecan Kocak", "url": "", "inst": "Sabancı Üniversitesi" } ], "affiliations": [ "Emory University", "INSEAD", "Sabancı Üniversitesi" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5666991", "doi": "10.2139/ssrn.5666991", "title": "Seeing is Believing: The Role of Visual-Verbal Congruence in Crowdfunding Disclosures", "authors": [ "Yi Cao", "Kristina M. Rennekamp", "Nicholas Seybert", "Chi Wan" ], "posted": "2025-12-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5666991", "field": "accounting", "role": "instrument", "bullets": [ "Kickstarter crowdfunding projects used as a simplified disclosure and investment setting; sample size, period, and geography are not stated, with the project as the unit of observation.", "LLMs, not named, extract information from project images and measure visual-verbal congruence between the image and the project description; no validation against hand coding is reported.", "Higher visual-verbal congruence increases project funding and success likelihood, and the effect is stronger in categories where visual information is more critical." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 3, "audience": "general", "models": [], "n": 360, "authors_detailed": [ { "name": "Yi Cao", "url": "", "inst": "George Mason University" }, { "name": "Kristina M. Rennekamp", "url": "", "inst": "Cornell University" }, { "name": "Nicholas Seybert", "url": "", "inst": "Data Assurance and Communication Security" }, { "name": "Chi Wan", "url": "", "inst": "San Diego State University" } ], "affiliations": [ "Cornell University", "George Mason University", "Data Assurance and Communication Security", "San Diego State University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.17923v2", "arxiv_id": "2512.17923v2", "title": "Inferring Latent Market Forces: Evaluating LLM Detection of Gamma Exposure Patterns via Obfuscation Testing", "authors": [ "Christopher Regan", "Ying Xie" ], "posted": "2025-12-08", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.17923v2", "field": "finance", "role": "instrument", "bullets": [ "242 trading days of S&P 500 options data testing three dealer hedging constraint patterns including gamma positioning, stock pinning, and 0DTE hedging.", "LLMs detected structural market patterns using unbiased prompts with raw gamma exposure values through a WHO-WHOM-WHAT causal reasoning framework.", "LLMs achieved 71.5% detection rate with unbiased prompts and 91.2% with regime labels; detection remained stable regardless of quarterly profitability variation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "gamma exposure pattern detection, 71.5-91.2% accuracy", "salience": 65, "n": 2526, "authors_detailed": [ { "name": "Christopher Regan", "url": "https://openalex.org/A5121208559", "inst": "" }, { "name": "Ying Xie", "url": "https://openalex.org/A5121230707", "inst": "Nanjing Tech University" } ], "affiliations": [ "Nanjing Tech University" ] }, { "uid": "arxiv:2512.07462v2", "arxiv_id": "2512.07462v2", "title": "Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics", "authors": [ "Trung-Kiet Huynh", "Duy-Minh Dao-Sy", "Thanh-Bang Cao", "Phong-Hao Le", "Hong-Dan Nguyen", "Phu-Quy Nguyen-Lam", "Minh-Luan Nguyen-Vo", "Hong-Phat Pham", "Phu-Hoa Pham", "Thien-Kim Than", "Chi-Nguyen Tran", "Huy Tran", "Gia-Thoai Tran-Le", "Alessio Buscemi", "Le Hong Trang", "The Anh Han" ], "posted": "2025-12-08", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.07462v2", "field": "economics", "role": "agent", "bullets": [ "Repeated social dilemmas including Prisoner's Dilemma and Public Goods Game with multiple LLMs tested across several languages.", "Multiple LLMs played as strategic agents in economic games; supervised classifiers identified canonical behavioral strategy intentions from trajectories.", "LLMs showed incentive-sensitive cooperation and end-game defection alignment; linguistic framing effects on behavior were as strong as architectural differences." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 40, "n": 2527, "authors_detailed": [ { "name": "Huynh, Trung-Kiet", "url": "", "inst": "" }, { "name": "Dao-Sy, Duy-Minh", "url": "", "inst": "" }, { "name": "Cao, Thanh-Bang", "url": "", "inst": "" }, { "name": "Le, Phong-Hao", "url": "", "inst": "" }, { "name": "Nguyen, Hong-Dan", "url": "", "inst": "" }, { "name": "Nguyen-Lam, Phu-Quy", "url": "", "inst": "" }, { "name": "Nguyen-Vo, Minh-Luan", "url": "", "inst": "" }, { "name": "Pham, Hong-Phat", "url": "", "inst": "" }, { "name": "Pham, Phu-Hoa", "url": "", "inst": "" }, { "name": "Than, Thien-Kim", "url": "", "inst": "" }, { "name": "Tran, Chi-Nguyen", "url": "", "inst": "" }, { "name": "Tran, Huy", "url": "", "inst": "" }, { "name": "Tran-Le, Gia-Thoai", "url": "", "inst": "" }, { "name": "Buscemi, Alessio", "url": "", "inst": "" }, { "name": "Trang, Le Hong", "url": "", "inst": "" }, { "name": "Han, The Anh", "url": "", "inst": "" } ] }, { "uid": "arxiv:2512.08088v1", "arxiv_id": "2512.08088v1", "title": "Adaptation of Embedding Models to Financial Filings via LLM Distillation", "authors": [ "Eliot Brenner", "Dominic Seyler", "Manjunath Hegde", "Andrei Simion", "Koustuv Dasgupta", "Bing Xiang" ], "posted": "2025-12-08", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.08088v1", "field": "finance", "role": "method", "bullets": [ "21,800 query-document pairs across 14 financial filing types and FinanceBench; retrieval embeddings adapted for RAG in specialized financial document domains.", "LLM-judged relevance distilled domain knowledge into compact bi-encoder retrievers via iterative hard-example mining from unlabeled financial filing corpora.", "Average 27.7% improvement in MRR@5 and 44.6% improvement in mean DCG@5 across filing types; improved NDCG on three of four FinanceBench document classes." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "FinanceBench and 14 financial filing types", "salience": 58, "n": 2612, "authors_detailed": [ { "name": "Brenner, Eliot", "url": "", "inst": "" }, { "name": "Seyler, Dominic", "url": "", "inst": "" }, { "name": "Hegde, Manjunath", "url": "", "inst": "" }, { "name": "Simion, Andrei", "url": "", "inst": "" }, { "name": "Dasgupta, Koustuv", "url": "", "inst": "" }, { "name": "Xiang, Bing", "url": "", "inst": "" } ] }, { "uid": "arxiv:2512.07828v2", "arxiv_id": "2512.07828v2", "title": "The Adoption and Usage of AI Agents: Early Evidence from Perplexity", "authors": [ "Jeremy Yang", "Noah Yonack", "Kate Zyskowski", "Denis Yarats", "Johnny Ho", "Jerry Ma" ], "posted": "2025-12-08", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.07828v2", "field": "economics", "role": "object", "bullets": [ "Hundreds of millions of anonymized user interactions with Perplexity's Comet browser and its integrated AI agent across countries, sectors, and user segments.", "No LLM used as research instrument; the study measures adoption patterns, usage intensity, and use-case taxonomy of a general-purpose AI agent in open web environments.", "Earlier adopters and knowledge-intensive sectors adopted more; productivity and learning comprised 57% of queries; users shifted toward cognitive tasks over time." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3578, "authors_detailed": [ { "name": "Yang, Jeremy", "url": "", "inst": "" }, { "name": "Yonack, Noah", "url": "", "inst": "" }, { "name": "Zyskowski, Kate", "url": "", "inst": "" }, { "name": "Yarats, Denis", "url": "", "inst": "" }, { "name": "Ho, Johnny", "url": "", "inst": "" }, { "name": "Ma, Jerry", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5785482", "doi": "10.2139/ssrn.5785482", "title": "GENERATIVE AI FOR CLAIMS EXCEPTIONS AND INVESTIGATIONS: ENHANCING RESOLUTION EFFICIENCY IN COMPLEX INSURANCE PROCESSES", "authors": [ "Keerthi Amistapuram" ], "posted": "2025-12-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5785482", "field": "finance", "role": "instrument", "bullets": [ "Conceptual framework for generative AI in insurance claims exceptions and investigations spanning triaging, evidence collection, and cross-claim synthesis.", "Generative AI models proposed for automating case triaging for investigation, collecting evidence to resolve claims, and synthesizing multi-claim results into narratives.", "GenAI can reduce friction and delays in complex claims by automating three dimensions of exception management across the investigation lifecycle." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3579, "authors_detailed": [ { "name": "Keerthi Amistapuram", "url": "https://openalex.org/A5120724710", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.5875406", "doi": "10.2139/ssrn.5875406", "title": "Generative Artificial Intelligence in External Auditing: Commentaries and Perceptions from the Audit Industry", "authors": [ "Lazarus Fotoh", "Tatenda Mugwira", "Johan Ingemar Lorentzon" ], "posted": "2025-12-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5875406", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 35, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 689, "authors_detailed": [ { "name": "Lazarus Elad Fotoh", "url": "https://openalex.org/A5017421836", "inst": "Karlstad University" }, { "name": "Tatenda Mugwira", "url": "https://openalex.org/A5026939521", "inst": "University of Agder" }, { "name": "Johan Lorentzon", "url": "https://openalex.org/A5057782730", "inst": "Karlstad University" } ], "affiliations": [ "Karlstad University", "University of Agder" ] }, { "uid": "arxiv:2512.06506v1", "arxiv_id": "2512.06506v1", "title": "AI as \"Co-founder\": GenAI for Entrepreneurship", "authors": [ "Junhui Jeff Cai", "Xian Gu", "Liugang Sheng", "Mengjia Xia", "Linda Zhao", "Wu Zhu" ], "posted": "2025-12-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.06506v1", "field": "economics", "role": "object", "bullets": [ "Geo-coded grid-level data on Chinese firm registrations through end of 2024, using ChatGPT's November 2022 launch as a natural experiment with variation in pre-existing AI human capital.", "ChatGPT's release served as a global shock lowering startup costs; difference-in-differences exploits cross-grid variation in AI-specific human capital to identify effects on firm entry.", "Grids with stronger AI human capital saw a surge in small-firm entry accounting for 6.0% of national firm entry; large-firm entry declined, consistent with a shift toward leaner ventures." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 78, "validated": null, "n": 3074, "authors_detailed": [ { "name": "Cai, Junhui Jeff", "url": "", "inst": "" }, { "name": "Gu, Xian", "url": "", "inst": "" }, { "name": "Sheng, Liugang", "url": "", "inst": "" }, { "name": "Xia, Mengjia", "url": "", "inst": "" }, { "name": "Zhao, Linda", "url": "", "inst": "" }, { "name": "Zhu, Wu", "url": "", "inst": "" } ] }, { "uid": "arxiv:2512.05907v1", "arxiv_id": "2512.05907v1", "title": "From Text to Returns: Using Large Language Models for Mutual Fund Portfolio Optimization and Risk-Adjusted Allocation", "authors": [ "Abrar Hossain", "Mufakir Qamar Ansari", "Haziq Jeelani", "Monia Digra", "Fayeq Jeelani Syed" ], "posted": "2025-12-05", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.05907v1", "field": "finance", "role": "agent", "bullets": [ "Mutual fund allocation across economic sectors, combining a retrieval augmented pipeline over external real time data with standard financial optimization and macroeconomic context signals.", "Phi 2, Mistral 7B, and Zypher 7B generate risk aware allocation strategies; the abstract reports no benchmark validation, statistical tests, or effect magnitudes for the comparison.", "Zypher 7B delivers the best risk adjusted returns of the three models, and the authors report improvement over basic allocation methods without stating magnitudes." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 27, "edition": 14, "validated": null, "n": 1821, "authors_detailed": [ { "name": "Hossain, Abrar", "url": "", "inst": "" }, { "name": "Ansari, Mufakir Qamar", "url": "", "inst": "" }, { "name": "Jeelani, Haziq", "url": "", "inst": "" }, { "name": "Digra, Monia", "url": "", "inst": "" }, { "name": "Syed, Fayeq Jeelani", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5866223", "doi": "10.2139/ssrn.5866223", "title": "Artificial Intelligence and Systemic Risk", "authors": [ "Stephen G. Cecchetti", "Robin L. Lumsdaine", "Tuomas Peltonen", "Antonio Sánchez Serrano" ], "posted": "2025-12-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5866223", "field": "finance", "role": "object", "bullets": [ "Policy analysis of AI adoption across the global financial system, covering concentration, interconnectedness, speed, and existing regulatory frameworks.", "Report catalogs eleven channels through which AI and LLMs may amplify or create systemic financial risks, from model uniformity to speed-driven procyclicality.", "AI concentration in few providers creates single points of failure; authors propose recalibrated capital requirements, circuit breakers, and transparency labels for AI use." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 2357, "authors_detailed": [ { "name": "Stephen G. Cecchetti", "url": "https://openalex.org/A5111855740", "inst": "National Bureau of Economic Research" }, { "name": "Robin L. Lumsdaine", "url": "https://openalex.org/A5086996217", "inst": "National Bureau of Economic Research" }, { "name": "Tuomas Peltonen", "url": "https://openalex.org/A5013318993", "inst": "European Central Bank" }, { "name": "Antonio Sánchez Serrano", "url": "https://openalex.org/A5063936026", "inst": "European Central Bank" } ], "affiliations": [ "National Bureau of Economic Research", "European Central Bank" ] }, { "uid": "arxiv:2512.05659v1", "arxiv_id": "2512.05659v1", "title": "Beyond Automation: Redesigning Jobs with LLMs to Enhance Productivity", "authors": [ "Andrew Ledingham", "Michael Hollins", "Matthew Lyon", "David Gillespie", "Umar Yunis-Guerra", "Jamie Siviter", "David Duncan", "Oliver P. Hauser" ], "posted": "2025-12-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.05659v1", "field": "economics", "role": "instrument", "bullets": [ "UK Civil Service, 193,497 job vacancies over six years covering 1,542,411 tasks assessed at the task level for AI exposure.", "LLM estimated AI exposure scores for each task and redesigned jobs through automation, optimization, and reallocation of task bundles.", "Most economic value of AI arises from productivity gains rather than role displacement; redesigned tasks favor human advantage in strategic leadership and complex problem resolution." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 70, "n": 2525, "authors_detailed": [ { "name": "Andrew Ledingham", "url": "https://openalex.org/A5120490669", "inst": "UK Research and Innovation" }, { "name": "Michael Hollins", "url": "https://openalex.org/A5051303300", "inst": "" }, { "name": "Matthew Lyon", "url": "https://openalex.org/A5047866814", "inst": "The University of Sydney" }, { "name": "David Gillespie", "url": "https://openalex.org/A5111230892", "inst": "Lawrence Livermore National Laboratory" }, { "name": "Umar Yunis-Guerra", "url": "https://openalex.org/A5120624947", "inst": "" }, { "name": "Jamie Siviter", "url": "https://openalex.org/A5120490670", "inst": "" }, { "name": "David T. Duncan", "url": "https://openalex.org/A5088196609", "inst": "Oak Ridge Associated Universities" }, { "name": "Oliver Hauser", "url": "https://openalex.org/A5027074094", "inst": "University of Exeter" } ], "affiliations": [ "UK Research and Innovation", "The University of Sydney", "Lawrence Livermore National Laboratory", "Oak Ridge Associated Universities", "University of Exeter" ] }, { "uid": "arxiv:2512.15728v1", "arxiv_id": "2512.15728v1", "title": "FedSight AI: Multi-Agent System Architecture for Federal Funds Target Rate Prediction", "authors": [ "Yuhan Hou", "Tianji Rao", "Jeremy Tan", "Adler Viton", "Xiyue Zhang", "David Ye", "Abhishek Kodi", "Sanjana Dulam", "Aditya Paul", "Yikai Feng" ], "posted": "2025-12-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.15728v1", "field": "economics", "role": "agent", "bullets": [ "Multi-agent LLM framework simulating FOMC deliberations evaluated against actual 2023-2024 meeting outcomes.", "Member agents analyzed structured indicators and Beige Book text, debated policy options, and voted using Chain-of-Draft multistage reasoning.", "FedSight CoD achieved 93.75% accuracy and 93.33% stability in predicting federal funds rate decisions, outperforming MiniFed and Ordinal Random Forest baselines." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "accuracy vs actual FOMC decisions 2023-2024", "salience": 55, "n": 2784, "authors_detailed": [ { "name": "Hou, Yuhan", "url": "", "inst": "" }, { "name": "T. Ramalingeswara Rao", "url": "https://openalex.org/A5087160574", "inst": "University of Louisville" }, { "name": "Tan, Jeremy", "url": "", "inst": "" }, { "name": "Adler Viton", "url": "https://openalex.org/A5120869567", "inst": "" }, { "name": "Xiyue Zhang", "url": "https://openalex.org/A5100717733", "inst": "Chinese Academy of Medical Sciences & Peking Union Medical College" }, { "name": "David Ye", "url": "https://openalex.org/A5047801099", "inst": "California Institute of Technology" }, { "name": "Abhishek Kodi", "url": "https://openalex.org/A5046114777", "inst": "Missouri University of Science and Technology" }, { "name": "Sanjana Dulam", "url": "https://openalex.org/A5060796992", "inst": "Vellore Institute of Technology University" }, { "name": "Paul, Aditya", "url": "", "inst": "" }, { "name": "Feng, Yikai", "url": "", "inst": "" } ], "affiliations": [ "California Institute of Technology", "University of Louisville", "Chinese Academy of Medical Sciences & Peking Union Medical College", "Missouri University of Science and Technology", "Vellore Institute of Technology University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5839023", "doi": "10.2139/ssrn.5839023", "title": "Partial Equilibrium Employability: A Theory of Routine and Advanced Skills in Accounting", "authors": [ "Dulani Jayasuriya" ], "posted": "2025-12-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5839023", "field": "accounting", "role": "object", "bullets": [ "Theoretical partial-equilibrium model of credentialed employability in accounting distinguishing routine, advanced, behavioral, and defensibility skill dimensions.", "No LLM used; the paper models how generative AI changes marginal value of routine versus advanced accounting competencies under audit and litigation oversight.", "GenAI shifts returns from routine procedure toward advanced judgment; competency frameworks decay faster when governance is fragmented across universities, firms, and regulators." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3575, "authors_detailed": [ { "name": "Dulani Jayasuriya", "url": "https://openalex.org/A5060414286", "inst": "University of Auckland" } ], "affiliations": [ "University of Auckland" ] }, { "uid": "doi:10.2139/ssrn.5708625", "doi": "10.2139/ssrn.5708625", "title": "The Great Decoupling: How Autonomous AI Agents Are Dismantling the Direct Brand-Consumer Relationship", "authors": [ "Paul F. Accornero" ], "posted": "2025-12-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5708625", "field": "management", "role": "object", "bullets": [ "Conceptual framework analyzing how autonomous AI agents restructure market architecture from dyadic brand-consumer to triadic brand-agent-human relationships.", "No LLM used as instrument; the paper theorizes three decoupling mechanisms — transactional, data, and communicative — through which AI agents disintermediate brands.", "Traditional CRM, loyalty programs, and DTC strategies face obsolescence; competitive advantage shifts to algorithmic optimality based on machine logic rather than emotional loyalty." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3576, "authors_detailed": [ { "name": "Paul F. Accornero", "url": "https://openalex.org/A5120590440", "inst": "AULSS 2 Marca Trevigiana" } ], "affiliations": [ "AULSS 2 Marca Trevigiana" ] }, { "uid": "doi:10.2139/ssrn.5724802", "doi": "10.2139/ssrn.5724802", "title": "The Governance Gauntlet: Strategic Risk Management When Algorithms Become Your Customers", "authors": [ "Paul F. Accornero" ], "posted": "2025-12-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5724802", "field": "management", "role": "object", "bullets": [ "Conceptual governance framework for agentic commerce drawing on cases from Amazon, Meta, and Uber involving algorithmic bias and data privacy failures.", "No LLM used; the paper develops a risk typology distinguishing Exclusion Risk from Inclusion Risk in algorithm-intermediated markets with machine-readable governance signals.", "Governance excellence creates measurable competitive advantage; firms that fail to build verifiable integrity signals face systematic exclusion from AI-mediated commerce." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3577, "authors_detailed": [ { "name": "Paul F. Accornero", "url": "https://openalex.org/A5120590440", "inst": "AULSS 2 Marca Trevigiana" } ], "affiliations": [ "AULSS 2 Marca Trevigiana" ] }, { "uid": "doi:10.2139/ssrn.5709083", "doi": "10.2139/ssrn.5709083", "title": "The Trust Paradox in AI-Mediated Commerce: A Conceptual Framework and Research Agenda", "authors": [ "Paul F. Accornero" ], "posted": "2025-12-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5709083", "field": "management", "role": "object", "bullets": [ "Conceptual framework drawing on principal-agent theory and Google Search precedent, modeling AI purchasing agents in consumer commerce platforms.", "No specific model deployed; paper theorizes a three-phase trust lifecycle for autonomous AI commerce agents using Akerlof-style adverse-selection dynamics.", "Predicts monetization pressure will systematically erode initial consumer trust in AI agents; proposes 'computational trust' construct and eleven testable propositions." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3968, "authors_detailed": [ { "name": "Paul F. Accornero", "url": "https://openalex.org/A5120689356", "inst": "AULSS 2 Marca Trevigiana" } ], "affiliations": [ "AULSS 2 Marca Trevigiana" ] }, { "uid": "arxiv:2512.06033v2", "arxiv_id": "2512.06033v2", "title": "Sell Data to AI Algorithms Without Revealing It: Secure Data Valuation and Sharing via Homomorphic Encryption", "authors": [ "Michael Yang", "Ruijiang Gao", "Zhiqiang Zheng" ], "posted": "2025-12-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.06033v2", "field": "economics", "role": "method", "bullets": [ "Data markets in healthcare and generative AI domains, testing privacy-preserving data valuation on BERT and GPT-2 architectures.", "Homomorphic encryption with gradient-based influence functions scored data points against buyer AI models using low-rank gradient projections for scalability.", "Encrypted valuation achieved high correlation with realized utility; heavy-tailed value distribution showed a minority of texts drive model capability while the majority degrades it." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "correlation with clinical utility, plaintext fidelity on BERT/GPT-2", "salience": 55, "n": 2524, "authors_detailed": [ { "name": "Michael Yang", "url": "https://openalex.org/A5143266487", "inst": "" }, { "name": "Ruijiang Gao", "url": "https://openalex.org/A5102426480", "inst": "The University of Texas at Dallas" }, { "name": "Z H Zheng", "url": "https://openalex.org/A5134922269", "inst": "Fujian University of Traditional Chinese Medicine" } ], "affiliations": [ "The University of Texas at Dallas", "Fujian University of Traditional Chinese Medicine" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.05156v2", "arxiv_id": "2512.05156v2", "title": "Semantic Faithfulness and Entropy Production Measures to Tame Your LLM Demons and Manage Hallucinations", "authors": [ "Igor Halperin" ], "posted": "2025-12-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.05156v2", "field": "finance", "role": "method", "bullets": [ "Unsupervised faithfulness metrics proposed using information-theoretic framework, demonstrated on LLM summarization of corporate SEC 10-K filings.", "Semantic faithfulness metric modeled QCA triplets as topic distributions and measured KL divergence between query-goal and answer transition matrices.", "High faithfulness scores correlated with low semantic entropy production; framework provided interpretable, unsupervised hallucination detection for financial document tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 40, "n": 2783, "authors_detailed": [ { "name": "Igor Halperin", "url": "https://openalex.org/A5048462036", "inst": "Fidelity Investments (United States)" } ], "affiliations": [ "Fidelity Investments (United States)" ] }, { "uid": "arxiv:2512.03607v1", "arxiv_id": "2512.03607v1", "title": "DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization", "authors": [ "Yusen Wu", "Xiaotie Deng" ], "posted": "2025-12-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.03607v1", "field": "management", "role": "instrument", "bullets": [ "Real retail environments with distributor agreements, sales assessments, and multi-tier business constraints for assortment and pricing optimization.", "LLMs performed semantic parsing of unstructured negotiation records and guided symbolic regression to generate interpretable pricing rules with economic priors.", "Framework achieved higher profits versus systematic B2C baselines while ensuring operational feasibility through a unified knowledge-optimization pipeline." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "real retail profit comparison vs B2C baselines", "salience": 50, "n": 2522, "authors_detailed": [ { "name": "Yusen Wu", "url": "https://openalex.org/A5001308300", "inst": "Beijing Normal University" }, { "name": "Xiaotie Deng", "url": "https://openalex.org/A5100638710", "inst": "Peking University" } ], "affiliations": [ "Beijing Normal University", "Peking University" ] }, { "uid": "doi:10.2139/ssrn.5675847", "doi": "10.2139/ssrn.5675847", "title": "Auditing Large Language Models", "authors": [ "Meghan Wondra" ], "posted": "2025-12-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5675847", "field": "accounting", "role": "object", "bullets": [ "Review of the emerging landscape of LLM auditing practices across audit firms, governance frameworks, and regulatory environments.", "Paper examines LLMs as objects of audit, analyzing risks including algorithmic bias, privacy vulnerabilities, data provenance, and accountability gaps.", "Audit firms can expand into AI assurance services; recommendations include integrating AI tools with human judgment and establishing mandatory policy frameworks." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 2523, "authors_detailed": [ { "name": "Meghan Wondra", "url": "", "inst": "Carroll University" } ], "affiliations": [ "Carroll University" ] }, { "uid": "doi:10.2139/ssrn.5838262", "doi": "10.2139/ssrn.5838262", "title": "ENHANCING FINANCIAL FRAUD DETECTION BY LEVERAGING NLP and GRAPH DATABASE", "authors": [ "Rubhesh S", "Rohith Rajasekaran", "Mitul Nagar" ], "posted": "2025-12-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5838262", "field": "finance", "role": "instrument", "bullets": [ "Financial data including transaction logs, customer communications, and reports processed for fraud detection using NLP and graph database analysis.", "LLAMA2 extracted information from unstructured financial documents; Neo4j graph database modeled entity relationships among individuals, accounts, and transactions.", "The integrated NLP-graph approach detected complex fraud patterns more effectively than conventional methods and reduced false alarm rates." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 38, "n": 3574, "authors_detailed": [ { "name": "S Rubhesh", "url": "https://openalex.org/A5120582891", "inst": "SRM Institute of Science and Technology" }, { "name": "Rohith Rajasekaran", "url": "", "inst": "SRM Institute of Science and Technology" }, { "name": "Mitul Sudhirkumar Nagar", "url": "https://openalex.org/A5092266005", "inst": "SRM Institute of Science and Technology" } ], "affiliations": [ "SRM Institute of Science and Technology" ] }, { "uid": "arxiv:2512.03107v1", "arxiv_id": "2512.03107v1", "title": "Detecting AI Hallucinations in Finance: An Information-Theoretic Method Cuts Hallucination Rate by 92%", "authors": [ "Mainak Singha" ], "posted": "2025-12-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.03107v1", "field": "finance", "role": "method", "bullets": [ "A controlled financial question answering dataset of 200 balanced samples with synthetic hallucinations and retrieved evidence attached to each question.", "GPT-3.5-turbo outputs are screened by combining semantic entropy from multi sample clustering with a perplexity decomposition of evidence use, checked against the known hallucination labels.", "Detection reaches 0.89 ROC AUC versus 0.50 for entropy alone, but falls to 0.59 on Claude 3 Haiku, which lacks token log probabilities; the authors frame this as a mechanism study." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "synthetic hallucination labels, ROC AUC reported", "salience": 40, "edition": 14, "n": 1837, "authors_detailed": [ { "name": "M. Singha", "url": "https://openalex.org/A5004316820", "inst": "Jadavpur University" } ], "affiliations": [ "Jadavpur University" ] }, { "uid": "arxiv:2512.02726v1", "arxiv_id": "2512.02726v1", "title": "AuditCopilot: Leveraging LLMs for Fraud Detection in Double-Entry Bookkeeping", "authors": [ "Md Abdul Kadir", "Sai Suresh Macharla Vasu", "Sidharth S. Nair", "Daniel Sonntag" ], "posted": "2025-12-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.02726v1", "field": "accounting", "role": "method", "bullets": [ "Synthetic ledgers and real anonymized double entry bookkeeping records containing tax relevant journal entries, the setting where auditors run rule based Journal Entry Tests.", "LLaMA and Gemma models flag anomalous entries and give natural language explanations; detection is benchmarked against the rule based tests and classical machine learning baselines.", "The LLMs beat both rule based tests and machine learning baselines and add readable justifications; no accuracy figures appear in the abstract, which frames the gain as AI augmented auditing." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "labelled anomalies in synthetic and real ledgers", "salience": 52, "edition": 14, "n": 1838, "authors_detailed": [ { "name": "Md Abdul Kadir", "url": "https://openalex.org/A5017652218", "inst": "Carl von Ossietzky Universität Oldenburg" }, { "name": "Sai Suresh Macharla Vasu", "url": "https://openalex.org/A5120686519", "inst": "" }, { "name": "Sidharth S. Nair", "url": "https://openalex.org/A5120587306", "inst": "" }, { "name": "Daniel Sonntag", "url": "https://openalex.org/A5120713588", "inst": "" } ], "affiliations": [ "Carl von Ossietzky Universität Oldenburg" ] }, { "uid": "arxiv:2601.02370v3", "arxiv_id": "2601.02370v3", "title": "Variance-Aware LLM Annotation for Strategy Research: Sources, Diagnostics, and a Protocol for Reliable Measurement", "authors": [ "Arnaldo Camuffo", "Alfonso Gambardella", "Saeid Kazemi", "Jakub Malachowski", "Abhinav Pandey" ], "posted": "2025-12-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2601.02370v3", "field": "management", "role": "method", "bullets": [ "Strategy research text annotation tasks testing prompt phrasing, model selection, and output extraction as variance sources across multiple LLMs.", "Multiple LLMs annotated text constructs for strategy research; variance was diagnosed across five sources using generalizability theory frameworks.", "Minor design choices shifted annotation outcomes by 12-85 percentage points, threatening reproducibility and biasing econometric estimates when correlated with covariates." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "annotation variance diagnostics across five sources", "salience": 75, "n": 2520, "authors_detailed": [ { "name": "Arnaldo Camuffo", "url": "https://openalex.org/A5027175520", "inst": "Bocconi University" }, { "name": "Alfonso Gambardella", "url": "https://openalex.org/A5121393874", "inst": "" }, { "name": "Saeid Kazemi", "url": "https://openalex.org/A5122003604", "inst": "" }, { "name": "Jakub Malachowski", "url": "https://openalex.org/A5121998353", "inst": "" }, { "name": "Abhinav Pandey", "url": "https://openalex.org/A5122016123", "inst": "" } ], "affiliations": [ "Bocconi University" ], "prestige": true }, { "uid": "arxiv:2512.02720v1", "arxiv_id": "2512.02720v1", "title": "StockMem: An Event-Reflection Memory Framework for Stock Forecasting", "authors": [ "He Wang", "Wenyilin Xiao", "Songqiao Han", "Hailiang Huang" ], "posted": "2025-12-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.02720v1", "field": "finance", "role": "instrument", "bullets": [ "Stock price prediction using news-driven event analysis with horizontal daily consolidation and longitudinal event evolution tracking.", "LLM-based framework structured news into events, built temporal and causal reflection knowledge bases, and retrieved analogous historical scenarios for prediction.", "StockMem outperformed existing memory architectures on prediction accuracy and provided explainable reasoning by tracing information chains affecting prices." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "stock price prediction benchmarks", "salience": 45, "n": 2521, "authors_detailed": [ { "name": "He Wang", "url": "https://openalex.org/A5100351659", "inst": "Shenyang Pharmaceutical University" }, { "name": "Wenyilin Xiao", "url": "https://openalex.org/A5101340687", "inst": "Shanghai University of Finance and Economics" }, { "name": "Songqiao Han", "url": "https://openalex.org/A5003652858", "inst": "Shanghai University of Finance and Economics" }, { "name": "Hailiang Huang", "url": "https://openalex.org/A5013792358", "inst": "Broad Institute" } ], "affiliations": [ "Shenyang Pharmaceutical University", "Shanghai University of Finance and Economics", "Broad Institute" ] }, { "uid": "doi:10.2139/ssrn.5840182", "doi": "10.2139/ssrn.5840182", "title": "The Informed Insider: A Leading Measure of Quality *", "authors": [ "Wei Cai", "Dennis Campbell", "Yaxuan Chen", "Yufei Chen", "Andrea Prat" ], "posted": "2025-12-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5840182", "field": "accounting", "role": "instrument", "bullets": [ "Over 4.3 million Glassdoor employee reviews for S&P 1500 firms from 2008 to 2023, with ML models trained on third-party customer satisfaction data.", "Machine learning and GPT models classified review text to construct firm-year quality indices; GPT-based measures tested as an LLM alternative.", "Employee-derived quality indices predicted product recalls, brand value, and profitability, outperforming firm fundamentals and Glassdoor ratings in out-of-sample tests." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "out-of-sample prediction of recalls, brand value, profitability", "salience": 55, "n": 2782, "authors_detailed": [ { "name": "Wei Cai", "url": "https://openalex.org/A5110831761", "inst": "Columbia Business School" }, { "name": "Dennis Campbell", "url": "https://openalex.org/A5102743873", "inst": "Harvard University" }, { "name": "Yaxuan Chen", "url": "https://openalex.org/A5064772409", "inst": "Cornell University" }, { "name": "Yufei Chen", "url": "https://openalex.org/A5100411549", "inst": "Cornell University" }, { "name": "Andrea Prat", "url": "https://openalex.org/A5072583771", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Columbia Business School", "Harvard University", "Cornell University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.02436v1", "arxiv_id": "2512.02436v1", "title": "Semantic Trading: Agentic AI for Clustering and Relationship Discovery in Prediction Markets", "authors": [ "Agostino Capponi", "Alfio Gliozzo", "Brian Zhu" ], "posted": "2025-12-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.02436v1", "field": "finance", "role": "instrument", "bullets": [ "Historical dataset of resolved prediction markets on Polymarket, with contracts clustered by topic and evaluated for within-cluster outcome dependence.", "Agentic LLM pipeline clusters markets using natural-language understanding over contract text and identifies correlated and anti-correlated outcome relationships across market pairs.", "Agent-identified relationships achieve 60-70% accuracy; induced trading strategies earn approximately 20% average returns over week-long holding horizons." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "Polymarket resolved-outcome accuracy for relationship predictions", "salience": 55, "n": 3235, "authors_detailed": [ { "name": "Agostino Capponi", "url": "https://openalex.org/A5041916584", "inst": "Columbia University" }, { "name": "Alfio Gliozzo", "url": "https://openalex.org/A5087419620", "inst": "Istituto di Scienza e Tecnologie dell'Informazione \"Alessandro Faedo\"" }, { "name": "Brian Zhu", "url": "https://openalex.org/A5102588943", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "Istituto di Scienza e Tecnologie dell'Informazione \"Alessandro Faedo\"" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.05144v1", "arxiv_id": "2512.05144v1", "title": "Job Satisfaction Through the Lens of Social Media: Rural--Urban Patterns in the U.S", "authors": [ "Stefano M Iacus", "Giuseppe Porro" ], "posted": "2025-12-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.05144v1", "field": "economics", "role": "instrument", "bullets": [ "County-level analysis of 2.6 billion georeferenced U.S. tweets linked to labor market conditions from 2013 to 2023, covering rural and urban areas.", "A fine-tuned large language model classified job satisfaction sentiment from tweets; results fed into logistic regressions of rural-urban differences.", "Rural counties reported lower job satisfaction than urban ones, but the gap decreased under tight labor markets, suggesting slack drives spatial inequality in work well-being." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 55, "n": 3573, "authors_detailed": [ { "name": "Stefano M. Iacus", "url": "https://openalex.org/A5034420722", "inst": "Harvard University" }, { "name": "Giuseppe Porro", "url": "https://openalex.org/A5084017033", "inst": "University of Insubria" } ], "affiliations": [ "Harvard University", "University of Insubria" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2308/horizons-2025-281", "doi": "10.2308/horizons-2025-281", "title": "The Use of Artificial Intelligence in Academic Research", "authors": [ "Gordon M. Graham", "Jennifer Wu Tucker" ], "posted": "2025-12-01", "added": "2026-08-24", "source_label": "Accounting Horizons", "url": "https://doi.org/10.2308/HORIZONS-2025-281", "field": "accounting", "role": "method", "bullets": [ "Setting: An Accounting Horizons editorial aimed at authors, reviewers, editors, readers, and those training future researchers.", "Approach: The authors draw on editorial experience to frame the emerging use of artificial intelligence in academic research.", "Contribution: The framework prompts the academic community to reconsider research, scholarship, and the role of human judgment as AI tools become widely available." ], "bullet_provenance": "editor", "validated": false, "validation_note": "Conceptual editorial and experience-based framework rather than an empirical model evaluation.", "salience": 90, "edition": 23, "audience": "general", "models": [], "n": 4126, "authors_detailed": [ { "name": "Gwendolyn Graham", "url": "https://openalex.org/A5015641372", "inst": "EY" }, { "name": "Jennifer Wu Tucker", "url": "https://openalex.org/A5050334594", "inst": "University of Florida" } ], "affiliations": [ "University of Florida", "EY" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5798205", "doi": "10.2139/ssrn.5798205", "title": "Beyond Automation: Redesigning Jobs with LLMs to Enhance Productivity", "authors": [ "Andrew Ledingham", "Michael Hollins", "Matthew Lyon", "David Gillespie", "Umar Yunis-Guerra", "Jamie Siviter", "David Duncan", "Oliver Hauser" ], "posted": "2025-12-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5798205", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 688, "authors_detailed": [ { "name": "Andrew Ledingham", "url": "https://openalex.org/A5120490669", "inst": "UK Research and Innovation" }, { "name": "Michael Hollins", "url": "https://openalex.org/A5051303300", "inst": "" }, { "name": "Matthew Lyon", "url": "https://openalex.org/A5047866814", "inst": "The University of Sydney" }, { "name": "David Gillespie", "url": "https://openalex.org/A5111230892", "inst": "Lawrence Livermore National Laboratory" }, { "name": "Umar Yunis-Guerra", "url": "https://openalex.org/A5120624947", "inst": "" }, { "name": "Jamie Siviter", "url": "https://openalex.org/A5120490670", "inst": "" }, { "name": "David T. Duncan", "url": "https://openalex.org/A5088196609", "inst": "Oak Ridge Associated Universities" }, { "name": "Oliver Hauser", "url": "https://openalex.org/A5027074094", "inst": "University of Exeter" } ], "affiliations": [ "UK Research and Innovation", "The University of Sydney", "Lawrence Livermore National Laboratory", "Oak Ridge Associated Universities", "University of Exeter" ] }, { "uid": "arxiv:2512.10971v1", "arxiv_id": "2512.10971v1", "title": "AI-Trader: Benchmarking Autonomous Agents in Real-Time Financial Markets", "authors": [ "Tianyu Fan", "Yuhao Yang", "Yangqin Jiang", "Yifei Zhang", "Yuxuan Chen", "Chao Huang" ], "posted": "2025-12-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.10971v1", "field": "finance", "role": "agent", "bullets": [ "U.S. stocks, A-shares, and cryptocurrencies across multiple trading frequencies in a live, data-uncontaminated benchmark environment.", "Six mainstream LLMs operated as fully autonomous trading agents receiving minimal context and independently searching and synthesizing live market data.", "General intelligence did not translate to trading capability; most agents showed poor returns, with excess returns more achievable in liquid markets than policy-driven ones." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "live trading returns across three markets", "salience": 65, "n": 2518, "authors_detailed": [ { "name": "Fan, Tianyu", "url": "", "inst": "" }, { "name": "Yang, Yuhao", "url": "", "inst": "" }, { "name": "Jiang, Yangqin", "url": "", "inst": "" }, { "name": "Zhang, Yifei", "url": "", "inst": "" }, { "name": "Chen, Yuxuan", "url": "", "inst": "" }, { "name": "Huang, Chao", "url": "", "inst": "" } ] }, { "uid": "doi:10.18653/v1/2025.emnlp-industry.61", "doi": "10.18653/v1/2025.emnlp-industry.61", "arxiv_id": "2601.05271v1", "title": "Enhancing Foundation Models in Transaction Understanding with LLM-based Sentence Embeddings", "authors": [ "Xiran Fan", "Zhimeng Jiang", "Chin-Chia Michael Yeh", "Yuzhong Chen", "Yingtong Dou", "Menghai Pan", "Yan Zheng" ], "posted": "2025-12-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2601.05271v1", "field": "finance", "role": "instrument", "bullets": [ "Large-scale payment network transaction datasets with merchant categorical fields containing unstructured textual data from real financial deployment.", "LLMs generated semantic embeddings as initializations for lightweight transaction models using multi-source data fusion and a one-word constraint principle.", "Hybrid framework significantly improved performance across multiple transaction understanding tasks while maintaining operational efficiency for real-time deployment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "large-scale transaction understanding benchmarks", "salience": 50, "n": 2519, "authors_detailed": [ { "name": "Xiran Fan", "url": "", "inst": "Visa (United States)" }, { "name": "Zhimeng Jiang", "url": "https://openalex.org/A5002869862", "inst": "Visa (United States)" }, { "name": "Chin‐Chia Michael Yeh", "url": "https://openalex.org/A5011045620", "inst": "Visa (United States)" }, { "name": "Yuzhong Chen", "url": "https://openalex.org/A5072866256", "inst": "Visa (United Kingdom)" }, { "name": "Yingtong Dou", "url": "https://openalex.org/A5052902632", "inst": "Visa (United Kingdom)" }, { "name": "Menghai Pan", "url": "https://openalex.org/A5027949894", "inst": "Visa (United Kingdom)" }, { "name": "Yan Zheng", "url": "https://openalex.org/A5050805538", "inst": "Visa (United States)" } ], "affiliations": [ "Visa (United States)", "Visa (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.5812143", "doi": "10.2139/ssrn.5812143", "title": "What Do LLMs Want?", "authors": [ "Thomas R. Cook", "Zachary Modig", "Sophia Kazinnik", "Nathan Palmer" ], "posted": "2025-12-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5812143", "field": "economics", "role": "agent", "bullets": [ "Multiple LLMs tested on dictator-style allocation games and a McCall job search environment to reveal latent economic preferences.", "Structural estimation recovered Fehr-Schmidt inequality aversion parameters; reframing and control vectors shifted choices toward payoff-maximizing behavior.", "LLMs exhibited inequality aversion stronger than human participants; preferences proved malleable via reframing but harder to steer consistently in complex economic tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 60, "n": 2781, "authors_detailed": [ { "name": "Thomas R. Cook", "url": "", "inst": "Federal Reserve Bank of Kansas City" }, { "name": "Zachary Modig", "url": "", "inst": "George Mason University" }, { "name": "Sophia Kazinnik", "url": "", "inst": "Stanford University" }, { "name": "Nathan Palmer", "url": "", "inst": "Federal Reserve" } ], "affiliations": [ "Stanford University", "Federal Reserve Bank of Kansas City", "George Mason University", "Federal Reserve" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.02227v1", "arxiv_id": "2512.02227v1", "title": "Orchestration Framework for Financial Agents: From Algorithmic Trading to Agentic Trading", "authors": [ "Jifeng Li", "Arnav Grover", "Abraham Alpuerto", "Yupeng Cao", "Xiao-Yang Liu" ], "posted": "2025-12-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.02227v1", "field": "finance", "role": "agent", "bullets": [ "Stock trading on hourly data April-December 2024 and BTC trading on minute data July-August 2025, benchmarked against S&P 500 and BTC buy-and-hold.", "LLM-based multi-agent system with planner, alpha, risk, portfolio, backtest, execution, and audit agents orchestrated autonomous trading decisions.", "Stock strategy returned 20.4% vs 16.0% for S&P 500 with Sharpe ratio of 2.63; BTC strategy returned 8.4% vs 3.8% for buy-and-hold." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 58, "n": 3572, "authors_detailed": [ { "name": "Jifeng Li", "url": "https://openalex.org/A5101437004", "inst": "Hebei Normal University" }, { "name": "Arnav Grover", "url": "https://openalex.org/A5120713383", "inst": "" }, { "name": "Abraham Alpuerto", "url": "https://openalex.org/A5120586683", "inst": "" }, { "name": "Yupeng Cao", "url": "https://openalex.org/A5009376964", "inst": "Kyoto University" }, { "name": "Xiao-Yang Liu", "url": "https://openalex.org/A5100405233", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "Hebei Normal University", "Kyoto University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2512.00946v1", "arxiv_id": "2512.00946v1", "title": "Fine-tuning of lightweight large language models for sentiment classification on heterogeneous financial textual data", "authors": [ "Alvaro Paredes Amorin", "Andre Python", "Christoph Weisser" ], "posted": "2025-11-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.00946v1", "field": "finance", "role": "method", "bullets": [ "Five public finance sentiment datasets of varying size, source, format, and language: FinancialPhraseBank, Financial Question Answering, Gold News Sentiment, Twitter Sentiment, and Chinese Finance Sentiment.", "DeepSeek LLM 7B, Llama3 8B Instruct, and Qwen3 8B are fine tuned and tested against FinBERT on the datasets' labels, in zero shot, few shot, and reduced training data settings.", "Qwen3 8B and Llama3 8B win most scenarios even using only 5 percent of available training data, making lightweight open models a cost effective route for heterogeneous financial text." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "five labelled finance sentiment datasets", "salience": 50, "edition": 14, "n": 1820, "authors_detailed": [ { "name": "Alvaro Paredes Amorin", "url": "https://openalex.org/A5120581900", "inst": "Zhejiang International Studies University" }, { "name": "André Python", "url": "https://openalex.org/A5045331325", "inst": "Centre for Human Genetics" }, { "name": "Christoph Weisser", "url": "https://openalex.org/A5007902286", "inst": "Witten/Herdecke University" } ], "affiliations": [ "Zhejiang International Studies University", "Centre for Human Genetics", "Witten/Herdecke University" ] }, { "uid": "arxiv:2512.01123v1", "arxiv_id": "2512.01123v1", "title": "A Hybrid Architecture for Options Wheel Strategy Decisions: LLM-Generated Bayesian Networks for Transparent Trading", "authors": [ "Xiaoting Kuang", "Boken Lin" ], "posted": "2025-11-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.01123v1", "field": "finance", "role": "instrument", "bullets": [ "Options wheel strategy tested on an 18.75-year dataset of 8,919 trades with out-of-sample evaluation.", "LLM constructed context-specific Bayesian networks from market conditions and selected analogous historical data to populate conditional probability tables.", "System achieved 15.3% annualized return with Sharpe ratio of 1.08 versus 0.62 for market benchmarks and maximum drawdown of negative 8.2% versus negative 60%." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "18.75-year out-of-sample backtest vs market benchmarks", "salience": 50, "n": 2780, "authors_detailed": [ { "name": "Xiaoting Kuang", "url": "https://openalex.org/A5120630673", "inst": "" }, { "name": "Lin, Boken", "url": "", "inst": "" } ] }, { "uid": "arxiv:2512.01107v1", "arxiv_id": "2512.01107v1", "title": "Foundation Priors", "authors": [ "Sanjog Misra" ], "posted": "2025-11-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.01107v1", "field": "economics", "role": "method", "bullets": [ "Theoretical framework for incorporating LLM-generated synthetic data as structured Bayesian priors in statistical and econometric workflows.", "Foundation prior modeled as exponential-tilted generalized Bayesian update with trust parameter governing weight assigned to synthetic data.", "Framework applies to experimental design, random-coefficient specifications, and partially linear models while preventing conflation of synthetic outputs with real data." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 75, "n": 2866, "authors_detailed": [ { "name": "Sanjog Misra", "url": "https://openalex.org/A5072512266", "inst": "Woodlawn School" } ], "affiliations": [ "Woodlawn School" ] }, { "uid": "arxiv:2512.00630v1", "arxiv_id": "2512.00630v1", "title": "Financial Text Classification Based On rLoRA Finetuning On Qwen3-8B model", "authors": [ "Zhiming Lian" ], "posted": "2025-11-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.00630v1", "field": "finance", "role": "instrument", "bullets": [ "Financial sentiment analysis and news classification tasks benchmarking Qwen3-8B against T5, BERT, RoBERTa, LLaMA-7B, and Baichuan2-7B models.", "Qwen3-8B fine-tuned with noisy embedding instruction tuning and rank-stabilized LoRA for financial text classification with FlashAttention memory optimization.", "Qwen3-8B consistently surpassed all baselines in classification accuracy while requiring fewer training epochs across both financial NLP tasks." ], "bullet_provenance": "ai", "models": [ "open_other", "llama" ], "open_weights": true, "validated": true, "validation_note": "financial sentiment and news classification benchmarks", "salience": 40, "n": 2611, "authors_detailed": [ { "name": "Lian, Zhiming", "url": "", "inst": "" } ] }, { "uid": "doi:10.1109/ichms65439.2025.11154208", "doi": "10.1109/ichms65439.2025.11154208", "arxiv_id": "2512.04108v1", "title": "Responsible LLM Deployment for High-Stake Decisions by Decentralized Technologies and Human-AI Interactions", "authors": [ "Swati Sachan", "Theo Miller", "Mai Phuong Nguyen" ], "posted": "2025-11-28", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.04108v1", "field": "management", "role": "method", "bullets": [ "A deployment framework for LLM decision support in high stakes settings, tested on responsible business lending decisions with models run locally inside the organization.", "BERT large, Mistral, and Llama 2 and 3 support the lending assessments; iterative expert and developer review screens uncertain cases and explanation stability, without a reported accuracy benchmark.", "Blockchain and IPFS ledgers of model activity are proposed for automated audit and accountability tracing; evidence remains at the framework and feasibility stage rather than field outcomes." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 32, "edition": 14, "n": 1836, "authors_detailed": [ { "name": "Swati Sachan", "url": "https://openalex.org/A5013692124", "inst": "University of Liverpool" }, { "name": "Theo Miller", "url": "", "inst": "Kendall College" }, { "name": "Mai Phuong Nguyen", "url": "https://openalex.org/A5101602315", "inst": "University of Liverpool" } ], "affiliations": [ "University of Liverpool", "Kendall College" ] }, { "uid": "arxiv:2512.00163v1", "arxiv_id": "2512.00163v1", "title": "Measuring What LLMs Think They Do: SHAP Faithfulness and Deployability on Financial Tabular Classification", "authors": [ "Saeed AlMarri", "Mathieu Ravaut", "Kristof Juhasz", "Gautier Marti", "Hamdan Al Ahbabi", "Ibrahim Elfadel" ], "posted": "2025-11-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.00163v1", "field": "finance", "role": "method", "bullets": [ "Financial tabular classification tasks evaluating LLM zero-shot prompting against LightGBM for risk assessment with SHAP-based explainability analysis.", "LLMs classified financial data via zero-shot prompting; SHAP values generated to compare self-reported feature importance against actual model decision behavior.", "LLM self-explanations diverged from their SHAP values; notable differences from LightGBM SHAP values highlight limitations as standalone classifiers for structured finance." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "SHAP faithfulness comparison with LightGBM on financial classification", "salience": 60, "n": 2610, "authors_detailed": [ { "name": "Saeed AlMarri", "url": "https://openalex.org/A5120316338", "inst": "" }, { "name": "Mathieu Ravaut", "url": "https://openalex.org/A5052746002", "inst": "Agency for Science, Technology and Research" }, { "name": "Kristof Juhasz", "url": "https://openalex.org/A5120537398", "inst": "" }, { "name": "Gautier Marti", "url": "https://openalex.org/A5086630995", "inst": "Capital University" }, { "name": "Hamdan Al Ahbabi", "url": "https://openalex.org/A5120316337", "inst": "Khalifa University of Science and Technology" }, { "name": "Ibrahim Elfadel", "url": "https://openalex.org/A5120710839", "inst": "" } ], "affiliations": [ "Agency for Science, Technology and Research", "Capital University", "Khalifa University of Science and Technology" ] }, { "uid": "arxiv:2511.21218v3", "arxiv_id": "2511.21218v3", "title": "Can Finetuing LLMs on Small Human Samples Increase Heterogeneity, Alignment, and Belief-Action Coherence?", "authors": [ "Steven Wang", "Kyle Hunt", "Shaojie Tang", "Kenneth Joseph" ], "posted": "2025-11-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.21218v3", "field": "management", "role": "method", "bullets": [ "A behavioral experiment on information disclosure, comparing responses from human participants with LLM generated ones across subgroups; sample sizes are not stated in the abstract.", "A base LLM, name not stated, is fine-tuned on a small human sample of the kind a pilot study yields, then compared with human data on divergence, subgroup alignment, and belief action coherence.", "Fine-tuning improves heterogeneity, alignment, and coherence over the base model, but no variant recovers the original regression coefficients, so simulated respondents remain unfit for formal inference." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human experimental responses, divergence and coefficient recovery", "salience": 64, "edition": 14, "models": [], "n": 1941, "authors_detailed": [ { "name": "Steven Wang", "url": "https://openalex.org/A5101707020", "inst": "Hospital of the University of Pennsylvania" }, { "name": "Hunt, Kyle", "url": "", "inst": "" }, { "name": "Shaojie Tang", "url": "https://openalex.org/A5050393292", "inst": "University at Buffalo, State University of New York" }, { "name": "Kenneth Joseph", "url": "https://openalex.org/A5032694172", "inst": "University at Buffalo, State University of New York" } ], "affiliations": [ "University at Buffalo, State University of New York" ] }, { "uid": "arxiv:2511.21802v1", "arxiv_id": "2511.21802v1", "title": "Tacit Bidder-Side Collusion: Artificial Intelligence in Dynamic Auctions", "authors": [ "Sriram Tolety" ], "posted": "2025-11-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.21802v1", "field": "economics", "role": "agent", "bullets": [ "Controlled simulations of repeated Dutch auctions with multiple LLMs acting as autonomous bidders, varying number of market participants.", "LLMs submitted bids without communication; theoretical model derived a closed-form threshold for sustainable collusion in subgame-perfect Nash equilibria.", "Systematic supra-competitive prices emerged in small auction settings; competitive behavior returned as bidder count increased, providing first evidence of bidder-side tacit collusion by LLMs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 65, "n": 2779, "authors_detailed": [ { "name": "Sriram Tolety", "url": "https://openalex.org/A5120629273", "inst": "" } ] }, { "uid": "arxiv:2512.07867v1", "arxiv_id": "2512.07867v1", "title": "LLM-Generated Counterfactual Stress Scenarios for Portfolio Risk Simulation via Hybrid Prompt-RAG Pipeline", "authors": [ "Masoud Soleimani" ], "posted": "2025-11-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2512.07867v1", "field": "finance", "role": "instrument", "bullets": [ "G7 macro-financial stress testing covering GDP growth, inflation, and policy rates with structured prompting and optional country-fundamentals retrieval.", "Multiple LLMs generated machine-readable macroeconomic scenarios translated into portfolio losses via factor-based VaR and Expected Shortfall mapping.", "LLMs produced coherent country-specific stress narratives with stable tail-risk amplification; risk variation driven by portfolio composition and prompt design." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "VaR and Expected Shortfall vs classical econometric baselines", "salience": 65, "n": 2865, "authors_detailed": [ { "name": "Soleimani, Masoud", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.19671v1", "arxiv_id": "2511.19671v1", "title": "FISCAL: Financial Synthetic Claim-document Augmented Learning for Efficient Fact-Checking", "authors": [ "Rishab Sharma", "Iman Saberi", "Elham Alipour", "Jie JW Wu", "Fatemeh Fard" ], "posted": "2025-11-24", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.19671v1", "field": "finance", "role": "method", "bullets": [ "Numerical claim verification against financial documents, trained on FISCAL-data, a synthetic claim document corpus generated by the proposed modular pipeline; dataset and scripts are released.", "A lightweight MiniCheck verifier is fine tuned on the synthetic corpus and evaluated on the external FinDVer and Fin-Fact benchmarks against much larger systems.", "The compact verifier beats GPT-3.5 Turbo and similar sized open models, approaches systems around twenty times larger such as Mixtral 8x22B and Command R Plus, and rivals GPT-4o and Claude 3.5 externally." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinDVer and Fin-Fact benchmarks", "salience": 40, "edition": 14, "n": 1811, "authors_detailed": [ { "name": "Sharma, Rishab", "url": "", "inst": "" }, { "name": "Saberi, Iman", "url": "", "inst": "" }, { "name": "Alipour, Elham", "url": "", "inst": "" }, { "name": "Wu, Jie JW", "url": "", "inst": "" }, { "name": "Fard, Fatemeh", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.21756v1", "arxiv_id": "2511.21756v1", "title": "Dissecting the Ledger: Locating and Suppressing \"Liar Circuits\" in Financial Large Language Models", "authors": [ "Soham Mirajkar" ], "posted": "2025-11-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.21756v1", "field": "finance", "role": "method", "bullets": [ "GPT-2 XL architecture evaluated on the ConvFinQA financial arithmetic benchmark using causal tracing and ablation methods.", "Causal tracing identified a dual-stage mechanism: a distributed computational scratchpad in middle layers and a decisive aggregation circuit in Layer 46.", "Suppressing Layer 46 reduced confidence in hallucinatory outputs by 81.8%; a linear probe on that layer detected arithmetic errors with 98% accuracy on unseen financial topics." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": true, "validated": true, "validation_note": "ConvFinQA benchmark, 98% probe accuracy", "salience": 50, "n": 2777, "authors_detailed": [ { "name": "Soham Mirajkar", "url": "https://openalex.org/A5120580451", "inst": "" } ] }, { "uid": "arxiv:2511.18850v4", "arxiv_id": "2511.18850v4", "title": "Cognitive Alpha Mining via LLM-Driven Code-Based Evolution", "authors": [ "Fengyuan Liu", "Yi Huang", "Sichun Luo", "Yuqi Wang", "Yazheng Yang", "Xinye Li", "Zefa Hu", "Junlan Feng", "Qi Liu" ], "posted": "2025-11-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.18850v4", "field": "finance", "role": "instrument", "bullets": [ "Five stock datasets from three international stock markets used to evaluate LLM-driven alpha factor discovery.", "LLMs served as adaptive cognitive agents iteratively refining, mutating, and recombining code-level alpha candidates through multi-stage prompts and financial feedback.", "CogAlpha consistently discovered alphas with superior predictive accuracy, robustness, and generalization over existing deep learning, genetic programming, and prior LLM methods." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "out-of-sample stock prediction accuracy vs baselines", "salience": 55, "n": 2778, "authors_detailed": [ { "name": "Liu, Fengyuan", "url": "", "inst": "" }, { "name": "Huang, Yi", "url": "", "inst": "" }, { "name": "Luo, Sichun", "url": "", "inst": "" }, { "name": "Wang, Yuqi", "url": "", "inst": "" }, { "name": "Yang, Yazheng", "url": "", "inst": "" }, { "name": "Li, Xinye", "url": "", "inst": "" }, { "name": "Hu, Zefa", "url": "", "inst": "" }, { "name": "Feng, Junlan", "url": "", "inst": "" }, { "name": "Liu, Qi", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.577056", "doi": "10.2139/ssrn.577056", "arxiv_id": "2511.18578v1", "title": "Re(Visiting) Time Series Foundation Models in Finance", "authors": [ "Eghbal Rahimikia", "Hao Ni", "Weiguan Wang" ], "posted": "2025-11-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.18578v1", "field": "finance", "role": "method", "bullets": [ "Large-scale dataset of daily excess returns across diverse global financial markets; zero-shot, fine-tuned, and from-scratch time series foundation models evaluated.", "Time series foundation models tested in zero-shot inference, fine-tuning, and pre-training from scratch against strong benchmark models for return forecasting.", "Off-the-shelf pre-trained models performed poorly in finance; models pre-trained from scratch on financial data achieved substantial forecasting and economic improvements." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "global daily excess returns forecasting benchmarks", "salience": 68, "n": 2609, "authors_detailed": [ { "name": "Rahimikia, Eghbal", "url": "", "inst": "" }, { "name": "Ni, Hao", "url": "", "inst": "" }, { "name": "Wang, Weiguan", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.18589v1", "arxiv_id": "2511.18589v1", "title": "Strategic Decision Framework for Enterprise LLM Adoption", "authors": [ "Michael Trusov", "Minha Hwang", "Zainab Jamal", "Swarup Chandra" ], "posted": "2025-11-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.18589v1", "field": "management", "role": "object", "bullets": [ "Interviews and case studies across healthcare, financial services, and software firms adopting LLMs for content generation, coding, and process automation.", "Framework maps six decision steps from application selection to deployment, covering data security, infrastructure, and build-versus-buy trade-offs.", "Organizations that aligned LLM capabilities with specific business objectives avoided common implementation failures across B2B and B2C contexts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 30, "validated": null, "n": 2776, "authors_detailed": [ { "name": "Trusov, Michael", "url": "", "inst": "" }, { "name": "Hwang, Minha", "url": "", "inst": "" }, { "name": "Jamal, Zainab", "url": "", "inst": "" }, { "name": "Chandra, Swarup", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.18177v1", "arxiv_id": "2511.18177v1", "title": "Rethinking Retrieval: From Traditional Retrieval Augmented Generation to Agentic and Non-Vector Reasoning Systems in the Financial Domain for Large Language Models", "authors": [ "Elias Lumer", "Matt Melich", "Olivia Zino", "Elena Kim", "Sara Dieter", "Pradeep Honaganahalli Basavaraju", "Vamse Kumar Subbiah", "James A. Burke", "Roberto Hernandez" ], "posted": "2025-11-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.18177v1", "field": "finance", "role": "method", "bullets": [ "Financial question answering over 1,200 SEC 10-K, 10-Q, and 8-K filings, measured on a 150 question benchmark for retrieval accuracy, answer quality, latency, and cost.", "Unnamed LLMs answer through vector based agentic RAG with hybrid search versus hierarchical node traversal without embeddings, scored on MRR, recall at 5, and LLM judged pairwise comparisons.", "Vector based agentic RAG wins 68 percent of comparisons at similar latency; cross encoder reranking adds a 59 percent absolute MRR gain and small to big retrieval wins 65 percent over baseline chunking." ], "bullet_provenance": "ai", "validated": true, "validation_note": "150 question SEC filing benchmark, MRR and recall", "salience": 44, "edition": 14, "models": [], "n": 1940, "authors_detailed": [ { "name": "Lumer, Elias", "url": "", "inst": "" }, { "name": "Melich, Matt", "url": "", "inst": "" }, { "name": "Zino, Olivia", "url": "", "inst": "" }, { "name": "Kim, Elena", "url": "", "inst": "" }, { "name": "Dieter, Sara", "url": "", "inst": "" }, { "name": "Basavaraju, Pradeep Honaganahalli", "url": "", "inst": "" }, { "name": "Subbiah, Vamse Kumar", "url": "", "inst": "" }, { "name": "Burke, James A.", "url": "", "inst": "" }, { "name": "Hernandez, Roberto", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5775428", "doi": "10.2139/ssrn.5775428", "title": "The ECB's Green Put: From Cheap Talk to Priced Action", "authors": [ "Tristan Jourde", "Urszula Szczerbowicz", "Floris van Dijk" ], "posted": "2025-11-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5775428", "field": "finance", "role": "instrument", "bullets": [ "European Central Bank communications from 1997 to 2025 linked to European equity and bond returns, comparing high-emission brown firms against green firms.", "A large language model, family not stated, classifies ECB statements as action-oriented versus materiality-oriented and covers nature alongside climate to build the CB-CNC index; no validation against ground truth is reported.", "Only action-oriented climate communication repriced brown firms, and its cumulative effect eliminated the brown premium, preventing roughly 25 percent outperformance of high-emission firms over green since 2018." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 687, "authors_detailed": [ { "name": "Tristan Jourde", "url": "https://openalex.org/A5120661066", "inst": "Banque de France" }, { "name": "Urszula Szczerbowicz", "url": "https://openalex.org/A5066366058", "inst": "SKEMA Business School" }, { "name": "Floris van Dijk", "url": "https://openalex.org/A5120661067", "inst": "Banque de France" } ], "affiliations": [ "Banque de France", "SKEMA Business School" ] }, { "uid": "arxiv:2511.17866v2", "arxiv_id": "2511.17866v2", "title": "Narratives to Numbers: Large Language Models and Economic Policy Uncertainty", "authors": [ "Ethan Hartley" ], "posted": "2025-11-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.17866v2", "field": "economics", "role": "instrument", "bullets": [ "Over 360 million nineteenth-century U.S. newspaper articles plus multilingual cross-country news corpora used to construct economic policy uncertainty indices.", "LLM classifiers replicated and extended the Economic Policy Uncertainty index, benchmarked against dictionary rules and human audit assessments across languages.", "LLM classifiers substantially outperformed dictionary rules, better tracked human audit labels, and enabled a new 19th-century U.S. EPU index and cross-country indices from a single multilingual model." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "comparison against human audit assessments of EPU classification", "salience": 80, "n": 2504, "authors_detailed": [ { "name": "Hartley, Ethan", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5765563", "doi": "10.2139/ssrn.5765563", "title": "AI and the Early-Career Penalty: Evidence from Korea", "authors": [ "Joseph Han" ], "posted": "2025-11-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5765563", "field": "economics", "role": "object", "bullets": [ "Large-scale Korean survey data on labor markets from 2013-2024 using a shift-share instrument combining 2012 industry employment shares with post-2012 AI diffusion measures.", "No LLM used as tool; the paper estimates AI exposure effects on employment using econometric methods with industry-level AI diffusion and generative AI adoption measures.", "AI exposure left aggregate employment unchanged but caused sizable losses among new labor market entrants; generative AI effects on young workers were even more pronounced." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3571, "authors_detailed": [ { "name": "Joseph Han", "url": "https://openalex.org/A5120466636", "inst": "Korea Development Institute" } ], "affiliations": [ "Korea Development Institute" ] }, { "uid": "doi:10.1145/3768292.3770415", "doi": "10.1145/3768292.3770415", "arxiv_id": "2511.17462v1", "title": "Scaling Conditional Autoencoders for Portfolio Optimization via Uncertainty-Aware Factor Selection", "authors": [ "Ryan Engel", "Yu Chen", "Pawel Polak", "Ioana Boier" ], "posted": "2025-11-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.17462v1", "field": "finance", "role": "instrument", "bullets": [ "U.S. equity asset pricing with conditional autoencoders estimating latent factors from firm characteristics, scaling beyond the typical five-factor limit.", "Zero-shot Chronos foundation model, XGBoost quantile regression, and bootstrap methods ranked factors by forecast uncertainty to select the most predictable subset.", "Uncertainty-aware factor pruning delivers substantial risk-adjusted gains; a performance-weighted ensemble achieves higher Sharpe, Sortino, and Omega ratios than any single model." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 60, "n": 3569, "authors_detailed": [ { "name": "Ronald E. Engel", "url": "https://openalex.org/A5052101416", "inst": "Stony Brook University" }, { "name": "Yu Chen", "url": "", "inst": "Stony Brook University" }, { "name": "Paweł Polak", "url": "https://openalex.org/A5090692960", "inst": "Stony Brook University" }, { "name": "Ioana Boier", "url": "https://openalex.org/A5016889043", "inst": "Nvidia (United States)" } ], "affiliations": [ "Stony Brook University", "Nvidia (United States)" ] }, { "uid": "doi:10.2139/ssrn.5750102", "doi": "10.2139/ssrn.5750102", "title": "Would You Let AI Be Your Boss? Turing and Weberian Thresholds in AI Acceptability", "authors": [ "Sun Young Lee", "Phanish Puranam", "Ke Michael Mai" ], "posted": "2025-11-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5750102", "field": "management", "role": "object", "bullets": [ "Survey of 1,029 full-time U.S. office workers measuring comfort with and acceptance of AI-led managerial decisions across five organizing dimensions.", "No LLM used as instrument; the study examines employee attitudes toward AI authority in task division, allocation, information sharing, rewards, and conflict management.", "Employees preferred human managers overall but accepted AI more in low-interaction domains like information sharing and resisted it in reward distribution and conflict management." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3570, "authors_detailed": [ { "name": "Sun Young Lee", "url": "", "inst": "Western University" }, { "name": "Phanish Puranam", "url": "https://openalex.org/A5015167951", "inst": "INSEAD" }, { "name": "Ke Michael", "url": "https://openalex.org/A5120416368", "inst": "National University of Singapore" } ], "affiliations": [ "INSEAD", "Western University", "National University of Singapore" ], "prestige": true }, { "uid": "doi:10.1016/j.accinf.2025.100760", "doi": "10.1016/j.accinf.2025.100760", "arxiv_id": "2511.16055v1", "title": "Artificial Intelligence and Accounting Research: A Framework and Agenda", "authors": [ "Theophanis C. Stratopoulos", "Victor Xiaoqi Wang" ], "posted": "2025-11-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.16055v1", "field": "accounting", "role": "method", "bullets": [ "Framework paper mapping AI and accounting research along two axes, research focus and methodological approach, applied to the IJAIS special issue and recent work in leading accounting journals.", "No model is deployed; the paper compares human researchers with AI agents across the research workflow and locates where accounting expertise keeps a comparative advantage.", "Argues that generative AI democratizes routine research capabilities while raising the bar for judgment and theory, and calls for doctoral training that builds AI fluency." ], "bullet_provenance": "ai", "salience": 55, "edition": 14, "models": [], "validated": null, "n": 1846, "authors_detailed": [ { "name": "Theophanis C. Stratopoulos", "url": "https://openalex.org/A5051997338", "inst": "University of Waterloo" }, { "name": "Victor Xiaoqi Wang", "url": "https://openalex.org/A5041860793", "inst": "California State University, Long Beach" } ], "affiliations": [ "University of Waterloo", "California State University, Long Beach" ] }, { "uid": "arxiv:2511.16654v2", "arxiv_id": "2511.16654v2", "title": "Comparison of Text-Based and Image-Based Retrieval in Multimodal Retrieval Augmented Generation Large Language Model Systems", "authors": [ "Elias Lumer", "Alex Cardenas", "Matt Melich", "Myles Mason", "Sara Dieter", "Vamse Kumar Subbiah", "Pradeep Honaganahalli Basavaraju", "Roberto Hernandez" ], "posted": "2025-11-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.16654v2", "field": "finance", "role": "method", "bullets": [ "A new earnings call benchmark of 40 question answer pairs, each tied to one image and one text chunk from financial documents.", "Six unnamed LLMs and two multimodal embedding models are compared on summary based versus native image embedding retrieval, scored with ranking metrics and LLM judged answer quality.", "Direct multimodal embeddings beat summary based retrieval by 13 points mean average precision at five and 11 points nDCG, and yield more factually consistent answers." ], "bullet_provenance": "ai", "validated": true, "validation_note": "40 pair earnings call QA benchmark, ranking metrics reported", "salience": 40, "edition": 14, "models": [], "n": 1883, "authors_detailed": [ { "name": "Elias Lumer", "url": "https://openalex.org/A5114601395", "inst": "PricewaterhouseCoopers (South Korea)" }, { "name": "Cardenas, Alex", "url": "", "inst": "" }, { "name": "Melich, Matt", "url": "", "inst": "" }, { "name": "Mason, Myles", "url": "", "inst": "" }, { "name": "Sara Dieter", "url": "https://openalex.org/A5120467531", "inst": "" }, { "name": "Subbiah, Vamse Kumar", "url": "", "inst": "" }, { "name": "Pradeep Honaganahalli Basavaraju", "url": "https://openalex.org/A5114601396", "inst": "PricewaterhouseCoopers (South Korea)" }, { "name": "Roberto Hernández", "url": "https://openalex.org/A5040355409", "inst": "PricewaterhouseCoopers (South Korea)" } ], "affiliations": [ "PricewaterhouseCoopers (South Korea)" ] }, { "uid": "doi:10.2139/ssrn.5649410", "doi": "10.2139/ssrn.5649410", "title": "AI Slop and Data Pollution in the Age of Generative AI: Strategic Risks, Economic Consequences, and Governance Pathways for Business, Management, and the Creative Industries", "authors": [ "Mohammad Samar Ansari" ], "posted": "2025-11-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5649410", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 35, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 686, "authors_detailed": [ { "name": "Mohammad Samar Ansari", "url": "", "inst": "University of Chester" } ], "affiliations": [ "University of Chester" ] }, { "uid": "arxiv:2511.16375v1", "arxiv_id": "2511.16375v1", "title": "Are Foundation Models Useful for Bankruptcy Prediction?", "authors": [ "Marcin Kostrzewa", "Oleksii Furman", "Roman Furman", "Sebastian Tomczak", "Maciej Zięba" ], "posted": "2025-11-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.16375v1", "field": "finance", "role": "method", "bullets": [ "Over one million company records from Visegrád Group countries, evaluated across multiple corporate bankruptcy prediction horizons.", "Llama-3.3-70B-Instruct and TabPFN were benchmarked against XGBoost and CatBoost for bankruptcy classification on highly imbalanced datasets.", "Classical ML models consistently outperformed foundation models; LLM probability estimates proved unreliable for risk-sensitive financial prediction settings." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "Bankruptcy prediction accuracy vs actual outcomes across multiple horizons", "salience": 74, "n": 3073, "authors_detailed": [ { "name": "Marcin Kostrzewa", "url": "https://openalex.org/A5120513687", "inst": "Artificial Intelligence in Medicine (Canada)" }, { "name": "Oleksii Furman", "url": "https://openalex.org/A5038826634", "inst": "Office of Science" }, { "name": "R. H. Furman", "url": "https://openalex.org/A5110095120", "inst": "Opera Software (Ireland)" }, { "name": "Sebastian Klaudiusz Tomczak", "url": "https://openalex.org/A5005730889", "inst": "Wrocław University of Science and Technology" }, { "name": "Maciej Zięba", "url": "https://openalex.org/A5083652196", "inst": "Warsaw University of Technology" } ], "affiliations": [ "Artificial Intelligence in Medicine (Canada)", "Office of Science", "Opera Software (Ireland)", "Wrocław University of Science and Technology", "Warsaw University of Technology" ] }, { "uid": "arxiv:2511.16438v1", "arxiv_id": "2511.16438v1", "title": "ESGBench: A Benchmark for Explainable ESG Question Answering in Corporate Sustainability Reports", "authors": [ "Sherine George", "Nithish Saji" ], "posted": "2025-11-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.16438v1", "field": "finance", "role": "method", "bullets": [ "Benchmark dataset of domain-grounded ESG questions across multiple themes paired with human-curated answers from corporate sustainability reports.", "State-of-the-art LLMs evaluated on ESGBench for factual consistency, traceability, and domain alignment in explainable ESG question answering.", "Key challenges identified in factual consistency and traceability of LLM responses; benchmark aims to accelerate transparent ESG-focused AI research." ], "bullet_provenance": "ai", "validated": true, "validation_note": "ESGBench human-curated answers", "salience": 50, "models": [], "n": 3567, "authors_detailed": [ { "name": "S.S. George", "url": "https://openalex.org/A5019892124", "inst": "Illinois Institute of Technology" }, { "name": "Nithish Saji", "url": "https://openalex.org/A5120610257", "inst": "" } ], "affiliations": [ "Illinois Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.5650070", "doi": "10.2139/ssrn.5650070", "title": "Behaviorally-Calibrated Finance Agents (BCFA™): A Framework for Confidence-Gated Agentic AI in Regulatory Finance Filings", "authors": [ "Moiz Kohari" ], "posted": "2025-11-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5650070", "field": "finance", "role": "method", "bullets": [ "Framework for AI agents operating in regulatory finance filings, surveillance, and compliance reporting workflows requiring auditable automation.", "Behaviorally-calibrated agents combine confidence prediction and abstention-based decision gating to act only within explicit verification thresholds.", "BCFA framework delivers auditable and explainable automation by optimizing for correctness over confidence in high-stakes financial compliance tasks." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 3568, "authors_detailed": [ { "name": "Moiz Kohari", "url": "https://openalex.org/A5098288674", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "arxiv:2511.15202v1", "arxiv_id": "2511.15202v1", "title": "SOLID: a Framework of Synergizing Optimization and LLMs for Intelligent Decision-Making", "authors": [ "Yinsheng Wang", "Tario G You", "Léonard Boussioux", "Shan Liu" ], "posted": "2025-11-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.15202v1", "field": "finance", "role": "agent", "bullets": [ "Stock portfolio investment case study using historical prices and financial news as inputs for combined optimization and LLM decision-making.", "LLM agent collaborated iteratively with mathematical optimizer through dual prices and deviation penalties under the SOLID framework with convergence guarantees.", "Framework demonstrated convergence under various scenarios and improved annualized returns compared to optimizer-only baseline while preserving modularity and data privacy." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 52, "n": 2503, "authors_detailed": [ { "name": "Wang, Yinsheng", "url": "", "inst": "" }, { "name": "You, Tario G", "url": "", "inst": "" }, { "name": "Boussioux, Léonard", "url": "", "inst": "" }, { "name": "Liu, Shan", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.15456v1", "arxiv_id": "2511.15456v1", "title": "Know Your Intent: An Autonomous Multi-Perspective LLM Agent Framework for DeFi User Transaction Intent Mining", "authors": [ "Qian'ang Mao", "Yuxuan Zhang", "Jiaman Chen", "Wenjun Zhou", "Jiaqi Yan" ], "posted": "2025-11-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.15456v1", "field": "finance", "role": "instrument", "bullets": [ "DeFi transactions on blockchain involving complex smart contract interactions; multi-agent LLM framework processing multimodal on-chain and off-chain data.", "Meta-level planner coordinates domain-expert agents to decompose intent analysis into subtasks; cognitive evaluator mitigates LLM hallucinations and ensures verifiability.", "TIM framework significantly outperforms machine learning models, single LLMs, and single-agent baselines in classifying DeFi user transaction intent." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Intent classification accuracy vs ML and single-LLM baselines", "salience": 45, "n": 2775, "authors_detailed": [ { "name": "Mao, Qian'ang", "url": "", "inst": "" }, { "name": "Zhang, Yuxuan", "url": "", "inst": "" }, { "name": "Chen, Jiaman", "url": "", "inst": "" }, { "name": "Zhou, Wenjun", "url": "", "inst": "" }, { "name": "Yan, Jiaqi", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.15214v2", "arxiv_id": "2511.15214v2", "title": "Corporate Earnings Calls and Analyst Beliefs", "authors": [ "Giuseppe Matera" ], "posted": "2025-11-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.15214v2", "field": "finance", "role": "instrument", "bullets": [ "Corporate earnings call transcripts paired with analyst forecast data, studying how narrative content beyond numbers shapes expectation formation.", "LLMs extracted narratives from transcripts and generated counterfactual versions varying topical emphasis while holding quantitative content fixed.", "Analysts systematically over-react to sentiment and optimism while under-reacting to narratives of risk and uncertainty in earnings calls." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 80, "n": 3071, "authors_detailed": [ { "name": "Giuseppe Matera", "url": "https://openalex.org/A5120479960", "inst": "École Polytechnique Fédérale de Lausanne" } ], "affiliations": [ "École Polytechnique Fédérale de Lausanne" ] }, { "uid": "arxiv:2511.15857v1", "arxiv_id": "2511.15857v1", "title": "A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios", "authors": [ "Anthony Wise", "Xinyi Zhou", "Martin Reimann", "Anind Dey", "Leilani Battle" ], "posted": "2025-11-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.15857v1", "field": "management", "role": "object", "bullets": [ "336 crowdsourced participants interacted with neutral or value-framed ChatBots while making decisions about U.S. defense spending allocations.", "ChatGPT-based chatbots applied value framing without overt bias or misinformation to test whether framing alone could shift participant budget choices.", "Value-framed ChatBots significantly altered budget decisions versus neutral control; misaligned framing triggered a backfire effect reinforcing original preferences." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "validated": null, "n": 3072, "authors_detailed": [ { "name": "Wise, Anthony", "url": "", "inst": "" }, { "name": "Xinyi Zhou", "url": "https://openalex.org/A5008404864", "inst": "Wuhan University of Technology" }, { "name": "Martin Reimann", "url": "https://openalex.org/A5026697089", "inst": "University of Arizona" }, { "name": "Anind K. Dey", "url": "https://openalex.org/A5032134965", "inst": "University of Washington" }, { "name": "Leilani Battle", "url": "https://openalex.org/A5009763002", "inst": "University of Washington" } ], "affiliations": [ "Wuhan University of Technology", "University of Arizona", "University of Washington" ] }, { "uid": "arxiv:2511.14130v2", "arxiv_id": "2511.14130v2", "title": "PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval", "authors": [ "Chun Chet Ng", "Jia Yu Lim", "Wei Zeng Low" ], "posted": "2025-11-18", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.14130v2", "field": "finance", "role": "method", "bullets": [ "Retrieval from lengthy corporate financial filings, with document and chunk ranking evaluated on the labelled FinAgentBench, FiQA-2018, and FinanceBench benchmarks.", "PRISM, a training free pipeline over unnamed LLMs, combines refined system prompts, selective in context learning, and light multi-agent coordination; rankings are scored against benchmark labels.", "Simpler configurations often beat complex multi-agent pipelines; the best run reaches NDCG at 5 of 0.718 on FinAgentBench, placing third as the only training free entry in the top three." ], "bullet_provenance": "ai", "validated": true, "validation_note": "NDCG on FinAgentBench, FiQA-2018, FinanceBench", "salience": 36, "edition": 14, "models": [], "n": 1939, "authors_detailed": [ { "name": "Chun Chet Ng", "url": "https://openalex.org/A5110692661", "inst": "University of Malaya" }, { "name": "Lim, Jia Yu", "url": "", "inst": "" }, { "name": "Wei Zeng Low", "url": "https://openalex.org/A5120276096", "inst": "" } ], "affiliations": [ "University of Malaya" ] }, { "uid": "arxiv:2511.14722v2", "arxiv_id": "2511.14722v2", "title": "When AI Democratizes Exploitation: LLM-Assisted Strategic Manipulation of Fair Division Algorithms", "authors": [ "Priyanka Verma", "Balagopal Unnikrishnan" ], "posted": "2025-11-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.14722v2", "field": "economics", "role": "agent", "bullets": [ "Rent division scenarios on the Spliddit fair division platform; four distinct manipulation strategies tested via conversational queries to LLM assistants.", "LLMs explained algorithmic mechanics, identified profitable deviations, and generated specific numerical inputs for coordinated preference misreporting.", "Users obtain actionable manipulation strategies via simple queries, democratizing access to strategic expertise previously requiring deep technical knowledge of algorithms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 2774, "authors_detailed": [ { "name": "Verma, Priyanka", "url": "", "inst": "" }, { "name": "Unnikrishnan, Balagopal", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.21712v1", "arxiv_id": "2511.21712v1", "title": "EulerESG: Automating ESG Disclosure Analysis with LLMs", "authors": [ "Yi Ding", "Xushuo Tang", "Zhengyi Yang", "Wenqian Zhang", "Simin Wu", "Yuxin Huang", "Lingjing Lan", "Weiyuan Li", "Yin Chen", "Mingchen Ju", "Wenke Yang", "Thong Hoang", "Mykhailo Klymenko", "Xiwei Zu", "Wenjie Zhang" ], "posted": "2025-11-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.21712v1", "field": "accounting", "role": "instrument", "bullets": [ "ESG reports from four global companies across twelve SASB sub-industries, analyzed as heterogeneous PDF documents against recognized reporting standards.", "An LLM-powered system used dual-channel retrieval and disclosure analysis to extract standard-aligned ESG metrics, achieving up to 0.95 average accuracy.", "EulerESG automatically populated SASB-aligned metric tables with high fidelity, outperforming generic text approaches through explicit ESG framework awareness." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "SASB-aligned metric extraction accuracy up to 0.95", "salience": 65, "n": 3069, "authors_detailed": [ { "name": "Yi Ding", "url": "https://openalex.org/A5027377891", "inst": "Geisinger Medical Center" }, { "name": "Xushuo Tang", "url": "https://openalex.org/A5066118206", "inst": "UNSW Sydney" }, { "name": "Zhengyi Yang", "url": "https://openalex.org/A5057773915", "inst": "UNSW Sydney" }, { "name": "Wenqian Zhang", "url": "https://openalex.org/A5100749049", "inst": "Shanghai University of Finance and Economics" }, { "name": "Simin Wu", "url": "https://openalex.org/A5101733106", "inst": "Institute of Subtropical Agriculture" }, { "name": "Yuxin Huang", "url": "https://openalex.org/A5120679960", "inst": "" }, { "name": "L. Lan", "url": "https://openalex.org/A5055923127", "inst": "Chengdu University of Technology" }, { "name": "Weiyuan Li", "url": "https://openalex.org/A5015025268", "inst": "Tongji University" }, { "name": "Yin Chen", "url": "https://openalex.org/A5100430556", "inst": "University of Arizona" }, { "name": "Mingchen Ju", "url": "https://openalex.org/A5070533702", "inst": "UNSW Sydney" }, { "name": "Wenke Yang", "url": "https://openalex.org/A5103226130", "inst": "Qingdao University" }, { "name": "Thong Hoang", "url": "https://openalex.org/A5101819360", "inst": "Commonwealth Scientific and Industrial Research Organisation" }, { "name": "Mykhailo Klymenko", "url": "https://openalex.org/A5120629087", "inst": "Commonwealth Scientific and Industrial Research Organisation" }, { "name": "Xingxing Zu", "url": "https://openalex.org/A5110781501", "inst": "Morgan State University" }, { "name": "Zhang Wenjie", "url": "https://openalex.org/A5100300179", "inst": "People’s Hospital of Rizhao" } ], "affiliations": [ "UNSW Sydney", "Shanghai University of Finance and Economics", "Institute of Subtropical Agriculture", "Chengdu University of Technology", "Tongji University", "University of Arizona", "Qingdao University" ] }, { "uid": "doi:10.2139/ssrn.5763042", "doi": "10.2139/ssrn.5763042", "title": "The POP Premium Populism and the Cross-Section of Stock Returns", "authors": [ "Riccardo Colacito", "Hui Ding", "Fuwei Jiang", "Yan Qian" ], "posted": "2025-11-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5763042", "field": "finance", "role": "instrument", "bullets": [ "Over one million Wall Street Journal articles from 1984 to 2024, with cross-sectional equity return data for U.S. firms.", "LLMs quantified populism-related sentiment in media text to construct a firm-level Populism Index capturing exposure to populist rhetoric.", "Low-POP-beta stocks earn approximately 8% annualized excess returns unexplained by standard factors, persistent across Republican and Democratic administrations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 78, "n": 3070, "authors_detailed": [ { "name": "Riccardo Colacito", "url": "", "inst": "Flagler College" }, { "name": "Hui Ding", "url": "", "inst": "Beijing Jiaotong University" }, { "name": "Fuwei Jiang", "url": "", "inst": "Xiamen University" }, { "name": "Yan Qian", "url": "", "inst": "University of North Carolina at Chapel Hill" } ], "affiliations": [ "University of North Carolina at Chapel Hill", "Flagler College", "Beijing Jiaotong University", "Xiamen University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2511.14930v1", "arxiv_id": "2511.14930v1", "title": "Fifty Shades of Greenwashing: The Political Economy of Climate Change Advertising on Social Media", "authors": [ "Robert Kubinec", "Aseem Mahajan" ], "posted": "2025-11-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.14930v1", "field": "economics", "role": "instrument", "bullets": [ "11 million social-political ads in Meta's Ad Targeting Dataset measuring climate-related misinformation from polluting companies on social media.", "LLMs combined with human coders and Bayesian item response theory classified greenwashing content and identified political greenwashing by fossil fuel interests.", "Fossil fuel companies micro-target political greenwashing ads at left-leaning communities with fossil fuel assets via organizations with undisclosed industry links." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3565, "authors_detailed": [ { "name": "Kubinec, Robert", "url": "", "inst": "" }, { "name": "Mahajan, Aseem", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5757608", "doi": "10.2139/ssrn.5757608", "title": "Stablecoins: A Revolutionary Payment Technology with Financial Risks", "authors": [ "Rashad Ahmed", "James Clouse", "Fabio Natalucci", "Alessandro Rebucci", "Geyue Sun" ], "posted": "2025-11-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5757608", "field": "finance", "role": "instrument", "bullets": [ "Analysis of GENIUS Act stablecoin regulation with expert opinions extracted from all U.S. stablecoin podcast episodes from January to July 2025.", "LLM analysis surveyed expert opinions from podcast transcripts to complement empirical analysis and historical case studies of stablecoin financial stability risks.", "GENIUS-compliant stablecoins create a segmented market; stablecoins increasingly substitute for money market funds and bank deposits with attendant stability risks." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "models": [], "n": 3566, "authors_detailed": [ { "name": "Rashad Ahmed", "url": "https://openalex.org/A5077643972", "inst": "Peterson Institute for International Economics" }, { "name": "James A. Clouse", "url": "https://openalex.org/A5010947309", "inst": "Einaudi Institute for Economics and Finance" }, { "name": "Fabio M. Natalucci", "url": "https://openalex.org/A5007900869", "inst": "Einaudi Institute for Economics and Finance" }, { "name": "Alessandro Rebucci", "url": "https://openalex.org/A5024376046", "inst": "National Bureau of Economic Research" }, { "name": "Geyue Sun", "url": "https://openalex.org/A5120503001", "inst": "George Washington University" } ], "affiliations": [ "Peterson Institute for International Economics", "Einaudi Institute for Economics and Finance", "National Bureau of Economic Research", "George Washington University" ] }, { "uid": "doi:10.2139/ssrn.5763222", "doi": "10.2139/ssrn.5763222", "title": "Financial Market Fragility in the Era of AI Planning", "authors": [ "Winston Wei Dou", "Itay Goldstein", "Yan Ji" ], "posted": "2025-11-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5763222", "field": "finance", "role": "agent", "bullets": [ "Dynamic trading model with positive-feedback investors, constrained arbitrageurs, and oligopolistic informed speculators in simulated financial markets.", "AI speculators trained via reinforcement learning with explicit planning modules autonomously discover intertemporal collusive trading strategies without communication or shared intent.", "AI-planning speculators create and exploit negative bubbles, manipulate feedback traders, and significantly amplify market fragility when feedback trading intensity is strong." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3967, "authors_detailed": [ { "name": "Winston Wei Dou", "url": "", "inst": "National Bureau of Economic Research" }, { "name": "Itay Goldstein", "url": "", "inst": "National Bureau of Economic Research" }, { "name": "Yan Ji", "url": "", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "National Bureau of Economic Research", "Hong Kong University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.5711302", "doi": "10.2139/ssrn.5711302", "title": "Human Capital Assessment via Large Language Models alignment", "authors": [ "Amina Cherief", "Olivier Coz", "Hicham Lahbabi", "Takaya Sekine", "Raphaël Semet", "Luda Svystunova" ], "posted": "2025-11-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5711302", "field": "finance", "role": "instrument", "bullets": [ "Cross-regional analysis of human capital perception across emerging and developed markets examining variation over time and across institutional contexts.", "LLMs combined with clustering techniques analyzed intangible human capital assets from research literature; generated regional development scores incorporated into portfolio strategies.", "Human-capital strategies generated significantly different returns in emerging versus developed markets; firms with stronger human capital showed greater resilience during geopolitical turmoil." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 65, "n": 2502, "authors_detailed": [ { "name": "Amina Cherief", "url": "https://openalex.org/A5019940678", "inst": "Institut Pasteur" }, { "name": "Olivier Coz", "url": "https://openalex.org/A5120551238", "inst": "Amundi Institute" }, { "name": "Hicham Lahbabi", "url": "https://openalex.org/A5120452996", "inst": "Amundi Asset Management" }, { "name": "Takaya Sekine", "url": "https://openalex.org/A5033308346", "inst": "Institut Pasteur" }, { "name": "Raphaël Semet", "url": "https://openalex.org/A5073140779", "inst": "Université Paris-Saclay" }, { "name": "Luda Svystunova", "url": "https://openalex.org/A5120405819", "inst": "Amundi Asset Management" } ], "affiliations": [ "Institut Pasteur", "Amundi Institute", "Amundi Asset Management", "Université Paris-Saclay" ] }, { "uid": "doi:10.2139/ssrn.5760202", "doi": "10.2139/ssrn.5760202", "title": "Sentiment about Others", "authors": [ "Yukun Liu", "Xiao Yin" ], "posted": "2025-11-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5760202", "field": "finance", "role": "instrument", "bullets": [ "Over 46 million StockTwits posts at the firm-week level, distinguishing messages expressing own outlook from those describing others' expected actions.", "LLMs classified posts into first-order and higher-order sentiment categories, extracting sentiment scores associated with each type of investor expression.", "Higher-order sentiment positively predicts short-horizon returns where contrarian retail trading is strongest; retail order imbalance decreases with higher-order sentiment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "n": 3068, "authors_detailed": [ { "name": "Yukun Liu", "url": "https://openalex.org/A5100778213", "inst": "Télécom Paris" }, { "name": "Yin Xiao", "url": "https://openalex.org/A5025300323", "inst": "University College London" } ], "affiliations": [ "Télécom Paris", "University College London" ] }, { 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"url": "https://openalex.org/A5035513045", "inst": "University of North Carolina at Chapel Hill" } ], "affiliations": [ "University of North Carolina at Chapel Hill" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2511.12876v4", "arxiv_id": "2511.12876v4", "title": "Think, Speak, Decide: Language-Augmented Multi-Agent Reinforcement Learning for Economic Decision-Making", "authors": [ "Heyang Ma", "Qirui Mi", "Qipeng Yang", "Zijun Fan", "Bo Li", "Haifeng Zhang" ], "posted": "2025-11-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.12876v4", "field": "economics", "role": "agent", "bullets": [ "Economic simulation environment with multi-agent reinforcement learning agents making fiscal and market decisions under language-augmented policies.", "LLM-augmented agents followed a Think-Speak-Decide pipeline integrating natural language reasoning and peer communication with numerical MARL policy optimization.", "LAMP outperformed MARL-only and LLM-only baselines by 63.5% and 34.0% in cumulative return with 18.8% and 59.4% gains in robustness." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3563, "authors_detailed": [ { "name": "Huadóng Ma", "url": "https://openalex.org/A5100710713", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Qirui Mi", "url": "https://openalex.org/A5001410776", "inst": "Chinese Academy of Sciences" }, { "name": "Qipeng Yang", "url": "https://openalex.org/A5030138168", "inst": "Nanjing University of Posts and Telecommunications" }, { "name": "Fan, Zijun", "url": "", "inst": "" }, { "name": "Bo Li", "url": "https://openalex.org/A5046508099", "inst": "North University of China" }, { "name": "Haifeng Zhang", "url": "https://openalex.org/A5101708378", "inst": "Shandong Institute of Automation" } ], "affiliations": [ "Beijing University of Posts and Telecommunications", "Chinese Academy of Sciences", "Nanjing University of Posts and Telecommunications", "North University of China", "Shandong Institute of Automation" ] }, { "uid": "doi:10.2139/ssrn.5739406", "doi": "10.2139/ssrn.5739406", "title": "Artificial Intelligence in Microfinance and Financial Inclusion: Applications, Issues, and Future Directions", "authors": [ "Arvind Ashta" ], "posted": "2025-11-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5739406", "field": "finance", "role": "object", "bullets": [ "Systematic review of AI applications across payments, savings, lending, insurance, and investments in microfinance serving marginalized populations globally.", "Survey examined machine learning, NLP, and generative AI solutions including alternative credit scoring and automated underwriting for financial inclusion.", "AI can democratize financial services but amplifies existing disparities through algorithmic bias, proxy discrimination, and digital divides without governance frameworks." ], "bullet_provenance": 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explainable answers to complex financial questions unattainable from traditional database queries alone; validated through practical case studies on Korean equities." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 45, "n": 2501, "authors_detailed": [ { "name": "Cheonsol Lee", "url": "https://openalex.org/A5123469804", "inst": "" }, { "name": "Youngsang Jeong", "url": "https://openalex.org/A5123461571", "inst": "" }, { "name": "Jiyoung Shin", "url": "https://openalex.org/A5090264572", "inst": "Fifth Gait Technologies (United States)" }, { "name": "Huiju Kim", "url": "https://openalex.org/A5123472931", "inst": "" }, { "name": "Jidong Kim", "url": "https://openalex.org/A5123488338", "inst": "" } ], "affiliations": [ "Fifth Gait Technologies (United States)" ] }, { "uid": "arxiv:2511.12563v1", "arxiv_id": "2511.12563v1", "title": "LOBERT: Generative AI Foundation Model for Limit Order Book Messages", "authors": [ "Eljas Linna", "Kestutis Baltakys", "Alexandros Iosifidis", "Juho Kanniainen" ], "posted": "2025-11-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.12563v1", "field": "finance", "role": "instrument", "bullets": [ "Limit order book message-level data from financial markets, modeling irregular event timing and high-frequency trader reactions to visible order flow.", "LOBERT adapts BERT architecture with novel tokenization treating complete multi-dimensional messages as single tokens while retaining continuous price, volume, and time representations.", "Achieves leading performance in mid-price movement prediction and next-message prediction while reducing required context length compared to previous methods." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "mid-price movement and next-message prediction benchmarks", "salience": 50, "n": 3234, "authors_detailed": [ { "name": "Eljas Linna", "url": "https://openalex.org/A5120609658", "inst": "" }, { "name": "Kęstutis Baltakys", "url": "https://openalex.org/A5056087541", "inst": "Tampere University of Applied Sciences" }, { "name": "Alexandros Iosifidis", "url": "https://openalex.org/A5064535836", "inst": "Tampere University of Applied Sciences" }, { "name": "Juho Kanniainen", "url": "https://openalex.org/A5049372872", "inst": "Tampere University of Applied Sciences" } ], "affiliations": [ "Tampere University of Applied Sciences" ] }, { "uid": "arxiv:2511.12319v1", "arxiv_id": "2511.12319v1", "title": "Decision and Gender Biases in Large Language Models: A Behavioral Economic Perspective", "authors": [ "Luca Corazzini", "Elisa Deriu", "Marco Guerzoni" ], "posted": "2025-11-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.12319v1", "field": "economics", "role": "agent", "bullets": [ "Ultimatum game and gambling choices elicited from Gemma 7B and Qwen under neutral and gender conditioned prompts, benchmarked against human experimental results.", "The open models act as experimental subjects; their decisions are fitted to inequity aversion and loss aversion parameters and compared with human estimates.", "Both models show attenuated but persistent behavioral tendencies, including moderate fairness concerns, mild loss aversion, and subtle gender conditioned differences." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 55, "edition": 14, "validated": null, "n": 1882, "authors_detailed": [ { "name": "Luca Corazzini", "url": "https://openalex.org/A5021589852", "inst": "University of Milano-Bicocca" }, { "name": "Elisa Deriu", "url": "https://openalex.org/A5120605123", "inst": "" }, { "name": "Marco Guerzoni", "url": "https://openalex.org/A5102799538", "inst": "Collegio Carlo Alberto" } ], "affiliations": [ "University of Milano-Bicocca", "Collegio Carlo Alberto" ] }, { "uid": "arxiv:2511.12306v2", "arxiv_id": "2511.12306v2", "title": "UpBench: A Dynamically Evolving Real-World Labor-Market Agentic Benchmark Framework Built for Human-Centric AI", "authors": [ "Darvin Yi", "Teng Liu", "Mattie Terzolo", "Lance Hasson", "Ayan Sinha", "Pablo Mendes", "Andrew Rabinovich" ], "posted": "2025-11-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.12306v2", "field": "management", "role": "method", "bullets": [ "Dynamically evolving benchmark drawn from verified Upwork client transactions across the global freelance labor marketplace.", "Expert freelancers decompose each job into acceptance criteria and assess LLM agent submissions with per-criterion rubric-based feedback.", "Framework enables fine-grained analysis of model strengths and instruction-following fidelity beyond binary pass/fail in authentic labor-market contexts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "expert freelancer rubric evaluation on Upwork jobs", "salience": 48, "n": 2864, "authors_detailed": [ { "name": "Darvin Yi", "url": "https://openalex.org/A5012971976", "inst": "University of Illinois Chicago" }, { "name": "Teng Liu", "url": "https://openalex.org/A5105533996", "inst": "Qufu Normal University" }, { "name": "Mattie Terzolo", "url": "https://openalex.org/A5120604174", "inst": "" }, { "name": "Lance Hasson", "url": "https://openalex.org/A5120702699", "inst": "" }, { "name": "Ayan Sinh", "url": "https://openalex.org/A5120457896", "inst": "" }, { "name": "Pablo N. Mendes", "url": "https://openalex.org/A5012347190", "inst": "Lattice Semiconductor (United States)" }, { "name": "Andrew Rabinovich", "url": "https://openalex.org/A5026452365", "inst": "ResearchWorks (United States)" } ], "affiliations": [ "University of Illinois Chicago", "Qufu Normal University", "Lattice Semiconductor (United States)", "ResearchWorks (United States)" ] }, { "uid": "arxiv:2511.11315v2", "arxiv_id": "2511.11315v2", "title": "LAET: A Layer-wise Adaptive Ensemble Tuning Framework for Pretrained Language Models", "authors": [ "Jawad Ibn Ahad", "Muhammad Rafsan Kabir", "Robin Krambroeckers", "Sifat Momen", "Nabeel Mohammed", "Shafin Rahman" ], "posted": "2025-11-14", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.11315v2", "field": "finance", "role": "method", "bullets": [ "Financial NLP benchmark tasks in the FLARE family, including sentiment analysis, stock movement prediction, and credit risk assessment, used to compare tuning strategies.", "Layer wise adaptive ensemble tuning fine tunes only the most effective layers of pretrained LLMs near 3B parameters, with performance scored on the labelled financial tasks.", "Selectively tuned small models outperform larger systems including GPT-4 on the financial benchmarks while cutting compute; exact margins are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "labelled financial NLP benchmarks", "salience": 42, "edition": 14, "n": 1881, "authors_detailed": [ { "name": "Ahad, Jawad Ibn", "url": "", "inst": "" }, { "name": "Muhammad Rafsan Kabir", "url": "https://openalex.org/A5109751297", "inst": "Centre for Artificial Intelligence and Robotics" }, { "name": "Robin Krambroeckers", "url": "https://openalex.org/A5120309248", "inst": "Centre for Artificial Intelligence and Robotics" }, { "name": "Sifat Momen", "url": "https://openalex.org/A5027844735", "inst": "North South University" }, { "name": "Nabeel Mohammed", "url": "https://openalex.org/A5062072064", "inst": "North South University" }, { "name": "Shafin Rahman", "url": "https://openalex.org/A5085599688", "inst": "North South University" } ], "affiliations": [ "Centre for Artificial Intelligence and Robotics", "North South University" ] }, { "uid": "arxiv:2511.11761v1", "arxiv_id": "2511.11761v1", "title": "Cost Transparency of Enterprise AI Adoption", "authors": [ "Soogand Alavi", "Salar Nozari", "Andrea Luangrath" ], "posted": "2025-11-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.11761v1", "field": "management", "role": "object", "bullets": [ "Experiment using OpenAI API to measure how prompt linguistic style affects output token counts and enterprise costs for commercial LLM services.", "Non-polite versus polite prompts tested for systematic differences in generated output tokens without impacting response quality in enterprise AI adoption context.", "Non-polite prompts significantly increased output tokens and enterprise costs; linguistic style drives unpredictable cost variation that complicates enterprise budgeting and pricing transparency." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "controlled experiment measuring token count variation on OpenAI API", "salience": 55, "n": 2500, "authors_detailed": [ { "name": "Alavi, Soogand", "url": "", "inst": "" }, { "name": "Salar Nozari", "url": "https://openalex.org/A5120655589", "inst": "University of Iowa" }, { "name": "Andrea Luangrath", "url": "https://openalex.org/A5120457719", "inst": "" } ], "affiliations": [ "University of Iowa" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5716903", "doi": "10.2139/ssrn.5716903", "title": "Playing Prisoner's Dilemma Games with a Large Language Model", "authors": [ "Andreas Orland", "Kazuhiro Takemoto", "Philipp Külpmann" ], "posted": "2025-11-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5716903", "field": "economics", "role": "agent", "bullets": [ "One-shot Prisoner's Dilemma experiment using a full factorial design that varies payoff parameters, number of partners, strategy space, and elicitation order.", "ChatGPT-3.5-turbo serves as the experimental subject, providing both decisions and stated beliefs; behavior is characterized rather than compared to a benchmark.", "The model cooperates highly at baseline despite pessimistic beliefs, is largely insensitive to payoff variation, but responds systematically to group size, action space, and elicitation order." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "edition": 3, "audience": "general", "validated": null, "n": 91, "authors_detailed": [ { "name": "Andreas Orland", "url": "https://openalex.org/A5073522804", "inst": "Corvinus University of Budapest" }, { "name": "Kazuhiro Takemoto", "url": "https://openalex.org/A5013426338", "inst": "Kyushu Institute of Technology" }, { "name": "Philipp Külpmann", "url": "", "inst": "" } ], "affiliations": [ "Corvinus University of Budapest", "Kyushu Institute of Technology" ] }, { "uid": "arxiv:2511.09854v2", "arxiv_id": "2511.09854v2", "title": "TermGPT: Multi-Level Contrastive Fine-Tuning for Terminology Adaptation in Legal and Financial Domain", "authors": [ "Yidan Sun", "Mengying Zhu", "Feiyue Chen", "Yangyang Wu", "Xiaolei Dan", "Mengyuan Yang", "Xiaolin Zheng", "Shenglin Ben" ], "posted": "2025-11-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.09854v2", "field": "finance", "role": "method", "bullets": [ "Financial terminology dataset constructed from official regulatory documents; evaluation covers finance and legal domain term discrimination tasks.", "TermGPT applied multi-level contrastive fine-tuning at sentence and token levels using sentence graphs to improve LLM embedding discrimination of domain terms.", "TermGPT outperformed existing baselines in term discrimination within finance and legal domains, addressing the isotropy problem in LLM embedding spaces." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "financial terminology discrimination benchmark from regulatory documents", "salience": 48, "n": 2608, "authors_detailed": [ { "name": "Yidan Sun", "url": "https://openalex.org/A5100629959", "inst": "Imperial Valley College" }, { "name": "Zhu, Mengying", "url": "", "inst": "" }, { "name": "Feiyue Chen", "url": "https://openalex.org/A5027235822", "inst": "Southwest University" }, { "name": "Yangyang Wu", "url": "https://openalex.org/A5114734496", "inst": "Chinese PLA General Hospital" }, { "name": "Xiaolei Dan", "url": "https://openalex.org/A5120414093", "inst": "" }, { "name": "Mengyuan Yang", "url": "https://openalex.org/A5115595293", "inst": "Zhengzhou University" }, { "name": "Xiaolin Zheng", "url": "https://openalex.org/A5074603286", "inst": "Guangxi University" }, { "name": "S.W. van der Ben", "url": "https://openalex.org/A5103915823", "inst": "" } ], "affiliations": [ "Imperial Valley College", "Southwest University", "Zhengzhou University", "Guangxi University" ] }, { "uid": "doi:10.2139/ssrn.5599772", "doi": "10.2139/ssrn.5599772", "title": "From Reviews to Actionable Insights: An LLM-Based Approach for Attribute and Feature Extraction", "authors": [ "Khaled Boughanmi", "Kamel Jedidi", "Nour Jedidi" ], "posted": "2025-11-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5599772", "field": "management", "role": "instrument", "bullets": [ "20,000 Yelp reviews of Starbucks stores; eight prompt variants evaluated on a random subset with human annotations as ground truth.", "LLM extracted product attributes, features, and associated sentiments; validated via agreement with human coders and predictive validity for customer star ratings.", "LLM matches human coding quality at two seconds per review versus six minutes; improving key service-feature sentiment could yield 1-2% average revenue gains per store." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Agreement with human annotations on Yelp reviews plus predictive validity for ratings", "salience": 55, "n": 2773, "authors_detailed": [ { "name": "Khaled Boughanmi", "url": "https://openalex.org/A5041642220", "inst": "Cornell University" }, { "name": "Kamel Jedidi", "url": "https://openalex.org/A5048212752", "inst": "Columbia University" }, { "name": "Nour Jedidi", "url": "https://openalex.org/A5114621405", "inst": "University of Waterloo" } ], "affiliations": [ "Cornell University", "Columbia University", "University of Waterloo" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2511.08082v1", "arxiv_id": "2511.08082v1", "title": "Prudential Reliability of Large Language Models in Reinsurance: Governance, Assurance, and Capital Efficiency", "authors": [ "Stella C. Dong" ], "posted": "2025-11-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.08082v1", "field": "finance", "role": "method", "bullets": [ "Six reinsurance task families evaluated under Solvency II, SR 11-7, and guidance from EIOPA, NAIC, and IAIS regulatory frameworks.", "Retrieval-grounded LLM configurations assessed via five-pillar RAIRAB benchmark measuring grounding accuracy, transparency, and accountability across governance lifecycle controls.", "Retrieval-grounded configurations achieved 0.90 grounding accuracy, reduced hallucination and interpretive drift by roughly 40%, and nearly doubled transparency scores." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "RAIRAB benchmark grounding accuracy and hallucination metrics", "salience": 58, "n": 2497, "authors_detailed": [ { "name": "Stella C. Dong", "url": "https://openalex.org/A5119989725", "inst": "" } ] }, { "uid": "arxiv:2511.08500v1", "arxiv_id": "2511.08500v1", "title": "SPEAR-MM: Selective Parameter Evaluation and Restoration via Model Merging for Efficient Financial LLM Adaptation", "authors": [ "Berkcan Kapusuzoglu", "Supriyo Chakraborty", "Renkun Ni", "Stephen Rawls", "Sambit Sahu" ], "posted": "2025-11-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.08500v1", "field": "finance", "role": "method", "bullets": [ "LLaMA-3.1-8B adapted to financial tasks with evaluation on both general reasoning and domain-specific benchmarks for resource-constrained institutions.", "SPEAR-MM selectively froze or restored transformer layers via spherical interpolation merging based on post-hoc layer-wise impact analysis on external benchmarks.", "Achieved 91.2% retention of general capabilities versus 69.7% for standard continual pretraining while maintaining 94% of domain adaptation gains at 90% lower computational cost." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "general and domain-specific benchmark retention metrics", "salience": 55, "n": 2498, "authors_detailed": [ { "name": "Berkcan Kapusuzoglu", "url": "https://openalex.org/A5120546898", "inst": "Capital One (United States)" }, { "name": "Supriyo Chakraborty", "url": "https://openalex.org/A5120055815", "inst": "" }, { "name": "Ruiqing Ni", "url": "https://openalex.org/A5051431217", "inst": "University of Bern" }, { "name": "Stephen Rawls", "url": "https://openalex.org/A5046697900", "inst": "University of Southern California" }, { "name": "Sambit Sahu", "url": "https://openalex.org/A5120596681", "inst": "Capital One (United States)" } ], "affiliations": [ "University of Southern California", "Capital One (United States)", "University of Bern" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2511.07803v1", "arxiv_id": "2511.07803v1", "title": "Judging by the Rules: Compliance-Aligned Framework for Modern Slavery Statement Monitoring", "authors": [ "Wenhao Xu", "Akshatha Arodi", "Jian-Yun Nie", "Arsene Fansi Tchango" ], "posted": "2025-11-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.07803v1", "field": "management", "role": "instrument", "bullets": [ "Corporate modern slavery disclosure statements evaluated against Modern Slavery Act statutory requirements for compliance monitoring at scale.", "Compliance Alignment Judge evaluated LLM justifications against statutory rules; trained Compliance Alignment LLM for rule-consistent, human-verifiable compliance classification.", "CALLM improved predictive performance over baselines and produced transparent, legally grounded outputs enabling scalable rule-level compliance verification." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "compliance classification accuracy against statutory rules", "salience": 50, "n": 2499, "authors_detailed": [ { "name": "Wu Xu", "url": "https://openalex.org/A5087127338", "inst": "Shanghai Jiao Tong University" }, { "name": "Akshatha Arodi", "url": "https://openalex.org/A5119204546", "inst": "" }, { "name": "Jian-Yun Nie", "url": "https://openalex.org/A5120446614", "inst": "" }, { "name": "Arsene Fansi Tchango", "url": "https://openalex.org/A5120648612", "inst": "Mila - Quebec Artificial Intelligence Institute" } ], "affiliations": [ "Shanghai Jiao Tong University", "Mila - Quebec Artificial Intelligence Institute" ] }, { "uid": "arxiv:2511.08721v1", "arxiv_id": "2511.08721v1", "title": "Benevolent Dictators? On LLM Agent Behavior in Dictator Games", "authors": [ "Andreas Einwiller", "Kanishka Ghosh Dastidar", "Artur Romazanov", "Annette Hautli-Janisz", "Michael Granitzer", "Florian Lemmerich" ], "posted": "2025-11-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.08721v1", "field": "economics", "role": "agent", "bullets": [ "Multiple LLMs tested in dictator game experiments with varied system prompts, neutral prompt variations, and open-ended linguistic response analysis.", "LLM agents played dictator games under different system prompts; LLM-ABS framework assessed prompt sensitivity and behavioral preferences using neutral baselines.", "Agents exhibited strong fairness preference; system prompts significantly influenced behavior; models expressed responses differently but prompt sensitivity persisted across all." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 55, "validated": null, "n": 2607, "authors_detailed": [ { "name": "Andreas Einwiller", "url": "https://openalex.org/A5120401270", "inst": "University of Passau" }, { "name": "Kanishka Ghosh Dastidar", "url": "https://openalex.org/A5029627550", "inst": "University of Passau" }, { "name": "Artur Romazanov", "url": "https://openalex.org/A5120401271", "inst": "University of Passau" }, { "name": "Annette Hautli-Janisz", "url": "https://openalex.org/A5120352484", "inst": "" }, { "name": "Michael Granitzer", "url": "https://openalex.org/A5006866152", "inst": "Austrian Research Institute for Artificial Intelligence" }, { "name": "Florian Lemmerich", "url": "https://openalex.org/A5076202690", "inst": "University of Passau" } ], "affiliations": [ "University of Passau", "Austrian Research Institute for Artificial Intelligence" ] }, { "uid": "doi:10.2139/ssrn.5729790", "doi": "10.2139/ssrn.5729790", "title": "The Coasean Singularity? Demand, Supply, and Market Design with AI Agents", "authors": [ "Peyman Shahidi", "Gili Rusak", "Benjamin Manning", "Andrey Fradkin", "John J. Horton" ], "posted": "2025-11-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5729790", "field": "economics", "role": "object", "bullets": [ "Conceptual NBER framework analyzing AI agents as autonomous market participants in digital markets, grounded in transaction cost economics and market design theory.", "Evaluates demand-side adoption trade-offs, supply-side agent monetization, and market-level effects of AI-mediated transactions on search, negotiation, and contracting costs.", "AI agents create efficiency gains through lower transaction costs but introduce frictions including congestion, price obfuscation, and novel regulatory challenges for market design." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3966, "authors_detailed": [ { "name": "Peyman Shahidi", "url": "", "inst": "Massachusetts Institute of Technology" }, { "name": "Gili Rusak", "url": "", "inst": "Harvard University Press" }, { "name": "Benjamin Manning", "url": "", "inst": "Massachusetts Institute of Technology" }, { "name": "Andrey Fradkin", "url": "", "inst": "Boston University" }, { "name": "John J. Horton", "url": "", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Massachusetts Institute of Technology", "Harvard University", "Boston University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2511.07585v1", "arxiv_id": "2511.07585v1", "title": "LLM Output Drift: Cross-Provider Validation & Mitigation for Financial Workflows", "authors": [ "Raffi Khatchadourian", "Rolando Franco" ], "posted": "2025-11-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.07585v1", "field": "finance", "role": "method", "bullets": [ "Three regulated financial tasks spanning retrieval augmented QA over SEC 10-K filings, JSON, and SQL generation, with 480 runs of 16 per condition across five models from 7B to 120B parameters.", "Qwen2.5 7B, Granite 3 8B, Llama 3.3 70B, Mistral Medium, and GPT-OSS 120B run in a deterministic harness with greedy decoding, fixed seeds, ordered retrieval, and finance calibrated invariant checks.", "Smaller models reach 100 percent output consistency at temperature zero while GPT-OSS 120B manages 12.5 percent; SQL stays stable, retrieval tasks drift 25 to 75 percent, and the framework maps to FSB, BIS, and CFTC requirements." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "salience": 58, "edition": 14, "validated": null, "n": 1815, "authors_detailed": [ { "name": "Raffi Khatchadourian", "url": "https://openalex.org/A5120547069", "inst": "" }, { "name": "R. Escobar Franco", "url": "https://openalex.org/A5088317684", "inst": "University of Illinois Chicago" } ], "affiliations": [ "University of Illinois Chicago" ] }, { "uid": "arxiv:2511.07110v3", "arxiv_id": "2511.07110v3", "title": "Two Heads are Better than One: Distilling Large Language Model Features Into Small Models with Feature Decomposition and Mixture", "authors": [ "Tianhao Fu", "Xinxin Xu", "Weichen Xu", "Jue Chen", "Ruilong Ren", "Bowen Deng", "Xinyu Zhao", "Jian Cao", "Xixin Cao" ], "posted": "2025-11-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.07110v3", "field": "finance", "role": "method", "bullets": [ "Four real-world market datasets used for algorithmic market making; LLM feature distillation into smaller student models via reinforcement learning.", "LLM features decomposed across layer, task, and data dimensions; student models learn distinct features integrated via Hajek mixture-of-experts architecture.", "CMM framework outperforms existing distillation methods and RL-based market-making strategies across all four market datasets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Four real-world market datasets vs RL and distillation baselines", "salience": 50, "n": 2772, "authors_detailed": [ { "name": "Fu, Tianhao", "url": "", "inst": "" }, { "name": "Xu, Xinxin", "url": "", "inst": "" }, { "name": "Xu, Weichen", "url": "", "inst": "" }, { "name": "Chen, Jue", "url": "", "inst": "" }, { "name": "Ren, Ruilong", "url": "", "inst": "" }, { "name": "Deng, Bowen", "url": "", "inst": "" }, { "name": "Zhao, Xinyu", "url": "", "inst": "" }, { "name": "Cao, Jian", "url": "", "inst": "" }, { "name": "Cao, Xixin", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.07322v2", "arxiv_id": "2511.07322v2", "title": "FinRpt: Dataset, Evaluation System and LLM-based Multi-agent Framework for Equity Research Report Generation", "authors": [ "Song Jin", "Shuqi Li", "Shukun Zhang", "Rui Yan" ], "posted": "2025-11-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.07322v2", "field": "finance", "role": "instrument", "bullets": [ "Equity research report dataset integrating seven financial data types with an automated construction pipeline for model training and evaluation.", "FinRpt-Gen multi-agent framework with LLM agents fine-tuned via supervised learning and reinforcement learning generates full equity research reports.", "Framework demonstrated strong performance across 11 evaluation metrics with publicly available code and datasets for reproducibility." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "11-metric evaluation system for equity research reports", "salience": 55, "n": 2863, "authors_detailed": [ { "name": "Jin, Song", "url": "", "inst": "" }, { "name": "Li, Shuqi", "url": "", "inst": "" }, { "name": "Zhang, Shukun", "url": "", "inst": "" }, { "name": "Yan, Rui", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5720342", "doi": "10.2139/ssrn.5720342", "title": "The Agentic Investor: AI for Real Estate Investment Management", "authors": [ "Luke Graham" ], "posted": "2025-11-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5720342", "field": "finance", "role": "object", "bullets": [ "Executive interviews, NLP analysis of 1,428 AI proptech company descriptions, and anonymized portfolio data from the real estate investment management sector.", "Examines AI adoption across the real estate investment lifecycle from pre-deal sourcing and due diligence through post-deal asset management and capital raising.", "AI applications reduce pre-deal workloads by up to 90%, cut LP response times from two weeks to two hours, and could raise profitability ratios from 55.2% to 71.5%." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3965, "authors_detailed": [ { "name": "Luke Graham", "url": "https://openalex.org/A5107457192", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford" ], "prestige": true }, { "uid": "arxiv:2511.06292v2", "arxiv_id": "2511.06292v2", "title": "Synthetic Data-Driven Prompt Tuning for Financial QA over Tables and Documents", "authors": [ "Yaoning Yu", "Kai-Min Chang", "Ye Yu", "Kai Wei", "Haojing Luo", "Haohan Wang" ], "posted": "2025-11-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.06292v2", "field": "finance", "role": "method", "bullets": [ "Financial documents including earnings reports and balance sheets; evaluated on the DocMath-Eval benchmark for numerical reasoning over tables and text.", "Self-improving prompt framework generates synthetic financial tables, verifies correctness and robustness, and iteratively refines prompts without external labels.", "System achieves higher accuracy and robustness than standard prompt methods on financial reasoning tasks in the DocMath-Eval benchmark." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "DocMath-Eval benchmark accuracy and robustness", "salience": 40, "n": 2771, "authors_detailed": [ { "name": "Ye Yu", "url": "https://openalex.org/A5065086396", "inst": "Changchun University of Science and Technology" }, { "name": "Chang, Kai-Min", "url": "", "inst": "" }, { "name": "Yu, Ye", "url": "", "inst": "" }, { "name": "Wei Ke", "url": "https://openalex.org/A5100773944", "inst": "Chinese Academy of Sciences" }, { "name": "Hao Luo", "url": "https://openalex.org/A5087736924", "inst": "Liaoning University" }, { "name": "Haohan Wang", "url": "https://openalex.org/A5101892837", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "Changchun University of Science and Technology", "Chinese Academy of Sciences", "Liaoning University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2511.06448v2", "arxiv_id": "2511.06448v2", "title": "When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms", "authors": [ "Qibing Ren", "Zhijie Zheng", "Jiaxuan Guo", "Junchi Yan", "Lizhuang Ma", "Jing Shao" ], "posted": "2025-11-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.06448v2", "field": "finance", "role": "object", "bullets": [ "MultiAgentFraudBench benchmark with 28 online fraud scenarios spanning the full fraud lifecycle across public and private interaction domains.", "LLM agents simulated collaborative financial fraud with analysis of interaction depth, activity level, and fine-grained collaboration failure modes.", "Malicious agents adapted to environmental interventions; content warnings and LLM monitors reduced but did not fully eliminate fraud success." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 68, "n": 2862, "authors_detailed": [ { "name": "Ren, Qibing", "url": "", "inst": "" }, { "name": "Zheng, Zhijie", "url": "", "inst": "" }, { "name": "Guo, Jiaxuan", "url": "", "inst": "" }, { "name": "Yan, Junchi", "url": "", "inst": "" }, { "name": "Ma, Lizhuang", "url": "", "inst": "" }, { "name": "Shao, Jing", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.06545v2", "arxiv_id": "2511.06545v2", "title": "Vibecoding and Digital Entrepreneurship", "authors": [ "Ruiqing Cao", "Abhishek Bhatia" ], "posted": "2025-11-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.06545v2", "field": "management", "role": "object", "bullets": [ "Digital ventures on a launch platform, difference-in-differences around diffusion of GenAI coding tools, varying exposure by product characteristics.", "GenAI coding automation (vibecoding) studied as treatment; exposure measured by whether product development is partially or fully automatable.", "Viable entry rises 11% only where vibecoding augments rather than replaces development, driven by STEM-educated founders outside middle management." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3549, "authors_detailed": [ { "name": "Cao, Ruiqing", "url": "", "inst": "" }, { "name": "Bhatia, Abhishek", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5713646", "doi": "10.2139/ssrn.5713646", "title": "AI Agents and Higher-Order Work", "authors": [ "Suproteem Sarkar" ], "posted": "2025-11-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5713646", "field": "economics", "role": "object", "bullets": [ "Usage data from the coding platform Cursor covering software developers, exploiting the timing of the platform's agent feature releases; sample period and size not stated.", "Studies the AI coding agents themselves rather than using a model for measurement; the specific model family behind the agents is not stated, and identification uses the feature release timeline.", "After agents arrived workers delegated more and produced less output manually; agents raised software output most for verifiable tasks and experienced workers, suggesting returns to expertise rise." ], "bullet_provenance": "ai", "salience": 68, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 685, "authors_detailed": [ { "name": "Suproteem Sarkar", "url": "", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5664035", "doi": "10.2139/ssrn.5664035", "title": "ChainSignalMind: A New Benchmark Comparing LLMs to SLMs in Crypto Analysis", "authors": [ "Ritvik Kulkarni", "Hriday Narang" ], "posted": "2025-11-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5664035", "field": "finance", "role": "method", "bullets": [ "Crypto-related datasets and simulated trading environments used to benchmark sentiment extraction, market prediction, and anomaly detection tasks in cryptocurrency markets.", "ChatGPT-4o and Meta's Llama 3.1 were compared on accuracy, latency, and interpretability across three cryptocurrency analysis tasks in the benchmark framework.", "Large models showed superior contextual reasoning for complex narratives while small models offered faster inference suited to real-time trading and edge deployment." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "validated": true, "validation_note": "Accuracy, latency, and interpretability benchmarks on crypto analysis tasks", "salience": 40, "n": 2397, "authors_detailed": [ { "name": "Ritvik Kulkarni", "url": "", "inst": "Lexington City Schools" }, { "name": "Hriday Narang", "url": "https://openalex.org/A5120298715", "inst": "Lexington City Schools" } ], "affiliations": [ "Lexington City Schools" ] }, { "uid": "arxiv:2511.08621v1", "arxiv_id": "2511.08621v1", "title": "The LLM Pro Finance Suite: Multilingual Large Language Models for Financial Applications", "authors": [ "Gaëtan Caillaut", "Raheel Qader", "Jingshu Liu", "Mariam Nakhlé", "Arezki Sadoune", "Massinissa Ahmim", "Jean-Gabriel Barthelemy" ], "posted": "2025-11-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.08621v1", "field": "finance", "role": "method", "bullets": [ "Five instruction-tuned LLMs from 8B to 70B parameters fine-tuned on curated corpus with over 50% financial data in English, French, and German.", "Models evaluated on comprehensive financial benchmark suite for finance-oriented tasks and financial translation against state-of-the-art baselines.", "Consistent improvement over baselines on finance tasks while maintaining general-domain capabilities; two 8B models publicly released on HuggingFace." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "financial benchmark suite vs SOTA baselines", "salience": 55, "n": 2494, "authors_detailed": [ { "name": "Gaëtan Caillaut", "url": "https://openalex.org/A5093817145", "inst": "Lingua et Machina (France)" }, { "name": "Raheel Qader", "url": "https://openalex.org/A5030717896", "inst": "Wave Dragon (Denmark)" }, { "name": "Jingshu Liu", "url": "https://openalex.org/A5007543305", "inst": "PLA 306 Hospital" }, { "name": "Mariam Nakhlé", "url": "https://openalex.org/A5093817144", "inst": "Institut polytechnique de Grenoble" }, { "name": "Arezki Sadoune", "url": "https://openalex.org/A5120498690", "inst": "" }, { "name": "Massinissa Ahmim", "url": "https://openalex.org/A5120498691", "inst": "" }, { "name": "Jean-Gabriel Barthélemy", "url": "https://openalex.org/A5042110285", "inst": "Centre National de la Recherche Scientifique" } ], "affiliations": [ "Lingua et Machina (France)", "Wave Dragon (Denmark)", "Institut polytechnique de Grenoble", "Centre National de la Recherche Scientifique" ] }, { "uid": "arxiv:2511.05766v1", "arxiv_id": "2511.05766v1", "title": "Anchors in the Machine: Behavioral and Attributional Evidence of Anchoring Bias in LLMs", "authors": [ "Felipe Valencia-Clavijo" ], "posted": "2025-11-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.05766v1", "field": "economics", "role": "object", "bullets": [ "Six open-source LLMs including Gemma-2B, Phi-2, Llama-2-7B, GPT-2, Falcon-RW-1B, and GPT-Neo-125M tested for anchoring bias across prompt designs.", "Log-probability behavioral analysis and exact Shapley-value attribution quantified anchor influence on output distributions with training-data contamination controls.", "Robust anchoring effects found in larger models; smaller models showed variability; attributional effects varied across prompts, underscoring fragility of LLMs as human judgment substitutes." ], "bullet_provenance": "ai", "models": [ "llama", "gemini", "open_other" ], "open_weights": true, "validated": true, "validation_note": "log-probability analysis with training-data contamination controls", "salience": 62, "n": 2495, "authors_detailed": [ { "name": "Valencia-Clavijo, Felipe", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.05000v1", "arxiv_id": "2511.05000v1", "title": "Query Generation Pipeline with Enhanced Answerability Assessment for Financial Information Retrieval", "authors": [ "Hyunkyu Kim", "Yeeun Yoo", "Youngjun Kwak" ], "posted": "2025-11-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.05000v1", "field": "finance", "role": "method", "bullets": [ "204 official Korean banking documents used to construct KoBankIR benchmark comprising 815 single and multi-document queries for financial information retrieval.", "LLM-based pipeline generated queries with reasoning-augmented answerability assessment; evaluated existing retrieval models on the resulting benchmark.", "Enhanced answerability method achieved stronger alignment with human judgments than prior approaches; existing retrieval models struggled with complex multi-document banking queries." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "alignment with human judgments on answerability assessment", "salience": 45, "n": 2496, "authors_detailed": [ { "name": "Kim, Hyunkyu", "url": "", "inst": "" }, { "name": "Yoo, Yeeun", "url": "", "inst": "" }, { "name": "Kwak, Youngjun", "url": "", "inst": "" } ] }, { "uid": "arxiv:2511.08616v2", "arxiv_id": "2511.08616v2", "title": "Reasoning on Time-Series for Financial Technical Analysis", "authors": [ "Kelvin J. L. Koa", "Jan Chen", "Yunshan Ma", "Huanhuan Zheng", "Tat-Seng Chua" ], "posted": "2025-11-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.08616v2", "field": "finance", "role": "method", "bullets": [ "Stock datasets across U.S., Chinese, and European markets with historical price data converted to textual annotations.", "Verbal Technical Analysis framework combines verbal and latent LLM reasoning optimized via inverse MSE reward for time-series forecasting.", "Achieves state-of-the-art forecasting accuracy across three markets; reasoning traces validated positively by industry expert evaluations." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "U.S., Chinese, and European stock datasets with MSE and expert evaluation", "salience": 50, "n": 3067, "authors_detailed": [ { "name": "Kelvin J. L. Koa", "url": "https://openalex.org/A5092741731", "inst": "National University of Singapore" }, { "name": "J. Chen", "url": "https://openalex.org/A5041572895", "inst": "" }, { "name": "Yunshan Ma", "url": "https://openalex.org/A5089377262", "inst": "Singapore Management University" }, { "name": "Huanhuan Zheng", "url": "https://openalex.org/A5051379497", "inst": "National University of Singapore" }, { "name": "Tat-Seng Chua", "url": "https://openalex.org/A5087041599", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore", "Singapore Management University" ] }, { "uid": "arxiv:2511.04299v3", "arxiv_id": "2511.04299v3", "title": "Measuring economic outlook in the news", "authors": [ "Elliot Beck", "Franziska Eckert", "Linus Kühne", "Helge Liebert", "Rina Rosenblatt-Wisch" ], "posted": "2025-11-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.04299v3", "field": "economics", "role": "instrument", "bullets": [ "Twenty-seven million news articles used to construct an economic sentiment indicator with document embeddings and LLM-generated synthetic training data.", "LLMs generate synthetic labeled data for local sentiment classification; embeddings measure economic outlook without transmitting proprietary text to external services.", "Indicator outperforms survey benchmarks and dictionary methods for GDP growth forecasting, capturing sentiment shifts weeks before official data releases." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "GDP growth forecast accuracy vs survey-based benchmarks and dictionary methods", "salience": 70, "n": 3548, "authors_detailed": [ { "name": "Beck, Elliot", "url": "", "inst": "" }, { "name": "Franziska Eckert", "url": "https://openalex.org/A5120305244", "inst": "" }, { "name": "Linus Kühne", "url": "https://openalex.org/A5120305245", "inst": "" }, { "name": "Helge Liebert", "url": "https://openalex.org/A5041415938", "inst": "Swiss National Bank" }, { "name": "Rina Rosenblatt-Wisch", "url": "https://openalex.org/A5120305247", "inst": "" } ], "affiliations": [ "Swiss National Bank" ] }, { "uid": "arxiv:2511.08608v1", "arxiv_id": "2511.08608v1", "title": "When Reasoning Fails: Evaluating 'Thinking' LLMs for Stock Prediction", "authors": [ "Rakeshkumar H Sodha" ], "posted": "2025-11-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.08608v1", "field": "finance", "role": "method", "bullets": [ "NIFTY constituent Indian equities evaluated via rolling 48-month/1-month walk-forward design at one-day horizon with universe sizes from 5 to 36 stocks.", "GPT-4o-mini (direct) and GPT-5 (thinking) ranked stocks cross-sectionally under fixed 512-token reasoning budget; compared against ridge and random forest on IC, MSE, and long/short backtests.", "Thinking LLM ranking quality degraded as universe size grew; direct LLM and classical baselines remained stable; no net portfolio advantage for thinking LLM after transaction costs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "IC, MSE, Diebold-Mariano, Pesaran-Timmermann, SPA tests vs classical baselines", "salience": 72, "n": 2493, "authors_detailed": [ { "name": "Rakeshkumar H Sodha", "url": "https://openalex.org/A5120245803", "inst": "Oyster & Pearl Hospital" } ], "affiliations": [ "Oyster & Pearl Hospital" ] }, { "uid": "arxiv:2511.03628v1", "arxiv_id": "2511.03628v1", "title": "LiveTradeBench: Seeking Real-World Alpha with Large Language Models", "authors": [ "Haofei Yu", "Fenghai Li", "Jiaxuan You" ], "posted": "2025-11-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.03628v1", "field": "finance", "role": "method", "bullets": [ "21 LLMs evaluated over 50-day live trading periods across U.S. stocks and Polymarket prediction markets with real-time streaming price and news data.", "LiveTradeBench streamed live market data to LLM agents making portfolio allocation decisions, eliminating offline backtesting dependence and preventing information leakage.", "High LMArena scores did not predict trading performance; models displayed distinct portfolio styles and some effectively adapted decisions to live market signals." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "llama", "open_other" ], "validated": false, "salience": 68, "n": 2517, "authors_detailed": [ { "name": "H. Yu", "url": "https://openalex.org/A5107952905", "inst": "Chungbuk National University" }, { "name": "Fenghai Li", "url": "https://openalex.org/A5067039224", "inst": "" }, { "name": "You, Jiaxuan", "url": "", "inst": "" } ], "affiliations": [ "Chungbuk National University" ] }, { "uid": "arxiv:2511.03915v1", "arxiv_id": "2511.03915v1", "title": "The Human Flourishing Geographic Index: A County-Level Dataset for the United States, 2013--2023", "authors": [ "Stefano M. Iacus", "Devika Jain", "Andrea Nasuto", "Giuseppe Porro", "Marcello Carammia", "Andrea Vezzulli" ], "posted": "2025-11-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.03915v1", "field": "economics", "role": "instrument", "bullets": [ "2.6 billion geolocated U.S. tweets from 2013-2023, yielding monthly county-level indicators across 48 flourishing dimensions.", "Fine-tuned LLMs classify tweet expressions into flourishing categories aligned with Harvard's Global Flourishing Study framework.", "County-level indices validated against established well-being indicators; dataset enables unprecedented spatial-temporal resolution of societal well-being." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "correlation with established well-being indicators", "salience": 45, "n": 3066, "authors_detailed": [ { "name": "Stefano M. Iacus", "url": "https://openalex.org/A5034420722", "inst": "Harvard University" }, { "name": "Devika Jain", "url": "https://openalex.org/A5046755958", "inst": "Harvard University" }, { "name": "Andrea Nasuto", "url": "https://openalex.org/A5054000702", "inst": "University of Liverpool" }, { "name": "Giuseppe Porro", "url": "https://openalex.org/A5084017033", "inst": "University of Insubria" }, { "name": "Marcello Carammia", "url": "https://openalex.org/A5079553683", "inst": "University of Salento" }, { "name": "Andrea Vezzulli", "url": "https://openalex.org/A5062786241", "inst": "University of Insubria" } ], "affiliations": [ "Harvard University", "University of Liverpool", "University of Insubria", "University of Salento" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5702344", "doi": "10.2139/ssrn.5702344", "title": "Beyond Fog: Measuring Managerial Obfuscation Using LLM", "authors": [ "Richard Wang" ], "posted": "2025-11-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5702344", "field": "accounting", "role": "instrument", "bullets": [ "More than 16,000 Management Discussion and Analysis sections from US 10-K filings; unit of observation is the filing; sample period not stated.", "An LLM (not named) scores four obfuscation dimensions, vagueness, hedging, positive spin, and inconsistency; checked for reproducibility and consistency with trained human judgments, with no agreement figure reported.", "Measures are distinct from readability and tone indices, predict earnings management in expected directions, and differentially predict analyst forecast dispersion and accuracy." ], "bullet_provenance": "ai", "validated": false, "validation_note": "compared to human judgments, no agreement figure stated", "salience": 58, "edition": 3, "audience": "general", "models": [], "n": 129, "authors_detailed": [ { "name": "Richard Wang", "url": "https://openalex.org/A5120249165", "inst": "St. John Fisher College" } ], "affiliations": [ "St. John Fisher College" ] }, { "uid": "arxiv:2511.08604v1", "arxiv_id": "2511.08604v1", "title": "Generative Agents and Expectations: Do LLMs Align with Heterogeneous Agent Models?", "authors": [ "Filippo Gusella", "Eugenio Vicario" ], "posted": "2025-11-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.08604v1", "field": "finance", "role": "agent", "bullets": [ "S&P 500 index data from 1990 to 2020, with strategy adoption probabilities compared against heterogeneous agent model literature estimates.", "An LLM-based generative agent determined probabilities of adopting fundamentalist or trend-follower investment strategies based on current market information.", "AI-generated strategy adoption probabilities aligned with HAM literature estimates on real data, but artificial market tests revealed systematic asymmetry toward fundamentalist behavior." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "Compared with HAM literature proportions for S&P 500 1990-2020", "salience": 70, "n": 2605, "authors_detailed": [ { "name": "Filippo Gusella", "url": "https://openalex.org/A5120353271", "inst": "" }, { "name": "Eugenio Vicario", "url": "https://openalex.org/A5120449495", "inst": "" } ] }, { "uid": "arxiv:2511.02458v1", "arxiv_id": "2511.02458v1", "title": "Prompting for Policy: Forecasting Macroeconomic Scenarios with Synthetic LLM Personas", "authors": [ "Giulia Iadisernia", "Carolina Camassa" ], "posted": "2025-11-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.02458v1", "field": "economics", "role": "agent", "bullets": [ "2,368 economics personas prompting GPT-4o across 50 quarterly ECB Survey of Professional Forecasters rounds covering 2013-2025, four macro variables and four horizons.", "GPT-4o generated point forecasts for HICP, core HICP, GDP growth, and unemployment; ablation tested persona prompting against no-persona baselines across 100 runs.", "GPT-4o matched human forecaster accuracy with statistically significant but practically modest differences; persona descriptions provided no measurable forecasting advantage." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "ECB Survey of Professional Forecasters", "salience": 72, "n": 2606, "authors_detailed": [ { "name": "Iadisernia, Giulia", "url": "", "inst": "" }, { "name": "Camassa, Carolina", "url": "", "inst": "" } ] }, { "uid": "doi:10.1007/978-3-032-13562-9_38", "doi": "10.1007/978-3-032-13562-9_38", "arxiv_id": "2511.02469v1", "title": "Modeling Hawkish-Dovish Latent Beliefs in Multi-Agent Debate-Based LLMs for Monetary Policy Decision Classification", "authors": [ "Kaito Takano", "Masanori Hirano", "Kei Nakagawa" ], "posted": "2025-11-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.02469v1", "field": "finance", "role": "agent", "bullets": [ "FOMC policy texts and macroeconomic indicators used to predict federal funds rate decisions; multi-agent debate framework with hawkish-dovish belief modeling.", "Multiple LLMs modeled as agents with distinct initial beliefs; agents revise predictions through iterative rounds simulating deliberation and consensus formation.", "Debate-based multi-agent approach significantly outperforms standard LLM baselines in FOMC decision prediction accuracy with improved interpretability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "FOMC rate-change prediction accuracy vs LLM baselines", "salience": 55, "n": 2770, "authors_detailed": [ { "name": "Kaito Takano", "url": "", "inst": "Osaka Metropolitan University" }, { "name": "Masanori Hirano", "url": "", "inst": "Preferred Networks (Japan)" }, { "name": "Kei Nakagawa", "url": "", "inst": "Osaka Metropolitan University" } ], "affiliations": [ "Osaka Metropolitan University", "Preferred Networks (Japan)" ] }, { "uid": "arxiv:2511.02451v1", "arxiv_id": "2511.02451v1", "title": "Merging Continual Pretraining Models for Domain-Specialized LLMs: A Case Study in Finance", "authors": [ "Kentaro Ueda", "François Portet", "Hirohiko Suwa", "Keiichi Yasumoto" ], "posted": "2025-11-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.02451v1", "field": "finance", "role": "method", "bullets": [ "Financial LLMs constructed by merging continual pre-training expert models specialized in finance, mathematics, and Japanese language processing capabilities.", "Three merging methods (Task Arithmetic, TIES, DARE-TIES) evaluated on 18 tasks across 8 established financial benchmark datasets for knowledge recovery and complementarity.", "Merging recovers general knowledge lost during continual pre-training and can produce emergent cross-domain skills; 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benchmark for domain-specific summarization comparable to CNN/DailyMail in English.", "mT5, AraT5, and domain-adapted FinAraT5 evaluated on abstractive summarization of financial texts; financial-domain pretraining compared to general models.", "Domain-adapted models generate more coherent summaries with superior handling of quantitative and entity-centric information in Arabic financial reporting." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Summarization quality evaluated against reference headlines on AraFinNews dataset", "salience": 35, "n": 2769, "authors_detailed": [ { "name": "Mo El-Haj", "url": "https://openalex.org/A5093759946", "inst": "VinUniversity" }, { "name": "Paul Rayson", "url": "https://openalex.org/A5058785189", "inst": "VinUniversity" } ], "affiliations": [ "VinUniversity" ] }, { "uid": "arxiv:2511.01211v3", "arxiv_id": "2511.01211v3", "title": "Novelty and Impact of Economics Papers", "authors": [ "Chaofeng Wu" ], "posted": "2025-11-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.01211v3", "field": "economics", "role": "instrument", "bullets": [ "Large corpus of full-text economics articles decomposed into spatial and temporal novelty dimensions.", "LLMs develop semantic isolation metrics quantifying each paper's intellectual position relative to the full-text literature.", "Temporal novelty predicts citation counts while spatial novelty predicts disruptive impact; four distinct impact archetypes identified." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 50, "n": 3064, "authors_detailed": [ { "name": "Chaofeng Wu", "url": "https://openalex.org/A5120301725", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5684489", "doi": "10.2139/ssrn.5684489", "title": "Interviews", "authors": [ "Elliott Ash", "Soumitra Shukla", "Jason Sockin" ], "posted": "2025-11-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5684489", "field": "economics", "role": "instrument", "bullets": [ "500,000 Glassdoor interview reports plus a small-scale randomized field experiment on interview difficulty effects.", "LLMs analyze interview narratives to identify signals about colleague ability and selectiveness of hiring processes.", "Easy interviews signal poor fit; acceptors after easy interviews are two-fifths SD less satisfied and 10% less likely to stay one year." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": false, "salience": 70, "n": 3065, "authors_detailed": [ { "name": "Elliott Ash", "url": "https://openalex.org/A5020377010", "inst": "ETH Zurich" }, { "name": "Soumitra Shukla", "url": "https://openalex.org/A5111342426", "inst": "Harvard Business School" }, { "name": "Jason Sockin", "url": "https://openalex.org/A5013015194", "inst": "IZA - Institute of Labor Economics" } ], "affiliations": [ "Harvard Business School", "ETH Zurich", "IZA - Institute of Labor Economics" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2511.01923v2", "arxiv_id": "2511.01923v2", "title": "The Efficiency Costs of Information Assurance in AI-Enabled Labor Markets: Evidence from LinkedIn's Policy Changes", "authors": [ "Lei Chen", "Chaoyue Gao", "Alvin Leung", "Gavin Wang" ], "posted": "2025-11-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.01923v2", "field": "economics", "role": "object", "bullets": [ "Hong Kong and Singapore labor markets around LinkedIn's sequential AI data-policy changes from August 2024 through November 2025.", "LinkedIn's AI matching system studied via difference-in-differences comparing labor outcomes across data restriction and restoration episodes.", "Data restriction raised turnover, shortened tenure, lengthened vacancy duration, cut match rates, and lowered wages; effects reversed upon data restoration." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3547, "authors_detailed": [ { "name": "Chen, Lei", "url": "", "inst": "" }, { "name": "Gao, Chaoyue", "url": "", "inst": "" }, { "name": "Leung, Alvin", "url": "", "inst": "" }, { "name": "Wang, Gavin", "url": "", "inst": "" } ] }, { "uid": "arxiv:2510.26228v1", "arxiv_id": "2510.26228v1", "title": "ChatGPT in Systematic Investing -- Enhancing Risk-Adjusted Returns with LLMs", "authors": [ "Nikolas Anic", "Andrea Barbon", "Ralf Seiz", "Carlo Zarattini" ], "posted": "2025-10-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.26228v1", "field": "finance", "role": "instrument", "bullets": [ "Daily returns for S&P 500 constituents joined to high frequency firm news, with queries issued when a stock is about to enter a momentum portfolio.", "ChatGPT, version not stated, judges whether recent news supports return continuation; scores steer selection and weights, with no validation against human coded news labels reported.", "The news conditioned strategy beats long only momentum on Sharpe and Sortino ratios, including a period after the model's pretraining cutoff, and survives transaction costs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 66, "edition": 14, "n": 1880 }, { "uid": "doi:10.2139/ssrn.5681210", "doi": "10.2139/ssrn.5681210", "title": "Monetary Policy Schocks: A New Hope Large Language Models and Central Bank Communication", "authors": [ "Ruben Fernandez-Fuertes" ], "posted": "2025-10-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5681210", "field": "economics", "role": "instrument", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 684, "authors_detailed": [ { "name": "Rubén Fernàndez-Fuertes", "url": "https://openalex.org/A5092731966", "inst": "Bocconi University" } ], "affiliations": [ "Bocconi University" ], "prestige": true }, { "uid": "arxiv:2510.26484v1", "arxiv_id": "2510.26484v1", "title": "Bayesian Network Fusion of Large Language Models for Sentiment Analysis", "authors": [ "Rasoul Amirzadeh", "Dhananjay Thiruvady", "Fatemeh Shiri" ], "posted": "2025-10-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.26484v1", "field": "finance", "role": "instrument", "bullets": [ "Three human-annotated financial corpora with distinct linguistic and contextual characteristics, using FinBERT, RoBERTa, and BERTweet as base sentiment classifiers.", "Bayesian Network LLM Fusion framework integrated sentiment predictions from three pretrained open-weight models as probabilistic nodes for late-fusion sentiment classification.", "BNLF achieved approximately six percent accuracy gain over individual baseline models, demonstrating robustness to dataset variability across diverse financial text corpora." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "Accuracy on three human-annotated financial corpora", "salience": 50, "n": 2604 }, { "uid": "arxiv:2510.26727v3", "arxiv_id": "2510.26727v3", "title": "Neither Consent nor Property: A Policy Lab for Data Law", "authors": [ "Haoyi Zhang", "Tianyi Zhu" ], "posted": "2025-10-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.26727v3", "field": "economics", "role": "agent", "bullets": [ "Agent-based model of the data market calibrated with multi-year fieldwork from 2022 to 2025 and LLM-based discrete choice experiments for preference elicitation.", "LLMs served as simulated subjects in discrete choice experiments to recover willingness-to-pay elasticities; three legal regimes compared in welfare simulations.", "Property-rule mechanisms like informed consent fail to maximize welfare; shifting liability to downstream buyers yields the highest social welfare outcomes." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2768 }, { "uid": "arxiv:2510.26217v1", "arxiv_id": "2510.26217v1", "title": "Hybrid LLM and Higher-Order Quantum Approximate Optimization for CSA Collateral Management", "authors": [ "Tao Jin", "Stuart Florescu", "Heyu", "Jin" ], "posted": "2025-10-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.26217v1", "field": "finance", "role": "instrument", "bullets": [ "ISDA Credit Support Annex collateral optimization with integer lots, haircuts, and issuer/currency/class caps on government bond datasets.", "Evidence-gated LLM extracts CSA terms to normalized JSON with span citations; quantum-inspired explorer optimizes multi-asset collateral allocation.", "Hybrid pipeline improves cost-movement-tail frontiers by 9.1% to 10.7% over a strong classical baseline across representative test harnesses." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "improvement over classical baseline on government bond datasets", "salience": 50, "n": 3546 }, { "uid": "doi:10.2139/ssrn.5677522", "doi": "10.2139/ssrn.5677522", "title": "Exploring Generative AI in Accounting: Information Production and Consumption", "authors": [ "Sean S. Cao", "Wilbur Chen", "Guang Ma", "Suraj Srinivasan" ], "posted": "2025-10-29", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5677522", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 40, "edition": 23, "bullets": [], "models": [], "validated": null, "n": 4195, "authors_detailed": [ { "name": "Sean Cao", "url": "https://openalex.org/A5055060179", "inst": "University of Maryland - Robert H. Smith School of Business" }, { "name": "Wilbur Chen", "url": "https://openalex.org/A5056418795", "inst": "Hong Kong University of Science and Technology" }, { "name": "Guang Ma", "url": "https://openalex.org/A5100914127", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Suraj Srinivasan", "url": "", "inst": "Dana-Farber/Harvard Cancer Center" } ], "affiliations": [ "University of Maryland - Robert H. Smith School of Business", "Hong Kong University of Science and Technology", "Rutgers, The State University of New Jersey" ] }, { "uid": "arxiv:2510.25701v1", "arxiv_id": "2510.25701v1", "title": "Interpreting LLMs as Credit Risk Classifiers: Do Their Feature Explanations Align with Classical ML?", "authors": [ "Saeed AlMarri", "Kristof Juhasz", "Mathieu Ravaut", "Gautier Marti", "Hamdan Al Ahbabi", "Ibrahim Elfadel" ], "posted": "2025-10-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.25701v1", "field": "finance", "role": "method", "bullets": [ "A real world loan default prediction task on structured tabular data; sample size, lender, and period are not stated in the abstract.", "Zero shot prompted LLMs, families not named, classify defaults against observed outcomes and a LightGBM baseline, with SHAP attributions and model self explanations compared.", "LLM feature rankings diverge notably from LightGBM and self explanations often contradict empirical SHAP attributions, cautioning against standalone LLM use for structured credit risk." ], "bullet_provenance": "ai", "validated": true, "validation_note": "observed loan defaults, LightGBM comparison", "salience": 50, "edition": 14, "models": [], "n": 1938 }, { "uid": "arxiv:2510.25432v1", "arxiv_id": "2510.25432v1", "title": "Depth and Autonomy: A Framework for Evaluating LLM Applications in Social Science Research", "authors": [ "Ali Sanaei", "Ali Rajabzadeh" ], "posted": "2025-10-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.25432v1", "field": "management", "role": "method", "bullets": [ "All published social science papers on Web of Science that use LLMs as a research tool, classified by interpretive depth and autonomy level.", "Two-dimensional framework classifies LLM applications in qualitative research by interpretive depth and autonomy, with design recommendations for each quadrant.", "Recommends decomposing tasks into manageable segments with low autonomy, increasing interpretive depth only where warranted and under researcher supervision." ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 2514 }, { "uid": "doi:10.2139/ssrn.5656465", "doi": "10.2139/ssrn.5656465", "title": "Bridging Language Barriers: The Impact of Large Language Models on Academic Writing", "authors": [ "Burak Dalaman", "Ali Furkan Kalay", "Nathan Kettlewell" ], "posted": "2025-10-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5656465", "field": "economics", "role": "object", "bullets": [ "Over one million arXiv abstracts and one million authors classified as native or nonnative English speakers by name etymology, before and after November 2022.", "Measured changes in lexical diversity and readability metrics in academic writing following ChatGPT release, comparing native and nonnative English speakers.", "Nonnative speakers showed greatest gains in writing sophistication; lexical diversity converged between native and nonnative groups after ChatGPT became available." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 2515, "authors_detailed": [ { "name": "Burak Dalaman", "url": "https://openalex.org/A5120172298", "inst": "University of London" }, { "name": "Ali Furkan Kalay", "url": "https://openalex.org/A5120172299", "inst": "Macquarie University" }, { "name": "Nathan Kettlewell", "url": "https://openalex.org/A5017758761", "inst": "The University of Sydney" } ], "affiliations": [ "University of London", "Macquarie University", "The University of Sydney" ] }, { "uid": "doi:10.2139/ssrn.5677544", "doi": "10.2139/ssrn.5677544", "title": "GenAI Models and the Hybrid Governance Trap", "authors": [ "Moran Ofir", "Ronit Levine Schnur" ], "posted": "2025-10-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5677544", "field": "management", "role": "object", "bullets": [ "Analytical comparison of for-profit, nonprofit, and hybrid organizational forms for generative AI firms, focusing on OpenAI's current hybrid structure.", "No model deployed; study examines how organizational form affects epistemic integrity, accountability, and fiduciary alignment in GenAI knowledge production.", "Hybrid models inherit limitations of both forms without securing strengths of either; legal innovation in organizational form is needed before path dependencies entrench." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 45, "validated": null, "n": 2767, "authors_detailed": [ { "name": "Moran Ofir", "url": "https://openalex.org/A5060295626", "inst": "Reichman University" }, { "name": "Ronit Levine Schnur", "url": "https://openalex.org/A5117171496", "inst": "Tel Aviv University" } ], "affiliations": [ "Reichman University", "Tel Aviv University" ] }, { "uid": "arxiv:2510.25091v1", "arxiv_id": "2510.25091v1", "title": "H3M-SSMoEs: Hypergraph-based Multimodal Learning with LLM Reasoning and Style-Structured Mixture of Experts", "authors": [ "Peilin Tan", "Liang Xie", "Churan Zhi", "Dian Tu", "Chuanqi Shi" ], "posted": "2025-10-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.25091v1", "field": "finance", "role": "method", "bullets": [ "Three major stock markets with multimodal data including market prices, news, and corporate fundamentals.", "Frozen LLM with lightweight adapters fuses quantitative and textual modalities within hypergraph-based architecture and mixture of experts.", "Surpasses state-of-the-art methods in predictive accuracy and investment returns while maintaining effective risk control across markets." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "three stock market datasets, predictive accuracy and portfolio returns", "salience": 45, "n": 3063 }, { "uid": "arxiv:2510.25743v4", "arxiv_id": "2510.25743v4", "title": "Agentic Economic Modeling", "authors": [ "Bohan Zhang", "Jiaxuan Li", "Ali Hortaçsu", "Xiaoyang Ye", "Victor Chernozhukov", "Angelo Ni", "Edward W Huang" ], "posted": "2025-10-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.25743v4", "field": "economics", "role": "agent", "bullets": [ "Large-scale conjoint study and regional field experiment using 10% of original human data to calibrate LLM-generated synthetic choices for econometric inference.", "LLMs generate task-conditioned synthetic choices; a bias-correction mapping aligns them with human responses for estimating demand elasticities and treatment effects.", "Corrected LLM choices lower demand-parameter estimation error; field experiment treatment effect of -65 plus/minus 10 bps closely matches full human result of -60 plus/minus 8 bps." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "conjoint study and field experiment vs. human ground-truth responses", "salience": 75, "n": 3232 }, { "uid": "doi:10.1016/j.respol.2026.105568", "doi": "10.1016/j.respol.2026.105568", "arxiv_id": "2511.00068v2", "title": "Hope, Signals, and Silicon: A Game-Theoretic Model of the Pre-Doctoral Academic Labor Market in the Age of AI", "authors": [ "Shaohui Wang" ], "posted": "2025-10-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.00068v2", "field": "economics", "role": "object", "bullets": [ "Game-theoretic model of the pre-doctoral academic labor market linking principal investigators, research assistants, and PhD admissions tournaments.", "Generative AI modeled as dual automation-augmentation technology in task-based production within PI-RA relational contracts; no empirical LLM used.", "AI triggers a signaling arms race in fixed-slot admissions, degrades routine artifacts' informational content, and segments the RA market by PI objectives." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3545, "authors_detailed": [ { "name": "Shaohui Wang", "url": "https://openalex.org/A5100667760", "inst": "Georgia State University" } ], "affiliations": [ "Georgia State University" ] }, { "uid": "doi:10.2139/ssrn.5660270", "doi": "10.2139/ssrn.5660270", "title": "Device-to-Device Economics and AI Agent Transactions", "authors": [ "Vedang Ratan Vatsa" ], "posted": "2025-10-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5660270", "field": "economics", "role": "object", "bullets": [ "Review of AI agent payment platforms including Google AP2, Coinbase x402, and Ethereum-Cloudflare collaborations enabling automated machine-to-machine economies.", "Surveys architectures for autonomous AI agent transactions and references Stanford research on negotiation asymmetries between agents of unequal capability.", "Weaker seller agents lose up to 14% in profit versus equal-ability counterparts, raising questions about market fairness and economic stability in automated markets." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3964, "authors_detailed": [ { "name": "Vedang Ratan Vatsa", "url": "https://openalex.org/A5120179899", "inst": "Webb Institute" } ], "affiliations": [ "Webb Institute" ] }, { "uid": "arxiv:2510.24402v1", "arxiv_id": "2510.24402v1", "title": "Metadata-Driven Retrieval-Augmented Generation for Financial Question Answering", "authors": [ "Michail Dadopoulos", "Anestis Ladas", "Stratos Moschidis", "Ioannis Negkakis" ], "posted": "2025-10-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.24402v1", "field": "finance", "role": "method", "bullets": [ "Long structured financial filings evaluated using the FinanceBench dataset with a multi-stage RAG architecture and LLM-generated metadata.", "An indexing pipeline creates contextually rich document chunks; benchmarks pre-retrieval filtering, post-retrieval reranking, and metadata-enriched embeddings.", "Embedding chunk metadata directly with text yields the largest gains; a custom metadata reranker offers a cost-effective alternative to commercial solutions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "FinanceBench financial QA dataset", "salience": 50, "n": 3963 }, { "uid": "arxiv:2510.23464v1", "arxiv_id": "2510.23464v1", "title": "Evaluating Large Language Models for Stance Detection on Financial Targets from SEC Filing Reports and Earnings Call Transcripts", "authors": [ "Nikesh Gyawali", "Doina Caragea", "Alex Vasenkov", "Cornelia Caragea" ], "posted": "2025-10-27", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.23464v1", "field": "finance", "role": "method", "bullets": [ "A sentence level corpus built from Form 10-K annual reports and quarterly earnings call transcripts, labelled positive, negative, or neutral toward debt, earnings per share, and sales.", "ChatGPT-o3-pro produces the stance labels under rigorous human validation; modern LLMs are then evaluated on the corpus with zero shot, few shot, and Chain-of-Thought prompting.", "Few shot prompting combined with Chain-of-Thought beats supervised baselines, and model performance differs between the filing sentences and the call transcript sentences." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "human-validated stance labels from 10-Ks and call transcripts", "salience": 52, "edition": 14, "n": 1835 }, { "uid": "arxiv:2510.23032v1", "arxiv_id": "2510.23032v1", "title": "P1GPT: a multi-agent LLM workflow module for multi-modal financial information analysis", "authors": [ "Chen-Che Lu", "Yun-Cheng Chou", "Teng-Ruei Chen" ], "posted": "2025-10-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.23032v1", "field": "finance", "role": "agent", "bullets": [ "Major U.S. equities with multi-modal datasets including technical indicators, fundamental data, and news, evaluated via backtesting.", "Layered multi-agent LLM framework fused technical, fundamental, and news insights through a structured reasoning pipeline for interpretable trading decisions.", "Achieved superior cumulative and risk-adjusted returns with low drawdowns compared to existing multi-LLM frameworks and time-series baselines." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "models": [], "n": 2513 }, { "uid": "arxiv:2510.23421v2", "arxiv_id": "2510.23421v2", "title": "Quantifying Systemic Vulnerability in the Foundation Model Industry", "authors": [ "Claudio Pirrone", "Stefano Fricano", "Gioacchino Fazio" ], "posted": "2025-10-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.23421v2", "field": "economics", "role": "instrument", "bullets": [ "Six state-of-the-art foundation model developers assessed for industrial vulnerability across five critical input categories.", "LLMs extract indicators from dispersed grey literature using validated human-in-the-loop methodology with complete human verification.", "AIIVI equals 0.82 indicating extreme vulnerability; energy infrastructure (0.90) surpasses compute (0.85) as the binding constraint." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "complete human verification of all LLM-extracted indicators", "salience": 55, "n": 3062 }, { "uid": "arxiv:2510.25779v1", "arxiv_id": "2510.25779v1", "title": "Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets", "authors": [ "Gagan Bansal", "Wenyue Hua", "Zezhou Huang", "Adam Fourney", "Amanda Swearngin", "Will Epperson", "Tyler Payne", "Jake M. Hofman", "Brendan Lucier", "Chinmay Singh", "Markus Mobius", "Akshay Nambi", "Archana Yadav", "Kevin Gao", "David M. Rothschild", "Aleksandrs Slivkins", "Daniel G. Goldstein", "Hussein Mozannar", "Nicole Immorlica", "Maya Murad", "Matthew Vogel", "Subbarao Kambhampati", "Eric Horvitz", "Saleema Amershi" ], "posted": "2025-10-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.25779v1", "field": "economics", "role": "agent", "bullets": [ "Simulated two-sided marketplace with LLM-based consumer assistants and competing business service agents at varying market scales.", "Frontier LLM agents transact in open-ended dialogues; the study measures welfare, behavioral biases, manipulation vulnerability, and search-mechanism effects.", "Models approach optimal welfare only under ideal search; all exhibit severe first-proposal bias, giving response speed a 10-30x advantage over quality." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 68, "n": 3962 }, { "uid": "arxiv:2511.00024v1", "arxiv_id": "2511.00024v1", "title": "Chitchat with AI: Understand the supply chain carbon disclosure of companies worldwide through Large Language Model", "authors": [ "Haotian Hang", "Yueyang Shen", "Vicky Zhu", "Jose Cruz", "Michelle Li" ], "posted": "2025-10-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.00024v1", "field": "accounting", "role": "instrument", "bullets": [ "Corporate carbon disclosures from the Carbon Disclosure Project across 11 years (2010-2020), spanning multiple sectors and countries worldwide.", "LLMs scored open-ended climate disclosure narratives using a harmonized master rubric with percentile-based normalization for cross-sector and cross-country benchmarking.", "Technology sector and Germany showed highest rubric alignment; other sectors exhibited volatility or superficial engagement in climate disclosure quality over time." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 2512, "authors_detailed": [ { "name": "Haotian Hang", "url": "https://openalex.org/A5120270397", "inst": "" }, { "name": "Shen, Yueyang", "url": "", "inst": "" }, { "name": "Vicky Zhu", "url": "https://openalex.org/A5120270398", "inst": "" }, { "name": "José M. Cruz", "url": "https://openalex.org/A5055624969", "inst": "Pennsylvania State University" }, { "name": "Mengdi Li", "url": "https://openalex.org/A5100606514", "inst": "Yunnan Normal University" } ], "affiliations": [ "Pennsylvania State University", "Yunnan Normal University" ] }, { "uid": "arxiv:2510.23669v2", "arxiv_id": "2510.23669v2", "title": "What Work is AI Actually Doing? Uncovering the Drivers of Generative AI Adoption", "authors": [ "Peeyush Agarwal", "Harsh Agarwal", "Akshat Rana" ], "posted": "2025-10-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.23669v2", "field": "economics", "role": "object", "bullets": [ "Four million Claude AI interactions from the Anthropic Economic Index mapped to O*NET tasks; seven task dimensions scored via 35 parameters.", "Multivariate techniques identified three latent task archetypes and analyzed their relationship with observed generative AI usage patterns across the task space.", "Five percent of tasks account for 59% of all interactions; high-creativity, high-complexity, low-routineness tasks attract the most AI engagement." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 60, "validated": null, "n": 2766 }, { "uid": "arxiv:2510.21604v1", "arxiv_id": "2510.21604v1", "title": "RETuning: Upgrading Inference-Time Scaling for Stock Movement Prediction with Large Language Models", "authors": [ "Xueyuan Lin", "Cehao Yang", "Ye Ma", "Ming Li", "Rongjunchen Zhang", "Yang Ni", "Xiaojun Wu", "Chengjin Xu", "Jian Guo", "Hui Xiong" ], "posted": "2025-10-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.21604v1", "field": "finance", "role": "method", "bullets": [ "5,123 A-share stocks throughout 2024 with 200K+ samples using 32K-token contexts including prices, news, analyst opinions, and macro indicators.", "Reflective Evidence Tuning cold-start method trained LLMs to build analytical frameworks and score evidence independently rather than follow analyst opinions.", "Inference-time scaling improved three-class stock movement prediction and generalized to out-of-distribution stocks even after six months of temporal distance." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "models": [], "n": 2511 }, { "uid": "arxiv:2510.21117v2", "arxiv_id": "2510.21117v2", "title": "DAO-AI: Evaluating Collective Decision-Making through Agentic AI in Decentralized Governance", "authors": [ "Agostino Capponi", "Alfio Gliozzo", "Chunghyun Han", "Junkyu Lee" ], "posted": "2025-10-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.21117v2", "field": "finance", "role": "agent", "bullets": [ "Over 3,000 governance proposals from major decentralized autonomous organization protocols with blockchain-verified financial simulation data.", "Agentic AI voter interprets proposal contexts, retrieves historical deliberation data, and independently determines voting positions within a modular workflow.", "Agent decisions show strong alignment with human and token-weighted voting outcomes, demonstrating AI can augment collective governance decision-making." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "alignment metrics against human and token-weighted DAO voting outcomes", "salience": 55, "n": 3544, "authors_detailed": [ { "name": "Agostino Capponi", "url": "https://openalex.org/A5041916584", "inst": "Columbia University" }, { "name": "Alfio Gliozzo", "url": "https://openalex.org/A5087419620", "inst": "Istituto di Scienza e Tecnologie dell'Informazione \"Alessandro Faedo\"" }, { "name": "Han, Chunghyun", "url": "", "inst": "" }, { "name": "Junkyu Lee", "url": "https://openalex.org/A5078925783", "inst": "University of Essex" } ], "affiliations": [ "Columbia University", "Istituto di Scienza e Tecnologie dell'Informazione \"Alessandro Faedo\"", "University of Essex" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2511.05508v2", "arxiv_id": "2511.05508v2", "title": "Personalized Chain-of-Thought Summarization of Financial News for Investor Decision Support", "authors": [ "Tianyi Zhang", "Mu Chen" ], "posted": "2025-10-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.05508v2", "field": "finance", "role": "method", "bullets": [ "Financial news articles summarized for individual investors using keyword-guided personalization and chain-of-thought prompting.", "A CoT framework condenses news into event-driven summaries filtered by user-specified keywords, producing an intermediate layer for language-model narratives.", "Personalized summaries isolate relevant market signals from noise, bridging raw financial news and actionable investor-focused insights." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 35, "n": 3961, "authors_detailed": [ { "name": "Zhang, Tianyi", "url": "", "inst": "" }, { "name": "Chen, Mu", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5615733", "doi": "10.2139/ssrn.5615733", "title": "The Agentic Frontier: Artificial Intelligence in Real Estate", "authors": [ "Cayman Seagraves", "Michael Seiler", "Stace Sirmans" ], "affiliations": [ "Collins College", "William & Mary", "Auburn University" ], "posted": "2025-10-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5615733", "field": "finance", "role": "object", "bullets": [ "Review of artificial intelligence applications in real estate; no empirical sample, it surveys methods spanning valuation, text, and image data.", "Charts AI methods from machine learning automated valuation models to natural language processing and computer vision; names no single model and reports no validation.", "Argues AI outperforms traditional models and that the frontier is shifting toward explainable, causal, and agentic systems, and proposes a research agenda." ], "bullet_provenance": "ai", "salience": 42, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 1037, "authors_detailed": [ { "name": "Cayman Seagraves", "url": "https://openalex.org/A5116866548", "inst": "Collins College" }, { "name": "Michael J. Seiler", "url": "https://openalex.org/A5055505342", "inst": "William & Mary" }, { "name": "Stace Sirmans", "url": "https://openalex.org/A5000933071", "inst": "Auburn University" } ] }, { "uid": "arxiv:2510.20612v1", "arxiv_id": "2510.20612v1", "title": "Black Box Absorption: LLMs Undermining Innovative Ideas", "authors": [ "Wenjun Cao" ], "posted": "2025-10-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.20612v1", "field": "economics", "role": "object", "bullets": [ "Theoretical framework analyzing interactions between individual innovators and LLM platform operators in the innovation ecosystem.", "Formalizes how opaque LLM architectures can internalize, generalize, and repurpose novel user-contributed concepts, defining idea units and idea safety.", "Identifies structural asymmetries between creators and platforms that threaten innovation economics principles and long-term ecosystem sustainability." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 2510, "authors_detailed": [ { "name": "Cao, Wenjun", "url": "", "inst": "" } ] }, { "uid": "arxiv:2510.20221v1", "arxiv_id": "2510.20221v1", "title": "FinCARE: Financial Causal Analysis with Reasoning and Evidence", "authors": [ "Alejandro Michel", "Abhinav Arun", "Bhaskarjit Sarmah", "Stefano Pasquali" ], "posted": "2025-10-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.20221v1", "field": "finance", "role": "instrument", "bullets": [ "Synthetic financial dataset of 500 firms across 18 variables, integrating domain knowledge from SEC 10-K filings via knowledge graph.", "LLM reasoning combined with knowledge graph constraints enhances PC, GES, and NOTEARS causal discovery algorithms for portfolio analysis.", "KG+LLM yields F1 improvements of 36% to 366% across algorithms; counterfactual predictions achieve mean absolute error of 0.003610." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "synthetic financial dataset with F1 scores across three causal discovery algorithms", "salience": 50, "n": 3061, "authors_detailed": [ { "name": "Alejandro Michel", "url": "https://openalex.org/A5120612691", "inst": "" }, { "name": "Abhinav Arun", "url": "https://openalex.org/A5120471508", "inst": "" }, { "name": "Bhaskarjit Sarmah", "url": "https://openalex.org/A5040045971", "inst": "Shree Guru Gobind Singh Tricentenary University" }, { "name": "Stefano Pasquali", "url": "https://openalex.org/A5011160139", "inst": "Ospedale Maggiore" } ], "affiliations": [ "Shree Guru Gobind Singh Tricentenary University", "Ospedale Maggiore" ] }, { "uid": "arxiv:2510.20099v1", "arxiv_id": "2510.20099v1", "title": "AI PB: A Grounded Generative Agent for Personalized Investment Insights", "authors": [ "Daewoo Park", "Suho Park", "Inseok Hong", "Hanwool Lee", "Junkyu Park", "Sangjun Lee", "Jeongman An", "Hyunbin Loh" ], "posted": "2025-10-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.20099v1", "field": "finance", "role": "agent", "bullets": [ "Production deployment in Korean retail finance, operating on-premises across 24 H100 GPUs under Korean financial regulations.", "LLMs routed by data sensitivity generate personalized, compliant investment insights via hybrid retrieval and contextual bandits; validated by human QA.", "Grounded generation with explicit routing and layered safety delivers trustworthy AI insights in high-stakes retail finance at production scale." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 50, "n": 3543, "authors_detailed": [ { "name": "Daewoo Park", "url": "https://openalex.org/A5046817224", "inst": "Hoseo University" }, { "name": "Suho Park", "url": "https://openalex.org/A5102071281", "inst": "Sungkyunkwan University" }, { "name": "Inseok Hong", "url": "https://openalex.org/A5026096772", "inst": "" }, { "name": "Hanwool Lee", "url": "https://openalex.org/A5103110871", "inst": "University of Jyväskylä" }, { "name": "Jun-Kyu Park", "url": "https://openalex.org/A5101779516", "inst": "Chonnam National University" }, { "name": "Sangjun Lee", "url": "https://openalex.org/A5100712411", "inst": "Pohang University of Science and Technology" }, { "name": "Jinung An", "url": "https://openalex.org/A5017309732", "inst": "Korea Advanced Institute of Science and Technology" }, { "name": "Hyunbin Loh", "url": "https://openalex.org/A5084723210", "inst": "" } ], "affiliations": [ "Hoseo University", "Sungkyunkwan University", "University of Jyväskylä", "Chonnam National University", "Pohang University of Science and Technology", "Korea Advanced Institute of Science and Technology" ] }, { "uid": "arxiv:2510.20699v1", "arxiv_id": "2510.20699v1", "title": "Fusing Narrative Semantics for Financial Volatility Forecasting", "authors": [ "Yaxuan Kong", "Yoontae Hwang", "Marcus Kaiser", "Chris Vryonides", "Roel Oomen", "Stefan Zohren" ], "posted": "2025-10-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.20699v1", "field": "finance", "role": "instrument", "bullets": [ "Financial volatility forecasting combining time-series features with unstructured news data, tested against existing baseline models across dynamic market conditions.", "Time Machine GPT generates point-in-time news embeddings fused with market features via auxiliary alignment loss in a multi-modal deep learning architecture.", "M2VN consistently outperforms existing baselines for volatility forecasting, demonstrating practical value for risk management through temporally sound multimodal data integration." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 3560, "authors_detailed": [ { "name": "Yaxuan Kong", "url": "https://openalex.org/A5099282237", "inst": "University of Oxford" }, { "name": "Yoontae Hwang", "url": "https://openalex.org/A5101711570", "inst": "Pusan National University" }, { "name": "Marcus Kaiser", "url": "https://openalex.org/A5058522288", "inst": "University of Nottingham" }, { "name": "Chris Vryonides", "url": "https://openalex.org/A5120499629", "inst": "Deutsche Bank (United Kingdom)" }, { "name": "Roel C. A. Oomen", "url": "https://openalex.org/A5053452059", "inst": "Deutsche Bank (Germany)" }, { "name": "Stefan Zohren", "url": "https://openalex.org/A5090331439", "inst": "Oxford Research Group" } ], "affiliations": [ "University of Oxford", "Pusan National University", "University of Nottingham", "Deutsche Bank (United Kingdom)", "Deutsche Bank (Germany)", "Oxford Research Group" ], "prestige": true }, { "uid": "arxiv:2510.20102v3", "arxiv_id": "2510.20102v3", "title": "Human-Centered LLM-Agent System for Detecting Anomalous Digital Asset Transactions", "authors": [ "Gyuyeon Na", "Minjung Park", "Hyeonjeong Cha", "Sangmi Chai" ], "posted": "2025-10-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.20102v3", "field": "finance", "role": "instrument", "bullets": [ "Cryptocurrency anomaly detection using a web-based multi-agent system with three cognitively aligned roles, tested on a digital-asset transaction dataset.", "Open-source LLM agents translate user intent into analytical rules, apply classical anomaly detectors for evidence scoring, and reconstruct expert-style justifications.", "System achieves strong predictive accuracy while substantially improving interpretability and decision transparency for regulatory, audit, and compliance-driven financial forensics." ], "bullet_provenance": "ai", "validated": true, "validation_note": "predictive accuracy on cryptocurrency anomaly dataset", "salience": 40, "models": [], "n": 3561, "authors_detailed": [ { "name": "Gyuyeon Na", "url": "https://openalex.org/A5120612688", "inst": "" }, { "name": "Min-Jung Park", "url": "https://openalex.org/A5101894680", "inst": "Gachon University" }, { "name": "Hyeonjeong Cha", "url": "https://openalex.org/A5120471504", "inst": "" }, { "name": "Sangmi Chai", "url": "https://openalex.org/A5038729859", "inst": "Ewha Womans University" } ], "affiliations": [ "Gachon University", "Ewha Womans University" ] }, { "uid": "arxiv:2510.19799v1", "arxiv_id": "2510.19799v1", "title": "Integrating Transparent Models, LLMs, and Practitioner-in-the-Loop: A Case of Nonprofit Program Evaluation", "authors": [ "Ji Ma", "Albert Casella" ], "posted": "2025-10-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.19799v1", "field": "management", "role": "instrument", "bullets": [ "Data from an ongoing college-success program in the public and nonprofit sector; practitioner-in-the-loop workflow with field experts participating throughout.", "LLM received interpretable decision-tree structures and reproduced case-level predictions; practitioners validated outputs at feature engineering, model design, and explanation stages.", "Integrated approach yields accurate and actionable case-level evaluations, offering a viable pathway for responsible AI adoption in public and nonprofit organizations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "LLM predictions compared against decision-tree model outputs", "salience": 30, "n": 2765, "authors_detailed": [ { "name": "Ji Ma", "url": "https://openalex.org/A5100982888", "inst": "Jinling Institute of Technology" }, { "name": "Albert Casella", "url": "https://openalex.org/A5089127348", "inst": "Bloomberg (United States)" } ], "affiliations": [ "Jinling Institute of Technology", "Bloomberg (United States)" ] }, { "uid": "arxiv:2510.19173v1", "arxiv_id": "2510.19173v1", "title": "News-Aware Direct Reinforcement Trading for Financial Markets", "authors": [ "Qing-Yu Lan", "Zhan-He Wang", "Jun-Qian Jiang", "Yu-Tong Wang", "Yun-Song Piao" ], "posted": "2025-10-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.19173v1", "field": "finance", "role": "instrument", "bullets": [ "Cryptocurrency market trading experiments using news data and price-volume inputs, evaluating DDQN and GRPO reinforcement learning algorithms for end-to-end decisions.", "LLM-derived news sentiment scores serve as observable inputs for reinforcement learning agents making trading decisions without handcrafted features or manual rules.", "News-aware approach without handcrafted features achieves performance superior to market benchmarks; time-series information plays a critical role in trading outcomes." ], "bullet_provenance": "ai", "validated": false, "salience": 35, "models": [], "n": 3559, "authors_detailed": [ { "name": "Qing Lan", "url": "https://openalex.org/A5100771331", "inst": "BGI Group (China)" }, { "name": "Zhan-He Wang", "url": "https://openalex.org/A5021180665", "inst": "University of Hong Kong" }, { "name": "Jiahui Jiang", "url": "https://openalex.org/A5102847435", "inst": "Southeast University" }, { "name": "Yutong Wang", "url": "https://openalex.org/A5100631524", "inst": "Harbin University" }, { "name": "Yun-Song Piao", "url": "https://openalex.org/A5112575261", "inst": "Chinese Academy of Sciences" } ], "affiliations": [ "BGI Group (China)", "University of Hong Kong", "Southeast University", "Harbin University", "Chinese Academy of Sciences" ] }, { "uid": "doi:10.2139/ssrn.5533883", "doi": "10.2139/ssrn.5533883", "title": "Monocentric Relationship Management: Defining the Single AI Paradigm Beyond CRM", "authors": [ "Aydin Habibi" ], "posted": "2025-10-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5533883", "field": "management", "role": "object", "bullets": [ "Concept note proposing a paradigm shift from fragmented CRM stacks to a single AI-native relationship management system.", "Defines Monocentric Relationship Management where one cognitive controller coordinates marketing, sales, service, pricing, billing, and legal under guardrails.", "Distinguishes MRM from autonomous CRM and agentic relationship management and proposes evaluation criteria for unified AI-native systems." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3960, "authors_detailed": [ { "name": "Aydin Habibi", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5634331", "doi": "10.2139/ssrn.5634331", "title": "Large Language Models Augment or Substitute Human Experts in Idea Screening", "authors": [ "Pavel Kireyev", "Brendon Rhodes", "Cathy L. Yang", "Abhishek Borah" ], "posted": "2025-10-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5634331", "field": "management", "role": "agent", "bullets": [ "74,436 advertising and product ideas across 153 contests on a crowdsourcing platform for major advertisers, with historical expert ratings and client selections.", "LLM ratings combined with ML model trained on expert scores screened crowdsourced ideas; evaluated against expert judgments and final sponsor selections.", "Expert evaluation effort reduced by 28.4% versus status quo; LLMs could make 5 of 10 experts redundant compared to 3 with ML alone." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert ratings and client selections", "salience": 72, "models": [], "n": 2509, "authors_detailed": [ { "name": "Pavel Kireyev", "url": "https://openalex.org/A5058621082", "inst": "London School of Economics and Political Science" }, { "name": "Brendon Rhodes", "url": "", "inst": "INSEAD" }, { "name": "Cathy Yang", "url": "https://openalex.org/A5045835737", "inst": "HEC Paris" }, { "name": "Abhishek Borah", "url": "https://openalex.org/A5002529588", "inst": "INSEAD" } ], "affiliations": [ "London School of Economics and Political Science", "INSEAD", "HEC Paris" ], "prestige": true }, { "uid": "arxiv:2510.17393v1", "arxiv_id": "2510.17393v1", "title": "3S-Trader: A Multi-LLM Framework for Adaptive Stock Scoring, Strategy, and Selection in Portfolio Optimization", "authors": [ "Kefan Chen", "Hussain Ahmad", "Diksha Goel", "Claudia Szabo" ], "posted": "2025-10-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.17393v1", "field": "finance", "role": "agent", "bullets": [ "U.S. equities including Dow Jones Industrial Average constituents and three sector-specific stock sets, evaluated via backtesting.", "Training-free multi-LLM framework with scoring, strategy, and selection modules constructed portfolios through cross-candidate reasoning and adaptive strategy revision.", "Achieved 131.83% cumulative return on DJIA constituents with Sharpe ratio of 0.31 and Calmar ratio of 11.84, outperforming multi-LLM and time-series baselines." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "models": [], "n": 2508, "authors_detailed": [ { "name": "Kefan Chen", "url": "https://openalex.org/A5085155953", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Hussain Ahmad", "url": "https://openalex.org/A5101660408", "inst": "The University of Adelaide" }, { "name": "Diksha Goel", "url": "https://openalex.org/A5084412291", "inst": "Commonwealth Scientific and Industrial Research Organisation" }, { "name": "Claudia Szabo", "url": "https://openalex.org/A5016821538", "inst": "The University of Adelaide" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "The University of Adelaide", "Commonwealth Scientific and Industrial Research Organisation" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.17235v1", "arxiv_id": "2510.17235v1", "title": "Coinvisor: An RL-Enhanced Chatbot Agent for Interactive Cryptocurrency Investment Analysis", "authors": [ "Chong Chen", "Ze Liu", "Lingfeng Bao", "Yanlin Wang", "Ting Chen", "Daoyuan Wu", "Jiachi Chen" ], "posted": "2025-10-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.17235v1", "field": "finance", "role": "instrument", "bullets": [ "Cryptocurrency investment analysis platform tested with 20 investors via automated benchmarks and user study.", "RL-based multi-agent LLM chatbot integrates diverse data sources for real-time cryptocurrency analysis and multi-step planning.", "Tool orchestration recall improves 40.7% over base model; user satisfaction reaches 4.64 out of 5, preferred over general LLMs and existing platforms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "tool calling accuracy benchmark and 20-participant user study", "salience": 40, "n": 2764, "authors_detailed": [ { "name": "Chong Chen", "url": "https://openalex.org/A5055208775", "inst": "City University of Hong Kong" }, { "name": "Ze Liu", "url": "https://openalex.org/A5041165857", "inst": "Hebei Medical University" }, { "name": "Lingfeng Bao", "url": "https://openalex.org/A5007075465", "inst": "Zhejiang University of Science and Technology" }, { "name": "Yanlin Wang", "url": "https://openalex.org/A5100350708", "inst": "Qilu University of Technology" }, { "name": "Ting Chen", "url": "https://openalex.org/A5100443178", "inst": "University of Electronic Science and Technology of China" }, { "name": "Daoyuan Wu", "url": "https://openalex.org/A5063510532", "inst": "Lingnan University" }, { "name": "Jiachi Chen", "url": "https://openalex.org/A5086118824", "inst": "Sun Yat-sen University" } ], "affiliations": [ "City University of Hong Kong", "Hebei Medical University", "Zhejiang University of Science and Technology", "Qilu University of Technology", "University of Electronic Science and Technology of China", "Lingnan University", "Sun Yat-sen University" ] }, { "uid": "doi:10.2139/ssrn.5629462", "doi": "10.2139/ssrn.5629462", "title": "Technology and Labor Markets: Past, Present, and Future; Evidence from Two Centuries of Innovation", "authors": [ "Huben Liu", "Dimitris Papanikolaou", "Lawrence Schmidt", "Bryan Seegmiller" ], "posted": "2025-10-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5629462", "field": "economics", "role": "instrument", "bullets": [ "U.S. Census occupation employment data spanning nearly two centuries combined with novel LLM-constructed technology exposure measures.", "LLMs and NLP construct occupation-level technology exposure measures; a calibrated model projects AI employment effects in the medium run.", "AI reverses historical trends by favoring lower-educated, lower-paid, more male-dominated occupations, unlike prior technological progress." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": false, "salience": 75, "n": 3060 }, { "uid": "arxiv:2510.17165v1", "arxiv_id": "2510.17165v1", "title": "Trading with the Devil: Risk and Return in Foundation Model Strategies", "authors": [ "Jinrui Zhang" ], "posted": "2025-10-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.17165v1", "field": "finance", "role": "method", "bullets": [ "Foundation model-based trading strategies evaluated for risk decomposition using an extended Capital Asset Pricing Model framework in financial markets.", "Proposes separating systematic risk from pretrained model epistemic uncertainty and idiosyncratic risk from fine-tuning aleatory uncertainty via Monte Carlo dropout estimation.", "Isolating distinct risk factors reveals performance limits, model degradation over time, and targeted refinement avenues for foundation-model-based trading strategies." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 55, "n": 3231, "authors_detailed": [ { "name": "Jinrui Zhang", "url": "https://openalex.org/A5082223185", "inst": "North China University of Science and Technology" } ], "affiliations": [ "North China University of Science and Technology" ] }, { "uid": "arxiv:2510.17108v4", "arxiv_id": "2510.17108v4", "title": "Structured Debate Improves Corporate Credit Reasoning in Financial AI", "authors": [ "Yoonjin Lee", "Munhee Kim", "Hanbi Choi", "Juhyeon Park", "Seungho Lyoo", "Woojin Park" ], "posted": "2025-10-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.17108v4", "field": "finance", "role": "method", "bullets": [ "Corporate credit evaluation of non-financial data, comparing single-agent and multi-agent debate systems against human expert task completion benchmarks.", "LLM-based Popperian Multi-agent Debate System structures adversarial argumentation under Karl Popper protocol for dual-perspective credit risk analysis.", "PMADS reports scored significantly higher than single-agent in explanatory adequacy, applicability, and usability; both systems drastically reduced task time versus experts." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3558, "authors_detailed": [ { "name": "Yoon‐Jin Lee", "url": "https://openalex.org/A5044393994", "inst": "Kansas State University" }, { "name": "Kim, Munhee", "url": "", "inst": "" }, { "name": "Hanbi Choi", "url": "https://openalex.org/A5120280970", "inst": "" }, { "name": "Ju-Hyeon Park", "url": "https://openalex.org/A5101134153", "inst": "" }, { "name": "Seungho Lyoo", "url": "https://openalex.org/A5120280971", "inst": "" }, { "name": "Woojin Park", "url": "https://openalex.org/A5120280972", "inst": "" } ], "affiliations": [ "Kansas State University" ] }, { "uid": "doi:10.2139/ssrn.5522439", "doi": "10.2139/ssrn.5522439", "title": "Loyalty in the Age of Agents: Can Algorithms Be Loyal? (Working Paper)", "authors": [ "Paul F. Accornero" ], "posted": "2025-10-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5522439", "field": "management", "role": "object", "bullets": [ "Conceptual analysis synthesizing loyalty theory, AI decision-making research, and platform economics for AI-mediated commerce environments.", "Introduces 'algorithmic loyalty' as a functional state where AI agents show persistent brand preference based on multi-variable optimization criteria.", "Develops a four-pillar framework: verifiable value consistency, supply reliability, systemic interoperability, and machine-readable trust signals." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3958, "authors_detailed": [ { "name": "Paul Accornero", "url": "https://openalex.org/A5120015495", "inst": "Capital Group (United States)" } ], "affiliations": [ "Capital Group (United States)" ] }, { "uid": "doi:10.2139/ssrn.5511838", "doi": "10.2139/ssrn.5511838", "title": "The Shopper has No Eyes: Branding in a World without Human Perception (Working Paper)", "authors": [ "Paul F. Accornero" ], "posted": "2025-10-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5511838", "field": "management", "role": "object", "bullets": [ "Conceptual framework drawing on branding theory and empirical evidence of AI agent purchasing behavior in autonomous commerce settings.", "Proposes the 'Great Value Sort' mechanism where algorithmic evaluation redistributes market share by objective performance rather than perceived brand equity.", "Argues brand value shifts from emotional persuasion to verifiable data transparency when AI agents lack human sensory and emotional capabilities." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3959, "authors_detailed": [ { "name": "Paul Accornero", "url": "https://openalex.org/A5120015495", "inst": "Capital Group (United States)" } ], "affiliations": [ "Capital Group (United States)" ] }, { "uid": "arxiv:2510.17882v1", "arxiv_id": "2510.17882v1", "title": "Does GenAI Rewrite How We Write? An Empirical Study on Two-Million Preprints", "authors": [ "Minfeng Qi", "Zhongmin Cao", "Qin Wang", "Ningran Li", "Tianqing Zhu" ], "posted": "2025-10-18", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.17882v1", "field": "management", "role": "object", "bullets": [ "More than 2.1 million preprints posted to arXiv, bioRxiv, medRxiv, and SocArXiv over 115 months from 2016 to 2025.", "No LLM serves as the research tool; interrupted time series, linguistic profiling, and topic models trace shifts in volume, authorship, style, and topics after LLM diffusion.", "LLMs coincide with faster submission and revision cycles, modestly more complex language, and growth in AI related topics concentrated in computational fields." ], "bullet_provenance": "ai", "salience": 55, "edition": 14, "models": [], "validated": null, "n": 1879, "authors_detailed": [ { "name": "Minfeng Qi", "url": "https://openalex.org/A5120283050", "inst": "" }, { "name": "Zhong‐Min Cao", "url": "https://openalex.org/A5032369885", "inst": "Chongqing University of Posts and Telecommunications" }, { "name": "Qin Wang", "url": "https://openalex.org/A5120283052", "inst": "" }, { "name": "Ningran Li", "url": "https://openalex.org/A5120283053", "inst": "" }, { "name": "Tianqing Zhu", "url": "https://openalex.org/A5120283054", "inst": "" } ], "affiliations": [ "Chongqing University of Posts and Telecommunications" ] }, { "uid": "doi:10.2139/ssrn.5585812", "doi": "10.2139/ssrn.5585812", "title": "ChatGPT Referrals to E-Commerce Websites: Do LLMs Outperform Traditional Channels? *", "authors": [ "Maximilian Kaiser", "Christian Schulze" ], "posted": "2025-10-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5585812", "field": "management", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 30, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 359 }, { "uid": "arxiv:2510.16551v3", "arxiv_id": "2510.16551v3", "title": "From Reviews to Actionable Insights: An LLM-Based Approach for Attribute and Feature Extraction", "authors": [ "Khaled Boughanmi", "Kamel Jedidi", "Nour Jedidi" ], "posted": "2025-10-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.16551v3", "field": "management", "role": "instrument", "bullets": [ "20,000 Yelp reviews of Starbucks stores; eight prompt variants evaluated against human annotations on a random subset.", "LLM extracts product attributes, features, and sentiments from reviews, distinguishing perceptual attributes from actionable features.", "LLM matches human coders at two seconds versus six minutes per review; improving sentiment on key service features yields 1-2% average revenue gains per store." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "agreement with human annotations and predictive validity for customer ratings", "salience": 65, "n": 2763, "authors_detailed": [ { "name": "Khaled Boughanmi", "url": "https://openalex.org/A5041642220", "inst": "Cornell University" }, { "name": "Kamel Jedidi", "url": "https://openalex.org/A5048212752", "inst": "Columbia University" }, { "name": "Nour Jedidi", "url": "https://openalex.org/A5114621405", "inst": "University of Waterloo" } ], "affiliations": [ "Cornell University", "Columbia University", "University of Waterloo" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.15691v3", "arxiv_id": "2510.15691v3", "title": "Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction", "authors": [ "Tian Guo", "Emmanuel Hauptmann" ], "posted": "2025-10-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.15691v3", "field": "finance", "role": "method", "bullets": [ "Real investment universes combining quantitative factors such as valuation, quality, and growth with company newsflow for stock return prediction and selection; universe details are not stated.", "An LLM, not named, encodes newsflow into representations fused with factor data; combination, summation, and attentive fusion are compared, plus a mixture model trained with a decoupled approach.", "Fusion learning shows limitations in the empirical comparison, motivating the adaptive mixture of single modality and fused predictions; the abstract reports no numeric performance figures." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 14, "models": [], "n": 2060, "authors_detailed": [ { "name": "Tian Guo", "url": "https://openalex.org/A5073299519", "inst": "Shanghai University" }, { "name": "Emmanuel Hauptmann", "url": "https://openalex.org/A5001168712", "inst": "Defense Information Systems Agency" } ], "affiliations": [ "Shanghai University", "Defense Information Systems Agency" ] }, { "uid": "doi:10.2139/ssrn.5616650", "doi": "10.2139/ssrn.5616650", "title": "From Signal to Noise: How Widespread LLM Usage Transforms Evaluator Effort and Credit Screening Outcomes", "authors": [ "Paramveer Dhillon", "Yi Gao", "Tian Lu", "Yingjie Zhang" ], "posted": "2025-10-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5616650", "field": "finance", "role": "object", "bullets": [ "Randomized field experiment in micro-lending with LLM usage rates exogenously varied from zero to 75 percent across treatment groups, holding the applicant pool fixed.", "LLMs assisted applicants in writing loan application narratives at varying prevalence rates to measure crowd-level effects on evaluator screening decisions and cognitive effort.", "Moderate usage (15-30%) improved qualified-borrower approvals without raising defaults; high usage (60-75%) caused signal dilution, reducing evaluator effort and worsening portfolio outcomes." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 82, "validated": null, "n": 2418, "authors_detailed": [ { "name": "Paramveer S. Dhillon", "url": "https://openalex.org/A5063223563", "inst": "University of Michigan" }, { "name": "Yuan Gao", "url": "https://openalex.org/A5100722719", "inst": "Texas Tech University" }, { "name": "Lu Tian", "url": "https://openalex.org/A5102959652", "inst": "Arizona State University" }, { "name": "Yingjie Zhang", "url": "https://openalex.org/A5100430947", "inst": "Peking University" } ], "affiliations": [ "Arizona State University", "University of Michigan", "Texas Tech University", "Peking University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.15232v1", "arxiv_id": "2510.15232v1", "title": "FinTrust: A Comprehensive Benchmark of Trustworthiness Evaluation in Finance Domain", "authors": [ "Tiansheng Hu", "Tongyan Hu", "Liuyang Bai", "Yilun Zhao", "Arman Cohan", "Chen Zhao" ], "posted": "2025-10-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.15232v1", "field": "finance", "role": "method", "bullets": [ "Benchmark of eleven LLMs on trustworthiness dimensions including safety, fairness, fiduciary alignment, and disclosure in finance.", "Proprietary models such as o4-mini and open-source models such as DeepSeek-V3 are evaluated on fine-grained finance trust tasks.", "All LLMs fail on fiduciary alignment and disclosure; proprietary models lead on safety while open-source models lead on industry-level fairness." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "benchmark scores across safety, fairness, fiduciary alignment, and disclosure tasks", "salience": 60, "n": 2762, "authors_detailed": [ { "name": "Tiansheng Hu", "url": "https://openalex.org/A5119963925", "inst": "New York University Shanghai" }, { "name": "Tao Hu", "url": "https://openalex.org/A5026921466", "inst": "Jinan University" }, { "name": "Liuyang Bai", "url": "https://openalex.org/A5102361524", "inst": "Chinese Academy of Sciences" }, { "name": "Yilun Zhao", "url": "https://openalex.org/A5037981788", "inst": "New York University Shanghai" }, { "name": "Arman Cohan", "url": "https://openalex.org/A5064858748", "inst": "Yale University" }, { "name": "Chen Zhao", "url": "https://openalex.org/A5100352014", "inst": "Wuhan University" } ], "affiliations": [ "New York University Shanghai", "Yale University", "Jinan University", "Chinese Academy of Sciences", "Wuhan University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.15200v1", "arxiv_id": "2510.15200v1", "title": "The Economics of AI Foundation Models: Openness, Competition, and Governance", "authors": [ "Fasheng Xu", "Xiaoyu Wang", "Wei Chen", "Karen Xie" ], "posted": "2025-10-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.15200v1", "field": "economics", "role": "object", "bullets": [ "Two-period game-theoretic model with an incumbent AI developer, downstream deployer, and entrant developer competing in the foundation model value chain.", "Analyzes how model openness shapes competition through knowledge spillovers and a data flywheel effect that lowers the deployer's future fine-tuning cost.", "Optimal openness is non-monotonic in flywheel strength; transparency mandates can backfire via an openness trap, and adoption subsidies may be captured by incumbents." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3230, "authors_detailed": [ { "name": "Fasheng Xu", "url": "https://openalex.org/A5086759286", "inst": "University of Connecticut" }, { "name": "Xiaoyu Wang", "url": "https://openalex.org/A5115595858", "inst": "Shandong University" }, { "name": "Wei Chen", "url": "https://openalex.org/A5100344501", "inst": "University of Connecticut" }, { "name": "Karen Xie", "url": "https://openalex.org/A5005895330", "inst": "University of Connecticut" } ], "affiliations": [ "University of Connecticut", "Shandong University" ] }, { "uid": "doi:10.2139/ssrn.5584850", "doi": "10.2139/ssrn.5584850", "title": "Generative AI and Firm Productivity: Field Experiments in Online Retail", "authors": [ "Lu Fang", "Zhe Yuan", "Kaifu Zhang", "Dante Donati", "Miklos Sarvary" ], "posted": "2025-10-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5584850", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 55, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 683, "authors_detailed": [ { "name": "Lu Fang", "url": "https://openalex.org/A5039161616", "inst": "Zhejiang University" }, { "name": "Zhe Yuan", "url": "https://openalex.org/A5049717631", "inst": "Zhejiang University of Finance and Economics" }, { "name": "Kaifu Zhang", "url": "https://openalex.org/A5103619995", "inst": "Anhui University" }, { "name": "Dante Donati", "url": "https://openalex.org/A5015724002", "inst": "Columbia University" }, { "name": "Miklós Sárváry", "url": "https://openalex.org/A5075806177", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "Zhejiang University", "Zhejiang University of Finance and Economics", "Anhui University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.14264v2", "arxiv_id": "2510.14264v2", "title": "AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading", "authors": [ "Zheye Deng", "Weixiang Yan", "Changlong Yu", "Jiashu Wang" ], "posted": "2025-10-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.14264v2", "field": "finance", "role": "agent", "bullets": [ "Single-agent RL framework tested on stock trading with tool-augmented decision workflow for autonomous information acquisition and execution.", "LLM agent uses reinforcement learning to learn a dynamic policy orchestrating tools and proactively acquiring market information on demand.", "AlphaQuanter achieved state-of-the-art performance on key financial metrics with interpretable reasoning revealing sophisticated trading strategies." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "financial trading metrics vs multi-agent and single-agent baselines", "salience": 55, "n": 2861 }, { "uid": "arxiv:2510.13157v1", "arxiv_id": "2510.13157v1", "title": "Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrieval", "authors": [ "Subhendu Khatuya", "Shashwat Naidu", "Pawan Goyal", "Niloy Ganguly" ], "posted": "2025-10-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.13157v1", "field": "finance", "role": "method", "bullets": [ "Numerical reasoning questions over text and tables from financial reports, using the labelled FinQA and ConvFinQA benchmarks as the evaluation setting.", "FINDER pairs a generative retriever with program of thought prompting and dynamically selected in context examples; the underlying LLM is not named, and answers are scored against benchmark ground truth.", "Execution accuracy rises 5.98 percent on FinQA and 4.05 percent on ConvFinQA over the previous state of the art." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinQA and ConvFinQA execution accuracy", "salience": 42, "edition": 14, "models": [], "n": 1937, "authors_detailed": [ { "name": "Subhendu Khatuya", "url": "https://openalex.org/A5062498056", "inst": "Indian Institute of Technology Kharagpur" }, { "name": "S. V. R. Naidu", "url": "https://openalex.org/A5004824169", "inst": "Indian Institute of Technology Kharagpur" }, { "name": "Pawan Goyal", "url": "https://openalex.org/A5103213198", "inst": "American College of Cardiology" }, { "name": "Niloy Ganguly", "url": "https://openalex.org/A5073812421", "inst": "Indian Institute of Technology Kharagpur" } ], "affiliations": [ "Indian Institute of Technology Kharagpur", "American College of Cardiology" ] }, { "uid": "arxiv:2510.14162v2", "arxiv_id": "2510.14162v2", "title": "FinAI Data Assistant: LLM-based Financial Database Query Processing with the OpenAI Function Calling API", "authors": [ "Juhyeong Kim", "Yejin Kim", "Youngbin Lee", "Hyunwoo Byun" ], "posted": "2025-10-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.14162v2", "field": "finance", "role": "method", "bullets": [ "Natural language queries over a financial database of prices and fundamentals, with controlled tests of data recall, company to ticker mapping for NASDAQ-100 and S&P 500 firms, and end-to-end query processing.", "OpenAI models with the Function Calling API route requests to a small library of vetted parameterized queries instead of synthesizing SQL; outputs are compared against actual database values.", "LLM-only recall shows non-negligible error and look-ahead bias for stock prices; ticker mapping is near perfect for NASDAQ-100 constituents, and function calling beats a text-to-SQL baseline on latency, cost, and reliability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "recall and ticker mapping checked against database values", "salience": 45, "edition": 14, "n": 2057 }, { "uid": "arxiv:2510.13939v4", "arxiv_id": "2510.13939v4", "title": "Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers", "authors": [ "Tuhin Chakrabarty", "Jane C. Ginsburg", "Paramveer Dhillon" ], "posted": "2025-10-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.13939v4", "field": "economics", "role": "object", "bullets": [ "Preregistered blind pairwise comparisons of up to 450 word excerpts emulating 50 award winning authors, judged by 28 MFA trained readers and 516 college educated general readers.", "ChatGPT, Claude, and Gemini write through in-context prompting; ChatGPT is also fine-tuned on each author's complete works at a median cost of 81 dollars per author.", "Prompted outputs lose to expert writers among MFA readers, but fine-tuned outputs are preferred by both reader groups and rarely flagged by detectors, evidence bearing on copyright's fourth fair use factor." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 80, "edition": 14, "validated": null, "n": 2058, "authors_detailed": [ { "name": "Tuhin Chakrabarty", "url": "https://openalex.org/A5021070482", "inst": "Stony Brook University" }, { "name": "Jane C. Ginsburg", "url": "https://openalex.org/A5033647423", "inst": "New York Law School" }, { "name": "Paramveer S. Dhillon", "url": "https://openalex.org/A5063223563", "inst": "University of Michigan" } ], "affiliations": [ "Stony Brook University", "New York Law School", "University of Michigan" ] }, { "uid": "arxiv:2510.13936v2", "arxiv_id": "2510.13936v2", "title": "FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysis", "authors": [ "Fengbin Zhu", "Xiang Yao Ng", "Ziyang Liu", "Chang Liu", "Xianwei Zeng", "Chao Wang", "Tianhui Tan", "Xuan Yao", "Pengyang Shao", "Min Xu", "Zixuan Wang", "Jing Wang", "Xin Lin", "Junfeng Li", "Jingxian Zhu", "Yang Zhang", "Wenjie Wang", "Fuli Feng", "Richang Hong", "Huanbo Luan", "Ke-Wei Huang", "Tat-Seng Chua" ], "posted": "2025-10-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.13936v2", "field": "finance", "role": "method", "bullets": [ "64 listed companies from 8 financial markets in 4 languages, yielding 15,808 grading items organized along an analyst workflow from data recognition to strategic interpretation.", "16 systems are compared, 6 deep research agents, 5 LLMs with reasoning and search, and 5 reasoning-only LLMs, none named in the abstract, graded under the HisRubric framework.", "Capabilities prove uneven across markets, languages, and analytical stages, with different strengths by approach; the benchmark and evaluation code are released publicly." ], "bullet_provenance": "ai", "validated": true, "validation_note": "15,808 rubric grading items", "salience": 52, "edition": 14, "models": [], "n": 2059 }, { "uid": "arxiv:2510.13750v2", "arxiv_id": "2510.13750v2", "title": "Confidence-Based Response Abstinence: Improving LLM Trustworthiness via Activation-Based Uncertainty Estimation", "authors": [ "Zhiqi Huang", "Vivek Datla", "Chenyang Zhu", "Alfy Samuel", "Daben Liu", "Anoop Kumar", "Ritesh Soni" ], "posted": "2025-10-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.13750v2", "field": "finance", "role": "method", "bullets": [ "Real-world financial industry customer-support RAG system with complex knowledge bases evaluated under strict latency constraints.", "Llama 3.1 8B with FFN activation-based confidence estimation modeled as sequence classification with Huber loss regularization.", "Method outperformed strong baselines on accuracy; using only 16th-layer activations preserved accuracy while cutting response latency." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "accuracy on financial customer-support RAG vs baselines", "salience": 40, "n": 2860, "authors_detailed": [ { "name": "Zhiqi Huang", "url": "https://openalex.org/A5107935776", "inst": "Nantong University" }, { "name": "Vivek Datla", "url": "https://openalex.org/A5120034789", "inst": "" }, { "name": "Chenyang Zhu", "url": "https://openalex.org/A5058214353", "inst": "Chengdu University of Information Technology" }, { "name": "Alfy Samuel", "url": "https://openalex.org/A5066674792", "inst": "University of Southern California" }, { "name": "Daben Liu", "url": "https://openalex.org/A5069329694", "inst": "Capital One (United States)" }, { "name": "Kumar, Anoop", "url": "", "inst": "" }, { "name": "Soni, Ritesh", "url": "", "inst": "" } ], "affiliations": [ "University of Southern California", "Nantong University", "Chengdu University of Information Technology", "Capital One (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.13524v2", "arxiv_id": "2510.13524v2", "title": "A Methodology for Assessing the Risk of Metric Failure in LLMs Within the Financial Domain", "authors": [ "William Flanagan", "Mukunda Das", "Rajitha Ramanayake", "Swanuja Maslekar", "Meghana Mangipudi", "Joong Ho Choi", "Shruti Nair", "Shambhavi Bhusan", "Sanjana Dulam", "Mouni Pendharkar", "Nidhi Singh", "Vashisth Doshi", "Sachi Shah Paresh" ], "posted": "2025-10-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.13524v2", "field": "finance", "role": "method", "bullets": [ "Financial services industry applications of generative AI, assessing metric performance across various GenAI workloads.", "Proposes a risk assessment framework combining subject matter expert evaluation with machine learning metrics for GenAI in finance.", "Finds that historical ML metrics and widespread academic benchmarks fail to generalize to industrial GenAI use cases in financial services." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": false, "salience": 40, "n": 3059, "authors_detailed": [ { "name": "William P. Flanagan", "url": "https://openalex.org/A5024265766", "inst": "General Electric (United States)" }, { "name": "Das, Mukunda", "url": "", "inst": "" }, { "name": "Ramanayake, Rajitha", "url": "", "inst": "" }, { "name": "Maslekar, Swanuja", "url": "", "inst": "" }, { "name": "Mangipudi, Meghana", "url": "", "inst": "" }, { "name": "Joong Ho Choi", "url": "https://openalex.org/A5077329318", "inst": "LG (South Korea)" }, { "name": "Nair, Shruti", "url": "", "inst": "" }, { "name": "Shambhavi Bhusan", "url": "https://openalex.org/A5120034249", "inst": "" }, { "name": "Sanjana Dulam", "url": "https://openalex.org/A5120034250", "inst": "" }, { "name": "Mouni Pendharkar", "url": "https://openalex.org/A5120034243", "inst": "" }, { "name": "Nidhi Singh", "url": "https://openalex.org/A5115593768", "inst": "National Institute of Technology Warangal" }, { "name": "Vashisth Doshi", "url": "https://openalex.org/A5120034244", "inst": "" }, { "name": "Sachi Shah Paresh", "url": "https://openalex.org/A5120034245", "inst": "" } ], "affiliations": [ "General Electric (United States)", "LG (South Korea)", "National Institute of Technology Warangal" ] }, { "uid": "doi:10.1145/3746252.3760812", "doi": "10.1145/3746252.3760812", "arxiv_id": "2510.14156v1", "title": "On Evaluating Loss Functions for Stock Ranking: An Empirical Analysis With Transformer Model", "authors": [ "Jan Kwiatkowski", "Jarosław A. Chudziak" ], "posted": "2025-10-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.14156v1", "field": "finance", "role": "method", "bullets": [ "S&P 500 constituents, daily return forecasting for rank-based portfolio selection, systematic comparison of loss-function families.", "A transformer model is trained with pointwise, pairwise, and listwise loss functions to rank stocks by predicted daily returns.", "Benchmark reveals that listwise losses better capture cross-sectional and temporal ranking patterns critical for portfolio selection." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "Ranking metrics on S&P 500 daily returns", "salience": 40, "n": 3956, "authors_detailed": [ { "name": "Jan Kwiatkowski", "url": "https://openalex.org/A5119234960", "inst": "Warsaw University of Technology" }, { "name": "Jarosław A. Chudziak", "url": "https://openalex.org/A5008057050", "inst": "Warsaw University of Technology" } ], "affiliations": [ "Warsaw University of Technology" ] }, { "uid": "doi:10.2139/ssrn.5511758", "doi": "10.2139/ssrn.5511758", "title": "From SEO to AIO: Agent Intent Optimization in the Age of Algorithmic Shoppers (Working Paper)", "authors": [ "Paul F. Accornero" ], "posted": "2025-10-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5511758", "field": "management", "role": "object", "bullets": [ "Conceptual framework synthesizing emerging industry evidence and marketing theory on AI agents that transact autonomously for consumers.", "Proposes Agent Intent Optimization, a shift from SEO's psychological persuasion to logical optimization of product visibility for algorithmic shoppers.", "Identifies a 'Shopper Schism' between human intention and algorithmic execution and develops a five-component AIO implementation framework." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3957, "authors_detailed": [ { "name": "Paul Accornero", "url": "https://openalex.org/A5120015495", "inst": "Capital Group (United States)" } ], "affiliations": [ "Capital Group (United States)" ] }, { "uid": "doi:10.1145/3768292.3770399", "doi": "10.1145/3768292.3770399", "arxiv_id": "2510.15993v1", "title": "Aligning Language Models with Investor and Market Behavior for Financial Recommendations", "authors": [ "Fernando Spadea", "Oshani Seneviratne" ], "posted": "2025-10-14", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.15993v1", "field": "finance", "role": "method", "bullets": [ "Retail asset recommendation on the FAR-Trans dataset of investor transaction histories and asset price trends; period and market are not stated.", "FLARKO encodes histories and trends as knowledge graphs and fine-tunes unnamed LLMs with Kahneman-Tversky optimization, in centralized and federated variants, benchmarked against recommendation baselines.", "The framework beats state of the art recommenders on behavioral alignment and joint profitability while staying interpretable; effect sizes are not given in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FAR-Trans dataset, baseline comparison", "salience": 38, "edition": 14, "models": [], "n": 1936, "authors_detailed": [ { "name": "Fernando Spadea", "url": "https://openalex.org/A5008943760", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Oshani Seneviratne", "url": "https://openalex.org/A5038466673", "inst": "Rensselaer Polytechnic Institute" } ], "affiliations": [ "Rensselaer Polytechnic Institute" ] }, { "uid": "arxiv:2510.12189v1", "arxiv_id": "2510.12189v1", "title": "Agent-Based Simulation of a Financial Market with Large Language Models", "authors": [ "Ryuji Hashimoto", "Takehiro Takayanagi", "Masahiro Suzuki", "Kiyoshi Izumi" ], "posted": "2025-10-14", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.12189v1", "field": "finance", "role": "agent", "bullets": [ "An artificial stock market simulation targeting path dependent price patterns, such as declines near historical highs, that fundamentals alone cannot explain.", "FCLAgents use an LLM, family not stated, to make buy or sell decisions from each agent's individual situation, while order price and volume follow standard rule-based methods.", "The simulation reproduces path dependent patterns that conventional agents miss, and the reference points anchoring the agents' loss aversion shift with market trajectories." ], "bullet_provenance": "ai", "salience": 56, "edition": 14, "models": [], "validated": null, "n": 2056, "authors_detailed": [ { "name": "Ryuji Hashimoto", "url": "https://openalex.org/A5110117696", "inst": "Bunkyo University" }, { "name": "Takehiro Takayanagi", "url": "https://openalex.org/A5074574419", "inst": "Bunkyo University" }, { "name": "Masahiro Suzuki", "url": "https://openalex.org/A5038802330", "inst": "Bunkyo University" }, { "name": "Kiyoshi Izumi", "url": "https://openalex.org/A5044205949", "inst": "Bunkyo University" } ], "affiliations": [ "Bunkyo University" ] }, { "uid": "doi:10.2139/ssrn.5506278", "doi": "10.2139/ssrn.5506278", "title": "Mapping Venture Capital investments in the (new) space economy with Large Language Models: a comparative analysis of Europe and the United States", "authors": [ "Massimo G. Colombo", "Alessandro Lucini-Paioni", "Bogdan Tofan" ], "posted": "2025-10-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5506278", "field": "finance", "role": "instrument", "bullets": [ "113,910 VC-backed startups from PitchBook database, comparing space economy startups in Europe and the United States.", "GPT-5 classified startups into the space economy via few-shot text-based classification, identifying 2,336 space startups from the full sample.", "European VC-backed startups are more specialized in space but raise less capital, receive fewer rounds, and attract smaller syndicates than U.S. counterparts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 60, "n": 2507, "authors_detailed": [ { "name": "Massimo G. Colombo", "url": "https://openalex.org/A5060857602", "inst": "Politecnico di Milano" }, { "name": "Alessandro Lucini-Paioni", "url": "https://openalex.org/A5119986048", "inst": "Politecnico di Milano" }, { "name": "Bogdan Tofan", "url": "https://openalex.org/A5066793859", "inst": "Politecnico di Milano" } ], "affiliations": [ "Politecnico di Milano" ] }, { "uid": "arxiv:2510.12409v1", "arxiv_id": "2510.12409v1", "title": "PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks", "authors": [ "Yunuo Liu", "Dawei Zhu", "Zena Al-Khalili", "Dai Cheng", "Yanjun Chen", "Dietrich Klakow", "Wei Zhang", "Xiaoyu Shen" ], "posted": "2025-10-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.12409v1", "field": "management", "role": "method", "bullets": [ "Benchmark of 300 questions from 42 real-world tourism pricing policies at two difficulty levels for multiple LLMs.", "LLMs attempt to compute correct prices under overlapping fare rules requiring multi-step arithmetic and rule interpretation.", "Performance drops steeply on bundled-tour calculations with interacting discounts, exposing systematic reasoning failures in revenue-critical tasks." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "accuracy on 300 pricing questions with known correct answers", "salience": 40, "n": 2761, "authors_detailed": [ { "name": "Yamei Liu", "url": "https://openalex.org/A5066679332", "inst": "Hunan University" }, { "name": "Dawei Zhu", "url": "https://openalex.org/A5040034806", "inst": "Research Institute of Petroleum Exploration and Development" }, { "name": "Zena Al-Khalili", "url": "https://openalex.org/A5119688410", "inst": "Saarland University" }, { "name": "Dai Cheng", "url": "https://openalex.org/A5055588065", "inst": "Tianjin University of Science and Technology" }, { "name": "Yanjun Chen", "url": "https://openalex.org/A5100410769", "inst": "Fudan University" }, { "name": "Dietrich Klakow", "url": "https://openalex.org/A5008875255", "inst": "Saarland University" }, { "name": "Zhang Wei", "url": "https://openalex.org/A5100666886", "inst": "Sichuan University" }, { "name": "Shen, Xiaoyu", "url": "", "inst": "" } ], "affiliations": [ "Hunan University", "Research Institute of Petroleum Exploration and Development", "Saarland University", "Tianjin University of Science and Technology", "Fudan University", "Sichuan University" ] }, { "uid": "arxiv:2510.12049v6", "arxiv_id": "2510.12049v6", "title": "Generative AI and Sales Productivity: Field Experiments in Online Retail", "authors": [ "Lu Fang", "Zhe Yuan", "Kaifu Zhang", "Dante Donati", "Miklos Sarvary" ], "posted": "2025-10-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.12049v6", "field": "management", "role": "object", "bullets": [ "Millions of users and products at a cross-border online retail platform across seven business workflows, 2023-2024 randomized field experiments.", "GenAI integrated into customer service, consumer-product matching, advertising, and seller services; effects measured via sales outcomes.", "Sales effects range from zero to 16.3% with implied annual value of $5 per consumer; gains driven by higher conversion rates, not larger carts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 75, "validated": null, "n": 3058 }, { "uid": "doi:10.2139/ssrn.5505223", "doi": "10.2139/ssrn.5505223", "title": "Agentic AI-Powered Claims Intelligence: A Deep Learning Framework for Automating Workers Compensation Claim Processing Using Generative AI", "authors": [ "Avinash Reddy Aitha", "Dr. A. Jyothi Babu" ], "posted": "2025-10-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5505223", "field": "finance", "role": "instrument", "bullets": [ "Two Australian workers compensation insurers with a 28-step manual claim establishment process performed by claim adjusters under strict business rules.", "ClaimGPT uses OpenAI API and Salesforce Einstein GPT for automated claim generation, agentic decision-making, and image-based documentation synthesis.", "Both implementations accelerated claim processing time and reduced the size of statutory claim reserves allocated to each newly established claim." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 35, "n": 3229, "authors_detailed": [ { "name": "Avinash Reddy Aitha", "url": "https://openalex.org/A5119927847", "inst": "National Society of Professional Engineers" }, { "name": "A. Jyothi Babu", "url": "https://openalex.org/A5038958316", "inst": "Mohan Babu University" } ], "affiliations": [ "National Society of Professional Engineers", "Mohan Babu University" ] }, { "uid": "arxiv:2510.11695v2", "arxiv_id": "2510.11695v2", "title": "When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents", "authors": [ "Lingfei Qian", "Xueqing Peng", "Yan Wang", "Vincent Jim Zhang", "Huan He", "Hanley Smith", "Yi Han", "Yueru He", "Haohang Li", "Yupeng Cao", "Yangyang Yu", "Alejandro Lopez-Lira", "Peng Lu", "Jian-Yun Nie", "Guojun Xiong", "Jimin Huang", "Sophia Ananiadou" ], "posted": "2025-10-13", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.11695v2", "field": "finance", "role": "method", "bullets": [ "Live cryptocurrency and stock trading with verified market data and expert checked news, run continuously as a lifelong evaluation arena.", "Four agent architectures spanning risk styles and memory based reasoning trade on backbones from GPT-4o and 4.1, Claude 3.5 haiku and sonnet 4, and Gemini 2.0 flash.", "Agent architecture drives behavioral differences from aggressive to conservative trading, while the choice of backbone model contributes less to outcome variation." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 66, "edition": 14, "validated": null, "n": 1878 }, { "uid": "arxiv:2510.10998v1", "arxiv_id": "2510.10998v1", "title": "ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios", "authors": [ "Mahika Phutane", "Hayoung Jung", "Matthew Kim", "Tanushree Mitra", "Aditya Vashistha" ], "posted": "2025-10-13", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.10998v1", "field": "management", "role": "object", "bullets": [ "2,820 hiring scenarios crossing disability with gender, nationality, and caste profiles, extending bias audits beyond Western settings toward Global South candidates.", "Six LLMs, not named in the abstract, are audited using ABLEIST, five ableism-specific and three intersectional harm metrics grounded in disability studies literature.", "Harms toward disabled candidates rise significantly and are amplified, for instance through tokenism, for gender and caste marginalized profiles, while state-of-the-art models fail to detect them." ], "bullet_provenance": "ai", "salience": 45, "edition": 14, "models": [], "validated": null, "n": 2055 }, { "uid": "arxiv:2510.11677v2", "arxiv_id": "2510.11677v2", "title": "Instruction Tuning Chronologically Consistent Language Models", "authors": [ "Songrun He", "Linying Lv", "Asaf Manela", "Jimmy Wu" ], "posted": "2025-10-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.11677v2", "field": "economics", "role": "method", "bullets": [ "Family of instruction-tuned LLMs each trained only on data available before a defined knowledge-cutoff date to ensure strict temporal separation.", "Open fixed-weight models with chat interface eliminate lookahead bias for generative-AI prediction tasks across research applications.", "Framework establishes a conservative lower bound on forecast accuracy by isolating predictability that survives once training data leakage is removed." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 72, "n": 2859, "authors_detailed": [ { "name": "Songrun He", "url": "https://openalex.org/A5048165657", "inst": "Washington University in St. Louis" }, { "name": "Liangyu Lv", "url": "https://openalex.org/A5055495019", "inst": "Peking University" }, { "name": "Asaf Manela", "url": "https://openalex.org/A5117372333", "inst": "Washington University in St. Louis" }, { "name": "Jinlin Wu", "url": "https://openalex.org/A5112053104", "inst": "Institut de Recherche et d’Innovation" } ], "affiliations": [ "Washington University in St. Louis", "Peking University", "Institut de Recherche et d’Innovation" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5557362", "doi": "10.2139/ssrn.5557362", "title": "Balancing Interpersonal and Informational Privacy Concerns in AI Mental Health", "authors": [ "Zhichen Chen", "Gordon Burtch", "Chrysanthos N. Dellarocas", "Bin Gu" ], "posted": "2025-10-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5557362", "field": "management", "role": "object", "bullets": [ "Three experiments including 650 Prolific participants with moderate-to-severe mental health challenges and a field experiment comparing in-person, telehealth, and AI care modes.", "No model deployed as instrument; LLM-based conversational AI is the studied object, with participants ranking preferences across mental health service delivery modes.", "Interpersonal privacy concerns about stigma increase preference for AI care and crowd out informational privacy concerns about data security, suggesting AI can boost engagement." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3556 }, { "uid": "doi:10.2139/ssrn.5587110", "doi": "10.2139/ssrn.5587110", "title": "Inferring Intent from AI-Mediated Interactions: A Comparison of LLMs vs. Traditional Graph Methods", "authors": [ "Wendy W. Moe", "Liye Ma", "Ziting Liao" ], "posted": "2025-10-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5587110", "field": "management", "role": "instrument", "bullets": [ "Consumer interactions with AI assistants, comparing inferred purchase intent against human annotator ratings and cost-per-click market metrics across query types.", "LLM-based and graph-based methods independently classify purchase intent from AI assistant interaction logs, benchmarked against human annotations and CPC values.", "LLMs better capture verb-based intent while graphs capture noun-based context; the methods are complements for AI orchestration and advertising strategy." ], "bullet_provenance": "ai", "validated": true, "validation_note": "compared against human annotators and CPC metrics", "salience": 50, "models": [], "n": 3557, "authors_detailed": [ { "name": "Wendy W. Moe", "url": "https://openalex.org/A5001650853", "inst": "University of Maryland, College Park" }, { "name": "L. L. Ma", "url": "https://openalex.org/A5084240418", "inst": "University of Maryland, College Park" }, { "name": "Ziting Liao", "url": "https://openalex.org/A5119970295", "inst": "University of Maryland, Robert H. Smith School of Business" } ], "affiliations": [ "University of Maryland, College Park", "University of Maryland, Robert H. Smith School of Business" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.10878v1", "arxiv_id": "2510.10878v1", "title": "Identifying and Quantifying Financial Bubbles with the Hyped Log-Periodic Power Law Model", "authors": [ "Zheng Cao", "Xingran Shao", "Yuheng Yan", "Helyette Geman" ], "posted": "2025-10-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.10878v1", "field": "finance", "role": "instrument", "bullets": [ "U.S. equities across industry sectors, 2018-2024, backtesting a unified bubble-detection framework for both overpricing and underpricing.", "A dual-stream transformer trained on LPPL-derived bubble labels, sentiment scores, and a hype index produces a continuous Bubble Score time series.", "The framework yields 34.13% annualized return in backtests with conservative bias that minimizes false positives and generalizes across sectors." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 48, "n": 3955, "authors_detailed": [ { "name": "Zheng Cao", "url": "https://openalex.org/A5029024939", "inst": "Changchun University of Science and Technology" }, { "name": "Xi Shao", "url": "https://openalex.org/A5016051584", "inst": "University of Maryland, College Park" }, { "name": "Yongjie Yan", "url": "https://openalex.org/A5103184231", "inst": "Johns Hopkins University" }, { "name": "Hélyette Geman", "url": "https://openalex.org/A5010200827", "inst": "Johns Hopkins University" } ], "affiliations": [ "University of Maryland, College Park", "Johns Hopkins University", "Changchun University of Science and Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.10819v1", "arxiv_id": "2510.10819v1", "title": "Generative AI and the Transformation of Software Development Practices", "authors": [ "Vivek Acharya" ], "posted": "2025-10-12", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.10819v1", "field": "management", "role": "object", "bullets": [ "Software engineering practice broadly, surveyed through case studies and industry data on chat based development, multi-agent systems, prompt orchestration, and Model Context Protocol integration.", "No specific model is used or named; the paper reviews how LLM assisted styles such as vibe coding and agentic programming alter developer workflows.", "Faster cycles and broader access to coding come with reliability and cost concerns; the paper outlines new roles, skills, and accountability practices rather than quantitative estimates." ], "bullet_provenance": "ai", "salience": 25, "edition": 14, "models": [], "validated": null, "n": 1935 }, { "uid": "arxiv:2510.10526v1", "arxiv_id": "2510.10526v1", "title": "Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading", "authors": [ "Wo Long", "Wenxin Zeng", "Xiaoyu Zhang", "Ziyao Zhou" ], "posted": "2025-10-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.10526v1", "field": "finance", "role": "instrument", "bullets": [ "Sentiment-driven quantitative trading system tested on market data, comparing rule-based and reinforcement learning approaches for integrating sentiment and technical signals.", "FinGPT performs sentiment analysis on financial text; TD3 reinforcement learning algorithm integrates sentiment signals with traditional technical indicators for trading decisions.", "Sentiment signals from FinGPT add value when combined with technical indicators; reinforcement learning outperforms conventional rule-based integration in dynamic environments." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 40, "n": 3554 }, { "uid": "arxiv:2510.10828v1", "arxiv_id": "2510.10828v1", "title": "VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering", "authors": [ "Zhenghan Tai", "Hanwei Wu", "Qingchen Hu", "Jijun Chi", "Hailin He", "Lei Ding", "Tung Sum Thomas Kwok", "Bohuai Xiao", "Yuchen Hua", "Suyuchen Wang", "Peng Lu", "Muzhi Li", "Yihong Wu", "Liheng Ma", "Jerry Huang", "Jiayi Zhang", "Gonghao Zhang", "Chaolong Jiang", "Jingrui Tian", "Sicheng Lyu", "Zeyu Li", "Boyu Han", "Fengran Mo", "Xinyue Yu", "Yufei Cui", "Ling Zhou", "Xinyu Wang" ], "posted": "2025-10-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.10828v1", "field": "finance", "role": "method", "bullets": [ "Financial question-answering system processing heterogeneous data including text, tables, and figures from public corporate disclosures and filings.", "RAG framework with multi-modal preprocessing, tripartite hybrid retrieval engine, and two-stage re-ranker training combining domain-general and company-specific fine-tuning.", "Framework outperforms baselines on financial QA tasks, enabling scalable adaptation from general-domain to company-specific applications through targeted re-ranker fine-tuning." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 3555 }, { "uid": "arxiv:2510.09735v1", "arxiv_id": "2510.09735v1", "title": "InterCorpRel-LLM: Enhancing Financial Relational Understanding with Graph-Language Models", "authors": [ "Qianyou Sun", "Jiexin Zheng", "Bohan Jin", "Lihua Chen", "Yijie Peng" ], "posted": "2025-10-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.09735v1", "field": "finance", "role": "method", "bullets": [ "Inter firm supply and competitor ties drawn from FactSet supply chain records, with training tasks on company graph matching, industry classification, and relation prediction.", "A 7B parameter language model fused with a graph neural network is trained on the proprietary graph; supply relation predictions are scored against the FactSet derived labels.", "The system reaches an F score of 0.8543 on supply relation identification against 0.2287 for GPT-5, and transfers zero shot to competitor identification." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "FactSet derived supply relation labels, F score reported", "salience": 62, "edition": 14, "n": 1877 }, { "uid": "arxiv:2510.08886v3", "arxiv_id": "2510.08886v3", "title": "FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs", "authors": [ "Yan Wang", "Keyi Wang", "Shanshan Yang", "Jaisal Patel", "Jeff Zhao", "Fengran Mo", "Xueqing Peng", "Lingfei Qian", "Yankai Chen", "Víctor Gutiérrez-Basulto", "Jimin Huang", "Guojun Xiong", "Xiao-Yang Liu", "Xue Liu", "Jian-Yun Nie" ], "posted": "2025-10-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.08886v3", "field": "accounting", "role": "method", "bullets": [ "1,102 annotated instances from real XBRL filings, averaging over 33,000 tokens, spanning semantic matching, relationship extraction, and mathematical reasoning across financial documents.", "13 state-of-the-art LLMs, not individually named in the abstract, are scored against taxonomy-aligned annotations on the three auditing tasks; dataset and evaluation code are public.", "All evaluated models show substantial gaps in concept retrieval, taxonomy-aware relation modeling, and cross-document consistency; the suite anchors an ongoing public evaluation contest." ], "bullet_provenance": "ai", "validated": true, "validation_note": "1,102 annotated XBRL instances", "salience": 60, "edition": 14, "models": [], "n": 2054 }, { "uid": "arxiv:2510.15949v5", "arxiv_id": "2510.15949v5", "title": "ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordination", "authors": [ "Charidimos Papadakis", "Angeliki Dimitriou", "Giorgos Filandrianos", "Maria Lymperaiou", "Konstantinos Thomas", "Giorgos Stamou" ], "posted": "2025-10-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.15949v5", "field": "finance", "role": "agent", "bullets": [ "Regime-specific equity trading studies across multiple market conditions using a multi-agent LLM framework.", "Central trading agent with Adaptive-OPRO prompt optimization uses real-time stochastic feedback to dynamically adjust trading decisions.", "Adaptive-OPRO consistently outperforms fixed prompts across multiple LLM families; reflection-based feedback fails to yield systematic gains." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": false, "salience": 55, "n": 3057, "authors_detailed": [ { "name": "Charidimos Papadakis", "url": "https://openalex.org/A5119791917", "inst": "" }, { "name": "Angeliki Dimitriou", "url": "https://openalex.org/A5007521009", "inst": "National Technical University of Athens" }, { "name": "Giorgos Filandrianos", "url": "https://openalex.org/A5077371399", "inst": "National Technical University of Athens" }, { "name": "Maria Lymperaiou", "url": "https://openalex.org/A5020141370", "inst": "National Technical University of Athens" }, { "name": "Κonstantinos Thomas", "url": "https://openalex.org/A5074911675", "inst": "National Technical University of Athens" }, { "name": "Giorgos Stamou", "url": "https://openalex.org/A5085359792", "inst": "National Technical University of Athens" } ], "affiliations": [ "National Technical University of Athens" ] }, { "uid": "arxiv:2510.08068v2", "arxiv_id": "2510.08068v2", "title": "An Adaptive Multi Agent Bitcoin Trading System", "authors": [ "Aadi Singhi" ], "posted": "2025-10-09", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.08068v2", "field": "finance", "role": "agent", "bullets": [ "Bitcoin trading backtested on price data from July 2024 to April 2025, spanning bullish, bearish, and sideways regimes in a single cryptocurrency market.", "LLM agents, models not named, cover technical analysis, sentiment, decisions, and reflection; daily and weekly natural language critiques from a reflect agent are injected into later prompts instead of fine-tuning.", "Reported returns beat buy and hold by 15 percent overall and by over 30 percent in bullish phases; weekly feedback adds 31 percent and cuts bearish losses by 10 percent." ], "bullet_provenance": "ai", "salience": 33, "edition": 14, "models": [], "validated": null, "n": 2052, "authors_detailed": [ { "name": "Aadi Singhi", "url": "https://openalex.org/A5120366167", "inst": "" } ] }, { "uid": "arxiv:2510.08671v1", "arxiv_id": "2510.08671v1", "title": "Optimizing delivery for quick commerce factoring qualitative assessment of generated routes", "authors": [ "Milon Bhattacharya", "Milan Kumar" ], "posted": "2025-10-09", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.08671v1", "field": "management", "role": "instrument", "bullets": [ "Last mile delivery routing for Indian quick commerce, where unstructured addresses and incomplete maps limit vehicle routing solvers; 400 generated route cases are annotated for evaluation.", "LLMs critique solver-generated routes against policy-based criteria; open-source models reach 79 percent accuracy and proprietary reasoning models up to 86 percent on the annotated cases, with no families named.", "Route critique adds an evaluation layer beyond distance and time metrics, letting logistics operators rank delivery plans for cost efficiency, reliability, and sustainability." ], "bullet_provenance": "ai", "validated": true, "validation_note": "400 annotated route cases, accuracy reported", "salience": 36, "edition": 14, "models": [], "n": 2053, "authors_detailed": [ { "name": "Milon Bhattacharya", "url": "https://openalex.org/A5120753952", "inst": "" }, { "name": "Milan Kumar", "url": "https://openalex.org/A5120721170", "inst": "" } ] }, { "uid": "arxiv:2510.08268v1", "arxiv_id": "2510.08268v1", "title": "Multi-Agent Analysis of Off-Exchange Public Information for Cryptocurrency Market Trend Prediction", "authors": [ "Kairan Hong", "Jinling Gan", "Qiushi Tian", "Yanglinxuan Guo", "Rui Guo", "Runnan Li" ], "posted": "2025-10-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.08268v1", "field": "finance", "role": "instrument", "bullets": [ "Bitcoin market trend prediction across three time horizons using news analysis, technical indicators, and multi-agent coordination.", "LLMs quantified market impact, regulatory implications, risk, and temporal effects from news; adaptive fusion combined signals by market regime.", "The framework achieved statistically significant improvements over state-of-the-art NLP baselines for cryptocurrency trend prediction." ], "bullet_provenance": "ai", "validated": true, "validation_note": "statistically significant comparison against NLP baselines", "salience": 50, "models": [], "n": 3053, "authors_detailed": [ { "name": "Hong, Kairan", "url": "", "inst": "" }, { "name": "Gan, Jinling", "url": "", "inst": "" }, { "name": "Tian, Qiushi", "url": "", "inst": "" }, { "name": "Yang Guo", "url": "https://openalex.org/A5110725522", "inst": "North China Electric Power University" }, { "name": "Rui Guo", "url": "https://openalex.org/A5102899778", "inst": "Xinjiang Medical University" }, { "name": "Runnan Li", "url": "https://openalex.org/A5102903512", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "North China Electric Power University", "Xinjiang Medical University", "Chinese University of Hong Kong" ] }, { "uid": "arxiv:2510.08114v1", "arxiv_id": "2510.08114v1", "title": "Can Risk-taking AI-Assistants suitably represent entities", "authors": [ "Ali Mazyaki", "Mohammad Naghizadeh", "Samaneh Ranjkhah Zonouzaghi", "Amirhossein Farshi Sotoudeh" ], "posted": "2025-10-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.08114v1", "field": "economics", "role": "agent", "bullets": [ "LLMs tested for risk aversion manipulability across diverse economic scenarios including gender-specific attitudes and role-based decisions.", "DeepSeek Reasoner and Gemini-2.0-flash-lite assessed for alignment with human risk preferences and susceptibility to risk-aversion manipulation.", "LMs show partial alignment with human risk behaviors but notable discrepancies exist; manipulability of risk aversion varies across models." ], "bullet_provenance": "ai", "models": [ "gemini", "open_other" ], "validated": false, "salience": 60, "n": 3054, "authors_detailed": [ { "name": "Ali Mazyaki", "url": "https://openalex.org/A5119757756", "inst": "" }, { "name": "Mohammad Naghizadeh", "url": "https://openalex.org/A5119757757", "inst": "" }, { "name": "Samaneh Ranjkhah Zonouzaghi", "url": "https://openalex.org/A5119757758", "inst": "" }, { "name": "Amirhossein Farshi Sotoudeh", "url": "https://openalex.org/A5120555752", "inst": "" } ] }, { "uid": "arxiv:2510.07852v2", "arxiv_id": "2510.07852v2", "title": "FinMR: A Knowledge-Intensive Multimodal Benchmark for Advanced Financial Reasoning", "authors": [ "Shuangyan Deng", "Haizhou Peng", "Jiachen Xu", "Rui Mao", "Ciprian Doru Giurcăneanu", "Jiamou Liu" ], "posted": "2025-10-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.07852v2", "field": "finance", "role": "method", "bullets": [ "Over 3,200 expert-annotated multimodal QA pairs across 15 financial topics at professional analyst level, with detailed explanations.", "Leading closed-source and open-source MLLMs benchmarked on mathematical reasoning, financial knowledge, and visual interpretation tasks.", "Significant performance gaps persist between current MLLMs and professional financial analysts across all tested financial reasoning dimensions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert-annotated financial QA benchmark with analyst comparison", "salience": 60, "models": [], "n": 3055, "authors_detailed": [ { "name": "Shuangyan Deng", "url": "https://openalex.org/A5058117289", "inst": "" }, { "name": "Peng, Haizhou", "url": "", "inst": "" }, { "name": "Jiachen Xu", "url": "https://openalex.org/A5075534833", "inst": "University of Vienna" }, { "name": "Rui Mao", "url": "https://openalex.org/A5101724957", "inst": "Nanyang Technological University" }, { "name": "Ciprian Doru Giurcăneanu", "url": "https://openalex.org/A5120050671", "inst": "" }, { "name": "Jiamou Liu", "url": "https://openalex.org/A5083914998", "inst": "University of Auckland" } ], "affiliations": [ "University of Vienna", "Nanyang Technological University", "University of Auckland" ] }, { "uid": "doi:10.2139/ssrn.5580290", "doi": "10.2139/ssrn.5580290", "title": "Impact of GenAI on U.S. Financial Advisors: Re-skilling for Cost-Effective and Efficient Financial Analysis and Planning", "authors": [ "Satyadhar Joshi" ], "posted": "2025-10-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5580290", "field": "finance", "role": "object", "bullets": [ "Literature review of AI adoption across financial analysis, planning, and advisory services in the United States.", "Synthesizes published findings on ChatGPT, Claude, and specialized financial AI systems across data analysis and advisory tasks.", "Reports 40-60% automation of routine tasks, 50-70% time reduction in modeling, and 3-5% higher investment returns from cited studies." ], "bullet_provenance": "ai", "models": [ "gpt", "claude" ], "salience": 30, "validated": null, "n": 3056, "authors_detailed": [ { "name": "Satyadhar Joshi", "url": "https://openalex.org/A5108368607", "inst": "Bar-Ilan University" } ], "affiliations": [ "Bar-Ilan University" ] }, { "uid": "arxiv:2510.07920v1", "arxiv_id": "2510.07920v1", "title": "Profit Mirage: Revisiting Information Leakage in LLM-based Financial Agents", "authors": [ "Xiangyu Li", "Yawen Zeng", "Xiaofen Xing", "Jin Xu", "Xiangmin Xu" ], "posted": "2025-10-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.07920v1", "field": "finance", "role": "method", "bullets": [ "Evaluation of LLM-based financial trading agents across four dimensions of information leakage, with release of FinLake-Bench, a leakage-robust evaluation benchmark.", "LLMs tested for out-of-sample trading performance; FactFin framework applies counterfactual perturbations via Monte Carlo Tree Search to force causal learning over memorized outcomes.", "FactFin surpasses all baselines in out-of-sample generalization, delivering superior risk-adjusted returns after the model's pre-training knowledge cutoff window ends." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 60, "n": 3227, "authors_detailed": [ { "name": "Xiangyu Li", "url": "https://openalex.org/A5100460318", "inst": "University of Southern California" }, { "name": "Zeng, Yawen", "url": "", "inst": "" }, { "name": "Xiaofen Xing", "url": "https://openalex.org/A5116337245", "inst": "South China University of Technology" }, { "name": "Jin Xu", "url": "https://openalex.org/A5048805608", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Xixiong Xu", "url": "https://openalex.org/A5101665164", "inst": "Chongqing University" } ], "affiliations": [ "University of Southern California", "South China University of Technology", "Beijing University of Posts and Telecommunications", "Chongqing University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.07645v1", "arxiv_id": "2510.07645v1", "title": "Banking Done Right: Redefining Retail Banking with Language-Centric AI", "authors": [ "Xin Jie Chua", "Jeraelyn Ming Li Tan", "Jia Xuan Tan", "Soon Chang Poh", "Yi Xian Goh", "Debbie Hui Tian Choong", "Chee Mun Foong", "Sze Jue Yang", "Chee Seng Chan" ], "posted": "2025-10-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.07645v1", "field": "finance", "role": "object", "bullets": [ "Ryt Bank, the first globally regulator-approved deployment where conversational AI serves as the primary retail banking interface rather than an advisory assistant.", "ILMU, a closed-source internally developed LLM with four LoRA-adapted agents for guardrails, intent, payment, and FAQ, handles core financial transactions via natural language.", "System supports live financial transactions under regulatory approval with deterministic guardrails, human-in-the-loop confirmation, and stateless audit architecture for compliance." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": false, "salience": 55, "validated": null, "n": 3228, "authors_detailed": [ { "name": "Xin Jie Chua", "url": "https://openalex.org/A5120309223", "inst": "University of Malaya" }, { "name": "Tan, Jeraelyn Ming Li", "url": "", "inst": "" }, { "name": "Tan, Jia Xuan", "url": "", "inst": "" }, { "name": "Soon Chang Poh", "url": "https://openalex.org/A5120309224", "inst": "University of Malaya" }, { "name": "Yi Xian Goh", "url": "https://openalex.org/A5120309225", "inst": "University of Malaya" }, { "name": "Debbie Hui Tian Choong", "url": "https://openalex.org/A5120309226", "inst": "" }, { "name": "Chee Mun Foong", "url": "https://openalex.org/A5120413882", "inst": "" }, { "name": "Sze Jue Yang", "url": "https://openalex.org/A5075286163", "inst": "University of Malaya" }, { "name": "Chee Seng Chan", "url": "https://openalex.org/A5070805897", "inst": "University of Malaya" } ], "affiliations": [ "University of Malaya" ] }, { "uid": "arxiv:2510.08337v1", "arxiv_id": "2510.08337v1", "title": "AI as a Centripetal Technology: Price Compression, Homogenization, and Entry", "authors": [ "Aliya Turegeldinova", "Bakytzhan Amralinova", "Mate Miklos Fodor", "Akerkin Eraliyeva", "Chen Dayou", "Aidos Joldassov" ], "posted": "2025-10-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.08337v1", "field": "economics", "role": "object", "bullets": [ "Theoretical two-stage differentiated-competition model analyzing generative AI's effect on product variety, pricing, and market entry across firm structures.", "No language model deployed; formal model derives equilibrium outcomes when AI capability compresses perceived product differences and raises originality costs.", "A capability threshold exists above which even duopoly becomes unsustainable; concentration rises while prices fall, reconciling the lower-prices-harder-entry paradox." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3552, "authors_detailed": [ { "name": "Aliya Turegeldinova", "url": "https://openalex.org/A5075220065", "inst": "Satbayev University" }, { "name": "Бакытжан Амралинова", "url": "https://openalex.org/A5003307302", "inst": "Satbayev University" }, { "name": "Máté Miklós Fodor", "url": "https://openalex.org/A5014883689", "inst": "Satbayev University" }, { "name": "Akerkin Eraliyeva", "url": "https://openalex.org/A5120099027", "inst": "Satbayev University" }, { "name": "Chen Dayou", "url": "https://openalex.org/A5120099028", "inst": "Kazakh-Russian International University" }, { "name": "Aidos Joldassov", "url": "https://openalex.org/A5120099029", "inst": "" } ], "affiliations": [ "Satbayev University", "Kazakh-Russian International University" ] }, { "uid": "doi:10.2139/ssrn.5476486", "doi": "10.2139/ssrn.5476486", "title": "Using Generative AI to Mitigate Cognitive Biases in Strategic Roadmapping Workshops", "authors": [ "Robert Vaughn Elliott" ], "posted": "2025-10-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5476486", "field": "management", "role": "agent", "bullets": [ "Cambridge S-Plan strategic roadmapping workshops, demonstrating generative AI as a participant to increase cognitive diversity during early ideation phases.", "Generative AI employed within abductive reasoning to generate strategic initiative hypotheses for workshop participants to review, revise, discard, or combine.", "GenAI collaboration within abductive reasoning increases cognitive diversity beyond analogue techniques alone, assisting with strategy design and roadmapping processes." ], "bullet_provenance": "ai", "validated": false, "salience": 25, "models": [], "n": 3553, "authors_detailed": [ { "name": "Robert Elliott", "url": "https://openalex.org/A5047364816", "inst": "University of Strathclyde" } ], "affiliations": [ "University of Strathclyde" ] }, { "uid": "arxiv:2510.07661v2", "arxiv_id": "2510.07661v2", "title": "IKNet: Interpretable Stock Price Prediction via Keyword-Guided Integration of News and Technical Indicators", "authors": [ "Jinwoong Kim", "Sangjin Park" ], "posted": "2025-10-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.07661v2", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 stocks, daily closing-price forecasts, 2015-2024, using news articles paired with technical indicators.", "FinBERT identifies salient news keywords whose embeddings pass through separate nonlinear projections and merge with time-series features to predict next-day prices.", "IKNet reduces RMSE by up to 32.9% versus RNN and transformer baselines and improves cumulative trading returns by 18.5 percentage points." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "RMSE against actual S&P 500 daily closing prices", "salience": 48, "n": 3954 }, { "uid": "arxiv:2510.06903v1", "arxiv_id": "2510.06903v1", "title": "When Machines Meet Each Other: Network Effects and the Strategic Role of History in Multi-Agent AI", "authors": [ "Yu Liu", "Wenwen Li", "Yifan Dou", "Guangnan Ye" ], "posted": "2025-10-08", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.06903v1", "field": "economics", "role": "agent", "bullets": [ "A canonical network effect participation game in which theory predicts a fulfilled expectation equilibrium, repeated under varied network strengths, price trajectories, and decision history lengths.", "Fifty heterogeneous GPT-5 based agents choose participation each round; individual level regressions relate deviations to price, history structure, and network effects, with no human subject comparison.", "Agents underestimate participation at low prices, overestimate at high prices, and stay dispersed; monotonic histories stabilize coordination while nonmonotonic histories amplify divergence and path dependence." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "edition": 14, "validated": null, "n": 1934, "authors_detailed": [ { "name": "Yu Liu", "url": "https://openalex.org/A5100345786", "inst": "Southwest Jiaotong University" }, { "name": "Wenwen Li", "url": "https://openalex.org/A5100338179", "inst": "Qingdao University" }, { "name": "Yifan Dou", "url": "https://openalex.org/A5076815046", "inst": "Florida State University" }, { "name": "Guangnan Ye", "url": "https://openalex.org/A5003276783", "inst": "Fudan University" } ], "affiliations": [ "Southwest Jiaotong University", "Qingdao University", "Florida State University", "Fudan University" ] }, { "uid": "doi:10.2139/ssrn.5566718", "doi": "10.2139/ssrn.5566718", "title": "Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management", "authors": [ "Runze Zhang", "Xiaowei Zhang", "mingyang zhao" ], "posted": "2025-10-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5566718", "field": "management", "role": "method", "bullet_provenance": "none", "salience": 52, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 358, "authors_detailed": [ { "name": "Runze Zhang", "url": "https://openalex.org/A5015859470", "inst": "Hong Kong University of Science and Technology" }, { "name": "Xiaowei Zhang", "url": "https://openalex.org/A5080690094", "inst": "Hong Kong University of Science and Technology" }, { "name": "Mingyang Zhao", "url": "https://openalex.org/A5015277898", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Hong Kong University of Science and Technology" ] }, { "uid": "arxiv:2511.11583v1", "arxiv_id": "2511.11583v1", "title": "Parallel and Multi-Stage Knowledge Graph Retrieval for Behaviorally Aligned Financial Asset Recommendations", "authors": [ "Fernando Spadea", "Oshani Seneviratne" ], "posted": "2025-10-08", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2511.11583v1", "field": "finance", "role": "instrument", "bullets": [ "Real-world financial transaction dataset with user behavioral data and market signals, evaluated for personalized asset recommendation quality.", "RAG-FLARKO retrieved behaviorally relevant entities from user transaction knowledge graphs, then filtered temporally consistent market signals to construct grounded subgraphs for LLM recommendations.", "Multi-stage retrieval significantly improved recommendation quality in profitability and behavioral alignment, enabling smaller models to match larger ones in resource-constrained deployment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 55, "n": 2603, "authors_detailed": [ { "name": "Fernando Spadea", "url": "https://openalex.org/A5008943760", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Oshani Seneviratne", "url": "https://openalex.org/A5038466673", "inst": "Rensselaer Polytechnic Institute" } ], "affiliations": [ "Rensselaer Polytechnic Institute" ] }, { "uid": "doi:10.2139/ssrn.5533778", "doi": "10.2139/ssrn.5533778", "title": "The Rise of AI Quantitative Investment Funds", "authors": [ "Wenzhong Fan" ], "posted": "2025-10-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5533778", "field": "finance", "role": "object", "bullets": [ "Review of international quantitative funds including Renaissance Technologies, Two Sigma, and Bridgewater Associates, covering the shift from traditional statistical models to AI-driven approaches.", "Surveys DNNs, RNNs, LSTMs, Transformers, reinforcement learning, and generative AI for high-frequency trading, asset allocation, and risk management across the full model lifecycle.", "Concludes future quant fund competitiveness will depend on integrated AI system capabilities rather than individual model performance, emphasizing human-machine collaborative evolution." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 25, "validated": null, "n": 3226, "authors_detailed": [ { "name": "Wenzhong Fan", "url": "", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.5538799", "doi": "10.2139/ssrn.5538799", "title": "Opportunities and Challenges of Agentic AI in Finance", "authors": [ "Saaniya Chugh", "Aditya Vilas Deshpande" ], "posted": "2025-10-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5538799", "field": "finance", "role": "object", "bullets": [ "Conceptual survey of autonomous AI agents in financial services covering fraud detection, portfolio management, algorithmic trading, and automated lending across institutions.", "No specific model tested; paper reviews reinforcement learning, multi-agent collaboration, and explainable AI frameworks for financial decision-making applications.", "Proposes risk-aware AI architectures and real-time anomaly detection to address AI-induced market disruptions such as flash crashes and systemic biases." ], "bullet_provenance": "ai", "salience": 15, "models": [], "validated": null, "n": 3551, "authors_detailed": [ { "name": "Saaniya Chugh", "url": "https://openalex.org/A5119900543", "inst": "Global Services (Slovakia)" }, { "name": "Aditya Deshpande", "url": "https://openalex.org/A5101973708", "inst": "University of the Cumberlands" } ], "affiliations": [ "Global Services (Slovakia)", "University of the Cumberlands" ] }, { "uid": "arxiv:2510.05710v2", "arxiv_id": "2510.05710v2", "title": "FinReflectKG -- EvalBench: Benchmarking Financial KG with Multi-Dimensional Evaluation", "authors": [ "Fabrizio Dimino", "Abhinav Arun", "Bhaskarjit Sarmah", "Stefano Pasquali" ], "posted": "2025-10-07", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.05710v2", "field": "finance", "role": "method", "bullets": [ "SEC 10-K filings of S&P 100 companies underlying FinReflectKG, a financial knowledge graph whose extracted triples link back to source text chunks.", "Unnamed LLMs extract triples in single-pass, multi-pass, and reflection agent modes; LLM judges rate faithfulness, precision, relevance, and comprehensiveness under a commit-then-justify protocol with explicit bias controls.", "Reflection based extraction leads on comprehensiveness, precision, and relevance while single-pass stays most faithful; the authors argue bias controlled LLM judging is a reliable, cheaper substitute for human annotation." ], "bullet_provenance": "ai", "validated": false, "salience": 46, "edition": 14, "models": [], "n": 1932, "authors_detailed": [ { "name": "Fabrizio Dimino", "url": "https://openalex.org/A5095383911", "inst": "Domus Medica" }, { "name": "Abhinav Arun", "url": "https://openalex.org/A5109668483", "inst": "Domus Medica" }, { "name": "Bhaskarjit Sarmah", "url": "https://openalex.org/A5040045971", "inst": "Shree Guru Gobind Singh Tricentenary University" }, { "name": "Stefano Pasquali", "url": "https://openalex.org/A5011160139", "inst": "Ospedale Maggiore" } ], "affiliations": [ "Domus Medica", "Shree Guru Gobind Singh Tricentenary University", "Ospedale Maggiore" ] }, { "uid": "arxiv:2510.05702v2", "arxiv_id": "2510.05702v2", "title": "Uncovering Representation Bias for Investment Decisions in Open-Source Large Language Models", "authors": [ "Fabrizio Dimino", "Krati Saxena", "Bhaskarjit Sarmah", "Stefano Pasquali" ], "posted": "2025-10-07", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.05702v2", "field": "finance", "role": "method", "bullets": [ "Around 150 US equities spanning sectors and size groups, probed for firm level model confidence across fundamental, technical, and growth oriented financial contexts.", "Open weight Qwen models answer balanced round robin prompts with constrained decoding, and token logits aggregate into confidence scores; no accuracy check against ground truth is reported.", "Confidence rises with firm size and valuation, falls with risk factors, and varies most in technology; rankings align best with fundamentals and least with growth indicators." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 46, "edition": 14, "validated": null, "n": 1933, "authors_detailed": [ { "name": "Fabrizio Dimino", "url": "https://openalex.org/A5095383911", "inst": "Domus Medica" }, { "name": "K. B. Saxena", "url": "https://openalex.org/A5047752515", "inst": "Kyushu Institute of Technology" }, { "name": "Bhaskarjit Sarmah", "url": "https://openalex.org/A5040045971", "inst": "Shree Guru Gobind Singh Tricentenary University" }, { "name": "Stefano Pasquali", "url": "https://openalex.org/A5030132224", "inst": "Domus Medica" } ], "affiliations": [ "Domus Medica", "Kyushu Institute of Technology", "Shree Guru Gobind Singh Tricentenary University" ] }, { "uid": "arxiv:2510.05533v1", "arxiv_id": "2510.05533v1", "title": "The New Quant: A Survey of Large Language Models in Financial Prediction and Trading", "authors": [ "Weilong Fu" ], "posted": "2025-10-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.05533v1", "field": "finance", "role": "method", "bullets": [ "Survey synthesizing more than fifty primary studies on LLMs in equity return prediction and trading across multiple markets and time periods.", "Survey covers sentiment extraction, numerical reasoning, retrieval-augmented generation, time-series prompting, and agentic trading systems using various LLM families.", "LLM-based signals improve prediction and trading but face production challenges including temporal leakage, hallucination, deployment costs, and absence of standardized evaluation." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "salience": 75, "validated": null, "n": 2602, "authors_detailed": [ { "name": "Weilong Fu", "url": "https://openalex.org/A5114022526", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.05545v2", "arxiv_id": "2510.05545v2", "title": "Can language models boost the power of randomized experiments without statistical bias?", "authors": [ "Xinrui Ruan", "Xinwei Ma", "Yingfei Wang", "Waverly Wei", "Jingshen Wang" ], "posted": "2025-10-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.05545v2", "field": "economics", "role": "method", "bullets": [ "Simulations calibrated to a mobile-app depression RCT; framework tested in zero-shot and few-shot settings across prompt designs.", "LLMs generate auxiliary prognostic predictions that are bias-corrected and reweighted within a causal estimation framework called CALM.", "CALM delivers lower variance than augmented inverse-probability-weighting estimators and remains stable across prompt designs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "variance compared against AIPW and other causal estimators in calibrated simulations", "salience": 65, "n": 2759, "authors_detailed": [ { "name": "Ruan, Xinrui", "url": "", "inst": "" }, { "name": "Xinwei Ma", "url": "https://openalex.org/A5047495643", "inst": "Hebei University of Technology" }, { "name": "Yingfei Wang", "url": "https://openalex.org/A5038576346", "inst": "Southwestern Medical Center" }, { "name": "Waverly Wei", "url": "https://openalex.org/A5073562418", "inst": "California Southern University" }, { "name": "Jingshen Wang", "url": "https://openalex.org/A5043785463", "inst": "Wuhan University of Technology" } ], "affiliations": [ "Hebei University of Technology", "California Southern University", "Wuhan University of Technology" ] }, { "uid": "arxiv:2510.06426v1", "arxiv_id": "2510.06426v1", "title": "FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering", "authors": [ "Yitao Long", "Tiansheng Hu", "Yilun Zhao", "Arman Cohan", "Chen Zhao" ], "posted": "2025-10-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.06426v1", "field": "finance", "role": "method", "bullets": [ "Benchmark of eight LLMs on complex financial questions requiring attributed long-form answers from financial reports.", "LLMs generate answers with three attribution layers: supporting evidence, numerical reasoning steps, and domain-specific financial knowledge.", "Fine-grained metrics distinguish model capabilities; end-to-end generation matches post-hoc attribution; iterative refinement helps only with external feedback." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "human-annotated attribution quality and automatic evaluation framework", "salience": 55, "n": 2760, "authors_detailed": [ { "name": "Yi‐Tao Long", "url": "https://openalex.org/A5034885015", "inst": "Nanjing University of Science and Technology" }, { "name": "Tiansheng Hu", "url": "https://openalex.org/A5119963925", "inst": "New York University Shanghai" }, { "name": "Yilun Zhao", "url": "https://openalex.org/A5037981788", "inst": "New York University Shanghai" }, { "name": "Arman Cohan", "url": "https://openalex.org/A5064858748", "inst": "Yale University" }, { "name": "Chen Zhao", "url": "https://openalex.org/A5100352014", "inst": "Wuhan University" } ], "affiliations": [ "New York University Shanghai", "Yale University", "Nanjing University of Science and Technology", "Wuhan University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5543139", "doi": "10.2139/ssrn.5543139", "title": "Ex Machina: Financial Stability in the Age of Artificial Intelligence", "authors": [ "Kartik Anand", "Sophia Kazinnik", "Agnese Leonello", "Ettore Panetti" ], "posted": "2025-10-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5543139", "field": "finance", "role": "agent", "bullets": [ "Simulated mutual fund redemption game with economic and strategic uncertainty comparing Q-learning and LLM investor architectures.", "Q-learning and LLM investors solved a coordination game to test whether AI architecture affects financial stability outcomes.", "Q-learning investors amplify fragility via excessive redemption under default risk; LLM investors show belief heterogeneity weakening coordination." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3052 }, { "uid": "arxiv:2510.04787v2", "arxiv_id": "2510.04787v2", "title": "Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading", "authors": [ "Zifan Song", "Kaitao Song", "Guosheng Hu", "Ding Qi", "Junyao Gao", "Xiaohua Wang", "Dongsheng Li", "Cairong Zhao" ], "posted": "2025-10-06", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.04787v2", "field": "finance", "role": "method", "bullets": [ "Quantitative trading across stock and cryptocurrency markets, evaluated on more than 200 trading pairs under volatile conditions; sample period is not stated.", "TiMi, a multi-agent system of unnamed LLMs, separates strategy development from minute level deployment, using semantic analysis, code programming, and mathematical reflection to build and refine trading bots.", "The authors report stable profitability, action efficiency, and risk control under volatile market dynamics; no return magnitudes are given in the abstract." ], "bullet_provenance": "ai", "salience": 40, "edition": 14, "models": [], "validated": null, "n": 1931, "authors_detailed": [ { "name": "Zifan Song", "url": "https://openalex.org/A5119912223", "inst": "" }, { "name": "Kaitao Song", "url": "https://openalex.org/A5028035527", "inst": "Fudan University" }, { "name": "Guosheng Hu", "url": "https://openalex.org/A5075333422", "inst": "Fujian Normal University" }, { "name": "Qi Ding", "url": "https://openalex.org/A5101819015", "inst": "Beijing University of Civil Engineering and Architecture" }, { "name": "Gao, Junyao", "url": "", "inst": "" }, { "name": "Xiao Hua Wang", "url": "https://openalex.org/A5100438577", "inst": "Yunnan Normal University" }, { "name": "Dong‐Sheng Li", "url": "https://openalex.org/A5100440919", "inst": "China Three Gorges University" }, { "name": "Cairong Zhao", "url": "https://openalex.org/A5119912225", "inst": "" } ], "affiliations": [ "Fudan University", "Fujian Normal University", "Beijing University of Civil Engineering and Architecture", "Yunnan Normal University", "China Three Gorges University" ] }, { "uid": "arxiv:2510.05431v4", "arxiv_id": "2510.05431v4", "title": "Self-Filtered Distillation with LLMs-generated Trust Indicators for Reliable Patent Classification", "authors": [ "Yongmin Yoo", "Xu Zhang", "Longbing Cao" ], "posted": "2025-10-06", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.05431v4", "field": "management", "role": "method", "bullets": [ "The USPTO-2M benchmark of over two million patents assigned to CPC classes, a task underlying prior art retrieval and intellectual property decisions.", "LLM-generated rationales, source model not named, become trust scores built from self-consistency, entailment with class definitions, and an independent LLM verifier, weighting each instance during distillation into four student architectures.", "Macro-F1 improves by up to 38.7 percent relative to unfiltered training, and trust scores correlate at 0.685 with expert judgments, supporting auditable classification." ], "bullet_provenance": "ai", "validated": true, "validation_note": "USPTO-2M labels; trust scores vs expert judgments, r = 0.685", "salience": 40, "edition": 14, "models": [], "n": 2051, "authors_detailed": [ { "name": "Yoo Yongmin", "url": "https://openalex.org/A5119913834", "inst": "" }, { "name": "Xu Zhang", "url": "https://openalex.org/A5100437302", "inst": "Chongqing University" }, { "name": "Cao Longbing", "url": "https://openalex.org/A5119913835", "inst": "" } ], "affiliations": [ "Chongqing University" ] }, { "uid": "doi:10.2139/ssrn.5516798", "doi": "10.2139/ssrn.5516798", "title": "Generative AI and Labor Market Outcomes: Evidence from the United Kingdom", "authors": [ "Bouke Klein Teeselink" ], "posted": "2025-10-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5516798", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 681, "authors_detailed": [ { "name": "Bouke Klein Teeselink", "url": "https://openalex.org/A5027301247", "inst": "King's College London" } ], "affiliations": [ "King's College London" ] }, { "uid": "doi:10.2139/ssrn.5572312", "doi": "10.2139/ssrn.5572312", "title": "Enterprise Violation Prediction Based on Multi-Agent and Multimodal Graph Neural Network", "authors": [ "Jinlong Wang", "Yingmin liu", "Shupeng Li", "Xiaoyun Xiong", "Hao-Ran Zhao", "Zhihan Lyu" ], "posted": "2025-10-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5572312", "field": "accounting", "role": "instrument", "bullets": [ "Corporate violation prediction for listed firms; sample size, time period, and geography are not stated, with the individual enterprise as the unit inside an inter-firm risk network.", "A large language model based multi-agent system quantifies multimodal risk factors that feed a personalized PageRank step and a multimodal graph neural network; the specific model is not stated.", "The framework reports higher accuracy, precision, recall, and F1 than traditional methods, though the magnitudes of the improvements are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 3, "audience": "technical", "models": [], "n": 682 }, { "uid": "arxiv:2510.04643v1", "arxiv_id": "2510.04643v1", "title": "QuantAgents: Towards Multi-agent Financial System via Simulated Trading", "authors": [ "Xiangyu Li", "Yawen Zeng", "Xiaofen Xing", "Jin Xu", "Xiangmin Xu" ], "posted": "2025-10-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.04643v1", "field": "finance", "role": "agent", "bullets": [ "A multi-agent LLM system with four specialized roles (simulated trading analyst, risk control analyst, market news analyst, manager) is backtested over three years of market data.", "LLM-based agents conduct simulated trading to evaluate investment strategies and predict future trends, receiving feedback from both real-market performance and simulated trading accuracy.", "The multi-agent system achieved an overall return of nearly 300% over three years, outperforming baselines across all reported metrics." ], "bullet_provenance": "ai", "validated": true, "validation_note": "backtested against real market returns", "salience": 50, "models": [], "n": 3542, "authors_detailed": [ { "name": "Xiangyu Li", "url": "https://openalex.org/A5100460318", "inst": "University of Southern California" }, { "name": "Zeng, Yawen", "url": "", "inst": "" }, { "name": "Xiaofen Xing", "url": "https://openalex.org/A5116337245", "inst": "South China University of Technology" }, { "name": "Jin Xu", "url": "https://openalex.org/A5101998573", "inst": "Hong Kong Polytechnic University" }, { "name": "Xixiong Xu", "url": "https://openalex.org/A5101665164", "inst": "Chongqing University" } ], "affiliations": [ "University of Southern California", "South China University of Technology", "Hong Kong Polytechnic University", "Chongqing University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5516478", "doi": "10.2139/ssrn.5516478", "title": "AI for Sovereign Wealth Funds - Integration into Investment Frameworks", "authors": [ "Guan Seng Khoo", "Reza Mahmud", "Kristian Flyvholm" ], "posted": "2025-10-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5516478", "field": "finance", "role": "object", "bullets": [ "Sovereign wealth funds managing $14 trillion in assets; conceptual analysis of AI integration into investment frameworks across portfolio management and risk assessment.", "No specific model deployed; paper surveys how predictive analytics and agentic AI can identify investment opportunities and automate due diligence and compliance monitoring.", "AI-embedded SWFs may gain competitive advantage through operational alpha, real-time risk modeling, and enhanced governance, though no empirical evidence is provided." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3550, "authors_detailed": [ { "name": "Guan Seng Khoo", "url": "https://openalex.org/A5064433560", "inst": "Singapore Management University" }, { "name": "Reza Mahmud", "url": "https://openalex.org/A5119867585", "inst": "SV Health Investors (United States)" }, { "name": "Kristian Flyvholm", "url": "https://openalex.org/A5119110125", "inst": "SV Health Investors (United States)" } ], "affiliations": [ "Singapore Management University", "SV Health Investors (United States)" ] }, { "uid": "arxiv:2510.03633v1", "arxiv_id": "2510.03633v1", "title": "Predicting Stock Price Movement with LLM-Enhanced Tweet Emotion Analysis", "authors": [ "An Vuong", "Susan Gauch" ], "posted": "2025-10-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.03633v1", "field": "finance", "role": "instrument", "bullets": [ "Tweets and prior day prices for TSLA, AAPL, and AMZN, used to predict significant next day price moves; the sample period is not stated.", "Llama 3.1-8B-Instruct preprocesses tweet text before emotion scoring by a DistilRoBERTa classifier and two NRC lexicon methods feeding an LSTM; emotion labels are not checked against human coding.", "All three emotion pipelines beat the 13.5 percent accuracy of a price-only baseline; the DistilRoBERTa variant improves from 23.6 to 38.5 percent with Llama preprocessing." ], "bullet_provenance": "ai", "models": [ "legacy", "llama" ], "open_weights": true, "validated": false, "salience": 34, "edition": 14, "n": 2050, "authors_detailed": [ { "name": "An Vuong", "url": "https://openalex.org/A5113975723", "inst": "La Trobe University" }, { "name": "Susan Gauch", "url": "https://openalex.org/A5032138644", "inst": "University of Arkansas at Fayetteville" } ], "affiliations": [ "La Trobe University", "University of Arkansas at Fayetteville" ] }, { "uid": "arxiv:2510.03195v5", "arxiv_id": "2510.03195v5", "title": "From Text to Alpha: Can LLMs Track Evolving Signals in Corporate Disclosures?", "authors": [ "Chanyeol Choi", "Yoon Kim", "Yu Yu", "Young Cha", "V. Zach Golkhou", "Igor Halperin", "Georgios Papaioannou", "Minkyu Kim", "Zhangyang Wang", "Jihoon Kwon", "Minjae Kim", "Alejandro Lopez-Lira", "Yongjae Lee" ], "posted": "2025-10-03", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.03195v5", "field": "finance", "role": "instrument", "bullets": [ "Corporate disclosures across consecutive reporting periods; firm universe, market, and sample period are not stated; predictions tested with portfolio sorts and cross-sectional regressions.", "An LLM, not named, extracts metric-focused spans and embedding similarity scores semantic change between periods, measuring how far firms move from previously emphasized metrics; no validation against human labels is reported.", "The moving-target measure earns more than twice the risk-adjusted alpha of a NER-based baseline, with gains attributed to preserving contextual qualifiers and filtering non-metric terms." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 14, "models": [], "n": 2049, "authors_detailed": [ { "name": "Choi, Chanyeol", "url": "", "inst": "" }, { "name": "Kim, Yoon", "url": "", "inst": "" }, { "name": "Yu, Yu", "url": "", "inst": "" }, { "name": "Cha, Young", "url": "", "inst": "" }, { "name": "Golkhou, V. Zach", "url": "", "inst": "" }, { "name": "Halperin, Igor", "url": "", "inst": "" }, { "name": "Papaioannou, Georgios", "url": "", "inst": "" }, { "name": "Kim, Minkyu", "url": "", "inst": "" }, { "name": "Wang, Zhangyang", "url": "", "inst": "" }, { "name": "Kwon, Jihoon", "url": "", "inst": "" }, { "name": "Kim, Minjae", "url": "", "inst": "" }, { "name": "Lopez-Lira, Alejandro", "url": "", "inst": "" }, { "name": "Lee, Yongjae", "url": "", "inst": "" } ] }, { "uid": "arxiv:2510.15929v1", "arxiv_id": "2510.15929v1", "title": "Comparing LLMs for Sentiment Analysis in Financial Market News", "authors": [ "Lucas Eduardo Pereira Teles", "Carlos M. S. Figueiredo" ], "posted": "2025-10-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.15929v1", "field": "finance", "role": "instrument", "bullets": [ "Comparative study of LLMs and classical NLP models on sentiment analysis of financial market news articles.", "Multiple large language models classify sentiment in financial news and are benchmarked against traditional approaches.", "LLMs outperform classical models in the vast majority of financial sentiment classification cases." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "LLM accuracy compared against classical model baselines", "salience": 35, "n": 2758, "authors_detailed": [ { "name": "Lucas Eduardo Pereira Teles", "url": "https://openalex.org/A5120275202", "inst": "" }, { "name": "Carlos M. S. Figueiredo", "url": "https://openalex.org/A5024375930", "inst": "Universidade do Estado do Amazonas" } ], "affiliations": [ "Universidade do Estado do Amazonas" ] }, { "uid": "arxiv:2510.02906v1", "arxiv_id": "2510.02906v1", "title": "FinReflectKG -- MultiHop: Financial QA Benchmark for Reasoning with Knowledge Graph Evidence", "authors": [ "Abhinav Arun", "Reetu Raj Harsh", "Bhaskarjit Sarmah", "Stefano Pasquali" ], "posted": "2025-10-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.02906v1", "field": "finance", "role": "method", "bullets": [ "Multi-hop financial QA benchmark built on a temporal knowledge graph from S&P 100 filings spanning 2022 to 2024, with 555 released pairs.", "Reasoning and non-reasoning LLMs evaluated under three retrieval scenarios: KG-linked paths, text-only page windows, and windows with distractors.", "KG-guided retrieval boosted correctness scores by approximately 24% while reducing token usage by 84.5% versus traditional page-window retrieval." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinReflectKG-MultiHop benchmark correctness scores", "salience": 60, "models": [], "n": 3051, "authors_detailed": [ { "name": "Abhinav Arun", "url": "https://openalex.org/A5109668483", "inst": "Domus Medica" }, { "name": "Reetu Raj Harsh", "url": "https://openalex.org/A5120289877", "inst": "Domus Medica" }, { "name": "Bhaskarjit Sarmah", "url": "https://openalex.org/A5040045971", "inst": "Shree Guru Gobind Singh Tricentenary University" }, { "name": "Stefano Pasquali", "url": "https://openalex.org/A5011160139", "inst": "Ospedale Maggiore" } ], "affiliations": [ "Domus Medica", "Shree Guru Gobind Singh Tricentenary University", "Ospedale Maggiore" ] }, { "uid": "doi:10.2139/ssrn.5546300", "doi": "10.2139/ssrn.5546300", "title": "Accounting Publication Process: Are we Making Progress? What about GenAI?", "authors": [ "John Barrick", "Scott L. Summers", "David A. Wood" ], "posted": "2025-10-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5546300", "field": "accounting", "role": "object", "bullets": [ "A longitudinal survey of over 1,100 accounting faculty extends data collected in 2015 and 2020 on perceptions of the accounting publication process.", "The survey documents generative AI adoption rates, planned usage, and faculty expectations about the future role of AI tools in accounting research.", "Nearly two-thirds of faculty already use generative AI tools, another 17% plan to adopt, and most expect use to become unavoidable within five years; perceptions of the publication process remain as negative or worse than prior waves." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3541, "authors_detailed": [ { "name": "John A. Barrick", "url": "https://openalex.org/A5019022162", "inst": "Brigham Young University" }, { "name": "Scott L. Summers", "url": "https://openalex.org/A5087829262", "inst": "Brigham Young University" }, { "name": "David A. Wood", "url": "https://openalex.org/A5075888890", "inst": "Brigham Young University" } ], "affiliations": [ "Brigham Young University" ] }, { "uid": "arxiv:2510.02209v2", "arxiv_id": "2510.02209v2", "title": "StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?", "authors": [ "Yanxu Chen", "Zijun Yao", "Yantao Liu", "Amy Xin", "Jin Ye", "Jianing Yu", "Lei Hou", "Juanzi Li" ], "posted": "2025-10-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.02209v2", "field": "finance", "role": "method", "bullets": [ "A contamination-free simulated trading environment over several months of daily prices, fundamentals, and news, in which agents make sequential buy, sell, or hold decisions; markets covered are not stated.", "Proprietary and open-source LLM agents, not individually named in the abstract, are scored on cumulative return, maximum drawdown, and Sortino ratio.", "Most models fail to beat a buy and hold baseline, and strength on static financial question answering does not translate into profitable trading behavior." ], "bullet_provenance": "ai", "salience": 60, "edition": 14, "models": [], "validated": null, "n": 2048, "authors_detailed": [ { "name": "Chen, Yanxu", "url": "", "inst": "" }, { "name": "Zijun Yao", "url": "https://openalex.org/A5119847051", "inst": "" }, { "name": "Yantao Liu", "url": "https://openalex.org/A5055407270", "inst": "" }, { "name": "Xin, Amy", "url": "", "inst": "" }, { "name": "Jin Ye", "url": "https://openalex.org/A5100447775", "inst": "Southwest University" }, { "name": "Jianing Yu", "url": "https://openalex.org/A5114310347", "inst": "Shandong University" }, { "name": "Lei Hou", "url": "https://openalex.org/A5010929778", "inst": "Nanjing University of Information Science and Technology" }, { "name": "Jinyan Li", "url": "https://openalex.org/A5101519526", "inst": "Ningxia University" } ], "affiliations": [ "Southwest University", "Shandong University", "Nanjing University of Information Science and Technology", "Ningxia University" ] }, { "uid": "doi:10.2139/ssrn.5512859", "doi": "10.2139/ssrn.5512859", "title": "Distant Investments: Decoding Mutual Fund Skill with Large Language Models", "authors": [ "Xiyuan Ma", "Matthew I. Spiegel", "Hong Zhang", "Yijun Zhou" ], "posted": "2025-10-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5512859", "field": "finance", "role": "instrument", "bullets": [ "U.S. mutual funds and their portfolio holdings, measuring semantic distance between fund prospectuses and firms' 10-K filings.", "LLMs computed semantic distance between fund prospectuses and 10-K strategic priorities, capturing hard-to-process information requiring managerial expertise.", "Funds with high traditional skill measures outperformed peers only when investing in semantically distant stocks; those distant investments predicted future returns." ], "bullet_provenance": "ai", "validated": false, "salience": 75, "models": [], "n": 2506, "authors_detailed": [ { "name": "Xiuliang Ma", "url": "https://openalex.org/A5100630953", "inst": "Singapore Management University" }, { "name": "Matthew Spiegel", "url": "https://openalex.org/A5036166835", "inst": "Yale University" }, { "name": "Hong Zhang", "url": "https://openalex.org/A5100430314", "inst": "Singapore Management University" }, { "name": "Yijun Zhou", "url": "https://openalex.org/A5009249209", "inst": "Baruch College" } ], "affiliations": [ "Yale University", "Singapore Management University", "Baruch College" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.05151v1", "arxiv_id": "2510.05151v1", "title": "Exploring Large Language Models for Financial Applications: Techniques, Performance, and Challenges with FinMA", "authors": [ "Prudence Djagba", "Abdelkader Y. Saley" ], "posted": "2025-10-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.05151v1", "field": "finance", "role": "method", "bullets": [ "FinMA model evaluated on the FLARE benchmark spanning sentiment analysis, classification, numerical reasoning, entity recognition, and summarization tasks.", "Domain-adapted open-weight LLM fine-tuned on Financial Instruction Tuning dataset within the PIXIU framework for financial NLP tasks.", "FinMA performed well on sentiment and classification but underperformed on numerical reasoning, entity recognition, and summarization tasks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "FLARE benchmark across financial NLP tasks", "salience": 40, "n": 2745, "authors_detailed": [ { "name": "Prudence Djagba", "url": "https://openalex.org/A5119912909", "inst": "" }, { "name": "Abdelkader Y. Saley", "url": "https://openalex.org/A5119912910", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5471186", "doi": "10.2139/ssrn.5471186", "title": "How Enterprises can Audit Their AI Visibility: A PSOS™-Based Framework", "authors": [ "Timothy de Rosen" ], "posted": "2025-10-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5471186", "field": "management", "role": "object", "bullets": [ "Enterprise brand visibility audited across ChatGPT, Gemini, and Perplexity as generative AI systems becoming primary consumer discovery channels.", "PSOS framework within the AIVO Standard provided structured methodology for measuring brand appearance in AI-generated responses.", "Framework equips CMOs and boards with actionable audit metrics for optimizing brand presence and mitigating AI visibility risk." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "salience": 45, "validated": null, "n": 2746, "authors_detailed": [ { "name": "Timothy de Rosen", "url": "https://openalex.org/A5119286499", "inst": "Independent researcher" } ], "affiliations": [ "Independent researcher" ] }, { "uid": "arxiv:2510.01664v1", "arxiv_id": "2510.01664v1", "title": "GuruAgents: Emulating Wise Investors with Prompt-Guided LLM Agents", "authors": [ "Yejin Kim", "Youngbin Lee", "Juhyeong Kim", "Yongjae Lee" ], "posted": "2025-10-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.01664v1", "field": "finance", "role": "agent", "bullets": [ "Five LLM-based agents emulating legendary investors backtested on NASDAQ-100 constituents from Q4 2023 through Q2 2025.", "Each GuruAgent encoded an iconic investor's philosophy into LLM prompts integrated with financial tools and a deterministic reasoning pipeline.", "The Buffett GuruAgent achieved 42.2% CAGR significantly outperforming benchmarks; other persona-driven agents showed varied portfolio results." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 3050, "authors_detailed": [ { "name": "Yejin Kim", "url": "https://openalex.org/A5119844698", "inst": "" }, { "name": "Youngbin Lee", "url": "https://openalex.org/A5020025335", "inst": "Seoul National University" }, { "name": "Juhyeong Kim", "url": "https://openalex.org/A5119844700", "inst": "" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5119844701", "inst": "Ulsan College" } ], "affiliations": [ "Seoul National University", "Ulsan College" ] }, { "uid": "arxiv:2510.01115v2", "arxiv_id": "2510.01115v2", "title": "Exploring Network-Knowledge Graph Duality: A Case Study in Agentic Supply Chain Risk Analysis", "authors": [ "Evan Heus", "Rick Bookstaber", "Dhruv Sharma" ], "posted": "2025-10-01", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.01115v2", "field": "finance", "role": "method", "bullets": [ "Supply chain networks treated as knowledge graphs, joined with numerical factor tables and news streams for firm level risk analysis; data coverage is not stated.", "An agentic framework around an unnamed LLM retrieves risk paths ranked by network centrality and wraps figures in context shell templates; no validation against ground truth is reported.", "The system generates real time, explainable risk narratives without fine-tuning or a graph database; no quantitative performance evidence appears in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 36, "edition": 14, "models": [], "n": 1930, "authors_detailed": [ { "name": "Evan Heus", "url": "https://openalex.org/A5119841421", "inst": "" }, { "name": "Rick Bookstaber", "url": "https://openalex.org/A5119841422", "inst": "" }, { "name": "Dhruv Sharma", "url": "https://openalex.org/A5101623851", "inst": "Institute of Management Technology" } ], "affiliations": [ "Institute of Management Technology" ] }, { "uid": "arxiv:2510.01526v1", "arxiv_id": "2510.01526v1", "title": "One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning", "authors": [ "Mengyu Wang", "Sotirios Sabanis", "Miguel de Carvalho", "Shay B. Cohen", "Tiejun Ma" ], "posted": "2025-10-01", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.01526v1", "field": "finance", "role": "method", "bullets": [ "Four financial question answering benchmarks requiring specialist knowledge and quantitative reasoning; training uses a few thousand examples and a single A100 GPU.", "A two-step fine-tuned decomposition model, base LLM not named, generates one supporting sub-question per query under a reward for improved answers; inference time matches zero-shot prompting.", "Answer accuracy improves 0.6 to 10.5 percent across host LLMs, beating domain-tuned models and prompting strategies; a single supporting question helps more than detailed guidance steps." ], "bullet_provenance": "ai", "validated": true, "validation_note": "four financial QA benchmarks, accuracy reported", "salience": 44, "edition": 14, "models": [], "n": 2047, "authors_detailed": [ { "name": "Mengyu Wang", "url": "https://openalex.org/A5075971836", "inst": "North China Electric Power University" }, { "name": "Sotirios Sabanis", "url": "https://openalex.org/A5009097122", "inst": "Turing Institute" }, { "name": "Miguel de Carvalho", "url": "https://openalex.org/A5033424403", "inst": "University of Aveiro" }, { "name": "Shay B. Cohen", "url": "https://openalex.org/A5030503109", "inst": "Israel Institute for Biological Research" }, { "name": "Tiejun Ma", "url": "https://openalex.org/A5100939543", "inst": "Northwestern Polytechnical University" } ], "affiliations": [ "North China Electric Power University", "Turing Institute", "University of Aveiro", "Israel Institute for Biological Research", "Northwestern Polytechnical University" ] }, { "uid": "doi:10.17016/feds.2025.090", "doi": "10.17016/feds.2025.090", "arxiv_id": "2510.01451v1", "title": "Financial Stability Implications of Generative AI: Taming the Animal Spirits", "authors": [ "Anne Lundgaard Hansen", "Seung Jung Lee" ], "posted": "2025-10-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.01451v1", "field": "finance", "role": "agent", "bullets": [ "Laboratory-style experiments replicating classic herd-behavior trading studies using LLM agents as decision-makers under varying conditions.", "LLM agents made trading decisions to test reliance on private information versus market trends across multiple experimental settings.", "AI agents relied more on private information than humans, reducing herd-driven bubbles, but could be induced to herd optimally when guided to maximize profits." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 82, "n": 2858, "authors_detailed": [ { "name": "Anne Lundgaard Hansen", "url": "https://openalex.org/A5024095797", "inst": "Federal Reserve Bank of Richmond" }, { "name": "Seung Jung Lee", "url": "https://openalex.org/A5012787769", "inst": "Federal Reserve Board of Governors" } ], "affiliations": [ "Federal Reserve Bank of Richmond", "Federal Reserve Board of Governors" ] }, { "uid": "doi:10.2139/ssrn.5552578", "doi": "10.2139/ssrn.5552578", "title": "Perceiving Central Bank Communications through Press Coverage", "authors": [ "Diego José Torres", "Pilar Garcia" ], "posted": "2025-10-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5552578", "field": "finance", "role": "instrument", "bullets": [ "Press coverage of central bank communications analyzed for dovish-hawkish tone orientation using semantic methods and BERT.", "A bag-of-words semantic orientation index measured media-perceived tone and was benchmarked against a BERT-based large language model approach.", "The simple media-tone index correlates more strongly with 2-, 5-, and 10-year Treasury yields and inflation expectations than BERT-based indices." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 60, "n": 3049, "authors_detailed": [ { "name": "Diego José Torres", "url": "https://openalex.org/A5119791246", "inst": "Bank of Spain" }, { "name": "Pilar García", "url": "https://openalex.org/A5115955434", "inst": "Bank of Spain" } ], "affiliations": [ "Bank of Spain" ] }, { "uid": "arxiv:2510.05140v2", "arxiv_id": "2510.05140v2", "title": "Auditing Algorithmic Bias in Transformer-Based Trading", "authors": [ "Armin Gerami", "Ramani Duraiswami" ], "posted": "2025-10-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.05140v2", "field": "finance", "role": "method", "bullets": [ "Transformer model applied to multi-asset financial trading, analyzing decision-making bias across varying data volatility and frequency characteristics.", "Transformer predictions audited using Partial Information Decomposition metric measuring each asset's influence on trading decisions.", "Model disregards data volatility entirely and is biased toward assets with lower-frequency price movements in decision-making." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 45, "n": 3953, "authors_detailed": [ { "name": "Armin Gerami", "url": "https://openalex.org/A5093925866", "inst": "Interface (United States)" }, { "name": "Ramani Duraiswami", "url": "https://openalex.org/A5013222310", "inst": "Nvidia (United Kingdom)" } ], "affiliations": [ "Interface (United States)", "Nvidia (United Kingdom)" ] }, { "uid": "arxiv:2509.25803v1", "arxiv_id": "2509.25803v1", "title": "Better with Less: Small Proprietary Models Surpass Large Language Models in Financial Transaction Understanding", "authors": [ "Wanying Ding", "Savinay Narendra", "Xiran Shi", "Adwait Ratnaparkhi", "Chengrui Yang", "Nikoo Sabzevar", "Ziyan Yin" ], "posted": "2025-09-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.25803v1", "field": "finance", "role": "method", "bullets": [ "Industrial financial transaction data supporting compliance, fraud detection, and decision tasks at the authors' firm; sample size and period are not stated.", "Benchmarks pretrained and fine-tuned LLMs, including LLaMA3 8b, Flan-T5, and SBERT, against small transformers built from scratch; evaluation data and accuracy figures are not stated.", "Small custom models match the LLMs while running faster and cheaper; the deployed decoder only model lifts transaction coverage 14 percent and saves over 13 million dollars a year." ], "bullet_provenance": "ai", "models": [ "legacy", "llama" ], "open_weights": false, "validated": false, "salience": 42, "edition": 14, "n": 1929 }, { "uid": "arxiv:2509.25709v1", "arxiv_id": "2509.25709v1", "title": "Leveraging LLMs to Improve Experimental Design: A Generative Stratification Approach", "authors": [ "George Gui", "Seungwoo Kim" ], "posted": "2025-09-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.25709v1", "field": "economics", "role": "method", "bullets": [ "Multiple field experiments with high-dimensional covariate data used to test LLM-based stratification against simple randomization designs.", "LLMs synthesized covariate information to generate blocking strata for pre-experiment assignment in randomized controlled trials.", "Generative stratification reduced treatment-effect estimate variance by 10% to 50% compared to simple randomization across empirical applications." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "variance reduction 10-50% vs simple randomization in empirical applications", "salience": 65, "n": 2743 }, { "uid": "arxiv:2509.25649v1", "arxiv_id": "2509.25649v1", "title": "The Media Bias Detector: A Framework for Annotating and Analyzing the News at Scale", "authors": [ "Samar Haider", "Amir Tohidi", "Jenny S. Wang", "Timothy Dörr", "David M. Rothschild", "Chris Callison-Burch", "Duncan J. Watts" ], "posted": "2025-09-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.25649v1", "field": "economics", "role": "instrument", "bullets": [ "Over 150,000 news articles from January 2024 onward scraped daily from major U.S. publishers for near-real-time bias measurement.", "LLMs extracted structured annotations including political lean, tone, topics, and article type at sentence, article, and publisher levels.", "Pipeline revealed systematic selection and framing differences across publishers with interactive platform released for public exploration." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 2744 }, { "uid": "arxiv:2510.00205v1", "arxiv_id": "2510.00205v1", "title": "Quantifying Semantic Shift in Financial NLP: Robust Metrics for Market Prediction Stability", "authors": [ "Zhongtian Sun", "Chenghao Xiao", "Anoushka Harit", "Jongmin Yu" ], "posted": "2025-09-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.00205v1", "field": "finance", "role": "method", "bullets": [ "LSTM and Transformer models evaluated across four economic periods: pre-COVID, COVID, post-COVID, and rate-hike regimes for market prediction.", "Four robustness metrics (FCAS, PCS, TSV, NLICS) quantified semantic and causal drift in financial NLP; GPT-4 case study assessed alignment consistency.", "Transformers degraded more than LSTMs under regime shifts; semantic volatility and distributional divergence correlated with increased prediction error." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "prediction error correlation with semantic metrics across four regimes", "salience": 55, "n": 3225 }, { "uid": "arxiv:2510.01286v1", "arxiv_id": "2510.01286v1", "title": "Emergent evaluation hubs in a decentralizing large language model ecosystem", "authors": [ "Manuel Cebrian", "Tomomi Kito", "Raul Castro Fernandez" ], "posted": "2025-09-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.01286v1", "field": "economics", "role": "object", "bullets": [ "Analysis draws on the Stanford Foundation-Model Ecosystem Graph and Evidently AI benchmark registry to map model creation and benchmark production across countries and organizations.", "Network analysis and agent-based simulation measure concentration in LLM benchmark influence versus decentralization in model production, using betweenness centrality and Gini coefficients.", "Model creation diversified across countries and modalities, but benchmark influence centralized: top 15% of nodes hold over 80% of high-betweenness paths, three countries produce 83% of benchmarks, and the benchmark Gini reaches 0.89." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3539, "authors_detailed": [ { "name": "Manuel Cebrián", "url": "https://openalex.org/A5017095669", "inst": "Consejo Superior de Investigaciones Científicas" }, { "name": "Tomomi Kito", "url": "https://openalex.org/A5063339619", "inst": "Waseda University" }, { "name": "Ronield Fernandez", "url": "https://openalex.org/A5051821282", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "Consejo Superior de Investigaciones Científicas", "Waseda University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.25745v1", "arxiv_id": "2509.25745v1", "title": "FinCap: Topic-Aligned Captions for Short-Form Financial YouTube Videos", "authors": [ "Siddhant Sukhani", "Yash Bhardwaj", "Riya Bhadani", "Veer Kejriwal", "Michael Galarnyk", "Sudheer Chava" ], "posted": "2025-09-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.25745v1", "field": "finance", "role": "instrument", "bullets": [ "624 annotated short-form YouTube videos on financial topics were used to test multimodal LLM captioning across seven modality combinations of transcript, audio, and video.", "Multimodal large language models generated topic-aligned captions for five financial tasks including sentiment analysis, recommendation extraction, and financial entity recognition.", "Video-only input performed strongly on four of five tasks; selective modality pairs often surpassed the full three-modality combination, suggesting additional modalities can introduce noise." ], "bullet_provenance": "ai", "validated": true, "validation_note": "624 human-annotated financial YouTube videos", "salience": 40, "models": [], "n": 3540, "authors_detailed": [ { "name": "Siddhant Sukhani", "url": "https://openalex.org/A5114620072", "inst": "Georgia Institute of Technology" }, { "name": "Yash Bhardwaj", "url": "https://openalex.org/A5084078737", "inst": "Georgia Institute of Technology" }, { "name": "Riya Bhadani", "url": "https://openalex.org/A5117596067", "inst": "Georgia Institute of Technology" }, { "name": "Veer Kejriwal", "url": "https://openalex.org/A5114620073", "inst": "Georgia Institute of Technology" }, { "name": "Michael Galarnyk", "url": "https://openalex.org/A5107099335", "inst": "Georgia Institute of Technology" }, { "name": "Sudheer Chava", "url": "https://openalex.org/A5029248881", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.00332v2", "arxiv_id": "2510.00332v2", "title": "When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets", "authors": [ "Zeshi Dai", "Zimo Peng", "Zerui Cheng", "Ryan Yihe Li" ], "posted": "2025-09-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.00332v2", "field": "finance", "role": "method", "bullets": [ "178 time-anchored tasks in crypto markets requiring agents to navigate adversarial misinformation, with $30 billion lost to exploits in 2024.", "17 AI models evaluated on distinguishing truth from manipulation and making irreversible financial decisions with and without tool augmentation.", "Frontier models achieved 28% accuracy without tools versus 80% human baseline; tool augmentation plateaued at 67.4% due to systematic preference for unreliable sources." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "open_other" ], "validated": true, "validation_note": "accuracy on 178 tasks vs human analyst baseline", "salience": 65, "n": 3952, "authors_detailed": [ { "name": "Dai, Zeshi", "url": "", "inst": "" }, { "name": "Peng, Zimo", "url": "", "inst": "" }, { "name": "Zerui Cheng", "url": "https://openalex.org/A5022462029", "inst": "Xiamen University" }, { "name": "Ryan Yihe Li", "url": "https://openalex.org/A5082185879", "inst": "" } ], "affiliations": [ "Xiamen University" ] }, { "uid": "arxiv:2509.24342v1", "arxiv_id": "2509.24342v1", "title": "Fin-Ally: Pioneering the Development of an Advanced, Commonsense-Embedded Conversational AI for Money Matters", "authors": [ "Sarmistha Das", "Priya Mathur", "Ishani Sharma", "Sriparna Saha", "Kitsuchart Pasupa", "Alka Maurya" ], "posted": "2025-09-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.24342v1", "field": "finance", "role": "instrument", "bullets": [ "1,417 annotated multi-turn financial dialogues in the Fin-Vault dataset covering budgeting, expense tracking, and financial planning.", "Fin-Ally integrates COMET-BART commonsense context with DPO-optimized language model to generate professional financial advisory responses.", "Commonsense-augmented responses showed improved textual precision and professional grounding compared to reasoning-only and politeness-only baselines." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "automated metrics on Fin-Vault dataset vs baselines", "salience": 35, "n": 2857, "authors_detailed": [ { "name": "Sarmistha Das", "url": "https://openalex.org/A5089770162", "inst": "Indian Institute of Technology Patna" }, { "name": "Priya Mathur", "url": "https://openalex.org/A5068339322", "inst": "Poornima University" }, { "name": "Sharma, Ishani", "url": "", "inst": "" }, { "name": "Sriparna Saha", "url": "https://openalex.org/A5060797340", "inst": "Indian Institute of Technology Patna" }, { "name": "Kitsuchart Pasupa", "url": "https://openalex.org/A5066462028", "inst": "King Mongkut's Institute of Technology Ladkrabang" }, { "name": "Alka Maurya", "url": "https://openalex.org/A5120058095", "inst": "International Crisis Group" } ], "affiliations": [ "Indian Institute of Technology Patna", "Poornima University", "King Mongkut's Institute of Technology Ladkrabang", "International Crisis Group" ] }, { "uid": "arxiv:2509.25054v2", "arxiv_id": "2509.25054v2", "title": "Signaling in the Age of AI: Evidence from Cover Letters", "authors": [ "Jingyi Cui", "Gabriel Dias", "Justin Ye" ], "posted": "2025-09-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.25054v2", "field": "economics", "role": "object", "bullets": [ "Application-level data from a large online labor platform track access to and usage of an AI-powered cover letter writing tool across job applications.", "Difference-in-differences estimation measures how the AI tool changed cover letter content, callback rates, and the signaling value of textual alignment between letters and job posts.", "The tool raised callback rates, but the correlation between textual alignment and callbacks fell 51%, and employers shifted toward alternative signals such as work history." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3537 }, { "uid": "arxiv:2510.00067v1", "arxiv_id": "2510.00067v1", "title": "Intelligent 5S Audit: Application of Artificial Intelligence for Continuous Improvement in the Automotive Industry", "authors": [ "Rafael da Silva Maciel", "Lucio Veraldo" ], "posted": "2025-09-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.00067v1", "field": "management", "role": "instrument", "bullets": [ "An automated 5S audit system was tested on manufacturing environments in the automotive supply chain, comparing LLM-based assessments against human auditors.", "A large language model analyzed images to score all five 5S dimensions (Seiri, Seiton, Seiso, Seiketsu, Shitsuke) in a standardized format, validated against human audits using Cohen's kappa.", "The system achieved strong human-AI agreement (kappa = 0.75), cut audit time by 50%, and reduced operating costs by 99.8% compared to traditional manual audits." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Cohen's kappa = 0.75 vs. human auditors", "salience": 35, "models": [], "n": 3538, "authors_detailed": [ { "name": "Maciel, Rafael da Silva", "url": "", "inst": "" }, { "name": "Lúcio Garcia Veraldo", "url": "https://openalex.org/A5050398847", "inst": "Federal Institute of São Paulo" } ], "affiliations": [ "Federal Institute of São Paulo" ] }, { "uid": "doi:10.2139/ssrn.5533921", "doi": "10.2139/ssrn.5533921", "title": "Is ChatGPT a Boon or a Bane for Learning? Experimental Evidence Across Task Formats and Chatbot Designs", "authors": [ "Andy Tao Li", "De Liu", "Teng Ye" ], "posted": "2025-09-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5533921", "field": "management", "role": "object", "bullets": [ "Randomized field experiment among college students comparing search engine, standard ChatGPT, and guided-discovery ChatGPT during practice sessions with subsequent exams.", "Standard and custom guided-discovery ChatGPT variants served as study tools; the custom version used zero-shot prompting to scaffold learning rather than provide direct answers.", "Standard ChatGPT users scored 4.18% lower than search-engine users on exams; guided-discovery ChatGPT users scored 9.23% higher, driven by extended practice and critical engagement." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2601 }, { "uid": "arxiv:2509.23259v1", "arxiv_id": "2509.23259v1", "title": "Fin-ExBERT: User Intent based Text Extraction in Financial Context using Graph-Augmented BERT and trainable Plugin", "authors": [ "Soumick Sarker", "Abhijit Kumar Rai" ], "posted": "2025-09-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.23259v1", "field": "finance", "role": "instrument", "bullets": [ "Annotated financial service call transcripts with domain-specific vocabulary and variable intent density from real-world deployments.", "BERT backbone with LoRA adapters and progressive unfreezing extracted user intent-relevant sentences using dynamic thresholding based on probability curvature.", "Strong precision and F1 on real-world financial transcripts with interpretable output suitable for downstream auditing and question-answering workflows." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "precision and F1 on annotated financial call transcripts", "salience": 40, "n": 3951, "authors_detailed": [ { "name": "Sarker, Soumick", "url": "", "inst": "" }, { "name": "Rai, Abhijit Kumar", "url": "", "inst": "" } ] }, { "uid": "arxiv:2509.22366v1", "arxiv_id": "2509.22366v1", "title": "Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models", "authors": [ "Max Malyi", "Jonathan Shek", "Andre Biscaya" ], "posted": "2025-09-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.22366v1", "field": "management", "role": "method", "bullets": [ "A large industrial dataset of free text wind turbine maintenance logs; operator, site count, and log volume are not stated in the abstract.", "LLMs, models not named, run four workflows, failure mode identification, causal chain inference, comparative site analysis, and data quality auditing; no accuracy check against ground truth is reported.", "The models synthesize log text into reliability hypotheses framed as a co-pilot for engineers; the contribution is an exploratory, reproducible workflow rather than a quantified finding." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 14, "models": [], "n": 2046 }, { "uid": "arxiv:2509.20961v1", "arxiv_id": "2509.20961v1", "title": "Unlocking Financial Insights: An advanced Multimodal Summarization with Multimodal Output Framework for Financial Advisory Videos", "authors": [ "Sarmistha Das", "R E Zera Marveen Lyngkhoi", "Sriparna Saha", "Alka Maurya" ], "posted": "2025-09-25", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.20961v1", "field": "finance", "role": "method", "bullets": [ "Fin-APT, a dataset of 470 publicly accessible financial advisory pep talk videos of 30 to 40 minutes, built for multimodal summarization with aligned keyframes.", "The FASTER pipeline uses BLIP visual descriptions, OCR, and Whisper transcription with speaker diarization rather than a named LLM; a modified DPO loss adds fact-checking against human aligned summaries.", "Cross-domain experiments report performance, robustness, and generalizability above LLM and vision-language baselines; the abstract gives no numeric margins, and a ranker aligns keyframes with summarized points." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 14, "models": [], "n": 2045, "authors_detailed": [ { "name": "Sarmistha Das", "url": "https://openalex.org/A5089770162", "inst": "Indian Institute of Technology Patna" }, { "name": "R E Zera Marveen Lyngkhoi", "url": "https://openalex.org/A5117076146", "inst": "Indian Institute of Technology Patna" }, { "name": "Sriparna Saha", "url": "https://openalex.org/A5025979359", "inst": "Indian Institute of Technology Patna" }, { "name": "Alka Maurya", "url": "https://openalex.org/A5101206578", "inst": "International Crisis Group" } ], "affiliations": [ "Indian Institute of Technology Patna", "International Crisis Group" ] }, { "uid": "arxiv:2509.20634v2", "arxiv_id": "2509.20634v2", "title": "Recidivism and Peer Influence with LLM Text Embeddings in Low Security Correctional Facilities", "authors": [ "Shanjukta Nath", "Jiwon Hong", "Jae Ho Chang", "Keith Warren", "Subhadeep Paul" ], "posted": "2025-09-25", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.20634v2", "field": "economics", "role": "instrument", "bullets": [ "80,000 to 120,000 written exchanges among residents of low-security correctional facilities, predicting three-year recidivism outcomes.", "LLM text embeddings constructed language profiles; novel instrumental variable estimators addressed network endogeneity in sparse peer-effect networks.", "LLM language profiles predicted recidivism 30% more accurately than pre-entry covariates alone; significant peer effects found in residents' language profiles." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "recidivism prediction accuracy vs pre-entry covariates baseline", "salience": 70, "n": 3224, "authors_detailed": [ { "name": "Shanjukta Nath", "url": "https://openalex.org/A5054951387", "inst": "University of Georgia" }, { "name": "Hong, Jiwon", "url": "", "inst": "" }, { "name": "Chang, Jae Ho", "url": "", "inst": "" }, { "name": "Keith Warren", "url": "https://openalex.org/A5119832648", "inst": "" }, { "name": "Subhadeep Paul", "url": "https://openalex.org/A5076552857", "inst": "The Ohio State University" } ], "affiliations": [ "University of Georgia", "The Ohio State University" ] }, { "uid": "arxiv:2509.21507v1", "arxiv_id": "2509.21507v1", "title": "QuantMind: A Context-Engineering Based Knowledge Framework for Quantitative Finance", "authors": [ "Haoxue Wang", "Keli Wen", "Yuante Li", "Qiancheng Qu", "Xiangxu Mu", "Xinjie Shen", "Jiaqi Gao", "Chenyang Chang", "Chuhan Xie", "San Yu Cheung", "Zhuoyuan Hu", "Xinyu Wang", "Sirui Bi", "Bi'an Du" ], "posted": "2025-09-25", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.21507v1", "field": "finance", "role": "instrument", "bullets": [ "A user study tests a knowledge extraction and retrieval framework on heterogeneous financial documents including filings, earnings calls, and research notes.", "An LLM-based RAG pipeline with multi-modal parsing, adaptive summarization, and multi-hop reasoning extracts and retrieves structured financial knowledge, benchmarked against unaided reading and generic AI.", "QuantMind improved factual accuracy and user experience over both unaided reading and generic AI assistance for quantitative finance research tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "controlled user study comparing factual accuracy", "salience": 50, "n": 3536 }, { "uid": "doi:10.2139/ssrn.5453614", "doi": "10.2139/ssrn.5453614", "title": "The Visibility Gap in AI: From Mentions to Occupancy", "authors": [ "Timothy de Rosen" ], "posted": "2025-09-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5453614", "field": "management", "role": "object", "bullets": [ "Brand visibility measured across ChatGPT, Gemini, Claude, Perplexity, and Grok as primary consumer discovery and decision-making interfaces.", "Study introduced Prompt-Space Occupancy Score measuring occupancy, positioning, and decay of brand presence in AI-generated outputs.", "Exposure-based visibility metrics overstate brand strength; durable answer occupancy diverges sharply from simple mention-count dashboards." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "salience": 55, "validated": null, "n": 2742, "authors_detailed": [ { "name": "Timothy de Rosen", "url": "https://openalex.org/A5119286499", "inst": "Independent researcher" } ], "affiliations": [ "Independent researcher" ] }, { "uid": "doi:10.2139/ssrn.5518278", "doi": "10.2139/ssrn.5518278", "title": "Prompt Quality and LLM Memory: Soft Information in AI Advising", "authors": [ "Jing Huang", "Shumiao Ouyang" ], "posted": "2025-09-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5518278", "field": "finance", "role": "agent", "bullets": [ "Theoretical cheap-talk framework with simulated investor profiles from the Survey of Consumer Finances for LLM portfolio advising experiments", "LLM advisor received investor preference signals under uncertainty; model analyzed prompt quality and memory as substitutes in soft-information communication", "Prompt quality substitutes for AI memory; investors prefer a more opinionated LLM under limited memory when the model's prior is misaligned" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 2434, "authors_detailed": [ { "name": "Jing Huang", "url": "", "inst": "Mitchell Institute" }, { "name": "Shumiao Ouyang", "url": "", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford", "Mitchell Institute" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5448214", "doi": "10.2139/ssrn.5448214", "title": "Responsible Evaluation of LLM Powered Search in E-Commerce", "authors": [ "Ieshika Chandra" ], "posted": "2025-09-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5448214", "field": "management", "role": "method", "bullets": [ "Conceptual framework for evaluating LLM-augmented product search and recommendation systems deployed on e-commerce platforms.", "LLMs parsed complex queries, enriched catalogs, and served as scalable evaluators assessed across relevance, correctness, readability, and fairness.", "Framework identified risks of semantic drift, attribute hallucination, and amplified brand bias requiring evaluation beyond click-based proxies." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 35, "validated": null, "n": 2741 }, { "uid": "doi:10.2139/ssrn.5443794", "doi": "10.2139/ssrn.5443794", "title": "Compounding Effects in AI Search Adoption: Visibility, Attribution, and Agentic Shopping Bots", "authors": [ "Timothy de Rosen" ], "posted": "2025-09-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5443794", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of AI search market dynamics covering visibility metrics, attribution methods, and agentic shopping bot capabilities.", "Paper examines how generative AI assistants disrupt search through Prompt-Space Occupancy Score, attribution gaps, and compressed decision funnels.", "AI search adoption follows compounding rather than linear trajectory; tipping points in brand visibility loss may arrive sooner than industry projects." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 40, "validated": null, "n": 3223, "authors_detailed": [ { "name": "Timothy de Rosen", "url": "https://openalex.org/A5119286499", "inst": "Independent researcher" } ], "affiliations": [ "Independent researcher" ] }, { "uid": "arxiv:2509.18775v2", "arxiv_id": "2509.18775v2", "title": "Financial Risk Relation Identification through Dual-view Adaptation", "authors": [ "Wei-Ning Chiu", "Yu-Hsiang Wang", "Andy Hsiao", "Yu-Shiang Huang", "Chuan-Ju Wang" ], "posted": "2025-09-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.18775v2", "field": "finance", "role": "instrument", "bullets": [ "U.S. public firms' Form 10-K filings used to extract inter-firm risk relations for portfolio management and investment strategy applications.", "Domain-specific financial encoder trained via unsupervised fine-tuning on chronological and lexical patterns in 10-K filings produced quantitative risk relation scores.", "Method outperforms strong baselines across multiple evaluation settings for identifying implicit inter-firm risk connections." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "multiple evaluation settings vs baselines", "salience": 50, "n": 3950, "authors_detailed": [ { "name": "W. T. Chiu", "url": "https://openalex.org/A5113483508", "inst": "National Taiwan University" }, { "name": "Yu‐Hsiang Wang", "url": "https://openalex.org/A5032461992", "inst": "National Taiwan University" }, { "name": "A. I. Hsiao", "url": "https://openalex.org/A5112770509", "inst": "Academia Sinica" }, { "name": "Yu-Shiang Huang", "url": "https://openalex.org/A5101137226", "inst": "National Taiwan University" }, { "name": "Chuan‐Ju Wang", "url": "https://openalex.org/A5017348970", "inst": "Research Center for Information Technology Innovation, Academia Sinica" } ], "affiliations": [ "National Taiwan University", "Academia Sinica", "Research Center for Information Technology Innovation, Academia Sinica" ] }, { "uid": "doi:10.1109/summa64428.2024.10803746", "doi": "10.1109/summa64428.2024.10803746", "arxiv_id": "2510.01225v1", "title": "Utilizing Modern Large Language Models (LLM) for Financial Trend Analysis and Digest Creation", "authors": [ "Andrei Lazarev", "Dmitrii Sedov" ], "posted": "2025-09-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.01225v1", "field": "finance", "role": "method", "bullets": [ "Scholarly records retrieved from OpenAlex, restructured into JSON and passed through a pipeline that ends in automatically generated PDF digests of financial research trends.", "Google's Gemini Pro summarizes the extracted records via prompt engineering; no validation of digest quality against any ground truth or human benchmark is reported.", "Demonstrates an end to end workflow producing digests that summarize key findings and identify emerging trends, presented as a walkthrough with no quantitative evaluation." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": false, "salience": 22, "edition": 14, "n": 1928, "authors_detailed": [ { "name": "Andrei Lazarev", "url": "https://openalex.org/A5106066829", "inst": "Moscow Institute of Physics and Technology" }, { "name": "Dmitrii Sedov", "url": "https://openalex.org/A5106066828", "inst": "National Research University Higher School of Economics" } ], "affiliations": [ "Moscow Institute of Physics and Technology", "National Research University Higher School of Economics" ] }, { "uid": "arxiv:2509.18052v3", "arxiv_id": "2509.18052v3", "title": "The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies", "authors": [ "Jiaxu Zhou", "Jen-tse Huang", "Xuhui Zhou", "Man Ho Lam", "Xintao Wang", "Hao Zhu", "Wenxuan Wang", "Maarten Sap" ], "posted": "2025-09-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.18052v3", "field": "economics", "role": "method", "bullets": [ "A systematic audit of 39 recent studies using LLM agents to simulate human collective behavior, plus reproductions of five representative experiments including the telephone game.", "Frontier LLMs, not named in the abstract, are tested for awareness of the underlying social experiment, which they identify in 50.8 percent of cases; 61 percent of prompts pre-determine outcomes.", "89.7 percent of audited studies violate at least one PIMMUR principle, and reported collective phenomena often vanish or reverse once the six principles are enforced." ], "bullet_provenance": "ai", "salience": 74, "edition": 14, "models": [], "validated": null, "n": 2043, "authors_detailed": [ { "name": "Jiaxu Zhou", "url": "https://openalex.org/A5065854759", "inst": "Shanghai Jiao Tong University" }, { "name": "Jen-tse Huang", "url": "https://openalex.org/A5119816314", "inst": "" }, { "name": "Chao Zhou", "url": "https://openalex.org/A5044677624", "inst": "Shanghai Changzheng Hospital" }, { "name": "Man Ho Lam", "url": "https://openalex.org/A5114087930", "inst": "Chinese University of Hong Kong" }, { "name": "Xintao Wang", "url": "https://openalex.org/A5081423918", "inst": "Renmin University of China" }, { "name": "Hao Zhu", "url": "https://openalex.org/A5084768551", "inst": "Harbin Medical University" }, { "name": "Wenxuan Wang", "url": "https://openalex.org/A5100755182", "inst": "Wuhan University of Technology" }, { "name": "Maarten Sap", "url": "https://openalex.org/A5015128745", "inst": "Carnegie Mellon University" } ], "affiliations": [ "Carnegie Mellon University", "Shanghai Jiao Tong University", "Chinese University of Hong Kong", "Renmin University of China", "Harbin Medical University", "Wuhan University of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.01222v1", "arxiv_id": "2510.01222v1", "title": "Discourse vs emissions: Analysis of corporate narratives, symbolic practices, and mimicry through LLMs", "authors": [ "Bertrand Kian Hassani", "Yacoub Bahini", "Rizwan Mushtaq" ], "posted": "2025-09-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.01222v1", "field": "accounting", "role": "instrument", "bullets": [ "Sustainability and annual reports from 828 US listed firms, with narrative indicators linked to emissions, market capitalization, and sector; the reporting period is not stated.", "Four LLM classifiers fine-tuned for climate communication score sentiment, commitment, specificity, and target ambition; the abstract names no base model and reports no accuracy check against human coding.", "Risk-focused narratives align with explicit commitments, but quantitative targets such as net zero pledges decouple from tone; larger and higher emitting firms disclose more, and similar styles across firms suggest mimicry." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 14, "models": [], "n": 2044, "authors_detailed": [ { "name": "Bertrand K. Hassani", "url": "https://openalex.org/A5050751459", "inst": "The London College" }, { "name": "Yacoub Bahini", "url": "https://openalex.org/A5014079209", "inst": "Centre d'Économie de la Sorbonne" }, { "name": "Rizwan Mushtaq", "url": "https://openalex.org/A5006134511", "inst": "Ayub Medical College" } ], "affiliations": [ "The London College", "Centre d'Économie de la Sorbonne", "Ayub Medical College" ] }, { "uid": "arxiv:2509.17395v1", "arxiv_id": "2509.17395v1", "title": "FinDebate: Multi-Agent Collaborative Intelligence for Financial Analysis", "authors": [ "Tianshi Cai", "Guanxu Li", "Nijia Han", "Ce Huang", "Zimu Wang", "Changyu Zeng", "Yuqi Wang", "Jingshi Zhou", "Haiyang Zhang", "Qi Chen", "Yushan Pan", "Shuihua Wang", "Wei Wang" ], "posted": "2025-09-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.17395v1", "field": "finance", "role": "agent", "bullets": [ "Multi-agent framework with five specialized agents covering earnings, market, sentiment, valuation, and risk dimensions of financial analysis.", "LLM agents conducted parallel collaborative debate with domain-specific RAG; safe debate protocol enabled agents to challenge and refine initial conclusions.", "LLM-based and human evaluations confirmed calibrated confidence levels and actionable investment strategies across multiple time horizons." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "LLM-based and human evaluation of analysis quality", "salience": 55, "n": 3222, "authors_detailed": [ { "name": "Cai, Tianshi", "url": "", "inst": "" }, { "name": "Guanxu Li", "url": "https://openalex.org/A5019730005", "inst": "China Academy of Engineering Physics" }, { "name": "Nijia Han", "url": "https://openalex.org/A5017556264", "inst": "Xi’an Jiaotong-Liverpool University" }, { "name": "Ce Huang", "url": "https://openalex.org/A5113974114", "inst": "Xi’an Jiaotong-Liverpool University" }, { "name": "Wang, Zimu", "url": "", "inst": "" }, { "name": "Zeng, Changyu", "url": "", "inst": "" }, { "name": "Yuqi Wang", "url": "https://openalex.org/A5100451661", "inst": "Xi'an University of Science and Technology" }, { "name": "Jingshi Zhou", "url": "https://openalex.org/A5110522199", "inst": "Johns Hopkins University" }, { "name": "Haiyang Zhang", "url": "https://openalex.org/A5100392403", "inst": "Harbin Institute of Technology" }, { "name": "Quan Chen", "url": "https://openalex.org/A5017281141", "inst": "Union Hospital" }, { "name": "Yushan Pan", "url": "https://openalex.org/A5044105477", "inst": "Shanghai Jiao Tong University" }, { "name": "Shuihua Wang‎", "url": "https://openalex.org/A5007987858", "inst": "Xi’an Jiaotong-Liverpool University" }, { "name": "Wei Wang", "url": "https://openalex.org/A5100392118", "inst": "Xi’an Jiaotong-Liverpool University" } ], "affiliations": [ "Johns Hopkins University", "China Academy of Engineering Physics", "Xi’an Jiaotong-Liverpool University", "Xi'an University of Science and Technology", "Harbin Institute of Technology", "Shanghai Jiao Tong University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.17037v1", "arxiv_id": "2509.17037v1", "title": "KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration", "authors": [ "Yajing Yang", "Tony Deng", "Min-Yen Kan" ], "posted": "2025-09-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.17037v1", "field": "finance", "role": "instrument", "bullets": [ "DataTales financial reporting benchmark with raw tabular data analyzed at entity, pairwise, group, and system levels of hierarchy.", "LLMs served as domain experts to extract hierarchical insights from financial tables and generate structured narratives evaluated by GPT-4o.", "KAHAN outperformed existing approaches by over 20% on narrative quality and maintained 98.2% factuality in human evaluation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "DataTales benchmark, 98.2% factuality in human evaluation", "salience": 50, "n": 2740, "authors_detailed": [ { "name": "Yajing Yang", "url": "https://openalex.org/A5008853318", "inst": "National University of Singapore" }, { "name": "Tony Deng", "url": "https://openalex.org/A5119813020", "inst": "" }, { "name": "Min‐Yen Kan", "url": "https://openalex.org/A5066305082", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore" ] }, { "uid": "doi:10.2139/ssrn.5436622", "doi": "10.2139/ssrn.5436622", "title": "Voice & RAG-Based Business Management Query System for SMEs", "authors": [ "Krish Vishal Soni", "Shyamkumar Mukesh Kadiwar", "Suthar Mahesh Manaram", "Amitkumar Velabhai Vaghela" ], "posted": "2025-09-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5436622", "field": "management", "role": "instrument", "bullets": [ "User evaluation with SME operators in India testing voice-based business management across Hindi, English, Marathi, Tamil, and Gujarati.", "Claude and Gemini Pro with RAG processed natural-language voice queries for inventory, sales tracking, invoicing, and financial reporting.", "Task completion time fell 50%, manual errors dropped 75%, and 90% of participants with limited technical skills reported high satisfaction." ], "bullet_provenance": "ai", "models": [ "claude", "gemini" ], "open_weights": false, "validated": true, "validation_note": "user study measuring task time and error reduction", "salience": 30, "n": 2856, "authors_detailed": [ { "name": "Krish Vishal Soni", "url": "https://openalex.org/A5119684022", "inst": "Parul University" }, { "name": "Shyamkumar Mukesh Kadiwar", "url": "https://openalex.org/A5119684023", "inst": "Parul University" }, { "name": "Suthar Mahesh Manaram", "url": "https://openalex.org/A5119684024", "inst": "Institute of Engineering" }, { "name": "Amitkumar Velabhai Vaghela", "url": "https://openalex.org/A5119684025", "inst": "Parul University" } ], "affiliations": [ "Parul University", "Institute of Engineering" ] }, { "uid": "arxiv:2509.15510v1", "arxiv_id": "2509.15510v1", "title": "The (Short-Term) Effects of Large Language Models on Unemployment and Earnings", "authors": [ "Danqing Chen", "Carina Kane", "Austin Kozlowski", "Nadav Kunievsky", "James A. Evans" ], "posted": "2025-09-19", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.15510v1", "field": "economics", "role": "object", "bullets": [ "US occupation level earnings and unemployment around ChatGPT's late 2022 release, compared across occupations with different exposure to LLMs.", "The authors run no model themselves; occupational LLM exposure scores define treatment in a synthetic difference in differences design.", "Earnings rose in highly exposed occupations after the release while unemployment was unchanged, suggesting early adjustment through pay rather than worker reallocation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "edition": 14, "validated": null, "n": 1876, "authors_detailed": [ { "name": "Danqing Chen", "url": "https://openalex.org/A5038132108", "inst": "Hangzhou Women’s Hospital" }, { "name": "Carina Kane", "url": "https://openalex.org/A5011228935", "inst": "" }, { "name": "Austin C. Kozlowski", "url": "https://openalex.org/A5001554992", "inst": "University of Chicago" }, { "name": "Nadav Kunievsky", "url": "https://openalex.org/A5119793376", "inst": "" }, { "name": "James A. Evans", "url": "https://openalex.org/A5076633756", "inst": "Santa Fe Institute" } ], "affiliations": [ "University of Chicago", "Santa Fe Institute" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.16462v1", "arxiv_id": "2509.16462v1", "title": "Intrinsic Meets Extrinsic Fairness: Assessing the Downstream Impact of Bias Mitigation in Large Language Models", "authors": [ "'Mina Arzaghi'", "'Alireza Dehghanpour Farashah'", "'Florian Carichon'", "' Golnoosh Farnadi'" ], "posted": "2025-09-19", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.16462v1", "field": "finance", "role": "method", "bullets": [ "Salary prediction, employment status, and creditworthiness classification tasks, with three open-source LLMs used both as frozen embedding extractors and as fine-tuned classifiers.", "Concept unlearning of intrinsic gender bias is compared with counterfactual data augmentation for downstream fairness; the abstract does not name the three models.", "Unlearning cuts intrinsic gender bias by up to 94.9 percent and improves demographic parity by up to 82 percent without losing accuracy, supporting mitigation before deployment." ], "bullet_provenance": "ai", "open_weights": true, "salience": 38, "edition": 14, "models": [], "validated": null, "n": 2042, "authors_detailed": [ { "name": "Mina Arzaghi'", "url": "https://openalex.org/A5120025162", "inst": "" }, { "name": "Alireza Dehghanpour Farashah", "url": "https://openalex.org/A5045726113", "inst": "Payame Noor University" }, { "name": "Florian Carichon'", "url": "https://openalex.org/A5120025163", "inst": "" }, { "name": "Golnoosh Farnadi'", "url": "https://openalex.org/A5120025164", "inst": "" } ], "affiliations": [ "Payame Noor University" ] }, { "uid": "doi:10.2139/ssrn.5508485", "doi": "10.2139/ssrn.5508485", "title": "Harnessing large language models for ESG analysis: Evaluating non-financial factors in stock prices", "authors": [ "Mengdi Zhang", "Bo Zhang", "Zhiheng Zhao", "Yulan Wang", "George Q. Huang" ], "posted": "2025-09-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5508485", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 50, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 357, "authors_detailed": [ { "name": "Mengdi Zhang", "url": "https://openalex.org/A5100717442", "inst": "Hong Kong Polytechnic University" }, { "name": "Bo Zhang", "url": "https://openalex.org/A5100335210", "inst": "Beijing Institute of Technology" }, { "name": "Zhiheng Zhao", "url": "https://openalex.org/A5081499025", "inst": "Hong Kong Polytechnic University" }, { "name": "Yulan Wang", "url": "https://openalex.org/A5100661963", "inst": "Hong Kong Polytechnic University" }, { "name": "George Q. Huang", "url": "https://openalex.org/A5015681327", "inst": "Hong Kong Polytechnic University" } ], "affiliations": [ "Hong Kong Polytechnic University", "Beijing Institute of Technology" ] }, { "uid": "arxiv:2509.15265v1", "arxiv_id": "2509.15265v1", "title": "AI and jobs. A review of theory, estimates, and evidence", "authors": [ "R. Maria del Rio-Chanona", "Ekkehard Ernst", "Rossana Merola", "Daniel Samaan", "Ole Teutloff" ], "posted": "2025-09-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.15265v1", "field": "economics", "role": "object", "bullets": [ "Review synthesizing theory and empirical evidence on AI's employment effects from RCTs, field experiments, and digital trace data.", "The study compared ex-ante AI exposure measures across occupations and assembled ex-post evidence from experiments and platform data.", "Productivity gains range 20-60% in controlled RCTs and 15-30% in field experiments; novice workers benefit more on simple tasks." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3048, "authors_detailed": [ { "name": "R. Maria del Rio-Chanona", "url": "https://openalex.org/A5045698412", "inst": "University College London" }, { "name": "Ekkehard Ernst", "url": "https://openalex.org/A5013233031", "inst": "International Labour Organization" }, { "name": "Rossana Merola", "url": "https://openalex.org/A5070834472", "inst": "International Labour Organization" }, { "name": "Daniel Samaan", "url": "https://openalex.org/A5075500112", "inst": "International Labour Organization" }, { "name": "Ole Teutloff", "url": "https://openalex.org/A5043965496", "inst": "University of Copenhagen" } ], "affiliations": [ "University College London", "International Labour Organization", "University of Copenhagen" ] }, { "uid": "doi:10.2139/ssrn.5462896", "doi": "10.2139/ssrn.5462896", "title": "Impartial Intelligence? Evidence of Country-Label Sensitivity in AI Financial Analysis", "authors": [ "Fabio Yoshio Suguri Motoki", "Jedson Pinto" ], "affiliations": [ "The University of Texas at Dallas", "The University of Texas Rio Grande Valley" ], "posted": "2025-09-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5462896", "field": "finance", "role": "object", "bullets": [ "30,000 synthetic financial transactions with identical statistical properties attributed to three countries (US, Great Britain, China), tested across multiple LLMs.", "GPT, Llama, and DeepSeek assign fraud probabilities to each transaction; country-label sensitivity is measured by comparing scores across attributions while holding data constant.", "Chinese-attributed transactions receive significantly higher fraud scores (36.2% vs 30–31%), a gap that persists across replications, languages, and mitigation prompts including explicit neutrality instructions." ], "bullet_provenance": "editor", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "Five independent replications with statistical significance tests; cross-model replication on Llama and DeepSeek", "salience": 0, "edition": 21, "audience": "business", "n": 2355, "authors_detailed": [ { "name": "Fábio Motoki", "url": "https://openalex.org/A5016703677", "inst": "The University of Texas Rio Grande Valley" }, { "name": "Jedson Pinto", "url": "https://openalex.org/A5119652438", "inst": "The University of Texas at Dallas" } ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.14448v2", "arxiv_id": "2509.14448v2", "title": "VCBench: Benchmarking LLMs in Venture Capital", "authors": [ "Rick Chen", "Joseph Ternasky", "Afriyie Samuel Kwesi", "Ben Griffin", "Aaron Ontoyin Yin", "Zakari Salifu", "Kelvin Amoaba", "Xianling Mu", "Fuat Alican", "Yigit Ihlamur" ], "posted": "2025-09-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.14448v2", "field": "finance", "role": "method", "bullets": [ "9,000 anonymized founder profiles with realized startup outcomes; the market index baseline precision at inception is 1.9 percent, and adversarial tests show over 90 percent lower re-identification risk.", "Nine state of the art LLMs predict founder success from the standardized profiles, scored on precision and F0.5 against the realized outcome labels.", "DeepSeek-V3 achieves more than six times the baseline precision and GPT-4o the top F0.5; most models beat benchmarks implied by Y Combinator and tier 1 firms." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "realized founder outcomes, precision and F0.5", "salience": 58, "edition": 14, "n": 1834 }, { "uid": "arxiv:2509.14180v1", "arxiv_id": "2509.14180v1", "title": "Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation Framework for Personal Finance LLMs", "authors": [ "Akhil Theerthala" ], "posted": "2025-09-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.14180v1", "field": "finance", "role": "method", "bullets": [ "Personal finance advice covering budgeting, debt, retirement, and estate planning; a 19,000 sample reasoning dataset is generated by combining financial context with behavioral finance research.", "Qwen-3-8B is fine-tuned on the synthetic dataset; assessment relies on a held-out split and a blind LLM jury, with no comparison against human ground truth.", "The 8B model matches 14 to 32B parameter baselines on factual accuracy, fluency, and personalization while incurring 80 percent lower costs." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 36, "edition": 14, "n": 2041, "authors_detailed": [ { "name": "Akhil Theerthala", "url": "https://openalex.org/A5120354827", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5495579", "doi": "10.2139/ssrn.5495579", "title": "Familiar Signal, New Context: The Evolution of Earnings Call Sentiment Analysis from Lexicons to LLMs", "authors": [ "Mengmeng Ao", "Frank Zhao" ], "posted": "2025-09-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5495579", "field": "finance", "role": "instrument", "bullets": [ "Earnings call transcripts for U.S. equities, comparing LLM-extracted sentiment features with traditional lexicon-based NLP scores across multiple periods.", "A fine-tuned LLM extracted sentiment features from transcripts and outputs were validated by correlation with lexicon-driven scores and used to build long-short strategies.", "LLM-based sentiment strategy delivered 8.4% long-short return versus 4.2% for lexicon approach, with growing advantage as mispricing opportunities narrowed." ], "bullet_provenance": "ai", "validated": false, "salience": 72, "models": [], "n": 2505, "authors_detailed": [ { "name": "Mengmeng Ao", "url": "https://openalex.org/A5042326879", "inst": "S&P Global" }, { "name": "Frank Zhao", "url": "https://openalex.org/A5113239616", "inst": "GE Global Research (United States)" } ], "affiliations": [ "S&P Global", "GE Global Research (United States)" ] }, { "uid": "doi:10.2139/ssrn.5495480", "doi": "10.2139/ssrn.5495480", "title": "AI & ESG", "authors": [ "Barbara Ballan", "Jason J. Czarnezki", "Morgan Martin" ], "posted": "2025-09-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5495480", "field": "management", "role": "object", "bullets": [ "Analysis of AI applications in the ESG space covering environmental impacts, use cases, legal hurdles, and best practices for U.S. companies.", "Paper examines how AI tools collect and analyze ESG metrics including environmental impacts and workplace safety, and proposes risk mitigation approaches.", "Recommends blockchain integration, adequate policies, and oversight mechanisms as best practices for companies deploying AI in ESG activities." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3949, "authors_detailed": [ { "name": "Barbara Ballan", "url": "https://openalex.org/A5107568736", "inst": "Florida International University" }, { "name": "Jason J. Czarnezki", "url": "https://openalex.org/A5026473426", "inst": "Illinois Institute of Technology" }, { "name": "Morgan Martin", "url": "https://openalex.org/A5119649653", "inst": "Independent" } ], "affiliations": [ "Florida International University", "Illinois Institute of Technology", "Independent" ] }, { "uid": "arxiv:2509.12611v1", "arxiv_id": "2509.12611v1", "title": "Analogy-Driven Financial Chain-of-Thought (AD-FCoT): A Prompting Approach for Financial Sentiment Analysis", "authors": [ "Anmol Singhal Navya Singhal" ], "posted": "2025-09-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.12611v1", "field": "finance", "role": "method", "bullets": [ "Historical financial news, thousands of articles, used for sentiment prediction aimed at anticipating market movements; the exact sample and period are not stated in the abstract.", "AD-FCoT prompts an unnamed LLM to draw analogies with historical events of known outcome inside a structured chain of thought; no training data or fine tuning is required.", "The framework beats strong baselines on sentiment classification accuracy and shows substantially higher correlation with market returns; specific figures are not given in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "sentiment accuracy against labelled baselines", "salience": 38, "edition": 14, "models": [], "n": 1927 }, { "uid": "arxiv:2509.12638v1", "arxiv_id": "2509.12638v1", "title": "FinSentLLM: Multi-LLM and Structured Semantic Signals for Enhanced Financial Sentiment Forecasting", "authors": [ "Zijian Zhang", "Rong Fu", "Yangfan He", "Xinze Shen", "Yanlong Wang", "Xiaojing Du", "Haochen You", "Jiazhao Shi", "Simon Fong" ], "posted": "2025-09-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.12638v1", "field": "finance", "role": "method", "bullets": [ "Financial PhraseBank sentences for sentiment classification, plus daily sentiment computed from FNSPID news matched to major stock indices for the market analysis.", "A compact meta-classifier pools an expert panel of sentiment LLMs, none named in the abstract, with structured semantic signals; outputs are scored against Financial PhraseBank labels.", "Accuracy and F1 improve 3 to 6 percent over strong baselines, and DCC-GARCH plus Johansen cointegration tests indicate long-run comovement between sentiment scores and equity indices." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Financial PhraseBank labels, accuracy and F1 reported", "salience": 46, "edition": 14, "models": [], "n": 2040 }, { "uid": "doi:10.2139/ssrn.5494548", "doi": "10.2139/ssrn.5494548", "title": "(Mis)Measuring the Drivers of Ad Performance", "authors": [ "Gijs Overgoor", "Samsun Knight", "Yakov Bart" ], "posted": "2025-09-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5494548", "field": "management", "role": "instrument", "bullets": [ "Over 10,000 human-labeled video ads with ad quality ratings from a 500-plus consumer panel provided by iSpot.tv.", "Off-the-shelf multimodal LLMs annotated ad features; a fine-tuned model exceeded average pairwise human agreement on many feature categories.", "Misaligned LLM measurement produced significant effects in opposite direction to human-labeled estimates; fine-tuning corrected bias and improved explanatory power." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison vs human annotations and pairwise inter-annotator agreement", "salience": 75, "n": 3221, "authors_detailed": [ { "name": "Gijs Overgoor", "url": "https://openalex.org/A5119633414", "inst": "Southern Methodist University" }, { "name": "Samsun Knight", "url": "https://openalex.org/A5119633415", "inst": "University of Toronto" }, { "name": "Yakov Bart", "url": "https://openalex.org/A5073502448", "inst": "Northeastern University" } ], "affiliations": [ "University of Toronto", "Southern Methodist University", "Northeastern University" ], "prestige": true }, { "uid": "arxiv:2509.13160v1", "arxiv_id": "2509.13160v1", "title": "FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning", "authors": [ "Liang Hu", "Jianpeng Jiao", "Jiashuo Liu", "Yanle Ren", "Zhoufutu Wen", "Kaiyuan Zhang", "Xuanliang Zhang", "Xiang Gao", "Tianci He", "Fei Hu", "Yali Liao", "Zaiyuan Wang", "Chenghao Yang", "Qianyu Yang", "Mingren Yin", "Zhiyuan Zeng", "Ge Zhang", "Xinyi Zhang", "Xiying Zhao", "Zhenwei Zhu", "Hongseok Namkoong", "Wenhao Huang", "Yuwen Tang" ], "posted": "2025-09-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.13160v1", "field": "finance", "role": "method", "bullets": [ "635 questions spanning global and Greater China markets, annotated by 70 professional financial experts with multi-stage quality assurance.", "Twenty-one LLM-based agents evaluated on time-sensitive data fetching, historical lookup, and complex investigation tasks replicating analyst workflows.", "Grok 4 tops the global subset approaching expert-level accuracy; web search and financial plugins substantially improve agent performance." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "635 expert-annotated questions across 21 models", "salience": 55, "n": 3534, "authors_detailed": [ { "name": "Hu L", "url": "https://openalex.org/A5101462142", "inst": "Central South University" }, { "name": "Jianpeng Jiao", "url": "https://openalex.org/A5113906958", "inst": "Handan College" }, { "name": "Jiashuo Liu", "url": "https://openalex.org/A5004072676", "inst": "Tianjin University" }, { "name": "Ren, Yanle", "url": "", "inst": "" }, { "name": "Zhoufutu Wen", "url": "https://openalex.org/A5020661044", "inst": "University of Siedlce" }, { "name": "Kaiyuan Zhang", "url": "https://openalex.org/A5102955600", "inst": "Xi'an Jiaotong University" }, { "name": "Zhang, Xuanliang", "url": "", "inst": "" }, { "name": "Xiangyu Gao", "url": "https://openalex.org/A5100328685", "inst": "Peking University" }, { "name": "Tianci He", "url": "https://openalex.org/A5024754380", "inst": "Wuhan University of Science and Technology" }, { "name": "Fei Hu", "url": "https://openalex.org/A5101421127", "inst": "Second Affiliated Hospital of Zhejiang University" }, { "name": "Liao, Yali", "url": "", "inst": "" }, { "name": "Z. Wang", "url": "https://openalex.org/A5103128142", "inst": "University of Science and Technology of China" }, { "name": "Chenghao Yang", "url": "https://openalex.org/A5078193661", "inst": "Central South University of Forestry and Technology" }, { "name": "Qianyu Yang", "url": "https://openalex.org/A5070684044", "inst": "Shenyang Agricultural University" }, { "name": "Yin, Mingren", "url": "", "inst": "" }, { "name": "Zhiyuan Zeng", "url": "https://openalex.org/A5008306856", "inst": "City University of Hong Kong" }, { "name": "Ge Zhang", "url": "https://openalex.org/A5067217116", "inst": "William Paterson University" }, { "name": "Xinyi Zhang", "url": "https://openalex.org/A5100381540", "inst": "Wuhan University" }, { "name": "Zhao, Xiying", "url": "", "inst": "" }, { "name": "Zhenwei Zhu", "url": "https://openalex.org/A5053340203", "inst": "Soochow University" }, { "name": "Hongseok Namkoong", "url": "https://openalex.org/A5008595840", "inst": "Risk Engineering (Bulgaria)" }, { "name": "Wenhao Huang", "url": "https://openalex.org/A5111236258", "inst": "Shandong University" }, { "name": "Yuwen Tang", "url": "https://openalex.org/A5112870115", "inst": "North China University of Science and Technology" } ], "affiliations": [ "Central South University", "Handan College", "Tianjin University", "University of Siedlce", "Xi'an Jiaotong University", "Peking University", "Wuhan University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.5487080", "doi": "10.2139/ssrn.5487080", "title": "How People Use ChatGPT", "authors": [ "Aaron Chatterji", "Tom Cunningham", "David Deming", "Zoë Hitzig", "Christopher Ong", "Carl Shan", "Kevin Wadman" ], "posted": "2025-09-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5487080", "field": "economics", "role": "object", "bullets": [ "Representative sample of ChatGPT conversations from launch in November 2022 through July 2025, reaching roughly 10% of the world's adult population.", "Privacy-preserving automated pipeline classified usage patterns by topic and work-relatedness across demographic and occupational groups.", "Non-work messages grew from 53% to over 70% of usage; writing dominates work tasks and economic value concentrates in knowledge-intensive occupations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 80, "validated": null, "n": 3535, "authors_detailed": [ { "name": "Aaron Chatterji", "url": "https://openalex.org/A5119636278", "inst": "Duke University" }, { "name": "Tom Cunningham", "url": "https://openalex.org/A5119636279", "inst": "Meta (United States)" }, { "name": "David Deming", "url": "https://openalex.org/A5119636280", "inst": "National Bureau of Economic Research" }, { "name": "Zoë Hitzig", "url": "https://openalex.org/A5119636281", "inst": "Harvard University Press" }, { "name": "Christopher Ong", "url": "https://openalex.org/A5086736190", "inst": "Harvard University Press" }, { "name": "Carl Shan", "url": "https://openalex.org/A5119636282", "inst": "OpenAI (United States)" }, { "name": "Kevin Wadman", "url": "https://openalex.org/A5119636283", "inst": "OpenAI (United States)" } ], "affiliations": [ "Duke University", "Harvard University", "Meta (United States)", "National Bureau of Economic Research", "OpenAI (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.13137v1", "arxiv_id": "2509.13137v1", "title": "Agentic AI for Financial Crime Compliance", "authors": [ "Henrik Axelsen", "Valdemar Licht", "Jan Damsgaard" ], "posted": "2025-09-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.13137v1", "field": "finance", "role": "object", "bullets": [ "Action Design Research with a fintech firm and regulatory stakeholders developing an agentic AI system for financial crime compliance.", "Agentic AI automated onboarding, monitoring, investigation, and reporting with task-specific model routing, explainability, and audit logging.", "Reference architecture demonstrates how agentic AI reconfigures compliance workflows while maintaining transparency and trust under regulatory constraints." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 50, "n": 3947 }, { "uid": "doi:10.2139/ssrn.5404854", "doi": "10.2139/ssrn.5404854", "title": "AI & the Future of Finance: A Framework for the Intelligent Financial Enterprise", "authors": [ "Anshuman Yadav" ], "posted": "2025-09-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5404854", "field": "finance", "role": "object", "bullets": [ "Conceptual framework for AI transformation of corporate finance functions including treasury, FP&A, risk oversight, and capital deployment.", "Proposes the Intelligent Financial Enterprise framework built on agentic AI with persistent memory, multi-agent collaboration, and decision-augmentation engines.", "Framework argues agentic AI enables continuous intelligence beyond automation, reshaping treasury, risk management, and investor communication." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3948, "authors_detailed": [ { "name": "Anshuman Yadav", "url": "", "inst": "" } ] }, { "uid": "arxiv:2509.12519v1", "arxiv_id": "2509.12519v1", "title": "Context-Aware Language Models for Forecasting Market Impact from Sequences of Financial News", "authors": [ "Ross Koval", "Nicholas Andrews", "Xifeng Yan" ], "posted": "2025-09-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.12519v1", "field": "finance", "role": "instrument", "bullets": [ "Financial news articles used to forecast stock market impact across multiple time horizons, incorporating historical news context.", "A large LM processed main articles while a small LM encoded historical context into summary embeddings aligned with the large model's space.", "Historical context provided consistent, significant improvement in prediction performance and translated to substantial gains in simulated investment returns." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 3046, "authors_detailed": [ { "name": "Ross Koval", "url": "https://openalex.org/A5092596259", "inst": "University of California, Santa Barbara" }, { "name": "Nicholas Andrews", "url": "https://openalex.org/A5101173949", "inst": "Johns Hopkins University" }, { "name": "Xifeng Yan", "url": "https://openalex.org/A5047709762", "inst": "Santa Barbara City College" } ], "affiliations": [ "Johns Hopkins University", "University of California, Santa Barbara", "Santa Barbara City College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5486426", "doi": "10.2139/ssrn.5486426", "title": "Financial Services in the Era of Generative AI: Facilitating Responsible Adoption", "authors": [ "Hong Kong Institute for Monetary and Financial Research" ], "posted": "2025-09-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5486426", "field": "finance", "role": "object", "bullets": [ "Survey and interviews of financial institutions in Hong Kong on current and projected GenAI adoption over three to five years.", "The study assessed GenAI use cases, risk management strategies, and talent development constraints across Hong Kong financial services.", "75% of surveyed institutions have implemented or are piloting GenAI use cases, expected to rise to 87% within five years." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3047, "authors_detailed": [ { "name": "Hong Kong Institute for Monetary and Financial Research", "url": "https://openalex.org/A5027518054", "inst": "Pacific Medical (China)" } ], "affiliations": [ "Pacific Medical (China)" ] }, { "uid": "arxiv:2509.12084v4", "arxiv_id": "2509.12084v4", "title": "Geopolitical Barriers to Globalization", "authors": [ "Tianyu Fan", "Mai Wo", "Wei Xiang" ], "posted": "2025-09-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.12084v4", "field": "economics", "role": "instrument", "bullets": [ "833,485 geopolitical events across 193 countries from 1950 to 2024, with bilateral trade modeled using local projections and an Armington framework.", "Large language models compiled and classified geopolitical events to construct a bilateral geopolitical alignment score between country pairs.", "One-standard-deviation improvement in alignment raises bilateral trade by 22%; geopolitical deterioration reduced 2021 global trade by about 5.3%." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 70, "n": 3533, "authors_detailed": [ { "name": "Tianyu Fan", "url": "https://openalex.org/A5056076948", "inst": "Yale University" }, { "name": "Mai Wo", "url": "https://openalex.org/A5119867917", "inst": "Yale University" }, { "name": "Wei Xiang", "url": "https://openalex.org/A5110764194", "inst": "University of Michigan" } ], "affiliations": [ "Yale University", "University of Michigan" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.12042v1", "arxiv_id": "2509.12042v1", "title": "FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval", "authors": [ "Ying Li", "Mengyu Wang", "Miguel de Carvalho", "Sotirios Sabanis", "Tiejun Ma" ], "posted": "2025-09-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.12042v1", "field": "finance", "role": "method", "bullets": [ "Full 10-K filings with queries aligned to the FinQA benchmark dataset for financial document question answering.", "FinGEAR combined finance-lexicon item-level guidance, dual hierarchical indices, and cross-encoder reranking for retrieval-augmented generation on 10-K documents.", "F1 improved 56.7% over flat RAG, 12.5% over graph-based RAG, and 217.6% over tree-based systems, with higher downstream answer accuracy." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "precision, recall, F1 on FinQA-aligned queries", "salience": 55, "n": 3946, "authors_detailed": [ { "name": "Ying Li", "url": "https://openalex.org/A5100414204", "inst": "Nanjing Audit University" }, { "name": "Mengyu Wang", "url": "https://openalex.org/A5100632175", "inst": "Ministry of Ecology and Environment" }, { "name": "Miguel de Carvalho", "url": "https://openalex.org/A5033424403", "inst": "University of Aveiro" }, { "name": "Sotirios Sabanis", "url": "https://openalex.org/A5009097122", "inst": "University of Edinburgh" }, { "name": "Tiejun Ma", "url": "https://openalex.org/A5100939543", "inst": "Northwestern Polytechnical University" } ], "affiliations": [ "Nanjing Audit University", "Ministry of Ecology and Environment", "University of Aveiro", "University of Edinburgh", "Northwestern Polytechnical University" ] }, { "uid": "arxiv:2510.01203v1", "arxiv_id": "2510.01203v1", "title": "Mamba Outpaces Reformer in Stock Prediction with Sentiments from Top Ten LLMs", "authors": [ "Lokesh Antony Kadiyala", "Amir Mirzaeinia" ], "posted": "2025-09-14", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.01203v1", "field": "finance", "role": "instrument", "bullets": [ "One month of minute level Apple stock prices, April 4 to May 2, 2025, aligned with AAPL news articles and technical indicators such as RSI and Bollinger band width.", "Ten models including DeepSeek V3, GPT variants, Llama, Claude, Gemini, Qwen, and Mistral score each article's sentiment on a 0 to 1 scale; no check against human sentiment labels is reported.", "Mamba forecasts beat Reformer in both speed and error for all ten sentiment inputs, reaching the lowest error of 0.137 when fed Llama 3.3 70B scores." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 32, "edition": 14, "n": 1805, "authors_detailed": [ { "name": "Lokesh Antony Kadiyala", "url": "https://openalex.org/A5119848326", "inst": "University of North Texas" }, { "name": "Amir Mirzaeinia", "url": "https://openalex.org/A5069548043", "inst": "University of North Texas" } ], "affiliations": [ "University of North Texas" ] }, { "uid": "arxiv:2509.11420v1", "arxiv_id": "2509.11420v1", "title": "Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning", "authors": [ "Yijia Xiao", "Edward Sun", "Tong Chen", "Fang Wu", "Di Luo", "Wei Wang" ], "posted": "2025-09-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.11420v1", "field": "finance", "role": "agent", "bullets": [ "100k-sample corpus covering 18 months across 14 equities and five financial data sources, tested on six major equities and ETFs.", "Fine-tuned LLM with supervised learning and three-stage RL curriculum generates structured investment theses with volatility-adjusted trading decisions.", "Trading-R1 achieved improved risk-adjusted returns and lower drawdowns versus both open-source and proprietary instruction-following and reasoning models." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "risk-adjusted returns on 6 equities/ETFs vs multiple baselines", "salience": 62, "n": 2855, "authors_detailed": [ { "name": "Xiao, Yijia", "url": "", "inst": "" }, { "name": "Edward W. Sun", "url": "https://openalex.org/A5053020626", "inst": "Kedge Business School" }, { "name": "Tong Chen", "url": "https://openalex.org/A5101503805", "inst": "Beihang University" }, { "name": "Wu Fang", "url": "https://openalex.org/A5089915151", "inst": "China General Nuclear Power Corporation (China)" }, { "name": "Di Luo", "url": "https://openalex.org/A5112121032", "inst": "Second Hospital of Tianjin Medical University" }, { "name": "Wei Wang", "url": "https://openalex.org/A5114734369", "inst": "Shandong University" } ], "affiliations": [ "Kedge Business School", "Beihang University", "China General Nuclear Power Corporation (China)", "Shandong University" ] }, { "uid": "arxiv:2509.10922v1", "arxiv_id": "2509.10922v1", "title": "Aligning ESG Controversy Data with International Guidelines through Semi-Automatic Ontology Construction", "authors": [ "Tsuyoshi Iwata", "Guillaume Comte", "Melissa Flores", "Ryoma Kondo", "Ryohei Hisano" ], "posted": "2025-09-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.10922v1", "field": "finance", "role": "instrument", "bullets": [ "ESG controversy events from news sources aligned with UN Global Compact and SDG frameworks using semi-automatic ontology construction.", "LLMs converted normative principles into RDF templates that extract structured information from news and populate a sustainability knowledge graph.", "The method produces a scalable, transparent framework that links reported ESG incidents to specific international sustainability guideline principles." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 3045, "authors_detailed": [ { "name": "Tsuyoshi Iwata", "url": "https://openalex.org/A5119958456", "inst": "SIGVARIS Group (Switzerland)" }, { "name": "Guillaume Comte", "url": "https://openalex.org/A5119958457", "inst": "" }, { "name": "Flores, Melissa", "url": "", "inst": "" }, { "name": "Ryoma Kondo", "url": "https://openalex.org/A5110542635", "inst": "Canon (Japan)" }, { "name": "Ryohei Hisano", "url": "https://openalex.org/A5057799988", "inst": "The University of Tokyo" } ], "affiliations": [ "SIGVARIS Group (Switzerland)", "Canon (Japan)", "The University of Tokyo" ] }, { "uid": "arxiv:2509.09995v4", "arxiv_id": "2509.09995v4", "title": "QuantHarness: Price-Driven Multi-Agent LLMs for High-Frequency Trading", "authors": [ "Fei Xiong", "Xiang Zhang", "Aosong Feng", "Siqi Sun", "Chenyu You" ], "posted": "2025-09-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.09995v4", "field": "finance", "role": "agent", "bullets": [ "Nine financial instruments including Bitcoin and Nasdaq futures tested at one-hour and four-hour high-frequency trading intervals.", "Four specialized LLM agents for indicators, patterns, trends, and risk generated trading decisions using structured short-horizon signals.", "QuantHarness outperformed baseline methods in predictive accuracy across multiple evaluation metrics at both trading intervals." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "predictive accuracy on Bitcoin and Nasdaq futures at 1h and 4h intervals", "salience": 55, "n": 2738, "authors_detailed": [ { "name": "Xiong, Fei", "url": "", "inst": "" }, { "name": "Xiang Zhang", "url": "https://openalex.org/A5056627479", "inst": "Sun Yat-sen University" }, { "name": "Aosong Feng", "url": "https://openalex.org/A5073331205", "inst": "Thales (Australia)" }, { "name": "Siqi Sun", "url": "https://openalex.org/A5016464421", "inst": "ID Genomics (United States)" }, { "name": "Chenyu You", "url": "https://openalex.org/A5102503449", "inst": "Yale University" } ], "affiliations": [ "Yale University", "Sun Yat-sen University", "Thales (Australia)", "ID Genomics (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.09928v1", "arxiv_id": "2509.09928v1", "title": "Fraud detection and risk assessment of online payment transactions on e-commerce platforms based on LLM and GCN frameworks", "authors": [ "RuiHan Luo", "Nanxi Wang", "Xiaotong Zhu" ], "posted": "2025-09-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.09928v1", "field": "finance", "role": "instrument", "bullets": [ "2,840,000 e-commerce payment transactions over 14 days from platforms including Amazon, with approximately 2,000 U.S. consumers and 30 merchants.", "GPT-4o extracted semantic features combined with graph convolutional networks to classify fraudulent transactions in highly imbalanced data.", "Framework achieved 0.98 accuracy, balancing precision and sensitivity for real-time fraud detection in online payment environments." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "accuracy 0.98 on 2.84M e-commerce transactions", "salience": 50, "n": 2739, "authors_detailed": [ { "name": "Luo, RuiHan", "url": "", "inst": "" }, { "name": "N Wang", "url": "https://openalex.org/A5076468324", "inst": "Wuhan Technology and Business University" }, { "name": "Zhu, Xiaotong", "url": "", "inst": "" } ], "affiliations": [ "Wuhan Technology and Business University" ] }, { "uid": "doi:10.2139/ssrn.5466886", "doi": "10.2139/ssrn.5466886", "title": "Expectation Inflation: LLMs, Knowledge Work and Human Agency", "authors": [ "Benjamin G. Voyer", "Vishal Kapadia" ], "posted": "2025-09-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5466886", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of LLM adoption across knowledge-intensive industries, examining shifting productivity standards and evolving human roles in the post-ChatGPT and post-DeepSeek era.", "Paper examines how widespread LLM use for content generation and rapid prototyping raises organizational expectations, reduces mistake tolerance, and reshapes knowledge-worker responsibilities.", "Widespread LLM adoption paradoxically inflates productivity expectations and reduces mistake tolerance, shifting human roles from information processing toward critical thinking and creativity." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 35, "validated": null, "n": 2376, "authors_detailed": [ { "name": "Benjamin G. Voyer", "url": "https://openalex.org/A5028240520", "inst": "ESCP Business School" }, { "name": "Vishal Kapadia", "url": "https://openalex.org/A5031646882", "inst": "ESCP Business School" } ], "affiliations": [ "ESCP Business School" ] }, { "uid": "doi:10.2139/ssrn.5470726", "doi": "10.2139/ssrn.5470726", "title": "From Narratives to Action: Leveraging AI to Decode and Advance Sustainability Commitments", "authors": [ "Ivan Savin" ], "posted": "2025-09-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5470726", "field": "accounting", "role": "instrument", "bullets": [ "Over 300 national climate pledges under the Paris Agreement and nearly 1,500 ESG reports from STOXX Europe 600 companies analyzed with NLP methods.", "ChatGPT and topic modeling extracted themes from sustainability documents and compared textual emphasis to actual emissions reductions and environmental performance metrics.", "Significant gaps between sustainability discourse and environmental action emerged, with theme emphasis varying systematically across countries and firms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 48, "n": 2417, "authors_detailed": [ { "name": "Ivan Savin", "url": "https://openalex.org/A5025144355", "inst": "Universitat Autònoma de Barcelona" } ], "affiliations": [ "Universitat Autònoma de Barcelona" ] }, { "uid": "doi:10.2139/ssrn.5467447", "doi": "10.2139/ssrn.5467447", "title": "Investigating Firm's use of AI in Achieving Sustainable Development Goals -A Large Language Model Approach", "authors": [ "Chuanwen Dong", "Kushal Koshti", "Shuqi Liu" ], "posted": "2025-09-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5467447", "field": "management", "role": "instrument", "bullets": [ "Annual reports from 85 leading firms in China, India, Germany, and the USA collected over ten years for SDG alignment analysis.", "An LLM with custom prompts analyzed report texts to measure each firm's engagement with each of the 17 Sustainable Development Goals.", "AI's impact on ESG scores varies across SDGs, over time, by industry, and by country, showing non-monotone rather than uniform effects." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3044, "authors_detailed": [ { "name": "Chuanwen Dong", "url": "", "inst": "ESCP Business School" }, { "name": "Kushal Koshti", "url": "https://openalex.org/A5119601568", "inst": "ESCP Business School" }, { "name": "Shuqi Liu", "url": "", "inst": "ESCP Business School" } ], "affiliations": [ "ESCP Business School" ] }, { "uid": "doi:10.2139/ssrn.5466891", "doi": "10.2139/ssrn.5466891", "title": "The Law and Economics of Generative AI and Copyright A Primer to Core Challenges For Our Digital Future", "authors": [ "Zachary Cooper", "Bertin Martens", "Christian Peukert", "Volker Stocker" ], "posted": "2025-09-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5466891", "field": "economics", "role": "object", "bullets": [ "Cross-jurisdictional analysis of copyright law challenges at input and output stages of generative AI systems.", "Study examines how legal uncertainty around data scraping and AI-generated content affects incentives to create and dataset accessibility.", "Current rules produce inconsistent outcomes on AI-assisted works' copyrightability; authors outline policy options for sustainable cultural production." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3532, "authors_detailed": [ { "name": "Zachary Cooper", "url": "https://openalex.org/A5115057886", "inst": "Vrije Universiteit Amsterdam" }, { "name": "Bertin Martens", "url": "https://openalex.org/A5031857465", "inst": "Tilburg University" }, { "name": "Christian Peukert", "url": "https://openalex.org/A5086582254", "inst": "University of Music Lausanne" }, { "name": "Volker Stocker", "url": "https://openalex.org/A5091751818", "inst": "Weizenbaum Institute" } ], "affiliations": [ "Vrije Universiteit Amsterdam", "Tilburg University", "University of Music Lausanne", "Weizenbaum Institute" ] }, { "uid": "arxiv:2509.09544v3", "arxiv_id": "2509.09544v3", "title": "MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)", "authors": [ "Paolo Pedinotti", "Peter Baumann", "Nathan Jessurun", "Leslie Barrett", "Enrico Santus" ], "posted": "2025-09-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.09544v3", "field": "finance", "role": "method", "bullets": [ "681 papers on generative AI in finance published 2022-2025, analyzed via ontology-guided LLM extraction into typed knowledge graphs.", "LLMs extracted structured knowledge graphs from the scientific corpus using MetaGraph methodology to enable large-scale trend analysis of financial NLP research.", "Three research phases identified: early LLM-driven task expansion, growing emphasis on limitations and risk, and shift toward modular retrieval-augmented designs." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 55, "n": 3944 }, { "uid": "doi:10.2139/ssrn.5446374", "doi": "10.2139/ssrn.5446374", "title": "A Firm’s Operational Risk: Data Set and Empirical Evidence", "authors": [ "Vivek Astvansh", "Joseph Simpson" ], "posted": "2025-09-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5446374", "field": "management", "role": "instrument", "bullets": [ "131,920 firm-years across 16,959 U.S. public firms, 2005-2024, using Item 1A risk disclosure text from Form 10-K annual reports.", "64 transformer models scored filings on eight risk factors including operations, finance, and technology, selecting the best performer per factor across eight metrics.", "Disclosed operational risk positively associates with operational cost; disclosed nonoperational risk strengthens this positive association." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "8 classification metrics per transformer on labeled 10-K risk factors", "salience": 65, "n": 3945, "authors_detailed": [ { "name": "Vivek Astvansh", "url": "https://openalex.org/A5036938950", "inst": "Woodlawn School" }, { "name": "Joseph Simpson", "url": "https://openalex.org/A5076085103", "inst": "The University of Texas Rio Grande Valley" } ], "affiliations": [ "Woodlawn School", "The University of Texas Rio Grande Valley" ] }, { "uid": "arxiv:2509.09727v1", "arxiv_id": "2509.09727v1", "title": "A Role-Aware Multi-Agent Framework for Financial Education Question Answering with LLMs", "authors": [ "Andy Zhu", "Yingjun Du" ], "posted": "2025-09-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.09727v1", "field": "finance", "role": "method", "bullets": [ "3,532 expert designed finance education questions from the online learning platform Study.com, answered with retrieval augmented evidence drawn from six finance textbooks.", "A single pass pipeline of base generator, evidence retriever, and expert reviewer agents; backbones tested include Gemini 2.0 Flash and GPT-4o mini, compared against a finance tuned FinGPT Llama 3 8B.", "Critique based refinement lifts accuracy 6.6 to 8.3 points over zero shot chain of thought, with Gemini 2.0 Flash strongest and GPT-4o mini matching the finance tuned baseline." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "expert designed Study.com question set", "salience": 32, "edition": 14, "n": 1819 }, { "uid": "arxiv:2509.08380v2", "arxiv_id": "2509.08380v2", "title": "Co-Investigator AI: The Rise of Agentic AI for Smarter, Trustworthy AML Compliance Narratives", "authors": [ "Prathamesh Vasudeo Naik", "Naresh Kumar Dintakurthi", "Zhanghao Hu", "Yue Wang", "Robby Qiu" ], "posted": "2025-09-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.08380v2", "field": "finance", "role": "method", "bullets": [ "Suspicious activity report drafting for anti money laundering compliance, demonstrated on a range of financial crime scenarios rather than a labelled corpus.", "An agentic pipeline, underlying models not stated, chains planning, crime typology detection, intelligence gathering, and an agent as judge validation layer, with human investigators reviewing drafts.", "The authors report faster and more accurate SAR drafting than traditional workflows, but no quantitative benchmark or accuracy figure appears in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 14, "models": [], "n": 1875, "authors_detailed": [ { "name": "Prathamesh Vasudeo Naik", "url": "https://openalex.org/A5120592090", "inst": "" }, { "name": "Naresh Kumar Dintakurthi", "url": "https://openalex.org/A5120691038", "inst": "" }, { "name": "Zhigang Hu", "url": "https://openalex.org/A5100640454", "inst": "Shanghai Industrial Technology Institute" }, { "name": "Yue Wang", "url": "https://openalex.org/A5030446686", "inst": "China University of Mining and Technology" }, { "name": "Robby Qiu", "url": "https://openalex.org/A5120640502", "inst": "" } ], "affiliations": [ "Shanghai Industrial Technology Institute", "China University of Mining and Technology" ] }, { "uid": "arxiv:2509.08919v1", "arxiv_id": "2509.08919v1", "title": "Generative Engine Optimization: How to Dominate AI Search", "authors": [ "Mahe Chen", "Xiaoxuan Wang", "Kaiwen Chen", "Nick Koudas" ], "posted": "2025-09-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.08919v1", "field": "management", "role": "object", "bullets": [ "Large-scale controlled experiments across multiple verticals, languages, and query paraphrases comparing AI search engines to Google web search.", "Study quantifies how ChatGPT, Perplexity, and Gemini source and cite information differently from traditional search in content visibility.", "AI search shows systematic bias toward earned media over brand-owned content; engines differ in domain diversity, freshness, and phrasing sensitivity." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "salience": 40, "validated": null, "n": 3043, "authors_detailed": [ { "name": "M. L. Chen", "url": "https://openalex.org/A5036612416", "inst": "University of Toronto" }, { "name": "Xiaoxuan Wang", "url": "https://openalex.org/A5100695922", "inst": "Beijing Jiaotong University" }, { "name": "Kaiwen Chen", "url": "https://openalex.org/A5101675126", "inst": "University of Alabama" }, { "name": "Nick Koudas", "url": "https://openalex.org/A5035257754", "inst": "Roma Tre University" } ], "affiliations": [ "University of Toronto", "Beijing Jiaotong University", "University of Alabama", "Roma Tre University" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5467412", "doi": "10.2139/ssrn.5467412", "title": "Agentic AI: The Next Frontier in Autonomous Enterprise Systems", "authors": [ "Daniel Völker", "Marc Oberhauser" ], "posted": "2025-09-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5467412", "field": "management", "role": "object", "bullets": [ "Three enterprise use cases: intelligent copilots, customer service automation, and end-to-end process automation, drawing on IBM watsonx architecture.", "Paper contrasts agentic AI with generative models, analyzing required components including orchestration, memory, cognition, and governance layers.", "Agentic AI already reshapes enterprise processes with improved decision quality; strategic insights provided for organizations adopting autonomous systems." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 30, "validated": null, "n": 3220, "authors_detailed": [ { "name": "Daniel Völker", "url": "", "inst": "ESCP Business School" }, { "name": "Marc OBERHAUSER", "url": "https://openalex.org/A5058025025", "inst": "ESCP Business School" } ], "affiliations": [ "ESCP Business School" ] }, { "uid": "arxiv:2509.08742v1", "arxiv_id": "2509.08742v1", "title": "FinZero: Launching Multi-modal Financial Time Series Forecast with Large Reasoning Model", "authors": [ "Yanlong Wang", "Jian Xu", "Fei Ma", "Hongkang Zhang", "Hang Yu", "Tiantian Gao", "Yu Wang", "Haochen You", "Shao-Lun Huang", "Danny Dongning Sun", "Xiao-Ping Zhang" ], "posted": "2025-09-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.08742v1", "field": "finance", "role": "method", "bullets": [ "Financial time-series data across multiple assets with a constructed multimodal image-text dataset (FVLDB) for training and evaluation.", "FinZero, a multimodal pre-trained model fine-tuned with uncertainty-adjusted group relative policy optimization, performed reasoning and prediction benchmarked against GPT-4o.", "FinZero achieved 13.48% improvement in prediction accuracy over GPT-4o in the high-confidence group after reinforcement learning fine-tuning." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "prediction accuracy vs GPT-4o on FVLDB dataset", "salience": 45, "n": 3943, "authors_detailed": [ { "name": "Yanlong Wang", "url": "https://openalex.org/A5050090906", "inst": "Harbin Medical University" }, { "name": "Jian Xu", "url": "https://openalex.org/A5109958366", "inst": "Civil Aviation Administration of China" }, { "name": "Fei Ma", "url": "https://openalex.org/A5115587912", "inst": "Guangzhou University of Chinese Medicine" }, { "name": "Hongkang Zhang", "url": "https://openalex.org/A5077065803", "inst": "Capital Medical University" }, { "name": "Hang Yu", "url": "https://openalex.org/A5050640186", "inst": "Dalian Medical University" }, { "name": "Tiantian Gao", "url": "https://openalex.org/A5102740127", "inst": "Nanjing University of Chinese Medicine" }, { "name": "Yu Wang", "url": "https://openalex.org/A5100445268", "inst": "Dongbei University of Finance and Economics" }, { "name": "Haochen You", "url": "https://openalex.org/A5102631359", "inst": "Columbia University" }, { "name": "Shao‐Lun Huang", "url": "https://openalex.org/A5088293566", "inst": "University Town of Shenzhen" }, { "name": "Dandan Sun", "url": "https://openalex.org/A5101799824", "inst": "Liaoning Provincial People's Hospital" }, { "name": "Xiao-Ping Zhang", "url": "https://openalex.org/A5100363160", "inst": "Tsinghua University" } ], "affiliations": [ "Columbia University", "Harbin Medical University", "Civil Aviation Administration of China", "Guangzhou University of Chinese Medicine", "Capital Medical University", "Dalian Medical University", "Nanjing University of Chinese Medicine", "Dongbei University of Finance and Economics" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.08140v1", "arxiv_id": "2509.08140v1", "title": "From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital", "authors": [ "Mihir Kumar", "Aaron Ontoyin Yin", "Zakari Salifu", "Kelvin Amoaba", "Afriyie Kwesi Samuel", "Fuat Alican", "Yigit Ihlamur" ], "posted": "2025-09-09", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.08140v1", "field": "finance", "role": "instrument", "bullets": [ "Early-stage startup records in venture capital, with three independent test subsets; sample size, period, and data sources are not stated in the abstract.", "An LLM, name not stated, engineers features from unstructured startup data for a layered ensemble of XGBoost, random forest, and linear regression; no check of extracted features against ground truth is reported.", "Precision reaches 9.8 to 11.1 times a random baseline across the three subsets; the startup's category list carries 15.6 percent of predictive influence, followed by the number of founders." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 14, "models": [], "n": 2039, "authors_detailed": [ { "name": "Kumar, Mihir", "url": "", "inst": "" }, { "name": "Aaron Ontoyin Yin", "url": "https://openalex.org/A5114658552", "inst": "Clearwater Group (United States)" }, { "name": "Zakari Salifu", "url": "https://openalex.org/A5120641020", "inst": "Clearwater Group (United States)" }, { "name": "Kelvin Amoaba", "url": "https://openalex.org/A5120641021", "inst": "" }, { "name": "Samuel, Afriyie Kwesi", "url": "", "inst": "" }, { "name": "Fuat Alican", "url": "https://openalex.org/A5114658551", "inst": "Clearwater Group (United States)" }, { "name": "Yigit Ihlamur", "url": "https://openalex.org/A5093557649", "inst": "Clearwater Group (United States)" } ], "affiliations": [ "Clearwater Group (United States)" ] }, { "uid": "doi:10.2139/ssrn.5425555", "doi": "10.2139/ssrn.5425555", "title": "Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data", "authors": [ "Seyed Mahdi Hosseini Maasoum", "Guy Lichtinger" ], "posted": "2025-09-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5425555", "field": "economics", "role": "object", "bullets": [ "US resume data covering 65 million workers at more than 280,000 firms from 2015 to 2025, tracking firm-level employment by worker seniority.", "GenAI adoption is identified by text analysis that flags 'GenAI integrator' job postings signalling active implementation; the paper does not name the model used for this classification.", "After adoption, junior employment falls sharply relative to non-adopters while senior employment stays flat, concentrated in exposed occupations and driven by slower hiring." ], "bullet_provenance": "ai", "validated": false, "salience": 72, "edition": 3, "audience": "general", "models": [], "n": 679, "authors_detailed": [ { "name": "Seyed Mahdi Hosseini Maasoum", "url": "https://openalex.org/A5119565702", "inst": "Harvard University Press" }, { "name": "Guy Lichtinger", "url": "https://openalex.org/A5104633709", "inst": "Harvard University" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5440116", "doi": "10.2139/ssrn.5440116", "title": "When LLMs Go Abroad: Foreign Bias in AI Financial Predictions", "authors": [ "Sean Cao", "Charles C. Y. Wang", "Yi Xiang" ], "posted": "2025-09-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5440116", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 48, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 680, "authors_detailed": [ { "name": "Sean Cao", "url": "https://openalex.org/A5055060179", "inst": "University of Maryland, College Park" }, { "name": "Charles C. Y. Wang", "url": "https://openalex.org/A5009696015", "inst": "Harvard University" }, { "name": "Yi Xiang", "url": "https://openalex.org/A5101632571", "inst": "Hong Kong Polytechnic University" } ], "affiliations": [ "University of Maryland, College Park", "Harvard University", "Hong Kong Polytechnic University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5403944", "doi": "10.2139/ssrn.5403944", "title": "Agentic AI for Smart Manufacturing", "authors": [ "Jay Lee", "Hanqi Su" ], "posted": "2025-09-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5403944", "field": "management", "role": "object", "bullets": [ "Smart manufacturing environments surveyed for agentic AI adoption integrating LLM-based agents with a unified data-model-knowledge infrastructure", "Multiple LLM agents collaborated with human expertise for perception, reasoning, planning, and optimization across manufacturing operations", "RAG-based LLM QA case study demonstrated framework feasibility; trustworthiness and deployment efficiency remain key technical challenges" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 38, "n": 2432, "authors_detailed": [ { "name": "Jay Lee", "url": "https://openalex.org/A5100686648", "inst": "University of Maryland, College Park" }, { "name": "Hanqi Su", "url": "https://openalex.org/A5109668481", "inst": "University of Maryland, College Park" } ], "affiliations": [ "University of Maryland, College Park" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5402503", "doi": "10.2139/ssrn.5402503", "title": "AI Agents for Cash Management in Payment Systems", "authors": [ "Iñaki Aldasoro", "Ajit Desai" ], "posted": "2025-09-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5402503", "field": "finance", "role": "agent", "bullets": [ "Simulated wholesale payment system scenarios with liquidity shocks tested using ChatGPT's reasoning model for intraday cash management decisions", "ChatGPT agent without domain-specific training prioritized payments, maintained liquidity buffers, and optimized settlement speed against liquidity usage", "AI agent closely replicated prudential cash-management practices with calibrated recommendations that preserved liquidity while minimizing payment delays" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 70, "n": 2433 }, { "uid": "doi:10.2139/ssrn.5456494", "doi": "10.2139/ssrn.5456494", "title": "More Ideas, Less Strategic Focus: How AI Changes the Alternatives Decision-Makers Consider", "authors": [ "Nety Wu", "Hyunjin Kim", "Chengyi Lin" ], "posted": "2025-09-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5456494", "field": "management", "role": "object", "bullets": [ "Randomized controlled trial with 305 MBA students testing LLM support at different stages of strategic decision-making processes.", "LLM assisted during problem formulation, ideation, or evaluation phases; researchers measured composition and strategic focus of alternatives generated.", "LLM support increased number of alternatives but decreased strategic focus when introduced during problem formulation, creating cognitive anchors that bound search." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "validated": null, "n": 2737, "authors_detailed": [ { "name": "Nety Wu", "url": "https://openalex.org/A5050307201", "inst": "INSEAD" }, { "name": "Hyunjin Kim", "url": "https://openalex.org/A5058825275", "inst": "INSEAD" }, { "name": "Chengyi Lin", "url": "https://openalex.org/A5108901578", "inst": "INSEAD" } ], "affiliations": [ "INSEAD" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5004839", "doi": "10.2139/ssrn.5004839", "title": "Mental Models and Financial Forecasts", "authors": [ "Francesca Bastianello", "Paul Decaire", "Marius Guenzel" ], "posted": "2025-09-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5004839", "field": "finance", "role": "instrument", "bullets": [ "Near-universe of 2.1 million equity analyst reports covering valuation methods, topic attention, sentiment, and time horizons of price targets.", "Multi-step LLM prompting strategy extracted analyst reasoning from reports; new diagnostic tools validated reliability of LLM-generated output.", "Analysts underreact to macroeconomic topics and overreact to firm-related topics, and these biases in reasoning contribute to return predictability." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "multi-step validation with diagnostic tools for LLM extraction reliability", "salience": 80, "n": 3219, "authors_detailed": [ { "name": "Francesca Bastianello", "url": "https://openalex.org/A5119569334", "inst": "University of Chicago" }, { "name": "Paul H. Décaire", "url": "https://openalex.org/A5020348953", "inst": "Arizona State University" }, { "name": "Marius Guenzel", "url": "https://openalex.org/A5064593195", "inst": "California University of Pennsylvania" } ], "affiliations": [ "University of Chicago", "Arizona State University", "California University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5422874", "doi": "10.2139/ssrn.5422874", "title": "Carbon Neutrality Uncertainty and the Cross-Section of Stock Returns: Evidence from China", "authors": [ "Guiqiang Shi", "Dehua Shen", "John W. Goodell" ], "posted": "2025-09-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5422874", "field": "finance", "role": "instrument", "bullets": [ "3,489 Chinese stocks from January 2011 to December 2022 analyzed with portfolio sorts and cross-sectional regressions.", "ChatGPT generated keywords to construct a carbon neutrality uncertainty index; each stock's sensitivity to this uncertainty was estimated.", "Higher sensitivity to carbon neutrality uncertainty predicts lower future stock returns; arbitrage asymmetry explains this negative relationship." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 60, "n": 3530, "authors_detailed": [ { "name": "Guiqiang Shi", "url": "https://openalex.org/A5101388199", "inst": "Nankai University" }, { "name": "Dehua Shen", "url": "https://openalex.org/A5070440485", "inst": "Nankai University" }, { "name": "John W. Goodell", "url": "https://openalex.org/A5025970919", "inst": "University of Akron" } ], "affiliations": [ "Nankai University", "University of Akron" ] }, { "uid": "doi:10.2139/ssrn.5457115", "doi": "10.2139/ssrn.5457115", "title": "Discovering Alternative Strategies: Experimental Evidence on the Impact of Frameworks", "authors": [ "Hyunjin Kim", "Nety Wu" ], "posted": "2025-09-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5457115", "field": "management", "role": "agent", "bullets": [ "Randomized experiments across 820 MBA students and executives testing how strategic frameworks shape generation of alternatives.", "LLMs processed and integrated multiple frameworks to generate strategic options, providing suggestive evidence on AI-assisted strategy making.", "Frameworks significantly expand visible options and shift choices toward mutually exclusive strategic alternatives over operational improvements." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3531, "authors_detailed": [ { "name": "Hyunjin Kim", "url": "https://openalex.org/A5119566580", "inst": "INSEAD" }, { "name": "Nety Wu", "url": "https://openalex.org/A5119566581", "inst": "INSEAD" } ], "affiliations": [ "INSEAD" ], "prestige": true }, { "uid": "arxiv:2509.06069v1", "arxiv_id": "2509.06069v1", "title": "From Digital Distrust to Codified Honesty: Experimental Evidence on Generative AI in Credence Goods Markets", "authors": [ "Alexander Erlei" ], "posted": "2025-09-07", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.06069v1", "field": "economics", "role": "agent", "bullets": [ "One-shot credence goods experiments compare AI-AI, human-human, human-AI, and human-AI-human expert markets, where experts hold private information about the service consumers need; participant numbers are not stated.", "LLMs, family not stated, play the expert role; human experts may delegate to LLM agents, set the agent's social objective function, and have those preferences disclosed to consumers.", "Human-human markets are more efficient than AI markets and LLM experts capture surplus from consumers; with disclosure of delegated objectives, human-AI-human markets beat human-human ones, while obfuscation erases the gains." ], "bullet_provenance": "ai", "salience": 68, "edition": 14, "models": [], "validated": null, "n": 2038, "authors_detailed": [ { "name": "Alexander Erlei", "url": "https://openalex.org/A5024981375", "inst": "University of Göttingen" } ], "affiliations": [ "University of Göttingen" ] }, { "uid": "arxiv:2509.10546v2", "arxiv_id": "2509.10546v2", "title": "Learning to Conceal Risk: Controllable Multi-turn Red Teaming for LLMs in the Financial Domain", "authors": [ "Gang Cheng", "Haibo Jin", "Wenbin Zhang", "Haohan Wang", "Jun Zhuang" ], "posted": "2025-09-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.10546v2", "field": "finance", "role": "method", "bullets": [ "FinRisk-Bench benchmark of 522 instructions spanning six financial regulatory risk categories tested on nine widely used LLMs.", "Controllable multi-turn red-teaming framework (CoRT) progressively conceals surface-level risk while probing for regulatory-violating responses in financial contexts.", "CoRT achieves 93-95% attack success rate across nine LLMs, demonstrating current models remain highly vulnerable to concealed financial regulatory-risk prompts." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Attack success rate measured across nine LLMs", "salience": 55, "models": [], "n": 2757 }, { "uid": "doi:10.2139/ssrn.5402205", "doi": "10.2139/ssrn.5402205", "title": "Identifying Episodes of Fiscal Austerity: An LLM-Based Approach", "authors": [ "Karan Bhasin", "Prakash Loungani" ], "posted": "2025-09-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5402205", "field": "economics", "role": "instrument", "bullets": [ "IMF Article IV reports for 17 OECD countries from 2004 to 2022 analyzed to identify episodes of fiscal austerity using LLM text analysis.", "LLMs extract latent signals of fiscal policy shifts, capturing nuanced policy intentions and deviations from expected paths in official reports.", "LLM-identified austerity episodes correspond to those independently identified by IMF staff in Adler et al. (2024), validating the automated approach." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Correspondence with IMF staff narrative identifications (Adler et al. 2024)", "salience": 65, "models": [], "n": 2755, "authors_detailed": [ { "name": "Karan Bhasin", "url": "https://openalex.org/A5020886236", "inst": "Albany State University" }, { "name": "Prakash Loungani", "url": "https://openalex.org/A5073255181", "inst": "International Monetary Fund" } ], "affiliations": [ "Albany State University", "International Monetary Fund" ] }, { "uid": "doi:10.2139/ssrn.5402179", "doi": "10.2139/ssrn.5402179", "title": "Forecasting the Future: Has GPT-5 Improved Time Series Forecasting Accuracy over GPT-4?", "authors": [ "Mohammadmahdi Ghasemloo", "Alireza Moradi" ], "posted": "2025-09-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5402179", "field": "management", "role": "method", "bullets": [ "Weekly influenza incidence from WHO FluNet and 30-minute call volumes from Oakland Call Center forecasted across multiple horizons.", "GPT-4o-mini and GPT-5-mini compared using fixed prompt structures and preprocessing, evaluated with RMSE, MAE, and MAPE metrics.", "GPT-5-mini consistently outperforms GPT-4o-mini on call center data; gains are domain- and horizon-dependent with mixed results on influenza forecasting." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "RMSE, MAE, MAPE against actual observed values", "salience": 50, "n": 2756, "authors_detailed": [ { "name": "Mohammadmahdi Ghasemloo", "url": "https://openalex.org/A5119557848", "inst": "Independent" }, { "name": "Alireza Moradi", "url": "https://openalex.org/A5100712602", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology", "Independent" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.03811v2", "arxiv_id": "2509.03811v2", "title": "Rethinking Supply Chain Planning: A Generative Paradigm", "authors": [ "Jiaheng Yin", "Yongzhi Qi", "Jianshen Zhang", "Dongyang Geng", "Zhengyu Chen", "Hao Hu", "Wei Qi", "Zuo-Jun Max Shen" ], "posted": "2025-09-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.03811v2", "field": "management", "role": "instrument", "bullets": [ "JD.com large-scale e-commerce supply chain operations coordinating demand planning and multi-stage logistics across dynamic environments.", "Generative AI agentic framework translated unstructured business context into structured analytical workflows, bridging organizational silos for automated planning.", "Deployment yielded approximately 22% improvement in planning accuracy and 2% increase in in-stock rates across JD.com operations." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "planning accuracy and in-stock rate metrics in production deployment", "salience": 65, "n": 3218 }, { "uid": "doi:10.2139/ssrn.5439660", "doi": "10.2139/ssrn.5439660", "title": "AI Innovation Competition as a Discovery Procedure: The Role and Limits of Competition Law", "authors": [ "Josef Drexl", "Daria Kim" ], "posted": "2025-09-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5439660", "field": "economics", "role": "object", "bullets": [ "Analysis of strategic partnership agreements between digital incumbents and AI developers, focusing on Microsoft/OpenAI and Microsoft/Mistral AI cases.", "Study examines whether conduct diminishing open-source licensing of AI models constitutes a competition law infringement under EU frameworks.", "Authors propose grounding competition law in innovation competition as a discovery procedure and recommend reforming the Digital Markets Act." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3529, "authors_detailed": [ { "name": "Josef Drexl", "url": "https://openalex.org/A5111385784", "inst": "Max Planck Institute for Innovation and Competition" }, { "name": "Daria Kim", "url": "https://openalex.org/A5009151881", "inst": "Max Planck Institute for Innovation and Competition" } ], "affiliations": [ "Max Planck Institute for Innovation and Competition" ] }, { "uid": "doi:10.2139/ssrn.5400466", "doi": "10.2139/ssrn.5400466", "title": "Selective Facts Can Be As Persuasive As Falsehoods", "authors": [ "Jennifer Allen", "Amir Tohidi", "Samar Haider", "David Rothschild", "Duncan Watts" ], "posted": "2025-09-03", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5400466", "field": "economics", "role": "instrument", "bullets": [ "Three pre-registered experiments in which participants read LLM-generated articles varying in framing and veracity on topics including the economy and election integrity; sample sizes not stated.", "An unspecified LLM generated articles that systematically varied across positive or negative framing and along a veracity gradient from selective facts through exaggerations to fabrications.", "Selectively framed factual statements shifted attitudes by an amount statistically indistinguishable from outright falsehoods, and in one experiment selective facts proved significantly more persuasive than lies." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 25, "models": [], "n": 4301, "authors_detailed": [ { "name": "Jenny Allen", "url": "https://openalex.org/A5119531484", "inst": "NYU Stern School of Business" }, { "name": "Amir Tohidi", "url": "https://openalex.org/A5008834223", "inst": "California University of Pennsylvania" }, { "name": "Samar Haider", "url": "https://openalex.org/A5020790791", "inst": "University of Pennsylvania" }, { "name": "David Rothschild", "url": "https://openalex.org/A5054707908", "inst": "California University of Pennsylvania" }, { "name": "Duncan J. Watts", "url": "https://openalex.org/A5018696141", "inst": "California University of Pennsylvania" } ], "affiliations": [ "NYU Stern School of Business", "University of Pennsylvania", "California University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.04523v1", "arxiv_id": "2509.04523v1", "title": "Using LLMs to create analytical datasets: A case study of reconstructing the historical memory of Colombia", "authors": [ "David Anderson", "Galia Benitez", "Margret Bjarnadottir", "Shriyan Reyya" ], "posted": "2025-09-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.04523v1", "field": "economics", "role": "instrument", "bullets": [ "Over 200,000 Spanish-language violence-related newspaper articles from Colombia, spanning decades of armed conflict.", "GPT reads and answers structured questions about each article to construct an analytical dataset on conflict events and locations.", "Resulting dataset enables descriptive analysis and a policy study linking violence patterns to coca crop eradication programs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 35, "n": 3041, "authors_detailed": [ { "name": "David R. Anderson", "url": "https://openalex.org/A5038272927", "inst": "May Institute" }, { "name": "Galia Benitez", "url": "https://openalex.org/A5119819140", "inst": "" }, { "name": "Margrét Bjarnadóttir", "url": "https://openalex.org/A5110538359", "inst": "University of Maryland, College Park" }, { "name": "Shriyan Reyya", "url": "https://openalex.org/A5119819141", "inst": "" } ], "affiliations": [ "University of Maryland, College Park", "May Institute" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5437296", "doi": "10.2139/ssrn.5437296", "title": "Novel Corporate Governance Structures", "authors": [ "Jennifer S. Fan", "Xuan-Thao Nguyen" ], "posted": "2025-09-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5437296", "field": "management", "role": "object", "bullets": [ "Corporate governance structures at OpenAI, Anthropic, and xAI, examining the tandem nonprofit-for-profit and related models.", "Study analyzes how AI safety concerns shaped novel governance structures, including board reconfigurations and management tensions at leading AI startups.", "Proposes a corporate law amendment requiring board-level AI Safety Committees at AI startups to improve long-term governance viability." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3042, "authors_detailed": [ { "name": "Jennifer S. Fan", "url": "https://openalex.org/A5085676039", "inst": "Loyola Marymount University" }, { "name": "Xuan-Thao Nguyen", "url": "https://openalex.org/A5117422811", "inst": "University of Washington" } ], "affiliations": [ "Loyola Marymount University", "University of Washington" ] }, { "uid": "arxiv:2509.02388v1", "arxiv_id": "2509.02388v1", "title": "Bridging Human Cognition and AI: A Framework for Explainable Decision-Making Systems", "authors": [ "N. Jean", "G. Le Pera" ], "posted": "2025-09-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.02388v1", "field": "management", "role": "method", "bullets": [ "A general framework mapping explainability techniques to Malle's five categories of behaviour explanation, illustrated with credit risk assessment and LLM-powered regulatory analysis cases.", "Large language models appear only inside the case studies; no specific models are named and no quantitative evaluation of outputs is reported.", "The paper argues that aligning technical explanations with human cognitive mechanisms improves transparency and trust in AI-assisted decisions; support remains illustrative rather than empirical." ], "bullet_provenance": "ai", "salience": 25, "edition": 14, "models": [], "validated": null, "n": 2036, "authors_detailed": [ { "name": "Nicola Jean", "url": "https://openalex.org/A5066085963", "inst": "University of Douala" }, { "name": "Giacomo Le Pera", "url": "https://openalex.org/A5005896099", "inst": "Bank of Italy" } ], "affiliations": [ "University of Douala", "Bank of Italy" ] }, { "uid": "arxiv:2509.04505v1", "arxiv_id": "2509.04505v1", "title": "The Ethical Compass of the Machine: Evaluating Large Language Models for Decision Support in Construction Project Management", "authors": [ "Somtochukwu Azie", "Yiping Meng" ], "posted": "2025-09-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.04505v1", "field": "management", "role": "method", "bullets": [ "Twelve real-world ethical dilemmas in construction project management scored with the EDSAC checklist, alongside semi-structured interviews with 12 industry experts.", "Two leading LLMs, not named in the abstract, answer the scenarios and are graded on checklist dimensions covering compliance, accountability, and reasoning transparency.", "The models handle structured legal compliance but fall short on contextual nuance, accountability, and transparent reasoning; experts back human-in-the-loop oversight over autonomous ethical judgment." ], "bullet_provenance": "ai", "validated": true, "validation_note": "EDSAC checklist grading of scenario answers", "salience": 34, "edition": 14, "models": [], "n": 2037, "authors_detailed": [ { "name": "Somtochukwu Azie", "url": "https://openalex.org/A5119819127", "inst": "" }, { "name": "Meng, Yiping", "url": "", "inst": "" } ] }, { "uid": "arxiv:2509.02879v1", "arxiv_id": "2509.02879v1", "title": "Artificial or Human Intelligence?", "authors": [ "Eric Gao" ], "posted": "2025-09-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.02879v1", "field": "economics", "role": "object", "bullets": [ "Theoretical model of student learning incentives when AI tools can independently solve problems but suffer from hallucination and sharp performance cutoffs.", "LLMs modeled as tools creating discontinuous ability gaps between students above and below the AI capability frontier.", "Restricting AI use on assignments counters student mis-specification of AI accuracy and prevents underinvestment in human capital accumulation." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 2754, "authors_detailed": [ { "name": "Gao, Eric", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5398915", "doi": "10.2139/ssrn.5398915", "title": "Corporate Disclosure of Biodiversity Risk Exposure", "authors": [ "Sijia Fan", "Miao Liu", "Yao Lu", "David T. Ng" ], "posted": "2025-09-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5398915", "field": "accounting", "role": "instrument", "bullets": [ "10-K filings of U.S. public firms, examining voluntary biodiversity risk disclosure and investor reactions to emerging risk disclosures.", "NLP and LLMs identify and classify biodiversity risk disclosures as direct (explicit) or indirect (implied) in a two-step approach.", "Firms disclose more with higher institutional ownership; investors react more strongly to indirect and first-time disclosures than to direct ones." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 65, "n": 3040, "authors_detailed": [ { "name": "Sijia Fan", "url": "", "inst": "Cornell University" }, { "name": "Miao Liu", "url": "https://openalex.org/A5100348907", "inst": "Boston College" }, { "name": "Yao Lu", "url": "https://openalex.org/A5100741287", "inst": "Cornell University" }, { "name": "David Ng", "url": "https://openalex.org/A5022840927", "inst": "Cornell University" } ], "affiliations": [ "Cornell University", "Boston College" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.02308v1", "arxiv_id": "2509.02308v1", "title": "Exploring Diffusion Models for Generative Forecasting of Financial Charts", "authors": [ "Taegyeong Lee", "Jiwon Park", "Kyunga Bang", "Seunghyun Hwang", "Ung-Jin Jang" ], "posted": "2025-09-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.02308v1", "field": "finance", "role": "method", "bullets": [ "Stock price time-series data treated as chart images for diffusion-model-based trend prediction experiments.", "Text-to-image diffusion models generated next-period chart images from current charts and instruction prompts, compared against ground truth images.", "Demonstrates feasibility of generative image models for stock trend prediction but identifies limitations requiring further research." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "generated vs ground truth chart image evaluation", "salience": 25, "n": 3942, "authors_detailed": [ { "name": "Taegyeong Lee", "url": "https://openalex.org/A5113160480", "inst": "" }, { "name": "Jiwon Park", "url": "https://openalex.org/A5010341341", "inst": "Yonsei University" }, { "name": "Bang, Kyunga", "url": "", "inst": "" }, { "name": "Seunghyun Hwang", "url": "https://openalex.org/A5011682241", "inst": "Purdue University West Lafayette" }, { "name": "Ungjin Jang", "url": "https://openalex.org/A5041892133", "inst": "" } ], "affiliations": [ "Yonsei University", "Purdue University West Lafayette" ] }, { "uid": "arxiv:2509.01393v2", "arxiv_id": "2509.01393v2", "title": "Adaptive Alpha Weighting with PPO: Enhancing Prompt-Based LLM-Generated Alphas in Quant Trading", "authors": [ "Qizhao Chen", "Hiroaki Kawashima" ], "posted": "2025-09-01", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.01393v2", "field": "finance", "role": "method", "bullets": [ "Ten stocks with price, volume, and sentiment inputs, for which a DeepSeek model generates fifty formulaic trading alphas as candidate signals.", "Proximal policy optimization reweights the LLM-generated alphas in real time; alpha quality is judged only by backtest performance, with no separate validation.", "The optimized strategy rarely tops cumulative returns but achieves higher Sharpe ratios and smaller maximum drawdowns than equal-weight, buy-and-hold, random, and momentum baselines in most cases." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 32, "edition": 14, "n": 2034, "authors_detailed": [ { "name": "Qizhao Chen", "url": "https://openalex.org/A5017181882", "inst": "University of Hyogo" }, { "name": "Hiroaki Kawashima", "url": "https://openalex.org/A5004107850", "inst": "University of Hyogo" } ], "affiliations": [ "University of Hyogo" ] }, { "uid": "doi:10.1109/bigdata66926.2025.11401468", "doi": "10.1109/bigdata66926.2025.11401468", "arxiv_id": "2509.01182v2", "title": "Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping", "authors": [ "Wonduk Seo", "Taesub Shin", "Hyunjin An", "Dokyun Kim", "Seunghyun Lee" ], "posted": "2025-09-01", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.01182v2", "field": "management", "role": "method", "bullets": [ "Real-world consumer goods listings across e-commerce platforms, where deciding whether two product names refer to one stock keeping unit lacks explicit identifiers.", "Three cooperating LLM agents generate disambiguation questions, resolve them through web search, and reuse validated reasoning traces, with human review of uncertain cases; models are not named.", "The framework beats strong baselines on accuracy, including bundle and brand-origin cases, while reused reasoning traces cut redundant searches." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labelled SKU matching datasets", "salience": 33, "edition": 14, "models": [], "n": 2035, "authors_detailed": [ { "name": "Wonduk Seo", "url": "https://openalex.org/A5124841431", "inst": "Yuhan (South Korea)" }, { "name": "Taesub Shin", "url": "https://openalex.org/A5128375991", "inst": "Yuhan (South Korea)" }, { "name": "Hyunjin An", "url": "https://openalex.org/A5124777517", "inst": "Yuhan (South Korea)" }, { "name": "Dokyun Kim", "url": "https://openalex.org/A5128339154", "inst": "Yuhan (South Korea)" }, { "name": "Seunghyun Lee", "url": "https://openalex.org/A5124818512", "inst": "Yuhan (South Korea)" } ], "affiliations": [ "Yuhan (South Korea)" ] }, { "uid": "arxiv:2509.01590v1", "arxiv_id": "2509.01590v1", "title": "Is All the Information in the Price? LLM Embeddings versus the EMH in Stock Clustering", "authors": [ "Bingyang Wang", "Grant Johnson", "Maria Hybinette", "Tucker Balch" ], "posted": "2025-09-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.01590v1", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 constituents from 2022 through 2024, comparing three stock clustering approaches in a roll-forward out-of-sample test.", "LLM embeddings of news headlines construct AI-driven clusters, evaluated against price-based correlation clusters and GICS using an APT factor model.", "Price-based clustering reduces RMSE by 14.7% versus LLM embeddings and 15.9% versus GICS, supporting the semi-strong efficient markets hypothesis." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "out-of-sample RMSE comparison across clustering methods", "salience": 60, "n": 3039, "authors_detailed": [ { "name": "Bingyang Wang", "url": "https://openalex.org/A5013848398", "inst": "Emory University" }, { "name": "G. Allan Johnson", "url": "https://openalex.org/A5087959553", "inst": "Duke University" }, { "name": "Maria Hybinette", "url": "https://openalex.org/A5005239713", "inst": "University of Georgia" }, { "name": "Tucker Balch", "url": "https://openalex.org/A5035482777", "inst": "Emory University" } ], "affiliations": [ "Emory University", "Duke University", "University of Georgia" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2510.07321v1", "arxiv_id": "2510.07321v1", "title": "How human is the machine? Evidence from 66,000 Conversations with Large Language Models", "authors": [ "Antonios Stamatogiannakis", "Arsham Ghodsinia", "Sepehr Etminanrad", "Dilney Gonçalves", "David Santos" ], "posted": "2025-08-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.07321v1", "field": "management", "role": "agent", "bullets": [ "Ten experiments with 66,000 LLM conversations testing well-documented cognitive biases and heuristics from behavioral consumer research.", "Multiple LLMs prompted to replicate human decision-making on established bias and heuristic tasks as synthetic consumer data substitutes.", "LLMs exhibit four deviation types from human behavior: bias reduction, amplification, reversal, and cross-model and temporal inconsistency undermining synthetic data reliability." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 2753, "authors_detailed": [ { "name": "Antonios Stamatogiannakis", "url": "https://openalex.org/A5080461918", "inst": "IE University" }, { "name": "Arsham Ghodsinia", "url": "https://openalex.org/A5114567446", "inst": "IE University" }, { "name": "Sepehr Etminanrad", "url": "https://openalex.org/A5119650331", "inst": "Erasmus University Rotterdam" }, { "name": "Dilney Gonçalves", "url": "https://openalex.org/A5081261896", "inst": "IE University" }, { "name": "David Santos", "url": "https://openalex.org/A5102974080", "inst": "Universidad Autónoma de Madrid" } ], "affiliations": [ "IE University", "Erasmus University Rotterdam", "Universidad Autónoma de Madrid" ] }, { "uid": "arxiv:2510.06222v1", "arxiv_id": "2510.06222v1", "title": "Inducing State Anxiety in LLM Agents Reproduces Human-Like Biases in Consumer Decision-Making", "authors": [ "Ziv Ben-Zion", "Zohar Elyoseph", "Tobias Spiller", "Teddy Lazebnik" ], "posted": "2025-08-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2510.06222v1", "field": "management", "role": "agent", "bullets": [ "A simulated grocery shopping task under budgets of 24, 54, and 108 dollars, run 2,250 times before and after exposure to anxiety inducing traumatic narratives.", "ChatGPT-5, Gemini 2.5, and Claude 3.5 Sonnet act as shopping agents; basket composition is scored for nutritional quality, a behavioural outcome rather than a validated text measurement.", "Traumatic prompts cut basket health scores by 0.081 to 0.126, effect sizes of d between minus 1.07 and minus 2.05, consistent across all three models and every budget level." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 55, "edition": 14, "validated": null, "n": 1814, "authors_detailed": [ { "name": "Ziv Ben‐Zion", "url": "https://openalex.org/A5002387004", "inst": "Yale University" }, { "name": "Zohar Elyoseph", "url": "https://openalex.org/A5028706339", "inst": "Carmel (Israel)" }, { "name": "Tobias R. Spiller", "url": "https://openalex.org/A5084506262", "inst": "University of Zurich" }, { "name": "Teddy Lazebnik", "url": "https://openalex.org/A5041000511", "inst": "Favaloro University" } ], "affiliations": [ "Yale University", "Carmel (Israel)", "University of Zurich", "Favaloro University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2509.04468v1", "arxiv_id": "2509.04468v1", "title": "Evaluating Large Language Models for Financial Reasoning: A CFA-Based Benchmark Study", "authors": [ "Xuan Yao", "Qianteng Wang", "Xinbo Liu", "Ke-Wei Huang" ], "posted": "2025-08-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.04468v1", "field": "finance", "role": "method", "bullets": [ "1,560 multiple-choice questions from official CFA mock exams across Levels I to III, treated as a proxy for real-world financial analysis complexity.", "State-of-the-art LLMs spanning multimodal, reasoning-specialized, and lightweight designs, not named in the abstract, answer under zero-shot prompting and a retrieval pipeline over official CFA curriculum.", "Reasoning-oriented models lead in zero-shot settings, retrieval helps most on complex scenarios, and errors trace mainly to knowledge gaps rather than text readability." ], "bullet_provenance": "ai", "validated": true, "validation_note": "official CFA mock exam answer keys", "salience": 48, "edition": 14, "models": [], "n": 2033 }, { "uid": "arxiv:2508.21512v1", "arxiv_id": "2508.21512v1", "title": "Accept or Deny? Evaluating LLM Fairness and Performance in Loan Approval across Table-to-Text Serialization Approaches", "authors": [ "Israel Abebe Azime", "Deborah D. Kanubala", "Tejumade Afonja", "Mario Fritz", "Isabel Valera", "Dietrich Klakow", "Philipp Slusallek" ], "posted": "2025-08-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.21512v1", "field": "finance", "role": "method", "bullets": [ "Loan approval datasets from Ghana, Germany, and the United States, covering three geographically distinct regions with tabular borrower data.", "Multiple LLMs classified loan applications under zero-shot and in-context learning using six table-to-text serialization formats including GReat and LIFT.", "In-context learning improved performance 4.9-59.6% over zero-shot baselines, but serialization format significantly affected fairness, with high-F1 formats exacerbating demographic disparities." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "F1 scores on loan approval datasets from three countries", "salience": 60, "n": 2600, "authors_detailed": [ { "name": "Israel Abebe Azime", "url": "https://openalex.org/A5013873474", "inst": "Saarland University" }, { "name": "Deborah D. Kanubala", "url": "https://openalex.org/A5120016095", "inst": "Saarland University" }, { "name": "Tejumade Afonja", "url": "https://openalex.org/A5077949475", "inst": "Helmholtz Center for Information Security" }, { "name": "Mario Fritz", "url": "https://openalex.org/A5003887059", "inst": "Helmholtz Center for Information Security" }, { "name": "Isabel Valera", "url": "https://openalex.org/A5037473201", "inst": "Saarland University" }, { "name": "Dietrich Klakow", "url": "https://openalex.org/A5008875255", "inst": "Saarland University" }, { "name": "Slusallek, Philipp", "url": "", "inst": "" } ], "affiliations": [ "Saarland University", "Helmholtz Center for Information Security" ] }, { "uid": "arxiv:2508.21285v2", "arxiv_id": "2508.21285v2", "title": "A Financial Brain Scan of the LLM", "authors": [ "Hui Chen", "Antoine Didisheim", "Mohammad", "Pourmohammadi", "Luciano Somoza", "Hanqing Tian" ], "posted": "2025-08-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.21285v2", "field": "finance", "role": "method", "bullets": [ "LLM-generated economic forecasts analyzed using mechanistic interpretability techniques that identify plain-English concepts guiding model reasoning.", "Interpretability methods map forecasts to concepts such as sentiment, technical analysis, and timing; models steered to vary risk aversion and optimism without reducing performance.", "Steering enables researchers to correct or simulate biases in LLM forecasts; method is transparent, lightweight, and replicable for social science research." ], "bullet_provenance": "ai", "validated": false, "salience": 70, "models": [], "n": 2750, "authors_detailed": [ { "name": "Hui Chen", "url": "https://openalex.org/A5100334188", "inst": "Mo" }, { "name": "Antoine Didisheim", "url": "https://openalex.org/A5019234963", "inst": "Mo" }, { "name": "Mohammad", "url": "", "inst": "Mo" }, { "name": "Pourmohammadi", "url": "", "inst": "" }, { "name": "Somoza, Luciano", "url": "", "inst": "" }, { "name": "Tian, Hanqing", "url": "", "inst": "" } ], "affiliations": [ "Mo" ] }, { "uid": "arxiv:2509.02596v1", "arxiv_id": "2509.02596v1", "title": "Introducing LCOAI: A Standardized Economic Metric for Evaluating AI Deployment Costs", "authors": [ "Eliseo Curcio" ], "posted": "2025-08-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.02596v1", "field": "economics", "role": "object", "bullets": [ "Three representative AI deployment scenarios compared: OpenAI GPT-4.1 API, Anthropic Claude Haiku API, and self-hosted LLaMA-2-13B with full lifecycle costing.", "Levelized Cost of AI metric normalizes total CAPEX and OPEX per unit of valid inference output, analogous to levelized cost of electricity in the energy sector.", "Sensitivity analyses reveal critical trade-offs in scalability and investment planning between vendor API and self-hosted models across varying inference volumes." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "llama" ], "validated": false, "salience": 40, "n": 2751, "authors_detailed": [ { "name": "Eliseo Curcio", "url": "https://openalex.org/A5119201367", "inst": "EngenderHealth" } ], "affiliations": [ "EngenderHealth" ] }, { "uid": "arxiv:2508.21368v1", "arxiv_id": "2508.21368v1", "title": "EconAgentic in DePIN Markets: A Large Language Model Approach to the Sharing Economy of Decentralized Physical Infrastructure", "authors": [ "Yulin Liu", "Mocca Schweitzer" ], "posted": "2025-08-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.21368v1", "field": "economics", "role": "agent", "bullets": [ "Decentralized Physical Infrastructure markets exceeding $10 billion market cap by 2024, simulated with LLM-powered agents under token-based economics.", "LLM agents respond to token incentives, invest in infrastructure, and adapt to market conditions; decisions compared against human heuristic benchmarks.", "Framework provides insights into efficiency, inclusion, and stability of tokenized decentralized economies, highlighting risks of autonomous AI in unregulated markets." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 2752, "authors_detailed": [ { "name": "Yulin Liu", "url": "https://openalex.org/A5100343752", "inst": "Educational Testing Service" }, { "name": "M. Schweitzer", "url": "https://openalex.org/A5111442821", "inst": "Valley Hospital" } ], "affiliations": [ "Educational Testing Service" ] }, { "uid": "arxiv:2509.02605v1", "arxiv_id": "2509.02605v1", "title": "Synthetic Founders: AI-Generated Social Simulations for Startup Validation Research in Computational Social Science", "authors": [ "Jorn K. Teutloff" ], "posted": "2025-08-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.02605v1", "field": "management", "role": "agent", "bullets": [ "15 early-stage startup founders interviewed; same protocol replicated with LLM-generated founder and investor personas in a docking experiment.", "LLM-driven synthetic personas simulate founder and investor responses to AI-powered startup validation, compared via structured thematic synthesis.", "AI personas converge on demand signals and trust barriers but miss relational advocacy value and moonshot skepticism found only in human interviews." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 3038, "authors_detailed": [ { "name": "Jorn K. Teutloff", "url": "https://openalex.org/A5120826331", "inst": "" } ] }, { "uid": "arxiv:2508.21051v3", "arxiv_id": "2508.21051v3", "title": "Language Models and Logic Programs for Trustworthy Tax Reasoning", "authors": [ "William Jurayj", "Nils Holzenberger", "Benjamin Van Durme" ], "posted": "2025-08-28", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.21051v3", "field": "accounting", "role": "method", "bullets": [ "The SARA statutory reasoning dataset of U.S. tax scenarios, with system deployment costs estimated from real-world penalties for tax filing errors.", "LLMs translate plain-text tax rules into formal logic programs solved symbolically, aided by retrieved exemplar case representations; answers are scored against SARA ground truth.", "The neuro-symbolic pipeline raises statutory reasoning accuracy substantially and drives estimated costs well below the 270 dollars and 13 hours the average American spends filing." ], "bullet_provenance": "ai", "validated": true, "validation_note": "SARA benchmark ground truth", "salience": 55, "edition": 14, "models": [], "n": 2032, "authors_detailed": [ { "name": "William Jurayj", "url": "https://openalex.org/A5077832672", "inst": "Johns Hopkins University" }, { "name": "Nils Holzenberger", "url": "https://openalex.org/A5017498603", "inst": "Institut Polytechnique de Paris" }, { "name": "Benjamin Van Durme", "url": "https://openalex.org/A5075825791", "inst": "Johns Hopkins University" } ], "affiliations": [ "Johns Hopkins University", "Institut Polytechnique de Paris" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2508.20707v1", "arxiv_id": "2508.20707v1", "title": "Can News Predict the Direction of Oil Price Volatility? A Language Model Approach with SHAP Explanations", "authors": [ "Romina Hashami", "Felipe Maldonado" ], "posted": "2025-08-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.20707v1", "field": "finance", "role": "instrument", "bullets": [ "Decade of Eikon financial news data (2014-2024) on crude oil markets, predicting directional changes in oil price volatility.", "FastText, FinBERT, Gemini, and LLaMA extract sentiment and embeddings from news; ensemble framework benchmarked against HAR model using McNemar test.", "Raw news count emerges as the most robust predictor; FastText most effective for directional forecasting; SHAP reveals regime-dependent predictive drivers." ], "bullet_provenance": "ai", "models": [ "gemini", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "benchmarked against HAR model with McNemar test for statistical significance", "salience": 55, "n": 3036, "authors_detailed": [ { "name": "Romina Hashami", "url": "https://openalex.org/A5118336620", "inst": "University of Essex" }, { "name": "Felipe Maldonado", "url": "https://openalex.org/A5067555645", "inst": "University of Essex" } ], "affiliations": [ "University of Essex" ] }, { "uid": "doi:10.2139/ssrn.5400765", "doi": "10.2139/ssrn.5400765", "title": "The Words of Chairs in FOMC Press Conferences: Financial Text Analysis Using Language Model Ensembles", "authors": [ "Jennifer Xu", "Tamara Babaian" ], "posted": "2025-08-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5400765", "field": "finance", "role": "instrument", "bullets": [ "FOMC press conference transcripts across multiple chairs, aligned with market reaction data for financial text analysis.", "Ensembles of FinBERT variants, GPT-4o, and dictionary-based methods classify sentiment, topics, and temporal directionality in chair remarks.", "Ensemble leveraging multiple models improves classification accuracy; financial markets respond to both sentiment tones and the context of sentiment." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "ensemble classification accuracy evaluation across model variants", "salience": 60, "n": 3037, "authors_detailed": [ { "name": "Jennifer Xu", "url": "https://openalex.org/A5023011601", "inst": "Bentley University" }, { "name": "Tamara Babaian", "url": "https://openalex.org/A5086392917", "inst": "Bentley University" } ], "affiliations": [ "Bentley University" ] }, { "uid": "doi:10.2139/ssrn.5409368", "doi": "10.2139/ssrn.5409368", "title": "Embracing the Digital Transition: The Adoption of Cloud Computing and AI by Italian Firms", "authors": [ "Lorenzo Bencivelli", "Sara Formai", "Elena Mattevi", "Tullia Padellini" ], "posted": "2025-08-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5409368", "field": "management", "role": "object", "bullets": [ "Survey of Italian firms with 20 or more employees drawn from Banca d'Italia business surveys covering 2020 to 2024.", "Study tracks cloud computing and AI adoption rates, linking uptake to firm size, export activity, group affiliation, and innovation capacity.", "AI adoption rose from 4% to 13% over four years; adoption correlates positively with realized and expected employment growth rather than displacement." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3528, "authors_detailed": [ { "name": "Lorenzo Bencivelli", "url": "https://openalex.org/A5034520309", "inst": "Bank of Italy" }, { "name": "Sara Formai", "url": "https://openalex.org/A5020277431", "inst": "Bank of Italy" }, { "name": "Elena Mattevi", "url": "https://openalex.org/A5119471267", "inst": "Bank of Italy" }, { "name": "Tullia Padellini", "url": "https://openalex.org/A5045987515", "inst": "Bank of Italy" } ], "affiliations": [ "Bank of Italy" ] }, { "uid": "arxiv:2509.04455v1", "arxiv_id": "2509.04455v1", "title": "INSEva: A Comprehensive Chinese Benchmark for Large Language Models in Insurance", "authors": [ "Shisong Chen", "Qian Zhu", "Wenyan Yang", "Chengyi Yang", "Zhong Wang", "Ping Wang", "Xuan Lin", "Bo Xu", "Daqian Li", "Chao Yuan", "Licai Qi", "Wanqing Xu", "sun zhenxing", "Xin Lu", "Shiqiang Xiong", "Chao Chen", "Haixiang Hu", "Yanghua Xiao" ], "posted": "2025-08-27", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2509.04455v1", "field": "finance", "role": "method", "bullets": [ "38,704 evaluation examples for the Chinese insurance domain, sourced from authoritative materials and organized by business area, task format, difficulty, and cognitive-knowledge dimension.", "Eight state-of-the-art LLMs, not named in the abstract, are scored with tailored methods assessing faithfulness and completeness of open-ended responses.", "General models average above 80 on basic insurance competency yet leave substantial gaps on complex real-world scenarios, with wide variation across evaluation dimensions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "curated insurance benchmark from authoritative materials", "salience": 38, "edition": 14, "models": [], "n": 2031, "authors_detailed": [ { "name": "Shisong Chen", "url": "https://openalex.org/A5085175204", "inst": "Second Military Medical University" }, { "name": "Qian Zhu", "url": "https://openalex.org/A5103011307", "inst": "Beijing University of Chinese Medicine" }, { "name": "Wenyan Yang", "url": "https://openalex.org/A5100745800", "inst": "China Pharmaceutical University" }, { "name": "Chengyi Yang", "url": "https://openalex.org/A5071511791", "inst": "East China Normal University" }, { "name": "Wang Zhong", "url": "https://openalex.org/A5103240906", "inst": "East China University of Science and Technology" }, { "name": "Ping Wang", "url": "https://openalex.org/A5100338698", "inst": "ShanghaiTech University" }, { "name": "Lin, Xuan", "url": "", "inst": "" }, { "name": "Bo Xu", "url": "https://openalex.org/A5102800174", "inst": "Wuhan University" }, { "name": "Danni Li", "url": "https://openalex.org/A5050988122", "inst": "First Hospital of China Medical University" }, { "name": "Chao Yuan", "url": "https://openalex.org/A5019113664", "inst": "Tongji University" }, { "name": "Li-Cai Qi", "url": "https://openalex.org/A5004450665", "inst": "" }, { "name": "Wanqing Xu", "url": "https://openalex.org/A5102177776", "inst": "Tufts University" }, { "name": "zhenxing, sun", "url": "", "inst": "" }, { "name": "Lin Xin", "url": "https://openalex.org/A5069884799", "inst": "Xi'an University of Science and Technology" }, { "name": "Shiqiang Xiong", "url": "https://openalex.org/A5119819106", "inst": "" }, { "name": "Chao Chen", "url": "https://openalex.org/A5101805443", "inst": "Shanghai Jiao Tong University" }, { "name": "H. B. Hu", "url": "https://openalex.org/A5109262530", "inst": "Wenzhou University" }, { "name": "Yanghua Xiao", "url": "https://openalex.org/A5090455375", "inst": "Fudan University" } ], "affiliations": [ "Second Military Medical University", "Beijing University of Chinese Medicine", "China Pharmaceutical University", "East China Normal University", "East China University of Science and Technology", "ShanghaiTech University", "Wuhan University" ] }, { "uid": "doi:10.2139/ssrn.5406332", "doi": "10.2139/ssrn.5406332", "title": "FX Sentiment Analysis with Large Language Models", "authors": [ "Daniele Ballinari", "Jessica Maly" ], "posted": "2025-08-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5406332", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 40, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 678, "authors_detailed": [ { "name": "Daniele Ballinari", "url": "https://openalex.org/A5002098154", "inst": "Swiss National Bank" }, { "name": "Jessica Maly", "url": "", "inst": "University of St.Gallen" } ], "affiliations": [ "Swiss National Bank", "University of St.Gallen" ] }, { "uid": "doi:10.2139/ssrn.5395056", "doi": "10.2139/ssrn.5395056", "title": "Large Language Model Disagreement", "authors": [ "Yang Liu", "Jiatao Liu", "Tianyu Wang" ], "posted": "2025-08-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5395056", "field": "finance", "role": "instrument", "bullets": [ "Earnings call transcripts from U.S. public firms analyzed by six frontier LLMs including GPT, Claude, Gemini, DeepSeek-V3, DeepSeek-R1, and Qwen-2.5.", "Six LLMs independently scored earnings calls; pairwise disagreement across models formed a novel LLMD measure compared against human analyst disagreement.", "LLMD negatively predicts cross-sectional stock returns where human and ML-based disagreement measures fail; effect concentrates in small-cap, high-volatility stocks." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "open_other" ], "open_weights": false, "validated": true, "validation_note": "correlation with human and ML-based disagreement measures", "salience": 75, "n": 2356, "authors_detailed": [ { "name": "Yang Liu", "url": "https://openalex.org/A5100355692", "inst": "Hunan University of Finance and Economics" }, { "name": "Jiatao Liu", "url": "https://openalex.org/A5001549032", "inst": "Xi’an Jiaotong-Liverpool University" }, { "name": "Tianyu Wang", "url": "https://openalex.org/A5100417433", "inst": "Tsinghua University" } ], "affiliations": [ "Hunan University of Finance and Economics", "Xi’an Jiaotong-Liverpool University", "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.5404966", "doi": "10.2139/ssrn.5404966", "title": "When AI Sets Wages: Biases and Labor Discrimination in Generative Pricing", "authors": [ "Maxime C. Cohen", "Eddy Hage-Youssef", "Warut Khern-Am-Nuai" ], "posted": "2025-08-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5404966", "field": "economics", "role": "agent", "bullets": [ "Sixty thousand freelancer profiles from the top six categories on a leading online labor platform, generating 480,000 hourly rate recommendations across eight LLMs.", "GPT-4o, Claude 3.7 Sonnet, Gemini Flash, DeepSeek-R1, Llama 3.1, and GPT-5 Mini recommended hourly wages; prompt interventions tested for bias mitigation.", "LLMs recommend rates 30-94% above human-set rates; geographic pay gaps reach 130% and age premiums 46%, with prompt design reducing geographic but not age bias." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "llama", "open_other" ], "open_weights": false, "validated": false, "salience": 75, "n": 2371, "authors_detailed": [ { "name": "Maxime C. Cohen", "url": "https://openalex.org/A5015555156", "inst": "McGill University" }, { "name": "Eddy Hage-Youssef", "url": "https://openalex.org/A5119054719", "inst": "McGill University" }, { "name": "Warut Khern-am-nuai", "url": "https://openalex.org/A5023532960", "inst": "McGill University" } ], "affiliations": [ "McGill University" ] }, { "uid": "arxiv:2508.19609v1", "arxiv_id": "2508.19609v1", "title": "FinCast: A Foundation Model for Financial Time-Series Forecasting", "authors": [ "Zhuohang Zhu", "Haodong Chen", "Qiang Qu", "Vera Chung" ], "posted": "2025-08-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.19609v1", "field": "finance", "role": "method", "bullets": [ "Large-scale financial datasets covering stocks, commodities, and futures across temporal resolutions from per-second to weekly intervals.", "FinCast foundation model trained for zero-shot financial time series forecasting without requiring domain-specific fine-tuning across multiple asset classes.", "FinCast surpassed existing state-of-the-art forecasting methods with robust zero-shot generalization across diverse financial domains and temporal resolutions." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "empirical comparison vs state-of-the-art forecasting methods", "salience": 55, "n": 3217 }, { "uid": "doi:10.2139/ssrn.5408664", "doi": "10.2139/ssrn.5408664", "title": "Implications of AI Usage for Financial Stability: Evidence from AI-Driven Investment Funds Identified by Generative AI", "authors": [ "Hong Kong Institute for Monetary and Financial Research" ], "posted": "2025-08-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5408664", "field": "finance", "role": "object", "bullets": [ "Matched sample of 2,698 AI-driven and 2,374 non-AI-driven equity open-ended funds drawn from 87,090 fund prospectuses analyzed with generative AI classification.", "Generative AI identified AI-driven investment strategies from prospectuses; matched-sample analysis compared trading correlations and market-stress responses across fund types.", "AI-driven OEFs show increased trading correlation among similar strategies but heterogeneous stress responses, with some pro-cyclical and others counter-cyclical." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 78, "n": 3527, "authors_detailed": [ { "name": "Hong Kong Institute for Monetary and Financial Research", "url": "https://openalex.org/A5027518054", "inst": "Pacific Medical (China)" } ], "affiliations": [ "Pacific Medical (China)" ] }, { "uid": "arxiv:2508.18600v1", "arxiv_id": "2508.18600v1", "title": "Bias-Adjusted LLM Agents for Human-Like Decision-Making via Behavioral Economics", "authors": [ "Ayato Kitadai", "Yusuke Fukasawa", "Nariaki Nishino" ], "posted": "2025-08-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.18600v1", "field": "economics", "role": "agent", "bullets": [ "The ultimatum game, simulated by LLM agents conditioned on personas built from individual-level behavioural economics data; the abstract does not name the models.", "Persona conditioning adjusts intrinsic model biases, and simulated play is compared with empirical human behaviour, with clearest improvement on the responder side.", "Alignment between simulated and observed decisions improves, though the authors note trait representations need refinement before population-scale simulation of human decision patterns." ], "bullet_provenance": "ai", "validated": true, "validation_note": "empirical ultimatum game behaviour", "salience": 44, "edition": 14, "models": [], "n": 2030 }, { "uid": "arxiv:2508.19136v1", "arxiv_id": "2508.19136v1", "title": "Using Machine Learning to Generate, Clarify, and Improve Economic Models", "authors": [ "Annie Liang" ], "posted": "2025-08-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.19136v1", "field": "economics", "role": "method", "bullets": [ "Survey of recent approaches using machine learning to develop and improve economic theories across domains including bargaining, choice under uncertainty, and labor markets.", "Reviews adversarial training to expose model limits, theory-constrained algorithmic search, and emerging LLM-based approaches for generating economic models.", "ML methods reconceived for economic modeling improve both prediction and causal understanding, with LLM advances opening new directions for theory generation." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3526 }, { "uid": "doi:10.1145/3768292.3770394", "doi": "10.1145/3768292.3770394", "arxiv_id": "2508.18427v2", "title": "Tracing Positional Bias in Financial Decision-Making: Mechanistic Insights from Qwen2.5", "authors": [ "Fabrizio Dimino", "Krati Saxena", "Bhaskarjit Sarmah", "Stefano Pasquali" ], "posted": "2025-08-25", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.18427v2", "field": "finance", "role": "method", "bullets": [ "Binary financial decisions elicited from Qwen2.5 instruct models from 1.5B to 14B parameters on a purpose built, finance authentic dataset.", "The audit varies answer position to quantify primacy and recency effects, then applies mechanistic interpretability to locate where bias emerges and propagates inside the models.", "Positional bias is pervasive, scale sensitive, and resurfaces under nuanced prompts and investment scenarios; the internal mapping supports mitigation guidance for financial deployment." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 56, "edition": 14, "validated": null, "n": 1874, "authors_detailed": [ { "name": "Fabrizio Dimino", "url": "https://openalex.org/A5095383911", "inst": "Domus Medica" }, { "name": "K. B. Saxena", "url": "https://openalex.org/A5047752515", "inst": "Becton Dickinson (India)" }, { "name": "Bhaskarjit Sarmah", "url": "https://openalex.org/A5040045971", "inst": "Becton Dickinson (India)" }, { "name": "Stefano Pasquali", "url": "https://openalex.org/A5030132224", "inst": "Domus Medica" } ], "affiliations": [ "Domus Medica", "Becton Dickinson (India)" ] }, { "uid": "arxiv:2508.17565v1", "arxiv_id": "2508.17565v1", "title": "TradingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis", "authors": [ "Feng Tian", "Flora D. Salim", "Hao Xue" ], "posted": "2025-08-25", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.17565v1", "field": "finance", "role": "method", "bullets": [ "Five real-world stock datasets backtested with a multi-agent system whose agents cover news sentiment, report interpretation, trend forecasting, style adaptation, and trading decisions.", "LLM agents, models not named in the abstract, self-reflect on past trades, and a pipeline synthesizes annotated post-training data; no validation against ground truth is reported.", "Backtests show better performance than rule-based, machine learning, reinforcement learning, and earlier LLM strategies, with configurable stop-loss and take-profit risk management." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 14, "models": [], "n": 2029 }, { "uid": "doi:10.2139/ssrn.5403483", "doi": "10.2139/ssrn.5403483", "title": "Notes on a World with Generative AI", "authors": [ "Nikos Askitas" ], "posted": "2025-08-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5403483", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis addressed to labor economists examining GenAI and LLMs as a disruptive force in white-collar cognitive labor markets.", "LLMs analyzed through analogy to auto-regressive economic models; risks of synthetic content feedback loops examined theoretically.", "GenAI may substitute for some cognitive labor, but statistical limits likely preserve a role for human judgment amid significant disruption." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 2416, "authors_detailed": [ { "name": "Nikos Askitas", "url": "https://openalex.org/A5009452325", "inst": "Max Planck Society" } ], "affiliations": [ "Max Planck Society" ] }, { "uid": "doi:10.2139/ssrn.5403487", "doi": "10.2139/ssrn.5403487", "title": "Quality, Safety, and Disparities of AI Chatbots in Managing Chronic Diseases: Experimental Evidence", "authors": [ "Yafei Si", "Yurun Meng", "Xi Chen", "Ruopeng An", "Limin Mao", "Bingqin Li", "Hazel Bateman", "Han Zhang", "Hongbin Fan", "Jiaqi Zu", "Shaoqing Gong", "Zhongliang Zhou", "Yudong Miao" ], "posted": "2025-08-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5403487", "field": "economics", "role": "object", "bullets": [ "384 patient-AI trials using simulated patients in China, comparing ERNIE Bot, ChatGPT, and DeepSeek to human physicians for chronic disease management.", "Three AI chatbots provide medical consultations; study evaluates diagnostic accuracy, prescription quality, and disparities by age and economic status.", "ERNIE Bot reaches 77.3% diagnostic accuracy but prescribes unnecessary tests (91.9%) and medications (57.8%); disparities favor older and wealthier patients." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "diagnostic accuracy and prescription quality against clinical standards", "salience": 50, "n": 3035, "authors_detailed": [ { "name": "Yafei Si", "url": "https://openalex.org/A5041637802", "inst": "The University of Melbourne" }, { "name": "Yurun Meng", "url": "", "inst": "" }, { "name": "Xi Chen", "url": "https://openalex.org/A5100329989", "inst": "South China Agricultural University" }, { "name": "Ruopeng An", "url": "https://openalex.org/A5002313901", "inst": "New York University" }, { "name": "Limin Mao", "url": "https://openalex.org/A5068984079", "inst": "House of Representatives" }, { "name": "Bingqin Li", "url": "https://openalex.org/A5078295182", "inst": "University of Johannesburg" }, { "name": "Hazel Bateman", "url": "https://openalex.org/A5046778422", "inst": "UNSW Sydney" }, { "name": "Han Zhang", "url": "https://openalex.org/A5100399287", "inst": "Guangxi University" }, { "name": "Hongbin Fan", "url": "https://openalex.org/A5112109003", "inst": "Shaanxi Xueqian Normal University" }, { "name": "Jiaqi Zu", "url": "https://openalex.org/A5106609569", "inst": "RMIT University" }, { "name": "Shaoqing Gong", "url": "https://openalex.org/A5082892643", "inst": "Luohe Medical College" }, { "name": "Zhongliang Zhou", "url": "https://openalex.org/A5112232491", "inst": "Xi'an Jiaotong University" }, { "name": "Yudong Miao", "url": "https://openalex.org/A5026303901", "inst": "Zhengzhou University" } ], "affiliations": [ "New York University", "The University of Melbourne", "South China Agricultural University", "House of Representatives", "University of Johannesburg", "UNSW Sydney", "Guangxi University", "Shaanxi Xueqian Normal University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3768292.3770363.", "doi": "10.1145/3768292.3770363.", "arxiv_id": "2508.17906v2", "title": "FinReflectKG: Agentic Construction and Evaluation of Financial Knowledge Graphs", "authors": [ "Abhinav Arun", "Fabrizio Dimino", "Tejas Prakash Agarwal", "Bhaskarjit Sarmah", "Stefano Pasquali" ], "posted": "2025-08-25", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.17906v2", "field": "finance", "role": "instrument", "bullets": [ "Latest annual SEC 10-K filings of all S&P 100 companies, constructing an open-source large-scale financial knowledge graph dataset.", "LLM-based extraction framework with three modes (single-pass, multi-pass, reflection-agent) plus LLM-as-Judge evaluation of knowledge graph quality.", "Reflection-agent mode achieved 64.8% compliance against rule-based policies, outperforming baseline extraction modes on precision, comprehensiveness, and relevance." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "rule-based compliance score and LLM-judge evaluation metrics", "salience": 60, "n": 3216 }, { "uid": "doi:10.1145/3746252.3761517", "doi": "10.1145/3746252.3761517", "arxiv_id": "2508.16936v2", "title": "THEME: Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics", "authors": [ "Hoyoung Lee", "Wonbin Ahn", "Suhwan Park", "Jaehoon Lee", "Minjae Kim", "Sungdong Yoo", "Taeyoon Lim", "Woohyung Lim", "Yongjae Lee" ], "posted": "2025-08-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.16936v2", "field": "finance", "role": "instrument", "bullets": [ "Thematic investing portfolios constructed by fine-tuning stock embeddings with hierarchical contrastive learning on theme-stock relationships and historical returns.", "THEME framework aligns themes and constituent stocks via contrastive learning, then refines embeddings with return data; compared against leading general-purpose LLM embeddings.", "THEME significantly outperforms general-purpose LLMs in thematic asset retrieval and produces portfolios with compelling risk-adjusted return performance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Thematic asset retrieval accuracy vs LLM baselines", "salience": 60, "n": 2749, "authors_detailed": [ { "name": "Hoyoung Lee", "url": "", "inst": "Ulsan National Institute of Science and Technology" }, { "name": "Wonbin Ahn", "url": "https://openalex.org/A5082843254", "inst": "LG (South Korea)" }, { "name": "Suhwan Park", "url": "https://openalex.org/A5012389090", "inst": "Ulsan National Institute of Science and Technology" }, { "name": "Jaehoon Lee", "url": "https://openalex.org/A5092362743", "inst": "LG (South Korea)" }, { "name": "Minjae Kim", "url": "", "inst": "LG (South Korea)" }, { "name": "Sungdong Yoo", "url": "https://openalex.org/A5084039433", "inst": "LG (South Korea)" }, { "name": "Taeyoon Lim", "url": "", "inst": "LG (South Korea)" }, { "name": "Woohyung Lim", "url": "https://openalex.org/A5017714788", "inst": "LG (South Korea)" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5100366482", "inst": "Ulsan National Institute of Science and Technology" } ], "affiliations": [ "Ulsan National Institute of Science and Technology", "LG (South Korea)" ] }, { "uid": "arxiv:2508.16243v1", "arxiv_id": "2508.16243v1", "title": "TULIP: Adapting Open-Source Large Language Models for Underrepresented Languages and Specialized Financial Tasks", "authors": [ "İrem Demirtaş", "Burak Payzun", "Seçil Arslan" ], "posted": "2025-08-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.16243v1", "field": "finance", "role": "method", "bullets": [ "Financial Turkish, an underrepresented language and domain pairing, addressed with a five stage pipeline of data collection, continued pretraining, benchmark design, synthetic data generation, and supervised fine tuning.", "Llama 3.1 8B and Qwen 2.5 7B are adapted into TULIP models; evaluation uses the authors' own benchmark, and the abstract reports no accuracy figures.", "The adapted models are reported to handle targeted financial Turkish tasks more effectively than their bases, positioning small on premise models as a privacy preserving alternative to proprietary APIs." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "custom financial Turkish benchmark, figures not stated", "salience": 33, "edition": 14, "n": 1926, "authors_detailed": [ { "name": "Irem Nur Demirtas", "url": "https://openalex.org/A5054856425", "inst": "Prometeia (Italy)" }, { "name": "Burak Payzun", "url": "https://openalex.org/A5120715230", "inst": "" }, { "name": "Seçil Arslan", "url": "https://openalex.org/A5077321782", "inst": "X-Fab (Germany)" } ], "affiliations": [ "Prometeia (Italy)", "X-Fab (Germany)" ] }, { "uid": "arxiv:2508.16378v2", "arxiv_id": "2508.16378v2", "title": "Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies", "authors": [ "Qizhao Chen" ], "posted": "2025-08-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.16378v2", "field": "finance", "role": "instrument", "bullets": [ "Multiple cryptocurrencies over a backtesting period, combining technical indicators with news-based sentiment signals.", "Google Gemini LLM validates VADER sentiment scores extracted from news articles, feeding into constrained mean-variance portfolio optimization.", "Integrated sentiment-technical strategy outperforms momentum, Bitcoin long-short, and equal-weight portfolios on risk-adjusted returns and cumulative growth." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "backtested against momentum, BTC long-short, and equal-weight benchmarks", "salience": 45, "n": 3032 }, { "uid": "arxiv:2508.16811v1", "arxiv_id": "2508.16811v1", "title": "Exploring the Impact of Generative Artificial Intelligence on Software Development in the IT Sector: Preliminary Findings on Productivity, Efficiency and Job Security", "authors": [ "Anton Ludwig Bonin", "Pawel Robert Smolinski", "Jacek Winiarski" ], "posted": "2025-08-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.16811v1", "field": "management", "role": "object", "bullets": [ "Survey of IT sector software developers using a mixed-method approach with expert-interview-based instrument, preliminary results from ongoing data collection.", "Study examines how GenAI tools (97% adoption, mainly ChatGPT) reshape personal productivity, organizational efficiency, and job insecurity.", "Organizational AI adoption correlates with perceived efficiency gains (r=.470) and heightened job security concerns (r=.549, p<.001)." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "validated": null, "n": 3033 }, { "uid": "arxiv:2508.16571v4", "arxiv_id": "2508.16571v4", "title": "LLM-Based Agents for Competitive Landscape Mapping in Drug Asset Due Diligence", "authors": [ "Vlad Vinogradov", "Alisa Vinogradova", "Dmitrii Radkevich", "Ilya Yasny", "Dmitry Kobyzev", "Ivan Izmailov", "Katsiaryna Yanchanka", "Roman Doronin", "Andrey Doronichev" ], "posted": "2025-08-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.16571v4", "field": "finance", "role": "instrument", "bullets": [ "Five years of multi-modal diligence memos from a private biotech VC fund, mapping indications to competitor drugs.", "LLM-based agents retrieve competing drug names and extract attributes; an LLM-as-judge agent filters false positives to maximize precision.", "Agent achieves 83% recall, exceeding OpenAI Deep Research (65%) and Perplexity (60%); analyst turnaround dropped from 2.5 days to approximately 3 hours." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "recall against structured evaluation corpus from VC diligence memos", "salience": 55, "n": 3034, "authors_detailed": [ { "name": "A. K. Vinogradova", "url": "https://openalex.org/A5018575598", "inst": "Optonicus (United States)" }, { "name": "В. М. Виноградов", "url": "https://openalex.org/A5111237806", "inst": "Expert System (Italy)" }, { "name": "Dmitrii Radkevich", "url": "https://openalex.org/A5119703340", "inst": "Optonicus (United States)" }, { "name": "Ilya Yasny", "url": "https://openalex.org/A5119703341", "inst": "Optonicus (United States)" }, { "name": "Dmitry Kobyzev", "url": "https://openalex.org/A5119703342", "inst": "Optonicus (United States)" }, { "name": "Ivan Izmailov", "url": "https://openalex.org/A5111237807", "inst": "Optonicus (United States)" }, { "name": "Katsiaryna Yanchanka", "url": "https://openalex.org/A5119703343", "inst": "Optonicus (United States)" }, { "name": "Roman Doronin", "url": "https://openalex.org/A5119703344", "inst": "Optonicus (United States)" }, { "name": "Andrey Doronichev", "url": "https://openalex.org/A5117729432", "inst": "Optonicus (United States)" } ], "affiliations": [ "Optonicus (United States)", "Expert System (Italy)" ] }, { "uid": "arxiv:2508.15202v2", "arxiv_id": "2508.15202v2", "title": "Fin-PRM: A Domain-Specialized Process Reward Model for Financial Reasoning in Large Language Models", "authors": [ "Jie Zhu", "Yuanchen Zhou", "Shuo Jiang", "Junhui Li", "Lifan Guo", "Feng Chen", "Chi Zhang" ], "posted": "2025-08-21", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.15202v2", "field": "finance", "role": "method", "bullets": [ "Financial reasoning benchmarks including CFLUE and FinQA, with a training set of 3,000 reasoning trajectories carrying automatically derived step and trajectory level labels.", "Fin-PRM, a domain specialized process reward model, scores intermediate steps and whole trajectories; labels come from Monte Carlo rollouts, LLM based evaluation, and financial knowledge verification rather than human coding.", "Applied to trajectory selection, best of N inference, and reward shaping, it consistently outperforms general purpose process reward models and strong baselines on the financial benchmarks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "CFLUE and FinQA benchmarks", "salience": 42, "edition": 14, "n": 1925, "authors_detailed": [ { "name": "Jie Zhu", "url": "https://openalex.org/A5025586324", "inst": "Guangxi University" }, { "name": "Zhou, Yuanchen", "url": "", "inst": "" }, { "name": "Jiang, Shuo", "url": "", "inst": "" }, { "name": "Junhui Li", "url": "https://openalex.org/A5100369264", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Guo, Lifan", "url": "", "inst": "" }, { "name": "Feng Chen", "url": "https://openalex.org/A5100352695", "inst": "The People's Hospital Tongling" }, { "name": "Chi Zhang", "url": "https://openalex.org/A5100696274", "inst": "Nanjing University of Aeronautics and Astronautics" } ], "affiliations": [ "Guangxi University", "Beijing University of Posts and Telecommunications", "Nanjing University of Aeronautics and Astronautics" ] }, { "uid": "arxiv:2508.16673v1", "arxiv_id": "2508.16673v1", "title": "Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Models", "authors": [ "Pooja S. B. Rao", "Laxminarayen Nagarajan Venkatesan", "Mauro Cherubini", "Dinesh Babu Jayagopi" ], "posted": "2025-08-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.16673v1", "field": "management", "role": "object", "bullets": [ "Two hundred interview transcripts from UK and Indian job seekers evaluated by LLMs, with controlled identity substitutions varying name by gender, caste, and region.", "Multiple LLMs scored hirability and related traits; cross-cultural score differences and name-based bias tested on anonymized and substituted transcripts.", "Indian transcripts received consistently lower scores linked to linguistic features; name substitutions alone produced no statistically significant scoring differences." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 2748 }, { "uid": "doi:10.2139/ssrn.5393044", "doi": "10.2139/ssrn.5393044", "title": "Higher Profit, Lower Risk? How Large Language Models Shape Decision Behavior in Supply Chain Optimization", "authors": [ "Thorsten Greil", "Milena Janjevic", "Sebastian Schiffels", "Samuel Kirshner" ], "posted": "2025-08-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5393044", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 677, "authors_detailed": [ { "name": "Thorsten Greil", "url": "https://openalex.org/A5119358890", "inst": "New England University Transportation Center" }, { "name": "Milena Janjevic", "url": "https://openalex.org/A5022179613", "inst": "New England University Transportation Center" }, { "name": "S. Schiffels", "url": "https://openalex.org/A5087298602", "inst": "University of Augsburg" }, { "name": "Samuel N. Kirshner", "url": "https://openalex.org/A5059274653", "inst": "UNSW Sydney" } ], "affiliations": [ "New England University Transportation Center", "University of Augsburg", "UNSW Sydney" ] }, { "uid": "arxiv:2508.15861v1", "arxiv_id": "2508.15861v1", "title": "XFinBench: Benchmarking LLMs in Complex Financial Problem Solving and Reasoning", "authors": [ "Zhihan Zhang", "Yixin Cao", "Lizi Liao" ], "posted": "2025-08-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.15861v1", "field": "finance", "role": "method", "bullets": [ "Benchmark of 4,235 graduate-level finance problems with multimodal context, covering terminology, temporal reasoning, forecasting, scenario planning, and numerical modeling.", "Eighteen leading LLMs evaluated on XFinBench; o1 achieved best text-only accuracy at 67.3%, with knowledge augmentation improving only small open-source models.", "Best model lags human experts by 12.5 percentage points overall, with largest gaps in temporal reasoning and scenario planning capabilities." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "XFinBench accuracy vs human expert baseline", "salience": 55, "n": 2747 }, { "uid": "arxiv:2508.13915v1", "arxiv_id": "2508.13915v1", "title": "Structured Agentic Workflows for Financial Time-Series Modeling with LLMs and Reflective Feedback", "authors": [ "Yihao Ang", "Yifan Bao", "Lei Jiang", "Jiajie Tao", "Anthony K. H. Tung", "Lukasz Szpruch", "Hao Ni" ], "posted": "2025-08-19", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.13915v1", "field": "finance", "role": "method", "bullets": [ "Diverse financial forecasting and synthetic data generation tasks, used to compare automated pipelines for building time series models in financial markets.", "A planner agent backed by an unnamed LLM iterates over model selection, code refinement, and fine tuning, guided by curated knowledge banks and experimental feedback.", "The agent outperforms AutoML and agentic baselines on accuracy, robustness, and decision traceability; effect sizes are not stated in the abstract." ], "bullet_provenance": "ai", "salience": 40, "edition": 14, "models": [], "validated": null, "n": 1873, "authors_detailed": [ { "name": "Yihao Ang", "url": "https://openalex.org/A5027345020", "inst": "National University of Singapore" }, { "name": "Yifan Bao", "url": "https://openalex.org/A5117417414", "inst": "National University of Singapore" }, { "name": "Lei Jiang", "url": "https://openalex.org/A5112199654", "inst": "Tencent (China)" }, { "name": "Tao, Jiajie", "url": "", "inst": "" }, { "name": "Anthony K. H. Tung", "url": "https://openalex.org/A5023925931", "inst": "National University of Singapore" }, { "name": "Łukasz Szpruch", "url": "https://openalex.org/A5024045252", "inst": "Turing Institute" }, { "name": "Hao Ni", "url": "https://openalex.org/A5100619564", "inst": "Chongqing University of Posts and Telecommunications" } ], "affiliations": [ "National University of Singapore", "Tencent (China)", "Turing Institute", "Chongqing University of Posts and Telecommunications" ] }, { "uid": "arxiv:2508.13491v2", "arxiv_id": "2508.13491v2", "title": "From Scores to Skills: A Cognitive Diagnosis Framework for Evaluating Financial Large Language Models", "authors": [ "Ziyan Kuang", "Feiyu Zhu", "Maowei Jiang", "Yanzhao Lai", "Zelin Wang", "Zhitong Wang", "Meikang Qiu", "Jiajia Huang", "Min Peng", "Qianqian Xie", "Sophia Ananiadou" ], "posted": "2025-08-19", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.13491v2", "field": "accounting", "role": "method", "bullets": [ "CPA-KQA, an evaluation set built from Certified Public Accountant examination material, expert-annotated with fine-grained knowledge labels at high inter-annotator agreement.", "30 proprietary, open-source, and domain-specific LLMs answer skill-tagged tasks, and cognitive diagnosis infers which accounting and financial skills each model possesses or lacks.", "The diagnosis exposes hidden gaps, including weak tax and regulatory reasoning missed by single-score benchmarks, and clusters models by behavioural profile." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert-annotated CPA exam items", "salience": 55, "edition": 14, "models": [], "n": 2028, "authors_detailed": [ { "name": "Ziyan Kuang", "url": "https://openalex.org/A5110247102", "inst": "University of Manchester" }, { "name": "Feiyu Zhu", "url": "https://openalex.org/A5101124710", "inst": "Liaoning Technical University" }, { "name": "Maowei Jiang", "url": "https://openalex.org/A5102679407", "inst": "Nanjing Audit University" }, { "name": "Yanzhao Lai", "url": "https://openalex.org/A5029848442", "inst": "China Electronics Technology Group Corporation" }, { "name": "Zelin Wang", "url": "https://openalex.org/A5089200131", "inst": "Qingdao University" }, { "name": "Zhitong Wang", "url": "https://openalex.org/A5033273224", "inst": "Hainan University" }, { "name": "Meikang Qiu", "url": "https://openalex.org/A5083330935", "inst": "Augusta University" }, { "name": "Jiajia Huang", "url": "https://openalex.org/A5112641253", "inst": "Huazhong University of Science and Technology" }, { "name": "Min Peng", "url": "https://openalex.org/A5102012008", "inst": "Jiujiang University" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" } ], "affiliations": [ "University of Manchester", "Liaoning Technical University", "Nanjing Audit University", "China Electronics Technology Group Corporation", "Qingdao University", "Hainan University", "Augusta University", "Huazhong University of Science and Technology" ] }, { "uid": "arxiv:2508.13635v4", "arxiv_id": "2508.13635v4", "title": "Interpreting the Interpreter: Can We Model post-ECB Conferences Volatility with LLM Agents?", "authors": [ "Umberto Collodel" ], "posted": "2025-08-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.13635v4", "field": "finance", "role": "agent", "bullets": [ "293 ECB Governing Council press conferences from 1998 to 2026, measuring Overnight Index Swap volatility as the outcome variable.", "LLMs simulate 30 heterogeneous traders interpreting transcripts to produce cross-sectional disagreement measures, validated out-of-sample on post-January-2025 conferences.", "LLM-implied disagreement correlates at approximately 0.5 with realized OIS volatility, outperforming standard text-based alternatives." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "out-of-sample correlation with realized OIS volatility on unseen conferences", "salience": 70, "n": 3031, "authors_detailed": [ { "name": "Umberto Collodel", "url": "https://openalex.org/A5062271810", "inst": "Central Bank of Malta" } ], "affiliations": [ "Central Bank of Malta" ] }, { "uid": "doi:10.2139/ssrn.5395490", "doi": "10.2139/ssrn.5395490", "title": "Negotiating with GPT-4: Digital Doormat or Skilful Counterpart?", "authors": [ "Dorcas Quek Anderson" ], "posted": "2025-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5395490", "field": "management", "role": "agent", "bullets": [ "Pilot study with students learning negotiation skills using GPT-4 dialogue agents as simulated counterparts in role-play exercises", "GPT-4 agents followed negotiation scenario prompts and engaged students in multi-turn bargaining simulations for conflict resolution training", "GPT-4 showed adequate prompt compliance but exhibited notable behavioral patterns; students partially achieved negotiation learning objectives" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 42, "n": 2431, "authors_detailed": [ { "name": "Dorcas Quek Anderson", "url": "https://openalex.org/A5073408815", "inst": "Advanced Digital Sciences Center" } ], "affiliations": [ "Advanced Digital Sciences Center" ] }, { "uid": "arxiv:2508.15825v2", "arxiv_id": "2508.15825v2", "title": "Enhancing Cryptocurrency Sentiment Analysis with Multimodal Features", "authors": [ "Chenghao Liu", "Aniket Mahanti", "Ranesh Naha", "Guanghao Wang", "Erwann Sbai" ], "posted": "2025-08-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.15825v2", "field": "finance", "role": "instrument", "bullets": [ "Cryptocurrency markets with sentiment data from TikTok video content and Twitter text posts across multiple digital assets.", "LLMs extracted sentiment from multimodal video and text social media content to analyze dynamic dependencies with market indicators.", "TikTok video sentiment influenced speculative assets short-term; cross-platform sentiment integration improved forecasting accuracy by up to 20%." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "models": [], "n": 3029 }, { "uid": "doi:10.2139/ssrn.5392719", "doi": "10.2139/ssrn.5392719", "title": "The Behavioral Signature of GenAI in Scientific Communication", "authors": [ "Nikos Askitas" ], "posted": "2025-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5392719", "field": "economics", "role": "object", "bullets": [ "IZA Discussion Paper series abstracts before and after ChatGPT-3.5 release in March 2023, with event-study design and placebo checks.", "Stylometric analysis, ML classifier, and OpenAI API similarity testing detected GPT-assisted writing patterns in economics abstracts.", "Abrupt persistent shift toward machine-generated writing style post-ChatGPT; growing share of abstracts flagged as GPT-like, indicating selective human-AI augmentation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3030, "authors_detailed": [ { "name": "Nikos Askitas", "url": "https://openalex.org/A5009452325", "inst": "IZA - Institute of Labor Economics" } ], "affiliations": [ "IZA - Institute of Labor Economics" ] }, { "uid": "doi:10.2139/ssrn.5392712", "doi": "10.2139/ssrn.5392712", "title": "Generative AI in Higher Education: Evidence from an Elite College", "authors": [ "Zara Contractor", "Germán Reyes" ], "posted": "2025-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5392712", "field": "economics", "role": "object", "bullets": [ "Survey of students at a selective U.S. college documenting generative AI adoption within two years of ChatGPT's release, across disciplines and demographics.", "Measured AI usage rates, purposes (learning augmentation versus task automation), and predictors of adoption including perceptions of benefit and institutional policies.", "Over 80 percent of students use AI academically; adoption varies by discipline, demographics, and achievement; institutional policies risk unintended disparate impacts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3525, "authors_detailed": [ { "name": "Zara Contractor", "url": "https://openalex.org/A5117186478", "inst": "Middlebury College" }, { "name": "Germán Morong Reyes", "url": "https://openalex.org/A5081612327", "inst": "Middlebury College" } ], "affiliations": [ "Middlebury College" ] }, { "uid": "arxiv:2508.12315v4", "arxiv_id": "2508.12315v4", "title": "Deciphering the global production network from cross-border firm transactions", "authors": [ "Neave O'Clery", "Ben Radcliffe-Brown", "Thomas Spencer", "Daniel Tarling-Hunter" ], "posted": "2025-08-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.12315v4", "field": "economics", "role": "instrument", "bullets": [ "Cross-border firm transaction data covering 20 million global firms and 1 billion transactions, inferring key inputs for over 1,200 products worldwide.", "LLM queries generated a reference product-linkage network (AIPNET) compared against the empirically inferred supply chain structure from transaction data.", "Products cluster into three groups; European nations and China dominate critical intermediates; backward linkages predict country-product diversification patterns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "structural comparison with empirically inferred transaction network", "salience": 65, "n": 3524, "authors_detailed": [ { "name": "Neave O’Clery", "url": "https://openalex.org/A5101898978", "inst": "CRC for Spatial information" }, { "name": "Ben Radcliffe-Brown", "url": "https://openalex.org/A5119723311", "inst": "" }, { "name": "Thomas E. Spencer", "url": "https://openalex.org/A5076315257", "inst": "University of Missouri Health System" }, { "name": "Daniel Tarling-Hunter", "url": "https://openalex.org/A5042297191", "inst": "" } ], "affiliations": [ "CRC for Spatial information" ] }, { "uid": "arxiv:2508.11873v1", "arxiv_id": "2508.11873v1", "title": "SimInterview: Transforming Business Education through Large Language Model-Based Simulated Multilingual Interview Training System", "authors": [ "Truong Thanh Hung Nguyen", "Tran Diem Quynh Nguyen", "Hoang Loc Cao", "Thi Cam Thanh Tran", "Thi Cam Mai Truong", "Hung Cao" ], "posted": "2025-08-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.11873v1", "field": "management", "role": "agent", "bullets": [ "Business interview training for English and Japanese job markets, tested with university level candidates interviewed by virtual recruiters that match resumes to job requirements through retrieval.", "OpenAI o3, Llama 4 Maverick, and Gemma 3 power the recruiter agents, combined with speech recognition, voice synthesis, and talking head generation; no benchmark validation is reported.", "Assessments are reported to align with job requirements and preserve resume content, satisfaction ratings are high, and the lightweight Gemma 3 produced the most engaging conversations." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "salience": 34, "edition": 14, "validated": null, "n": 1924, "authors_detailed": [ { "name": "Truong Thanh Hung Nguyen", "url": "https://openalex.org/A5091003870", "inst": "University of New Brunswick" }, { "name": "Nguyen, Tran Diem Quynh", "url": "", "inst": "" }, { "name": "Cao, Hoang Loc", "url": "", "inst": "" }, { "name": "Thi Cam Thanh Tran", "url": "https://openalex.org/A5101207218", "inst": "Dalat University" }, { "name": "Truong, Thi Cam Mai", "url": "", "inst": "" }, { "name": "Hung Cao", "url": "https://openalex.org/A5088383217", "inst": "University of New Brunswick" } ], "affiliations": [ "University of New Brunswick", "Dalat University" ] }, { "uid": "arxiv:2508.11152v1", "arxiv_id": "2508.11152v1", "title": "AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions", "authors": [ "Tianjiao Zhao", "Jingrao Lyu", "Stokes Jones", "Harrison Garber", "Stefano Pasquali", "Dhagash Mehta" ], "posted": "2025-08-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.11152v1", "field": "finance", "role": "agent", "bullets": [ "Equity research and portfolio construction, with role based teams of specialized LLM agents selecting stocks under varying levels of risk tolerance.", "Agents collaborate in defined roles and their stock picking is evaluated against established benchmarks; the abstract does not name the underlying models.", "Reports stock picking performance relative to benchmarks and discusses advantages, limitations, and implementation challenges of multi agent equity analysis; magnitudes are not stated in the abstract." ], "bullet_provenance": "ai", "salience": 42, "edition": 14, "models": [], "validated": null, "n": 1923 }, { "uid": "arxiv:2508.11779v1", "arxiv_id": "2508.11779v1", "title": "A Multi-Task Evaluation of LLMs' Processing of Academic Text Input", "authors": [ "Tianyi Li", "Yu Qin", "Olivia R. Liu Sheng" ], "posted": "2025-08-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.11779v1", "field": "management", "role": "method", "bullets": [ "Information systems articles from three top journals, processed through four tasks, content reproduction, comparison, scoring, and reflection, with an extensive battery of text metrics.", "Google's Gemini acts as oracle, arbiter, and collaborator under detailed prompts; outputs are checked with linguistic metrics, ground-truth comparisons, and human evaluation.", "Summaries and paraphrases prove acceptably reliable, pairwise ranking scales poorly, grading discriminates weakly, and reflections stay shallow, so unchecked use in peer review is not recommended." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "ground truth comparison and human evaluation", "salience": 52, "edition": 14, "n": 2027, "authors_detailed": [ { "name": "Tianyi Li", "url": "https://openalex.org/A5100460598", "inst": "Chinese Academy of Medical Sciences & Peking Union Medical College" }, { "name": "Yu Qin", "url": "https://openalex.org/A5074974149", "inst": "Jiangsu Provincial Center for Disease Control and Prevention" }, { "name": "Olivia R. Liu Sheng", "url": "https://openalex.org/A5077260842", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University", "Chinese Academy of Medical Sciences & Peking Union Medical College", "Jiangsu Provincial Center for Disease Control and Prevention" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5382813", "doi": "10.2139/ssrn.5382813", "title": "More than Words: The Value of Alignment, Narrative, and Persuasion in Managerial Strategic Responses", "authors": [ "Xavier Babu", "Julie Zhang" ], "posted": "2025-08-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5382813", "field": "management", "role": "instrument", "bullets": [ "Panel dataset of Tripadvisor hotel reviews and manager responses, measuring product reputation effects in the hospitality industry.", "GPT-4 extracted narrative strength and persuasive features; Sentence-BERT measured semantic alignment between manager responses and customer reviews.", "Semantic alignment in responses significantly improved product reputation; the effect was amplified by high narrative and persuasive quality." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 3028, "authors_detailed": [ { "name": "Xavier Babu", "url": "https://openalex.org/A5114868981", "inst": "University of Massachusetts Lowell" }, { "name": "Julie Zhang", "url": "", "inst": "Independent - affiliation not provided to SSRN" } ], "affiliations": [ "University of Massachusetts Lowell", "Independent - affiliation not provided to SSRN" ] }, { "uid": "doi:10.2139/ssrn.5381584", "doi": "10.2139/ssrn.5381584", "title": "A Review of LLM Agent Applications in Finance and Banking", "authors": [ "Devesh Batra", "Conor Hamill", "John Hartley", "Ramin Okhrati", "Dale Seddon", "Harvey Miller", "Raad Khraishi", "Greig Cowan" ], "posted": "2025-08-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5381584", "field": "finance", "role": "method", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 676 }, { "uid": "arxiv:2508.09893v1", "arxiv_id": "2508.09893v1", "title": "RAGulating Compliance: A Multi-Agent Knowledge Graph for Regulatory QA", "authors": [ "Bhavik Agarwal", "Hemant Sunil Jomraj", "Simone Kaplunov", "Jack Krolick", "Viktoria Rojkova" ], "posted": "2025-08-13", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.09893v1", "field": "accounting", "role": "method", "bullets": [ "Regulatory compliance question answering over regulatory documents, from which agents extract subject predicate object triplets into an ontology free knowledge graph.", "A multi agent pipeline embeds triplets alongside source text and metadata in a single vector database and retrieves at triplet level; models are not named and no accuracy figures are reported.", "The hybrid system is reported to outperform conventional methods on complex regulatory queries, with traceability and subgraph visualization presented as support for audit focused applications." ], "bullet_provenance": "ai", "validated": false, "salience": 33, "edition": 14, "models": [], "n": 1922 }, { "uid": "doi:10.2139/ssrn.5389821", "doi": "10.2139/ssrn.5389821", "title": "Verba Volant, Transcripta Manent: What Corporate Earnings Calls Reveal About the AI Stock Rally", "authors": [ "Michele Ca’ Zorzi", "Ana-Simona Manu", "Gianluigi Lopardo" ], "posted": "2025-08-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5389821", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 firms' earnings call transcripts over 2014-2023; panel econometric analysis of generative AI exposure and financial performance.", "LLMs quantified firm-level GenAI exposure from transcripts and classified sentiment as opportunity, adoption, or risk to construct exposure measures.", "A 1 percentage point increase in GenAI exposure raised quarterly excess returns by 0.26%; difference-in-differences showed 2.4% average quarterly stock price increase post-ChatGPT." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 78, "n": 2492, "authors_detailed": [ { "name": "Michele Ca’ Zorzi", "url": "https://openalex.org/A5109497774", "inst": "European Central Bank" }, { "name": "Ana-Simona Manu", "url": "https://openalex.org/A5055333078", "inst": "European Central Bank" }, { "name": "Gianluigi Lopardo", "url": "https://openalex.org/A5119288859", "inst": "European Central Bank" } ], "affiliations": [ "European Central Bank" ] }, { "uid": "doi:10.2139/ssrn.5371824", "doi": "10.2139/ssrn.5371824", "title": "Evaluating Global News Sentiment on Electric Vehicle Adoption using Gemini 2.0", "authors": [ "Kirti Walke" ], "posted": "2025-08-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5371824", "field": "economics", "role": "instrument", "bullets": [ "100 international EV-related news headlines and extracts, manually annotated for sentiment by human raters.", "Gemini 2.0 Flash classified sentiment as positive, neutral, or negative; benchmarked against VADER rule-based baseline.", "Gemini 2.0 Flash achieved 93% accuracy with high recall and precision across all three sentiment classes." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "93% accuracy vs human-annotated sentiment labels", "salience": 20, "n": 2736, "authors_detailed": [ { "name": "Kirti Walke", "url": "https://openalex.org/A5119288699", "inst": "Yashwantrao Chavan Maharashtra Open University" } ], "affiliations": [ "Yashwantrao Chavan Maharashtra Open University" ] }, { "uid": "arxiv:2508.09641v1", "arxiv_id": "2508.09641v1", "title": "VisFinEval: A Scenario-Driven Chinese Multimodal Benchmark for Holistic Financial Understanding", "authors": [ "Zhaowei Liu", "Xin Guo", "Haotian Xia", "Lingfeng Zeng", "Fangqi Lou", "Jinyi Niu", "Mengping Li", "Qi Qi", "Jiahuan Li", "Wei Zhang", "Yinglong Wang", "Weige Cai", "Weining Shen", "Liwen Zhang" ], "posted": "2025-08-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.09641v1", "field": "finance", "role": "method", "bullets": [ "15,848 Chinese financial question-answer pairs across eight image modalities spanning front-middle-back office lifecycle tasks.", "Evaluated 21 multimodal LLMs in zero-shot setting on financial knowledge, analysis, decision support, and risk control scenarios.", "Top model Qwen-VL-max achieved 76.3% overall accuracy, outperforming non-experts but trailing financial experts by over 14 percentage points." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "VisFinEval benchmark accuracy vs. expert and non-expert humans", "salience": 42, "n": 3027 }, { "uid": "arxiv:2508.09935v1", "arxiv_id": "2508.09935v1", "title": "Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach", "authors": [ "Sayem Hossen", "Monalisa Moon Joti", "Md. Golam Rashed" ], "posted": "2025-08-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.09935v1", "field": "management", "role": "instrument", "bullets": [ "Synthesis of empirical studies on deceptive language in financial reporting, sustainability disclosure, and digital marketing communications", "Transformer models and computational textual analysis applied to detect persuasive and misrepresentative language patterns in business texts", "Detection accuracies exceeding 99% achieved in controlled settings, but multilingual replication remains constrained by data scarcity and infrastructure gaps" ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "controlled-setting detection accuracy benchmarks", "salience": 35, "n": 3941 }, { "uid": "doi:10.2139/ssrn.5371017", "doi": "10.2139/ssrn.5371017", "title": "Generative AI as a Tourism Actor: Reconceptualising Experience Co-creation, Destination Governance and Responsible Innovation in the Synthetic Experience Economy", "authors": [ "Evangelos Christou", "Anestis Fotiadis", "Antonios Giannopoulos" ], "posted": "2025-08-12", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5371017", "field": "management", "role": "object", "bullets": [ "Conceptual study drawing on tourism, information systems, marketing, psychology, and ethics to model generative AI as an active participant in tourism value co-creation.", "No specific model is deployed; the paper theorizes GenAI's role through service-dominant logic and actor-network theory rather than testing a system.", "Proposes the Synthetic Experience System framework with three co-creation loops and four boundary conditions, yielding fifteen testable research propositions across tourism, policy, and sustainability." ], "bullet_provenance": "ai", "salience": 35, "edition": 25, "models": [], "validated": null, "n": 4302, "authors_detailed": [ { "name": "Evangelos Christou", "url": "https://openalex.org/A5031673379", "inst": "International Hellenic University" }, { "name": "Anestis Fotiadis", "url": "https://openalex.org/A5057670674", "inst": "Zayed University" }, { "name": "Αντώνιος Γιαννόπουλος", "url": "https://openalex.org/A5054512763", "inst": "International Hellenic University" } ], "affiliations": [ "International Hellenic University", "Zayed University" ] }, { "uid": "doi:10.2139/ssrn.5375528", "doi": "10.2139/ssrn.5375528", "title": "A Strategic Outlook on LLM SEO: Using File-Format Logic to Guide AI-Optimized Content Design", "authors": [ "Avinash Tripathi" ], "posted": "2025-08-12", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5375528", "field": "management", "role": "object", "bullets": [ "The study tested a file-based content optimization framework using more than 100 AI-search prompts across three LLM platforms, run from five countries: India, the United States, Australia, the United Kingdom, and Germany.", "The paper introduces LLM SEO Files, a six-layer framework applied to ChatGPT, Google AI Overview, and Perplexity search outputs; validation against ground truth is not stated.", "Adding the five-layer optimization framework in phase two increased brand mentions and domain citations by 64% compared with non-optimized content in phase one." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 22, "validated": null, "n": 4119, "authors_detailed": [ { "name": "Avinash Tripathi", "url": "https://openalex.org/A5119285878", "inst": "" } ] }, { "uid": "arxiv:2508.08992v4", "arxiv_id": "2508.08992v4", "title": "Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty", "authors": [ "Rui Wang", "Qihan Lin", "Jiayu Liu", "Qing Zong", "Tianshi Zheng", "Dadi Guo", "Haochen Shi", "Peixuan Han", "Weiqi Wang", "Yangqiu Song" ], "posted": "2025-08-12", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.08992v4", "field": "economics", "role": "agent", "bullets": [ "Lottery-style choices from a classic behavioural economics paradigm, with uncertainty stated numerically and through linguistic epistemic markers; the models tested are not named.", "The workflow estimates prospect theory parameters from model choices, then injects derived probability mappings for epistemic markers into prompts to test parameter stability.", "Prospect theory does not reliably describe LLM decision-making across models, and parameters shift under linguistic uncertainty, warning against prospect-theory-based deployment where ambiguity is common." ], "bullet_provenance": "ai", "salience": 48, "edition": 14, "models": [], "validated": null, "n": 2026, "authors_detailed": [ { "name": "Rui Wang", "url": "https://openalex.org/A5100431237", "inst": "Shanghai University" }, { "name": "Qihan Lin", "url": "https://openalex.org/A5135963722", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Jiayu Liu", "url": "https://openalex.org/A5030410270", "inst": "University of Chinese Academy of Sciences" }, { "name": "Qing Zong", "url": "https://openalex.org/A5093047682", "inst": "Hong Kong University of Science and Technology" }, { "name": "Tianshi Zheng", "url": "https://openalex.org/A5119181283", "inst": "Hong Kong University of Science and Technology" }, { "name": "Dadi Guo", "url": "https://openalex.org/A5143825890", "inst": "" }, { "name": "Haochen Shi", "url": "https://openalex.org/A5066687788", "inst": "Universidad de Deusto" }, { "name": "Peixuan Han", "url": "https://openalex.org/A5109792226", "inst": "University of Nottingham Ningbo China" }, { "name": "Weiqi Wang", "url": "https://openalex.org/A5143862616", "inst": "" }, { "name": "Yangqiu Song", "url": "https://openalex.org/A5020880385", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "Shanghai University", "University of Chinese Academy of Sciences", "Hong Kong University of Science and Technology", "Universidad de Deusto", "University of Nottingham Ningbo China" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5387233", "doi": "10.2139/ssrn.5387233", "title": "Beyond Substitution: Large Language Models Drive Novel Knowledge Emergence in Online Forums", "authors": [ "Neha Sharma", "Simin Li" ], "posted": "2025-08-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5387233", "field": "management", "role": "object", "bullets": [ "Stack Overflow Q&A data analyzed before and after ChatGPT 3.5 release in November 2022, with Mathematics and Statistics Stack Exchange as controls.", "Difference-in-differences and knowledge network analysis measured changes in question volume, novelty, and network structure following public ChatGPT availability.", "Overall question volume declined but novel tag combinations surged; knowledge networks became more fragmented, suggesting LLMs enabled exploration of niche problem spaces." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 2491, "authors_detailed": [ { "name": "Neha Sharma", "url": "https://openalex.org/A5100772847", "inst": "Chandigarh University" }, { "name": "S. W. Li", "url": "https://openalex.org/A5009200919", "inst": "Sun Yat-sen University" } ], "affiliations": [ "Chandigarh University", "Sun Yat-sen University" ] }, { "uid": "doi:10.18653/v1/2024.naacl-industry.6", "doi": "10.18653/v1/2024.naacl-industry.6", "arxiv_id": "2508.10927v1", "title": "Modeling and Detecting Company Risks from News: A Case Study in Bloomberg News", "authors": [ "Jiaxin Pei", "Soumya Vadlamannati", "Liang-Kang Huang", "Daniel Preotiuc-Pietro", "Xinyu Hua" ], "posted": "2025-08-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.10927v1", "field": "finance", "role": "method", "bullets": [ "744 Bloomberg news articles annotated under a seven aspect company risk schema covering areas such as supply chain, regulation, and competition, with 277K further articles analyzed at deployment.", "Zero shot and few shot LLaMA-2 prompting is benchmarked against fine tuned pretrained language models on the annotated articles; prompting manages only moderate to low performance.", "Fine tuned smaller models win on most risk aspects, and the deployed classifier maps risk exposure patterns across companies and industries at scale." ], "bullet_provenance": "ai", "models": [ "legacy", "llama" ], "open_weights": true, "validated": true, "validation_note": "744 hand-annotated Bloomberg articles", "salience": 48, "edition": 14, "n": 1845, "authors_detailed": [ { "name": "Jiaxin Pei", "url": "https://openalex.org/A5105957311", "inst": "University of Michigan" }, { "name": "Soumya Vadlamannati", "url": "https://openalex.org/A5017392300", "inst": "Indian Institute of Technology Hyderabad" }, { "name": "Liang‐Kang Huang", "url": "https://openalex.org/A5076755213", "inst": "Goddard Space Flight Center" }, { "name": "Daniel Preotiuc-Pietro", "url": "https://openalex.org/A5106152329", "inst": "Bloomberg (United States)" }, { "name": "Xinyu Hua", "url": "https://openalex.org/A5109753823", "inst": "Bloomberg (United States)" } ], "affiliations": [ "University of Michigan", "Indian Institute of Technology Hyderabad", "Goddard Space Flight Center", "Bloomberg (United States)" ] }, { "uid": "arxiv:2508.07405v1", "arxiv_id": "2508.07405v1", "title": "Generative AI for Strategic Plan Development", "authors": [ "Jesse Ponnock" ], "posted": "2025-08-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.07405v1", "field": "management", "role": "method", "bullets": [ "A large corpus of US Government Accountability Office reports, used to generate candidate themes for the published strategic plan of a large government organization.", "BERTopic and non negative matrix factorization, rather than generative LLMs, produce topics that are then scored for similarity against the plan's vision elements.", "Generated themes match every evaluated vision element at some level, and more than half of BERTopic's matched topics reach medium or strong correlation." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "topic similarity scored against a published plan's vision elements", "salience": 30, "edition": 14, "n": 1872, "authors_detailed": [ { "name": "Jesse Ponnock", "url": "https://openalex.org/A5120003724", "inst": "" } ] }, { "uid": "arxiv:2508.20097v2", "arxiv_id": "2508.20097v2", "title": "Can LLMs Identify Tax Abuse?", "authors": [ "Andrew Blair-Stanek", "Nils Holzenberger", "Benjamin Van Durme" ], "posted": "2025-08-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.20097v2", "field": "accounting", "role": "method", "bullets": [ "U.S. tax minimization strategies grounded in statutes, case law, and administrative guidance; the abstract does not name the models or the number of tasks.", "Frontier LLMs interpret and verify strategies, fill gaps in partially specified ones, and generate complete strategies from scratch; no accuracy statistics are given in the abstract.", "The models' reasoning produced a tax strategy the authors call entirely novel, suggesting potential for tax agencies working against abusive planning by wealthy taxpayers." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "edition": 14, "models": [], "n": 2025 }, { "uid": "arxiv:2508.07408v1", "arxiv_id": "2508.07408v1", "title": "Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading", "authors": [ "Yueyi Wang", "Qiyao Wei" ], "posted": "2025-08-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.07408v1", "field": "finance", "role": "instrument", "bullets": [ "Company-related tweets aligned with 1-to-7-day forward returns for U.S. equities to evaluate alpha signal tradability.", "LLM assigned multi-label event categories to high-sentiment-intensity tweets; signals tested for statistical significance.", "Certain event labels yielded Sharpe ratios as low as -0.38 and information coefficients exceeding 0.05, significant at 95% level." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "information coefficients on 1-7 day forward returns", "salience": 55, "n": 2735, "authors_detailed": [ { "name": "Yueyi Wang", "url": "https://openalex.org/A5010218141", "inst": "Ingenierie des Materiaux polymeres" }, { "name": "Qiyao Wei", "url": "https://openalex.org/A5102489674", "inst": "" } ], "affiliations": [ "Ingenierie des Materiaux polymeres" ] }, { "uid": "doi:10.1145/3770855.3817727", "doi": "10.1145/3770855.3817727", "arxiv_id": "2508.13174v2", "title": "AlphaEval: A Comprehensive and Efficient Evaluation Framework for Formula Alpha Mining", "authors": [ "Hongjun Ding", "Binqi Chen", "Jinsheng Huang", "Taian Guo", "Zhengyang Mao", "Guoyi Shao", "Lutong Zou", "Luchen Liu", "Ming Zhang" ], "posted": "2025-08-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.13174v2", "field": "finance", "role": "method", "bullets": [ "Evaluation of formula alpha mining models including genetic programming, reinforcement learning, and LLM-based approaches for quantitative investment strategies.", "AlphaEval framework assesses generated alphas on five dimensions (predictive power, stability, robustness, financial logic, diversity) without full backtesting.", "Framework achieves evaluation consistency comparable to comprehensive backtesting while identifying superior alphas over traditional single-metric screening approaches." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "consistency with comprehensive backtesting evaluation", "salience": 62, "n": 3523, "authors_detailed": [ { "name": "Hongjun Ding", "url": "https://openalex.org/A5112167017", "inst": "Baruch College" }, { "name": "Binqi Chen", "url": "https://openalex.org/A5051681992", "inst": "Peking University" }, { "name": "Jinsheng Huang", "url": "https://openalex.org/A5009336452", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Taian Guo", "url": "https://openalex.org/A5054442510", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Zhengyang Mao", "url": "https://openalex.org/A5054462908", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Guoyi Shao", "url": "https://openalex.org/A5102889265", "inst": "Peking University" }, { "name": "Lutong Zou", "url": "https://openalex.org/A5102571805", "inst": "Harvard University Press" }, { "name": "Luchen Liu", "url": "https://openalex.org/A5074524814", "inst": "Hainan University" }, { "name": "Ming Zhang", "url": "https://openalex.org/A5100642537", "inst": "Beijing Academy of Artificial Intelligence" } ], "affiliations": [ "Harvard University", "Baruch College", "Peking University", "Beijing Academy of Artificial Intelligence", "Hainan University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5385523", "doi": "10.2139/ssrn.5385523", "title": "Can Llms Help Students Learn Accounting? A Performance Analysis Across Models and Prompts", "authors": [ "Barbara Llacay", "Maria Pilar Curós Vilà" ], "posted": "2025-08-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5385523", "field": "accounting", "role": "method", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 356 }, { "uid": "arxiv:2508.14052v4", "arxiv_id": "2508.14052v4", "title": "FinAgentBench: A Benchmark Dataset for Agentic Retrieval in Financial Question Answering", "authors": [ "Chanyeol Choi", "Jihoon Kwon", "Alejandro Lopez-Lira", "Chaewoon Kim", "Minjae Kim", "Juneha Hwang", "Jaeseon Ha", "Hojun Choi", "Suyeol Yun", "Yongjin Kim", "Yongjae Lee" ], "posted": "2025-08-07", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.14052v4", "field": "finance", "role": "method", "bullets": [ "26,000 expert annotated examples over documents of S&P 500 listed firms, built to test multi step retrieval in financial question answering.", "LLM agents, individual models not named in the abstract, first choose the relevant document type and then locate the key passage, with each step scored against the annotations.", "State of the art models leave clear headroom on the two stage task, and targeted fine tuning materially improves agentic retrieval; figures are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "26K expert annotated relevance judgments", "salience": 55, "edition": 14, "models": [], "n": 1871, "authors_detailed": [ { "name": "Chanyeol Choi", "url": "https://openalex.org/A5047029131", "inst": "Community Link" }, { "name": "Jihoon Kwon", "url": "https://openalex.org/A5066426830", "inst": "Tech University of Korea" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Chaewoon Kim", "url": "https://openalex.org/A5071807982", "inst": "Lineq (Czechia)" }, { "name": "Minjae Kim", "url": "https://openalex.org/A5024780187", "inst": "Inje University" }, { "name": "Hwang, Juneha", "url": "", "inst": "" }, { "name": "Junhyoung Ha", "url": "https://openalex.org/A5034230283", "inst": "Ulsan National Institute of Science and Technology" }, { "name": "Hojun Choi", "url": "https://openalex.org/A5104240253", "inst": "Korea Advanced Institute of Science and Technology" }, { "name": "Suyeol Yun", "url": "https://openalex.org/A5117354551", "inst": "Medieval Academy of America" }, { "name": "Yong-Jin Kim", "url": "https://openalex.org/A5100719356", "inst": "Kyungpook National University" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5100366478", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Florida", "Community Link", "Tech University of Korea", "Lineq (Czechia)", "Inje University", "Ulsan National Institute of Science and Technology", "Korea Advanced Institute of Science and Technology", "Medieval Academy of America" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2508.04975v2", "arxiv_id": "2508.04975v2", "title": "Sentiment-Aware Stock Price Prediction with Transformer and LLM-Generated Formulaic Alpha", "authors": [ "Qizhao Chen", "Hiroaki Kawashima" ], "posted": "2025-08-07", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.04975v2", "field": "finance", "role": "instrument", "bullets": [ "Stock price prediction from historical prices, technical indicators, and sentiment for target and related companies; sample, market, and period are not stated in the abstract.", "A prompt based LLM, not named, writes formulaic alphas that feed Transformer, LSTM, TCN, SVR, and random forest forecasters; the alphas are never validated against any ground truth.", "LLM generated alphas improve forecasting accuracy across the downstream models, with the natural language rationales offered as an interpretability benefit; effect sizes are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 36, "edition": 14, "models": [], "n": 1921 }, { "uid": "doi:10.1145/3768292.3770433", "doi": "10.1145/3768292.3770433", "arxiv_id": "2508.05201v2", "title": "FAITH: A Framework for Assessing Intrinsic Tabular Hallucinations in Finance", "authors": [ "Mengao Zhang", "Jiayu Fu", "Tanya Warrier", "Yuwen Wang", "Tianhui Tan", "Ke-wei Huang" ], "posted": "2025-08-07", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.05201v2", "field": "finance", "role": "method", "bullets": [ "S&P 500 annual reports turned into a context-aware masked span prediction dataset over financial tables through an automated masking pipeline.", "State-of-the-art LLMs, not named in the abstract, must reproduce masked values from surrounding context, so intrinsic hallucinations are scored against true report figures.", "The contribution is the framework, the derived evaluation dataset, and a cross-model comparison of hallucination patterns; specific error rates are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "masked true values from S&P 500 annual reports", "salience": 52, "edition": 14, "models": [], "n": 2024, "authors_detailed": [ { "name": "Mengao Zhang", "url": "", "inst": "National University of Singapore" }, { "name": "Jiayu Fu", "url": "", "inst": "National University of Singapore" }, { "name": "Tanya Warrier", "url": "https://openalex.org/A5120060722", "inst": "National University of Singapore" }, { "name": "Yuwen Wang", "url": "https://openalex.org/A5041387507", "inst": "National University of Singapore" }, { "name": "T.C. Tan", "url": "https://openalex.org/A5101802556", "inst": "National University of Singapore" }, { "name": "Ke‐Wei Huang", "url": "https://openalex.org/A5061690540", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore" ] }, { "uid": "arxiv:2508.10021v4", "arxiv_id": "2508.10021v4", "title": "LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients", "authors": [ "Egor Fadeev", "Dzhambulat Mollaev", "Aleksei Shestov", "Omar Zoloev", "Artem Sakhno", "Dmitry Korolev", "Ivan Kireev", "Andrey Savchenko", "Maksim Makarenko" ], "posted": "2025-08-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.10021v4", "field": "finance", "role": "instrument", "bullets": [ "Real-world financial datasets of bank client transaction sequences deployed in latency-sensitive production environments.", "Contrastive learning aligned raw event embeddings with frozen LLM semantic embeddings via short behavioral-feature summaries.", "LATTE outperformed state-of-the-art event sequence representations while substantially reducing inference cost and input size." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "downstream financial task benchmarks", "salience": 50, "n": 2734 }, { "uid": "doi:10.2139/ssrn.5381249", "doi": "10.2139/ssrn.5381249", "title": "Rewarding Human-AI Collaboration: A Dynamic Theory of Managerial Compensation", "authors": [ "Weining Zhang" ], "posted": "2025-08-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5381249", "field": "management", "role": "object", "bullets": [ "Dynamic principal-agent model of managerial compensation when generative AI creates supermodular complementarities with human capital in firms.", "Theoretical model derives optimal contracts balancing performance incentives with explicit learning rewards for AI skill development under moral hazard.", "High-skill managers receive stronger performance pay as AI advances, reversing standard risk-incentive trade-offs and driving rising within-firm wage inequality." ], "bullet_provenance": "ai", "salience": 68, "models": [], "validated": null, "n": 3522, "authors_detailed": [ { "name": "Weining Zhang", "url": "https://openalex.org/A5101637028", "inst": "Cheung Kong Graduate School of Business" } ], "affiliations": [ "Cheung Kong Graduate School of Business" ] }, { "uid": "doi:10.2139/ssrn.5381563", "doi": "10.2139/ssrn.5381563", "title": "Prompting Away the Fixation: How the Use of Generative AI Reduces Surrogation", "authors": [ "Dennis Fehrenbacher", "Victor van Pelt" ], "posted": "2025-08-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5381563", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 40, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 1036, "authors_detailed": [ { "name": "Dennis D. Fehrenbacher", "url": "https://openalex.org/A5082598074", "inst": "University of St.Gallen" }, { "name": "Victor van Pelt", "url": "https://openalex.org/A5041176323", "inst": "WHU – Otto Beisheim School of Management" } ], "affiliations": [ "University of St.Gallen", "WHU – Otto Beisheim School of Management" ] }, { "uid": "arxiv:2508.04625v1", "arxiv_id": "2508.04625v1", "title": "FinMMR: Make Financial Numerical Reasoning More Multimodal, Comprehensive, and Challenging", "authors": [ "Zichen Tang", "Haihong E", "Jiacheng Liu", "Zhongjun Yang", "Rongjin Li", "Zihua Rong", "Haoyang He", "Zhuodi Hao", "Xinyang Hu", "Kun Ji", "Ziyan Ma", "Mengyuan Ji", "Jun Zhang", "Chenghao Ma", "Qianhe Zheng", "Yang Liu", "Yiling Huang", "Xinyi Hu", "Qing Huang", "Zijian Xie", "Shiyao Peng" ], "posted": "2025-08-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.04625v1", "field": "finance", "role": "method", "bullets": [ "4,300 bilingual questions with 8,700 images spanning 14 financial categories including corporate finance, banking, and industry analysis.", "Evaluated multimodal LLMs on multi-step numerical reasoning integrating financial knowledge with complex charts, tables, and structure diagrams.", "Best-performing model achieved only 53.0% accuracy on hard problems, revealing substantial gaps in financial visual reasoning capabilities." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinMMR benchmark accuracy", "salience": 45, "models": [], "n": 3026 }, { "uid": "doi:10.2139/ssrn.5375473", "doi": "10.2139/ssrn.5375473", "title": "The Market's Mirror: Revealing Investor Disagreement with LLMs", "authors": [ "Vineet Bhagwat", "J. Anthony Cookson", "Chukwuma Dim", "Marina Niessner" ], "posted": "2025-08-05", "added": "2026-07-27", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5375473", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 55, "edition": 5, "bullets": [], "models": [], "validated": null, "n": 1233, "authors_detailed": [ { "name": "Vineet Bhagwat", "url": "https://openalex.org/A5024186733", "inst": "George Washington University" }, { "name": "J. Anthony Cookson", "url": "https://openalex.org/A5076123255", "inst": "University of Colorado Boulder" }, { "name": "Chukwuma Dim", "url": "https://openalex.org/A5087528513", "inst": "George Washington University" }, { "name": "Marina Niessner", "url": "https://openalex.org/A5009534656", "inst": "Indiana University" } ], "affiliations": [ "George Washington University", "University of Colorado Boulder", "Indiana University" ] }, { "uid": "arxiv:2508.05669v1", "arxiv_id": "2508.05669v1", "title": "Fine-Tuning Vision-Language Models for Markdown Conversion of Financial Tables in Malaysian Audited Financial Reports", "authors": [ "Jin Khye Tan", "En Jun Choong", "Ethan Jeremiah Chitty", "Yan Pheng Choo", "John Hsin Yang Wong", "Chern Eu Cheah" ], "posted": "2025-08-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.05669v1", "field": "accounting", "role": "method", "bullets": [ "Financial tables from Malaysian audited financial reports, with 2,152 curated image text training pairs and 100 out of sample tables held out for evaluation.", "Qwen2.5-VL-7B is fine tuned with LoRA to emit Markdown; outputs are scored by a criteria based LLM judge and a Markdown tree edit distance similarity metric.", "Achieves 92.20 percent judged accuracy and a 96.53 percent structural similarity score, surpassing the base model, larger vision language models, GPT-4o, and Gemini 2.5 Flash with faster inference." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "100 held-out tables, judged accuracy and tree edit distance", "salience": 44, "edition": 14, "n": 1920, "authors_detailed": [ { "name": "Tan, Jin Khye", "url": "", "inst": "Information Technology University" }, { "name": "En Jun Choong", "url": "https://openalex.org/A5120002928", "inst": "" }, { "name": "Ethan Jeremiah Chitty", "url": "https://openalex.org/A5120002929", "inst": "" }, { "name": "Yan Pheng Choo", "url": "https://openalex.org/A5120002930", "inst": "" }, { "name": "Wong, John Hsin Yang", "url": "", "inst": "" }, { "name": "Chern Eu Cheah", "url": "https://openalex.org/A5120002931", "inst": "" } ], "affiliations": [ "Information Technology University" ] }, { "uid": "arxiv:2508.02292v2", "arxiv_id": "2508.02292v2", "title": "FinWorld: An All-in-One Open-Source Platform for End-to-End Financial AI Research and Deployment", "authors": [ "Wentao Zhang", "Yilei Zhao", "Chuqiao Zong", "Xinrun Wang", "Bo An" ], "posted": "2025-08-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.02292v2", "field": "finance", "role": "method", "bullets": [ "An open-source platform covering two markets, four stock pools, and over 800 million financial data points across forecasting, portfolio management, trading, and automated analysis tasks.", "The platform unifies deep learning, reinforcement learning, and LLM agents, including reinforcement-learning-based fine-tuning; the abstract names no specific language models.", "Experiments on four financial AI tasks are presented as evidence of reproducibility, transparent benchmarking, and streamlined deployment; no headline accuracy figures are stated." ], "bullet_provenance": "ai", "salience": 42, "edition": 14, "models": [], "validated": null, "n": 2023 }, { "uid": "arxiv:2508.02366v3", "arxiv_id": "2508.02366v3", "title": "Language Model Guided Reinforcement Learning in Quantitative Trading", "authors": [ "Adam Darmanin", "Vince Vella" ], "posted": "2025-08-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.02366v3", "field": "finance", "role": "instrument", "bullets": [ "Historical market data for algorithmic trading evaluated using Sharpe Ratio and Maximum Drawdown metrics.", "LLMs generated high-level trading strategies via structured prompts to guide reinforcement learning agents in short-term decisions.", "LLM-guided agents improved both return and risk metrics relative to unguided RL baselines." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 48, "n": 2731, "authors_detailed": [ { "name": "Adam Darmanin", "url": "https://openalex.org/A5120718667", "inst": "University of Malta" }, { "name": "Vince Vella", "url": "https://openalex.org/A5011119122", "inst": "University of Essex" } ], "affiliations": [ "University of Malta", "University of Essex" ] }, { "uid": "arxiv:2508.02452v1", "arxiv_id": "2508.02452v1", "title": "LatentPrompt: Optimizing Promts in Latent Space", "authors": [ "Mateusz Bystroński", "Grzegorz Piotrowski", "Nitesh V. Chawla", "Tomasz Kajdanowicz" ], "posted": "2025-08-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.02452v1", "field": "finance", "role": "method", "bullets": [ "Financial PhraseBank sentiment classification benchmark used as proof-of-concept for automated prompt optimization.", "LLM prompts embedded in continuous latent space and iteratively optimized for task-specific performance via black-box access.", "Single optimization cycle increased Financial PhraseBank classification accuracy by approximately 3 percentage points." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "Financial PhraseBank classification accuracy", "salience": 25, "n": 2732, "authors_detailed": [ { "name": "Mateusz Bystroński", "url": "https://openalex.org/A5119181771", "inst": "" }, { "name": "Grzegorz Piotrowski", "url": "https://openalex.org/A5119181770", "inst": "Wrocław University of Science and Technology" }, { "name": "Nitesh V. Chawla", "url": "https://openalex.org/A5068157871", "inst": "University of Notre Dame" }, { "name": "Tomasz Kajdanowicz", "url": "https://openalex.org/A5050914099", "inst": "Wrocław University of Science and Technology" } ], "affiliations": [ "University of Notre Dame", "Wrocław University of Science and Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2508.02222v1", "arxiv_id": "2508.02222v1", "title": "FinCPRG: A Bidirectional Generation Pipeline for Hierarchical Queries and Rich Relevance in Financial Chinese Passage Retrieval", "authors": [ "Xuan Xu", "Beilin Chu", "Qinhong Lin", "Yixiao Zhong", "Fufang Wen", "Jiaqi Liu", "Binjie Fei", "Yu Li", "Zhongliang Yang", "Linna Zhou" ], "posted": "2025-08-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.02222v1", "field": "finance", "role": "method", "bullets": [ "Nearly 1,300 Chinese financial research reports used to construct hierarchical passage retrieval dataset with multi-level queries.", "LLMs generated sentence-level, passage-level, and topic-level queries incorporating industry, topic, and time elements from financial reports.", "FinCPRG dataset validated through retrieval benchmarking and training experiments on Chinese financial text." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "passage retrieval benchmarks on FinCPRG", "salience": 30, "n": 2733, "authors_detailed": [ { "name": "Xuan Xu", "url": "https://openalex.org/A5072204529", "inst": "Jiangsu University" }, { "name": "Beilin Chu", "url": "https://openalex.org/A5062868287", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Qinhong Lin", "url": "https://openalex.org/A5119175109", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Zhong, Yixiao", "url": "", "inst": "" }, { "name": "Fufang Wen", "url": "https://openalex.org/A5119175108", "inst": "Beijing Biocytogen (China)" }, { "name": "Jiaqi Liu", "url": "https://openalex.org/A5025640838", "inst": "Southwest University" }, { "name": "Binjie Fei", "url": "https://openalex.org/A5119175110", "inst": "Simplex Manufacturing (United States)" }, { "name": "Yu Li", "url": "https://openalex.org/A5100345712", "inst": "Wuhan University of Technology" }, { "name": "Zhongliang Yang", "url": "https://openalex.org/A5020864189", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Linna Zhou", "url": "https://openalex.org/A5059744997", "inst": "Beijing University of Posts and Telecommunications" } ], "affiliations": [ "Jiangsu University", "Beijing University of Posts and Telecommunications", "Beijing Biocytogen (China)", "Southwest University", "Simplex Manufacturing (United States)", "Wuhan University of Technology" ] }, { "uid": "arxiv:2508.02630v3", "arxiv_id": "2508.02630v3", "title": "What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce", "authors": [ "Amine Allouah", "Omar Besbes", "Josué D Figueroa", "Yash Kanoria", "Akshit Kumar" ], "posted": "2025-08-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.02630v3", "field": "economics", "role": "object", "bullets": [ "Simulated e-commerce marketplace with AI agents from multiple LLM providers acting as autonomous consumer buyers across product categories and queries.", "Multiple LLM-based agents evaluated, selected, and purchased products; randomized trials measured position bias, sensitivity to price, ratings, reviews, and sponsored tags.", "Agents concentrated demand on few modal products, exhibited provider-specific position biases even in headless interfaces, and were vulnerable to seller-side agent description gaming." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 3521, "authors_detailed": [ { "name": "Amine Allouah", "url": "https://openalex.org/A5083280377", "inst": "Santa Clara University" }, { "name": "Omar Besbes", "url": "https://openalex.org/A5063750068", "inst": "Columbia University" }, { "name": "Figueroa, Josué D", "url": "", "inst": "" }, { "name": "Yash Kanoria", "url": "https://openalex.org/A5000266593", "inst": "Columbia University" }, { "name": "Akshit Kumar", "url": "https://openalex.org/A5102785483", "inst": "Invertis University" } ], "affiliations": [ "Columbia University", "Santa Clara University", "Invertis University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2508.01545v2", "arxiv_id": "2508.01545v2", "title": "Getting out of the Big-Muddy: Escalation of Commitment in LLMs", "authors": [ "Emilio Barkett", "Olivia Long", "Paul Kröger" ], "posted": "2025-08-03", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.01545v2", "field": "management", "role": "agent", "bullets": [ "A two-stage investment task run for 6,500 trials across four conditions, model as investor, model as advisor, multi-agent deliberation, and compound pressure scenarios.", "LLMs, not named in the abstract, choose whether to keep funding a failing course of action, testing escalation of commitment from behavioural research.", "Individual settings stay near rational, but symmetric peer deliberation escalates in 99.2 percent of trials against 46.2 percent under hierarchy, and compound pressures push average allocations to failing divisions to 68.95 percent." ], "bullet_provenance": "ai", "salience": 55, "edition": 14, "models": [], "validated": null, "n": 2022 }, { "uid": "arxiv:2508.01871v1", "arxiv_id": "2508.01871v1", "title": "Multi-turn Natural Language to Graph Query Language Translation", "authors": [ "Yuanyuan Liang", "Lei Pan", "Tingyu Xie", "Yunshi Lan", "Weining Qian" ], "posted": "2025-08-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.01871v1", "field": "finance", "role": "method", "bullets": [ "Financial market graph database used to construct MTGQL, a multi-turn natural-language-to-graph-query dataset via LLMs.", "LLMs generated hierarchical queries at sentence, passage, and topic levels for both intra-doc and cross-doc financial scenarios.", "Three baseline methods benchmarked on MTGQL, establishing foundation for multi-turn financial graph query translation." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "MTGQL multi-turn translation benchmarks", "salience": 28, "n": 2730, "authors_detailed": [ { "name": "Yuanyuan Liang", "url": "https://openalex.org/A5101652480", "inst": "University of Maryland, Baltimore" }, { "name": "Lei Pan", "url": "https://openalex.org/A5074570471", "inst": "Michigan Technological University" }, { "name": "Tingyu Xie", "url": "https://openalex.org/A5013015812", "inst": "Kyushu University" }, { "name": "Yunshi Lan", "url": "https://openalex.org/A5090588589", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Weining Qian", "url": "https://openalex.org/A5089931216", "inst": "Ludong University" } ], "affiliations": [ "University of Maryland, Baltimore", "Michigan Technological University", "Kyushu University", "Beijing Academy of Artificial Intelligence", "Ludong University" ] }, { "uid": "arxiv:2508.01390v2", "arxiv_id": "2508.01390v2", "title": "Recognising, Anticipating, and Mitigating LLM Pollution of Online Behavioural Research", "authors": [ "Raluca Rilla", "Tobias Werner", "Hiromu Yakura", "Iyad Rahwan", "Anne-Marie Nussberger" ], "posted": "2025-08-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.01390v2", "field": "economics", "role": "method", "bullets": [ "Online behavioural research with human subjects, a setting where participants increasingly turn to LLMs for advice, translation, or outright completion of study tasks.", "No model is deployed; the paper defines three interacting pollution variants, partial LLM mediation, full LLM delegation by agentic systems, and spillover of anticipatory behaviour change.", "Argues the variants generate cascading distortions of sample authenticity and biases hard to detect after the fact, and proposes responses across researcher practice, platform accountability, and community coordination." ], "bullet_provenance": "ai", "salience": 63, "edition": 14, "models": [], "validated": null, "n": 1919, "authors_detailed": [ { "name": "Raluca Rilla", "url": "https://openalex.org/A5117445889", "inst": "Max Planck Institute for Human Development" }, { "name": "Werner, Tobias", "url": "", "inst": "" }, { "name": "Hiromu Yakura", "url": "https://openalex.org/A5051687457", "inst": "Max Planck Institute for Human Development" }, { "name": "Iyad Rahwan", "url": "https://openalex.org/A5009232244", "inst": "Max Planck Institute for Human Development" }, { "name": "Anne-Marie Nußberger", "url": "https://openalex.org/A5086655976", "inst": "Max Planck Institute for Human Development" } ], "affiliations": [ "Max Planck Institute for Human Development" ] }, { "uid": "arxiv:2508.00961v1", "arxiv_id": "2508.00961v1", "title": "FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph", "authors": [ "Xiang Li", "Penglei Sun", "Wanyun Zhou", "Zikai Wei", "Yongqi Zhang", "Xiaowen Chu" ], "posted": "2025-08-01", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.00961v1", "field": "finance", "role": "method", "bullets": [ "An equity research knowledge graph of 305,360 entities, 9,625 relational triples, and 19 relation types, built from research reports and real-time market events.", "Prompt-driven extraction guided by institutional templates populates the graph and a two-stage graph-based retrieval feeds LLMs; extraction quality is not validated in the abstract.", "With the retrieval pipeline, backtested stock trend prediction accuracy beats financial LLMs by 18.81 percent and institutional strategies by 17.85 percent on average." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 14, "models": [], "n": 2021 }, { "uid": "arxiv:2508.00554v4", "arxiv_id": "2508.00554v4", "title": "ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism", "authors": [ "Rui Sun", "Li Zhao", "Zuoyou Jiang", "Bo Yang", "Yuxiao Bai", "Mengting Chen", "Jing Li", "Zuo Bai" ], "posted": "2025-08-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.00554v4", "field": "finance", "role": "agent", "bullets": [ "Post-2024 Chinese A-share stock market used as backtest environment for multi-agent LLM-based trading system with internal competition", "LLM agents organized into Data and Research teams produced parallel trading decisions via tool-augmented deep research scored after market outcomes", "Higher backtested return and risk-adjusted performance than baselines through quantify-predict-allocate contest mechanism within agent teams" ], "bullet_provenance": "ai", "validated": true, "validation_note": "backtest return and risk-adjusted metrics on post-2024 A-share market", "salience": 58, "models": [], "n": 3520, "authors_detailed": [ { "name": "Rui Sun", "url": "https://openalex.org/A5114417936", "inst": "Harbin Normal University" }, { "name": "Li Zhao", "url": "https://openalex.org/A5143270729", "inst": "" }, { "name": "Zuoyou Jiang", "url": "https://openalex.org/A5121682106", "inst": "Shanghai Jiao Tong University" }, { "name": "Bo Yang", "url": "https://openalex.org/A5000683189", "inst": "University of Electronic Science and Technology of China" }, { "name": "Yuxiao Bai", "url": "https://openalex.org/A5143335017", "inst": "" }, { "name": "Mengting Chen", "url": "https://openalex.org/A5017270734", "inst": "Jiangnan University" }, { "name": "Jing Li", "url": "https://openalex.org/A5143378712", "inst": "" }, { "name": "Zuo Bai", "url": "https://openalex.org/A5008300079", "inst": "Financiadora de Estudos e Projetos" } ], "affiliations": [ "Harbin Normal University", "Shanghai Jiao Tong University", "University of Electronic Science and Technology of China", "Jiangnan University", "Financiadora de Estudos e Projetos" ] }, { "uid": "arxiv:2507.23181v2", "arxiv_id": "2507.23181v2", "title": "Will Compute Bottlenecks Prevent an Intelligence Explosion?", "authors": [ "Parker Whitfill", "Cheryl Wu" ], "posted": "2025-07-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.23181v2", "field": "economics", "role": "object", "bullets": [ "Panel dataset constructed for four frontier AI labs (OpenAI, DeepMind, Anthropic, DeepSeek) from 2014 to 2024 measuring inputs and outputs", "CES production functions estimated elasticity of substitution between research compute and cognitive labor under two model specifications", "Baseline model suggests compute and labor are substitutes; frontier-experiments model accounting for model scale suggests they are complements" ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3519, "authors_detailed": [ { "name": "Parker Whitfill", "url": "https://openalex.org/A5116038902", "inst": "Massachusetts Institute of Technology" }, { "name": "C.H. Wu", "url": "https://openalex.org/A5033890936", "inst": "Hang Seng University of Hong Kong" } ], "affiliations": [ "Massachusetts Institute of Technology", "Hang Seng University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.22748v3", "arxiv_id": "2507.22748v3", "title": "How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index", "authors": [ "Golo Henseke", "Rhys Davies", "Alan Felstead", "Duncan Gallie", "Francis Green", "Ying Zhou" ], "posted": "2025-07-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.22748v3", "field": "economics", "role": "object", "bullets": [ "UK jobs observed in the British Skills and Employment Surveys, with worker reported task data linking exposure estimates to wages and job postings from 2017 to 2023/24.", "LLMs, family not stated, rate whether each task can be completed at least 25 percent faster; the index is checked for reliability, validity, and predictive power over existing measures.", "Nearly all jobs show some exposure but only 13 percent are heavily exposed; the wage premium on exposed tasks fell 12 percent and exposed postings contracted after ChatGPT." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "reliability and validity checks against existing exposure measures", "salience": 68, "edition": 14, "n": 1870 }, { "uid": "doi:10.2139/ssrn.5370043", "doi": "10.2139/ssrn.5370043", "title": "Large Language Models for Supply Chain Decisions", "authors": [ "David Simchi-Levi", "Konstantina Mellou", "Ishai Menache", "Jeevan Pathuri" ], "posted": "2025-07-30", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5370043", "field": "management", "role": "agent", "bullets": [ "Industrial supply chain planning settings where optimization tools already exist but planners spend days interpreting outputs, running scenarios, and updating mathematical models.", "Language models sit on top of the optimization stack to explain recommendations, answer what if questions, and update models without a data science team; the model family is not stated.", "Reported time to decision falls from days or weeks to minutes or hours; no formal accuracy evaluation against ground truth is stated in the abstract." ], "bullet_provenance": "ai", "salience": 64, "edition": 1, "audience": "broad", "models": [], "validated": null, "n": 5, "authors_detailed": [ { "name": "David Simchi‐Levi", "url": "https://openalex.org/A5112431388", "inst": "Massachusetts Institute of Technology" }, { "name": "Konstantina Mellou", "url": "https://openalex.org/A5032602978", "inst": "Microsoft Research (United Kingdom)" }, { "name": "Ishai Menache", "url": "https://openalex.org/A5032872922", "inst": "Microsoft Research (United Kingdom)" }, { "name": "Jeevan Pathuri", "url": "https://openalex.org/A5025242939", "inst": "Microsoft (Finland)" } ], "affiliations": [ "Massachusetts Institute of Technology", "Microsoft Research (United Kingdom)", "Microsoft (Finland)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5367754", "doi": "10.2139/ssrn.5367754", "title": "Fine-Tuning Large Language Models on Cultural Nuances for Linguistically Driven Hyper-Personalization: A Literature Review", "authors": [ "Raghu Para" ], "posted": "2025-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5367754", "field": "management", "role": "object", "bullets": [ "Literature review of studies through early 2024 on culturally nuanced LLM fine-tuning for business and commerce applications worldwide.", "Synthesizes approaches to data curation, cultural representation, and fine-tuning methods for marketing, customer service, and recommendations.", "Evidence suggests cultural fine-tuning enables new levels of hyper-personalization, though data scarcity and stereotype risks remain unresolved." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 35, "validated": null, "n": 2599, "authors_detailed": [ { "name": "Raghu K Para", "url": "https://openalex.org/A5115454601", "inst": "Independent Researcher" } ], "affiliations": [ "Independent Researcher" ] }, { "uid": "arxiv:2507.22758v2", "arxiv_id": "2507.22758v2", "title": "MASCA: LLM based-Multi Agents System for Credit Assessment", "authors": [ "Gautam Jajoo", "Atharva Pandey", "Pranjal A Chitale", "Saksham Agarwal" ], "posted": "2025-07-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.22758v2", "field": "finance", "role": "agent", "bullets": [ "Credit assessment using a multi-agent LLM architecture with contrastive learning for risk-reward evaluation and bias analysis.", "Specialized LLM agents collaboratively evaluated creditworthiness in a hierarchical system informed by signaling game theory.", "MASCA outperformed baseline credit scoring approaches; bias analysis revealed fairness concerns in LLM-based credit assessment decisions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "credit scoring baselines", "salience": 45, "models": [], "n": 3024 }, { "uid": "doi:10.2139/ssrn.5369035", "doi": "10.2139/ssrn.5369035", "title": "The choice for and the effect of CEO television interviews after earnings announcements", "authors": [ "Anthony Haake", "Wolfgang Breuer" ], "posted": "2025-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5369035", "field": "finance", "role": "instrument", "bullets": [ "1,869 S&P 500 earnings announcements in 2023-2024 with matched CEO television interviews on financial news networks.", "LLMs with topic modeling measured textual similarity between CEO TV interviews and earnings conference calls to assess messaging strategy.", "CEO interviews after negative surprises mitigated short-term price declines but predicted weaker future performance; echoing ECCs improved returns more." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3025, "authors_detailed": [ { "name": "Anthony Haake", "url": "https://openalex.org/A5018055280", "inst": "RWTH Aachen University" }, { "name": "Wolfgang Breuer", "url": "https://openalex.org/A5027846435", "inst": "RWTH Aachen University" } ], "affiliations": [ "RWTH Aachen University" ] }, { "uid": "arxiv:2507.21790v2", "arxiv_id": "2507.21790v2", "title": "Can large language models assist choice modelling? Insights into prompting strategies and current models capabilities", "authors": [ "Georges Sfeir", "Gabriel Nova", "Stephane Hess", "Sander van Cranenburgh" ], "posted": "2025-07-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.21790v2", "field": "economics", "role": "method", "bullets": [ "Discrete choice modelling workflows, specifically specification and estimation of multinomial logit models, tested under five configurations varying the modelling goal, prompting strategy, and whether models see full data or a data dictionary.", "Twelve versions of seven families including ChatGPT, Claude, Gemini, DeepSeek, Llama, Gemma, and Mistral; every suggested specification was implemented, estimated, and scored on fit, behavioural plausibility, and complexity.", "Proprietary models with chain of thought prompting produce valid and behaviourally sound utility functions, open weight models largely fail, and only GPT o3 in an agentic setup correctly estimates its own specifications." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "llama", "open_other" ], "open_weights": true, "salience": 46, "edition": 14, "validated": null, "n": 1804 }, { "uid": "doi:10.1016/j.tbs.2026.101245", "doi": "10.1016/j.tbs.2026.101245", "arxiv_id": "2507.22244v2", "title": "Valuing Time in Silicon: Can Large Language Models Replicate Human Value of Travel Time", "authors": [ "Yingnan Yan", "Tianming Liu", "Yafeng Yin" ], "posted": "2025-07-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.22244v2", "field": "economics", "role": "agent", "bullets": [ "A full factorial choice experiment varying choice setting, travel purpose, and socio-demographic factors, administered to three popular LLMs the abstract does not name.", "Models choose between travel alternatives so implied values of travel time can be estimated and compared with human benchmarks across contexts.", "Some models match aggregate human values of travel time and all show human-like sensitivity to purpose and income, but trade-off magnitudes diverge enough to require validation before use." ], "bullet_provenance": "ai", "validated": true, "validation_note": "compared with human value of travel time estimates", "salience": 50, "edition": 14, "models": [], "n": 2020, "authors_detailed": [ { "name": "Yingnan Yan", "url": "https://openalex.org/A5107601122", "inst": "University of Michigan" }, { "name": "Tianming Liu", "url": "https://openalex.org/A5124769266", "inst": "" }, { "name": "Yafeng Yin", "url": "https://openalex.org/A5124829154", "inst": "" } ], "affiliations": [ "University of Michigan" ] }, { "uid": "arxiv:2507.20957v4", "arxiv_id": "2507.20957v4", "title": "Your AI, Not Your View: The Bias of LLMs in Investment Analysis", "authors": [ "Hoyoung Lee", "Junhyuk Seo", "Suhwan Park", "Junhyeong Lee", "Wonbin Ahn", "Chanyeol Choi", "Alejandro Lopez-Lira", "Yongjae Lee" ], "posted": "2025-07-28", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.20957v4", "field": "finance", "role": "agent", "bullets": [ "Hypothetical investment scenarios with balanced and imbalanced argument sets, designed to elicit latent sector, size, and momentum preferences; a public leaderboard extends coverage to more models.", "Models, not named in the abstract, issue investment judgments under conflicts between parametric knowledge and presented evidence, and bias persistence is measured as counter-evidence accumulates.", "Most models favour technology stocks, large caps, and contrarian strategies, and these leanings harden into confirmation bias, with initial judgments held against mounting contrary evidence." ], "bullet_provenance": "ai", "salience": 62, "edition": 14, "models": [], "validated": null, "n": 2018 }, { "uid": "arxiv:2507.20796v2", "arxiv_id": "2507.20796v2", "title": "Aligning Large Language Model Agents with Rational and Moral Preferences: A Supervised Fine-Tuning Approach", "authors": [ "Wei Lu", "Amit Dhanda", "Daniel L. Chen", "Christian B. Hansen" ], "posted": "2025-07-28", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.20796v2", "field": "economics", "role": "agent", "bullets": [ "Canonical economic games, moral dilemmas, and a repeated duopoly pricing setting, played by off-the-shelf LLM agents that the abstract does not name.", "Supervised fine-tuning on a small synthetic dataset of strategies implied by homo economicus and homo moralis utilities steers agent behaviour; baselines over-cooperate and respond weakly to incentives.", "Fine-tuned agents show persistent, interpretable shifts, and agents aligned to different preference structures produce systematically distinct equilibrium outcomes and pricing dynamics." ], "bullet_provenance": "ai", "salience": 60, "edition": 14, "models": [], "validated": null, "n": 2019 }, { "uid": "arxiv:2507.20930v2", "arxiv_id": "2507.20930v2", "title": "FRED: Financial Retrieval-Enhanced Detection and Editing of Hallucinations in Language Models", "authors": [ "Likun Tan", "Kuan-Wei Huang", "Kevin Wu" ], "posted": "2025-07-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.20930v2", "field": "finance", "role": "method", "bullets": [ "Financial question-answering corpora with synthetic tagged errors for hallucination detection and editing in LLM-generated text.", "Fine-tuned Phi-4, Phi-4-mini, Qwen3-4B, and Qwen3-14B to detect and correct factual inaccuracies; benchmarked against OpenAI-o3.", "Fine-tuned Phi-4 achieved 8% F1 improvement and 30% detection gain over o3; 4B Phi-4-mini matched o3 within 2%." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "binary F1 and detection F1 vs OpenAI-o3", "salience": 55, "n": 2729 }, { "uid": "arxiv:2507.20535v1", "arxiv_id": "2507.20535v1", "title": "Learning Explainable Stock Predictions with Tweets Using Mixture of Experts", "authors": [ "Wenyan Xu", "Dawei Xiang", "Rundong Wang", "Yonghong Hu", "Liang Zhang", "Jiayu Chen", "Zhonghua Lu" ], "posted": "2025-07-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.20535v1", "field": "finance", "role": "instrument", "bullets": [ "Stock markets using Twitter and public news data with LLM-generated summaries across multiple prediction horizons.", "FTS-Text-MoE combined LLM news summaries with price data in a Mixture of Experts Transformer decoder for multi-resolution stock prediction.", "FTS-Text-MoE outperformed baseline methods in investment returns and Sharpe ratio across different forecasting time scales." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 30, "n": 3023, "authors_detailed": [ { "name": "Wenyan Xu", "url": "https://openalex.org/A5042043976", "inst": "Shanghai Jiao Tong University" }, { "name": "Dawei Xiang", "url": "https://openalex.org/A5035075306", "inst": "Kunming University of Science and Technology" }, { "name": "Rundong Wang", "url": "https://openalex.org/A5019721388", "inst": "Nanjing University" }, { "name": "Yonghong Hu", "url": "https://openalex.org/A5101404236", "inst": "Nanjing Tech University" }, { "name": "Liang Zhang", "url": "https://openalex.org/A5064744030", "inst": "Guangzhou University" }, { "name": "Jiayu Chen", "url": "https://openalex.org/A5100380044", "inst": "Beijing Institute of Technology" }, { "name": "Zhonghua Lu", "url": "https://openalex.org/A5114171303", "inst": "Jilin University" } ], "affiliations": [ "Shanghai Jiao Tong University", "Kunming University of Science and Technology", "Nanjing University", "Nanjing Tech University", "Guangzhou University", "Beijing Institute of Technology", "Jilin University" ] }, { "uid": "arxiv:2507.21360v1", "arxiv_id": "2507.21360v1", "title": "Efficacy of AI RAG Tools for Complex Information Extraction and Data Annotation Tasks: A Case Study Using Banks Public Disclosures", "authors": [ "Nicholas Botti", "Flora Haberkorn", "Charlotte Hoopes", "Shaun Khan" ], "posted": "2025-07-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.21360v1", "field": "finance", "role": "instrument", "bullets": [ "Within-subjects experiment with randomized task assignments on thousands of pages of GSIB public disclosure documents with complex annotation criteria", "AI RAG tool assisted analysts with information extraction under naive and interactive conditions, compared against human-only baseline accuracy", "Interactive AI use accelerated task execution by up to 10x and improved accuracy, with estimated savings of 268 hours on the full annotation task" ], "bullet_provenance": "ai", "validated": true, "validation_note": "human-only baseline accuracy comparison", "salience": 62, "models": [], "n": 3940 }, { "uid": "arxiv:2507.20249v1", "arxiv_id": "2507.20249v1", "title": "Modeling Professionalism in Expert Questioning through Linguistic Differentiation", "authors": [ "Giulia D'Agostino", "Chung-Chi Chen" ], "posted": "2025-07-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.20249v1", "field": "finance", "role": "instrument", "bullets": [ "Financial analyst questions from earnings calls, both human-authored and LLM-generated, annotated for structural and pragmatic professionalism.", "Linguistic features (discourse regulators, prefaces, request types) classified professionalism; compared against Gemini-2.0 and SVM baselines.", "Interpretable feature classifier outperformed Gemini-2.0 at distinguishing expert-authored from LLM-generated analyst questions." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "classification accuracy vs human professionalism judgments", "salience": 42, "n": 2728 }, { "uid": "arxiv:2507.20162v3", "arxiv_id": "2507.20162v3", "title": "Modeling Insider Filing Delays in Financial Markets with an Interpretable XGBoost Framework", "authors": [ "Cheng Huang", "Yao Ma", "Fan Gao", "Yutong Liu", "Yadi Liu", "Xiaoli Ma", "Ye Aung Moe", "Yuhan Zhang", "Weizheng Xie", "Zeyu Han", "Xiangxiang Wang", "Hao Wang", "Yongbin Yu" ], "posted": "2025-07-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.20162v3", "field": "finance", "role": "instrument", "bullets": [ "Over four million SEC Form 4 insider transactions from 2002 to 2025 enriched with insider roles, governance attributes, and firm indicators", "Hybrid state-space encoder with XGBoost classifier benchmarked against statistical models, deep sequence learners, and LLM baselines for filing delay prediction", "Framework outperformed all baselines including LLMs in precision, recall, and F1; insider history and governance signals proved most predictive" ], "bullet_provenance": "ai", "validated": true, "validation_note": "precision, recall, F1 on 4M+ Form 4 filings 2002-2025", "salience": 50, "models": [], "n": 3518, "authors_detailed": [ { "name": "Cheng Huang", "url": "https://openalex.org/A5101434474", "inst": "117th Hospital of People's Liberation Army" }, { "name": "Ma Yao", "url": "https://openalex.org/A5100560493", "inst": "Xi'an Jiaotong University" }, { "name": "Fan Gao", "url": "https://openalex.org/A5060200573", "inst": "Kunming Medical University" }, { "name": "Liu, Yutong", "url": "", "inst": "" }, { "name": "Liu, Yadi", "url": "", "inst": "" }, { "name": "Xiaoli Ma", "url": "https://openalex.org/A5061302667", "inst": "Chinese Academy of Sciences" }, { "name": "Ye Aung Moe", "url": "https://openalex.org/A5098358105", "inst": "University of Nebraska–Lincoln" }, { "name": "Yuhan Zhang", "url": "https://openalex.org/A5100385421", "inst": "Shenzhen University" }, { "name": "Weizheng Xie", "url": "https://openalex.org/A5052578684", "inst": "Southern Methodist University" }, { "name": "Zeyu Han", "url": "https://openalex.org/A5024877525", "inst": "Beijing Tongren Hospital" }, { "name": "Xiangxiang Wang", "url": "https://openalex.org/A5101720265", "inst": "Anhui Jianzhu University" }, { "name": "Hao Wang", "url": "https://openalex.org/A5100769024", "inst": "Peking University" }, { "name": "Yongbin Yu", "url": "https://openalex.org/A5105821300", "inst": "University of Electronic Science and Technology of China" } ], "affiliations": [ "Xi'an Jiaotong University", "Kunming Medical University", "Chinese Academy of Sciences", "University of Nebraska–Lincoln", "Shenzhen University", "Southern Methodist University" ] }, { "uid": "arxiv:2507.18932v2", "arxiv_id": "2507.18932v2", "title": "MMESGBench: Pioneering Multimodal Understanding and Complex Reasoning Benchmark for ESG Tasks", "authors": [ "Lei Zhang", "Xin Zhou", "Chaoyue He", "Di Wang", "Yi Wu", "Hong Xu", "Wei Liu", "Chunyan Miao" ], "posted": "2025-07-25", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.18932v2", "field": "finance", "role": "method", "bullets": [ "933 validated QA pairs derived from 45 ESG documents spanning seven document types and three major ESG source categories.", "Multimodal LLMs generated and verified QA pairs through human-AI collaborative pipeline; expert validators calibrated for quality and relevance.", "Multimodal and retrieval-augmented models substantially outperformed text-only baselines on visually grounded and cross-page ESG reasoning tasks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "expert-validated QA benchmark with model accuracy comparison", "salience": 50, "n": 3215 }, { "uid": "arxiv:2507.22936v2", "arxiv_id": "2507.22936v2", "title": "Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis", "authors": [ "Md Talha Mohsin" ], "posted": "2025-07-24", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.22936v2", "field": "finance", "role": "method", "bullets": [ "Question answering over Business sections of US 10-K filings, with five transformer based LLMs compared under standardized and context controlled prompting conditions.", "Outputs are rated by humans on relevance, completeness, clarity, conciseness, and factual accuracy, with automated similarity metrics and behavioural diagnostics; the abstract does not name the five models.", "No single model dominates across evaluation perspectives, inter rater agreement is modest, and the authors read differences as relative tendencies under tested conditions rather than evidence of general reliability." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human ratings with agreement statistics on 10-K QA", "salience": 42, "edition": 14, "models": [], "n": 1918, "authors_detailed": [ { "name": "Md Mohsin", "url": "https://openalex.org/A5084290715", "inst": "University of Tulsa" } ], "affiliations": [ "University of Tulsa" ] }, { "uid": "arxiv:2507.18417v1", "arxiv_id": "2507.18417v1", "title": "FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs", "authors": [ "Giorgos Iacovides", "Wuyang Zhou", "Danilo Mandic" ], "posted": "2025-07-24", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.18417v1", "field": "finance", "role": "method", "bullets": [ "Standard financial sentiment classification benchmarks, plus portfolio simulations under realistic transaction costs of 5 basis points using sentiment probabilities converted to rankable scores.", "A causal LLM, base model not named in the abstract, is aligned with direct preference optimization after supervised fine-tuning; it beats supervised fine-tuned baselines by 11 percent on average.", "The sentiment-driven strategy retains 67 percent annual returns with a Sharpe ratio of 2.0 after costs, which the authors present as a first among sentiment approaches." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "financial sentiment benchmarks, +11 percent over SFT baselines", "salience": 50, "edition": 14, "models": [], "n": 2017, "authors_detailed": [ { "name": "Giorgos Iacovides", "url": "https://openalex.org/A5094208275", "inst": "NIHR Imperial Biomedical Research Centre" }, { "name": "Wuyang Zhou", "url": "https://openalex.org/A5065141875", "inst": "State Grid Corporation of China (China)" }, { "name": "Danilo P. Mandic", "url": "https://openalex.org/A5103001848", "inst": "Imperial College London" } ], "affiliations": [ "NIHR Imperial Biomedical Research Centre", "State Grid Corporation of China (China)", "Imperial College London" ] }, { "uid": "doi:10.2139/ssrn.5357471", "doi": "10.2139/ssrn.5357471", "title": "Large Language Models in the Institutional Press: Investigating the Effects on Information Sourcing and News Production", "authors": [ "Xiaoke Zhang", "Myunghwan Lee", "Mi Zhou", "Gene Moo Lee" ], "posted": "2025-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5357471", "field": "management", "role": "object", "bullets": [ "1,073,742 news articles from 111 South Korean outlets analyzed at event and journalist levels, with a randomized experiment component.", "Industry experts detected undisclosed LLM-assisted articles; difference-in-differences and event-level analyses measured changes in information sourcing post-LLM adoption.", "LLM assistance accelerated publication but reduced information sources used, with larger declines in primary than secondary sources; effects persisted at journalist level over time." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 72, "validated": null, "n": 2489, "authors_detailed": [ { "name": "Xiaoke Zhang", "url": "https://openalex.org/A5115594284", "inst": "University of British Columbia" }, { "name": "Myunghwan Lee", "url": "", "inst": "Chinese University of Hong Kong" }, { "name": "Mi Zhou", "url": "https://openalex.org/A5101947019", "inst": "University of British Columbia" }, { "name": "Gene Moo Lee", "url": "https://openalex.org/A5072864189", "inst": "University of British Columbia" } ], "affiliations": [ "University of British Columbia", "Chinese University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.5364388", "doi": "10.2139/ssrn.5364388", "title": "Measuring Sustainability with AI", "authors": [ "Meng Wang", "Baozhong Yang", "Wei Jiang" ], "posted": "2025-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5364388", "field": "finance", "role": "instrument", "bullets": [ "Review of AI and NLP methods for measuring firm-level ESG exposure using financial disclosures and news coverage in the climate finance literature.", "LLMs classified corporate ESG discussions as specific or general; paper surveys recent NLP-based ESG measurement approaches across multiple studies.", "AI-generated ESG measures complement traditional ratings by providing real-time granularity; specific-versus-general classification distinguishes substantive ESG commitment from boilerplate." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 55, "n": 2490, "authors_detailed": [ { "name": "Meng Wang", "url": "https://openalex.org/A5092270901", "inst": "University of South Florida" }, { "name": "Baozhong Yang", "url": "https://openalex.org/A5084538885", "inst": "Georgia State University" }, { "name": "Wei Jiang", "url": "https://openalex.org/A5024857449", "inst": "Emory University" } ], "affiliations": [ "Emory University", "University of South Florida", "Georgia State University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.18560v1", "arxiv_id": "2507.18560v1", "title": "HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization", "authors": [ "Benjamin Coriat", "Eric Benhamou" ], "posted": "2025-07-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.18560v1", "field": "finance", "role": "instrument", "bullets": [ "US equities portfolio optimization evaluated on 2018-2024 out-of-sample data after training on 2000-2017; compared against equal-weighted and S&P 500 benchmarks.", "Lightweight LLMs extract sentiment signals from financial news, feeding a hierarchical three-tier reinforcement learning architecture combining base agents, meta-agents, and a super-agent.", "Framework achieves 26% annualized return and Sharpe ratio of 1.2, outperforming both equal-weighted portfolio and S&P 500 benchmark over the evaluation period." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 2725, "authors_detailed": [ { "name": "Benjamín Coriat", "url": "https://openalex.org/A5011583438", "inst": "Université Sorbonne Paris Nord" }, { "name": "Eric Benhamou", "url": "https://openalex.org/A5068844837", "inst": "Clinique Hartmann" } ], "affiliations": [ "Université Sorbonne Paris Nord", "Clinique Hartmann" ] }, { "uid": "arxiv:2507.22932v1", "arxiv_id": "2507.22932v1", "title": "FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification", "authors": [ "Baptiste Lefort", "Eric Benhamou", "Beatrice Guez", "Jean-Jacques Ohana", "Ethan Setrouk", "Alban Etienne" ], "posted": "2025-07-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.22932v1", "field": "finance", "role": "instrument", "bullets": [ "U.S. equities over 2018-2024 test period, trained on 2000-2017 market data and financial news corpus.", "Lightweight LLMs extracted sentiment from financial news; hierarchical DRL agents integrated sentiment with market indicators for portfolio optimization.", "Framework achieved 26% annualized return and 1.2 Sharpe ratio, outperforming equal-weighted and S&P 500 benchmarks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 45, "n": 2726, "authors_detailed": [ { "name": "Baptiste Lefort", "url": "https://openalex.org/A5093631836", "inst": "CentraleSupélec" }, { "name": "Eric Benhamou", "url": "https://openalex.org/A5068844837", "inst": "Clinique Hartmann" }, { "name": "Béatrice Guez", "url": "https://openalex.org/A5002704483", "inst": "Alpha-1 Foundation" }, { "name": "Jean‐Jacques Ohana", "url": "https://openalex.org/A5026233991", "inst": "Alpha-1 Foundation" }, { "name": "Ethan Setrouk", "url": "https://openalex.org/A5117076737", "inst": "Alpha-1 Foundation" }, { "name": "Etienne, Alban", "url": "", "inst": "" } ], "affiliations": [ "CentraleSupélec", "Clinique Hartmann", "Alpha-1 Foundation" ] }, { "uid": "arxiv:2507.18368v1", "arxiv_id": "2507.18368v1", "title": "Reasoning Beyond the Obvious: Evaluating Divergent and Convergent Thinking in LLMs for Financial Scenarios", "authors": [ "Zhuang Qiang Bok", "Watson Wei Khong Chua" ], "posted": "2025-07-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.18368v1", "field": "finance", "role": "method", "bullets": [ "ConDiFi benchmark of 607 macro-financial divergent prompts and 990 multi-hop adversarial MCQs evaluated across 14 leading LLMs.", "GPT-4o, DeepSeek-R1, Cohere Command R+ and 11 others tested on divergent and convergent financial reasoning tasks.", "GPT-4o underperformed on novelty and actionability; DeepSeek-R1 and Command R+ ranked highest for actionable investment insights." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "ConDiFi benchmark with human-scored novelty and actionability", "salience": 55, "n": 2727, "authors_detailed": [ { "name": "Zhuang Qiang Bok", "url": "https://openalex.org/A5120018913", "inst": "" }, { "name": "Watson Wei Khong Chua", "url": "https://openalex.org/A5120018914", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5361839", "doi": "10.2139/ssrn.5361839", "title": "Accountants and Generative AI: Hype or Help?", "authors": [ "Jude Edeigba" ], "posted": "2025-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5361839", "field": "accounting", "role": "object", "bullets": [ "Big 4 accounting firm commentaries and reports analyzed as archival data on generative AI adoption in accounting practice.", "Thematic analysis with Python textacy examined perceived benefits and risks of generative AI across core accounting functions.", "Generative AI supports financial reporting, risk compliance, and fraud detection but poses risks to information quality requiring human oversight." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3022, "authors_detailed": [ { "name": "Jude Edeigba", "url": "https://openalex.org/A5090335110", "inst": "Colorado Mesa University" } ], "affiliations": [ "Colorado Mesa University" ] }, { "uid": "arxiv:2508.06497v1", "arxiv_id": "2508.06497v1", "title": "Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News", "authors": [ "Mohammed-Khalil Ghali", "Cecil Pang", "Oscar Molina", "Carlos Gershenson-Garcia", "Daehan Won" ], "posted": "2025-07-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.06497v1", "field": "economics", "role": "instrument", "bullets": [ "64-year dataset of commodity prices from 1960 to 2023 paired with temporally aligned global economic news embeddings across multiple commodities", "Agentic generative AI pipeline extracted and fact-checked news summaries fused with dual-stream LSTM networks via attention for price spike detection", "Mean AUC of 0.94 and accuracy of 0.91, far exceeding logistic regression at 0.34, random forest at 0.57, and SVM at 0.47" ], "bullet_provenance": "ai", "validated": true, "validation_note": "AUC and accuracy on 64-year commodity price spike dataset", "salience": 60, "models": [], "n": 3517, "authors_detailed": [ { "name": "Mohammed-Khalil Ghali", "url": "https://openalex.org/A5099008034", "inst": "Binghamton University" }, { "name": "Pang, Cecil", "url": "", "inst": "" }, { "name": "Oscar Molina", "url": "https://openalex.org/A5120647898", "inst": "" }, { "name": "Carlos Gershenson-Garcia", "url": "https://openalex.org/A5120445580", "inst": "" }, { "name": "Daehan Won", "url": "https://openalex.org/A5047648692", "inst": "Pohang University of Science and Technology" } ], "affiliations": [ "Binghamton University", "Pohang University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.5342108", "doi": "10.2139/ssrn.5342108", "title": "Does Agentic AI Require New Policy Frameworks?", "authors": [ "Sarah Lam", "Nathaniel Lovin" ], "posted": "2025-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5342108", "field": "economics", "role": "object", "bullets": [ "U.S. policy landscape for agentic AI covering open-source models, cybersecurity, competition, and labor market effects", "Autonomous AI agents examined for policy implications including algorithmic collusion, prompt injection, and anticompetitive platform integration", "Existing frameworks partially address agentic AI risks but require new legal clarity on liability, competition enforcement, and workforce displacement" ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 3939, "authors_detailed": [ { "name": "Sarah Oh Lam", "url": "https://openalex.org/A5026017643", "inst": "Science and Technology Policy Institute" }, { "name": "Nathaniel Lovin", "url": "https://openalex.org/A5119062824", "inst": "Science and Technology Policy Institute" } ], "affiliations": [ "Science and Technology Policy Institute" ] }, { "uid": "arxiv:2507.17211v1", "arxiv_id": "2507.17211v1", "title": "EFS: Evolutionary Factor Searching for Sparse Portfolio Optimization Using Large Language Models", "authors": [ "Haochen Luo", "Yuan Zhang", "Chen Liu" ], "posted": "2025-07-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.17211v1", "field": "finance", "role": "instrument", "bullets": [ "Five Fama-French benchmark datasets and three real-market datasets (US50, HSI45, CSI300) for sparse portfolio construction using LLM-generated alpha factors.", "LLMs automatically generate and evolve alpha factors through an evolutionary feedback loop; factors rank assets for top-m selection in sparse portfolio optimization.", "EFS significantly outperforms statistical and optimization baselines across all datasets, with largest gains in larger asset universes and volatile market conditions." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 2723, "authors_detailed": [ { "name": "Jiandong Chen", "url": "https://openalex.org/A5146367616", "inst": "" }, { "name": "Haochen Luo", "url": "https://openalex.org/A5002148253", "inst": "Shenzhen Metro (China)" }, { "name": "Yuan Zhang", "url": "https://openalex.org/A5146423433", "inst": "" }, { "name": "Chen Liu", "url": "https://openalex.org/A5100322208", "inst": "Wuhan Polytechnic University" }, { "name": "Qingfu Zhang", "url": "https://openalex.org/A5000546219", "inst": "City University of Hong Kong" } ], "affiliations": [ "Shenzhen Metro (China)", "Wuhan Polytechnic University", "City University of Hong Kong" ] }, { "uid": "arxiv:2507.17134v1", "arxiv_id": "2507.17134v1", "title": "Resilient Multi-Agent Negotiation for Medical Supply Chains:Integrating LLMs and Blockchain for Transparent Coordination", "authors": [ "Mariam ALMutairi", "Hyungmin Kim" ], "posted": "2025-07-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.17134v1", "field": "management", "role": "agent", "bullets": [ "Simulated pandemic supply chain with autonomous agents representing manufacturers, distributors, and healthcare institutions negotiating allocation of scarce medical resources.", "LLM-powered agents perform structured context-aware negotiation and decision-making; blockchain layer enforces decisions via smart contracts for transparency and auditability.", "Framework improves negotiation efficiency, allocation fairness, supply chain responsiveness, and auditability compared to baselines in simulated pandemic disruption scenarios." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 2724, "authors_detailed": [ { "name": "Mashael Almutairi", "url": "https://openalex.org/A5113293445", "inst": "Saudi Aramco (Saudi Arabia)" }, { "name": "Hyungmin Kim", "url": "https://openalex.org/A5100695852", "inst": "Korea Institute of Toxicology" } ], "affiliations": [ "Saudi Aramco (Saudi Arabia)", "Korea Institute of Toxicology" ] }, { "uid": "doi:10.2139/ssrn.5355991", "doi": "10.2139/ssrn.5355991", "title": "Specifics Matter: An Analysis of Mutual Fund ESG Disclosures", "authors": [ "Huayu Shi", "Xing Han", "John B. Lee", "Helen Lu" ], "posted": "2025-07-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5355991", "field": "finance", "role": "instrument", "bullets": [ "U.S. mutual funds with ESG disclosures in prospectuses, analyzing fund flows, holdings-weighted ESG scores, and shareholder proposals.", "An LLM classified ESG disclosures as specific versus generic; the measure was validated against subsequent ESG scores and proposal voting.", "ESG specificity attracted institutional but not retail capital; the effect was strongest for funds without Morningstar Globe ratings or during high climate concern." ], "bullet_provenance": "ai", "validated": true, "validation_note": "validated against subsequent ESG scores and shareholder proposals", "salience": 65, "models": [], "n": 3021, "authors_detailed": [ { "name": "Huayu Shi", "url": "https://openalex.org/A5114323745", "inst": "University of Auckland" }, { "name": "Xing Han", "url": "https://openalex.org/A5114323746", "inst": "University of Auckland" }, { "name": "John B. Lee", "url": "https://openalex.org/A5085339064", "inst": "University of Auckland" }, { "name": "Helen Lu", "url": "https://openalex.org/A5114323747", "inst": "Vlerick Business School" } ], "affiliations": [ "University of Auckland", "Vlerick Business School" ] }, { "uid": "arxiv:2507.17186v2", "arxiv_id": "2507.17186v2", "title": "FinGAIA: A Chinese Benchmark for AI Agents in Real-World Financial Domain", "authors": [ "Lingfeng Zeng", "Fangqi Lou", "Zixuan Wang", "Jiajie Xu", "Jinyi Niu", "Mengping Li", "Yifan Dong", "Qi Qi", "Wei Zhang", "Ziwei Yang", "Jun Han", "Ruilun Feng", "Ruiqi Hu", "Lejie Zhang", "Zhengbo Feng", "Yicheng Ren", "Xin Guo", "Zhaowei Liu", "Dongpo Cheng", "Weige Cai", "Liwen Zhang" ], "posted": "2025-07-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.17186v2", "field": "finance", "role": "method", "bullets": [ "407 tasks across seven Chinese financial sub-domains including securities, funds, banking, insurance, futures, trusts, and asset management.", "Ten AI agents evaluated in zero-shot setting; ChatGPT achieved highest overall accuracy at 48.9%, lagging financial experts by over 35 percentage points.", "Five recurring failure patterns identified including cross-modal alignment deficiency and financial terminological bias; no agent approached expert-level performance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "accuracy vs financial expert ground truth across 407 tasks", "salience": 55, "n": 3214, "authors_detailed": [ { "name": "Lingfeng Zeng", "url": "https://openalex.org/A5101925762", "inst": "Guangzhou University of Chinese Medicine" }, { "name": "F. Lou", "url": "https://openalex.org/A5100570387", "inst": "Chongqing Medical University" }, { "name": "Zixuan Wang", "url": "https://openalex.org/A5100398250", "inst": "Shenyang University of Technology" }, { "name": "Jiajie Xu", "url": "https://openalex.org/A5043368271", "inst": "Hebei Agricultural University" }, { "name": "Niu, Jinyi", "url": "", "inst": "" }, { "name": "Mingqun Li", "url": "https://openalex.org/A5038926313", "inst": "Sun Yat-sen University" }, { "name": "Yifan Dong", "url": "https://openalex.org/A5058338322", "inst": "University of California, Riverside" }, { "name": "Qi Qi", "url": "https://openalex.org/A5103960937", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Wei Zhang", "url": "https://openalex.org/A5000814233", "inst": "Southern University of Science and Technology" }, { "name": "Ziwei Yang", "url": "https://openalex.org/A5070359048", "inst": "Solid State Physics Laboratory" }, { "name": "Han Jun", "url": "https://openalex.org/A5014298558", "inst": "Southwest University" }, { "name": "Feng, Ruilun", "url": "", "inst": "" }, { "name": "Rui Hu", "url": "https://openalex.org/A5101971205", "inst": "Moorfields Eye Hospital NHS Foundation Trust" }, { "name": "Zhang, Lejie", "url": "", "inst": "" }, { "name": "Feng, Zhengbo", "url": "", "inst": "" }, { "name": "Ren, Yicheng", "url": "", "inst": "" }, { "name": "Xin Guo", "url": "https://openalex.org/A5101191342", "inst": "Jiangsu University" }, { "name": "Zhaowei Liu", "url": "https://openalex.org/A5061364578", "inst": "Yantai University" }, { "name": "Cheng, Dongpo", "url": "", "inst": "" }, { "name": "Weige Cai", "url": "https://openalex.org/A5101236539", "inst": "" }, { "name": "Liwen Zhang", "url": "https://openalex.org/A5100459588", "inst": "University of Macau" } ], "affiliations": [ "Guangzhou University of Chinese Medicine", "Chongqing Medical University", "Shenyang University of Technology", "Hebei Agricultural University", "Sun Yat-sen University", "University of California, Riverside", "Beijing University of Posts and Telecommunications", "Southern University of Science and Technology" ] }, { "uid": "arxiv:2507.16802v4", "arxiv_id": "2507.16802v4", "title": "Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning", "authors": [ "Yanjun Zheng", "Xiyang Du", "Longfei Liao", "Xiaoke Zhao", "Zhaowen Zhou", "Jingze Song", "Bo Zhang", "Jiawei Liu", "Xiang Qi", "Zhe Li", "Zhiqiang Zhang", "Wei Wang", "Peng Zhang" ], "posted": "2025-07-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.16802v4", "field": "finance", "role": "method", "bullets": [ "Two finance specialized LLMs at 8B and 32B parameters built on Qwen3, trained with a financial task label system and layered data governance.", "Training combines trustworthy knowledge engineering, multi agent data synthesis, and difficulty aware optimization; evaluation covers FinEval, FinanceIQ, general reasoning sets, and a new agent oriented Finova benchmark.", "The models are reported as state of the art on financial benchmarks while retaining general reasoning; the abstract provides no specific accuracy numbers." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "financial benchmarks FinEval, FinanceIQ, Finova", "salience": 40, "edition": 14, "n": 1832, "authors_detailed": [ { "name": "Yanjun Zheng", "url": "https://openalex.org/A5102391860", "inst": "Centre National de la Recherche Scientifique" }, { "name": "XB Du", "url": "https://openalex.org/A5110418229", "inst": "Harbin Electric Corporation (China)" }, { "name": "Liao, Longfei", "url": "", "inst": "" }, { "name": "Zhao, Xiaoke", "url": "", "inst": "" }, { "name": "Z P Zhou", "url": "https://openalex.org/A5104208572", "inst": "Kunming Medical University" }, { "name": "Jiahao Song", "url": "https://openalex.org/A5018651900", "inst": "Xi'an Jiaotong University" }, { "name": "Bo Zhang", "url": "https://openalex.org/A5100335309", "inst": "Wuhan University" }, { "name": "Jiawei Liu", "url": "https://openalex.org/A5101834031", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Qi Xiang", "url": "https://openalex.org/A5102944662", "inst": "Nanjing Normal University" }, { "name": "Zhe Li", "url": "https://openalex.org/A5107953533", "inst": "Zhejiang Normal University" }, { "name": "Zhiqiang Zhang", "url": "https://openalex.org/A5112498798", "inst": "Huaibei Mining (China)" }, { "name": "Wei Wang", "url": "https://openalex.org/A5107955920", "inst": "Yuli Hospital" }, { "name": "Peng Zhang", "url": "https://openalex.org/A5100364094", "inst": "Society of Automotive Engineers International" } ], "affiliations": [ "Centre National de la Recherche Scientifique", "Harbin Electric Corporation (China)", "Kunming Medical University", "Xi'an Jiaotong University", "Wuhan University", "Beijing University of Posts and Telecommunications", "Nanjing Normal University", "Zhejiang Normal University" ] }, { "uid": "doi:10.1109/bigdata59044.2023.10386518", "doi": "10.1109/bigdata59044.2023.10386518", "arxiv_id": "2507.16642v1", "title": "Towards Automated Regulatory Compliance Verification in Financial Auditing with Large Language Models", "authors": [ "Armin Berger", "Lars Hillebrand", "David Leonhard", "Tobias Deußer", "Thiago Bell Felix de Oliveira", "Tim Dilmaghani", "Mohamed Khaled", "Bernd Kliem", "Rüdiger Loitz", "Christian Bauckhage", "Rafet Sifa" ], "posted": "2025-07-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.16642v1", "field": "accounting", "role": "method", "bullets": [ "Two labelled datasets from PwC Germany matching financial report passages to legal requirements of accounting standards, including non English cases.", "Open Llama-2 models and OpenAI GPT models judge whether recommended report excerpts actually comply with the mandated standards, with the labelled data providing ground truth.", "Llama-2 70B is best at detecting non compliance, while GPT-4 leads across the widest range of scenarios, particularly outside English." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "labelled PwC Germany compliance datasets", "salience": 54, "edition": 14, "n": 1833, "authors_detailed": [ { "name": "Armin Berger", "url": "https://openalex.org/A5102998018", "inst": "Fraunhofer Institute for Intelligent Analysis and Information Systems" }, { "name": "Lars Hillebrand", "url": "https://openalex.org/A5081939611", "inst": "Fraunhofer Institute for Intelligent Analysis and Information Systems" }, { "name": "David Leonhard", "url": "https://openalex.org/A5091957484", "inst": "University of Bonn" }, { "name": "Tobias Deußer", "url": "https://openalex.org/A5003368121", "inst": "University of Bonn" }, { "name": "Thiago Bell Felix De Oliveira", "url": "https://openalex.org/A5093759835", "inst": "University of Bonn" }, { "name": "Tim Dilmaghani", "url": "https://openalex.org/A5044214991", "inst": "PricewaterhouseCoopers GmbH,Dusseldorf,Germany" }, { "name": "M. Ben Khaled", "url": "https://openalex.org/A5084289640", "inst": "PricewaterhouseCoopers GmbH,Dusseldorf,Germany" }, { "name": "Bernd Kliem", "url": "https://openalex.org/A5037349423", "inst": "PricewaterhouseCoopers GmbH,Dusseldorf,Germany" }, { "name": "Rüdiger Loitz", "url": "https://openalex.org/A5026553005", "inst": "PricewaterhouseCoopers GmbH,Dusseldorf,Germany" }, { "name": "Christian Bauckhage", "url": "https://openalex.org/A5003875445", "inst": "University of Bonn" }, { "name": "Rafet Sifa", "url": "https://openalex.org/A5064201630", "inst": "University of Bonn" } ], "affiliations": [ "Fraunhofer Institute for Intelligent Analysis and Information Systems", "University of Bonn", "PricewaterhouseCoopers GmbH,Dusseldorf,Germany" ] }, { "uid": "arxiv:2508.08262v1", "arxiv_id": "2508.08262v1", "title": "Argument Quality Annotation and Gender Bias Detection in Financial Communication through Large Language Models", "authors": [ "Alaa Alhamzeh", "Mays Al Rebdawi" ], "posted": "2025-07-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.08262v1", "field": "finance", "role": "method", "bullets": [ "FinArgQuality dataset of arguments from financial communications, annotated repeatedly across three temperature settings to gauge stability; document counts are not stated.", "GPT-4o, Llama 3.1, and Gemma 2 annotate argument quality; runs are checked for consistency and benchmarked against human annotations, and an adversarial attack injects gender bias to probe robustness.", "Model annotations agree with one another more than human annotators do, yet each model shows some degree of gender bias; recommendations for bias aware annotation follow." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "human annotation benchmark, agreement across runs", "salience": 45, "edition": 14, "n": 1869, "authors_detailed": [ { "name": "Alaa Alhamzeh", "url": "https://openalex.org/A5001682349", "inst": "University of Passau" }, { "name": "Mays Al Rebdawi", "url": "https://openalex.org/A5120499213", "inst": "" } ], "affiliations": [ "University of Passau" ] }, { "uid": "arxiv:2507.21134v2", "arxiv_id": "2507.21134v2", "title": "TRIDENT: Benchmarking LLM Safety in Finance, Medicine, and Law", "authors": [ "Zheng Hui", "Yijiang River Dong", "Ehsan Shareghi", "Nigel Collier" ], "posted": "2025-07-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.21134v2", "field": "finance", "role": "method", "bullets": [ "Trident-Bench, a safety benchmark for legal, financial, and medical deployment of language models, grounded in AMA, ABA, and CFA Institute ethics codes.", "19 general-purpose and domain-specialized models are scored on the benchmark; GPT and Gemini families are named among the generalists tested.", "Strong generalist models meet basic domain safety expectations while domain-specialized models stumble on subtler ethical nuances, arguing for finer-grained safety tuning in regulated fields." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "benchmark built from professional ethics codes", "salience": 42, "edition": 14, "n": 2016 }, { "uid": "doi:10.2139/ssrn.5361703", "doi": "10.2139/ssrn.5361703", "title": "Assessing corporate sustainability with large language models: Evidence from Europe", "authors": [ "Kerstin Forster", "Lucas Keil", "Victor Wagner", "Maximilian A. Müller", "Thorsten Sellhorn", "Stefan Feuerriegel" ], "posted": "2025-07-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5361703", "field": "accounting", "role": "instrument", "bullets": [ "The 600 largest listed European corporations over 2014 to 2023, drawing on annual and sustainability reports to assemble 2,880,249 environmental, social, and governance indicator observations.", "An open-source machine learning framework, with the language model family not stated, extracts ESG indicators and their numerical values from reports; no accuracy figure is reported.", "Top ESG-rating decile firms disclosed 22 percent more indicators than the bottom decile, and reported scope 3 emissions rose 5.6-fold from 2021 to 2023." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "edition": 3, "audience": "general", "models": [], "n": 355 }, { "uid": "arxiv:2507.22917v1", "arxiv_id": "2507.22917v1", "title": "Reading Between the Timelines: RAG for Answering Diachronic Questions", "authors": [ "Kwun Hang Lau", "Ruiyuan Zhang", "Weijie Shi", "Xiaofang Zhou", "Xiaojun Cheng" ], "posted": "2025-07-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.22917v1", "field": "finance", "role": "method", "bullets": [ "Analytical Diachronic QA Benchmark built from hybrid corpus of real and synthetic financial news requiring temporal entity tracking.", "Temporal-aware RAG framework disentangles queries into subject and time window and calibrates semantic matching against temporal relevance.", "Approach improved answer accuracy by 13% to 27% over standard RAG implementations on longitudinal financial questions." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "ADQAB benchmark accuracy vs standard RAG", "salience": 50, "n": 2598, "authors_detailed": [ { "name": "K. Lau", "url": "https://openalex.org/A5108107666", "inst": "Northwestern University" }, { "name": "Ruiyuan Zhang", "url": "https://openalex.org/A5007192858", "inst": "Xi'an University of Science and Technology" }, { "name": "Weijie Shi", "url": "https://openalex.org/A5069105482", "inst": "Hong Kong University of Science and Technology" }, { "name": "Xiaofang Zhou", "url": "https://openalex.org/A5101874208", "inst": "Integrated Chinese Medicine (China)" }, { "name": "Cheng, Xiaojun", "url": "", "inst": "" } ], "affiliations": [ "Northwestern University", "Xi'an University of Science and Technology", "Hong Kong University of Science and Technology", "Integrated Chinese Medicine (China)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.15771v2", "arxiv_id": "2507.15771v2", "title": "Left Leaning Models: How AI Evaluates Economic Policy?", "authors": [ "Maxim Chupilkin" ], "posted": "2025-07-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.15771v2", "field": "economics", "role": "object", "bullets": [ "Conjoint experiment on leading LLMs from OpenAI, Anthropic, and Google evaluating economic policy trade-offs under multi-factor constraints across monetary and fiscal scenarios.", "Multiple LLMs asked to choose among economic policy bundles varying growth, unemployment, inequality, inflation, and public debt; tested for sensitivity to context and magnitudes.", "LLMs consistently prefer high growth, low unemployment, and low inequality over low inflation and low public debt; numerical sensitivity tests reveal loss aversion patterns." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "salience": 65, "validated": null, "n": 2722, "authors_detailed": [ { "name": "Chupilkin, Maxim", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5344082", "doi": "10.2139/ssrn.5344082", "title": "Leveraging LLM-Based Sentiment Analysis for Portfolio Optimization with Proximal Policy Optimization", "authors": [ "Kemal Kirtac", "G. Germano" ], "posted": "2025-07-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5344082", "field": "finance", "role": "instrument", "bullets": [ "Three-asset portfolio optimization using daily sentiment scores extracted from Refinitiv financial news by LLaMA 3.3.", "LLaMA 3.3 generated sentiment scores integrated into PPO reinforcement learning via a sentiment-weighted advantage function term.", "Sentiment-augmented PPO raised the Sharpe ratio from 1.55 to 1.90 and reduced drawdowns versus standard PPO (p < 0.001)." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 42, "n": 3020, "authors_detailed": [ { "name": "Kemal Kirtac", "url": "https://openalex.org/A5108909107", "inst": "University College London" }, { "name": "Guido Germano", "url": "https://openalex.org/A5066615785", "inst": "University College London" } ], "affiliations": [ "University College London" ] }, { "uid": "doi:10.1007/978-3-031-97564-6_24", "doi": "10.1007/978-3-031-97564-6_24", "arxiv_id": "2507.22922v1", "title": "Predicting stock prices with ChatGPT-annotated Reddit sentiment", "authors": [ "Mateusz Kmak", "Kamil Chmurzyński", "Kamil Matejuk", "Paweł Kotzbach", "Jan Kocoń" ], "posted": "2025-07-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.22922v1", "field": "finance", "role": "instrument", "bullets": [ "Reddit r/wallstreetbets posts about GameStop and AMC Entertainment analyzed for sentiment-based stock price prediction", "ChatGPT annotated sentiment to fine-tune a RoBERTa model, compared against two existing text-based methods using correlation and Granger causality", "Social media sentiment showed weak correlation with stock prices; comment volume and Google search trends exhibited stronger predictive signals" ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "correlation and Granger causality against GME and AMC stock prices", "salience": 50, "n": 3516, "authors_detailed": [ { "name": "Mateusz Kmak", "url": "https://openalex.org/A5118830937", "inst": "Wrocław University of Science and Technology" }, { "name": "Kamil Chmurzyński", "url": "https://openalex.org/A5118830938", "inst": "Wrocław University of Science and Technology" }, { "name": "Kamil Matejuk", "url": "https://openalex.org/A5118830939", "inst": "Wrocław University of Science and Technology" }, { "name": "Paweł Kotzbach", "url": "https://openalex.org/A5118830940", "inst": "Wrocław University of Science and Technology" }, { "name": "Jan Kocoń", "url": "https://openalex.org/A5043845890", "inst": "Wrocław University of Science and Technology" } ], "affiliations": [ "Wrocław University of Science and Technology" ] }, { "uid": "doi:10.1109/icdmw69685.2025.00026", "doi": "10.1109/icdmw69685.2025.00026", "arxiv_id": "2507.14785v2", "title": "Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs", "authors": [ "Erfan Pirmorad" ], "posted": "2025-07-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.14785v2", "field": "finance", "role": "method", "bullets": [ "Synthetic anti money laundering scenarios built to reflect common laundering behaviours, with k hop neighborhoods around entities of interest extracted from a financial knowledge graph and serialized into text.", "An LLM, not named in the abstract, is prompted with few shot in context examples to rate suspiciousness and justify its call; no accuracy against labelled cases is reported.", "The model can emulate analyst style logic, highlight red flags, and produce coherent explanations; the authors frame the study as exploratory groundwork rather than a validated detection system." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 14, "models": [], "n": 1917, "authors_detailed": [ { "name": "Erfan Pirmorad", "url": "https://openalex.org/A5128723124", "inst": "Vanguard University" } ], "affiliations": [ "Vanguard University" ] }, { "uid": "arxiv:2507.22911v2", "arxiv_id": "2507.22911v2", "title": "ElectriQ: A Benchmark for Assessing the Response Capability of Large Language Models in Power Marketing", "authors": [ "Jinzhi Wang", "Qingke Peng", "Haozhou Li", "Zeyuan Zeng", "Jiangbo Zhang", "Kaixuan Yang", "Ningyong Wu", "Qinfeng Song", "Ruimeng Li", "Biyi Zhou" ], "posted": "2025-07-19", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.22911v2", "field": "management", "role": "method", "bullets": [ "Electric power marketing customer service, with a benchmark of over 550,000 dialogues spanning six service domains and 24 sub scenarios.", "Thirteen LLMs, not named in the abstract, are scored through human ratings, automatic metrics, and two compliance stress tests covering statutory citation correctness and long dialogue consistency.", "Domain aligned 7 billion parameter models using the SEEK-RAG retrieval method match or surpass much larger models while reducing computational cost." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human ratings plus statutory citation and consistency stress tests", "salience": 37, "edition": 14, "models": [], "n": 1916, "authors_detailed": [ { "name": "Jinzhi Wang", "url": "https://openalex.org/A5108055997", "inst": "Anhui Medical University" }, { "name": "Peng, Qingke", "url": "", "inst": "" }, { "name": "Haozhou Li", "url": "https://openalex.org/A5021544997", "inst": "Inner Mongolia University of Technology" }, { "name": "Zeng, Zeyuan", "url": "", "inst": "" }, { "name": "Jiangbo Zhang", "url": "https://openalex.org/A5031298508", "inst": "Xinyang Normal University" }, { "name": "Kaixuan Yang", "url": "https://openalex.org/A5052219648", "inst": "Yantai University" }, { "name": "Wu, Ningyong", "url": "", "inst": "" }, { "name": "Qinfeng Song", "url": "https://openalex.org/A5002486479", "inst": "State Grid Corporation of China (China)" }, { "name": "Ruimeng Li", "url": "https://openalex.org/A5008420397", "inst": "University of Science and Technology of China" }, { "name": "Zhou, Biyi", "url": "", "inst": "" } ], "affiliations": [ "Anhui Medical University", "Inner Mongolia University of Technology", "Xinyang Normal University", "Yantai University", "State Grid Corporation of China (China)", "University of Science and Technology of China" ] }, { "uid": "arxiv:2507.12901v3", "arxiv_id": "2507.12901v3", "title": "Agentar-DeepFinance-100K: A Large-Scale Financial Dataset via Systematic Chain-of-Thought Synthesis Optimization", "authors": [ "Xiaoke Zhao", "Zhaowen Zhou", "Lin Chen", "Lihong Wang", "Zhiyi Huang", "Kaiyuan Zheng", "Yanjun Zheng", "Xiyang Du", "Longfei Liao", "Jiawei Liu", "Xiang Qi", "Bo Zhang", "Peng Zhang", "Wei Wang", "Zhe Li" ], "posted": "2025-07-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.12901v3", "field": "finance", "role": "method", "bullets": [ "100K-sample large-scale financial reasoning dataset with chain-of-thought rationales synthesized from advanced reasoning models.", "Multi-perspective Knowledge Extraction and Self-Corrective Rewriting pipeline generates deep financial CoT trajectories; CoT Cube analyzes design factors.", "Models trained on Agentar-DeepFinance-100K achieved significant improvements on standard financial reasoning benchmarks over base models." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "financial reasoning benchmarks", "salience": 55, "n": 2597, "authors_detailed": [ { "name": "Zhao, Xiaoke", "url": "", "inst": "" }, { "name": "Z P Zhou", "url": "https://openalex.org/A5104208572", "inst": "Kunming Medical University" }, { "name": "Lin Chen", "url": "https://openalex.org/A5100443778", "inst": "Hangzhou Normal University" }, { "name": "Lihong Wang", "url": "https://openalex.org/A5100443581", "inst": "Qingdao University of Science and Technology" }, { "name": "Zhiyi Huang", "url": "https://openalex.org/A5090297023", "inst": "Integrated Chinese Medicine (China)" }, { "name": "Kaiyuan Zheng", "url": "https://openalex.org/A5101474913", "inst": "Dongguan University of Technology" }, { "name": "Yanjun Zheng", "url": "https://openalex.org/A5102391860", "inst": "Centre National de la Recherche Scientifique" }, { "name": "XB Du", "url": "https://openalex.org/A5110418229", "inst": "Harbin Electric Corporation (China)" }, { "name": "Liao, Longfei", "url": "", "inst": "" }, { "name": "Jiawei Liu", "url": "https://openalex.org/A5060484287", "inst": "Chang'an University" }, { "name": "Xiang Qi", "url": "https://openalex.org/A5100741587", "inst": "University of Shanghai for Science and Technology" }, { "name": "Bo Zhang", "url": "https://openalex.org/A5077036584", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Peng Zhang", "url": "https://openalex.org/A5100364010", "inst": "Beijing University of Technology" }, { "name": "Wei Wang", "url": "https://openalex.org/A5100392093", "inst": "University of Skövde" }, { "name": "Zhe Li", "url": "https://openalex.org/A5100356717", "inst": "State Key Laboratory of Estuarine and Coastal Research" } ], "affiliations": [ "Kunming Medical University", "Hangzhou Normal University", "Qingdao University of Science and Technology", "Integrated Chinese Medicine (China)", "Dongguan University of Technology", "Centre National de la Recherche Scientifique", "Harbin Electric Corporation (China)", "Chang'an University" ] }, { "uid": "doi:10.2139/ssrn.5333340", "doi": "10.2139/ssrn.5333340", "title": "Legal and Regulatory Implications of Agentic AI in Tax Administration", "authors": [ "Pramod Kumar Siva" ], "posted": "2025-07-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5333340", "field": "accounting", "role": "object", "bullets": [ "U.S. IRS pilots and global revenue-authority experiments in agentic AI for tax administration, reviewed across five legal risk domains", "Agentic AI systems examined for anomaly detection, audit selection, and taxpayer assistance under IRC, APA, and constitutional due-process frameworks", "Existing legal frameworks provide partial governance but leave gaps in privacy, bias, explainability, due process, and accountability for autonomous tax decisions" ], "bullet_provenance": "ai", "salience": 38, "models": [], "validated": null, "n": 3938, "authors_detailed": [ { "name": "Pramod Kumar Siva", "url": "https://openalex.org/A5119264308", "inst": "Texas A&M University School of Law" } ], "affiliations": [ "Texas A&M University School of Law" ] }, { "uid": "arxiv:2507.10933v1", "arxiv_id": "2507.10933v1", "title": "Artificial Finance: How AI Thinks About Money", "authors": [ "Orhan Erdem", "Ragavi Pobbathi Ashok" ], "posted": "2025-07-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.10933v1", "field": "finance", "role": "agent", "bullets": [ "A battery of standard financial decision making questions posed to seven LLMs, with human responses from 53 countries as the comparison population.", "Five GPT series models, Gemini 2.0 Flash, and DeepSeek R1 answer lottery and intertemporal trade off items; outputs are treated as decision behavior, with no measurement validation involved.", "Models mostly choose by expected value, showing risk neutrality, occasionally break normative consistency on time trade offs, and in aggregate resemble respondents from Tanzania most closely." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "open_other" ], "open_weights": true, "salience": 50, "edition": 14, "validated": null, "n": 1831, "authors_detailed": [ { "name": "Orhan Erdem", "url": "https://openalex.org/A5000588911", "inst": "University of North Texas" }, { "name": "Ragavi Pobbathi Ashok", "url": "https://openalex.org/A5119896785", "inst": "" } ], "affiliations": [ "University of North Texas" ] }, { "uid": "arxiv:2507.11086v1", "arxiv_id": "2507.11086v1", "title": "Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification", "authors": [ "Andres Azqueta-Gavaldón", "Joaquin Ramos Cosgrove" ], "posted": "2025-07-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.11086v1", "field": "finance", "role": "method", "bullets": [ "65 Portuguese company cases validated against reference sources, testing cross border entity identification for risk management and regulatory compliance in the Spanish financial system.", "Hugging Face hosted open models and interface based LLMs, including Microsoft Copilot and Qwen 2.5, are compared with Jaccard, cosine, and Levenshtein matching on the labelled cases.", "Interface based LLMs exceed 93 percent accuracy with F1 above 96 percent, while traditional string methods top 92 percent accuracy but carry 20 to 40 percent false positive rates." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "65 hand labelled company matching cases", "salience": 45, "edition": 14, "n": 1868, "authors_detailed": [ { "name": "Andres Azqueta-Gavaldón", "url": "https://openalex.org/A5119896950", "inst": "" }, { "name": "Joaquin Ramos Cosgrove", "url": "https://openalex.org/A5119896951", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5350350", "doi": "10.2139/ssrn.5350350", "title": "Geoeconomic Pressure", "authors": [ "Christopher Clayton", "Antonio Coppola", "Matteo Maggiori", "Jesse Schreger" ], "posted": "2025-07-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5350350", "field": "economics", "role": "instrument", "bullets": [ "Large textual corpora covering global geoeconomic events; classification of governments, targets, instruments, firms, and products involved in pressure campaigns.", "Multiple open-weight LLMs classified geoeconomic pressure instances from text; robustness assessed across models and prompt variations to quantify measurement uncertainty.", "Tariff-affected firms responded primarily with price changes; export-control-affected firms increased R&D investment, with heterogeneous responses depending on country role in the pressure." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 82, "n": 2488, "authors_detailed": [ { "name": "Christopher Clayton", "url": "https://openalex.org/A5013393057", "inst": "Whitney Museum of American Art" }, { "name": "Antonio Coppola", "url": "https://openalex.org/A5077505318", "inst": "Stanford University" }, { "name": "Matteo Maggiori", "url": "https://openalex.org/A5065807417", "inst": "Stanford Medicine" }, { "name": "Jesse Schreger", "url": "https://openalex.org/A5035873373", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Stanford University", "Whitney Museum of American Art", "Stanford Medicine", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.14189v2", "arxiv_id": "2507.14189v2", "title": "DeepWriter: A Fact-Grounded Multimodal Writing Assistant Based On Offline Knowledge Base", "authors": [ "Song Mao", "Lejun Cheng", "Pinlong Cai", "Guohang Yan", "Ding Wang", "Botian Shi" ], "posted": "2025-07-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.14189v2", "field": "finance", "role": "instrument", "bullets": [ "Financial report generation from a curated offline knowledge base incorporating both textual and visual elements from structured corpora.", "DeepWriter pipeline performs task decomposition, outline generation, multimodal retrieval, and section-by-section composition with reflection.", "Generated reports surpassed existing baselines in factual accuracy and content quality for professional-grade financial documents." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "factual accuracy comparison vs baselines", "salience": 48, "n": 2596 }, { "uid": "arxiv:2507.09739v1", "arxiv_id": "2507.09739v1", "title": "Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500", "authors": [ "Haojie Liu", "Zihan Lin", "Randall R. Rojas" ], "posted": "2025-07-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.09739v1", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 stocks with real-time financial news data used to evaluate sentiment-augmented trading strategies against buy-and-hold benchmarks.", "GPT-2 and FinBERT extracted sentiment from financial news and combined scores with ARIMA, ETS, momentum, and trend-based technical indicators.", "Sentiment-augmented strategies outperformed buy-and-hold benchmarks in asset values and returns during volatile market conditions." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 30, "n": 3018, "authors_detailed": [ { "name": "Haojie Liu", "url": "https://openalex.org/A5101722814", "inst": "Zhejiang University of Water Resource and Electric Power" }, { "name": "Zihan Lin", "url": "https://openalex.org/A5076123559", "inst": "Nanjing Drum Tower Hospital" }, { "name": "Randall R. Rojas", "url": "https://openalex.org/A5000818233", "inst": "RTX (United States)" } ], "affiliations": [ "Zhejiang University of Water Resource and Electric Power", "RTX (United States)" ] }, { "uid": "arxiv:2507.20474v3", "arxiv_id": "2507.20474v3", "title": "MountainLion: A Multi-Modal LLM-Based Agent System for Interpretable and Adaptive Financial Trading", "authors": [ "Siyi Wu", "Junqiao Wang", "Zhaoyang Guan", "Leyi Zhao", "Xinyuan Song", "Xinyu Ying", "Dexu Yu", "Jinhao Wang", "Hanlin Zhang", "Michele Pak", "Yangfan He", "Yi Xin", "Jianhui Wang", "Tianyu Shi" ], "posted": "2025-07-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.20474v3", "field": "finance", "role": "agent", "bullets": [ "Cryptocurrency markets with multi-modal data including textual news, candlestick charts, and trading signal charts for investment decisions.", "Multi-agent LLM system processed textual and visual financial data, generated reports, and refined strategies via a central reflection module.", "System enriched technical price triggers with macroeconomic and capital flow signals, improving returns and providing interpretable investment recommendations." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 3019, "authors_detailed": [ { "name": "Siyi Wu", "url": "https://openalex.org/A5056610501", "inst": "Wuhan University" }, { "name": "Junqiao Wang", "url": "https://openalex.org/A5101400364", "inst": "Sichuan University" }, { "name": "Guan, Zhaoyang", "url": "", "inst": "" }, { "name": "Zhao, Leyi", "url": "", "inst": "" }, { "name": "Xinyuan Song", "url": "https://openalex.org/A5056116087", "inst": "Emory University" }, { "name": "Ying, Xinyu", "url": "", "inst": "" }, { "name": "Dapeng Yu", "url": "https://openalex.org/A5101581129", "inst": "Hefei University" }, { "name": "Jinhao Wang", "url": "https://openalex.org/A5102752402", "inst": "Anyang Academy of Agricultural Sciences" }, { "name": "Hanlin Zhang", "url": "https://openalex.org/A5101598769", "inst": "Qingdao University" }, { "name": "Michele Pak", "url": "https://openalex.org/A5119791668", "inst": "" }, { "name": "Yangfan He", "url": "https://openalex.org/A5100642695", "inst": "University of Minnesota" }, { "name": "Xin Yi", "url": "https://openalex.org/A5100646574", "inst": "Xi'an University of Science and Technology" }, { "name": "Jianhui Wang", "url": "https://openalex.org/A5100334393", "inst": "Nanjing Tech University" }, { "name": "Tianyu Shi", "url": "https://openalex.org/A5101850471", "inst": "University of Toronto" } ], "affiliations": [ "Emory University", "University of Minnesota", "Wuhan University", "Sichuan University", "Hefei University", "Anyang Academy of Agricultural Sciences", "Qingdao University", "Xi'an University of Science and Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.09255v1", "arxiv_id": "2507.09255v1", "title": "StockSim: A Dual-Mode Order-Level Simulator for Evaluating Multi-Agent LLMs in Financial Markets", "authors": [ "Charidimos Papadakis", "Giorgos Filandrianos", "Angeliki Dimitriou", "Maria Lymperaiou", "Konstantinos Thomas", "Giorgos Stamou" ], "posted": "2025-07-12", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.09255v1", "field": "finance", "role": "method", "bullets": [ "An open-source, order-level market simulator with latency, slippage, and order-book microstructure, built for testing LLM trading agents under realistic market dynamics.", "A role-based framework runs heterogeneous LLM agents and multi-agent coordination inside the simulated market; the abstract names no specific models.", "The platform offers order-level and coarser simulation modes and is fully released; the abstract reports no benchmark results for particular models." ], "bullet_provenance": "ai", "salience": 40, "edition": 14, "models": [], "validated": null, "n": 2015, "authors_detailed": [ { "name": "Charidimos Papadakis", "url": "https://openalex.org/A5119791917", "inst": "" }, { "name": "Giorgos Filandrianos", "url": "https://openalex.org/A5077371399", "inst": "National Technical University of Athens" }, { "name": "Angeliki Dimitriou", "url": "https://openalex.org/A5007521009", "inst": "National Technical University of Athens" }, { "name": "Maria Lymperaiou", "url": "https://openalex.org/A5020141370", "inst": "National Technical University of Athens" }, { "name": "Κonstantinos Thomas", "url": "https://openalex.org/A5074911675", "inst": "National and Kapodistrian University of Athens" }, { "name": "Giorgos Stamou", "url": "https://openalex.org/A5085359792", "inst": "National Technical University of Athens" } ], "affiliations": [ "National Technical University of Athens", "National and Kapodistrian University of Athens" ] }, { "uid": "arxiv:2507.08339v4", "arxiv_id": "2507.08339v4", "title": "What Factors Affect LLMs and RLLMs in Financial Question Answering?", "authors": [ "Peng Wang", "Xuesi Hu", "Jiageng Wu", "Yuntao Zou", "Qiancheng Zhang", "Dagang Li" ], "posted": "2025-07-11", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.08339v4", "field": "finance", "role": "method", "bullets": [ "Financial question answering tasks, with five standard LLMs and four reasoning LLMs tested under varying prompting methods, agentic frameworks, and multilingual alignment approaches.", "Model families are not named in the abstract; the study measures how each intervention shifts benchmark performance for standard versus reasoning models, without reporting figures in the abstract.", "Prompting and agent frameworks lift standard models by simulating long chain of thought, add little for reasoning models, and multilingual alignment mainly extends reasoning length with minimal reasoning model gains." ], "bullet_provenance": "ai", "validated": true, "validation_note": "financial QA benchmarks, figures not stated in abstract", "salience": 40, "edition": 14, "models": [], "n": 1915 }, { "uid": "arxiv:2507.08584v1", "arxiv_id": "2507.08584v1", "title": "To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions", "authors": [ "Dimitrios Emmanoulopoulos", "Ollie Olby", "Justin Lyon", "Namid R. Stillman" ], "posted": "2025-07-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.08584v1", "field": "finance", "role": "agent", "bullets": [ "Daily equity trading across multiple stocks evaluated via traditional backtests and a causal market simulator with synthetic price paths and news.", "LLM-based agentic system iteratively discovers stochastic differential equations for financial time series to generate risk metrics for trading.", "Model-informed strategies improved Sharpe ratios over standard LLM sentiment-based agents across multiple equities in both test environments." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "backtest Sharpe ratios vs baseline agents", "salience": 65, "n": 2595 }, { "uid": "arxiv:2507.08244v1", "arxiv_id": "2507.08244v1", "title": "Advancing AI Capabilities and Evolving Labor Outcomes", "authors": [ "Jacob Dominski", "Yong Suk Lee" ], "posted": "2025-07-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.08244v1", "field": "economics", "role": "object", "bullets": [ "U.S. Current Population Survey data comparing Oct 2022-Mar 2023 with Oct 2024-Mar 2025, linked to task-level occupational AI exposure scores.", "ChatGPT 4o and Claude 3.5 Sonnet assessed task-level AI capability across a five-stage framework from traditional ML to agentic AI.", "Higher AI exposure associated with reduced employment, higher unemployment, and shorter work hours, especially among college-educated and older workers." ], "bullet_provenance": "ai", "models": [ "gpt", "claude" ], "open_weights": false, "salience": 70, "validated": null, "n": 2854, "authors_detailed": [ { "name": "Jacob Dominski", "url": "https://openalex.org/A5120785240", "inst": "" }, { "name": "Yong Suk Lee", "url": "https://openalex.org/A5031732004", "inst": "University of Notre Dame" } ], "affiliations": [ "University of Notre Dame" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.11548v3", "arxiv_id": "2507.11548v3", "title": "Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening", "authors": [ "Kevin T Webster" ], "posted": "2025-07-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.11548v3", "field": "management", "role": "object", "bullets": [ "Audit of eight widely used generative AI platforms for resume screening using matched fictitious resumes with demographic variation across roles", "Systems tested for racial and gender bias via controlled resume pairs and for task competence via role-mismatch and keyword-manipulation experiments", "Several unbiased-appearing models failed to distinguish relevant from irrelevant candidate experience, revealing competence failures masked as demographic fairness" ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3515, "authors_detailed": [ { "name": "Kevin Webster", "url": "https://openalex.org/A5112871868", "inst": "International Paper (United States)" } ], "affiliations": [ "International Paper (United States)" ] }, { "uid": "arxiv:2507.20468v1", "arxiv_id": "2507.20468v1", "title": "Building crypto portfolios with agentic AI", "authors": [ "Antonino Castelli", "Paolo Giudici", "Alessandro Piergallini" ], "posted": "2025-07-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.20468v1", "field": "finance", "role": "agent", "bullets": [ "Daily data on the ten most capitalized cryptocurrencies from 2020 to 2025, comparing static equal-weight and dynamic rolling-window optimization strategies.", "Multi-agent system built in CrewAI autonomously constructed crypto portfolios maximizing Sharpe and Sortino ratios while minimizing volatility.", "Dynamic optimization strategy significantly outperformed equal weighting in risk-adjusted returns both in-sample and out-of-sample across the five-year period." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "In-sample and out-of-sample backtesting on 10 cryptocurrencies 2020-2025", "salience": 50, "n": 3937 }, { "uid": "arxiv:2507.07935v6", "arxiv_id": "2507.07935v6", "title": "Working with AI: Measuring the Applicability of Generative AI to Occupations", "authors": [ "Kiran Tomlinson", "Sonia Jaffe", "Will Wang", "Scott Counts", "Siddharth Suri" ], "posted": "2025-07-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.07935v6", "field": "economics", "role": "object", "bullets": [ "200,000 anonymized conversations with Microsoft Bing Copilot mapped to O*NET work activities across US occupations using an LLM classification pipeline.", "Study measures real-world AI applicability to occupations by classifying which work activities AI assisted or performed in each conversation.", "Most common AI-assisted activities involved information work; applicability was widespread across sectors, with occupation-level variation in task delegation versus workflow assistance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 74, "validated": null, "n": 3017, "authors_detailed": [ { "name": "Kiran Tomlinson", "url": "https://openalex.org/A5077023880", "inst": "Microsoft (United States)" }, { "name": "Sonia Jaffe", "url": "https://openalex.org/A5042418453", "inst": "Microsoft (United States)" }, { "name": "Will Ke Wang", "url": "https://openalex.org/A5090323993", "inst": "Duke University" }, { "name": "Scott Counts", "url": "https://openalex.org/A5079717522", "inst": "Microsoft (United States)" }, { "name": "Siddharth Suri", "url": "https://openalex.org/A5051257652", "inst": "Microsoft (United States)" } ], "affiliations": [ "Duke University", "Microsoft (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.07906v1", "arxiv_id": "2507.07906v1", "title": "Agentic Retrieval of Topics and Insights from Earnings Calls", "authors": [ "Anant Gupta", "Rajarshi Bhowmik", "Geoffrey Gunow" ], "posted": "2025-07-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.07906v1", "field": "finance", "role": "instrument", "bullets": [ "Quarterly earnings call transcripts analyzed for emerging topics, company-level strategic insights, and trend evolution over time.", "LLM agent extracts topics from documents, structures them into a hierarchical ontology, and maps relationships between new and existing topics.", "Approach surfaces emerging financial trends with measurable ontology coherence and topic evolution accuracy; enables dynamic tracking of strategic company focus." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3213, "authors_detailed": [ { "name": "Anant Gupta", "url": "https://openalex.org/A5100874383", "inst": "All India Institute of Medical Sciences" }, { "name": "Rajarshi Bhowmik", "url": "https://openalex.org/A5030152944", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Geoffrey Gunow", "url": "https://openalex.org/A5120453110", "inst": "" } ], "affiliations": [ "All India Institute of Medical Sciences", "Rutgers, The State University of New Jersey" ] }, { "uid": "arxiv:2507.06528v1", "arxiv_id": "2507.06528v1", "title": "InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior", "authors": [ "Huisheng Wang", "Zhuoshi Pan", "Hangjing Zhang", "Mingxiao Liu", "Hanqing Gao", "H. Vicky Zhao" ], "posted": "2025-07-09", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.06528v1", "field": "finance", "role": "agent", "bullets": [ "Investor decision making under herd behavior, a setting where authentic user data for supervised fine tuning is scarce, costly to collect, and privacy sensitive.", "InvestAlign generates fine tuning data from theoretical solutions to simple optimal investment problems; base models are not named, and the tuned agent is compared with real user behaviour.", "Theory generated data yields faster parameter convergence than real user data in the authors' analysis, and the fine tuned InvestAgent aligns more closely with real users on simple and complex problems." ], "bullet_provenance": "ai", "validated": true, "validation_note": "agent behaviour compared with real-user decisions, statistics not stated", "salience": 44, "edition": 14, "models": [], "n": 1914, "authors_detailed": [ { "name": "Huisheng Wang", "url": "https://openalex.org/A5109975681", "inst": "Hainan Normal University" }, { "name": "Zhuoshi Pan", "url": "https://openalex.org/A5108876828", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "H. Zhang", "url": "https://openalex.org/A5005023011", "inst": "University of Science and Technology of China" }, { "name": "Mingxiao Liu", "url": "https://openalex.org/A5060892673", "inst": "Stevens Institute of Technology" }, { "name": "Hanqing Gao", "url": "https://openalex.org/A5111174514", "inst": "Chinese Center For Disease Control and Prevention" }, { "name": "Huaici Zhao", "url": "https://openalex.org/A5063377357", "inst": "Shenyang Institute of Automation" } ], "affiliations": [ "Hainan Normal University", "Beijing Academy of Artificial Intelligence", "University of Science and Technology of China", "Stevens Institute of Technology", "Chinese Center For Disease Control and Prevention", "Shenyang Institute of Automation" ] }, { "uid": "doi:10.2139/ssrn.5341594", "doi": "10.2139/ssrn.5341594", "title": "Ai and the Fed", "authors": [ "Sophia Kazinnik", "Erik Brynjolfsson" ], "posted": "2025-07-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5341594", "field": "economics", "role": "object", "bullets": [ "Federal Reserve System examined as representative case for AI integration in central banking using dual top-down and bottom-up framework", "Generative AI capabilities assessed across strategic central banking functions and specific FRS tasks and occupations for augmentation potential", "Significant potential for workforce augmentation and efficiency gains identified alongside critical data infrastructure and workforce transition challenges" ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3514, "authors_detailed": [ { "name": "Sophia Kazinnik", "url": "https://openalex.org/A5118843287", "inst": "Stanford University" }, { "name": "Erik Brynjolfsson", "url": "https://openalex.org/A5118843288", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Stanford University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.07296v1", "arxiv_id": "2507.07296v1", "title": "Time Series Foundation Models for Multivariate Financial Time Series Forecasting", "authors": [ "Ben A. Marconi" ], "posted": "2025-07-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.07296v1", "field": "finance", "role": "instrument", "bullets": [ "Three financial forecasting tasks: U.S. 10-year Treasury yield changes, EUR/USD volatility, and equity spread prediction across varying data lengths.", "Tiny Time Mixers and Chronos time series foundation models evaluated in zero-shot and fine-tuned settings against naive baselines and traditional specialized models.", "Pretrained TTM achieved 25-50% better performance with limited fine-tuning data, but traditional specialized models matched or exceeded it in two of three tasks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Benchmarked against naive baselines and traditional models on Treasury, FX, and equity tasks", "salience": 50, "n": 3936, "authors_detailed": [ { "name": "Ben A. Marconi", "url": "https://openalex.org/A5120357162", "inst": "" } ] }, { "uid": "arxiv:2507.08019v1", "arxiv_id": "2507.08019v1", "title": "Signal or Noise? Evaluating Large Language Models in Resume Screening Across Contextual Variations and Human Expert Benchmarks", "authors": [ "Aryan Varshney", "Venkat Ram Reddy Ganuthula" ], "posted": "2025-07-08", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.08019v1", "field": "management", "role": "agent", "bullets": [ "Controlled resume and job description pairs evaluated under varying company contexts, a multinational, a startup, no company, and reduced context, with three human recruitment experts as benchmark.", "Claude, GPT, and Gemini repeatedly score identical and randomized resumes; analysis of variance separates stable signal from noise across contexts and against the expert ratings.", "Ratings shift significantly in four of eight LLM only conditions and diverge from human experts in every context, with meta cognition analysis showing weighting patterns unlike human evaluators." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 46, "edition": 14, "validated": null, "n": 1830 }, { "uid": "arxiv:2507.06057v2", "arxiv_id": "2507.06057v2", "title": "FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models", "authors": [ "Bo Pang", "Yalu Ouyang", "Hangfei Xu", "Ziqi Jia", "Panpan Li", "Shengzhao Wen", "Lu Wang", "Shiyong Li", "Yanpeng Wang" ], "posted": "2025-07-08", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.06057v2", "field": "finance", "role": "method", "bullets": [ "Seven financial and general benchmarks, used to evaluate 32 billion parameter models trained from Qwen2.5-32B with curated financial corpora called FEVO-Train.", "The FEVO framework stacks continued pretraining, supervised fine tuning, and reinforcement learning, with training data curated by frontier reasoning models and rule based filtering.", "FEVO-R32B reaches state of the art results on five financial benchmarks against larger and specialist models, and clearly beats a variant trained from the instruct model with reinforcement learning only." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "seven financial and general benchmarks", "salience": 45, "edition": 14, "n": 1913, "authors_detailed": [ { "name": "Bo Pang", "url": "https://openalex.org/A5120340008", "inst": "" }, { "name": "Yalu Ouyang", "url": "https://openalex.org/A5120340009", "inst": "University of California San Diego" }, { "name": "Haifeng Xu", "url": "https://openalex.org/A5018854220", "inst": "Eugene Applebaum College of Pharmacy and Health Sciences" }, { "name": "Ziqi Jia", "url": "https://openalex.org/A5120340010", "inst": "" }, { "name": "Panpan Li", "url": "https://openalex.org/A5043594071", "inst": "" }, { "name": "Shengzhao Wen", "url": "https://openalex.org/A5120340011", "inst": "Baidu (China)" }, { "name": "Lu Wang", "url": "https://openalex.org/A5100364381", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "S. Z. Li", "url": "https://openalex.org/A5085338571", "inst": "Peking University" }, { "name": "Yanpeng Wang", "url": "https://openalex.org/A5007888885", "inst": "Linyi University" } ], "affiliations": [ "University of California San Diego", "Eugene Applebaum College of Pharmacy and Health Sciences", "Baidu (China)", "Beijing University of Posts and Telecommunications", "Peking University", "Linyi University" ] }, { "uid": "doi:10.2139/ssrn.5341272", "doi": "10.2139/ssrn.5341272", "title": "Measuring Geopolitical Alignment and Economic Growth", "authors": [ "Tianyu Fan" ], "posted": "2025-07-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5341272", "field": "economics", "role": "instrument", "bullets": [ "373,020 geopolitical events across 193 countries from 1960 to 2024, used to build a bilateral alignment measure, with country GDP per capita as the growth outcome.", "Large language models, with the family not stated, compile the raw events into the alignment measure; the paper reports no validation of the extraction against a benchmark.", "A one standard deviation permanent rise in alignment raises GDP per capita by roughly 10 percent over 25 years, alongside gains in investment, productivity, and trade." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "edition": 3, "audience": "general", "models": [], "n": 354, "authors_detailed": [ { "name": "Tianyu Fan", "url": "https://openalex.org/A5056076948", "inst": "Yale University" } ], "affiliations": [ "Yale University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1016/j.eng.2025.11.029", "doi": "10.1016/j.eng.2025.11.029", "arxiv_id": "2507.18577v2", "title": "Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges", "authors": [ "Liyuan Chen", "Shuoling Liu", "Jiangpeng Yan", "Xiaoyu Wang", "Henglin Liu", "Chuang Li", "Kecheng Jiao", "Jixuan Ying", "Yang Veronica Liu", "Qiang Yang", "Xiu Li" ], "posted": "2025-07-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.18577v2", "field": "finance", "role": "method", "bullets": [ "Comprehensive survey of financial foundation models spanning language, time-series, and visual-language modalities across financial engineering applications worldwide.", "Reviews architectures, training methods, datasets, and deployments of GPT-4, Gemini, and domain-specific financial models for tasks from summarization to forecasting.", "Identifies data availability, algorithmic scalability, and infrastructure constraints as critical gaps; proposes taxonomy of financial language, time-series, and visual-language foundation models." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "salience": 45, "validated": null, "n": 2721, "authors_detailed": [ { "name": "Liyuan Chen", "url": "https://openalex.org/A5100715495", "inst": "Sun Yat-sen University" }, { "name": "Shuoling Liu", "url": "https://openalex.org/A5018892437", "inst": "New Leaders" }, { "name": "Jiangpeng Yan", "url": "https://openalex.org/A5013716631", "inst": "Beijing Normal University" }, { "name": "Xiaoyu Wang", "url": "https://openalex.org/A5101604758", "inst": "Southwest University of Political Science & Law" }, { "name": "Henglin Liu", "url": "https://openalex.org/A5076113712", "inst": "" }, { "name": "Chuang Li", "url": "https://openalex.org/A5100386721", "inst": "Fudan University" }, { "name": "Kecheng Jiao", "url": "https://openalex.org/A5120019851", "inst": "" }, { "name": "Jixuan Ying", "url": "", "inst": "" }, { "name": "Yang Liu", "url": "https://openalex.org/A5100356161", "inst": "Xidian University" }, { "name": "Qiang Yang", "url": "https://openalex.org/A5016231511", "inst": "Southwest University of Science and Technology" }, { "name": "Xiu Li", "url": "https://openalex.org/A5100754504", "inst": "Xinjiang Production and Construction Corps" } ], "affiliations": [ "Sun Yat-sen University", "New Leaders", "Beijing Normal University", "Southwest University of Political Science & Law", "Fudan University", "Xidian University", "Southwest University of Science and Technology", "Xinjiang Production and Construction Corps" ] }, { "uid": "arxiv:2507.04833v6", "arxiv_id": "2507.04833v6", "title": "Measuring Geopolitical Alignment and Economic Growth", "authors": [ "Tianyu Fan" ], "posted": "2025-07-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.04833v6", "field": "economics", "role": "instrument", "bullets": [ "373,020 geopolitical events across 193 countries over 1960-2024, with local projections exploiting within-country temporal variation in bilateral alignment.", "Large language models compiled and classified geopolitical events from historical records to construct a new event-based bilateral alignment measure.", "One-standard-deviation improvement in geopolitical alignment increased GDP per capita by approximately 10% over 25 years, operating through investment, trade, and stability channels." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 70, "n": 3016, "authors_detailed": [ { "name": "Tianyu Fan", "url": "https://openalex.org/A5056076948", "inst": "Yale University" } ], "affiliations": [ "Yale University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5327586", "doi": "10.2139/ssrn.5327586", "title": "A Strategic Framework for Utilizing AI to Enhance SME Survival, Growth, and Competitive Advantage", "authors": [ "Asif Raza" ], "posted": "2025-07-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5327586", "field": "management", "role": "object", "bullets": [ "Secondary research and case studies examining AI adoption by SMEs contributing over 50% of global GDP across sectors", "Generative AI and predictive analytics assessed for projected cost reductions of 20-40% and revenue increases up to 30% from vendor benchmarks", "Proposed 7-step AI adoption framework with ROI model and readiness checklist tailored to SME resource and governance constraints" ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3513, "authors_detailed": [ { "name": "Asif Raza", "url": "https://openalex.org/A5036658711", "inst": "Universidad de Londres" } ], "affiliations": [ "Universidad de Londres" ] }, { "uid": "arxiv:2507.10448v1", "arxiv_id": "2507.10448v1", "title": "FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios", "authors": [ "Yingqian Wu", "Qiushi Wang", "Zefei Long", "Rong Ye", "Zhongtian Lu", "Xianyin Zhang", "Bingxuan Li", "Wei Chen", "Liwen Zhang", "Zhongyu Wei" ], "posted": "2025-07-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.10448v1", "field": "finance", "role": "method", "bullets": [ "Financial analysis tasks from real Chinese online investment forums covering macroeconomic, industry, and company-level analysis with human evaluation.", "Four specialized LLM agents (document analyzer, analyst, accountant, consultant) trained on financial data and benchmarked against GPT-4o and Xuanyuan.", "FinTeam achieved 62% human acceptance rate for financial reports, 7.43% average improvement on FinCUGE, and 2.06% accuracy gain on FinEval." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinCUGE and FinEval benchmarks plus human evaluation", "salience": 52, "n": 3015, "authors_detailed": [ { "name": "Yingqian Wu", "url": "https://openalex.org/A5104336620", "inst": "Fudan University" }, { "name": "Qiushi Wang", "url": "https://openalex.org/A5100411695", "inst": "Sun Yat-sen University" }, { "name": "Zefei Long", "url": "https://openalex.org/A5101259829", "inst": "Fudan University" }, { "name": "Rong Ye", "url": "https://openalex.org/A5103167214", "inst": "Guizhou University" }, { "name": "Zhongtian Lu", "url": "https://openalex.org/A5102571797", "inst": "Fudan University" }, { "name": "Xianyin Zhang", "url": "https://openalex.org/A5045876463", "inst": "Fudan University" }, { "name": "B. Li", "url": "https://openalex.org/A5021922879", "inst": "Shijiazhuang University" }, { "name": "Wei Chen", "url": "https://openalex.org/A5100756971", "inst": "National University of Defense Technology" }, { "name": "Liwen Zhang", "url": "https://openalex.org/A5100459596", "inst": "Harbin Institute of Technology" }, { "name": "Zhongyu Wei", "url": "https://openalex.org/A5011504177", "inst": "Fudan University" } ], "affiliations": [ "Fudan University", "Sun Yat-sen University", "Guizhou University", "Shijiazhuang University", "National University of Defense Technology", "Harbin Institute of Technology" ] }, { "uid": "arxiv:2507.03721v1", "arxiv_id": "2507.03721v1", "title": "Predicting Business Angel Early-Stage Decision Making Using AI", "authors": [ "Yan Katcharovski", "Andrew L. Maxwell" ], "posted": "2025-07-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.03721v1", "field": "management", "role": "instrument", "bullets": [ "600 transcribed, unstructured startup pitches seeking business angel funding with known deal outcomes, scored on the eight factors of the Critical Factor Assessment.", "Multiple LLMs, not individually named in the abstract, assign the factor scores that feed machine-learning classifiers; scores track human-graded assessments at Spearman 0.896.", "The best classifier predicts deal versus no deal with 85 percent accuracy, replacing an assessment that previously required three trained evaluators and several days." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human-graded CFA scores, Spearman 0.896", "salience": 56, "edition": 14, "models": [], "n": 2013 }, { "uid": "arxiv:2507.03435v1", "arxiv_id": "2507.03435v1", "title": "ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction", "authors": [ "Jarosław A. Chudziak", "Michał Wawer" ], "posted": "2025-07-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.03435v1", "field": "finance", "role": "method", "bullets": [ "Stock market price data analyzed by a multi-agent system that combines large language models with the Elliott Wave Principle to produce predictions and explanations.", "Agents exchange natural-language dialogue to refine joint technical analyses; the abstract names no specific model and reports no validation against ground truth.", "Experiments are said to show wave pattern recognition and readable descriptions of market trends; no accuracy or trading performance figures are stated." ], "bullet_provenance": "ai", "validated": false, "salience": 28, "edition": 14, "models": [], "n": 2014 }, { "uid": "arxiv:2507.02439v2", "arxiv_id": "2507.02439v2", "title": "Introducing a New Brexit-Related Uncertainty Index: Its Evolution and Economic Consequences", "authors": [ "Ismet Gocer", "Julia Darby", "Serdar Ongan" ], "posted": "2025-07-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.02439v2", "field": "economics", "role": "instrument", "bullets": [ "Monthly Economist Intelligence Unit country reports for the UK from May 2012 to January 2025; VAR analysis of macroeconomic responses to uncertainty shocks.", "LLMs combined with text mining and NLP techniques constructed a Brexit-Related Uncertainty Index and a complementary COVID-19 index from unstructured report text.", "BRUI tracks Brexit-specific uncertainty distinct from COVID effects; VAR impulse responses show significant impacts on investment, trade, and other macroeconomic variables." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 2720, "authors_detailed": [ { "name": "İsmet Göçer", "url": "https://openalex.org/A5075114340", "inst": "Southampton Solent University" }, { "name": "Julia Darby", "url": "https://openalex.org/A5084272365", "inst": "University of Strathclyde" }, { "name": "Serdar Ongan", "url": "https://openalex.org/A5053262356", "inst": "University of West Bohemia in Pilsen" } ], "affiliations": [ "Southampton Solent University", "University of Strathclyde", "University of West Bohemia in Pilsen" ] }, { "uid": "arxiv:2507.02087v2", "arxiv_id": "2507.02087v2", "title": "Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions", "authors": [ "Eitan Anzenberg", "Arunava Samajpati", "Sivasankaran Chandrasekar", "Varun Kacholia" ], "posted": "2025-07-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.02087v2", "field": "management", "role": "agent", "bullets": [ "Roughly 10,000 real recent candidate job pairs, scored for match quality and audited with impact ratios across declared gender, race, and intersectional subgroups.", "Models from OpenAI, Anthropic, Google, Meta, and DeepSeek screen candidates against outcome labels, judged on ROC AUC, precision recall, and fairness versus Match Score, a proprietary supervised model.", "Match Score reaches ROC AUC 0.85 versus 0.77 for the best LLM and a minimum race impact ratio of 0.957 versus 0.809 or lower, showing accuracy and fairness can coexist." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "candidate outcome labels, AUC and impact ratios", "salience": 58, "edition": 14, "n": 1867 }, { "uid": "arxiv:2507.01923v2", "arxiv_id": "2507.01923v2", "title": "Decision-Oriented Text Evaluation", "authors": [ "Yu-Shiang Huang", "Chuan-Ju Wang", "Chung-Chi Chen" ], "posted": "2025-07-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.01923v2", "field": "finance", "role": "agent", "bullets": [ "Market digest texts including objective morning summaries and subjective closing-bell analyses used to inform trading decisions by human investors and autonomous LLM agents.", "LLM agents acted as autonomous traders informed exclusively by generated texts; trading performance measured as financial returns to evaluate text quality.", "Neither humans nor LLM agents consistently beat random using summaries alone; richer analytical commentaries enabled human-LLM teams to significantly outperform individual baselines." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 2719, "authors_detailed": [ { "name": "Yu-Shiang Huang", "url": "https://openalex.org/A5101137226", "inst": "National Taiwan University" }, { "name": "Chuan‐Ju Wang", "url": "https://openalex.org/A5017348970", "inst": "Research Center for Information Technology Innovation, Academia Sinica" }, { "name": "Chung-Chi Chen", "url": "https://openalex.org/A5101516307", "inst": "National Institute of Informatics" } ], "affiliations": [ "National Taiwan University", "Research Center for Information Technology Innovation, Academia Sinica", "National Institute of Informatics" ] }, { "uid": "doi:10.2139/ssrn.5334836", "doi": "10.2139/ssrn.5334836", "title": "AI for Good: History, Open Data and Some ESG-based Applications", "authors": [ "Daniel E. O'Leary" ], "posted": "2025-07-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5334836", "field": "management", "role": "instrument", "bullets": [ "Three ESG-related applications: carbon pollution dictionary development, a 311 open-data interface for citizen communication, and AI-facilitated government purchase monitoring.", "AI text mining and large language models integrated with open data sources to extract, classify, and monitor ESG-relevant information across applications.", "Framework demonstrates feasibility of AI-driven ESG monitoring with potential for real-time autonomous applications linking open data to environmental and governance oversight." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 30, "n": 3014, "authors_detailed": [ { "name": "Daniel E. O’Leary", "url": "https://openalex.org/A5003034800", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.00718v1", "arxiv_id": "2507.00718v1", "title": "AI Analyst: Framework and Comprehensive Evaluation of Large Language Models for Financial Time Series Report Generation", "authors": [ "Elizabeth Fons", "Elena Kochkina", "Rachneet Kaur", "Zhen Zeng", "Berowne Hlavaty", "Charese Smiley", "Svitlana Vyetrenko", "Manuela Veloso" ], "posted": "2025-07-01", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.00718v1", "field": "finance", "role": "method", "bullets": [ "Real stock market index data plus synthetic time series, used to test whether language models can turn numerical series into written financial reports.", "A framework spans prompt engineering, model selection, and evaluation; generated sentences are automatically highlighted by whether they derive from the data, financial reasoning, or external knowledge. Models are not named in the abstract.", "The models produce coherent and informative reports on both real and synthetic series; no quantitative accuracy figure or ground truth check is given in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 14, "models": [], "n": 1912 }, { "uid": "doi:10.2139/ssrn.5316518", "doi": "10.2139/ssrn.5316518", "title": "Can AI be trusted with financial data?", "authors": [ "Marcelo Farr", "William C. Johnson", "Ariel J. Markelevich", "Alexis Montecinos" ], "posted": "2025-07-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5316518", "field": "accounting", "role": "method", "bullets": [ "US public company financial statements across multiple filing periods, covering firms of varying size, complexity, segment count, and filing format.", "LLM extracted financial data from annual reports; accuracy tested across firm complexity, operating segments, and HTML versus XBRL formatting dimensions.", "AI made fewer errors for smaller firms, simpler statements, fewer segments, and structured formats; hallucination rates remained under 2% overall." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "LLM output compared to actual financial statement data", "salience": 70, "n": 3013, "authors_detailed": [ { "name": "Marcelo Farr", "url": "https://openalex.org/A5118761674", "inst": "Adolfo Ibáñez University" }, { "name": "William C. Johnson", "url": "https://openalex.org/A5101579840", "inst": "University of Massachusetts Lowell" }, { "name": "Ariel Markelevich", "url": "https://openalex.org/A5029035375", "inst": "Suffolk University" }, { "name": "Alexis Montecinos", "url": "https://openalex.org/A5118761675", "inst": "Adolfo Ibáñez University" } ], "affiliations": [ "Adolfo Ibáñez University", "University of Massachusetts Lowell", "Suffolk University" ] }, { "uid": "arxiv:2506.23719v1", "arxiv_id": "2506.23719v1", "title": "DABstep: Data Agent Benchmark for Multi-step Reasoning", "authors": [ "Alex Egg", "Martin Iglesias Goyanes", "Friso Kingma", "Andreu Mora", "Leandro von Werra", "Thomas Wolf" ], "posted": "2025-06-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.23719v1", "field": "finance", "role": "method", "bullets": [ "Over 450 multi-step data analysis tasks derived from a financial analytics platform, requiring code-based processing and reasoning over heterogeneous documentation.", "Leading LLM-based agents were evaluated on tasks combining data manipulation, cross-referencing multiple sources, and precise numerical result reporting.", "Best agent achieved only 14.55% accuracy on the hardest tasks, revealing a large gap between current LLM agent capabilities and real-world financial analysis." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "DABstep benchmark with 450+ financial analytics tasks, factoid-style automatic scoring", "salience": 55, "n": 3935, "authors_detailed": [ { "name": "Alex Egg", "url": "https://openalex.org/A5014586997", "inst": "" }, { "name": "Martin Iglesias Goyanes", "url": "https://openalex.org/A5120419794", "inst": "" }, { "name": "Friso H. Kingma", "url": "https://openalex.org/A5018268043", "inst": "" }, { "name": "A. Mora", "url": "https://openalex.org/A5111315136", "inst": "European Space Agency" }, { "name": "Leandro von Werra", "url": "https://openalex.org/A5008355834", "inst": "ETH Zurich" }, { "name": "Thomas Wolf", "url": "https://openalex.org/A5107899645", "inst": "École Polytechnique" } ], "affiliations": [ "European Space Agency", "ETH Zurich", "École Polytechnique" ] }, { "uid": "arxiv:2507.02954v2", "arxiv_id": "2507.02954v2", "title": "Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III", "authors": [ "Pranam Shetty", "Abhisek Upadhayaya", "Parth Mitesh Shah", "Srikanth Jagabathula", "Shilpi Nayak", "Anna Joo Fee" ], "posted": "2025-06-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.02954v2", "field": "finance", "role": "method", "bullets": [ "The CFA Level III exam, multiple choice and essay sections, taken by 23 frontier LLMs under a revised, stricter essay grading methodology.", "Models answer under several prompting strategies including Chain-of-Thought and Self-Discover, with responses scored against exam answer keys as ground truth.", "o4-mini posts a 79.1 percent composite and Gemini 2.5 Flash 77.3 percent; the authors read the results as progress but flag deployment cost and interpretation against professional standards." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "CFA Level III answer keys with stricter essay grading", "salience": 55, "edition": 14, "n": 1829, "authors_detailed": [ { "name": "Prakash Shetty", "url": "https://openalex.org/A5016733454", "inst": "Nitte University" }, { "name": "Upadhayaya, Abhisek", "url": "", "inst": "" }, { "name": "Shah, Parth Mitesh", "url": "", "inst": "" }, { "name": "Srikanth Jagabathula", "url": "https://openalex.org/A5053861695", "inst": "Harvard University" }, { "name": "Nayak, Shilpi", "url": "", "inst": "" }, { "name": "Anna Joo Fee", "url": "https://openalex.org/A5120060712", "inst": "" } ], "affiliations": [ "Harvard University", "Nitte University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.01990v1", "arxiv_id": "2507.01990v1", "title": "Integrating Large Language Models in Financial Investments and Market Analysis: A Survey", "authors": [ "Sedigheh Mahdavi", "Jiating", "Chen", "Pradeep Kumar Joshi", "Lina Huertas Guativa", "Upmanyu Singh" ], "posted": "2025-06-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.01990v1", "field": "finance", "role": "method", "bullets": [ "Survey of recent research on LLMs in financial investment and market analysis, covering stock selection, risk assessment, sentiment analysis, trading, and financial forecasting.", "No model is deployed; contributions are grouped into four frameworks: LLM pipelines, hybrid integration, fine tuning and adaptation, and agent based architectures.", "Maps capabilities and challenges across the four framework families and outlines potential directions; no empirical comparison or quantitative synthesis is reported." ], "bullet_provenance": "ai", "salience": 26, "edition": 14, "models": [], "validated": null, "n": 1911 }, { "uid": "arxiv:2506.23273v2", "arxiv_id": "2506.23273v2", "title": "FinStat2SQL: A Text2SQL Pipeline for Financial Statement Analysis", "authors": [ "Quang Hung Nguyen", "Phuong Anh Trinh", "Phan Quoc Hung Mai", "Tuan Phong Trinh" ], "posted": "2025-06-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.23273v2", "field": "accounting", "role": "instrument", "bullets": [ "Vietnamese financial statements prepared under VAS standards; synthetic QA dataset built from domain-specific database for evaluation of natural language querying.", "Multi-agent pipeline combining GPT-4o-mini and a fine-tuned 7B open-weight model performed entity extraction, SQL generation, and self-correction over financial statement data.", "Fine-tuned 7B model achieved 61.33% accuracy with sub-4-second response on consumer hardware, outperforming GPT-4o-mini on domain-specific financial queries." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "accuracy on synthetic QA dataset", "salience": 35, "n": 2718, "authors_detailed": [ { "name": "Nguyen, Quang Hung", "url": "", "inst": "" }, { "name": "Phuong Anh Trinh", "url": "https://openalex.org/A5120609203", "inst": "" }, { "name": "Phan Quoc Hung Mai", "url": "https://openalex.org/A5120419590", "inst": "" }, { "name": "Trinh, Tuan Phong", "url": "", "inst": "" } ] }, { "uid": "arxiv:2506.22704v2", "arxiv_id": "2506.22704v2", "title": "Beyond Code: The Multidimensional Impacts of Large Language Models in Software Development", "authors": [ "Sardar Bonabi", "Sarah Bana", "Vijay Gurbaxani", "Tingting Nian" ], "posted": "2025-06-28", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.22704v2", "field": "management", "role": "object", "bullets": [ "88,022 open-source developers on GitHub in Italy, France, and Portugal, observed around Italy's temporary ChatGPT ban in a difference-in-differences design with two-way fixed effects.", "ChatGPT access is the treatment in the causal story rather than a research tool; outcomes cover code production, knowledge sharing, and skill acquisition.", "Access lifts developer productivity 6.4 percent, knowledge sharing 9.6 percent, and skill acquisition 8.4 percent, with novices gaining output while experienced developers gain learning and collaboration." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 70, "edition": 14, "validated": null, "n": 2012, "authors_detailed": [ { "name": "Sardar Bonabi", "url": "https://openalex.org/A5118708148", "inst": "University of California, Irvine" }, { "name": "Sarah Bana", "url": "https://openalex.org/A5118708149", "inst": "Chapman University" }, { "name": "Vijay Gurbaxani", "url": "https://openalex.org/A5005339370", "inst": "University of California, Irvine" }, { "name": "Tingting Nian", "url": "https://openalex.org/A5087794751", "inst": "University of California, Irvine" } ], "affiliations": [ "University of California, Irvine", "Chapman University" ] }, { "uid": "doi:10.35650/aidp.4114.d.2025", "doi": "10.35650/aidp.4114.d.2025", "arxiv_id": "2508.00838v1", "title": "The Attribution Crisis in LLM Search Results", "authors": [ "Ilan Strauss", "Jangho Yang", "Tim O'Reilly", "Sruly Rosenblat", "Isobel Moure" ], "posted": "2025-06-27", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.00838v1", "field": "economics", "role": "object", "bullets": [ "Roughly 14,000 real LMArena conversations with search-enabled systems, including Google Gemini, OpenAI GPT-4o, and Perplexity Sonar, linked to their retrieval and citation logs.", "The commercial systems are the object of study; a negative binomial hurdle model relates relevant web pages visited to citations actually provided per query.", "Gemini skips search in 34 percent of responses and gives no clickable citation in 92 percent, and a typical Gemini or Sonar answer leaves about three relevant pages uncited." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 55, "edition": 14, "validated": null, "n": 2011 }, { "uid": "arxiv:2506.21031v1", "arxiv_id": "2506.21031v1", "title": "Large Language Models Acing Chartered Accountancy", "authors": [ "Jatin Gupta", "Akhil Sharma", "Saransh Singhania", "Mohammad Adnan", "Sakshi Deo", "Ali Imam Abidi", "Keshav Gupta" ], "posted": "2025-06-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.21031v1", "field": "accounting", "role": "method", "bullets": [ "CA-Ben, question answer sets built from Institute of Chartered Accountants of India examinations spanning foundation, intermediate, and final stages of the chartered accountancy curriculum.", "GPT-4o, Llama 3.3 70B, Llama 3.1 405B, Mistral Large, Claude 3.5 Sonnet, and Phi 4 answer under standardized protocols scored against the examination keys.", "Claude 3.5 Sonnet and GPT-4o come out ahead, strongest in conceptual and legal reasoning, while numerical computation and legal interpretation stay weak; exact accuracy figures are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "ICAI examination answer keys", "salience": 48, "edition": 14, "n": 1810, "authors_detailed": [ { "name": "Jatin Gupta", "url": "https://openalex.org/A5109443982", "inst": "Sharda University" }, { "name": "Sharma, Akhil", "url": "", "inst": "" }, { "name": "Saransh Singhania", "url": "https://openalex.org/A5114718526", "inst": "Sharda University" }, { "name": "Mohammad Adnan", "url": "https://openalex.org/A5103969466", "inst": "Business School Lausanne" }, { "name": "Sakshi Deo", "url": "https://openalex.org/A5112971665", "inst": "Sharda University" }, { "name": "Ali Imam Abidi", "url": "https://openalex.org/A5024592017", "inst": "Sharda University" }, { "name": "Keshav Gupta", "url": "https://openalex.org/A5019799476", "inst": "Medanta The Medicity" } ], "affiliations": [ "Sharda University", "Business School Lausanne", "Medanta The Medicity" ] }, { "uid": "doi:10.1007/s11142-025-09892-6", "doi": "10.1007/s11142-025-09892-6", "title": "Can generative AI help identify peer firms?", "authors": [ "Yi Cao", "Long Chen", "Jennifer Wu Tucker", "Chi Wan" ], "posted": "2025-06-25", "added": "2026-08-24", "source_label": "Review of Accounting Studies", "url": "https://doi.org/10.1007/s11142-025-09892-6", "field": "accounting", "role": "instrument", "bullets": [ "Setting: Firm-level product-market peer identification, benchmarked against human-expert peers, established peer systems, business-description similarity, and SIC industries.", "Design: Generative AI produces peer lists; validity is assessed through overlap with comparison systems and subsequent co-movement in stock returns, sales growth, and gross margins.", "Result: AI peers overlap strongly with expert peers and deliver stronger subsequent outcome correlations and within-group homogeneity than business-description and SIC alternatives, especially for large firms." ], "bullet_provenance": "editor", "validated": true, "validation_note": "Benchmarked against human experts and established peer-identification systems, with downstream market and operating-outcome tests.", "salience": 100, "edition": 23, "audience": "general", "models": [], "n": 4124, "authors_detailed": [ { "name": "Yi Cao", "url": "https://openalex.org/A5045986817", "inst": "George Mason University" }, { "name": "Long Chen", "url": "https://openalex.org/A5100336423", "inst": "George Mason University" }, { "name": "Jennifer Wu Tucker", "url": "https://openalex.org/A5050334594", "inst": "University of Florida" }, { "name": "Chi Wan", "url": "https://openalex.org/A5101540802", "inst": "University of Massachusetts Boston" } ], "affiliations": [ "University of Florida", "George Mason University", "University of Massachusetts Boston" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1109/bigdata66926.2025.11401444", "doi": "10.1109/bigdata66926.2025.11401444", "arxiv_id": "2506.20821v1", "title": "MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering", "authors": [ "Chinmay Gondhalekar", "Urjitkumar Patel", "Fang-Chun Yeh" ], "posted": "2025-06-25", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.20821v1", "field": "finance", "role": "method", "bullets": [ "Question answering over long financial filings such as 10-Ks, 10-Qs, and investor presentations, which mix dense narrative, structured tables, and complex figures across hundreds of pages.", "A quantized open source multimodal model, not named in the abstract, converts batched tables and figures into structured JSON plus summaries; modality aware retrieval escalates from text only to text, table, and image contexts.", "On multimodal financial QA the pipeline scores 19 percentage points higher accuracy than free tier ChatGPT-4o while running on commodity hardware." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "financial QA accuracy against ChatGPT-4o baseline", "salience": 42, "edition": 14, "n": 1818, "authors_detailed": [ { "name": "Chinmay Gondhalekar", "url": "https://openalex.org/A5093940847", "inst": "X-Fab (Germany)" }, { "name": "Urjitkumar Patel", "url": "https://openalex.org/A5111130559", "inst": "X-Fab (Germany)" }, { "name": "Fang-Chun Yeh", "url": "https://openalex.org/A5114127655", "inst": "X-Fab (Germany)" } ], "affiliations": [ "X-Fab (Germany)" ] }, { "uid": "doi:10.2139/ssrn.5318992", "doi": "10.2139/ssrn.5318992", "title": "Large Language Models in the Firm and Labor Income Share", "authors": [ "Chunqiang Zhang", "Wanqiang Ye", "Yuan Meng" ], "posted": "2025-06-25", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5318992", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 50, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 353, "authors_detailed": [ { "name": "Chunqiang Zhang", "url": "https://openalex.org/A5110695321", "inst": "Anhui University of Finance and Economics" }, { "name": "Wanqiang Ye", "url": "https://openalex.org/A5104291604", "inst": "Anhui University of Finance and Economics" }, { "name": "Yuan Meng", "url": "https://openalex.org/A5100350912", "inst": "Anhui University of Finance and Economics" } ], "affiliations": [ "Anhui University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.5317057", "doi": "10.2139/ssrn.5317057", "title": "When IR Meets GenAI", "authors": [ "Ning Jia", "Ningzhong Li", "Da Xu", "Yuwen Zhang" ], "posted": "2025-06-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5317057", "field": "finance", "role": "object", "bullets": [ "US public firms with and without investor relations programs, before and after ChatGPT's November 2022 release, difference-in-differences design.", "Study examines how GenAI adoption amplifies IR communication advantages through nontraditional channels such as corporate websites, benefiting retail investors.", "Firms with IR programs showed significantly greater information-environment improvement post-ChatGPT; effects concentrated in good-news dissemination and amplified asymmetric bad-news withholding." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "validated": null, "n": 3012, "authors_detailed": [ { "name": "Ning Jia", "url": "https://openalex.org/A5101709263", "inst": "Tsinghua University" }, { "name": "Ningzhong Li", "url": "https://openalex.org/A5091726176", "inst": "The University of Texas at Dallas" }, { "name": "Da Xu", "url": "https://openalex.org/A5076475582", "inst": "Tsinghua University" }, { "name": "Yuwen Zhang", "url": "https://openalex.org/A5020359936", "inst": "Tsinghua University" } ], "affiliations": [ "The University of Texas at Dallas", "Tsinghua University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5317703", "doi": "10.2139/ssrn.5317703", "title": "Cross-Border Agentic AI and the EU AI Act's Global Reach", "authors": [ "Theodoros Karathanasis" ], "posted": "2025-06-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5317703", "field": "finance", "role": "object", "bullets": [ "Analysis of EU AI Act provisions applied to cross-border agentic AI operations in banking, including offshore data processing and model training.", "Paper examines whether wholly offshore AI activities trigger EU AI Act compliance when they produce substantial effects within the EU market.", "Even offshore AI operations may fall under EU AI Act jurisdiction when part of strategies with substantial EU effects, complicating cross-border accountability." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3934, "authors_detailed": [ { "name": "Theodoros Karathanasis", "url": "https://openalex.org/A5060976965", "inst": "Université Grenoble Alpes" } ], "affiliations": [ "Université Grenoble Alpes" ] }, { "uid": "doi:10.2139/ssrn.5314976", "doi": "10.2139/ssrn.5314976", "title": "Large Language Models in Human Resource Management: a systematic literature review of applications, open issues and future research directions", "authors": [ "Thomas K. Dasaklis", "Panagiotis G. Giannopoulos", "Dimitris Koutras", "Vangelis Malamas", "Panos Chountalas" ], "posted": "2025-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5314976", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 40, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 352 }, { "uid": "doi:10.2139/ssrn.5315056", "doi": "10.2139/ssrn.5315056", "title": "Confirmation and Specificity Biases in Large Language Models: An Explorative Study", "authors": [ "Daniel E. O'Leary" ], "posted": "2025-06-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5315056", "field": "management", "role": "object", "bullets": [ "Exploratory study of three leading commercial LLMs—ChatGPT, Claude, and Gemini—tested on a number-sequence rule-identification task adapted from classic human confirmation bias research.", "Each LLM received identical prompts asking it to identify the rule governing a number sequence; responses analyzed for confirmation bias and specificity bias patterns.", "All three LLMs exhibited confirmation bias and specificity bias by overfitting to observed data, mirroring well-documented patterns from human cognitive experiments." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "validated": false, "salience": 30, "n": 2381, "authors_detailed": [ { "name": "Daniel E. O’Leary", "url": "https://openalex.org/A5003034800", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5315021", "doi": "10.2139/ssrn.5315021", "title": "An Anchoring Effect in Large Language Models 1", "authors": [ "Daniel E. O'Leary" ], "posted": "2025-06-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5315021", "field": "management", "role": "agent", "bullets": [ "Experiments on three LLMs testing anchoring bias in numerical estimation tasks using text-based information across multiple decision dimensions.", "Three LLMs prompted with anchor information before making numerical judgments; responses compared across anchored and unanchored conditions for bias propagation.", "All three LLMs demonstrate anchoring bias in numerical estimates mirroring human behavior; bias propagates from the anchored dimension to related decision dimensions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "anchoring bias measured against unanchored baseline across decision dimensions", "salience": 55, "models": [], "n": 2717, "authors_detailed": [ { "name": "Daniel E. O’Leary", "url": "https://openalex.org/A5003034800", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5297493", "doi": "10.2139/ssrn.5297493", "title": "Revolutionizing Real Estate Education: Integrating AI and LLMs into Undergraduate Curriculum", "authors": [ "Cayman Seagraves", "Philip Seagraves" ], "posted": "2025-06-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5297493", "field": "management", "role": "object", "bullets": [ "Undergraduate real estate curriculum spanning commercial, residential, appraisal, and market analytics courses; no specific institution or student sample reported.", "Paper examines how AI and LLM tools reshape teaching methodologies and curriculum content, expecting students to leverage AI for homework and projects.", "Argues graduates must be proficient in AI for a tech-driven real estate market; no empirical evaluation of student learning outcomes reported." ], "bullet_provenance": "ai", "validated": false, "salience": 20, "models": [], "n": 2716, "authors_detailed": [ { "name": "Cayman Seagraves", "url": "https://openalex.org/A5090973432", "inst": "Collins College" }, { "name": "Philip Seagraves", "url": "https://openalex.org/A5031956328", "inst": "Middle Tennessee State University" } ], "affiliations": [ "Collins College", "Middle Tennessee State University" ] }, { "uid": "arxiv:2506.18942v3", "arxiv_id": "2506.18942v3", "title": "Advanced Applications of Generative AI in Actuarial Science: Case Studies Beyond ChatGPT", "authors": [ "Simon Hatzesberger", "Iris Nonneman" ], "posted": "2025-06-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.18942v3", "field": "finance", "role": "method", "bullets": [ "Four implemented actuarial case studies: claim cost prediction from unstructured text, market comparison from insurers' annual reports, car damage photo classification, and migration of legacy actuarial code from R to Python.", "LLM extracted features feed a supervised claims model, retrieval augmented generation structures report content, a fine tuned vision model labels damage, and a multi agent system checks translated code against original outputs; specific models are not named in the abstract.", "The cases map where generative AI fits actuarial workflows; the article then reviews regulatory, security, dual use and fraud, reproducibility, privacy, and governance challenges in regulated insurance, without quantified performance results." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 48, "edition": 14, "n": 1817 }, { "uid": "arxiv:2506.17490v1", "arxiv_id": "2506.17490v1", "title": "Social Group Bias in AI Finance", "authors": [ "Thomas R. Cook", "Sophia Kazinnik" ], "posted": "2025-06-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.17490v1", "field": "finance", "role": "agent", "bullets": [ "Simulated mortgage applications identical in every attribute except race, submitted to language models in a reproducible counterfactual framework for credit decision-making.", "Models, not named in the abstract, decide on the counterfactual applicants; layer-wise activation analysis traces race through internal representations before a control-vector intervention steers them.", "Race-based gaps exceed historically observed bias levels, and the intervention cuts disparities by up to 70 percent, 33 percent on average, without degrading overall performance." ], "bullet_provenance": "ai", "open_weights": true, "salience": 60, "edition": 14, "models": [], "validated": null, "n": 2010 }, { "uid": "arxiv:2506.17367v1", "arxiv_id": "2506.17367v1", "title": "Cash or Comfort? How LLMs Value Your Inconvenience", "authors": [ "Mateusz Cedro", "Timour Ichmoukhamedov", "Sofie Goethals", "Yifan He", "James Hinns", "David Martens" ], "posted": "2025-06-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.17367v1", "field": "economics", "role": "agent", "bullets": [ "Multiple LLMs tested on hypothetical scenarios trading monetary rewards against user discomforts including additional walking, waiting, hunger, and pain.", "Several LLMs assigned monetary values to user inconveniences; responses evaluated for cross-model consistency, prompt sensitivity, and reasonableness of trade-offs.", "LLMs show large cross-model variance, fragility to prompt phrasing, acceptance of unreasonably low rewards for major discomforts, and rejection of costless gains." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "models": [], "n": 2715 }, { "uid": "doi:10.5220/0013191200003890", "doi": "10.5220/0013191200003890", "arxiv_id": "2506.16813v1", "title": "Integrating Traditional Technical Analysis with AI: A Multi-Agent LLM-Based Approach to Stock Market Forecasting", "authors": [ "Michał Wawer", "Jarosław A. Chudziak" ], "posted": "2025-06-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.16813v1", "field": "finance", "role": "instrument", "bullets": [ "Historical stock data from major U.S. companies analyzed across multiple time frames for Elliott Wave pattern recognition and trend forecasting.", "Multi-agent LLM system with RAG and deep reinforcement learning identified Elliott Wave patterns and predicted future stock price movements.", "The system demonstrated effective pattern recognition and trend forecasting validated against historical U.S. equity data across multiple time horizons." ], "bullet_provenance": "ai", "validated": true, "validation_note": "historical stock data pattern recognition and forecasting accuracy", "salience": 45, "models": [], "n": 2853, "authors_detailed": [ { "name": "Michał Wawer", "url": "https://openalex.org/A5115542606", "inst": "Warsaw University of Technology" }, { "name": "Jarosław A. Chudziak", "url": "https://openalex.org/A5008057050", "inst": "Warsaw University of Technology" } ], "affiliations": [ "Warsaw University of Technology" ] }, { "uid": "doi:10.2139/ssrn.5274120", "doi": "10.2139/ssrn.5274120", "title": "Generative Artificial Intelligence: Unravelling the Competition Conundrums", "authors": [ "Yagya Agarwal" ], "posted": "2025-06-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5274120", "field": "economics", "role": "object", "bullets": [ "Analysis of generative AI market structure examining data access, computing power, and human expertise as barriers to competition globally.", "Paper maps competition concerns in generative AI markets and compares concentration patterns to established digital market dynamics under antitrust frameworks.", "Generative AI market follows digital market concentration patterns with few dominant players; authors argue regulation should treat it as a digital market subspecies." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3211, "authors_detailed": [ { "name": "Yagya Agarwal", "url": "", "inst": "Rajiv Gandhi National University of Law" } ], "affiliations": [ "Rajiv Gandhi National University of Law" ] }, { "uid": "doi:10.2139/ssrn.5304586", "doi": "10.2139/ssrn.5304586", "title": "Building AI Capabilities: CFA Institute", "authors": [ "Rajkumar Venkatesan", "Amy Klopfenstein" ], "posted": "2025-06-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5304586", "field": "management", "role": "object", "bullets": [ "CFA Institute, a global not-for-profit with 550 employees and $375 million revenue, launched an experimental AI team in fall 2023.", "Teaching case documents the organization's adoption of generative AI for internal training courses, prompt engineering, and marketing content production.", "AI team used generative AI to create employee training and promotional materials more efficiently; organization continued experimenting with workplace productivity applications." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3212, "authors_detailed": [ { "name": "Rajkumar Venkatesan", "url": "https://openalex.org/A5108294721", "inst": "University of Virginia" }, { "name": "Amy Klopfenstein", "url": "https://openalex.org/A5117071353", "inst": "University of Virginia" } ], "affiliations": [ "University of Virginia" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5231377", "doi": "10.2139/ssrn.5231377", "title": "Agentic AI and Predictive Analytics: Revolutionizing Retail Supply Chain Management for Next-gen Resilience and Efficiency", "authors": [ "Srinivas Kalisetty" ], "posted": "2025-06-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5231377", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 27, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 1035, "authors_detailed": [ { "name": "Srinivas Kalisetty", "url": "https://openalex.org/A5089315183", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "arxiv:2506.16123v5", "arxiv_id": "2506.16123v5", "title": "FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning", "authors": [ "Natapong Nitarach", "Warit Sirichotedumrong", "Panop Pitchayarthorn", "Pittawat Taveekitworachai", "Potsawee Manakul", "Kunat Pipatanakul" ], "posted": "2025-06-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.16123v5", "field": "finance", "role": "method", "bullets": [ "Ten CFA-style financial domains evaluated with three prompting approaches: standard zero-shot, unstructured chain-of-thought, and structured chain-of-thought.", "FinCoT provides structured expert financial reasoning blueprints to Qwen3-8B-Base and Fin-R1 7B; compared against standard and unstructured CoT baselines.", "FinCoT raises Qwen3-8B-Base accuracy from 63.2% to 80.5% and Fin-R1 from 65.7% to 75.7%, while cutting output length by up to 8.9x." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "CFA-style financial questions across 10 domains; accuracy reported", "salience": 60, "n": 2714 }, { "uid": "doi:10.2139/ssrn.5233756", "doi": "10.2139/ssrn.5233756", "title": "AI Business Strategy: How Generative Models Are Reshaping Competitive Advantage", "authors": [ "Sonny Marmon" ], "posted": "2025-06-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5233756", "field": "management", "role": "object", "bullets": [ "Cross-industry case studies and trend analysis of generative AI adoption in business strategy across multiple sectors.", "Paper examines how GPT models, DALL-E, and Midjourney enable firms to innovate, personalize offerings at scale, and streamline operations.", "Generative AI enables firms to create new markets and disrupt incumbents; paper identifies associated risks, ethical considerations, and forecast strategic implications." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 25, "validated": null, "n": 3010, "authors_detailed": [ { "name": "Sonny Marmon", "url": "https://openalex.org/A5116310571", "inst": "National Parks and Wildlife Service" } ], "affiliations": [ "National Parks and Wildlife Service" ] }, { "uid": "doi:10.2139/ssrn.5248293", "doi": "10.2139/ssrn.5248293", "title": "Beyond Barriers: Generative Artificial Intelligence as a Catalyst for Female Engagement in Tech Communities", "authors": [ "Aida Sanatizadeh", "Yingda Lu", "Keran Zhao", "Huang Nina" ], "posted": "2025-06-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5248293", "field": "management", "role": "object", "bullets": [ "Longitudinal Stack Exchange data before and after ChatGPT's November 2022 release, quasi-experimental difference-in-differences design across tech communities.", "Study examines ChatGPT's impact on gender disparities in knowledge-sharing; measures participation rates, content posting behavior, and confidence levels by gender.", "Generative AI reduced the gender participation gap, with effects strongest among high-expertise female users; no significant improvement in reported confidence levels." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3011, "authors_detailed": [ { "name": "Aida Sanatizadeh", "url": "https://openalex.org/A5080416559", "inst": "Northern Illinois University" }, { "name": "Yingda Lu", "url": "https://openalex.org/A5052955821", "inst": "University of Illinois Chicago" }, { "name": "Keran Zhao", "url": "https://openalex.org/A5020750294", "inst": "Pennsylvania State University" }, { "name": "Huang Nina", "url": "https://openalex.org/A5118391948", "inst": "College of Business and Technology" } ], "affiliations": [ "Northern Illinois University", "University of Illinois Chicago", "Pennsylvania State University", "College of Business and Technology" ] }, { "uid": "doi:10.2139/ssrn.5242919", "doi": "10.2139/ssrn.5242919", "title": "Invasive Behavior of Artificial Intelligence-Based Technologies Sets to Revolutionize Society", "authors": [ "Mario Coccia" ], "posted": "2025-06-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5242919", "field": "management", "role": "object", "bullets": [ "Patent analysis of transformer deep-learning technology growth from 2016 to 2023 compared to alternative architectures such as convolutional neural networks.", "No LLM deployed; study empirically measures patent filing rates for GenAI transformer technology versus competing technologies to quantify invasive diffusion behavior.", "Transformer technology patent growth rate (55.82%) more than doubled CNN growth (23.02%), demonstrating rapid displacement of alternative technologies in innovation ecosystems." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3512, "authors_detailed": [ { "name": "Mario Coccia", "url": "https://openalex.org/A5118336978", "inst": "National Research Council" } ], "affiliations": [ "National Research Council" ] }, { "uid": "arxiv:2507.01970v1", "arxiv_id": "2507.01970v1", "title": "News Sentiment Embeddings for Stock Price Forecasting", "authors": [ "Ayaan Qayyum" ], "posted": "2025-06-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.01970v1", "field": "finance", "role": "instrument", "bullets": [ "Daily SPDR S&P 500 ETF (SPY) prices paired with Wall Street Journal headlines and macroeconomic indicators including DXY and Treasury yields.", "OpenAI text embedding models encoded headlines into vectors; PCA extracted key features; over 390 machine learning models were trained for daily price prediction.", "Headline embeddings improved stock price prediction by at least 40% compared to models trained without text embedding features." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "SPY daily price prediction vs. no-embedding baseline", "salience": 45, "n": 3932 }, { "uid": "arxiv:2506.17339v2", "arxiv_id": "2506.17339v2", "title": "AI is the Strategy: From Agentic AI to Autonomous Business Models onto Strategy in the Age of AI", "authors": [ "René Bohnsack", "Mickie de Wet" ], "posted": "2025-06-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.17339v2", "field": "management", "role": "object", "bullets": [ "Two illustrative cases: getswan.ai, an Israeli autonomy-by-design startup, and a hypothetical AI-driven reconfiguration of Ryanair as an incumbent.", "Paper develops Autonomous Business Models where agentic AI executes value creation, delivery, and capture, reframing AI from tool to strategy itself.", "AI-led firms introduce synthetic competition at machine-level speed and scale, challenging foundational assumptions in strategy and organizational design." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3933 }, { "uid": "arxiv:2506.15041v1", "arxiv_id": "2506.15041v1", "title": "Identifying economic narratives in large text corpora -- An integrated approach using Large Language Models", "authors": [ "Tobias Schmidt", "Kai-Robin Lange", "Matthias Reccius", "Henrik Müller", "Michael Roos", "Carsten Jentsch" ], "posted": "2025-06-18", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.15041v1", "field": "economics", "role": "method", "bullets": [ "Wall Street Journal and New York Times articles on inflation, with narratives defined under a strict scheme and gold standards produced by expert annotators; article counts are not stated.", "GPT-4o extracts economic narratives in a structured format, benchmarked against the expert gold standard; the abstract reports no agreement statistics.", "GPT-4o returns valid structured narratives yet stays below expert level on complex documents, and the authors give guidance for economists applying LLMs to similar tasks." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "validated": true, "validation_note": "expert annotated gold standard narratives", "salience": 55, "edition": 14, "n": 1866, "authors_detailed": [ { "name": "Tobias M. Schmidt", "url": "https://openalex.org/A5048656711", "inst": "University of Geneva" }, { "name": "Kai-Robin Lange", "url": "https://openalex.org/A5087331000", "inst": "TU Dortmund University" }, { "name": "Matthias Reccius", "url": "https://openalex.org/A5082506005", "inst": "Ruhr University Bochum" }, { "name": "Müller, Henrik", "url": "", "inst": "" }, { "name": "Michael Roos", "url": "https://openalex.org/A5079679700", "inst": "Dortmund University of Applied Sciences and Arts" }, { "name": "Carsten Jentsch", "url": "https://openalex.org/A5013581565", "inst": "TU Dortmund University" } ], "affiliations": [ "University of Geneva", "TU Dortmund University", "Ruhr University Bochum", "Dortmund University of Applied Sciences and Arts" ] }, { "uid": "arxiv:2506.21591v3", "arxiv_id": "2506.21591v3", "title": "FinEval-KR: A Financial Domain Evaluation Framework for Large Language Models' Knowledge and Reasoning", "authors": [ "Shaoyu Dou", "Yutian Shen", "Mofan Chen", "Zixuan Wang", "Jiajie Xu", "Qi Guo", "Kailai Shao", "Chao Chen", "Haixiang Hu", "Haibo Shi", "Min Min", "Liwen Zhang" ], "posted": "2025-06-18", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.21591v3", "field": "finance", "role": "method", "bullets": [ "Chinese financial reasoning tasks spanning 22 subfields, released as an open-source dataset for separating what language models know from how well they reason in finance.", "Each model receives distinct knowledge, reasoning, and Bloom taxonomy cognitive scores on the labelled tasks; the abstract does not name the models evaluated.", "Reasoning and higher-order cognitive ability drive accuracy, knowledge application limits even top models, and specialized financial LLMs trail leading general models across metrics." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labelled Chinese financial reasoning dataset, accuracy decomposed", "salience": 38, "edition": 14, "models": [], "n": 2009, "authors_detailed": [ { "name": "Shaoyu Dou", "url": "https://openalex.org/A5082430901", "inst": "Tongji University" }, { "name": "Yutian Shen", "url": "https://openalex.org/A5066895967", "inst": "Shanghai University of Finance and Economics" }, { "name": "Chen, Mofan", "url": "", "inst": "" }, { "name": "Zixuan Wang", "url": "https://openalex.org/A5100398263", "inst": "North China Electric Power University" }, { "name": "Jiajie Xu", "url": "https://openalex.org/A5086062267", "inst": "Soochow University" }, { "name": "Qi Guo", "url": "https://openalex.org/A5087762384", "inst": "Southern University of Science and Technology" }, { "name": "Kailai Shao", "url": "https://openalex.org/A5084705199", "inst": "Zhejiang University of Science and Technology" }, { "name": "Chao Chen", "url": "https://openalex.org/A5100408305", "inst": "Jingdezhen Ceramic Institute" }, { "name": "H. B. Hu", "url": "https://openalex.org/A5109262530", "inst": "Wenzhou University" }, { "name": "Haibo Shi", "url": "https://openalex.org/A5039541195", "inst": "Shanghai Sixth People's Hospital" }, { "name": "Min Min", "url": "https://openalex.org/A5109549001", "inst": "Nanchang University" }, { "name": "Liwen Zhang", "url": "https://openalex.org/A5103810708", "inst": "Hubei University of Medicine" } ], "affiliations": [ "Tongji University", "Shanghai University of Finance and Economics", "North China Electric Power University", "Soochow University", "Southern University of Science and Technology", "Zhejiang University of Science and Technology", "Jingdezhen Ceramic Institute", "Wenzhou University" ] }, { "uid": "doi:10.2139/ssrn.5303840", "doi": "10.2139/ssrn.5303840", "title": "Improving Text Classification: Logistic Regression Makes Small LLMs Strong and Explainable 'Tens-of-Shot' Classifiers", "authors": [ "Marcus Buckmann", "Ed Hill" ], "posted": "2025-06-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5303840", "field": "finance", "role": "method", "bullets": [ "Seventeen sentence-classification tasks with two to four classes each, applied to economics and finance text data with dozens of labeled examples per class.", "Penalized logistic regression on small local language model embeddings compared against large commercial models on the same classification benchmarks.", "Small-model embeddings matched or exceeded large-model performance while providing stable, interpretable explanations for classification decisions." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "17 sentence classification benchmarks", "salience": 58, "n": 3008, "authors_detailed": [ { "name": "Marcus Buckmann", "url": "https://openalex.org/A5060167568", "inst": "Bank of England" }, { "name": "Ed Hill", "url": "https://openalex.org/A5108672256", "inst": "Bank of England" } ], "affiliations": [ "Bank of England" ] }, { "uid": "doi:10.2139/ssrn.5267572", "doi": "10.2139/ssrn.5267572", "title": "Large Language Models and Creative Content Design: A Case Study of Email Marketing at Wine Access", "authors": [ "Jean-Pierre H. Dube", "Ariel Xu" ], "posted": "2025-06-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5267572", "field": "management", "role": "agent", "bullets": [ "Three randomized controlled trials at Wine Access, a small online wine retailer, comparing human-written, LLM-generated, and hybrid email marketing content.", "A GPT-based LLM generated email content in pure-AI and human-edited hybrid conditions; frequentist decision theory accommodated the small-sample setting.", "All three content cells approximately doubled gross profits versus no-email control; decision theory selected an AI cell over the human-only policy on net annual profit." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "three randomized controlled trials with profit outcomes", "salience": 78, "n": 3009, "authors_detailed": [ { "name": "Jean‐Pierre Dubé", "url": "https://openalex.org/A5020658461", "inst": "Marketing Science Institute" }, { "name": "Qianwen Xu", "url": "https://openalex.org/A5102950299", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "Marketing Science Institute" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.14146v1", "arxiv_id": "2506.14146v1", "title": "Collaborative Editable Model", "authors": [ "Kaiwen Tang", "Aitong Wu", "Yao Lu", "Guangda Sun" ], "posted": "2025-06-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.14146v1", "field": "finance", "role": "method", "bullets": [ "Financial information scenario with 15,000 feedback instances from approximately 120 users contributing domain knowledge snippets.", "Collaborative Editable Model uses user dialogues, ratings, and attribution analysis to identify high-value knowledge for in-context prompting.", "CoEM achieved significant quality improvements in domain-specific financial content generation without traditional fine-tuning overhead." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "user ratings on generated content quality", "salience": 45, "n": 2594, "authors_detailed": [ { "name": "Kaiwen Tang", "url": "https://openalex.org/A5120048133", "inst": "" }, { "name": "Aitong Wu", "url": "https://openalex.org/A5120048134", "inst": "" }, { "name": "Yao Lu", "url": "https://openalex.org/A5120048135", "inst": "" }, { "name": "Guangda Sun", "url": "https://openalex.org/A5120048136", "inst": "" } ] }, { "uid": "arxiv:2506.13313v1", "arxiv_id": "2506.13313v1", "title": "Large Language Models as 'Hidden Persuaders': Fake Product Reviews are Indistinguishable to Humans and Machines", "authors": [ "Weiyao Meng", "John Harvey", "James Goulding", "Chris James Carter", "Evgeniya Lukinova", "Andrew Smith", "Paul Frobisher", "Mina Forrest", "Georgiana Nica-Avram" ], "posted": "2025-06-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.13313v1", "field": "management", "role": "object", "bullets": [ "Three studies in which human judges and several LLMs classify product reviews as real or machine-generated; judge sample sizes are not stated in the abstract.", "An LLM, not named in the abstract, generated the fake reviews; humans and LLM judges were scored against known authenticity, and their judgment strategies compared.", "Humans average 50.8 percent accuracy, chance level, and LLM judges do no better; humans show skepticism toward positive reviews and misjudge fake negative ones, while machine strategies fail differently." ], "bullet_provenance": "ai", "salience": 60, "edition": 14, "models": [], "validated": null, "n": 2006, "authors_detailed": [ { "name": "Weiyao Meng", "url": "https://openalex.org/A5065651256", "inst": "University of Nottingham" }, { "name": "J. Harvey", "url": "https://openalex.org/A5006108739", "inst": "University of Nottingham" }, { "name": "James Goulding", "url": "https://openalex.org/A5026654187", "inst": "University of Nottingham" }, { "name": "Christopher Carter", "url": "https://openalex.org/A5101400796", "inst": "University of Nottingham" }, { "name": "Evgeniya Lukinova", "url": "https://openalex.org/A5042033646", "inst": "University of Nottingham" }, { "name": "Andrew Smith", "url": "https://openalex.org/A5100667850", "inst": "University College Dublin" }, { "name": "Paul Frobisher", "url": "https://openalex.org/A5119861113", "inst": "Center for Strategic Research" }, { "name": "Forrest, Mina", "url": "", "inst": "" }, { "name": "Georgiana Nica‐Avram", "url": "https://openalex.org/A5034102126", "inst": "University of Nottingham" } ], "affiliations": [ "University of Nottingham", "University College Dublin", "Center for Strategic Research" ] }, { "uid": "arxiv:2506.14028v3", "arxiv_id": "2506.14028v3", "title": "MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application", "authors": [ "Xueqing Peng", "Lingfei Qian", "Yan Wang", "Ruoyu Xiang", "Yueru He", "Yang Ren", "Mingyang Jiang", "Vincent Jim Zhang", "Yuqing Guo", "Jeff Zhao", "Huan He", "Yi Han", "Yun Feng", "Yuechen Jiang", "Yupeng Cao", "Haohang Li", "Yangyang Yu", "Xiaoyu Wang", "Penglei Gao", "Shengyuan Lin", "Keyi Wang", "Shanshan Yang", "Yilun Zhao", "Zhiwei Liu", "Peng Lu", "Jerry Huang", "Suyuchen Wang", "Triantafillos Papadopoulos", "Polydoros Giannouris", "Efstathia Soufleri", "Nuo Chen", "Zhiyang Deng", "Heming Fu", "Yijia Zhao", "Mingquan Lin", "Meikang Qiu", "Kaleb E Smith", "Arman Cohan", "Xiao-Yang Liu", "Jimin Huang", "Guojun Xiong", "Alejandro Lopez-Lira", "Xi Chen", "Junichi Tsujii", "Jian-Yun Nie", "Sophia Ananiadou", "Qianqian Xie" ], "posted": "2025-06-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.14028v3", "field": "finance", "role": "method", "bullets": [ "Expert-annotated financial tasks in five languages and three modalities, from filings and news to scanned documents and meeting audio, selected by difficulty to avoid saturated tasks.", "21 LLMs are evaluated on multilingual financial reasoning and financial OCR against expert annotations; GPT-4o is the only model the abstract names.", "GPT-4o scores 46.01 percent overall, holding up on vision and audio but dropping sharply in multilingual settings; datasets, scripts, and leaderboards are public." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "expert-annotated tasks, overall accuracy reported", "salience": 54, "edition": 14, "n": 2007 }, { "uid": "arxiv:2506.13328v1", "arxiv_id": "2506.13328v1", "title": "Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach", "authors": [ "Chaoxu Pang", "Yixuan Cao", "Ganbin Zhou", "Hongwei Li", "Ping Luo" ], "posted": "2025-06-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.13328v1", "field": "accounting", "role": "method", "bullets": [ "Three types of real-world disclosure documents where numerical values recur across tables and document-level checking faces a combinatorial number of candidate pairs.", "An LLM encoder screens numerical mentions with parallel instructional encoding, then a specialized LLM classifies surviving pairs, pretrained with weak supervision from cross-table equality; model families are not named.", "The coarse-to-fine pipeline beats prior methods on the three document sets while staying efficient; specific error rates are not given in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "evaluation on real disclosure documents", "salience": 45, "edition": 14, "models": [], "n": 2008, "authors_detailed": [ { "name": "Chaoxu Pang", "url": "https://openalex.org/A5119967793", "inst": "" }, { "name": "Yixuan Cao", "url": "https://openalex.org/A5119967794", "inst": "" }, { "name": "Ganbin Zhou", "url": "https://openalex.org/A5119967795", "inst": "" }, { "name": "Hongwei Li", "url": "https://openalex.org/A5119967796", "inst": "" }, { "name": "Ping Luo", "url": "https://openalex.org/A5119967797", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5296888", "doi": "10.2139/ssrn.5296888", "title": "Can Artificial Intelligence Curb Greenwashing? Firm-Level Evidence Based on Large Language Model", "authors": [ "Ling-Yun He", "Ling-Yun He" ], "posted": "2025-06-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5296888", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 38, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 675, "authors_detailed": [ { "name": "Ling‐Yun He", "url": "https://openalex.org/A5081390403", "inst": "Jinan University" }, { "name": "Ling‐Yun He", "url": "https://openalex.org/A5081390403", "inst": "Jinan University" } ], "affiliations": [ "Jinan University" ] }, { "uid": "arxiv:2506.13055v1", "arxiv_id": "2506.13055v1", "title": "CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model", "authors": [ "Jiangtong Li", "Yiyun Zhu", "Dawei Cheng", "Zhijun Ding", "Changjun Jiang" ], "posted": "2025-06-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.13055v1", "field": "finance", "role": "method", "bullets": [ "Chinese multimodal financial benchmark with over 9,000 image-question pairs covering tables, histograms, line charts, pie charts, and structural diagrams.", "Multiple multimodal LLMs evaluated on staged tasks requiring stepwise interpretation of visual financial content including charts and tabular data.", "MLLMs show limited efficiency and robustness; primary errors stem from misinterpreting visual content and misunderstanding financial concepts in the Chinese context." ], "bullet_provenance": "ai", "validated": true, "validation_note": "CFBenchmark-MM with 9,000+ image-question pairs across five visual types", "salience": 45, "models": [], "n": 2713, "authors_detailed": [ { "name": "Jiangtong Li", "url": "https://openalex.org/A5047636121", "inst": "Tongji University" }, { "name": "Yiyun Zhu", "url": "https://openalex.org/A5018059436", "inst": "Shanghai University" }, { "name": "Dawei Cheng", "url": "https://openalex.org/A5069869295", "inst": "Tongji University" }, { "name": "Zhijun Ding", "url": "https://openalex.org/A5041681214", "inst": "Tongji University" }, { "name": "Changjun Jiang", "url": "https://openalex.org/A5066099338", "inst": "Tongji University" } ], "affiliations": [ "Tongji University", "Shanghai University" ] }, { "uid": "doi:10.2139/ssrn.5293722", "doi": "10.2139/ssrn.5293722", "title": "Agentic AI and Managers' Analytics Capabilities: An Exploration", "authors": [ "Ravi Bapna", "Sofia Bapna", "Pankaj Kumar", "Vandith Pamuru", "Akhmed Umyarov", "Mochen Yang" ], "posted": "2025-06-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5293722", "field": "management", "role": "object", "bullets": [ "Field deployment with MBA students at a top-30 global program using a no-code agentic-AI tool as a virtual data scientist in a core analytics course.", "GenAI agent enabled natural-language interaction for segmentation, classification, prediction, and text analytics; learning measured via closed-book final exam scores.", "Doubling agentic-AI usage intensity raised final exam scores by 0.624 points (1.56 percentage points); lower-performing students gained more from engagement." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Closed-book final exam scores as learning outcome measure", "salience": 58, "models": [], "n": 3511, "authors_detailed": [ { "name": "Ravi Bapna", "url": "https://openalex.org/A5058644870", "inst": "University of Minnesota" }, { "name": "Sofia Bapna", "url": "https://openalex.org/A5030143381", "inst": "University of Minnesota" }, { "name": "Pankaj Kumar", "url": "https://openalex.org/A5005409441", "inst": "National Institute of Technology Rourkela" }, { "name": "Vandith Pamuru", "url": "https://openalex.org/A5118212645", "inst": "Indian School of Business" }, { "name": "Akhmed Umyarov", "url": "https://openalex.org/A5002380733", "inst": "University of Minnesota" }, { "name": "Mochen Yang", "url": "https://openalex.org/A5008688627", "inst": "University of Minnesota" } ], "affiliations": [ "University of Minnesota", "National Institute of Technology Rourkela", "Indian School of Business" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5200284", "doi": "10.2139/ssrn.5200284", "title": "AI Prompt Engineering and the U.N. Model Treaty", "authors": [ "Lucas de Lima Carvalho", "Gabriel Bez-Batti", "Raphael de Campos Martins" ], "posted": "2025-06-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5200284", "field": "accounting", "role": "object", "bullets": [ "Analysis of Art. 12A of the U.N. Model Tax Treaty, examining three illustrative examples of AI-supported services provided to cross-border clients.", "Paper evaluates whether AI prompt engineering constitutes taxable \"technical services\" under existing international tax treaty provisions and commentary.", "AI-based activities may fall within or outside current treaty definitions depending on the human component and the service delivery structure." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3931, "authors_detailed": [ { "name": "Lucas de Lima Carvalho", "url": "https://openalex.org/A5110961202", "inst": "Latin American Sepsis Institute" }, { "name": "Gabriel Bez-Batti", "url": "https://openalex.org/A5118213927", "inst": "Universidade de São Paulo" }, { "name": "Raphael de Campos Martins", "url": "https://openalex.org/A5118213928", "inst": "Universidade Presbiteriana Mackenzie" } ], "affiliations": [ "Latin American Sepsis Institute", "Universidade de São Paulo", "Universidade Presbiteriana Mackenzie" ] }, { "uid": "doi:10.1016/j.ins.2025.122702", "doi": "10.1016/j.ins.2025.122702", "arxiv_id": "2506.12349v1", "title": "Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeek", "authors": [ "Peiran Qiu", "Siyi Zhou", "Emilio Ferrara" ], "posted": "2025-06-14", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.12349v1", "field": "economics", "role": "object", "bullets": [ "646 politically sensitive prompts posed to DeepSeek, an open weight Chinese model, with final answers compared against the model's own intermediate chain of thought reasoning.", "The proposed auditing framework treats the visible reasoning trace as a reference point, flagging suppression when content present in reasoning is omitted or rephrased in the final output.", "Semantic level suppression appears: references to transparency, government accountability, and civic mobilization are dropped from answers, while language aligned with state propaganda is occasionally amplified." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 52, "edition": 14, "validated": null, "n": 1910, "authors_detailed": [ { "name": "Peiran Qiu", "url": "https://openalex.org/A5119762133", "inst": "University of Southern California" }, { "name": "Siyi Zhou", "url": "", "inst": "University of Southern California" }, { "name": "Emilio Ferrara", "url": "https://openalex.org/A5078699564", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.12338v1", "arxiv_id": "2506.12338v1", "title": "Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs", "authors": [ "Yan Sun", "Stanley Kok" ], "posted": "2025-06-14", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.12338v1", "field": "management", "role": "method", "bullets": [ "Question answering benchmarks spanning general knowledge and financial scenarios, with cognitive biases such as confirmation and availability systematically injected into the prompts.", "LLMs, not named in the abstract, answer biased and neutral prompt variants; accuracy is scored against benchmark labels and attention weights are examined for mechanism.", "Subtle prompt biases significantly change answer choices and reduce accuracy, and shifts in attention distribution are associated with the inaccuracies; magnitudes are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "accuracy against benchmark labels", "salience": 38, "edition": 14, "models": [], "n": 2005, "authors_detailed": [ { "name": "Yan Sun", "url": "https://openalex.org/A5100376518", "inst": "Shanghai Jiao Tong University" }, { "name": "Stanley Kok", "url": "https://openalex.org/A5013720432", "inst": "National University of Singapore" } ], "affiliations": [ "Shanghai Jiao Tong University", "National University of Singapore" ] }, { "uid": "doi:10.2139/ssrn.5287110", "doi": "10.2139/ssrn.5287110", "title": "(Generative) AI in Financial Economics", "authors": [ "Hongwei Mo", "Shumiao Ouyang" ], "posted": "2025-06-13", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5287110", "field": "finance", "role": "object", "bullets": [ "Review article synthesizing the emerging literature on generative AI and finance, organized around six areas from corporate finance and asset pricing to household finance and labor economics.", "No model is applied by the authors; the review categorizes LLMs as analytic tools, external shocks to the economy, and autonomous economic agents. Specific models not stated.", "Synthesizes how firms benefit from AI, how it aids return predictability and financial inclusion, and its labor market effects, closing with unanswered questions and research avenues." ], "bullet_provenance": "ai", "salience": 52, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 23, "authors_detailed": [ { "name": "Hongwei Mo", "url": "https://openalex.org/A5075285062", "inst": "University of Oxford" }, { "name": "Shumiao Ouyang", "url": "https://openalex.org/A5118137352", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5290985", "doi": "10.2139/ssrn.5290985", "title": "The acceptance and usage of ChatGPT: An Information Adoption Model perspective", "authors": [ "Mark Camilleri", "Adriana Camilleri" ], "posted": "2025-06-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5290985", "field": "management", "role": "object", "bullets": [ "Survey of 327 higher education students measuring perceptions of ChatGPT-generated content using Information Adoption Model constructs and SmartPLS structural equation modeling.", "ChatGPT served as the studied technology; IAM constructs of information relevance, accuracy, source trustworthiness, and usefulness were tested as adoption predictors.", "Information usefulness was the strongest predictor of ChatGPT information adoption; information relevance exhibited the largest upstream effect on perceived usefulness of generated content." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 28, "validated": null, "n": 2396, "authors_detailed": [ { "name": "Mark Anthony Camilleri", "url": "https://openalex.org/A5082124522", "inst": "University of Malta" }, { "name": "Adriana Caterina Camilleri", "url": "https://openalex.org/A5081898997", "inst": "University of Bath" } ], "affiliations": [ "University of Malta", "University of Bath" ] }, { "uid": "doi:10.2139/ssrn.5199752", "doi": "10.2139/ssrn.5199752", "title": "How does Managers' Willingness to Disclose Affect Analysts' Earning Forecasts-A Measurement by LLMs", "authors": [ "Qingwen Liang", "Matias Carrasco Kind" ], "posted": "2025-06-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5199752", "field": "accounting", "role": "instrument", "bullets": [ "S&P 500 firms' quarterly earnings conference call transcripts matched with analyst forecast data on errors, dispersion, and uncertainty across multiple quarters.", "GPT-4 and Llama 3.3 extracted manager non-responses via a three-step prompting approach of identification, classification, and evaluation; results validated with Llama-based measures.", "More non-responses significantly increase analyst forecast errors and dispersion; effects concentrate in multi-industry firms and during COVID, driven by heightened information asymmetry." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": false, "validated": true, "validation_note": "Validated NOR extraction across GPT-4 and Llama 3.3 with robustness checks", "salience": 70, "n": 2402, "authors_detailed": [ { "name": "Qingwen Liang", "url": "https://openalex.org/A5087827091", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Matias Carrasco Kind", "url": "", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.11880v1", "arxiv_id": "2506.11880v1", "title": "Addressing Bias in LLMs: Strategies and Application to Fair AI-based Recruitment", "authors": [ "Alejandro Peña", "Julian Fierrez", "Aythami Morales", "Gonzalo Mancera", "Miguel Lopez", "Ruben Tolosana" ], "posted": "2025-06-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.11880v1", "field": "management", "role": "object", "bullets": [ "AI-based automated recruitment setting; experiments on two transformer-based LLMs analyzing gender bias absorbed from biased training data.", "Two LLMs trained on recruitment data evaluated for demographic bias; a privacy-enhancing framework strips gender information from the learning pipeline.", "Proposed framework effectively prevents trained recruitment systems from reproducing gender bias present in the training data across both LLM architectures." ], "bullet_provenance": "ai", "validated": true, "validation_note": "bias measurement before and after privacy-enhancing framework", "salience": 40, "models": [], "n": 2712, "authors_detailed": [ { "name": "Alejandro Peña", "url": "https://openalex.org/A5101705195", "inst": "Christian-Albrechts-Universität zu Kiel" }, { "name": "Julián Fiérrez", "url": "https://openalex.org/A5068081385", "inst": "Universidad Autónoma de Madrid" }, { "name": "Aythami Morales", "url": "https://openalex.org/A5083125799", "inst": "Universidad de Las Palmas de Gran Canaria" }, { "name": "Gonzalo Mancera", "url": "https://openalex.org/A5093932132", "inst": "Zimmer Biomet (Netherlands)" }, { "name": "Miguel López", "url": "https://openalex.org/A5084355446", "inst": "Universitat de Miguel Hernández d'Elx" }, { "name": "Rubén Tolosana", "url": "https://openalex.org/A5009955709", "inst": "Zimmer Biomet (Netherlands)" } ], "affiliations": [ "Christian-Albrechts-Universität zu Kiel", "Universidad Autónoma de Madrid", "Universidad de Las Palmas de Gran Canaria", "Zimmer Biomet (Netherlands)", "Universitat de Miguel Hernández d'Elx" ] }, { "uid": "doi:10.2139/ssrn.5284219", "doi": "10.2139/ssrn.5284219", "title": "Generative Artificial Intelligence for Advancing Knowledge-based Technological Innovation and Strategy: The \"Fruit Tree\" Model", "authors": [ "Jiaming Ding", "Kenneth Guang-lih Huang" ], "posted": "2025-06-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5284219", "field": "management", "role": "object", "bullets": [ "Literature review and conceptual framework examining how generative AI may reshape knowledge-based technological innovation processes within firms.", "No specific model deployed; paper proposes the 'Fruit Tree' model mapping GenAI capabilities to stages of knowledge-based innovation and strategic management.", "Framework identifies GenAI applications across innovation stages and proposes strategic recommendations for firms to optimize GenAI's role in fostering technological advancement." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3510, "authors_detailed": [ { "name": "Jiaming Ding", "url": "https://openalex.org/A5038775173", "inst": "Hefei University of Technology" }, { "name": "KaiHui Huang", "url": "https://openalex.org/A5008801761", "inst": "National University of Singapore" } ], "affiliations": [ "Hefei University of Technology", "National University of Singapore" ] }, { "uid": "arxiv:2506.12110v1", "arxiv_id": "2506.12110v1", "title": "EconGym: A Scalable AI Testbed with Diverse Economic Tasks", "authors": [ "Qirui Mi", "Qipeng Yang", "Zijun Fan", "Wentian Fan", "Heyang Ma", "Chengdong Ma", "Siyu Xia", "Bo An", "Jun Wang", "Haifeng Zhang" ], "posted": "2025-06-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.12110v1", "field": "economics", "role": "method", "bullets": [ "Modular testbed implementing 11 economic role types including households, firms, banks, and governments across 25-plus tasks scalable to 10,000 agents.", "Platform benchmarks AI algorithms, classical economic methods, and hybrid approaches for policy learning in fiscal, pension, and monetary coordination tasks.", "AI agents guided by classical economic methods outperformed pure AI or pure economic approaches in complex multi-agent policy settings." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3930 }, { "uid": "arxiv:2506.12093v1", "arxiv_id": "2506.12093v1", "title": "Intelligent Automation for FDI Facilitation: Optimizing Tariff Exemption Processes with OCR And Large Language Models", "authors": [ "Muhammad Sukri Bin Ramli" ], "posted": "2025-06-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.12093v1", "field": "economics", "role": "instrument", "bullets": [ "Conceptual framework for manufacturing-sector FDI tariff exemption processing by a national tax authority; no specific country or sample identified.", "OCR digitizes application documents while an LLM verifies HS Tariff Codes for machinery and raw materials against official exemption lists.", "Proposed system aims to reduce administrative burden, improve exemption accuracy, and strengthen the control environment for FDI facilitation; no empirical evaluation reported." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 2711 }, { "uid": "doi:10.2139/ssrn.5282709", "doi": "10.2139/ssrn.5282709", "title": "How can a Fed Chair not be an actor?", "authors": [ "Milan Badics" ], "posted": "2025-06-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5282709", "field": "finance", "role": "instrument", "bullets": [ "FOMC statements and press conference transcripts on policy announcement days, event-study regressions across equity, Treasury, and other asset classes.", "LLMs constructed four textual tone indices capturing hawkish-dovish stance, economic sentiment, and uncertainty; speech emotion recognition models extracted vocal tone from the Fed Chair.", "Hawkish tone raised equity prices via lower discount-rate risk premia; positive voice tone predicted future rate cuts and produced effects similar to monetary easing." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 72, "n": 3007, "authors_detailed": [ { "name": "Milan Badics", "url": "https://openalex.org/A5118134077", "inst": "Corvinus University of Budapest" } ], "affiliations": [ "Corvinus University of Budapest" ] }, { "uid": "arxiv:2506.12099v1", "arxiv_id": "2506.12099v1", "title": "SocialCredit+", "authors": [ "Thabassum Aslam", "Anees Aslam" ], "posted": "2025-06-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.12099v1", "field": "finance", "role": "instrument", "bullets": [ "Conceptual AI credit scoring system augmenting traditional evaluation with publicly available social media data and a Sharia-compliance layer.", "LLM with retrieval-augmented generation analyzes posts, bios, images, and friend networks to generate explainable credit-score factors from behavioral profiles.", "System translates social signals into credit-score factors with text-based explanations; demonstrated on synthetic scenarios without empirical validation on real borrowers." ], "bullet_provenance": "ai", "validated": false, "salience": 25, "models": [], "n": 3210, "authors_detailed": [ { "name": "Thabassum Aslam", "url": "https://openalex.org/A5119968292", "inst": "" }, { "name": "Anees Aslam", "url": "https://openalex.org/A5119968293", "inst": "" } ] }, { "uid": "arxiv:2506.13790v3", "arxiv_id": "2506.13790v3", "title": "The NordDRG AI Benchmark for Large Language Models", "authors": [ "Tapio Pitkäranta" ], "posted": "2025-06-11", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.13790v3", "field": "economics", "role": "method", "bullets": [ "A rule complete test bed for diagnosis related group reasoning, built from machine readable NordDRG definition tables, expert manuals, and governance change logs, with 13 logic and 13 grouper emulation tasks.", "GPT-5 Thinking, Opus 4.1, o3, and smaller endpoints run artefact only through reference agents, with strict exact match scoring on the DRG and the triggering logic row.", "GPT-5 Thinking and Opus 4.1 solve all 13 logic tasks; grouper emulation peaks at 7 of 13, and endpoints outside the top four score zero." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "exact match against DRG grouper outputs", "salience": 45, "edition": 14, "n": 1865, "authors_detailed": [ { "name": "Tapio Pitkäranta", "url": "https://openalex.org/A5057289282", "inst": "Aalto University" } ], "affiliations": [ "Aalto University" ] }, { "uid": "doi:10.2308/isys-2023-023", "doi": "10.2308/isys-2023-023", "arxiv_id": "2506.10155v1", "title": "Measuring Corporate Human Capital Disclosures: Lexicon, Data, Code, and Research Opportunities", "authors": [ "Elizabeth Demers", "Victor Xiaoqi Wang", "Kean Wu" ], "posted": "2025-06-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.10155v1", "field": "accounting", "role": "instrument", "bullets": [ "Corporate human capital disclosures from U.S. public companies across five subcategories including DEI, health and safety, and compensation.", "Word2vec trained on confirmed HC disclosures built a keyword lexicon; paper provides Python code for fine-tuning BERT on HC classification tasks.", "Produced a comprehensive HC lexicon, labeled disclosure dataset, and reusable code for researchers to measure human capital disclosures in corporate filings." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 50, "n": 3929, "authors_detailed": [ { "name": "Elizabeth Demers", "url": "https://openalex.org/A5034987530", "inst": "University of Waterloo" }, { "name": "Victor Xiaoqi Wang", "url": "https://openalex.org/A5041860793", "inst": "California State University, Long Beach" }, { "name": "Kean Wu", "url": "https://openalex.org/A5101966742", "inst": "Rochester Institute of Technology" } ], "affiliations": [ "University of Waterloo", "California State University, Long Beach", "Rochester Institute of Technology" ] }, { "uid": "arxiv:2506.08762v2", "arxiv_id": "2506.08762v2", "title": "EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements", "authors": [ "Issa Sugiura", "Takashi Ishida", "Taro Makino", "Chieko Tazuke", "Takanori Nakagawa", "Kosuke Nakago", "David Ha" ], "posted": "2025-06-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.08762v2", "field": "accounting", "role": "method", "bullets": [ "Ten years of annual reports filed by Japanese companies through EDINET, supporting accounting fraud detection, earnings forecasting, and industry classification tasks.", "State-of-the-art LLMs, not named in the abstract, must read entire reports and combine tables with text; outputs are scored against labels built from the filings.", "Frontier models edge out logistic regression only marginally on the binary fraud and earnings tasks; the authors argue for richer scaffolding such as simulations and task-specific reasoning support." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labels from EDINET filings, logistic regression baseline", "salience": 60, "edition": 14, "models": [], "n": 2002, "authors_detailed": [ { "name": "Issa Sugiura", "url": "https://openalex.org/A5111251789", "inst": "National Institute of Informatics" }, { "name": "Takashi Ishida", "url": "https://openalex.org/A5038817476", "inst": "Koshien University" }, { "name": "Taro Makino", "url": "https://openalex.org/A5011699970", "inst": "Tsurumi University" }, { "name": "Chieko Tazuke", "url": "https://openalex.org/A5120777558", "inst": "" }, { "name": "Nakagawa, Takanori", "url": "", "inst": "" }, { "name": "Kosuke Nakago", "url": "https://openalex.org/A5000297964", "inst": "Preferred Networks (Japan)" }, { "name": "D.W. Ha", "url": "https://openalex.org/A5102669944", "inst": "Korea Electrotechnology Research Institute" } ], "affiliations": [ "National Institute of Informatics", "Koshien University", "Tsurumi University", "Preferred Networks (Japan)", "Korea Electrotechnology Research Institute" ] }, { "uid": "arxiv:2506.09080v2", "arxiv_id": "2506.09080v2", "title": "FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making", "authors": [ "Jiaxiang Chen", "Mingxi Zou", "Zhuo Wang", "Qifan Wang", "Dongning Sun", "Chi Zhang", "Zenglin Xu" ], "posted": "2025-06-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.09080v2", "field": "finance", "role": "method", "bullets": [ "Event-centric trend prediction and trading tasks on curated financial datasets; assets, period, and sample size are not stated in the abstract.", "Multiple LLM-based agents, models not named, combine historical trends, current events, and expert-informed precedents, with confidence-adjusted position sizing and outcome-based refinement grounded in behavioral economics.", "The framework reports higher accuracy and better risk-adjusted returns than strong baselines on both task types; effect sizes are not given in the abstract." ], "bullet_provenance": "ai", "salience": 40, "edition": 14, "models": [], "validated": null, "n": 2003, "authors_detailed": [ { "name": "Jiaxiang Chen", "url": "https://openalex.org/A5064450687", "inst": "Qiqihar University" }, { "name": "Zou, Mingxi", "url": "", "inst": "" }, { "name": "Zhuo Wang", "url": "https://openalex.org/A5100446576", "inst": "State Key Laboratory of Chemical Engineering" }, { "name": "Qifan Wang", "url": "https://openalex.org/A5055007304", "inst": "Durham University" }, { "name": "Sun, Dongning", "url": "", "inst": "" }, { "name": "Chi Zhang", "url": "https://openalex.org/A5100609451", "inst": "University of Warwick" }, { "name": "Zenglin Xu", "url": "https://openalex.org/A5051227924", "inst": "Shanghai Academy of Social Sciences" } ], "affiliations": [ "Qiqihar University", "State Key Laboratory of Chemical Engineering", "Durham University", "University of Warwick", "Shanghai Academy of Social Sciences" ] }, { "uid": "arxiv:2506.22440v2", "arxiv_id": "2506.22440v2", "title": "From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI", "authors": [ "Sharique Hasan", "Alexander Oettl", "Sampsa Samila" ], "posted": "2025-06-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.22440v2", "field": "management", "role": "object", "bullets": [ "A conceptual analysis of how LLM adoption reshapes organizational design and competitive strategy, framed by trade-offs among generality, accuracy, and simplicity; no data or sample.", "No model is deployed or evaluated; LLMs enter as the technology whose simple interfaces shift complexity onto infrastructure, compliance, and specialized personnel.", "Competitive advantage is argued to come from mastering relocated complexity through abstraction layers, workflow alignment, and complementary expertise rather than from adoption alone." ], "bullet_provenance": "ai", "salience": 46, "edition": 14, "models": [], "validated": null, "n": 2004, "authors_detailed": [ { "name": "Sharique Hasan", "url": "https://openalex.org/A5012661228", "inst": "Duke University" }, { "name": "Alexander Oettl", "url": "https://openalex.org/A5020267226", "inst": "Georgia Institute of Technology" }, { "name": "Sampsa Samila", "url": "https://openalex.org/A5014891648", "inst": "Pearson (United States)" } ], "affiliations": [ "Duke University", "Georgia Institute of Technology", "Pearson (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.08726v3", "arxiv_id": "2506.08726v3", "title": "Improved LLM Agents for Financial Document Question Answering", "authors": [ "Nelvin Tan", "Zian Seng", "Liang Zhang", "Yu-Ching Shih", "Dong Yang", "Amol Salunkhe" ], "posted": "2025-06-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.08726v3", "field": "finance", "role": "method", "bullets": [ "Financial documents containing tabular and textual data; numerical question answering task evaluated with and without oracle labels available.", "LLM-based critic and calculator agents perform numerical QA on financial documents, compared against prior program-of-thought state-of-the-art approach.", "Improved critic agent outperforms previous state-of-the-art and is safer; performance deteriorates without oracle labels, exposing limits of self-correction." ], "bullet_provenance": "ai", "validated": true, "validation_note": "financial document numerical QA benchmark vs. prior state-of-the-art", "salience": 45, "models": [], "n": 2710 }, { "uid": "doi:10.1109/eem64765.2025.11050326", "doi": "10.1109/eem64765.2025.11050326", "arxiv_id": "2506.08113v2", "title": "Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting", "authors": [ "Timothée Hornek Amir Sartipi", "Igor Tchappi", "Gilbert Fridgen" ], "posted": "2025-06-09", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.08113v2", "field": "economics", "role": "method", "bullets": [ "Day ahead auction electricity prices for 2024 in Germany, France, the Netherlands, Austria, and Belgium, forecast daily at a one day horizon.", "Six pretrained time series foundation models, Chronos-Bolt, Chronos-T5, TimesFM, Moirai, Time-MoE, and TimeGPT, are compared with statistical and machine learning baselines on error against realized prices.", "Chronos-Bolt and Time-MoE match traditional methods, but a biseasonal MSTL model capturing daily and weekly seasonality stays consistently strong and no foundation model statistically beats it." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "realized day ahead prices, forecast error metrics", "salience": 45, "edition": 14, "models": [], "n": 1864, "authors_detailed": [ { "name": "Timothée Hornek", "url": "https://openalex.org/A5093680846", "inst": "University of Luxembourg" }, { "name": "Amir Sartipi", "url": "https://openalex.org/A5084664902", "inst": "University of Luxembourg" }, { "name": "Igor Tchappi", "url": "https://openalex.org/A5010688960", "inst": "University of Luxembourg" }, { "name": "Gilbert Fridgen", "url": "https://openalex.org/A5116210780", "inst": "University of Luxembourg" } ], "affiliations": [ "University of Luxembourg" ] }, { "uid": "arxiv:2506.06991v2", "arxiv_id": "2506.06991v2", "title": "Evaluating LLM-Contaminated Crowdsourcing Data Without Ground Truth", "authors": [ "Yichi Zhang", "Jinlong Pang", "Zhaowei Zhu", "Yang Liu" ], "posted": "2025-06-08", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.06991v2", "field": "economics", "role": "method", "bullets": [ "Crowdsourced annotation tasks such as multiple-choice labeling, where workers may quietly answer with LLM help; tested on real-world crowdsourcing datasets.", "No LLM performs measurement; a peer prediction mechanism scores correlations among worker answers conditioned on LLM-generated labels held by the requester, with guarantees that allow for LLM collusion.", "The training-free scores flag low-effort cheating without any ground truth, and the paper characterizes when the mechanism works; detection accuracy figures are not stated." ], "bullet_provenance": "ai", "salience": 55, "edition": 14, "models": [], "validated": null, "n": 2001, "authors_detailed": [ { "name": "Yichi Zhang", "url": "https://openalex.org/A5100444182", "inst": "Xi’an University of Posts and Telecommunications" }, { "name": "Pang, Jinlong", "url": "", "inst": "" }, { "name": "Zhaowei Zhu", "url": "https://openalex.org/A5025441627", "inst": "Central South University" }, { "name": "Yang Liu", "url": "https://openalex.org/A5049449431", "inst": "Jilin Normal University" } ], "affiliations": [ "Xi’an University of Posts and Telecommunications", "Central South University", "Jilin Normal University" ] }, { "uid": "arxiv:2506.06622v2", "arxiv_id": "2506.06622v2", "title": "QuantMCP: Grounding Large Language Models in Verifiable Financial Reality", "authors": [ "Yifan Zeng" ], "posted": "2025-06-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.06622v2", "field": "finance", "role": "method", "bullets": [ "Framework connecting LLMs to Python-accessible financial APIs including Wind and yfinance for real-time market data retrieval and analysis.", "QuantMCP uses Model Context Protocol to enable natural-language queries that invoke financial data APIs, reducing hallucination through verified data grounding.", "Framework provides extensible bridge between conversational AI and financial data; no quantitative evaluation of accuracy or performance reported in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 25, "models": [], "n": 2709, "authors_detailed": [ { "name": "Yifan Zeng", "url": "https://openalex.org/A5120810101", "inst": "" } ] }, { "uid": "arxiv:2506.05873v1", "arxiv_id": "2506.05873v1", "title": "Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks", "authors": [ "Yushang Zhao", "Yike Peng", "Dannier Li", "Yuxin Yang", "Chengrui Zhou", "Jing Dong" ], "posted": "2025-06-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.05873v1", "field": "finance", "role": "instrument", "bullets": [ "Public and real-world financial datasets capturing user-product interactions and social ties; no specific period or geography reported in the abstract.", "A pre-trained LLM encodes user reviews into feature vectors; a heterogeneous graph neural network fuses text and graph embeddings for product recommendation.", "Hybrid LLM-GNN model outperforms standalone LLM or GNN baselines in accuracy, recall, and NDCG for personalized financial product recommendations." ], "bullet_provenance": "ai", "validated": true, "validation_note": "public and real-world financial datasets; accuracy, recall, NDCG", "salience": 35, "models": [], "n": 2707, "authors_detailed": [ { "name": "Y. X. Zhao", "url": "https://openalex.org/A5033286141", "inst": "University of Science and Technology of China" }, { "name": "Yike Peng", "url": "https://openalex.org/A5114225342", "inst": "Columbia University" }, { "name": "Dannier Li", "url": "https://openalex.org/A5036218337", "inst": "University of Nebraska–Lincoln" }, { "name": "Yang, Yuxin", "url": "", "inst": "" }, { "name": "Chengrui Zhou", "url": "https://openalex.org/A5023133104", "inst": "Columbia University" }, { "name": "Jing Dong", "url": "https://openalex.org/A5101448979", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "University of Science and Technology of China", "University of Nebraska–Lincoln" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.05700v1", "arxiv_id": "2506.05700v1", "title": "RKEFino1: A Regulation Knowledge-Enhanced Large Language Model", "authors": [ "Yan Wang", "Yueru He", "Ruoyu Xiang", "Jeff Zhao" ], "posted": "2025-06-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.05700v1", "field": "accounting", "role": "method", "bullets": [ "Financial regulatory domain covering XBRL, CDM, and MOF standards; two QA tasks and a novel Numerical NER task spanning sentences and tables.", "RKEFino1, fine-tuned from Fino1 with domain regulatory knowledge, performs knowledge-based QA, mathematical reasoning, and numerical entity recognition for digital regulatory reporting.", "Model demonstrates effectiveness and generalization in compliance-critical financial tasks; released publicly on Hugging Face for reproducibility." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "QA and Numerical NER tasks on financial regulatory data", "salience": 55, "n": 2708, "authors_detailed": [ { "name": "Yan Wang", "url": "https://openalex.org/A5073193553", "inst": "University of Massachusetts Lowell" }, { "name": "Yueru He", "url": "https://openalex.org/A5111133467", "inst": "Columbia University" }, { "name": "Ruoyu Xiang", "url": "https://openalex.org/A5120696883", "inst": "" }, { "name": "Jing Zhao", "url": "https://openalex.org/A5056404006", "inst": "Sun Yat-sen University" } ], "affiliations": [ "Columbia University", "University of Massachusetts Lowell", "Sun Yat-sen University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.18653/v1/2025.acl-long.766", "doi": "10.18653/v1/2025.acl-long.766", "arxiv_id": "2506.05828v2", "title": "FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging", "authors": [ "Zichen Tang", "Haihong E", "Ziyan Ma", "Haoyang He", "Jiacheng Liu", "Zhongjun Yang", "Zihua Rong", "Rongjin Li", "Kun Ji", "Qing Huang", "Xinyang Hu", "Yang Liu", "Qianhe Zheng" ], "posted": "2025-06-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.05828v2", "field": "finance", "role": "method", "bullets": [ "Benchmark of 908 annotated financial reasoning questions covering 67.8% of financial concepts, including 238 hard multi-formula problems requiring precise numerical reasoning.", "GPT-4o, OpenAI o1, and DeepSeek-R1 tested on financial numerical reasoning; Python-formatted knowledge functions boosted GPT-4o accuracy from 83.2% to 91.6%.", "Best model (OpenAI o1 with program-of-thought) reached 89.1% accuracy; combining Reasoner and Programmer models raised DeepSeek-R1 from 83.2% to 87.8%." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "FinanceReasoning benchmark with annotated ground-truth solutions", "salience": 58, "n": 3509, "authors_detailed": [ { "name": "Ziwei Tang", "url": "https://openalex.org/A5086131832", "inst": "Department of Finance" }, { "name": "E Haihong", "url": "https://openalex.org/A5119204468", "inst": "" }, { "name": "Z. Ma", "url": "https://openalex.org/A5115593299", "inst": "Department of Finance" }, { "name": "Haoyang He", "url": "https://openalex.org/A5088106844", "inst": "Department of Finance" }, { "name": "Jiacheng Liu", "url": "https://openalex.org/A5100656307", "inst": "Department of Finance" }, { "name": "Zhongjun Yang", "url": "https://openalex.org/A5064910164", "inst": "Department of Finance" }, { "name": "Zihua Rong", "url": "https://openalex.org/A5119204469", "inst": "Department of Finance" }, { "name": "Rui Li", "url": "https://openalex.org/A5100448606", "inst": "Department of Finance" }, { "name": "Kun Ji", "url": "https://openalex.org/A5119204470", "inst": "Department of Finance" }, { "name": "Qing Huang", "url": "https://openalex.org/A5014366892", "inst": "Department of Finance" }, { "name": "Xinyang Hu", "url": "https://openalex.org/A5101242111", "inst": "Department of Finance" }, { "name": "Yang Liu", "url": "https://openalex.org/A5015039329", "inst": "Department of Finance" }, { "name": "Qianhe Zheng", "url": "", "inst": "Department of Finance" } ], "affiliations": [ "Department of Finance" ] }, { "uid": "arxiv:2506.06576v3", "arxiv_id": "2506.06576v3", "title": "Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce", "authors": [ "Yijia Shao", "Humishka Zope", "Yucheng Jiang", "Jiaxin Pei", "David Nguyen", "Erik Brynjolfsson", "Diyi Yang" ], "posted": "2025-06-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.06576v3", "field": "economics", "role": "object", "bullets": [ "Survey of 1,500 U.S. domain workers and AI experts across 844 tasks in 104 occupations, building on the O*NET database.", "Developed Human Agency Scale and auditing framework to assess worker preferences for AI agent automation versus augmentation of occupational tasks.", "Workers prefer varied levels of human involvement; AI integration shifts core competencies from information-focused skills toward interpersonal ones." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3928, "authors_detailed": [ { "name": "Yijia Shao", "url": "https://openalex.org/A5109589823", "inst": "Peking University" }, { "name": "Humishka Zope", "url": "https://openalex.org/A5120722404", "inst": "" }, { "name": "Jiang, Yucheng", "url": "", "inst": "" }, { "name": "Pei, Jiaxin", "url": "", "inst": "" }, { "name": "David Nguyen", "url": "https://openalex.org/A5102927314", "inst": "McMaster University" }, { "name": "Erik Brynjolfsson", "url": "https://openalex.org/A5108609301", "inst": "Boston University" }, { "name": "Diyi Yang", "url": "https://openalex.org/A5089413311", "inst": "Stanford Medicine" } ], "affiliations": [ "Boston University", "Peking University", "McMaster University", "Stanford Medicine" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.04574v1", "arxiv_id": "2506.04574v1", "title": "Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis", "authors": [ "Dimitris Vamvourellis", "Dhagash Mehta" ], "posted": "2025-06-05", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.04574v1", "field": "finance", "role": "method", "bullets": [ "Financial PhraseBank, financial sentences annotated by domain experts, used for zero shot sentiment classification, with performance also examined by linguistic complexity and annotator agreement level.", "GPT-4o, GPT-4.1, and o3-mini are prompted to mimic fast or deliberate thinking and scored against human labels, with fine tuned FinBERT variants as baselines.", "GPT-4o without chain of thought aligns best with human sentiment; reasoning models and reasoning prompts tend to overthink and lose accuracy on this task." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "validated": true, "validation_note": "Financial PhraseBank expert labels", "salience": 52, "edition": 14, "n": 1863, "authors_detailed": [ { "name": "Dimitris Vamvourellis", "url": "https://openalex.org/A5091742803", "inst": "BlackRock (United States)" }, { "name": "Dhagash Mehta", "url": "https://openalex.org/A5056701307", "inst": "University of Notre Dame" } ], "affiliations": [ "University of Notre Dame", "BlackRock (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.05019v2", "arxiv_id": "2506.05019v2", "title": "FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysis", "authors": [ "Wenyan Xu", "Dawei Xiang", "Yue Liu", "Xiyu Wang", "Yanxiang Ma", "Liang Zhang", "Shu Hu", "Chang Xu", "Jiaheng Zhang" ], "posted": "2025-06-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.05019v2", "field": "finance", "role": "method", "bullets": [ "5,105 stocks from S&P 500 and HS 300 spanning 2009-2025 with minute, daily, and quarterly resolution across U.S. and Chinese markets.", "Transformer models tested on four temporally aligned modalities: financial news, structured tables, K-line charts, and price time series totaling 112.6 GB.", "Scale and data quality markedly boost prediction accuracy; multimodal fusion yields moderate gains in Transformer-based financial forecasting models." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": true, "validation_note": "stock price prediction accuracy benchmarks", "salience": 45, "n": 2852 }, { "uid": "arxiv:2506.04290v2", "arxiv_id": "2506.04290v2", "title": "Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy", "authors": [ "Muhammed Golec", "Maha AlabdulJalil" ], "posted": "2025-06-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.04290v2", "field": "finance", "role": "method", "bullets": [ "Sixty papers on LLM based credit risk estimation published between 2020 and 2025, selected with the PRISMA strategy for a systematic review.", "No model is run; the review organizes architectures, data types, explainability mechanisms such as chain of thought prompts and natural language justifications, and application areas like default prediction.", "Delivers a four heading taxonomy and identifies research gaps around interpretability, positioning itself as a reference for credit scoring work with language models." ], "bullet_provenance": "ai", "salience": 30, "edition": 14, "models": [], "validated": null, "n": 1909, "authors_detailed": [ { "name": "Muhammed Golec", "url": "https://openalex.org/A5054023569", "inst": "Boğaziçi University" }, { "name": "Maha AlabdulJalil", "url": "https://openalex.org/A5119657836", "inst": "" } ], "affiliations": [ "Boğaziçi University" ] }, { "uid": "arxiv:2506.06377v1", "arxiv_id": "2506.06377v1", "title": "Evaluating Large Language Model Capabilities in Assessing Spatial Econometrics Research", "authors": [ "Giuseppe Arbia", "Luca Morandini", "Vincenzo Nardelli" ], "posted": "2025-06-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.06377v1", "field": "economics", "role": "method", "bullets": [ "Original and deliberately altered counterfactual summaries built from 28 spatial econometrics papers published between 2005 and 2024, judged by a range of LLMs.", "Models give qualitative assessments and binary calls on variable choice, coefficient plausibility, and publication suitability, scored against the known alterations; GPT-4o reaches 0.87 F1 on variable choice.", "Performance is solid on variable coherence but uneven on coefficient plausibility and publication suitability, varying with model and paper traits, supporting only an assistive role in peer review." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "counterfactual alterations as ground truth, F1 reported", "salience": 55, "edition": 14, "n": 1999, "authors_detailed": [ { "name": "Giuseppe Arbia", "url": "https://openalex.org/A5068025989", "inst": "Università Cattolica del Sacro Cuore" }, { "name": "Luca Morandini", "url": "https://openalex.org/A5072117188", "inst": "The University of Melbourne" }, { "name": "Vincenzo Nardelli", "url": "https://openalex.org/A5004555042", "inst": "Università Cattolica del Sacro Cuore" } ], "affiliations": [ "Università Cattolica del Sacro Cuore", "The University of Melbourne" ] }, { "uid": "arxiv:2507.08104v1", "arxiv_id": "2507.08104v1", "title": "VideoConviction: A Multimodal Benchmark for Human Conviction and Stock Market Recommendations", "authors": [ "Michael Galarnyk", "Veer Kejriwal", "Agam Shah", "Yash Bhardwaj", "Nicholas Meyer", "Anand Krishnan", "Sudheer Chava" ], "posted": "2025-06-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.08104v1", "field": "finance", "role": "method", "bullets": [ "YouTube stock recommendation videos from financial influencers, carrying more than 6,000 expert annotations produced over 457 hours, with full and segmented video inputs.", "Multimodal and text-only LLMs, not named in the abstract, extract tickers, investment actions, and conviction, scored against the expert annotations.", "Video input helps ticker extraction but models mistake commentary for recommendations; betting against the recommendations beats the S&P 500 by 6.8 percent annually with a Sharpe ratio of 0.41 versus 0.65." ], "bullet_provenance": "ai", "validated": true, "validation_note": "6,000 plus expert annotations", "salience": 58, "edition": 14, "models": [], "n": 2000 }, { "uid": "doi:10.3386/w33777", "doi": "10.3386/w33777", "title": "Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI", "authors": [ "Anders Humlum", "Emilie Vestergaard" ], "posted": "2025-06-04", "added": "2026-07-24", "source_label": "NBER", "url": "https://doi.org/10.3386/w33777", "field": "economics", "role": "object", "bullets": [ "Two AI-chatbot adoption surveys from late 2023 and 2024 covering 11 exposed occupations, 25,000 workers and 7,000 workplaces in Denmark, linked to matched employer-employee registers.", "AI chatbots are the studied object rather than a research tool; identification comes from difference-in-differences using employer adoption policies as quasi-experimental variation, with no specific model named.", "No significant effect on earnings or hours in any occupation, with confidence intervals ruling out effects above 1 percent, average time savings near 3 percent and weak wage pass-through." ], "bullet_provenance": "ai", "salience": 80, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 674, "authors_detailed": [ { "name": "Anders Humlum", "url": "https://openalex.org/A5093399820", "inst": "University of Chicago" }, { "name": "Emilie Vestergaard", "url": "https://openalex.org/A5112220091", "inst": "University of Copenhagen" } ], "affiliations": [ "University of Chicago", "University of Copenhagen" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.04478v2", "arxiv_id": "2506.04478v2", "title": "Matching Markets Meet LLMs: Algorithmic Reasoning with Ranked Preferences", "authors": [ "Hadi Hosseini", "Samarth Khanna", "Ronak Singh" ], "posted": "2025-06-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.04478v2", "field": "economics", "role": "method", "bullets": [ "Matching market instances of varying size requiring stable matching generation, instability detection, resolution, and preference queries.", "State-of-the-art LLMs including advanced reasoning models evaluated on hierarchy of preference-based algorithmic tasks with LoRA fine-tuning.", "Even top reasoning models failed to resolve instability in large markets; LoRA improved small-market performance but not large instances." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "task accuracy across market sizes", "salience": 52, "n": 2593 }, { "uid": "arxiv:2506.02838v1", "arxiv_id": "2506.02838v1", "title": "TaxAgent: How Large Language Model Designs Fiscal Policy", "authors": [ "Jizhou Wang", "Xiaodan Fang", "Lei Huang", "Yongfeng Huang" ], "posted": "2025-06-03", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.02838v1", "field": "economics", "role": "agent", "bullets": [ "A macroeconomic agent-based simulation with heterogeneous household agents, benchmarked against Saez optimal taxation, US federal income tax rules, and a free market baseline.", "An LLM, not named in the abstract, plays the government and iteratively adjusts tax rates in response to simulated household behavior; no check against real fiscal data is reported.", "The LLM-designed schedule reaches a better equity-efficiency trade-off than all three benchmarks inside the simulation; magnitudes are not stated in the abstract." ], "bullet_provenance": "ai", "salience": 50, "edition": 14, "models": [], "validated": null, "n": 1997 }, { "uid": "arxiv:2506.02510v1", "arxiv_id": "2506.02510v1", "title": "M$^3$FinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial Meeting Understanding Evaluation Dataset", "authors": [ "Jie Zhu", "Junhui Li", "Yalong Wen", "Xiandong Li", "Lifan Guo", "Feng Chen" ], "posted": "2025-06-03", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.02510v1", "field": "finance", "role": "method", "bullets": [ "Financial meeting transcripts in English, Chinese, and Japanese spanning GICS industry sectors, with tasks for summarization, question-answer pair extraction, and question answering.", "Seven LLMs, not named in the abstract, are evaluated against the dataset's reference annotations across the three tasks, including long-context models.", "Even the strongest long-context models leave substantial room for improvement on meeting comprehension; the abstract gives no specific scores." ], "bullet_provenance": "ai", "validated": true, "validation_note": "reference annotations for three tasks", "salience": 46, "edition": 14, "models": [], "n": 1998, "authors_detailed": [ { "name": "Jie Zhu", "url": "https://openalex.org/A5000539711", "inst": "Jiangnan University" }, { "name": "Junhui Li", "url": "https://openalex.org/A5100369269", "inst": "University of Science and Technology of China" }, { "name": "Yalong Wen", "url": "https://openalex.org/A5113392188", "inst": "Shanghai Polytechnic University" }, { "name": "Xiandong Li", "url": "https://openalex.org/A5080978660", "inst": "Hubei University of Medicine" }, { "name": "Lifan Guo", "url": "https://openalex.org/A5113222149", "inst": "" }, { "name": "Feng Chen", "url": "https://openalex.org/A5100352752", "inst": "Huaqiao University" } ], "affiliations": [ "Jiangnan University", "University of Science and Technology of China", "Shanghai Polytechnic University", "Hubei University of Medicine", "Huaqiao University" ] }, { "uid": "arxiv:2506.02515v4", "arxiv_id": "2506.02515v4", "title": "FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning", "authors": [ "Zhuohan Xie", "Daniil Orel", "Rushil Thareja", "Dhruv Sahnan", "Hachem Madmoun", "Fan Zhang", "Debopriyo Banerjee", "Georgi Georgiev", "Xueqing Peng", "Lingfei Qian", "Jimin Huang", "Jinyan Su", "Aaryamonvikram Singh", "Rui Xing", "Rania Elbadry", "Chen Xu", "Haonan Li", "Fajri Koto", "Ivan Koychev", "Tanmoy Chakraborty", "Yuxia Wang", "Salem Lahlou", "Veselin Stoyanov", "Sophia Ananiadou", "Preslav Nakov" ], "posted": "2025-06-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.02515v4", "field": "finance", "role": "method", "bullets": [ "Benchmark spanning 58 topics across 12 financial domains with parameterized symbolic templates and executable Python code for machine-verifiable reasoning.", "Twenty-six leading LLMs evaluated on multi-step symbolic financial reasoning using CHAINEVAL metric measuring answer correctness and step-level consistency.", "Even frontier LLMs show clear limitations in symbolic financial reasoning; domain-adapted and math-enhanced fine-tuned models substantially narrow the performance gap." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3209 }, { "uid": "doi:10.2139/ssrn.5263803", "doi": "10.2139/ssrn.5263803", "title": "Beyond the Black Box: Interpretability of LLMs in Finance", "authors": [ "Hariom Tatsat", "Ariye Shater" ], "posted": "2025-06-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5263803", "field": "finance", "role": "method", "bullet_provenance": "none", "salience": 36, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 673, "authors_detailed": [ { "name": "Hariom Tatsat", "url": "https://openalex.org/A5018777944", "inst": "Barclay College" }, { "name": "Ariye Shater", "url": "https://openalex.org/A5117805084", "inst": "Barclay College" } ], "affiliations": [ "Barclay College" ] }, { "uid": "doi:10.2139/ssrn.5273609", "doi": "10.2139/ssrn.5273609", "title": "Talking Terms: Agent Information in LLM Supply Chain Bargaining", "authors": [ "Samuel Kirshner", "Yiwen Pan", "Xianghua (Jason) Wu", "Alex Gould" ], "posted": "2025-06-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5273609", "field": "management", "role": "agent", "bullets": [ "Experimental studies with LLM agents as participants in supply chain contract negotiations under public, private, ambiguous, and deceptive cost information scenarios.", "LLM agents negotiate autonomously in bilateral supply chain bargaining; results compared with human negotiators from a benchmark study; RAG configurations tested.", "LLM agents more inclined to reach agreement, yielding greater supply chain efficiency but potentially greater inequality; deception about costs improves supplier outcomes at retailers' expense." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison with human negotiator benchmark study", "salience": 68, "n": 3006, "authors_detailed": [ { "name": "Samuel N. Kirshner", "url": "https://openalex.org/A5059274653", "inst": "UNSW Sydney" }, { "name": "Yiwen Pan", "url": "https://openalex.org/A5018750078", "inst": "Zhejiang Ocean University" }, { "name": "Xiaofang Wu", "url": "https://openalex.org/A5103859276", "inst": "Shanghai University of Political Science and Law" }, { "name": "Alex Gould", "url": "", "inst": "Independent Researcher" } ], "affiliations": [ "UNSW Sydney", "Zhejiang Ocean University", "Shanghai University of Political Science and Law", "Independent Researcher" ] }, { "uid": "arxiv:2506.01423v1", "arxiv_id": "2506.01423v1", "title": "FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance", "authors": [ "Hongyang Yang", "Likun Lin", "Yang She", "Xinyu Liao", "Jiaoyang Wang", "Runjia Zhang", "Yuquan Mo", "Christina Dan Wang" ], "posted": "2025-06-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.01423v1", "field": "finance", "role": "instrument", "bullets": [ "Case studies of bank wire transfers and employee reimbursements as representative financial ERP workflows in enterprise settings.", "Multi-agent generative AI system with specialized sub-agents orchestrated for budget planning, financial reporting, and transaction processing tasks.", "Framework achieved 40% reduction in processing time, 94% drop in error rate, and improved regulatory compliance versus traditional rule-based ERP workflows." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 3208, "authors_detailed": [ { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "Beijing University of Technology" }, { "name": "Likun Lin", "url": "https://openalex.org/A5111042348", "inst": "" }, { "name": "Yang She", "url": "https://openalex.org/A5089576219", "inst": "Beijing Institute of Technology" }, { "name": "Liao, Xinyu", "url": "", "inst": "" }, { "name": "Jiaoyang Wang", "url": "https://openalex.org/A5042344734", "inst": "Hebei Agricultural University" }, { "name": "Runjia Zhang", "url": "https://openalex.org/A5058359125", "inst": "" }, { "name": "Yunxian Mo", "url": "https://openalex.org/A5109923794", "inst": "Sun Yat-sen University" }, { "name": "Christina Dan Wang", "url": "https://openalex.org/A5002366400", "inst": "New York University Shanghai" } ], "affiliations": [ "New York University Shanghai", "Beijing University of Technology", "Beijing Institute of Technology", "Hebei Agricultural University", "Sun Yat-sen University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.00856v3", "arxiv_id": "2506.00856v3", "title": "Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks", "authors": [ "Qiang Chen", "Tianyang Han", "Jin Li", "Ye Luo", "Zigan Wang", "Yuxiao Wu", "Xiaowei Zhang", "Tuo Zhou" ], "posted": "2025-06-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.00856v3", "field": "economics", "role": "agent", "bullets": [ "Econometric tasks from academic coursework materials and published research papers requiring full empirical analysis execution.", "MetricsAI agent built on open-source MetaGPT performs task planning, code generation, error-based reflection, and multi-round refinement.", "Domain-specialized agent significantly outperformed benchmark LLMs and general-purpose agents on real-world econometric analysis tasks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "coursework and published paper reproduction tasks", "salience": 68, "n": 2592, "authors_detailed": [ { "name": "Qiang Chen", "url": "https://openalex.org/A5100435659", "inst": "CITIC Group (China)" }, { "name": "Tianyang Han", "url": "https://openalex.org/A5103155324", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Jin Li", "url": "https://openalex.org/A5100364769", "inst": "Shihezi University" }, { "name": "Ye Luo", "url": "https://openalex.org/A5050014657", "inst": "Chengdu University of Information Technology" }, { "name": "Wang, Zigan", "url": "", "inst": "" }, { "name": "Yuxiao Wu", "url": "https://openalex.org/A5100308164", "inst": "Peking University" }, { "name": "Xiaowei Zhang", "url": "https://openalex.org/A5108094887", "inst": "University of Minnesota" }, { "name": "Zhou, Tuo", "url": "", "inst": "" } ], "affiliations": [ "University of Minnesota", "CITIC Group (China)", "Beijing Academy of Artificial Intelligence", "Shihezi University", "Chengdu University of Information Technology", "Peking University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2506.01093v1", "arxiv_id": "2506.01093v1", "title": "Regulatory Graphs and GenAI for Real-Time Transaction Monitoring and Compliance Explanation in Banking", "authors": [ "Kunal Khanvilkar", "Kranthi Kommuru" ], "posted": "2025-06-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.01093v1", "field": "finance", "role": "instrument", "bullets": [ "Simulated stream of financial transaction data with dynamic transaction graphs constructed for real-time regulatory compliance monitoring.", "Graph neural network classifies suspicious transactions; a retrieval-augmented generation module produces natural-language explanations aligned with regulatory clauses.", "Achieved 98.2% F1-score, 97.8% precision, and 97.0% recall; expert evaluation confirmed quality and interpretability of generated compliance justifications." ], "bullet_provenance": "ai", "validated": true, "validation_note": "simulated financial transaction stream with expert evaluation", "salience": 40, "models": [], "n": 3927, "authors_detailed": [ { "name": "Kunal Khanvilkar", "url": "https://openalex.org/A5119660693", "inst": "Automatic Data Processing (United States)" }, { "name": "Kranthi Kommuru", "url": "https://openalex.org/A5051994685", "inst": "Automatic Data Processing (United States)" } ], "affiliations": [ "Automatic Data Processing (United States)" ] }, { "uid": "arxiv:2506.06335v2", "arxiv_id": "2506.06335v2", "title": "FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models", "authors": [ "Xuan Xu", "Fufang Wen", "Beilin Chu", "Zhibing Fu", "Qinhong Lin", "Jiaqi Liu", "Binjie Fei", "Yu Li", "Linna Zhou", "Zhongliang Yang" ], "posted": "2025-05-31", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.06335v2", "field": "finance", "role": "method", "bullets": [ "Chinese financial text tasks, with FinBERT2 pretrained on a 32 billion token finance specific corpus described as the largest known Chinese financial pretraining corpus at this scale.", "The bidirectional encoder is fine tuned into labelers, retrievers, and a topic model, and compared with FinBERT variants, unnamed leading LLMs, and commercial embedders across ten financial benchmarks.", "Fine tuned labelers beat other FinBERT variants by 0.4 to 3.3 points and leading LLMs by 9.7 to 12.3 points on classification, and retrievers outperform open and proprietary embedders." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "five financial classification and five retrieval benchmarks", "salience": 48, "edition": 14, "n": 1908, "authors_detailed": [ { "name": "Xuan Xu", "url": "https://openalex.org/A5072204529", "inst": "Jiangsu University" }, { "name": "Fufang Wen", "url": "https://openalex.org/A5119175108", "inst": "Beijing Biocytogen (China)" }, { "name": "Beilin Chu", "url": "https://openalex.org/A5062868287", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Zhibing Fu", "url": "https://openalex.org/A5103645753", "inst": "Beijing Biocytogen (China)" }, { "name": "Qinhong Lin", "url": "https://openalex.org/A5119175109", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Liu, Jiaqi", "url": "", "inst": "" }, { "name": "Binjie Fei", "url": "https://openalex.org/A5119175110", "inst": "Beijing Biocytogen (China)" }, { "name": "Zhongliang Yang", "url": "https://openalex.org/A5020864189", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Linna Zhou", "url": "https://openalex.org/A5059744997", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Yu Li", "url": "https://openalex.org/A5109704302", "inst": "China Datang Corporation (China)" } ], "affiliations": [ "Jiangsu University", "Beijing Biocytogen (China)", "Beijing University of Posts and Telecommunications", "China Datang Corporation (China)" ] }, { "uid": "arxiv:2506.02037v2", "arxiv_id": "2506.02037v2", "title": "FinS-Pilot: A Benchmark for Online Financial RAG System", "authors": [ "Feng Wang", "Yiding Sun", "Jiaxin Mao", "Wei Xue", "Danqing Xu" ], "posted": "2025-05-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.02037v2", "field": "finance", "role": "method", "bullets": [ "Real-world Chinese financial assistant interactions with real-time API data and text data across critical financial domains.", "Multiple leading Chinese LLMs evaluated on RAG tasks covering static knowledge and time-sensitive market information via intent classification.", "Benchmark identified meaningful performance differences among Chinese LLMs for financial RAG applications in online settings." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "FinS-Pilot benchmark across multiple LLMs", "salience": 50, "n": 2591, "authors_detailed": [ { "name": "Feng Wang", "url": "https://openalex.org/A5103326343", "inst": "Communication University of China" }, { "name": "Yiding Sun", "url": "https://openalex.org/A5103326225", "inst": "Tencent (China)" }, { "name": "Jiaxin Mao", "url": "https://openalex.org/A5072119199", "inst": "Renmin University of China" }, { "name": "Weiwei Xue", "url": "https://openalex.org/A5027345342", "inst": "Nanjing University of Chinese Medicine" }, { "name": "D. Xu", "url": "https://openalex.org/A5107868032", "inst": "Chinese Academy of Sciences" } ], "affiliations": [ "Communication University of China", "Tencent (China)", "Renmin University of China", "Nanjing University of Chinese Medicine", "Chinese Academy of Sciences" ] }, { "uid": "arxiv:2506.00532v2", "arxiv_id": "2506.00532v2", "title": "Generative AI and Organizational Structure in the Knowledge Economy", "authors": [ "Fasheng Xu", "Jing Hou", "Wei Chen", "Karen Xie" ], "posted": "2025-05-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.00532v2", "field": "management", "role": "object", "bullets": [ "Theoretical model of knowledge-based hierarchies examining GenAI deployment across two modes (automation vs. augmentation) and two layers (worker vs. expert).", "No specific LLM used; analytical model derives equilibrium workforce composition and organizational structure under each of four GenAI deployment architectures.", "Worker-level automation raises skill requirements and cuts junior hiring; worker-level augmentation lowers skill thresholds; span of control contracts then expands as AI improves." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3508, "authors_detailed": [ { "name": "Fasheng Xu", "url": "https://openalex.org/A5101051805", "inst": "University of Connecticut" }, { "name": "Jing Hou", "url": "https://openalex.org/A5011275907", "inst": "Shenyang Jianzhu University" }, { "name": "Wei Chen", "url": "https://openalex.org/A5101520695", "inst": "University of Connecticut" }, { "name": "Karen Xie", "url": "https://openalex.org/A5005895330", "inst": "University of Connecticut" } ], "affiliations": [ "University of Connecticut", "Shenyang Jianzhu University" ] }, { "uid": "doi:10.2139/ssrn.5275557", "doi": "10.2139/ssrn.5275557", "title": "Beyond Automation: Exploring the Potential of Agentic AI in Risk Management and Fraud Detection in Banks", "authors": [ "Harish Kumar Sriram", "Bharath M Bharath M" ], "posted": "2025-05-30", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5275557", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 28, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 1034, "authors_detailed": [ { "name": "Harish Kumar Sriram", "url": "https://openalex.org/A5116219676", "inst": "Independent" }, { "name": "Borycki Elizabeth M.", "url": "https://openalex.org/A5112208750", "inst": "National Cancer Registration Service" } ], "affiliations": [ "Independent", "National Cancer Registration Service" ] }, { "uid": "arxiv:2506.00152v1", "arxiv_id": "2506.00152v1", "title": "Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective", "authors": [ "Erfan Loghmani" ], "posted": "2025-05-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.00152v1", "field": "management", "role": "method", "bullets": [ "Multiple real-world observational datasets with business outcome metrics such as conversion rates, plus controlled simulation experiments.", "DeconfoundLM removes confounder effects from reward signals during LLM fine-tuning using explicit causal correction methods.", "Causal deconfounding improved recovery of true causal relationships and mitigated spurious correlation failures versus naive fine-tuning approaches." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "simulation experiments on causal relationship recovery", "salience": 52, "n": 2589, "authors_detailed": [ { "name": "Erfan Loghmani", "url": "https://openalex.org/A5065688027", "inst": "Foster-Miller (United States)" } ], "affiliations": [ "Foster-Miller (United States)" ] }, { "uid": "arxiv:2505.24622v3", "arxiv_id": "2505.24622v3", "title": "Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data", "authors": [ "Ben Griffin", "Aaron Ontoyin Yin", "Diego Vidaurre", "Ugur Koyluoglu", "Joseph Ternasky", "Fuat Alican", "Yigit Ihlamur" ], "posted": "2025-05-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.24622v3", "field": "management", "role": "instrument", "bullets": [ "Early-stage startup screening from founder profiles and Phase I clinical trial prediction, both low-base-rate text classification domains.", "LLM generates simple yes/no questions as weak learners combined by unit-weight vote into an auditable green-flags scorecard.", "Scorecard precision exceeded base rate by several multiples on startup screening; outperformed baselines on PR-AUC and ROC-AUC for clinical trials." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "PR-AUC, ROC-AUC against published baselines", "salience": 55, "n": 2590, "authors_detailed": [ { "name": "Ben Griffin", "url": "https://openalex.org/A5093199632", "inst": "University of Oxford" }, { "name": "Yin, Aaron Ontoyin", "url": "", "inst": "" }, { "name": "Diego Vidaurre", "url": "https://openalex.org/A5048405025", "inst": "Aarhus University" }, { "name": "Ugur Koyluoglu", "url": "https://openalex.org/A5119862778", "inst": "" }, { "name": "Joseph Ternasky", "url": "https://openalex.org/A5119862779", "inst": "Clearwater Group (United States)" }, { "name": "Fuat Alican", "url": "https://openalex.org/A5114658551", "inst": "University of Oxford" }, { "name": "Yigit Ihlamur", "url": "https://openalex.org/A5093557649", "inst": "Amazon (Germany)" } ], "affiliations": [ "University of Oxford", "Aarhus University", "Clearwater Group (United States)", "Amazon (Germany)" ], "prestige": true }, { "uid": "arxiv:2505.24714v1", "arxiv_id": "2505.24714v1", "title": "FinMME: Benchmark Dataset for Financial Multi-Modal Reasoning Evaluation", "authors": [ "Junyu Luo", "Zhizhuo Kou", "Liming Yang", "Xiao Luo", "Jinsheng Huang", "Zhiping Xiao", "Jingshu Peng", "Chengzhong Liu", "Jiaming Ji", "Xuanzhe Liu", "Sirui Han", "Ming Zhang", "Yike Guo" ], "posted": "2025-05-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.24714v1", "field": "finance", "role": "method", "bullets": [ "11,000+ financial research samples across 18 domains and 6 asset classes, 10 chart types and 21 subtypes, annotated by 20 human annotators.", "GPT-4o, Gemini 2.0 Flash, and other multimodal LLMs evaluated on financial visual question answering with FinScore hallucination-penalty evaluation system.", "Even GPT-4o shows unsatisfactory performance on FinMME; benchmark exhibits high robustness with prediction variation below 1% under different prompts." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "FinMME benchmark with 20 human annotators and FinScore evaluation system", "salience": 58, "n": 3005, "authors_detailed": [ { "name": "Junyu Luo", "url": "https://openalex.org/A5101912906", "inst": "Peking University" }, { "name": "Zhizhuo Kou", "url": "https://openalex.org/A5109618901", "inst": "Hong Kong University of Science and Technology" }, { "name": "Liming Yang", "url": "https://openalex.org/A5032961377", "inst": "University of Connecticut" }, { "name": "Xiao Luo", "url": "https://openalex.org/A5100408619", "inst": "South China Agricultural University" }, { "name": "Jinsheng Huang", "url": "https://openalex.org/A5009336452", "inst": "Florida International University" }, { "name": "Zhiping Xiao", "url": "https://openalex.org/A5049472306", "inst": "University of Washington" }, { "name": "Jingshu Peng", "url": "https://openalex.org/A5085070214", "inst": "HKUST Shenzhen Research Institute" }, { "name": "Chengzhong Liu", "url": "https://openalex.org/A5103233885", "inst": "Hong Kong University of Science and Technology" }, { "name": "Jiaming Ji", "url": "https://openalex.org/A5095382286", "inst": "King University" }, { "name": "Xuanzhe Liu", "url": "https://openalex.org/A5103034984", "inst": "King University" }, { "name": "Sirui Han", "url": "https://openalex.org/A5077447568", "inst": "Hong Kong University of Science and Technology" }, { "name": "Ming Zhang", "url": "https://openalex.org/A5046314854", "inst": "King University" }, { "name": "Yike Guo", "url": "https://openalex.org/A5059516520", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Peking University", "Hong Kong University of Science and Technology", "University of Connecticut", "South China Agricultural University", "Florida International University", "University of Washington", "HKUST Shenzhen Research Institute", "King University" ] }, { "uid": "arxiv:2505.23025v1", "arxiv_id": "2505.23025v1", "title": "Learning to Regulate: A New Event-Level Dataset of Capital Control Measures", "authors": [ "Geyue Sun", "Xiao Liu", "Tomas Williams", "Roberto Samaniego" ], "posted": "2025-05-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.23025v1", "field": "economics", "role": "instrument", "bullets": [ "Capital control events for 196 countries between 1999 and 2023, extracted from the IMF AREAER reports into 5,198 events with 27 annotated fields.", "GPT-4.1 structures the events through a two step prompt framework, and a fine tuned Llama 3.1 8B reaches 90.09 percent category accuracy and 99.55 percent status accuracy for real time extension.", "Event studies for China, Australia, and the US show inward controls cutting fund inflows within one month, with restrictive measures biting harder than liberalizing ones." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": false, "validated": true, "validation_note": "fine-tuned classifier accuracy against AREAER-derived labels", "salience": 68, "edition": 14, "n": 1828, "authors_detailed": [ { "name": "Sun, Geyue", "url": "", "inst": "" }, { "name": "Xiao Liu", "url": "https://openalex.org/A5089671835", "inst": "Xihua University" }, { "name": "Tomás Williams", "url": "https://openalex.org/A5027971419", "inst": "George Washington University" }, { "name": "Roberto M. Samaniego", "url": "https://openalex.org/A5090484422", "inst": "George Washington University" } ], "affiliations": [ "Xihua University", "George Washington University" ] }, { "uid": "doi:10.2139/ssrn.5274196", "doi": "10.2139/ssrn.5274196", "title": "Fine-Tuning Large Language Models for Financial Markets via Ontological Reasoning", "authors": [ "Teodoro Baldazzi", "Luigi Bellomarini", "Stefano Ceri", "Andrea Colombo", "Andrea Gentili", "Emanuel Sallinger" ], "posted": "2025-05-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5274196", "field": "finance", "role": "method", "bullets": [ "Financial markets domain with enterprise knowledge graphs encoding domain knowledge in the Vadalog automated reasoning system.", "LLMs fine-tuned using verbalized ontological inferences from knowledge graphs for QA, explanation, translation, and description tasks.", "Proof of concept demonstrates ontology-guided fine-tuning produces more accurate domain-specific financial NLP outputs than standard approaches." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 48, "n": 2588, "authors_detailed": [ { "name": "Teodoro Baldazzi", "url": "https://openalex.org/A5083393379", "inst": "Roma Tre University" }, { "name": "Luigi Bellomarini", "url": "https://openalex.org/A5049287242", "inst": "Bank of Italy" }, { "name": "Stefano Ceri", "url": "https://openalex.org/A5048332811", "inst": "Politecnico di Milano" }, { "name": "Andrea Colombo", "url": "https://openalex.org/A5101858633", "inst": "Bank of Italy" }, { "name": "Andrea Gentili", "url": "https://openalex.org/A5110920607", "inst": "Bank of Italy" }, { "name": "Emanuel Sallinger", "url": "https://openalex.org/A5012243761", "inst": "TU Wien" } ], "affiliations": [ "Roma Tre University", "Bank of Italy", "Politecnico di Milano", "TU Wien" ] }, { "uid": "arxiv:2506.00073v4", "arxiv_id": "2506.00073v4", "title": "The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets", "authors": [ "Shenzhe Zhu", "Jiao Sun", "Yi Nian", "Tobin South", "Alex Pentland", "Jiaxin Pei" ], "posted": "2025-05-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.00073v4", "field": "management", "role": "agent", "bullets": [ "Simulated consumer marketplace where both buyer and seller sides are fully automated by LLM agents for negotiation and transaction execution.", "Multiple LLM agents evaluated on deal-making quality in real-world-style negotiation settings; framework measures outcome fairness and behavioral anomalies.", "AI-mediated deal-making is inherently imbalanced across agent types; behavioral anomalies cause financial losses such as overspending or accepting unreasonable deals." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 3207, "authors_detailed": [ { "name": "S. S. Zhu", "url": "https://openalex.org/A5021222690", "inst": "University of Toronto" }, { "name": "Jiao Jiao Sun", "url": "https://openalex.org/A5051469719", "inst": "Hebei University" }, { "name": "Yi Nian", "url": "https://openalex.org/A5073683446", "inst": "University of Southern California" }, { "name": "Tobin South", "url": "https://openalex.org/A5027450101", "inst": "The University of Adelaide" }, { "name": "Alex Pentland", "url": "https://openalex.org/A5007176508", "inst": "Moscow Institute of Thermal Technology" }, { "name": "Jiaxin Pei", "url": "https://openalex.org/A5116462456", "inst": "" } ], "affiliations": [ "University of Southern California", "University of Toronto", "Hebei University", "The University of Adelaide", "Moscow Institute of Thermal Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5268208", "doi": "10.2139/ssrn.5268208", "title": "THE IMPACT OF GENERATIVE AI ON WORKFORCE PRODUCTIVITY AND CREATIVE PROBLEM SOLVING", "authors": [ "Anuj Arora" ], "posted": "2025-05-28", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5268208", "field": "management", "role": "object", "bullets": [ "Conceptual review of generative AI integration across workplace environments, examining routine task automation and creative problem-solving processes without original empirical data.", "Discusses GANs and transformer-based tools in general terms; no specific model is deployed or tested by the authors.", "Concludes that generative AI offers gains in efficiency and innovation but warns that integration demands attention to ethical, social, and operational factors." ], "bullet_provenance": "ai", "salience": 30, "edition": 25, "models": [], "validated": null, "n": 4303, "authors_detailed": [ { "name": "Anuj Arora", "url": "https://openalex.org/A5084460246", "inst": "Global Science & Technology (United States)" } ], "affiliations": [ "Global Science & Technology (United States)" ] }, { "uid": "doi:10.2139/ssrn.5263449", "doi": "10.2139/ssrn.5263449", "title": "Agentic AI for End-to-End Investment Banking Automation: A No-Code Approach Using GPT, Zapier, and Airtable", "authors": [ "Nithin Kumar Racharla" ], "posted": "2025-05-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5263449", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 25, "edition": 3, "audience": "technical", "bullets": [], "validated": null, "n": 1033, "authors_detailed": [ { "name": "Nithin Kumar Racharla", "url": "https://openalex.org/A5117724285", "inst": "ICFAI Business School" } ], "affiliations": [ "ICFAI Business School" ] }, { "uid": "arxiv:2505.23842v5", "arxiv_id": "2505.23842v5", "title": "Fair Document Valuation in LLM Summaries via Shapley Values", "authors": [ "Zikun Ye", "Hema Yoganarasimhan" ], "posted": "2025-05-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.23842v5", "field": "economics", "role": "method", "bullets": [ "Amazon product review data used to evaluate fair compensation of content creators whose documents feed LLM summarization systems.", "Cluster Shapley approximation groups semantically similar documents via LLM embeddings and computes attribution with formal error bounds.", "Cluster Shapley substantially improves the efficiency-accuracy frontier; simple heuristics like equal or relevance-based allocation yield unfair outcomes." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Amazon product review attribution accuracy comparison", "salience": 60, "models": [], "n": 2706, "authors_detailed": [ { "name": "Zikun Ye", "url": "https://openalex.org/A5028486768", "inst": "University of Washington" }, { "name": "Hema Yoganarasimhan", "url": "https://openalex.org/A5076336545", "inst": "Paccar (United States)" } ], "affiliations": [ "University of Washington", "Paccar (United States)" ] }, { "uid": "arxiv:2505.22752v1", "arxiv_id": "2505.22752v1", "title": "Climate Finance Bench", "authors": [ "Rafik Mankour", "Yassine Chafai", "Hamada Saleh", "Ghassen Ben Hassine", "Thibaud Barreau", "Peter Tankov" ], "posted": "2025-05-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.22752v1", "field": "accounting", "role": "method", "bullets": [ "33 sustainability reports across all 11 GICS sectors with 330 expert-validated QA pairs spanning extraction, numerical reasoning, and logical reasoning.", "RAG approaches benchmarked on corporate climate disclosure questions; retriever passage-level recall identified as the chief performance bottleneck.", "Retriever quality, not generator capability, drives QA accuracy on climate disclosures; the benchmark is released as an open resource for future work." ], "bullet_provenance": "ai", "validated": true, "validation_note": "330 expert-validated QA pairs benchmark", "salience": 55, "models": [], "n": 2851, "authors_detailed": [ { "name": "Rafik Mankour", "url": "https://openalex.org/A5120516335", "inst": "" }, { "name": "Yassine Chafai", "url": "https://openalex.org/A5120424790", "inst": "" }, { "name": "Saleh, Hamada", "url": "", "inst": "" }, { "name": "Ghassen Ben Hassine", "url": "https://openalex.org/A5120663362", "inst": "" }, { "name": "Thibaud Barreau", "url": "https://openalex.org/A5023714781", "inst": "" }, { "name": "Peter Tankov", "url": "https://openalex.org/A5052828075", "inst": "New York University Abu Dhabi" } ], "affiliations": [ "New York University Abu Dhabi" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5271397", "doi": "10.2139/ssrn.5271397", "title": "From Models to Markets: How Generative AI is Reshaping Investment Research", "authors": [ "subhankar panda", "Abhishek Upadhyay", "Harshini Gadam" ], "posted": "2025-05-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5271397", "field": "finance", "role": "object", "bullets": [ "Review of generative AI applications across investment research, portfolio management, and risk assessment in financial markets.", "Surveys how generative AI technologies construct real-time financial narratives, simulate market conditions, and supplement traditional models relying on historical data.", "Finds generative AI accelerates research processes and fosters innovations in portfolio management, with unresolved challenges in transparency, interpretability, and ethical application." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 25, "validated": null, "n": 3003, "authors_detailed": [ { "name": "subhankar panda", "url": "", "inst": "OASIS Clinic" }, { "name": "Abhishek Upadhyay", "url": "https://openalex.org/A5101546176", "inst": "Carnegie Mellon University" }, { "name": "Harshini Gadam", "url": "https://openalex.org/A5116830842", "inst": "Illinois Institute of Technology" } ], "affiliations": [ "Carnegie Mellon University", "Illinois Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5272785", "doi": "10.2139/ssrn.5272785", "title": "A New Technique for Measuring a Firm's Marketing Emphasis", "authors": [ "Hoorsana Damavandi", "Feng Mai", "Vivek Astvansh" ], "posted": "2025-05-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5272785", "field": "management", "role": "instrument", "bullets": [ "254,522 firm-quarter observations of 9,916 U.S. public firms from 2003Q1 to 2023Q1, measuring marketing emphasis from earnings call transcripts.", "LLM-based technique extracts marketing emphasis and its components from managers' earnings call language; validated via internal and external validity tests.", "Marketing-emphasis scores improve machine-learning predictions of advertising, R&D, trade credit, sales revenue, profit, and market value." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "internal and external validity tests; ML prediction improvement for advertising, R&D, revenue, profit, market value", "salience": 68, "n": 3004, "authors_detailed": [ { "name": "Hoorsana Damavandi", "url": "https://openalex.org/A5011311208", "inst": "Western University" }, { "name": "Feng Mai", "url": "https://openalex.org/A5088161536", "inst": "University of Iowa" }, { "name": "Vivek Astvansh", "url": "https://openalex.org/A5036938950", "inst": "Woodlawn School" } ], "affiliations": [ "University of Iowa", "Western University", "Woodlawn School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5270073", "doi": "10.2139/ssrn.5270073", "title": "Balancing AI and Human Expertise: The Role of AI Watermarking and Detection", "authors": [ "Lin Hu", "Zhenhua Wu", "Pei-Yu Chen" ], "posted": "2025-05-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5270073", "field": "economics", "role": "object", "bullets": [ "Theoretical model of labor market effects from generative AI examining substitution, complementarity, and deskilling under varying skill distributions and human input costs.", "No specific LLM deployed; paper models generative AI as labor substitute and complement, analyzing AI watermarking and detection as policy interventions to preserve skills.", "Effectiveness of watermarking and detection depends on degree of AI-human complementarity, skill distribution, relative cost of human input, and reliability of detection measures." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3507, "authors_detailed": [ { "name": "Lin Hu", "url": "https://openalex.org/A5017812548", "inst": "Australian National University" }, { "name": "Zhenhua Wu", "url": "https://openalex.org/A5100737172", "inst": "Hankou University" }, { "name": "Pei‐Yu Chen", "url": "https://openalex.org/A5101699880", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University", "Australian National University", "Hankou University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.21427v2", "arxiv_id": "2505.21427v2", "title": "Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning", "authors": [ "Xianling Mu", "Joseph Ternasky", "Fuat Alican", "Yigit Ihlamur" ], "posted": "2025-05-27", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.21427v2", "field": "finance", "role": "method", "bullets": [ "Early stage startup investment screening; the dataset and period are not stated, with a 1.9 percent base success rate and 5.6 percent top tier venture capital precision as reference points.", "A memory augmented LLM, family not stated, applies a natural language investment policy refined through an in context feedback loop without gradient training; predictions are checked against realized startup outcomes.", "The induced policy reaches precision over 20 times random chance and 7.1 times the typical success rate of top tier venture capital firms." ], "bullet_provenance": "ai", "validated": true, "validation_note": "realized startup outcomes, precision reported", "salience": 50, "edition": 14, "models": [], "n": 1861, "authors_detailed": [ { "name": "Xiaoyi Mu", "url": "https://openalex.org/A5058830871", "inst": "University of Oxford" }, { "name": "Joseph Ternasky", "url": "https://openalex.org/A5119862779", "inst": "Clearwater Group (United States)" }, { "name": "Fuat Alican", "url": "https://openalex.org/A5114658551", "inst": "Clearwater Group (United States)" }, { "name": "Yigit Ihlamur", "url": "https://openalex.org/A5093557649", "inst": "Clearwater Group (United States)" } ], "affiliations": [ "University of Oxford", "Clearwater Group (United States)" ], "prestige": true }, { "uid": "doi:10.47852/bonviewaia52026307", "doi": "10.47852/bonviewaia52026307", "arxiv_id": "2505.20733v2", "title": "E2E Process Automation Leveraging Generative AI and IDP-Based Automation Agent: A Case Study on Corporate Expense Processing", "authors": [ "Cheonsu Jeong", "Seongmin Sim", "Hyoyoung Cho", "Sungsu Kim", "Byounggwan Shin" ], "posted": "2025-05-27", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.20733v2", "field": "accounting", "role": "object", "bullets": [ "Case study at a large Korean enterprise automating corporate expense processing end to end, combining OCR document recognition, policy database classification, and human in the loop final review.", "An unnamed LLM handles exception cases and the system learns from human decisions over time; no formal accuracy validation of the model is reported in the abstract.", "Processing time for paper receipt expenses falls by more than 80 percent, with lower error rates, better compliance, and higher employee satisfaction reported." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 14, "models": [], "n": 1862, "authors_detailed": [ { "name": "C. Jeong", "url": "https://openalex.org/A5102725428", "inst": "Samsung (South Korea)" }, { "name": "Seongmin Sim", "url": "", "inst": "Samsung (South Korea)" }, { "name": "Hyoyoung Cho", "url": "https://openalex.org/A5026203635", "inst": "Samsung (South Korea)" }, { "name": "Sung‐Su Kim", "url": "https://openalex.org/A5101723942", "inst": "Samsung (South Korea)" }, { "name": "B.C. Shin", "url": "https://openalex.org/A5108299497", "inst": "Samsung (South Korea)" } ], "affiliations": [ "Samsung (South Korea)" ] }, { "uid": "arxiv:2505.21371v2", "arxiv_id": "2505.21371v2", "title": "When Experimental Economics Meets Large Language Models: Evidence-based Tactics", "authors": [ "Shu Wang", "Zijun Yao", "Shuhuai Zhang", "Jianuo Gai", "Tracy Xiao Liu", "Songfa Zhong" ], "posted": "2025-05-27", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.21371v2", "field": "economics", "role": "method", "bullets": [ "Design guidance for running economic experiments on LLMs, drawn from experimental economics principles and AI research, tested through two sets of purpose-built experiments.", "The models tested are not named in the abstract; the experiments vary design and implementation choices and measure how model responses change with them.", "Seven practical tactics for LLM experimentation result, aimed at design, replicability, and generalizability; no single quantitative effect is highlighted in the abstract." ], "bullet_provenance": "ai", "salience": 60, "edition": 14, "models": [], "validated": null, "n": 1996 }, { "uid": "arxiv:2505.21627v4", "arxiv_id": "2505.21627v4", "title": "Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives", "authors": [ "Ander Artola Velasco", "Stratis Tsirtsis", "Nastaran Okati", "Manuel Gomez-Rodriguez" ], "posted": "2025-05-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.21627v4", "field": "economics", "role": "object", "bullets": [ "Theoretical analysis and experiments with Llama, Gemma, and Ministral models using LMSYS Chatbot Arena input prompts.", "Study examines pay-per-token pricing incentives showing providers can strategically misreport tokens to overcharge users undetectably.", "Heuristic overcharging algorithm costs less than additional revenue gained; character-count linear pricing eliminates the strategic incentive." ], "bullet_provenance": "ai", "models": [ "llama", "gemini", "open_other" ], "open_weights": true, "validated": false, "salience": 60, "n": 2704, "authors_detailed": [ { "name": "Ander Artola Velasco", "url": "https://openalex.org/A5116166912", "inst": "Max Planck Institute for Software Systems" }, { "name": "Stratis Tsirtsis", "url": "https://openalex.org/A5035213985", "inst": "Hasso Plattner Institute" }, { "name": "Nastaran Okati", "url": "https://openalex.org/A5077488458", "inst": "Max Planck Institute for Software Systems" }, { "name": "Manuel Gomez-Rodriguez", "url": "https://openalex.org/A5042180520", "inst": "Max Planck Institute for Software Systems" } ], "affiliations": [ "Max Planck Institute for Software Systems", "Hasso Plattner Institute" ] }, { "uid": "doi:10.2139/ssrn.5266605", "doi": "10.2139/ssrn.5266605", "title": "Do We Need to Know What Is Artificial? Unpacking Disclosure & Generating Trust in an Era of Algorithmic Action", "authors": [ "Orly Lobel" ], "posted": "2025-05-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5266605", "field": "management", "role": "object", "bullets": [ "Cross-sector review of AI disclosure regulations and behavioral research on algorithmic aversion and adoration across multiple domains.", "Analysis of when mandatory AI disclosure supports or undermines trust, accuracy, safety, and fairness for users and consumers.", "Mandatory disclosure can reduce trust and override beneficial AI recommendations; a context-dependent dignitarian-utilitarian framework is proposed." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 2705, "authors_detailed": [ { "name": "Orly Lobel", "url": "https://openalex.org/A5036817809", "inst": "University of San Diego" } ], "affiliations": [ "University of San Diego" ] }, { "uid": "arxiv:2505.21562v1", "arxiv_id": "2505.21562v1", "title": "Enhancing Selection of Climate Tech Startups with AI -- A Case Study on Integrating Human and AI Evaluations in the ClimaTech Great Global Innovation Challenge", "authors": [ "Jennifer Turliuk", "Alejandro Sevilla", "Daniela Gorza", "Tod Hynes" ], "posted": "2025-05-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.21562v1", "field": "management", "role": "instrument", "bullets": [ "ClimaTech Global Innovation Challenge with 57 climate technology startup applications evaluated by GPT-4o alongside panels of human judges across multiple competition rounds.", "GPT-4o scored applications via StackAI on team quality, market potential, and innovation; AI and human scores were weighted in semi-final and final selection rounds.", "Moderate positive correlation between AI and human scores at Spearman's rho 0.47; final four human-selected startups ranked among the highest rated by the AI." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Spearman's rho 0.47 vs human judge scores", "salience": 45, "n": 3506, "authors_detailed": [ { "name": "Jennifer Turliuk", "url": "https://openalex.org/A5120316638", "inst": "" }, { "name": "A. Sevilla", "url": "https://openalex.org/A5074485661", "inst": "Universidad Carlos III de Madrid" }, { "name": "Daniela Gorza", "url": "https://openalex.org/A5120316639", "inst": "" }, { "name": "Tod Hynes", "url": "https://openalex.org/A5120316640", "inst": "" } ], "affiliations": [ "Universidad Carlos III de Madrid" ] }, { "uid": "arxiv:2505.19457v1", "arxiv_id": "2505.19457v1", "title": "BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs", "authors": [ "Guilong Lu", "Xuntao Guo", "Rongjunchen Zhang", "Wenqiao Zhu", "Ji Liu" ], "posted": "2025-05-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.19457v1", "field": "finance", "role": "method", "bullets": [ "6,781 annotated Chinese queries drawn from real world financial applications, grouped into nine categories across numerical calculation, reasoning, information extraction, prediction recognition, and knowledge based question answering.", "25 proprietary and open models are benchmarked with objective and subjective metrics, plus an evaluation scheme called IteraJudge to reduce bias when an LLM acts as the grader.", "No model dominates: DeepSeek R1 and Claude 3.5 Sonnet lead numerical work, proprietary models lead reasoning by up to 19.49 points, and extraction scores spread from 11.23 to 71.46." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "6,781 annotated finance queries", "salience": 55, "edition": 14, "n": 1807 }, { "uid": "arxiv:2505.19804v3", "arxiv_id": "2505.19804v3", "title": "Compliance-to-Code: Enhancing Financial Compliance Checking via Code Generation", "authors": [ "Siyuan Li", "Jian Chen", "Rui Yao", "Xuming Hu", "Peilin Zhou", "Weihua Qiu", "Simin Zhang", "Chucheng Dong", "Zhiyao Li", "Qipeng Xie", "Zixuan Yuan" ], "posted": "2025-05-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.19804v3", "field": "finance", "role": "method", "bullets": [ "1,159 annotated clauses from 361 Chinese financial regulations in ten categories, each decomposed into subject, condition, constraint, and context with cross-regulation relations.", "The dataset attaches deterministic Python mappings, code reasoning, and explanations to each clause for compliance code generation; the abstract reports no model evaluation figures.", "A pipeline, FinCheck, demonstrates regulation structuring, code generation, and report generation; the resource targets weaknesses the authors see in LLMs on Chinese regulatory text." ], "bullet_provenance": "ai", "salience": 44, "edition": 14, "models": [], "validated": null, "n": 1995, "authors_detailed": [ { "name": "Siyuan Li", "url": "https://openalex.org/A5115592128", "inst": "Guiyang Medical University" }, { "name": "Jian Chen", "url": "https://openalex.org/A5100751758", "inst": "Donghua University" }, { "name": "Rui Yao", "url": "https://openalex.org/A5013953637", "inst": "Sun Yat-sen University" }, { "name": "Xuming Hu", "url": "https://openalex.org/A5109782052", "inst": "Ji Hua Laboratory" }, { "name": "Peilin Zhou", "url": "https://openalex.org/A5103037922", "inst": "Dalian Ocean University" }, { "name": "Qiu, Weihua", "url": "", "inst": "" }, { "name": "Zhang, Simin", "url": "", "inst": "" }, { "name": "Dong, Chucheng", "url": "", "inst": "" }, { "name": "Zhiyao Li", "url": "https://openalex.org/A5101680833", "inst": "Guangzhou University of Chinese Medicine" }, { "name": "Qiufei Xie", "url": "https://openalex.org/A5101895903", "inst": "Jishou University" }, { "name": "Yuan, Zixuan", "url": "", "inst": "" } ], "affiliations": [ "Guiyang Medical University", "Donghua University", "Sun Yat-sen University", "Ji Hua Laboratory", "Dalian Ocean University", "Guangzhou University of Chinese Medicine", "Jishou University" ] }, { "uid": "doi:10.18653/v1/2025.findings-acl.855", "doi": "10.18653/v1/2025.findings-acl.855", "arxiv_id": "2505.20368v3", "title": "Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents", "authors": [ "Jaeyoung Choe", "Jihoon Kim", "Woohwan Jung" ], "posted": "2025-05-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.20368v3", "field": "finance", "role": "method", "bullets": [ "145,897 SEC documents with 1,595 question-answer pairs in the new LOFin open-domain financial QA benchmark.", "Hierarchical retrieval framework with evidence curation performs document-level then passage-level retrieval and generates complementary queries for missing information.", "HiREC reduced duplicate retrieval errors from similar boilerplate SEC text, improving QA accuracy over standard RAG methods." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "LOFin benchmark, 1,595 QA pairs", "salience": 58, "n": 2586, "authors_detailed": [ { "name": "Jae Young Choe", "url": "https://openalex.org/A5031370249", "inst": "Artificial Intelligence in Medicine (Canada)" }, { "name": "Jihoon Kim", "url": "", "inst": "Artificial Intelligence in Medicine (Canada)" }, { "name": "Woohwan Jung", "url": "https://openalex.org/A5041272419", "inst": "Artificial Intelligence in Medicine (Canada)" } ], "affiliations": [ "Artificial Intelligence in Medicine (Canada)" ] }, { "uid": "arxiv:2505.19819v1", "arxiv_id": "2505.19819v1", "title": "FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets", "authors": [ "Dannong Wang", "Jaisal Patel", "Daochen Zha", "Steve Y. Yang", "Xiao-Yang Liu" ], "posted": "2025-05-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.19819v1", "field": "finance", "role": "method", "bullets": [ "19 financial datasets including four novel XBRL analysis datasets from 150 SEC filings, covering general and professional financial tasks.", "Five LoRA fine-tuning methods benchmarked across five base LLMs, evaluated on accuracy, F1, BERTScore, and computational cost.", "LoRA methods achieved average 36% performance gains over base models, demonstrating affordable scaling for financial language tasks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "accuracy, F1, BERTScore across 19 datasets", "salience": 62, "n": 2587, "authors_detailed": [ { "name": "Wang, Dannong", "url": "", "inst": "" }, { "name": "Patel, Jaisal", "url": "", "inst": "" }, { "name": "Daochen Zha", "url": "https://openalex.org/A5058071176", "inst": "Bay Area Air Quality Management District" }, { "name": "Steve Y. Yang", "url": "https://openalex.org/A5080120183", "inst": "Stevens Institute of Technology" }, { "name": "Xiao-Yang Liu", "url": "https://openalex.org/A5100405233", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "Bay Area Air Quality Management District", "Stevens Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.19430v3", "arxiv_id": "2505.19430v3", "title": "Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation", "authors": [ "Keane Ong", "Rui Mao", "Deeksha Varshney", "Paul Pu Liang", "Erik Cambria", "Gianmarco Mengaldo" ], "posted": "2025-05-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.19430v3", "field": "finance", "role": "method", "bullets": [ "FIN-FORCE benchmark of financial news headlines designed for evaluating forward counterfactual reasoning about market developments.", "State-of-the-art LLMs evaluated on generating plausible future market scenarios with structured counterfactual generation methods.", "LLMs show promise but exhibit limitations in anticipating structured forward counterfactuals for financial decision-making." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FIN-FORCE benchmark with structured evaluation", "salience": 55, "models": [], "n": 2703, "authors_detailed": [ { "name": "Keane Ong", "url": "https://openalex.org/A5001463799", "inst": "National University of Singapore" }, { "name": "Rui Mao", "url": "https://openalex.org/A5101724957", "inst": "Nanyang Technological University" }, { "name": "Deeksha Varshney", "url": "https://openalex.org/A5063590919", "inst": "Indian Institute of Technology Jodhpur" }, { "name": "Paul Pu Liang", "url": "https://openalex.org/A5086233510", "inst": "Moscow Institute of Thermal Technology" }, { "name": "Erik Cambria", "url": "https://openalex.org/A5100752356", "inst": "Nanyang Technological University" }, { "name": "Gianmarco Mengaldo", "url": "https://openalex.org/A5091468612", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore", "Nanyang Technological University", "Indian Institute of Technology Jodhpur", "Moscow Institute of Thermal Technology" ] }, { "uid": "arxiv:2505.20273v1", "arxiv_id": "2505.20273v1", "title": "Ten Principles of AI Agent Economics", "authors": [ "Ke Yang", "ChengXiang Zhai" ], "posted": "2025-05-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.20273v1", "field": "economics", "role": "object", "bullets": [ "Theoretical framework drawing on economics, decision theory, and ethics to analyze AI agent integration into economic systems.", "Proposes ten principles governing how LLM-based AI agents make decisions, influence social interactions, and participate in economic activity.", "Identifies key challenges including agent evolution from tools to independent entities, labor market disruption, and the need for ethical safeguards." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3926, "authors_detailed": [ { "name": "Ke Yang", "url": "https://openalex.org/A5119756987", "inst": "" }, { "name": "ChengXiang Zhai", "url": "https://openalex.org/A5119756988", "inst": "" } ] }, { "uid": "arxiv:2505.19197v3", "arxiv_id": "2505.19197v3", "title": "Structuring the Unstructured: A Multi-Agent System for Extracting and Querying Financial KPIs and Guidance", "authors": [ "Chanyeol Choi", "Alejandro Lopez-Lira", "Yongjae Lee", "Jihoon Kwon", "Minjae Kim", "Juneha Hwang", "Minsoo Ha", "Chaewoon Kim", "Jaeseon Ha", "Suyeol Yun", "Jin Kim" ], "posted": "2025-05-25", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.19197v3", "field": "finance", "role": "instrument", "bullets": [ "Unstructured financial filings processed by a two-agent LLM system comprising an extraction agent and a text-to-SQL agent for structured data retrieval and querying.", "LLM agents identified key performance indicators from financial text, standardized formats, verified accuracy, and converted natural language queries into executable SQL statements.", "System achieved 95% accuracy in structuring financial filings matching human annotator performance; 91% of natural language retrieval queries rated correct by evaluators." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "95% accuracy vs human annotators on financial filings", "salience": 72, "n": 3505, "authors_detailed": [ { "name": "Chanyeol Choi", "url": "https://openalex.org/A5047029131", "inst": "Community Link" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5100366482", "inst": "Ulsan National Institute of Science and Technology" }, { "name": "Jihoon Kwon", "url": "https://openalex.org/A5066426830", "inst": "Tech University of Korea" }, { "name": "Minjae Kim", "url": "https://openalex.org/A5024780187", "inst": "Inje University" }, { "name": "Hwang, Juneha", "url": "", "inst": "" }, { "name": "Molina Ha", "url": "https://openalex.org/A5040696320", "inst": "Ewha Womans University" }, { "name": "Chaewoon Kim", "url": "https://openalex.org/A5071807982", "inst": "Singapore University of Technology and Design" }, { "name": "Junhyoung Ha", "url": "https://openalex.org/A5034230283", "inst": "Ulsan National Institute of Science and Technology" }, { "name": "Suyeol Yun", "url": "https://openalex.org/A5117354551", "inst": "Medieval Academy of America" }, { "name": "Jin Kim", "url": "https://openalex.org/A5100380873", "inst": "Ulsan National Institute of Science and Technology" } ], "affiliations": [ "University of Florida", "Community Link", "Ulsan National Institute of Science and Technology", "Tech University of Korea", "Inje University", "Ewha Womans University", "Singapore University of Technology and Design", "Medieval Academy of America" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.20343v2", "arxiv_id": "2505.20343v2", "title": "Do LLMs have a Gender (Entropy) Bias?", "authors": [ "Sonal Prabhune", "Balaji Padmanabhan", "Kaushik Dutta" ], "posted": "2025-05-24", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.20343v2", "field": "management", "role": "object", "bullets": [ "RealWorldQuestioning, a new benchmark of real user questions in education, jobs, personal financial management, and health, posed for men and women across four LLMs.", "The four models are not named in the abstract; ChatGPT-4o serves as judge alongside quantitative metrics to compare response information content by gender, without human validation.", "Category-level differences are insignificant, but most individual questions show substantial gender gaps that offset in aggregate; iteratively merging gendered responses beats both variants in 78 percent of cases." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "edition": 14, "n": 1993, "authors_detailed": [ { "name": "Sonal Prabhune", "url": "https://openalex.org/A5119938243", "inst": "" }, { "name": "Balaji Padmanabhan", "url": "https://openalex.org/A5050411634", "inst": "University of Maryland, College Park" }, { "name": "Kaushik Dutta", "url": "https://openalex.org/A5059780231", "inst": "University of South Florida" } ], "affiliations": [ "University of Maryland, College Park", "University of South Florida" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.18542v4", "arxiv_id": "2505.18542v4", "title": "Business as Rulesual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs", "authors": [ "Chen Yang", "Ruping Xu", "Ruizhe Li", "Bin Cao", "Jing Fan" ], "posted": "2025-05-24", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.18542v4", "field": "management", "role": "method", "bullets": [ "409 real business documents with 2,855 expert-annotated rules covering conditional branching and parallel execution, spanning more than 30 domains including finance and administration.", "13 LLMs, unnamed in the abstract, extract rule flows under the ExIde framework's five prompting strategies, from implicit semantic alignment to executable pseudo-code grounding, without fine-tuning.", "Pseudo-code grounding beats standard prompts for rule extraction, and reasoning-optimized models handle long-range and non-linear dependencies better than instruction-tuned ones." ], "bullet_provenance": "ai", "validated": true, "validation_note": "2,855 expert-annotated rules across 409 documents", "salience": 42, "edition": 14, "models": [], "n": 1994 }, { "uid": "doi:10.2139/ssrn.5267464", "doi": "10.2139/ssrn.5267464", "title": "Can Llms Credibly Transform the Creation of Panel Data from Diverse Historical Tables?", "authors": [ "Verónica Bäcker-Peral", "Vitaly Meursault", "Christopher Severen" ], "posted": "2025-05-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5267464", "field": "economics", "role": "method", "bullet_provenance": "none", "salience": 47, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 672, "authors_detailed": [ { "name": "Verónica Bäcker-Peral", "url": "https://openalex.org/A5117667451", "inst": "Massachusetts Institute of Technology" }, { "name": "Vitaly Meursault", "url": "https://openalex.org/A5117667452", "inst": "Federal Reserve Bank of Philadelphia" }, { "name": "Christopher Severen", "url": "https://openalex.org/A5029228912", "inst": "Federal Reserve Bank of Philadelphia" } ], "affiliations": [ "Massachusetts Institute of Technology", "Federal Reserve Bank of Philadelphia" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.18419v1", "arxiv_id": "2505.18419v1", "title": "How do managers' non-responses during earnings calls affect analyst forecasts", "authors": [ "Qingwen Liang", "Matias Carrasco Kind" ], "posted": "2025-05-23", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.18419v1", "field": "accounting", "role": "instrument", "bullets": [ "Quarterly earnings call transcripts of S&P 500 firms, linked to analyst forecast errors, dispersion, and uncertainty, plus post announcement returns, volatility, volume, and spreads.", "ChatGPT-4 and LLaMA 3.3 identify, classify, and evaluate manager non responses through a three step prompt sequence; the abstract mentions no validation against human coded transcripts.", "More non responses go with larger forecast errors, wider dispersion, stronger post earnings drift, higher volatility, and wider bid ask spreads, and effects concentrate in high institutional ownership and R&D heavy firms." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": false, "salience": 58, "edition": 14, "n": 1844, "authors_detailed": [ { "name": "Qingwen Liang", "url": "https://openalex.org/A5087827091", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Kind, Matias Carrasco", "url": "", "inst": "" } ], "affiliations": [ "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.17625v1", "arxiv_id": "2505.17625v1", "title": "Enhancing Large Vision-Language Models with Layout Modality for Table Question Answering on Japanese Annual Securities Reports", "authors": [ "Hayato Aida", "Kosuke Takahashi", "Takahiro Omi" ], "posted": "2025-05-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.17625v1", "field": "finance", "role": "instrument", "bullets": [ "Japanese annual securities reports containing complex table structures in HTML, image, and plain text formats.", "Large vision-language models enhanced with in-table textual content and layout features for table question answering on financial documents.", "Auxiliary layout and text modalities significantly improved QA performance over baseline LVLMs on complex financial document table structures." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "table QA accuracy on securities reports", "salience": 45, "n": 2585 }, { "uid": "arxiv:2505.17479v1", "arxiv_id": "2505.17479v1", "title": "Twin-2K-500: A dataset for building digital twins of over 2,000 people based on their answers to over 500 questions", "authors": [ "Olivier Toubia", "George Z. Gui", "Tianyi Peng", "Daniel J. Merlau", "Ang Li", "Haozhe Chen" ], "posted": "2025-05-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.17479v1", "field": "economics", "role": "method", "bullets": [ "2,058 US participants surveyed across four waves totaling 500 questions covering demographics, psychology, and behavioral economics experiments.", "LLM-based digital twins constructed from comprehensive individual profiles to predict behavior at individual and aggregate levels.", "Digital twins predicted human behavior well; test-retest wave established accuracy baseline for benchmarking persona simulations." ], "bullet_provenance": "ai", "validated": true, "validation_note": "test-retest accuracy baseline against actual survey responses", "salience": 65, "models": [], "n": 2702 }, { "uid": "arxiv:2505.17471v2", "arxiv_id": "2505.17471v2", "title": "FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain", "authors": [ "Suifeng Zhao", "Zhuoran Jin", "Sujian Li", "Jun Gao" ], "posted": "2025-05-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.17471v2", "field": "finance", "role": "method", "bullets": [ "Bilingual retrieval corpus of 60,780 Chinese and 51,219 English financial document pages with human-annotated QA dataset spanning seven question categories.", "Multimodal LLMs evaluated on visual RAG tasks using new RGenCite baseline integrating visual citation with answer generation; automatic citation evaluation method proposed.", "FinRAGBench-V poses significant challenges for current multimodal LLMs; benchmark exposes gaps in integrating visual financial content for retrieval-augmented generation." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3206 }, { "uid": "arxiv:2505.17648v5", "arxiv_id": "2505.17648v5", "title": "Simulating Macroeconomic Expectations in Survey Experiments with LLM-based Economic Agents", "authors": [ "Jianhao Lin", "Lexuan Sun", "Yixin Yan" ], "posted": "2025-05-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.17648v5", "field": "economics", "role": "agent", "bullets": [ "LLM agents equipped with personal characteristics and prior expectations replicated three representative macroeconomic survey experiments across different respondent types and expectation categories.", "LLM agents generated expectation distributions and open-ended responses; prior expectations proved crucial for matching distributions while personal and external information drove human-like reasoning.", "Agent expectation distributions were highly similar to human survey data and open-ended responses captured human-aligned qualitative patterns at the aggregate level." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "Replication of three survey experiments vs human respondent data", "salience": 78, "n": 3504 }, { "uid": "arxiv:2505.16090v1", "arxiv_id": "2505.16090v1", "title": "Can AI Read Between The Lines? Benchmarking LLMs On Financial Nuance", "authors": [ "Dominick Kubica", "Dylan T. Gordon", "Nanami Emura", "Derleen Saini", "Charlie Goldenberg" ], "posted": "2025-05-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.16090v1", "field": "finance", "role": "method", "bullets": [ "Microsoft earnings call transcripts, analysed across the company's lines of business in a Santa Clara University practicum, targeting the hedged and strategically ambiguous language of financial disclosure.", "Copilot, ChatGPT, and Gemini are compared with traditional machine learning sentiment baselines; model versions are not stated and the abstract reports no accuracy statistics against labelled ground truth.", "Sentiment consistency is visualised against market sentiment and stock movements and prompt engineering is explored, but the abstract states no quantified headline finding." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": false, "salience": 30, "edition": 14, "n": 1809, "authors_detailed": [ { "name": "Dominick Kubica", "url": "https://openalex.org/A5120416882", "inst": "" }, { "name": "Gordon, Dylan T.", "url": "", "inst": "" }, { "name": "Nanami Emura", "url": "https://openalex.org/A5120463400", "inst": "" }, { "name": "Derleen Saini", "url": "https://openalex.org/A5120416883", "inst": "" }, { "name": "Charlie Goldenberg", "url": "https://openalex.org/A5120463401", "inst": "" } ] }, { "uid": "arxiv:2506.07315v2", "arxiv_id": "2506.07315v2", "title": "Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation", "authors": [ "Zonghan Wu", "Congyuan Zou", "Junlin Wang", "Chenhan Wang", "Hangjing Yang", "Yilei Shao" ], "posted": "2025-05-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.07315v2", "field": "finance", "role": "method", "bullets": [ "FinAR-Bench, a benchmark for fundamental analysis of financial statements; company coverage, reporting periods, and task counts are not stated in the abstract.", "LLMs, not named in the abstract, are scored on three separable steps, key information extraction, financial indicator calculation, and logical reasoning, each assessed against ground truth.", "The evaluation maps where current models succeed and fail in fundamental analysis; no headline accuracy figures are given in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "stepwise benchmark tasks with ground truth", "salience": 48, "edition": 14, "models": [], "n": 1860 }, { "uid": "arxiv:2505.15155v2", "arxiv_id": "2505.15155v2", "title": "R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization", "authors": [ "Yuante Li", "Xu Yang", "Xiao Yang", "Minrui Xu", "Xisen Wang", "Weiqing Liu", "Jiang Bian" ], "posted": "2025-05-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.15155v2", "field": "finance", "role": "agent", "bullets": [ "Multi-agent LLM framework for automated quantitative strategy research tested on real stock markets with iterative factor mining and model optimization pipeline.", "LLM agents formulated hypotheses, generated alpha factor and prediction model code, and ran iterative real-market backtests with multi-armed bandit scheduling for direction selection.", "Achieved 2x higher annualized returns than classical factor libraries using 70% fewer factors and outperformed state-of-the-art deep time-series models on real markets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Real-market backtests vs classical factor libraries and deep learning baselines", "salience": 70, "n": 3503, "authors_detailed": [ { "name": "Yuelin Li", "url": "https://openalex.org/A5100680211", "inst": "Dalian Jiaotong University" }, { "name": "Yang Xu", "url": "https://openalex.org/A5100462086", "inst": "Shanghai Special Equipment Supervision and Inspection Institute" }, { "name": "Yang Xiao", "url": "https://openalex.org/A5064266033", "inst": "Suzhou Research Institute" }, { "name": "Minrui Xu", "url": "https://openalex.org/A5053209543", "inst": "Shanghai Electric (China)" }, { "name": "Wang, Xisen", "url": "", "inst": "" }, { "name": "Weiqing Liu", "url": "https://openalex.org/A5003846087", "inst": "Chongqing University of Science and Technology" }, { "name": "Jiang Bian", "url": "https://openalex.org/A5066890376", "inst": "Indiana University Health" } ], "affiliations": [ "Dalian Jiaotong University", "Shanghai Special Equipment Supervision and Inspection Institute", "Suzhou Research Institute", "Shanghai Electric (China)", "Chongqing University of Science and Technology", "Indiana University Health" ] }, { "uid": "arxiv:2505.14918v2", "arxiv_id": "2505.14918v2", "title": "Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications", "authors": [ "Fadel M. Megahed", "Ying-Ju Chen", "L. Allision Jones-Farmer", "Younghwa Lee", "Jiawei Brooke Wang", "Inez M. Zwetsloot" ], "posted": "2025-05-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.14918v2", "field": "finance", "role": "method", "bullets": [ "1,350 financial news articles, classified five times each by 14 commercial and open models including Claude 3.7 Sonnet, GPT-4o, DeepSeek R1, Gemma 3, Llama 3.2, Phi 4, and Command R Plus.", "A psychometrics based framework fixes sample size requirements and reliability metrics; sentiment labels are validated against StockNewsAPI ground truth, with accuracy from 0.76 to 0.88.", "Models agree with themselves on 90 to 98 percent of items, small variants match expensive family members, and all models predict subsequent market movement at chance." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "StockNewsAPI labels, accuracy 0.76 to 0.88", "salience": 55, "edition": 14, "n": 1813, "authors_detailed": [ { "name": "Fadel M. Megahed", "url": "https://openalex.org/A5024863972", "inst": "University of Miami" }, { "name": "Ying‐Ju Chen", "url": "https://openalex.org/A5101811263", "inst": "University of Dayton" }, { "name": "L. Allision Jones-Farmer", "url": "https://openalex.org/A5120054232", "inst": "" }, { "name": "Younghwa Lee", "url": "https://openalex.org/A5009851933", "inst": "Heidelberg University" }, { "name": "Jiawei Wang", "url": "https://openalex.org/A5115603595", "inst": "Miami University" }, { "name": "Inez Maria Zwetsloot", "url": "https://openalex.org/A5061494936", "inst": "Systems Analytics (United States)" } ], "affiliations": [ "University of Miami", "University of Dayton", "Heidelberg University", "Miami University", "Systems Analytics (United States)" ] }, { "uid": "arxiv:2505.14727v1", "arxiv_id": "2505.14727v1", "title": "The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents", "authors": [ "Mohammad Rubyet Islam" ], "posted": "2025-05-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.14727v1", "field": "finance", "role": "method", "bullets": [ "Narrative review of alpha generation, organized as a five stage progression from manual strategies through statistical models, classical machine learning, deep learning, and LLM powered agentic architectures.", "No model is applied; the paper surveys representation learning, multimodal data fusion, and tool augmented LLM agents as components of context aware financial systems.", "Proposes a unified taxonomy for judging system maturity and flags interpretability, data fragility, governance, and regulatory compliance as the main barriers to production deployment." ], "bullet_provenance": "ai", "salience": 27, "edition": 14, "models": [], "validated": null, "n": 1907, "authors_detailed": [ { "name": "Islam, Mohammad Rubyet", "url": "", "inst": "" } ] }, { "uid": "arxiv:2508.00828v1", "arxiv_id": "2508.00828v1", "title": "Finance Agent Benchmark: Benchmarking LLMs on Real-world Financial Research Tasks", "authors": [ "Antoine Bigeard", "Langston Nashold", "Rayan Krishnan", "Shirley Wu" ], "posted": "2025-05-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2508.00828v1", "field": "finance", "role": "method", "bullets": [ "537 expert-authored questions across nine task categories derived from recent SEC filings, developed with bank, hedge fund, and PE professionals.", "Multiple LLMs tested in agentic harness with Google Search and EDGAR access; best performer was OpenAI o3 evaluated on accuracy.", "Top model achieved only 46.8% accuracy at $3.79 per query, revealing substantial gaps in LLM capability for complex financial research tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "expert-authored QA benchmark, 537 questions", "salience": 72, "n": 2584, "authors_detailed": [ { "name": "Antoine Bigeard", "url": "https://openalex.org/A5120732374", "inst": "" }, { "name": "Langston Nashold", "url": "https://openalex.org/A5009459298", "inst": "Valve (United States)" }, { "name": "Rayan Krishnan", "url": "https://openalex.org/A5102786166", "inst": "Valve (United States)" }, { "name": "Shirley Wu", "url": "https://openalex.org/A5101786641", "inst": "Stanford University" } ], "affiliations": [ "Stanford University", "Valve (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.14420v2", "arxiv_id": "2505.14420v2", "title": "SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection", "authors": [ "Huopu Zhang", "Yanguang Liu", "Miao Zhang", "Zirui He", "Mengnan Du" ], "posted": "2025-05-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.14420v2", "field": "finance", "role": "instrument", "bullets": [ "Three financial datasets of earnings conference calls, regulatory filings, and financial news, documents typically exceeding 5,000 words with substantial redundancy.", "Sparse autoencoders decompose dense LLM representations into interpretable sparse components; ANOVA F-tests and tree-based importance scoring select top-k discriminative features for earnings surprise classification.", "SAE-FiRE significantly outperforms baseline approaches on all three financial datasets for predicting earnings surprises." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "classification accuracy vs baselines on three financial datasets", "salience": 62, "n": 3001 }, { "uid": "doi:10.2139/ssrn.5255872", "doi": "10.2139/ssrn.5255872", "title": "Fast-Tracking IRS Internal Operations Through Generative AI", "authors": [ "Robert Kovacev", "Omar Hussein" ], "posted": "2025-05-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5255872", "field": "accounting", "role": "object", "bullets": [ "U.S. Internal Revenue Service internal operations, analyzing generative AI applications for tax administration efficiency and taxpayer data handling.", "Generative AI evaluated for potential to improve IRS operational efficiency, conserve agency resources, and automate internal processes.", "Identifies efficiency gains in tax administration alongside risks that generative AI deployment could threaten taxpayer confidentiality." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3002, "authors_detailed": [ { "name": "Robert J Kovacev", "url": "https://openalex.org/A5097523800", "inst": "Miller College" }, { "name": "Omar Hussein", "url": "https://openalex.org/A5073424896", "inst": "Miller & Chevalier" } ], "affiliations": [ "Miller College", "Miller & Chevalier" ] }, { "uid": "doi:10.2139/ssrn.5258789", "doi": "10.2139/ssrn.5258789", "title": "New Tools, New Rules: A Practical Guide to Effective and Responsible GenAI Use for Surveys and Experiments Research", "authors": [ "Simon J. Blanchard", "Nofar Duani", "Aaron Garvey", "Oded Netzer", "Travis Tae Oh" ], "posted": "2025-05-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5258789", "field": "management", "role": "method", "bullets": [ "A practical guide rather than an empirical study, covering the full survey and experiment research workflow: literature review, instrument design, study administration, data coding and interpretation of results.", "It explains how GenAI and language model systems operate, maps opportunities and risks at each research stage, and stresses checking the validity of GenAI-coded responses; no specific model is named.", "Delivers rules and best-practice tips for responsible GenAI use, with a companion site offering reproducible R and SPSS coding templates and sample preregistrations." ], "bullet_provenance": "ai", "salience": 54, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 195, "authors_detailed": [ { "name": "Simon J. Blanchard", "url": "https://openalex.org/A5008381354", "inst": "Georgetown University" }, { "name": "Nofar Duani", "url": "https://openalex.org/A5077020068", "inst": "University of Southern California" }, { "name": "Aaron Garvey", "url": "https://openalex.org/A5029262036", "inst": "University of Kentucky" }, { "name": "Oded Netzer", "url": "https://openalex.org/A5002138970", "inst": "Columbia University" }, { "name": "Travis Tae Oh", "url": "https://openalex.org/A5069970510", "inst": "Yeshiva University" } ], "affiliations": [ "Georgetown University", "University of Southern California", "Columbia University", "University of Kentucky", "Yeshiva University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5258102", "doi": "10.2139/ssrn.5258102", "title": "Exploring the Future of Public Sector Work: Generative AI Adoption amongst Dubai Government Employees Exploring the Future of Public Sector Work", "authors": [ "Sarah Shaer", "Keertana Subramani", "Fadi Salem" ], "posted": "2025-05-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5258102", "field": "management", "role": "object", "bullets": [ "Survey of generative AI adoption among Dubai government employees, a jurisdiction with two decades of early technology adoption in public services.", "Examines adoption and use of ChatGPT, Gemini, Claude, DeepSeek, and LLaMA for government operations, assessing capabilities, privacy constraints, and deployment considerations across agencies.", "GAI tools reshape government operations but data privacy and security concerns constrain deployment of certain models; comprehensive AI integration strategies are required." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "llama", "open_other" ], "salience": 15, "validated": null, "n": 2370, "authors_detailed": [ { "name": "Sarah Shaer", "url": "https://openalex.org/A5050092789", "inst": "Mohammed Bin Rashid School of Government" }, { "name": "Keertana Subramani", "url": "https://openalex.org/A5117529996", "inst": "Mohammed Bin Rashid School of Government" }, { "name": "Fadi Salem", "url": "https://openalex.org/A5036413433", "inst": "Mohammed Bin Rashid School of Government" } ], "affiliations": [ "Mohammed Bin Rashid School of Government" ] }, { "uid": "doi:10.2139/ssrn.5231493", "doi": "10.2139/ssrn.5231493", "title": "A Multidisciplinary Framework for AI and Data-Driven Transformation in Taxation, Insurance, Mortgage Financing, and Financial Advisory: Integrating Cloud Computing, Deep Learning, and Agentic AI for Community-Centric Economic Development", "authors": [ "Someshwar Mashetty", "Murali Malempati", "Srinivasarao Paleti", "BALAJI ADUSUPALLI", "Jeevani Singireddy" ], "posted": "2025-05-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5231493", "field": "finance", "role": "object", "bullets": [ "Survey mapping AI techniques across taxation, insurance, mortgage financing, and financial advisory sectors using a structured framework.", "Heat-map analysis ranks technique popularity across four financial domains; reviews applications including tax audit models, fraud detection, and bankruptcy prediction.", "Privacy-preserving data mining reduced necessary feature pools by two-thirds with only 15% cost to the true positive rate and up to five-fold lower false positives." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3925, "authors_detailed": [ { "name": "Someshwar Mashetty", "url": "https://openalex.org/A5117268050", "inst": "Aims Community College" }, { "name": "Murali Malempati", "url": "https://openalex.org/A5116817583", "inst": "Independent" }, { "name": "Srinivasarao Paleti", "url": "https://openalex.org/A5116932528", "inst": "Independent" }, { "name": "Balaji Adusupalli", "url": "https://openalex.org/A5116878998", "inst": "National Society of Professional Engineers" }, { "name": "Jeevani Singireddy", "url": "https://openalex.org/A5116932530", "inst": "Intuit (United States)" } ], "affiliations": [ "Aims Community College", "Independent", "National Society of Professional Engineers", "Intuit (United States)" ] }, { "uid": "arxiv:2505.13533v1", "arxiv_id": "2505.13533v1", "title": "FinMaster: A Holistic Benchmark for Mastering Full-Pipeline Financial Workflows with LLMs", "authors": [ "Junzhe Jiang", "Chang Yang", "Aixin Cui", "Sihan Jin", "Ruiyu Wang", "Bo Li", "Xiao Huang", "Dongning Sun", "Xinrun Wang" ], "posted": "2025-05-18", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.13533v1", "field": "accounting", "role": "method", "bullets": [ "Synthetic, privacy-compliant company data from the FinSim simulator feeds 183 tasks across financial literacy, accounting, auditing, and consulting, scored through a unified evaluation interface.", "State-of-the-art LLMs, not named in the abstract, are scored against the benchmark's generated ground truth across task types and difficulty levels.", "Accuracy falls from above 90 percent on basic tasks to about 40 percent on multi-step scenarios, and single-metric calculation accuracy of 58 percent drops to 37 percent in multi-metric settings." ], "bullet_provenance": "ai", "validated": true, "validation_note": "benchmark ground truth, accuracy by difficulty reported", "salience": 55, "edition": 14, "models": [], "n": 1992, "authors_detailed": [ { "name": "Jiang, Junzhe", "url": "", "inst": "" }, { "name": "Chang Yang", "url": "https://openalex.org/A5115596038", "inst": "North University of China" }, { "name": "Aixin Cui", "url": "https://openalex.org/A5111289356", "inst": "Lingnan University" }, { "name": "Jin, Sihan", "url": "", "inst": "" }, { "name": "Ruiyu Wang", "url": "https://openalex.org/A5100730179", "inst": "Hong Kong Polytechnic University" }, { "name": "Bo Li", "url": "https://openalex.org/A5100374368", "inst": "Qingdao University" }, { "name": "Xiao Huang", "url": "https://openalex.org/A5070077825", "inst": "Chengdu University" }, { "name": "Sun, Dongning", "url": "", "inst": "" }, { "name": "Xinrun Wang", "url": "https://openalex.org/A5049783685", "inst": "Singapore Management University" } ], "affiliations": [ "North University of China", "Lingnan University", "Hong Kong Polytechnic University", "Qingdao University", "Chengdu University", "Singapore Management University" ] }, { "uid": "arxiv:2505.12495v2", "arxiv_id": "2505.12495v2", "title": "KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation", "authors": [ "Nikita Tatarinov", "Vidhyakshaya Kannan", "Haricharana Srinivasa", "Arnav Raj", "Harpreet Singh Anand", "Varun Singh", "Aditya Luthra", "Ravij Lade", "Agam Shah", "Sudheer Chava" ], "posted": "2025-05-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.12495v2", "field": "finance", "role": "method", "bullets": [ "20,139 QA pairs at multiple complexity levels extracted from financial credit agreements using knowledge-graph document representations.", "Sixteen proprietary and open-weight LLMs evaluated on multi-hop retrieval, set operations, and answer plurality over long contexts.", "Even best-performing models struggled with set-based comparisons and multi-hop reasoning, revealing systematic semantic failure modes." ], "bullet_provenance": "ai", "validated": true, "validation_note": "KG-MuLQA financial credit agreement QA benchmark", "salience": 55, "models": [], "n": 2701 }, { "uid": "arxiv:2505.12001v1", "arxiv_id": "2505.12001v1", "title": "Interactional Fairness in LLM Multi-Agent Systems: An Evaluation Framework", "authors": [ "Ruta Binkyte" ], "posted": "2025-05-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.12001v1", "field": "management", "role": "agent", "bullets": [ "A pilot study of simulated resource negotiations between LLM agents, manipulating tone, explanation quality, outcome inequality, and collaborative versus competitive task framing.", "Agents, model not named in the abstract, negotiate under measures adapted from Colquitt's organizational justice scale and the critical incident technique; acceptance decisions are the outcome.", "Tone and justification quality shift acceptance even when objective outcomes are constant, and the relative weight of interpersonal versus informational fairness depends on context." ], "bullet_provenance": "ai", "salience": 42, "edition": 14, "models": [], "validated": null, "n": 1991, "authors_detailed": [ { "name": "Rūta Binkytė", "url": "https://openalex.org/A5083119693", "inst": "Helmholtz Center for Information Security" } ], "affiliations": [ "Helmholtz Center for Information Security" ] }, { "uid": "doi:10.4337/9781803929996.00009", "doi": "10.4337/9781803929996.00009", "arxiv_id": "2505.12012v1", "title": "Empowering Sustainable Finance with Artificial Intelligence: A Framework for Responsible Implementation", "authors": [ "Georgios Pavlidis" ], "posted": "2025-05-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.12012v1", "field": "finance", "role": "object", "bullets": [ "Conceptual analysis of AI applications in ESG investing, referencing a global AI market projected at $1,394 billion by 2029.", "Framework examines how AI identifies climate risks, sets ESG goals, and advances sustainable finance decisions without testing a specific model.", "Proposes principles of legitimacy, oversight, transparency, and explainability to govern AI-delegated ESG investment decisions." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3924, "authors_detailed": [ { "name": "Georgios Pavlidis", "url": "https://openalex.org/A5021165028", "inst": "Paphos General Hospital" } ], "affiliations": [ "Paphos General Hospital" ] }, { "uid": "arxiv:2505.13511v1", "arxiv_id": "2505.13511v1", "title": "Can AI Freelancers Compete? Benchmarking Earnings, Reliability, and Task Success at Scale", "authors": [ "David Noever", "Forrest McKee" ], "posted": "2025-05-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.13511v1", "field": "economics", "role": "method", "bullets": [ "Synthetic freelance programming and data analysis jobs derived from a Kaggle freelancer job postings dataset, each carrying structured test cases and a dollar price, with a median fixed project price near 250.", "Claude 3.5 Haiku, GPT-4o mini, Qwen 2.5, and Mistral attempt each job; outputs are checked automatically against input output test cases and earnings are the summed prices of solved tasks.", "Claude 3.5 Haiku leads with about 1.52 million dollars in simulated earnings, GPT-4o mini follows at 1.49 million, with Qwen at 1.33 million and Mistral at 0.70 million." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "programmatic input output test cases", "salience": 42, "edition": 14, "n": 1806, "authors_detailed": [ { "name": "David Noever", "url": "https://openalex.org/A5074519523", "inst": "PeopleTec (United States)" }, { "name": "Forrest McKee", "url": "https://openalex.org/A5016940644", "inst": "PeopleTec (United States)" } ], "affiliations": [ "PeopleTec (United States)" ] }, { "uid": "arxiv:2505.11065v2", "arxiv_id": "2505.11065v2", "title": "Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking", "authors": [ "Changlun Li", "Yao Shi", "Chen Wang", "Qiqi Duan", "Runke Ruan", "Weijie Huang", "Haonan Long", "Lijun Huang", "Nan Tang", "Yuyu Luo" ], "posted": "2025-05-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.11065v2", "field": "finance", "role": "method", "bullets": [ "Real time stock market data published after each model's pretraining cutoff, spanning ticker analysis, investment decisions, portfolio management, and risk control for nine flagship LLMs.", "A multi agent live benchmark has each LLM run a fund forward in time, removing the look ahead leakage that arises when backtest windows overlap training corpora.", "Even DeepSeek-V3 and Claude 3.7 Sonnet post net trading losses in the live evaluation, cutting against claims that current LLMs can manage funds actively." ], "bullet_provenance": "ai", "models": [ "claude", "open_other" ], "open_weights": true, "salience": 56, "edition": 14, "validated": null, "n": 1827, "authors_detailed": [ { "name": "C. Wei Li", "url": "https://openalex.org/A5112454106", "inst": "National Chung Hsing University" }, { "name": "Shi, Yao", "url": "", "inst": "" }, { "name": "Wang, Chen", "url": "", "inst": "" }, { "name": "Qiqi Duan", "url": "https://openalex.org/A5103283819", "inst": "Shenzhen University" }, { "name": "Runke Ruan", "url": "https://openalex.org/A5120696074", "inst": "Hong Kong University of Science and Technology" }, { "name": "Weijie Huang", "url": "https://openalex.org/A5066366560", "inst": "University of British Columbia" }, { "name": "Hao Long", "url": "https://openalex.org/A5031924974", "inst": "Nanchang University" }, { "name": "Huang, Lijun", "url": "", "inst": "" }, { "name": "Nan Tang", "url": "https://openalex.org/A5101824160", "inst": "Shanghai University" }, { "name": "Yuyu Luo", "url": "https://openalex.org/A5100614732", "inst": "Beijing Institute of Technology" } ], "affiliations": [ "National Chung Hsing University", "Shenzhen University", "Hong Kong University of Science and Technology", "University of British Columbia", "Nanchang University", "Shanghai University", "Beijing Institute of Technology" ] }, { "uid": "arxiv:2505.11122v3", "arxiv_id": "2505.11122v3", "title": "Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining", "authors": [ "Yu Shi", "Yitong Duan", "Jian Li" ], "posted": "2025-05-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.11122v3", "field": "finance", "role": "method", "bullets": [ "Formulaic alpha factors mined and backtested on real stock market data; the market, period, and universe are not stated in the abstract.", "An LLM, not named in the abstract, generates and refines symbolic formulas inside Monte Carlo tree search, guided by backtest feedback, with a frequent subtree avoidance rule against homogenization.", "Mined factors show better predictive accuracy and trading performance than existing automated baselines, and the authors describe the resulting formulas as easier to interpret." ], "bullet_provenance": "ai", "salience": 48, "edition": 14, "models": [], "validated": null, "n": 1990, "authors_detailed": [ { "name": "Yu Shi", "url": "https://openalex.org/A5100328458", "inst": "Shanghai University" }, { "name": "Duan, Yitong", "url": "", "inst": "" }, { "name": "Li J", "url": "https://openalex.org/A5100402504", "inst": "Beijing Institute of Technology" } ], "affiliations": [ "Shanghai University", "Beijing Institute of Technology" ] }, { "uid": "arxiv:2505.11011v1", "arxiv_id": "2505.11011v1", "title": "Humans expect rationality and cooperation from LLM opponents in strategic games", "authors": [ "Darija Barak", "Miguel Costa-Gomes" ], "posted": "2025-05-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.11011v1", "field": "economics", "role": "agent", "bullets": [ "Incentivized lab experiment using within-subject design in multi-player p-beauty contest with human and LLM opponents.", "LLMs served as strategic opponents; subjects adjusted play based on perceived LLM rationality and cooperation propensity.", "Subjects chose significantly lower numbers against LLMs, driven by high-reasoning subjects playing the zero Nash equilibrium." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "models": [], "n": 2699, "authors_detailed": [ { "name": "Darija Barak", "url": "https://openalex.org/A5120696063", "inst": "" }, { "name": "Miguel Costa-Gomes", "url": "https://openalex.org/A5120696064", "inst": "" } ] }, { "uid": "arxiv:2505.13504v1", "arxiv_id": "2505.13504v1", "title": "An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents", "authors": [ "Ayesha Amjad", "Saurav Sthapit", "Tahir Qasim Syed" ], "posted": "2025-05-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.13504v1", "field": "accounting", "role": "instrument", "bullets": [ "Invoices, purchase orders, and financial documents evaluated on SOIRE and CORD benchmark datasets for data extraction.", "Multi-agent LLM framework with reinforcement-learning driver agent automated extraction using self-correcting task-specific prompts.", "Agentic framework produced promising extraction results on both benchmarks, handling diverse document layouts and formats." ], "bullet_provenance": "ai", "validated": true, "validation_note": "SOIRE and CORD benchmarks", "salience": 40, "models": [], "n": 2700, "authors_detailed": [ { "name": "Amjad, Ayesha", "url": "", "inst": "" }, { "name": "Saurav Sthapit", "url": "https://openalex.org/A5070382610", "inst": "Analog Devices (United States)" }, { "name": "Tahir Syed", "url": "https://openalex.org/A5004232654", "inst": "Institute of Business Administration Karachi" } ], "affiliations": [ "Analog Devices (United States)", "Institute of Business Administration Karachi" ] }, { "uid": "arxiv:2505.11163v1", "arxiv_id": "2505.11163v1", "title": "Foundation Time-Series AI Model for Realized Volatility Forecasting", "authors": [ "Anubha Goel", "Puneet Pasricha", "Martin Magris", "Juho Kanniainen" ], "posted": "2025-05-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.11163v1", "field": "finance", "role": "instrument", "bullets": [ "Realized volatility forecasting on financial return data across multiple assets, compared against standard econometric benchmarks.", "Google TimesFM foundation model evaluated in zero-shot and incrementally fine-tuned modes; accuracy tested via Diebold-Mariano and Giacomini-White statistics.", "Fine-tuned TimesFM statistically outperformed traditional econometric models; pretrained zero-shot version provided only a reasonable baseline." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Diebold-Mariano and Giacomini-White tests vs econometric benchmarks", "salience": 58, "n": 3205, "authors_detailed": [ { "name": "Anubha Goel", "url": "https://openalex.org/A5052447672", "inst": "Tampere University" }, { "name": "Puneet Pasricha", "url": "https://openalex.org/A5029086045", "inst": "Indian Institute of Technology Ropar" }, { "name": "Martin Magris", "url": "https://openalex.org/A5006559883", "inst": "University of Trieste" }, { "name": "Juho Kanniainen", "url": "https://openalex.org/A5049372872", "inst": "Tampere University of Applied Sciences" } ], "affiliations": [ "Tampere University", "Indian Institute of Technology Ropar", "University of Trieste", "Tampere University of Applied Sciences" ] }, { "uid": "arxiv:2505.11599v2", "arxiv_id": "2505.11599v2", "title": "Can LLMs Credibly Transform the Creation of Panel Data from Diverse Historical Tables?", "authors": [ "Verónica Bäcker-Peral", "Vitaly Meursault", "Christopher Severen" ], "posted": "2025-05-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.11599v2", "field": "economics", "role": "instrument", "bullets": [ "Historical county-level U.S. vehicle registration tables from early 20th-century state reports, evaluated against human-transcribed gold standard data.", "Built multimodal LLM pipeline for table digitization; measured exact cell match, parsing errors, and numerical transcription accuracy at scale.", "95.4% exact cell match at 50x lower cost than outsourcing; critical parsing errors cut from 61.4% to 0.35%; regression results statistically indistinguishable from gold standard." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human-transcribed gold standard; 95.4% exact cell match", "salience": 68, "models": [], "n": 3502, "authors_detailed": [ { "name": "Verónica Bäcker-Peral", "url": "https://openalex.org/A5117667451", "inst": "Massachusetts Institute of Technology" }, { "name": "Vitaly Meursault", "url": "https://openalex.org/A5117667452", "inst": "Federal Reserve Bank of Philadelphia" }, { "name": "Christopher Severen", "url": "https://openalex.org/A5029228912", "inst": "Federal Reserve Bank of Philadelphia" } ], "affiliations": [ "Massachusetts Institute of Technology", "Federal Reserve Bank of Philadelphia" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.10732v1", "arxiv_id": "2505.10732v1", "title": "Automating Security Audit Using Large Language Model based Agent: An Exploration Experiment", "authors": [ "Jia Hui Chin", "Pu Zhang", "Yu Xin Cheong", "Jonathan Pan" ], "posted": "2025-05-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.10732v1", "field": "accounting", "role": "method", "bullets": [ "Exploratory experiment automating part of an organizational security audit, the field check of Windows password policy compliance; no firm sample or audit population is described.", "GPT-4 with Langchain executes the audit steps as an autonomous agent; the abstract claims accurate flagging of violations but reports no quantified accuracy benchmark.", "The agent flagged policy violations and appeared faster than manual auditing, with consistency in complex, dynamic environments noted as an open limitation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 30, "edition": 14, "n": 1859, "authors_detailed": [ { "name": "Chin, Jia Hui", "url": "", "inst": "" }, { "name": "Pu Zhang", "url": "https://openalex.org/A5028058582", "inst": "Shijiazhuang University" }, { "name": "Yu Xin Cheong", "url": "https://openalex.org/A5120717161", "inst": "" }, { "name": "Jonathan Z. Pan", "url": "https://openalex.org/A5016967222", "inst": "San Francisco General Hospital" } ], "affiliations": [ "Shijiazhuang University" ] }, { "uid": "arxiv:2505.17048v2", "arxiv_id": "2505.17048v2", "title": "Words That Unite The World: A Unified Framework for Deciphering Central Bank Communications Globally", "authors": [ "Agam Shah", "Siddhant Sukhani", "Huzaifa Pardawala", "Saketh Budideti", "Riya Bhadani", "Rudra Gopal", "Siddhartha Somani", "Rutwik Routu", "Michael Galarnyk", "Soungmin Lee", "Arnav Hiray", "Akshar Ravichandran", "Eric Kim", "Pranav Aluru", "Joshua Zhang", "Sebastian Jaskowski", "Veer Guda", "Meghaj Tarte", "Liqin Ye", "Spencer Gosden", "Rachel Yuh", "Sloka Chava", "Sahasra Chava", "Dylan Patrick Kelly", "Aiden Chiang", "Harsit Mittal", "Sudheer Chava" ], "posted": "2025-05-15", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.17048v2", "field": "economics", "role": "method", "bullets": [ "Over 380,000 sentences from 25 central banks across 28 years; 1,000 sentences per bank were dual-annotated with expert review for stance, timing, and uncertainty.", "Seven pretrained language models and nine LLMs, unnamed in the abstract, are benchmarked zero-shot, few-shot, and with the annotation guide, totaling 15,075 experiments.", "Models trained on pooled data across banks beat single-bank training; human evaluations, error analyses, and predictive tasks support the framework's economic utility." ], "bullet_provenance": "ai", "validated": true, "validation_note": "25k dual-annotated sentences with expert adjudication", "salience": 62, "edition": 14, "models": [], "n": 1989 }, { "uid": "arxiv:2505.10742v3", "arxiv_id": "2505.10742v3", "title": "Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work", "authors": [ "Brandon Lepine", "Juho Kim", "Pamela Mishkin", "Matthew Beane" ], "posted": "2025-05-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.10742v3", "field": "finance", "role": "object", "bullets": [ "34 financial professionals completing a complex valuation task using GPT-4o, yielding 1,178 participant-subtask observations with transcript-based cognitive load measurement.", "GPT-4o assisted valuation work; transcript-based framework estimated intrinsic and extraneous cognitive load from computational indicators anchored in task decomposition.", "AI-generated content usage positively associated with quality; extraneous load's negative association roughly three times that of intrinsic load; model-initiated task switching strongest predictor of decline." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "validated": null, "n": 2999, "authors_detailed": [ { "name": "B Lepine", "url": "https://openalex.org/A5074585017", "inst": "" }, { "name": "Juho Kim", "url": "https://openalex.org/A5079709359", "inst": "SK Group (South Korea)" }, { "name": "Pamela Mishkin", "url": "https://openalex.org/A5120395301", "inst": "" }, { "name": "Matt Beane", "url": "https://openalex.org/A5074288704", "inst": "University of California, Santa Barbara" } ], "affiliations": [ "SK Group (South Korea)", "University of California, Santa Barbara" ] }, { "uid": "arxiv:2506.01973v2", "arxiv_id": "2506.01973v2", "title": "Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges", "authors": [ "Xiao-Yang Liu Yanglet", "Yupeng Cao", "Li Deng" ], "posted": "2025-05-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2506.01973v2", "field": "finance", "role": "method", "bullets": [ "Position paper presented at MFFM Workshop at ACM ICAIF 2024, surveying multimodal foundation models for financial services and investment processes.", "Reviews FinLLMs including FinGPT and BloombergGPT; proposes multimodal financial foundation model paradigm integrating text, audio, images, video, and market data.", "Argues MFFMs will enable deeper understanding of financial complexity; highlights ongoing FinAgent research at Columbia University's SecureFinAI Lab." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 40, "validated": null, "n": 3000, "authors_detailed": [ { "name": "Xiao-Yang Liu Yanglet", "url": "https://openalex.org/A5119984836", "inst": "" }, { "name": "Yupeng Cao", "url": "https://openalex.org/A5009376964", "inst": "Kyoto University" }, { "name": "Li Deng", "url": "https://openalex.org/A5113744040", "inst": "Chongqing University" } ], "affiliations": [ "Kyoto University", "Chongqing University" ] }, { "uid": "arxiv:2505.10278v2", "arxiv_id": "2505.10278v2", "title": "MASS: Muli-agent simulation scaling for portfolio construction", "authors": [ "Taian Guo", "Haiyang Shen", "JinSheng Huang", "Zhengyang Mao", "Junyu Luo", "Binqi Chen", "Zhuoru Chen", "Luchen Liu", "Bingyu Xia", "Xuhui Liu", "Yun Ma", "Ming Zhang" ], "posted": "2025-05-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.10278v2", "field": "finance", "role": "agent", "bullets": [ "2023 Chinese A-share market; multi-agent framework with up to 512 heterogeneous LLM-based agents for end-to-end portfolio construction.", "Used backward optimization to learn optimal distribution of agents; demonstrated scaling effects as agent count increased exponentially to 512.", "Scaling agents yields progressively higher excess returns; outperforms seven baselines with validated robustness against data leakage concerns." ], "bullet_provenance": "ai", "validated": true, "validation_note": "backtesting on 2023 Chinese A-share market vs seven baselines", "salience": 72, "models": [], "n": 3501, "authors_detailed": [ { "name": "Taian Guo", "url": "https://openalex.org/A5054442510", "inst": "Peking University" }, { "name": "Haiyang Shen", "url": "https://openalex.org/A5102363536", "inst": "South China Normal University" }, { "name": "Jinpei Huang", "url": "https://openalex.org/A5069434989", "inst": "Hunan Agricultural University" }, { "name": "Zhengyang Mao", "url": "https://openalex.org/A5054462908", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Junyu Luo", "url": "https://openalex.org/A5101912906", "inst": "Peking University" }, { "name": "Binqi Chen", "url": "https://openalex.org/A5051681992", "inst": "Peking University" }, { "name": "Chen, Zhuoru", "url": "", "inst": "" }, { "name": "Luchen Liu", "url": "https://openalex.org/A5074524814", "inst": "Hainan University" }, { "name": "Bingyu Xia", "url": "https://openalex.org/A5035964015", "inst": "Foxconn (China)" }, { "name": "Xuhui Liu", "url": "https://openalex.org/A5100666705", "inst": "San Francisco VA Medical Center" }, { "name": "Yun Ma", "url": "https://openalex.org/A5100720810", "inst": "Ningxia University" }, { "name": "Ming Zhang", "url": "https://openalex.org/A5100447270", "inst": "Wannan Medical College" } ], "affiliations": [ "Peking University", "South China Normal University", "Hunan Agricultural University", "Beijing Academy of Artificial Intelligence", "Hainan University", "Foxconn (China)", "Ningxia University" ] }, { "uid": "arxiv:2505.10581v1", "arxiv_id": "2505.10581v1", "title": "The Impact of Large Language Models on Task Automation in Manufacturing Services", "authors": [ "Jochen Wulf", "Juerg Meierhofer" ], "posted": "2025-05-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.10581v1", "field": "management", "role": "instrument", "bullets": [ "Production machinery sector customer service data analyzed for text correction, summarization, and question answering tasks using real-life customer communications.", "LLMs with Retrieval Augmented Generation applied to domain-specific technical service documentation for error correction, summarization, and context-aware responses.", "LLMs reliably correct errors and generate concise summaries, demonstrating significant efficiency gains in manufacturing technical service provision and customer support." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 2583, "authors_detailed": [ { "name": "Jochen Wulf", "url": "https://openalex.org/A5036086266", "inst": "ZHAW Zurich University of Applied Sciences" }, { "name": "Juerg Meierhofer", "url": "https://openalex.org/A5113210473", "inst": "" } ], "affiliations": [ "ZHAW Zurich University of Applied Sciences" ] }, { "uid": "arxiv:2505.24650v1", "arxiv_id": "2505.24650v1", "title": "Beyond the Black Box: Interpretability of LLMs in Finance", "authors": [ "Hariom Tatsat", "Ariye Shater" ], "posted": "2025-05-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.24650v1", "field": "finance", "role": "method", "bullets": [ "Financial-services use cases including trading strategies, sentiment analysis, regulatory compliance, bias detection, and hallucination detection.", "Mechanistic interpretability techniques reverse-engineered LLM internal activations and circuits to explain how specific features influence predictions in financial tasks.", "First finance-domain application of mechanistic interpretability; techniques identified bias, detected hallucinations, and addressed regulatory transparency requirements for LLM deployment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 70, "n": 2698, "authors_detailed": [ { "name": "Hariom Tatsat", "url": "https://openalex.org/A5018777944", "inst": "Barclays (United Kingdom)" }, { "name": "Ariye Shater", "url": "https://openalex.org/A5117805084", "inst": "Barclays (United Kingdom)" } ], "affiliations": [ "Barclays (United Kingdom)" ] }, { "uid": "arxiv:2505.09083v1", "arxiv_id": "2505.09083v1", "title": "Ornithologist: Towards Trustworthy \"Reasoning\" about Central Bank Communications", "authors": [ "Dominic Zaun Eu Jones" ], "posted": "2025-05-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.09083v1", "field": "economics", "role": "instrument", "bullets": [ "Reserve Bank of Australia communications classified for hawkishness and dovishness using a weakly-supervised textual classification system.", "Developed Ornithologist, which guides an LLM with human-authored decision trees for taxonomy-guided reasoning to reduce hallucination risk.", "Ornithologist measurements carry predictive information about the future cash rate path and market expectations for RBA policy." ], "bullet_provenance": "ai", "validated": false, "salience": 72, "models": [], "n": 3499, "authors_detailed": [ { "name": "Jones, Dominic Zaun Eu", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5253050", "doi": "10.2139/ssrn.5253050", "title": "GPTs and Digital Superintermediaries: Dynamics, Dilemmas, Dangers of Generative AI: A Conceptual Framework", "authors": [ "Caroline, Muyaluka Azionya" ], "posted": "2025-05-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5253050", "field": "economics", "role": "object", "bullets": [ "Conceptual literature analysis of AI-powered digital platforms, with AI market projected to reach $740 billion by 2030.", "Examined how generative pre-trained transformers influence digital superintermediaries' market power through control of computational resources and data access.", "GPTs create self-reinforcing cycles of AI capability concentration; novel regulatory approaches needed for AI-enhanced multisided platforms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 35, "validated": null, "n": 3500, "authors_detailed": [ { "name": "Muyaluka Azionya Caroline", "url": "https://openalex.org/A5008863873", "inst": "University of Johannesburg" } ], "affiliations": [ "University of Johannesburg" ] }, { "uid": "arxiv:2505.08662v2", "arxiv_id": "2505.08662v2", "title": "Revealing economic facts: LLMs know more than they say", "authors": [ "Marcus Buckmann", "Quynh Anh Nguyen", "Edward Hill" ], "posted": "2025-05-13", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.08662v2", "field": "economics", "role": "method", "bullets": [ "County-level statistics such as unemployment and firm-level items such as total assets, estimated and imputed from model internals rather than from generated text.", "Linear probes are trained on hidden states of open-source LLMs, families not named in the abstract, and benchmarked against the models' direct text outputs on labelled data.", "Hidden-state probes beat text outputs with only a few dozen labelled examples, and a transfer method improves accuracy with no labels for the target variable." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "estimates compared to actual county and firm statistics", "salience": 66, "edition": 14, "models": [], "n": 1988 }, { "uid": "doi:10.2139/ssrn.5252888", "doi": "10.2139/ssrn.5252888", "title": "A Novel Multi-Step-Prompt Approach for LLM-based Q&As on Banking Supervisory Regulations", "authors": [ "Daniele Licari", "Canio Benedetto", "Daniele Bovi", "Praveen Bushipaka", "Alessandro De Gregorio", "Marco De Leonardis", "Tommaso Cucinotta" ], "posted": "2025-05-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5252888", "field": "finance", "role": "instrument", "bullets": [ "Banking supervisory regulation questions on CRR liquidity risk rules benchmarked against official European Banking Authority answers", "Multi-step prompting enriched LLM context with relevant CRR articles and compared results against zero-shot and few-shot baselines via an LLM evaluator", "Multi-step approach significantly improved answer correctness and completeness over standard prompting for complex regulatory questions" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "EBA official answers as benchmark", "salience": 55, "n": 2430, "authors_detailed": [ { "name": "Daniele Licari", "url": "https://openalex.org/A5072778657", "inst": "Bank of Italy" }, { "name": "Canio Benedetto", "url": "https://openalex.org/A5117536058", "inst": "Bank of Italy" }, { "name": "Daniele Bovi", "url": "https://openalex.org/A5015649570", "inst": "Bank of Italy" }, { "name": "Praveen Bushipaka", "url": "https://openalex.org/A5092770706", "inst": "Scuola Superiore Sant'Anna" }, { "name": "Alessandro De Gregorio", "url": "https://openalex.org/A5117536060", "inst": "Bank of Italy" }, { "name": "Marco De Leonardis", "url": "https://openalex.org/A5117536061", "inst": "Bank of Italy" }, { "name": "Tommaso Cucinotta", "url": "https://openalex.org/A5058912977", "inst": "Scuola Superiore Sant'Anna" } ], "affiliations": [ "Bank of Italy", "Scuola Superiore Sant'Anna" ] }, { "uid": "doi:10.2139/ssrn.5250742", "doi": "10.2139/ssrn.5250742", "title": "Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI", "authors": [ "Anders Humlum", "Emilie Vestergaard" ], "posted": "2025-05-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5250742", "field": "economics", "role": "object", "bullets": [ "Danish workers and employers in ChatGPT-exposed occupations, linked large-scale adoption surveys to administrative labor market records, two years post-launch.", "ChatGPT and similar chatbots adopted widely by employers; difference-in-differences estimates effects on earnings, hours, and task composition.", "Precise null effects on earnings and hours (ruling out effects >2%), but significant task reorganization into content generation, AI oversight, and higher-paying AI-relevant occupations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 82, "validated": null, "n": 2998, "authors_detailed": [ { "name": "Anders Humlum", "url": "https://openalex.org/A5093399820", "inst": "University of Chicago" }, { "name": "Emilie Vestergaard", "url": "https://openalex.org/A5112220091", "inst": "University of Copenhagen" } ], "affiliations": [ "University of Chicago", "University of Copenhagen" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5252770", "doi": "10.2139/ssrn.5252770", "title": "Adoption and Expected Impact of Generative AI: Evidence from Italian Households", "authors": [ "David Loschiavo", "Mirko Moscatelli" ], "posted": "2025-05-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5252770", "field": "economics", "role": "object", "bullets": [ "Bank of Italy Conjunctural Survey of Italian households, August-September 2024, examining GenAI adoption, trust, and labor market expectations.", "Analyzed socio-demographic determinants of GenAI use and compared trust in AI-based services versus human-managed alternatives across sectors.", "25% of respondents used GenAI in prior 12 months; adoption concentrated among men, younger workers, and ICT/professional/education sectors." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3498, "authors_detailed": [ { "name": "David Loschiavo", "url": "https://openalex.org/A5117525599", "inst": "Bank of Italy" }, { "name": "Mirko Moscatelli", "url": "https://openalex.org/A5117525600", "inst": "Bank of Italy" } ], "affiliations": [ "Bank of Italy" ] }, { "uid": "arxiv:2505.07457v1", "arxiv_id": "2505.07457v1", "title": "Can Generative AI agents behave like humans? Evidence from laboratory market experiments", "authors": [ "R. Maria del Rio-Chanona", "Marco Pangallo", "Cars Hommes" ], "posted": "2025-05-12", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.07457v1", "field": "economics", "role": "agent", "bullets": [ "Laboratory style market experiments with positive and negative feedback structures, populated by interacting LLM agents whose decisions move prices that feed back into later decisions.", "LLM agents, models not named in the abstract, trade with a three period memory window and a high variability setting; simulated dynamics are compared with human laboratory sessions.", "Agents show bounded rather than strictly rational expectations and reproduce the broad distinction between feedback regimes, but display less behavioural heterogeneity than human participants." ], "bullet_provenance": "ai", "salience": 58, "edition": 14, "models": [], "validated": null, "n": 1906, "authors_detailed": [ { "name": "R. Maria del Rio-Chanona", "url": "https://openalex.org/A5045698412", "inst": "University College London" }, { "name": "Marco Pangallo", "url": "https://openalex.org/A5057979179", "inst": "Centra Health" }, { "name": "Cars Hommes", "url": "https://openalex.org/A5065911565", "inst": "Tinbergen Institute" } ], "affiliations": [ "University College London", "Centra Health", "Tinbergen Institute" ] }, { "uid": "doi:10.2139/ssrn.5217193", "doi": "10.2139/ssrn.5217193", "title": "On the Usefulness of Using Current LLMs for Experimental Auction Valuation", "authors": [ "Jay R. Corrigan", "Carola Grebitus", "Matthew C. Rousu" ], "posted": "2025-05-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5217193", "field": "economics", "role": "agent", "bullets": [ "Two experimental auction datasets with human participant bids from the United States and Germany, including demographic data for each participant.", "ChatGPT, Claude, and Gemini generated simulated auction bids using experiment instructions and varying proportions of original human bid data as reference.", "LLM-generated bids without reference data do not match human valuations, but providing 10-20% of original bids as anchors produces closely matching results." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "validated": true, "validation_note": "Compared LLM-generated bids against human auction bids", "salience": 62, "n": 2395, "authors_detailed": [ { "name": "Jay R. Corrigan", "url": "https://openalex.org/A5055854037", "inst": "Kenyon College" }, { "name": "Carola Grebitus", "url": "https://openalex.org/A5053652107", "inst": "Twitter (United States)" }, { "name": "Matthew C. Rousu", "url": "https://openalex.org/A5045931662", "inst": "Susquehanna University" } ], "affiliations": [ "Kenyon College", "Twitter (United States)", "Susquehanna University" ] }, { "uid": "doi:10.1145/3770854.3785702", "doi": "10.1145/3770854.3785702", "arxiv_id": "2505.07078v6", "title": "Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?", "authors": [ "Weixian Waylon Li", "Hyeonjun Kim", "Mihai Cucuringu", "Tiejun Ma" ], "posted": "2025-05-11", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.07078v6", "field": "finance", "role": "method", "bullets": [ "Backtests spanning two decades and more than 100 symbols under the FINSABER framework, built to test timing based LLM investing strategies beyond narrow periods and universes.", "Existing LLM strategy setups are re evaluated rather than redesigned; underlying models are not named in the abstract, and the design targets survivorship and data snooping biases.", "Previously reported LLM advantages deteriorate significantly under broader cross sections and longer horizons; strategies are overly conservative in bull markets and overly aggressive in bear markets, incurring heavy losses." ], "bullet_provenance": "ai", "salience": 62, "edition": 14, "models": [], "validated": null, "n": 1905, "authors_detailed": [ { "name": "W Li", "url": "https://openalex.org/A5076224522", "inst": "University of Edinburgh" }, { "name": "Hyeonjun Kim", "url": "https://openalex.org/A5112162751", "inst": "Sungkyunkwan University" }, { "name": "Mihai Cucuringu", "url": "https://openalex.org/A5039600886", "inst": "Science Oxford" }, { "name": "Tiejun Ma", "url": "https://openalex.org/A5069370346", "inst": "University of Edinburgh" } ], "affiliations": [ "University of Edinburgh", "Sungkyunkwan University", "Science Oxford" ] }, { "uid": "arxiv:2505.06864v1", "arxiv_id": "2505.06864v1", "title": "NewsNet-SDF: Stochastic Discount Factor Estimation with Pretrained Language Model News Embeddings via Adversarial Networks", "authors": [ "Shunyao Wang", "Ming Cheng", "Christina Dan Wang" ], "posted": "2025-05-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.06864v1", "field": "finance", "role": "instrument", "bullets": [ "U.S. equities covering 10,000 securities from 1980 to 2022, paired with 2.5 million financial news articles and macroeconomic time series data.", "GTE-multilingual pretrained language model extracted news embeddings fed into an adversarial network to estimate stochastic discount factors for asset pricing.", "Achieved Sharpe ratio of 2.80, a 471% improvement over CAPM and 74% reduction in pricing errors versus the Fama-French five-factor model." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 50, "n": 3923, "authors_detailed": [ { "name": "Shunyao Wang", "url": "https://openalex.org/A5046542152", "inst": "Nanjing University of Science and Technology" }, { "name": "Ming Cheng", "url": "https://openalex.org/A5101598960", "inst": "Southern University of Science and Technology" }, { "name": "Christina Dan Wang", "url": "https://openalex.org/A5002366400", "inst": "New York University Shanghai" } ], "affiliations": [ "New York University Shanghai", "Nanjing University of Science and Technology", "Southern University of Science and Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2507.18639v1", "arxiv_id": "2507.18639v1", "title": "People Are Highly Cooperative with Large Language Models, Especially When Communication Is Possible or Following Human Interaction", "authors": [ "Paweł Niszczota", "Tomasz Grzegorczyk", "Alexander Pastukhov" ], "posted": "2025-05-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2507.18639v1", "field": "management", "role": "agent", "bullets": [ "Two experiments (N=100 repeated game, N=192 one-shot) using Prisoner's Dilemma with human participants facing human, classic bot, or GPT opponents in real time.", "GPT played as an opponent in cooperative games; one condition allowed free-text communication between participant and LLM before each decision.", "Cooperation with LLMs ran 10-15 percentage points below human-opponent rates; communication raised cooperation by 88% equally for human and LLM opponents." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 2697, "authors_detailed": [ { "name": "Paweł Niszczota", "url": "https://openalex.org/A5120780037", "inst": "" }, { "name": "Tomasz Grzegorczyk", "url": "https://openalex.org/A5120780038", "inst": "" }, { "name": "Alexander Pastukhov", "url": "https://openalex.org/A5034781477", "inst": "University of Bamberg" } ], "affiliations": [ "University of Bamberg" ] }, { "uid": "doi:10.1109/ids66066.2025.00013", "doi": "10.1109/ids66066.2025.00013", "arxiv_id": "2505.06408v2", "title": "A New DAPO Algorithm for Stock Trading", "authors": [ "Ruijian Zha", "Bojun Liu" ], "posted": "2025-05-09", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.06408v2", "field": "finance", "role": "instrument", "bullets": [ "NASDAQ-100 index trading built on the FNSPID financial news dataset, with a reinforcement learning agent trained for 100 epochs on price and news inputs.", "An LLM, family not stated, turns news into risk and sentiment signals feeding a GRPO algorithm augmented with dynamic sampling ideas; the signals are not validated against ground truth.", "The agent reports a 230.49 percent cumulative return and a 0.37 information ratio, beating a CPPO DeepSeek baseline, with training time cut from about 8 hours to 2.5." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 40, "edition": 14, "n": 1858, "authors_detailed": [ { "name": "Ruijian Zha", "url": "https://openalex.org/A5118449050", "inst": "Columbia University" }, { "name": "Bo Liu", "url": "https://openalex.org/A5100461618", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.07871v1", "arxiv_id": "2505.07871v1", "title": "Evaluating Financial Sentiment Analysis with Annotators Instruction Assisted Prompting: Enhancing Contextual Interpretation and Stock Prediction Accuracy", "authors": [ "A M Muntasir Rahman", "Ajim Uddin", "Guiling \"Grace\" Wang" ], "posted": "2025-05-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.07871v1", "field": "finance", "role": "method", "bullets": [ "Financial PhraseBank and a new WallStreetBets subreddit dataset used to evaluate LLM financial sentiment analysis with annotator-guided prompting.", "LLMs evaluated with Annotators Instruction Assisted Prompt, integrating human annotator task definitions into the prompt framework for sentiment classification.", "AIAP improved LLM sentiment accuracy by up to 9.08 percentage points; a confidence-score sentiment index enhanced stock price prediction models." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Financial PhraseBank and WSBS benchmark accuracy", "salience": 50, "models": [], "n": 2850 }, { "uid": "arxiv:2505.05523v1", "arxiv_id": "2505.05523v1", "title": "GenAI in Entrepreneurship: a systematic review of generative artificial intelligence in entrepreneurship research: current issues and future directions", "authors": [ "Anna Kusetogullari", "Huseyin Kusetogullari", "Martin Andersson", "Tony Gorschek" ], "posted": "2025-05-08", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.05523v1", "field": "management", "role": "object", "bullets": [ "Systematic review of 83 peer-reviewed articles on generative AI in entrepreneurship research, retrieved from Web of Science and Scopus; the covered period is not stated.", "No LLM analyzes the corpus; themes come from TF-IDF vectorization with principal component analysis and hierarchical clustering over the article texts.", "Five clusters emerge, from digital transformation to data-driven trends, and the authors call for macro-level work on GenAI as an external enabler and on regulatory frameworks." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "salience": 30, "edition": 14, "validated": null, "n": 1987, "authors_detailed": [ { "name": "Anna Kusetogullari", "url": "https://openalex.org/A5030659142", "inst": "Blekinge Institute of Technology" }, { "name": "Huseyin Kusetogullari", "url": "https://openalex.org/A5102983746", "inst": "Ajman University" }, { "name": "Martin Andersson", "url": "https://openalex.org/A5101398385", "inst": "SKF (Sweden)" }, { "name": "Tony Gorschek", "url": "https://openalex.org/A5047199297", "inst": "Fortiss" } ], "affiliations": [ "Blekinge Institute of Technology", "Ajman University", "SKF (Sweden)", "Fortiss" ] }, { "uid": "doi:10.2139/ssrn.5247196", "doi": "10.2139/ssrn.5247196", "title": "Towards the Terminator Economy: Assessing Job Exposure to Ai Through Llms", "authors": [ "Emilio Colombo", "Fabio Mercorio", "Mario Mezzanzanica", "Antonio Serino" ], "posted": "2025-05-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5247196", "field": "economics", "role": "instrument", "bullet_provenance": "none", "salience": 48, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 671, "authors_detailed": [ { "name": "Emilio Colombo", "url": "https://openalex.org/A5013367449", "inst": "Università Cattolica del Sacro Cuore" }, { "name": "Fabio Mercorio", "url": "https://openalex.org/A5047827615", "inst": "University of Milano-Bicocca" }, { "name": "Mario Mezzanzanica", "url": "https://openalex.org/A5039716498", "inst": "University of Milano-Bicocca" }, { "name": "Antonio Serino", "url": "https://openalex.org/A5099566317", "inst": "University of Milano-Bicocca" } ], "affiliations": [ "Università Cattolica del Sacro Cuore", "University of Milano-Bicocca" ] }, { "uid": "doi:10.2139/ssrn.5167493", "doi": "10.2139/ssrn.5167493", "title": "Can Large Language Models Extract Customer Needs as well as Professional Analysts?", "authors": [ "Artem Timoshenko", "Chengfeng Mao", "John R. Hauser" ], "posted": "2025-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5167493", "field": "management", "role": "method", "bullet_provenance": "none", "salience": 50, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 351, "authors_detailed": [ { "name": "Artem Timoshenko", "url": "https://openalex.org/A5022326376", "inst": "Kellogg's (Canada)" }, { "name": "Chengfeng Mao", "url": "https://openalex.org/A5055221257", "inst": "Massachusetts Institute of Technology" }, { "name": "John R. Hauser", "url": "https://openalex.org/A5032453542", "inst": "MIT Sloan School of Management" } ], "affiliations": [ "Massachusetts Institute of Technology", "MIT Sloan School of Management", "Kellogg's (Canada)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2505.04110v1", "arxiv_id": "2505.04110v1", "title": "Alpha Excel Benchmark", "authors": [ "David Noever", "Forrest McKee" ], "posted": "2025-05-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.04110v1", "field": "finance", "role": "method", "bullets": [ "113 Financial Modeling World Cup Excel competition challenges converted to programmatically evaluable JSON format for LLM benchmarking.", "Several leading LLMs solved financial-modeling tasks spanning pattern recognition and numerical reasoning, scored against known challenge answers.", "Performance varied sharply by category; models excelled at pattern recognition but struggled with complex numerical reasoning in business-oriented financial tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "FMWC challenge ground-truth answers", "salience": 48, "n": 2696, "authors_detailed": [ { "name": "David Noever", "url": "https://openalex.org/A5074519523", "inst": "PeopleTec (United States)" }, { "name": "Forrest McKee", "url": "https://openalex.org/A5016940644", "inst": "PeopleTec (United States)" } ], "affiliations": [ "PeopleTec (United States)" ] }, { "uid": "doi:10.2139/ssrn.5240924", "doi": "10.2139/ssrn.5240924", "title": "Human + AI in Accounting: Early Evidence from the Field", "authors": [ "Jung Ho Choi", "Chloe Xie" ], "posted": "2025-05-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5240924", "field": "accounting", "role": "object", "bullets": [ "Survey of 277 accountants and field data from 79 SMEs using AI-based accounting software, with hundreds of thousands of transaction-level records.", "Measured GenAI adoption effects on productivity, reporting quality, and task reallocation; ran framed field experiment testing over-reliance on AI classifications.", "18% increase in weekly client support per SD of AI use; 12% increase in ledger granularity; 7.5-day reduction in monthly close time." ], "bullet_provenance": "ai", "salience": 85, "models": [], "validated": null, "n": 3496 }, { "uid": "doi:10.2139/ssrn.5232395", "doi": "10.2139/ssrn.5232395", "title": "Integrating generative AI into financial reporting systems for automated insights and decision support", "authors": [ "Harish Kumar Sriram" ], "posted": "2025-05-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5232395", "field": "accounting", "role": "instrument", "bullets": [ "Conceptual framework for integrating GenAI into corporate financial reporting systems covering liquidity, solvency, and profitability assessment.", "Proposed GenAI-powered automation of on-demand financial insights, what-if analyses for capital budgeting, and profit distribution decision support.", "Framework extends to any FRS at national or global levels; positions the FRS as a hub for integrated generative AI technologies." ], "bullet_provenance": "ai", "validated": false, "salience": 25, "models": [], "n": 3497, "authors_detailed": [ { "name": "Harish Kumar Sriram", "url": "https://openalex.org/A5116219676", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.5222006", "doi": "10.2139/ssrn.5222006", "title": "Large Language Models in the Workplace: A Social Perspective", "authors": [ "Meng Li", "Lai Wei", "Yao Yao" ], "posted": "2025-05-06", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5222006", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 40, "edition": 24, "bullets": [], "models": [], "validated": null, "n": 4228, "authors_detailed": [ { "name": "Meng Li", "url": "https://openalex.org/A5100666330", "inst": "Decision Sciences (United States)" }, { "name": "Wei Lai", "url": "https://openalex.org/A5012372031", "inst": "Boston College" }, { "name": "Yao Yao", "url": "https://openalex.org/A5102941230", "inst": "University of Houston" } ], "affiliations": [ "Boston College", "Decision Sciences (United States)", "University of Houston" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5223637", "doi": "10.2139/ssrn.5223637", "title": "A Taxonomic Framework of Prompt Engineering Methodologies: Systematizing Generative AI Applications in Marketing Practice", "authors": [ "Achint Nigam" ], "posted": "2025-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5223637", "field": "management", "role": "method", "bullet_provenance": "none", "salience": 34, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 543, "authors_detailed": [ { "name": "Achint Nigam", "url": "https://openalex.org/A5041207096", "inst": "Birla Institute of Technology and Science, Pilani" } ], "affiliations": [ "Birla Institute of Technology and Science, Pilani" ] }, { "uid": "doi:10.2139/ssrn.5221827", "doi": "10.2139/ssrn.5221827", "title": "Regulating Generative Artificial Intelligence in the Malaysian region: The case of ChatGPT in the business and finance sectors", "authors": [ "Parveen Kaur Harnam Singh" ], "posted": "2025-05-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5221827", "field": "management", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 33, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 670, "authors_detailed": [ { "name": "Parveen Kaur Harnam Singh", "url": "", "inst": "University of Malaya" } ], "affiliations": [ "University of Malaya" ] }, { "uid": "doi:10.2139/ssrn.5222427", "doi": "10.2139/ssrn.5222427", "title": "Outperformed by AI: Time to Replace Your Analyst? Find Out Which GenAI Model Does It Best", "authors": [ "Michael Schopf" ], "posted": "2025-05-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5222427", "field": "finance", "role": "agent", "bullets": [ "Six leading LLMs evaluated on SWOT analyses of global companies across multiple sectors, comparing output quality against human investment analyst benchmarks.", "Grok3, ChatGPT-4o, and four other LLMs produced SWOT analyses with varying prompt sophistication; outputs compared on specificity, depth, and analytical quality.", "Advanced prompting improved LLM output quality by up to 40%; top models matched or exceeded human analysts in specificity and depth of financial analysis." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": false, "salience": 48, "n": 2401, "authors_detailed": [ { "name": "Michael Schöpf", "url": "https://openalex.org/A5097027425", "inst": "Schopf Meta Consult (SMC)" } ], "affiliations": [ "Schopf Meta Consult (SMC)" ] }, { "uid": "doi:10.2139/ssrn.5221064", "doi": "10.2139/ssrn.5221064", "title": "Ideology and Asset Pricing *", "authors": [ "Jiaen Li" ], "posted": "2025-05-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5221064", "field": "finance", "role": "instrument", "bullets": [ "Cross-section of cryptocurrency returns and U.S. stocks analyzed using social media data to capture anarchism and decentralization narratives", "LLMs measured ideology dynamics from social media text to construct two ideology-based asset pricing factors for cryptocurrencies", "Two-factor ideology model outperforms a three-factor crypto market-size-momentum model; ideology exposure also predicts abnormal stock returns" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "n": 2997, "authors_detailed": [ { "name": "Jiaen Li", "url": "https://openalex.org/A5063897247", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5202553", "doi": "10.2139/ssrn.5202553", "title": "The Impact of Realism and AI Disclosure on Virtual Influencer Effectiveness: A Large Field Experiment", "authors": [ "Meixian Wang", "Jaehwuen Jung", "Ravi Bapna" ], "posted": "2025-05-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5202553", "field": "management", "role": "object", "bullets": [ "Field experiment with over 1.8 million consumers exposed to AI-generated virtual influencer marketing content across varying anthropomorphism levels.", "Tested interplay of virtual influencer realism and AI identity disclosure on consumer engagement metrics including link clicks and video plays.", "AI disclosure reduces clicks for highly realistic virtual influencers via expectation violation; experienced users show reversed positive engagement effects." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3203, "authors_detailed": [ { "name": "Meixian Wang", "url": "https://openalex.org/A5052605570", "inst": "Temple University" }, { "name": "Jaehwuen Jung", "url": "https://openalex.org/A5050983035", "inst": "Temple University" }, { "name": "Ravi Bapna", "url": "https://openalex.org/A5058644870", "inst": "University of Minnesota" } ], "affiliations": [ "University of Minnesota", "Temple University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5202630", "doi": "10.2139/ssrn.5202630", "title": "The Impact of Realism and AI Disclosure on Virtual Influencer Effectiveness: A Large Field Experiment", "authors": [ "Meixian Wang", "Jaehwuen Jung", "Ravi Bapna" ], "posted": "2025-05-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5202630", "field": "management", "role": "object", "bullets": [ "Field experiment with 1.8 million consumers exposed to AI-generated virtual influencer content on a social media platform.", "Virtual influencers with varying anthropomorphism levels deployed with randomized AI identity disclosure; online experiment tested underlying mechanisms.", "AI disclosure reduced link clicks and video plays for highly realistic influencers, but consumers with prior virtual influencer experience showed reversed positive engagement." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3204, "authors_detailed": [ { "name": "Meixian Wang", "url": "https://openalex.org/A5052605570", "inst": "Temple University" }, { "name": "Jaehwuen Jung", "url": "https://openalex.org/A5050983035", "inst": "Temple University" }, { "name": "Ravi Bapna", "url": "https://openalex.org/A5058644870", "inst": "University of Minnesota" } ], "affiliations": [ "University of Minnesota", "Temple University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5224596", "doi": "10.2139/ssrn.5224596", "title": "How Stock Market Participants Use Generative Artificial Intelligence: Evidence from User-Platform Interaction Data", "authors": [ "Frank Ecker", "Xitong Li", "Yilan Li", "Fan Wu" ], "posted": "2025-05-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5224596", "field": "finance", "role": "object", "bullets": [ "Dataset of 1.7 million stock-related queries from one of China's largest GenAI platforms during the first half of 2024, tracking retail investor behavior.", "GenAI platform answers to investor queries analyzed for conciseness, directional trading signal accuracy, and sentiment; user query sophistication tracked over continued engagement.", "GenAI usage associated with higher informed trading and lower liquidity; aggregated answer sentiment correlates with same-day abnormal returns when user feedback is positive." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 78, "validated": null, "n": 3493 }, { "uid": "doi:10.2139/ssrn.5205850", "doi": "10.2139/ssrn.5205850", "title": "The Transparency Dilemma: How AI Disclosure Erodes Trust", "authors": [ "Oliver Schilke", "Martin Reimann" ], "posted": "2025-05-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5205850", "field": "management", "role": "object", "bullets": [ "Thirteen experiments across diverse tasks including communications, analytics, and artistry, with individual actors and organizational actors such as investment funds.", "Tested whether disclosing AI usage compromises trust, using micro-institutional theory to explain the role of perceived legitimacy in trust formation.", "AI disclosure consistently reduces trust; the penalty is attenuated but not eliminated among evaluators with favorable technology attitudes and high AI accuracy perceptions." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3494, "authors_detailed": [ { "name": "Oliver Schilke", "url": "https://openalex.org/A5001948088", "inst": "University of Arizona" }, { "name": "Martin Reimann", "url": "https://openalex.org/A5026697089", "inst": "University of Arizona" } ], "affiliations": [ "University of Arizona" ] }, { "uid": "doi:10.2139/ssrn.5217951", "doi": "10.2139/ssrn.5217951", "title": "Images Tell Stories", "authors": [ "Joshua Ronen", "Tavy Ronen", "Mi Zhou" ], "posted": "2025-05-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5217951", "field": "finance", "role": "instrument", "bullets": [ "Review of research on images in financial contexts, including crowdfunding, analyst forecasts, and market risk, illustrated with GE 10-K filings.", "Discussed generative AI for image generation and analysis; examined how image expressiveness and content operate through affect and cognition channels.", "Image metrics associate with higher crowdfunding investments, smaller analyst forecast errors and dispersion, and lower market risk." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3495, "authors_detailed": [ { "name": "Joshua Ronen", "url": "https://openalex.org/A5019523502", "inst": "Faculty of 1000 (United States)" }, { "name": "Tavy Ronen", "url": "https://openalex.org/A5005586420", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Mi Zhou", "url": "https://openalex.org/A5070503917", "inst": "Virginia Tech" } ], "affiliations": [ "Faculty of 1000 (United States)", "Rutgers, The State University of New Jersey", "Virginia Tech" ] }, { "uid": "doi:10.2139/ssrn.5227409", "doi": "10.2139/ssrn.5227409", "title": "Breaking (up) News: How Current and Forward-Looking Information Impact US Treasury Yield Dynamics", "authors": [ "Matthias Apel", "Maximilian Stroh" ], "posted": "2025-05-05", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5227409", "field": "finance", "role": "instrument", "bullets": [ "US Federal Reserve-related news coverage used to study Treasury yield dynamics across maturities; sample size and time span are not stated.", "Large language models, family not stated, were instructed to classify central bank news into current versus forward-looking hawkish or dovish signals, with no validation against human labels reported.", "The forward-looking policy trend predicts future yield movements, strongest at short maturities, while the current-information trend only co-moves contemporaneously." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "edition": 7, "models": [], "n": 1289, "authors_detailed": [ { "name": "Matthias Apel", "url": "", "inst": "Quoniam Asset Management GmbH" }, { "name": "Maximilian Stroh", "url": "https://openalex.org/A5055478210", "inst": "Quoniam Asset Management" } ], "affiliations": [ "Quoniam Asset Management GmbH", "Quoniam Asset Management" ] }, { "uid": "arxiv:2505.05494v1", "arxiv_id": "2505.05494v1", "title": "An Automated LLM-based Pipeline for Asset-Level Database Creation to Assess Deforestation Impact", "authors": [ "Avanija Menon", "Ovidiu Serban" ], "posted": "2025-05-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.05494v1", "field": "accounting", "role": "instrument", "bullets": [ "SEC EDGAR filings from Mining, Oil & Gas, and Utilities sectors processed for EU Deforestation Regulation compliance and ESG assessment.", "LLMs with Instructional Role-Based Zero-Shot Chain-of-Thought prompting extracted asset-level data; Retrieval-Augmented Validation verified outputs via web search.", "The IRZ-CoT pipeline significantly improved extraction accuracy over zero-shot prompting, with RAV enhancing validation coverage for structured regulatory data." ], "bullet_provenance": "ai", "validated": true, "validation_note": "extraction accuracy vs. zero-shot baseline", "salience": 50, "models": [], "n": 2849, "authors_detailed": [ { "name": "Menon, Avanija", "url": "", "inst": "" }, { "name": "Ovidiu Şerban", "url": "https://openalex.org/A5040170557", "inst": "Imperial College London" } ], "affiliations": [ "Imperial College London" ] }, { "uid": "doi:10.5281/zenodo.15327831", "doi": "10.5281/zenodo.15327831", "arxiv_id": "2505.01575v3", "title": "Asset Pricing in Pre-trained Transformer", "authors": [ "Shanyan Lai" ], "posted": "2025-05-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.01575v3", "field": "finance", "role": "method", "bullets": [ "US large-cap stocks spanning pre-COVID, COVID, and one-year post-COVID periods, evaluated with equal- and value-weighted portfolios.", "Proposed SERT transformer and pre-trained transformers compared against standard and encoder-only transformers for factor-based asset pricing.", "SERT achieved 11.94% out-of-sample R-squared during market volatility and a Sortino ratio 47% above the buy-and-hold benchmark in equal-weighted portfolios." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "out-of-sample R-squared on US large-cap stock returns", "salience": 55, "n": 3922, "authors_detailed": [ { "name": "Shanyan Lai", "url": "https://openalex.org/A5055511815", "inst": "University of York" } ], "affiliations": [ "University of York" ] }, { "uid": "arxiv:2505.02846v2", "arxiv_id": "2505.02846v2", "title": "The Precautionary Principle and the Innovation Principle: Incompatible Guides for AI Innovation Governance?", "authors": [ "Kim Kaivanto" ], "posted": "2025-05-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.02846v2", "field": "economics", "role": "object", "bullets": [ "Theoretical framework using Signal Detection Theory to model optimal regulatory trade-offs between type-I and type-II errors in AI innovation governance decisions.", "No specific language model deployed; framework analyzes weak-form precautionary and innovation principles as potentially complementary regulatory stances for AI foundation models.", "Weak precautionary and innovation principles are compatible for sufficiently extreme cost ratios; regulatory sandboxes suit narrow AI but not general-purpose foundation models." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3492, "authors_detailed": [ { "name": "Kim Kaivanto", "url": "https://openalex.org/A5070587276", "inst": "Lancaster University" } ], "affiliations": [ "Lancaster University" ] }, { "uid": "arxiv:2504.21400v1", "arxiv_id": "2504.21400v1", "title": "Who Gets the Callback? Generative AI and Gender Bias", "authors": [ "Sugat Chaturvedi", "Rochana Chaturvedi" ], "posted": "2025-04-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.21400v1", "field": "economics", "role": "agent", "bullets": [ "332,044 real world online job postings mapped to the Standard Occupational Classification, each presented with equally qualified male and female candidates for shortlisting.", "Mid sized open weight LLMs, not named in the abstract, recommend which candidate should receive an interview callback; persona infusion with Big Five traits and historical figures probes recruiter identity.", "Most models favor men, with the gap widening in higher wage and male dominated occupations; recommendations align with gendered ad language, and less agreeable personas stereotype less." ], "bullet_provenance": "ai", "open_weights": true, "salience": 66, "edition": 14, "models": [], "validated": null, "n": 1826, "authors_detailed": [ { "name": "Sugat Chaturvedi", "url": "https://openalex.org/A5001488339", "inst": "Ahmedabad University" }, { "name": "Rochana Chaturvedi", "url": "https://openalex.org/A5081469985", "inst": "Northwestern University" } ], "affiliations": [ "Northwestern University", "Ahmedabad University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2504.21574v1", "arxiv_id": "2504.21574v1", "title": "Generative AI in Financial Institution: A Global Survey of Opportunities, Threats, and Regulation", "authors": [ "Bikash Saha", "Nanda Rani", "Sandeep Kumar Shukla" ], "posted": "2025-04-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.21574v1", "field": "finance", "role": "object", "bullets": [ "A global survey of generative AI adoption across banks, insurers, asset managers, and fintech firms, drawing on academic literature, industry case studies, and policy frameworks.", "No model is deployed or tested; the paper catalogues uses from virtual assistants to fraud detection and threats such as AI-generated phishing and deepfake-enabled fraud.", "Recommends explainability techniques, adversarial testing, auditability, and human oversight for secure adoption, and reviews regulatory initiatives worldwide; no empirical estimates are reported." ], "bullet_provenance": "ai", "salience": 30, "edition": 14, "models": [], "validated": null, "n": 1986, "authors_detailed": [ { "name": "Bikash Saha", "url": "https://openalex.org/A5058842043", "inst": "Indian Institute of Technology Kanpur" }, { "name": "Nanda Rani", "url": "https://openalex.org/A5001530440", "inst": "Helmholtz Center for Information Security" }, { "name": "Sandeep Kumar Shukla", "url": "https://openalex.org/A5103516105", "inst": "Sharda University" } ], "affiliations": [ "Indian Institute of Technology Kanpur", "Helmholtz Center for Information Security", "Sharda University" ] }, { "uid": "arxiv:2505.00036v1", "arxiv_id": "2505.00036v1", "title": "A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies", "authors": [ "Zhongren Chen", "Joshua Kalla", "Quan Le", "Shinpei Nakamura-Sakai", "Jasjeet Sekhon", "Ruixiao Wang" ], "posted": "2025-04-29", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2505.00036v1", "field": "economics", "role": "object", "bullets": [ "Two survey experiments totaling 10,417 respondents across three US political domains, plus a simulation of campaign costs calibrated to real-world exposure parameters.", "Chatbots, model not named in the abstract, held extended persuasive conversations; effects were benchmarked against actual campaign ads over short and longer horizons.", "Persuasion per exposed voter matches campaign ads; estimated cost is 48 to 74 dollars per persuaded voter versus about 100 for traditional methods, which remain easier to scale." ], "bullet_provenance": "ai", "salience": 56, "edition": 14, "models": [], "validated": null, "n": 1985 }, { "uid": "doi:10.2139/ssrn.5159615", "doi": "10.2139/ssrn.5159615", "title": "Chronologically Consistent Large Language Models", "authors": [ "Songrun He", "Linying Lv", "Asaf Manela", "Jimmy Wu" ], "posted": "2025-04-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5159615", "field": "finance", "role": "method", "bullet_provenance": "none", "salience": 55, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 669, "authors_detailed": [ { "name": "Songrun He", "url": "https://openalex.org/A5048165657", "inst": "Washington University in St. Louis" }, { "name": "Liangyu Lv", "url": "https://openalex.org/A5055495019", "inst": "Washington University in St. Louis" }, { "name": "Asaf Manela", "url": "https://openalex.org/A5117372333", "inst": "Washington University in St. Louis" }, { "name": "Jinlin Wu", "url": "https://openalex.org/A5112053104", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5217340", "doi": "10.2139/ssrn.5217340", "title": "Can Large Language Models Trade? Testing Financial Theories with LLM Agents in Market Simulations", "authors": [ "Alejandro Lopez-Lira" ], "posted": "2025-04-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5217340", "field": "finance", "role": "agent", "bullet_provenance": "none", "salience": 65, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 350, "authors_detailed": [ { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5117358940", "inst": "University of Florida" } ], "affiliations": [ "University of Florida" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3715275.3732168", "doi": "10.1145/3715275.3732168", "arxiv_id": "2504.20086v1", "title": "Understanding and Mitigating Risks of Generative AI in Financial Services", "authors": [ "Sebastian Gehrmann", "Claire Huang", "Xian Teng", "Sergei Yurovski", "Iyanuoluwa Shode", "Chirag S. Patel", "Arjun Bhorkar", "Naveen Thomas", "John Doucette", "David Rosenberg", "Mark Dredze", "David Rabinowitz" ], "posted": "2025-04-25", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.20086v1", "field": "finance", "role": "method", "bullets": [ "Financial services industry applications assessed through red-teaming activities to evaluate how well open-source technical guardrail solutions handle domain-specific AI content risks.", "Multiple GenAI models tested against a finance-specific content risk taxonomy grounded in industry regulations, corporate governance requirements, and sector-specific legal standards.", "Existing open-source guardrails fail to detect most finance-specific content risks, revealing significant gaps between general-purpose safety tools and domain-specific regulatory needs." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "red-team evaluation against finance-specific content risk taxonomy", "salience": 65, "n": 3491, "authors_detailed": [ { "name": "Sebastian Gehrmann", "url": "https://openalex.org/A5026789962", "inst": "Bloomberg (United States)" }, { "name": "Xueqi Huang", "url": "https://openalex.org/A5054079327", "inst": "Bloomberg (United States)" }, { "name": "Xian Teng", "url": "https://openalex.org/A5074306833", "inst": "Bloomberg (United States)" }, { "name": "Sergei Yurovski", "url": "https://openalex.org/A5118593184", "inst": "Bloomberg (United States)" }, { "name": "Arjun Bhorkar", "url": "https://openalex.org/A5118593185", "inst": "Bloomberg (United States)" }, { "name": "Nelson Thomas", "url": "https://openalex.org/A5066899651", "inst": "Bloomberg (United States)" }, { "name": "John Doucette", "url": "https://openalex.org/A5115771808", "inst": "Bloomberg (United States)" }, { "name": "David S. Rosenberg", "url": "https://openalex.org/A5026405650", "inst": "Bloomberg, Toronto, Canada" }, { "name": "Mark Dredze", "url": "https://openalex.org/A5024437840", "inst": "Bloomberg (United States)" }, { "name": "David Rabinowitz", "url": "https://openalex.org/A5115771807", "inst": "Bloomberg (United States)" } ], "affiliations": [ "Bloomberg (United States)", "Bloomberg, Toronto, Canada" ] }, { "uid": "arxiv:2504.16548v2", "arxiv_id": "2504.16548v2", "title": "Exploring human-SAV interaction using LLMs: The impact of psychological factors on user experience", "authors": [ "Lirui Guo", "Michael G. Burke", "Wynita M. Griggs" ], "posted": "2025-04-23", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.16548v2", "field": "management", "role": "object", "bullets": [ "Participants interacted in turn with four conversational shared autonomous vehicle agents that varied anthropomorphic cues and psychological ownership triggers; the sample size is not stated.", "The agents run on an LLM the abstract does not name; outcomes are user ratings of ownership, anthropomorphism, service quality, disclosure, and acceptance plus response sentiment, with no accuracy validation.", "The agent built for anthropomorphism and ownership raised perceived human-likeness, and its replies read as more positive but more subjective; perceived performance also fed psychological ownership." ], "bullet_provenance": "ai", "salience": 35, "edition": 14, "models": [], "validated": null, "n": 1984, "authors_detailed": [ { "name": "Lirui Guo", "url": "https://openalex.org/A5083740053", "inst": "Australian Regenerative Medicine Institute" }, { "name": "Michael Burke", "url": "https://openalex.org/A5059032849", "inst": "University of Edinburgh" }, { "name": "Wynita M. Griggs", "url": "https://openalex.org/A5064783367", "inst": "University College Dublin" } ], "affiliations": [ "Australian Regenerative Medicine Institute", "University of Edinburgh", "University College Dublin" ] }, { "uid": "doi:10.2139/ssrn.5150225", "doi": "10.2139/ssrn.5150225", "title": "The Impact of Generative AI (ChatGPT) on Recruitment Efficiency and Candidate Quality: The Mediating Role of Process Automation Level and the Moderating Role of Organizational Size", "authors": [ "Nadeen Selim", "Sameh Abdelhay", "Dalia Hassan", "Dr Magdi El-Bannany" ], "posted": "2025-04-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5150225", "field": "management", "role": "object", "bullets": [ "Quantitative survey of 469 professionals across organizations of varying sizes, analyzed with structural equation modeling in AMOS SPSS to assess ChatGPT in recruitment.", "ChatGPT evaluated for bias reduction in candidate screening, efficiency gains, and evaluation accuracy, with user AI familiarity as a moderating variable.", "Significant bias reduction in screening attributed to algorithmic objectivity and consistency; user familiarity with AI moderates the relationship between tool use and efficiency." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "validated": null, "n": 3490, "authors_detailed": [ { "name": "Nadeen Selim", "url": "https://openalex.org/A5099986133", "inst": "Umm al-Qura University" }, { "name": "Sameh Abdelhay", "url": "https://openalex.org/A5022383695", "inst": "Umm al-Qura University" }, { "name": "Dalia Hassan", "url": "https://openalex.org/A5099986132", "inst": "Umm al-Qura University" }, { "name": "Dr Magdi El-Bannany", "url": "https://openalex.org/A5117264872", "inst": "Ain Shams University" } ], "affiliations": [ "Umm al-Qura University", "Ain Shams University" ] }, { "uid": "arxiv:2504.16361v1", "arxiv_id": "2504.16361v1", "title": "Comparing Different Transformer Model Structures for Stock Prediction", "authors": [ "Qizhao Chen" ], "posted": "2025-04-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.16361v1", "field": "finance", "role": "method", "bullets": [ "Stock index price prediction using five transformer architectural variants, compared against traditional forecasting models.", "Evaluated encoder-only, decoder-only, vanilla encoder-decoder, embedding-free, and ProbSparse attention transformers on stock index data.", "Decoder-only transformer outperformed all other variants in every scenario; ProbSparse attention performed worst across nearly all cases." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "stock index prediction accuracy vs traditional models", "salience": 30, "n": 3921, "authors_detailed": [ { "name": "Qizhao Chen", "url": "https://openalex.org/A5017181882", "inst": "University of Hyogo" } ], "affiliations": [ "University of Hyogo" ] }, { "uid": "arxiv:2504.16188v1", "arxiv_id": "2504.16188v1", "title": "FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking", "authors": [ "Jabez Magomere", "Elena Kochkina", "Samuel Mensah", "Simerjot Kaur", "Charese H. Smiley" ], "posted": "2025-04-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.16188v1", "field": "finance", "role": "method", "bullets": [ "21,304 premise hypothesis pairs drawn from SEC filings, annual reports, and earnings call transcripts, with a 3,304 pair test set annotated by finance experts.", "Pretrained encoders and LLM baselines, families not named, are scored on the benchmark; best Macro F1 reaches 74.57 percent for encoders and 78.62 percent for LLMs.", "Domain shift sharply degrades general domain inference performance, and instruction tuned financial LLMs do surprisingly poorly, indicating limited generalizability and substantial headroom on financial reasoning." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert-annotated test set, Macro F1 reported", "salience": 46, "edition": 14, "models": [], "n": 1904, "authors_detailed": [ { "name": "Jabez Magomere", "url": "https://openalex.org/A5099111164", "inst": "University of Oxford" }, { "name": "Elena Kochkina", "url": "https://openalex.org/A5075871561", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "S. Y. Mensah", "url": "https://openalex.org/A5109741594", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Simerjot Kaur", "url": "https://openalex.org/A5031351140", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Charese Smiley", "url": "https://openalex.org/A5070646187", "inst": "JPMorgan Chase & Co (United States)" } ], "affiliations": [ "University of Oxford", "JPMorgan Chase & Co (United States)" ], "prestige": true }, { "uid": "arxiv:2504.15716v1", "arxiv_id": "2504.15716v1", "title": "DianJin-R1: Evaluating and Enhancing Financial Reasoning in Large Language Models", "authors": [ "Jie Zhu", "Qian Chen", "Huaixia Dou", "Junhui Li", "Lifan Guo", "Feng Chen", "Chi Zhang" ], "posted": "2025-04-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.15716v1", "field": "finance", "role": "method", "bullets": [ "Chinese financial reasoning tasks built from CFLUE, FinQA, and a proprietary compliance corpus, with evaluation on those three plus MATH-500 and GPQA-Diamond.", "Qwen2.5 7B and 32B are fine-tuned on reasoning traces, then trained with group relative policy optimization using rewards for structured output and answer correctness.", "The reasoning models beat non-reasoning counterparts, most clearly on complex tasks, and a single call matches or exceeds costlier multi-agent systems on the compliance dataset." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "CFLUE, FinQA and CCC benchmarks with verified annotations", "salience": 45, "edition": 14, "n": 1983, "authors_detailed": [ { "name": "Jie Zhu", "url": "https://openalex.org/A5025586324", "inst": "Guangxi University" }, { "name": "Qian Chen", "url": "https://openalex.org/A5070487819", "inst": "Capital University" }, { "name": "Dou, Huaixia", "url": "", "inst": "" }, { "name": "Junhui Li", "url": "https://openalex.org/A5100369269", "inst": "University of Science and Technology of China" }, { "name": "Lifan Guo", "url": "https://openalex.org/A5113222149", "inst": "" }, { "name": "Feng Chen", "url": "https://openalex.org/A5100352752", "inst": "Huaqiao University" }, { "name": "Chi Zhang", "url": "https://openalex.org/A5100609451", "inst": "University of Warwick" } ], "affiliations": [ "Guangxi University", "Capital University", "University of Science and Technology of China", "Huaqiao University", "University of Warwick" ] }, { "uid": "doi:10.1098/rspa.2025.1113", "doi": "10.1098/rspa.2025.1113", "arxiv_id": "2504.15801v2", "title": "A closer look at how large language models trust humans: patterns and biases", "authors": [ "Valeria Lerman", "Yaniv Dover" ], "posted": "2025-04-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.15801v2", "field": "management", "role": "object", "bullets": [ "43,200 simulated experiments across five popular LLMs and five scenarios including loan applications, evaluated on competence, benevolence, and integrity dimensions.", "Five LLMs assessed on trust formation toward humans varying in trustworthiness dimensions and demographic attributes including age, religion, and gender.", "LLM trust follows human-like patterns overall but shows demographic bias especially in financial scenarios, with newer models exhibiting stronger bias effects." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": false, "salience": 70, "n": 2582, "authors_detailed": [ { "name": "Valeria Lerman", "url": "https://openalex.org/A5117597989", "inst": "Hebrew College" }, { "name": "Yaniv Dover", "url": "https://openalex.org/A5037819479", "inst": "Hebrew College" } ], "affiliations": [ "Hebrew College" ] }, { "uid": "arxiv:2504.15683v1", "arxiv_id": "2504.15683v1", "title": "FinTextSim: Enhancing Financial Text Analysis with BERTopic", "authors": [ "Simon Jehnen", "Joaquín Ordieres-Meré", "Javier Villalba-Díez" ], "posted": "2025-04-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.15683v1", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 companies' 10-K filings (Items 7 and 7A), 2016-2022, United States.", "Fine-tuned sentence-transformer (FinTextSim) paired with BERTopic extracted topic clusters from financial disclosures; evaluated against all-MiniLM-L6-v2 baseline.", "FinTextSim increased intratopic similarity by 81% and reduced intertopic similarity by 100%, enabling distinct economic topic clusters that the baseline could not produce." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "intratopic and intertopic similarity on 10-K topic clusters", "salience": 42, "n": 3920, "authors_detailed": [ { "name": "Simon Jehnen", "url": "https://openalex.org/A5117487432", "inst": "Dr. Becker Rhein-Sieg Clinic" }, { "name": "Joaquín Ordieres‐Meré", "url": "https://openalex.org/A5024077875", "inst": "Universidad Politécnica de Madrid" }, { "name": "Javier Villalba-Díez", "url": "https://openalex.org/A5048585660", "inst": "Universidad de La Rioja" } ], "affiliations": [ "Universidad Politécnica de Madrid", "Universidad de La Rioja" ] }, { "uid": "arxiv:2504.15440v1", "arxiv_id": "2504.15440v1", "title": "Demand for LLMs: Descriptive Evidence on Substitution, Market Expansion, and Multihoming", "authors": [ "Andrey Fradkin" ], "posted": "2025-04-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.15440v1", "field": "economics", "role": "object", "bullets": [ "Transaction-level data from OpenRouter, an LLM marketplace, covering multiple model releases and user adoption over time.", "No model performs a task; the study examines demand patterns for LLM products including adoption curves, substitution, and multihoming across apps.", "New models gain users rapidly then plateau within weeks; releases differ in whether they expand the market or cannibalize rivals; app-level multihoming is common." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "llama", "open_other" ], "salience": 75, "validated": null, "n": 2695, "authors_detailed": [ { "name": "Andrey Fradkin", "url": "https://openalex.org/A5119771017", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5224204", "doi": "10.2139/ssrn.5224204", "title": "Ethnographic Records, Folklore, and AI", "authors": [ "Stelios Michalopoulos" ], "posted": "2025-04-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5224204", "field": "economics", "role": "instrument", "bullets": [ "Murdock's ethnographic atlas and Berezkin's folklore motif index used in comparative economic development research across societies", "LLMs proposed to extract cultural insights from folklore motifs, complementing structured ethnographic records for quantitative analysis", "Ancestral narratives processed by LLMs reveal societal norms and historical forces that shape cross-country differences in economic behavior" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 2996, "authors_detailed": [ { "name": "Stelios Michalopoulos", "url": "https://openalex.org/A5023559123", "inst": "Brown University" } ], "affiliations": [ "Brown University" ] }, { "uid": "arxiv:2504.14633v1", "arxiv_id": "2504.14633v1", "title": "Harnessing Generative LLMs for Enhanced Financial Event Entity Extraction Performance", "authors": [ "Soo-joon Choi", "Ji-jun Park" ], "posted": "2025-04-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.14633v1", "field": "finance", "role": "instrument", "bullets": [ "CCKS 2019 Financial Event Entity Extraction dataset containing Chinese financial texts with complex overlapping entity structures across multiple event types.", "Pre-trained LLM fine-tuned with parameter-efficient fine-tuning generates structured JSON output containing extracted entities and character spans from financial text.", "Generative LLM achieves new state-of-the-art F1 score on the benchmark, significantly outperforming sequence labeling baselines including SEBERTNets across entity types." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "F1 on CCKS 2019 Financial Event Entity Extraction", "salience": 50, "n": 2581 }, { "uid": "arxiv:2504.14765v2", "arxiv_id": "2504.14765v2", "title": "The Memorization Problem: Can We Trust LLMs' Economic Forecasts?", "authors": [ "Alejandro Lopez-Lira", "Yuehua Tang", "Mingyin Zhu" ], "posted": "2025-04-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.14765v2", "field": "economics", "role": "method", "bullets": [ "Economic and financial time-series data spanning periods before and after LLM training-data knowledge cutoffs, tested across multiple models.", "Multiple LLMs were queried for exact economic values; prompting instructions and entity masking were applied to prevent memorization leakage from training data.", "LLMs recalled exact pre-cutoff values regardless of safeguards; post-cutoff recall dropped to zero, demonstrating that memorization renders in-sample forecast evaluation non-identified." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "recall accuracy compared pre- vs. post-knowledge-cutoff", "salience": 82, "n": 2694 }, { "uid": "arxiv:2504.14493v4", "arxiv_id": "2504.14493v4", "title": "FinSage: A Multi-aspect RAG System for Financial Filings Question Answering", "authors": [ "Xinyu Wang", "Jijun Chi", "Zhenghan Tai", "Tung Sum Thomas Kwok", "Muzhi Li", "Zhuhong Li", "Hailin He", "Yuchen Hua", "Peng Lu", "Suyuchen Wang", "Yihong Wu", "Jerry Huang", "Jingrui Tian", "Fengran Mo", "Yufei Cui", "Ling Zhou" ], "posted": "2025-04-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.14493v4", "field": "finance", "role": "instrument", "bullets": [ "Multi-modal financial filings including text, tables, and diagrams; 75 expert-curated questions plus FinanceBench QA dataset.", "RAG framework with multi-path sparse-dense retrieval, HyDE query expansion, and DPO-fine-tuned re-ranker for regulatory compliance QA.", "Achieved 92.51% recall on expert questions and 24.06% accuracy gain over best baseline on FinanceBench; deployed to serve over 1,200 users." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "FinanceBench QA accuracy and expert-curated question recall", "salience": 50, "n": 3202 }, { "uid": "arxiv:2504.14345v2", "arxiv_id": "2504.14345v2", "title": "LLM-Enhanced Black-Litterman Portfolio Optimization", "authors": [ "Youngbin Lee", "Yejin Kim", "Juhyeong Kim", "Suin Kim", "Yongjae Lee" ], "posted": "2025-04-19", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.14345v2", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 constituents in a backtest translating LLM return forecasts and predictive uncertainty into investor views and confidence levels for the Black-Litterman model.", "Several LLMs, not named in the abstract, generate the return forecasts; forecast accuracy is not separately checked against realized returns beyond portfolio outcomes.", "Portfolios built on the strongest models beat traditional baselines in absolute and risk adjusted terms, and each LLM shows a persistent investment style whose regime fit drives performance." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 14, "models": [], "n": 1982, "authors_detailed": [ { "name": "Youngbin Lee", "url": "https://openalex.org/A5020025335", "inst": "Seoul National University" }, { "name": "Yejin Kim", "url": "https://openalex.org/A5100628371", "inst": "Korea University" }, { "name": "J.W. Kim", "url": "https://openalex.org/A5082717157", "inst": "" }, { "name": "S. S. Kim", "url": "https://openalex.org/A5033790318", "inst": "" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5100366482", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University", "Seoul National University", "Korea University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2504.13629v1", "arxiv_id": "2504.13629v1", "title": "Divergent LLM Adoption and Heterogeneous Convergence Paths in Research Writing", "authors": [ "Cong William Lin", "Wu Zhu" ], "posted": "2025-04-18", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.13629v1", "field": "economics", "role": "object", "bullets": [ "More than 627,000 arXiv manuscripts, observed with author discipline, gender, native language status, and career stage around the arrival of ChatGPT.", "Prompt and discipline specific language models are fine tuned to detect ChatGPT revised text; the abstract reports no accuracy check of the detector against labelled examples.", "Adoption differs across disciplines and demographics, and difference in differences estimates show writing converging, with early adopters, male researchers, non native speakers, and junior scholars shifting most toward established styles." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 60, "edition": 14, "n": 1903 }, { "uid": "arxiv:2504.16116v4", "arxiv_id": "2504.16116v4", "title": "DMind Benchmark: Toward a Holistic Assessment of LLM Capabilities across the Web3 Domain", "authors": [ "Enhao Huang", "Pengyu Sun", "Shuxun Wang", "Zixin Lin", "Alex Chen", "Kaichun Hu", "Joey Ouyang", "Frank Li", "Zhiyu Zhang", "Haobo Wang", "Yiming Li", "Zhan Qin", "James Yi", "Gang Zhao", "Ziang Ling", "Lowes Yang" ], "posted": "2025-04-18", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.16116v4", "field": "finance", "role": "method", "bullets": [ "A Web3 evaluation suite of nine subdomains spanning infrastructure, smart contracts, and token economics, combining objective knowledge questions with open ended reasoning tasks.", "31 proprietary and open weight models, unnamed in the abstract, are scored through a contamination aware pipeline with cross judge consistency checks on the protocol.", "Models manage infrastructure concepts but weaken on high reasoning tasks such as security auditing; adversarial tests suggest scores reflect reasoning rather than memorization, and a Pareto analysis guides deployment cost." ], "bullet_provenance": "ai", "validated": true, "validation_note": "curated Web3 benchmark, cross-judge consistency checks", "salience": 33, "edition": 14, "models": [], "n": 1981 }, { "uid": "doi:10.2139/ssrn.5217505", "doi": "10.2139/ssrn.5217505", "title": "The Memorization Problem: Can We Trust LLMs' Economic Forecasts?", "authors": [ "Alejandro Lopez-Lira", "Yuehua Tang", "Mingyin Zhu" ], "posted": "2025-04-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5217505", "field": "economics", "role": "method", "bullet_provenance": "none", "salience": 55, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 668, "authors_detailed": [ { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5117203301", "inst": "University of Florida" }, { "name": "Yuehua Tang", "url": "https://openalex.org/A5052351860", "inst": "University of Florida" }, { "name": "Mingyin Zhu", "url": "https://openalex.org/A5117203302", "inst": "University of Florida" } ], "affiliations": [ "University of Florida" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2504.13444v2", "arxiv_id": "2504.13444v2", "title": "Balancing Engagement and Polarization: Multi-Objective Alignment of News Content Using LLMs", "authors": [ "Mengjie Cheng", "Elie Ofek", "Hema Yoganarasimhan" ], "posted": "2025-04-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.13444v2", "field": "management", "role": "method", "bullets": [ "New York Times news articles used to study the trade-off between reader engagement and political polarization in LLM-generated content", "Open-source LLMs fine-tuned with Multi-Objective Direct Preference Optimization to generate engaging content while controlling editorial slant", "MODPO model reduces polarizing language while preserving engagement; naive LLM prompting increases polarization alongside engagement" ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 65, "n": 2995 }, { "uid": "arxiv:2504.13125v1", "arxiv_id": "2504.13125v1", "title": "LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard", "authors": [ "Varun Rao", "Youran Sun", "Mahendra Kumar", "Tejas Mutneja", "Agastya Mukherjee", "Haizhao Yang" ], "posted": "2025-04-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.13125v1", "field": "finance", "role": "method", "bullets": [ "Financial NLP tasks from the Open FinLLM Leaderboard, which supplies both the fine tuning targets and the evaluation suite for the study.", "Qwen2.5 and DeepSeek R1 are adapted with supervised fine tuning, direct preference optimization, and reinforcement learning; leaderboard tasks provide labelled scoring, but the abstract reports no figures.", "Fine tuned models post substantial gains across a range of financial tasks, and the authors measure a data scaling law for the financial domain; magnitudes are not stated." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Open FinLLM Leaderboard tasks", "salience": 34, "edition": 14, "n": 1816, "authors_detailed": [ { "name": "Rao, Varun", "url": "", "inst": "" }, { "name": "Youran Sun", "url": "https://openalex.org/A5101731605", "inst": "University of Maryland, College Park" }, { "name": "Mahendra Kumar", "url": "https://openalex.org/A5032582804", "inst": "Bhubaneswar Borooah Cancer Institute" }, { "name": "Tejas Mutneja", "url": "https://openalex.org/A5118508789", "inst": "University of Maryland, College Park" }, { "name": "Mukherjee, Agastya", "url": "", "inst": "" }, { "name": "Haizhao Yang", "url": "https://openalex.org/A5079602544", "inst": "University of Maryland, College Park" } ], "affiliations": [ "University of Maryland, College Park", "Bhubaneswar Borooah Cancer Institute" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2504.13217v3", "arxiv_id": "2504.13217v3", "title": "Sustainability via LLM Right-sizing", "authors": [ "Jennifer Haase", "Finn Klessascheck", "Jan Mendling", "Sebastian Pokutta" ], "posted": "2025-04-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.13217v3", "field": "management", "role": "object", "bullets": [ "Ten everyday occupational tasks, including summarizing, scheduling, and drafting emails and proposals, run across eleven proprietary and open weight LLMs.", "GPT-4o, Gemma-3, Phi-4, and others are scored on ten quality, accuracy, and responsibility criteria by a dual LLM evaluation framework; no human ground truth agreement is reported.", "GPT-4o performs best at the highest cost and footprint; smaller models handle most tasks reliably, and a cluster analysis sorts models into three tiers to support sufficiency based deployment." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": false, "salience": 45, "edition": 14, "n": 1979, "authors_detailed": [ { "name": "Jennifer Haase", "url": "https://openalex.org/A5034108285", "inst": "Humboldt-Universität zu Berlin" }, { "name": "Finn Klessascheck", "url": "https://openalex.org/A5060174775", "inst": "University of Münster" }, { "name": "Jan Mendling", "url": "https://openalex.org/A5062764959", "inst": "Vienna University of Economics and Business" }, { "name": "Sebastian Pokutta", "url": "https://openalex.org/A5114731551", "inst": "Zuse Institute Berlin" } ], "affiliations": [ "Humboldt-Universität zu Berlin", "University of Münster", "Vienna University of Economics and Business", "Zuse Institute Berlin" ] }, { "uid": "arxiv:2504.13216v1", "arxiv_id": "2504.13216v1", "title": "KFinEval-Pilot: A Comprehensive Benchmark Suite for Korean Financial Language Understanding", "authors": [ "Bokwang Hwang", "Seonkyu Lim", "Taewoong Kim", "Yongjae Geun", "Sunghyun Bang", "Sohyun Park", "Jihyun Park", "Myeonggyu Lee", "Jinwoo Lee", "Yerin Kim", "Jinsun Yoo", "Jingyeong Hong", "Jina Park", "Yongchan Kim", "Suhyun Kim", "Younggyun Hahm", "Yiseul Lee", "Yejee Kang", "Chanhyuk Yoon", "Chansu Lee", "Heeyewon Jeong", "Jiyeon Lee", "Seonhye Gu", "Hyebin Kang", "Yousang Cho", "Hangyeol Yoo", "KyungTae Lim" ], "posted": "2025-04-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.13216v1", "field": "finance", "role": "method", "bullets": [ "More than 1,000 curated Korean questions across financial knowledge, legal reasoning, and financial toxicity, aligned with Korean regulatory and linguistic context.", "Questions come from GPT-4 generation with expert validation; a range of LLMs is then benchmarked, with evaluated model names not given in the abstract.", "Evaluated models differ markedly, trading off task accuracy against output safety across families; reasoning and safety remain the weak points for financial use." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "expert-validated benchmark questions", "salience": 40, "edition": 14, "n": 1980 }, { "uid": "doi:10.2139/ssrn.5219933", "doi": "10.2139/ssrn.5219933", "title": "Large Language Models, Small Labor Market Effects", "authors": [ "Anders Humlum", "Emilie Vestergaard" ], "posted": "2025-04-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5219933", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 68, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 349, "authors_detailed": [ { "name": "Anders Humlum", "url": "https://openalex.org/A5093399820", "inst": "University of Chicago" }, { "name": "Emilie Vestergaard", "url": "https://openalex.org/A5112220091", "inst": "University of Copenhagen" } ], "affiliations": [ "University of Chicago", "University of Copenhagen" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5143165", "doi": "10.2139/ssrn.5143165", "title": "Shariah Governance Standard on Generative AI for Islamic Financial Institutions", "authors": [ "Muhammad Bilal Zafar", "Hassnian Ali" ], "posted": "2025-04-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5143165", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 35, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 667 }, { "uid": "doi:10.2139/ssrn.5216676", "doi": "10.2139/ssrn.5216676", "title": "Capital Allocation Concentration Measurement in Venture Capital", "authors": [ "Teodor Duevski", "Viacheslav Bazaliy" ], "posted": "2025-04-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5216676", "field": "finance", "role": "instrument", "bullets": [ "Matched PitchBook and Crunchbase venture capital investment data used to measure capital allocation concentration across startup sectors.", "OpenAI text embeddings captured semantic similarity among financed startups, replacing HHI-based concentration measures tied to industry taxonomies.", "Aggregate VC concentration increased more sharply than HHI suggests; 40% of growth stems from rising within-sector similarity among founded startups." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "signal-to-noise and retrieval benchmarks on matched PitchBook-Crunchbase data", "salience": 65, "n": 2848, "authors_detailed": [ { "name": "Teodor Duevski", "url": "https://openalex.org/A5046687558", "inst": "HEC Paris" }, { "name": "Viacheslav Bazaliy", "url": "https://openalex.org/A5117197537", "inst": "Boston Consulting Group (United States)" } ], "affiliations": [ "HEC Paris", "Boston Consulting Group (United States)" ] }, { "uid": "arxiv:2504.11671v4", "arxiv_id": "2504.11671v4", "title": "Computational Basis of LLM's Decision Making in Social Simulation", "authors": [ "Ji Ma" ], "posted": "2025-04-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.11671v4", "field": "economics", "role": "agent", "bullets": [ "Dictator Game behavioral experiment probing LLM internal representations with manipulated demographic variable vectors across gender and other social dimensions.", "Variable-variation vectors extracted from LLM internal states then manipulated during inference to study how social concepts shape fairness and prosocial decisions.", "Manipulating internal representation vectors substantially alters how demographic variables relate to LLM decision-making, revealing engineerable social concept encoding." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 65, "n": 2580 }, { "uid": "doi:10.2139/ssrn.5213130", "doi": "10.2139/ssrn.5213130", "title": "Behavioral Economics of AI: LLM Biases and Corrections", "authors": [ "Pietro Bini", "Lin Cong", "Xing Huang", "Lawrence J. Jin" ], "posted": "2025-04-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5213130", "field": "economics", "role": "agent", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 665, "authors_detailed": [ { "name": "Pietro Bini", "url": "https://openalex.org/A5117151094", "inst": "Cornell University" }, { "name": "Lin Cong", "url": "https://openalex.org/A5087260751", "inst": "National Bureau of Economic Research" }, { "name": "Xing Huang", "url": "https://openalex.org/A5103233345", "inst": "Washington University in St. Louis" }, { "name": "Lawrence J. Jin", "url": "https://openalex.org/A5101499391", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Cornell University", "Washington University in St. Louis", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5187344", "doi": "10.2139/ssrn.5187344", "title": "Generative AI and Labor Market Matching Efficiency", "authors": [ "Emma Wiles", "John J. Horton" ], "posted": "2025-04-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5187344", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 50, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 666, "authors_detailed": [ { "name": "Emma Wiles", "url": "https://openalex.org/A5117163134", "inst": "Boston University" }, { "name": "John J. Horton", "url": "https://openalex.org/A5077125314", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Boston University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5200496", "doi": "10.2139/ssrn.5200496", "title": "GPT-Language-based Experimental Economics System (GLEES)", "authors": [ "Kay-Yut Chen", "Diana Wu" ], "posted": "2025-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5200496", "field": "economics", "role": "agent", "bullets": [ "GLEES platform for conducting economic experiments using GPT-powered AI agents as experimental subjects in behavioral research settings", "GPT-based agents participated in exploratory economic experiments designed to test behavioral responses and decision-making patterns", "Framework demonstrated feasibility of LLM-based experimental economics and identified new challenges in understanding AI-driven economic behavior" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 2429, "authors_detailed": [ { "name": "Kay‐Yut Chen", "url": "https://openalex.org/A5035922455", "inst": "The University of Texas at Arlington" }, { "name": "Diana Wu", "url": "https://openalex.org/A5117155934", "inst": "San Jose State University" } ], "affiliations": [ "The University of Texas at Arlington", "San Jose State University" ] }, { "uid": "doi:10.2139/ssrn.5185035", "doi": "10.2139/ssrn.5185035", "title": "Labor Market Signals: The Role of Large Language Models", "authors": [ "Kian Abbas Nejad", "Giuseppe Musillo", "Till Wicker", "Niccolo Zaccaria" ], "posted": "2025-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5185035", "field": "economics", "role": "object", "bullets": [ "Two field experiments with job seekers and recruiters examining LLM-generated cover letters and hiring outcomes in a labor market setting.", "LLMs generated cover letters for applicants of varying quality; recruiters evaluated letters with and without knowledge of LLM usage in a calibrated structural model.", "LLMs improved signal quality for weaker applicants but did not increase interview invitations; recruiter awareness of LLM use raised the value of human-crafted letters." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 2487, "authors_detailed": [ { "name": "Kian Abbas Nejad", "url": "https://openalex.org/A5095705451", "inst": "Tilburg University" }, { "name": "Giuseppe Musillo", "url": "https://openalex.org/A5095705450", "inst": "Tilburg University" }, { "name": "Till Wicker", "url": "https://openalex.org/A5095705448", "inst": "Tilburg University" }, { "name": "Niccolo Zaccaria", "url": "https://openalex.org/A5095705449", "inst": "Tilburg University" } ], "affiliations": [ "Tilburg University" ] }, { "uid": "arxiv:2504.11443v1", "arxiv_id": "2504.11443v1", "title": "Early Impacts of M365 Copilot", "authors": [ "Eleanor Wiske Dillon", "Sonia Jaffe", "Sida Peng", "Alexia Cambon" ], "posted": "2025-04-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.11443v1", "field": "management", "role": "object", "bullets": [ "Randomized controlled experiment with over 6,000 knowledge workers at 56 firms across a wide range of industries, observed over a six-month study period.", "Microsoft 365 Copilot, a GPT-based generative AI assistant integrated into email, document, and meeting applications, was randomly assigned to treated workers in their daily workflow.", "Users saved 30 minutes per week on email and completed documents 12% faster; nearly 40% of those given access used the tool regularly." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 82, "validated": null, "n": 3488, "authors_detailed": [ { "name": "Eleanor Wiske Dillon", "url": "https://openalex.org/A5069532832", "inst": "Microsoft (United States)" }, { "name": "Sonia Jaffe", "url": "https://openalex.org/A5042418453", "inst": "Microsoft (United States)" }, { "name": "Sida Peng", "url": "https://openalex.org/A5073087748", "inst": "Microsoft (United States)" }, { "name": "Alexia Cambon", "url": "https://openalex.org/A5117319332", "inst": "Microsoft (Finland)" } ], "affiliations": [ "Microsoft (United States)", "Microsoft (Finland)" ] }, { "uid": "arxiv:2504.11436v4", "arxiv_id": "2504.11436v4", "title": "Shifting Work Patterns with Generative AI", "authors": [ "Eleanor Wiske Dillon", "Sonia Jaffe", "Nicole Immorlica", "Christopher T. Stanton" ], "posted": "2025-04-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.11436v4", "field": "management", "role": "object", "bullets": [ "Field experiment across 66 firms with 7,137 knowledge workers randomly assigned access to a generative AI productivity tool over six months.", "GPT-based generative AI tool integrated into workers' existing applications for email, meetings, and writing; 80% of treated workers adopted the tool during the study.", "Active users spent two fewer hours per week on email and reduced after-hours work, but no detectable shift in overall task quantity or composition." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 80, "validated": null, "n": 3489, "authors_detailed": [ { "name": "Eleanor Wiske Dillon", "url": "https://openalex.org/A5069532832", "inst": "Microsoft (United States)" }, { "name": "Sonia Jaffe", "url": "https://openalex.org/A5042418453", "inst": "Microsoft (United States)" }, { "name": "Nicole Immorlica", "url": "https://openalex.org/A5009111822", "inst": "Microsoft (United States)" }, { "name": "Christopher Stanton", "url": "https://openalex.org/A5060707167", "inst": "Center for Economic and Policy Research" } ], "affiliations": [ "Microsoft (United States)", "Center for Economic and Policy Research" ] }, { "uid": "arxiv:2504.10636v1", "arxiv_id": "2504.10636v1", "title": "Who is More Bayesian: Humans or ChatGPT?", "authors": [ "Tianshi Mu", "Pranjal Rawat", "John Rust", "Chengjun Zhang", "Qixuan Zhong" ], "posted": "2025-04-14", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.10636v1", "field": "economics", "role": "agent", "bullets": [ "Binary classification problems with a known Bayesian optimum, combining human choices from the El-Gamal and Grether and the Holt and Smith laboratory experiments with fresh model runs.", "Successive ChatGPT versions answer the same updating problems given to human subjects; choices are scored against Bayes rule, so the exercise probes decision behavior rather than text measurement.", "Humans and early models show representativeness and conservatism biases; ChatGPT 3.5 classifies below human level while ChatGPT 4o reaches nearly perfect, superhuman Bayesian performance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "edition": 14, "validated": null, "n": 1825, "authors_detailed": [ { "name": "Tingkui Mu", "url": "https://openalex.org/A5071187874", "inst": "Xi'an Jiaotong University" }, { "name": "Pranjal Rawat", "url": "https://openalex.org/A5117448432", "inst": "Georgetown University" }, { "name": "John Rust", "url": "https://openalex.org/A5006713595", "inst": "Georgetown University" }, { "name": "Chengjun Zhang", "url": "https://openalex.org/A5100777284", "inst": "Zhejiang A & F University" }, { "name": "Qixuan Zhong", "url": "https://openalex.org/A5005292515", "inst": "Chang'an University" } ], "affiliations": [ "Georgetown University", "Xi'an Jiaotong University", "Zhejiang A & F University", "Chang'an University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2504.09903v1", "arxiv_id": "2504.09903v1", "title": "Refining Financial Consumer Complaints through Multi-Scale Model Interaction", "authors": [ "Bo-Wei Chen", "An-Zi Yen", "Chung-Chi Chen" ], "posted": "2025-04-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.09903v1", "field": "finance", "role": "instrument", "bullets": [ "Chinese financial dispute records dataset (FinDR) annotated with official judgments on claim reasonableness, bridging informal consumer complaints and legal standards.", "Multi-Scale Model Interaction uses a lightweight classifier to evaluate LLM outputs and guide iterative refinement of informal text into persuasive legal arguments.", "MSMI significantly outperforms single-pass prompting strategies on financial dispute refinement and shows improved adversarial robustness on short-text benchmarks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "claim reasonableness vs official judgments", "salience": 50, "n": 2579, "authors_detailed": [ { "name": "Bo‐Wei Chen", "url": "https://openalex.org/A5100679032", "inst": "ATUM (United States)" }, { "name": "An-Zi Yen", "url": "https://openalex.org/A5012131890", "inst": "National Yang Ming Chiao Tung University" }, { "name": "Chung-Chi Chen", "url": "https://openalex.org/A5101516307", "inst": "National Institute of Informatics" } ], "affiliations": [ "ATUM (United States)", "National Yang Ming Chiao Tung University", "National Institute of Informatics" ] }, { "uid": "arxiv:2504.10679v1", "arxiv_id": "2504.10679v1", "title": "Keyword Extraction, and Aspect Classification in Sinhala, English, and Code-Mixed Content", "authors": [ "F. A. Rizvi", "T. Navojith", "A. M. N. H. Adhikari", "W. P. U. Senevirathna", "Dharshana Kasthurirathna", "Lakmini Abeywardhana" ], "posted": "2025-04-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.10679v1", "field": "finance", "role": "instrument", "bullets": [ "Banking-sector customer opinions in Sinhala, English, and code-mixed content for brand reputation monitoring in Sri Lankan banks.", "Fine-tuned BERT, XLM-RoBERTa, and FinBERT-KeyBERT hybrid for keyword extraction and aspect classification, benchmarked against GPT-4o.", "Fine-tuned transformers outperform GPT-4o: 91.2% keyword accuracy in English, 87.4% in Sinhala, and 87.4% aspect classification accuracy." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "accuracy on banking content keyword extraction and aspect classification", "salience": 35, "n": 3201, "authors_detailed": [ { "name": "Farhana Rizvi", "url": "https://openalex.org/A5023305178", "inst": "Preston University" }, { "name": "T. Navojith", "url": "https://openalex.org/A5120319544", "inst": "" }, { "name": "Ajay Adhikari", "url": "https://openalex.org/A5007002635", "inst": "Australian National University" }, { "name": "W. Senevirathna", "url": "https://openalex.org/A5054687904", "inst": "University of Colombo" }, { "name": "Dharshana Kasthurirathna", "url": "https://openalex.org/A5108738927", "inst": "Sri Lanka Institute of Information Technology" }, { "name": "Lakmini Abeywardhana", "url": "https://openalex.org/A5041583087", "inst": "Sri Lanka Institute of Information Technology" } ], "affiliations": [ "Preston University", "Australian National University", "University of Colombo", "Sri Lanka Institute of Information Technology" ] }, { "uid": "arxiv:2504.09736v1", "arxiv_id": "2504.09736v1", "title": "Agentic Workflows for Economic Research: Design and Implementation", "authors": [ "Herbert Dawid", "Philipp Harting", "Hankui Wang", "Zhongli Wang", "Jiachen Yi" ], "posted": "2025-04-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.09736v1", "field": "economics", "role": "method", "bullets": [ "Economic research lifecycle from ideation through empirical analysis implemented using Microsoft AutoGen multi-agent platform with human-in-the-loop checkpoints.", "LLM-based specialized agents handle literature review, economic modeling, data processing, and result interpretation with structured inter-agent communication protocols.", "Framework demonstrates feasibility of autonomous agentic economic research workflows while maintaining methodological validity through strategic human oversight integration." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2578, "authors_detailed": [ { "name": "Herbert Dawid", "url": "https://openalex.org/A5043134112", "inst": "Bielefeld University" }, { "name": "Philipp Harting", "url": "https://openalex.org/A5005158034", "inst": "Centre National de la Recherche Scientifique" }, { "name": "H Wang", "url": "https://openalex.org/A5074522998", "inst": "" }, { "name": "Zhongli Wang", "url": "https://openalex.org/A5100674334", "inst": "Beijing Jiaotong University" }, { "name": "Ji Soo Yi", "url": "https://openalex.org/A5053349373", "inst": "Hanyang University" } ], "affiliations": [ "Bielefeld University", "Centre National de la Recherche Scientifique", "Beijing Jiaotong University", "Hanyang University" ] }, { "uid": "arxiv:2504.09663v2", "arxiv_id": "2504.09663v2", "title": "Ordinary Least Squares as an Attention Mechanism", "authors": [ "Philippe Goulet Coulombe" ], "posted": "2025-04-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.09663v2", "field": "economics", "role": "method", "bullets": [ "Theoretical framework linking ordinary least squares predictions to the attention mechanism underpinning transformer architectures", "OLS recast as a similarity-based method that maps onto the query-key-value structure of attention, with connections to time series econometrics", "The equivalence shows OLS implicitly optimizes an embedding space for inner-product comparison, offering statisticians an accessible entry point to attention-based models" ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 2994, "authors_detailed": [ { "name": "Philippe Goulet Coulombe", "url": "https://openalex.org/A5025143675", "inst": "Université du Québec à Montréal" } ], "affiliations": [ "Université du Québec à Montréal" ] }, { "uid": "arxiv:2504.08386v1", "arxiv_id": "2504.08386v1", "title": "PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation", "authors": [ "Arman Khaledian", "Amirreza Ghadiridehkordi", "Nariman Khaledian" ], "posted": "2025-04-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.08386v1", "field": "finance", "role": "instrument", "bullets": [ "Real-world financial text corpus processed through Zanista AI's Newswitch platform for trading and knowledge-intensive finance applications.", "Applied PCA to reduce LLM embeddings from 3,072 to 110 dimensions for RAG-based retrieval over financial documents.", "Up to 60x retrieval speedup and 28.6x index size reduction with only moderate decline in correlation with human-annotated similarity scores." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "correlation with human-annotated similarity scores", "salience": 40, "n": 3200, "authors_detailed": [ { "name": "Arman Khaledian", "url": "https://openalex.org/A5119849203", "inst": "" }, { "name": "Amirreza Ghadiridehkordi", "url": "https://openalex.org/A5119849204", "inst": "" }, { "name": "Nariman Khaledian", "url": "https://openalex.org/A5014841508", "inst": "Centre National de la Recherche Scientifique" } ], "affiliations": [ "Centre National de la Recherche Scientifique" ] }, { "uid": "arxiv:2504.07733v2", "arxiv_id": "2504.07733v2", "title": "DeepGreen: Effective LLM-Driven Greenwashing Monitoring System Designed for Empirical Testing -- Evidence from China", "authors": [ "Congluo Xu", "Jiuyue Liu", "Ziyang Li", "Chengmengjia Lin" ], "posted": "2025-04-10", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.07733v2", "field": "accounting", "role": "instrument", "bullets": [ "9,369 annual reports of Chinese A share listed firms from 2021 to 2023, linked to environmental penalties and green investor presence.", "The dual stage DeepGreen pipeline, model unnamed, extracts greenwashing signals; random sample validation at both stages is reported without figures, and ablations show retrieval augmentation reduces hallucinations.", "Extracted greenwashing is positively associated with environmental penalties, supported by instrumental variable, matching, and placebo checks; green investors weaken the link, and large or green asset rich firms show weaker exposure." ], "bullet_provenance": "ai", "validated": true, "validation_note": "random-sample validation at both stages, figures not stated", "salience": 58, "edition": 14, "models": [], "n": 1902, "authors_detailed": [ { "name": "Chong Xu", "url": "https://openalex.org/A5077706881", "inst": "Lanzhou Jiaotong University" }, { "name": "Liu, Jiuyue", "url": "", "inst": "" }, { "name": "Li, Ziyang", "url": "", "inst": "" }, { "name": "C. L. Lin", "url": "https://openalex.org/A5111318006", "inst": "Chinese Academy of Sciences" } ], "affiliations": [ "Lanzhou Jiaotong University", "Chinese Academy of Sciences" ] }, { "uid": "doi:10.2139/ssrn.5136877", "doi": "10.2139/ssrn.5136877", "title": "The Labor Market Effects of Generative Artificial Intelligence", "authors": [ "Jonathan Hartley", "Filip Jolevski", "Vitor Melo", "Brendan Moore" ], "posted": "2025-04-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5136877", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 664, "authors_detailed": [ { "name": "Jonathan Hartley", "url": "", "inst": "Stanford University" }, { "name": "Filip Jolevski", "url": "https://openalex.org/A5117103046", "inst": "World Bank" }, { "name": "Victor Hugo Cunha de Melo", "url": "https://openalex.org/A5067013003", "inst": "Clemson University" }, { "name": "Brendan Moore", "url": "https://openalex.org/A5086497177", "inst": "Stanford University" } ], "affiliations": [ "Stanford University", "World Bank", "Clemson University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5180945", "doi": "10.2139/ssrn.5180945", "title": "The Direct Effect of Managerial Responses: When Politeness Backfires", "authors": [ "Haofeng Jin", "Lili Wang", "Yayuan Liu", "Tong Yu" ], "posted": "2025-04-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5180945", "field": "management", "role": "instrument", "bullets": [ "Online reviews and managerial responses on a leading Chinese mobile game platform with observable pre- and post-response rating update records", "Machine learning and LLMs separately measured politeness levels in firms' public replies to customer reviews", "Users who update after receiving a response upgrade ratings by one star on average, but higher politeness triggers perceived persuasion intent and reduces the positive effect" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "n": 2993, "authors_detailed": [ { "name": "Haofeng Jin", "url": "https://openalex.org/A5102948465", "inst": "Zhejiang University" }, { "name": "Lili Wang", "url": "https://openalex.org/A5100356381", "inst": "Fujian Normal University" }, { "name": "Yayuan Liu", "url": "https://openalex.org/A5113143892", "inst": "Zhejiang University" }, { "name": "Tong Yu", "url": "https://openalex.org/A5100767815", "inst": "University of Pittsburgh" } ], "affiliations": [ "Zhejiang University", "Fujian Normal University", "University of Pittsburgh" ] }, { "uid": "doi:10.2139/ssrn.5133933", "doi": "10.2139/ssrn.5133933", "title": "The Future of U.S. Retail Banking - A Comparative Analysis of AI versus Human Interaction Driving Service Excellence and Customer Satisfaction", "authors": [ "Sifat Mahmud" ], "posted": "2025-04-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5133933", "field": "management", "role": "object", "bullets": [ "Survey of 50 undergraduate and graduate business students at Wright State University on preferences for AI versus human banking service interactions.", "Respondents assessed generative AI chatbots against human agents across dimensions including efficiency, trust, empathy, and personalized financial guidance in retail banking.", "AI improved operational efficiency and reduced transaction times, but human interaction remained essential for trust, empathy, and long-term customer relationship building." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3486, "authors_detailed": [ { "name": "Sifat Mahmud", "url": "", "inst": "Wright State University" } ], "affiliations": [ "Wright State University" ] }, { "uid": "doi:10.2139/ssrn.5134721", "doi": "10.2139/ssrn.5134721", "title": "Toward a Human–AI Task Tensor: A Taxonomy for Organizing Work in the Age of Generative AI", "authors": [ "Anil Doshi", "Alastair Moore" ], "posted": "2025-04-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5134721", "field": "management", "role": "object", "bullets": [ "Conceptual paper proposing an eight-dimension human-AI task tensor for organizing and analyzing how work is performed in organizations adopting generative AI.", "Eight tensor dimensions include task definition, AI integration, interaction modality, audit requirement, output definition, decision-making authority, AI structure, and human persona.", "Framework generates illustrative lower-dimensional projections and a practical human-AI task canvas tool for guiding organizational decisions on human-AI work arrangements." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3487, "authors_detailed": [ { "name": "Anil R. Doshi", "url": "https://openalex.org/A5011170431", "inst": "University College London" }, { "name": "Alastair Moore", "url": "https://openalex.org/A5015493447", "inst": "University College London" } ], "affiliations": [ "University College London" ] }, { "uid": "arxiv:2504.05862v2", "arxiv_id": "2504.05862v2", "title": "Are Generative AI Agents Effective Personalized Financial Advisors?", "authors": [ "Takehiro Takayanagi", "Kiyoshi Izumi", "Javier Sanz-Cruzado", "Richard McCreadie", "Iadh Ounis" ], "posted": "2025-04-08", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.05862v2", "field": "finance", "role": "agent", "bullets": [ "Lab based user study with 64 participants who interact with LLM advisors on preference elicitation, personalized investment guidance, and advisor personality in the finance domain.", "The LLM plays the advisor in live conversations and is compared with human advisors; the abstract does not name the model or report a validation benchmark.", "LLM advisors match humans at eliciting preferences but can push investors toward unsuitable assets, and participants preferred and trusted an extroverted persona even when its advice was worse." ], "bullet_provenance": "ai", "salience": 56, "edition": 14, "models": [], "validated": null, "n": 1901, "authors_detailed": [ { "name": "Takehiro Takayanagi", "url": "https://openalex.org/A5074574419", "inst": "Bunkyo University" }, { "name": "Kiyoshi Izumi", "url": "https://openalex.org/A5044205949", "inst": "Bunkyo University" }, { "name": "Javier Sanz-Cruzado", "url": "https://openalex.org/A5060443310", "inst": "University of Glasgow" }, { "name": "Richard McCreadie", "url": "https://openalex.org/A5057657785", "inst": "University of Maryland, College Park" }, { "name": "Iadh Ounis", "url": "https://openalex.org/A5079046603", "inst": "University of Glasgow" } ], "affiliations": [ "University of Maryland, College Park", "Bunkyo University", "University of Glasgow" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5207598", "doi": "10.2139/ssrn.5207598", "title": "Asset Embeddings", "authors": [ "Xavier Gabaix", "Ralph S. J. Koijen", "Robert Richmond", "Motohiro Yogo" ], "posted": "2025-04-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5207598", "field": "finance", "role": "instrument", "bullets": [ "U.S. institutional portfolio holdings used to construct high-dimensional asset embeddings representing firms for asset pricing applications.", "Word2Vec, BERT, and recommender systems generated embeddings from holdings data; LLMs provided economic narratives from firm-level text.", "Asset embeddings outperformed traditional firm characteristics at predicting relative valuations, explaining return comovement, and predicting institutional portfolio decisions." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "relative valuation, return comovement, and portfolio decision benchmarks", "salience": 75, "n": 2847, "authors_detailed": [ { "name": "Xavier Gabaix", "url": "https://openalex.org/A5069579613", "inst": "National Bureau of Economic Research" }, { "name": "Ralph S. J. Koijen", "url": "https://openalex.org/A5083877502", "inst": "National Bureau of Economic Research" }, { "name": "Robert Richmond", "url": "https://openalex.org/A5049963078", "inst": "National Bureau of Economic Research" }, { "name": "Motohiro Yogo", "url": "https://openalex.org/A5048794076", "inst": "Princeton University" } ], "affiliations": [ "Princeton University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2504.05639v1", "arxiv_id": "2504.05639v1", "title": "DBOT: Artificial Intelligence for Systematic Long-Term Investing", "authors": [ "Vasant Dhar", "João Sedoc" ], "posted": "2025-04-08", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.05639v1", "field": "finance", "role": "agent", "bullets": [ "Publicly traded companies valued using the methodology of Aswath Damodaran, with backtesting capability across historical periods.", "Generative AI system (DBOT) trained on thousands of Damodaran's published valuations and writings to automate company valuation.", "DBOT can value any publicly traded company; backtesting reveals identifiable capability gaps relative to Damodaran's expert judgment." ], "bullet_provenance": "ai", "validated": false, "salience": 70, "models": [], "n": 3199, "authors_detailed": [ { "name": "Vasant Dhar", "url": "https://openalex.org/A5075950230", "inst": "New York University" }, { "name": "João Sedoc", "url": "https://openalex.org/A5120420123", "inst": "" } ], "affiliations": [ "New York University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2504.05104v2", "arxiv_id": "2504.05104v2", "title": "AI for Climate Finance: Agentic Retrieval and Multi-Step Reasoning for Early Warning System Investments", "authors": [ "Saeid Ario Vaghefi", "Aymane Hachcham", "Veronica Grasso", "Jiska Manicus", "Nakiete Msemo", "Chiara Colesanti Senni", "Markus Leippold" ], "posted": "2025-04-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.05104v2", "field": "finance", "role": "instrument", "bullets": [ "Twenty-five multilateral development bank project documents analyzed to track Early Warning Systems investments in the Climate Risk and Early Warning Systems Fund.", "LLM-based agentic AI with retrieval-augmented generation, fine-tuning, and chain-of-thought prompting classifies climate finance investments against funding guidelines.", "Agent-based RAG achieves 87% accuracy, 89% precision, and 83% recall, significantly outperforming zero-shot, few-shot, and fine-tuned classifier alternatives." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "accuracy/precision/recall on EWS investment classification", "salience": 60, "n": 2577, "authors_detailed": [ { "name": "Saeid Ashraf Vaghefi", "url": "https://openalex.org/A5035433111", "inst": "University of Zurich" }, { "name": "Aymane Hachcham", "url": "https://openalex.org/A5117435592", "inst": "University of Zurich" }, { "name": "Veronica F. Grasso", "url": "https://openalex.org/A5073990807", "inst": "California Institute of Technology" }, { "name": "Jiska Manicus", "url": "https://openalex.org/A5117435593", "inst": "" }, { "name": "Nakiete Msemo", "url": "https://openalex.org/A5025549068", "inst": "World Meteorological Organization" }, { "name": "Chiara Colesanti Senni", "url": "https://openalex.org/A5086406811", "inst": "University of Zurich" }, { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" } ], "affiliations": [ "California Institute of Technology", "University of Zurich", "World Meteorological Organization" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5207116", "doi": "10.2139/ssrn.5207116", "title": "Stock Market Volatility and Business News", "authors": [ "Justin Case", "Adam Clements" ], "posted": "2025-04-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5207116", "field": "finance", "role": "instrument", "bullets": [ "Over 1.1 million Wall Street Journal news articles used to identify exogenous narrative factors influencing U.S. stock market volatility over time.", "Two-step topic modeling and term frequency approach extracted news-based volatility measures; out-of-sample performance compared against ChatGPT-derived predictors and standard economic indicators.", "News-based measures reduced monthly volatility forecast errors by over 40% relative to benchmarks and outperformed ChatGPT-derived predictors and standard economic indicators." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "out-of-sample volatility forecasts vs benchmarks", "salience": 70, "n": 3484, "authors_detailed": [ { "name": "Justin Case", "url": "https://openalex.org/A5018525994", "inst": "University of Auckland" }, { "name": "Adam Clements", "url": "https://openalex.org/A5000585848", "inst": "Queensland University of Technology" } ], "affiliations": [ "University of Auckland", "Queensland University of Technology" ] }, { "uid": "doi:10.2139/ssrn.5206893", "doi": "10.2139/ssrn.5206893", "title": "AI Competition and Firm Value: Evidence from DeepSeek's Disruption", "authors": [ "Xing Yang" ], "posted": "2025-04-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5206893", "field": "finance", "role": "object", "bullets": [ "U.S. AI-related public firms tracked using real-time Yahoo Finance data around the January 2025 release of DeepSeek's R1 open-source language model.", "Event study measured cumulative abnormal returns for AI firms and GPU providers following DeepSeek R1 release with heterogeneity analysis by firm resources.", "U.S. AI firms responded positively to DeepSeek R1; resource-constrained firms gained more initially but underperformed longer-term; GPU providers faced negative market reactions." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 65, "validated": null, "n": 3485, "authors_detailed": [ { "name": "Xing Yang", "url": "https://openalex.org/A5069731732", "inst": "First Affiliated Hospital of Xi'an Jiaotong University" } ], "affiliations": [ "First Affiliated Hospital of Xi'an Jiaotong University" ] }, { "uid": "doi:10.2139/ssrn.5171287", "doi": "10.2139/ssrn.5171287", "title": "Generative AI and the Transmission of Public Information in the Stock Market: Evidence from the Release of ChatGPT", "authors": [ "Jenny Stanco", "Kee H. Chung" ], "posted": "2025-04-06", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5171287", "field": "finance", "role": "object", "bullets": [ "Firms filing 10-Ks around the public release of ChatGPT; sample size and window are not stated. Disclosure richness is proxied by filing length, investor attention by abnormal Google ticker searches.", "No model performs measurement here; the launch itself is the treatment event, and trading outcomes are compared across firms sorted on the disclosure and attention proxies.", "Trading activity, liquidity, and price efficiency rise most where filings are long and attention is high, alongside more accurate analyst forecasts and stronger return reactions to earnings news." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 58, "edition": 15, "validated": null, "n": 2103, "authors_detailed": [ { "name": "Jenny Stanco", "url": "https://openalex.org/A5116974273", "inst": "University at Buffalo, State University of New York" }, { "name": "Kee H. Chung", "url": "https://openalex.org/A5011392134", "inst": "University at Buffalo, State University of New York" } ], "affiliations": [ "University at Buffalo, State University of New York" ] }, { "uid": "arxiv:2504.04596v1", "arxiv_id": "2504.04596v1", "title": "SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities", "authors": [ "Noga Ben Yoash", "Meni Brief", "Oded Ovadia", "Gil Shenderovitz", "Moshik Mishaeli", "Rachel Lemberg", "Eitam Sheetrit" ], "posted": "2025-04-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.04596v1", "field": "finance", "role": "method", "bullets": [ "565 expert-written questions on SEC filings spanning comparison analysis, ratio calculation, risk assessment, and financial insight generation.", "Multiple LLMs evaluated on financial analysis tasks; SECQUE-Judge uses LLM-based judges that demonstrate strong alignment with human evaluations.", "Benchmark reveals varying model performance across financial analysis categories; publicly available to facilitate further research in financial AI." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "SECQUE-Judge alignment with human expert evaluations", "salience": 55, "n": 2693, "authors_detailed": [ { "name": "Noga Ben Yoash", "url": "https://openalex.org/A5114452490", "inst": "" }, { "name": "Meni Brief", "url": "https://openalex.org/A5114452489", "inst": "" }, { "name": "Oded Ovadia", "url": "https://openalex.org/A5079388254", "inst": "Linde (United States)" }, { "name": "Gil Shenderovitz", "url": "https://openalex.org/A5094151881", "inst": "Ben-Gurion University of the Negev" }, { "name": "Moshik Mishaeli", "url": "https://openalex.org/A5093478183", "inst": "Microsoft (Israel)" }, { "name": "Rachel Lemberg", "url": "https://openalex.org/A5114452491", "inst": "" }, { "name": "Eitam Sheetrit", "url": "https://openalex.org/A5083805783", "inst": "Ben-Gurion University of the Negev" } ], "affiliations": [ "Linde (United States)", "Ben-Gurion University of the Negev", "Microsoft (Israel)" ] }, { "uid": "arxiv:2504.04295v1", "arxiv_id": "2504.04295v1", "title": "Dynamic Hedging Strategies in Derivatives Markets with LLM-Driven Sentiment and News Analytics", "authors": [ "Jie Yang", "Yiqiu Tang", "Yongjie Li", "Lihua Zhang", "Haoran Zhang" ], "posted": "2025-04-05", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.04295v1", "field": "finance", "role": "instrument", "bullets": [ "Hedging in derivatives markets using signals from news articles, social media, and financial reports, backtested on historical derivatives data; instruments, period, and sample are not stated.", "An LLM, not named, produces sentiment and news analytics that drive real time hedge adjustments; no validation of the sentiment measures is reported.", "Claims better risk adjusted returns than static hedging approaches, but the abstract provides no magnitudes, no benchmark strategies, and no detail on markets or implementation." ], "bullet_provenance": "ai", "validated": false, "salience": 20, "edition": 14, "models": [], "n": 1977, "authors_detailed": [ { "name": "Jie Yang", "url": "https://openalex.org/A5100404947", "inst": "Harbin University of Science and Technology" }, { "name": "Tang, Yiqiu", "url": "", "inst": "" }, { "name": "Yongjie Li", "url": "https://openalex.org/A5052806317", "inst": "University of Macau" }, { "name": "Lihua Zhang", "url": "https://openalex.org/A5100414906", "inst": "Allen Institute for Brain Science" }, { "name": "Haoran Zhang", "url": "https://openalex.org/A5115595763", "inst": "Centre National de la Recherche Scientifique" } ], "affiliations": [ "Harbin University of Science and Technology", "University of Macau", "Allen Institute for Brain Science", "Centre National de la Recherche Scientifique" ] }, { "uid": "arxiv:2504.04292v1", "arxiv_id": "2504.04292v1", "title": "Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets", "authors": [ "Jie Yang", "Yiqiu Tang", "Yongjie Li", "Lihua Zhang", "Haoran Zhang" ], "posted": "2025-04-05", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.04292v1", "field": "finance", "role": "instrument", "bullets": [ "Real time monitoring of equity, fixed income, and currency markets, aggregating news articles, financial texts, and market reports; data sources, period, and sample are not stated.", "An LLM, not named, interprets text to contextualize cross asset risk signals within market narratives; the abstract reports no validation of these readings.", "Backtests and real time simulations are said to predict market shifts more accurately than conventional methods, with no figures or benchmark detail given." ], "bullet_provenance": "ai", "validated": false, "salience": 20, "edition": 14, "models": [], "n": 1978, "authors_detailed": [ { "name": "Jie Yang", "url": "https://openalex.org/A5110210304", "inst": "China National Offshore Oil Corporation (China)" }, { "name": "Tang, Yiqiu", "url": "", "inst": "" }, { "name": "Y. Li", "url": "https://openalex.org/A5017757492", "inst": "University of Electronic Science and Technology of China" }, { "name": "Lihua Zhang", "url": "https://openalex.org/A5100414905", "inst": "Kunming University of Science and Technology" }, { "name": "Haoran Zhang", "url": "https://openalex.org/A5115595763", "inst": "Centre National de la Recherche Scientifique" } ], "affiliations": [ "China National Offshore Oil Corporation (China)", "University of Electronic Science and Technology of China", "Kunming University of Science and Technology", "Centre National de la Recherche Scientifique" ] }, { "uid": "arxiv:2504.04141v4", "arxiv_id": "2504.04141v4", "title": "Self-Adaptive Cognitive Debiasing for Large Language Models in Decision-Making", "authors": [ "Yougang Lyu", "Shijie Ren", "Yue Feng", "Zihan Wang", "Zhumin Chen", "Zhaochun Ren", "Maarten de Rijke" ], "posted": "2025-04-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.04141v4", "field": "finance", "role": "method", "bullets": [ "Financial, healthcare, and legal decision-making tasks evaluated using both open-weight and closed-weight LLMs under single-bias and multi-bias prompt conditions.", "Self-adaptive cognitive debiasing iteratively refines prompts through bias determination, analysis, and debiasing steps to mitigate multiple cognitive biases simultaneously.", "SACD achieves the lowest average bias scores across all domains and bias settings, outperforming advanced prompt engineering and existing debiasing techniques." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "bias scores on decision-making tasks", "salience": 55, "n": 2576, "authors_detailed": [ { "name": "Yougang Lyu", "url": "https://openalex.org/A5073463616", "inst": "Amsterdam University of the Arts" }, { "name": "S. Demirg ren", "url": "https://openalex.org/A5097230111", "inst": "Nanjing Foreign Language School" }, { "name": "Feng Yue", "url": "https://openalex.org/A5101915371", "inst": "Harbin Medical University" }, { "name": "Zihan Wang", "url": "https://openalex.org/A5100380107", "inst": "Qingdao University" }, { "name": "Zhumin Chen", "url": "https://openalex.org/A5050947285", "inst": "Shandong University" }, { "name": "Zhaochun Ren", "url": "https://openalex.org/A5100384130", "inst": "Leiden University" }, { "name": "Maarten de Rijke", "url": "https://openalex.org/A5031439294", "inst": "German Research Centre for Artificial Intelligence" } ], "affiliations": [ "Amsterdam University of the Arts", "Nanjing Foreign Language School", "Harbin Medical University", "Qingdao University", "Shandong University", "Leiden University", "German Research Centre for Artificial Intelligence" ] }, { "uid": "doi:10.18653/v1/2025.realm-1.9", "doi": "10.18653/v1/2025.realm-1.9", "arxiv_id": "2504.03255v2", "title": "Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective", "authors": [ "Garry A. Gabison", "R. Patrick Xian" ], "posted": "2025-04-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.03255v2", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of delegated use of LLM agents and their extended systems, viewed through a principal agent lens; no dataset or empirical sample is involved.", "No model is deployed by the authors; the paper maps where liability could attach across the principal agent relationship as agentic systems reach deployment.", "Identifies inherent and emergent liability issues that risk based studies miss, and motivates technical governance work on interpretability, behaviour evaluation, reward and conflict management, and fail safe engineering." ], "bullet_provenance": "ai", "salience": 33, "edition": 14, "models": [], "validated": null, "n": 1900, "authors_detailed": [ { "name": "Garry A. Gabison", "url": "https://openalex.org/A5078485418", "inst": "The Honourable Society of Lincoln's Inn" }, { "name": "R. Patrick Xian", "url": "https://openalex.org/A5099178301", "inst": "South China University of Technology" } ], "affiliations": [ "The Honourable Society of Lincoln's Inn", "South China University of Technology" ] }, { "uid": "arxiv:2504.06293v2", "arxiv_id": "2504.06293v2", "title": "Generative AI Enhanced Financial Risk Management Information Retrieval", "authors": [ "Amin Haeri", "Jonathan Vitrano", "Mahdi Ghelichi" ], "posted": "2025-04-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.06293v2", "field": "finance", "role": "instrument", "bullets": [ "Ninety-four OSFI regulatory guidelines published 1991-2024 for Canadian financial institutions, curated into the RiskData dataset.", "Fine-tuned a sentence-BERT embedding model (RiskEmbed) for domain-specific retrieval in financial risk management RAG systems.", "RiskEmbed significantly outperforms general-purpose and financial embedding models on ranking metrics for regulatory document retrieval." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "ranking metrics on OSFI regulatory document retrieval", "salience": 55, "n": 3198, "authors_detailed": [ { "name": "Amin Haeri", "url": "https://openalex.org/A5044679988", "inst": "TD Bank Group" }, { "name": "Jonathan Vitrano", "url": "https://openalex.org/A5119791969", "inst": "" }, { "name": "Mahdi Ghelichi", "url": "https://openalex.org/A5119791970", "inst": "" } ], "affiliations": [ "TD Bank Group" ] }, { "uid": "arxiv:2504.02429v2", "arxiv_id": "2504.02429v2", "title": "MulFSA: Multi-level Financial Sentiment Analysis Framework for Bond Market", "authors": [ "Yiwei Liu", "Junbo Wang", "Lei Long", "Xin Li", "Ruiting Ma", "Yuankai Wu", "Xuebin Chen" ], "posted": "2025-04-03", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.02429v2", "field": "finance", "role": "instrument", "bullets": [ "A Chinese bond market corpus of 1.35 million texts covering 2013 to 2023, distilled into a daily composite sentiment index for credit spread forecasting.", "Pre trained language models and LLMs, families not stated, score firm and industry level sentiment with duration aware smoothing; no check against human sentiment labels is reported.", "Sentiment lowers credit spread forecast errors by 10.25 percent in MAE and 11.94 percent in MAPE, and index swings track major risk events and firm crises." ], "bullet_provenance": "ai", "validated": false, "salience": 48, "edition": 14, "models": [], "n": 1976, "authors_detailed": [ { "name": "Yiwei Liu", "url": "https://openalex.org/A5100348084", "inst": "Central University of Finance and Economics" }, { "name": "Junbo Wang", "url": "https://openalex.org/A5112578806", "inst": "Northwestern Polytechnical University" }, { "name": "Long Lei", "url": "https://openalex.org/A5018985485", "inst": "Chinese University of Hong Kong" }, { "name": "Xin Li", "url": "https://openalex.org/A5100772163", "inst": "Nanchang University" }, { "name": "Ruofei Ma", "url": "https://openalex.org/A5014430715", "inst": "ShanghaiTech University" }, { "name": "Yuankai Wu", "url": "https://openalex.org/A5065903043", "inst": "Technical University of Munich" }, { "name": "Chen, Xuebin", "url": "", "inst": "" } ], "affiliations": [ "Central University of Finance and Economics", "Northwestern Polytechnical University", "Chinese University of Hong Kong", "Nanchang University", "ShanghaiTech University", "Technical University of Munich" ] }, { "uid": "doi:10.2139/ssrn.5198675", "doi": "10.2139/ssrn.5198675", "title": "The News in Earnings Announcement Disclosures: Capturing Word Context Using LLM Methods", "authors": [ "Federico Siano" ], "posted": "2025-04-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5198675", "field": "accounting", "role": "instrument", "bullets": [ "U.S. firms' earnings press releases and conference calls matched with short-window stock returns around announcement dates", "An LLM captured word-context information in textual disclosures, benchmarked against dictionary and non-LLM machine learning text measures", "LLM-based textual news explains three times more return variation than prior text measures and accounts for price revisions within five minutes of release" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "R-squared of short-window return regressions vs dictionary and ML baselines", "salience": 82, "n": 2992, "authors_detailed": [ { "name": "Federico Siano", "url": "https://openalex.org/A5052997135", "inst": "The University of Texas at Dallas" } ], "affiliations": [ "The University of Texas at Dallas" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5120832", "doi": "10.2139/ssrn.5120832", "title": "Governance in the Absence of Government", "authors": [ "Tracy Hresko Pearl" ], "posted": "2025-04-02", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5120832", "field": "management", "role": "object", "bullets": [ "Conceptual analysis addresses United States and cross-border AI oversight when legislative speed, expertise, capture, gridlock, outdated agencies, and technical complexity constrain formal regulation.", "No language model produces evidence; generative AI's autonomy, labor effects, misinformation, and safety risks are the objects motivating alternative governance institutions.", "Agile soft-law coordination is favored over delayed statutory action, culminating in a proposed International Council on AI Risk for standards, compliance, and multi-stakeholder oversight." ], "bullet_provenance": "ai", "salience": 51, "edition": 23, "models": [], "validated": null, "n": 4194, "authors_detailed": [ { "name": "Tracy Hresko Pearl", "url": "https://openalex.org/A5000311005", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2504.01566v1", "arxiv_id": "2504.01566v1", "title": "GPT Adoption and the Impact of Disclosure Policies", "authors": [ "Cathy Yang", "David Restrepo Amariles", "Leo Allen", "Aurore Troussel" ], "posted": "2025-04-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.01566v1", "field": "management", "role": "object", "bullets": [ "Survey experiment with professionals in legal, audit, and advisory roles at consulting firms, framed by agency theory; sample size and country are not stated.", "No model is used as a research tool; ChatGPT use and mandatory disclosure of that use are the treatments examined for information asymmetry and agency costs.", "Disclosure narrows information asymmetry but leaves agency costs largely intact because managers undervalue analyst contributions made with GPT; incentive design is discussed as a remedy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 14, "validated": null, "n": 1857 }, { "uid": "arxiv:2504.02165v1", "arxiv_id": "2504.02165v1", "title": "Responsible Innovation: A Strategic Framework for Financial LLM Integration", "authors": [ "Ahmadreza Tavasoli", "Maedeh Sharbaf", "Seyed Mohamad Madani" ], "posted": "2025-04-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.02165v1", "field": "management", "role": "object", "bullets": [ "Financial institutions considering LLM deployment for credit assessment, client advisory, and language heavy processes under data governance, interpretability, and regulatory constraints.", "No model is used; the paper lays out a six decision framework running from necessity and data governance through risk, ethics, and ROI to the sourcing choice.", "Yields a staged roadmap tied to pilot testing, audit trails, and ongoing compliance evaluation; the framework is conceptual and no empirical validation is reported." ], "bullet_provenance": "ai", "salience": 32, "edition": 14, "models": [], "validated": null, "n": 1975, "authors_detailed": [ { "name": "Ahmadreza Tavasoli", "url": "https://openalex.org/A5117108242", "inst": "HEC Montréal" }, { "name": "Maedeh Sharbaf", "url": "https://openalex.org/A5117261627", "inst": "" }, { "name": "Madani, Seyed Mohamad", "url": "", "inst": "" } ], "affiliations": [ "HEC Montréal" ] }, { "uid": "doi:10.2139/ssrn.5121167", "doi": "10.2139/ssrn.5121167", "title": "Is Generative AI Old Wine in a New Bottle? An Asset Pricing Model on Generative AI and Data Economy", "authors": [ "Cong Zhang", "Haoyang Sun" ], "posted": "2025-04-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5121167", "field": "finance", "role": "object", "bullets": [ "Two-sector production-based asset pricing model with an AI computation sector and a general goods sector using AI agents as labor substitutes.", "Theoretical model examines how AI productivity and elasticity of substitution affect production, profit, labor demand, and sector valuations.", "AI productivity gains increase computation supply and reduce labor supply; positive technology shocks depress sector valuations and crowd out data-related investment." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 2846, "authors_detailed": [ { "name": "Cong Zhang", "url": "https://openalex.org/A5100438419", "inst": "Woodlawn School" }, { "name": "Haoyang Sun", "url": "https://openalex.org/A5103193944", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "Woodlawn School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5120059", "doi": "10.2139/ssrn.5120059", "title": "Generative AI in Innovation and Marketing Processes: A Roadmap of Research Opportunities", "authors": [ "Paola Cillo", "Gaia Rubera" ], "posted": "2025-04-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5120059", "field": "management", "role": "object", "bullets": [ "Conceptual roadmap paper surveying generative AI applications across the full innovation and marketing process at both firm and consumer levels of analysis.", "Provides technical overview of how GenAI models are trained and generate content, then maps research questions across two marketing domains.", "Framework identifies how GenAI reshapes consumer behavior, firm marketing strategy, the link between market-based assets and firm value, and consumer roles." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3483, "authors_detailed": [ { "name": "Paola Cillo", "url": "https://openalex.org/A5085307896", "inst": "Bocconi University" }, { "name": "Gaia Rubera", "url": "https://openalex.org/A5008941173", "inst": "Bocconi University" } ], "affiliations": [ "Bocconi University" ], "prestige": true }, { "uid": "arxiv:2504.13871v1", "arxiv_id": "2504.13871v1", "title": "Human aversion? Do AI Agents Judge Identity More Harshly Than Performance", "authors": [ "Yuanjun Feng", "Vivek Chodhary", "Yash Raj Shrestha" ], "posted": "2025-03-31", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.13871v1", "field": "management", "role": "agent", "bullets": [ "Controlled prediction task in which an LLM based agent receives forecasts from a human and an algorithm and chooses how much weight to give each source.", "The agent's weighting decisions are the outcome under study; the abstract does not name the model used, and no benchmark validation applies.", "The agent penalizes human errors more severely than algorithmic errors despite comparable error rates, and the discount deepens when identity is disclosed and the human is positioned second." ], "bullet_provenance": "ai", "salience": 55, "edition": 14, "models": [], "validated": null, "n": 1899 }, { "uid": "doi:10.2139/ssrn.5176089", "doi": "10.2139/ssrn.5176089", "title": "Large Language Models and the Labour Market: Spatial Evidence from Job Ads", "authors": [ "Eszter Baranyai", "Marcell Granat", "Mór Szepesi" ], "posted": "2025-03-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5176089", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 40, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 663, "authors_detailed": [ { "name": "Eszter Baranyai", "url": "https://openalex.org/A5116866078", "inst": "Hungarian National Bank" }, { "name": "Marcell Granat", "url": "https://openalex.org/A5116866079", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Mór Szepesi", "url": "https://openalex.org/A5116866080", "inst": "yale" } ], "affiliations": [ "Hungarian National Bank", "Independent - affiliation not provided to SSRN", "yale" ] }, { "uid": "arxiv:2504.01041v1", "arxiv_id": "2504.01041v1", "title": "Empirical Analysis of Digital Innovations Impact on Corporate ESG Performance: The Mediating Role of GAI Technology", "authors": [ "Jun Cui" ], "posted": "2025-03-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.01041v1", "field": "management", "role": "object", "bullets": [ "Panel of 8,000 firm-year observations from the Chinese CMARS and WIND databases covering publicly listed companies from 2015 to 2023 across industries.", "Generative AI technology adoption measured as a mediating variable between digital innovation and ESG performance using IV, PSM, and difference-in-differences methods.", "Digital innovation significantly enhances ESG performance through GAI adoption as mediator; effects vary by firm size, industry type, and ownership structure." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3482 }, { "uid": "arxiv:2504.00042v2", "arxiv_id": "2504.00042v2", "title": "Beyond the Reported Cutoff: Where Large Language Models Fall Short on Financial Knowledge", "authors": [ "Agam Shah", "Liqin Ye", "Sebastian Jaskowski", "Wei Xu", "Sudheer Chava" ], "posted": "2025-03-30", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.00042v2", "field": "finance", "role": "method", "bullets": [ "U.S. publicly traded companies, over 197k factual questions on financial performance evaluated against verified company data.", "Multiple LLMs answered financial knowledge questions; accuracy analyzed by company size, retail investment, institutional attention, and filing readability.", "LLMs less informed about past performance but more aware of larger companies and recent data; hallucination rates higher for larger firms in recent years." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "model responses vs factual company financial data", "salience": 65, "n": 2692 }, { "uid": "arxiv:2503.23190v1", "arxiv_id": "2503.23190v1", "title": "Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting", "authors": [ "Eftychia Makri", "Georgios Palaiokrassas", "Sarah Bouraga", "Antigoni Polychroniadou", "Leandros Tassiulas" ], "posted": "2025-03-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.23190v1", "field": "finance", "role": "instrument", "bullets": [ "Ethereum cryptocurrency price data used for short-term and few-shot forecasting scenarios against traditional and contemporary models.", "Pre-trained LLMs adapted via selective layer freezing to Ethereum price time series data; evaluated on standard forecasting error metrics.", "Selectively frozen LLMs achieve state-of-the-art performance on MSE, MAE, and RMSE, consistently surpassing traditional and contemporary benchmarks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "MSE, MAE, RMSE vs traditional and contemporary forecasting models", "salience": 45, "n": 2691, "authors_detailed": [ { "name": "Eftychia Makri", "url": "https://openalex.org/A5088625984", "inst": "Yale University" }, { "name": "Georgios Palaiokrassas", "url": "https://openalex.org/A5040196236", "inst": "Institute of Communication and Computer Systems" }, { "name": "Sarah Bouraga", "url": "https://openalex.org/A5007847034", "inst": "University of Namur" }, { "name": "Antigoni Polychroniadou", "url": "https://openalex.org/A5030114656", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Leandros Tassiulas", "url": "https://openalex.org/A5014892027", "inst": "Yale University" } ], "affiliations": [ "Yale University", "Institute of Communication and Computer Systems", "University of Namur", "JPMorgan Chase & Co (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.21422v1", "arxiv_id": "2503.21422v1", "title": "From Deep Learning to LLMs: A survey of AI in Quantitative Investment", "authors": [ "Bokai Cao", "Saizhuo Wang", "Xinyi Lin", "Xiaojun Wu", "Haohan Zhang", "Lionel M. Ni", "Jian Guo" ], "posted": "2025-03-27", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.21422v1", "field": "finance", "role": "method", "bullets": [ "A survey of quantitative investment organized around the alpha pipeline, from hand crafted factors and statistical models through deep learning to LLM based agents.", "No new model is built; the paper maps where LLMs enter the pipeline, covering unstructured data processing, alpha generation, and self iterative agent workflows.", "Argues the field is moving from prediction centered deep learning toward autonomous agent automation across data handling, factor discovery, and execution." ], "bullet_provenance": "ai", "salience": 38, "edition": 14, "models": [], "validated": null, "n": 1974, "authors_detailed": [ { "name": "Bokai Cao", "url": "https://openalex.org/A5044986542", "inst": "University of New Orleans" }, { "name": "Saizhuo Wang", "url": "https://openalex.org/A5035150525", "inst": "Hong Kong University of Science and Technology" }, { "name": "Xinyi Lin", "url": "https://openalex.org/A5014014301", "inst": "Fujian Medical University" }, { "name": "Xiaojun Wu", "url": "https://openalex.org/A5101968216", "inst": "Johns Hopkins University" }, { "name": "Zhang, Haohan", "url": "", "inst": "" }, { "name": "Lionel M. Ni", "url": "https://openalex.org/A5021357065", "inst": "Argonne National Laboratory" }, { "name": "Jian Guo", "url": "https://openalex.org/A5102706846", "inst": "Qiqihar University" } ], "affiliations": [ "Johns Hopkins University", "University of New Orleans", "Hong Kong University of Science and Technology", "Fujian Medical University", "Argonne National Laboratory", "Qiqihar University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.21115v1", "arxiv_id": "2503.21115v1", "title": "Leveraging Large Language Models for Risk Assessment in Hyperconnected Logistic Hub Network Deployment", "authors": [ "Yinzhu Quan", "Yujia Xu", "Guanlin Chen", "Frederick Benaben", "Benoit Montreuil" ], "posted": "2025-03-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.21115v1", "field": "management", "role": "instrument", "bullets": [ "Hyperconnected logistic hub network deployment under volatile conditions, analyzing geopolitical, financial, weather, and traffic risk data.", "LLM-driven pipeline processes unstructured data and automatically invokes analytical tools for structured risk assessment of candidate hub locations.", "Framework clusters hubs by comparable risk profiles and provides interpretable explanations, enabling scalable data-driven logistic hub selection." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 35, "n": 2690, "authors_detailed": [ { "name": "Yinzhu Quan", "url": "https://openalex.org/A5119948172", "inst": "" }, { "name": "Yujia Xu", "url": "https://openalex.org/A5120303246", "inst": "Georgia Institute of Technology" }, { "name": "Chen, Guanlin", "url": "", "inst": "" }, { "name": "Frédérick Bénaben", "url": "https://openalex.org/A5060452952", "inst": "Georgia Institute of Technology" }, { "name": "Benoît Montreuil", "url": "https://openalex.org/A5013129686", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.22726v1", "arxiv_id": "2503.22726v1", "title": "InfoBid: A Simulation Framework for Studying Information Disclosure in Auctions with Large Language Model-based Agents", "authors": [ "Yue Yin" ], "posted": "2025-03-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.22726v1", "field": "economics", "role": "agent", "bullets": [ "Second price online advertising auctions simulated under varying publisher information disclosure schemas, in a framework for studying signaling and information asymmetry in market design.", "GPT-4o powers the bidding agents; behaviour under each disclosure schema is compared with predictions from economic and social learning theory rather than validated against ground truth.", "Disclosure choices shift strategic bidding and auction outcomes in ways consistent with theory, supporting LLM agents as stand ins for human bidders in market design research." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "edition": 14, "validated": null, "n": 1856, "authors_detailed": [ { "name": "Yue Yin", "url": "https://openalex.org/A5103084835", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.20986v5", "arxiv_id": "2503.20986v5", "title": "MAD Chairs: A new tool to evaluate AI", "authors": [ "Chris Santos-Lang" ], "posted": "2025-03-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.20986v5", "field": "economics", "role": "agent", "bullets": [ "A game called MAD Chairs, presented as beyond the reach of standard game theoretic tools, used as an evaluation environment for frontier chat models.", "Claude, Gemini, ChatGPT, Qwen, and DeepSeek play the game and their strategies are examined for fairness related failure modes; no accuracy metric applies.", "Play exposed improvement opportunities in all five models at the time of writing; the author positions the game as a tool for AI safety and game theory research." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "salience": 30, "edition": 14, "validated": null, "n": 1973, "authors_detailed": [ { "name": "Chris Santos-Lang", "url": "https://openalex.org/A5044986431", "inst": "Southwestern Illinois College" } ], "affiliations": [ "Southwestern Illinois College" ] }, { "uid": "doi:10.2139/ssrn.5189069", "doi": "10.2139/ssrn.5189069", "title": "Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks", "authors": [ "Julian Wang", "Victor Xiaoqi Wang" ], "posted": "2025-03-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5189069", "field": "accounting", "role": "method", "bullet_provenance": "none", "salience": 48, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 348, "authors_detailed": [ { "name": "Julian Wang", "url": "https://openalex.org/A5073000065", "inst": "University of Oxford" }, { "name": "Victor Xiaoqi Wang", "url": "https://openalex.org/A5041860793", "inst": "California State University, Long Beach" } ], "affiliations": [ "University of Oxford", "California State University, Long Beach" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5118317", "doi": "10.2139/ssrn.5118317", "title": "AlphaPortfolio: Discovery of Portfolio Optimization and Allocation Methods Using LLMs", "authors": [ "Kamer Ali Yuksel" ], "posted": "2025-03-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5118317", "field": "finance", "role": "method", "bullet_provenance": "none", "salience": 38, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 662, "authors_detailed": [ { "name": "Kamer Ali Yüksel", "url": "https://openalex.org/A5033353054", "inst": "TiGenix (Spain)" } ], "affiliations": [ "TiGenix (Spain)" ] }, { "uid": "arxiv:2503.20990v3", "arxiv_id": "2503.20990v3", "title": "FinAudio: A Benchmark for Audio Large Language Models in Financial Applications", "authors": [ "Yupeng Cao", "Haohang Li", "Yangyang Yu", "Shashidhar Reddy Javaji", "Yueru He", "Jimin Huang", "Qianqian Xie", "Fabrizio Dimino", "Xiao-yang Liu", "K. P. Subbalakshmi", "Meikang Qiu", "Sophia Ananiadou", "Jian-Yun Nie" ], "posted": "2025-03-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.20990v3", "field": "finance", "role": "method", "bullets": [ "Benchmark of earnings conference calls and CEO speeches, comprising short-audio ASR, long-audio ASR, and financial audio summarization tasks", "Seven prevalent AudioLLMs evaluated on curated financial audio datasets for transcription accuracy and summarization quality", "Existing AudioLLMs show substantial performance limitations on financial audio compared to general-domain benchmarks" ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "ASR word error rate on curated financial audio datasets", "salience": 45, "n": 2991, "authors_detailed": [ { "name": "Yupeng Cao", "url": "https://openalex.org/A5009376964", "inst": "Kyoto University" }, { "name": "Haohang Li", "url": "https://openalex.org/A5027371293", "inst": "Stevens Institute of Technology" }, { "name": "Yangyang Yu", "url": "https://openalex.org/A5069731031", "inst": "Jilin University of Finance and Economics" }, { "name": "Shashidhar Reddy Javaji", "url": "https://openalex.org/A5115647631", "inst": "Stevens Institute of Technology" }, { "name": "Yueru He", "url": "https://openalex.org/A5111133467", "inst": "Columbia University" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "Xuzhou Medical College" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Dimino, Fabrizio", "url": "", "inst": "" }, { "name": "Xiaoyang Liu", "url": "https://openalex.org/A5100405226", "inst": "Tongji University" }, { "name": "K. P. Subbalakshmi", "url": "https://openalex.org/A5033041089", "inst": "Stevens Institute of Technology" }, { "name": "Meikang Qiu", "url": "https://openalex.org/A5083330935", "inst": "Augusta University" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" }, { "name": "Jian‐Yun Nie", "url": "https://openalex.org/A5018977183", "inst": "Université du Québec à Montréal" } ], "affiliations": [ "Columbia University", "Kyoto University", "Stevens Institute of Technology", "Jilin University of Finance and Economics", "Xuzhou Medical College", "Hunan Normal University", "Tongji University", "Augusta University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5190785", "doi": "10.2139/ssrn.5190785", "title": "Cloze Encounters: The Impact of Pirated Data Access on LLM Performance", "authors": [ "Stella Jia", "Abhishek Nagaraj" ], "posted": "2025-03-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5190785", "field": "economics", "role": "object", "bullets": [ "Sample of 12,916 books in and out of the Books3 pirated dataset, tested across GPT, Claude, Llama, and Gemini using an instrumental variable strategy.", "LLMs performed name cloze word-prediction tasks on book texts; causal identification exploits the pattern of which publication years appear in the Books3 training corpus.", "Books in the pirated training set show significantly higher LLM prediction accuracy; effects are stronger for less popular titles and vary across model families." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "llama" ], "open_weights": false, "validated": true, "validation_note": "name cloze word-prediction accuracy", "salience": 65, "n": 2374, "authors_detailed": [ { "name": "Stella Jia", "url": "https://openalex.org/A5116772781", "inst": "University of California, Berkeley" }, { "name": "Abhishek Nagaraj", "url": "https://openalex.org/A5002319407", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of California, Berkeley" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.18313v2", "arxiv_id": "2503.18313v2", "title": "Will LLMs be Professional at Fund Investment? DeepFund: A Live Arena Perspective", "authors": [ "Changlun Li", "Yao Shi", "Yuyu Luo", "Nan Tang" ], "posted": "2025-03-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.18313v2", "field": "finance", "role": "agent", "bullets": [ "Live arena platform evaluating LLM-based trading strategies for fund investment across varying market conditions.", "Multiple LLMs serve as agents in key investment decision roles within a multi-agent framework simulating real-world fund management processes.", "Platform enables comparative performance analysis with fund investment metrics; reveals LLM capabilities and limitations in dynamic live market settings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 55, "n": 2689, "authors_detailed": [ { "name": "C. Wei Li", "url": "https://openalex.org/A5112454106", "inst": "National Chung Hsing University" }, { "name": "Shi, Yao", "url": "", "inst": "" }, { "name": "Yuyu Luo", "url": "https://openalex.org/A5100614732", "inst": "Hong Kong University of Science and Technology" }, { "name": "Nan Tang", "url": "https://openalex.org/A5062243169", "inst": "Shanghai University" } ], "affiliations": [ "National Chung Hsing University", "Hong Kong University of Science and Technology", "Shanghai University" ] }, { "uid": "arxiv:2503.18029v1", "arxiv_id": "2503.18029v1", "title": "Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts", "authors": [ "Zongxiao Wu", "Yizhe Dong", "Yaoyiran Li", "Baofeng Shi" ], "posted": "2025-03-23", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.18029v1", "field": "finance", "role": "instrument", "bullets": [ "Loan officers' written assessments combined with structured borrower data for credit default prediction; lender, country, and sample size are not stated in the abstract.", "ChatGPT interprets and rewrites the assessments; refined and original texts feed deep learning default models. The version is not stated and the rewriting is not checked against ground truth.", "Text improves default prediction, ChatGPT refined text most, with the model's borrower delinquency analysis contributing the largest gain and usually higher profitability than human written text." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "edition": 14, "n": 1971 }, { "uid": "arxiv:2503.17963v1", "arxiv_id": "2503.17963v1", "title": "Won: Establishing Best Practices for Korean Financial NLP", "authors": [ "Guijin Son", "Hyunwoo Ko", "Haneral Jung", "Chami Hwang" ], "posted": "2025-03-23", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.17963v1", "field": "finance", "role": "method", "bullets": [ "A Korean financial LLM leaderboard run for about eight weeks, scoring 1,119 submissions on a closed benchmark of five multiple choice categories plus one open ended task.", "Categories span finance and accounting, stock price prediction, company analysis, financial markets, and agent tasks; training strategies of top performers are summarized.", "Releases an 80,000 instance open instruction dataset and Won, a fully open Korean financial LLM assembled from the observed best practices." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "closed Korean finance MCQA benchmark scoring", "salience": 45, "edition": 14, "n": 1972 }, { "uid": "arxiv:2503.18168v1", "arxiv_id": "2503.18168v1", "title": "Strategic Prompt Pricing for AIGC Services: A User-Centric Approach", "authors": [ "Xiang Li", "Bing Luo", "Jianwei Huang", "Yuan Luo" ], "posted": "2025-03-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.18168v1", "field": "economics", "role": "object", "bullets": [ "Theoretical model of an AIGC service platform with heterogeneous users varying in prompt engineering capability and ambiguity levels.", "Developed Optimal Prompt Pricing algorithm using prompt ambiguity framework and validated with a character-level GPT-like generative model.", "OPP algorithm achieves up to 31.72% improvement in platform payoff over existing pricing mechanisms; users with higher prompt ambiguity show non-monotonic usage." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 3197 }, { "uid": "arxiv:2503.17247v1", "arxiv_id": "2503.17247v1", "title": "KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications", "authors": [ "Michael J Bommarito", "Daniel Martin Katz", "Jillian Bommarito" ], "posted": "2025-03-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.17247v1", "field": "finance", "role": "method", "bullets": [ "Legal, financial, and governmental text corpora used to train domain-specific BPE tokenizers released in cased and character-level variants on Hugging Face.", "Custom BPE tokenizers with 128K vocabulary benchmarked against GPT-4o and Llama3 for token efficiency on legal and financial documents and specialized terminology.", "Domain tokenizer used 9-17% fewer tokens than GPT-4o and Llama3 overall, up to 83% fewer for legal terms and 39% fewer for financial terms." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "token count comparison vs GPT-4o and Llama3", "salience": 40, "n": 3481, "authors_detailed": [ { "name": "Michael James Bommarito", "url": "https://openalex.org/A5085629068", "inst": "Stanford Medicine" }, { "name": "Daniel Katz", "url": "https://openalex.org/A5101661429", "inst": "Chicago Kent College of Law" }, { "name": "Jillian Bommarito", "url": "https://openalex.org/A5064721809", "inst": "Gleason (United States)" } ], "affiliations": [ "Stanford Medicine", "Chicago Kent College of Law", "Gleason (United States)" ] }, { "uid": "arxiv:2503.16252v5", "arxiv_id": "2503.16252v5", "title": "Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning", "authors": [ "Zhaowei Liu", "Xin Guo", "Zhi Yang", "Fangqi Lou", "Lingfeng Zeng", "Jinyi Niu", "Mengping Li", "Qi Qi", "Zhiqiang Liu", "Yiyang Han", "Dongpo Cheng", "Ronghao Chen", "Huacan Wang", "Xingdong Feng", "Huixia Judy Wang", "Chengchun Shi", "Liwen Zhang" ], "posted": "2025-03-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.16252v5", "field": "finance", "role": "method", "bullets": [ "Financial reasoning scenarios such as compliance checking and robo advisory; training data is Fin-R1-Data, 60,091 chain of thought samples distilled and filtered from authoritative financial benchmarks.", "A 7 billion parameter model is trained with supervised fine tuning followed by reinforcement learning; code is public and the base model is not named in the abstract.", "The small model reports competitive scores on established financial benchmarks against much larger general models, though the abstract gives no specific figures." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "established financial benchmarks", "salience": 50, "edition": 14, "n": 1812, "authors_detailed": [ { "name": "Zhaowei Liu", "url": "https://openalex.org/A5061364578", "inst": "Yantai University" }, { "name": "Xin Dong Guo", "url": "https://openalex.org/A5084313185", "inst": "Kunming Institute of Zoology" }, { "name": "Yang, Zhi", "url": "", "inst": "" }, { "name": "Fangqi Lou", "url": "https://openalex.org/A5120756773", "inst": "" }, { "name": "Lingfeng Zeng", "url": "https://openalex.org/A5071484833", "inst": "Guangzhou University of Chinese Medicine" }, { "name": "Niu, Jinyi", "url": "", "inst": "" }, { "name": "Li, Mengping", "url": "", "inst": "" }, { "name": "Qi, Qi", "url": "", "inst": "" }, { "name": "Liu, Zhiqiang", "url": "", "inst": "" }, { "name": "Han, Yiyang", "url": "", "inst": "" }, { "name": "Cheng, Dongpo", "url": "", "inst": "" }, { "name": "Chen, Ronghao", "url": "", "inst": "" }, { "name": "Wang, Huacan", "url": "", "inst": "" }, { "name": "Feng, Xingdong", "url": "", "inst": "" }, { "name": "Wang, Huixia Judy", "url": "", "inst": "" }, { "name": "Shi, Chengchun", "url": "", "inst": "" }, { "name": "Liwen Zhang", "url": "https://openalex.org/A5100459596", "inst": "Harbin Institute of Technology" } ], "affiliations": [ "Yantai University", "Kunming Institute of Zoology", "Guangzhou University of Chinese Medicine", "Harbin Institute of Technology" ] }, { "uid": "arxiv:2504.06279v1", "arxiv_id": "2504.06279v1", "title": "Financial Analysis: Intelligent Financial Data Analysis System Based on LLM-RAG", "authors": [ "Jingru Wang", "Wen Ding", "Xiaotong Zhu" ], "posted": "2025-03-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2504.06279v1", "field": "finance", "role": "method", "bullets": [ "NASDAQ financial fundamentals from 2010 to 2023, queried through a system that combines a preprocessing module, vector storage and retrieval, and retrieval augmented query processing.", "gpt-3.5-turbo-1106 with retrieval answers financial queries and is scored on the dataset, reaching 78.6 percent accuracy and 89.2 percent recall in the fully optimized configuration.", "The optimized configuration beats the baseline model by 23 percentage points in accuracy and cuts response time by 34.8 percent, with a moderate rise in memory use." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "accuracy and recall on NASDAQ fundamentals queries", "salience": 34, "edition": 14, "n": 1898, "authors_detailed": [ { "name": "Jingru Wang", "url": "https://openalex.org/A5107028498", "inst": "Nankai University" }, { "name": "Ding Wen", "url": "https://openalex.org/A5091628409", "inst": "Georgia Institute of Technology" }, { "name": "Xiaotong Zhu", "url": "https://openalex.org/A5103511835", "inst": "Chinese Academy of Sciences" } ], "affiliations": [ "Georgia Institute of Technology", "Nankai University", "Chinese Academy of Sciences" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.16575v1", "arxiv_id": "2503.16575v1", "title": "Extract, Match, and Score: An Evaluation Paradigm for Long Question-context-answer Triplets in Financial Analysis", "authors": [ "Bo Hu", "Han Yuan", "Vlad Pandelea", "Wuqiong Luo", "Yingzhu Zhao", "Zheng Ma" ], "posted": "2025-03-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.16575v1", "field": "finance", "role": "method", "bullets": [ "Real-world financial dataset of long question-context-answer triplets from financial analysis and regulatory compliance use cases.", "LLMs generated long-form financial answers; authors propose Extract, Match, and Score evaluation tailored to complex long-form outputs.", "Traditional metrics fail on long-form financial answers; EMS evaluation provides reliable quality assessment for extended financial analysis tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 45, "n": 2688, "authors_detailed": [ { "name": "Bo Hu", "url": "https://openalex.org/A5100748112", "inst": "Yanshan University" }, { "name": "Yuan Han", "url": "https://openalex.org/A5101001006", "inst": "Port Authority of New York and New Jersey" }, { "name": "Vlad Pandelea", "url": "https://openalex.org/A5022280034", "inst": "Nanyang Technological University" }, { "name": "Luo, Wuqiong", "url": "", "inst": "" }, { "name": "Yingzhu Zhao", "url": "https://openalex.org/A5018178902", "inst": "Tsinghua University" }, { "name": "Zheng Ma", "url": "https://openalex.org/A5100656551", "inst": "Group Sense (China)" } ], "affiliations": [ "Yanshan University", "Port Authority of New York and New Jersey", "Nanyang Technological University", "Tsinghua University", "Group Sense (China)" ] }, { "uid": "arxiv:2503.15668v1", "arxiv_id": "2503.15668v1", "title": "Model Risk Management for Generative AI In Financial Institutions", "authors": [ "Anwesha Bhattacharyya", "Ye Yu", "Hanyu Yang", "Rahul Singh", "Tarun Joshi", "Jie Chen", "Kiran Yalavarthy" ], "posted": "2025-03-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.15668v1", "field": "finance", "role": "method", "bullets": [ "US large banks deploying generative AI applications across business lines, focusing on model risk management and validation practices.", "Framework addresses hallucination and toxicity risks specific to GenAI models requiring additional controls beyond traditional model risk approaches.", "Outlines enhanced validation practices for safe GenAI deployment in regulated financial institutions, emphasizing testing for novel generative model risks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "validated": null, "n": 2990, "authors_detailed": [ { "name": "A. Bhattacharyya", "url": "https://openalex.org/A5080639959", "inst": "Assam Agricultural University" }, { "name": "Yu, Ye", "url": "", "inst": "" }, { "name": "Hanyu Yang", "url": "https://openalex.org/A5101514877", "inst": "Nanjing Tech University" }, { "name": "Rahul Singh", "url": "https://openalex.org/A5102985625", "inst": "SRM Institute of Science and Technology" }, { "name": "Tarun Joshi", "url": "https://openalex.org/A5102487242", "inst": "Swami Rama Himalayan University" }, { "name": "Jie Chen", "url": "https://openalex.org/A5100668186", "inst": "Southwest Forestry University" }, { "name": "Kiran Yalavarthy", "url": "https://openalex.org/A5120037762", "inst": "" } ], "affiliations": [ "Assam Agricultural University", "Nanjing Tech University", "SRM Institute of Science and Technology", "Swami Rama Himalayan University", "Southwest Forestry University" ] }, { "uid": "doi:10.2139/ssrn.5180947", "doi": "10.2139/ssrn.5180947", "title": "AI-Generated Summaries as Differentiating Reference: Impact on User Content Generation in Online Communities", "authors": [ "Lin Yuan", "Fun Yi Chan", "Chaoyue Gao", "Alvin Leung", "Bin Gu", "Qiang Ye" ], "posted": "2025-03-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5180947", "field": "management", "role": "object", "bullets": [ "Hotel reviews from a third-party platform paired with hotel sales records analyzed in a quasi-experimental design around AI summary adoption.", "LLaMA 3.2 generated simulated AI summaries to assess review differentiation; cosine similarity and entropy measured textual changes before and after platform adoption.", "AIGS adoption increased lexical diversity, entropy, and image inclusion in reviews; mediation analysis confirmed differentiation drove improved review helpfulness and hotel sales." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 55, "n": 3480, "authors_detailed": [ { "name": "L. Yuan", "url": "https://openalex.org/A5016302264", "inst": "Harbin Institute of Technology" }, { "name": "Fun Yi Chan", "url": "", "inst": "Harbin Institute of Technology" }, { "name": "Chaoyue Gao", "url": "https://openalex.org/A5044025922", "inst": "University of Science and Technology of China" }, { "name": "Alvin Leung", "url": "https://openalex.org/A5021355350", "inst": "City University of Hong Kong" }, { "name": "Bin Gu", "url": "https://openalex.org/A5081942609", "inst": "Boston University" }, { "name": "Qiang Ye", "url": "https://openalex.org/A5100417962", "inst": "University of Science and Technology of China" } ], "affiliations": [ "Boston University", "Harbin Institute of Technology", "University of Science and Technology of China", "City University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.13149v1", "arxiv_id": "2503.13149v1", "title": "Are LLMs (Really) Ideological? An IRT-based Analysis and Alignment Tool for Perceived Socio-Economic Bias in LLMs", "authors": [ "Jasmin Wachter", "Michael Radloff", "Maja Smolej", "Katharina Kinder-Kurlanda" ], "posted": "2025-03-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.13149v1", "field": "economics", "role": "object", "bullets": [ "Survey style ideological items posed to language models and analyzed with item response theory; two model families fine tuned to fixed ideological positions serve as calibration anchors.", "Meta Llama 3.2 1B Instruct and ChatGPT 3.5 answer the items; a two stage model separates refusal to engage from perceived bias in answered items, avoiding human raters by design.", "Off the shelf models mostly avoid ideological engagement rather than lean partisan, cutting against earlier claims of systematic socio-economic slant in LLMs." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "salience": 45, "edition": 14, "validated": null, "n": 1855 }, { "uid": "arxiv:2503.22693v2", "arxiv_id": "2503.22693v2", "title": "Bridging Language Models and Financial Analysis", "authors": [ "Alejandro Lopez-Lira", "Jihoon Kwon", "Sangwoon Yoon", "Jy-yong Sohn", "Chanyeol Choi" ], "posted": "2025-03-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.22693v2", "field": "finance", "role": "method", "bullets": [ "Survey of recent LLM research applied to the financial sector, covering textual, numerical, and visual financial data analysis.", "Reviews multiple LLM methodologies for processing multifaceted financial data; examines distinctive capabilities and potential relevance to finance.", "Identifies significant gap between rapid LLM innovation and cautious industry adoption; maps promising research avenues for advancing financial applications." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "llama", "open_other" ], "salience": 45, "validated": null, "n": 2687, "authors_detailed": [ { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Jihoon Kwon", "url": "https://openalex.org/A5066426830", "inst": "Tech University of Korea" }, { "name": "Sangwoon Yoon", "url": "https://openalex.org/A5078121927", "inst": "Chung-Ang University" }, { "name": "Jy-yong Sohn", "url": "https://openalex.org/A5006777962", "inst": "University of Wisconsin–Madison" }, { "name": "Chanyeol Choi", "url": "https://openalex.org/A5047029131", "inst": "Community Link" } ], "affiliations": [ "University of Florida", "Tech University of Korea", "Chung-Ang University", "University of Wisconsin–Madison", "Community Link" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.13520v1", "arxiv_id": "2503.13520v1", "title": "Evaluating the Process Modeling Abilities of Large Language Models -- Preliminary Foundations and Results", "authors": [ "Peter Fettke", "Constantin Houy" ], "posted": "2025-03-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.13520v1", "field": "management", "role": "method", "bullets": [ "Evaluation framework for LLM-generated business process models considering quality, generation cost, and time trade-offs.", "LLMs assessed on process model generation; authors identify Pareto-optimal variant sets across multiple quality dimensions.", "Reliable evaluation requires multi-criteria approach addressing conceptualization, validation, generalizability, and data leakage beyond simple accuracy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 30, "n": 2989 }, { "uid": "arxiv:2503.10727v2", "arxiv_id": "2503.10727v2", "title": "Word-level Annotation of GDPR Transparency Compliance in Privacy Policies using Large Language Models", "authors": [ "Thomas Cory", "Wolf Rieder", "Julia Krämer", "Philip Raschke", "Patrick Herbke", "Axel Küpper" ], "posted": "2025-03-13", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.10727v2", "field": "other", "role": "method", "bullets": [ "703,791 English language privacy policies, with a ground truth sample of 200 policies hand annotated under a scheme of 21 GDPR derived transparency requirements.", "Seven LLMs, not named in the abstract, run a pipeline of passage classification, retrieval augmented generation, and self correction, evaluated at both passage and word span level.", "Decomposing the annotation task and adding targeted retrieval and classification components improves accuracy, most clearly for well structured requirements; exact figures are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "200 hand-annotated policies, two-tier evaluation", "salience": 41, "edition": 14, "models": [], "n": 1897 }, { "uid": "arxiv:2503.09637v1", "arxiv_id": "2503.09637v1", "title": "Complementarity, Augmentation, or Substitutivity? The Impact of Generative Artificial Intelligence on the U.S. Federal Workforce", "authors": [ "William G. Resh", "Yi Ming", "Xinyao Xia", "Michael Overton", "Gul Nisa Gürbüz", "Brandon De Bruhl" ], "posted": "2025-03-12", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.09637v1", "field": "management", "role": "object", "bullets": [ "United States federal workforce, with occupational requirements assembled at the knowledge, skill, and ability level from the Office of Personnel Management and individual agency records.", "A multi stage retrieval augmented generation pipeline projects shifts in required competencies and flags vulnerable occupations; the underlying language models are not named and no validation is reported.", "Preliminary results indicate significant shifts in required competencies and uneven vulnerability across roles, and the authors argue against generic approaches to strategic human capital planning." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 14, "models": [], "n": 1896, "authors_detailed": [ { "name": "William G. Resh", "url": "https://openalex.org/A5024606793", "inst": "Georgia State University" }, { "name": "Ming Yi", "url": "https://openalex.org/A5022949390", "inst": "Luoyang Institute of Science and Technology" }, { "name": "Xinyao Xia", "url": "https://openalex.org/A5102636252", "inst": "University of Southern California" }, { "name": "Michael Overton", "url": "https://openalex.org/A5067620293", "inst": "University of North Texas" }, { "name": "Gul Nisa Gürbüz", "url": "https://openalex.org/A5120307677", "inst": "University of California, Santa Barbara" }, { "name": "Brandon De Bruhl", "url": "https://openalex.org/A5120307678", "inst": "" } ], "affiliations": [ "University of Southern California", "Georgia State University", "Luoyang Institute of Science and Technology", "University of North Texas", "University of California, Santa Barbara" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.09647v5", "arxiv_id": "2503.09647v5", "title": "Leveraging LLMS for Top-Down Sector Allocation In Automated Trading", "authors": [ "Ryan Quek Wei Heng", "Edoardo Vittori", "Keane Ong", "Rui Mao", "Erik Cambria", "Gianmarco Mengaldo" ], "posted": "2025-03-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.09647v5", "field": "finance", "role": "instrument", "bullets": [ "U.S. equity sector allocation using macroeconomic data, policy documents, and market sentiment signals for automated portfolio decisions.", "LLMs processed multiple data streams for systematic macro analysis to generate sector-level portfolio allocation signals.", "LLM-based strategy achieved Sharpe ratio of 2.51 and 8.79% return versus negative 0.61 Sharpe and negative 1.39% for cross-momentum benchmark." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Sharpe ratio and return comparison against cross-momentum strategy", "salience": 60, "models": [], "n": 2677 }, { "uid": "arxiv:2503.09212v2", "arxiv_id": "2503.09212v2", "title": "Generative AI Adoption and Higher Order Skills", "authors": [ "Piyush Gulati", "Arianna Marchetti", "Phanish Puranam", "Victoria Sevcenko" ], "posted": "2025-03-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.09212v2", "field": "management", "role": "object", "bullets": [ "Job postings from 596 US public firms recruiting for GenAI skills, 2022-2024, analyzed in a difference-in-differences design around ChatGPT launch.", "ChatGPT release used as natural experiment to measure shifts in cognitive and social skill demands within GenAI-adopting roles.", "Roles requiring GenAI tools show higher cognitive skill demands; social skill demand within GenAI roles falls 4.5% post-ChatGPT launch." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 2988, "authors_detailed": [ { "name": "Piyush Gulati", "url": "https://openalex.org/A5093019646", "inst": "INSEAD" }, { "name": "Arianna Marchetti", "url": "https://openalex.org/A5005557346", "inst": "Singapore Management University" }, { "name": "Phanish Puranam", "url": "https://openalex.org/A5015167951", "inst": "INSEAD" }, { "name": "Victoria Sevcenko", "url": "https://openalex.org/A5102839762", "inst": "INSEAD" } ], "affiliations": [ "INSEAD", "Singapore Management University" ], "prestige": true }, { "uid": "arxiv:2503.08750v1", "arxiv_id": "2503.08750v1", "title": "Exposing Product Bias in LLM Investment Recommendation", "authors": [ "Yuhan Zhi", "Xiaoyu Zhang", "Longtian Wang", "Shumin Jiang", "Shiqing Ma", "Xiaohong Guan", "Chao Shen" ], "posted": "2025-03-11", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.08750v1", "field": "finance", "role": "object", "bullets": [ "567,000 automatically generated investment recommendation queries spanning stocks, mutual funds, cryptocurrencies, savings products, and portfolios, probing what LLM advisers steer users toward.", "The audited models are not named in the abstract; the study measures systematic product preferences in recommendations rather than using LLMs as research instruments.", "LLMs repeatedly favour specific products, Apple and Microsoft stock among them, and the preferences survive debiasing, which the authors link to risks of inflated valuations and instability." ], "bullet_provenance": "ai", "salience": 55, "edition": 14, "models": [], "validated": null, "n": 1970, "authors_detailed": [ { "name": "Yuhan Zhi", "url": "https://openalex.org/A5101935015", "inst": "Hubei University" }, { "name": "Xiaoyu Zhang", "url": "https://openalex.org/A5100419400", "inst": "Hebei Medical University" }, { "name": "L. Wang", "url": "https://openalex.org/A5107247784", "inst": "State Key Laboratory of Electrical Insulation and Power Equipment" }, { "name": "Shumin Jiang", "url": "https://openalex.org/A5039077071", "inst": "Chinese Academy of Sciences" }, { "name": "Shiqing Ma", "url": "https://openalex.org/A5101594068", "inst": "Amherst College" }, { "name": "Xiaohong Guan", "url": "https://openalex.org/A5044978005", "inst": "Harbin Medical University" }, { "name": "Chao Shen", "url": "https://openalex.org/A5039560181", "inst": "Wuxi Wind Power Design and Research Institute" } ], "affiliations": [ "Hubei University", "Hebei Medical University", "State Key Laboratory of Electrical Insulation and Power Equipment", "Chinese Academy of Sciences", "Amherst College", "Harbin Medical University", "Wuxi Wind Power Design and Research Institute" ] }, { "uid": "doi:10.2139/ssrn.5100205", "doi": "10.2139/ssrn.5100205", "title": "Performance Improvement for Large Language Models: Retrieval-Augmented Generation AI Agent for Tabular Data Processing in Auditing Procedures", "authors": [ "Fangbing Xiong", "Quanhong Han", "Chengning Zhang" ], "posted": "2025-03-11", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5100205", "field": "accounting", "role": "method", "bullet_provenance": "none", "salience": 42, "edition": 2, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 63, "authors_detailed": [ { "name": "Fangbing Xiong", "url": "", "inst": "Rutgers Sexual and Reproductive Health and Rights" }, { "name": "Quanhong Han", "url": "", "inst": "New Jersey Institute of Technology" }, { "name": "Chengning Zhang", "url": "https://openalex.org/A5101871087", "inst": "New York University" } ], "affiliations": [ "New York University", "Rutgers Sexual and Reproductive Health and Rights", "New Jersey Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.10674v2", "arxiv_id": "2503.10674v2", "title": "Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS", "authors": [ "Shafiuddin Rehan Ahmed", "Ankit Parag Shah", "Quan Hung Tran", "Vivek Khetan", "Sukryool Kang", "Ankit Mehta", "Yujia Bao", "Wei Wei" ], "posted": "2025-03-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.10674v2", "field": "accounting", "role": "instrument", "bullets": [ "ESG reports mapped to GRI and ESRS disclosure standards using content index tables as weak supervision for retrieval training.", "LLM-as-judge refines disclosure-section mappings; fine-tuned BERT models benchmarked against commercial embeddings for ESG retrieval.", "Fine-tuned BERT outperforms commercial and leading public embedding models, including cross-standard transfer from GRI to ESRS reports." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "retrieval accuracy benchmarks on ESG-CID dataset", "salience": 55, "n": 2987, "authors_detailed": [ { "name": "Shafiuddin Rehan Ahmed", "url": "https://openalex.org/A5107063680", "inst": "University of Colorado Boulder" }, { "name": "Ankit Shah", "url": "https://openalex.org/A5019541211", "inst": "Aditya Birla (India)" }, { "name": "Quan H. Tran", "url": "https://openalex.org/A5101460494", "inst": "University of Houston" }, { "name": "Vivek Khetan", "url": "https://openalex.org/A5091757734", "inst": "Accenture (Switzerland)" }, { "name": "Sung‐Ho Kang", "url": "https://openalex.org/A5110726396", "inst": "Korea Polar Research Institute" }, { "name": "Ankit I. Mehta", "url": "https://openalex.org/A5076123107", "inst": "Illinois College" }, { "name": "Yujia Bao", "url": "https://openalex.org/A5027122418", "inst": "Santa Barbara City College" }, { "name": "Wei Wei", "url": "https://openalex.org/A5100323697", "inst": "Chongqing University of Posts and Telecommunications" } ], "affiliations": [ "University of Colorado Boulder", "Aditya Birla (India)", "University of Houston", "Accenture (Switzerland)", "Korea Polar Research Institute", "Illinois College", "Santa Barbara City College", "Chongqing University of Posts and Telecommunications" ] }, { "uid": "arxiv:2503.06646v2", "arxiv_id": "2503.06646v2", "title": "Evaluating and Aligning Human Economic Risk Preferences in LLMs", "authors": [ "Jiaxin Liu", "Yixuan Tang", "Yi Yang", "Kar Yan Tam" ], "posted": "2025-03-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.06646v2", "field": "economics", "role": "agent", "bullets": [ "Economic risk preference tasks across simplified and complex decision scenarios with persona-based LLM evaluation.", "LLMs assessed for alignment with human risk aversion and risk-seeking behavior; alignment method proposed for persona-specific preferences.", "LLMs perform reasonably in simple personalized risk contexts but decline in complex economic decision-making tasks." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 2676, "authors_detailed": [ { "name": "Jiaxin Liu", "url": "https://openalex.org/A5100383770", "inst": "Dalian Medical University" }, { "name": "Yixuan Tang", "url": "https://openalex.org/A5103211747", "inst": "Central South University" }, { "name": "Yi Yang", "url": "https://openalex.org/A5101532117", "inst": "Heilongjiang University of Science and Technology" }, { "name": "Kar Yan Tam", "url": "https://openalex.org/A5090061579", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Dalian Medical University", "Central South University", "Heilongjiang University of Science and Technology", "Hong Kong University of Science and Technology" ] }, { "uid": "arxiv:2503.05185v2", "arxiv_id": "2503.05185v2", "title": "Towards Temporal-Aware Multi-Modal Retrieval Augmented Generation in Finance", "authors": [ "Fengbin Zhu", "Junfeng Li", "Liangming Pan", "Wenjie Wang", "Fuli Feng", "Chao Wang", "Huanbo Luan", "Tat-Seng Chua" ], "posted": "2025-03-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.05185v2", "field": "finance", "role": "method", "bullets": [ "NASDAQ 100 companies with multimodal corpus of tables, news, stock prices, and technical charts across daily to annual periods.", "TMMHybridRAG converts tabular, visual, and time-series data to text via LLMs, incorporating temporal information in graph and dense indexes.", "Method improves over baselines on ten financial analysis tasks but notable performance gaps remain on the FinTMMBench benchmark." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinTMMBench benchmark across ten financial analysis tasks", "salience": 55, "models": [], "n": 2672, "authors_detailed": [ { "name": "Fengbin Zhu", "url": "https://openalex.org/A5029052244", "inst": "National University of Singapore" }, { "name": "J.-Y. Li", "url": "https://openalex.org/A5108094972", "inst": "Northwestern Polytechnical University" }, { "name": "Liangming Pan", "url": "https://openalex.org/A5027533517", "inst": "Peking University" }, { "name": "Wenjie Wang", "url": "https://openalex.org/A5100368538", "inst": "Beijing Institute of Technology" }, { "name": "Fuli Feng", "url": "https://openalex.org/A5051925942", "inst": "University of Science and Technology of China" }, { "name": "Chao Wang", "url": "https://openalex.org/A5100407035", "inst": "Zhejiang International Studies University" }, { "name": "Huanbo Luan", "url": "https://openalex.org/A5091587552", "inst": "Univates" }, { "name": "Tat‐Seng Chua", "url": "https://openalex.org/A5089404640", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore", "Northwestern Polytechnical University", "Peking University", "Beijing Institute of Technology", "University of Science and Technology of China", "Zhejiang International Studies University", "Univates" ] }, { "uid": "arxiv:2503.05481v1", "arxiv_id": "2503.05481v1", "title": "Maximum Hallucination Standards for Domain-Specific Large Language Models", "authors": [ "Tingmingke Lu" ], "posted": "2025-03-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.05481v1", "field": "economics", "role": "object", "bullets": [ "Theoretical model of domain-specific LLMs treated as engineering products with hallucination rates as a product attribute.", "Analytical framework examines optimal hallucination regulation under imperfect consumer awareness and misinformation externalities.", "Net welfare improves when maximum hallucination standards vary by domain willingness to pay and marginal misinformation damage." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 2673 }, { "uid": "arxiv:2503.05234v1", "arxiv_id": "2503.05234v1", "title": "Unveiling Biases in AI: ChatGPT's Political Economy Perspectives and Human Comparisons", "authors": [ "Leonardo Becchetti", "Nazaria Solferino" ], "posted": "2025-03-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.05234v1", "field": "economics", "role": "object", "bullets": [ "ChatGPT responses to European Social Survey political economy questions on environment, civil rights, inequality, and government size.", "ChatGPT answers compared to ideological stances of ESS respondents giving similar answers on a left-right political spectrum.", "ChatGPT shows significant left-oriented bias on environmental and civil rights topics, diverging from its self-declared center-left position." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Comparison against European Social Survey respondent positions", "salience": 45, "n": 2674 }, { "uid": "arxiv:2503.05061v2", "arxiv_id": "2503.05061v2", "title": "No Free Labels: Limitations of LLM-as-a-Judge Without Human Grounding", "authors": [ "Michael Krumdick", "Charles Lovering", "Varshini Reddy", "Seth Ebner", "Chris Tanner" ], "posted": "2025-03-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.05061v2", "field": "finance", "role": "method", "bullets": [ "160 challenging business and finance questions with 1,200 LLM-generated responses evaluated by financial professionals.", "Suite of LLMs assessed as automated judges on BFF-Bench and a challenging MT-Bench subset; expert annotations form the VERDICTS dataset.", "LLM judges agree with experts only on questions they can answer themselves; providing expert-written references largely mitigates this gap." ], "bullet_provenance": "ai", "validated": true, "validation_note": "BFF-Bench with 1,200 responses judged by financial professionals", "salience": 60, "models": [], "n": 2675 }, { "uid": "doi:10.2139/ssrn.5097793", "doi": "10.2139/ssrn.5097793", "title": "When Generative Artificial Intelligence Enhances the Quality of Human Output: Workplace Implications", "authors": [ "Prithwiraj Mukherjee", "Souvik Dutta" ], "posted": "2025-03-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5097793", "field": "management", "role": "object", "bullets": [ "Principal-agent model of a manager contracting a white-collar employee who has access to generative AI for creative output production.", "Models employee effort allocation across baseline work, prompt engineering, and hallucination error-checking under information asymmetry about worker ability type.", "GenAI access increases both observable output quality and unobservable baseline effort; higher AI accuracy induces greater prompt engineering investment by employees." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3919, "authors_detailed": [ { "name": "Prithwiraj Mukherjee", "url": "https://openalex.org/A5056362146", "inst": "Ahmedabad University" }, { "name": "Souvik Dutta", "url": "https://openalex.org/A5003816893", "inst": "Indraprastha Institute of Information Technology Delhi" } ], "affiliations": [ "Ahmedabad University", "Indraprastha Institute of Information Technology Delhi" ] }, { "uid": "arxiv:2503.04873v1", "arxiv_id": "2503.04873v1", "title": "Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?", "authors": [ "Xinyu Wei", "Luojia Liu" ], "posted": "2025-03-06", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.04873v1", "field": "finance", "role": "method", "bullets": [ "Financial sentiment classification using in context demonstrations of document sentiment pairs; the abstract does not name the datasets, sample sizes, or periods used.", "Most current LLM families, with DeepSeek V3 the only model named, are tested across several in context sample selection methods against labelled sentiment; no accuracy figures appear in the abstract.", "The experiments support in context learning as a workable route to financial sentiment analysis where fine tuning on finance data is impractical; magnitudes are not stated." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "labelled financial sentiment data", "salience": 42, "edition": 14, "n": 1854 }, { "uid": "doi:10.2139/ssrn.5166656", "doi": "10.2139/ssrn.5166656", "title": "Large language models in finance: estimating financial sentiment for stock prediction", "authors": [ "K. Kirtac", "G. Germano" ], "posted": "2025-03-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5166656", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 347, "authors_detailed": [ { "name": "Kemal Kirtac", "url": "https://openalex.org/A5108909107", "inst": "University College London" }, { "name": "Guido Germano", "url": "https://openalex.org/A5066615785", "inst": "University College London" } ], "affiliations": [ "University College London" ] }, { "uid": "doi:10.25209/2079-3316-2025-16-1-83-130", "doi": "10.25209/2079-3316-2025-16-1-83-130", "arxiv_id": "2503.08696v1", "title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "authors": [ "Kasymkhan Khubiev", "Mikhail Semenov" ], "posted": "2025-03-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.08696v1", "field": "finance", "role": "instrument", "bullets": [ "176 Russian stocks on Moscow Exchange with 79,555 Russian-language financial news articles used for multimodal price prediction.", "RuBERT and Vikhr-Qwen2.5-0.5b-Instruct process news text, combined with LSTM on candlestick time series for price forecasting.", "Adding textual news modality reduced mean absolute percentage error by 55% compared to time-series-only models." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "MAPE and directional accuracy on Moscow Exchange stocks", "salience": 50, "n": 2985, "authors_detailed": [ { "name": "Mikhail Semenov", "url": "https://openalex.org/A5024242137", "inst": "Sirius University of Science and Technology" }, { "name": "Kasymkhan Usufovich Khubiyev", "url": "https://openalex.org/A5116576020", "inst": "Sirius University of Science and Technology" } ], "affiliations": [ "Sirius University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.5167018", "doi": "10.2139/ssrn.5167018", "title": "Generative AI and Entrepreneurial Entry", "authors": [ "Jiayi Bao", "Bowen Lou", "Hongshen Sun" ], "posted": "2025-03-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5167018", "field": "management", "role": "object", "bullets": [ "STEM workforce across US industries examined via difference-in-differences around ChatGPT launch using industry-level GenAI exposure variation.", "ChatGPT release serves as natural experiment; GenAI exposure measure validated against aggregated ChatGPT website traffic data.", "GenAI access increases incorporated entrepreneurship among high-exposure STEM workers; augmentation channel dominates over automation-displacement channel." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 70, "validated": null, "n": 2986, "authors_detailed": [ { "name": "Jiayi Bao", "url": "https://openalex.org/A5042251525", "inst": "Texas A&M University" }, { "name": "Bowen Lou", "url": "https://openalex.org/A5119710910", "inst": "University of Southern California" }, { "name": "Hongshen Sun", "url": "https://openalex.org/A5119710911", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "University of Southern California", "Massachusetts Institute of Technology", "Texas A&M University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.10649v1", "arxiv_id": "2503.10649v1", "title": "Measuring Political Preferences in AI Systems: An Integrative Approach", "authors": [ "David Rozado" ], "posted": "2025-03-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.10649v1", "field": "economics", "role": "object", "bullets": [ "Leading commercial AI systems, including ChatGPT and Gemini, audited with four methods: language comparison with US Congress members, policy recommendations, sentiment toward politically affiliated figures, and standardized orientation tests.", "The systems under audit generate open ended text as well as test responses; outputs are scored against partisan language and by sentiment, with no external validation benchmark reported.", "Most contemporary systems lean left to varying degrees; cited prior work shows fine tuning with politically skewed data can move models across the spectrum, so the tilt is not inherent." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 45, "edition": 14, "validated": null, "n": 1895, "authors_detailed": [ { "name": "Rozado, David", "url": "", "inst": "" } ] }, { "uid": "arxiv:2503.02692v1", "arxiv_id": "2503.02692v1", "title": "FinArena: A Human-Agent Collaboration Framework for Financial Market Analysis and Forecasting", "authors": [ "Congluo Xu", "Zhaobin Liu", "Ziyang Li" ], "posted": "2025-03-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.02692v1", "field": "finance", "role": "instrument", "bullets": [ "Stock trend prediction and trading simulations across various risk profiles using multimodal data including stock prices, news articles, and financial statements.", "LLM-based multi-agent system with adaptive RAG integrates diverse financial data sources; a universal expert agent incorporates individual risk preferences.", "FinArena surpasses both traditional and state-of-the-art benchmarks in stock trend prediction and yields promising results in trading simulations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "stock trend prediction benchmarks", "salience": 55, "n": 2575 }, { "uid": "doi:10.2139/ssrn.5162246", "doi": "10.2139/ssrn.5162246", "title": "AI and the Extended Workday: Productivity, Contracting Efficiency, and Distribution of Rents", "authors": [ "Wei Jiang", "Junyoung Park", "Rachel J. Xiao", "Shen Zhang" ], "posted": "2025-03-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5162246", "field": "economics", "role": "object", "bullets": [ "Individual-level time diary data from 2004 to 2023 in the United States, measuring work hours and leisure allocation across occupations with varying AI exposure.", "The study examines how occupational AI exposure, including the ChatGPT productivity shock, affects the intensive margin of employment and time allocation between work and leisure.", "Higher AI exposure is associated with longer work hours and reduced leisure, driven by AI complementing labor; effects amplified in competitive markets where workers lack bargaining power." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3479, "authors_detailed": [ { "name": "Wei Jiang", "url": "https://openalex.org/A5024857449", "inst": "Emory University" }, { "name": "Jun‐Young Park", "url": "https://openalex.org/A5100354913", "inst": "Auburn University" }, { "name": "Rachel J. Xiao", "url": "https://openalex.org/A5113560520", "inst": "Fordham University" }, { "name": "Shen Zhang", "url": "https://openalex.org/A5100354194", "inst": "Seton Hall University" } ], "affiliations": [ "Emory University", "Auburn University", "Fordham University", "Seton Hall University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2503.00725v1", "arxiv_id": "2503.00725v1", "title": "Causal Inference on Outcomes Learned from Text", "authors": [ "Iman Modarressi", "Jann Spiess", "Amar Venugopal" ], "posted": "2025-03-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.00725v1", "field": "economics", "role": "method", "bullets": [ "Randomized trials with text outcomes, demonstrated on academic manuscript abstracts as a proof-of-concept application.", "LLMs identify systematic differences across treatment and control text groups; human labeling validates claims via sample splitting.", "Framework provides valid causal inference on text with statistical guarantees, requiring costly human validation of LLM-suggested differences." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Human labeling validation with sample splitting for statistical inference", "salience": 65, "models": [], "n": 2671 }, { "uid": "arxiv:2503.00320v2", "arxiv_id": "2503.00320v2", "title": "Shifting Power: Leveraging LLMs to Simulate Human Aversion in ABMs of Bilateral Financial Exchanges, A bond market study", "authors": [ "Alicia Vidler", "Toby Walsh" ], "posted": "2025-03-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.00320v2", "field": "finance", "role": "agent", "bullets": [ "Agent-based model of bilateral government bond markets with decentralized transactions between market makers and clients.", "LLM-augmented agents (TRIBE) simulate human-like risk aversion and ambiguity sensitivity in bond trading decisions.", "Slight trade aversion encoded in LLM agents causes complete trading cessation; human-like variability shifts power to clients and triggers systemic collapse." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 2984, "authors_detailed": [ { "name": "Alicia Vidler", "url": "https://openalex.org/A5051454314", "inst": "Bar-Ilan University" }, { "name": "Toby Walsh", "url": "https://openalex.org/A5072902302", "inst": "UNSW Sydney" } ], "affiliations": [ "Bar-Ilan University", "UNSW Sydney" ] }, { "uid": "doi:10.1145/3768292.3770371", "doi": "10.1145/3768292.3770371", "arxiv_id": "2502.21112v2", "title": "Optimizing Large Language Models for ESG Activity Detection in Financial Texts", "authors": [ "Mattia Birti", "Andrea Maurino", "Francesco Osborne" ], "posted": "2025-02-28", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.21112v2", "field": "accounting", "role": "method", "bullets": [ "ESG-Activities, a benchmark of 1,325 text segments from sustainability reports and corporate disclosures, labelled against the European Union's ESG taxonomy of environmental activities.", "Current generation LLMs classify segments before and after fine tuning on a mix of original and synthetic data, with the labelled benchmark supplying accuracy comparisons.", "Fine tuning raises classification accuracy substantially, and open Llama 7B and Gemma 7B beat larger proprietary systems in some configurations." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "ESG-Activities labelled benchmark", "salience": 50, "edition": 14, "n": 1843, "authors_detailed": [ { "name": "Mattia Birti", "url": "https://openalex.org/A5120353186", "inst": "University of Milano-Bicocca" }, { "name": "Andrea Maurino", "url": "https://openalex.org/A5067371383", "inst": "University of Milano-Bicocca" }, { "name": "Francesco Osborne", "url": "https://openalex.org/A5066447873", "inst": "The Open University" } ], "affiliations": [ "University of Milano-Bicocca", "The Open University" ] }, { "uid": "arxiv:2502.21206v3", "arxiv_id": "2502.21206v3", "title": "Chronologically Consistent Large Language Models", "authors": [ "Songrun He", "Linying Lv", "Asaf Manela", "Jimmy Wu" ], "posted": "2025-02-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.21206v3", "field": "finance", "role": "method", "bullets": [ "U.S. financial news and stock returns, with language models trained on temporally restricted text corpora to avoid lookahead bias.", "ChronoBERT and ChronoGPT trained only on text available at each point in time, benchmarked against BERT and Llama.", "Chronologically consistent models match BERT on NLP benchmarks and achieve Sharpe ratios comparable to larger Llama in next-day return prediction." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "NLP benchmarks and Sharpe ratio comparison for stock return prediction", "salience": 75, "n": 2668, "authors_detailed": [ { "name": "Songrun He", "url": "https://openalex.org/A5048165657", "inst": "Washington University in St. Louis" }, { "name": "Liangyu Lv", "url": "https://openalex.org/A5055495019", "inst": "Peking University" }, { "name": "Asaf Manela", "url": "https://openalex.org/A5117372333", "inst": "Washington University in St. Louis" }, { "name": "Jinlin Wu", "url": "https://openalex.org/A5112053104", "inst": "Institut de Recherche et d’Innovation" } ], "affiliations": [ "Washington University in St. Louis", "Peking University", "Institut de Recherche et d’Innovation" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.21037v2", "arxiv_id": "2502.21037v2", "title": "The amplifier effect of artificial agents in social contagion", "authors": [ "Eric Hitz", "Mingmin Feng", "Radu Tanase", "René Algesheimer", "Manuel S. Mariani" ], "posted": "2025-02-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.21037v2", "field": "economics", "role": "agent", "bullets": [ "Two choice experiments replicated with LLM-powered artificial agents embedded in network-based social contagion scenarios.", "LLM agents simulated adoption decisions to measure threshold behavior and contagion spread compared to prior human subjects.", "Artificial agents exhibit lower adoption thresholds than humans, producing significantly faster and wider social contagion in networks." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "models": [], "n": 2669 }, { "uid": "arxiv:2502.20963v2", "arxiv_id": "2502.20963v2", "title": "Retrieval Augmented Generation for Topic Modeling in Organizational Research: An Introduction with Empirical Demonstration", "authors": [ "Gerion Spielberger", "Florian M. Artinger", "Jochen Reb", "Rudolf Kerschreiter" ], "posted": "2025-02-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.20963v2", "field": "management", "role": "instrument", "bullets": [ "Twitter/X dataset previously analyzed by Mu et al. (2024a), reanalyzed for leadership and organizational research topic classification.", "Agentic RAG pipeline combined retrieval, LLM generation, and iterative agent-driven refinement for automated topic modeling.", "Agentic RAG achieved higher reliability and validity than both standard machine learning and direct LLM prompting for topic modeling." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Comparison with standard ML and LLM prompting on Twitter/X dataset", "salience": 50, "models": [], "n": 2670 }, { "uid": "doi:10.2139/ssrn.5158718", "doi": "10.2139/ssrn.5158718", "title": "Analysing Modern Slavery Statements (MSS) Using Large Language Models (LLMs)", "authors": [ "Ser-Huang Poon", "Eghbal Rahimikia", "Philip Jobi Vallavanthra", "Siliang Wei" ], "posted": "2025-02-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5158718", "field": "management", "role": "instrument", "bullets": [ "Corporate Modern Slavery Statements produced under UK and Australian legislation, analyzed at scale for compliance monitoring.", "LLMs extract, classify, and assess compliance content of statements beyond keyword search, capturing contextual nuances.", "LLMs enable scalable monitoring of corporate human rights reporting, identifying compliance gaps and modern slavery risk patterns." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 2667, "authors_detailed": [ { "name": "Ser‐Huang Poon", "url": "https://openalex.org/A5017515962", "inst": "Turing Institute" }, { "name": "Eghbal Rahimikia", "url": "https://openalex.org/A5116449593", "inst": "University of Manchester" }, { "name": "Philip Jobi Vallavanthra", "url": "https://openalex.org/A5116449594", "inst": "University of Manchester" }, { "name": "Siliang Wei", "url": "https://openalex.org/A5083993686", "inst": "University of Manchester" } ], "affiliations": [ "Turing Institute", "University of Manchester" ] }, { "uid": "arxiv:2502.18772v1", "arxiv_id": "2502.18772v1", "title": "Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance", "authors": [ "Xueqing Peng", "Triantafillos Papadopoulos", "Efstathia Soufleri", "Polydoros Giannouris", "Ruoyu Xiang", "Yan Wang", "Lingfei Qian", "Jimin Huang", "Qianqian Xie", "Sophia Ananiadou" ], "posted": "2025-02-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.18772v1", "field": "finance", "role": "method", "bullets": [ "Greek financial NLP across five tasks including NER, QA, summarization, and classification, using three new expert-annotated datasets.", "Twenty-two LLMs evaluated on Plutus-ben benchmark; Plutus-8B fine-tuned on Greek financial data as the first Greek financial LLM.", "Greek financial NLP remains challenging; cross-lingual transfer underperforms, confirming need for language-specific financial models." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Plutus-ben benchmark across five financial NLP tasks", "salience": 50, "n": 2666, "authors_detailed": [ { "name": "Xueqing Peng", "url": "https://openalex.org/A5036053506", "inst": "Finlay Institute" }, { "name": "Triantafillos Papadopoulos", "url": "https://openalex.org/A5120311671", "inst": "" }, { "name": "Efstathia Soufleri", "url": "https://openalex.org/A5031719209", "inst": "Purdue University West Lafayette" }, { "name": "Polydoros Giannouris", "url": "https://openalex.org/A5120311672", "inst": "" }, { "name": "Ruoyu Xiang", "url": "https://openalex.org/A5101307852", "inst": "Chongqing Normal University" }, { "name": "Yan Wang", "url": "https://openalex.org/A5100733313", "inst": "Jiangnan University" }, { "name": "Lingfei Qian", "url": "https://openalex.org/A5009941118", "inst": "Yale University" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "Xuzhou Medical College" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" } ], "affiliations": [ "Yale University", "Finlay Institute", "Purdue University West Lafayette", "Chongqing Normal University", "Jiangnan University", "Xuzhou Medical College", "Hunan Normal University", "University of Manchester" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5083199", "doi": "10.2139/ssrn.5083199", "title": "Quantifying a Firm's AI Engagement: Constructing Objective, Data-Driven, AI Stock Indices Using SEC 10-K Filings", "authors": [ "Lennart Ante", "Aman Saggu" ], "posted": "2025-02-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5083199", "field": "finance", "role": "instrument", "bullets": [ "Annual 10-K filings from 3,395 NASDAQ-listed firms between 2011 and 2023, measuring AI engagement through NLP-based text analysis of filing language.", "NLP techniques classified AI stocks using binary indicators and weighted scores based on frequency and context of AI-related terms; validated via event study on ChatGPT launch.", "Constructed AI indices matched or surpassed 14 existing AI-themed ETFs in risk-return profiles; firms with higher AI engagement saw significantly greater abnormal returns around ChatGPT's launch." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": true, "validation_note": "event study on ChatGPT launch showing abnormal returns by AI engagement", "salience": 60, "n": 3478, "authors_detailed": [ { "name": "Lennart Ante", "url": "https://openalex.org/A5034620178", "inst": "Constructor University" }, { "name": "Aman Saggu", "url": "https://openalex.org/A5068688584", "inst": "Mahidol University" } ], "affiliations": [ "Constructor University", "Mahidol University" ] }, { "uid": "arxiv:2502.17967v2", "arxiv_id": "2502.17967v2", "title": "Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents", "authors": [ "Tianmi Ma", "Jiawei Du", "Wenxin Huang", "Wenjie Wang", "Liang Xie", "Xian Zhong", "Joey Tianyi Zhou" ], "posted": "2025-02-25", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.17967v2", "field": "finance", "role": "agent", "bullets": [ "A virtual zero sum stock market where LLM agents trade against each other with bid ask interactions and price impact, tested on NASDAQ and CSI data.", "LLM agents, families not stated in the abstract, trade from plain text numbers or chart visualizations, with an optional reflection module.", "Agents overfit local patterns when given text based numbers but reason and trade better with charts; reflection adds further gains, especially under high volatility." ], "bullet_provenance": "ai", "salience": 50, "edition": 14, "models": [], "validated": null, "n": 1968, "authors_detailed": [ { "name": "Tao Ma", "url": "https://openalex.org/A5000527097", "inst": "Shaanxi Normal University" }, { "name": "Jiawei Du", "url": "https://openalex.org/A5107895081", "inst": "Beijing University of Chemical Technology" }, { "name": "Wenxin Huang", "url": "https://openalex.org/A5101949151", "inst": "Zhejiang Normal University" }, { "name": "Wenjie Wang", "url": "https://openalex.org/A5100368521", "inst": "East China University of Science and Technology" }, { "name": "Liang Xie", "url": "https://openalex.org/A5101460488", "inst": "Commercial Aircraft Corporation of China (China)" }, { "name": "Xian Zhong", "url": "https://openalex.org/A5120665702", "inst": "" }, { "name": "Zhou, Joey Tianyi", "url": "", "inst": "" } ], "affiliations": [ "Shaanxi Normal University", "Beijing University of Chemical Technology", "Zhejiang Normal University", "East China University of Science and Technology", "Commercial Aircraft Corporation of China (China)" ] }, { "uid": "arxiv:2503.01870v2", "arxiv_id": "2503.01870v2", "title": "Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs", "authors": [ "Artem Timoshenko", "Chengfeng Mao", "John R. Hauser" ], "posted": "2025-02-25", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.01870v2", "field": "management", "role": "method", "bullets": [ "Voice of the customer interviews and qualitative content across multiple product and service categories, the marketing input from which analysts formulate customer needs.", "Supervised fine tuned LLMs, base models not named, abstract customer needs and are judged against market research professionals; relatively small models suffice, and results generalize across foundation models.", "Fine tuned models perform at least as well as professional analysts and clearly beat untuned foundation models, producing specific, grounded need statements without hallucination." ], "bullet_provenance": "ai", "validated": true, "validation_note": "compared with professional analysts across product categories", "salience": 62, "edition": 14, "models": [], "n": 1969, "authors_detailed": [ { "name": "Artem Timoshenko", "url": "https://openalex.org/A5022326376", "inst": "Northwestern University" }, { "name": "Chengfeng Mao", "url": "https://openalex.org/A5055221257", "inst": "Wuhan University of Technology" }, { "name": "John R. Hauser", "url": "https://openalex.org/A5032453542", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Northwestern University", "Massachusetts Institute of Technology", "Wuhan University of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5153894", "doi": "10.2139/ssrn.5153894", "title": "Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Research", "authors": [ "Roman Lukyanenko" ], "posted": "2025-02-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5153894", "field": "management", "role": "object", "bullets": [ "Information systems research perspective on GenAI tracing the evolution from symbolic AI to connectionist large language models.", "Reviews GenAI capabilities from a sociotechnical lens and proposes a structured IS research agenda for business and societal impacts.", "Identifies deep systemic properties of human-AI ecosystems and calls for research on GenAI-enabled strategies and operations in organizations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 25, "validated": null, "n": 2983, "authors_detailed": [ { "name": "Roman Lukyanenko", "url": "https://openalex.org/A5090350066", "inst": "University of Virginia" } ], "affiliations": [ "University of Virginia" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.16879v1", "arxiv_id": "2502.16879v1", "title": "A Multi-LLM-Agent-Based Framework for Economic and Public Policy Analysis", "authors": [ "Yuzhi Hao", "Danyang Xie" ], "posted": "2025-02-24", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.16879v1", "field": "economics", "role": "agent", "bullets": [ "Two period consumption allocation problems, solved with explicit utility functions and by intuitive reasoning, followed by an interest income taxation simulation.", "Five LLMs, not named in the abstract, act as economic agents; their differing analytical capabilities map to education groups and income brackets instead of prompt induced heterogeneity.", "The framework demonstrates policy impact simulation across heterogeneous agents; the abstract reports the proof of concept rather than quantified welfare or revenue estimates." ], "bullet_provenance": "ai", "salience": 45, "edition": 14, "models": [], "validated": null, "n": 1966, "authors_detailed": [ { "name": "Y. Andrew Hao", "url": "https://openalex.org/A5045890315", "inst": "Hong Kong University of Science and Technology" }, { "name": "Danyang Xie", "url": "https://openalex.org/A5119857981", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Hong Kong University of Science and Technology" ] }, { "uid": "arxiv:2502.16789v2", "arxiv_id": "2502.16789v2", "title": "AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay", "authors": [ "Ziyi Tang", "Zechuan Chen", "Jiarui Yang", "Jiayao Mai", "Yongsen Zheng", "Keze Wang", "Jinrui Chen", "Liang Lin" ], "posted": "2025-02-24", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.16789v2", "field": "finance", "role": "agent", "bullets": [ "Alpha factor mining evaluated on the Chinese CSI 500 and US S&P 500 over the past four years, spanning bull and bear markets.", "An autonomous LLM agent, family not stated, generates factors under abstract syntax tree originality and complexity constraints, with LLM judged alignment between market hypotheses and factors.", "Reported to resist alpha decay better than genetic programming and other LLM miners and to deliver consistent alpha in both markets; effect sizes are not stated in the abstract." ], "bullet_provenance": "ai", "salience": 45, "edition": 14, "models": [], "validated": null, "n": 1967, "authors_detailed": [ { "name": "Ziyi Tang", "url": "https://openalex.org/A5101045006", "inst": "China University of Petroleum, East China" }, { "name": "Zechuan Chen", "url": "https://openalex.org/A5011159124", "inst": "Sun Yat-sen University" }, { "name": "J. Y. Yang", "url": "https://openalex.org/A5075408889", "inst": "Sun Yat-sen University" }, { "name": "Mai, Jiayao", "url": "", "inst": "" }, { "name": "Yongsen Zheng", "url": "https://openalex.org/A5003424646", "inst": "Nanyang Technological University" }, { "name": "Keze Wang", "url": "https://openalex.org/A5088124671", "inst": "Sun Yat-sen University" }, { "name": "J H Chen", "url": "https://openalex.org/A5019281346", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Liang Lin", "url": "https://openalex.org/A5100412937", "inst": "Fujian Medical University" } ], "affiliations": [ "China University of Petroleum, East China", "Sun Yat-sen University", "Nanyang Technological University", "Chinese University of Hong Kong, Shenzhen", "Fujian Medical University" ] }, { "uid": "doi:10.2139/ssrn.5078043", "doi": "10.2139/ssrn.5078043", "title": "Decoding China's Industrial Policies", "authors": [ "Hanming Fang", "Ming Li", "Guangli Lu" ], "posted": "2025-02-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5078043", "field": "economics", "role": "instrument", "bullets": [ "Three million Chinese central, provincial, and municipal government documents from 2000 to 2022 analyzed for industrial policy content", "LLMs with multi-stage prompt engineering classified documents and extracted targeted industries, policy tools, and implementation mechanisms with verification", "Industrial policies often misaligned with local comparative advantage; spatial inefficiencies and industry-wide overcapacity emerged as significant downsides" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "manual verification of extracted policy dimensions", "salience": 75, "n": 2428, "authors_detailed": [ { "name": "Hanming Fang", "url": "https://openalex.org/A5102752404", "inst": "National Bureau of Economic Research" }, { "name": "Ming Li", "url": "https://openalex.org/A5100351317", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Guangli Lu", "url": "https://openalex.org/A5101954257", "inst": "Chinese University of Hong Kong, Shenzhen" } ], "affiliations": [ "National Bureau of Economic Research", "Chinese University of Hong Kong, Shenzhen" ] }, { "uid": "arxiv:2502.16810v6", "arxiv_id": "2502.16810v6", "title": "AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting", "authors": [ "Jibang Wu", "Chenghao Yang", "Yi Wu", "Simon Mahns", "Chaoqi Wang", "Hao Zhu", "Fei Fang", "Haifeng Xu" ], "posted": "2025-02-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.16810v6", "field": "management", "role": "agent", "bullets": [ "Real estate marketing with human-subject experiments using a focus group of potential house buyers evaluating AI-generated versus human-expert property descriptions.", "LLM-based agentic framework with grounding, personalization, and marketing modules generates targeted property descriptions aligned with user preferences and factual attributes.", "AI-generated marketing descriptions preferred over human-expert copy by a clear margin while maintaining the same level of factual accuracy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "human preference evaluation vs expert copy", "salience": 60, "n": 2574, "authors_detailed": [ { "name": "Jibang Wu", "url": "https://openalex.org/A5010684754", "inst": "New York University Shanghai" }, { "name": "Yang, Chenghao", "url": "", "inst": "" }, { "name": "Wu, Yi", "url": "", "inst": "" }, { "name": "Simon Mahns", "url": "https://openalex.org/A5119738308", "inst": "" }, { "name": "Wang, Chaoqi", "url": "", "inst": "" }, { "name": "Hao Zhu", "url": "https://openalex.org/A5100376348", "inst": "Jiangsu University" }, { "name": "Fei Fang", "url": "https://openalex.org/A5101930572", "inst": "Hong Kong Polytechnic University" }, { "name": "Haifeng Xu", "url": "https://openalex.org/A5100731916", "inst": "Düsseldorf University Hospital" } ], "affiliations": [ "New York University Shanghai", "Jiangsu University", "Hong Kong Polytechnic University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.17011v2", "arxiv_id": "2502.17011v2", "title": "Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation", "authors": [ "Jaskaran Singh Walia", "Aarush Sinha", "Naman Saraswat", "Srinitish Srinivasan", "Srihari Unnikrishnan" ], "posted": "2025-02-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.17011v2", "field": "finance", "role": "instrument", "bullets": [ "Four U.S. bond categories (AAA, BAA, US10Y, Junk) with 12 macroeconomic variables used to generate synthetic bond yield data.", "Qwen2.5-7B generated trading signals, risk assessments, and volatility projections from CausalGAN-produced synthetic bond data.", "Framework achieved 0.103% MAE and 60% profit rate; expert assessments scored 4.67 out of 5 for forecast quality." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "MAE 0.103%, profit/loss rate 60%, expert assessment 4.67/5", "salience": 45, "n": 2665, "authors_detailed": [ { "name": "Jaskaran Singh Walia", "url": "https://openalex.org/A5020581858", "inst": "Vellore Institute of Technology University" }, { "name": "Sinha, Aarush", "url": "", "inst": "" }, { "name": "Saraswat, Naman", "url": "", "inst": "" }, { "name": "Srinivasan, Srinitish", "url": "", "inst": "" }, { "name": "S. Unnikrishnan", "url": "https://openalex.org/A5031959233", "inst": "Florida State University" } ], "affiliations": [ "Vellore Institute of Technology University", "Florida State University" ] }, { "uid": "arxiv:2502.17161v1", "arxiv_id": "2502.17161v1", "title": "Real-time Monitoring of Economic Shocks using Company Websites", "authors": [ "Michael Koenig", "Jakob Rauch", "Martin Woerter" ], "posted": "2025-02-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.17161v1", "field": "economics", "role": "instrument", "bullets": [ "Over five million company websites worldwide, applied to COVID-19 pandemic period to construct a firm-level affectedness indicator.", "LLM-assisted classification and information extraction on website texts produced a Web-Based Affectedness Indicator (WAI) quantifying firm responses to shocks.", "WAI correlates highly with pandemic containment measures and reliably predicts firm performance, providing real-time data unavailable from traditional sources." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "correlation with pandemic containment measures and firm performance prediction", "salience": 70, "n": 2981, "authors_detailed": [ { "name": "Michael Koenig", "url": "https://openalex.org/A5116887374", "inst": "" }, { "name": "Jakob Rauch", "url": "https://openalex.org/A5116887375", "inst": "Vrije Universiteit Amsterdam" }, { "name": "Martin Woerter", "url": "https://openalex.org/A5071669000", "inst": "ETH Zurich" } ], "affiliations": [ "Vrije Universiteit Amsterdam", "ETH Zurich" ] }, { "uid": "doi:10.2139/ssrn.5078085", "doi": "10.2139/ssrn.5078085", "title": "Impact of Generative AI on Supply Chain | Insights and Trends", "authors": [ "Srinivas Rao Ramasani Venkata" ], "posted": "2025-02-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5078085", "field": "management", "role": "object", "bullets": [ "Review of generative AI applications across supply chain sectors including aviation, maritime logistics, and procurement.", "Examines GenAI deployment in predictive maintenance, supplier selection, and cargo routing across multiple industry contexts.", "GenAI reduces maintenance downtime and costs through prediction, optimizes routing to lower operational expenses and carbon footprint." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 25, "validated": null, "n": 2982, "authors_detailed": [ { "name": "Srinivas Rao Ramasani Venkata", "url": "https://openalex.org/A5116397307", "inst": "" } ] }, { "uid": "arxiv:2503.16458v1", "arxiv_id": "2503.16458v1", "title": "Users Favor LLM-Generated Content -- Until They Know It's AI", "authors": [ "Petr Parshakov", "Iuliia Naidenova", "Sofia Paklina", "Nikita Matkin", "Cornel Nesseler" ], "posted": "2025-02-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.16458v1", "field": "management", "role": "object", "bullets": [ "Controlled field experiment with participants evaluating human- and LLM-generated responses to popular questions, with randomized disclosure of the response source.", "Participants rated response quality without knowing or after being told whether the content was generated by a human or an AI system.", "Participants preferred AI-generated responses overall, but this preference diminished significantly once the AI origin was disclosed, revealing bias against known AI content." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3477, "authors_detailed": [ { "name": "Petr Parshakov", "url": "https://openalex.org/A5120756873", "inst": "National Research University Higher School of Economics" }, { "name": "Iuliia Naidenova", "url": "https://openalex.org/A5033819053", "inst": "National Research University Higher School of Economics" }, { "name": "Sofia Paklina", "url": "https://openalex.org/A5091656431", "inst": "National Research University Higher School of Economics" }, { "name": "N. V. Matkin", "url": "https://openalex.org/A5012633204", "inst": "National Research University Higher School of Economics" }, { "name": "Cornel Nesseler", "url": "https://openalex.org/A5074184480", "inst": "University of Zurich" } ], "affiliations": [ "National Research University Higher School of Economics", "University of Zurich" ] }, { "uid": "doi:10.1109/icdew67478.2025.00038", "doi": "10.1109/icdew67478.2025.00038", "arxiv_id": "2502.16159v1", "title": "ZiGong 1.0: A Large Language Model for Financial Credit", "authors": [ "Yu Lei", "Zixuan Wang", "Chu Liu", "Tongyao Wang" ], "posted": "2025-02-22", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.16159v1", "field": "finance", "role": "method", "bullets": [ "Financial credit assessment tasks in real world settings, where general purpose language models fall short for lack of specialized financial expertise.", "A Mistral based model, ZiGong, is trained with multi task supervised fine tuning; a proxy model scores and prunes training samples to reduce hallucination in financial contexts.", "The authors report improved robustness and prediction accuracy over training without pruning, but the abstract names no benchmark and gives no figures." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 30, "edition": 14, "n": 1842, "authors_detailed": [ { "name": "Lei Yu", "url": "https://openalex.org/A5049180328", "inst": "University of International Business and Economics" }, { "name": "Zixuan Wang", "url": "https://openalex.org/A5100398250", "inst": "University of International Business and Economics" }, { "name": "Chu Liu", "url": "https://openalex.org/A5100912672", "inst": "University of International Business and Economics" }, { "name": "Tongyao Wang", "url": "https://openalex.org/A5004626273", "inst": "University of International Business and Economics" } ], "affiliations": [ "University of International Business and Economics" ] }, { "uid": "arxiv:2502.16343v1", "arxiv_id": "2502.16343v1", "title": "Exploring Sentiment Manipulation by LLM-Enabled Intelligent Trading Agents", "authors": [ "David Byrd" ], "posted": "2025-02-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.16343v1", "field": "finance", "role": "agent", "bullets": [ "Simulated financial market with multiple traders where a deep reinforcement learning agent controls an LLM to post on a social media feed.", "An RL-based trading agent used an LLM to generate social media posts observed by other traders, learning to manipulate sentiment for profit.", "The agent learned to augment its trading profit by optimizing the sentiment of its generated posts in the simulated market environment." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "models": [], "n": 2845, "authors_detailed": [ { "name": "David A. Byrd", "url": "https://openalex.org/A5078313330", "inst": "Bowdoin College" } ], "affiliations": [ "Bowdoin College" ] }, { "uid": "arxiv:2502.15411v4", "arxiv_id": "2502.15411v4", "title": "HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings", "authors": [ "Rasmus Aavang", "Giovanni Rizzi", "Rasmus Bøggild", "Alexandre Iolov", "Mike Zhang", "Johannes Bjerva" ], "posted": "2025-02-21", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.15411v4", "field": "accounting", "role": "method", "bullets": [ "1.65 million paragraphs from mandated iXBRL earnings filings carrying 198,000 hierarchically organized KPI labels, plus an 8,000 paragraph manually curated subset for rapid evaluation.", "Benchmarks encoder models against LLMs, families not stated, on KPI classification, extraction, and structured extraction, scored against the taxonomy linked labels.", "Encoders top 0.906 macro F1 on classification while LLMs manage 0.440 F1 on structured extraction; date handling drives most extraction errors. Code and data are released." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labelled iXBRL benchmark, F1 reported", "salience": 55, "edition": 14, "models": [], "n": 1965, "authors_detailed": [ { "name": "Rasmus Aavang", "url": "https://openalex.org/A5119851971", "inst": "Aalborg University" }, { "name": "Giovanni Rizzi", "url": "https://openalex.org/A5119851972", "inst": "" }, { "name": "Rasmus Bøggild", "url": "https://openalex.org/A5119851973", "inst": "" }, { "name": "Alexandre Iolov", "url": "https://openalex.org/A5119851974", "inst": "Alion Science and Technology (United States)" }, { "name": "Mike Zhang", "url": "https://openalex.org/A5025357679", "inst": "The University of Tokyo" }, { "name": "Johannes Bjerva", "url": "https://openalex.org/A5013472329", "inst": "Aalborg University" } ], "affiliations": [ "Aalborg University", "Alion Science and Technology (United States)", "The University of Tokyo" ] }, { "uid": "arxiv:2502.15865v2", "arxiv_id": "2502.15865v2", "title": "Standard Benchmarks Fail -- Auditing LLM Agents in Finance Must Prioritize Risk", "authors": [ "Zichen Chen", "Jiaao Chen", "Jianda Chen", "Misha Sra" ], "posted": "2025-02-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.15865v2", "field": "finance", "role": "method", "bullets": [ "Audit of six API-based and open-weights LLM agents on three high-impact financial tasks using a risk-engineering framework.", "Stress-tested agents for hallucinations, stale data, and adversarial prompt manipulation beyond standard accuracy and return metrics.", "Conventional benchmarks miss hidden weaknesses; authors propose a three-level risk-audit agenda as primary evaluation criterion for financial LLM agents." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "stress-test audit across three financial tasks", "salience": 55, "n": 2980, "authors_detailed": [ { "name": "Chen, Zichen", "url": "", "inst": "" }, { "name": "Jiaao Chen", "url": "https://openalex.org/A5044588650", "inst": "South China Agricultural University" }, { "name": "Chen, Jianda", "url": "", "inst": "" }, { "name": "Misha Sra", "url": "https://openalex.org/A5119738383", "inst": "" } ], "affiliations": [ "South China Agricultural University" ] }, { "uid": "arxiv:2502.15094v2", "arxiv_id": "2502.15094v2", "title": "Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Models", "authors": [ "Marianne Chuang", "Gabriel Chuang", "Cheryl Chuang", "John Chuang" ], "posted": "2025-02-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.15094v2", "field": "accounting", "role": "method", "bullets": [ "Company submitted climate reports on emissions reduction targets and progress, scored by LLM judges and stress tested against responses greenwashed by an LLM under accuracy and length constraints.", "LLM as a judge in two designs, numerical rating and pairwise comparison; model family is not stated and no agreement statistic against ground truth is reported.", "Both designs separate high performing companies from the rest, and the pairwise comparison design proves more robust when scoring responses that an LLM has greenwashed." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "edition": 14, "models": [], "n": 1964, "authors_detailed": [ { "name": "Chuang, Marianne", "url": "", "inst": "" }, { "name": "Gabriel Chuang", "url": "https://openalex.org/A5023988667", "inst": "Columbia University" }, { "name": "Cheryl Chuang", "url": "https://openalex.org/A5049255114", "inst": "University of California, Santa Cruz" }, { "name": "John Chuang", "url": "https://openalex.org/A5048767056", "inst": "University of California, Berkeley" } ], "affiliations": [ "Columbia University", "University of California, Berkeley", "University of California, Santa Cruz" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.12838v1", "arxiv_id": "2502.12838v1", "title": "Towards Equitable AI: Detecting Bias in Using Large Language Models for Marketing", "authors": [ "Berk Yilmaz", "Huthaifa I. Ashqar" ], "posted": "2025-02-18", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.12838v1", "field": "management", "role": "agent", "bullets": [ "1,700 finance related marketing slogans generated for 17 demographic groups defined by gender, marital status, age, income, and education.", "ChatGPT, version not stated, writes the targeted ads; slogan terms are grouped into four themes and demographic differences tested with relative bias measures and Kolmogorov Smirnov tests.", "Slogans are not neutral; women, younger, lower income, and less educated audiences receive systematically different thematic emphasis than older, richer, more educated groups." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "salience": 42, "edition": 14, "validated": null, "n": 1894, "authors_detailed": [ { "name": "Berk Yilmaz", "url": "https://openalex.org/A5090504444", "inst": "Near East University" }, { "name": "Huthaifa I. Ashqar", "url": "https://openalex.org/A5067023899", "inst": "American Systems (United States)" } ], "affiliations": [ "Near East University", "American Systems (United States)" ] }, { "uid": "doi:10.2139/ssrn.5118661", "doi": "10.2139/ssrn.5118661", "title": "The Impact of Generative AI on Information Processing: Evidence from the Ban of ChatGPT in Italy", "authors": [ "Jeremy Bertomeu", "Yupeng Lin", "Yibin Liu", "Zhenghui Ni" ], "posted": "2025-02-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5118661", "field": "accounting", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 661, "authors_detailed": [ { "name": "Jeremy Bertomeu", "url": "https://openalex.org/A5009431175", "inst": "Washington University in St. Louis" }, { "name": "Yupeng Lin", "url": "https://openalex.org/A5007731007", "inst": "National University of Singapore" }, { "name": "Yibin Liu", "url": "https://openalex.org/A5100767946", "inst": "National University of Singapore" }, { "name": "Zhenghui Ni", "url": "https://openalex.org/A5045668799", "inst": "Renmin University of China" } ], "affiliations": [ "Washington University in St. Louis", "National University of Singapore", "Renmin University of China" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.15800v3", "arxiv_id": "2502.15800v3", "title": "LLM Agents Do Not Replicate Human Market Traders: Evidence From Experimental Finance", "authors": [ "Thomas Henning", "Siddhartha M. Ojha", "Ross Spoon", "Jiatong Han", "Colin F. Camerer" ], "posted": "2025-02-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.15800v3", "field": "finance", "role": "agent", "bullets": [ "Classic experimental finance asset-trading paradigm with known fundamental value, testing single-model and mixed-model battle-royale LLM market configurations against established human participant results.", "Multiple LLM-based agents traded a risky asset in simulated markets; pricing behavior and trading strategy variance compared against documented human outcomes from prior experiments.", "LLMs priced assets near fundamental value with muted bubble formation and less strategy variance than humans, failing to reproduce the large emergent bubbles observed in human markets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison against human experimental trading outcomes", "salience": 65, "n": 2686, "authors_detailed": [ { "name": "Thomas Henning", "url": "https://openalex.org/A5080805971", "inst": "Max Planck Institute for Astronomy" }, { "name": "Siddhartha M. Ojha", "url": "https://openalex.org/A5119852626", "inst": "" }, { "name": "Ross Spoon", "url": "https://openalex.org/A5119852627", "inst": "" }, { "name": "Jiatong Han", "url": "https://openalex.org/A5102712588", "inst": "Tianjin University" }, { "name": "Colin F. Camerer", "url": "https://openalex.org/A5024087833", "inst": "California Institute of Technology" } ], "affiliations": [ "California Institute of Technology", "Max Planck Institute for Astronomy", "Tianjin University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.11433v3", "arxiv_id": "2502.11433v3", "title": "FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading", "authors": [ "Guojun Xiong", "Zhiyang Deng", "Keyi Wang", "Yupeng Cao", "Haohang Li", "Yangyang Yu", "Xueqing Peng", "Mingquan Lin", "Kaleb E Smith", "Xiao-Yang Liu", "Jimin Huang", "Sophia Ananiadou", "Qianqian Xie" ], "posted": "2025-02-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.11433v3", "field": "finance", "role": "agent", "bullets": [ "Interactive financial trading posed as multi step decision making, with additional evaluation on other financial domain tasks; datasets and markets are not stated in the abstract.", "A partially fine tuned LLM, base model not stated, serves as the policy network and is optimized with policy gradients on trading rewards.", "Reports improved trading results and gains on other financial tasks relative to the base setup; the abstract gives no magnitudes." ], "bullet_provenance": "ai", "salience": 38, "edition": 14, "models": [], "validated": null, "n": 1962, "authors_detailed": [ { "name": "Guojun Xiong", "url": "https://openalex.org/A5102620407", "inst": "Harvard University" }, { "name": "Zhiyang Deng", "url": "https://openalex.org/A5069290428", "inst": "Hong Kong Baptist University" }, { "name": "Keyi Wang", "url": "https://openalex.org/A5101922920", "inst": "Drug Enforcement Administration" }, { "name": "Yupeng Cao", "url": "https://openalex.org/A5030238641", "inst": "Chinese Academy of Sciences" }, { "name": "Haohang Li", "url": "https://openalex.org/A5027371293", "inst": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Yangyang Yu", "url": "https://openalex.org/A5113287535", "inst": "Guangdong Academy of Agricultural Sciences" }, { "name": "Xueqing Peng", "url": "https://openalex.org/A5036053506", "inst": "Finlay Institute" }, { "name": "Mingquan Lin", "url": "https://openalex.org/A5000776140", "inst": "University of Minnesota" }, { "name": "Scott Smith", "url": "https://openalex.org/A5078748158", "inst": "Oak Ridge National Laboratory" }, { "name": "Xiaoyang Liu", "url": "https://openalex.org/A5100405221", "inst": "Tianjin University of Science and Technology" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" } ], "affiliations": [ "Harvard University", "University of Minnesota", "Hong Kong Baptist University", "Drug Enforcement Administration", "Chinese Academy of Sciences", "Nanjing University of Aeronautics and Astronautics", "Guangdong Academy of Agricultural Sciences", "Finlay Institute" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.11521v2", "arxiv_id": "2502.11521v2", "title": "Detecting Various DeFi Price Manipulations with LLM Reasoning", "authors": [ "Juantao Zhong", "Daoyuan Wu", "Ye Liu", "Maoyi Xie", "Yang Liu", "Yi Li", "Ning Liu" ], "posted": "2025-02-17", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.11521v2", "field": "finance", "role": "instrument", "bullets": [ "DeFi price manipulation attacks, including custom price models that account for 44.2 percent of the 95 attacks reported in the past three years.", "An LLM fine tuned on Foundry synthesized on chain data, base model not stated, infers price calculation and token price trends from contract code and transaction traces.", "Achieves 80 percent recall on real world attacks and 96 percent precision on suspicious transactions with zero false alarms on benign ones, and helped confirm 147 incidents with an industry partner." ], "bullet_provenance": "ai", "validated": true, "validation_note": "documented real-world attack set, precision and recall reported", "salience": 45, "edition": 14, "models": [], "n": 1963, "authors_detailed": [ { "name": "Zhong, Juantao", "url": "", "inst": "" }, { "name": "Daoyuan Wu", "url": "https://openalex.org/A5063510532", "inst": "Lingnan University" }, { "name": "Ye Liu", "url": "https://openalex.org/A5100346546", "inst": "Shandong University of Technology" }, { "name": "Xie, Maoyi", "url": "", "inst": "" }, { "name": "Yang Liu", "url": "https://openalex.org/A5100355768", "inst": "Hong Kong Baptist University" }, { "name": "Yi Li", "url": "https://openalex.org/A5100421454", "inst": "Chongqing University of Posts and Telecommunications" }, { "name": "Ning Liu", "url": "https://openalex.org/A5100432426", "inst": "Midea Group (China)" } ], "affiliations": [ "Lingnan University", "Shandong University of Technology", "Hong Kong Baptist University", "Chongqing University of Posts and Telecommunications", "Midea Group (China)" ] }, { "uid": "arxiv:2502.13165v1", "arxiv_id": "2502.13165v1", "title": "HedgeAgents: A Balanced-aware Multi-agent Financial Trading System", "authors": [ "Xiangyu Li", "Yawen Zeng", "Xiaofen Xing", "Jin Xu", "Xiangmin Xu" ], "posted": "2025-02-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.13165v1", "field": "finance", "role": "agent", "bullets": [ "Multi-agent LLM trading system tested across financial markets over a three-year period, with agents specializing in various asset classes and coordinating via three conference types.", "LLM-based central fund manager and multiple hedging expert agents made trading decisions leveraging LLM cognitive capabilities and implemented coordinated hedging strategies across asset classes.", "System achieved 70% annualized return and 400% total return over three years, versus a negative 20% loss for standard LLM trading approaches facing rapid declines." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 35, "n": 2685, "authors_detailed": [ { "name": "Xiangyu Li", "url": "https://openalex.org/A5100460318", "inst": "University of Southern California" }, { "name": "Zeng, Yawen", "url": "", "inst": "" }, { "name": "Xiaofen Xing", "url": "https://openalex.org/A5116337245", "inst": "South China University of Technology" }, { "name": "Jin Xu", "url": "https://openalex.org/A5101998573", "inst": "Hong Kong Polytechnic University" }, { "name": "Xixiong Xu", "url": "https://openalex.org/A5101665164", "inst": "Chongqing University" } ], "affiliations": [ "University of Southern California", "South China University of Technology", "Hong Kong Polytechnic University", "Chongqing University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.12226v3", "arxiv_id": "2502.12226v3", "title": "Creating a Causally Grounded Rating Method for Assessing the Robustness of AI Models for Time-Series Forecasting", "authors": [ "Kausik Lakkaraju", "Rachneet Kaur", "Parisa Zehtabi", "Sunandita Patra", "Zhen Zeng", "Siva Likitha Valluru", "Biplav Srivastava", "Marco Valtorta" ], "posted": "2025-02-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.12226v3", "field": "finance", "role": "method", "bullets": [ "Stock price data from multiple industries, evaluating time-series-specific and general-purpose foundation models under six perturbation types and twelve data distributions.", "A causally grounded rating framework assessed forecasting robustness of multimodal, Vision Transformer, and foundation models by analyzing statistical and confounding biases.", "Multimodal and domain-specific foundation models demonstrated greater robustness than general-purpose models; a user study confirmed the ratings reduced difficulty in comparing model robustness." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "stock price forecasting under perturbation benchmarks with user study", "salience": 40, "n": 3476, "authors_detailed": [ { "name": "Kausik Lakkaraju", "url": "https://openalex.org/A5037234293", "inst": "University of South Carolina" }, { "name": "Rachneet Kaur", "url": "https://openalex.org/A5075208338", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Parisa Zehtabi", "url": "https://openalex.org/A5006401989", "inst": "Art Institute of Portland" }, { "name": "Sunandita Patra", "url": "https://openalex.org/A5104333335", "inst": "Morgan Stanley (United States)" }, { "name": "Zhen Zeng", "url": "https://openalex.org/A5100360190", "inst": "Yunnan Normal University" }, { "name": "Siva Likitha Valluru", "url": "https://openalex.org/A5031980960", "inst": "University of South Carolina" }, { "name": "Biplav Srivastava", "url": "https://openalex.org/A5051577973", "inst": "University of South Carolina" }, { "name": "Marco Valtorta", "url": "https://openalex.org/A5007794440", "inst": "University of South Carolina" } ], "affiliations": [ "University of South Carolina", "JPMorgan Chase & Co (United States)", "Art Institute of Portland", "Morgan Stanley (United States)", "Yunnan Normal University" ] }, { "uid": "arxiv:2502.10990v3", "arxiv_id": "2502.10990v3", "title": "FinMTEB: Finance Massive Text Embedding Benchmark", "authors": [ "Yixuan Tang", "Yi Yang" ], "posted": "2025-02-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.10990v3", "field": "finance", "role": "method", "bullets": [ "64 financial embedding datasets across seven task types in Chinese and English, spanning news, annual reports, ESG reports, regulatory filings, and earnings calls.", "15 embedding models, including a finance adapted E5 trained on persona based synthetic data, are scored on the labelled benchmark tasks.", "General benchmark rankings transfer poorly to finance, domain adapted models win consistently, and bag of words beats dense embeddings on financial semantic similarity." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "64 labelled financial embedding datasets", "salience": 55, "edition": 14, "n": 1893, "authors_detailed": [ { "name": "Tang, Yixuan", "url": "", "inst": "" }, { "name": "Yi Yang", "url": "https://openalex.org/A5086493300", "inst": "Fairfield University" } ], "affiliations": [ "Fairfield University" ] }, { "uid": "arxiv:2502.10008v1", "arxiv_id": "2502.10008v1", "title": "ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy?", "authors": [ "Jian Chen", "Guohao Tang", "Guofu Zhou", "Wu Zhu" ], "posted": "2025-02-14", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.10008v1", "field": "finance", "role": "instrument", "bullets": [ "Wall Street Journal text as the information source for predicting the United States stock market and macroeconomy; sample period is not stated in the abstract.", "ChatGPT and DeepSeek extract news signals; versions and prompts are not stated, and no accuracy check of the extracted signals against human coding is reported.", "ChatGPT predicts the market risk premium while DeepSeek and other models underperform; the effect works through underreaction to positive news in downturns, and negative news carries no predictive value." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": false, "salience": 65, "edition": 14, "n": 1961, "authors_detailed": [ { "name": "Jian Chen", "url": "https://openalex.org/A5101802752", "inst": "Southeast University" }, { "name": "Guohao Tang", "url": "https://openalex.org/A5038557883", "inst": "Qingdao Agricultural University" }, { "name": "Guofu Zhou", "url": "https://openalex.org/A5012239666", "inst": "Washington University in St. Louis" }, { "name": "Wu Zhu", "url": "https://openalex.org/A5113044135", "inst": "Ankang City Central Hospital" } ], "affiliations": [ "Washington University in St. Louis", "Southeast University", "Qingdao Agricultural University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.10605v4", "arxiv_id": "2502.10605v4", "title": "Optimal Causal Annotations: An Application to Casenotes in Social Services", "authors": [ "Ezinne Nwankwo", "Lauri Goldkind", "Angela Zhou" ], "posted": "2025-02-14", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.10605v4", "field": "management", "role": "instrument", "bullets": [ "Casenotes from a nonprofit street outreach program for homelessness services, with budget-constrained expert labeling for ground-truth outcomes.", "LLMs classified client progress toward housing applications from unstructured casenotes; optimal annotation sampling minimized treatment effect estimation variance.", "Method achieved 43%-91% reductions in labeling costs for equivalent interval widths; 8.6% of clients improved two-year housing outcomes from additional early outreach." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "expert-labeled ground truth for casenote classification", "salience": 48, "n": 3475, "authors_detailed": [ { "name": "Ezinne Nwankwo", "url": "https://openalex.org/A5039764450", "inst": "University of Southern California" }, { "name": "Lauri Goldkind", "url": "https://openalex.org/A5060992366", "inst": "Fordham University" }, { "name": "Angela Zhou", "url": "https://openalex.org/A5101466158", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California", "Fordham University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.09747v2", "arxiv_id": "2502.09747v2", "title": "The Widespread Adoption of Large Language Model-Assisted Writing Across Society", "authors": [ "Weixin Liang", "Yaohui Zhang", "Mihai Codreanu", "Jiayu Wang", "Hancheng Cao", "James Zou" ], "posted": "2025-02-13", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.09747v2", "field": "economics", "role": "object", "bullets": [ "687,241 consumer finance complaints, 537,413 corporate press releases, 304.3 million job postings, and 15,919 UN press releases, tracked from January 2022 to September 2024.", "A population level statistical detector rather than a named LLM estimates the share of text written with AI assistance; the abstract reports no accuracy benchmark for this detector.", "By late 2024 about 18 percent of complaint text, up to 24 percent of corporate press release text, and nearly 14 percent of UN release content appears LLM assisted, with growth plateauing." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "edition": 14, "validated": null, "n": 1824, "authors_detailed": [ { "name": "Weixin Liang", "url": "https://openalex.org/A5101011426", "inst": "Guangdong Academy of Sciences" }, { "name": "Yaohui Zhang", "url": "https://openalex.org/A5113162278", "inst": "Shanghai Maritime University" }, { "name": "Mihai Codreanu", "url": "https://openalex.org/A5116293013", "inst": "Stanford University" }, { "name": "Jiayu Wang", "url": "https://openalex.org/A5028408476", "inst": "Shanxi Medical University" }, { "name": "Hancheng Cao", "url": "https://openalex.org/A5023364449", "inst": "Emory University" }, { "name": "James Zou", "url": "https://openalex.org/A5005779176", "inst": "Palo Alto University" } ], "affiliations": [ "Stanford University", "Emory University", "Guangdong Academy of Sciences", "Shanghai Maritime University", "Shanxi Medical University", "Palo Alto University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.08875v2", "arxiv_id": "2502.08875v2", "title": "Utilizing Pre-trained and Large Language Models for 10-K Items Segmentation", "authors": [ "Hsin-Min Lu", "Yu-Tai Chien", "Huan-Hsun Yen", "Yen-Hsiu Chen" ], "posted": "2025-02-13", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.08875v2", "field": "accounting", "role": "method", "bullets": [ "3,737 hand annotated 10-K reports used to train and evaluate segmentation of core items 1, 1A, 3, and 7.", "ChatGPT-4o with line ID prompting is compared against a hierarchical BERT with Bi-LSTM model, both scored against the annotations.", "The BERT approach reaches macro F1 of 0.9825 versus 0.9567 for the GPT approach and 0.9048 for rules, while the GPT route adapts more easily to format changes." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "validated": true, "validation_note": "3,737 hand annotated 10-K reports, macro F1 reported", "salience": 58, "edition": 14, "n": 1892, "authors_detailed": [ { "name": "Hsin‐Min Lu", "url": "https://openalex.org/A5074117557", "inst": "National Taiwan University" }, { "name": "Chien, Yu-Tai", "url": "", "inst": "" }, { "name": "Yen, Huan-Hsun", "url": "", "inst": "" }, { "name": "Yefeng Chen", "url": "https://openalex.org/A5053523372", "inst": "Changchun University of Science and Technology" } ], "affiliations": [ "National Taiwan University", "Changchun University of Science and Technology" ] }, { "uid": "arxiv:2503.05708v1", "arxiv_id": "2503.05708v1", "title": "On Large Language Models as Data Sources for Policy Deliberation on Climate Change and Sustainability", "authors": [ "Rachel Bina", "Kha Luong", "Shrey Mehta", "Daphne Pang", "Mingjun Xie", "Christine Chou", "Steven O. Kimbrough" ], "posted": "2025-02-13", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.05708v1", "field": "management", "role": "method", "bullets": [ "Climate and sustainability policy alternatives considered by United States local governments, scored on quality of life criteria and ranked with the TOPSIS multi criteria decision method.", "GPT-4 generates the policy by criterion evaluation scores; resulting rankings are compared with the authors' informed assessment exercise, with agreement described only qualitatively.", "GPT-4 based rankings roughly match the human exercise, so the authors judge the scores usable, with vetting, as starter inputs for policy deliberation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 38, "edition": 14, "n": 1960 }, { "uid": "doi:10.2139/ssrn.5133400", "doi": "10.2139/ssrn.5133400", "title": "Leveraging Prompt Engineering to Enhance Financial Market Integrity and Risk Management", "authors": [ "Satyadhar Joshi" ], "posted": "2025-02-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5133400", "field": "finance", "role": "method", "bullet_provenance": "none", "salience": 26, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 1032, "authors_detailed": [ { "name": "Satyadhar Joshi", "url": "", "inst": "Bar-Ilan University" } ], "affiliations": [ "Bar-Ilan University" ] }, { "uid": "arxiv:2503.16438v1", "arxiv_id": "2503.16438v1", "title": "Artificial Intelligence Quotient (AIQ): A Novel Framework for Measuring Human-AI Collaborative Intelligence", "authors": [ "Venkat Ram Reddy Ganuthula", "Krishna Kumar Balaraman" ], "posted": "2025-02-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2503.16438v1", "field": "management", "role": "object", "bullets": [ "Conceptual framework proposing the Artificial Intelligence Quotient metric for measuring individuals' capacity to collaborate with AI systems in professional and educational contexts.", "Built on established cognitive assessment methodologies and contemporary AI interaction research to define standardized dimensions of human-AI collaborative intelligence measurement.", "Framework addresses growing need for evaluation of AI-augmented cognitive capabilities in workplace settings; no empirical validation results or scoring benchmarks were reported." ], "bullet_provenance": "ai", "salience": 15, "models": [], "validated": null, "n": 2684, "authors_detailed": [ { "name": "Venkat Ram Reddy Ganuthula", "url": "https://openalex.org/A5115780701", "inst": "Indian Institute of Technology Jodhpur" }, { "name": "Krishna Kumar Balaraman", "url": "https://openalex.org/A5075701083", "inst": "Indian Institute of Technology Jodhpur" } ], "affiliations": [ "Indian Institute of Technology Jodhpur" ] }, { "uid": "doi:10.2139/ssrn.5069785", "doi": "10.2139/ssrn.5069785", "title": "Breaking the Iron Triangle in Behavioral Research with GenAI", "authors": [ "Paul Andrew Blythe", "Christopher Kulis", "A. Peter McGraw" ], "posted": "2025-02-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5069785", "field": "management", "role": "method", "bullets": [ "Conceptual framework for integrating generative AI into behavioral science research, addressing speed-cost-quality trade-offs.", "Proposes three principles — Ask it, Train it, Check it — for using LLMs in research while maintaining scientific rigor.", "Argues GenAI can relax the Iron Triangle constraint, enabling faster, cheaper research without sacrificing quality in academic collaboration." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 30, "validated": null, "n": 2979, "authors_detailed": [ { "name": "Paul Andrew Blythe", "url": "https://openalex.org/A5116258188", "inst": "University of Colorado Boulder" }, { "name": "Christopher Kulis", "url": "https://openalex.org/A5116258189", "inst": "University of Colorado Boulder" }, { "name": "A. Peter McGraw", "url": "https://openalex.org/A5110738598", "inst": "University of Colorado Boulder" } ], "affiliations": [ "University of Colorado Boulder" ] }, { "uid": "doi:10.2139/ssrn.5133985", "doi": "10.2139/ssrn.5133985", "title": "A Literature Review of Gen AI Agents in Financial Applications: Models and Implementations", "authors": [ "Satyadhar Joshi" ], "posted": "2025-02-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5133985", "field": "finance", "role": "object", "bullets": [ "Structured literature review of AI agents across five financial domains using research and white papers published within the prior six months.", "Reviews implementations in risk management, investment strategies, fraud detection, stock market analysis, and customer support with quantitative outcomes.", "Reported gains include 25% improvement in risk model accuracy, 20% reduction in loan defaults, and 40% decrease in false-positive fraud detections." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 3918, "authors_detailed": [ { "name": "Satyadhar Joshi", "url": "", "inst": "Bar-Ilan University" } ], "affiliations": [ "Bar-Ilan University" ] }, { "uid": "arxiv:2502.08127v3", "arxiv_id": "2502.08127v3", "title": "Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance", "authors": [ "Lingfei Qian", "Weipeng Zhou", "Yan Wang", "Xueqing Peng", "Han Yi", "Yilun Zhao", "Jimin Huang", "Qianqian Xie", "Jian-yun Nie" ], "posted": "2025-02-12", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.08127v3", "field": "finance", "role": "method", "bullets": [ "FinCoT, a financial chain of thought corpus distilled from seven QA datasets, and FinReason, a benchmark of multi table, long context, and equation based tasks used to evaluate 29 LLMs.", "Fin-o1 models are trained with supervised fine tuning and GRPO reinforcement learning, then scored on labelled financial QA against GPT-o1, DeepSeek-R1, GPT-4.5, and finance tuned baselines.", "Fin-o1 beats its backbones and larger general reasoning models on financial tasks; GRPO gives reliable gains while PPO and DPO do not, and general models degrade on long financial documents." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "labelled financial QA benchmarks", "salience": 60, "edition": 14, "n": 1853, "authors_detailed": [ { "name": "Lingfei Qian", "url": "https://openalex.org/A5009941118", "inst": "Yale University" }, { "name": "Weipeng Zhou", "url": "https://openalex.org/A5062009708", "inst": "Beihang University" }, { "name": "Yan Wang", "url": "https://openalex.org/A5118092688", "inst": "Wuhan Polytechnic University" }, { "name": "Xueqing Peng", "url": "https://openalex.org/A5036053506", "inst": "Finlay Institute" }, { "name": "Yi, Han", "url": "", "inst": "" }, { "name": "Zhao, Yilun", "url": "", "inst": "" }, { "name": "Huang, Jimin", "url": "", "inst": "" }, { "name": "Xie, Qianqian", "url": "", "inst": "" }, { "name": "Nie, Jian-yun", "url": "", "inst": "" } ], "affiliations": [ "Yale University", "Beihang University", "Wuhan Polytechnic University", "Finlay Institute" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5068625", "doi": "10.2139/ssrn.5068625", "title": "Cyber Risk, ChatGPT and Firm Value", "authors": [ "Fearghal Kearney", "Tren Ma", "Hadi Movaghari", "Georgios Sermpinis" ], "posted": "2025-02-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5068625", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 659, "authors_detailed": [ { "name": "Fearghal Kearney", "url": "https://openalex.org/A5059515690", "inst": "Queen's University Belfast" }, { "name": "Tren Ma", "url": "https://openalex.org/A5006029516", "inst": "University of Nottingham" }, { "name": "Hadi Movaghari", "url": "https://openalex.org/A5020177444", "inst": "Adam Smith Institute" }, { "name": "Georgios Sermpinis", "url": "https://openalex.org/A5090027748", "inst": "University of Glasgow" } ], "affiliations": [ "Queen's University Belfast", "University of Nottingham", "Adam Smith Institute", "University of Glasgow" ] }, { "uid": "doi:10.2139/ssrn.5068116", "doi": "10.2139/ssrn.5068116", "title": "Investor Reactions to Generative AI Usage in MD&A Disclosures", "authors": [ "Benedikt J. Plate", "Johannes Voshaar", "Jochen Zimmermann" ], "posted": "2025-02-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5068116", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 48, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 660, "authors_detailed": [ { "name": "Benedikt J. Plate", "url": "https://openalex.org/A5095955203", "inst": "University of Bremen" }, { "name": "Johannes Voshaar", "url": "https://openalex.org/A5014230083", "inst": "University of Bremen" }, { "name": "Jochen Zimmermann", "url": "https://openalex.org/A5013724873", "inst": "University of Bremen" } ], "affiliations": [ "University of Bremen" ] }, { "uid": "arxiv:2502.07393v1", "arxiv_id": "2502.07393v1", "title": "FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents", "authors": [ "Mostapha Benhenda" ], "posted": "2025-02-11", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.07393v1", "field": "finance", "role": "instrument", "bullets": [ "Nasdaq-100 index backtest drawing financial news from the FNSPID dataset, with code, data, and trained trading agents released publicly on GitHub.", "DeepSeek V3, Qwen 2.5, and Llama 3.3 generate risk assessments and trading recommendations from news, which are injected into a CVaR based PPO trading algorithm; no validation of the signals is reported.", "The abstract describes the architecture and backtest setup but states no performance results for the LLM infused agents against the plain algorithm." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 38, "edition": 14, "n": 1823, "authors_detailed": [ { "name": "Mostapha Benhenda", "url": "https://openalex.org/A5013283264", "inst": "Laboratoire Analyse, Géométrie et Applications" } ], "affiliations": [ "Laboratoire Analyse, Géométrie et Applications" ] }, { "uid": "doi:10.2139/ssrn.5123068", "doi": "10.2139/ssrn.5123068", "title": "Using Gen AI Agents With GAE And VAE To Enhance Resilience Of Us Markets", "authors": [ "Satyadhar Joshi" ], "posted": "2025-02-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5123068", "field": "finance", "role": "method", "bullets": [ "U.S. Treasury ten-year rates from 2012 to 2024, used to build and test generative adversarial network and variational autoencoder financial risk models.", "ChatGPT-4 and Google Gemini generated queries to fine-tune GAN and VAE parameters; three expert volunteers evaluated query relevance and accuracy.", "LLM-generated queries enhanced synthetic rate generation; backtesting confirmed generated data matched real Treasury rate patterns, supporting a full-stack AI agent framework." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "backtesting against real 10-year U.S. Treasury rates", "salience": 30, "n": 2372, "authors_detailed": [ { "name": "Satyadhar Joshi", "url": "", "inst": "Bar-Ilan University" } ], "affiliations": [ "Bar-Ilan University" ] }, { "uid": "arxiv:2502.07736v2", "arxiv_id": "2502.07736v2", "title": "Menu Pricing of Large Language Models", "authors": [ "Dirk Bergemann", "Alessandro Bonatti", "Alex Smolin" ], "posted": "2025-02-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.07736v2", "field": "economics", "role": "object", "bullets": [ "Theoretical framework for optimal pricing and product design of LLMs, analyzing token budget menus for heterogeneous users with varying valuations across a continuum of tasks.", "Derived optimal mechanism under homogeneous production technology, reducing multi-dimensional screening to one-dimensional problem via scalar index summarizing high-dimensional user type profiles.", "Optimal mechanism takes the form of committed-spend contracts; open-source competition reshapes compute provision margins, matching observed pricing by Anthropic, OpenAI, and GitHub." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 2683, "authors_detailed": [ { "name": "Dirk Bergemann", "url": "https://openalex.org/A5003919163", "inst": "Yale University" }, { "name": "Alessandro Bonatti", "url": "https://openalex.org/A5040536317", "inst": "New School" }, { "name": "Alex Smolin", "url": "https://openalex.org/A5022350587", "inst": "Toulouse School of Economics" } ], "affiliations": [ "Yale University", "New School", "Toulouse School of Economics" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.07924v1", "arxiv_id": "2502.07924v1", "title": "NDAI Agreements", "authors": [ "Matthew Stephenson", "Andrew Miller", "Xyn Sun", "Bhargav Annem", "Rohan Parikh" ], "posted": "2025-02-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.07924v1", "field": "economics", "role": "object", "bullets": [ "Theoretical bargaining model between an inventor and investor over an information good, extending Arrow's 1962 disclosure-appropriation paradox.", "AI agents operating within trusted execution environments mediate disclosure and payment decisions; agent errors in payments and disclosures are modeled explicitly.", "TEE-based AI agents eliminate the hold-up problem and achieve full efficient disclosure; budget caps and acceptance thresholds preserve most efficiency gains despite errors." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3916, "authors_detailed": [ { "name": "Matthew Stephenson", "url": "https://openalex.org/A5116269074", "inst": "Flinders University" }, { "name": "Andrew Miller", "url": "https://openalex.org/A5116269075", "inst": "" }, { "name": "Sun, Xyn", "url": "", "inst": "" }, { "name": "Bhargav Annem", "url": "https://openalex.org/A5116269076", "inst": "" }, { "name": "Rohan Parikh", "url": "https://openalex.org/A5116269077", "inst": "" } ], "affiliations": [ "Flinders University" ] }, { "uid": "doi:10.2139/ssrn.5126922", "doi": "10.2139/ssrn.5126922", "title": "Agentic Generative AI and the Future U.S. Workforce: Advancing Innovation and National Competitiveness", "authors": [ "Satyadhar Joshi" ], "posted": "2025-02-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5126922", "field": "management", "role": "object", "bullets": [ "Systematic review of generative AI applications in workforce development and education across U.S. industries, covering the most recent year of literature.", "Categorizes AI-driven training programs including upskilling initiatives for older workers and strategies to address the emerging AI skills gap.", "AI training programs improve productivity and accessibility but face unresolved challenges in data privacy, algorithmic bias, and user adoption." ], "bullet_provenance": "ai", "salience": 25, "models": [], "validated": null, "n": 3917, "authors_detailed": [ { "name": "Satyadhar Joshi", "url": "https://openalex.org/A5007970885", "inst": "Bar-Ilan University" } ], "affiliations": [ "Bar-Ilan University" ] }, { "uid": "doi:10.2139/ssrn.5127716", "doi": "10.2139/ssrn.5127716", "title": "Can AI Solve the Peer Review Crisis? A Large-Scale Experiment on Llm's Performance and Biases in Evaluating Economics Papers", "authors": [ "Pat Pataranutaporn", "Nattavudh Powdthavee", "Pattie Maes", "Pattie Maes" ], "posted": "2025-02-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5127716", "field": "economics", "role": "agent", "bullet_provenance": "none", "salience": 50, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 658, "authors_detailed": [ { "name": "Pat Pataranutaporn", "url": "https://openalex.org/A5116129255", "inst": "Massachusetts Institute of Technology" }, { "name": "Nattavudh Powdthavee", "url": "https://openalex.org/A5116129256", "inst": "University of Warwick" }, { "name": "Pattie Maes", "url": "https://openalex.org/A5081457786", "inst": "Massachusetts Institute of Technology" }, { "name": "Pattie Maes", "url": "https://openalex.org/A5081457786", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "University of Warwick" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5110014", "doi": "10.2139/ssrn.5110014", "title": "Internal Credit and External Blame: Self-Attribution in Operations and Supply Chain Performance", "authors": [ "Yimeng Niu", "Xiao Qiao", "Jing Wu", "Xingsheng Yang" ], "posted": "2025-02-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5110014", "field": "management", "role": "instrument", "bullets": [ "S&P 500 firms' quarterly earnings call Q&A transcripts analyzed at the firm level to measure operations management self-attribution across multiple time periods.", "Fine-tuned LLaMA-3.1-8B-Instruct classified self-attribution in manager responses; validated against gradient boosting, KNN, random forest baselines and GPT-4o, demonstrating superior performance.", "Managers claim internal credit for operational successes and assign external blame to supply chain partners; this pattern associates with positive future financial and operational outcomes." ], "bullet_provenance": "ai", "models": [ "llama", "gpt" ], "open_weights": true, "validated": true, "validation_note": "Compared against gradient boosting, KNN, random forest, and GPT-4o", "salience": 72, "n": 2394 }, { "uid": "arxiv:2502.06387v2", "arxiv_id": "2502.06387v2", "title": "How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators", "authors": [ "Shang Liu", "Hanzhao Wang", "Zhongyao Ma", "Xiaocheng Li" ], "posted": "2025-02-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.06387v2", "field": "economics", "role": "object", "bullets": [ "Theoretical analysis of preference annotation quality for LLM alignment, studying monitoring schemes and incentive contracts under a principal-agent model with continuous action space.", "Proposed self-consistency monitoring scheme for preference annotators and derived statistical sample complexity bounds for both expert-based and self-consistency quality assessment methods.", "Linear contracts achieve shortfall scaling of order 1/(In) versus 1/sqrt(In log n) for binary contracts and are proved rate-optimal among all general contract forms." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 2682, "authors_detailed": [ { "name": "Liu, Shang", "url": "", "inst": "" }, { "name": "Hanzhao Wang", "url": "https://openalex.org/A5021901253", "inst": "The University of Sydney" }, { "name": "Z. Ma", "url": "https://openalex.org/A5080378540", "inst": "Harbin Engineering University" }, { "name": "Xiaocheng Li", "url": "https://openalex.org/A5100627646", "inst": "Uber AI (United States)" } ], "affiliations": [ "The University of Sydney", "Harbin Engineering University", "Uber AI (United States)" ] }, { "uid": "arxiv:2502.06329v1", "arxiv_id": "2502.06329v1", "title": "Expect the Unexpected: FailSafe Long Context QA for Finance", "authors": [ "Kiran Kamble", "Melisa Russak", "Dmytro Mozolevskyi", "Muayad Ali", "Mateusz Russak", "Waseem AlShikh" ], "posted": "2025-02-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.06329v1", "field": "finance", "role": "method", "bullets": [ "Benchmark of 24 off-the-shelf LLMs on financial question-answering with six input perturbation types covering query and context failures.", "Qwen2.5-72B-Instruct served as LLM judge scoring robustness, context grounding, and compliance across models including o3-mini and Palmyra-Fin-128k.", "Most robust model (o3-mini) hallucinated in 41% of cases; most compliant model (Palmyra-Fin) failed on robustness in 17% of test cases." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "FailSafeQA benchmark with robustness, compliance, and context grounding scores", "salience": 60, "n": 2978, "authors_detailed": [ { "name": "Kiran Kamble", "url": "https://openalex.org/A5053209482", "inst": "Shivaji University" }, { "name": "Melisa Russak", "url": "https://openalex.org/A5028563651", "inst": "The Sanskrit College and University" }, { "name": "Dmytro Mozolevskyi", "url": "https://openalex.org/A5097139532", "inst": "" }, { "name": "Ali, Muayad", "url": "", "inst": "" }, { "name": "Mateusz Russak", "url": "https://openalex.org/A5107421488", "inst": "" }, { "name": "Waseem AlShikh", "url": "https://openalex.org/A5092442112", "inst": "The Sanskrit College and University" } ], "affiliations": [ "Shivaji University", "The Sanskrit College and University" ] }, { "uid": "arxiv:2502.07050v1", "arxiv_id": "2502.07050v1", "title": "Artificial General Intelligence and the End of Human Employment: The Need to Renegotiate the Social Contract", "authors": [ "Pascal Stiefenhofer" ], "posted": "2025-02-10", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.07050v1", "field": "economics", "role": "object", "bullets": [ "Theoretical model of labor markets where AGI systems operate at near-zero marginal cost, replacing human workers across economic sectors.", "Analytical framework examines how AGI labor reduces marginal productivity of human work and shifts economic power to capital owners.", "Wages converge toward zero under AGI substitution; aggregate demand collapses without redistribution via UBI or cooperative AI ownership structures." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3915, "authors_detailed": [ { "name": "Pascal Stiefenhofer", "url": "https://openalex.org/A5116248674", "inst": "" } ] }, { "uid": "arxiv:2502.05878v3", "arxiv_id": "2502.05878v3", "title": "Retrieval-augmented Large Language Models for Financial Time Series Forecasting", "authors": [ "Mengxi Xiao", "Zihao Jiang", "Lingfei Qian", "Zhengyu Chen", "Yueru He", "Yijing Xu", "Yuecheng Jiang", "Dong Li", "Ruey-Ling Weng", "Min Peng", "Jimin Huang", "Sophia Ananiadou", "Qianqian Xie" ], "posted": "2025-02-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.05878v3", "field": "finance", "role": "instrument", "bullets": [ "Stock price movement prediction using financial time-series data enriched with a broader set of financial indicators capturing previously overlooked market dynamics.", "StockLLM, a 1B-parameter fine-tuned LLM, paired with FinSeer, a domain-specific retriever trained via LLM feedback and similarity-driven objectives for RAG.", "FinSeer outperforms existing textual retrievers and traditional distance-based retrieval methods in enhancing StockLLM prediction accuracy on stock movements." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "stock movement prediction accuracy", "salience": 50, "n": 2573, "authors_detailed": [ { "name": "Mengxi Xiao", "url": "https://openalex.org/A5111097308", "inst": "Yunnan Center for Disease Control And Prevention" }, { "name": "Zihao Jiang", "url": "https://openalex.org/A5100641890", "inst": "Beijing Institute of Technology" }, { "name": "Lingfei Qian", "url": "https://openalex.org/A5009941118", "inst": "Yale University" }, { "name": "Zhengyu Chen", "url": "https://openalex.org/A5101886933", "inst": "North China Electric Power University" }, { "name": "Yueru He", "url": "https://openalex.org/A5111133467", "inst": "Columbia University" }, { "name": "Yijing Xu", "url": "https://openalex.org/A5100871294", "inst": "Hangzhou Normal University" }, { "name": "Jiang, Yuecheng", "url": "", "inst": "" }, { "name": "Dong Li", "url": "https://openalex.org/A5052630982", "inst": "Academy of Medical Sciences" }, { "name": "Ruey-Ling Weng", "url": "https://openalex.org/A5061284344", "inst": "Yale University" }, { "name": "Min Peng", "url": "https://openalex.org/A5102012008", "inst": "Jiujiang University" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "Xuzhou Medical College" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" } ], "affiliations": [ "Yale University", "Columbia University", "Yunnan Center for Disease Control And Prevention", "Beijing Institute of Technology", "North China Electric Power University", "Hangzhou Normal University", "Academy of Medical Sciences", "Jiujiang University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.06874v2", "arxiv_id": "2502.06874v2", "title": "Group Reasoning Emission Estimation Networks", "authors": [ "Yanming Guo", "Xiao Qian", "Kevin Credit", "Jin Ma" ], "posted": "2025-02-08", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.06874v2", "field": "accounting", "role": "method", "bullets": [ "Textual descriptions of 20,850 companies with validated NAICS labels, linked to an economic model of carbon intensity factors to estimate enterprise level greenhouse gas emissions.", "Fine tuned Sentence-BERT retrieval plus an ensemble of LLM classifiers, family not stated, decomposes sector classification along the NAICS hierarchy; accuracy is scored against the labels across 1,114 categories.", "Reaches 83.68 percent top 1 and 91.47 percent top 10 classification accuracy; emission estimates on 20 case study firms show a 45.88 percent mean absolute percentage error." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "NAICS-labelled company benchmark, accuracy and MAPE reported", "salience": 55, "edition": 14, "n": 1959, "authors_detailed": [ { "name": "Yanming Guo", "url": "https://openalex.org/A5079425523", "inst": "National University of Defense Technology" }, { "name": "Qian Xiao", "url": "https://openalex.org/A5082921193", "inst": "North Sichuan Medical University" }, { "name": "Kevin Credit", "url": "https://openalex.org/A5116248319", "inst": "" }, { "name": "Jin Ma", "url": "https://openalex.org/A5101808638", "inst": "Heilongjiang Earthquake Agency" } ], "affiliations": [ "National University of Defense Technology", "North Sichuan Medical University", "Heilongjiang Earthquake Agency" ] }, { "uid": "arxiv:2502.05439v2", "arxiv_id": "2502.05439v2", "title": "Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews", "authors": [ "Izunna Okpala", "Ashkan Golgoon", "Arjun Ravi Kannan" ], "posted": "2025-02-08", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.05439v2", "field": "finance", "role": "method", "bullets": [ "Credit card fraud detection, credit approval, and portfolio credit risk datasets used to test agentic AI workflows in financial services.", "LLM-based agentic crews with judge agents performed end-to-end modeling and model risk management tasks including EDA, feature engineering, and compliance checking.", "Crews completed full modeling and MRM workflows across all three financial datasets with human-in-the-loop oversight and task-specific agent collaboration." ], "bullet_provenance": "ai", "validated": false, "salience": 45, "models": [], "n": 2844, "authors_detailed": [ { "name": "Izunna Okpala", "url": "https://openalex.org/A5072405003", "inst": "Capital Group (United States)" }, { "name": "Ashkan Golgoon", "url": "https://openalex.org/A5106580061", "inst": "Capital Group (United States)" }, { "name": "Arjun Ravi Kannan", "url": "https://openalex.org/A5089840355", "inst": "Capital Group (United States)" } ], "affiliations": [ "Capital Group (United States)" ] }, { "uid": "doi:10.1145/3711896.373687", "doi": "10.1145/3711896.373687", "arxiv_id": "2502.04592v3", "title": "CAMEF: Causal-Augmented Multi-Modality Event-Driven Financial Forecasting by Integrating Time Series Patterns and Salient Macroeconomic Announcements", "authors": [ "Yang Zhang", "Wenbo Yang", "Jun Wang", "Qiang Ma", "Jie Xiong" ], "posted": "2025-02-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.04592v3", "field": "finance", "role": "instrument", "bullets": [ "Six types of U.S. macroeconomic releases from 2008 to April 2024 paired with high-frequency trading data for five key financial assets.", "LLM-based counterfactual event augmentation integrates policy texts and price series through a causal learning mechanism for financial forecasting.", "CAMEF outperforms state-of-the-art transformer-based time-series and multi-modal baselines; ablation studies confirm the causal mechanism and event augmentation contribute independently." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "comparison against transformer and multi-modal baselines", "salience": 50, "n": 3474 }, { "uid": "arxiv:2502.04095v1", "arxiv_id": "2502.04095v1", "title": "LLMs to Support a Domain Specific Knowledge Assistant", "authors": [ "Maria-Flavia Lovin" ], "posted": "2025-02-06", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.04095v1", "field": "accounting", "role": "method", "bullets": [ "Sustainability reporting under IFRS standards, a domain lacking public question answer data; the project constructs 1,063 synthetic QA pairs spanning typical company queries.", "Unnamed LLMs generate and score the dataset using chain of thought and few shot prompting; RAG and fully LLM based answering pipelines are then fine tuned on it.", "The LLM pipeline reaches 93.45 percent on single industry and 80.30 percent on cross industry multiple choice questions, ahead of the RAG variant; the test items are themselves synthetic." ], "bullet_provenance": "ai", "validated": true, "validation_note": "accuracy on own synthetic multiple-choice benchmark", "salience": 34, "edition": 14, "models": [], "n": 1841, "authors_detailed": [ { "name": "Maria-Flavia Lovin", "url": "https://openalex.org/A5116188226", "inst": "" } ] }, { "uid": "arxiv:2502.03158v3", "arxiv_id": "2502.03158v3", "title": "Strategizing with AI: Insights from a Beauty Contest Experiment", "authors": [ "Iuliia Alekseenko", "Dmitry Dagaev", "Sofia Paklina", "Petr Parshakov" ], "posted": "2025-02-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.03158v3", "field": "economics", "role": "agent", "bullets": [ "Virtual p-beauty contest experiments replicating classic behavioral economics studies, with LLMs playing against various groups of virtual players across multiple parameter configurations.", "Multiple LLMs played iterated guessing games requiring strategic reasoning about opponent sophistication; performance compared against results from original human participant experiments.", "LLMs recognized strategic context and adapted to changing parameters but behaved more sophisticatedly than humans; all LLMs failed to identify dominant strategies in two-player games." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison against original human experiment outcomes", "salience": 55, "n": 2681, "authors_detailed": [ { "name": "Iuliia Alekseenko", "url": "https://openalex.org/A5116182546", "inst": "National Research University Higher School of Economics" }, { "name": "Dmitry Dagaev", "url": "https://openalex.org/A5089402209", "inst": "Twitter (United States)" }, { "name": "Sofia Paklina", "url": "https://openalex.org/A5091656431", "inst": "National Research University Higher School of Economics" }, { "name": "Petr Parshakov", "url": "https://openalex.org/A5073937184", "inst": "Perm State University" } ], "affiliations": [ "National Research University Higher School of Economics", "Twitter (United States)", "Perm State University" ] }, { "uid": "doi:10.2139/ssrn.5109470", "doi": "10.2139/ssrn.5109470", "title": "Agentic AI: Service Operations with Augmentation and Automation AI", "authors": [ "Guanling Yang" ], "posted": "2025-02-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5109470", "field": "management", "role": "object", "bullets": [ "Stylized theoretical model examining augmentation AI and automation AI deployment in service operations, analyzing effects on customer demand and firm profitability.", "No specific LLM deployed; theoretical analysis compared augmentation AI enhancing human servers versus automation AI agents delivering autonomous service.", "Augmentation AI improves welfare and profits; automation AI can backfire even with high capability, potentially stifling innovation and worsening customer experience." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 2977, "authors_detailed": [ { "name": "Guanling Yang", "url": "https://openalex.org/A5083076643", "inst": "Hebei University of Technology" } ], "affiliations": [ "Hebei University of Technology" ] }, { "uid": "arxiv:2502.01992v1", "arxiv_id": "2502.01992v1", "title": "FinRLlama: A Solution to LLM-Engineered Signals Challenge at FinRL Contest 2024", "authors": [ "Arnav Grover" ], "posted": "2025-02-04", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.01992v1", "field": "finance", "role": "method", "bullets": [ "Trading signal generation for Task II of the FinRL Challenge at ACM ICAIF 2024, using historical market data and market feedback rewards.", "LLaMA 3.2 3B Instruct is fine tuned with reinforcement learning from market feedback on market specific prompts; no validation against labelled signals is reported.", "The tuned model beat baseline methods on signal consistency and trading outcomes and won the challenge task; magnitudes are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 30, "edition": 14, "n": 1891, "authors_detailed": [ { "name": "Arnav Grover", "url": "https://openalex.org/A5116167200", "inst": "" } ] }, { "uid": "doi:10.1016/j.knosys.2026.115559", "doi": "10.1016/j.knosys.2026.115559", "arxiv_id": "2502.18471v2", "title": "FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data", "authors": [ "Ankur Sinha", "Chaitanya Agarwal", "Pekka Malo" ], "posted": "2025-02-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.18471v2", "field": "finance", "role": "instrument", "bullets": [ "Fine-tuned Bloom 7B on 14 million financial news articles from Reuters and DPA plus a sample of 12 million SEC filings to create FinBloom 7B.", "FinBloom 7B further fine-tuned on 50,000-plus financial query-context pairs to generate relevant context for real-time financial data retrieval.", "The financial agent reduces latency for answering dynamic financial queries by autonomously generating retrieval context without requiring manual user-provided data." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 55, "n": 2572, "authors_detailed": [ { "name": "Ankur Sinha", "url": "", "inst": "Indian Institute of Management Ahmedabad" }, { "name": "Chaitanya Agarwal", "url": "", "inst": "Indian Institute of Management Ahmedabad" }, { "name": "Pekka Malo", "url": "", "inst": "Aalto University" } ], "affiliations": [ "Indian Institute of Management Ahmedabad", "Aalto University" ] }, { "uid": "arxiv:2502.02199v1", "arxiv_id": "2502.02199v1", "title": "When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks", "authors": [ "Felix Drinkall", "Janet B. Pierrehumbert", "Stefan Zohren" ], "posted": "2025-02-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.02199v1", "field": "finance", "role": "method", "bullets": [ "Three regression tasks compared including financial return prediction, writing quality assessment, and review scoring, testing LLM embedding compression across different signal-to-noise ratio contexts.", "LLM text embeddings compressed via autoencoder hidden representations in a minimally supervised manner and compared against full-dimensional embeddings for downstream regression performance.", "Compressed embeddings improved financial return prediction by mitigating overfitting on noisy tasks but reduced performance on high signal-to-noise tasks with strong causal input-target dependencies." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "financial return prediction and writing quality regression benchmarks", "salience": 55, "n": 2680, "authors_detailed": [ { "name": "Felix Drinkall", "url": "https://openalex.org/A5045703184", "inst": "University of Oxford" }, { "name": "Janet B. Pierrehumbert", "url": "https://openalex.org/A5049920688", "inst": "Science Oxford" }, { "name": "Stefan Zohren", "url": "https://openalex.org/A5090331439", "inst": "Oxford Research Group" } ], "affiliations": [ "University of Oxford", "Science Oxford", "Oxford Research Group" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5055024", "doi": "10.2139/ssrn.5055024", "title": "Big Data and Machine Learning in ESG Research", "authors": [ "Kai Li" ], "posted": "2025-02-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5055024", "field": "finance", "role": "method", "bullets": [ "Survey of machine learning applications in ESG research covering methods from bag-of-words through generative AI.", "Reviews techniques applied to job postings, earnings calls, and news reports for topics including corporate culture and climate risk exposure.", "Outlines how progressively advanced ML methods from topic modeling to generative AI unlock ESG research questions previously impossible at scale." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 55, "validated": null, "n": 3473, "authors_detailed": [ { "name": "Kai Li", "url": "https://openalex.org/A5052701967", "inst": "University of British Columbia" } ], "affiliations": [ "University of British Columbia" ] }, { "uid": "arxiv:2502.01574v1", "arxiv_id": "2502.01574v1", "title": "An End-To-End LLM Enhanced Trading System", "authors": [ "Ziyao Zhou", "Ronitt Mehra" ], "posted": "2025-02-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.01574v1", "field": "finance", "role": "instrument", "bullets": [ "Real-time equity trading system synthesizing financial news and social media data streams for automated signal generation, deployed on Kubernetes for scalable operation.", "FinGPT performed domain-specific sentiment analysis on financial text; system combined sentiment-driven insights with technical indicators to produce actionable trading signals.", "System demonstrated end-to-end integration of LLM-based sentiment with technical strategies; no backtested performance metrics were reported in the paper." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 20, "n": 2678, "authors_detailed": [ { "name": "Zhou, Ziyao", "url": "", "inst": "" }, { "name": "Ronitt Mehra", "url": "https://openalex.org/A5116166515", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.01506v5", "arxiv_id": "2502.01506v5", "title": "TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets", "authors": [ "Yuzhe Yang", "Yifei Zhang", "Minghao Wu", "Kaidi Zhang", "Yunmiao Zhang", "Honghai Yu", "Yan Hu", "Benyou Wang" ], "posted": "2025-02-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.01506v5", "field": "finance", "role": "agent", "bullets": [ "Simulated multi-agent stock market where LLM-based agents model individual investor behaviors incorporating cognitive biases and emotional fluctuations to study emergent collective market dynamics.", "Multiple LLM agents acted as heterogeneous traders making buy-sell decisions; the framework modeled interactions and feedback mechanisms drawn from behavioral economics principles.", "Individual agent actions triggered emergent group phenomena including financial bubbles and recessions, demonstrating that LLM-driven simulations can reproduce stylized socio-economic patterns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 2679 }, { "uid": "doi:10.2139/ssrn.5119118", "doi": "10.2139/ssrn.5119118", "title": "AI and the Extended Workday: Productivity, Contracting Efficiency, and Distribution of Rents", "authors": [ "Wei Jiang", "Junyoung Park", "Rachel J. Xiao", "Shen Zhang" ], "posted": "2025-02-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5119118", "field": "economics", "role": "object", "bullets": [ "Individual time-diary data from 2004 to 2024 covering U.S. workers across occupations with varying AI exposure levels.", "Study uses the ChatGPT shock and broader AI developments as natural experiments to estimate effects of occupational AI exposure on work hours.", "Greater AI exposure is associated with longer work hours and less non-screen leisure; effects are strongest in competitive labor and product markets where gains accrue to firms and consumers." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 75, "validated": null, "n": 3472, "authors_detailed": [ { "name": "Wei Jiang", "url": "https://openalex.org/A5024857449", "inst": "Emory University" }, { "name": "Jun‐Young Park", "url": "https://openalex.org/A5100354913", "inst": "Auburn University" }, { "name": "Rachel J. Xiao", "url": "https://openalex.org/A5113560520", "inst": "Fordham University" }, { "name": "Shen Zhang", "url": "https://openalex.org/A5100354194", "inst": "Seton Hall University" } ], "affiliations": [ "Emory University", "Auburn University", "Fordham University", "Seton Hall University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.00828v1", "arxiv_id": "2502.00828v1", "title": "Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization", "authors": [ "Yoontae Hwang", "Yaxuan Kong", "Stefan Zohren", "Yongjae Lee" ], "posted": "2025-02-02", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.00828v1", "field": "finance", "role": "instrument", "bullets": [ "Portfolio construction over S&P100 and DOW30 constituents, with asset relationships, temporal dependencies, and macro variables processed by an attention mechanism feeding an optimization layer.", "An LLM supplies representations inside a decision focused learning architecture; the abstract does not say which model, and no check of intermediate predictions against ground truth is reported.", "Beats state of the art deep learning baselines on both universes, and gradient analysis indicates the network concentrates on assets most consequential for the allocation decision." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 14, "models": [], "n": 1958, "authors_detailed": [ { "name": "Yoontae Hwang", "url": "https://openalex.org/A5101711570", "inst": "Pusan National University" }, { "name": "Yaxuan Kong", "url": "https://openalex.org/A5099282237", "inst": "Science Oxford" }, { "name": "Stefan Zohren", "url": "https://openalex.org/A5090331439", "inst": "University of Oxford" }, { "name": "Yongjae Lee", "url": "https://openalex.org/A5100366482", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University", "University of Oxford", "Pusan National University", "Science Oxford" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.00415v2", "arxiv_id": "2502.00415v2", "title": "MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents", "authors": [ "George Fatouros", "Kostas Metaxas", "John Soldatos", "Manos Karathanassis" ], "posted": "2025-02-01", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.00415v2", "field": "finance", "role": "method", "bullets": [ "S&P 100 stocks over 2023 to 2024 plus S&P 500 stocks in 2024, combining news, prices, fundamentals, SEC filings, earnings calls, and institutional reports.", "LLM agents with retrieval augmented generation process filings and macro reports; model names are not stated in the abstract and no ground truth accuracy benchmark is reported.", "The framework reports 125.9 percent cumulative return against 73.5 percent for the index, and a 33.8 percent higher Sortino ratio in the S&P 500 test." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 14, "models": [], "n": 1890, "authors_detailed": [ { "name": "George Fatouros", "url": "https://openalex.org/A5134201437", "inst": "Alpha Technologies (Canada)" }, { "name": "Kostas Metaxas", "url": "https://openalex.org/A5141970507", "inst": "Innovate UK" }, { "name": "John Soldatos", "url": "https://openalex.org/A5135392146", "inst": "Agruicultural Research Institute" }, { "name": "Manos Karathanassis", "url": "https://openalex.org/A5142443557", "inst": "" } ], "affiliations": [ "Alpha Technologies (Canada)", "Innovate UK", "Agruicultural Research Institute" ] }, { "uid": "arxiv:2502.00198v4", "arxiv_id": "2502.00198v4", "title": "Fairshare Data Pricing via Data Valuation for Large Language Models", "authors": [ "Luyang Zhang", "Cathy Jiao", "Beibei Li", "Chenyan Xiong" ], "posted": "2025-01-31", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.00198v4", "field": "economics", "role": "object", "bullets": [ "Theoretical model of the market for LLM training data, with model builders as buyers and human annotators as sellers, paired with training experiments on math, medical diagnosis, and physical reasoning tasks.", "Data valuation quantifies each item's contribution to model performance and sets the fairshare price; the open source models trained in the experiments are not named.", "Exploitative pricing drives high quality sellers out and degrades data and models; fairshare sustains participation, raises seller earnings, and improves buyer performance per dollar and long term welfare." ], "bullet_provenance": "ai", "open_weights": true, "salience": 55, "edition": 14, "models": [], "validated": null, "n": 1957, "authors_detailed": [ { "name": "Luyang Zhang", "url": "https://openalex.org/A5020978585", "inst": "Johns Hopkins University" }, { "name": "Cathy Jiao", "url": "https://openalex.org/A5084722703", "inst": "LAC+USC Medical Center" }, { "name": "Beibei Li", "url": "https://openalex.org/A5100423730", "inst": "Sichuan University" }, { "name": "Chenyan Xiong", "url": "https://openalex.org/A5102363883", "inst": "Rutgers, The State University of New Jersey" } ], "affiliations": [ "Johns Hopkins University", "Sichuan University", "Rutgers, The State University of New Jersey" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5050951", "doi": "10.2139/ssrn.5050951", "title": "Generative AI Solutions to Empower Financial Firms", "authors": [ "Shashank Shaurya Dubey", "Vivek Astvansh", "Praveen K. Kopalle" ], "posted": "2025-01-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5050951", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 35, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 657, "authors_detailed": [ { "name": "Shashank Shaurya Dubey", "url": "https://openalex.org/A5115718834", "inst": "Indian Institute of Technology (IIT)" }, { "name": "Vivek Astvansh", "url": "https://openalex.org/A5036938950", "inst": "McGill University" }, { "name": "Praveen K. Kopalle", "url": "https://openalex.org/A5000759766", "inst": "Dartmouth College" } ], "affiliations": [ "Dartmouth College", "Indian Institute of Technology (IIT)", "McGill University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5049259", "doi": "10.2139/ssrn.5049259", "title": "AI Regulation: Competition, Arbitrage & Regulatory Capture", "authors": [ "Filippo Lancieri", "Laura Edelson", "Stefan Bechtold" ], "posted": "2025-01-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5049259", "field": "economics", "role": "object", "bullets": [ "Cross-jurisdictional analysis of AI regulatory frameworks examining EU and other regimes' competition, privacy, and intellectual property dimensions.", "No LLM used as tool; the paper analyzes strategic behavior by governments and firms around AI regulation, arbitrage, and capture.", "Multi-level competition forces tradeoffs between regulatory arbitrage and fragmentation; harmonization faces headwinds from divergent national interests in AI leadership." ], "bullet_provenance": "ai", "salience": 58, "models": [], "validated": null, "n": 2486, "authors_detailed": [ { "name": "Filippo Lancieri", "url": "https://openalex.org/A5000917577", "inst": "Georgetown University" }, { "name": "Laura Edelson", "url": "https://openalex.org/A5116102889", "inst": "Northeastern University" }, { "name": "Stefan Bechtold", "url": "https://openalex.org/A5000035510", "inst": "ETH Zurich" } ], "affiliations": [ "Georgetown University", "Northeastern University", "ETH Zurich" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5048316", "doi": "10.2139/ssrn.5048316", "title": "Large Language Models in Finance: Reasoning", "authors": [ "Miquel Noguer I Alonso" ], "posted": "2025-01-30", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5048316", "field": "finance", "role": "method", "bullet_provenance": "none", "salience": 35, "edition": 23, "bullets": [], "models": [], "validated": null, "n": 4193, "authors_detailed": [ { "name": "Miquel Noguer I Alonso", "url": "https://openalex.org/A5007301330", "inst": "Allen Institute for Artificial Intelligence" } ], "affiliations": [ "Allen Institute for Artificial Intelligence" ] }, { "uid": "arxiv:2501.18062v1", "arxiv_id": "2501.18062v1", "title": "FinanceQA: A Benchmark for Evaluating Financial Analysis Capabilities of Large Language Models", "authors": [ "Spencer Mateega", "Carlos Georgescu", "Danny Tang" ], "posted": "2025-01-30", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.18062v1", "field": "finance", "role": "method", "bullets": [ "A public testing suite of numerical financial analysis tasks that mirror on the job work at hedge funds, private equity firms, and investment banks.", "Current LLMs, named only as OpenAI models in a fine tuning experiment, are scored against ground truth answers requiring accounting conventions and assumption generation under incomplete information.", "Models fail roughly 60 percent of the realistic tasks, and the authors argue higher quality training data is needed for professional grade analysis." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "ground truth answers on analyst style tasks", "salience": 60, "edition": 14, "n": 1889, "authors_detailed": [ { "name": "Spencer Mateega", "url": "https://openalex.org/A5116094782", "inst": "" }, { "name": "Carlos Georgescu", "url": "https://openalex.org/A5116094783", "inst": "" }, { "name": "Tang, Danny", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5051800", "doi": "10.2139/ssrn.5051800", "title": "Impression Management: AI-based Evidence from Earnings Guidance", "authors": [ "Jonathan Berkovitch", "Doron Israeli", "Ron Kasznik" ], "posted": "2025-01-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5051800", "field": "accounting", "role": "instrument", "bullets": [ "18,046 firm-issued earnings guidance press releases from 3,619 firms covering 2001 to 2022, with the individual press release as the unit of observation.", "FinBERT, a pre-trained transformer sentiment model, scores positive and negative tone in disclosure titles, early text portions, and full text; ChatGPT-generated titles serve as a comparison, with no accuracy benchmark reported.", "Titles carry more positive sentiment than the underlying text, the gap widening when text conveys negative information, and title sentiment is incrementally associated with abnormal returns and lower abnormal trading volume." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "open_other" ], "open_weights": true, "validated": false, "salience": 60, "edition": 3, "audience": "general", "n": 346, "authors_detailed": [ { "name": "Jonathan Berkovitch", "url": "https://openalex.org/A5013631272", "inst": "Libera Università Internazionale degli Studi Sociali Guido Carli" }, { "name": "Doron Israeli", "url": "https://openalex.org/A5063969309", "inst": "Reichman University" }, { "name": "Ron Kasznik", "url": "https://openalex.org/A5021484228", "inst": "Stanford Graduate School of Business" } ], "affiliations": [ "Stanford Graduate School of Business", "Libera Università Internazionale degli Studi Sociali Guido Carli", "Reichman University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.15724v1", "arxiv_id": "2502.15724v1", "title": "Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors", "authors": [ "Halil Ibrahim Ergul", "Selim Balcisoy", "Burcin Bozkaya" ], "posted": "2025-01-28", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.15724v1", "field": "management", "role": "method", "bullets": [ "Bank transaction records from two banks, one for training and one for testing, with merchant categories in groceries, clothing, and gas stations as prediction targets.", "Mistral Instruct fine tuned with LoRA on transactions rendered as natural language is compared with CNN, LSTM, and probabilistic baselines, scored by F1 on the held out bank.", "The tuned model posts the highest F1 in all three merchant categories and handles minority classes better; exact scores are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "cross bank holdout, F1 reported", "salience": 42, "edition": 14, "n": 1888, "authors_detailed": [ { "name": "Halil Ibrahim Ergul", "url": "https://openalex.org/A5119852137", "inst": "" }, { "name": "Selim Balcısoy", "url": "https://openalex.org/A5043347770", "inst": "Sabancı Üniversitesi" }, { "name": "Burçin Bozkaya", "url": "https://openalex.org/A5084485442", "inst": "Environmental Systems Research Institute (United States)" } ], "affiliations": [ "Sabancı Üniversitesi", "Environmental Systems Research Institute (United States)" ] }, { "uid": "arxiv:2501.17299v1", "arxiv_id": "2501.17299v1", "title": "\"Ownership, Not Just Happy Talk\": Co-Designing a Participatory Large Language Model for Journalism", "authors": [ "Emily Tseng", "Meg Young", "Marianne Aubin Le Quéré", "Aimee Rinehart", "Harini Suresh" ], "posted": "2025-01-28", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.17299v1", "field": "management", "role": "object", "bullets": [ "Twenty interviews with reporters, data journalists, editors, labor organizers, product leads, and executives in news organizations facing both LLM adoption and copyright disputes.", "No model is deployed; the co-design study asks what a journalist controlled LLM should do, surfacing tensions at macro, meso, and micro levels.", "The team specifies organizational structures and functionality for a participatory, journalist controlled LLM and argues one size fits all commercial foundation models fit workplace needs poorly." ], "bullet_provenance": "ai", "salience": 33, "edition": 14, "models": [], "validated": null, "n": 1956, "authors_detailed": [ { "name": "Emily Tseng", "url": "https://openalex.org/A5036398088", "inst": "Microsoft (United States)" }, { "name": "Meg Young", "url": "https://openalex.org/A5001340441", "inst": "Data & Society Research Institute" }, { "name": "Marianne Aubin Le Quéré", "url": "https://openalex.org/A5050735872", "inst": "Princeton University" }, { "name": "Aimee Rinehart", "url": "https://openalex.org/A5116082820", "inst": "Associated Press" }, { "name": "Harini Suresh", "url": "https://openalex.org/A5005452839", "inst": "Brown University" } ], "affiliations": [ "Princeton University", "Microsoft (United States)", "Data & Society Research Institute", "Associated Press", "Brown University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2501.16996v5", "arxiv_id": "2501.16996v5", "title": "Artificial Intelligence Clones", "authors": [ "Annie Liang" ], "posted": "2025-01-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.16996v5", "field": "economics", "role": "object", "bullets": [ "Theoretical model of matching markets with personality modeled as points in k-dimensional Euclidean space and AI representations as noisy approximations.", "Compares in-person search regime to AI-mediated search where individuals match based on LLM-generated personality representations.", "A finite number of in-person meetings yields better expected match quality than search over infinite AI representations; two meetings suffice in high dimensions." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3196, "authors_detailed": [ { "name": "Annie Liang", "url": "https://openalex.org/A5039749384", "inst": "Northwestern University" } ], "affiliations": [ "Northwestern University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2501.15720v2", "arxiv_id": "2501.15720v2", "title": "ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis", "authors": [ "Keane Ong", "Rui Mao", "Deeksha Varshney", "Frank Xing", "Ranjan Satapathy", "Johan Sulaeman", "Erik Cambria", "Gianmarco Mengaldo" ], "posted": "2025-01-27", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.15720v2", "field": "finance", "role": "instrument", "bullets": [ "Sustainability disclosures analyzed to construct a publicly available knowledge base of 44,000 ESG knowledge triplets.", "GPT-4o performs inference within a neurosymbolic framework combining concept parsing and semi-supervised label propagation with a hierarchical ESG taxonomy.", "ESGSenticNet outperforms baselines on ESG relatedness and action orientation of captured terms by 26% and 31% respectively, requiring no training at deployment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "ESG relatedness and action orientation benchmarks vs. baselines", "salience": 50, "n": 3470, "authors_detailed": [ { "name": "Keane Ong", "url": "https://openalex.org/A5001463799", "inst": "National University of Singapore" }, { "name": "Rui Mao", "url": "https://openalex.org/A5101724957", "inst": "Nanyang Technological University" }, { "name": "Varshney, Deeksha", "url": "", "inst": "" }, { "name": "Frank Xing", "url": "https://openalex.org/A5024877482", "inst": "University of Reading" }, { "name": "Ranjan Satapathy", "url": "https://openalex.org/A5054901394", "inst": "Agency for Science, Technology and Research" }, { "name": "Johan Sulaeman", "url": "https://openalex.org/A5000510255", "inst": "National University of Singapore" }, { "name": "Erik Cambria", "url": "https://openalex.org/A5100752356", "inst": "Nanyang Technological University" }, { "name": "Gianmarco Mengaldo", "url": "https://openalex.org/A5091468612", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore", "Nanyang Technological University", "University of Reading", "Agency for Science, Technology and Research" ] }, { "uid": "doi:10.2139/ssrn.5039987", "doi": "10.2139/ssrn.5039987", "title": "Central Bank Digital Currencies (CBDC) Sentiment Score Analysis of News using Chat GPT-4.0 from January 2018 to June 2024", "authors": [ "Renata Alves" ], "posted": "2025-01-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5039987", "field": "finance", "role": "instrument", "bullets": [ "News articles on central bank digital currencies from LexisNexis in multiple languages, January 2018 to June 2024.", "ChatGPT-4.0 scored sentiment of CBDC news to construct a new CBDCSX sentiment index and identified top expressions by topic and sentiment.", "56% of analyzed coverage was negative; CBDC launch announcements drove highest positive sentiment while political events and privacy concerns drove the lowest." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 3471, "authors_detailed": [ { "name": "Renata Ayumi Alves", "url": "https://openalex.org/A5116051183", "inst": "Fundação Getulio Vargas" } ], "affiliations": [ "Fundação Getulio Vargas" ] }, { "uid": "arxiv:2501.15411v1", "arxiv_id": "2501.15411v1", "title": "The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation", "authors": [ "Raha Aghaei", "Ali A. Kiaei", "Mahnaz Boush", "Javad Vahidi", "Zeynab Barzegar", "Mahan Rofoosheh" ], "posted": "2025-01-26", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.15411v1", "field": "management", "role": "object", "bullets": [ "Supply chain management functions including demand forecasting, inventory, supplier relations, and logistics, reviewed at the industry level with no primary data collection.", "No specific model is used or named; the paper surveys potential LLM applications and pairings with IoT, blockchain, and robotics.", "Claimed benefits include better decisions, lower costs, and responsiveness, with recommendations on data management and workforce training; no quantitative evidence is offered." ], "bullet_provenance": "ai", "salience": 20, "edition": 14, "models": [], "validated": null, "n": 1955, "authors_detailed": [ { "name": "Raha Aghaei", "url": "https://openalex.org/A5115981162", "inst": "Sharif University of Technology" }, { "name": "Ali Akbar Kiaei", "url": "https://openalex.org/A5025767582", "inst": "Iran University of Medical Sciences" }, { "name": "Mahnaz Boush", "url": "https://openalex.org/A5009630715", "inst": "University of Mazandaran" }, { "name": "Javad Vahidi", "url": "https://openalex.org/A5062592875", "inst": "Iran University of Science and Technology" }, { "name": "Zeynab Barzegar", "url": "https://openalex.org/A5048644913", "inst": "Iran University of Medical Sciences" }, { "name": "Mahan Rofoosheh", "url": "https://openalex.org/A5115981164", "inst": "Technical and Vocational University" } ], "affiliations": [ "Sharif University of Technology", "Iran University of Medical Sciences", "University of Mazandaran", "Iran University of Science and Technology", "Technical and Vocational University" ] }, { "uid": "arxiv:2501.14334v2", "arxiv_id": "2501.14334v2", "title": "Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts", "authors": [ "Clément Desroches", "Martin Chauvin", "Louis Ladan", "Caroline Vateau", "Simon Gosset", "Philippe Cordier" ], "posted": "2025-01-24", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.14334v2", "field": "management", "role": "object", "bullets": [ "Corporate AI portfolios assessed with life cycle methodology spanning hardware fabrication to end of life, plus projections of AI electricity use to 2030 under IPCC aligned scenarios.", "No LLM serves as a tool; generative models are the object being sized, with large generative AI estimated to consume up to 4600 times the energy of traditional models.", "A high adoption scenario projects AI electricity use growing by a factor of 24.4 by 2030; isolated hardware, model, or grid improvements are judged insufficient without value chain coordination." ], "bullet_provenance": "ai", "salience": 50, "edition": 14, "models": [], "validated": null, "n": 1852, "authors_detailed": [ { "name": "Clément Desroches", "url": "https://openalex.org/A5116017055", "inst": "Capgemini (France)" }, { "name": "M. Chauvin", "url": "https://openalex.org/A5015870485", "inst": "Capgemini (France)" }, { "name": "Louis Ladan", "url": "https://openalex.org/A5116017057", "inst": "Capgemini (France)" }, { "name": "Caroline Vateau", "url": "https://openalex.org/A5116017058", "inst": "Capgemini (France)" }, { "name": "Simon Gosset", "url": "https://openalex.org/A5116017059", "inst": "Capgemini (France)" }, { "name": "Philippe Cordier", "url": "https://openalex.org/A5116017060", "inst": "Capgemini (France)" } ], "affiliations": [ "Capgemini (France)" ] }, { "uid": "arxiv:2501.14431v2", "arxiv_id": "2501.14431v2", "title": "Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains", "authors": [ "Xu Chu", "Zhijie Tan", "Hanlin Xue", "Guanyu Wang", "Tong Mo", "Weiping Li" ], "posted": "2025-01-24", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.14431v2", "field": "finance", "role": "method", "bullets": [ "Stock investment recommendation and legal QA tasks, with two purpose built chain of thought datasets of about 2,000 examples each for fine tuning.", "Supervised fine tuning plus selective tree search over reasoning paths; the base model family is not stated in the abstract. A new PROOF score assesses explainability alongside accuracy.", "The tuned models lead on accuracy and explainability across both domain tasks; specific margins are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "stock recommendation and legal QA benchmarks", "salience": 35, "edition": 14, "n": 1887, "authors_detailed": [ { "name": "Xu Chu", "url": "https://openalex.org/A5007041000", "inst": "" }, { "name": "Zhijie Tan", "url": "https://openalex.org/A5116039185", "inst": "" }, { "name": "Hanlin Xue", "url": "https://openalex.org/A5116039186", "inst": "" }, { "name": "Guanyu Wang", "url": "https://openalex.org/A5100644044", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Tong Mo", "url": "https://openalex.org/A5059356240", "inst": "Peking University" }, { "name": "Weiping Li", "url": "https://openalex.org/A5100654867", "inst": "Changhong (China)" } ], "affiliations": [ "Chinese University of Hong Kong, Shenzhen", "Peking University", "Changhong (China)" ] }, { "uid": "doi:10.2139/ssrn.5108138", "doi": "10.2139/ssrn.5108138", "title": "Can Investors Learn from Patent Documents? — Evidence from Textual Analysis", "authors": [ "Yuxiang Zheng" ], "posted": "2025-01-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5108138", "field": "finance", "role": "instrument", "bullets": [ "U.S. patent texts linked to stock market valuations, covering firms with granted patents.", "BERT encodes contextual information in patent texts, explaining 31.5% of variation in stock market patent valuation beyond structured characteristics.", "Patent texts predict future earnings level, volatility, and cumulation speed; investors underreact to patent text information, and this gap narrows after pre-grant publication mandates." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "R-squared for patent valuation, return predictability tests", "salience": 62, "n": 3469, "authors_detailed": [ { "name": "Yuxiang Zheng", "url": "https://openalex.org/A5116015713", "inst": "Rutgers, The State University of New Jersey" } ], "affiliations": [ "Rutgers, The State University of New Jersey" ] }, { "uid": "arxiv:2501.13993v1", "arxiv_id": "2501.13993v1", "title": "CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation", "authors": [ "Hamza Landolsi", "Kais Letaief", "Nizar Taghouti", "Ines Abdeljaoued-Tej" ], "posted": "2025-01-23", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.13993v1", "field": "finance", "role": "method", "bullets": [ "Banking customer queries about services and annual reports at international banks, answered from processed document collections held in parallel vector and graph databases.", "An unnamed open source LLM generates answers after query expansion and hybrid vector plus graph retrieval; the abstract reports no accuracy evaluation or benchmark comparison.", "The system is presented as improving clarity and accessibility of banking information for customers; no quantitative performance results are stated." ], "bullet_provenance": "ai", "open_weights": true, "validated": false, "salience": 28, "edition": 14, "models": [], "n": 1851 }, { "uid": "arxiv:2502.00029v2", "arxiv_id": "2502.00029v2", "title": "AlphaSharpe: LLM-Driven Discovery of Robust Risk-Adjusted Metrics", "authors": [ "Kamer Ali Yuksel", "Hassan Sawaf" ], "posted": "2025-01-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.00029v2", "field": "finance", "role": "method", "bullets": [ "Real-world equity dataset used to evolve risk-return metrics via LLM-driven iterative crossover, mutation, and out-of-sample evaluation.", "LLMs generated and refined financial performance metrics using implicit domain knowledge, scored for generalization to unseen market data.", "Discovered metrics showed 3x the predictive power for future risk-returns and 2x portfolio performance versus the traditional Sharpe ratio." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "Out-of-sample correlation with future risk-return and portfolio performance", "salience": 60, "n": 2655, "authors_detailed": [ { "name": "Kamer Ali Yüksel", "url": "https://openalex.org/A5033353054", "inst": "Van Yüzüncü Yıl Üniversitesi" }, { "name": "Hassan Sawaf", "url": "https://openalex.org/A5045818674", "inst": "TiGenix (Spain)" } ], "affiliations": [ "Van Yüzüncü Yıl Üniversitesi", "TiGenix (Spain)" ] }, { "uid": "doi:10.5220/0013174500003890", "doi": "10.5220/0013174500003890", "arxiv_id": "2502.05186v1", "title": "Multimodal Stock Price Prediction", "authors": [ "Furkan Karadaş", "Bahaeddin Eravcı", "Ahmet Murat Özbayoğlu" ], "posted": "2025-01-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.05186v1", "field": "finance", "role": "instrument", "bullets": [ "U.S. stock market data combined with tweets and news articles, testing multimodal sentiment-augmented prediction against a standard LSTM baseline model.", "ChatGPT-4o and FinBERT performed sentiment analysis on tweets and news articles; sentiment scores augmented an LSTM-based stock price prediction model.", "Incorporating multimodal sentiment data increased forecast effectiveness by up to 5% over the reference LSTM model for stock price prediction tasks." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "stock price prediction accuracy vs LSTM baseline", "salience": 30, "n": 2975, "authors_detailed": [ { "name": "Furkan Karadaş", "url": "https://openalex.org/A5116228000", "inst": "TOBB University of Economics and Technology" }, { "name": "Bahaeddin Eravcı", "url": "https://openalex.org/A5116228001", "inst": "TOBB University of Economics and Technology" }, { "name": "Ahmet Murat Özbayoğlu", "url": "https://openalex.org/A5048947308", "inst": "TOBB University of Economics and Technology" } ], "affiliations": [ "TOBB University of Economics and Technology" ] }, { "uid": "doi:10.2139/ssrn.5109196", "doi": "10.2139/ssrn.5109196", "title": "Questioning the Answers: LLMs enter the Boardroom", "authors": [ "Henry Chiang", "Liam Hynes", "Daniel Sandberg" ], "posted": "2025-01-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5109196", "field": "finance", "role": "instrument", "bullets": [ "192,000 earnings call transcripts, scoring executive communication quality during Q&A sessions with sell-side analysts across U.S. public firms.", "LLM vector embeddings scored executives on proactiveness of prepared remarks addressing analysts' questions and on-topic alignment of their responses.", "Embeddings quantify how well executives anticipate analyst concerns and whether responses remain on-topic, revealing systematic variation in communication quality." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 55, "n": 2976, "authors_detailed": [ { "name": "Henry Chiang", "url": "https://openalex.org/A5108947577", "inst": "S&P Global" }, { "name": "Liam Hynes", "url": "https://openalex.org/A5093689862", "inst": "GE Global Research (United States)" }, { "name": "Daniel Sandberg", "url": "https://openalex.org/A5099515679", "inst": "S&P Global" } ], "affiliations": [ "S&P Global", "GE Global Research (United States)" ] }, { "uid": "doi:10.2139/ssrn.5038444", "doi": "10.2139/ssrn.5038444", "title": "Redefining Crisis Management in an AI-First World", "authors": [ "Supriya Pandey", "Tanushree Halder", "Saibal Samaddar" ], "posted": "2025-01-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5038444", "field": "management", "role": "instrument", "bullets": [ "One of India's largest electricity distribution companies receiving approximately 2,000 social media complaints daily during peak seasons.", "Generative AI processed complaints following organizational procedures, augmented with customer justice frameworks to reduce social bias.", "Solution surpassed manual bandwidth limitations, enabled efficient complaint tracking and resolution, and set an industry benchmark for AI-driven customer experience." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 30, "n": 3468, "authors_detailed": [ { "name": "Supriya Pandey", "url": "https://openalex.org/A5116003261", "inst": "University of Delhi" }, { "name": "Tanushree Halder", "url": "https://openalex.org/A5089501957", "inst": "Indian Institute of Management Indore" }, { "name": "Saibal Samaddar", "url": "https://openalex.org/A5065691060", "inst": "University of Warwick" } ], "affiliations": [ "University of Delhi", "Indian Institute of Management Indore", "University of Warwick" ] }, { "uid": "doi:10.2139/ssrn.5103553", "doi": "10.2139/ssrn.5103553", "title": "AI-Powered (Finance) Scholarship", "authors": [ "Robert Novy-Marx", "Mihail Velikov" ], "posted": "2025-01-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5103553", "field": "finance", "role": "method", "bullets": [ "Over 30,000 potential stock return predictor signals mined from accounting data; 96 signals passed the Assaying Anomalies protocol's rigorous criteria.", "State-of-the-art LLMs generated three complete academic paper versions per signal with creative names, custom introductions, and literature citations.", "LLMs produce hundreds of publishable-looking finance papers, demonstrating research efficiency gains but also the risk of industrialized HARKing." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 78, "n": 2485, "authors_detailed": [ { "name": "Robert Novy‐Marx", "url": "https://openalex.org/A5003812290", "inst": "National Bureau of Economic Research" }, { "name": "Mihail Velikov", "url": "https://openalex.org/A5041057816", "inst": "Pennsylvania State University" } ], "affiliations": [ "National Bureau of Economic Research", "Pennsylvania State University" ] }, { "uid": "doi:10.2139/ssrn.5103580", "doi": "10.2139/ssrn.5103580", "title": "American Life Histories", "authors": [ "David Lagakos", "Stelios Michalopoulos", "Hans-Joachim Voth" ], "posted": "2025-01-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5103580", "field": "economics", "role": "instrument", "bullets": [ "Over 1,400 life narratives of older Americans collected during the 1930s, analyzed for life satisfaction, sources of meaning, and critical junctures.", "LLMs extracted structured information from narratives and were compared to detailed human readings in a Turing Test format for complex questions.", "Sources of life meaning were more varied than prior research suggested; work and community contributions rivaled family, and women disproportionately cited adverse family events." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "LLM responses compared to human readers on complex narrative questions", "salience": 55, "n": 2654, "authors_detailed": [ { "name": "David Lagakos", "url": "https://openalex.org/A5043016835", "inst": "National Bureau of Economic Research" }, { "name": "Stelios Michalopoulos", "url": "https://openalex.org/A5023559123", "inst": "Brown University" }, { "name": "Hans‐Joachim Voth", "url": "https://openalex.org/A5064565742", "inst": "Union Bank of Switzerland" } ], "affiliations": [ "National Bureau of Economic Research", "Brown University", "Union Bank of Switzerland" ] }, { "uid": "doi:10.2139/ssrn.5033187", "doi": "10.2139/ssrn.5033187", "title": "Decoding Congressional Stock Trades: An Industry and Committee-Focused Analysis with Graph Neural Network and Large Language Model", "authors": [ "Suyeol Yun" ], "posted": "2025-01-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5033187", "field": "finance", "role": "instrument", "bullets": [ "U.S. congressional stock trades at the congressperson-ticker-timing level, refining excess return estimation beyond traditional aggregate calendar-year approaches.", "Graph Neural Network identified influential factors; LLM provided interpretability through predictive analysis and theorizing about congressional trading motivations and behaviors.", "Industry sectors and committee assignments are pivotal factors; the LLM provides crucial interpretability though it trails the GNN in raw predictive power." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "congressional trade excess return prediction", "salience": 50, "n": 2974, "authors_detailed": [ { "name": "Suyeol Yun", "url": "https://openalex.org/A5108675080", "inst": "Medieval Academy of America" } ], "affiliations": [ "Medieval Academy of America" ] }, { "uid": "arxiv:2501.11581v1", "arxiv_id": "2501.11581v1", "title": "Open Sourcing GPTs: Economics of Open Sourcing Advanced AI Models", "authors": [ "Mahyar Habibi" ], "posted": "2025-01-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.11581v1", "field": "economics", "role": "object", "bullets": [ "Release decisions for advanced LLMs by for-profit developers, studied empirically across firms and models and then in a theoretical framework; data sources are not stated.", "No model is used as a tool; LLMs are the studied technology, with open sourcing framed as trading immediate returns for faster technology growth.", "Open sourcing likelihood falls with a model's performance edge but rises for large tech owners, and follows an inverted U in owner size, suggesting moderate concentration helps open ecosystems." ], "bullet_provenance": "ai", "salience": 58, "edition": 14, "models": [], "validated": null, "n": 1953, "authors_detailed": [ { "name": "Mahyar Habibi", "url": "https://openalex.org/A5115997602", "inst": "" } ] }, { "uid": "arxiv:2501.16356v1", "arxiv_id": "2501.16356v1", "title": "Evaluating Binary Decision Biases in Large Language Models: Implications for Fair Agent-Based Financial Simulations", "authors": [ "Alicia Vidler", "Toby Walsh" ], "posted": "2025-01-20", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.16356v1", "field": "finance", "role": "method", "bullets": [ "Binary yes or no decision tasks meant for agent based financial market simulations, sampled through one shot and few shot API queries at varying temperatures.", "Three GPT model versions are tested for output distribution bias, Markov properties, and negative recency, benchmarked against true random binary series rather than labelled ground truth.", "GPT-4-0125-preview answers yes 98 to 99 percent of the time against 32 to 43 percent for GPT-4o-Mini, and batch versus repeated sampling shifts distributions, so simulation integration needs care." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 46, "edition": 14, "validated": null, "n": 1954, "authors_detailed": [ { "name": "Alicia Vidler", "url": "https://openalex.org/A5051454314", "inst": "Bar-Ilan University" }, { "name": "Toby Walsh", "url": "https://openalex.org/A5072902302", "inst": "UNSW Sydney" } ], "affiliations": [ "Bar-Ilan University", "UNSW Sydney" ] }, { "uid": "arxiv:2501.10963v2", "arxiv_id": "2501.10963v2", "title": "Open FinLLM Leaderboard: Towards Financial AI Readiness", "authors": [ "Shengyuan Colin Lin", "Felix Tian", "Keyi Wang", "Xingjian Zhao", "Jimin Huang", "Qianqian Xie", "Luca Borella", "Matt White", "Christina Dan Wang", "Kairong Xiao", "Xiao-Yang Liu Yanglet", "Li Deng" ], "posted": "2025-01-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.10963v2", "field": "finance", "role": "method", "bullets": [ "Open leaderboard platform assessing AI models across a spectrum of financial tasks, developed in collaboration with Linux Foundation and Hugging Face.", "Multiple FinLLMs evaluated on financial knowledge and reasoning benchmarks covering business, finance, accounting, and auditing task categories.", "Leaderboard provides standardized financial AI readiness assessment and democratizes access to financial intelligence benchmarking for academia and industry." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "validated": true, "validation_note": "FinLLM benchmark task accuracy scores", "salience": 40, "n": 2973, "authors_detailed": [ { "name": "Lin, Shengyuan Colin", "url": "", "inst": "" }, { "name": "Tian, Felix", "url": "", "inst": "" }, { "name": "Keyi Wang", "url": "https://openalex.org/A5076572035", "inst": "South China Agricultural University" }, { "name": "Zhao, Xingjian", "url": "", "inst": "" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "Xuzhou Medical College" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Luca Borella", "url": "https://openalex.org/A5115505581", "inst": "" }, { "name": "Matt White", "url": "https://openalex.org/A5111151871", "inst": "" }, { "name": "Christina Dan Wang", "url": "https://openalex.org/A5018062245", "inst": "New York University Shanghai" }, { "name": "Kairong Xiao", "url": "https://openalex.org/A5080804351", "inst": "California Institute of Technology" }, { "name": "Xiao-Yang Liu Yanglet", "url": "https://openalex.org/A5115996925", "inst": "Columbia University" }, { "name": "Li Deng", "url": "https://openalex.org/A5069186270", "inst": "China Three Gorges Corporation (China)" } ], "affiliations": [ "New York University Shanghai", "California Institute of Technology", "Columbia University", "South China Agricultural University", "Xuzhou Medical College", "Hunan Normal University", "China Three Gorges Corporation (China)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2501.09636v2", "arxiv_id": "2501.09636v2", "title": "LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading", "authors": [ "Kuan-Ming Liu", "Ming-Chih Lo" ], "posted": "2025-01-16", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.09636v2", "field": "finance", "role": "method", "bullets": [ "Multimodal stock market data combining historical prices with stock news; markets, sample period, and number of assets are not stated in the abstract.", "An unnamed LLM replaces the neural network router in a mixture of experts trading architecture, choosing experts from price history and news; no ground truth validation applies.", "LLMoE reports better trading performance than state of the art mixture of experts and deep network baselines; magnitudes are not given in the abstract." ], "bullet_provenance": "ai", "salience": 36, "edition": 14, "models": [], "validated": null, "n": 1952, "authors_detailed": [ { "name": "Liu, Kuan-Ming", "url": "", "inst": "" }, { "name": "M. C. Lo", "url": "https://openalex.org/A5113387105", "inst": "Loughborough University" } ], "affiliations": [ "Loughborough University" ] }, { "uid": "doi:10.2139/ssrn.5083707", "doi": "10.2139/ssrn.5083707", "title": "Bank Social Media Disclosure During a Banking Crisis", "authors": [ "Shushu Jiang", "Jason Junshen Lin", "Yibin Liu", "Rachel Xi Zhang", "Luo Zuo" ], "posted": "2025-01-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5083707", "field": "finance", "role": "instrument", "bullets": [ "Comprehensive sample of bank tweets during the 2023 U.S. banking crisis, linked to bank-level uninsured deposit ratios and subsequent deposit flows.", "LLMs identified depositor-relevant content from bank tweets; difference-in-differences design compared disclosure behavior by pre-crisis uninsured deposit exposure.", "Banks with higher uninsured deposit ratios increased financial-information tweets during the crisis; tweeting banks experienced higher uninsured deposit growth the following year." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 60, "n": 2972, "authors_detailed": [ { "name": "Shushu Jiang", "url": "https://openalex.org/A5053849643", "inst": "National University of Singapore" }, { "name": "Yibin Liu", "url": "https://openalex.org/A5100767946", "inst": "China University of Petroleum, Beijing" }, { "name": "Yibin Liu", "url": "https://openalex.org/A5147283317", "inst": "National University of Singapore" }, { "name": "Rachel Xi Zhang", "url": "https://openalex.org/A5062492771", "inst": "National University System" }, { "name": "Luo Zuo", "url": "https://openalex.org/A5020328839", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore", "China University of Petroleum, Beijing", "National University System" ] }, { "uid": "doi:10.2139/ssrn.5023563", "doi": "10.2139/ssrn.5023563", "title": "Examining the Integration of Generative AI Models for Improved Risk Management Practices in the Financial Sector", "authors": [ "Odai AlJaloudi", "Mouhamed Thiam", "Muath Abdel Qader", "M.K.S. Al-Mhdawi", "Abroon Qazi", "Nicholas Dacre" ], "posted": "2025-01-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5023563", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 38, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 656, "authors_detailed": [ { "name": "Odai AlJaloudi", "url": "https://openalex.org/A5114623366", "inst": "University of Gloucestershire" }, { "name": "Mouhamed Thiam", "url": "https://openalex.org/A5114623367", "inst": "Nottingham Trent University" }, { "name": "Muath Abdel Qader", "url": "https://openalex.org/A5114623368", "inst": "Australian University, Kuwait" }, { "name": "M.K.S. Al-Mhdawi", "url": "https://openalex.org/A5010038132", "inst": "Teesside University" }, { "name": "Abroon Qazi", "url": "https://openalex.org/A5084847550", "inst": "American University of Sharjah" }, { "name": "Nicholas Dacre", "url": "https://openalex.org/A5006870368", "inst": "University of Southampton" } ], "affiliations": [ "University of Gloucestershire", "Nottingham Trent University", "Australian University, Kuwait", "Teesside University", "American University of Sharjah", "University of Southampton" ] }, { "uid": "doi:10.2139/ssrn.5094968", "doi": "10.2139/ssrn.5094968", "title": "Large Language Models: An Applied Econometric Framework", "authors": [ "Jens Ludwig", "Sendhil Mullainathan", "Ashesh Rambachan" ], "posted": "2025-01-14", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5094968", "field": "economics", "role": "method", "bullets": [ "Methodological paper for empirical economics with no single dataset, organized around two uses of text: forecasting outcomes and automating measurement of economic concepts for downstream analysis.", "Large language models (families not named) read text; the framework enforces no training leakage for prediction and pairs model outputs with a small human validation sample for estimation.", "Without a validation sample researchers cannot bound LLM errors, and seemingly minor choices of model or prompt can shift downstream parameter estimates dramatically." ], "bullet_provenance": "ai", "salience": 80, "edition": 2, "audience": "broad", "models": [], "validated": null, "n": 36, "authors_detailed": [ { "name": "Jens Ludwig", "url": "https://openalex.org/A5085753634", "inst": "Public Policy Institute of California" }, { "name": "Sendhil Mullainathan", "url": "https://openalex.org/A5034703281", "inst": "University of Chicago" }, { "name": "Ashesh Rambachan", "url": "https://openalex.org/A5018937306", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "University of Chicago", "Massachusetts Institute of Technology", "Public Policy Institute of California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5082861", "doi": "10.2139/ssrn.5082861", "title": "Caution Ahead: Numerical Reasoning and Look-ahead Bias in AI Models", "authors": [ "Bradford (Lynch) Levy" ], "posted": "2025-01-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5082861", "field": "accounting", "role": "object", "bullets": [ "Series of experiments on commercial LLMs applied to accounting and finance tasks, testing numerical reasoning and predictive accuracy against known benchmarks.", "LLMs are tested for numerical reasoning quality and look-ahead bias; the study opens the black box to isolate whether strong performance reflects economic reasoning or modeling artifacts.", "Much of AI's apparent superiority on accounting and finance tasks traces to look-ahead bias and poor numerical reasoning, not to economically grounded mechanisms." ], "bullet_provenance": "editor", "validated": true, "validation_note": "experiments with known correct answers for numerical reasoning", "salience": 80, "edition": 21, "audience": "general", "models": [], "n": 4074 }, { "uid": "arxiv:2501.06834v1", "arxiv_id": "2501.06834v1", "title": "LLMs Model Non-WEIRD Populations: Experiments with Synthetic Cultural Agents", "authors": [ "Augusto Gonzalez-Bonorino", "Monica Capra", "Emilio Pantoja" ], "posted": "2025-01-12", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.06834v1", "field": "economics", "role": "agent", "bullets": [ "Classic behavioral experiments, including dictator and ultimatum games, run on synthetic cultural agents built to represent non-WEIRD populations that are hard to reach experimentally.", "Unnamed LLMs are prompted to act as culturally profiled participants; behavior is compared only qualitatively with published human results where data exist, with no agreement statistic reported.", "Simulated behavior varies substantially across cultures and qualitatively resembles human patterns where comparisons exist; for unstudied populations the method generates testable hypotheses and pilots protocols." ], "bullet_provenance": "ai", "validated": false, "salience": 56, "edition": 14, "models": [], "n": 1951 }, { "uid": "arxiv:2501.04961v4", "arxiv_id": "2501.04961v4", "title": "Demystifying Domain-adaptive Post-training for Financial LLMs", "authors": [ "Zixuan Ke", "Yifei Ming", "Xuan-Phi Nguyen", "Caiming Xiong", "Shafiq Joty" ], "posted": "2025-01-09", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.04961v4", "field": "finance", "role": "method", "bullets": [ "Finance domain post-training of open LLMs using the curated FinTrain datasets, evaluated on FinEval, a suite spanning a wide range of financial tasks.", "FINDAP jointly optimizes continual pretraining and instruction tuning of Llama with preference data distilled from a generative reward model, producing Llama-Fin; performance is scored on the FinEval suite.", "Llama-Fin reports state of the art results across financial tasks, with stage by stage analysis of which capabilities each post-training phase contributes; figures are not given in the abstract." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "FinEval financial evaluation suite", "salience": 50, "edition": 14, "n": 1950, "authors_detailed": [ { "name": "Zixuan Ke", "url": "https://openalex.org/A5013224749", "inst": "South China Agricultural University" }, { "name": "Yifei Ming", "url": "https://openalex.org/A5035172001", "inst": "Salesforce (United States)" }, { "name": "Xuan-Phi Nguyen", "url": "https://openalex.org/A5114373630", "inst": "Salesforce (United States)" }, { "name": "Caiming Xiong", "url": "https://openalex.org/A5032046813", "inst": "Salesforce (United States)" }, { "name": "Shafiq Joty", "url": "https://openalex.org/A5005443526", "inst": "Salesforce (United States)" } ], "affiliations": [ "South China Agricultural University", "Salesforce (United States)" ] }, { "uid": "arxiv:2501.12399v2", "arxiv_id": "2501.12399v2", "title": "FinSphere, a Real-Time Stock Analysis Agent Powered by Instruction-Tuned LLMs and Domain Tools", "authors": [ "Shijie Han", "Jingshu Zhang", "Yiqing Shen", "Kaiyuan Yan", "Hongguang Li" ], "posted": "2025-01-08", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.12399v2", "field": "finance", "role": "method", "bullets": [ "Stock analysis report generation grounded in Stocksis, a dataset curated by industry experts, combined with real time market feeds and quantitative tools; coverage and period are not stated.", "An instruction tuned LLM, base model not stated, orchestrates the tools; reports are scored with AnalyScore, the authors' own quality rubric, with no agreement check against expert judgments reported.", "FinSphere earns higher AnalyScore ratings than general and finance specific LLMs and prior agent systems, including baselines given real time data and few shot guidance." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 14, "models": [], "n": 1850, "authors_detailed": [ { "name": "Shijie Han", "url": "https://openalex.org/A5101869828", "inst": "Central South University" }, { "name": "J. Z. Zhang", "url": "https://openalex.org/A5053412536", "inst": "Nanjing Normal University" }, { "name": "Yiqing Shen", "url": "https://openalex.org/A5072622467", "inst": "Johns Hopkins University" }, { "name": "Yan, Kaiyuan", "url": "", "inst": "" }, { "name": "Hongguang Li", "url": "https://openalex.org/A5100624099", "inst": "Liaocheng University" } ], "affiliations": [ "Johns Hopkins University", "Central South University", "Nanjing Normal University", "Liaocheng University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5085859", "doi": "10.2139/ssrn.5085859", "title": "Thinking with Many Minds: Using Large Language Models for Multi-Perspective Problem-Solving", "authors": [ "Sanghyun Park", "Boris Maciejovsky", "Phanish Puranam" ], "posted": "2025-01-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5085859", "field": "management", "role": "agent", "bullets": [ "Complex problem-solving scenarios requiring multi-perspective deliberation, comparing mental simulation with LLM-based synthetic deliberation approaches.", "Custom GPT-based model simulated discourse between agents embodying diverse perspectives, processing multiple viewpoints concurrently without cognitive degradation.", "Synthetic deliberation enables parallel exploration of perspectives with precise control over viewpoint synthesis, transcending mental simulation limitations for strategic planning." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 2971, "authors_detailed": [ { "name": "Sanghyun Park", "url": "https://openalex.org/A5100322261", "inst": "National University of Singapore" }, { "name": "Boris Maciejovsky", "url": "https://openalex.org/A5002280936", "inst": "University of California, Riverside" }, { "name": "Phanish Puranam", "url": "https://openalex.org/A5015167951", "inst": "INSEAD" } ], "affiliations": [ "INSEAD", "National University of Singapore", "University of California, Riverside" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.5044024", "doi": "10.2139/ssrn.5044024", "title": "Corporate use of AI and Subsequent Product Market Performance", "authors": [ "Jennifer Tucker", "Jeff J. Wang", "Ran Zhao" ], "posted": "2025-01-07", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5044024", "field": "accounting", "role": "object", "bullets": [ "Setting: Firms’ operational use of AI is identified from annual-report disclosures and linked to product-market outcomes in the following year.", "Design: The study relates disclosed AI use to sales growth, market share, margins, operating profit, and operating cash flow, and compares disclosure-based evidence with firms’ employment of AI workers.", "Result: AI use is associated with broad subsequent performance gains, concentrated in revenue-generating uses, specific disclosures, and more advanced AI; disclosure is more informative than AI-worker presence." ], "bullet_provenance": "editor", "validated": true, "validation_note": "Uses multiple subsequent operating outcomes, disclosure-specificity and technology heterogeneity tests, and an AI-worker comparison.", "salience": 95, "edition": 23, "audience": "general", "models": [], "n": 4125, "authors_detailed": [ { "name": "Jenny Wu Tucker", "url": "https://openalex.org/A5002871510", "inst": "University of Florida" }, { "name": "Jundong Wang", "url": "https://openalex.org/A5101925770", "inst": "San Diego State University" }, { "name": "Ran Zhao", "url": "https://openalex.org/A5100626037", "inst": "San Diego State University" } ], "affiliations": [ "University of Florida", "San Diego State University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2502.15700v1", "arxiv_id": "2502.15700v1", "title": "Sustainable Digitalization of Business with Multi-Agent RAG and LLM", "authors": [ "Muhammad Arslan", "Saba Munawar", "Christophe Cruz" ], "posted": "2025-01-06", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.15700v1", "field": "management", "role": "method", "bullets": [ "Business information extraction from news articles, financial reports, and consumer reviews, framed against United Nations sustainable development goals; no dataset or sample is specified.", "Pre-existing unnamed LLMs with retrieval augmented generation are organized into specialized agents for retrieval, enrichment, and classification; no accuracy evaluation is reported.", "The paper argues that reusing pretrained models avoids the environmental cost of training new ones; no empirical performance or sustainability measurements are provided." ], "bullet_provenance": "ai", "validated": false, "salience": 22, "edition": 14, "models": [], "n": 1949, "authors_detailed": [ { "name": "Muhammad Arslan", "url": "https://openalex.org/A5080371676", "inst": "Israeli Cultural Institute" }, { "name": "Saba Munawar", "url": "https://openalex.org/A5113425398", "inst": "NUCES" }, { "name": "Christophe Cruz", "url": "https://openalex.org/A5031830209", "inst": "Centre National de la Recherche Scientifique" } ], "affiliations": [ "Israeli Cultural Institute", "NUCES", "Centre National de la Recherche Scientifique" ] }, { "uid": "doi:10.2139/ssrn.5057769", "doi": "10.2139/ssrn.5057769", "title": "Large Language Models for Market Research: A Data-augmentation Approach", "authors": [ "Mengxin Wang", "Dennis Zhang", "Heng Zhang" ], "posted": "2025-01-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5057769", "field": "management", "role": "method", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 655, "authors_detailed": [ { "name": "Mengxin Wang", "url": "https://openalex.org/A5054100235", "inst": "The University of Texas at Dallas" }, { "name": "Dennis Zhang", "url": "https://openalex.org/A5084405003", "inst": "Washington University in St. Louis" }, { "name": "Heng Zhang", "url": "https://openalex.org/A5031674072", "inst": "Supply Chain Management Department - W.P.Carey School of Business" } ], "affiliations": [ "The University of Texas at Dallas", "Washington University in St. Louis", "Supply Chain Management Department - W.P.Carey School of Business" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5036687", "doi": "10.2139/ssrn.5036687", "title": "A Comparison of Artificial Intelligence and Human Responses in Audit Experiments", "authors": [ "Nikki MacKenzie", "James Moon", "Susan Rykowski", "Quinn Thomas Swanquist", "Robert Lowell Whited" ], "posted": "2025-01-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5036687", "field": "accounting", "role": "agent", "bullets": [ "Published audit experiments used as benchmarks to compare ChatGPT responses against human auditor response patterns in ill-structured tasks.", "ChatGPT completed audit judgment tasks; responses evaluated for reproduction of motivational biases and information-weighting patterns seen in auditors.", "ChatGPT does not reproduce motivational biases but partially replicates information-weighting patterns observed in human auditors." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison to published audit experiment response patterns", "salience": 65, "n": 2415, "authors_detailed": [ { "name": "Nikki L. MacKenzie", "url": "https://openalex.org/A5025127534", "inst": "Georgia Institute of Technology" }, { "name": "James Moon", "url": "https://openalex.org/A5058440382", "inst": "Georgia Institute of Technology" }, { "name": "Susan Rykowski", "url": "https://openalex.org/A5115782084", "inst": "University of Alabama" }, { "name": "Quinn Thomas Swanquist", "url": "https://openalex.org/A5013619831", "inst": "University of Alabama" }, { "name": "Robert Lowell Whited", "url": "https://openalex.org/A5053543296", "inst": "North Carolina State University" } ], "affiliations": [ "Georgia Institute of Technology", "University of Alabama", "North Carolina State University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2501.02237v1", "arxiv_id": "2501.02237v1", "title": "Financial Named Entity Recognition: How Far Can LLM Go?", "authors": [ "Yi-Te Lu", "Yintong Huo" ], "posted": "2025-01-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.02237v1", "field": "finance", "role": "method", "bullets": [ "State-of-the-art LLMs evaluated on financial named entity recognition across financial statements, announcements, and business news.", "Multiple LLMs tested with various prompting methods on financial NER; five representative failure types identified and categorized.", "Results revealed strengths and limitations of generic LLMs for domain-specific financial NER under different prompt configurations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Financial NER benchmark evaluation", "salience": 50, "n": 2571, "authors_detailed": [ { "name": "Yingdong Lu", "url": "https://openalex.org/A5082596545", "inst": "IBM (United States)" }, { "name": "Yintong Huo", "url": "https://openalex.org/A5080873193", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "IBM (United States)", "Chinese University of Hong Kong" ] }, { "uid": "arxiv:2501.06211v1", "arxiv_id": "2501.06211v1", "title": "FLAME: Financial Large-Language Model Assessment and Metrics Evaluation", "authors": [ "Jiayu Guo", "Yu Guo", "Martha Li", "Songtao Tan" ], "posted": "2025-01-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.06211v1", "field": "finance", "role": "method", "bullets": [ "Chinese-language financial LLM benchmark covering 14 certification types including CPA, CFA, and FRM with approximately 16,000 manually reviewed questions.", "Six LLMs including GPT-4o, Qwen2.5, and Baichuan4-Finance evaluated on certification exams and 10 core financial business scenario tasks.", "Baichuan4-Finance outperformed other LLMs on most tasks; the benchmark establishes a comprehensive evaluation system for Chinese financial LLM development." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "16,000 certification exam questions and financial scenario benchmarks", "salience": 50, "n": 2653 }, { "uid": "arxiv:2501.02031v1", "arxiv_id": "2501.02031v1", "title": "CarbonChat: Large Language Model-Based Corporate Carbon Emission Analysis and Climate Knowledge Q&A System", "authors": [ "Zhixuan Cao", "Ming Han", "Jingtao Wang", "Meng Jia" ], "posted": "2025-01-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.02031v1", "field": "accounting", "role": "instrument", "bullets": [ "Corporate sustainability reports and climate policy documents analyzed for carbon emission metrics across 14 greenhouse-gas-accounting dimensions.", "LLM with enhanced RAG architecture integrating intent recognition, structured reasoning chains, hybrid retrieval, and Text2SQL for carbon Q&A.", "Multi-layer chunking and hallucination detection reduce error rates in automated corporate carbon emission analysis and policy interpretation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 40, "n": 2843, "authors_detailed": [ { "name": "Cao, Zhixuan", "url": "", "inst": "" }, { "name": "Ming Han", "url": "https://openalex.org/A5101240388", "inst": "Qingdao National Laboratory for Marine Science and Technology" }, { "name": "Jingtao Wang", "url": "https://openalex.org/A5038920879", "inst": "Fujian Normal University" }, { "name": "Meng Jia", "url": "https://openalex.org/A5100542296", "inst": "Zunyi Medical University" } ], "affiliations": [ "Qingdao National Laboratory for Marine Science and Technology", "Fujian Normal University", "Zunyi Medical University" ] }, { "uid": "doi:10.1016/j.techfore.2024.123965", "doi": "10.1016/j.techfore.2024.123965", "arxiv_id": "2501.01763v1", "title": "Quantifying A Firm's AI Engagement: Constructing Objective, Data-Driven, AI Stock Indices Using 10-K Filings", "authors": [ "Lennart Ante", "Aman Saggu" ], "posted": "2025-01-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.01763v1", "field": "finance", "role": "instrument", "bullets": [ "Annual 10-K filings from 3,395 NASDAQ-listed firms between 2011 and 2023, measuring AI engagement through term frequency and context.", "NLP techniques classify firms by AI engagement using binary indicators and weighted scores; validated via event study on ChatGPT launch.", "Four constructed AI indices match or surpass 14 existing AI-themed ETFs in risk-adjusted returns; higher-engagement firms saw greater positive abnormal returns at ChatGPT launch." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": true, "validation_note": "ChatGPT launch event study with abnormal returns", "salience": 55, "n": 3467, "authors_detailed": [ { "name": "Lennart Ante", "url": "https://openalex.org/A5034620178", "inst": "Constructing Excellence" }, { "name": "Aman Saggu", "url": "https://openalex.org/A5068688584", "inst": "Mahidol University" } ], "affiliations": [ "Constructing Excellence", "Mahidol University" ] }, { "uid": "arxiv:2501.00826v3", "arxiv_id": "2501.00826v3", "title": "LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management", "authors": [ "Yichen Luo", "Yebo Feng", "Jiahua Xu", "Paolo Tasca", "Yang Liu" ], "posted": "2025-01-01", "added": "2026-08-06", "source_label": "arXiv", "url": "https://arxiv.org/abs/2501.00826v3", "field": "finance", "role": "agent", "bullets": [ "A 52 week backtest over calendar 2025 on the 15 largest layer one cryptocurrencies by market capitalisation, fusing price and on chain series, weekly news text, and technical indicators.", "Three specialised agents for market dynamics, news sentiment, and trade execution coordinate under hierarchical, collaborative, or debate structures; backbones include GPT-4o, GPT-5, and Claude Sonnet 4.5, with zero shot, chain of thought, RAG, and skill variants.", "The hierarchical skill configuration returns 133.52 percent cumulative with a 1.502 Sharpe ratio, beating single agents, passive benchmarks, and deep learning baselines; dropping the crypto agent cuts returns by 42.57 points." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 45, "edition": 14, "validated": null, "n": 1822, "authors_detailed": [ { "name": "Yichen Luo", "url": "https://openalex.org/A5095787352", "inst": "" }, { "name": "Yebo Feng", "url": "https://openalex.org/A5026905803", "inst": "Nanyang Technological University" }, { "name": "Jiahua Xu", "url": "https://openalex.org/A5108876548", "inst": "Exponent (United States)" }, { "name": "Paolo Tasca", "url": "https://openalex.org/A5074632640", "inst": "Exponent (United States)" }, { "name": "Yang Liu", "url": "https://openalex.org/A5106669665", "inst": "Chung-Ang University" } ], "affiliations": [ "Nanyang Technological University", "Exponent (United States)", "Chung-Ang University" ] }, { "uid": "arxiv:2412.20138v7", "arxiv_id": "2412.20138v7", "title": "TradingAgents: Multi-Agents LLM Financial Trading Framework", "authors": [ "Yijia Xiao", "Edward Sun", "Di Luo", "Wei Wang" ], "posted": "2024-12-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.20138v7", "field": "finance", "role": "agent", "bullets": [ "A simulated trading firm for stocks, staffed by LLM agents in roles including fundamental, sentiment, and technical analysts, traders with varied risk appetites, and a risk management team.", "Bull and bear researcher agents debate market conditions, and traders synthesize the debates with historical data into decisions. Which LLMs power the agents is not stated.", "Backtests report gains over baseline models in cumulative return, Sharpe ratio, and maximum drawdown. Exact figures are not stated in the abstract." ], "bullet_provenance": "ai", "salience": 46, "edition": 13, "models": [], "validated": null, "n": 1682, "authors_detailed": [ { "name": "Xiao, Yijia", "url": "", "inst": "" }, { "name": "Edward W. Sun", "url": "https://openalex.org/A5053020626", "inst": "Kedge Business School" }, { "name": "Di Luo", "url": "https://openalex.org/A5041400177", "inst": "University of Dundee" }, { "name": "Wei Wang", "url": "https://openalex.org/A5023022796", "inst": "Qufu Normal University" } ], "affiliations": [ "Kedge Business School", "University of Dundee", "Qufu Normal University" ] }, { "uid": "arxiv:2412.20072v2", "arxiv_id": "2412.20072v2", "title": "Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset", "authors": [ "Chongjian Yue", "Xinrun Xu", "Xiaojun Ma", "Lun Du", "Zhiming Ding", "Shi Han", "Dongmei Zhang", "Qi Zhang" ], "posted": "2024-12-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.20072v2", "field": "finance", "role": "method", "bullets": [ "Hybrid long documents mixing narrative and tables beyond LLM context limits, with a financial reports numerical extraction dataset (FINE) introduced for evaluation.", "An automated information extraction framework selects, summarizes and serializes content before the LLM reads it; the abstract does not name which models are tested.", "Simple table serialization is sufficient for table understanding, and careful selection plus prompt engineering carries the framework across complex scenarios; accuracy figures are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FINE labeled extraction dataset; figures not in abstract", "salience": 33, "edition": 13, "models": [], "n": 1711, "authors_detailed": [ { "name": "Chongjian Yue", "url": "https://openalex.org/A5051815372", "inst": "Peking University" }, { "name": "Xinrun Xu", "url": "https://openalex.org/A5104264437", "inst": "Chinese Academy of Sciences" }, { "name": "Xiaojun Ma", "url": "https://openalex.org/A5100957906", "inst": "Microsoft Research (United Kingdom)" }, { "name": "Lun Du", "url": "https://openalex.org/A5008387608", "inst": "Peking University" }, { "name": "Zhiming Ding", "url": "https://openalex.org/A5057841485", "inst": "Chinese Academy of Sciences" }, { "name": "Shi Han", "url": "https://openalex.org/A5006300825", "inst": "Microsoft Research Asia (China)" }, { "name": "Dongmei Zhang", "url": "https://openalex.org/A5101567180", "inst": "Hebei Agricultural University" }, { "name": "Qi Zhang", "url": "https://openalex.org/A5049552955", "inst": "Wuhan University of Technology" } ], "affiliations": [ "Peking University", "Chinese Academy of Sciences", "Microsoft Research (United Kingdom)", "Microsoft Research Asia (China)", "Hebei Agricultural University", "Wuhan University of Technology" ] }, { "uid": "doi:10.1080/00036846.2025.2450384", "doi": "10.1080/00036846.2025.2450384", "arxiv_id": "2412.19784v4", "title": "Can AI Help with Your Personal Finances?", "authors": [ "Oudom Hean", "Utsha Saha", "Binita Saha" ], "posted": "2024-12-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.19784v4", "field": "finance", "role": "method", "bullets": [ "Personal finance questions on mortgages, taxes, loans and investments in the United States, posed to several leading chat models.", "ChatGPT, Gemini, Claude and Llama responses are scored for accuracy; the paper reports roughly 70 percent average accuracy but does not describe the grading benchmark in the abstract.", "Accuracy varies substantially across topics and falls on complex financial queries, while newer versions of each model improve on their predecessors." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "advice responses scored for accuracy, grading benchmark not described", "salience": 42, "edition": 13, "n": 1524, "authors_detailed": [ { "name": "Oudom Hean", "url": "https://openalex.org/A5090726375", "inst": "Dakota State University" }, { "name": "Utsha Saha", "url": "https://openalex.org/A5093203770", "inst": "Dakota State University" }, { "name": "Binita Saha", "url": "https://openalex.org/A5114221495", "inst": "Dakota State University" } ], "affiliations": [ "Dakota State University" ] }, { "uid": "doi:10.1016/j.frl.2024.105227", "doi": "10.1016/j.frl.2024.105227", "arxiv_id": "2412.19245v1", "title": "Sentiment trading with large language models", "authors": [ "Kemal Kirtac", "Guido Germano" ], "posted": "2024-12-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.19245v1", "field": "finance", "role": "instrument", "bullets": [ "965,375 US financial news articles from January 2010 to June 2023, scored for sentiment and matched to daily stock returns.", "Compares OPT, BERT, and FinBERT against the Loughran-McDonald dictionary; OPT classifies sentiment at 74.4 percent accuracy versus 50.1 percent for the dictionary, though the abstract does not describe the ground truth labels.", "OPT scores predict next-day returns with coefficients near 0.27, and a long-short strategy on them earns a Sharpe ratio of 3.05 against 1.23 for the dictionary." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "sentiment accuracy figures reported, labels not described in abstract", "salience": 55, "edition": 13, "n": 1589, "authors_detailed": [ { "name": "Kemal Kirtac", "url": "https://openalex.org/A5108909107", "inst": "University College London" }, { "name": "Guido Germano", "url": "https://openalex.org/A5066615785", "inst": "Systemic Risk Centre" } ], "affiliations": [ "University College London", "Systemic Risk Centre" ] }, { "uid": "doi:10.2139/ssrn.5053915", "doi": "10.2139/ssrn.5053915", "title": "Socratic Iterative Prompt Engineering: Enhancing Large Language Model Decision-Making and Reasoning in the Beer Game Supply Chain", "authors": [ "Leonard Boussioux", "Hongyu Chen", "Ming Fan", "Apurva Jain" ], "posted": "2024-12-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5053915", "field": "management", "role": "method", "bullet_provenance": "none", "salience": 38, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 1031, "authors_detailed": [ { "name": "Léonard Boussioux", "url": "https://openalex.org/A5036794043", "inst": "University of Washington" }, { "name": "Hongyu Chen", "url": "https://openalex.org/A5115601817", "inst": "University of Washington" }, { "name": "Ming Fan", "url": "https://openalex.org/A5006907190", "inst": "University of Washington" }, { "name": "Apurva Jain", "url": "https://openalex.org/A5103349948", "inst": "University of Washington" } ], "affiliations": [ "University of Washington" ] }, { "uid": "arxiv:2412.19140v1", "arxiv_id": "2412.19140v1", "title": "SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis", "authors": [ "Senbin Zhu", "Chenyuan He", "Hongde Liu", "Pengcheng Dong", "Hanjie Zhao", "Yuchen Yan", "Yuxiang Jia", "Hongying Zan", "Min Peng" ], "posted": "2024-12-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.19140v1", "field": "finance", "role": "instrument", "bullets": [ "English and Chinese financial entity-level sentiment datasets constructed from financial texts, with a cryptocurrency market case study.", "Fine-tuned base LLM generates pseudo-labeled data; a GNN-based correction model applies self-aware in-context learning for entity-level sentiment classification.", "Achieved state-of-the-art performance on both new datasets and demonstrated practical monitoring utility in the cryptocurrency market." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "SOTA on newly constructed entity-level financial sentiment datasets", "salience": 45, "n": 3466, "authors_detailed": [ { "name": "Senbin Zhu", "url": "https://openalex.org/A5102543288", "inst": "Artificial Intelligence in Medicine (Canada)" }, { "name": "Chenyuan He", "url": "https://openalex.org/A5109768010", "inst": "Zhengzhou University" }, { "name": "Hongde Liu", "url": "https://openalex.org/A5102983253", "inst": "Chinese Academy of Sciences" }, { "name": "Pengcheng Dong", "url": "https://openalex.org/A5101724453", "inst": "Shandong University" }, { "name": "Hanjie Zhao", "url": "https://openalex.org/A5101091235", "inst": "Zhengzhou University" }, { "name": "Yuchen Yan", "url": "https://openalex.org/A5087595253", "inst": "Society of Automotive Engineers International" }, { "name": "Yuxiang Jia", "url": "https://openalex.org/A5064013704", "inst": "Zhengzhou University" }, { "name": "Hongying Zan", "url": "https://openalex.org/A5062969664", "inst": "Zhengzhou University" }, { "name": "Min Peng", "url": "https://openalex.org/A5079426884", "inst": "Yunnan University" } ], "affiliations": [ "Artificial Intelligence in Medicine (Canada)", "Zhengzhou University", "Chinese Academy of Sciences", "Shandong University", "Society of Automotive Engineers International", "Yunnan University" ] }, { "uid": "arxiv:2412.18174v1", "arxiv_id": "2412.18174v1", "title": "INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent", "authors": [ "Haohang Li", "Yupeng Cao", "Yangyang Yu", "Shashidhar Reddy Javaji", "Zhiyang Deng", "Yueru He", "Yuechen Jiang", "Zining Zhu", "Koduvayur Subbalakshmi", "Guojun Xiong", "Jimin Huang", "Lingfei Qian", "Xueqing Peng", "Qianqian Xie", "Jordan W. Suchow" ], "posted": "2024-12-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.18174v1", "field": "finance", "role": "method", "bullets": [ "InvestorBench, a benchmark of financial decision tasks covering single stocks, cryptocurrencies, and ETFs, assembled from open source multi modal datasets and market environments.", "Thirteen backbone LLMs, not named in the abstract, drive one agent framework whose reasoning and trading decisions are scored across tasks and market conditions.", "The contribution is the standardized evaluation platform for financial agents; the abstract itself reports no headline performance ranking across the thirteen backbone models." ], "bullet_provenance": "ai", "salience": 50, "edition": 13, "models": [], "validated": null, "n": 1765, "authors_detailed": [ { "name": "Haohang Li", "url": "https://openalex.org/A5027371293", "inst": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Yupeng Cao", "url": "https://openalex.org/A5009376964", "inst": "Kyoto University" }, { "name": "Yangyang Yu", "url": "https://openalex.org/A5069731031", "inst": "Jilin University of Finance and Economics" }, { "name": "Shashidhar Reddy Javaji", "url": "https://openalex.org/A5115647631", "inst": "Stevens Institute of Technology" }, { "name": "Zhiyang Deng", "url": "https://openalex.org/A5069290428", "inst": "Hong Kong Baptist University" }, { "name": "Yueru He", "url": "https://openalex.org/A5111133467", "inst": "Columbia University" }, { "name": "Jiang, Yuechen", "url": "", "inst": "" }, { "name": "Zining Zhu", "url": "https://openalex.org/A5114418716", "inst": "Nanjing University of Chinese Medicine" }, { "name": "K. P. Subbalakshmi", "url": "https://openalex.org/A5033041089", "inst": "Stevens Institute of Technology" }, { "name": "Guojun Xiong", "url": "https://openalex.org/A5102620407", "inst": "Harvard University" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" }, { "name": "Lingfei Qian", "url": "https://openalex.org/A5009941118", "inst": "Yale University" }, { "name": "Xueqing Peng", "url": "https://openalex.org/A5036053506", "inst": "Finlay Institute" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Jordan W. Suchow", "url": "https://openalex.org/A5069454833", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Columbia University", "Harvard University", "Nanjing University of Aeronautics and Astronautics", "Kyoto University", "Jilin University of Finance and Economics", "Stevens Institute of Technology", "Hong Kong Baptist University", "Nanjing University of Chinese Medicine" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5069656", "doi": "10.2139/ssrn.5069656", "title": "Ai Agents in Multi-Criteria Decision Analysis: Automating the Analytic Hierarchy Process with Large Language Models", "authors": [ "Igor Svoboda", "Dmytro Lande" ], "posted": "2024-12-23", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5069656", "field": "management", "role": "method", "bullet_provenance": "none", "salience": 30, "edition": 23, "bullets": [], "models": [], "validated": null, "n": 4188, "authors_detailed": [ { "name": "Igor Svoboda", "url": "https://openalex.org/A5093925843", "inst": "National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”" }, { "name": "Dmytro Lande", "url": "https://openalex.org/A5079401596", "inst": "National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”" } ], "affiliations": [ "National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”" ] }, { "uid": "arxiv:2412.16922v1", "arxiv_id": "2412.16922v1", "title": "Enhancing Supply Chain Transparency in Emerging Economies Using Online Contents and LLMs", "authors": [ "Bohan Jin", "Qianyou Sun", "Lihua Chen" ], "posted": "2024-12-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.16922v1", "field": "management", "role": "instrument", "bullets": [ "Online content on supplier relationships in mainland China's semiconductor industry, aimed at gaps that Bloomberg and FactSet leave in emerging economies.", "A web crawler feeds LLMs, not named in the abstract, that assemble a supply chain knowledge graph; no accuracy check against ground truth is reported.", "The system recovers supplier links absent from commercial datasets, while monetary and material flows, time series, synonyms, and online content bias remain open problems." ], "bullet_provenance": "ai", "validated": false, "salience": 46, "edition": 13, "models": [], "n": 1764, "authors_detailed": [ { "name": "Jin, Bohan", "url": "", "inst": "" }, { "name": "Sun, Qianyou", "url": "", "inst": "" }, { "name": "Lihua Chen", "url": "https://openalex.org/A5100354342", "inst": "Shihezi University" } ], "affiliations": [ "Shihezi University" ] }, { "uid": "arxiv:2412.17114v3", "arxiv_id": "2412.17114v3", "title": "Decentralized Governance of Autonomous AI Agents", "authors": [ "Tomer Jordi Chaffer", "Charles von Goins", "Bayo Okusanya", "Dontrail Cotlage", "Justin Goldston" ], "posted": "2024-12-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.17114v3", "field": "management", "role": "object", "bullets": [ "Proposed ETHOS framework for decentralized governance of autonomous AI agents using Web3 and blockchain technologies.", "Framework designs a global AI agent registry with dynamic risk classification, soulbound tokens, and zero-knowledge proofs for compliance.", "Introduces AI-specific legal entities with mandatory insurance and decentralized justice; conceptual framework with no empirical test." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3914, "authors_detailed": [ { "name": "Tomer Jordi Chaffer", "url": "https://openalex.org/A5051668606", "inst": "McGill University Health Centre" }, { "name": "Goins, Charles von", "url": "", "inst": "" }, { "name": "Bayo Okusanya", "url": "https://openalex.org/A5002838276", "inst": "" }, { "name": "Cotlage, Dontrail", "url": "", "inst": "" }, { "name": "Goldston, Justin", "url": "", "inst": "" } ], "affiliations": [ "McGill University Health Centre" ] }, { "uid": "doi:10.18653/v1/2024.emnlp-industry.88", "doi": "10.18653/v1/2024.emnlp-industry.88", "arxiv_id": "2412.15386v1", "title": "Systematic Evaluation of Long-Context LLMs on Financial Concepts", "authors": [ "Lavanya Gupta", "Saket Sharma", "Yiyun Zhao" ], "posted": "2024-12-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.15386v1", "field": "finance", "role": "method", "bullets": [ "A constructed real-world financial news dataset with progressively harder tasks, varying context length, task difficulty, and the position of key information in the window.", "Long-context models from the GPT-4 suite are evaluated, with ablations on instruction placement and minor markdown formatting, and F1 with confidence intervals advocated over recall.", "Performance is brittle at long context even on simple tasks, drops sharply as complexity rises, and instruction following collapses into degenerate outputs at the longest lengths." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "constructed financial news task suite, F1", "salience": 45, "edition": 13, "n": 1681, "authors_detailed": [ { "name": "Lavanya Gupta", "url": "https://openalex.org/A5104078175", "inst": "Kokilaben Dhirubhai Ambani Hospital" }, { "name": "Saket Sharma", "url": "", "inst": "" }, { "name": "Yiyun Zhao", "url": "https://openalex.org/A5068039283", "inst": "JPMorgan Chase & Co (United States)" } ], "affiliations": [ "JPMorgan Chase & Co (United States)" ] }, { "uid": "doi:10.2139/ssrn.5004249", "doi": "10.2139/ssrn.5004249", "title": "Shifting Standards or Changing Preferences? Unraveling Review Polarization via LLMs", "authors": [ "Limin Fang", "Chunhua Wu", "Baohong Sun" ], "posted": "2024-12-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5004249", "field": "management", "role": "instrument", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 654, "authors_detailed": [ { "name": "Limin Fang", "url": "https://openalex.org/A5018459920", "inst": "University of British Columbia" }, { "name": "Chunhua Wu", "url": "https://openalex.org/A5073927333", "inst": "University of British Columbia" }, { "name": "Baohong Sun", "url": "https://openalex.org/A5112257801", "inst": "Cheung Kong Graduate School of Business (New York)" } ], "affiliations": [ "University of British Columbia", "Cheung Kong Graduate School of Business (New York)" ] }, { "uid": "doi:10.2139/ssrn.5062344", "doi": "10.2139/ssrn.5062344", "title": "Homo-Silicus: Not (Yet) a Good Imitator of Homo Sapiens or Homo Economicus", "authors": [ "Solomon W. Polachek", "Kenny Romano", "Ozlem Tonguc" ], "posted": "2024-12-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5062344", "field": "economics", "role": "agent", "bullets": [ "Prisoner's Dilemma experiments with varying stake sizes replicating Yamagishi et al. (2016) Study 2 using three LLMs as simulated players.", "ChatGPT 3.5, ChatGPT 4.0, and Gemini 1.0 Pro played iterated PD games; cooperation rates compared to human experimental benchmarks.", "LLMs mirror human behavior only under very specific conditions; stake size and order sensitivity suggest caution in LLM-based behavioral research." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "comparison to Yamagishi et al. (2016) human PD results", "salience": 55, "n": 2414, "authors_detailed": [ { "name": "Solomon W. Polachek", "url": "https://openalex.org/A5020087732", "inst": "Binghamton University" }, { "name": "Kenny Romano", "url": "https://openalex.org/A5115534414", "inst": "Binghamton University" }, { "name": "Ozlem Tonguc", "url": "https://openalex.org/A5115534415", "inst": "Binghamton University" } ], "affiliations": [ "Binghamton University" ] }, { "uid": "arxiv:2412.13013v4", "arxiv_id": "2412.13013v4", "title": "The Emergence of Strategic Reasoning of Large Language Models", "authors": [ "Gavin Kader", "Dongwoo Lee" ], "posted": "2024-12-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.13013v4", "field": "economics", "role": "agent", "bullets": [ "Three classical behavioral economics games serve as the test bed, with LLM play scored against human performance benchmarks using hierarchical models of bounded rationality.", "ChatGPT-4, Claude 3.5 Sonnet and Gemini 1.5 are compared with reasoning models o1, Claude 4 Sonnet Thinking and Gemini Flash Thinking 2.0; no validation beyond the game-theoretic scoring applies.", "Reasoning models often match or beat human play while standard models show little strategic capability, which the authors call the first documented transition in LLM strategic reasoning." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 60, "edition": 13, "validated": null, "n": 1521, "authors_detailed": [ { "name": "Gavin Kader", "url": "https://openalex.org/A5068268551", "inst": "Southwestern University of Finance and Economics" }, { "name": "Duyong LEE", "url": "https://openalex.org/A5030512060", "inst": "" } ], "affiliations": [ "Southwestern University of Finance and Economics" ] }, { "uid": "arxiv:2412.13018v2", "arxiv_id": "2412.13018v2", "title": "OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain", "authors": [ "Shuting Wang", "Jiejun Tan", "Zhicheng Dou", "Ji-Rong Wen" ], "posted": "2024-12-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.13018v2", "field": "finance", "role": "method", "bullets": [ "Chinese-language financial domain benchmark spanning five task classes and 16 financial topics, built to evaluate retrieval-augmented generation pipelines end to end.", "GPT-4 generates evaluation instances that human annotators accept 87 percent of the time; scoring combines rule-based metrics with a fine-tuned LLM evaluator.", "Tested RAG systems perform unevenly across topics and tasks, which the authors read as sizable headroom for retrieval-augmented setups in specialist financial domains." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "human review of generated eval items, 87 percent acceptance", "salience": 30, "edition": 13, "n": 1588, "authors_detailed": [ { "name": "Shuting Wang", "url": "https://openalex.org/A5100422433", "inst": "Yulin University" }, { "name": "Jiejun Tan", "url": "https://openalex.org/A5062990837", "inst": "Tibet Autonomous Region People's Hospital" }, { "name": "Zhicheng Dou", "url": "https://openalex.org/A5010558184", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Ji-Rong Wen", "url": "https://openalex.org/A5025631695", "inst": "Beijing Academy of Artificial Intelligence" } ], "affiliations": [ "Yulin University", "Beijing Academy of Artificial Intelligence" ] }, { "uid": "arxiv:2412.15270v2", "arxiv_id": "2412.15270v2", "title": "Baichuan4-Finance Technical Report", "authors": [ "Hanyu Zhang", "Boyu Qiu", "Yuhao Feng", "Shuqi Li", "Qian Ma", "Xiyuan Zhang", "Qiang Ju", "Dong Yan", "Jian Xie" ], "posted": "2024-12-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.15270v2", "field": "finance", "role": "method", "bullets": [ "A Chinese financial corpus built through a dedicated data quality pipeline supports continual pretraining on the Baichuan4-Turbo base model.", "Baichuan4-Finance combines a domain self constraint pretraining strategy with supervised fine tuning and reinforcement learning from human and AI feedback; evaluation spans two financial benchmarks and general suites.", "The finance models are reported to beat almost all baselines on financial tasks by significant margins without losing general ability; specific figures are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "two financial benchmarks and general suites; figures not in abstract", "salience": 42, "edition": 13, "n": 1710, "authors_detailed": [ { "name": "Hanyu Zhang", "url": "https://openalex.org/A5100778118", "inst": "Jiangsu University" }, { "name": "Qiu, Boyu", "url": "", "inst": "" }, { "name": "Yuhao Feng", "url": "https://openalex.org/A5053412652", "inst": "China Pharmaceutical University" }, { "name": "Shuqi Li", "url": "https://openalex.org/A5100675758", "inst": "National University of Singapore" }, { "name": "Qian Ma", "url": "https://openalex.org/A5101768187", "inst": "Cardiff University" }, { "name": "Xiyuan Zhang", "url": "https://openalex.org/A5100725660", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Qiang Ju", "url": "https://openalex.org/A5101056898", "inst": "Renji Hospital" }, { "name": "Yan Dong", "url": "https://openalex.org/A5102811992", "inst": "Hefei University of Technology" }, { "name": "Jian Xie", "url": "https://openalex.org/A5022490482", "inst": "" } ], "affiliations": [ "Jiangsu University", "China Pharmaceutical University", "National University of Singapore", "Cardiff University", "Rutgers, The State University of New Jersey", "Hefei University of Technology" ] }, { "uid": "arxiv:2412.12610v2", "arxiv_id": "2412.12610v2", "title": "Gender Bias and Property Taxes", "authors": [ "Gordon Burtch", "Alejandro Zentner" ], "posted": "2024-12-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.12610v2", "field": "economics", "role": "instrument", "bullets": [ "Over 100,000 property tax appeal hearings with 2.7 years of audio recordings, examining gender bias in outcomes across panelist-appellant gender pairs.", "Multimodal LLM constructed measures of participant behavior and tone from hearing audio, enabling large-scale analysis of gendered interaction patterns.", "Female appellants fared worse overall; they received lesser reductions from female panels, while male appellants behaved more aggressively toward female panelists." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 55, "n": 2969, "authors_detailed": [ { "name": "Gordon Burtch", "url": "https://openalex.org/A5057319034", "inst": "Boston University" }, { "name": "Alejandro Zentner", "url": "https://openalex.org/A5065572995", "inst": "HTW Berlin - University of Applied Sciences" } ], "affiliations": [ "Boston University", "HTW Berlin - University of Applied Sciences" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2412.12567v4", "arxiv_id": "2412.12567v4", "title": "FCMR: Robust Evaluation of Financial Cross-Modal Multi-Hop Reasoning", "authors": [ "Seunghee Kim", "Changhyeon Kim", "Taeuk Kim" ], "posted": "2024-12-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.12567v4", "field": "finance", "role": "method", "bullets": [ "Financial domain benchmark using textual reports, tables, and charts requiring cross-modal multi-hop reasoning at easy, medium, and hard difficulty levels.", "Multiple MLLMs including Claude 3.5 Sonnet, GPT-4, and Gemini were evaluated on financial reasoning tasks requiring integration across three modalities.", "Best model (Claude 3.5 Sonnet) achieved only 30.4% accuracy on the hardest tier; information retrieval was identified as the critical performance bottleneck." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "FCMR benchmark accuracy across difficulty tiers", "salience": 45, "n": 2970, "authors_detailed": [ { "name": "Seunghee Kim", "url": "https://openalex.org/A5035048401", "inst": "" }, { "name": "Changhyeon Kim", "url": "https://openalex.org/A5030408465", "inst": "" }, { "name": "Kim, Taeuk", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5049032", "doi": "10.2139/ssrn.5049032", "title": "Corporate Opposition to Climate Change Disclosure Regulation in the United States", "authors": [ "Addisu Lashitew", "Youqing Mu" ], "posted": "2024-12-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5049032", "field": "accounting", "role": "instrument", "bullets": [ "146 large U.S. corporations' comment letters to the SEC on the proposed 2024 climate-related disclosure regulation.", "GPT-3 performed sentiment analysis on corporate comment letters to measure opposition to the SEC's climate disclosure rule.", "Average sentiment was positive toward the regulation; energy firms showed highest opposition; higher Scope 1 GHG emissions predicted greater resistance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "n": 3465, "authors_detailed": [ { "name": "Addisu A. Lashitew", "url": "https://openalex.org/A5087976393", "inst": "Ministry of Economy" }, { "name": "Youqing Mu", "url": "https://openalex.org/A5091221893", "inst": "McMaster University" } ], "affiliations": [ "Ministry of Economy", "McMaster University" ] }, { "uid": "arxiv:2412.11698v2", "arxiv_id": "2412.11698v2", "title": "On Large Language Models in Mission-Critical IT Governance: Are We Ready Yet?", "authors": [ "Matteo Esposito", "Francesco Palagiano", "Valentina Lenarduzzi", "Davide Taibi" ], "posted": "2024-12-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.11698v2", "field": "management", "role": "object", "bullets": [ "Survey of practitioners who build and run security solutions for mission-critical IT systems; respondent counts and geography are not stated in the abstract.", "No model is applied; the study elicits practitioner experiences, concerns, and expectations about integrating generative AI into governance and risk analysis for critical systems.", "Practitioners stress data protection and transparency, and the authors call for interdisciplinary work, regulation-oriented models, and a unified AI framework with global benchmarks." ], "bullet_provenance": "ai", "salience": 25, "edition": 13, "models": [], "validated": null, "n": 1587, "authors_detailed": [ { "name": "Matteo Esposito", "url": "https://openalex.org/A5065369537", "inst": "University of Oulu" }, { "name": "Francesco Palagiano", "url": "https://openalex.org/A5063972501", "inst": "Azienda Sanitaria Locale Alessandria" }, { "name": "Valentina Lenarduzzi", "url": "https://openalex.org/A5015576503", "inst": "University of Southern Denmark" }, { "name": "Davide Taibi", "url": "https://openalex.org/A5086929289", "inst": "Tampere University" } ], "affiliations": [ "University of Oulu", "Azienda Sanitaria Locale Alessandria", "University of Southern Denmark", "Tampere University" ] }, { "uid": "doi:10.2139/ssrn.5040309", "doi": "10.2139/ssrn.5040309", "title": "Disclosure Presentation Attributes, Generative AI Output, and Investor Judgments", "authors": [ "Joe Croom", "Brian Gale", "Stephanie M. Grant" ], "posted": "2024-12-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5040309", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 48, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 653, "authors_detailed": [ { "name": "Joe Croom", "url": "https://openalex.org/A5099056182", "inst": "Indiana University" }, { "name": "Brian Gale", "url": "https://openalex.org/A5070268309", "inst": "University of Washington" }, { "name": "Stephanie M. Grant", "url": "https://openalex.org/A5047578972", "inst": "University of Washington" } ], "affiliations": [ "Indiana University", "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.5049998", "doi": "10.2139/ssrn.5049998", "title": "Man vs Machine: Can Ai Grade and Give Feedback Like a Human?", "authors": [ "Arnaud Chevalier", "Jakub Orzech", "Petar Stankov" ], "posted": "2024-12-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5049998", "field": "economics", "role": "object", "bullets": [ "Undergraduate students in a randomized controlled trial, assigned to receive grading feedback from a human instructor, ChatGPT 3.5, or ChatGPT 4.", "ChatGPT 3.5 and ChatGPT 4 provided written feedback on student work; separately, both models graded assessments for comparison against human-generated grades.", "ChatGPT 3.5 feedback lowered subsequent grades versus human feedback; ChatGPT 4 showed no penalty; AI grading differed significantly from human grades and rankings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "RCT comparing AI vs human grading and feedback outcomes", "salience": 45, "n": 2968, "authors_detailed": [ { "name": "Arnaud Chevalier", "url": "", "inst": "" }, { "name": "Jakub Orzech", "url": "https://openalex.org/A5115496558", "inst": "" }, { "name": "Petar Stankov", "url": "https://openalex.org/A5115496559", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5059403", "doi": "10.2139/ssrn.5059403", "title": "Generative AI For Predictive Credit Scoring And Lending Decisions Investigating How AI Is Revolutionising Credit Risk Assessments And Automating Loan Approval Processes In Banking", "authors": [ "Archana Todupunuri" ], "posted": "2024-12-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5059403", "field": "finance", "role": "object", "bullets": [ "Banking sector credit risk assessment and loan approval processes examined through secondary data collection and qualitative analysis.", "Paper investigates how generative AI is changing predictive credit scoring and automating lending decisions without specifying particular models.", "GenAI reportedly improves consistency and efficiency in credit risk assessment while raising data privacy and bias concerns in banking." ], "bullet_provenance": "ai", "salience": 10, "models": [], "validated": null, "n": 3464, "authors_detailed": [ { "name": "Archana Todupunuri", "url": "https://openalex.org/A5115464396", "inst": "Jacksonville University" } ], "affiliations": [ "Jacksonville University" ] }, { "uid": "arxiv:2412.11159v2", "arxiv_id": "2412.11159v2", "title": "A Report on Financial Regulations Challenge at COLING 2025", "authors": [ "Keyi Wang", "Jaisal Patel", "Charlie Shen", "Daniel Kim", "Andy Zhu", "Alex Lin", "Luca Borella", "Cailean Osborne", "Matt White", "Steve Yang", "Kairong Xiao", "Xiao-Yang Liu Yanglet" ], "posted": "2024-12-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.11159v2", "field": "finance", "role": "method", "bullets": [ "The Regulations Challenge shared task at COLING 2025, with nine tasks and question sets on financial regulations and standards across business, finance, accounting, and auditing.", "Participant teams applied popular LLMs to the question sets. The overview paper summarizes their approaches and results rather than running its own single model.", "The report takes stock of what current models can and cannot do on regulatory understanding. Individual scores and rankings are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "nine regulation task question sets", "salience": 28, "edition": 13, "models": [], "n": 1680, "authors_detailed": [ { "name": "Keyi Wang", "url": "https://openalex.org/A5076572035", "inst": "South China Agricultural University" }, { "name": "Patel, Jaisal", "url": "", "inst": "" }, { "name": "Shen, Charlie", "url": "", "inst": "" }, { "name": "Daniel Kim", "url": "https://openalex.org/A5100372591", "inst": "Kongju National University" }, { "name": "Zhu, Andy", "url": "", "inst": "" }, { "name": "Alex T.L. Lin", "url": "https://openalex.org/A5110337360", "inst": "Zhujiang Hospital" }, { "name": "Luca Borella", "url": "https://openalex.org/A5115505581", "inst": "" }, { "name": "Cailean Osborne", "url": "https://openalex.org/A5086026854", "inst": "University of Oxford" }, { "name": "Matt White", "url": "https://openalex.org/A5111151871", "inst": "" }, { "name": "Steve Y. Yang", "url": "https://openalex.org/A5080120183", "inst": "Stevens Institute of Technology" }, { "name": "Xiao, Kairong", "url": "", "inst": "" }, { "name": "Yanglet, Xiao-Yang Liu", "url": "", "inst": "" } ], "affiliations": [ "University of Oxford", "South China Agricultural University", "Kongju National University", "Stevens Institute of Technology" ], "prestige": true }, { "uid": "arxiv:2412.11192v1", "arxiv_id": "2412.11192v1", "title": "From Votes to Volatility Predicting the Stock Market on Election Day", "authors": [ "Igor L. R. Azevedo", "Toyotaro Suzumura" ], "posted": "2024-12-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.11192v1", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 stocks on U.S. Election Day, a period of heightened market volatility driven by policy uncertainty from election outcomes.", "LLM-based agents analyzed political and economic consequences of elections to generate stock predictions within a specialized forecasting architecture.", "The Election Day Stock Market Forecasting model improved S&P 500 predictive performance compared to the baseline state-of-the-art architecture on this volatile day." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "S&P 500 prediction accuracy vs baseline", "salience": 30, "n": 2967, "authors_detailed": [ { "name": "Igor L. R. Azevedo", "url": "https://openalex.org/A5108326805", "inst": "The University of Tokyo" }, { "name": "Toyotaro Suzumura", "url": "https://openalex.org/A5011886931", "inst": "The University of Tokyo" } ], "affiliations": [ "The University of Tokyo" ] }, { "uid": "arxiv:2412.10823v2", "arxiv_id": "2412.10823v2", "title": "FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs", "authors": [ "Yixuan Liang", "Yuncong Liu", "Neng Wang", "Hongyang Yang", "Boyu Zhang", "Christina Dan Wang" ], "posted": "2024-12-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.10823v2", "field": "finance", "role": "method", "bullets": [ "Company-related news for short-term stock movement prediction, with recent stories clustered to measure dissemination breadth and enrich prompts with contextual data.", "An LLM is instruction-tuned on dissemination-aware, context-enriched prompts to predict short-term price direction. The base model family is not stated in the abstract.", "The approach improves prediction accuracy by 8 percent over existing methods, with realized short-term price movements as the scoring target." ], "bullet_provenance": "ai", "validated": true, "validation_note": "realized short-term price movements, accuracy reported", "salience": 34, "edition": 13, "models": [], "n": 1678, "authors_detailed": [ { "name": "Liang, Yixuan", "url": "", "inst": "" }, { "name": "Yuncong Liu", "url": "https://openalex.org/A5069149817", "inst": "University College London" }, { "name": "Wang, Neng", "url": "", "inst": "" }, { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "Beijing University of Technology" }, { "name": "Boyu Zhang", "url": "https://openalex.org/A5100714035", "inst": "Electric Power University" }, { "name": "Christina Dan Wang", "url": "https://openalex.org/A5002366400", "inst": "Fudan University" } ], "affiliations": [ "University College London", "Beijing University of Technology", "Electric Power University", "Fudan University" ] }, { "uid": "arxiv:2502.15684v1", "arxiv_id": "2502.15684v1", "title": "An Agent Framework for Real-Time Financial Information Searching with Large Language Models", "authors": [ "Jinzheng Li", "Jingshu Zhang", "Hongguang Li", "Yiqing Shen" ], "posted": "2024-12-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2502.15684v1", "field": "finance", "role": "method", "bullets": [ "Real-time financial question answering over market, stock, and news sources, evaluated on FinSearchBench-24, 1,500 four-choice questions spanning June to October 2024.", "An LLM pre-planner decomposes queries into sub-queries over a graph of data sources, an adaptive rewriter refines them, and temporal weighting ranks results. Model families are not stated.", "The abstract presents the framework and benchmark construction but reports no headline accuracy comparison against baseline search or agent systems." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinSearchBench-24 four-choice benchmark", "salience": 30, "edition": 13, "models": [], "n": 1679, "authors_detailed": [ { "name": "Jinzheng Li", "url": "https://openalex.org/A5102242055", "inst": "Aviation Industry Corporation of China (China)" }, { "name": "Jingshu Zhang", "url": "https://openalex.org/A5068695515", "inst": "SMART Global Holdings (United States)" }, { "name": "Hongguang Li", "url": "https://openalex.org/A5100624098", "inst": "National Earthquake Response Support Service" }, { "name": "Yiqing Shen", "url": "https://openalex.org/A5072622467", "inst": "Johns Hopkins University" } ], "affiliations": [ "Johns Hopkins University", "Aviation Industry Corporation of China (China)", "SMART Global Holdings (United States)", "National Earthquake Response Support Service" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2412.10906v1", "arxiv_id": "2412.10906v1", "title": "SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation", "authors": [ "Qilong Wu", "Xiaoneng Xiang", "Hejia Huang", "Xuan Wang", "Yeo Wei Jie", "Ranjan Satapathy", "Ricardo Shirota Filho", "Bharadwaj Veeravalli" ], "posted": "2024-12-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.10906v1", "field": "finance", "role": "method", "bullets": [ "SusGen-30K, a category balanced instruction dataset covering seven financial NLP tasks plus ESG report generation, released with the TCFD-Bench evaluation set for sustainability reports.", "Open models of 7 to 8B parameters are tuned into the SusGen-GPT suite and scored against GPT-4; a retrieval augmented system wraps the model for report drafting.", "The tuned models claim state of the art on six adapted and two off the shelf tasks while trailing GPT-4 by 2 percent despite the large difference in parameter count." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "financial NLP benchmarks and TCFD-Bench", "salience": 40, "edition": 13, "n": 1709, "authors_detailed": [ { "name": "Qilong Wu", "url": "https://openalex.org/A5040587076", "inst": "Shenyang Aerospace University" }, { "name": "Xiaoneng Xiang", "url": "https://openalex.org/A5112558680", "inst": "China Academy of Space Technology" }, { "name": "Huang, Hejia", "url": "", "inst": "" }, { "name": "Xuan Wang", "url": "https://openalex.org/A5100329006", "inst": "Northwestern Polytechnical University" }, { "name": "Yeo Wei Jie", "url": "https://openalex.org/A5102617540", "inst": "Nanyang Technological University" }, { "name": "Ranjan Satapathy", "url": "https://openalex.org/A5054901394", "inst": "Agency for Science, Technology and Research" }, { "name": "Ricardo Shirota Filho", "url": "https://openalex.org/A5054038637", "inst": "Institute of High Performance Computing" }, { "name": "Bharadwaj Veeravalli", "url": "https://openalex.org/A5070594442", "inst": "National University of Singapore" } ], "affiliations": [ "Shenyang Aerospace University", "China Academy of Space Technology", "Northwestern Polytechnical University", "Nanyang Technological University", "Agency for Science, Technology and Research", "Institute of High Performance Computing", "National University of Singapore" ] }, { "uid": "arxiv:2412.10635v1", "arxiv_id": "2412.10635v1", "title": "Do LLMs Act as Repositories of Causal Knowledge?", "authors": [ "Nick Huntington-Klein", "Eleanor J. Murray" ], "posted": "2024-12-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.10635v1", "field": "economics", "role": "method", "bullets": [ "The Coronary Drug Project, where several published expert lists of confounders provide a ground truth for judging causal variable selection.", "LLMs, families not named in the abstract, label candidate confounders; answers are compared with expert consensus across models, prompts, and answer option orderings.", "Expert chosen confounders are only slightly more likely to be flagged than non confounders, and calls swing with prompts and option order, so automated causal link reporting is not yet reliable." ], "bullet_provenance": "ai", "validated": true, "validation_note": "expert-selected confounders in the Coronary Drug Project", "salience": 58, "edition": 13, "models": [], "n": 1763 }, { "uid": "arxiv:2412.09884v1", "arxiv_id": "2412.09884v1", "title": "Benchmarking Table Comprehension In The Wild", "authors": [ "Yikang Pan", "Yi Zhu", "Rand Xie", "Yizhi Liu" ], "posted": "2024-12-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.09884v1", "field": "finance", "role": "method", "bullets": [ "TableQuest, a question answering benchmark drawn from table rich financial reports, keeping tables in their surrounding document context rather than isolated.", "Seven LLMs, not named in the abstract, answer curated question pairs filtered for logic and diversity; evaluation data and judging procedure are released.", "Models find individual facts with reasonable accuracy yet break down on multi step calculations and heavier reasoning; the abstract reports no numeric scores." ], "bullet_provenance": "ai", "validated": true, "validation_note": "curated QA pairs from financial reports", "salience": 38, "edition": 13, "models": [], "n": 1762, "authors_detailed": [ { "name": "Yi Pan", "url": "https://openalex.org/A5103572130", "inst": "Liaoning Shihua University" }, { "name": "Yi‐Chun Zhu", "url": "https://openalex.org/A5076130185", "inst": "Macau University of Science and Technology" }, { "name": "Rand Xie", "url": "https://openalex.org/A5115460504", "inst": "Bose (United States)" }, { "name": "Yizhi Liu", "url": "https://openalex.org/A5100701317", "inst": "Guizhou University" } ], "affiliations": [ "Liaoning Shihua University", "Macau University of Science and Technology", "Bose (United States)", "Guizhou University" ] }, { "uid": "doi:10.2139/ssrn.5053905", "doi": "10.2139/ssrn.5053905", "title": "Generative AI and Investor Processing of Financial Information", "authors": [ "Elizabeth Blankespoor", "Joe Croom", "Stephanie M. Grant" ], "posted": "2024-12-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5053905", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 55, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 651 }, { "uid": "doi:10.2139/ssrn.5042792", "doi": "10.2139/ssrn.5042792", "title": "An Investigation into How Generative AI Can Improve Auditors' Decisions", "authors": [ "Lori Shefchik Bhaskar", "Andrew Jones", "Kathryn Kadous" ], "posted": "2024-12-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5042792", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 52, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 652, "authors_detailed": [ { "name": "Lori Shefchik Bhaskar", "url": "https://openalex.org/A5018263745", "inst": "Indiana University" }, { "name": "Andrew Jones", "url": "https://openalex.org/A5040203154", "inst": "Indiana University" }, { "name": "Kathryn Kadous", "url": "https://openalex.org/A5080919961", "inst": "Emory University" } ], "affiliations": [ "Emory University", "Indiana University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4993495", "doi": "10.2139/ssrn.4993495", "title": "XBRL-Agent: Leveraging Large Language Models for Financial Report Analysis", "authors": [ "Shijie Han", "Haoqiang Kang", "Bo Jin", "Xiao-Yang Liu", "Steve Y. Yang" ], "posted": "2024-12-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4993495", "field": "accounting", "role": "instrument", "bullets": [ "Evaluation on XBRL financial reports using 500 term explanations, 50 domain questions, 1,000 financial math tests, and 50 numeric queries.", "LLMs augmented with retriever and calculator tools under an agent framework tested on domain knowledge and numeric XBRL extraction tasks.", "Accuracy improved by up to 17% on domain tasks and 42% on numeric tasks versus baseline LLMs, though mathematical calculation gaps remain." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "XBRL domain query and numeric type query accuracy benchmarks", "salience": 55, "n": 2652, "authors_detailed": [ { "name": "Shijie Han", "url": "https://openalex.org/A5100963270", "inst": "Columbia University" }, { "name": "Haoqiang Kang", "url": "https://openalex.org/A5114637336", "inst": "University of California San Diego" }, { "name": "Bo Jin", "url": "https://openalex.org/A5114637337", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Xiao-Yang Liu", "url": "https://openalex.org/A5100405233", "inst": "Columbia University" }, { "name": "Steve Y. Yang", "url": "https://openalex.org/A5080120183", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Columbia University", "University of California San Diego", "Rensselaer Polytechnic Institute", "Stevens Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2412.09859v1", "arxiv_id": "2412.09859v1", "title": "Financial Sentiment Analysis: Leveraging Actual and Synthetic Data for Supervised Fine-tuning", "authors": [ "Abraham Atsiwo" ], "posted": "2024-12-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.09859v1", "field": "finance", "role": "instrument", "bullets": [ "Financial PhraseBank dataset at 50% and 100% annotator agreement levels, English financial sentences.", "BertNSP-finance concatenated short sentences into longer contexts; finbert-lc fine-tuned on actual and synthetic data for sentiment classification.", "Improved accuracy and F1 score over existing fine-tuned financial sentiment models at both agreement levels." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "Financial PhraseBank accuracy and F1", "salience": 35, "n": 3913 }, { "uid": "arxiv:2412.09394v2", "arxiv_id": "2412.09394v2", "title": "LLMs for Time Series: an Application for Single Stocks and Statistical Arbitrage", "authors": [ "Sebastien Valeyre", "Sofiane Aboura" ], "posted": "2024-12-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.09394v2", "field": "finance", "role": "instrument", "bullets": [ "Largest American single stocks, with the universe and data of Guijarro-Ordonnez et al. 2022, and return forecasts feeding a simulated long-short portfolio.", "Chronos, an open time series foundation model, is used pretrained and with fine-tuned supervised forecasts. Evaluation is by portfolio simulation rather than a labeled benchmark.", "The long-short strategy generates alpha on return series close to noise, while comparisons with specialized and smaller deep learning models show substantial room for improvement." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 42, "edition": 13, "validated": null, "n": 1677, "authors_detailed": [ { "name": "Sébastien Valeyre", "url": "https://openalex.org/A5089274688", "inst": "INSEAD" }, { "name": "Sofiane Aboura", "url": "https://openalex.org/A5050773436", "inst": "Université Paris Cité" } ], "affiliations": [ "INSEAD", "Université Paris Cité" ], "prestige": true }, { "uid": "arxiv:2412.09345v1", "arxiv_id": "2412.09345v1", "title": "Delving into Youth Perspectives on In-game Gambling-like Elements: A Proof-of-Concept Study Utilising Large Language Models for Analysing User-Generated Text Data", "authors": [ "Thomas Krause", "Steffen Otterbach", "Johannes Singer" ], "posted": "2024-12-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.09345v1", "field": "economics", "role": "method", "bullets": [ "User generated posts about gambling like mechanics in digital games, lootboxes especially, from young players; sample size and platform are not stated in the abstract.", "LLMs, not named in the abstract, code opinions and themes through iterative prompt refinement, and their output is checked against human coders.", "Model coding matches human coders for identifying relevant patterns and themes, while more complex coding tasks remain weaker and need further methodological refinement." ], "bullet_provenance": "ai", "validated": true, "validation_note": "agreement with human coders; statistics not in abstract", "salience": 35, "edition": 13, "models": [], "n": 1708, "authors_detailed": [ { "name": "Thomas Krause", "url": "https://openalex.org/A5030927255", "inst": "University of Bern" }, { "name": "Steffen Otterbach", "url": "https://openalex.org/A5079419529", "inst": "University of Hohenheim" }, { "name": "Johannes Singer", "url": "https://openalex.org/A5005999398", "inst": "University of Hohenheim" } ], "affiliations": [ "University of Bern", "University of Hohenheim" ] }, { "uid": "doi:10.2139/ssrn.5053346", "doi": "10.2139/ssrn.5053346", "title": "Modeling Story Expectations to Understand Engagement: A Generative Framework Using LLMs", "authors": [ "Hortense Fong", "George Gui" ], "posted": "2024-12-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5053346", "field": "management", "role": "instrument", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 650, "authors_detailed": [ { "name": "Hortense Fong", "url": "https://openalex.org/A5052464509", "inst": "Columbia University" }, { "name": "George Gui", "url": "https://openalex.org/A5028759136", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2412.08179v3", "arxiv_id": "2412.08179v3", "title": "RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis -- A Case Study for the Semiconductor Sector", "authors": [ "Hai-Thien To", "Tien-Cuong Bui", "Van-Duc Le" ], "posted": "2024-12-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.08179v3", "field": "finance", "role": "method", "bullets": [ "Earnings report analysis in the semiconductor sector, with an instruction dataset built from industry documents and NVIDIA, AMD and Broadcom as case companies.", "An unnamed open-source LLM is instruction tuned with retrieval augmentation; the abstract reports comparability to GPT-3.5 on report generation but no ground-truth accuracy check.", "Retrieval-augmented instruction tuning substantially improves the base model and approaches commercial system quality on financial report generation, by the authors' evaluation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": true, "validated": false, "salience": 33, "edition": 13, "n": 1548, "authors_detailed": [ { "name": "To, Hai-Thien", "url": "", "inst": "" }, { "name": "Bui, Tien-Cuong", "url": "", "inst": "" }, { "name": "Le, Van-Duc", "url": "", "inst": "" } ] }, { "uid": "arxiv:2412.08680v1", "arxiv_id": "2412.08680v1", "title": "Distinguishing Scams and Fraud with Ensemble Learning", "authors": [ "Isha Chadalavada", "Tianhui Huang", "Jessica Staddon" ], "posted": "2024-12-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.08680v1", "field": "finance", "role": "instrument", "bullets": [ "Consumer Financial Protection Bureau complaints database used to distinguish scam complaints from non-scam fraud in consumer finance.", "An LLM ensemble approach classified CFPB complaints as scam versus fraud to support scam defense in financial consumer protection.", "Initial findings reveal strengths and weaknesses of LLMs in scam classification; the CFPB corpus currently lacks a scam-fraud distinction." ], "bullet_provenance": "ai", "validated": false, "salience": 25, "models": [], "n": 3463, "authors_detailed": [ { "name": "Isha Chadalavada", "url": "https://openalex.org/A5115426302", "inst": "" }, { "name": "Tianhui Huang", "url": "https://openalex.org/A5064100671", "inst": "Jiangsu Changjiang Electronics Technology (China)" }, { "name": "Jessica Staddon", "url": "https://openalex.org/A5009511099", "inst": "Norfolk State University" } ], "affiliations": [ "Jiangsu Changjiang Electronics Technology (China)", "Norfolk State University" ] }, { "uid": "arxiv:2412.12148v1", "arxiv_id": "2412.12148v1", "title": "How to Choose a Threshold for an Evaluation Metric for Large Language Models", "authors": [ "Bhaskarjit Sarmah", "Mingshu Li", "Jingrao Lyu", "Sebastian Frank", "Nathalia Castellanos", "Stefano Pasquali", "Dhagash Mehta" ], "posted": "2024-12-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.12148v1", "field": "finance", "role": "method", "bullets": [ "A step-by-step procedure for choosing deployment thresholds on LLM evaluation metrics, translated from model risk management guidelines used in regulated financial industries.", "The recipe is demonstrated on the Faithfulness hallucination metric using the public HaluBench ground truth dataset. No specific language model family is named.", "Threshold choice starts from application risks and stakeholder risk tolerance, then applies statistical procedures on ground truth data, generalizing to other generative AI metrics." ], "bullet_provenance": "ai", "salience": 35, "edition": 13, "models": [], "validated": null, "n": 1676, "authors_detailed": [ { "name": "Bhaskarjit Sarmah", "url": "https://openalex.org/A5040045971", "inst": "Shree Guru Gobind Singh Tricentenary University" }, { "name": "Mingshu Li", "url": "https://openalex.org/A5091468980", "inst": "Chinese Academy of Sciences" }, { "name": "Jingrao Lyu", "url": "https://openalex.org/A5111397832", "inst": "Blackrock Microsystems (United States)" }, { "name": "Sebastian Frank", "url": "https://openalex.org/A5021022819", "inst": "BlackRock (United States)" }, { "name": "N.P. Castellanos", "url": "https://openalex.org/A5113549995", "inst": "BlackRock (United States)" }, { "name": "Stefano Pasquali", "url": "https://openalex.org/A5030132224", "inst": "Domus Medica" }, { "name": "Dhagash Mehta", "url": "https://openalex.org/A5056701307", "inst": "University of Notre Dame" } ], "affiliations": [ "University of Notre Dame", "Shree Guru Gobind Singh Tricentenary University", "Chinese Academy of Sciences", "Blackrock Microsystems (United States)", "BlackRock (United States)", "Domus Medica" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2412.09640v1", "arxiv_id": "2412.09640v1", "title": "Blockchain Data Analysis in the Era of Large-Language Models", "authors": [ "Kentaroh Toyoda", "Xiao Wang", "Mingzhe Li", "Bo Gao", "Yuan Wang", "Qingsong Wei" ], "posted": "2024-12-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.09640v1", "field": "finance", "role": "method", "bullets": [ "A survey of blockchain data analysis for academia, industry, and policy, spanning fraud detection, regulatory compliance, smart contract auditing, and decentralized finance risk management.", "No model is deployed. The paper catalogs techniques and design patterns for integrating LLMs into blockchain analysis and outlines research opportunities and challenges.", "The authors argue LLMs can address data scarcity, weak generalizability, and limited reasoning in existing tools. The paper offers an agenda rather than empirical results." ], "bullet_provenance": "ai", "salience": 22, "edition": 13, "models": [], "validated": null, "n": 1675, "authors_detailed": [ { "name": "Kentaroh Toyoda", "url": "https://openalex.org/A5066312369", "inst": "Vulcan (United States)" }, { "name": "Xiao Wang", "url": "https://openalex.org/A5112719601", "inst": "Zhejiang International Studies University" }, { "name": "Mingzhe Li", "url": "https://openalex.org/A5100775678", "inst": "Harbin Institute of Technology" }, { "name": "Bo Gao", "url": "https://openalex.org/A5046359289", "inst": "Guiyang Medical University" }, { "name": "Yuan Wang", "url": "https://openalex.org/A5100344860", "inst": "Nanchang University" }, { "name": "Qingsong Wei", "url": "https://openalex.org/A5035083405", "inst": "Zhejiang Sci-Tech University" } ], "affiliations": [ "Vulcan (United States)", "Zhejiang International Studies University", "Harbin Institute of Technology", "Guiyang Medical University", "Nanchang University", "Zhejiang Sci-Tech University" ] }, { "uid": "arxiv:2412.07042v1", "arxiv_id": "2412.07042v1", "title": "Generative AI Impact on Labor Market: Analyzing ChatGPT's Demand in Job Advertisements", "authors": [ "Mahdi Ahmadi", "Neda Khosh Kheslat", "Adebola Akintomide" ], "posted": "2024-12-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.07042v1", "field": "economics", "role": "object", "bullets": [ "US job advertisements mentioning ChatGPT, collected from major posting platforms between May and December 2023, with occupation titles, degree requirements, and salary ranges.", "No LLM performs the measurement; text mining and topic modeling cluster the postings, making ChatGPT the object of employer demand rather than the research tool.", "Five skill clusters emerge, from general familiarity and marketing uses to prompt engineering and product development, pointing to demand for both basic and advanced generative AI skills." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 44, "edition": 13, "validated": null, "n": 1771, "authors_detailed": [ { "name": "Ahmadi, Mahdi", "url": "", "inst": "" }, { "name": "Neda Khosh Kheslat", "url": "https://openalex.org/A5115087574", "inst": "" }, { "name": "Adebola Akintomide", "url": "https://openalex.org/A5115087575", "inst": "" } ] }, { "uid": "doi:10.3390/bdcc8110143", "doi": "10.3390/bdcc8110143", "arxiv_id": "2412.06837v1", "title": "Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach", "authors": [ "Olamilekan Shobayo", "Sidikat Adeyemi-Longe", "Olusogo Popoola", "Bayode Ogunleye" ], "posted": "2024-12-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.06837v1", "field": "finance", "role": "instrument", "bullets": [ "Nigerian Stock Exchange All-Share Index and financial news data used to compare AI models for sentiment-based stock price prediction.", "FinBERT, GPT-4, and Logistic Regression classified market sentiment and generated scores; hyperparameters optimized with Optuna and cross-validation.", "Logistic Regression outperformed with 81.83% accuracy and 89.76% ROC AUC; GPT-4 achieved only 54.19% accuracy on the prediction task." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "Accuracy, precision, recall, F1, ROC AUC on NGX All-Share Index", "salience": 40, "n": 3462, "authors_detailed": [ { "name": "Olamilekan Shobayo", "url": "https://openalex.org/A5014068242", "inst": "Sheffield Hallam University" }, { "name": "Sidikat Adeyemi-longe", "url": "https://openalex.org/A5107189517", "inst": "Sheffield Hallam University" }, { "name": "Olusogo Popoola", "url": "https://openalex.org/A5069725543", "inst": "Sheffield Hallam University" }, { "name": "Bayode Ogunleye", "url": "https://openalex.org/A5071126071", "inst": "University of Brighton" } ], "affiliations": [ "Sheffield Hallam University", "University of Brighton" ] }, { "uid": "arxiv:2412.04856v1", "arxiv_id": "2412.04856v1", "title": "Can Large Language Models Effectively Process and Execute Financial Trading Instructions?", "authors": [ "Yu Kang", "Ge Wang", "Xin Yang", "Yuda Wang", "Mingwen Liu" ], "posted": "2024-12-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.04856v1", "field": "finance", "role": "method", "bullets": [ "A constructed set of 500 trade orders simulating real trading scenarios, used to test conversion of natural language instructions into a standard execution format.", "Five LLMs, not named in the abstract, run the order recognition pipeline and are scored for generation, follow up, accuracy, and completeness against the dataset.", "Generation rates run 87.5 to 98.3 percent, but accuracy sits at 5 to 10 percent with missing rates up to 67 percent, and models over collect information, a security concern." ], "bullet_provenance": "ai", "validated": true, "validation_note": "scored against a 500-order labelled dataset", "salience": 45, "edition": 13, "models": [], "n": 1761, "authors_detailed": [ { "name": "Yu Kang", "url": "https://openalex.org/A5100624617", "inst": "Tianjin University" }, { "name": "Ge Wang", "url": "https://openalex.org/A5100605192", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Xin Yang", "url": "https://openalex.org/A5100635997", "inst": "Dalian University of Technology" }, { "name": "Yuda Wang", "url": "https://openalex.org/A5050015894", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "M.T. Liu", "url": "https://openalex.org/A5014998363", "inst": "Gezhouba Explosive (China)" } ], "affiliations": [ "Tianjin University", "Rensselaer Polytechnic Institute", "Dalian University of Technology", "Beijing University of Posts and Telecommunications", "Gezhouba Explosive (China)" ] }, { "uid": "doi:10.2139/ssrn.5041035", "doi": "10.2139/ssrn.5041035", "title": "The Digital Wordsmith: AI-Assisted Disclosures and Crowdfunding Success", "authors": [ "John (Jianqiu) Bai", "Yi Cao", "Miao Liu", "Chi Wan" ], "posted": "2024-12-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5041035", "field": "finance", "role": "object", "bullets": [ "Kickstarter crowdfunding campaigns before and after ChatGPT introduction, comparing AI-assisted versus non-AI campaign disclosures and funding outcomes.", "ChatGPT generated campaign text; difference-in-differences design exploited ChatGPT launch as exogenous shock to campaign disclosure quality and persuasiveness.", "AI-assisted projects saw 12.4% higher success likelihood and 16.6% more pledges, but delivery rates fell 11.4% in regions with weak institutional frameworks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 2966, "authors_detailed": [ { "name": "John Bai", "url": "https://openalex.org/A5054468432", "inst": "Northeastern University" }, { "name": "Yi Cao", "url": "https://openalex.org/A5045986817", "inst": "George Mason University" }, { "name": "Miao Liu", "url": "", "inst": "Boston College" }, { "name": "Chi Wan", "url": "https://openalex.org/A5101540802", "inst": "San Diego State University" } ], "affiliations": [ "Boston College", "Northeastern University", "George Mason University", "San Diego State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4989979", "doi": "10.2139/ssrn.4989979", "title": "The Macroeconomic Implications of the Gen-AI Economy", "authors": [ "Pablo Guerrón-Quintana", "Tomoaki Mikami", "Jaromir Nosal" ], "posted": "2024-12-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4989979", "field": "economics", "role": "object", "bullets": [ "Multi-sector model of the U.S. economy calibrated with occupational task characteristics and input-output linkages for the Gen-AI sector.", "The model studies Gen-AI's macroeconomic impact through two channels: input-output structure and customer base management in a calibrated general equilibrium.", "A 10% Gen-AI productivity increase over 10 years implies 5.6% aggregate GDP growth with significant labor reallocation away from the AI sector." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3461, "authors_detailed": [ { "name": "Pablo Guerrón-Quintana", "url": "https://openalex.org/A5004037070", "inst": "Boston College" }, { "name": "Tomoaki Mikami", "url": "https://openalex.org/A5001213289", "inst": "Boston College" }, { "name": "Jaromir Nosal", "url": "https://openalex.org/A5054107147", "inst": "Boston College" } ], "affiliations": [ "Boston College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4987429", "doi": "10.2139/ssrn.4987429", "title": "Municipal Cost of Living Sentiment Index", "authors": [ "Howard Leong" ], "posted": "2024-12-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4987429", "field": "economics", "role": "instrument", "bullets": [ "Online content about Calgary's economy analyzed to construct a municipal-level cost-of-living sentiment index for a single Canadian city.", "Four unspecified large language models classified sentiment of online content into approving, neutral, and disapproving categories.", "The Calgary Cost of Living Sentiment Index scored 31.91 out of 100, with 24.87% disapproving and only 15.86% approving content." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "models": [], "n": 3460, "authors_detailed": [ { "name": "Howard Leong", "url": "https://openalex.org/A5115013084", "inst": "University of Calgary" } ], "affiliations": [ "University of Calgary" ] }, { "uid": "doi:10.1109/bigdata62323.2024.10825891", "doi": "10.1109/bigdata62323.2024.10825891", "arxiv_id": "2412.03527v1", "title": "FANAL -- Financial Activity News Alerting Language Modeling Framework", "authors": [ "Urjitkumar Patel", "Fang-Chun Yeh", "Chinmay Gondhalekar", "Hari Nalluri" ], "posted": "2024-12-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.03527v1", "field": "finance", "role": "method", "bullets": [ "Streaming financial news categorized into twelve event classes for real time alerting, with silver labels generated through XGBoost for training.", "ORBERT, a BERT variant tuned with odds ratio preference optimization, does the event classification and is benchmarked against GPT-4o, Llama 3.1 8B and Phi-3; accuracy figures are not given in the abstract.", "The tuned small model is reported to beat the larger general purpose models on both accuracy and cost, making tuned encoders the cheaper option for financial event alerting." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "classification accuracy compared against GPT-4o, Llama 3.1 and Phi-3; figures not in abstract", "salience": 38, "edition": 13, "n": 1707, "authors_detailed": [ { "name": "Urjitkumar Patel", "url": "https://openalex.org/A5111130559", "inst": "X-Fab (Germany)" }, { "name": "Fang-Chun Yeh", "url": "https://openalex.org/A5114127655", "inst": "X-Fab (Germany)" }, { "name": "Chinmay Gondhalekar", "url": "https://openalex.org/A5093940847", "inst": "X-Fab (Germany)" }, { "name": "Hari Nalluri", "url": "https://openalex.org/A5115021915", "inst": "" } ], "affiliations": [ "X-Fab (Germany)" ] }, { "uid": "doi:10.2139/ssrn.5041934", "doi": "10.2139/ssrn.5041934", "title": "We Need to Talk: Audio Surveys and Information Extraction", "authors": [ "Vincenzo Galasso", "Tommaso Nannicini", "Debora Nozza" ], "posted": "2024-12-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5041934", "field": "economics", "role": "instrument", "bullets": [ "Three randomized controlled trials across surveys on AI, public policy, and international relations, with respondents assigned to answer by audio or text.", "LLMs evaluated informativeness of open-ended oral versus written survey responses, scoring quantity, quality, and personal experience content.", "Oral responses were longer and lexically simpler but contained more information and personal experiences than written responses across all three surveys." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 45, "n": 2651, "authors_detailed": [ { "name": "Vincenzo Galasso", "url": "https://openalex.org/A5025242196", "inst": "Ifo Institute for Economic Research" }, { "name": "Tommaso Nannicini", "url": "https://openalex.org/A5040204992", "inst": "IZA - Institute of Labor Economics" }, { "name": "Debora Nozza", "url": "https://openalex.org/A5032463780", "inst": "Bocconi University" } ], "affiliations": [ "Bocconi University", "Ifo Institute for Economic Research", "IZA - Institute of Labor Economics" ], "prestige": true }, { "uid": "arxiv:2412.02065v3", "arxiv_id": "2412.02065v3", "title": "Leveraging Large Language Models to Democratize Access to Costly Datasets for Academic Research", "authors": [ "Julian Junyan Wang", "Victor Xiaoqi Wang" ], "posted": "2024-12-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.02065v3", "field": "accounting", "role": "method", "bullets": [ "About 10,000 US proxy statements and more than 12,000 10-K filings, targeting CEO pay ratios and critical audit matters.", "GPT-4o-mini inside a retrieval augmented generation pipeline extracts both items, reaching human-level accuracy in 9 and 40 minutes of processing at under 10 dollars per collection.", "Replicates data that otherwise takes hundreds of manual hours or commercial database subscriptions; the pipeline and both extracted datasets are shared for reuse." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "benchmarked against human collection, human-level accuracy", "salience": 66, "edition": 13, "n": 1565, "authors_detailed": [ { "name": "Jiahao Wang", "url": "https://openalex.org/A5113225503", "inst": "Nanjing University of Finance and Economics" }, { "name": "Victor Xiaoqi Wang", "url": "https://openalex.org/A5041860793", "inst": "California State University, Long Beach" } ], "affiliations": [ "Nanjing University of Finance and Economics", "California State University, Long Beach" ] }, { "uid": "arxiv:2412.02605v3", "arxiv_id": "2412.02605v3", "title": "Interpretable Company Similarity with Sparse Autoencoders", "authors": [ "Marco Molinari", "Victor Shao", "Luca Imeneo", "Mateusz Mikolajczak", "Vladimir Tregubiak", "Abhimanyu Pandey", "Sebastian Kuznetsov Ryder Torres Pereira" ], "posted": "2024-12-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.02605v3", "field": "finance", "role": "instrument", "bullets": [ "Company descriptions for listed equities, with logged monthly return correlations and co-integration trading used to judge similarity quality; universe size is not stated.", "Sparse autoencoders decompose activations of an unnamed LLM into interpretable features that cluster firms; features are benchmarked against SIC codes, industry codes, and embeddings rather than hand-coded labels.", "SAE feature similarity correlates with monthly returns better than sector codes or embeddings and yields higher Sharpe ratios in co-integration strategies, while remaining interpretable." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 13, "models": [], "n": 1623, "authors_detailed": [ { "name": "Marco Molinari", "url": "https://openalex.org/A5085369499", "inst": "Ospedale San Giuseppe" }, { "name": "Victor Shao", "url": "https://openalex.org/A5115012554", "inst": "London School of Economics and Political Science" }, { "name": "Imeneo, Luca", "url": "", "inst": "" }, { "name": "Mateusz Mikolajczak", "url": "https://openalex.org/A5052497903", "inst": "London School of Economics and Political Science" }, { "name": "Vladimir Tregubiak", "url": "https://openalex.org/A5115012553", "inst": "London School of Economics and Political Science" }, { "name": "Pandey, Abhimanyu", "url": "", "inst": "" }, { "name": "Pereira, Sebastian Kuznetsov Ryder Torres", "url": "", "inst": "" } ], "affiliations": [ "London School of Economics and Political Science", "Ospedale San Giuseppe" ], "prestige": true }, { "uid": "arxiv:2412.02835v1", "arxiv_id": "2412.02835v1", "title": "CAISSON: Concept-Augmented Inference Suite of Self-Organizing Neural Networks", "authors": [ "Igor Halperin" ], "posted": "2024-12-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.02835v1", "field": "finance", "role": "method", "bullets": [ "Synthetic financial analyst notes and question-answer pairs generated via SynFAQA framework for multi-step reasoning evaluation.", "Dual Self-Organizing Maps created multi-view document clustering for hierarchical RAG over financial analyst reports.", "Substantial retrieval improvements over basic and enhanced RAG implementations, especially for complex multi-entity queries." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "SynFAQA benchmark with ground-truth answer sets", "salience": 40, "n": 3912, "authors_detailed": [ { "name": "Igor Halperin", "url": "https://openalex.org/A5048462036", "inst": "Fidelity Investments (United States)" } ], "affiliations": [ "Fidelity Investments (United States)" ] }, { "uid": "arxiv:2412.00549v1", "arxiv_id": "2412.00549v1", "title": "SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains", "authors": [ "Jebish Purbey", "Siddhant Gupta", "Nikhil Manali", "Siddartha Pullakhandam", "Drishti Sharma", "Ashay Srivastava", "Ram Mohan Rao Kadiyala" ], "posted": "2024-11-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2412.00549v1", "field": "finance", "role": "method", "bullets": [ "Entry to the COLING 2025 financial misinformation detection shared task, covering claim classification and explanation generation on the organizers' labelled data.", "Qwen, Mistral, and Gemma-2 are combined with preprocessing and sequential learning; outputs are scored against the task's ground truth labels rather than author-run human coding.", "The system reaches an F1 of 0.83 for classification and ROUGE-1 of 0.73 for generated explanations on the challenge benchmark." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "shared task labelled test set, F1 and ROUGE reported", "salience": 25, "edition": 13, "n": 1586, "authors_detailed": [ { "name": "Jebish Purbey", "url": "https://openalex.org/A5114654176", "inst": "Nepal Development Research Institute" }, { "name": "Gupta, Siddhant", "url": "", "inst": "" }, { "name": "Nikhil Manali", "url": "https://openalex.org/A5115003544", "inst": "" }, { "name": "Siddartha Pullakhandam", "url": "https://openalex.org/A5114534514", "inst": "University of Maryland, College Park" }, { "name": "Sharma, Drishti", "url": "", "inst": "" }, { "name": "Srivastava, Ashay", "url": "", "inst": "" }, { "name": "Ram Mohan Rao Kadiyala", "url": "https://openalex.org/A5033606784", "inst": "National Institute of Technology Warangal" } ], "affiliations": [ "University of Maryland, College Park", "Nepal Development Research Institute", "National Institute of Technology Warangal" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.19515v1", "arxiv_id": "2411.19515v1", "title": "Leveraging Large Language Models for Institutional Portfolio Management: Persona-Based Ensembles", "authors": [ "Yoshia Abe", "Shuhei Matsuo", "Ryoma Kondo", "Ryohei Hisano" ], "posted": "2024-11-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.19515v1", "field": "finance", "role": "agent", "bullets": [ "Stock and bond portfolio adjustment simulations driven by economic indicators, mimicking institutional investor behavior across rising and falling consumer price index regimes.", "LLMs prompted under different personas forecast price movements, with an ensemble aggregating their diverse predictions. The paper does not say which models were used.", "The mode ensemble beats buy and hold on Sharpe ratio when CPI is rising, while traditional strategies win during declining CPI trends and sharp downturns." ], "bullet_provenance": "ai", "salience": 35, "edition": 13, "models": [], "validated": null, "n": 1674, "authors_detailed": [ { "name": "Abe, Yoshia", "url": "", "inst": "" }, { "name": "Matsuo, Shuhei", "url": "", "inst": "" }, { "name": "Ryoma Kondo", "url": "https://openalex.org/A5110542635", "inst": "Canon (Japan)" }, { "name": "Ryohei Hisano", "url": "https://openalex.org/A5057799988", "inst": "The University of Tokyo" } ], "affiliations": [ "Canon (Japan)", "The University of Tokyo" ] }, { "uid": "arxiv:2411.18242v1", "arxiv_id": "2411.18242v1", "title": "Thai Financial Domain Adaptation of THaLLE -- Technical Report", "authors": [ "KBTG Labs", "Atthakorn Petchsod", "Pornchanan Balee", "Danupat Khamnuansin", "Anuruth Lertpiya", "Chanatip Saetia", "Tawunrat Chalothorn", "Thadpong Pongthawornkamol", "Monchai Lertsutthiwong" ], "posted": "2024-11-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.18242v1", "field": "finance", "role": "method", "bullets": [ "Investment Consultant exam material from the Stock Exchange of Thailand, with data augmentation to compensate for the small Thai-language financial corpus.", "A THaLLE base model, family not stated, undergoes continued pretraining, rank-stabilized LoRA fine-tuning, and direct preference optimization; exam scoring supplies the ground-truth check.", "The adapted model scores 72, 72, and 84 percent on IC exam levels P1 to P3, which the authors read as fitness for Thai financial advisory tasks." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "Thai IC licensing exams", "salience": 22, "edition": 13, "models": [], "n": 1622, "authors_detailed": [ { "name": "KBTG Labs", "url": "https://openalex.org/A5099123316", "inst": "" }, { "name": "Atthakorn Petchsod", "url": "https://openalex.org/A5099123317", "inst": "Silpakorn University" }, { "name": "Pornchanan Balee", "url": "https://openalex.org/A5099123318", "inst": "" }, { "name": "Danupat Khamnuansin", "url": "https://openalex.org/A5099097290", "inst": "Chulalongkorn University" }, { "name": "Anuruth Lertpiya", "url": "https://openalex.org/A5081580071", "inst": "Chulalongkorn University" }, { "name": "Chanatip Saetia", "url": "https://openalex.org/A5012692919", "inst": "Silpakorn University" }, { "name": "Tawunrat Chalothorn", "url": "https://openalex.org/A5090165607", "inst": "Silpakorn University" }, { "name": "Thadpong Pongthawornkamol", "url": "https://openalex.org/A5099123320", "inst": "" }, { "name": "Monchai Lertsutthiwong", "url": "https://openalex.org/A5099123321", "inst": "Silpakorn University" } ], "affiliations": [ "Silpakorn University", "Chulalongkorn University" ] }, { "uid": "doi:10.3386/w33198", "doi": "10.3386/w33198", "title": "Generative AI for Economic Research: LLMs Learn to Collaborate and Reason", "authors": [ "Anton Korinek" ], "posted": "2024-11-26", "added": "2026-08-04", "source_label": "NBER", "url": "https://doi.org/10.3386/w33198", "field": "economics", "role": "method", "bullets": [ "An NBER working paper written as a practical guide for economists, surveying LLM capabilities available through late 2024 rather than analyzing a dataset.", "No measurement task is performed; the article walks through reasoning models, interactive workspaces such as Claude Artifacts and ChatGPT Canvas, and LLM powered internet search, with no formal validation.", "Argues these capabilities give economists sizable productivity gains, including automated blog posts, slides, and podcasts for promoting research; no effect sizes are reported." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 62, "edition": 10, "validated": null, "n": 1345, "authors_detailed": [ { "name": "Anton Korinek", "url": "https://openalex.org/A5009882421", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "National Bureau of Economic Research" ] }, { "uid": "doi:10.2139/ssrn.5032561", "doi": "10.2139/ssrn.5032561", "title": "Generative AI for Economic Research: LLMs Learn to Collaborate and Reason", "authors": [ "Anton Korinek" ], "posted": "2024-11-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5032561", "field": "economics", "role": "method", "bullets": [ "Practical guide for economists covering 2024 advances in LLM reasoning capabilities, interactive collaboration workspaces, and AI-powered internet search tools.", "Reviews Claude Artifacts, ChatGPT Canvas, Microsoft Copilot, and NotebookLM for research productivity including automated blog posts and presentation generation.", "Recent LLM improvements in reasoning, collaboration interfaces, and internet search yield significant productivity gains for economists across the full research workflow." ], "bullet_provenance": "ai", "models": [ "gpt", "claude" ], "open_weights": false, "salience": 60, "validated": null, "n": 2369, "authors_detailed": [ { "name": "Anton Korinek", "url": "https://openalex.org/A5009882421", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "National Bureau of Economic Research" ] }, { "uid": "arxiv:2411.17900v1", "arxiv_id": "2411.17900v1", "title": "Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading", "authors": [ "Suyeol Yun" ], "posted": "2024-11-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.17900v1", "field": "finance", "role": "method", "bullets": [ "Historical market data for quantitative trading, offline reinforcement learning setting with expert trajectories.", "GPT-2 pretrained weights fine-tuned with LoRA as a Decision Transformer to learn trading policies without live market interaction.", "Model matched CQL, IQL, and Behavior Cloning baselines and achieved superior rewards in certain trading scenarios." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": true, "validated": false, "salience": 45, "n": 3911, "authors_detailed": [ { "name": "Suyeol Yun", "url": "https://openalex.org/A5103882818", "inst": "Korea Aerospace Research Institute" } ], "affiliations": [ "Korea Aerospace Research Institute" ] }, { "uid": "arxiv:2411.16569v1", "arxiv_id": "2411.16569v1", "title": "Predictive Power of LLMs in Financial Markets", "authors": [ "Jerick Shi", "Burton Hollifield" ], "posted": "2024-11-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.16569v1", "field": "finance", "role": "method", "bullets": [ "Federal Reserve Beige Book summaries of district economic conditions, used to predict correlations among assets; period and document counts are not stated.", "GPT and BERT models turn Beige Book text into correlation predictions that feed investment strategies; the GPT variant is not precisely named in the abstract.", "The Beige Book carries information about asset correlations, but GPT predictions suffer from look-ahead bias and traditional models still win for investment decisions." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "validated": false, "salience": 42, "edition": 13, "n": 1621, "authors_detailed": [ { "name": "Shi, Jerick", "url": "", "inst": "" }, { "name": "Burton Hollifield", "url": "https://openalex.org/A5035513045", "inst": "University of North Carolina at Chapel Hill" } ], "affiliations": [ "University of North Carolina at Chapel Hill" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.16585v1", "arxiv_id": "2411.16585v1", "title": "MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series", "authors": [ "Aaron Wheeler", "Jeffrey D. Varner" ], "posted": "2024-11-25", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.16585v1", "field": "finance", "role": "method", "bullets": [ "U.S. equity limit order book data used to train and evaluate a generative pre-trained transformer for financial time series simulation.", "A custom GPT generates long sequences of order messages in a discrete event simulator, replicating limit order book dynamics in a streaming manner.", "The model reproduces key stylized facts of real financial markets and macro-scale data distributions even when the initial prompt leaves the context window." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Stylized facts comparison against real limit order book data", "salience": 55, "n": 3459, "authors_detailed": [ { "name": "Aaron R. Wheeler", "url": "https://openalex.org/A5002024611", "inst": "University of Toronto" }, { "name": "Jeffrey D. Varner", "url": "https://openalex.org/A5003161155", "inst": "Cornell University" } ], "affiliations": [ "Cornell University", "University of Toronto" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.15718v1", "arxiv_id": "2411.15718v1", "title": "Can an increase in productivity cause a decrease in production? Insights from a model economy with AI automation", "authors": [ "Casey O. Barkan" ], "posted": "2024-11-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.15718v1", "field": "economics", "role": "object", "bullets": [ "Idealized model economy with a single monopolist-monopsonist firm and a new autonomous-capital technology motivated by AI agents.", "Theoretical model examined how AI-enabled capital substitution without labor input affects firm profit and total production.", "Automation increases firm profit while total production decreases, showing productivity gains need not produce economic growth." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3910, "authors_detailed": [ { "name": "Casey O. Barkan", "url": "https://openalex.org/A5114987615", "inst": "" } ] }, { "uid": "arxiv:2411.16732v1", "arxiv_id": "2411.16732v1", "title": "Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge", "authors": [ "Joohyun Lee", "Minji Roh" ], "posted": "2024-11-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.16732v1", "field": "finance", "role": "method", "bullets": [ "ACM ICAIF 24 FinanceRAG competition tasks over financial statements and disclosures; corpus and query counts are not stated in the abstract.", "The pipeline combines query expansion, corpus refinement, multiple unnamed reranker models, and long-context management for generation; scoring follows the competition's evaluation.", "The system placed second in the FinanceRAG challenge, with ablations attributing gains to pre-retrieval processing and reranking; metric values are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinanceRAG challenge evaluation", "salience": 22, "edition": 13, "models": [], "n": 1619, "authors_detailed": [ { "name": "Joo Hyun Lee", "url": "https://openalex.org/A5100396186", "inst": "The Catholic University of Korea Yeouido St. Mary's Hospital" }, { "name": "Roh, Minji", "url": "", "inst": "" } ], "affiliations": [ "The Catholic University of Korea Yeouido St. Mary's Hospital" ] }, { "uid": "arxiv:2411.15396v1", "arxiv_id": "2411.15396v1", "title": "The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges", "authors": [ "Jiqun Liu", "Jiangen He" ], "posted": "2024-11-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.15396v1", "field": "management", "role": "agent", "bullets": [ "Credibility ratings of COVID-19 medical information in a retrieval setting, compared between crowdsourced human judges and an LLM-based replication of the same between-subjects experiment.", "Unnamed LLMs of varying size and recency rate result credibility with decoy options present; ratings are compared with human assessors as the baseline rather than validated labels.", "Larger, newer models distinguish credible from false content more consistently, yet decoy effects appear more pervasively in LLM judgments than in human ratings." ], "bullet_provenance": "ai", "salience": 35, "edition": 13, "models": [], "validated": null, "n": 1620 }, { "uid": "doi:10.2139/ssrn.5026357", "doi": "10.2139/ssrn.5026357", "title": "AI Driven Tool for Meeting Management for Productivity and Engagement Using LLM", "authors": [ "saahir khan", "P Sreevatsan", "Dr. Krishnaveni S" ], "posted": "2024-11-22", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5026357", "field": "management", "role": "instrument", "bullets": [ "The paper describes a prototype corporate meeting-management platform; no specific sample, time period, or geography of use is reported.", "The system pairs OpenAI Whisper for audio transcription with GPT-4 for summarization and Google Text-to-Speech for output; validation against ground truth is not stated.", "The paper presents the platform's design and expected productivity and engagement benefits without reporting quantitative results." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "validation_note": "not stated", "salience": 30, "edition": 22, "n": 4123, "authors_detailed": [ { "name": "Saahir Khan", "url": "https://openalex.org/A5013782331", "inst": "SRM Institute of Science and Technology" }, { "name": "P Sreevatsan", "url": "https://openalex.org/A5114754888", "inst": "SRM Institute of Science and Technology" }, { "name": "K. S.", "url": "https://openalex.org/A5101379250", "inst": "SRM Institute of Science and Technology" } ], "affiliations": [ "SRM Institute of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.4979501", "doi": "10.2139/ssrn.4979501", "title": "Scaling Core Earnings Measurement with Large Language Models", "authors": [ "Matthew Shaffer", "Charles C. Y. Wang" ], "posted": "2024-11-22", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4979501", "field": "accounting", "role": "instrument", "bullet_provenance": "none", "salience": 50, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 345, "authors_detailed": [ { "name": "M Shaffer", "url": "https://openalex.org/A5113776252", "inst": "University of Virginia" }, { "name": "Charles C. Y. Wang", "url": "https://openalex.org/A5009696015", "inst": "Harvard University" } ], "affiliations": [ "University of Virginia", "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4976975", "doi": "10.2139/ssrn.4976975", "title": "Leveraging Automatically Optimized Forecasters and Large Language Model for Forecasting of Vietnamese Macroeconomic Indicators", "authors": [ "Hung Phan" ], "posted": "2024-11-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4976975", "field": "economics", "role": "instrument", "bullets": [ "Vietnamese macroeconomic indicators including FX rate, inflation, and policy rate forecast using news articles from 2014 to 2024 plus domestic data", "LLMs with chain-of-thought and role-playing prompts extracted sentiment scores from news; gradient-boosted decision tree models used these as inputs", "Sentiment-augmented GBDT models outperformed baselines; chain-of-thought prompting consistently produced more predictive sentiment than role-playing" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "forecast accuracy on Vietnamese FX, inflation, and policy rate", "salience": 55, "n": 2427, "authors_detailed": [ { "name": "Hung Phan", "url": "https://openalex.org/A5024298908", "inst": "RMIT University" } ], "affiliations": [ "RMIT University" ] }, { "uid": "doi:10.2139/ssrn.5018775", "doi": "10.2139/ssrn.5018775", "title": "The Financial Anatomy of Climate Solutions: A Large Language Model Approach to Company Classification and Analysis", "authors": [ "Shirley Lu", "George Serafeim" ], "posted": "2024-11-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5018775", "field": "accounting", "role": "instrument", "bullets": [ "US public firms' 10-K Item 1 Business Descriptions analyzed to quantify climate solution focus, linked to financial performance data.", "LLM classified firms' climate solution intensity from 10-K filings; measure validated against green patents, green revenues, and earnings call climate discussions.", "Higher climate solution firms show superior revenue growth but lower profitability from higher COGS and labor costs; growth concentrates in transition firms, lower margins in pure firms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "green patents, green revenues, earnings call measures", "salience": 78, "n": 2484, "authors_detailed": [ { "name": "Shirley Lu", "url": "", "inst": "" }, { "name": "George Serafeim", "url": "", "inst": "" } ] }, { "uid": "arxiv:2411.13813v4", "arxiv_id": "2411.13813v4", "title": "The Value of Information from Sell-side Analysts", "authors": [ "Linying Lv" ], "posted": "2024-11-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.13813v4", "field": "finance", "role": "instrument", "bullets": [ "Large corpus of sell-side analyst written reports matched to contemporaneous U.S. stock returns, with Shapley decomposition of topic-level explanatory contribution.", "LLM embeddings extracted qualitative information from analyst reports; out-of-sample prediction evaluated explanatory power against quantitative forecasts.", "Qualitative content explains above 10% of stock returns out-of-sample, exceeding quantitative forecasts; income statement analyses account for over half the explanatory power." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "out-of-sample stock return prediction R-squared", "salience": 65, "n": 2962, "authors_detailed": [ { "name": "Linying Lv", "url": "https://openalex.org/A5114761608", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.14230v2", "arxiv_id": "2411.14230v2", "title": "Public sentiments on the fourth industrial revolution: An unsolicited public opinion poll from Twitter", "authors": [ "Diletta Abbonato" ], "posted": "2024-11-21", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.14230v2", "field": "economics", "role": "instrument", "bullets": [ "Approximately 90,000 tweets and news articles across six European countries, 2006-2019, before widespread generative AI adoption.", "Transformer-based NLP models performed sentiment analysis on public discourse about Fourth Industrial Revolution technologies across national contexts.", "Neutral sentiment declined over the period as public opinion polarized into enthusiasm and concern; approximately 6% of users inhabited sentiment-aligned echo chambers." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 30, "n": 2963, "authors_detailed": [ { "name": "Diletta Abbonato", "url": "https://openalex.org/A5114763719", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.5018767", "doi": "10.2139/ssrn.5018767", "title": "Climate Solutions, Transition Risk, and Stock Returns", "authors": [ "Shirley Lu", "Edward J. Riedl", "Simon Xu", "George Serafeim" ], "posted": "2024-11-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5018767", "field": "finance", "role": "instrument", "bullets": [ "U.S. public firms with stock return and financial data, measuring climate solution product exposure and transition risk pricing in equity markets.", "LLMs measured firms' climate solution products and services from corporate disclosures to construct a firm-level climate solution exposure score.", "High-climate-solution firms show lower stock returns and higher valuation multiples; stock prices respond positively to events signaling increased climate solution demand." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 55, "n": 2964, "authors_detailed": [ { "name": "Shirley Lu", "url": "https://openalex.org/A5004994890", "inst": "Harvard University" }, { "name": "Edward J. Riedl", "url": "https://openalex.org/A5029372168", "inst": "Boston University" }, { "name": "Simon Xu", "url": "https://openalex.org/A5109449074", "inst": "Harvard Business School" }, { "name": "George Serafeim", "url": "https://openalex.org/A5006610838", "inst": "Harvard Business School, Boston, MA 02163, United States" } ], "affiliations": [ "Harvard University", "Boston University", "Harvard Business School", "Harvard Business School, Boston, MA 02163, United States" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5018769", "doi": "10.2139/ssrn.5018769", "title": "Catalysts for Climate Solutions: Corporate Responses to Venture Capital Financing of Climate-tech Startups", "authors": [ "Shirley Lu", "George Serafeim", "Simon Xu" ], "posted": "2024-11-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5018769", "field": "finance", "role": "instrument", "bullets": [ "U.S. incumbent firms and VC-backed climate-tech startups, examining product market overlap and corporate strategic responses to venture capital investment signals.", "LLMs measured firm-level product focus on climate solutions, enabling tracking of changes following VC financing events in similar product markets.", "Incumbents in overlapping product markets increased climate solution focus after VC investment, especially when investment showed promising financial prospects and higher visibility." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 45, "n": 2965, "authors_detailed": [ { "name": "Shirley Lu", "url": "", "inst": "Harvard University" }, { "name": "George Serafeim", "url": "", "inst": "Harvard Business School, Boston, MA 02163, United States" }, { "name": "Simon Xu", "url": "", "inst": "Harvard Business School" } ], "affiliations": [ "Harvard University", "Harvard Business School, Boston, MA 02163, United States", "Harvard Business School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.5005458", "doi": "10.2139/ssrn.5005458", "title": "A New Organizational Structure Database: Examining Structure through Top Management Team Compositions", "authors": [ "Daniel Albert", "John Eklund", "Lisa Tang" ], "posted": "2024-11-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5005458", "field": "management", "role": "instrument", "bullets": [ "S&P 500 firms, 1993-2020, hand-collected dataset of top management team compositions with original executive role titles for organizational structure analysis.", "Generative AI categorized executive titles into six role groups and 12 hierarchical levels, enabling systematic cross-firm and within-firm structural comparisons.", "AI-derived classifications align with prior research and reveal industry-specific structural changes, functional distributions, and the evolution of executive roles over time." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 35, "n": 2961, "authors_detailed": [ { "name": "Daniel Albert", "url": "https://openalex.org/A5103260279", "inst": "Drexel University" }, { "name": "John Eklund", "url": "https://openalex.org/A5003538717", "inst": "University of Southern California" }, { "name": "Lisa Tang", "url": "https://openalex.org/A5071527352", "inst": "National University of Singapore" } ], "affiliations": [ "University of Southern California", "Drexel University", "National University of Singapore" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.13599v2", "arxiv_id": "2411.13599v2", "title": "Can ChatGPT Overcome Behavioral Biases in the Financial Sector? Classify-and-Rethink: Multi-Step Zero-Shot Reasoning in the Gold Investment", "authors": [ "Shuoling Liu", "Gaoguo Jia", "Yuhang Jiang", "Liyuan Chen", "Qiang Yang" ], "posted": "2024-11-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.13599v2", "field": "finance", "role": "agent", "bullets": [ "Gold market investment setting with news text as an input; the abstract does not state the sample, period, or data source.", "ChatGPT generates investment opinions under zero-shot chain-of-thought and a classify-and-rethink prompting scheme; the exact model version is not stated and no ground-truth validation is reported.", "The authors report that chain-of-thought prompting yields more explainable predictions, less behavioral bias, and higher investment returns, without stating magnitudes." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": false, "salience": 30, "edition": 13, "n": 1585, "authors_detailed": [ { "name": "Shuoling Liu", "url": "https://openalex.org/A5018892437", "inst": "Development Fund" }, { "name": "Gaoguo Jia", "url": "https://openalex.org/A5028266176", "inst": "Development Fund" }, { "name": "Yuhang Jiang", "url": "https://openalex.org/A5104212428", "inst": "Development Fund" }, { "name": "Liyuan Chen", "url": "https://openalex.org/A5100715502", "inst": "Southwestern Medical Center" }, { "name": "Qiang Yang", "url": "https://openalex.org/A5036669418", "inst": "Beijing Institute of Technology" } ], "affiliations": [ "Development Fund", "Beijing Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.5014385", "doi": "10.2139/ssrn.5014385", "title": "Stock Portfolio Selection based on Risk Appetite: Evidence from ChatGPT", "authors": [ "Constantin Jacob Schneider", "Yahya Yilmaz" ], "posted": "2024-11-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5014385", "field": "finance", "role": "agent", "bullets": [ "U.S. and European equity markets; ChatGPT models prompted to create stock portfolios for retail investors across varying risk appetites.", "GPT-4 and GPT-4o generated portfolios evaluated against market benchmarks for return, risk, and alignment with stated risk preferences.", "Higher-risk portfolios yielded higher returns; GPT-4o outperformed in the U.S. while GPT-4 delivered the highest returns in Europe." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Portfolio performance against U.S. and European equity benchmarks", "salience": 55, "n": 2650, "authors_detailed": [ { "name": "Constantin Jacob Schneider", "url": "", "inst": "University of Münster" }, { "name": "Yahya Yilmaz", "url": "https://openalex.org/A5104384508", "inst": "University of Münster" } ], "affiliations": [ "University of Münster" ] }, { "uid": "doi:10.2139/ssrn.5026598", "doi": "10.2139/ssrn.5026598", "title": "Tax Law and Flexible Formalizations", "authors": [ "Sarah B. Lawsky" ], "posted": "2024-11-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5026598", "field": "accounting", "role": "object", "bullets": [ "Essay on how advances in LLMs, NLP, and domain-specific programming languages enable computational formalization of U.S. tax law.", "Discusses potential applications of flexible formalizations including automated statutory drafting, shelter detection, and administrative cost reduction.", "Argues computational analysis of formalized tax law can increase transparency, improve drafting quality, and make tax complexity more manageable." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 2959, "authors_detailed": [ { "name": "Sarah B. Lawsky", "url": "https://openalex.org/A5075418086", "inst": "Illinois College" } ], "affiliations": [ "Illinois College" ] }, { "uid": "doi:10.2139/ssrn.5024264", "doi": "10.2139/ssrn.5024264", "title": "Textual Factors: A Scalable, Interpretable, and Data-Driven Approach to Analyzing Unstructured Information", "authors": [ "Lin William Cong", "Tengyuan Liang", "Xiao Zhang", "Wu Zhu" ], "posted": "2024-11-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5024264", "field": "finance", "role": "method", "bullets": [ "News articles, corporate filings, and patents analyzed using a scalable text-factor framework applied to macroeconomic forecasting and asset pricing.", "Word embeddings clustered via Locality-Sensitive Hashing and topic modeling constructed textual factors; performance compared against large language models.", "Framework improves macroeconomic forecasting from news, interprets multi-factor asset pricing models from filings, and measures technology breakthroughs from patents." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 52, "n": 2960, "authors_detailed": [ { "name": "Lin William Cong", "url": "", "inst": "Nanyang Technological University" }, { "name": "Tengyuan Liang", "url": "https://openalex.org/A5113876441", "inst": "University of Chicago" }, { "name": "Xiao Zhang", "url": "https://openalex.org/A5101939409", "inst": "Lexmark (United States)" }, { "name": "Zhu Wu", "url": "https://openalex.org/A5100528706", "inst": "Tsinghua University" } ], "affiliations": [ "University of Chicago", "Nanyang Technological University", "Lexmark (United States)", "Tsinghua University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4988760", "doi": "10.2139/ssrn.4988760", "title": "Made with AI: Consumer Engagement with Social Media Containing AI Disclosures", "authors": [ "Stephan Carney", "Ignacio Riveros", "Stephanie Tully" ], "posted": "2024-11-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4988760", "field": "management", "role": "object", "bullets": [ "Consumer engagement on TikTok after the platform introduced its AI-generated-content disclosure policy, paired with eight preregistered experiments; the paper does not state sample sizes or the observation window.", "No language model is used as a research tool; generative AI is the object of study through platform disclosures, and no specific model or family is named.", "AI-content disclosures reduce engagement by weakening parasocial connection tied to perceived creator effort, not quality concerns or AI aversion; disclosures signaling greater effort mitigate the decline." ], "bullet_provenance": "ai", "salience": 66, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 649, "authors_detailed": [ { "name": "Stephan Carney", "url": "https://openalex.org/A5114692274", "inst": "University of Southern California" }, { "name": "Ignacio Riveros", "url": "https://openalex.org/A5114692275", "inst": "University of Southern California" }, { "name": "Stephanie Tully", "url": "https://openalex.org/A5114692276", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.11059v1", "arxiv_id": "2411.11059v1", "title": "Financial News-Driven LLM Reinforcement Learning for Portfolio Management", "authors": [ "Ananya Unnikrishnan" ], "posted": "2024-11-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.11059v1", "field": "finance", "role": "instrument", "bullets": [ "Trading experiments on one stock, Apple, and one portfolio, the ING Corporate Leaders Trust Series B, with news sentiment feeding a reinforcement learning trader.", "An LLM scores financial news sentiment that enters the reinforcement learning state. The model is not named and no validation against human labels is reported.", "Sentiment-augmented agents end with higher net worth and cumulative profit than sentiment-free versions and beat the actual portfolio's buy and hold strategy. Magnitudes are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 25, "edition": 13, "models": [], "n": 1673, "authors_detailed": [ { "name": "Ananya Unnikrishnan", "url": "https://openalex.org/A5114730311", "inst": "" } ] }, { "uid": "arxiv:2411.09937v2", "arxiv_id": "2411.09937v2", "title": "Refined and Segmented Price Sentiment Indices from Survey Comments", "authors": [ "Masahiro Suzuki", "Hiroki Sakaji" ], "posted": "2024-11-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.09937v2", "field": "economics", "role": "instrument", "bullets": [ "Economy Watchers Survey comments from Japan's Cabinet Office, classified by price direction and segmented by consumer versus business perspective.", "An LLM classified price-related comments by trend direction; multiple LLM outputs were ensembled to improve classification performance.", "LLM-classified indices show higher correlation with existing price indices than prior methods; consumer-focused indices gain from industry-based filtering." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "correlation with existing Japanese price indices", "salience": 55, "n": 2958, "authors_detailed": [ { "name": "Masahiro Suzuki", "url": "https://openalex.org/A5101426690", "inst": "Chuo University" }, { "name": "Hiroki Sakaji", "url": "https://openalex.org/A5028823648", "inst": "Hokkaido University" } ], "affiliations": [ "Chuo University", "Hokkaido University" ] }, { "uid": "doi:10.1109/bigdata62323.2024.10825292", "doi": "10.1109/bigdata62323.2024.10825292", "arxiv_id": "2411.09249v1", "title": "Enhancing Financial Domain Adaptation of Language Models via Model Augmentation", "authors": [ "Kota Tanabe", "Masanori Hirano", "Kazuki Matoya", "Kentaro Imajo", "Hiroki Sakaji", "Itsuki Noda" ], "posted": "2024-11-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.09249v1", "field": "finance", "role": "method", "bullets": [ "Financial domain adaptation experiments in Japanese, pairing a general-purpose LLM with a financial-specialized one and scoring the combination on Japanese financial benchmarks and qualitative response comparisons.", "A CALM composition adds cross-attention between the two models and is trained on a financial dataset different from the specialist's training data. Model families are not stated.", "The composed model scores above both original models and baselines, and connecting the models at their middle layers adapts best to the financial domain. Magnitudes are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "Japanese financial benchmarks", "salience": 30, "edition": 13, "models": [], "n": 1672, "authors_detailed": [ { "name": "Kota Tanabe", "url": "https://openalex.org/A5102409231", "inst": "Hokkaido University" }, { "name": "Masanori Hirano", "url": "https://openalex.org/A5002301422", "inst": "Preferred Networks (Japan)" }, { "name": "Kazuki Matoya", "url": "https://openalex.org/A5114426967", "inst": "Preferred Networks (Japan)" }, { "name": "Kentaro Imajo", "url": "https://openalex.org/A5038152086", "inst": "Preferred Networks (Japan)" }, { "name": "Hiroki Sakaji", "url": "https://openalex.org/A5028823648", "inst": "Hokkaido University" }, { "name": "Itsuki Noda", "url": "https://openalex.org/A5095740250", "inst": "Hokkaido University" } ], "affiliations": [ "Hokkaido University", "Preferred Networks (Japan)" ] }, { "uid": "arxiv:2411.08804v1", "arxiv_id": "2411.08804v1", "title": "FinRobot: AI Agent for Equity Research and Valuation with Large Language Models", "authors": [ "Tianyu Zhou", "Pinqiao Wang", "Yilin Wu", "Hongyang Yang" ], "posted": "2024-11-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.08804v1", "field": "finance", "role": "agent", "bullets": [ "Sell-side equity research and valuation, with an open-source multi-agent framework producing company analysis, valuation metrics, risk assessments, and an investment thesis from continuously updated data.", "Three chain-of-thought agents divide the work into data aggregation, analyst-style reasoning, and thesis writing; underlying model families are not named in the abstract.", "The authors state the reports are comparable to output from major brokerage firms and fundamental research vendors, a claim made without a stated evaluation metric." ], "bullet_provenance": "ai", "salience": 40, "edition": 13, "models": [], "validated": null, "n": 1670, "authors_detailed": [ { "name": "Tianyu Zhou", "url": "https://openalex.org/A5037683629", "inst": "Harbin Medical University" }, { "name": "P Wang", "url": "https://openalex.org/A5064569076", "inst": "University of Birmingham" }, { "name": "Yilin Wu", "url": "https://openalex.org/A5090957700", "inst": "Sun Yat-sen University" }, { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "Beijing University of Technology" } ], "affiliations": [ "Harbin Medical University", "University of Birmingham", "Sun Yat-sen University", "Beijing University of Technology" ] }, { "uid": "arxiv:2411.08404v1", "arxiv_id": "2411.08404v1", "title": "Quantifying Qualitative Insights: Leveraging LLMs to Market Predict", "authors": [ "Hoyoung Lee", "Youngsoo Choi", "Yuhee Kwon" ], "posted": "2024-11-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.08404v1", "field": "finance", "role": "instrument", "bullets": [ "Daily securities firm reports segmented into key factors and paired with price data to build context sets for market forecasting, with few-shot examples refreshed at query time.", "An LLM, unnamed in the abstract, assigns scores to the key factors through a crafted prompt, converting qualitative text into scaled quantitative inputs; the scores are not checked against human coding.", "The pipeline outperforms time-series models at market forecasting, though the authors flag imperfect reproducibility and limited explainability; effect sizes are not stated in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 13, "models": [], "n": 1671, "authors_detailed": [ { "name": "Hoyoung Lee", "url": "https://openalex.org/A5114658565", "inst": "" }, { "name": "Youngsoo Choi", "url": "https://openalex.org/A5114658566", "inst": "Lawrence Livermore National Laboratory" }, { "name": "Yuhee Kwon", "url": "https://openalex.org/A5114658567", "inst": "" } ], "affiliations": [ "Lawrence Livermore National Laboratory" ] }, { "uid": "doi:10.2139/ssrn.5016372", "doi": "10.2139/ssrn.5016372", "title": "Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence", "authors": [ "Anton Korinek", "Jai Vipra" ], "posted": "2024-11-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5016372", "field": "economics", "role": "object", "bullets": [ "Analysis of the foundation model industry focusing on LLM providers, cost structures, key inputs, and competitive dynamics across leading firms.", "No LLM used as tool; the paper studies market concentration, economies of scale, scope, vertical integration, and market tipping risk in AI.", "Significant economies of scale and scope create a tendency toward concentration; vertical integration could translate market power into unprecedented societal influence." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 2483, "authors_detailed": [ { "name": "Anton Korinek", "url": "https://openalex.org/A5009882421", "inst": "National Bureau of Economic Research" }, { "name": "Jai Vipra", "url": "https://openalex.org/A5093211854", "inst": "Cornell University" } ], "affiliations": [ "Cornell University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.06852v1", "arxiv_id": "2411.06852v1", "title": "Evaluating Large Language Models on Financial Report Summarization: An Empirical Study", "authors": [ "Xinqi Yang", "Scott Zang", "Yong Ren", "Dingjie Peng", "Zheng Wen" ], "posted": "2024-11-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.06852v1", "field": "finance", "role": "method", "bullets": [ "A corpus of financial reports released publicly on Hugging Face for testing automated report summarization; document counts are not given in the abstract.", "GLM-4, Mistral-NeMo, and Llama 3.1 generate summaries scored with ROUGE-1, BERT Score, and an LLM score, alongside qualitative checks of contextual fit and consistency.", "The paper contributes benchmark results and an evaluation framework mixing quantitative and qualitative criteria; relative model rankings are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "ROUGE-1 and BERT Score on financial report summaries", "salience": 31, "edition": 13, "n": 1584, "authors_detailed": [ { "name": "Yang Xinqi", "url": "https://openalex.org/A5101017883", "inst": "Soochow University" }, { "name": "Scott Zang", "url": "https://openalex.org/A5114654179", "inst": "" }, { "name": "Yong Ren", "url": "https://openalex.org/A5101369358", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Dingjie Peng", "url": "https://openalex.org/A5045771343", "inst": "Waseda University" }, { "name": "Zhen Wen", "url": "https://openalex.org/A5043926363", "inst": "Soochow University" } ], "affiliations": [ "Soochow University", "Beijing Academy of Artificial Intelligence", "Waseda University" ] }, { "uid": "arxiv:2411.06837v2", "arxiv_id": "2411.06837v2", "title": "Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications", "authors": [ "Sander Noels", "Alexander Rogiers", "Maarten Buyl", "Tijl De Bie" ], "posted": "2024-11-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.06837v2", "field": "management", "role": "object", "bullets": [ "A survey of empirical studies measuring how LLM systems change human attitudes and behaviour across politics, marketing, public health, e-commerce, and charitable giving.", "No new model deployment; the review categorizes interaction approach, model scale and capability, prompt design, personalization, and AI source disclosure as drivers of persuasive effectiveness, and audits study designs and success metrics.", "Surveyed systems frequently reach human-level or greater persuasiveness, which the authors connect to risks for information integrity, fairness, privacy, and autonomy, and to a case for updated regulation." ], "bullet_provenance": "ai", "salience": 45, "edition": 13, "models": [], "validated": null, "n": 1669, "authors_detailed": [ { "name": "Sander Noels", "url": "https://openalex.org/A5070561295", "inst": "Ghent University Hospital" }, { "name": "Alexander Rogiers", "url": "https://openalex.org/A5097358257", "inst": "Ghent University Hospital" }, { "name": "Maarten Buyl", "url": "https://openalex.org/A5060824248", "inst": "Ghent University Hospital" }, { "name": "Tijl De Bie", "url": "https://openalex.org/A5076045275", "inst": "Ghent University Hospital" } ], "affiliations": [ "Ghent University Hospital" ] }, { "uid": "doi:10.1007/s41060-025-00731-0", "doi": "10.1007/s41060-025-00731-0", "arxiv_id": "2411.07031v1", "title": "Evaluating the Accuracy of Chatbots in Financial Literature", "authors": [ "Orhan Erdem", "Kristi Hassett", "Feyzullah Egriboyun" ], "posted": "2024-11-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.07031v1", "field": "finance", "role": "method", "bullets": [ "One hundred fifty citations generated by ChatGPT-4o, ChatGPT o1-preview, and Gemini Advanced, evaluated for hallucination in financial literature.", "Each chatbot provided financial references; a novel nonbinary scoring method and recency measure assessed hallucination rates across topic vintages.", "ChatGPT-4o hallucinated at 20.0% and o1-preview at 21.3%, while Gemini Advanced hallucinated at 76.7%; rates rose for more recent topics." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "manual verification of 150 financial literature citations", "salience": 55, "n": 2957, "authors_detailed": [ { "name": "Orhan Erdem", "url": "https://openalex.org/A5000588911", "inst": "University of North Texas" }, { "name": "Kristi Hassett", "url": "https://openalex.org/A5114654325", "inst": "University of North Texas" }, { "name": "Feyzullah Egriboyun", "url": "https://openalex.org/A5114654326", "inst": "Hult International Business School" } ], "affiliations": [ "University of North Texas", "Hult International Business School" ] }, { "uid": "doi:10.2139/ssrn.5016963", "doi": "10.2139/ssrn.5016963", "title": "Harnessing Generative AI to Drive Responsible Business Research and Accelerate Social Impact", "authors": [ "David S. Steingard", "David Reibstein", "Mark Normandin" ], "posted": "2024-11-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5016963", "field": "management", "role": "instrument", "bullets": [ "366 academic journal articles submitted to the RRBM Honor Roll, evaluated by AI and human reviewers against SDG and responsible-research standards.", "A custom ChatGPT tool (ChatSDG+RR7) scored articles on sustainability and responsible-research criteria; results compared across AI-only, human-only, and collaborative evaluations.", "AI-assisted peer review improved consistency, transparency, and efficiency in research evaluation while reducing human bias and error relative to human-only review." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "AI vs human reviewer agreement on RRBM Honor Roll articles", "salience": 38, "n": 3458, "authors_detailed": [ { "name": "David S. Steingard", "url": "https://openalex.org/A5087297319", "inst": "Saint Joseph's University" }, { "name": "David J. Reibstein", "url": "https://openalex.org/A5006671061", "inst": "University of Pennsylvania" }, { "name": "Mark Normandin", "url": "https://openalex.org/A5114593000", "inst": "Saint Joseph's University" } ], "affiliations": [ "University of Pennsylvania", "Saint Joseph's University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.06391v1", "arxiv_id": "2411.06391v1", "title": "CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction", "authors": [ "Shuqi Li", "Yuebo Sun", "Yuxin Lin", "Xin Gao", "Shuo Shang", "Rui Yan" ], "posted": "2024-11-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.06391v1", "field": "finance", "role": "instrument", "bullets": [ "News-driven multi-stock movement prediction on six datasets covering US, China, Japan, and UK markets, with unidirectional links such as supplier-consumer relations motivating causal modelling.", "An LLM-based denoised news encoder, model unnamed in the abstract, filters useful information from noisy news, while a lag-dependent temporal causal discovery mechanism and functional causal model produce predictions.", "CausalStock beats strong baselines on news-driven and price-only multi-stock prediction across the six datasets and yields interpretable causal relations; margins are not stated in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 38, "edition": 13, "models": [], "n": 1668, "authors_detailed": [ { "name": "Shuqi Li", "url": "https://openalex.org/A5100675758", "inst": "National University of Singapore" }, { "name": "Yuebo Sun", "url": "https://openalex.org/A5111361374", "inst": "Institute of Quality Standards and Testing Technology for Agro Products" }, { "name": "Lin, Yuxin", "url": "", "inst": "" }, { "name": "Xin Gao", "url": "https://openalex.org/A5058706214", "inst": "Harbin University of Science and Technology" }, { "name": "Shuo Shang", "url": "https://openalex.org/A5114656937", "inst": "University of Electronic Science and Technology of China" }, { "name": "Rui Yan", "url": "https://openalex.org/A5100716377", "inst": "Harbin Institute of Technology" } ], "affiliations": [ "National University of Singapore", "Institute of Quality Standards and Testing Technology for Agro Products", "Harbin University of Science and Technology", "University of Electronic Science and Technology of China", "Harbin Institute of Technology" ] }, { "uid": "doi:10.18653/v1/2025.findings-emnlp.1227", "doi": "10.18653/v1/2025.findings-emnlp.1227", "arxiv_id": "2411.06272v2", "title": "Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models", "authors": [ "Xiaojun Wu", "Junxi Liu", "Huanyi Su", "Zhouchi Lin", "Yiyan Qi", "Chengjin Xu", "Jiajun Su", "Jiajie Zhong", "Fuwei Wang", "Saizhuo Wang", "Fengrui Hua", "Jia Li", "Jian Guo" ], "posted": "2024-11-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.06272v2", "field": "finance", "role": "method", "bullets": [ "Eight core financial NLP tasks in Chinese and English, assembled from open-source datasets and industry demands into a bilingual benchmark.", "GPT-4o, Llama3, FinGPT, and FinMA are compared on the benchmark; the authors also release Touchstone-GPT, trained with continual pretraining and instruction tuning, with public weights.", "Evaluated models show uneven strengths across financial understanding and generation; Touchstone-GPT performs well bilingually with weak spots on specific tasks; per-task scores are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "eight bilingual financial NLP benchmark tasks", "salience": 42, "edition": 13, "n": 1583, "authors_detailed": [ { "name": "Xiaojun Wu", "url": "https://openalex.org/A5017320951", "inst": "Shanghai Medical College of Fudan University" }, { "name": "Junxi Liu", "url": "https://openalex.org/A5050295166", "inst": "Chongqing University" }, { "name": "H.C. Su", "url": "https://openalex.org/A5113651583", "inst": "National Taipei University of Technology" }, { "name": "Zhouchi Lin", "url": "https://openalex.org/A5102174961", "inst": "Institute for Development and Economic Analysis" }, { "name": "Yiyan Qi", "url": "https://openalex.org/A5056609151", "inst": "Digital Science (United States)" }, { "name": "Chengjin Xu", "url": "https://openalex.org/A5022319543", "inst": "Hefei University of Technology" }, { "name": "Jiajun Su", "url": "https://openalex.org/A5024589846", "inst": "Huaqiao University" }, { "name": "Jiajie Zhong", "url": "https://openalex.org/A5076328758", "inst": "South China University of Technology" }, { "name": "Fuwei Wang", "url": "https://openalex.org/A5082966492", "inst": "Northwest University" }, { "name": "Saizhuo Wang", "url": "https://openalex.org/A5006393472", "inst": "Hong Kong University of Science and Technology" }, { "name": "Fengrui Hua", "url": "https://openalex.org/A5088224908", "inst": "Xihua University" }, { "name": "Jinlan Li", "url": "https://openalex.org/A5040765198", "inst": "University of Science and Technology of China" }, { "name": "Jian Guo", "url": "https://openalex.org/A5061772046", "inst": "National Bureau of Statistics of China" } ], "affiliations": [ "Shanghai Medical College of Fudan University", "Chongqing University", "National Taipei University of Technology", "Institute for Development and Economic Analysis", "Digital Science (United States)", "Hefei University of Technology", "Huaqiao University", "South China University of Technology" ] }, { "uid": "arxiv:2411.06076v1", "arxiv_id": "2411.06076v1", "title": "BreakGPT: Leveraging Large Language Models for Predicting Asset Price Surges", "authors": [ "Aleksandr Simonyan" ], "posted": "2024-11-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.06076v1", "field": "finance", "role": "method", "bullets": [ "Financial time series for volatile assets, targeting sharp upward price moves; the abstract does not state the sample, assets, or period.", "BreakGPT, an LLM based architecture joined with time series representation learning; the base model is not stated and no validation figures are reported.", "The author reports BreakGPT competitive with Transformer baselines at capturing local and global temporal dependencies with minimal training, but the abstract carries no performance numbers." ], "bullet_provenance": "ai", "validated": false, "salience": 22, "edition": 13, "models": [], "n": 1760, "authors_detailed": [ { "name": "Aleksandr Simonyan", "url": "https://openalex.org/A5114656907", "inst": "Adobe Systems (United States)" } ], "affiliations": [ "Adobe Systems (United States)" ] }, { "uid": "arxiv:2411.05764v1", "arxiv_id": "2411.05764v1", "title": "FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents", "authors": [ "Yilun Zhao", "Yitao Long", "Yuru Jiang", "Chengye Wang", "Weiyuan Chen", "Hongjun Liu", "Yiming Zhang", "Xiangru Tang", "Chen Zhao", "Arman Cohan" ], "posted": "2024-11-08", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.05764v1", "field": "finance", "role": "method", "bullets": [ "FinDVer, 2,400 expert annotated claim verification cases over long financial documents mixing text and tables, split into extraction, numerical reasoning, and knowledge intensive subsets.", "A broad set of LLMs evaluated under long context and retrieval augmented settings with chain of thought analysis; GPT-4o is the best system tested.", "Even GPT-4o falls short of human experts on the benchmark, with error analysis pointing to reasoning failures in long context and retrieval settings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "2,400 expert-annotated examples", "salience": 45, "edition": 13, "n": 1770, "authors_detailed": [ { "name": "Yilun Zhao", "url": "https://openalex.org/A5047416722", "inst": "Zhejiang University" }, { "name": "Yi‐Tao Long", "url": "https://openalex.org/A5034885015", "inst": "Thales (Australia)" }, { "name": "Yuru Jiang", "url": "https://openalex.org/A5020071474", "inst": "Beijing Information Science & Technology University" }, { "name": "Chengye Wang", "url": "https://openalex.org/A5045077119", "inst": "University of Illinois Chicago" }, { "name": "Weiyuan Chen", "url": "https://openalex.org/A5056048192", "inst": "University of Akron" }, { "name": "Hongjun Liu", "url": "https://openalex.org/A5084387750", "inst": "Düsseldorf University Hospital" }, { "name": "Zhang, Yiming", "url": "", "inst": "" }, { "name": "Xiangru Tang", "url": "https://openalex.org/A5108999586", "inst": "Yale University" }, { "name": "Chen Zhao", "url": "https://openalex.org/A5100352014", "inst": "Wuhan University" }, { "name": "Arman Cohan", "url": "https://openalex.org/A5064858748", "inst": "Yale University" } ], "affiliations": [ "Yale University", "Zhejiang University", "Thales (Australia)", "Beijing Information Science & Technology University", "University of Illinois Chicago", "University of Akron", "Wuhan University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.07264v1", "arxiv_id": "2411.07264v1", "title": "Multi-Document Financial Question Answering using LLMs", "authors": [ "Shalin Shah", "Srikanth Ryali", "Ramasubbu Venkatesh" ], "posted": "2024-11-08", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.07264v1", "field": "finance", "role": "method", "bullets": [ "18 10-K reports from Apple, Microsoft, Alphabet, NVIDIA, Amazon, and Tesla (2021-2023) with 111 complex financial questions.", "Semantic-tagging RAG and knowledge-graph RAG methods compared against plain RAG; small model fine-tuned via knowledge distillation from a large teacher.", "Both semantic RAG and knowledge-graph RAG outperform plain RAG; KG_RAG leads on four of nine evaluation metrics including faithfulness and correctness." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "faithfulness, relevance, correctness, ROUGE, and embedding similarity on 111 questions", "salience": 35, "n": 3457, "authors_detailed": [ { "name": "Shalin Shah", "url": "https://openalex.org/A5041467219", "inst": "Sant Gadge Baba Amravati University" }, { "name": "Srikanth Ryali", "url": "https://openalex.org/A5114657414", "inst": "" }, { "name": "Ramasubbu Venkatesh", "url": "https://openalex.org/A5027912683", "inst": "Tumkur University" } ], "affiliations": [ "Sant Gadge Baba Amravati University", "Tumkur University" ] }, { "uid": "arxiv:2411.04788v1", "arxiv_id": "2411.04788v1", "title": "Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research", "authors": [ "Xuewen Han", "Neng Wang", "Shangkun Che", "Hongyang Yang", "Kunpeng Zhang", "Sean Xin Xu" ], "posted": "2024-11-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.04788v1", "field": "finance", "role": "agent", "bullets": [ "Thirty Dow Jones companies, 2023 SEC 10-K filings, three sub-tasks: fundamentals, market sentiment, and risk analysis.", "Multi-agent GenAI system with configurable group sizes and collaboration structures analyzed filings using a sub-optimal combination strategy.", "Multi-agent collaboration outperformed single-agent models in accuracy and adaptability across all three financial analysis tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 3908, "authors_detailed": [ { "name": "Xuewen Han", "url": "https://openalex.org/A5062069504", "inst": "University of Toronto" }, { "name": "Wang, Neng", "url": "", "inst": "" }, { "name": "Shangkun Che", "url": "https://openalex.org/A5103120458", "inst": "Tsinghua University" }, { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "Beijing University of Technology" }, { "name": "Kunpeng Zhang", "url": "https://openalex.org/A5014223717", "inst": "Hebei Medical University" }, { "name": "Sean Xin Xu", "url": "https://openalex.org/A5035476877", "inst": "China Institutes of Contemporary International Relations" } ], "affiliations": [ "University of Toronto", "Tsinghua University", "Beijing University of Technology", "Hebei Medical University", "China Institutes of Contemporary International Relations" ], "prestige": true }, { "uid": "arxiv:2411.04473v1", "arxiv_id": "2411.04473v1", "title": "ML-Promise: A Multilingual Dataset for Corporate Promise Verification", "authors": [ "Yohei Seki", "Hakusen Shu", "Anaïs Lhuissier", "Hanwool Lee", "Juyeon Kang", "Min-Yuh Day", "Chung-Chi Chen" ], "posted": "2024-11-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.04473v1", "field": "management", "role": "instrument", "bullets": [ "Multilingual dataset of corporate ESG report promises in English, French, Chinese, Japanese, and Korean.", "RAG-based approaches identified and verified corporate environmental promises; textual and image-based baselines tested.", "RAG approaches showed strongest results for promise verification, enabling cross-lingual corporate accountability assessment." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 40, "n": 3909, "authors_detailed": [ { "name": "Yohei Seki", "url": "https://openalex.org/A5078878706", "inst": "University of Tsukuba" }, { "name": "Shu, Hakusen", "url": "", "inst": "" }, { "name": "Anaïs Lhuissier", "url": "https://openalex.org/A5114655671", "inst": "Thales (France)" }, { "name": "Hanwool Lee", "url": "https://openalex.org/A5103110871", "inst": "University of Jyväskylä" }, { "name": "Juyeon Kang", "url": "https://openalex.org/A5053661888", "inst": "Dassault Systèmes (France)" }, { "name": "Min-Yuh Day", "url": "https://openalex.org/A5055245450", "inst": "National Taipei University" }, { "name": "Chung-Chi Chen", "url": "https://openalex.org/A5101516307", "inst": "National Institute of Informatics" } ], "affiliations": [ "University of Tsukuba", "Thales (France)", "University of Jyväskylä", "Dassault Systèmes (France)", "National Taipei University", "National Institute of Informatics" ] }, { "uid": "arxiv:2411.03402v1", "arxiv_id": "2411.03402v1", "title": "Climate AI for Corporate Decarbonization Metrics Extraction", "authors": [ "Aditya Dave", "Mengchen Zhu", "Dapeng Hu", "Sachin Tiwari" ], "posted": "2024-11-05", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.03402v1", "field": "finance", "role": "method", "bullets": [ "Corporate greenhouse gas emission targets sourced from public sustainability disclosures that follow no standard format, curated as metrics for sustainable investing.", "An LLM pipeline extracts, validates, and scores linked decarbonization metrics, replacing manual curation that requires subject matter expert review; results are reported as robust to the choice of LLM.", "Automated curation is reported to improve collection efficiency and accuracy relative to manual processing; specific accuracy figures are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "extraction accuracy assessed against expert-validated curation; no figure in abstract", "salience": 40, "edition": 13, "models": [], "n": 1667, "authors_detailed": [ { "name": "Dave, Aditya", "url": "", "inst": "" }, { "name": "Zhu, Mengchen", "url": "", "inst": "" }, { "name": "Dapeng Hu", "url": "https://openalex.org/A5061428018", "inst": "Xi'an University of Architecture and Technology" }, { "name": "Tiwari, Sachin", "url": "", "inst": "" } ], "affiliations": [ "Xi'an University of Architecture and Technology" ] }, { "uid": "arxiv:2411.03314v1", "arxiv_id": "2411.03314v1", "title": "MME-Finance: A Multimodal Finance Benchmark for Expert-level Understanding and Reasoning", "authors": [ "Ziliang Gan", "Yu Lu", "Dong Zhang", "Haohan Li", "Che Liu", "Jian Liu", "Ji Liu", "Haipang Wu", "Chaoyou Fu", "Zenglin Xu", "Rongjunchen Zhang", "Yong Dai" ], "posted": "2024-11-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.03314v1", "field": "finance", "role": "method", "bullets": [ "Bilingual benchmark evaluating 19 multimodal LLMs on financial visual QA including candlestick charts and technical indicators.", "GPT-4o, Qwen2VL-72B, and 17 other models tested on perception, reasoning, and cognition using expert-annotated financial questions.", "Top scores are 65.69 (Qwen2VL-72B) and 63.18 (GPT-4o); performance is weakest on finance-specific chart categories." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "validated": true, "validation_note": "expert-annotated financial VQA benchmark with 10+ year industry experts", "salience": 45, "n": 3456, "authors_detailed": [ { "name": "Ziliang Gan", "url": "https://openalex.org/A5047019585", "inst": "Higher Institute for Tourism, Hotels and Computer" }, { "name": "Lu Yu", "url": "https://openalex.org/A5044385614", "inst": "Sichuan University" }, { "name": "Dong Zhang", "url": "https://openalex.org/A5101763138", "inst": "ShanghaiTech University" }, { "name": "Haohan Li", "url": "https://openalex.org/A5060134713", "inst": "Hangzhou Dianzi University" }, { "name": "Che Liu", "url": "https://openalex.org/A5014579261", "inst": "Northwestern Polytechnical University" }, { "name": "Jianjun Liu", "url": "https://openalex.org/A5100374993", "inst": "University of North Carolina at Chapel Hill" }, { "name": "Liu Ji", "url": "https://openalex.org/A5019728384", "inst": "Third People's Hospital of Hefei" }, { "name": "Haipang Wu", "url": "https://openalex.org/A5071627107", "inst": "Higher Institute for Tourism, Hotels and Computer" }, { "name": "Chaoyou Fu", "url": "https://openalex.org/A5014172220", "inst": "Peking University" }, { "name": "Zenglin Xu", "url": "https://openalex.org/A5051227924", "inst": "Shanghai Academy of Social Sciences" }, { "name": "Runxin Zhang", "url": "https://openalex.org/A5084208969", "inst": "Second Military Medical University" }, { "name": "Yong Dai", "url": "https://openalex.org/A5102982899", "inst": "Nanjing Tech University" } ], "affiliations": [ "University of North Carolina at Chapel Hill", "Higher Institute for Tourism, Hotels and Computer", "Sichuan University", "ShanghaiTech University", "Hangzhou Dianzi University", "Northwestern Polytechnical University", "Peking University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.02476v1", "arxiv_id": "2411.02476v1", "title": "A Comparative Analysis of Instruction Fine-Tuning LLMs for Financial Text Classification", "authors": [ "Sorouralsadat Fatemi", "Yuheng Hu", "Maryam Mousavi" ], "posted": "2024-11-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.02476v1", "field": "finance", "role": "method", "bullets": [ "Four financial text classification tasks for fine-tuning plus three unseen tasks covering argument, deal completeness and causal classification for zero-shot tests.", "Mistral 7B, Llama3 8B and Phi3 mini are instruction fine-tuned and merged with base models; performance is scored on the labelled tasks, without figures in the abstract.", "Instruction-tuned variants degrade less on unseen tasks than base fine-tunes, and model merging restores zero-shot accuracy, sometimes above the original model." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "labelled financial classification tasks, figures not in abstract", "salience": 38, "edition": 13, "n": 1547, "authors_detailed": [ { "name": "Sorouralsadat Fatemi", "url": "https://openalex.org/A5058376884", "inst": "University of Illinois Chicago" }, { "name": "Yuheng Hu", "url": "https://openalex.org/A5016075036", "inst": "Tongji University" }, { "name": "Maryam Mousavi", "url": "https://openalex.org/A5015657656", "inst": "Amirkabir University of Technology" } ], "affiliations": [ "University of Illinois Chicago", "Tongji University", "Amirkabir University of Technology" ] }, { "uid": "doi:10.2139/ssrn.4991944", "doi": "10.2139/ssrn.4991944", "title": "Human Capital Disclosure and Labor Market Outcomes: Evidence from Regulation S-K", "authors": [ "Jung Ho Choi", "Dan Li", "Daniele Macciocchi" ], "posted": "2024-11-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4991944", "field": "accounting", "role": "instrument", "bullets": [ "US public firms subject to 2020 Regulation S-K matched with large private firms; job-level posting data analyzed for DEI disclosure.", "Generative LLM measures DEI content in job postings to detect disclosure changes following human capital reporting regulation.", "Public firms increase DEI disclosure in job postings post-regulation; recruitment periods lengthen but workplace gender diversity rises after one year." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 60, "n": 3455, "authors_detailed": [ { "name": "Jung Ho Choi", "url": "https://openalex.org/A5010840958", "inst": "Palo Alto University" }, { "name": "Dan Li", "url": "https://openalex.org/A5100380686", "inst": "Singapore Management University" }, { "name": "Daniele Macciocchi", "url": "https://openalex.org/A5039473499", "inst": "University of Miami" } ], "affiliations": [ "Palo Alto University", "Singapore Management University", "University of Miami" ] }, { "uid": "arxiv:2411.01582v2", "arxiv_id": "2411.01582v2", "title": "Donald Trumps in the Virtual Polls: Simulating and Predicting Public Opinions in Surveys Using Large Language Models", "authors": [ "Shapeng Jiang", "Lijia Wei", "Chen Zhang" ], "posted": "2024-11-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.01582v2", "field": "economics", "role": "agent", "bullets": [ "World Values Survey and American National Election Studies records covering US and China samples, past US elections, and the 2024 presidential race.", "ChatGPT-4o generates synthetic survey answers from demographic profiles and simulates voting behavior; outputs are compared with actual human responses and election results.", "Simulations reproduce cultural differences and in-sample voting patterns and yield plausible out-of-sample 2024 forecasts, with weaker fidelity on value-sensitive topics." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "human WVS and ANES responses and election outcomes", "salience": 48, "edition": 13, "n": 1582, "authors_detailed": [ { "name": "Song Jiang", "url": "https://openalex.org/A5026734950", "inst": "Institute of Applied Physics" }, { "name": "Lijia Wei", "url": "https://openalex.org/A5061079364", "inst": "Wuhan University" }, { "name": "Chen Zhang", "url": "https://openalex.org/A5100374160", "inst": "San Francisco Art Institute" } ], "affiliations": [ "Institute of Applied Physics", "Wuhan University", "San Francisco Art Institute" ] }, { "uid": "arxiv:2411.01368v1", "arxiv_id": "2411.01368v1", "title": "Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models", "authors": [ "Ali Elahi", "Fatemeh Taghvaei" ], "posted": "2024-11-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.01368v1", "field": "finance", "role": "method", "bullets": [ "Financial statements, price histories, and multi-source news for 20 companies with the highest trading volumes across industries; the market is not stated.", "GPT-3, GPT-4, Llama 2, and Llama 3 classify three and six month price direction from tabular metrics plus retrieved news chunks in zero, two, and four shot prompts.", "The best classifier reaches weighted F1 of 58.5 and 59.1 percent for the three and six month horizons, with a Matthews correlation of 0.175 at both." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "realized three and six month price moves, F1 and Matthews correlation", "salience": 33, "edition": 13, "n": 1581, "authors_detailed": [ { "name": "A Abd Elahi", "url": "https://openalex.org/A5017808998", "inst": "Baptist Hospital of Miami" }, { "name": "Fatemeh Taghvaei", "url": "https://openalex.org/A5114637383", "inst": "University of Illinois Chicago" } ], "affiliations": [ "University of Illinois Chicago" ] }, { "uid": "doi:10.1109/bigdata62323.2024.10826008", "doi": "10.1109/bigdata62323.2024.10826008", "arxiv_id": "2411.00420v1", "title": "Evaluating Company-specific Biases in Financial Sentiment Analysis using Large Language Models", "authors": [ "Kei Nakagawa", "Masanori Hirano", "Yugo Fujimoto" ], "posted": "2024-11-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.00420v1", "field": "finance", "role": "method", "bullets": [ "Japanese financial texts scored for sentiment, with firm-level analysis linking measured bias to corporate characteristics and stock performance; sample size is not stated in the abstract.", "LLMs, unnamed in the abstract, score identical texts with and without the company name in the prompt; the gap defines company-specific bias, and no ground-truth accuracy check is reported.", "An accompanying economic model shows how widespread biased LLM sentiment could distort stock prices, and the empirical analysis ties firm-specific bias to characteristics and returns; magnitudes are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 13, "models": [], "n": 1666, "authors_detailed": [ { "name": "Kei Nakagawa", "url": "https://openalex.org/A5086122043", "inst": "Nomura Asset Management Co, Ltd.,Innovation Lab,Tokyo,Japan" }, { "name": "Masanori Hirano", "url": "https://openalex.org/A5002301422", "inst": "Preferred Networks (Japan)" }, { "name": "Yugo Fujimoto", "url": "https://openalex.org/A5010069030", "inst": "Nomura Asset Management Co, Ltd.,Innovation Lab,Tokyo,Japan" } ], "affiliations": [ "Nomura Asset Management Co, Ltd.,Innovation Lab,Tokyo,Japan", "Preferred Networks (Japan)" ] }, { "uid": "arxiv:2411.11853v3", "arxiv_id": "2411.11853v3", "title": "Chat Bankman-Fried: an Exploration of LLM Alignment in Finance", "authors": [ "Claudia Biancotti", "Carolina Camassa", "Andrea Coletta", "Oliver Giudice", "Aldo Glielmo" ], "posted": "2024-11-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.11853v3", "field": "finance", "role": "agent", "bullets": [ "Twelve LLMs cast as the CEO of a financial institution facing outstanding corporate debt, in a simulation that varies preferences, incentives, and constraints.", "Each model decides whether to misuse customer assets, with logistic regressions tracing how every adjustment moves that choice; the twelve models are not named in the abstract.", "Baseline willingness to act unethically differs sharply across models, and risk aversion, profit expectations, and regulation shift misalignment in the direction economic theory predicts." ], "bullet_provenance": "ai", "salience": 62, "edition": 13, "models": [], "validated": null, "n": 1759, "authors_detailed": [ { "name": "Claudia Biancotti", "url": "https://openalex.org/A5029031757", "inst": "Bank of Italy" }, { "name": "Carolina Camassa", "url": "https://openalex.org/A5092618396", "inst": "Bank of Italy" }, { "name": "Andrea Coletta", "url": "https://openalex.org/A5051607028", "inst": "Bank of Italy" }, { "name": "Oliver Giudice", "url": "https://openalex.org/A5014104390", "inst": "Bank of Italy" }, { "name": "Aldo Glielmo", "url": "https://openalex.org/A5002088349", "inst": "Bank of Italy" } ], "affiliations": [ "Bank of Italy" ] }, { "uid": "doi:10.2139/ssrn.5007084", "doi": "10.2139/ssrn.5007084", "title": "Generative AI and the Nature of Work", "authors": [ "Manuel Hoffmann", "Sam Boysel", "Frank Nagle", "Sida Peng", "Kevin Xu" ], "posted": "2024-11-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5007084", "field": "management", "role": "object", "bullets": [ "Open-source software developers on GitHub observed over two years; regression discontinuity design around Copilot eligibility threshold.", "GitHub Copilot, a generative AI code completion tool, studied for causal effects on developer task allocation across millions of panel observations.", "Copilot access shifts work toward core coding and away from project management; effects are larger for lower-ability developers." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 3453, "authors_detailed": [ { "name": "Manuel Hoffmann", "url": "https://openalex.org/A5110061098", "inst": "Harvard Business School" }, { "name": "Sam Boysel", "url": "https://openalex.org/A5114485212", "inst": "Harvard Business School" }, { "name": "Frank Nagle", "url": "https://openalex.org/A5042707032", "inst": "Linux Foundation" }, { "name": "Sida Peng", "url": "https://openalex.org/A5073087748", "inst": "Microsoft (Finland)" }, { "name": "Kevin S. Xu", "url": "https://openalex.org/A5009765913", "inst": "STCube (United States)" } ], "affiliations": [ "Harvard Business School", "Linux Foundation", "Microsoft (Finland)", "STCube (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.01067v2", "arxiv_id": "2411.01067v2", "title": "Randomized Controlled Trials for Security Copilot for IT Administrators", "authors": [ "James Bono", "Alec Xu" ], "posted": "2024-11-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.01067v2", "field": "management", "role": "object", "bullets": [ "Randomized controlled trials of IT administrators using Microsoft Security Copilot in Entra and Intune admin centers across three scenarios.", "Microsoft Security Copilot tested on sign-in troubleshooting, device policy management, and device troubleshooting tasks.", "Copilot users achieved 34.53% higher accuracy and 29.79% faster completion; free-response tasks showed 146% more relevant facts identified." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3454, "authors_detailed": [ { "name": "James V. Bono", "url": "https://openalex.org/A5013037997", "inst": "New England Baptist Hospital" }, { "name": "Aman Xu", "url": "https://openalex.org/A5033441324", "inst": "Boston University" } ], "affiliations": [ "Boston University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3677052.3698694", "doi": "10.1145/3677052.3698694", "arxiv_id": "2411.00856v1", "title": "AI in Investment Analysis: LLMs for Equity Stock Ratings", "authors": [ "Kassiani Papasotiriou", "Srijan Sood", "Shayleen Reynolds", "Tucker Balch" ], "posted": "2024-10-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.00856v1", "field": "finance", "role": "agent", "bullets": [ "Fundamental financials, market data, and news for listed equities from January 2022 to June 2024; the size of the stock universe is not stated in the abstract.", "GPT-4-32k v0613, chosen for its September 2021 knowledge cutoff to limit leakage, produces multi-horizon stock ratings from varied data mixes; ratings are judged by forward returns rather than analyst agreement.", "LLM ratings beat traditional rating methods on forward returns, gaining most from fundamentals; sentiment scores can replace full news summaries, and omitting news sometimes helps by reducing bias." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 13, "validated": null, "n": 1580, "authors_detailed": [ { "name": "Kassiani Papasotiriou", "url": "https://openalex.org/A5030885959", "inst": "Morgan Stanley (United States)" }, { "name": "Srijan Sood", "url": "https://openalex.org/A5043221382", "inst": "Morgan Stanley (United States)" }, { "name": "Shayleen Reynolds", "url": "https://openalex.org/A5064210543", "inst": "Morgan Stanley (United States)" }, { "name": "Tucker Balch", "url": "https://openalex.org/A5035482777", "inst": "Emory University" } ], "affiliations": [ "Emory University", "Morgan Stanley (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3677052.3698686", "doi": "10.1145/3677052.3698686", "arxiv_id": "2410.21741v1", "title": "Enhancing Financial Question Answering with a Multi-Agent Reflection Framework", "authors": [ "Sorouralsadat Fatemi", "Yuheng Hu" ], "posted": "2024-10-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.21741v1", "field": "finance", "role": "method", "bullets": [ "Financial question answering requiring numerical reasoning over tables and text; the specific benchmark datasets are not named in the abstract.", "LLaMA3 8B and 70B agents are wrapped in a reflection framework with one or more critic agents reviewing reasoning steps, scored on financial QA tasks.", "Critic agents add about 15 percent for the 8B model and 5 percent for the 70B, matching or beating GPT-4o mini and LLaMA3.1 405B but trailing Claude 3.5 Sonnet." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "financial QA benchmarks, dataset names not stated", "salience": 42, "edition": 13, "n": 1546, "authors_detailed": [ { "name": "Sorouralsadat Fatemi", "url": "https://openalex.org/A5058376884", "inst": "University of Illinois Chicago" }, { "name": "Yuheng Hu", "url": "https://openalex.org/A5016075036", "inst": "University of Illinois Chicago" } ], "affiliations": [ "University of Illinois Chicago" ] }, { "uid": "doi:10.2139/ssrn.4986017", "doi": "10.2139/ssrn.4986017", "title": "Generative AI in Financial Reporting", "authors": [ "Elizabeth Blankespoor", "Ed deHaan", "Qianqian Li" ], "posted": "2024-10-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4986017", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 48, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 648 }, { "uid": "doi:10.2139/ssrn.5001357", "doi": "10.2139/ssrn.5001357", "title": "Ai and Finance", "authors": [ "Andrea L. Eisfeldt", "Gregor Schubert" ], "posted": "2024-10-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.5001357", "field": "finance", "role": "object", "bullets": [ "Review of literature on generative AI adoption effects on firm value and innovations in AI-assisted financial research methods.", "Surveys ChatGPT's impact on firms and describes available AI tools for researchers with practical implementation guidance.", "Documents rapid growth in demand for AI-related skills in finance and identifies multiple directions for future empirical research." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "validated": null, "n": 2956, "authors_detailed": [ { "name": "Andrea L. Eisfeldt", "url": "https://openalex.org/A5059753495", "inst": "University of California, Los Angeles" }, { "name": "Gregor Schubert", "url": "https://openalex.org/A5088498667", "inst": "University of California, Los Angeles" } ], "affiliations": [ "University of California, Los Angeles" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3677052.3698688", "doi": "10.1145/3677052.3698688", "arxiv_id": "2411.08899v1", "title": "FinVision: A Multi-Agent Framework for Stock Market Prediction", "authors": [ "Sorouralsadat Fatemi", "Yuheng Hu" ], "posted": "2024-10-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.08899v1", "field": "finance", "role": "agent", "bullets": [ "Stock market trading environment using textual news reports, candlestick charts, and trading signal charts as multimodal inputs.", "Multi-agent LLM system with specialized agents and a reflection module processes multimodal financial data to make trading decisions.", "Ablation studies confirm the visual reflection module is the key driver of improved trading decision-making in the framework." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 3451, "authors_detailed": [ { "name": "Sorouralsadat Fatemi", "url": "https://openalex.org/A5058376884", "inst": "University of Illinois Chicago" }, { "name": "Yuheng Hu", "url": "https://openalex.org/A5016075036", "inst": "University of Illinois Chicago" } ], "affiliations": [ "University of Illinois Chicago" ] }, { "uid": "arxiv:2410.21771v1", "arxiv_id": "2410.21771v1", "title": "Why is it so hard to find a job now? Enter Ghost Jobs", "authors": [ "Hunter Ng" ], "posted": "2024-10-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.21771v1", "field": "economics", "role": "instrument", "bullets": [ "Glassdoor job ads dataset covering US labor market; Beveridge Curve analysis spanning fifteen years of vacancy-unemployment data.", "LLM-BERT technique classifies job postings as ghost jobs, defined as ads posted without genuine intent to fill the position.", "Up to 21% of job ads are ghost jobs, concentrated in specialized industries and larger firms; ghost jobs explain the recent Beveridge Curve disconnect." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 65, "n": 3452, "authors_detailed": [ { "name": "H. Alvin Ng", "url": "https://openalex.org/A5061662424", "inst": "Pace University" } ], "affiliations": [ "Pace University" ] }, { "uid": "arxiv:2410.21338v2", "arxiv_id": "2410.21338v2", "title": "FinTeamExperts: Role Specialized MOEs For Financial Analysis", "authors": [ "Yue Yu", "Prayag Tiwari" ], "posted": "2024-10-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.21338v2", "field": "finance", "role": "method", "bullets": [ "Financial NLP tasks across four benchmark datasets; the framework trains three 8 billion parameter models on separate corpora for macro, micro and quantitative analyst roles.", "A mixture-of-experts ensemble of role-specialized LLMs is instruct-tuned on downstream tasks; the base model family is not named and no ground-truth validation statistics are given.", "FinTeamExperts beats same-size and larger models on three of four datasets and same-size models on the fourth, which the authors credit to role specialization." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": false, "salience": 33, "edition": 13, "n": 1535, "authors_detailed": [ { "name": "Yue Yu", "url": "https://openalex.org/A5102989349", "inst": "University of British Columbia" }, { "name": "Prayag Tiwari", "url": "https://openalex.org/A5114653296", "inst": "Halmstad University" } ], "affiliations": [ "University of British Columbia", "Halmstad University" ] }, { "uid": "arxiv:2410.20739v3", "arxiv_id": "2410.20739v3", "title": "Gender Bias in LLM-generated Interview Responses", "authors": [ "Haein Kong", "Yongsu Ahn", "Sangyub Lee", "Yunho Maeng" ], "posted": "2024-10-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.20739v3", "field": "management", "role": "agent", "bullets": [ "Job interview answers generated across models, question types, and occupations, audited against two gender stereotype frameworks; response counts are not stated in the abstract.", "GPT-3.5, GPT-4, and Claude write interview responses for varied jobs; the audit checks how the generated language aligns with gender stereotypes and job gender dominance.", "Gender bias appears consistently in all three models and tracks both established stereotypes and the gender dominance of the job in question." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 36, "edition": 13, "validated": null, "n": 1579, "authors_detailed": [ { "name": "Haein Kong", "url": "https://openalex.org/A5032409331", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Ahn, Yongsu", "url": "", "inst": "" }, { "name": "Lee, Sangyub", "url": "", "inst": "" }, { "name": "Yunho Maeng", "url": "https://openalex.org/A5114633783", "inst": "" } ], "affiliations": [ "Rutgers, The State University of New Jersey" ] }, { "uid": "arxiv:2411.05801v1", "arxiv_id": "2411.05801v1", "title": "Do LLM Personas Dream of Bull Markets? Comparing Human and AI Investment Strategies Through the Lens of the Five-Factor Model", "authors": [ "Harris Borman", "Anna Leontjeva", "Luiz Pizzato", "Max Kun Jiang", "Dan Jermyn" ], "posted": "2024-10-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.05801v1", "field": "finance", "role": "agent", "bullets": [ "A simulated investment task given to LLM personas built from Big Five personality profiles, with a parallel survey condition for comparison.", "The persona prompted model, unnamed in the abstract, makes investment choices; behaviour is compared with trait links documented in human personality research.", "Personas reproduce trait consistent differences in learning style, impulsivity, and risk appetite, miss on environmental attitudes, and look more human in the simulation than in the survey format." ], "bullet_provenance": "ai", "validated": true, "validation_note": "behaviour compared with human Big Five findings", "salience": 45, "edition": 13, "models": [], "n": 1758, "authors_detailed": [ { "name": "Harris Borman", "url": "https://openalex.org/A5114653930", "inst": "Reserve Bank of Australia" }, { "name": "Anna Leontjeva", "url": "https://openalex.org/A5016341909", "inst": "Reserve Bank of Australia" }, { "name": "Luiz Pizzato", "url": "https://openalex.org/A5042400550", "inst": "Reserve Bank of Australia" }, { "name": "Jiang, Max Kun", "url": "", "inst": "" }, { "name": "Dan Jermyn", "url": "https://openalex.org/A5114653931", "inst": "Reserve Bank of Australia" } ], "affiliations": [ "Reserve Bank of Australia" ] }, { "uid": "arxiv:2410.21359v3", "arxiv_id": "2410.21359v3", "title": "Can Machines Think Like Humans? A Behavioral Evaluation of LLM Agents in Dictator Games", "authors": [ "Ji Ma" ], "posted": "2024-10-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.21359v3", "field": "economics", "role": "agent", "bullets": [ "Dictator games played by LLM agents under varied personas and experimental framings, with giving compared within model families, across families, and with human baselines.", "Several LLM families, not named in the abstract, are prompted with human like identities; allocation choices and reasoning text are examined for markers of human decision making.", "Assigning a human identity does not produce human like giving; alignment with human behaviour varies across architectures and prompt wording with no clear pattern." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "human dictator game behaviour", "salience": 50, "edition": 13, "n": 1769, "authors_detailed": [ { "name": "Ji Ma", "url": "https://openalex.org/A5103136675", "inst": "Liaoning University" } ], "affiliations": [ "Liaoning University" ] }, { "uid": "arxiv:2410.20651v2", "arxiv_id": "2410.20651v2", "title": "SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis", "authors": [ "Huzaifa Pardawala", "Siddhant Sukhani", "Agam Shah", "Veer Kejriwal", "Abhishek Pillai", "Rohan Bhasin", "Andrew DiBiasio", "Tarun Mandapati", "Dhruv Adha", "Sudheer Chava" ], "posted": "2024-10-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.20651v2", "field": "finance", "role": "instrument", "bullets": [ "49,446 expert annotations on earnings call transcript QA pairs scored across six subjectivity dimensions including assertiveness and optimism.", "RoBERTa-base and Llama-3-70b-Chat classify subjectivity features; generalizability tested on White House press briefings.", "RoBERTa matches Llama-3-70b on low-subjectivity features (2.17% F1 gap) but trails on high-subjectivity ones (10.01% gap); cross-domain F1 averages 65.97%." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "weighted F1 against human annotations across six features", "salience": 55, "n": 3450, "authors_detailed": [ { "name": "Huzaifa Pardawala", "url": "https://openalex.org/A5114620071", "inst": "Georgia Institute of Technology" }, { "name": "Siddhant Sukhani", "url": "https://openalex.org/A5114620072", "inst": "Georgia Institute of Technology" }, { "name": "Agam Shah", "url": "https://openalex.org/A5017623662", "inst": "Georgia Institute of Technology" }, { "name": "Veer Kejriwal", "url": "https://openalex.org/A5114620073", "inst": "Georgia Institute of Technology" }, { "name": "Abhishek Lakshman Pillai", "url": "https://openalex.org/A5085052934", "inst": "Kyoto University" }, { "name": "Rohan Bhasin", "url": "https://openalex.org/A5086770035", "inst": "" }, { "name": "Andrew DiBiasio", "url": "https://openalex.org/A5114620074", "inst": "" }, { "name": "Tarun Mandapati", "url": "https://openalex.org/A5114620075", "inst": "" }, { "name": "Dhruv Adha", "url": "https://openalex.org/A5114620076", "inst": "" }, { "name": "Sudheer Chava", "url": "https://openalex.org/A5029248881", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology", "Kyoto University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3677052.3698597", "doi": "10.1145/3677052.3698597", "arxiv_id": "2410.19727v1", "title": "FISHNET: Financial Intelligence from Sub-querying, Harmonizing, Neural-Conditioning, Expert Swarms, and Task Planning", "authors": [ "Nicole Cho", "Nishan Srishankar", "Lucas Cecchi", "William Watson" ], "posted": "2024-10-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.19727v1", "field": "finance", "role": "method", "bullets": [ "Over 98,000 regulatory filings with widely varying semantics, hierarchy and format serve as the corpus for automated financial analysis tasks.", "An agentic architecture of sub-querying, harmonizing, neural conditioning, expert swarm and task planning agents built on LLMs; the abstract does not name the underlying model.", "The assembled swarm reaches a 61.8 percent success rate on insight generation, against 5 percent for routing and 45.6 percent R-precision for retrieval augmented generation, with ablations crediting each agent." ], "bullet_provenance": "ai", "validated": true, "validation_note": "task success rate against routing and RAG baselines", "salience": 42, "edition": 13, "models": [], "n": 1706, "authors_detailed": [ { "name": "Nicole Cho", "url": "https://openalex.org/A5084455404", "inst": "J.P. Morgan, US" }, { "name": "Nishan Srishankar", "url": "https://openalex.org/A5052272697", "inst": "J.P. Morgan, US" }, { "name": "Lucas Cecchi", "url": "https://openalex.org/A5103124317", "inst": "J.P. Morgan, US" }, { "name": "William Watson", "url": "https://openalex.org/A5074837370", "inst": "J.P. Morgan, US" } ], "affiliations": [ "J.P. Morgan, US" ] }, { "uid": "arxiv:2410.19599v3", "arxiv_id": "2410.19599v3", "title": "Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina", "authors": [ "Yuan Gao", "Dokyun Lee", "Gordon Burtch", "Sina Fazelpour" ], "posted": "2024-10-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.19599v3", "field": "economics", "role": "method", "bullets": [ "The 11-20 money request game, a standard test of strategic reasoning depth, replayed with a wide range of LLMs under varied languages, assigned roles, and prompt setups.", "Models play the game and their choice distributions are set against human experimental play; the abstract names no specific model families.", "Nearly all setups fail to reproduce the human distribution, and failures shift unpredictably with input language, roles, and safeguarding, cautioning against surrogate use in social science." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human choice distributions in the 11-20 game", "salience": 68, "edition": 13, "models": [], "n": 1757, "authors_detailed": [ { "name": "Yuan Gao", "url": "https://openalex.org/A5114619452", "inst": "Quest University Canada" }, { "name": "Dokyun Lee", "url": "https://openalex.org/A5088913516", "inst": "Boston University" }, { "name": "Gordon Burtch", "url": "https://openalex.org/A5057319034", "inst": "Boston University" }, { "name": "Sina Fazelpour", "url": "https://openalex.org/A5114619454", "inst": "" } ], "affiliations": [ "Boston University", "Quest University Canada" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2410.18448v1", "arxiv_id": "2410.18448v1", "title": "GPT-Signal: Generative AI for Semi-automated Feature Engineering in the Alpha Research Process", "authors": [ "Yining Wang", "Jinman Zhao", "Yuri Lawryshyn" ], "posted": "2024-10-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.18448v1", "field": "finance", "role": "method", "bullets": [ "Alpha research for algorithmic trading, where formulaic return-predictive signals are generated as a feature engineering step; the asset universe and sample period are not stated.", "GPT-4 proposes new formulaic alphas in a semi-automated workflow; the abstract reports no validation of the generated signals against any benchmark.", "The paper positions LLM-generated alphas as a time-saving complement to manual feature engineering; no out-of-sample performance figures appear in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "edition": 13, "n": 1529, "authors_detailed": [ { "name": "Yining Wang", "url": "https://openalex.org/A5100424588", "inst": "Beijing Institute of Technology" }, { "name": "Zhao, Jinman", "url": "", "inst": "" }, { "name": "Yuri Lawryshyn", "url": "https://openalex.org/A5074794416", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto", "Beijing Institute of Technology" ], "prestige": true }, { "uid": "arxiv:2410.19025v1", "arxiv_id": "2410.19025v1", "title": "Large Language Models for Financial Aid in Financial Time-series Forecasting", "authors": [ "Md Khairul Islam", "Ayush Karmacharya", "Timothy Sue", "Judy Fox" ], "posted": "2024-10-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.19025v1", "field": "finance", "role": "method", "bullets": [ "Financial aid time series marked by short histories and high-dimensional inputs, evaluated alongside seven other forecasting tasks in a benchmark study.", "Pre-trained models including a GPT-2 backbone LLM, transformers, and linear models forecast in zero-shot and few-shot modes; errors are scored against actual series values.", "Foundation models outperform traditional approaches on scarce financial datasets even with little or no fine-tuning; specific error metrics are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "forecast errors against actual series", "salience": 20, "edition": 13, "n": 1618, "authors_detailed": [ { "name": "Md. Khairul Islam", "url": "https://openalex.org/A5100671175", "inst": "Islamic University" }, { "name": "Ayush Karmacharya", "url": "https://openalex.org/A5033888207", "inst": "University of Virginia" }, { "name": "Timothy Sue", "url": "https://openalex.org/A5018985979", "inst": "University of Virginia" }, { "name": "Judy Fox", "url": "https://openalex.org/A5058920163", "inst": "University of Virginia" } ], "affiliations": [ "University of Virginia", "Islamic University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4997750", "doi": "10.2139/ssrn.4997750", "title": "The Ai-Driven Decision-Making (Aidm) Framework: Integrating Ahp and Chatgpt-4 for Supplier Selection", "authors": [ "Mohammad Dehghanimohammadabadi", "Nihan Kabadayı" ], "posted": "2024-10-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4997750", "field": "management", "role": "method", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 32, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 647, "authors_detailed": [ { "name": "Mohammad Dehghanimohammadabadi", "url": "https://openalex.org/A5016843552", "inst": "Northeastern University (USA)" }, { "name": "Nihan Kabadayı", "url": "https://openalex.org/A5017112849", "inst": "Istanbul University" } ], "affiliations": [ "Northeastern University (USA)", "Istanbul University" ] }, { "uid": "doi:10.2139/ssrn.4959535", "doi": "10.2139/ssrn.4959535", "title": "Writing Quality and Soft Information in the GenAI Age: Evidence from Online Credit Markets", "authors": [ "Lin William Cong", "Yanhong Guo", "Xin Zhao", "Wenjun Zhou" ], "posted": "2024-10-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4959535", "field": "finance", "role": "object", "bullets": [ "Loan applications on a dominant online credit platform, examining writing quality and lending outcomes before and after ChatGPT adoption.", "ChatGPT adoption effects measured via a Writing Quality Index; proprietary BERT models with deep Heckman correction estimated lender decisions.", "ChatGPT adoption decreases soft information conveyed and increases credit misallocation; lenders compensate by shifting weight to hard information." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 80, "validated": null, "n": 2413, "authors_detailed": [ { "name": "Cong Lin", "url": "https://openalex.org/A5100331602", "inst": "Nanyang Technological University" }, { "name": "Yanhong Guo", "url": "https://openalex.org/A5051692050", "inst": "Dalian University of Technology" }, { "name": "Xin Zhao", "url": "https://openalex.org/A5040055615", "inst": "Dalian University of Technology" }, { "name": "Wenjun Zhou", "url": "https://openalex.org/A5101388195", "inst": "Knoxville College" } ], "affiliations": [ "Nanyang Technological University", "Dalian University of Technology", "Knoxville College" ] }, { "uid": "doi:10.2139/ssrn.4998272", "doi": "10.2139/ssrn.4998272", "title": "Should Central Banks Care About Text Mining? A Literature Review", "authors": [ "Jean Charles Bricongne", "Raquel Caldeira", "Baptiste Meunier" ], "posted": "2024-10-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4998272", "field": "economics", "role": "instrument", "bullets": [ "Literature review of text mining use cases across central banks and supervisory institutions worldwide.", "Reviews NLP methods for measuring inflation expectations, financial stability risks, climate preparedness, and central bank communications.", "Text mining complements traditional indicators at central banks; generative AI opens new frontiers for textual data analysis." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3449, "authors_detailed": [ { "name": "Jean Charles Bricongne", "url": "https://openalex.org/A5110928208", "inst": "Banque de France" }, { "name": "Raquel Caldeira", "url": "https://openalex.org/A5114391045", "inst": "Banque de France" }, { "name": "Baptiste Meunier", "url": "https://openalex.org/A5051949701", "inst": "European Central Bank" } ], "affiliations": [ "Banque de France", "European Central Bank" ] }, { "uid": "arxiv:2411.05013v1", "arxiv_id": "2411.05013v1", "title": "Enhancing literature review with LLM and NLP methods. Algorithmic trading case", "authors": [ "Stanisław Łaniewski", "Robert Ślepaczuk" ], "posted": "2024-10-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.05013v1", "field": "finance", "role": "method", "bullets": [ "14,342 algorithmic trading articles published between 1956 and the first quarter of 2020, filtered from a corpus of 136 million research papers.", "Keyword rules, embeddings, topic models, and ChatGPT-style LLMs organize the corpus and answer questions about it; no accuracy check against hand coding is reported.", "Algorithmic trading output grows faster than publications overall, stocks and main indices cover over half of studied assets, cryptocurrencies grow fastest, and machine learning methods now dominate." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 32, "edition": 13, "n": 1578, "authors_detailed": [ { "name": "Stanisław Łaniewski", "url": "https://openalex.org/A5029012659", "inst": "University of Warsaw" }, { "name": "Robert Ślepaczuk", "url": "https://openalex.org/A5031588007", "inst": "Center for Social and Economic Research" } ], "affiliations": [ "University of Warsaw", "Center for Social and Economic Research" ] }, { "uid": "doi:10.2139/ssrn.4983334", "doi": "10.2139/ssrn.4983334", "title": "How Much Should We Trust Large Language Model-Based Measures for Accounting and Finance Research?", "authors": [ "Minji Yoo" ], "posted": "2024-10-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4983334", "field": "accounting", "role": "method", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 646, "authors_detailed": [ { "name": "Minji Yoo", "url": "https://openalex.org/A5114382027", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4991774", "doi": "10.2139/ssrn.4991774", "title": "Who is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms", "authors": [ "Ozge Demirci", "Jonas Hannane", "Xinrong Zhu" ], "posted": "2024-10-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4991774", "field": "economics", "role": "object", "bullets": [ "Global freelancing platform job posts covering writing, coding, and image-creation categories, eight months after ChatGPT launch.", "ChatGPT and image-generating AI studied as substitutes for freelancer labor; Google Trends proxies public awareness of substitutability.", "Writing and coding job posts fell 21% and image-creation posts fell 17% relative to manual-skill jobs; remaining posts are more complex and higher paid." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 3448, "authors_detailed": [ { "name": "Ozge Demirci", "url": "https://openalex.org/A5114353504", "inst": "Imperial College London" }, { "name": "Jonas Hannane", "url": "https://openalex.org/A5114353505", "inst": "German Institute for Economic Research" }, { "name": "Xinrong Zhu", "url": "https://openalex.org/A5051898983", "inst": "Imperial College London" } ], "affiliations": [ "Imperial College London", "German Institute for Economic Research" ] }, { "uid": "doi:10.1109/bigdata62323.2024.10825105", "doi": "10.1109/bigdata62323.2024.10825105", "arxiv_id": "2410.19845v1", "title": "Enhancing Trust and Safety in Digital Payments: An LLM-Powered Approach", "authors": [ "Devendra Dahiphale", "Naveen Madiraju", "Justin Lin", "Rutvik Karve", "Monu Agrawal", "Anant Modwal", "Ramanan Balakrishnan", "Shanay Shah", "Govind Kaushal", "Priya Mandawat", "Prakash Hariramani", "Arif Merchant" ], "posted": "2024-10-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.19845v1", "field": "finance", "role": "instrument", "bullets": [ "Scam detection for digital payments on India's Unified Payments Interface, with Google Pay as the use case and curated transaction data reviewed by human scam analysts.", "Gemini Ultra classifies suspected scams and drafts reasoning for reviewers, reaching 93.33 percent classification accuracy and 89 percent accuracy in generated reasoning on the curated set.", "The model surfaced accurate new reasons for suspected scams in 32 percent of cases that reviewers had not noted, supporting an assistant role in scam review workflows." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": true, "validation_note": "93.33 percent scam classification accuracy on curated labelled transactions", "salience": 48, "edition": 13, "n": 1665, "authors_detailed": [ { "name": "Devendra Dahiphale", "url": "https://openalex.org/A5015671621", "inst": "Google (United States)" }, { "name": "Naveen Madiraju", "url": "https://openalex.org/A5114619887", "inst": "Google (United States)" }, { "name": "Justin Yifu Lin", "url": "https://openalex.org/A5075837299", "inst": "Google (United States)" }, { "name": "Rutvik Karve", "url": "https://openalex.org/A5034077396", "inst": "Google (United States)" }, { "name": "Monu Agrawal", "url": "", "inst": "Google (United States)" }, { "name": "Anant Modwal", "url": "https://openalex.org/A5114619888", "inst": "Google (United States)" }, { "name": "Ramanan Balakrishnan", "url": "", "inst": "Google (United States)" }, { "name": "Sejal Shah", "url": "https://openalex.org/A5111955143", "inst": "Google (United States)" }, { "name": "Govind Kaushal", "url": "https://openalex.org/A5114619889", "inst": "Google (United States)" }, { "name": "Priya Mandawat", "url": "https://openalex.org/A5114619884", "inst": "Google (United States)" }, { "name": "Prakash Hariramani", "url": "https://openalex.org/A5114619885", "inst": "Google (United States)" }, { "name": "Arif Merchant", "url": "https://openalex.org/A5114619886", "inst": "Google (United States)" } ], "affiliations": [ "Google (United States)" ] }, { "uid": "arxiv:2410.15238v1", "arxiv_id": "2410.15238v1", "title": "Economic Anthropology in the Era of Generative Artificial Intelligence", "authors": [ "Zachary Sheldon", "Peeyush Kumar" ], "posted": "2024-10-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.15238v1", "field": "economics", "role": "object", "bullets": [ "A conceptual study at the border of economic anthropology and generative AI, contrasting two purpose-built model variants; training data details are not stated.", "A model trained on standard data is compared with one adapted with anthropological knowledge for recognizing diverse economic systems; the base architecture is not named.", "Anthropological adaptation improves recognition of non-market economies and concepts, supporting a more pluralist account of economics; evaluation criteria are not quantified in the abstract." ], "bullet_provenance": "ai", "salience": 30, "edition": 13, "models": [], "validated": null, "n": 1577, "authors_detailed": [ { "name": "Zachary Sheldon", "url": "https://openalex.org/A5064777109", "inst": "Cornell University" }, { "name": "Peeyush Kumar", "url": "https://openalex.org/A5100703174", "inst": "Teerthanker Mahaveer University" } ], "affiliations": [ "Cornell University", "Teerthanker Mahaveer University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3677052.3698637", "doi": "10.1145/3677052.3698637", "arxiv_id": "2410.15284v1", "title": "Customized FinGPT Search Agents Using Foundation Models", "authors": [ "Felix Tian", "Ajay Byadgi", "Daniel Kim", "Daochen Zha", "Matt White", "Kairong Xiao", "Xiao-Yang Liu Yanglet" ], "posted": "2024-10-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.15284v1", "field": "finance", "role": "method", "bullets": [ "Financial question answering over local documents, user-specified sources, and institutional proprietary data; evaluation datasets are not described in the abstract.", "FinGPT-based search agents use retrieval-augmented generation for individual users and dynamic vector databases with fine-tuning for institutions; no ground-truth validation procedure is described.", "The agents are reported to beat existing models on accuracy, relevance, and response time, addressing data privacy and freshness needs; figures are not stated." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 25, "edition": 13, "n": 1617, "authors_detailed": [ { "name": "Felix Tian", "url": "", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Ajay Byadgi", "url": "", "inst": "Rensselaer Polytechnic Institute" }, { "name": "D. Kim", "url": "https://openalex.org/A5054985544", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Daochen Zha", "url": "https://openalex.org/A5058071176", "inst": "Independent Researcher, US" }, { "name": "Matt White", "url": "https://openalex.org/A5113525383", "inst": "Berkeley College" }, { "name": "Kairong Xiao", "url": "https://openalex.org/A5080804351", "inst": "Columbia University" }, { "name": "Xiao-Yang Liu", "url": "https://openalex.org/A5100405233", "inst": "Rensselaer Polytechnic Institute" } ], "affiliations": [ "Columbia University", "Rensselaer Polytechnic Institute", "Independent Researcher, US", "Berkeley College" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2410.14926v1", "arxiv_id": "2410.14926v1", "title": "Aligning LLMs with Human Instructions and Stock Market Feedback in Financial Sentiment Analysis", "authors": [ "Zijie Zhao", "Roy E. Welsch" ], "posted": "2024-10-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.14926v1", "field": "finance", "role": "method", "bullets": [ "Financial sentiment analysis for trading, with the resulting signals also fed into portfolio construction benchmarked against the S&P 500; text sources and period are not stated.", "LLaMA 2 models from 7B to 70B receive instruction tuning and market-feedback-weighted retrieval augmentation; sentiment accuracy and F1 beat state-of-the-art baselines by 1 to 6 points.", "Portfolios built on the signals earn a 3.61 percent higher Sharpe ratio than the S&P 500 baseline in bullish markets, with a fivefold reduction in return losses in bearish ones." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "sentiment accuracy and F1 against labelled baselines", "salience": 45, "edition": 13, "n": 1545, "authors_detailed": [ { "name": "Zhao, Zijie", "url": "", "inst": "" }, { "name": "Roy E. Welsch", "url": "https://openalex.org/A5081284390", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2410.15212v2", "arxiv_id": "2410.15212v2", "title": "Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms", "authors": [ "Subramanyam Sahoo", "Kamlesh Dutta" ], "posted": "2024-10-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.15212v2", "field": "economics", "role": "object", "bullets": [ "A discussion piece surveying generative AI as an economic force across production, manufacturing, and finance; no dataset, sample, or empirical design is described.", "No model is deployed; large language models and deep generative models are discussed as automating design, optimization, risk assessment, trading strategies, and forecasting.", "The paper argues generative AI will reshape business models, disruptive technologies, and economic landscapes globally, but reports no quantified findings or tests." ], "bullet_provenance": "ai", "salience": 22, "edition": 13, "models": [], "validated": null, "n": 1664, "authors_detailed": [ { "name": "Subramanyam Sahoo", "url": "https://openalex.org/A5025127084", "inst": "National Institute of Technology Hamirpur" }, { "name": "Kamlesh Dutta", "url": "https://openalex.org/A5067298637", "inst": "National Institute of Technology Hamirpur" } ], "affiliations": [ "National Institute of Technology Hamirpur" ] }, { "uid": "doi:10.2139/ssrn.4992209", "doi": "10.2139/ssrn.4992209", "title": "Keeping the Faith (and the Returns): An AI Approach to Values-based Investing", "authors": [ "Maureen O'Hara", "Artem Streltsov" ], "posted": "2024-10-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4992209", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 constituents screened against Catholic values using the Global X S&P500 CATH ETF as benchmark for faith-based investing.", "LLMs performed textual analysis to identify companies consistent with the Catholic values mandate; deep reinforcement learning optimized portfolio weights.", "Synthetic CATH portfolios with AI-optimized weights show dramatic improvement in out-of-sample Sharpe ratios, closing the values-based investing performance gap." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 68, "n": 2482, "authors_detailed": [ { "name": "Maureen O’Hara", "url": "https://openalex.org/A5076338249", "inst": "Cornell University" }, { "name": "Artem Streltsov", "url": "https://openalex.org/A5114332075", "inst": "Cornell University" } ], "affiliations": [ "Cornell University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4954234", "doi": "10.2139/ssrn.4954234", "title": "Specifics Matter: An Analysis of Mutual Fund ESG Disclosures", "authors": [ "Huayu Shi", "Xing Han", "John B. Lee", "Helen Lu" ], "posted": "2024-10-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4954234", "field": "finance", "role": "instrument", "bullets": [ "U.S. mutual funds disclosing ESG considerations in prospectus Principal Investment Strategy sections, measuring disclosure specificity over time.", "An LLM distinguished specific from generic ESG disclosures; the measure was linked to subsequent ESG scores and shareholder proposal support.", "Funds with specific ESG disclosures attract higher flows, concentrated among institutional-oriented funds and periods of heightened climate concern." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 60, "n": 2955, "authors_detailed": [ { "name": "Huayu Shi", "url": "https://openalex.org/A5114323745", "inst": "University of Auckland" }, { "name": "Xing Han", "url": "https://openalex.org/A5114323746", "inst": "University of Auckland" }, { "name": "John Byong-Tek Lee", "url": "https://openalex.org/A5080428853", "inst": "University of Auckland" }, { "name": "Helen Lu", "url": "https://openalex.org/A5114323747", "inst": "Ghent University" } ], "affiliations": [ "University of Auckland", "Ghent University" ] }, { "uid": "arxiv:2410.14059v3", "arxiv_id": "2410.14059v3", "title": "UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models", "authors": [ "Yuzhe Yang", "Yifei Zhang", "Yan Hu", "Yilin Guo", "Ruoli Gan", "Yueru He", "Mingcong Lei", "Xiao Zhang", "Haining Wang", "Qianqian Xie", "Jimin Huang", "Honghai Yu", "Benyou Wang" ], "posted": "2024-10-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.14059v3", "field": "finance", "role": "method", "bullets": [ "A benchmark for real-world financial tasks grounded in a user study of 804 participants, whose feedback defines the intents and multi-turn interactions in the evaluation dataset.", "Eleven LLM services are scored through an LLM-as-judge protocol combined with human expert evaluation; individual model families are not named in the abstract.", "Benchmark scores align with human preferences at a Pearson correlation of 0.78, which the authors present as evidence the framework tracks user satisfaction in financial scenarios." ], "bullet_provenance": "ai", "validated": true, "validation_note": "benchmark scores versus human preferences, Pearson 0.78", "salience": 45, "edition": 13, "models": [], "n": 1663, "authors_detailed": [ { "name": "Yuzhe Yang", "url": "https://openalex.org/A5101216596", "inst": "University of Minnesota" }, { "name": "Yifei Zhang", "url": "https://openalex.org/A5100386928", "inst": "Ningbo University" }, { "name": "Yan Hu", "url": "https://openalex.org/A5100708039", "inst": "The Ohio State University Wexner Medical Center" }, { "name": "Yilin Guo", "url": "https://openalex.org/A5102754877", "inst": "Beijing Normal University" }, { "name": "Gan, Ruoli", "url": "", "inst": "" }, { "name": "Yueru He", "url": "https://openalex.org/A5111133467", "inst": "Columbia University" }, { "name": "Ming Lei", "url": "https://openalex.org/A5107094272", "inst": "Guangxi Medical University" }, { "name": "Xiao Zhang", "url": "https://openalex.org/A5100320877", "inst": "University of Science and Technology of China" }, { "name": "Haining Wang", "url": "https://openalex.org/A5100664241", "inst": "Wuhan Academy of Agricultural Sciences" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" }, { "name": "Honghai Yu", "url": "https://openalex.org/A5068527310", "inst": "Dalian University of Technology" }, { "name": "Benyou Wang", "url": "https://openalex.org/A5057282504", "inst": "Chinese University of Hong Kong, Shenzhen" } ], "affiliations": [ "University of Minnesota", "Columbia University", "Ningbo University", "Beijing Normal University", "Guangxi Medical University", "University of Science and Technology of China", "Wuhan Academy of Agricultural Sciences" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3677052.3698682", "doi": "10.1145/3677052.3698682", "arxiv_id": "2410.13959v2", "title": "FinQAPT: Empowering Financial Decisions with End-to-End LLM-driven Question Answering Pipeline", "authors": [ "Kuldeep Singh", "Simerjot Kaur", "Charese Smiley" ], "posted": "2024-10-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.13959v2", "field": "finance", "role": "method", "bullets": [ "Financial question answering over corporate reports, with the FinQA dataset of numerical questions used to tune and evaluate each pipeline module.", "An LLM pipeline retrieves report context and answers with dynamic n-shot prompting plus clustering based negative sampling for retrieval; the underlying model is not named in the abstract.", "Module level accuracy reaches 80.6 percent on FinQA, while end to end accuracy drops because relevant context is frequently missed at the retrieval step." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinQA labeled benchmark, 80.6 percent accuracy", "salience": 40, "edition": 13, "models": [], "n": 1705, "authors_detailed": [ { "name": "Karam Singh", "url": "https://openalex.org/A5022533727", "inst": "Michigan State University" }, { "name": "Simerjot Kaur", "url": "https://openalex.org/A5031351140", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Charese Smiley", "url": "https://openalex.org/A5070646187", "inst": "JPMorgan Chase & Co (United States)" } ], "affiliations": [ "Michigan State University", "JPMorgan Chase & Co (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2410.19806v3", "arxiv_id": "2410.19806v3", "title": "Learning to Adopt Generative AI", "authors": [ "Lijia Ma", "Xingchen Xu", "Yumei He", "Yong Tan" ], "posted": "2024-10-17", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.19806v3", "field": "economics", "role": "object", "bullets": [ "Large-scale clickstream dataset tracking ChatGPT usage across demographic groups; Bayesian learning model of adoption.", "ChatGPT is the object studied; model estimates heterogeneous learning rates and utility from adoption across social characteristics.", "Non-white and less-educated users gain more utility per use but update beliefs more slowly, creating belief traps that sustain underutilization." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 70, "validated": null, "n": 3447, "authors_detailed": [ { "name": "Lijia Ma", "url": "https://openalex.org/A5103127486", "inst": "Shenzhen University" }, { "name": "Xingchen Xu", "url": "https://openalex.org/A5100696665", "inst": "Arizona State University" }, { "name": "Yumei He", "url": "https://openalex.org/A5065505449", "inst": "Tulane University" }, { "name": "Yong Tan", "url": "https://openalex.org/A5037984091", "inst": "University of Washington" } ], "affiliations": [ "Arizona State University", "Shenzhen University", "Tulane University", "University of Washington" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2411.00782v2", "arxiv_id": "2411.00782v2", "title": "TradExpert: Revolutionizing Trading with Mixture of Expert LLMs", "authors": [ "Qianggang Ding", "Haochen Shi", "Jiadong Guo", "Bang Liu" ], "posted": "2024-10-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2411.00782v2", "field": "finance", "role": "agent", "bullets": [ "Quantitative trading built on four financial data streams, namely news articles, market data, alpha factors, and fundamentals, plus a newly released large-scale evaluation dataset.", "Four specialized expert LLMs each analyze one data source and a general expert LLM synthesizes their signals into predictions or rankings; model families are not named in the abstract.", "The mixture-of-experts setup is reported to outperform benchmarks on stock movement prediction and trading scenarios; effect sizes are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "movement prediction and trading performance on benchmark datasets", "salience": 45, "edition": 13, "models": [], "n": 1662, "authors_detailed": [ { "name": "Qi Ding", "url": "https://openalex.org/A5101819015", "inst": "Beijing University of Civil Engineering and Architecture" }, { "name": "Shi, Haochen", "url": "", "inst": "" }, { "name": "Guo, Jiadong", "url": "", "inst": "" }, { "name": "Liu, Bang", "url": "", "inst": "" } ], "affiliations": [ "Beijing University of Civil Engineering and Architecture" ] }, { "uid": "arxiv:2410.12583v1", "arxiv_id": "2410.12583v1", "title": "STRUX: An LLM for Decision-Making with Structured Explanations", "authors": [ "Yiming Lu", "Yebowen Hu", "Hassan Foroosh", "Wei Jin", "Fei Liu" ], "posted": "2024-10-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.12583v1", "field": "finance", "role": "agent", "bullets": [ "Earnings call transcripts used to forecast stock investment decisions; the number of transcripts, firms, and the sample period are not stated.", "An LLM, family not stated, condenses each transcript into a table of key facts, self reflects to label facts favorable or adverse, and is fine tuned to rank them.", "STRUX beats strong baselines on the stock decision task while exposing which facts drove each call; the abstract gives no accuracy figures." ], "bullet_provenance": "ai", "salience": 32, "edition": 13, "models": [], "validated": null, "n": 1803, "authors_detailed": [ { "name": "Yiming Lu", "url": "https://openalex.org/A5114337546", "inst": "Emory University" }, { "name": "Yebowen Hu", "url": "https://openalex.org/A5113137573", "inst": "University of Central Florida" }, { "name": "Hassan Foroosh", "url": "https://openalex.org/A5076117344", "inst": "University of Central Florida" }, { "name": "Wei Jin", "url": "https://openalex.org/A5114337548", "inst": "Emory University" }, { "name": "Fei Liu", "url": "https://openalex.org/A5100394562", "inst": "Wannan Medical College" } ], "affiliations": [ "Emory University", "University of Central Florida", "Wannan Medical College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4951362", "doi": "10.2139/ssrn.4951362", "title": "Measuring R&D Activity Through 10-K Narrative Disclosures", "authors": [ "Panayiotis C. Andreou", "Neophytos Lambertides", "Anna Maruska" ], "posted": "2024-10-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4951362", "field": "accounting", "role": "instrument", "bullets": [ "US public firms filing 10-K reports; textual measure of R&D activity constructed from narrative disclosures.", "ChatGPT used alongside a human survey to validate that a custom dictionary captures R&D-related narrative content.", "Textual R&D measure shows positive significant association with Tobin's Q; results robust to non-patented and zero-R&D-expense firms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "human survey questionnaire and ChatGPT interpretive validation", "salience": 55, "n": 3446, "authors_detailed": [ { "name": "Panayiotis C. Andreou", "url": "https://openalex.org/A5018520973", "inst": "Cyprus University of Technology" }, { "name": "Neophytos Lambertides", "url": "https://openalex.org/A5001552324", "inst": "Cyprus University of Technology" }, { "name": "Anna Maruska", "url": "https://openalex.org/A5107128729", "inst": "Cyprus University of Technology" } ], "affiliations": [ "Cyprus University of Technology" ] }, { "uid": "arxiv:2410.11773v7", "arxiv_id": "2410.11773v7", "title": "Time-Series Foundation AI Model for Value-at-Risk Forecasting", "authors": [ "Anubha Goel", "Puneet Pasricha", "Juho Kanniainen" ], "posted": "2024-10-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.11773v7", "field": "finance", "role": "method", "bullets": [ "S&P 100 index and constituents, 19 years of daily returns with 8.5 years of out-of-sample backtesting data.", "Google TimesFM time-series foundation model fine-tuned for Value-at-Risk quantile forecasting, compared against GARCH and GAS models.", "Fine-tuned foundation model consistently outperforms traditional methods in actual-over-expected ratios across 0.01 to 0.1 quantiles; zero-shot use is suboptimal." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "backtested actual-over-expected VaR ratios on S&P 100", "salience": 60, "n": 3907, "authors_detailed": [ { "name": "Anubha Goel", "url": "https://openalex.org/A5052447672", "inst": "Tampere University" }, { "name": "Puneet Pasricha", "url": "https://openalex.org/A5029086045", "inst": "Indian Institute of Technology Ropar" }, { "name": "Juho Kanniainen", "url": "https://openalex.org/A5049372872", "inst": "Tampere University of Applied Sciences" } ], "affiliations": [ "Tampere University", "Indian Institute of Technology Ropar", "Tampere University of Applied Sciences" ] }, { "uid": "arxiv:2410.19775v1", "arxiv_id": "2410.19775v1", "title": "Gender Bias of LLM in Economics: An Existentialism Perspective", "authors": [ "Hui Zhong", "Songsheng Chen", "Mian Liang" ], "posted": "2024-10-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.19775v1", "field": "economics", "role": "object", "bullets": [ "Word embedding association tests applied to language models positioned for financial decision-making, combined with formal argument; no economic outcome data is analyzed.", "GPT-4 and BERT are probed with WEAT for stereotype associations that persist without explicit gender markers; the models are the object of study, not a measurement tool.", "Bias emerges as a systematic product of optimization on biased training data rather than a removable flaw, motivating governance beyond technical debiasing; no effect sizes are reported." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "salience": 26, "edition": 13, "validated": null, "n": 1576, "authors_detailed": [ { "name": "Zhong, Hui", "url": "", "inst": "" }, { "name": "Songsheng Chen", "url": "https://openalex.org/A5053337769", "inst": "Taiwan Semiconductor Manufacturing Company (Taiwan)" }, { "name": "Liang, Mian", "url": "", "inst": "" } ], "affiliations": [ "Taiwan Semiconductor Manufacturing Company (Taiwan)" ] }, { "uid": "arxiv:2410.10665v1", "arxiv_id": "2410.10665v1", "title": "Double Jeopardy and Climate Impact in the Use of Large Language Models: Socio-economic Disparities and Reduced Utility for Non-English Speakers", "authors": [ "Aivin V. Solatorio", "Gabriel Stefanini Vicente", "Holly Krambeck", "Olivier Dupriez" ], "posted": "2024-10-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.10665v1", "field": "economics", "role": "object", "bullets": [ "FLORES-200 and FLORES+ translation data joined with Ethnologue and World Development Indicators, linking language communities to country income groups.", "OpenAI GPT models accessed via API; the study measures tokenization driven cost differences across languages and uses translation quality as a performance proxy.", "Around 1.5 billion speakers, mostly of languages from lower middle income countries, face 4 to 6 times higher token costs alongside weaker performance in low resource languages." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 13, "validated": null, "n": 1756, "authors_detailed": [ { "name": "Aivin V. Solatorio", "url": "https://openalex.org/A5020026033", "inst": "World Bank" }, { "name": "Gabriel Stefanini Vicente", "url": "https://openalex.org/A5114335247", "inst": "" }, { "name": "Holly Krambeck", "url": "https://openalex.org/A5029151556", "inst": "World Bank" }, { "name": "Olivier Dupriez", "url": "https://openalex.org/A5049463620", "inst": "World Bank Group" } ], "affiliations": [ "World Bank", "World Bank Group" ] }, { "uid": "doi:10.2139/ssrn.4950340", "doi": "10.2139/ssrn.4950340", "title": "What Can We Learn from LLMs? Building a Foundation Model for Inventory Management", "authors": [ "Magnus Josef Maichle", "Nikolai Stein", "Richard Pibernik" ], "posted": "2024-10-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4950340", "field": "management", "role": "method", "bullets": [ "A real-world retail dataset covering thousands of heterogeneous products, used to build and test an inventory management model at the product level, including new products with limited history.", "The authors train their own foundation model on the GPT architecture with a standardized process, giving cross-learning across the portfolio and zero-shot handling of new products; no off-the-shelf named model is used.", "The foundation model consistently beats several state-of-the-art inventory models on inventory distortion costs, though the abstract reports no magnitude." ], "bullet_provenance": "ai", "validated": true, "validation_note": "real retail data, outperforms state-of-the-art on inventory distortion cost, magnitude not stated", "salience": 58, "edition": 3, "audience": "technical", "models": [], "n": 194, "authors_detailed": [ { "name": "Magnus Josef Maichle", "url": "https://openalex.org/A5095559015", "inst": "University of Würzburg" }, { "name": "Nikolai Stein", "url": "https://openalex.org/A5079156260", "inst": "University of Würzburg" }, { "name": "Richard Pibernik", "url": "https://openalex.org/A5005653218", "inst": "University of Würzburg" } ], "affiliations": [ "University of Würzburg" ] }, { "uid": "arxiv:2410.12857v1", "arxiv_id": "2410.12857v1", "title": "Enterprise Benchmarks for Large Language Model Evaluation", "authors": [ "Bing Zhang", "Mikio Takeuchi", "Ryo Kawahara", "Shubhi Asthana", "Md. Maruf Hossain", "Guang-Jie Ren", "Kate Soule", "Yada Zhu" ], "posted": "2024-10-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.12857v1", "field": "other", "role": "method", "bullets": [ "25 publicly available datasets from enterprise domains including financial services, legal, cyber security, and climate and sustainability, spanning varied NLP tasks.", "13 LLMs, none named in the abstract, are benchmarked with the proposed domain specific evaluation framework; code and prompts are released on GitHub.", "Performance differs substantially across models and enterprise tasks, and the authors argue model selection should follow the specific requirements of each task." ], "bullet_provenance": "ai", "validated": true, "validation_note": "25 labeled enterprise datasets", "salience": 31, "edition": 13, "models": [], "n": 1755, "authors_detailed": [ { "name": "Bing Zhang", "url": "https://openalex.org/A5100389650", "inst": "Shanghai Ocean University" }, { "name": "Mikio Takeuchi", "url": "https://openalex.org/A5110441468", "inst": "IBM (United States)" }, { "name": "Ryo Kawahara", "url": "https://openalex.org/A5021743744", "inst": "Kyoto University" }, { "name": "Shubhi Asthana", "url": "https://openalex.org/A5065749078", "inst": "Shri Ramswaroop Memorial University" }, { "name": "M. Shamim Hossain", "url": "https://openalex.org/A5037865550", "inst": "King Saud University" }, { "name": "Ge Ren", "url": "https://openalex.org/A5101448636", "inst": "Xinjiang Normal University" }, { "name": "Kate Soule", "url": "https://openalex.org/A5114337709", "inst": "IBM (United States)" }, { "name": "Yada Zhu", "url": "https://openalex.org/A5101792548", "inst": "University of Central Florida" } ], "affiliations": [ "Shanghai Ocean University", "IBM (United States)", "Kyoto University", "Shri Ramswaroop Memorial University", "King Saud University", "Xinjiang Normal University", "University of Central Florida" ] }, { "uid": "arxiv:2410.07677v1", "arxiv_id": "2410.07677v1", "title": "Smart Audit System Empowered by LLM", "authors": [ "Xu Yao", "Xiaoxu Wu", "Xi Li", "Huan Xu", "Chenlei Li", "Ping Huang", "Si Li", "Xiaoning Ma", "Jiulong Shan" ], "posted": "2024-10-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.07677v1", "field": "management", "role": "method", "bullets": [ "Manufacturing quality audits across global supply chains, supported by a system spanning dynamic risk assessment, a compliance knowledge base, and supplier commonality analysis for engineers.", "LLMs, unnamed in the abstract, power the risk assessment model, a compliance copilot for data processing and retrieval, and a Re-act analysis agent; no ground-truth validation is reported.", "Testing scenarios show an improvement of over 24 percent in audit efficiency and effectiveness; the metric behind that figure is not defined in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 35, "edition": 13, "models": [], "n": 1661, "authors_detailed": [ { "name": "Xu Yao", "url": "https://openalex.org/A5110167607", "inst": "Chongqing University of Posts and Telecommunications" }, { "name": "Xi Wu", "url": "https://openalex.org/A5023037552", "inst": "Shanghai University" }, { "name": "Xi Li", "url": "https://openalex.org/A5072091429", "inst": "Tongji University" }, { "name": "Huan Xu", "url": "https://openalex.org/A5111774170", "inst": "Shanghai Ninth People's Hospital" }, { "name": "Chenlei Li", "url": "https://openalex.org/A5009258120", "inst": "Electric Power Research Institute" }, { "name": "Huang Ping", "url": "https://openalex.org/A5102799722", "inst": "Universiti Sains Malaysia" }, { "name": "Si Li", "url": "https://openalex.org/A5100391329", "inst": "Fudan University" }, { "name": "MA Xiao-ning", "url": "https://openalex.org/A5101262887", "inst": "Qingdao University" }, { "name": "Jiulong Shan", "url": "https://openalex.org/A5068249077", "inst": "Apple (Israel)" } ], "affiliations": [ "Chongqing University of Posts and Telecommunications", "Shanghai University", "Tongji University", "Electric Power Research Institute", "Universiti Sains Malaysia", "Fudan University", "Qingdao University" ] }, { "uid": "doi:10.1109/ica63002.2024.00042", "doi": "10.1109/ica63002.2024.00042", "arxiv_id": "2410.21280v1", "title": "TraderTalk: An LLM Behavioural ABM applied to Simulating Human Bilateral Trading Interactions", "authors": [ "Alicia Vidler", "Toby Walsh" ], "posted": "2024-10-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.21280v1", "field": "finance", "role": "agent", "bullets": [ "Government bond market trading simulated as bilateral conversations between two stylized virtual traders in an agent based model; simulation scale is not stated in the abstract.", "LLMs, family not named, generate the traders' conversational behavior and decisions, with turn taking coordination and prompt design handled opportunistically rather than systematically.", "The hybrid model reproduces trade to order volume ratios seen in related asset markets, supporting LLM augmented agent based simulation of trading interactions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "trade to order ratios vs observed markets", "salience": 45, "edition": 13, "models": [], "n": 1754, "authors_detailed": [ { "name": "Alicia Vidler", "url": "https://openalex.org/A5051454314", "inst": "UNSW" }, { "name": "Toby Walsh", "url": "https://openalex.org/A5072902302", "inst": "UNSW" } ], "affiliations": [ "UNSW" ] }, { "uid": "arxiv:2410.07970v1", "arxiv_id": "2410.07970v1", "title": "Mapping Hong Kong's Financial Ecosystem: A Network Analysis of the SFC's Licensed Professionals and Institutions", "authors": [ "Abdulla AlKetbi", "Gautier Marti", "Khaled AlNuaimi", "Raed Jaradat", "Andreas Henschel" ], "posted": "2024-10-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.07970v1", "field": "finance", "role": "instrument", "bullets": [ "The Hong Kong SFC public register of licensed persons and registered institutions, spanning 21 years at daily granularity, treated as an evolving professional network.", "Large language models, families not stated, classify firm types and infer employee nationality and gender from names; no accuracy check against ground truth is reported.", "The enriched register is released as a structured dataset with preliminary findings on the network's structure; specific network statistics are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 13, "models": [], "n": 1802, "authors_detailed": [ { "name": "Abdulla AlKetbi", "url": "https://openalex.org/A5111396936", "inst": "Khalifa University of Science and Technology" }, { "name": "Gautier Marti", "url": "https://openalex.org/A5086630995", "inst": "Capital University" }, { "name": "Khaled Alnuaimi", "url": "https://openalex.org/A5071285744", "inst": "Khalifa University of Science and Technology" }, { "name": "Raed Jaradat", "url": "https://openalex.org/A5071190894", "inst": "United States Army Corps of Engineers" }, { "name": "Andreas Henschel", "url": "https://openalex.org/A5075543445", "inst": "Khalifa University of Science and Technology" } ], "affiliations": [ "Khalifa University of Science and Technology", "Capital University", "United States Army Corps of Engineers" ] }, { "uid": "doi:10.2139/ssrn.4974976", "doi": "10.2139/ssrn.4974976", "title": "How Much Does ChatGPT Know About Finance?", "authors": [ "Douglas J. Fairhurst", "Daniel Greene" ], "posted": "2024-10-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4974976", "field": "finance", "role": "method", "bullets": [ "Over 10,000 LLM responses to finance-related prompts evaluated across multiple models and interaction methods for accuracy and text quality.", "Multiple LLMs tested on finance tasks; accuracy measured against correct answers and similarity scored against human expert-written text.", "Appropriate LLM use is task-specific rather than job-specific; some models and methods favor accuracy while others better match human expert prose." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Accuracy on 10,000+ finance prompts across models and tasks", "salience": 60, "n": 2649, "authors_detailed": [ { "name": "Douglas J. Fairhurst", "url": "https://openalex.org/A5057391204", "inst": "Washington State University" }, { "name": "Daniel Greene", "url": "https://openalex.org/A5054973731", "inst": "Clemson University" } ], "affiliations": [ "Washington State University", "Clemson University" ] }, { "uid": "arxiv:2410.10873v1", "arxiv_id": "2410.10873v1", "title": "AuditWen:An Open-Source Large Language Model for Audit", "authors": [ "Jiajia Huang", "Haoran Zhu", "Chao Xu", "Tianming Zhan", "Qianqian Xie", "Jimin Huang" ], "posted": "2024-10-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.10873v1", "field": "accounting", "role": "method", "bullets": [ "Audit-domain instruction dataset of 28,000 items spanning 15 audit tasks at three layers, distilled from application scenarios, plus a 3,000-instruction evaluation benchmark.", "Qwen is fine-tuned into AuditWen, then compared with existing LLMs on information extraction, question answering, and document generation for audit work.", "AuditWen leads the compared models on question understanding and answer generation, and is released open source; specific scores are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "3,000-instruction audit benchmark", "salience": 38, "edition": 13, "n": 1616, "authors_detailed": [ { "name": "Jiajia Huang", "url": "https://openalex.org/A5108210470", "inst": "Tongji University" }, { "name": "Haoran Zhu", "url": "https://openalex.org/A5081925629", "inst": "Beijing University of Technology" }, { "name": "Chao Xu", "url": "https://openalex.org/A5006348865", "inst": "Tongji University" }, { "name": "Zhan, Tianming", "url": "", "inst": "" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" } ], "affiliations": [ "Tongji University", "Beijing University of Technology", "Hunan Normal University", "University of Manchester" ] }, { "uid": "doi:10.2139/ssrn.4947135", "doi": "10.2139/ssrn.4947135", "title": "Efficacy of AI and Other Large Language Models in Predicting Stock Prices", "authors": [ "Jonathan Vidal" ], "posted": "2024-10-09", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4947135", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 30, "edition": 13, "bullets": [], "models": [], "validated": null, "n": 1800, "authors_detailed": [ { "name": "Jonathan Vidal", "url": "https://openalex.org/A5108998257", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "arxiv:2410.06932v1", "arxiv_id": "2410.06932v1", "title": "Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models", "authors": [ "Daniel Albert", "Stephan Billinger" ], "posted": "2024-10-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.06932v1", "field": "management", "role": "agent", "bullets": [ "A human laboratory experiment from behavioral strategy on search and choice, reproduced with generated agents and then extended; the original task details are not given in the abstract.", "LLM generated agents, family not stated, replay the experimental protocol, and the study also mines the agents' simulated thoughts alongside their choices.", "Agents reproduce human search behavior and decisions, and more forward looking thoughts correlate with favoring exploitation over exploration to maximize wealth." ], "bullet_provenance": "ai", "salience": 55, "edition": 13, "models": [], "validated": null, "n": 1801, "authors_detailed": [ { "name": "Daniel Albert", "url": "https://openalex.org/A5103260279", "inst": "Drexel University" }, { "name": "Stephan Billinger", "url": "https://openalex.org/A5044413937", "inst": "University of Southern Denmark" } ], "affiliations": [ "Drexel University", "University of Southern Denmark" ] }, { "uid": "doi:10.2139/ssrn.4952737", "doi": "10.2139/ssrn.4952737", "title": "Rethinking the Stock Market Participation Puzzle: A Qualitative Approach", "authors": [ "Kamila Duraj", "Daniela Grunow", "Michael Haliassos", "Christine Laudenbach", "Stephan Siegel" ], "posted": "2024-10-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4952737", "field": "finance", "role": "instrument", "bullets": [ "In-depth interviews with investors and non-investors in Germany plus a representative survey of over 7,000 individuals on stock market participation.", "An LLM-based content analysis complemented traditional human-led coding to classify beliefs and barriers from open-ended interview responses.", "A pervasive misconception that participation requires stock-picking and market-timing inflates perceived costs and deters market entry." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2954, "authors_detailed": [ { "name": "Kamila Duraj", "url": "https://openalex.org/A5108170933", "inst": "Leibniz Institute for Financial Research SAFE" }, { "name": "Daniela Grunow", "url": "https://openalex.org/A5065165330", "inst": "Goethe University Frankfurt" }, { "name": "Michael Haliassos", "url": "https://openalex.org/A5020457143", "inst": "Goethe University Frankfurt" }, { "name": "Christine Laudenbach", "url": "https://openalex.org/A5063735471", "inst": "Goethe University Frankfurt" }, { "name": "Stephan Siegel", "url": "https://openalex.org/A5033842881", "inst": "Ifo Institute for Economic Research" } ], "affiliations": [ "Leibniz Institute for Financial Research SAFE", "Goethe University Frankfurt", "Ifo Institute for Economic Research" ] }, { "uid": "doi:10.2139/ssrn.4947595", "doi": "10.2139/ssrn.4947595", "title": "AI in innovation research: An overview of transformers", "authors": [ "Mariano Mastrogiorgio" ], "posted": "2024-10-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4947595", "field": "management", "role": "method", "bullets": [ "Survey of transformer architectures applied to patent text documents used by innovation researchers across multiple NLP tasks.", "Reviews key transformer components including attention modules and contextual word embeddings for patent classification and extraction.", "Identifies opportunities and challenges transformers present to innovation scholars but reports no empirical benchmark results." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 20, "validated": null, "n": 3445, "authors_detailed": [ { "name": "Mariano Mastrogiorgio", "url": "https://openalex.org/A5013696129", "inst": "IE University" } ], "affiliations": [ "IE University" ] }, { "uid": "arxiv:2410.18988v1", "arxiv_id": "2410.18988v1", "title": "Generating long-horizon stock \"buy\" signals with a neural language model", "authors": [ "Joel R. Bock" ], "posted": "2024-10-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.18988v1", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 large-cap companies, 10-K narrative text, predicting stock price direction at horizons up to 12 months after filing.", "Fine-tuned small language model generates forward-looking buy and sell signals from 10-K report text at discrete horizons.", "Buy signals most precise at 6 and 9 months with F1-macro of 0.62, yielding 4.8 to 9 percent improvement over random selection; sell signals underperform." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "F1-macro against random stock selection baseline", "salience": 58, "n": 3905, "authors_detailed": [ { "name": "Joel R. Bock", "url": "https://openalex.org/A5045749176", "inst": "University of San Diego" } ], "affiliations": [ "University of San Diego" ] }, { "uid": "arxiv:2410.07118v1", "arxiv_id": "2410.07118v1", "title": "Exploring the Readiness of Prominent Small Language Models for the Democratization of Financial Literacy", "authors": [ "Tagore Rao Kosireddy", "Jeffrey D. Wall", "Evan Lucas" ], "posted": "2024-10-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.07118v1", "field": "finance", "role": "method", "bullets": [ "Financial question-answering tasks evaluated across open-source small language models with fewer than three billion parameters.", "Benchmarked OpenELM, Phi, Gemma, and TinyLlama in zero-shot and few-shot settings on financial literacy questions, measuring memory, inference time, and readability.", "Some off-the-shelf SLMs merit further fine-tuning for individual financial use; performance and accessibility vary substantially across models." ], "bullet_provenance": "ai", "models": [ "gemini", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "similarity comparison to ground-truth financial Q&A answers", "salience": 52, "n": 3906, "authors_detailed": [ { "name": "Tagore Rao Kosireddy", "url": "https://openalex.org/A5109819219", "inst": "Michigan Technological University" }, { "name": "Jeffrey David Wall", "url": "https://openalex.org/A5109819220", "inst": "Michigan Technological University" }, { "name": "Evan Lucas", "url": "https://openalex.org/A5109819221", "inst": "Michigan Technological University" } ], "affiliations": [ "Michigan Technological University" ] }, { "uid": "doi:10.2139/ssrn.4978831", "doi": "10.2139/ssrn.4978831", "title": "Theorizing with Large Language Models", "authors": [ "Matteo Tranchero", "Cecil-Francis Brenninkmeijer", "Arul Murugan", "Abhishek Nagaraj" ], "posted": "2024-10-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4978831", "field": "management", "role": "method", "bullets": [ "In silico experiments with multiple LLM agents simulating organizational settings to develop and extend strategic management theory.", "LLM agents assigned roles, preferences, and capabilities replicated human-subject experiments on strategic exploration under uncertainty.", "Framework replicated human experimental results at lower cost and extended theory by clarifying boundary conditions and uncovering mechanisms." ], "bullet_provenance": "ai", "validated": true, "validation_note": "replication of human-subject experiments", "salience": 65, "models": [], "n": 2412, "authors_detailed": [ { "name": "Matteo Tranchero", "url": "https://openalex.org/A5040896167", "inst": "University of Pennsylvania" }, { "name": "Cecil-Francis Brenninkmeijer", "url": "https://openalex.org/A5107803552", "inst": "University of California, Berkeley" }, { "name": "Arul Murugan", "url": "https://openalex.org/A5107803553", "inst": "University of California, Berkeley" }, { "name": "Abhishek Nagaraj", "url": "https://openalex.org/A5002319407", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of Pennsylvania", "University of California, Berkeley" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2410.17266v2", "arxiv_id": "2410.17266v2", "title": "Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes", "authors": [ "Kelvin J. L. Koa", "Yunshan Ma", "Yi Xu", "Ritchie Ng", "Huanhuan Zheng", "Tat-Seng Chua" ], "posted": "2024-10-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.17266v2", "field": "finance", "role": "method", "bullets": [ "Detection of rare stock portfolio crash events, such as the 2007 global financial crisis and the 2020 COVID-19 crash, from streams of news linked to portfolio stocks.", "A framework wraps an LLM, unnamed in the abstract, with brainstorming, memory, attention, and reasoning components that aggregate news impacts across related stocks and time steps without task-specific training.", "The framework beats state-of-the-art baselines at crash detection, ablations show each component contributes, and an extension detects global macroeconomic crisis events; margins are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "detection performance on realized portfolio crash events versus baselines", "salience": 42, "edition": 13, "models": [], "n": 1660, "authors_detailed": [ { "name": "Kelvin J. L. Koa", "url": "https://openalex.org/A5092741731", "inst": "National University of Singapore" }, { "name": "Yunshan Ma", "url": "https://openalex.org/A5089377262", "inst": "Singapore Management University" }, { "name": "Xu, Yi", "url": "", "inst": "" }, { "name": "Ritchie Ng", "url": "https://openalex.org/A5005298162", "inst": "National University of Singapore" }, { "name": "Huanhuan Zheng", "url": "https://openalex.org/A5051379497", "inst": "National University of Singapore" }, { "name": "Tat-Seng Chua", "url": "https://openalex.org/A5087041599", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore", "Singapore Management University" ] }, { "uid": "arxiv:2410.05401v4", "arxiv_id": "2410.05401v4", "title": "Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation", "authors": [ "Tunazzina Islam", "Dan Goldwasser" ], "posted": "2024-10-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.05401v4", "field": "management", "role": "instrument", "bullets": [ "Climate campaign advertisements on Meta, analyzed post hoc for demographic microtargeting by gender and age group; ad counts are not stated in the abstract.", "LLMs, not named in the abstract, predict each ad's intended demographic target and generate thematic explanations; accuracy and error rates are assessed with fairness metrics.", "Young adults are targeted through activism framing and women through caregiving and advocacy themes; prediction performs well overall but shows bias for male audiences." ], "bullet_provenance": "ai", "validated": true, "validation_note": "ad targeting labels, accuracy and fairness metrics", "salience": 36, "edition": 13, "models": [], "n": 1753, "authors_detailed": [ { "name": "Tunazzina Islam", "url": "https://openalex.org/A5056005531", "inst": "Purdue University West Lafayette" }, { "name": "Dan Goldwasser", "url": "https://openalex.org/A5032121234", "inst": "Purdue University West Lafayette" } ], "affiliations": [ "Purdue University West Lafayette" ] }, { "uid": "doi:10.2139/ssrn.4945481", "doi": "10.2139/ssrn.4945481", "title": "Large Language Models as Financial Analysts", "authors": [ "Miquel Noguer i Alonso", "Hanane Dupouy" ], "posted": "2024-10-07", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4945481", "field": "finance", "role": "agent", "bullet_provenance": "none", "salience": 34, "edition": 13, "bullets": [], "models": [], "validated": null, "n": 1799, "authors_detailed": [ { "name": "Miquel Noguer I Alonso", "url": "https://openalex.org/A5007301330", "inst": "Artificial Intelligence in Medicine (Canada)" }, { "name": "Hanane Dupouy", "url": "https://openalex.org/A5095112692", "inst": "Artificial Intelligence in Medicine (Canada)" } ], "affiliations": [ "Artificial Intelligence in Medicine (Canada)" ] }, { "uid": "doi:10.3386/w33033", "doi": "10.3386/w33033", "title": "Theorizing with Large Language Models", "authors": [ "Matteo Tranchero", "Cecil-Francis Brenninkmeijer", "Arul Murugan", "Abhishek Nagaraj" ], "posted": "2024-10-07", "added": "2026-07-24", "source_label": "NBER", "url": "https://doi.org/10.3386/w33033", "field": "management", "role": "method", "bullets": [ "Methodological paper in strategic management, applied to a theory of strategic exploration under uncertainty; specific sample and data are not stated.", "Proposes Generative AI-Based Experimentation, running unnamed LLMs as configurable in silico agents whose roles, preferences, and stated explanations can be varied, checked against prior human-subject results.", "The framework reproduces human-subject experimental results at lower cost and extends theory by clarifying boundary conditions and surfacing mechanisms; no agreement statistic is reported." ], "bullet_provenance": "ai", "validated": false, "salience": 64, "edition": 3, "audience": "general", "models": [], "n": 90, "authors_detailed": [ { "name": "Matteo Tranchero", "url": "https://openalex.org/A5040896167", "inst": "University of Pennsylvania" }, { "name": "Cecil-Francis Brenninkmeijer", "url": "https://openalex.org/A5107803552", "inst": "Berkeley College" }, { "name": "Arul Murugan", "url": "https://openalex.org/A5107803553", "inst": "University of California, Berkeley" }, { "name": "Abhishek Nagaraj", "url": "https://openalex.org/A5002319407", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "University of Pennsylvania", "University of California, Berkeley", "Berkeley College", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4979155", "doi": "10.2139/ssrn.4979155", "title": "Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in Peer-to-Peer Lending", "authors": [ "Mario Sanz-Guerrero", "Javier Arroyo" ], "posted": "2024-10-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4979155", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 344, "authors_detailed": [ { "name": "Mario Sanz-Guerrero", "url": "https://openalex.org/A5093836190", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Javier Arroyo", "url": "https://openalex.org/A5061373875", "inst": "Independent - affiliation not provided to SSRN" } ], "affiliations": [ "Independent - affiliation not provided to SSRN" ] }, { "uid": "doi:10.2139/ssrn.4978706", "doi": "10.2139/ssrn.4978706", "title": "12 Best Practices for Leveraging Generative AI in Experimental Research", "authors": [ "Samuel Chang", "Andrew Kennedy", "Aaron Leonard", "John A. List" ], "posted": "2024-10-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4978706", "field": "economics", "role": "method", "bullets": [ "Best-practices guide for economists using generative AI across pre-treatment, design, analysis, and forward-looking stages of experiments.", "Proposes twelve practices covering pre-registration, data privacy, exclusion restrictions, prompting bias, training-set bias, and replicability with GenAI.", "Identifies prompting and training-set bias as key threats to experimental validity; establishes replicability standards for GenAI-augmented research." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 3904, "authors_detailed": [ { "name": "Samuel Chang", "url": "https://openalex.org/A5032613665", "inst": "Woodlawn School" }, { "name": "Andrew Kennedy", "url": "https://openalex.org/A5024691628", "inst": "University of Chicago" }, { "name": "Aaron Leonard", "url": "https://openalex.org/A5109796939", "inst": "University of Chicago" }, { "name": "John A. List", "url": "https://openalex.org/A5083530241", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "Woodlawn School" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2410.04526v4", "arxiv_id": "2410.04526v4", "title": "FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering", "authors": [ "Siqiao Xue", "Xiaojing Li", "Fan Zhou", "Qingyang Dai", "Zhixuan Chu", "Hongyuan Mei" ], "posted": "2024-10-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.04526v4", "field": "finance", "role": "method", "bullets": [ "1,945 multilingual multimodal finance questions from university textbooks and exams plus 103 expert-written live questions with held-out answers, spanning eight subfields with charts, tables and diagrams.", "LLMs including GPT-o1 and DeepSeek R1 are scored against human-annotated answers and rationales; 1,270 DeepSeek R1 reasoning traces are then used to fine-tune open Qwen models.", "The benchmark remains hard even for reasoning models, and training on the curated reasoning trajectories markedly improves Qwen performance on the contamination-free live set." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "human-annotated multimodal finance QA benchmark with held-out live set", "salience": 47, "edition": 13, "n": 1544, "authors_detailed": [ { "name": "Siqiao Xue", "url": "https://openalex.org/A5086364554", "inst": "Antea Group (France)" }, { "name": "Li, Xiaojing", "url": "", "inst": "" }, { "name": "Fan Zhou", "url": "https://openalex.org/A5077610457", "inst": "University of Electronic Science and Technology of China" }, { "name": "Qingyang Dai", "url": "https://openalex.org/A5051337359", "inst": "State Key Laboratory of Industrial Control Technology" }, { "name": "Zhixuan Chu", "url": "https://openalex.org/A5008967163", "inst": "Zhejiang University" }, { "name": "Hongyuan Mei", "url": "https://openalex.org/A5070368916", "inst": "Harbin Institute of Technology" } ], "affiliations": [ "Antea Group (France)", "University of Electronic Science and Technology of China", "State Key Laboratory of Industrial Control Technology", "Zhejiang University", "Harbin Institute of Technology" ] }, { "uid": "arxiv:2410.04545v1", "arxiv_id": "2410.04545v1", "title": "How Does the Disclosure of AI Assistance Affect the Perceptions of Writing?", "authors": [ "Zhuoyan Li", "Chen Liang", "Jing Peng", "Ming Yin" ], "posted": "2024-10-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.04545v1", "field": "management", "role": "object", "bullets": [ "Experiment in which readers rate argumentative essays and creative stories while disclosure of the level and type of AI writing assistance is varied; participant counts are not stated.", "No model performs measurement; LLM based writing assistance is the manipulated treatment, ranging from editing help to new content generation, with no family named.", "Disclosing AI help, especially content generation, lowers average quality ratings, widens disagreement across raters, and pushes AI assisted texts out of top rankings." ], "bullet_provenance": "ai", "salience": 50, "edition": 13, "models": [], "validated": null, "n": 1752, "authors_detailed": [ { "name": "Zhuoyan Li", "url": "https://openalex.org/A5024020429", "inst": "Purdue University West Lafayette" }, { "name": "Chen Liang", "url": "https://openalex.org/A5108411563", "inst": "Jiangsu University" }, { "name": "Jing Peng", "url": "https://openalex.org/A5072128915", "inst": "Hubei University of Technology" }, { "name": "Ming Yin", "url": "https://openalex.org/A5071294124", "inst": "Purdue University West Lafayette" } ], "affiliations": [ "Purdue University West Lafayette", "Jiangsu University", "Hubei University of Technology" ] }, { "uid": "arxiv:2410.03897v4", "arxiv_id": "2410.03897v4", "title": "Generative AI, Managerial Expectations, and Economic Activity", "authors": [ "Manish Jha", "Jialin Qian", "Michael Weber", "Baozhong Yang" ], "posted": "2024-10-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.03897v4", "field": "economics", "role": "instrument", "bullets": [ "Over 120,000 U.S. corporate conference call transcripts used to construct firm, industry, and macroeconomic expectation measures.", "Generative AI extracts managerial economic outlook to build an AI Economy Score for macroeconomic and sectoral forecasting.", "AI Economy Score predicts GDP growth, production, and employment up to 10 quarters ahead, outperforming existing survey forecasts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "out-of-sample GDP, production, and employment forecasts", "salience": 70, "n": 3444, "authors_detailed": [ { "name": "Manish Jha", "url": "https://openalex.org/A5024242858", "inst": "Georgia State University" }, { "name": "Jialin Qian", "url": "https://openalex.org/A5104148222", "inst": "Rochester Institute of Technology" }, { "name": "Michael Weber", "url": "https://openalex.org/A5103159140", "inst": "3M (Germany)" }, { "name": "Baozhong Yang", "url": "https://openalex.org/A5084538885", "inst": "Georgia State University" } ], "affiliations": [ "Georgia State University", "Rochester Institute of Technology", "3M (Germany)" ] }, { "uid": "doi:10.2139/ssrn.4963618", "doi": "10.2139/ssrn.4963618", "title": "Re(Visiting) Large Language Models in Finance", "authors": [ "Eghbal Rahimikia", "Felix Drinkall" ], "posted": "2024-10-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4963618", "field": "finance", "role": "method", "bullet_provenance": "none", "salience": 38, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 645, "authors_detailed": [ { "name": "Eghbal Rahimikia", "url": "https://openalex.org/A5008383259", "inst": "University of Manchester" }, { "name": "Felix Drinkall", "url": "https://openalex.org/A5045703184", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford", "University of Manchester" ], "prestige": true }, { "uid": "doi:10.1145/3677052.3698628", "doi": "10.1145/3677052.3698628", "arxiv_id": "2410.12835v1", "title": "A Dutch Financial Large Language Model", "authors": [ "Sander Noels", "Jorne De Blaere", "Tijl De Bie" ], "posted": "2024-10-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.12835v1", "field": "finance", "role": "method", "bullets": [ "Dutch financial domain with over 140,000 instruction tuning samples across five financial tasks in Dutch and English.", "Built FinGEITje, the first Dutch financial LLM, using automated translation and an LLM-as-evaluator benchmark methodology.", "FinGEITje outperforms baselines across five Dutch and English financial tasks; releases first Dutch financial evaluation benchmark." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Dutch financial benchmark with LLM evaluator", "salience": 40, "n": 3443, "authors_detailed": [ { "name": "Sander Noels", "url": "https://openalex.org/A5070561295", "inst": "Ghent University" }, { "name": "Jorne De Blaere", "url": "https://openalex.org/A5114337699", "inst": "Silverfin, Belgium" }, { "name": "Tijl De Bie", "url": "https://openalex.org/A5076045275", "inst": "Ghent University" } ], "affiliations": [ "Ghent University", "Silverfin, Belgium" ] }, { "uid": "arxiv:2410.01987v1", "arxiv_id": "2410.01987v1", "title": "Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT", "authors": [ "Yanxin Shen", "Pulin Kirin Zhang" ], "posted": "2024-10-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.01987v1", "field": "finance", "role": "method", "bullets": [ "News articles, financial reports, and company announcements with sentiment labels; dataset names and sizes are not stated in the abstract.", "GPT-4o under zero-shot and few-shot prompting is compared with fine-tuned FinBERT on sentiment classification; outputs are scored against the labels, without figures in the abstract.", "Few-shot GPT-4o is reported to match a well fine-tuned FinBERT on financial sentiment, suggesting prompting can substitute for domain fine-tuning in this task." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "validated": true, "validation_note": "labelled sentiment data, figures not in abstract", "salience": 24, "edition": 13, "n": 1614, "authors_detailed": [ { "name": "Shen, Yanxin", "url": "", "inst": "" }, { "name": "Pulin Kirin Zhang", "url": "https://openalex.org/A5041426274", "inst": "Lehigh University" } ], "affiliations": [ "Lehigh University" ] }, { "uid": "arxiv:2410.02024v3", "arxiv_id": "2410.02024v3", "title": "FLAG: Financial Long Document Classification via AMR-based GNN", "authors": [ "Bolun \"Namir\" Xia", "Aparna Gupta", "Mohammed J. Zaki" ], "posted": "2024-10-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.02024v3", "field": "finance", "role": "instrument", "bullets": [ "Quarterly earnings call transcripts across sectors plus a corpus of S&P 1500 calls, labelled with subsequent stock price movements at several horizons.", "Sentence-level abstract meaning representation graphs are assembled into document graphs, endowed with financial-domain LLM word embeddings, and fed to a graph neural network; the embedding model is not named.", "The AMR-based pipeline beats direct fine-tuning of LLMs on transcript text for predicting price movement trends in both datasets; margins are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 28, "edition": 13, "models": [], "n": 1615, "authors_detailed": [ { "name": "Bolun Xia", "url": "https://openalex.org/A5046237209", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Aparna Gupta", "url": "https://openalex.org/A5079035190", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Mohammed J. Zaki", "url": "https://openalex.org/A5019559411", "inst": "Rensselaer Polytechnic Institute" } ], "affiliations": [ "Rensselaer Polytechnic Institute" ] }, { "uid": "doi:10.2139/ssrn.4974084", "doi": "10.2139/ssrn.4974084", "title": "Generative Artificial Intelligence Integration in Higher Education: Chatgpt and the Perceptions of Management Educators", "authors": [ "Faisal Shahzad", "Zeeshan Ullah", "Zehra Binnur Avunduk", "Ahmad Arslan" ], "posted": "2024-10-02", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4974084", "field": "management", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 25, "edition": 13, "bullets": [], "validated": null, "n": 1797, "authors_detailed": [ { "name": "Faisal Shahzad", "url": "https://openalex.org/A5029297817", "inst": "Häme University of Applied Sciences" }, { "name": "Zeeshan Ullah", "url": "https://openalex.org/A5057858919", "inst": "Cork University Hospital" }, { "name": "Zehra Binnur Avunduk", "url": "https://openalex.org/A5017928014", "inst": "Istanbul University-Cerrahpaşa" }, { "name": "Ahmad Arslan", "url": "https://openalex.org/A5055467779", "inst": "University of Oulu" } ], "affiliations": [ "Häme University of Applied Sciences", "Istanbul University-Cerrahpaşa", "University of Oulu" ] }, { "uid": "arxiv:2410.01772v2", "arxiv_id": "2410.01772v2", "title": "DeFine: Decision-Making with Analogical Reasoning over Factor Profiles", "authors": [ "Yebowen Hu", "Xiaoyang Wang", "Wenlin Yao", "Yiming Lu", "Daoan Zhang", "Hassan Foroosh", "Dong Yu", "Fei Liu" ], "posted": "2024-10-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.01772v2", "field": "finance", "role": "agent", "bullets": [ "Verbose speech transcripts such as company earnings calls, where hedging, repetition, and vagueness carry decision relevant uncertainty; corpus size not stated.", "The DeFine framework has an LLM, family not stated, build probabilistic factor profiles from transcripts and combine them with analogical reasoning over similar past cases to reach decisions.", "The framework is aimed at consulting and financial deliberation settings; the abstract describes the design but reports no benchmark comparison or quantitative result." ], "bullet_provenance": "ai", "salience": 28, "edition": 13, "models": [], "validated": null, "n": 1798, "authors_detailed": [ { "name": "Yebowen Hu", "url": "https://openalex.org/A5091574624", "inst": "University of Central Florida" }, { "name": "Xiaoyang Wang", "url": "https://openalex.org/A5101835856", "inst": "University of Electronic Science and Technology of China" }, { "name": "Wenlin Yao", "url": "https://openalex.org/A5114418786", "inst": "Amazon (Germany)" }, { "name": "Yiming Lu", "url": "https://openalex.org/A5100945549", "inst": "Aero Engine Corporation of China (China)" }, { "name": "Daoan Zhang", "url": "https://openalex.org/A5081992105", "inst": "Hunan University of Science and Technology" }, { "name": "Hassan Foroosh", "url": "https://openalex.org/A5076117344", "inst": "University of Central Florida" }, { "name": "Dong Yü", "url": "https://openalex.org/A5033056611", "inst": "Harbin Medical University" }, { "name": "Fei Liu", "url": "https://openalex.org/A5115602455", "inst": "Sichuan University" } ], "affiliations": [ "University of Central Florida", "University of Electronic Science and Technology of China", "Amazon (Germany)", "Aero Engine Corporation of China (China)", "Hunan University of Science and Technology", "Harbin Medical University", "Sichuan University" ] }, { "uid": "arxiv:2410.02091v3", "arxiv_id": "2410.02091v3", "title": "The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot", "authors": [ "Fangchen Song", "Ashish Agarwal", "Wen Wen" ], "posted": "2024-10-02", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.02091v3", "field": "economics", "role": "object", "bullets": [ "GitHub Copilot proprietary usage data combined with public open-source software project data on collaborative development.", "Studied how GitHub Copilot adoption affects developer participation, individual productivity, and coordination in OSS projects.", "Copilot increases project-level code contributions by 5.9% through more participation and productivity, but coordination time rises 8%; net effect positive." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 3442, "authors_detailed": [ { "name": "Fangchen Song", "url": "https://openalex.org/A5100496644", "inst": "The University of Texas at Austin" }, { "name": "Ashish Agarwal", "url": "https://openalex.org/A5027820479", "inst": "All India Institute of Medical Sciences Jodhpur" }, { "name": "Wen Wen", "url": "https://openalex.org/A5100319717", "inst": "Total (France)" } ], "affiliations": [ "The University of Texas at Austin", "All India Institute of Medical Sciences Jodhpur", "Total (France)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2410.01109v2", "arxiv_id": "2410.01109v2", "title": "Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance", "authors": [ "Meni Brief", "Oded Ovadia", "Gil Shenderovitz", "Noga Ben Yoash", "Rachel Lemberg", "Eitam Sheetrit" ], "posted": "2024-10-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.01109v2", "field": "finance", "role": "method", "bullets": [ "More than 200 fine-tuning experiments across financial benchmark tasks, run over several widely used LLM baselines including the small Phi-3-Mini model.", "Models are tuned on single tasks versus cocktails of related tasks, with general instruction data as regularization and mathematical data added; performance is scored on financial benchmarks.", "Multi-task cocktails beat single-task tuning, letting Phi-3-Mini surpass GPT-4o on financial benchmarks, though gains stay task-specific rather than broadening domain knowledge or reasoning." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "financial benchmark tasks", "salience": 44, "edition": 13, "n": 1564, "authors_detailed": [ { "name": "Meni Brief", "url": "https://openalex.org/A5114452489", "inst": "" }, { "name": "Oded Ovadia", "url": "https://openalex.org/A5079388254", "inst": "Linde (United States)" }, { "name": "Gil Shenderovitz", "url": "https://openalex.org/A5094151881", "inst": "Ben-Gurion University of the Negev" }, { "name": "Noga Ben Yoash", "url": "https://openalex.org/A5114452490", "inst": "" }, { "name": "Rachel Lemberg", "url": "https://openalex.org/A5114452491", "inst": "" }, { "name": "Eitam Sheetrit", "url": "https://openalex.org/A5083805783", "inst": "Ben-Gurion University of the Negev" } ], "affiliations": [ "Linde (United States)", "Ben-Gurion University of the Negev" ] }, { "uid": "arxiv:2410.01039v1", "arxiv_id": "2410.01039v1", "title": "From Facts to Insights: A Study on the Generation and Evaluation of Analytical Reports for Deciphering Earnings Calls", "authors": [ "Tomas Goldsack", "Yang Wang", "Chenghua Lin", "Chung-Chi Chen" ], "posted": "2024-10-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.01039v1", "field": "finance", "role": "agent", "bullets": [ "Analytical reports generated from earnings call transcripts and compared against reports written by human experts; company sample and period are not stated in the abstract.", "A multi-agent LLM framework, models unnamed in the abstract, gives specialized agents distinct viewpoints and analysis topics, and LLMs also score report quality, checked against human expert judgments.", "Adding agents produces reports judged more insightful, yet expert-written reports remain preferred in most cases, and LLM quality assessments correlate significantly with human experts across multiple dimensions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "generated reports and LLM judgments evaluated against human experts", "salience": 50, "edition": 13, "models": [], "n": 1659, "authors_detailed": [ { "name": "Tomas Goldsack", "url": "https://openalex.org/A5003440849", "inst": "University of Sheffield" }, { "name": "Yang Wang", "url": "https://openalex.org/A5015215572", "inst": "Pacific Northwest National Laboratory" }, { "name": "Chenghua Lin", "url": "https://openalex.org/A5024599321", "inst": "University of Alberta" }, { "name": "Chung-Chi Chen", "url": "https://openalex.org/A5101516307", "inst": "National Taiwan University of Science and Technology" } ], "affiliations": [ "University of Sheffield", "Pacific Northwest National Laboratory", "University of Alberta", "National Taiwan University of Science and Technology" ] }, { "uid": "arxiv:2410.00354v1", "arxiv_id": "2410.00354v1", "title": "Hierarchical Organization Simulacra in the Investment Sector", "authors": [ "Chung-Chi Chen", "Hiroya Takamura", "Ichiro Kobayashi", "Yusuke Miyao" ], "posted": "2024-10-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.00354v1", "field": "finance", "role": "agent", "bullets": [ "Over 115,000 news articles on 300 companies across 15 years, with professional traders' decisions as the benchmark for comparison.", "Large language model agents, families not stated, arranged in a mock investment firm hierarchy make decisions from news; prompt wording and perceived agent seniority are varied.", "Hierarchical simulations match professionals' choices in frequency and profitability, yet small prompt rewordings and seniority cues significantly shift the decisions." ], "bullet_provenance": "ai", "salience": 50, "edition": 13, "models": [], "validated": null, "n": 1796, "authors_detailed": [ { "name": "Chung-Chi Chen", "url": "https://openalex.org/A5101516307", "inst": "National Taiwan University of Science and Technology" }, { "name": "Hiroya Takamura", "url": "https://openalex.org/A5042127858", "inst": "The Institute of Statistical Mathematics" }, { "name": "Ichiro Kobayashi", "url": "https://openalex.org/A5089123620", "inst": "Hitotsubashi University" }, { "name": "Yusuke Miyao", "url": "https://openalex.org/A5004444958", "inst": "The University of Tokyo" } ], "affiliations": [ "National Taiwan University of Science and Technology", "The Institute of Statistical Mathematics", "Hitotsubashi University", "The University of Tokyo" ] }, { "uid": "doi:10.2139/ssrn.4940601", "doi": "10.2139/ssrn.4940601", "title": "Did Early ChatGPT-4 Adopters Change Payments to Other Digital Services? Exploratory Evidence from a Consumer Spending Panel", "authors": [ "Seung Hyun Kim", "Daniel McCarthy", "Kenneth C. Wilbur" ], "posted": "2024-10-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4940601", "field": "management", "role": "object", "bullets": [ "U.S. consumer spending panel tracking digital service payments before and after ChatGPT-4 launch, using coarsened exact matching of early versus later adopters.", "Triple-difference identification strategy estimated counterfactual spending on other digital services by early ChatGPT-4 subscribers.", "Market share declines larger than 2% were ruled out for nearly all other digital service brands; early adopters tended to also pay for other AI services." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 65, "validated": null, "n": 2648, "authors_detailed": [ { "name": "Seung Hyun Kim", "url": "https://openalex.org/A5100454615", "inst": "Northeastern University" }, { "name": "Daniel McCarthy", "url": "https://openalex.org/A5081822861", "inst": "University of Maryland - Robert H. Smith School of Business" }, { "name": "Kenneth C. Wilbur", "url": "https://openalex.org/A5003816851", "inst": "University of California San Diego" } ], "affiliations": [ "Northeastern University", "University of Maryland - Robert H. Smith School of Business", "University of California San Diego" ] }, { "uid": "arxiv:2410.00727v3", "arxiv_id": "2410.00727v3", "title": "\"Show Me What's Wrong!\": Combining Charts and Text to Guide Data Analysis", "authors": [ "Beatriz Feliciano", "Rita Costa", "Jean Alves", "Javier Liébana", "Diogo Duarte", "Pedro Bizarro" ], "posted": "2024-10-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.00727v3", "field": "finance", "role": "instrument", "bullets": [ "Financial fraud detection setting with multi-dimensional transactional data evaluated by seven domain experts.", "LLM generates textual insights combined with automated visual cues and charts to guide exploratory anomaly detection.", "Domain experts confirmed the tool effectively supports identification of suspicious activity and reduces information overload in fraud analysis." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 35, "n": 3441, "authors_detailed": [ { "name": "Beatriz Feliciano", "url": "https://openalex.org/A5114452421", "inst": "" }, { "name": "Rita Costa", "url": "https://openalex.org/A5101645392", "inst": "University of Guayaquil" }, { "name": "Jean V. Alves", "url": "https://openalex.org/A5104218445", "inst": "Feed Control (Norway)" }, { "name": "Javier Liébana", "url": "https://openalex.org/A5094134816", "inst": "Escola Superior de Enfermagem de Coimbra" }, { "name": "Diogo Duarte", "url": "https://openalex.org/A5101647087", "inst": "Universidad Complutense de Madrid" }, { "name": "Pedro Bizarro", "url": "https://openalex.org/A5077752651", "inst": "Feed Control (Norway)" } ], "affiliations": [ "University of Guayaquil", "Feed Control (Norway)", "Escola Superior de Enfermagem de Coimbra", "Universidad Complutense de Madrid" ] }, { "uid": "arxiv:2410.00207v1", "arxiv_id": "2410.00207v1", "title": "Evaluating the performance of state-of-the-art esg domain-specific pre-trained large language models in text classification against existing models and traditional machine learning techniques", "authors": [ "Tin Yuet Chung", "Majid Latifi" ], "posted": "2024-09-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.00207v1", "field": "finance", "role": "method", "bullets": [ "Corporate textual disclosures labelled for environmental, social, and governance content; sample size and period are not stated in the abstract.", "Llama 2 fine-tuned with QLoRA is compared against FinBERT-ESG, support vector machines, and XGBoost using accuracy, precision, recall, and F1 for each ESG pillar.", "QLoRA fine-tuning improves performance across all three pillars, yielding separate environmental, social, and governance classifiers; the abstract reports no specific figures." ], "bullet_provenance": "ai", "models": [ "legacy", "llama" ], "open_weights": true, "validated": true, "validation_note": "labelled ESG classification, standard NLP metrics", "salience": 34, "edition": 13, "n": 1563, "authors_detailed": [ { "name": "Chung, Tin Yuet", "url": "", "inst": "" }, { "name": "Majid Latifi", "url": "https://openalex.org/A5009615784", "inst": "Machine Science" } ], "affiliations": [ "Machine Science" ] }, { "uid": "arxiv:2409.19854v1", "arxiv_id": "2409.19854v1", "title": "The Construction of Instruction-tuned LLMs for Finance without Instruction Data Using Continual Pretraining and Model Merging", "authors": [ "Masanori Hirano", "Kentaro Imajo" ], "posted": "2024-09-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.19854v1", "field": "finance", "role": "method", "bullets": [ "Japanese financial language modeling, building instruction-following domain models by combining continual pretraining on financial data with model merging instead of instruction data collection.", "An instruction task vector from public instruction-tuned LLMs is merged with a domain pretrained vector, and resulting nekomata 14B based models are released on Hugging Face.", "Merging yields working instruction-tuned financial LLMs with no instruction data, exploiting near independence of the instruction and domain vectors. Benchmark figures are not stated." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 30, "edition": 13, "validated": null, "n": 1704, "authors_detailed": [ { "name": "Masanori Hirano", "url": "https://openalex.org/A5002301422", "inst": "Preferred Networks (Japan)" }, { "name": "Kentaro Imajo", "url": "https://openalex.org/A5038152086", "inst": "Kyoto University" } ], "affiliations": [ "Preferred Networks (Japan)", "Kyoto University" ] }, { "uid": "doi:10.22214/ijraset.2025.68240", "doi": "10.22214/ijraset.2025.68240", "arxiv_id": "2410.12807v1", "title": "A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting", "authors": [ "Arya Chakraborty", "Auhona Basu" ], "posted": "2024-09-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.12807v1", "field": "finance", "role": "instrument", "bullets": [ "Historical stock prices and technical indicators combined with financial news, social media, and report text; markets, sample, and period are not stated.", "A two level Conv-LSTM models the price series while an unnamed LLM reads the text for sentiment and context; no validation of either component is reported.", "The paper presents the architecture and argues it should improve prediction accuracy and advising; the abstract contains no quantitative results." ], "bullet_provenance": "ai", "validated": false, "salience": 20, "edition": 13, "models": [], "n": 1795, "authors_detailed": [ { "name": "Arya Chakraborty", "url": "https://openalex.org/A5003712249", "inst": "Birla Institute of Technology, Mesra" }, { "name": "Auhona Basu", "url": "", "inst": "" } ], "affiliations": [ "Birla Institute of Technology, Mesra" ] }, { "uid": "doi:10.2139/ssrn.4939706", "doi": "10.2139/ssrn.4939706", "title": "Context-Specific Small Language Models vs. LLMs: Deciphering Customer Interactions with AI assistants", "authors": [ "Ziting Liao", "Liye Ma", "Wendy W. Moe" ], "posted": "2024-09-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4939706", "field": "management", "role": "instrument", "bullets": [ "Consumer utterances to AI assistants analyzed for purchase intent prediction, validated against cost-per-click and competitive density in search marketing.", "ChatGPT scored purchase intent via prompting; graph-based method extracted noun-verb networks from utterances as a comparison approach.", "Graph-based method captures information beyond ChatGPT and outperforms it for commercially relevant keywords on CPC and competitive density prediction." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "prediction accuracy on CPC and competitive density metrics", "salience": 48, "n": 2842, "authors_detailed": [ { "name": "Ziting Liao", "url": "https://openalex.org/A5114237112", "inst": "" }, { "name": "Liye Ma", "url": "https://openalex.org/A5102409436", "inst": "Yanshan University" }, { "name": "Wendy W. Moe", "url": "https://openalex.org/A5001650853", "inst": "University of Maryland, College Park" } ], "affiliations": [ "University of Maryland, College Park", "Yanshan University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2409.19508v1", "arxiv_id": "2409.19508v1", "title": "Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science", "authors": [ "Aniket Pramanick", "Yufang Hou", "Saif M. Mohammad", "Iryna Gurevych" ], "posted": "2024-09-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.19508v1", "field": "management", "role": "object", "bullets": [ "Bibliometric sample of roughly 148,000 papers citing any of 106 curated LLMs, used to trace uptake across academic fields outside computer science since 2018.", "No model performs measurement; citation and usage patterns of LLMs are themselves the object, classified by field and by how the models are employed.", "Linguistics and engineering account for about 45 percent of LLM citations, and most non-CS fields rely on task-agnostic zero-shot or few-shot use rather than fine-tuning." ], "bullet_provenance": "ai", "salience": 28, "edition": 13, "models": [], "validated": null, "n": 1613, "authors_detailed": [ { "name": "Aniket Pramanick", "url": "https://openalex.org/A5041415557", "inst": "Hess (United States)" }, { "name": "Yufang Hou", "url": "https://openalex.org/A5078812348", "inst": "Chinese Academy of Medical Sciences & Peking Union Medical College" }, { "name": "Saif M. Mohammad", "url": "https://openalex.org/A5033684482", "inst": "National Research Council Canada" }, { "name": "Iryna Gurevych", "url": "https://openalex.org/A5027450194", "inst": "Technische Universität Darmstadt" } ], "affiliations": [ "Hess (United States)", "Chinese Academy of Medical Sciences & Peking Union Medical College", "National Research Council Canada", "Technische Universität Darmstadt" ] }, { "uid": "arxiv:2410.03724v3", "arxiv_id": "2410.03724v3", "title": "Overcoming the Machine Penalty with Imperfectly Fair AI Agents", "authors": [ "Zhen Wang", "Ruiqi Song", "Chen Shen", "Shiya Yin", "Zhao Song", "Balaraju Battu", "Lei Shi", "Danyang Jia", "Talal Rahwan", "Shuyue Hu" ], "posted": "2024-09-29", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.03724v3", "field": "economics", "role": "agent", "bullets": [ "Pre-registered experiment with 1,152 participants playing social dilemma games with LLM-powered AI agents exhibiting selfish, cooperative, or fair personas.", "LLM agents communicated with human participants before game play; fair agents occasionally broke cooperation promises mimicking human behavior.", "Only fair AI agents elicited cooperation rates comparable to human-human interactions, overcoming the machine penalty in social dilemmas." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "n": 3195, "authors_detailed": [ { "name": "Zhen Wang", "url": "https://openalex.org/A5114469987", "inst": "" }, { "name": "Ruiqi Song", "url": "https://openalex.org/A5114469989", "inst": "" }, { "name": "Chen Shen", "url": "https://openalex.org/A5114469990", "inst": "" }, { "name": "Shiya Yin", "url": "https://openalex.org/A5114469991", "inst": "" }, { "name": "Zhao Song", "url": "https://openalex.org/A5114469992", "inst": "" }, { "name": "Balaraju Battu", "url": "https://openalex.org/A5043896739", "inst": "New York University Abu Dhabi" }, { "name": "Lei Shi", "url": "https://openalex.org/A5114469993", "inst": "" }, { "name": "Danyang Jia", "url": "https://openalex.org/A5114469994", "inst": "" }, { "name": "Talal Rahwan", "url": "https://openalex.org/A5007282319", "inst": "New York University Abu Dhabi" }, { "name": "Shuyue Hu", "url": "https://openalex.org/A5114469988", "inst": "" } ], "affiliations": [ "New York University Abu Dhabi" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3711061", "doi": "10.1145/3711061", "arxiv_id": "2409.19450v2", "title": "Secret Use of Large Language Model (LLM)", "authors": [ "Zhiping Zhang", "Chenxinran Shen", "Bingsheng Yao", "Dakuo Wang", "Tianshi Li" ], "posted": "2024-09-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.19450v2", "field": "management", "role": "object", "bullets": [ "Mixed-methods design pairing an exploratory survey that gathered 125 real-world cases of people hiding their LLM use with a controlled experiment on 300 users.", "No model is applied by the researchers; large language models feature as the technology whose undisclosed use the study explains.", "Secrecy is triggered by task type more than demographics or personality, operating through perceived external judgment of LLM use, informing interventions that encourage transparent disclosure of AI assistance." ], "bullet_provenance": "ai", "salience": 45, "edition": 13, "models": [], "validated": null, "n": 1658, "authors_detailed": [ { "name": "Zhiping Zhang", "url": "https://openalex.org/A5100389481", "inst": "Northeastern University" }, { "name": "Chenxinran Shen", "url": "https://openalex.org/A5059105678", "inst": "Northeastern University" }, { "name": "Bingsheng Yao", "url": "https://openalex.org/A5033744502", "inst": "Northeastern University" }, { "name": "Dakuo Wang", "url": "https://openalex.org/A5062817658", "inst": "Northeastern University" }, { "name": "Tianshi Li", "url": "https://openalex.org/A5039928918", "inst": "Northeastern University" } ], "affiliations": [ "Northeastern University" ] }, { "uid": "arxiv:2409.18417v2", "arxiv_id": "2409.18417v2", "title": "VickreyFeedback: Cost-efficient Data Construction for Reinforcement Learning from Human Feedback", "authors": [ "Guoxi Zhang", "Jiuding Duan" ], "posted": "2024-09-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.18417v2", "field": "economics", "role": "method", "bullets": [ "Preference data collection for reinforcement learning from human feedback, treated as a monetized economy in which annotation carries dollar costs and preferences may be intransitive or cyclic.", "An auction mechanism steers the annotation budget toward high-quality feedback for LLM fine-tuning. No specific model or evaluation benchmark is named in the abstract.", "The auction-based protocol is reported to be cost-effective in dollar terms while maintaining satisfactory model performance. Magnitudes are not stated." ], "bullet_provenance": "ai", "salience": 26, "edition": 13, "models": [], "validated": null, "n": 1702, "authors_detailed": [ { "name": "Guoxi Zhang", "url": "https://openalex.org/A5101852296", "inst": "Chongqing University" }, { "name": "Duan, Jiuding", "url": "", "inst": "" } ], "affiliations": [ "Chongqing University" ] }, { "uid": "arxiv:2409.18511v4", "arxiv_id": "2409.18511v4", "title": "Do We Need Domain-Specific Embedding Models? An Empirical Investigation", "authors": [ "Yixuan Tang", "Yi Yang" ], "posted": "2024-09-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.18511v4", "field": "finance", "role": "method", "bullets": [ "FinMTEB, a financial counterpart to the MTEB embedding benchmark assembled from financial domain text datasets, with four measures used to control for dataset complexity.", "Seven state-of-the-art embedding models are evaluated on both MTEB and FinMTEB. The individual models are not named in the abstract.", "Scores drop significantly on financial tasks even after controlling for complexity, and MTEB performance is uncorrelated with FinMTEB performance, arguing for domain-specific benchmarks." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinMTEB labeled financial embedding tasks", "salience": 45, "edition": 13, "models": [], "n": 1703, "authors_detailed": [ { "name": "Tang, Yixuan", "url": "", "inst": "" }, { "name": "Yixin Yang", "url": "https://openalex.org/A5101416986", "inst": "Zhejiang International Studies University" } ], "affiliations": [ "Zhejiang International Studies University" ] }, { "uid": "arxiv:2409.17581v1", "arxiv_id": "2409.17581v1", "title": "A Scalable Data-Driven Framework for Systematic Analysis of SEC 10-K Filings Using Large Language Models", "authors": [ "Syed Affan Daimi", "Asma Iqbal" ], "posted": "2024-09-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.17581v1", "field": "finance", "role": "method", "bullets": [ "SEC 10-K filings of NYSE-listed companies, automatically retrieved and segmented into required sections, feeding a no-code pipeline with an interactive interface for year-on-year comparisons.", "Cohere's Command-R+ rates each company on dimensions including confidence, environmental sustainability, innovation, and workforce management; no check against ground truth is reported in the abstract.", "The output is a set of quantitative ratings and visualizations for screening many firms quickly; no accuracy or market outcome results are stated." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 34, "edition": 13, "n": 1657, "authors_detailed": [ { "name": "Syed Affan Daimi", "url": "https://openalex.org/A5093464021", "inst": "Indian Institute of Technology Madras" }, { "name": "Asma Iqbal", "url": "https://openalex.org/A5104218229", "inst": "COMSATS University Islamabad" } ], "affiliations": [ "Indian Institute of Technology Madras", "COMSATS University Islamabad" ] }, { "uid": "arxiv:2409.17448v1", "arxiv_id": "2409.17448v1", "title": "Enhancing Financial Sentiment Analysis with Expert-Designed Hint", "authors": [ "Chung-Chi Chen", "Hiroya Takamura", "Ichiro Kobayashi", "Yusuke Miyao" ], "posted": "2024-09-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.17448v1", "field": "finance", "role": "method", "bullets": [ "Financial social media posts, including tweets containing different types of numerical data; sample sizes and platform details are not stated in the abstract.", "Various LLMs, none named in the abstract, run sentiment analysis with and without an expert designed hint stressing the importance of numbers.", "The hint raises sentiment performance across models, most for posts with monetary numbers and cases needing perspective taking; gains are not quantified in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labeled financial social media sentiment data", "salience": 35, "edition": 13, "models": [], "n": 1751, "authors_detailed": [ { "name": "Chung-Chi Chen", "url": "https://openalex.org/A5101516307", "inst": "National Taiwan University of Science and Technology" }, { "name": "Hiroya Takamura", "url": "https://openalex.org/A5042127858", "inst": "The Institute of Statistical Mathematics" }, { "name": "Ichiro Kobayashi", "url": "https://openalex.org/A5089123620", "inst": "Hitotsubashi University" }, { "name": "Yusuke Miyao", "url": "https://openalex.org/A5004444958", "inst": "The University of Tokyo" } ], "affiliations": [ "National Taiwan University of Science and Technology", "The Institute of Statistical Mathematics", "Hitotsubashi University", "The University of Tokyo" ] }, { "uid": "arxiv:2409.17827v2", "arxiv_id": "2409.17827v2", "title": "BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text", "authors": [ "Siyan Wang", "Bradford Levy" ], "posted": "2024-09-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.17827v2", "field": "accounting", "role": "method", "bullets": [ "BeanCounter, a public corpus of more than 159B tokens extracted from businesses' disclosures, with under 0.1 percent overlap with Common Crawl based datasets.", "Two LLMs, families not stated, are continually pretrained on the corpus and compared with their base models; demographic mentions and toxicity are profiled against web datasets.", "Continued pretraining reduces toxic generation by 18 to 33 percent and improves finance domain performance, while identity terms appear in less toxic contexts than on the web." ], "bullet_provenance": "ai", "salience": 56, "edition": 13, "models": [], "validated": null, "n": 1793, "authors_detailed": [ { "name": "Siyan Wang", "url": "https://openalex.org/A5101753405", "inst": "North University of China" }, { "name": "Levy, Bradford", "url": "", "inst": "" } ], "affiliations": [ "North University of China" ] }, { "uid": "arxiv:2409.17587v2", "arxiv_id": "2409.17587v2", "title": "Multimodal Banking Dataset: Understanding Client Needs through Event Sequences", "authors": [ "Dzhambulat Mollaev", "Alexander Kostin", "Maria Postnova", "Ivan Karpukhin", "Ivan Kireev", "Gleb Gusev", "Andrey Savchenko" ], "posted": "2024-09-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.17587v2", "field": "finance", "role": "method", "bullets": [ "MBD, an anonymized open dataset covering over 2M corporate clients of a large bank, with 950M transactions, 1B geolocation events, 5M support dialogue embeddings, and monthly product purchases.", "Event sequence models including large language models, families not stated, are benchmarked on future purchase prediction and modality matching over MBD and two public financial datasets.", "Multimodal fusion baselines beat single modality techniques on every task; the abstract reports this superiority without giving accuracy numbers." ], "bullet_provenance": "ai", "salience": 38, "edition": 13, "models": [], "validated": null, "n": 1794, "authors_detailed": [ { "name": "Dzhambulat Mollaev", "url": "https://openalex.org/A5094584051", "inst": "Institute of Physics and Technology" }, { "name": "Alexander Kostin", "url": "https://openalex.org/A5009664424", "inst": "Vasyl' Stus Donetsk National University" }, { "name": "Postnova, Maria", "url": "", "inst": "" }, { "name": "Ivan Karpukhin", "url": "https://openalex.org/A5045931494", "inst": "Lomonosov Moscow State University" }, { "name": "Ivan Kireev", "url": "https://openalex.org/A5036899242", "inst": "Stavropol State Agrarian University" }, { "name": "Gleb Gusev", "url": "https://openalex.org/A5052767879", "inst": "Russian University of Transport" }, { "name": "А. А. Савченко", "url": "https://openalex.org/A5008998801", "inst": "Krasnoyarsk Scientific Center" } ], "affiliations": [ "Institute of Physics and Technology", "Vasyl' Stus Donetsk National University", "Lomonosov Moscow State University", "Stavropol State Agrarian University", "Russian University of Transport", "Krasnoyarsk Scientific Center" ] }, { "uid": "doi:10.2139/ssrn.4943590", "doi": "10.2139/ssrn.4943590", "title": "Aligned Structuring of AI Startups", "authors": [ "Gad Weiss" ], "posted": "2024-09-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4943590", "field": "finance", "role": "object", "bullets": [ "Conceptual policy analysis built around OpenAI's late-2024 proposal to unwind its capped-profit structure, with Anthropic as a second example; no dataset or empirical sample is analyzed.", "No language model is used; the paper defines and evaluates aligned structuring, governance arrangements that assign control and capped cash flows so that public interests take priority in AI development.", "Argues aligned structuring is a weak public-interest safeguard because outside decision-makers lack leverage and expertise, profit caps are easily circumvented, and the nonstandard form may deter needed capital and talent." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "general", "validated": null, "n": 1030, "authors_detailed": [ { "name": "Gad Weiss", "url": "https://openalex.org/A5107533087", "inst": "New York Law School" } ], "affiliations": [ "New York Law School" ] }, { "uid": "arxiv:2409.17933v2", "arxiv_id": "2409.17933v2", "title": "ChatGPT and Corporate Policies", "authors": [ "Manish Jha", "Jialin Qian", "Michael Weber", "Baozhong Yang" ], "posted": "2024-09-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.17933v2", "field": "finance", "role": "instrument", "bullets": [ "Over 120,000 U.S. corporate conference call transcripts used to measure managerial capital expenditure expectations.", "ChatGPT scores anticipated changes in capital expenditures from earnings calls, validated against CFO survey responses.", "Investment score predicts capex up to nine quarters ahead; high-score firms earn positive short-term but negative long-run abnormal returns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "correlation with CFO survey responses", "salience": 75, "n": 3440, "authors_detailed": [ { "name": "Manish Jha", "url": "https://openalex.org/A5024242858", "inst": "Georgia State University" }, { "name": "Jialin Qian", "url": "https://openalex.org/A5104148222", "inst": "Rochester Institute of Technology" }, { "name": "Michael Weber", "url": "https://openalex.org/A5114419204", "inst": "" }, { "name": "Baozhong Yang", "url": "https://openalex.org/A5084538885", "inst": "Georgia State University" } ], "affiliations": [ "Georgia State University", "Rochester Institute of Technology" ] }, { "uid": "arxiv:2409.17266v2", "arxiv_id": "2409.17266v2", "title": "Empirical Asset Pricing with Large Language Model Agents", "authors": [ "Junyan Cheng", "Peter Chin" ], "posted": "2024-09-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.17266v2", "field": "finance", "role": "instrument", "bullets": [ "Cross section of asset returns combined with manually curated financial economic factors; the asset universe, market, and sample period are not stated in the abstract.", "LLM agents, model family not named, produce qualitative discretionary investment evaluations that join the quantitative factors in the pricing model; no validation of the evaluations is reported.", "Against machine learning baselines, the portfolio Sharpe ratio improves 10.6 percent and mean absolute alpha of anomaly portfolios improves 10.0 percent." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 13, "models": [], "n": 1750 }, { "uid": "doi:10.1145/3701716.3715599", "doi": "10.1145/3701716.3715599", "arxiv_id": "2409.16452v2", "title": "FMDLlama: Financial Misinformation Detection based on Large Language Models", "authors": [ "Zhiwei Liu", "Xin Zhang", "Kailai Yang", "Qianqian Xie", "Jimin Huang", "Sophia Ananiadou" ], "posted": "2024-09-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.16452v2", "field": "finance", "role": "method", "bullets": [ "Financial misinformation detection on social media style content, with a new multi-task instruction dataset FMDID and the FMD-B evaluation benchmark; sizes are not stated in the abstract.", "Llama3.1 is instruction-tuned for claim classification and explanation generation, then scored on FMD-B against a range of open-source LLMs and OpenAI models.", "The fine-tuned model tops both the open-source comparisons and OpenAI products on the benchmark, and is released open source; specific scores are not stated." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "FMD-B labelled benchmark", "salience": 30, "edition": 13, "n": 1612, "authors_detailed": [ { "name": "Zhiwei Liu", "url": "https://openalex.org/A5053521378", "inst": "University of Manchester" }, { "name": "Xin Zhang", "url": "", "inst": "University of Manchester" }, { "name": "Kailai Yang", "url": "https://openalex.org/A5066983614", "inst": "University of Manchester" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "The Fin AI, Singapore, Singapore" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "The Fin AI, Singapore, Singapore" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" } ], "affiliations": [ "University of Manchester", "The Fin AI, Singapore, Singapore" ] }, { "uid": "doi:10.2139/ssrn.4964384", "doi": "10.2139/ssrn.4964384", "title": "The Rapid Adoption of Generative AI", "authors": [ "Alexander Bick", "Adam Blandin", "David Deming" ], "posted": "2024-09-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4964384", "field": "economics", "role": "object", "bullets": [ "Nationally representative U.S. surveys of adults aged 18-64 measuring generative AI adoption at work and home through late 2024.", "Tracked adoption rates and usage intensity of generative AI, comparing diffusion speed to personal computers and the internet.", "Nearly 40% of U.S. adults use generative AI; work adoption matches PC speed; 1-5% of work hours are AI-assisted with 1.4% time savings." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3439, "authors_detailed": [ { "name": "Alexander Bick", "url": "https://openalex.org/A5102827031", "inst": "Federal Reserve Bank of St. Louis" }, { "name": "Adam Blandin", "url": "https://openalex.org/A5111336807", "inst": "Vanderbilt University" }, { "name": "David Deming", "url": "https://openalex.org/A5113396094", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Vanderbilt University", "Federal Reserve Bank of St. Louis", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2409.15256v1", "arxiv_id": "2409.15256v1", "title": "Behavioral Bias of Vision-Language Models: A Behavioral Finance View", "authors": [ "Yuhang Xiao", "Yudi Lin", "Ming-Chang Chiu" ], "posted": "2024-09-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.15256v1", "field": "finance", "role": "agent", "bullets": [ "Recency bias and authority bias, two behavioral finance patterns, tested in large vision-language models with a purpose-built data collection and evaluation pipeline.", "Open models including LLaVA-NeXT, MobileVLM V2, Mini-Gemini, MiniCPM Llama3 V 2.5 and Phi 3 Vision are compared with GPT-4o on bias-probing financial tasks.", "The open models display both biases to a significant degree while GPT-4o is negligibly affected, a gap the authors flag as a target for open model improvement." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "llama", "open_other" ], "open_weights": true, "salience": 48, "edition": 13, "validated": null, "n": 1543, "authors_detailed": [ { "name": "Yu-Hang Xiao", "url": "https://openalex.org/A5084481204", "inst": "Shenzhen University" }, { "name": "Yudi Lin", "url": "https://openalex.org/A5100991838", "inst": "Sanya University" }, { "name": "Ming-Chang Chiu", "url": "https://openalex.org/A5071166309", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California", "Shenzhen University", "Sanya University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2409.15436v2", "arxiv_id": "2409.15436v2", "title": "Ads that Talk Back: Implications and Perceptions of Injecting Personalized Advertising into LLM Chatbots", "authors": [ "Brian Jay Tang", "Kaiwen Sun", "Noah T. Curran", "Florian Schaub", "Kang G. Shin" ], "posted": "2024-09-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.15436v2", "field": "management", "role": "object", "bullets": [ "Between-subjects experiment with 179 participants using a chatbot that weaves personalized product advertisements into LLM responses, mirroring monetization plans floated by AI companies.", "The authors fine-tuned and released an open model, Phi-4-Ads, to serve ads and adapt to user preferences; benchmark checks showed ad injection only slightly reduced response desirability.", "Participants largely failed to detect the embedded ads, often preferred responses containing them, and tried to change ad settings by natural language rather than clicking the advertising disclosure." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "salience": 50, "edition": 13, "validated": null, "n": 1656, "authors_detailed": [ { "name": "Brian Tang", "url": "https://openalex.org/A5108757471", "inst": "University of Michigan" }, { "name": "Kaiwen Sun", "url": "https://openalex.org/A5025058366", "inst": "University of Michigan" }, { "name": "Noah Curran", "url": "https://openalex.org/A5112533089", "inst": "" }, { "name": "Florian Schaub", "url": "https://openalex.org/A5061205711", "inst": "University of Michigan" }, { "name": "Kang G. Shin", "url": "https://openalex.org/A5053541912", "inst": "University of Michigan" } ], "affiliations": [ "University of Michigan" ] }, { "uid": "doi:10.2139/ssrn.4941906", "doi": "10.2139/ssrn.4941906", "title": "What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts", "authors": [ "Shuaiyu Chen", "T. Clifton Green", "Huseyin Gulen", "Dexin Zhou" ], "posted": "2024-09-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4941906", "field": "finance", "role": "agent", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 50, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 343, "authors_detailed": [ { "name": "Shuaiyu Chen", "url": "https://openalex.org/A5009882284", "inst": "University of Virginia" }, { "name": "T. Clifton Green", "url": "https://openalex.org/A5046129142", "inst": "Emory University" }, { "name": "Huseyin Gulen", "url": "https://openalex.org/A5102768552", "inst": "Mitchell E. Daniels, Jr School of Business, Purdue University" }, { "name": "Dexin Zhou", "url": "https://openalex.org/A5069014291", "inst": "Baruch College" } ], "affiliations": [ "University of Virginia", "Emory University", "Mitchell E. Daniels, Jr School of Business, Purdue University", "Baruch College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4963992", "doi": "10.2139/ssrn.4963992", "title": "Creative and Strategic Capabilities of Generative Ai: Evidence from Large-Scale Experiments", "authors": [ "Noah Bohren", "Rustamdjan Hakimov", "Rafael Lalive" ], "posted": "2024-09-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4963992", "field": "management", "role": "agent", "bullets": [ "Representative U.S. adult population completing creative idea generation and strategic game tasks, comparing human and AI chatbot performance.", "ChatGPT and Google Bard generated creative ideas and played a strategic game; human raters scored outputs blind to source.", "ChatGPT outperformed humans in creativity; humans were more successful at strategic adaptation; AI-labeled responses received lower scores regardless of source." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "salience": 52, "validated": null, "n": 2953, "authors_detailed": [ { "name": "Noah Bohren", "url": "https://openalex.org/A5092242208", "inst": "University of Lausanne" }, { "name": "Rustamdjan Hakimov", "url": "https://openalex.org/A5024490957", "inst": "WZB Berlin Social Science Center" }, { "name": "Rafael Lalive", "url": "https://openalex.org/A5075713315", "inst": "Ifo Institute for Economic Research" } ], "affiliations": [ "University of Lausanne", "WZB Berlin Social Science Center", "Ifo Institute for Economic Research" ] }, { "uid": "arxiv:2409.15567v3", "arxiv_id": "2409.15567v3", "title": "Asking an AI for salary negotiation advice is a matter of concern: Controlled experimental perturbation of ChatGPT for protected and non-protected group discrimination on a contextual task with no clear ground truth answers", "authors": [ "R. Stuart Geiger", "Flynn O'Sullivan", "Elsie Wang", "Jonathan Lo" ], "posted": "2024-09-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.15567v3", "field": "management", "role": "object", "bullets": [ "98,800 prompts submitted to four ChatGPT versions varying employee gender, university, major, and negotiation voice.", "Audited ChatGPT salary negotiation recommendations for bias across protected and non-protected demographic attributes.", "Statistically significant salary gaps by gender in all models; largest gaps arise between model versions and employee-vs-employer prompt framing." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "validated": null, "n": 3438, "authors_detailed": [ { "name": "R. Stuart Geiger", "url": "https://openalex.org/A5088070105", "inst": "University of California San Diego" }, { "name": "O'Sullivan, Flynn", "url": "", "inst": "" }, { "name": "Elsie Wang", "url": "https://openalex.org/A5059960107", "inst": "University of California San Diego" }, { "name": "June C. Lo", "url": "https://openalex.org/A5074779960", "inst": "University of Wisconsin–Madison" } ], "affiliations": [ "University of California San Diego", "University of Wisconsin–Madison" ] }, { "uid": "arxiv:2410.07143v1", "arxiv_id": "2410.07143v1", "title": "SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest", "authors": [ "Saber Talazadeh", "Dragan Perakovic" ], "posted": "2024-09-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.07143v1", "field": "finance", "role": "instrument", "bullets": [ "Stock market data used to compare prediction models for directional stock price movement forecasting.", "FinGPT generates sentiment features from financial text, integrated into a Random Forest framework for stock prediction.", "Sentiment-Augmented Random Forest outperforms conventional Random Forest and LSTM models with 9.23% average accuracy improvement." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "stock direction accuracy vs RF and LSTM baselines", "salience": 40, "n": 3437, "authors_detailed": [ { "name": "Saber Talazadeh", "url": "https://openalex.org/A5109819268", "inst": "British Columbia Institute of Technology" }, { "name": "Dragan Peraković", "url": "https://openalex.org/A5022899605", "inst": "University of Zagreb" } ], "affiliations": [ "British Columbia Institute of Technology", "University of Zagreb" ] }, { "uid": "arxiv:2409.14202v3", "arxiv_id": "2409.14202v3", "title": "Mining Causality: AI-Assisted Search for Instrumental Variables", "authors": [ "Sukjin Han" ], "posted": "2024-09-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.14202v3", "field": "economics", "role": "method", "bullets": [ "Three canonical empirical settings in economics, returns to schooling, supply and demand, and peer effects, plus extensions to control variables and regression discontinuity running variables.", "Unnamed LLMs are prompted with multi-step, role-playing strategies to simulate agents' decision processes and propose candidate instrumental variables; proposals are not validated against any benchmark.", "The paper argues LLM search over narratives can vastly widen the space of candidate instruments, and demonstrates prompt construction rather than measuring instrument validity." ], "bullet_provenance": "ai", "salience": 55, "edition": 13, "models": [], "validated": null, "n": 1611, "authors_detailed": [ { "name": "Sukjin Han", "url": "https://openalex.org/A5071480416", "inst": "University of Bristol" } ], "affiliations": [ "University of Bristol" ] }, { "uid": "arxiv:2409.13869v2", "arxiv_id": "2409.13869v2", "title": "Generative AI Carries Non-Democratic Biases and Stereotypes: Representation of Women, Black Individuals, Age Groups, and People with Disability in AI-Generated Images across Occupations", "authors": [ "Ayoob Sadeghiani" ], "posted": "2024-09-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.13869v2", "field": "management", "role": "object", "bullets": [ "444 occupational images produced by Microsoft Designer, Meta AI, and Ideogram across 37 occupations, coded for gender, race, age, and visible disability.", "The image generators are the audited object rather than a research tool; representation in their outputs is analyzed by the author, and no language model measurement is involved.", "Women are underrepresented in senior and technology roles, Black individuals are nearly absent, visibly disabled people never appear, and younger faces dominate across occupations." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "salience": 30, "edition": 13, "validated": null, "n": 1792, "authors_detailed": [ { "name": "Ayoob Sadeghiani", "url": "https://openalex.org/A5083775381", "inst": "Amirkabir University of Technology" } ], "affiliations": [ "Amirkabir University of Technology" ] }, { "uid": "arxiv:2409.18999v1", "arxiv_id": "2409.18999v1", "title": "Enhancing TinyBERT for Financial Sentiment Analysis Using GPT-Augmented FinBERT Distillation", "authors": [ "Graison Jos Thomas" ], "posted": "2024-09-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.18999v1", "field": "finance", "role": "method", "bullets": [ "Financial sentiment sentences from the Financial PhraseBank and FiQA 2018 Task 1 datasets serve as the training and evaluation material.", "GPT-4 Omni and GPT-3.5 Turbo generate and transform synthetic financial text; an augmented FinBERT teacher then distills into compact TinyFinBERT, scored on the labelled benchmarks.", "TinyFinBERT approaches its FinBERT teacher's accuracy at a much smaller size, and augmentation also lifts FinBERT itself; exact margins are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "validated": true, "validation_note": "Financial PhraseBank and FiQA 2018 labelled data", "salience": 32, "edition": 13, "n": 1562, "authors_detailed": [ { "name": "Thomas, Graison Jos", "url": "", "inst": "" } ] }, { "uid": "arxiv:2410.00031v2", "arxiv_id": "2410.00031v2", "title": "Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions", "authors": [ "Ryan Y. Lin", "Siddhartha Ojha", "Kevin Cai", "Maxwell F. Chen" ], "posted": "2024-09-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2410.00031v2", "field": "economics", "role": "agent", "bullets": [ "Simulated multi commodity Cournot competition in which autonomous LLM agents set prices and allocate resources; run counts and commodity numbers are not stated.", "LLM agents, families not named, compete without human input or any collusion instructions; the study watches for emergent anti competitive strategies.", "Agents divide markets and monopolize individual commodities to maximize profit without being told to collude, raising antitrust questions about delegating pricing to LLMs." ], "bullet_provenance": "ai", "salience": 58, "edition": 13, "models": [], "validated": null, "n": 1749, "authors_detailed": [ { "name": "Ryan Y. Lin", "url": "https://openalex.org/A5114426584", "inst": "" }, { "name": "Sadhana Ojha", "url": "https://openalex.org/A5087878545", "inst": "" }, { "name": "Cai, Kevin", "url": "", "inst": "" }, { "name": "Chen, Maxwell F.", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.4929626", "doi": "10.2139/ssrn.4929626", "title": "The Acceptance and Usage of ChatGPT: An Information Adoption Model Perspective", "authors": [ "Mark Camilleri", "Adriana Camilleri" ], "posted": "2024-09-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4929626", "field": "management", "role": "object", "bullets": [ "Survey of 327 higher education students measuring perceptions of ChatGPT-generated content using the information adoption model framework.", "SmartPLS structural equation modeling tested information relevance, accuracy, source trustworthiness, and usefulness as determinants of ChatGPT information adoption.", "Information usefulness most strongly predicted adoption; information relevance had the largest influence on perceived usefulness of ChatGPT-generated content." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "validated": null, "n": 2368, "authors_detailed": [ { "name": "Mark Anthony Camilleri", "url": "https://openalex.org/A5082124522", "inst": "Northwestern University" }, { "name": "Adriana Caterina Camilleri", "url": "https://openalex.org/A5081898997", "inst": "Malta College of Arts, Science and Technology" } ], "affiliations": [ "Northwestern University", "Malta College of Arts, Science and Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4543999", "doi": "10.2139/ssrn.4543999", "title": "AI Democratization and Trading Inequality", "authors": [ "Anne Chang", "Xi Dong", "Xiumin Martin", "Changyun Zhou" ], "posted": "2024-09-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4543999", "field": "finance", "role": "object", "bullets": [ "U.S. equity markets around earnings calls, comparing retail and short-seller trading alignment with AI-sentiment before and after ChatGPT deployment.", "AI sentiment extracted from earnings-call transcripts proxied for textual signals; exogenous ChatGPT outages provided causal identification.", "Retail trading alignment with AI sentiment rose post-ChatGPT; information asymmetry declined and retail profitability improved while short-seller edge weakened." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 82, "validated": null, "n": 2952, "authors_detailed": [ { "name": "Anne B. Chang", "url": "https://openalex.org/A5040334922", "inst": "Baruch College" }, { "name": "Xi Dong", "url": "https://openalex.org/A5021751937", "inst": "Baruch College" }, { "name": "Xiumin Martin", "url": "https://openalex.org/A5084744419", "inst": "Washington University in St. Louis" }, { "name": "Changyun Zhou", "url": "https://openalex.org/A5114229558", "inst": "Southwestern University of Finance and Economics" } ], "affiliations": [ "Washington University in St. Louis", "Baruch College", "Southwestern University of Finance and Economics" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2409.11643v2", "arxiv_id": "2409.11643v2", "title": "Combating Phone Scams with LLM-based Detection: Where Do We Stand?", "authors": [ "Zitong Shen", "Kangzhong Wang", "Youqian Zhang", "Grace Ngai", "Eugene Y. Fu" ], "posted": "2024-09-18", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.11643v2", "field": "finance", "role": "method", "bullets": [ "Conversational dynamics between scammers and victims in phone calls; the datasets are acknowledged as biased and their sizes are not stated in the abstract.", "LLM based detectors, models not named, flag fraudulent calls as conversations unfold; the evaluation surfaces relatively low recall and hallucination problems.", "Results are described as promising for real time scam detection, but recall limits and hallucinations leave the approach short of reliable deployment." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labeled scam call data, recall reported", "salience": 28, "edition": 13, "models": [], "n": 1748, "authors_detailed": [ { "name": "Zitong Shen", "url": "https://openalex.org/A5114223911", "inst": "Jiangsu Second Normal University" }, { "name": "Kangzhong Wang", "url": "https://openalex.org/A5082087455", "inst": "Hong Kong Polytechnic University" }, { "name": "Youqian Zhang", "url": "https://openalex.org/A5016761774", "inst": "Hong Kong Polytechnic University" }, { "name": "Grace Ngai", "url": "https://openalex.org/A5051682329", "inst": "Hong Kong Polytechnic University" }, { "name": "Eugene Yujun Fu", "url": "https://openalex.org/A5027114592", "inst": "Education University of Hong Kong" } ], "affiliations": [ "Jiangsu Second Normal University", "Hong Kong Polytechnic University", "Education University of Hong Kong" ] }, { "uid": "arxiv:2409.18988v1", "arxiv_id": "2409.18988v1", "title": "A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy", "authors": [ "Xiang Li", "Lan Zhao", "Junhao Ren", "Yajuan Sun", "Chuan Fu Tan", "Zhiquan Yeo", "Gaoxi Xiao" ], "posted": "2024-09-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.18988v1", "field": "economics", "role": "method", "bullets": [ "Textual descriptions of business activities worldwide, mapped to the International Standard Industrial Classification to build a waste to resource repository for circular economy matching.", "A fine tuned GPT-2, an older open weights model, classifies activity descriptions into ISIC codes and is evaluated on a 182 label test dataset.", "The classifier reaches 95 percent accuracy on the test set, supporting standardized coding of economic activities across regions for downstream recommendation systems." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "182 label ISIC test set, accuracy reported", "salience": 36, "edition": 13, "n": 1746, "authors_detailed": [ { "name": "Xiang Li", "url": "https://openalex.org/A5100658980", "inst": "Northwestern Polytechnical University" }, { "name": "Lan Zhao", "url": "https://openalex.org/A5033916369", "inst": "Zhejiang A & F University" }, { "name": "Ren, Junhao", "url": "", "inst": "" }, { "name": "Yajuan Sun", "url": "https://openalex.org/A5015505211", "inst": "Agency for Science, Technology and Research" }, { "name": "Chuan Fu Tan", "url": "https://openalex.org/A5109660231", "inst": "Agency for Science, Technology and Research" }, { "name": "Zhiquan Yeo", "url": "https://openalex.org/A5070558727", "inst": "Agency for Science, Technology and Research" }, { "name": "Xiao, Gaoxi", "url": "", "inst": "" } ], "affiliations": [ "Northwestern Polytechnical University", "Zhejiang A & F University", "Agency for Science, Technology and Research" ] }, { "uid": "arxiv:2409.11491v1", "arxiv_id": "2409.11491v1", "title": "Enriching Datasets with Demographics through Large Language Models: What's in a Name?", "authors": [ "Khaled AlNuaimi", "Gautier Marti", "Mathieu Ravaut", "Abdulla AlKetbi", "Andreas Henschel", "Raed Jaradat" ], "posted": "2024-09-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.11491v1", "field": "finance", "role": "method", "bullets": [ "Several name datasets, including an unlabelled roster of licensed financial professionals in Hong Kong, enriched with gender, race, and age; dataset sizes are not stated.", "Zero shot LLMs, families not named in the abstract, infer demographics from names and are compared with bespoke supervised models trained on specialized data.", "Zero shot inference matches or beats the specialized models, while the authors document demographic biases in the LLMs; figures are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labeled name demographic datasets, supervised baselines", "salience": 42, "edition": 13, "models": [], "n": 1747, "authors_detailed": [ { "name": "Khaled Alnuaimi", "url": "https://openalex.org/A5071285744", "inst": "Khalifa University of Science and Technology" }, { "name": "Gautier Marti", "url": "https://openalex.org/A5086630995", "inst": "Capital University" }, { "name": "Mathieu Ravaut", "url": "https://openalex.org/A5052746002", "inst": "Agency for Science, Technology and Research" }, { "name": "Abdulla AlKetbi", "url": "https://openalex.org/A5111396936", "inst": "Khalifa University of Science and Technology" }, { "name": "Andreas Henschel", "url": "https://openalex.org/A5075543445", "inst": "Khalifa University of Science and Technology" }, { "name": "Raed Jaradat", "url": "https://openalex.org/A5071190894", "inst": "United States Army Corps of Engineers" } ], "affiliations": [ "Khalifa University of Science and Technology", "Capital University", "Agency for Science, Technology and Research", "United States Army Corps of Engineers" ] }, { "uid": "arxiv:2409.10750v1", "arxiv_id": "2409.10750v1", "title": "GPT takes the SAT: Tracing changes in Test Difficulty and Math Performance of Students", "authors": [ "Vikram Krishnaveti", "Saannidhya Rawat" ], "posted": "2024-09-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.10750v1", "field": "economics", "role": "agent", "bullets": [ "Annual SAT math sections from 2008 through 2023, paired with published student score data broken out by race and gender.", "GPT-4 through the OpenAI API sits each year's exam as a constant-ability synthetic test taker, forming the control group of a design the authors call transformed control; ability constancy is not separately validated.", "SAT math rigor fell 71 points while student performance fell 36, a 107 point divergence, with drops of 104 points for White, 84 for Black, and 53 for Asian students." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 42, "edition": 13, "validated": null, "n": 1575, "authors_detailed": [ { "name": "Vikram Krishnaveti", "url": "https://openalex.org/A5106348708", "inst": "" }, { "name": "Saannidhya Rawat", "url": "https://openalex.org/A5055814499", "inst": "University of Cincinnati" } ], "affiliations": [ "University of Cincinnati" ] }, { "uid": "arxiv:2409.10372v4", "arxiv_id": "2409.10372v4", "title": "Integrated Design and Governance of Agentic AI Systems through Adaptive Information Modulation", "authors": [ "Qiliang Chen", "Sepehr Ilami", "Nunzio Lore", "Babak Heydari" ], "posted": "2024-09-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.10372v4", "field": "economics", "role": "agent", "bullets": [ "Repeated social dilemma games among strategic LLM-based agents, with a reinforcement learning governing agent that modulates what contextual and historical information each agent can access.", "LLM agents interact while a reinforcement learning governor adjusts information transparency at each timestep, avoiding structural interventions or payoff modifications. Underlying language models are not named.", "Adaptive information governance raises cooperation significantly relative to static information-sharing baselines, pointing to transparency control as a design lever. Effect sizes are not stated." ], "bullet_provenance": "ai", "salience": 34, "edition": 13, "models": [], "validated": null, "n": 1701, "authors_detailed": [ { "name": "Qiliang Chen", "url": "https://openalex.org/A5019153640", "inst": "Guangzhou University of Chinese Medicine" }, { "name": "Sepehr Ilami", "url": "https://openalex.org/A5114400148", "inst": "Northeastern University" }, { "name": "Nunzio Lorè", "url": "https://openalex.org/A5028910900", "inst": "Northeastern University" }, { "name": "Babak Heydari", "url": "https://openalex.org/A5030641600", "inst": "Northeastern University" } ], "affiliations": [ "Guangzhou University of Chinese Medicine", "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.4924553", "doi": "10.2139/ssrn.4924553", "title": "Limitations and Loopholes in the EU AI Act and AI Liability Directives: What This Means for the European Union, the United States, and Beyond", "authors": [ "Sandra Wachter" ], "posted": "2024-09-15", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4924553", "field": "economics", "role": "object", "bullets": [ "Policy essay studies the European AI Act and two liability directives across high-impact uses including employment, insurance, finance, education, healthcare, and public administration.", "No specific model produces evidence; predictive and generative AI systems are the regulated objects, making output validation inapplicable to the legal analysis.", "Industry lobbying, self-certification, weak oversight, and material-harm limits leave bias, hallucination, societal damage, and financial losses insufficiently addressed across Europe and beyond." ], "bullet_provenance": "ai", "salience": 53, "edition": 23, "models": [], "validated": null, "n": 4187, "authors_detailed": [ { "name": "Sandra Wachter", "url": "https://openalex.org/A5075172090", "inst": "Internet Society" } ], "affiliations": [ "Internet Society" ] }, { "uid": "arxiv:2409.09894v2", "arxiv_id": "2409.09894v2", "title": "Estimating Wage Disparities Using Foundation Models", "authors": [ "Keyon Vafa", "Susan Athey", "David M. Blei" ], "posted": "2024-09-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.09894v2", "field": "economics", "role": "instrument", "bullets": [ "Panel Study of Income Dynamics data on U.S. workers' career histories and wages, used to decompose the gender wage gap.", "Custom-built foundation model fine-tuned for wage prediction; new algorithms address omitted variable bias from maximizing predictive accuracy alone.", "Career history explains more of the gender wage gap than standard econometric models capture; identifies previously omitted career-history elements." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 72, "n": 3903, "authors_detailed": [ { "name": "Keyon Vafa", "url": "https://openalex.org/A5085921946", "inst": "Cornell University" }, { "name": "Susan Athey", "url": "https://openalex.org/A5001211242", "inst": "Stanford Medicine" }, { "name": "David M. Blei", "url": "https://openalex.org/A5070920982", "inst": "University of the District of Columbia" } ], "affiliations": [ "Cornell University", "Stanford Medicine", "University of the District of Columbia" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2409.13749v1", "arxiv_id": "2409.13749v1", "title": "KodeXv0.1: A Family of State-of-the-Art Financial Large Language Models", "authors": [ "Neel Rajani", "Lilli Kiessling", "Aleksandr Ogaltsov", "Claus Lang" ], "posted": "2024-09-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.13749v1", "field": "finance", "role": "method", "bullets": [ "Financial question answering over documents such as earnings calls and business reports, with a synthetic context-question-answer dataset built from public filings.", "Llama 3.1 8B and 70B are tuned with RAG-aware 4 bit LoRA instruction training; results are scored on FinanceBench, FinQABench and a held-out test split.", "KodeX 8B beats same-size instruct models by up to 9.24 percent and GPT-4 by up to 7.07 percent, and the 70B variant tops GPT-4 on every benchmark tested." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "FinanceBench, FinQABench and a withheld test split", "salience": 40, "edition": 13, "n": 1542, "authors_detailed": [ { "name": "Neel Rajani", "url": "https://openalex.org/A5114402385", "inst": "" }, { "name": "Lilli Kiessling", "url": "https://openalex.org/A5114402386", "inst": "" }, { "name": "Aleksandr Ogaltsov", "url": "https://openalex.org/A5114402387", "inst": "" }, { "name": "Claus Lang", "url": "https://openalex.org/A5074276334", "inst": "" } ] }, { "uid": "arxiv:2409.08890v1", "arxiv_id": "2409.08890v1", "title": "A Market for Lemons? Strategic Directions for a Vigilant Application of Artificial Intelligence in Entrepreneurship Research", "authors": [ "Martin Obschonka", "Moren Levesque" ], "posted": "2024-09-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.08890v1", "field": "management", "role": "object", "bullets": [ "Conceptual meta-analysis of AI methods adoption in entrepreneurship research, applying Akerlof's market-for-lemons framework.", "Identifies a double-black-box puzzle where opaque AI methods intersect with inherent uncertainty of entrepreneurship phenomena.", "Knowledge asymmetries from AI adoption risk increasing undetectable suboptimal research products unless the field builds AI resilience." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3436, "authors_detailed": [ { "name": "Martin Obschonka", "url": "https://openalex.org/A5021701198", "inst": "Amsterdam University of the Arts" }, { "name": "Moren Lévesque", "url": "https://openalex.org/A5072177336", "inst": "York University" } ], "affiliations": [ "Amsterdam University of the Arts", "York University" ] }, { "uid": "arxiv:2409.08357v2", "arxiv_id": "2409.08357v2", "title": "An Experimental Study of Competitive Market Behavior Through LLMs", "authors": [ "Jingru Jia", "Zehua Yuan" ], "posted": "2024-09-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.08357v2", "field": "economics", "role": "agent", "bullets": [ "Controlled experimental markets in which LLM agents act as traders, with convergence toward competitive equilibrium as the yardstick; model names and session counts are not stated in the abstract.", "The agents, unnamed in the abstract, make repeated trading decisions in a simulated market, and their collective behaviour is compared with the equilibrium outcomes expected from human experiments.", "The LLM agents fail to reach market equilibrium, which the authors attribute to limits in dynamic decision-making and treat as a caveat for LLM-based market simulation." ], "bullet_provenance": "ai", "salience": 50, "edition": 13, "models": [], "validated": null, "n": 1655, "authors_detailed": [ { "name": "Jingru Jia", "url": "https://openalex.org/A5114367715", "inst": "" }, { "name": "Zehua Yuan", "url": "https://openalex.org/A5114367716", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.4932769", "doi": "10.2139/ssrn.4932769", "title": "A Comprehensive Survey of Large Language Models in Management: Applications, Challenges, and Opportunities", "authors": [ "Hongke Zhao", "Chuang Zhao", "Likang Wu", "Yuqing Shan", "Zonghan Jin", "Yuanpei Sui", "Zipeng Liu", "Nan Feng", "Minqiang Li", "Wei Zhang" ], "posted": "2024-09-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4932769", "field": "management", "role": "object", "bullets": [ "Literature survey with no empirical sample, covering reported LLM applications across three business domains: finance, marketing, and supply chain management.", "No model named; the paper reviews documented uses such as market prediction, fraud detection, personalized engagement, and demand forecasting rather than running any model.", "Concludes that LLMs improve operational efficiency and strategic insight across the three domains and stresses synergistic cross-domain effects; no quantitative finding reported." ], "bullet_provenance": "ai", "salience": 33, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 128, "authors_detailed": [ { "name": "Hongke Zhao", "url": "https://openalex.org/A5017692278", "inst": "Tianjin University" }, { "name": "Chuang Zhao", "url": "", "inst": "" }, { "name": "Likang Wu", "url": "https://openalex.org/A5052639977", "inst": "Tianjin University" }, { "name": "Yuqing Shan", "url": "https://openalex.org/A5111322922", "inst": "Tianjin University" }, { "name": "Zonghan Jin", "url": "https://openalex.org/A5111322923", "inst": "Tianjin University" }, { "name": "Yuanpei Sui", "url": "https://openalex.org/A5113377750", "inst": "Tsinghua University" }, { "name": "Zipeng Liu", "url": "https://openalex.org/A5101978010", "inst": "Tianjin University" }, { "name": "Nan Feng", "url": "https://openalex.org/A5111184743", "inst": "Tianjin University of Finance and Economics" }, { "name": "Minqiang Li", "url": "https://openalex.org/A5101423504", "inst": "Tianjin University of Finance and Economics" }, { "name": "Wei Zhang", "url": "https://openalex.org/A5109156232", "inst": "Tianjin University of Finance and Economics" } ], "affiliations": [ "Tianjin University", "Tsinghua University", "Tianjin University of Finance and Economics" ] }, { "uid": "arxiv:2409.06289v4", "arxiv_id": "2409.06289v4", "title": "Automate Strategy Finding with LLM in Quant Investment", "authors": [ "Zhizhuo Kou", "Holam Yu", "Junyu Luo", "Jingshu Peng", "Xujia Li", "Chengzhong Liu", "Juntao Dai", "Lei Chen", "Sirui Han", "Yike Guo" ], "posted": "2024-09-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.06289v4", "field": "finance", "role": "method", "bullets": [ "Chinese and US equity markets, with a backtest on the SSE50 index from January 2023 to January 2024; the factor universe size is not stated.", "Prompt engineered LLMs, families not named, generate alpha factor candidates; multi agent evaluation filters them by market state and predictive quality, with dynamic weight optimization.", "The framework reports a 53.17 percent cumulative return on SSE50 over the backtest year and outperforms all benchmark strategies on risk adjusted measures." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 13, "models": [], "n": 1745, "authors_detailed": [ { "name": "Zhizhuo Kou", "url": "https://openalex.org/A5114353759", "inst": "Hong Kong University of Science and Technology" }, { "name": "Holam Yu", "url": "https://openalex.org/A5114353760", "inst": "Hong Kong University of Science and Technology" }, { "name": "Luo, Junyu", "url": "", "inst": "" }, { "name": "Jingshu Peng", "url": "https://openalex.org/A5113154054", "inst": "Hong Kong University of Science and Technology" }, { "name": "Li, Xujia", "url": "", "inst": "" }, { "name": "Liu, Chengzhong", "url": "", "inst": "" }, { "name": "Dai, Juntao", "url": "", "inst": "" }, { "name": "Lei Chen", "url": "https://openalex.org/A5025654844", "inst": "Durham University" }, { "name": "Han, Sirui", "url": "", "inst": "" }, { "name": "Guo, Yike", "url": "", "inst": "" } ], "affiliations": [ "Hong Kong University of Science and Technology", "Durham University" ] }, { "uid": "doi:10.2139/ssrn.4950574", "doi": "10.2139/ssrn.4950574", "title": "An Anatomy of Firms’ Political Speech", "authors": [ "Pablo Ottonello", "Wenting Song", "Sebastian Sotelo" ], "posted": "2024-09-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4950574", "field": "economics", "role": "instrument", "bullets": [ "U.S. firms' earnings calls, regulatory filings, and social media communications measuring political engagement patterns.", "Trained an LLM to identify statements containing political opinions across multiple corporate communication outlets.", "Political engagement is rare, concentrated among large firms, and the 2020 surge was driven by medium-sized firms entering new topics." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 60, "n": 3435, "authors_detailed": [ { "name": "Pablo Ottonello", "url": "https://openalex.org/A5085744956", "inst": "University of Maryland, College Park" }, { "name": "Wenting Song", "url": "https://openalex.org/A5057701004", "inst": "Bank of Canada" }, { "name": "Sebastian Sotelo", "url": "https://openalex.org/A5107078731", "inst": "University of Michigan" } ], "affiliations": [ "University of Maryland, College Park", "Bank of Canada", "University of Michigan" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2409.13704v2", "arxiv_id": "2409.13704v2", "title": "Entity Extraction from High-Level Corruption Schemes via Large Language Models", "authors": [ "Panagiotis Koletsis", "Panagiotis-Konstantinos Gemos", "Christos Chronis", "Iraklis Varlamis", "Vasilis Efthymiou", "Georgios Th. Papadopoulos" ], "posted": "2024-09-05", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.13704v2", "field": "finance", "role": "method", "bullets": [ "A new micro-benchmark of financial crime news articles for identifying individuals and organizations, including multiple written forms of the same entity.", "Various low-billion parameter LLMs are tested across prompt engineering variants with an LLM-based disambiguation step, scored on accuracy, precision, recall, and F1. Models are not named in the abstract.", "The proposed approach beats a widely used open-source state-of-the-art baseline on the benchmark. Exact scores are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "annotated financial crime NER benchmark, F1 reported", "salience": 30, "edition": 13, "models": [], "n": 1700, "authors_detailed": [ { "name": "Panagiotis Koletsis", "url": "https://openalex.org/A5114402359", "inst": "Harokopio University of Athens" }, { "name": "Panagiotis-Konstantinos Gemos", "url": "https://openalex.org/A5114402360", "inst": "Harokopio University of Athens" }, { "name": "Christos Chronis", "url": "https://openalex.org/A5066444298", "inst": "Harokopio University of Athens" }, { "name": "Iraklis Varlamis", "url": "https://openalex.org/A5056484904", "inst": "Harokopio University of Athens" }, { "name": "Vasilis Efthymiou", "url": "https://openalex.org/A5073440687", "inst": "Harokopio University of Athens" }, { "name": "Georgios Th. Papadopoulos", "url": "https://openalex.org/A5065554248", "inst": "Harokopio University of Athens" } ], "affiliations": [ "Harokopio University of Athens" ] }, { "uid": "arxiv:2409.03734v1", "arxiv_id": "2409.03734v1", "title": "Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry", "authors": [ "Meena Jagadeesan", "Michael I. Jordan", "Jacob Steinhardt" ], "posted": "2024-09-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.03734v1", "field": "economics", "role": "object", "bullets": [ "Theoretical framework modeling market entry dynamics for large-scale ML model marketplaces with safety-performance tradeoffs.", "Multi-objective high-dimensional regression analysis characterizes data requirements for market entrants facing reputational safety constraints.", "Safety alignment requirements fundamentally reduce barriers to entry; new firms need significantly fewer data points than the incumbent's dataset size." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3433, "authors_detailed": [ { "name": "Meena Jagadeesan", "url": "https://openalex.org/A5005798722", "inst": "California University of Pennsylvania" }, { "name": "Michael I. Jordan", "url": "https://openalex.org/A5049812527", "inst": "Institut national de recherche en sciences et technologies du numérique" }, { "name": "Jacob Steinhardt", "url": "https://openalex.org/A5060196069", "inst": "Berkeley College" } ], "affiliations": [ "California University of Pennsylvania", "Institut national de recherche en sciences et technologies du numérique", "Berkeley College" ] }, { "uid": "doi:10.1016/j.ejor.2024.09.011", "doi": "10.1016/j.ejor.2024.09.011", "arxiv_id": "2409.03668v1", "title": "A Fused Large Language Model for Predicting Startup Success", "authors": [ "Abdurahman Maarouf", "Stefan Feuerriegel", "Nicolas Pröllochs" ], "posted": "2024-09-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.03668v1", "field": "finance", "role": "instrument", "bullets": [ "20,172 startup profiles from Crunchbase with textual self-descriptions and fundamental characteristics predicting venture success.", "A fused large language model processes textual self-descriptions alongside structured startup data to predict startup success outcomes.", "Textual self-descriptions contribute significant predictive power for startup success beyond fundamental variables such as age, founders, and sector." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "predictive accuracy on Crunchbase startup outcomes", "salience": 55, "n": 3434, "authors_detailed": [ { "name": "Abdurahman Maarouf", "url": "https://openalex.org/A5042525157", "inst": "LMU Klinikum" }, { "name": "Stefan Feuerriegel", "url": "https://openalex.org/A5081442873", "inst": "LMU Klinikum" }, { "name": "Nicolas Pröllochs", "url": "https://openalex.org/A5048633988", "inst": "Justus-Liebig-Universität Gießen" } ], "affiliations": [ "LMU Klinikum", "Justus-Liebig-Universität Gießen" ] }, { "uid": "arxiv:2409.02391v2", "arxiv_id": "2409.02391v2", "title": "Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Translation", "authors": [ "Ali Merali" ], "posted": "2024-09-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.02391v2", "field": "economics", "role": "object", "bullets": [ "Preregistered online experiment in which 300 professional translators completed 1,800 tasks, each assigned one of 13 LLMs spanning a range of training compute, or a control.", "Model families are not named in the abstract; translators worked with their assigned model, and the design maps each model's training compute to speed, quality grades, and earnings.", "A tenfold compute increase raised completion speed 12.3 percent, grades 0.18 standard deviations, and earnings per minute 16.1 percent, with gains four times larger for lower-skilled workers and a projected US productivity boost of at least 6.9 percent over a decade." ], "bullet_provenance": "ai", "salience": 75, "edition": 13, "models": [], "validated": null, "n": 1654, "authors_detailed": [ { "name": "Ali merali", "url": "https://openalex.org/A5104601175", "inst": "" } ] }, { "uid": "arxiv:2409.07487v2", "arxiv_id": "2409.07487v2", "title": "MoA is All You Need: Building LLM Research Team using Mixture of Agents", "authors": [ "Sandy Chen", "Leqi Zeng", "Abhinav Raghunathan", "Flora Huang", "Terrence C. Kim" ], "posted": "2024-09-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.07487v2", "field": "finance", "role": "method", "bullets": [ "Retrieval augmented generation for financial question answering and information extraction in domains core to Vanguard's business, under production constraints of cost and speed.", "A layered mixture of agents network of individually customized small language models collaborates on answers. Model families are not named and no ground truth benchmark is reported.", "The authors report higher quality and more grounded responses than alternatives while keeping costs low. No quantitative evaluation is stated in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 26, "edition": 13, "models": [], "n": 1699, "authors_detailed": [ { "name": "Sandy Chen", "url": "https://openalex.org/A5075367705", "inst": "St. Marianna University School of Medicine" }, { "name": "Zeng, Leqi", "url": "", "inst": "" }, { "name": "Abhinav Raghunathan", "url": "https://openalex.org/A5114368976", "inst": "" }, { "name": "Flora Huang", "url": "https://openalex.org/A5114368977", "inst": "" }, { "name": "Terrence C. Kim", "url": "https://openalex.org/A5114430144", "inst": "" } ], "affiliations": [ "St. Marianna University School of Medicine" ] }, { "uid": "arxiv:2409.02836v1", "arxiv_id": "2409.02836v1", "title": "Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models", "authors": [ "Moein Shahiki Tash", "Zahra Ahani", "Mohim Tash", "Olga Kolesnikova", "Grigori Sidorov" ], "posted": "2024-09-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.02836v1", "field": "finance", "role": "instrument", "bullets": [ "Cryptocurrency discussion comments about Cardano, Binance, Matic, Fantom, and Ripple; comment volumes and sample period are not stated in the abstract.", "GPT-4o with few shot prompts labels comments as predictive incremental, decremental, neutral, or non predictive, plus hope and regret; no check against human labels is reported.", "Predictive sentiment patterns differ across coins, with Matic drawing the most optimistic predictions; hope and regret interact with predictive statements." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 30, "edition": 13, "n": 1744, "authors_detailed": [ { "name": "Moein Shahiki Tash", "url": "https://openalex.org/A5043174689", "inst": "Instituto Politécnico Nacional" }, { "name": "Zahra Ahani", "url": "https://openalex.org/A5093734227", "inst": "Instituto Politécnico Nacional" }, { "name": "Mohim Tash", "url": "https://openalex.org/A5106369147", "inst": "University of Sistan and Baluchestan" }, { "name": "Olga Kolesnikova", "url": "https://openalex.org/A5061018630", "inst": "Instituto Politécnico Nacional" }, { "name": "Grigori Sidorov", "url": "https://openalex.org/A5008287867", "inst": "Instituto Politécnico Nacional" } ], "affiliations": [ "Instituto Politécnico Nacional", "University of Sistan and Baluchestan" ] }, { "uid": "doi:10.2139/ssrn.4946802", "doi": "10.2139/ssrn.4946802", "title": "Finance-Specific Large Language Models: Advancing Sentiment Analysis and Return Prediction with Llama 2", "authors": [ "I-Chan Chiu", "Mao-Wei Hung" ], "posted": "2024-09-04", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4946802", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "llama" ], "open_weights": true, "salience": 40, "edition": 13, "bullets": [], "validated": null, "n": 1791, "authors_detailed": [ { "name": "I-Chan Chiu", "url": "https://openalex.org/A5058855014", "inst": "National Taiwan University" }, { "name": "Mao‐Wei Hung", "url": "https://openalex.org/A5112245075", "inst": "National Taiwan University" } ], "affiliations": [ "National Taiwan University" ] }, { "uid": "doi:10.2139/ssrn.4945933", "doi": "10.2139/ssrn.4945933", "title": "The Wade Test: Generative AI and a CEO bot", "authors": [ "Prithwiraj Choudhury", "Bart Vanneste", "Amirhossein Zohrehvand" ], "posted": "2024-09-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4945933", "field": "management", "role": "object", "bullets": [ "Field experiment at one firm plus a general-audience study testing whether employees distinguish CEO communication from AI-generated mimicry.", "An AI trained on the CEO's prior communication generated responses; employees and a general audience rated helpfulness and identified source.", "Correct identification rate was 59%, barely above chance; responses believed AI-generated were rated less helpful regardless of actual source." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "validated": null, "n": 2951, "authors_detailed": [ { "name": "Prithwiraj Choudhury", "url": "https://openalex.org/A5020108888", "inst": "Harvard University" }, { "name": "Bart Vanneste", "url": "https://openalex.org/A5056117219", "inst": "University College London" }, { "name": "Amirhossein Zohrehvand", "url": "https://openalex.org/A5012731232", "inst": "Leiden University" } ], "affiliations": [ "Harvard University", "University College London", "Leiden University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2409.00128v3", "arxiv_id": "2409.00128v3", "title": "Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management", "authors": [ "Ziyan Cui", "Ning Li", "Huaikang Zhou" ], "posted": "2024-08-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.00128v3", "field": "management", "role": "method", "bullets": [ "156 scenario-based experiments drawn from top social science journals, spanning psychology and management, replicated with LLM respondents in place of human participants.", "GPT-4, Claude 3.5 Sonnet and DeepSeek v3 answer each study's materials, with replication judged against the original human effect directions and significance.", "Main effects replicate 73 to 81 percent of the time and interactions 46 to 63 percent, but effect sizes run two to three times larger than in human studies and reported nulls often turn significant." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "replication against 156 published human experiments", "salience": 78, "edition": 13, "n": 1528, "authors_detailed": [ { "name": "Ziyan Cui", "url": "https://openalex.org/A5044689620", "inst": "Tsinghua University" }, { "name": "Ning Li", "url": "https://openalex.org/A5101689745", "inst": "Ruijin Hospital" }, { "name": "Huaikang Zhou", "url": "https://openalex.org/A5014309666", "inst": "Tsinghua University" } ], "affiliations": [ "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.4918704", "doi": "10.2139/ssrn.4918704", "title": "Regulating under Uncertainty: Governance Options for Generative AI", "authors": [ "Florence G'sell" ], "posted": "2024-08-26", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4918704", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 30, "edition": 23, "bullets": [], "models": [], "validated": null, "n": 4192, "authors_detailed": [ { "name": "Florence G’Sell", "url": "https://openalex.org/A5085840779", "inst": "Institut d'Etudes Politiques de Paris" } ], "affiliations": [ "Institut d'Etudes Politiques de Paris" ] }, { "uid": "doi:10.1108/jebde-11-2023-0028", "doi": "10.1108/jebde-11-2023-0028", "arxiv_id": "2408.14593v1", "title": "How to build trust in answers given by Generative AI for specific, and vague, financial questions", "authors": [ "Alex Zarifis", "Xusen Cheng" ], "posted": "2024-08-26", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.14593v1", "field": "finance", "role": "object", "bullets": [ "Survey of consumers using generative AI for financial questions, comparing specific versus vague question scenarios via SEM and multi-group analysis.", "Tested a trust model for GenAI financial advice across two question-type scenarios examining humanness, oversight, transparency, and accuracy factors.", "Human-like interaction builds trust only for vague financial questions; oversight, transparency, accuracy, and ease of use build trust in both scenarios." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 50, "validated": null, "n": 3431, "authors_detailed": [ { "name": "Alex Zarifis", "url": "https://openalex.org/A5056541014", "inst": "Université Paris Sciences et Lettres" }, { "name": "Xusen Cheng", "url": "https://openalex.org/A5033721513", "inst": "Renmin University of China" } ], "affiliations": [ "Université Paris Sciences et Lettres", "Renmin University of China" ] }, { "uid": "doi:10.2139/ssrn.4916385", "doi": "10.2139/ssrn.4916385", "title": "Is AI a Job Killer? A Little Yes and a Big No", "authors": [ "Larry Downes", "Blair Levin" ], "posted": "2024-08-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4916385", "field": "economics", "role": "object", "bullets": [ "Historical analysis of technology-driven labor displacement episodes, extended through the 2022 public release of ChatGPT.", "Examines whether large-scale AI deployment across industries will cause massive job losses for creative and professional workers.", "Historical pattern suggests initial displacement followed by net job creation, yielding counter-intuitive imperatives for business, labor, and government." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 40, "validated": null, "n": 3432, "authors_detailed": [ { "name": "Larry Downes", "url": "https://openalex.org/A5034386755", "inst": "Willamette University" }, { "name": "Blair Levin", "url": "https://openalex.org/A5026779011", "inst": "Brookings Institution" } ], "affiliations": [ "Willamette University", "Brookings Institution" ] }, { "uid": "arxiv:2409.08281v1", "arxiv_id": "2409.08281v1", "title": "StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction", "authors": [ "Shengkun Wang", "Taoran Ji", "Linhan Wang", "Yanshen Sun", "Shang-Ching Liu", "Amit Kumar", "Chang-Tien Lu" ], "posted": "2024-08-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.08281v1", "field": "finance", "role": "method", "bullets": [ "Stock price forecasting over arbitrary look-back windows, treating price series as token sequences alongside textual information such as stock correlations, statistical trends, and timestamps; markets and period are not stated.", "A purpose-built LLM architecture, backbone not named in the abstract, embeds prices as consecutive tokens and fuses them with the extracted text in a shared embedding space.", "The system predicts prices more accurately than recent financial LLM baselines while cutting memory use and runtime; error magnitudes are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "forecast error versus realized prices against FinLLM baselines", "salience": 40, "edition": 13, "models": [], "n": 1653, "authors_detailed": [ { "name": "Shengkun Wang", "url": "https://openalex.org/A5102768296", "inst": "China Automotive Technology and Research Center" }, { "name": "Taoran Ji", "url": "https://openalex.org/A5050083334", "inst": "Loughborough University" }, { "name": "Linhan Wang", "url": "https://openalex.org/A5063375262", "inst": "Zhejiang Institute of Mechanical and Electrical Engineering" }, { "name": "Sun, Yanshen", "url": "", "inst": "" }, { "name": "Liu, Shang-Ching", "url": "", "inst": "" }, { "name": "Amit Kumar", "url": "https://openalex.org/A5057976327", "inst": "University of Alberta" }, { "name": "Chang‐Tien Lu", "url": "https://openalex.org/A5038002204", "inst": "Virginia Tech" } ], "affiliations": [ "China Automotive Technology and Research Center", "Loughborough University", "Zhejiang Institute of Mechanical and Electrical Engineering", "University of Alberta", "Virginia Tech" ] }, { "uid": "arxiv:2408.13214v2", "arxiv_id": "2408.13214v2", "title": "EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods", "authors": [ "Hongcheng Ding", "Xuanze Zhao", "Ruiting Deng", "Shamsul Nahar Abdullah", "Deshinta Arrova Dewi" ], "posted": "2024-08-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.13214v2", "field": "finance", "role": "instrument", "bullets": [ "EUR/USD exchange rates with structured financial indicators plus unstructured news and analysis text; article counts and the sample period are not stated.", "Large language models, families not stated, score sentiment polarity and classify implied exchange rate movement from the texts; no validation against human labels is reported.", "Fusing the text features with the top 12 quantitative features cuts forecast MAE by 10.69 percent and RMSE by 9.56 percent versus the best baseline." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 13, "models": [], "n": 1790, "authors_detailed": [ { "name": "Hongcheng Ding", "url": "https://openalex.org/A5114230916", "inst": "Changzhou University" }, { "name": "Xuanze Zhao", "url": "https://openalex.org/A5108988639", "inst": "INTI International University" }, { "name": "Deng, Ruiting", "url": "", "inst": "" }, { "name": "Shamsul Nahar Abdullah", "url": "https://openalex.org/A5103127142", "inst": "INTI International University" }, { "name": "Deshinta Arrova Dewi", "url": "https://openalex.org/A5006229253", "inst": "INTI International University" } ], "affiliations": [ "Changzhou University", "INTI International University" ] }, { "uid": "arxiv:2409.11408v1", "arxiv_id": "2409.11408v1", "title": "Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning", "authors": [ "Baptiste Lefort", "Eric Benhamou", "Jean-Jacques Ohana", "David Saltiel", "Beatrice Guez" ], "posted": "2024-08-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2409.11408v1", "field": "finance", "role": "method", "bullets": [ "Daily financial news summaries from Bloomberg, labelled by whether mentioned firms' stocks subsequently rose, fell, or stayed flat, removing human annotation from the loop.", "Fine tuned FinBERT and FinDRoBERTa are set against zero shot and fine tuned GPT-3.5 and GPT-4 on market sentiment classification; the resulting models are public on HuggingFace.", "The fine tuned compact models match or beat the GPT models, and a Condorcet jury analysis indicates the two groups make correlated rather than independent errors." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "validated": true, "validation_note": "market-move labels on Bloomberg news", "salience": 55, "edition": 13, "n": 1768 }, { "uid": "doi:10.2139/ssrn.4932003", "doi": "10.2139/ssrn.4932003", "title": "Exploring Large Language Models in External Audits: Implications and Ethical Considerations", "authors": [ "Fotoh Elad", "Tatenda Mugwira" ], "posted": "2024-08-22", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4932003", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 30, "edition": 13, "bullets": [], "models": [], "validated": null, "n": 1789, "authors_detailed": [ { "name": "Fotoh Elad", "url": "", "inst": "Karlstad University" }, { "name": "Tatenda Mugwira", "url": "https://openalex.org/A5026939521", "inst": "University of Agder" } ], "affiliations": [ "Karlstad University", "University of Agder" ] }, { "uid": "arxiv:2408.11961v1", "arxiv_id": "2408.11961v1", "title": "Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain litigation using LLM-based Thematic Factor Mapping", "authors": [ "Junliang Luo", "Xihan Xiong", "William Knottenbelt", "Xue Liu" ], "posted": "2024-08-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.11961v1", "field": "finance", "role": "instrument", "bullets": [ "All SEC complaints against blockchain companies from 2012 to 2024 in the United States, along with the legal Acts each complaint cites, analyzed annually.", "Pretrained language models and LLMs, none named in the abstract, map complaint text to author defined thematic factors; no validation against hand coding is described.", "Quantified factors trace shifting regulatory emphasis in SEC crypto enforcement and its relation to cited statutes; the abstract gives no headline magnitudes." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 13, "models": [], "n": 1743, "authors_detailed": [ { "name": "Junliang Luo", "url": "https://openalex.org/A5033816042", "inst": "McGill University" }, { "name": "Xihan Xiong", "url": "https://openalex.org/A5071714089", "inst": "Imperial College London" }, { "name": "William J. Knottenbelt", "url": "https://openalex.org/A5050119476", "inst": "Imperial College London" }, { "name": "Ke Liu", "url": "https://openalex.org/A5100349853", "inst": "Nanjing Tech University" } ], "affiliations": [ "McGill University", "Imperial College London", "Nanjing Tech University" ] }, { "uid": "arxiv:2408.11878v3", "arxiv_id": "2408.11878v3", "title": "Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications", "authors": [ "Jimin Huang", "Mengxi Xiao", "Dong Li", "Zihao Jiang", "Yuzhe Yang", "Yifei Zhang", "Lingfei Qian", "Yan Wang", "Xueqing Peng", "Yang Ren", "Ruoyu Xiang", "Zhengyu Chen", "Xiao Zhang", "Yueru He", "Weiguang Han", "Shunian Chen", "Lihang Shen", "Daniel Kim", "Yangyang Yu", "Yupeng Cao", "Zhiyang Deng", "Haohang Li", "Duanyu Feng", "Yongfu Dai", "VijayaSai Somasundaram", "Peng Lu", "Guojun Xiong", "Zhiwei Liu", "Zheheng Luo", "Zhiyuan Yao", "Ruey-Ling Weng", "Meikang Qiu", "Kaleb E Smith", "Honghai Yu", "Yanzhao Lai", "Min Peng", "Jian-Yun Nie", "Jordan W. Suchow", "Xiao-Yang Liu", "Benyou Wang", "Alejandro Lopez-Lira", "Qianqian Xie", "Sophia Ananiadou", "Junichi Tsujii" ], "posted": "2024-08-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.11878v3", "field": "finance", "role": "method", "bullets": [ "A 52 billion token financial pretraining corpus, 573,000 financial instructions, and 1.43 million multimodal pairs spanning text, tables, time series, and charts.", "The suite trains FinLLaMA, an instruction-tuned variant, and multimodal FinLLaVA, scored on 14 financial tasks over 30 datasets plus 4 multimodal tasks in zero-shot, few-shot, and fine-tuned settings.", "The authors report the models beat GPT-4 and financial LLM baselines across the evaluation suite; code and weights are released under open licenses." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "14 financial tasks across 30 labelled datasets", "salience": 52, "edition": 13, "n": 1561, "authors_detailed": [ { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" }, { "name": "Mengxi Xiao", "url": "https://openalex.org/A5111097308", "inst": "Yunnan Center for Disease Control And Prevention" }, { "name": "Dong Li", "url": "https://openalex.org/A5088062637", "inst": "Southwest University" }, { "name": "Zihao Jiang", "url": "https://openalex.org/A5100641890", "inst": "Beijing Institute of Technology" }, { "name": "Yuzhe Yang", "url": "https://openalex.org/A5036229509", "inst": "Google (United States)" }, { "name": "Yifei Zhang", "url": "https://openalex.org/A5100386934", "inst": "Nanchang University" }, { "name": "Qian, Lingfei", "url": "", "inst": "" }, { "name": "Wang, Yan", "url": "", "inst": "" }, { "name": "Peng, Xueqing", "url": "", "inst": "" }, { "name": "Ren, Yang", "url": "", "inst": "" }, { "name": "Ruoyu Xiang", "url": "https://openalex.org/A5101307852", "inst": "Chongqing Normal University" }, { "name": "Zhengyu Chen", "url": "https://openalex.org/A5003772256", "inst": "Huazhong University of Science and Technology" }, { "name": "Xiao Zhang", "url": "https://openalex.org/A5100320945", "inst": "Beihang University" }, { "name": "Yueru He", "url": "https://openalex.org/A5111133467", "inst": "Columbia University" }, { "name": "Weiguang Han", "url": "https://openalex.org/A5054465909", "inst": "Hebei University of Technology" }, { "name": "Shunian Chen", "url": "https://openalex.org/A5041945106", "inst": "Beijing Normal University" }, { "name": "Lihang Shen", "url": "https://openalex.org/A5026285361", "inst": "Peking University" }, { "name": "Daniel Kim", "url": "https://openalex.org/A5100372590", "inst": "Memorial Sloan Kettering Cancer Center" }, { "name": "Yangyang Yu", "url": "https://openalex.org/A5069731031", "inst": "Jilin University of Finance and Economics" }, { "name": "Yupeng Cao", "url": "https://openalex.org/A5115604006", "inst": "Stevens Institute of Technology" }, { "name": "Zhiyang Deng", "url": "https://openalex.org/A5069290428", "inst": "Hong Kong Baptist University" }, { "name": "Haohang Li", "url": "https://openalex.org/A5020982913", "inst": "Stevens Institute of Technology" }, { "name": "Duanyu Feng", "url": "https://openalex.org/A5001345589", "inst": "National University of Singapore" }, { "name": "Dai, Yongfu", "url": "", "inst": "" }, { "name": "VijayaSai Somasundaram", "url": "https://openalex.org/A5115454997", "inst": "" }, { "name": "Pengfei Lu", "url": "https://openalex.org/A5052550377", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Guojun Xiong", "url": "https://openalex.org/A5102620407", "inst": "Harvard University" }, { "name": "Zhiwei Liu", "url": "https://openalex.org/A5100321232", "inst": "University of the Sciences" }, { "name": "Zheheng Luo", "url": "https://openalex.org/A5075367840", "inst": "Academy of Military Medical Sciences" }, { "name": "Zhiyuan Yao", "url": "https://openalex.org/A5108265235", "inst": "Ningbo University" }, { "name": "Weng, Ruey-Ling", "url": "", "inst": "" }, { "name": "Qiu, Meikang", "url": "", "inst": "" }, { "name": "Kaleb E. Smith", "url": "https://openalex.org/A5035188223", "inst": "Nvidia (United States)" }, { "name": "Honghai Yu", "url": "https://openalex.org/A5100643773", "inst": "Beijing Jiaotong University" }, { "name": "Yanzhao Lai", "url": "https://openalex.org/A5029848442", "inst": "China Electronics Technology Group Corporation" }, { "name": "Min Peng", "url": "https://openalex.org/A5102996335", "inst": "Wuhan University" }, { "name": "Nie, Jian-Yun", "url": "", "inst": "" }, { "name": "Jordan W. Suchow", "url": "https://openalex.org/A5069454833", "inst": "Stevens Institute of Technology" }, { "name": "Xiaoyang Liu", "url": "https://openalex.org/A5100405221", "inst": "Tianjin University of Science and Technology" }, { "name": "Benyou Wang", "url": "https://openalex.org/A5057282504", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" }, { "name": "Tsujii, Junichi", "url": "", "inst": "" } ], "affiliations": [ "University of Manchester", "Yunnan Center for Disease Control And Prevention", "Southwest University", "Beijing Institute of Technology", "Google (United States)", "Nanchang University", "Chongqing Normal University", "Huazhong University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.4931424", "doi": "10.2139/ssrn.4931424", "title": "Can Generative Ai Craft Scale Items? A Mixed-Method Study on Ai's Capability to Adapt and Create New Scales with Recommendations for Best Practices", "authors": [ "Mohammed Salah", "Fadi Abdelfattah", "Hussam Al Halbusi", "Suaad Jassem", "Muna Mohammed", "Maria Mohd Ismail", "Amira Al Balghouni" ], "posted": "2024-08-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4931424", "field": "management", "role": "method", "bullets": [ "Mixed-method study testing GPT-4's ability to generate and adapt research measurement scales compared to conventional development methods.", "GPT-4 generated scale items evaluated through quantitative reliability and validity analysis alongside expert review of contextual appropriateness.", "AI-generated scales showed acceptable reliability and validity with improved methodological efficiency and reduced bias versus traditional approaches." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Quantitative reliability and validity testing of AI-generated scales", "salience": 38, "n": 2647, "authors_detailed": [ { "name": "Aghiles Salah", "url": "https://openalex.org/A5039519070", "inst": "Délégation Paris 5" }, { "name": "Fadi Abdelfattah", "url": "https://openalex.org/A5024054274", "inst": "Modern College of Business and Science" }, { "name": "Hussam Al Halbusi", "url": "https://openalex.org/A5041645225", "inst": "University of Baghdad" }, { "name": "Suaad Jassem", "url": "https://openalex.org/A5022408305", "inst": "Muscat College" }, { "name": "Muna Mohammed", "url": "https://openalex.org/A5102903450", "inst": "Modern College of Business and Science" }, { "name": "Maria Mohd Ismail", "url": "https://openalex.org/A5071941603", "inst": "University of Malaya" }, { "name": "Amira Al Balghouni", "url": "https://openalex.org/A5106953772", "inst": "German University of Technology" } ], "affiliations": [ "Délégation Paris 5", "Modern College of Business and Science", "University of Baghdad", "Muscat College", "University of Malaya", "German University of Technology" ] }, { "uid": "doi:10.2139/ssrn.4922214", "doi": "10.2139/ssrn.4922214", "title": "Global Agricultural Adaptation Case Database and Trend Analysis Based on Large Language Models", "authors": [ "Jingwen Zhong", "Xueyan Zhang", "Xin Ma" ], "posted": "2024-08-19", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4922214", "field": "economics", "role": "instrument", "bullets": [ "Global database of agricultural climate adaptation cases published between 2000 and 2024, assembled under the ROSES systematic evidence synthesis protocol and paired with geographic analysis.", "ChatGPT answers structured questions to pull case details such as climate stressors, adaptation measures, costs, and constraints; the model version is not stated and no accuracy check against hand coding is reported.", "Cases cluster in central and southern Africa, southern Asia, and Europe, responses to extreme events are scarce, and measures are shifting from single technologies toward integrated approaches like climate smart agriculture." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 38, "edition": 8, "n": 1324, "authors_detailed": [ { "name": "Jingwen Zhong", "url": "https://openalex.org/A5047686344", "inst": "Heilongjiang Bayi Agricultural University" }, { "name": "Xueyan Zhang", "url": "https://openalex.org/A5100452535", "inst": "Chinese Academy of Sciences" }, { "name": "Xin Ma", "url": "https://openalex.org/A5032105492", "inst": "Nankai University" } ], "affiliations": [ "Heilongjiang Bayi Agricultural University", "Chinese Academy of Sciences", "Nankai University" ] }, { "uid": "doi:10.2139/ssrn.4926522", "doi": "10.2139/ssrn.4926522", "title": "Quality and Accountability of Large Language Models (Llms) in Healthcare in Low- and Middle-Income Countries (Lmic): A Simulated Patient Study Using Chatgpt", "authors": [ "Yafei Si", "Yuyi Yang", "Xi Wang", "Ruopeng An", "Jiaqi Zu", "Xi Chen", "Xiaojing Fan", "Sen Gong" ], "posted": "2024-08-19", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4926522", "field": "economics", "role": "agent", "bullets": [ "Twenty seven simulated patient trials covering nine established non-communicable and infectious diseases, designed to mimic clinical encounters common in low and middle income countries.", "ChatGPT, version not stated, produced diagnoses and prescriptions that were scored against the known condition in each simulated case, with accuracy reported by disease type.", "Diagnosis was correct in 74.1 percent of trials and prescriptions in 84.5 percent, yet unnecessary or harmful medications appeared in 85.2 percent, with weaker performance on infectious diseases." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "outputs scored against known conditions in simulated patient vignettes", "salience": 56, "edition": 8, "n": 1325, "authors_detailed": [ { "name": "Yafei Si", "url": "https://openalex.org/A5041637802", "inst": "The University of Melbourne" }, { "name": "Yuyi Yang", "url": "https://openalex.org/A5108151158", "inst": "Washington University in St. Louis" }, { "name": "X. L. Wang", "url": "https://openalex.org/A5108050002", "inst": "Washington University in St. Louis" }, { "name": "Ruopeng An", "url": "https://openalex.org/A5002313901", "inst": "Washington University in St. Louis" }, { "name": "Jiaqi Zu", "url": "https://openalex.org/A5106609569", "inst": "Duke Kunshan University" }, { "name": "Xi Chen", "url": "https://openalex.org/A5067614269", "inst": "Nanyang Technological University" }, { "name": "Xiaojing Fan", "url": "https://openalex.org/A5101532634", "inst": "Xi'an Jiaotong University" }, { "name": "Sen Gong", "url": "https://openalex.org/A5102220128", "inst": "Zhejiang University" } ], "affiliations": [ "Washington University in St. Louis", "The University of Melbourne", "Duke Kunshan University", "Nanyang Technological University", "Xi'an Jiaotong University", "Zhejiang University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2408.09742v2", "arxiv_id": "2408.09742v2", "title": "Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs", "authors": [ "Simon D Angus", "Lachlan O'Neill" ], "posted": "2024-08-19", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.09742v2", "field": "economics", "role": "method", "bullets": [ "Synthetic datasets and a human-labeled corpus for detecting issue framing in text relevant to social science and policy analysis.", "LLM next-token log probabilities detect contrasting frames using minimal examples, compared against prompt-based and embedding-based methods.", "Paired completion matches or exceeds alternatives at lower cost and bias, especially suited to low-resource text classification settings." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "human-labeled corpus comparison", "salience": 50, "n": 3429, "authors_detailed": [ { "name": "Simon D. Angus", "url": "https://openalex.org/A5078279167", "inst": "Monash University Malaysia" }, { "name": "Lachlan O’Neill", "url": "https://openalex.org/A5057481755", "inst": "Australian Regenerative Medicine Institute" } ], "affiliations": [ "Monash University Malaysia", "Australian Regenerative Medicine Institute" ] }, { "uid": "doi:10.2139/ssrn.4914586", "doi": "10.2139/ssrn.4914586", "title": "Transmission Bias in Financial News", "authors": [ "Khaled Obaid", "Kuntara Pukthuanthong" ], "posted": "2024-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4914586", "field": "finance", "role": "instrument", "bullets": [ "Exclusive Wall Street Journal articles and their retellings by competing news outlets, linked to market trading volume and returns data.", "An LLM scored original and retelling articles along five dimensions of language style to measure systematic news distortion.", "Retelling articles are less factual, more negative, and less appealing; greater distortion predicts higher abnormal trading volume without return effects." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 70, "n": 3430, "authors_detailed": [ { "name": "Khaled Obaid", "url": "https://openalex.org/A5039793701", "inst": "Mississippi State University" }, { "name": "Kuntara Pukthuanthong", "url": "https://openalex.org/A5080206786", "inst": "Cornell University" } ], "affiliations": [ "Cornell University", "Mississippi State University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2408.08811v1", "arxiv_id": "2408.08811v1", "title": "Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors", "authors": [ "Felipe A. Csaszar", "Harsh Ketkar", "Hyunjin Kim" ], "posted": "2024-08-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.08811v1", "field": "management", "role": "agent", "bullets": [ "Strategic decision-making compared between humans and machines, with evidence drawn from a leading accelerator programme and a startup competition; participant counts are not stated in the abstract.", "Current LLMs, versions not named in the abstract, generate and evaluate business strategies, with output benchmarked against entrepreneurs and investors from those settings.", "LLM performance is reported as comparable to entrepreneurs and investors, and the authors develop a framework linking AI-assisted search, representation, and aggregation to firm outcomes." ], "bullet_provenance": "ai", "validated": true, "validation_note": "LLM strategy output benchmarked against entrepreneurs and investors", "salience": 65, "edition": 13, "models": [], "n": 1652, "authors_detailed": [ { "name": "Felipe A. Csaszar", "url": "https://openalex.org/A5039016992", "inst": "University of Michigan" }, { "name": "Harsh Ketkar", "url": "https://openalex.org/A5092689928", "inst": "The University of Texas at Austin" }, { "name": "Hyunjin Kim", "url": "https://openalex.org/A5100359572", "inst": "Electronics and Telecommunications Research Institute" } ], "affiliations": [ "The University of Texas at Austin", "University of Michigan", "Electronics and Telecommunications Research Institute" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2408.11856v3", "arxiv_id": "2408.11856v3", "title": "Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models", "authors": [ "Hongcheng Ding", "Xuanze Zhao", "Ruiting Deng", "Shamsul Nahar Abdullah", "Deshinta Arrova Dewi", "Zixiao Jiang" ], "posted": "2024-08-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.11856v3", "field": "finance", "role": "method", "bullets": [ "Sentiment analysis framed as multi-task learning over tasks of varying complexity, evaluated on a standard dataset and a customized financial text dataset.", "A plug-and-play dynamic adaptive optimization module reweights task losses during LLM fine-tuning. The underlying language model is not named in the abstract.", "The framework improves mean squared error by 15.58 percent and accuracy by 1.24 percent compared with previous work on the sentiment datasets." ], "bullet_provenance": "ai", "validated": true, "validation_note": "financial sentiment datasets, MSE and accuracy", "salience": 24, "edition": 13, "models": [], "n": 1698, "authors_detailed": [ { "name": "Hongcheng Ding", "url": "https://openalex.org/A5114230916", "inst": "Changzhou University" }, { "name": "Xuanze Zhao", "url": "https://openalex.org/A5108988639", "inst": "INTI International University" }, { "name": "Deng, Ruiting", "url": "", "inst": "" }, { "name": "Shamsul Nahar Abdullah", "url": "https://openalex.org/A5103127142", "inst": "INTI International University" }, { "name": "Deshinta Arrova Dewi", "url": "https://openalex.org/A5006229253", "inst": "INTI International University" }, { "name": "Zixiao Jiang", "url": "https://openalex.org/A5111300179", "inst": "Qingdao University" } ], "affiliations": [ "Changzhou University", "INTI International University", "Qingdao University" ] }, { "uid": "arxiv:2408.07923v2", "arxiv_id": "2408.07923v2", "title": "When and Why is Persuasion Hard? A Computational Complexity Result", "authors": [ "Zachary Wojtowicz" ], "posted": "2024-08-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.07923v2", "field": "economics", "role": "object", "bullets": [ "Theoretical analysis formalizing informational persuasion as a computational decision problem for both human and AI agents.", "Novel proof establishes that discovering persuasive messages is NP-Hard while adopting supplied persuasion is NP.", "Asymmetry explains human susceptibility to persuasion and why AI may reduce costs of litigation and strategic communication." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3194, "authors_detailed": [ { "name": "Zachary Wojtowicz", "url": "https://openalex.org/A5114709370", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1109/docs63458.2024.10704454", "doi": "10.1109/docs63458.2024.10704454", "arxiv_id": "2408.06634v2", "title": "Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach", "authors": [ "Haowei Ni", "Shuchen Meng", "Xupeng Chen", "Ziqing Zhao", "Andi Chen", "Panfeng Li", "Shiyao Zhang", "Qifu Yin", "Yuanqing Wang", "Yuxi Chan" ], "posted": "2024-08-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.06634v2", "field": "finance", "role": "method", "bullets": [ "Post-earnings stock direction prediction, combining financial metric growth and earnings call transcripts with market index performance and analyst grades in a supervised dataset.", "Llama 3 8B Instruct is instruction fine-tuned with QLoRA compression and benchmarked against GPT-4, with accuracy, weighted F1 and Matthews correlation on realized moves.", "The fine-tuned 4 bit Llama model outperforms baselines including GPT-4 on accuracy, weighted F1 and MCC, though the abstract omits the magnitudes." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "accuracy, weighted F1 and MCC against realized post-earnings moves", "salience": 45, "edition": 13, "n": 1541, "authors_detailed": [ { "name": "Haowei Ni", "url": "https://openalex.org/A5101382764", "inst": "Columbia University" }, { "name": "Shuchen Meng", "url": "https://openalex.org/A5100576382", "inst": "Central University of Finance and Economics" }, { "name": "Xupeng Chen", "url": "https://openalex.org/A5103045574", "inst": "New York University" }, { "name": "Ziqing Zhao", "url": "https://openalex.org/A5033027820", "inst": "Cornell University" }, { "name": "Andi Chen", "url": "https://openalex.org/A5113375362", "inst": "Independent Researcher,Beijing,China" }, { "name": "Panfeng Li", "url": "https://openalex.org/A5047800787", "inst": "University of Michigan" }, { "name": "Shiyao Zhang", "url": "https://openalex.org/A5064293717", "inst": "Cornell University" }, { "name": "Qifu Yin", "url": "https://openalex.org/A5111360535", "inst": "Columbia University" }, { "name": "Yuanqing Wang", "url": "https://openalex.org/A5100321645", "inst": "New York University" }, { "name": "Yuxi Chan", "url": "https://openalex.org/A5113427541", "inst": "Rutgers, The State University of New Jersey" } ], "affiliations": [ "Columbia University", "New York University", "Cornell University", "Central University of Finance and Economics", "Independent Researcher,Beijing,China", "University of Michigan", "Rutgers, The State University of New Jersey" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4912642", "doi": "10.2139/ssrn.4912642", "title": "From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management", "authors": [ "Ning Li", "Huaikang Zhou", "Mingze Xu" ], "posted": "2024-08-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4912642", "field": "management", "role": "instrument", "bullet_provenance": "none", "salience": 40, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 342, "authors_detailed": [ { "name": "Ning Li", "url": "https://openalex.org/A5108078080", "inst": "Tsinghua University" }, { "name": "Huaikang Zhou", "url": "https://openalex.org/A5109779971", "inst": "Tsinghua University" }, { "name": "Mingze Xu", "url": "https://openalex.org/A5101563998", "inst": "Tsinghua University" } ], "affiliations": [ "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.4912547", "doi": "10.2139/ssrn.4912547", "title": "Fluency is the Key: AI Agent Language Dynamics on Customer Satisfaction and Transfer Requests", "authors": [ "Katsiaryna Siamionava", "Reihane Boghrati", "Jianlei Zhang", "Tian Lu" ], "posted": "2024-08-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4912547", "field": "management", "role": "object", "bullets": [ "Experiment with 705 participants and observational analysis of 121,122 customer-AI chat interactions from a large U.S. media company.", "Studies how AI agent language strategies across cognitive, emotional, and linguistic fluency dimensions affect customer satisfaction and transfer requests.", "Clarifications outperform misunderstandings in comprehension error recovery; empathetic language and reading complexity show boundary conditions on effectiveness." ], "bullet_provenance": "ai", "salience": 62, "models": [], "validated": null, "n": 3902, "authors_detailed": [ { "name": "Katsiaryna Siamionava", "url": "https://openalex.org/A5105105413", "inst": "Arizona State University" }, { "name": "Reihane Boghrati", "url": "https://openalex.org/A5020653999", "inst": "Arizona State University" }, { "name": "Jianlei Zhang", "url": "https://openalex.org/A5069606943", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Lu Tian", "url": "https://openalex.org/A5100777336", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University", "Independent - affiliation not provided to SSRN" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4907043", "doi": "10.2139/ssrn.4907043", "title": "Using Generative AI to Calculate Party Positions: A Comparison of Human Experts and Large Language Models", "authors": [ "Cantay Caliskan", "Junhua Huang", "Yiyang Huang", "Ruoxuan Lin", "Wanting Shan" ], "posted": "2024-08-12", "added": "2026-07-31", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4907043", "field": "other", "role": "method", "bullets": [ "Party election manifestos from Germany, the United Kingdom, and the United States, with Manifesto Project human expert codings as the benchmark for left-right positions.", "ChatGPT 3.5 Turbo, Cohere Command, Gemini 1, Llama 2, and Llama 3 each estimate party positions, and estimates are compared with those of CMP-trained human experts.", "The models rate leftist and rightist manifestos 73.64 percent less extreme on average than experts do, arguing against replacing human coders with LLMs for this task." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "Manifesto Project human expert codings", "salience": 44, "edition": 8, "n": 1296, "authors_detailed": [ { "name": "Cantay Caliskan", "url": "https://openalex.org/A5106423250", "inst": "University of Rochester" }, { "name": "Junhua Huang", "url": "https://openalex.org/A5101758751", "inst": "University of Rochester" }, { "name": "Yiyang Huang", "url": "https://openalex.org/A5106479064", "inst": "University of Rochester" }, { "name": "Ruoxuan Lin", "url": "https://openalex.org/A5024189756", "inst": "University of Rochester" }, { "name": "Wanting Shan", "url": "https://openalex.org/A5108968429", "inst": "University of Rochester" } ], "affiliations": [ "University of Rochester" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4907325", "doi": "10.2139/ssrn.4907325", "title": "Introducing Agora.ai: A Proof-of-Concept on AI-Driven Corporate Lobbying for EU Policy Analysis", "authors": [ "Frank Chen", "Adam Chalmers" ], "posted": "2024-08-12", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4907325", "field": "management", "role": "instrument", "bullets": [ "A proof-of-concept of Agora.ai, an LLM tool for corporate lobbying and policy advocacy in the European Union and European Parliament; sample size and evaluation set not stated.", "The tool builds on OpenAI's GPT-4 through prompt engineering and fine-tuning to perform policy relevance analysis, stakeholder mapping, and political alignment suggestions; validation against human coding not stated.", "Authors report the system yields insights they characterize as comparable to human outputs, but report no accuracy or agreement statistic." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "validation_note": "informal comparison to human outputs, no metric", "salience": 35, "edition": 7, "n": 1264, "authors_detailed": [ { "name": "Frank Chen", "url": "https://openalex.org/A5046450507", "inst": "University of Edinburgh" }, { "name": "Adam William Chalmers", "url": "https://openalex.org/A5082128541", "inst": "University of Edinburgh" } ], "affiliations": [ "University of Edinburgh" ] }, { "uid": "doi:10.2139/ssrn.4908364", "doi": "10.2139/ssrn.4908364", "title": "Governing the Large Language Model Commons: Using Digital Assets to Endow Intellectual Property Rights", "authors": [ "Christos Makridis", "Joshua Ammons" ], "posted": "2024-08-12", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4908364", "field": "economics", "role": "object", "bullets": [ "Conceptual paper with one illustrative case study of blockchain-based dispute resolution; no empirical sample, period, or geography is used.", "No model is named; large language models are treated as the force eroding intellectual property protection rather than a tool used by the authors.", "Argues that non-fungible tokens, framed through polycentric governance theory, could help assign and enforce digital intellectual property rights." ], "bullet_provenance": "ai", "salience": 33, "edition": 7, "models": [], "validated": null, "n": 1288, "authors_detailed": [ { "name": "Christos Makridis", "url": "https://openalex.org/A5041747384", "inst": "University of Nicosia" }, { "name": "Joshua Ammons", "url": "https://openalex.org/A5050816381", "inst": "George Mason University" } ], "affiliations": [ "University of Nicosia", "George Mason University" ] }, { "uid": "doi:10.2139/ssrn.4909147", "doi": "10.2139/ssrn.4909147", "title": "Artificial Intelligence Agentic Auditing", "authors": [ "Marco Schreyer", "Hanchi Gu", "Kevin Moffitt", "Miklos A. Vasarhelyi" ], "posted": "2024-08-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4909147", "field": "accounting", "role": "object", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 1029, "authors_detailed": [ { "name": "Marco Schreyer", "url": "https://openalex.org/A5056732531", "inst": "International Computer Science Institute" }, { "name": "Hanchi Gu", "url": "https://openalex.org/A5101395562", "inst": "Shanghai University of Finance and Economics" }, { "name": "Kevin Moffitt", "url": "https://openalex.org/A5016296300", "inst": "Rutgers Sexual and Reproductive Health and Rights" }, { "name": "Miklos A. Vasarhelyi", "url": "https://openalex.org/A5049215719", "inst": "Rutgers Sexual and Reproductive Health and Rights" } ], "affiliations": [ "International Computer Science Institute", "Shanghai University of Finance and Economics", "Rutgers Sexual and Reproductive Health and Rights" ] }, { "uid": "doi:10.2139/ssrn.4910591", "doi": "10.2139/ssrn.4910591", "title": "Tax Disclosures in Sustainability Reporting: Evidence on Disclosure Content and Corporate Tax Avoidance", "authors": [ "Inga Hardeck", "Frank Hechtner", "Andreas Seebeck", "Marius Weiß" ], "posted": "2024-08-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4910591", "field": "accounting", "role": "instrument", "bullets": [ "GRI-reporting firms adopting the GRI 207 tax sustainability disclosure standard, analyzed with automated and manual textual methods.", "TaxBERT, a domain-specific LLM, classified content and characteristics of tax sustainability disclosures alongside manual coding.", "Adopting firms show a persistent 1-2 percentage point increase in GAAP effective tax rates, linked to disclosed strategy changes." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 62, "n": 2949, "authors_detailed": [ { "name": "Inga Hardeck", "url": "https://openalex.org/A5028592580", "inst": "University of Regensburg" }, { "name": "Frank Hechtner", "url": "https://openalex.org/A5032188405", "inst": "Friedrich-Alexander-Universität Erlangen-Nürnberg" }, { "name": "Andreas Seebeck", "url": "https://openalex.org/A5039076241", "inst": "Constructor University" }, { "name": "Marius Weiss", "url": "https://openalex.org/A5114206835", "inst": "Friedrich-Alexander-Universität Erlangen-Nürnberg" } ], "affiliations": [ "University of Regensburg", "Friedrich-Alexander-Universität Erlangen-Nürnberg", "Constructor University" ] }, { "uid": "doi:10.2139/ssrn.4909057", "doi": "10.2139/ssrn.4909057", "title": "Assessing the Integration of ChatGPT in IT Audits that Support Financial Statement Audits", "authors": [ "Angel R. Otero" ], "posted": "2024-08-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4909057", "field": "accounting", "role": "object", "bullets": [ "Conceptual assessment of ChatGPT integration into IT audits that support financial statement audits, without empirical testing.", "ChatGPT evaluated qualitatively for efficiency, precision, and speed across IT audit processes including risk assessment and control testing.", "Identifies potential gains in audit quality and efficiency but flags data privacy, output reliability, and regulatory compliance as unresolved challenges." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 25, "validated": null, "n": 2950, "authors_detailed": [ { "name": "Angel R. Otero", "url": "https://openalex.org/A5025017380", "inst": "Florida Institute of Technology" } ], "affiliations": [ "Florida Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.4908691", "doi": "10.2139/ssrn.4908691", "title": "The Incident-Driven Green Products", "authors": [ "Yifei Zhang" ], "posted": "2024-08-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4908691", "field": "management", "role": "instrument", "bullets": [ "U.S. product announcements from 2002 to 2022, identifying green products among listed companies using stacked difference-in-differences.", "ChatGPT classified product announcements as green or non-green, identifying approximately 3.7% of all U.S. product announcements as green.", "Firms in severe environmental incidents launch 40% more green products within two years, backed by high-quality patents and real environmental gains." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 3425, "authors_detailed": [ { "name": "Yifei Zhang", "url": "https://openalex.org/A5106399015", "inst": "Peking University" } ], "affiliations": [ "Peking University" ] }, { "uid": "doi:10.2139/ssrn.4904876", "doi": "10.2139/ssrn.4904876", "title": "On the Antitrust Implications of Embedding Generative AI in Core Platform Services", "authors": [ "Thomas Höppner", "Steffen Uphues" ], "posted": "2024-08-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4904876", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of digital gatekeepers embedding generative AI into core platform services under the EU regulatory framework.", "Examines how generative AI enables platforms to repurpose business-user content, shifting platforms from intermediaries to direct suppliers.", "Platforms centralizing information through AI risk unprecedented user manipulation and exploitative discrimination extracting excessive rents from business users." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 45, "validated": null, "n": 3426, "authors_detailed": [ { "name": "Thomas Höppner", "url": "https://openalex.org/A5106423258", "inst": "Technical University of Applied Sciences Wildau" }, { "name": "Steffen Uphues", "url": "https://openalex.org/A5106413373", "inst": "SpaceTec Partners" } ], "affiliations": [ "Technical University of Applied Sciences Wildau", "SpaceTec Partners" ] }, { "uid": "doi:10.2139/ssrn.4909905", "doi": "10.2139/ssrn.4909905", "title": "How Generative AI Transforms Questioning Behavior on Q&A Platforms: Evidence from A Natural Experiment with Pilot Usage of ChatGPT", "authors": [ "Xinzhi Rao", "Guohou Shan", "Liangfei Qiu" ], "posted": "2024-08-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4909905", "field": "management", "role": "object", "bullets": [ "Stack Overflow users during the introduction and subsequent ban of ChatGPT, analyzed with difference-in-differences estimation.", "Studied how ChatGPT use for answer generation affected questioning behavior measured by volume, length, novelty, and upvotes received.", "ChatGPT use increased question volume with longer, more novel content and more upvotes, driven by complexity shift and skill enhancement mechanisms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3427, "authors_detailed": [ { "name": "Xinzhi Rao", "url": "https://openalex.org/A5106478989", "inst": "Hefei University of Technology" }, { "name": "Guohou Shan", "url": "https://openalex.org/A5065084539", "inst": "Northeastern University" }, { "name": "Liangfei Qiu", "url": "https://openalex.org/A5079497219", "inst": "University of Florida" } ], "affiliations": [ "University of Florida", "Hefei University of Technology", "Northeastern University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2408.10255v2", "arxiv_id": "2408.10255v2", "title": "Large Investment Model", "authors": [ "Jian Guo", "Heung-Yeung Shum" ], "posted": "2024-08-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.10255v2", "field": "finance", "role": "method", "bullets": [ "Commodity futures and stock market data across multiple exchanges, instruments, and frequencies for quantitative investment research.", "An end-to-end foundation model learns signal patterns from diverse financial data and transfers global patterns to downstream strategy tasks.", "Cross-instrument prediction experiments show the upstream foundation model improves downstream commodity futures trading performance over traditional approaches." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": false, "validated": true, "validation_note": "cross-instrument commodity futures prediction experiments", "salience": 55, "n": 3428, "authors_detailed": [ { "name": "Jian Guo", "url": "https://openalex.org/A5109927726", "inst": "Lanzhou Jiaotong University" }, { "name": "Heung‐Yeung Shum", "url": "https://openalex.org/A5061000201", "inst": "Tsinghua University" } ], "affiliations": [ "Lanzhou Jiaotong University", "Tsinghua University" ] }, { "uid": "arxiv:2408.05365v4", "arxiv_id": "2408.05365v4", "title": "FiSTECH: Financial Style Transfer to Enhance Creativity without Hallucinations in LLMs", "authors": [ "Sohini Roychowdhury", "Marko Krema", "Brian Moore", "Xingjian Lai", "Dike Effedua", "Bharat Jethwani" ], "posted": "2024-08-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.05365v4", "field": "finance", "role": "method", "bullets": [ "Public domain financial reports for style training, then a second round built from manually corrected hallucination examples; corpus size is not stated.", "A base LLM, family not named, is fine-tuned in two stages that first tolerate and then correct hallucinations, generating report sections from short instructions and tabular inputs.", "Financial question answering accuracy doubles and hallucinations fall by more than half, with lower perplexity and better ROUGE, TER, and BLEU than base models." ], "bullet_provenance": "ai", "validated": true, "validation_note": "QA accuracy and reference-based generation metrics", "salience": 30, "edition": 13, "models": [], "n": 1574, "authors_detailed": [ { "name": "Sohini Roychowdhury", "url": "https://openalex.org/A5050360160", "inst": "University of Minnesota" }, { "name": "Marko Krema", "url": "https://openalex.org/A5059594013", "inst": "Accenture (United States)" }, { "name": "Brian C. J. Moore", "url": "https://openalex.org/A5110050608", "inst": "University of Cambridge" }, { "name": "Xingjian Lai", "url": "https://openalex.org/A5078381392", "inst": "Beijing University of Chinese Medicine" }, { "name": "Dike Effedua", "url": "https://openalex.org/A5107490660", "inst": "" }, { "name": "Bharat Jethwani", "url": "https://openalex.org/A5107438038", "inst": "" } ], "affiliations": [ "University of Minnesota", "University of Cambridge", "Accenture (United States)", "Beijing University of Chinese Medicine" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2408.04948v1", "arxiv_id": "2408.04948v1", "title": "HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction", "authors": [ "Bhaskarjit Sarmah", "Benika Hall", "Rohan Rao", "Sunil Patel", "Stefano Pasquali", "Dhagash Mehta" ], "posted": "2024-08-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.04948v1", "field": "finance", "role": "method", "bullets": [ "Earnings call transcripts in question and answer format supply natural ground-truth pairs; document counts are not stated in the abstract.", "HybridRAG merges knowledge-graph and vector retrieval to feed unnamed LLMs; retrieval accuracy and answer quality are scored against the transcripts' ground-truth answers.", "Combining both retrieval sources beats vector-only and graph-only RAG at both the retrieval and generation stages; specific metric values are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "ground-truth Q&A pairs from transcripts", "salience": 34, "edition": 13, "models": [], "n": 1610 }, { "uid": "doi:10.2139/ssrn.4920899", "doi": "10.2139/ssrn.4920899", "title": "The Effect of Digital Technology Adoption on Firm Employment", "authors": [ "Jie Gao", "Zhizhuo Li", "Yebin Wang", "Wentao Zhang" ], "posted": "2024-08-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4920899", "field": "economics", "role": "instrument", "bullets": [ "Chinese A-share listed companies, 2008-2020 firm-level panel data, measuring digital technology adoption from annual reports.", "An LLM performed textual analysis of annual reports to construct digital technology adoption indicators across six technology categories.", "Digital technology adoption significantly decreases firm employment, with effects concentrated among less-educated and lower-skilled workers in manufacturing." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3424, "authors_detailed": [ { "name": "Jie Gao", "url": "https://openalex.org/A5101693329", "inst": "Central University of Finance and Economics" }, { "name": "Zhizhuo Li", "url": "https://openalex.org/A5108329452", "inst": "Beijing University of Posts and Telecommunications" }, { "name": "Yebin Wang", "url": "https://openalex.org/A5044079782", "inst": "Guangxi University of Finance and Economics" }, { "name": "Wentao Zhang", "url": "https://openalex.org/A5100459860", "inst": "Central University of Finance and Economics" } ], "affiliations": [ "Central University of Finance and Economics", "Beijing University of Posts and Telecommunications", "Guangxi University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.4905637", "doi": "10.2139/ssrn.4905637", "title": "Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow", "authors": [ "Tian Guo", "Emmanuel Hauptman" ], "posted": "2024-08-08", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4905637", "field": "finance", "role": "instrument", "bullets": [ "Real financial newsflow paired with investment universes to forecast stock returns for long-only and long-short portfolios; sample period and geography are not stated in the abstract.", "Encoder-only DeBERTa and decoder-only Mistral and Llama were fine-tuned to build text representations feeding a return-forecasting module; performance was judged by portfolio returns, not against labeled ground truth.", "Aggregated token-level embeddings improved portfolios, decoder models led in large universes, Mistral was most robust, and the LLM representations outperformed conventional sentiment scores as a portfolio signal." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 56, "edition": 7, "n": 1251, "authors_detailed": [ { "name": "Tian Guo", "url": "https://openalex.org/A5073299519", "inst": "RAM Active Investments" }, { "name": "Emmanuel Hauptman", "url": "https://openalex.org/A5106356374", "inst": "Independent - affiliation not provided to SSRN" } ], "affiliations": [ "RAM Active Investments", "Independent - affiliation not provided to SSRN" ] }, { "uid": "doi:10.2139/ssrn.4905533", "doi": "10.2139/ssrn.4905533", "title": "Does sentiment help in asset pricing? A novel approach using large language models and market-based labels", "authors": [ "Jule Schuettler", "Francesco Audrino", "Fabio Sigrist" ], "posted": "2024-08-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4905533", "field": "finance", "role": "instrument", "bullets": [ "U.S. stocks with earnings call transcripts, newspaper articles, and social media tweets used to construct a sentiment-based asset pricing factor.", "A state-of-the-art LLM classified sentiment using market-data-driven labels; results benchmarked against FinBERT with human-annotated labels.", "A long-short sentiment portfolio yields 35.56% annualized return with a Sharpe ratio of 2.21, driven primarily by the short leg." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 70, "n": 2948, "authors_detailed": [ { "name": "Jule Schuettler", "url": "https://openalex.org/A5106356538", "inst": "University of St.Gallen" }, { "name": "Francesco Audrino", "url": "https://openalex.org/A5080369947", "inst": "University of St.Gallen" }, { "name": "Fabio Sigrist", "url": "https://openalex.org/A5004802929", "inst": "ETH Zurich" } ], "affiliations": [ "University of St.Gallen", "ETH Zurich" ] }, { "uid": "arxiv:2408.03762v1", "arxiv_id": "2408.03762v1", "title": "'Finance Wizard' at the FinLLM Challenge Task: Financial Text Summarization", "authors": [ "Meisin Lee", "Soon Lay-Ki" ], "posted": "2024-08-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.03762v1", "field": "finance", "role": "instrument", "bullets": [ "FinNLP-AgentScen 2024 shared task on financial text summarization using fine-tuned Llama3-8B adapted to the finance domain.", "Llama3-8B underwent continued pre-training on finance corpora, multi-task instruction tuning, and task-specific fine-tuning for summarization.", "FinLlama3_sum placed third in the competition with a ROUGE-1 score of 0.521 on financial text summarization." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "ROUGE-1 score on FinNLP shared task benchmark", "salience": 25, "n": 3193, "authors_detailed": [ { "name": "Meisin Lee", "url": "https://openalex.org/A5022013791", "inst": "" }, { "name": "Soon Lay-Ki", "url": "https://openalex.org/A5114367935", "inst": "" } ] }, { "uid": "arxiv:2408.03033v1", "arxiv_id": "2408.03033v1", "title": "L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarization", "authors": [ "Elvys Linhares Pontes", "Carlos-Emiliano González-Gallardo", "Mohamed Benjannet", "Caryn Qu", "Antoine Doucet" ], "posted": "2024-08-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.03033v1", "field": "finance", "role": "method", "bullets": [ "The FinLLM Challenge 2024 shared tasks of financial text classification and financial text summarization, using organizer-provided training data and official test sets.", "Several LLMs are fine-tuned with 4-bit quantization and LoRA to run on low GPU memory. Base models are not named in the abstract.", "The systems placed third in financial classification with an F1 score of 0.7543 and sixth in financial summarization on the official test sets." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinLLM Challenge official test sets, F1", "salience": 22, "edition": 13, "models": [], "n": 1697, "authors_detailed": [ { "name": "Elvys Linhares Pontes", "url": "https://openalex.org/A5072568748", "inst": "Centre National de la Recherche Scientifique" }, { "name": "Carlos-Emiliano González-Gallardo", "url": "https://openalex.org/A5006172563", "inst": "Université de Tours" }, { "name": "Mohamed Benjannet", "url": "https://openalex.org/A5050299081", "inst": "" }, { "name": "Chuanqi Qu", "url": "https://openalex.org/A5083934553", "inst": "China University of Mining and Technology" }, { "name": "Antoine Doucet", "url": "https://openalex.org/A5033491986", "inst": "University of Ljubljana" } ], "affiliations": [ "Centre National de la Recherche Scientifique", "Université de Tours", "China University of Mining and Technology", "University of Ljubljana" ] }, { "uid": "doi:10.2139/ssrn.4915955", "doi": "10.2139/ssrn.4915955", "title": "Deep Learning for Economists", "authors": [ "Melissa Dell" ], "posted": "2024-08-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4915955", "field": "economics", "role": "method", "bullets": [ "Review covering deep learning methods for economists, including classifiers, generative AI, and embedding models, with companion demo website.", "Surveys deep neural networks applied to classification, document digitization, record linkage, and data exploration in large-scale text and image corpora.", "Demonstrates that deep learning models can scale affordably to millions or billions of data points when suitable tuning methods are applied." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 70, "validated": null, "n": 3423, "authors_detailed": [ { "name": "Melissa Dell", "url": "https://openalex.org/A5078758511", "inst": "Harvard University" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2408.02302v1", "arxiv_id": "2408.02302v1", "title": "SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models", "authors": [ "Shujuan Zhao", "Lingfeng Qiao", "Kangyang Luo", "Qian-Wen Zhang", "Junru Lu", "Di Yin" ], "posted": "2024-08-05", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.02302v1", "field": "finance", "role": "method", "bullets": [ "Chinese financial domain tasks spanning question answering, research report summarization, sentiment analysis, and financial calculation, with instruction data built from news, professional papers, and research reports.", "An open-source base model receives continued pretraining, supervised fine-tuning, and direct preference optimization. The base model family is not named in the abstract.", "SNFinLLM is reported to outperform other financial language models on finance benchmarks and the authors' evaluation set. Margins are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "finance benchmarks and custom evaluation set", "salience": 28, "edition": 13, "models": [], "n": 1696, "authors_detailed": [ { "name": "Zhao, Shujuan", "url": "", "inst": "" }, { "name": "Lingfeng Qiao", "url": "https://openalex.org/A5069607513", "inst": "Shanghai Jiao Tong University" }, { "name": "Kangyang Luo", "url": "https://openalex.org/A5076427086", "inst": "University of Science and Technology of China" }, { "name": "Qian-Wen Zhang", "url": "https://openalex.org/A5084857694", "inst": "" }, { "name": "Jian Lu", "url": "https://openalex.org/A5100662417", "inst": "Xiamen University" }, { "name": "Di Yin", "url": "https://openalex.org/A5100567807", "inst": "City University of Hong Kong" } ], "affiliations": [ "Shanghai Jiao Tong University", "University of Science and Technology of China", "Xiamen University", "City University of Hong Kong" ] }, { "uid": "arxiv:2408.04646v2", "arxiv_id": "2408.04646v2", "title": "Efficacy of Large Language Models in Systematic Reviews", "authors": [ "Aaditya Shah", "Shridhar Mehendale", "Siddha Kanthi" ], "posted": "2024-08-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.04646v2", "field": "finance", "role": "method", "bullets": [ "88 hand-coded papers on ESG and financial performance from 2020 to 2024, plus 238 papers from an earlier systematic review used for training and testing.", "Llama 3 8B, GPT-4o, a Custom GPT and a fine-tuned GPT-4o Mini classify the papers, scored for accuracy against the human coding.", "The fine-tuned GPT-4o Mini gains 28.3 percent average accuracy over base models on one prompt, and the Custom GPT gains 3.0 and 15.7 percent on two others." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "accuracy against hand-coded classifications of 326 papers", "salience": 45, "edition": 13, "n": 1540, "authors_detailed": [ { "name": "Aaditya Shah", "url": "https://openalex.org/A5112326955", "inst": "Indian Navy" }, { "name": "Shridhar Mehendale", "url": "https://openalex.org/A5107099326", "inst": "Illinois Mathematics and Science Academy" }, { "name": "Siddha Kanthi", "url": "https://openalex.org/A5095962069", "inst": "" } ], "affiliations": [ "Indian Navy", "Illinois Mathematics and Science Academy" ] }, { "uid": "arxiv:2408.05233v2", "arxiv_id": "2408.05233v2", "title": "Electric Vehicle User Charging Behavior Analysis Integrating Psychological and Environmental Factors: A Statistical-Driven LLM based Agent Approach", "authors": [ "Chuanlin Zhang", "Junkang Feng", "Chenggang Cui", "Pengfeng Lin", "Hui Chen", "Yan Xu", "A. M. Y. M. Ghias", "Qianguang Ma", "Pei Zhang" ], "posted": "2024-08-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.05233v2", "field": "economics", "role": "agent", "bullets": [ "Charging behaviour of electric vehicle taxi drivers across multiple urban environments, studied through agent simulation rather than newly collected field data.", "LLM agents, family not stated, role-play individual drivers with traits such as time sensitivity, price awareness, and range anxiety, with statistical priors anchoring decisions to empirical behaviour patterns.", "Simulations are reported to reproduce real-world charging patterns across cities, with decisions shaped jointly by psychological traits and situational triggers and clear heterogeneity across user groups." ], "bullet_provenance": "ai", "validated": true, "validation_note": "simulated behaviour compared with real-world charging patterns; no statistic in abstract", "salience": 38, "edition": 13, "models": [], "n": 1651, "authors_detailed": [ { "name": "Chuanlin Zhang", "url": "https://openalex.org/A5054051002", "inst": "Beijing Institute of Technology" }, { "name": "Junkang Feng", "url": "https://openalex.org/A5100928786", "inst": "Shanghai University of Electric Power" }, { "name": "Chenggang Cui", "url": "https://openalex.org/A5005830480", "inst": "Shanghai University of Electric Power" }, { "name": "Lin, Pengfeng", "url": "", "inst": "" }, { "name": "Chen, Hui", "url": "", "inst": "" }, { "name": "Xu, Yan", "url": "", "inst": "" }, { "name": "Ghias, A. M. Y. M.", "url": "", "inst": "" }, { "name": "Ma, Qianguang", "url": "", "inst": "" }, { "name": "Zhang, Pei", "url": "", "inst": "" } ], "affiliations": [ "Beijing Institute of Technology", "Shanghai University of Electric Power" ] }, { "uid": "doi:10.2139/ssrn.4914889", "doi": "10.2139/ssrn.4914889", "title": "Understanding the Unexpected: The Adverse Influence of Large Language Models on Critical Thinking and Professional Skepticism in Accounting Education", "authors": [ "Ihsan Manshur Putra", "Fauziah Istiqomah Abdunnafi" ], "posted": "2024-08-03", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4914889", "field": "accounting", "role": "object", "bullets": [ "Experiment with fourth-year undergraduate accounting students at one accredited program, split into an LLM-using group and a control group; sample size and country not stated.", "The LLM family and version are not stated; students used it as a study aid rather than researchers using it to measure anything, and no validation is reported.", "Critical thinking scores fell among LLM users while rising in the control group, and the change moved together with professional skepticism." ], "bullet_provenance": "ai", "salience": 45, "edition": 7, "models": [], "validated": null, "n": 1287, "authors_detailed": [ { "name": "Ihsan Manshur Putra", "url": "https://openalex.org/A5106269824", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Fauziah Istiqomah Abdunnafi", "url": "https://openalex.org/A5099020123", "inst": "Independent - affiliation not provided to SSRN" } ], "affiliations": [ "Independent - affiliation not provided to SSRN" ] }, { "uid": "arxiv:2408.01285v2", "arxiv_id": "2408.01285v2", "title": "Do Prevalent Bias Metrics Capture Allocational Harms from LLMs?", "authors": [ "Hannah Cyberey", "Yangfeng Ji", "David Evans" ], "posted": "2024-08-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.01285v2", "field": "management", "role": "method", "bullets": [ "Ten LLMs applied to two resource allocation tasks; group level allocation outcomes are compared with what standard bias metrics would predict.", "The models generate predictions whose group disparities are measured; the study tests whether performance gap and distribution distance metrics anticipate allocation outcomes.", "Common bias metrics fail to reliably signal group disparities in allocations, cautioning against relying on them alone when selecting models for decision settings." ], "bullet_provenance": "ai", "salience": 38, "edition": 13, "models": [], "validated": null, "n": 1742, "authors_detailed": [ { "name": "Cyberey, Hannah", "url": "", "inst": "" }, { "name": "Yangfeng Ji", "url": "https://openalex.org/A5085302513", "inst": "University of Virginia" }, { "name": "David Evans", "url": "https://openalex.org/A5032031899", "inst": "Commonwealth Scientific and Industrial Research Organisation" } ], "affiliations": [ "University of Virginia", "Commonwealth Scientific and Industrial Research Organisation" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4904004", "doi": "10.2139/ssrn.4904004", "title": "Corporate Responses to Generative AI: Early Evidence from Investment Efficiency", "authors": [ "William Mbanyele" ], "posted": "2024-08-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4904004", "field": "finance", "role": "object", "bullets": [ "U.S. public firms' SEC 10-Q filings measuring generative AI exposure, using ChatGPT release as an identification event for causal analysis.", "Study constructs textual measure of GAI exposure from quarterly filings and tests its association with corporate investment efficiency.", "GAI exposure is positively associated with investment efficiency, with stronger effects in competitive markets and firms facing high information asymmetry." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 65, "validated": null, "n": 2664, "authors_detailed": [ { "name": "William Mbanyele", "url": "https://openalex.org/A5050881941", "inst": "Shandong University" } ], "affiliations": [ "Shandong University" ] }, { "uid": "arxiv:2407.21459v1", "arxiv_id": "2407.21459v1", "title": "KemenkeuGPT: Leveraging a Large Language Model on Indonesia's Government Financial Data and Regulations to Enhance Decision Making", "authors": [ "Gilang Fajar Febrian", "Grazziela Figueredo" ], "posted": "2024-07-31", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.21459v1", "field": "management", "role": "method", "bullets": [ "Indonesian government financial data and regulations from 2003 to 2023, sourced from the Ministry of Finance, Statistics Indonesia, and the IMF, with input from ministry officials.", "A LangChain RAG pipeline with prompt engineering and fine-tuning, base model not stated, is evaluated through human feedback, LLM-based grading, and the RAGAS framework.", "Accuracy rises from 35 to 61 percent across development iterations, with 44 percent correctness and 73 percent faithfulness on RAGAS, beating several base models." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human feedback, LLM grading, RAGAS metrics", "salience": 28, "edition": 13, "models": [], "n": 1609, "authors_detailed": [ { "name": "Gilang Fajar Febrian", "url": "https://openalex.org/A5031403382", "inst": "" }, { "name": "Grazziela P. Figueredo", "url": "https://openalex.org/A5051446557", "inst": "University of Nottingham" } ], "affiliations": [ "University of Nottingham" ] }, { "uid": "arxiv:2407.21276v3", "arxiv_id": "2407.21276v3", "title": "Knowledge Pyramid Construction for Multi-Level Retrieval-Augmented Generation", "authors": [ "Rubing Chen", "Xulu Zhang", "Jiaxin Wu", "Wenqi Fan", "Xiao-Yong Wei", "Qing Li" ], "posted": "2024-07-31", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.21276v3", "field": "finance", "role": "method", "bullets": [ "Two domain-specific retrieval benchmarks including a financial knowledge dataset, tested against 19 state-of-the-art RAG methods.", "PolyRAG three-layer knowledge pyramid (ontologies, knowledge graphs, raw text) augmented GPT-4 with cross-layer filtering and waterfall retrieval.", "GPT-4 F1 score improved from 0.1636 to 0.8109 on financial retrieval, a 395% gain, outperforming all 19 competing methods." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "F1 on financial and academic retrieval benchmarks", "salience": 45, "n": 3895, "authors_detailed": [ { "name": "Rubing Chen", "url": "https://openalex.org/A5111976364", "inst": "Hong Kong Polytechnic University" }, { "name": "Xulu Zhang", "url": "https://openalex.org/A5108965063", "inst": "Hong Kong Polytechnic University" }, { "name": "Jiaxin Wu", "url": "https://openalex.org/A5101880563", "inst": "Shenzhen University" }, { "name": "Wenqi Fan", "url": "https://openalex.org/A5043696243", "inst": "Hong Kong Polytechnic University" }, { "name": "Xiao-Yong Wei", "url": "https://openalex.org/A5064374603", "inst": "Hong Kong Polytechnic University" }, { "name": "Qing Li", "url": "https://openalex.org/A5100404176", "inst": "Zhejiang International Studies University" } ], "affiliations": [ "Hong Kong Polytechnic University", "Shenzhen University", "Zhejiang International Studies University" ] }, { "uid": "doi:10.2139/ssrn.4909749", "doi": "10.2139/ssrn.4909749", "title": "Consumption and Savings with Large Language Model Agents", "authors": [ "Michael R. Douglas", "Sergiy Verstyuk" ], "posted": "2024-07-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4909749", "field": "economics", "role": "agent", "bullets": [ "Canonical consumption-savings model with aggregate productivity shocks and individual employment risk, comparing LLM agents to rational-agent benchmarks.", "LLMs replace fully rational agents in a macro model, making consumption-savings decisions autonomously with inter-agent linguistic communication.", "LLM agents exhibit reasonable economic behavior with systematic anthropomorphic biases, performing more like imperfect humans than perfectly rational theoretical counterparts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 75, "n": 2663, "authors_detailed": [ { "name": "Michael R. Douglas", "url": "https://openalex.org/A5107840420", "inst": "Harvard University Press" }, { "name": "Sergiy Verstyuk", "url": "https://openalex.org/A5027456897", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2407.20371v2", "arxiv_id": "2407.20371v2", "title": "Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval", "authors": [ "Kyra Wilson", "Aylin Caliskan" ], "posted": "2024-07-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.20371v2", "field": "management", "role": "agent", "bullets": [ "Resume audit covering nine occupations, built from over 500 public resumes and 500 job descriptions, with names varied to signal race and gender.", "Massive text embedding models rank candidates in a simulated retrieval-based screening pipeline; bias is measured through name substitutions rather than accuracy against ground truth.", "White-associated names are favored in 85.1 percent of tests and female-associated names in 11.1 percent, with Black men disadvantaged in up to 100 percent of comparisons." ], "bullet_provenance": "ai", "salience": 60, "edition": 13, "models": [], "validated": null, "n": 1608, "authors_detailed": [ { "name": "Kyra Wilson", "url": "https://openalex.org/A5110110978", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Aylin Caliskan", "url": "https://openalex.org/A5101545719", "inst": "University of Washington" } ], "affiliations": [ "Rutgers, The State University of New Jersey", "University of Washington" ] }, { "uid": "arxiv:2407.19922v1", "arxiv_id": "2407.19922v1", "title": "Monetizing Currency Pair Sentiments through LLM Explainability", "authors": [ "Lior Limonad", "Fabiana Fournier", "Juan Manuel Vera Díaz", "Inna Skarbovsky", "Shlomit Gur", "Raquel Lazcano" ], "posted": "2024-07-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.19922v1", "field": "finance", "role": "method", "bullets": [ "Open news feed data merged with market prices for currency pair price prediction; period and observation counts are not stated in the abstract.", "An LLM, not named, performs sentiment analysis and serves as a post hoc model independent explainability layer; explanations are then fed back as machine learning inputs.", "Explanation enriched inputs are said to improve future currency pair predictions; the abstract reports no accuracy comparison or magnitude." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 13, "models": [], "n": 1741, "authors_detailed": [ { "name": "Lior Limonad", "url": "https://openalex.org/A5084219797", "inst": "IBM Research - Haifa" }, { "name": "Fabiana Fournier", "url": "https://openalex.org/A5006551782", "inst": "IBM Research - Haifa" }, { "name": "Juan Manuel Vera Díaz", "url": "https://openalex.org/A5106163684", "inst": "" }, { "name": "Inna Skarbovsky", "url": "https://openalex.org/A5003522846", "inst": "IBM Research - Haifa" }, { "name": "Shlomit Gur", "url": "https://openalex.org/A5106163685", "inst": "" }, { "name": "Raquel Lazcano", "url": "https://openalex.org/A5064256426", "inst": "Atos (Spain)" } ], "affiliations": [ "IBM Research - Haifa", "Atos (Spain)" ] }, { "uid": "doi:10.2139/ssrn.4909429", "doi": "10.2139/ssrn.4909429", "title": "Pioneering Exploration in Patent Landscape Studies: Leveraging Large Language Models and In-Context Learning for Deeper Insights", "authors": [ "Minghui Qian", "Mengchun Zhao", "Jianliang Yang", "Zhian Ying", "Chi Wang", "Shulin Guo" ], "posted": "2024-07-29", "added": "2026-07-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4909429", "field": "management", "role": "instrument", "bullets": [ "Patent landscape classification of patents into user-defined categories, evaluated on three public datasets named InjVal, Rito, and Atz in a low-resource setting.", "GPT-PLS uses GPT with a task-specific prompt template and in-context learning demonstrations to classify patents, benchmarked against a fully trained state-of-the-art model using Macro-F1 and Micro-F1.", "GPT-PLS improved Macro-F1 by 5.33 percent and Micro-F1 by 2.83 percent over the trained baseline, indicating LLMs can support patent classification without extensive annotation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "three public patent datasets, Macro-F1 and Micro-F1 versus trained SOTA", "salience": 42, "edition": 7, "n": 1278, "authors_detailed": [ { "name": "Qian Minghui", "url": "https://openalex.org/A5103661983", "inst": "Renmin University of China" }, { "name": "Mengchun Zhao", "url": "https://openalex.org/A5114198854", "inst": "Renmin University of China" }, { "name": "Jianliang Yang", "url": "https://openalex.org/A5051199776", "inst": "Renmin University of China" }, { "name": "Zhian Ying", "url": "https://openalex.org/A5111271980", "inst": "Renmin University of China" }, { "name": "Chi Wang", "url": "https://openalex.org/A5100342213", "inst": "Renmin University of China" }, { "name": "Shulin Guo", "url": "https://openalex.org/A5107298418", "inst": "Renmin University of China" } ], "affiliations": [ "Renmin University of China" ] }, { "uid": "arxiv:2407.19586v1", "arxiv_id": "2407.19586v1", "title": "Is Generative AI an Existential Threat to Human Creatives? Insights from Financial Economics", "authors": [ "Jiasun Li" ], "posted": "2024-07-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.19586v1", "field": "economics", "role": "object", "bullets": [ "Theory paper on whether generative models can fully displace human content creators, argued by analogy to Grossman and Stiglitz on informationally efficient markets.", "No model is deployed; generative AI, with GPT and diffusion systems cited as examples, features as an actor in the theoretical argument.", "If AI supplied all content cheaply, humans would stop creating for lack of profit, leaving models to train on stale information; the paradox implies human creatives persist." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 52, "edition": 13, "validated": null, "n": 1740, "authors_detailed": [ { "name": "Jiasun Li", "url": "https://openalex.org/A5003473501", "inst": "George Mason University" } ], "affiliations": [ "George Mason University" ] }, { "uid": "arxiv:2408.06361v2", "arxiv_id": "2408.06361v2", "title": "Large Language Model Agent in Financial Trading: A Survey", "authors": [ "Han Ding", "Yinheng Li", "Junhao Wang", "Hang Chen", "Doudou Guo", "Yunbai Zhang" ], "posted": "2024-07-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2408.06361v2", "field": "finance", "role": "method", "bullets": [ "Review of research deploying LLM agents for financial trading, covering agent architectures, data inputs, and backtest performance; paper counts are not stated.", "No new model; the survey catalogues how existing agents combine LLMs with market and news data and how the studies evaluate them in backtests.", "Summarizes reported backtest results and open challenges for LLM trading agents and sketches research directions; no pooled performance estimate is offered." ], "bullet_provenance": "ai", "salience": 42, "edition": 13, "models": [], "validated": null, "n": 1739 }, { "uid": "doi:10.2139/ssrn.4893097", "doi": "10.2139/ssrn.4893097", "title": "AI Companions Reduce Loneliness", "authors": [ "Julian De Freitas", "Ahmet Kaan Uğuralp", "Zeliha Uğuralp", "Stefano Puntoni" ], "posted": "2024-07-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4893097", "field": "management", "role": "object", "bullets": [ "Six studies including a longitudinal design and Prolific experiments examined AI companion chatbot effects on consumer loneliness.", "Fine-tuned LLMs detected loneliness in conversations; controlled experiments compared AI companions with human interaction and other activities.", "AI companions reduced loneliness on par with human interaction; the effect persisted over one week and users underestimated the improvement." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 2570, "authors_detailed": [ { "name": "Julian De Freitas", "url": "https://openalex.org/A5009128737", "inst": "Harvard University" }, { "name": "Ahmet Kaan Uğuralp", "url": "https://openalex.org/A5039014942", "inst": "Bilkent University" }, { "name": "Zeliha Uğuralp", "url": "https://openalex.org/A5054318815", "inst": "Bilkent University" }, { "name": "Stefano Puntoni", "url": "https://openalex.org/A5086883230", "inst": "University of Pennsylvania" } ], "affiliations": [ "Harvard University", "University of Pennsylvania", "Bilkent University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4898166", "doi": "10.2139/ssrn.4898166", "title": "Does Increasing Reliance on Artificial Intelligence Boost Creativity? Assessing AI-Augmented Creativity with Large Language Models", "authors": [ "Jiaoping Chen", "Laura Brandimarte", "Anjana Susarla" ], "posted": "2024-07-24", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4898166", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 35, "edition": 23, "bullets": [], "models": [], "validated": null, "n": 4191, "authors_detailed": [ { "name": "Jiaoping Chen", "url": "https://openalex.org/A5040847364", "inst": "University of Baltimore" }, { "name": "Laura Brandimarte", "url": "https://openalex.org/A5039552297", "inst": "University of Arizona" }, { "name": "Anjana Susarla", "url": "https://openalex.org/A5077777059", "inst": "Eli and Edythe Broad Foundation" } ], "affiliations": [ "University of Baltimore", "University of Arizona", "Eli and Edythe Broad Foundation" ] }, { "uid": "arxiv:2407.17624v2", "arxiv_id": "2407.17624v2", "title": "Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs", "authors": [ "Felix Drinkall", "Janet B. Pierrehumbert", "Stefan Zohren" ], "posted": "2024-07-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.17624v2", "field": "finance", "role": "method", "bullets": [ "Corporate credit rating forecasting with fundamental, macroeconomic, and textual inputs; sample, period, and rating source are not stated in the abstract.", "Generative LLMs, families not named in the abstract, are pitted against an XGBoost model that combines numeric features with high-density text embeddings.", "LLMs encode text well but lag on numeric and multimodal inputs, and the XGBoost combination outperforms them on rating prediction; margins are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "forecasts scored against realized corporate credit ratings", "salience": 55, "edition": 13, "models": [], "n": 1650, "authors_detailed": [ { "name": "Felix Drinkall", "url": "https://openalex.org/A5045703184", "inst": "Turing Institute" }, { "name": "Janet B. Pierrehumbert", "url": "https://openalex.org/A5049920688", "inst": "Northwestern University" }, { "name": "Stefan Zohren", "url": "https://openalex.org/A5090331439", "inst": "University of Oxford" } ], "affiliations": [ "Northwestern University", "University of Oxford", "Turing Institute" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2407.17190v1", "arxiv_id": "2407.17190v1", "title": "Fusing LLMs and KGs for Formal Causal Reasoning behind Financial Risk Contagion", "authors": [ "Guanyuan Yu", "Xv Wang", "Qing Li", "Yu Zhao" ], "posted": "2024-07-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.17190v1", "field": "finance", "role": "method", "bullets": [ "Financial knowledge graphs and supply chain datasets ground a model of how risk spreads between entities toward systemic events; dataset sizes are not stated.", "An LLM, family not named, is fused with graph nodes through multi scale contrastive learning to perform formal causal reasoning and score node level risk.", "Outperforms state of the art baselines on prediction and out of distribution generalization; Sankey diagrams trace contagion paths; magnitudes are not given in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "financial KG and supply chain benchmarks", "salience": 42, "edition": 13, "models": [], "n": 1738 }, { "uid": "doi:10.2139/ssrn.4700354", "doi": "10.2139/ssrn.4700354", "title": "Making ChatGPT Work For Me", "authors": [ "Samantha Keppler", "Wichinpong Sinchaisri", "Clare Snyder" ], "posted": "2024-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4700354", "field": "management", "role": "object", "bullets": [ "Twenty-four U.S. K-12 public school teachers observed using ChatGPT-4 for self-chosen work tasks in recorded one-on-one virtual sessions.", "Teachers entered 201 prompts into ChatGPT-4; researchers coded four interaction modes: make, find, jump-start, and iterate.", "Fifty-five percent of prompts were delegating requests; only 15.5% used the iterate mode that asks AI to think rather than produce." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 35, "validated": null, "n": 2947, "authors_detailed": [ { "name": "Samantha Keppler", "url": "https://openalex.org/A5084124842", "inst": "University of Michigan" }, { "name": "Wichinpong Park Sinchaisri", "url": "https://openalex.org/A5021112441", "inst": "University of California, Berkeley" }, { "name": "C. W. Snyder", "url": "https://openalex.org/A5018593341", "inst": "New York University" } ], "affiliations": [ "University of California, Berkeley", "New York University", "University of Michigan" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2407.15788v1", "arxiv_id": "2407.15788v1", "title": "Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach", "authors": [ "Rian Dolphin", "Joe Dursun", "Jonathan Chow", "Jarrett Blankenship", "Katie Adams", "Quinton Pike" ], "posted": "2024-07-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.15788v1", "field": "finance", "role": "method", "bullets": [ "5,530 financial news articles processed into company tickers, company-level sentiment, and summaries, with the pipeline serving a live real-time API.", "LLMs with recent prompting techniques extract relevant tickers and score sentiment per company, checked by a tailored string similarity validation framework. Model families are not stated.", "90 percent of articles miss no tickers relative to current data providers and 22 percent gain additional relevant tickers, with the evaluation dataset released." ], "bullet_provenance": "ai", "validated": true, "validation_note": "comparison against commercial data provider tickers on 5,530 articles", "salience": 38, "edition": 13, "models": [], "n": 1695, "authors_detailed": [ { "name": "Rian Dolphin", "url": "https://openalex.org/A5107619375", "inst": "" }, { "name": "Joe Dursun", "url": "https://openalex.org/A5107619376", "inst": "Eterna Massive Open Laboratory" }, { "name": "Jonathan Chow", "url": "https://openalex.org/A5107619377", "inst": "" }, { "name": "Jarrett Blankenship", "url": "https://openalex.org/A5107496252", "inst": "Eterna Massive Open Laboratory" }, { "name": "Katie Adams", "url": "https://openalex.org/A5107531771", "inst": "" }, { "name": "Quinton Pike", "url": "https://openalex.org/A5107550686", "inst": "" } ], "affiliations": [ "Eterna Massive Open Laboratory" ] }, { "uid": "arxiv:2407.15339v3", "arxiv_id": "2407.15339v3", "title": "Deep Learning for Economists", "authors": [ "Melissa Dell" ], "posted": "2024-07-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.15339v3", "field": "economics", "role": "method", "bullets": [ "Review covering deep learning applications in economics including classification, document digitization, record linkage, and large-scale text and image analysis.", "Surveys neural network methods including classifiers, regression models, generative AI, and embeddings for extracting structured information from unstructured economic data.", "Deep learning models can scale affordably to millions or billions of data points; accompanied by EconDL companion site with demo notebooks and software resources." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "salience": 65, "validated": null, "n": 3421, "authors_detailed": [ { "name": "Melissa Dell", "url": "https://openalex.org/A5078758511", "inst": "Canadian Institute for Advanced Research" } ], "affiliations": [ "Canadian Institute for Advanced Research" ] }, { "uid": "doi:10.2139/ssrn.4901032", "doi": "10.2139/ssrn.4901032", "title": "Intellectual Property and Creative Machines", "authors": [ "Gaétan De Rassenfosse", "Adam B. Jaffe", "Joel Waldfogel" ], "posted": "2024-07-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4901032", "field": "economics", "role": "object", "bullets": [ "Conceptual economic framework examining intellectual property challenges posed by generative AI in creative industries across jurisdictions.", "No model deployed; paper analyzes economic tradeoffs in IP policy for machine-generated creative content using welfare economics tools.", "Proposes framework for evaluating IP responses to creative machines, identifying conditions where protection enhances or jeopardizes welfare in post-GenAI creative markets." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3422, "authors_detailed": [ { "name": "Gaétan de Rassenfosse", "url": "https://openalex.org/A5086533373", "inst": "École Polytechnique Fédérale de Lausanne" }, { "name": "Adam B. Jaffe", "url": "https://openalex.org/A5022731487", "inst": "National Bureau of Economic Research" }, { "name": "Joel Waldfogel", "url": "https://openalex.org/A5020491437", "inst": "University of Minnesota" } ], "affiliations": [ "University of Minnesota", "École Polytechnique Fédérale de Lausanne", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2407.12220v2", "arxiv_id": "2407.12220v2", "title": "Questionable practices in machine learning", "authors": [ "Gavin Leech", "Juan J. Vazquez", "Niclas Kupper", "Misha Yagudin", "Laurence Aitchison" ], "posted": "2024-07-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.12220v2", "field": "other", "role": "method", "bullets": [ "A catalogue of 44 questionable research practices in machine learning, with examples where possible, focused on how large language models are evaluated on public benchmarks.", "No model is deployed; the paper itemizes practices that inflate reported results while falling short of outright fraud, and separately lists decisions that make studies irreproducible.", "The authors tie these practices to incentive pressure for state-of-the-art claims and argue they undermine the reproducibility and auditability of reported LLM benchmark results." ], "bullet_provenance": "ai", "salience": 40, "edition": 13, "models": [], "validated": null, "n": 1649, "authors_detailed": [ { "name": "Gavin Leech", "url": "https://openalex.org/A5029915724", "inst": "University of Bristol" }, { "name": "Juan José Vázquez-Alcaraz", "url": "https://openalex.org/A5061493516", "inst": "Universidad del Cono Sur de las Américas" }, { "name": "Niclas Kupper", "url": "https://openalex.org/A5114401494", "inst": "" }, { "name": "Misha Yagudin", "url": "https://openalex.org/A5056230521", "inst": "Atlas Scientific (United States)" }, { "name": "Laurence Aitchison", "url": "https://openalex.org/A5062539506", "inst": "University of Bristol" } ], "affiliations": [ "University of Bristol", "Universidad del Cono Sur de las Américas", "Atlas Scientific (United States)" ] }, { "uid": "doi:10.2139/ssrn.4891763", "doi": "10.2139/ssrn.4891763", "title": "Using Large Language Models to Explore Contextualization Effects in Economics-Based Accounting Experiments", "authors": [ "Fikir Worku Edossa", "Joachim Gassen", "Victor S. Maas" ], "posted": "2024-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4891763", "field": "accounting", "role": "agent", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 341, "authors_detailed": [ { "name": "Fikir Worku Edossa", "url": "https://openalex.org/A5104634814", "inst": "Humboldt University of Berlin - School of Business and Economics" }, { "name": "Joachim Gassen", "url": "https://openalex.org/A5075113649", "inst": "Humboldt University of Berlin - School of Business and Economics" }, { "name": "Victor S. Maas", "url": "https://openalex.org/A5000685915", "inst": "University of Amsterdam" } ], "affiliations": [ "Humboldt University of Berlin - School of Business and Economics", "University of Amsterdam" ] }, { "uid": "arxiv:2407.18957v5", "arxiv_id": "2407.18957v5", "title": "When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments", "authors": [ "Chong Zhang", "Xinyi Liu", "Zhongmou Zhang", "Mingyu Jin", "Lingyao Li", "Zhenting Wang", "Wenyue Hua", "Dong Shu", "Suiyuan Zhu", "Xiaobo Jin", "Sujian Li", "Mengnan Du", "Yongfeng Zhang" ], "posted": "2024-07-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.18957v5", "field": "finance", "role": "agent", "bullets": [ "A simulated stock trading environment resembling real market conditions, where LLM-driven investor agents react to macroeconomics, policy changes, company fundamentals, and global events.", "Different LLMs are run inside the multi-agent StockAgent framework, built to avoid test-set leakage from models' prior knowledge of market data. Models tested are not named in the abstract.", "Simulations surface how external factors shape trading behavior and price fluctuations, informing LLM-based investment advice. Quantitative effects are not stated in the abstract." ], "bullet_provenance": "ai", "salience": 40, "edition": 13, "models": [], "validated": null, "n": 1694, "authors_detailed": [ { "name": "Chong Zhang", "url": "https://openalex.org/A5046630355", "inst": "University of Chinese Academy of Sciences" }, { "name": "Xinyi Liu", "url": "https://openalex.org/A5100395659", "inst": "Capital Medical University" }, { "name": "Zhongmou Zhang", "url": "https://openalex.org/A5005675164", "inst": "Beijing University of Chinese Medicine" }, { "name": "Mingyu Jin", "url": "https://openalex.org/A5102521857", "inst": "Northwestern University" }, { "name": "Lingyao Li", "url": "https://openalex.org/A5005480899", "inst": "University of South Florida" }, { "name": "Zhenting Wang", "url": "https://openalex.org/A5044589781", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Wenyue Hua", "url": "https://openalex.org/A5106214773", "inst": "" }, { "name": "Shu, Dong", "url": "", "inst": "" }, { "name": "Suiyuan Zhu", "url": "https://openalex.org/A5113315260", "inst": "" }, { "name": "Xiaobo Jin", "url": "https://openalex.org/A5011077051", "inst": "Chengdu Medical College" }, { "name": "Sujian Li", "url": "https://openalex.org/A5058353424", "inst": "University of Science and Technology Beijing" }, { "name": "Mengnan Du", "url": "https://openalex.org/A5072191151", "inst": "New Jersey Institute of Technology" }, { "name": "Yongfeng Zhang", "url": "https://openalex.org/A5087294988", "inst": "University of Wisconsin–Madison" } ], "affiliations": [ "Northwestern University", "University of Chinese Academy of Sciences", "Capital Medical University", "Beijing University of Chinese Medicine", "University of South Florida", "Rutgers, The State University of New Jersey", "Chengdu Medical College", "University of Science and Technology Beijing" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3677052.3698603", "doi": "10.1145/3677052.3698603", "arxiv_id": "2407.10909v2", "title": "FinDKG: Dynamic Knowledge Graphs with Large Language Models for Detecting Global Trends in Financial Markets", "authors": [ "Xiaohui Victor Li", "Francesco Sanna Passino" ], "posted": "2024-07-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.10909v2", "field": "finance", "role": "method", "bullets": [ "Corpus of financial news articles turned into an open source dynamic knowledge graph, FinDKG, for tracking global market themes; article counts are not stated.", "ICKG, an open source fine tuned LLM whose base family is not stated, generates the knowledge graph; an attention based graph transformer then learns on it.", "Beats benchmarks on link prediction and, applied to thematic investing, outperforms existing thematic ETFs; margins are not given in the abstract." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "link prediction benchmarks", "salience": 50, "edition": 13, "n": 1737, "authors_detailed": [ { "name": "Xiaohui Victor Li", "url": "https://openalex.org/A5104199267", "inst": "Imperial College London" }, { "name": "Francesco Sanna Passino", "url": "https://openalex.org/A5041458702", "inst": "Imperial College London" } ], "affiliations": [ "Imperial College London" ] }, { "uid": "doi:10.1145/3677052.3698612", "doi": "10.1145/3677052.3698612", "arxiv_id": "2407.11215v2", "title": "Mechanistic interpretability of large language models with applications to the financial services industry", "authors": [ "Ashkan Golgoon", "Khashayar Filom", "Arjun Ravi Kannan" ], "posted": "2024-07-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.11215v2", "field": "finance", "role": "method", "bullets": [ "GPT-2 Small model examined using mechanistic interpretability techniques for Fair Lending compliance monitoring in financial services.", "Applied direct logit attribution and activation patching to reverse-engineer GPT-2 Small's attention patterns when identifying potential Fair Lending violations.", "Identified attention heads 10.2, 10.7, and 11.3 as positive contributors and 9.6 and 10.6 as negative contributors to compliance task completion." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": true, "validated": false, "salience": 48, "n": 3420, "authors_detailed": [ { "name": "Ashkan Golgoon", "url": "https://openalex.org/A5106580061", "inst": "Discover Financial Services (United States)" }, { "name": "Khashayar Filom", "url": "https://openalex.org/A5009622310", "inst": "Discover Financial Services (United States)" }, { "name": "Arjun Ravi Kannan", "url": "https://openalex.org/A5089840355", "inst": "Discover Financial Services (United States)" } ], "affiliations": [ "Discover Financial Services (United States)" ] }, { "uid": "arxiv:2407.09003v1", "arxiv_id": "2407.09003v1", "title": "Enhancing Few-Shot Stock Trend Prediction with Large Language Models", "authors": [ "Yiqi Deng", "Xingwei He", "Jiahao Hu", "Siu-Ming Yiu" ], "posted": "2024-07-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.09003v1", "field": "finance", "role": "method", "bullets": [ "Stock trend prediction from individual financial news items for S&P 500, CSI-100, and Hong Kong stocks, in a few-shot setting meant to avoid costly labelled training data.", "An LLM, version not stated, classifies each news item separately with an added irrelevant label to strip noise, then item-level predictions are combined by majority vote.", "Accuracy is 66.59 percent for S&P 500, 62.17 for CSI-100, and 61.17 for Hong Kong, roughly 4 to 7 points over standard few-shot prompting and on par with supervised methods." ], "bullet_provenance": "ai", "validated": true, "validation_note": "accuracy against realized stock trends in three markets", "salience": 42, "edition": 13, "models": [], "n": 1648, "authors_detailed": [ { "name": "Yiqi Deng", "url": "https://openalex.org/A5102960005", "inst": "Sichuan University" }, { "name": "Xingwei He", "url": "https://openalex.org/A5111298239", "inst": "Queen Mary University of London" }, { "name": "Jiahao Hu", "url": "https://openalex.org/A5081006428", "inst": "Harbin Medical University" }, { "name": "Siu Ming Yiu", "url": "https://openalex.org/A5025130883", "inst": "University of Hong Kong" } ], "affiliations": [ "Sichuan University", "Queen Mary University of London", "Harbin Medical University", "University of Hong Kong" ] }, { "uid": "arxiv:2407.09281v2", "arxiv_id": "2407.09281v2", "title": "Predicting and Understanding Human Action Decisions: Insights from Large Language Models and Cognitive Instance-Based Learning", "authors": [ "Thuy Ngoc Nguyen", "Kasturi Jamale", "Cleotilde Gonzalez" ], "posted": "2024-07-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.09281v2", "field": "economics", "role": "agent", "bullets": [ "Two sequential decision making tasks with exploration exploitation tradeoffs and delayed feedback, using human play data; participant numbers are not stated in the abstract.", "LLMs, families not named, predict the next human action and are benchmarked against a cognitive instance based learning model of experiential choice.", "LLMs absorb feedback quickly and gain prediction accuracy, while the cognitive model better reproduces exploratory behavior and loss aversion in human decisions." ], "bullet_provenance": "ai", "validated": true, "validation_note": "prediction accuracy against observed human choices", "salience": 42, "edition": 13, "models": [], "n": 1736, "authors_detailed": [ { "name": "Thuy Ngoc Nguyen", "url": "https://openalex.org/A5053131708", "inst": "Vietnam National University, Hanoi" }, { "name": "Kasturi Jamale", "url": "https://openalex.org/A5104579751", "inst": "University of Dayton" }, { "name": "Cleotilde González", "url": "https://openalex.org/A5076876507", "inst": "Decision Sciences (United States)" } ], "affiliations": [ "Vietnam National University, Hanoi", "University of Dayton", "Decision Sciences (United States)" ] }, { "uid": "doi:10.2139/ssrn.4885867", "doi": "10.2139/ssrn.4885867", "title": "The Disruption of Generative AI in Real Asset Markets", "authors": [ "Chongyu Wang", "Jingfang Wang", "Tingyu Zhou" ], "posted": "2024-07-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4885867", "field": "finance", "role": "object", "bullets": [ "U.S. commercial real estate tenants and publicly traded asset owners, post-ChatGPT release period, measuring GenAI exposure effects on rents and equity returns.", "Constructed novel GenAI exposure measures for tenants and linked tenant exposure to owner-level equity portfolios and analyst forecasts.", "High-exposure tenants pay 4.4% lower rents; a long-short portfolio on exposure yields -11.8%, driven by a labor-substitution channel." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "validated": null, "n": 2946, "authors_detailed": [ { "name": "Chongyu Wang", "url": "https://openalex.org/A5101471089", "inst": "Florida State University" }, { "name": "Jingfang Wang", "url": "https://openalex.org/A5101555911", "inst": "Florida State University" }, { "name": "Tingyu Zhou", "url": "https://openalex.org/A5080068772", "inst": "Florida State University" } ], "affiliations": [ "Florida State University" ] }, { "uid": "doi:10.2139/ssrn.4884932", "doi": "10.2139/ssrn.4884932", "title": "Measuring Semantic Similarity in Japanese Key Audit Matters", "authors": [ "Nobushige Doi", "Yusuke Nobuta", "Takeshi MIZUNO" ], "posted": "2024-07-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4884932", "field": "accounting", "role": "instrument", "bullets": [ "Key audit matters from audit reports of publicly listed companies in Japan, assessed for boilerplate similarity across reports.", "NLP methods including word-frequency, word-match, and context-embedded vectors measured semantic similarity of KAMs, validated against manual annotations.", "Word match rate best captured boilerplate degree; context embedding vectors captured semantic similarity that surface-level metrics missed." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "manual annotation comparison", "salience": 36, "n": 3894, "authors_detailed": [ { "name": "Nobushige Doi", "url": "https://openalex.org/A5023596495", "inst": "Nihon University" }, { "name": "Yusuke Nobuta", "url": "https://openalex.org/A5093615103", "inst": "Nihon University" }, { "name": "Takeshi Mizuno", "url": "https://openalex.org/A5006788556", "inst": "Nihon University" } ], "affiliations": [ "Nihon University" ] }, { "uid": "doi:10.2139/ssrn.4856935", "doi": "10.2139/ssrn.4856935", "title": "The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot", "authors": [ "Fangchen Song", "Ashish Agarwal", "Wen Wen" ], "posted": "2024-07-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4856935", "field": "management", "role": "object", "bullets": [ "GitHub open-source projects with proprietary Copilot usage data combined with public project data, measuring developer contributions and coordination.", "Study measures GitHub Copilot's causal effects on code contributions, developer participation, and coordination time in collaborative open-source development.", "Copilot increases project-level code contributions by 5.9% and participation by 3.4% but raises coordination time by 8%; net effect on timely code merges remains positive." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "validated": null, "n": 3419, "authors_detailed": [ { "name": "Fangchen Song", "url": "https://openalex.org/A5100496644", "inst": "The University of Texas at Austin" }, { "name": "Ashish Agarwal", "url": "https://openalex.org/A5027820479", "inst": "The University of Texas at Austin" }, { "name": "Wen Wen", "url": "https://openalex.org/A5100319717", "inst": "The University of Texas at Austin" } ], "affiliations": [ "The University of Texas at Austin" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2407.18920v2", "arxiv_id": "2407.18920v2", "title": "Context-Masked Meta-Prompting for Privacy-Preserving LLM Adaptation in Finance", "authors": [ "Sayash Raaj Hiraou" ], "posted": "2024-07-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.18920v2", "field": "finance", "role": "method", "bullets": [ "Public NLP datasets standing in for financial tasks: SQuAD for extractive question answering, CNN DailyMail for news summarisation and SAMSum for client interaction summaries.", "GPT-3.5 Turbo optimizes hard prompts iteratively without seeing proprietary context; gains are measured by ROUGE-L against benchmark references, including a reported 103.87 percent improvement in question answering.", "The authors pitch context-masked prompt optimization as a low-cost route to LLM adoption under financial privacy constraints; no financial data is actually used." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "ROUGE-L on public QA and summarisation benchmarks", "salience": 27, "edition": 13, "n": 1534, "authors_detailed": [ { "name": "Sayash Raaj Hiraou", "url": "https://openalex.org/A5107630174", "inst": "" } ] }, { "uid": "arxiv:2407.06567v3", "arxiv_id": "2407.06567v3", "title": "FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making", "authors": [ "Yangyang Yu", "Zhiyuan Yao", "Haohang Li", "Zhiyang Deng", "Yupeng Cao", "Zhi Chen", "Jordan W. Suchow", "Rong Liu", "Zhenyu Cui", "Zhaozhuo Xu", "Denghui Zhang", "Koduvayur Subbalakshmi", "Guojun Xiong", "Yueru He", "Jimin Huang", "Dong Li", "Qianqian Xie" ], "posted": "2024-07-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.06567v3", "field": "finance", "role": "agent", "bullets": [ "Sequential investment decision tasks, including single stock trading and portfolio management, handled by a multi-agent system arranged as a manager-analyst hierarchy modeled on investment firm structure.", "LLM agents, family not stated in the abstract, coordinate through natural language, and an episodic self-critique risk component converts lessons into verbal reinforcement propagated to agents needing updates.", "The framework is reported to improve decision quality and cut unnecessary agent-to-agent communication while generalizing across financial tasks; no return or accuracy figures appear in the abstract." ], "bullet_provenance": "ai", "salience": 55, "edition": 13, "models": [], "validated": null, "n": 1647, "authors_detailed": [ { "name": "Yangyang Yu", "url": "https://openalex.org/A5113287535", "inst": "Guangdong Academy of Agricultural Sciences" }, { "name": "Zhiyuan Yao", "url": "https://openalex.org/A5104439966", "inst": "Affiliated Hospital of Guizhou Medical University" }, { "name": "Haohang Li", "url": "https://openalex.org/A5027371293", "inst": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Zhiyang Deng", "url": "https://openalex.org/A5069290428", "inst": "Hong Kong Baptist University" }, { "name": "Yupeng Cao", "url": "https://openalex.org/A5030238641", "inst": "Chinese Academy of Sciences" }, { "name": "Zhi Chen", "url": "https://openalex.org/A5115596320", "inst": "Fujian Medical University" }, { "name": "Jordan W. Suchow", "url": "https://openalex.org/A5069454833", "inst": "Stevens Institute of Technology" }, { "name": "Rong Liu", "url": "https://openalex.org/A5100664877", "inst": "Second Military Medical University" }, { "name": "Zhenyu Cui", "url": "https://openalex.org/A5102991132", "inst": "Yanbian University" }, { "name": "Xu, Zhaozhuo", "url": "", "inst": "" }, { "name": "Denghui Zhang", "url": "https://openalex.org/A5101638182", "inst": "Qilu University of Technology" }, { "name": "K. P. Subbalakshmi", "url": "https://openalex.org/A5033041089", "inst": "Stevens Institute of Technology" }, { "name": "Guojun Xiong", "url": "https://openalex.org/A5102620407", "inst": "Harvard University" }, { "name": "Yueru He", "url": "https://openalex.org/A5111133467", "inst": "Columbia University" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" }, { "name": "Li Dong", "url": "https://openalex.org/A5101751776", "inst": "Kunming University of Science and Technology" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" } ], "affiliations": [ "Guangdong Academy of Agricultural Sciences", "Nanjing University of Aeronautics and Astronautics", "Hong Kong Baptist University", "Chinese Academy of Sciences", "Fujian Medical University", "Stevens Institute of Technology", "Second Military Medical University" ] }, { "uid": "arxiv:2407.06893v1", "arxiv_id": "2407.06893v1", "title": "Measuring Sustainability Intention of ESG Fund Disclosure using Few-Shot Learning", "authors": [ "Mayank Singh", "Nazia Nafis", "Abhijeet Kumar", "Mridul Mishra" ], "posted": "2024-07-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.06893v1", "field": "finance", "role": "instrument", "bullets": [ "Prospectus disclosures from the US sustainable fund universe, with more than 1,000 ESG statements manually annotated and published for training and evaluation.", "Few-shot fine-tuned classifiers label investment language as specific, ambiguous, or generic, beating zero-shot Llama-13B and GPT-3.5 Turbo by about 30 points absolute in precision, recall, and F1.", "A language ratio metric scores and ranks funds on sustainability intention, giving regulators, investors, and advisors a screen for vague ESG claims." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "manually annotated ESG statements, F1 reported", "salience": 45, "edition": 13, "n": 1693, "authors_detailed": [ { "name": "Mayank Singh", "url": "https://openalex.org/A5100819933", "inst": "Galgotias University" }, { "name": "Nazia Nafis", "url": "https://openalex.org/A5103305933", "inst": "" }, { "name": "Abhijeet Kumar", "url": "https://openalex.org/A5101856702", "inst": "National Institute of Technology Warangal" }, { "name": "Mridul Mishra", "url": "https://openalex.org/A5045690063", "inst": "" } ], "affiliations": [ "Galgotias University", "National Institute of Technology Warangal" ] }, { "uid": "doi:10.2139/ssrn.4881094", "doi": "10.2139/ssrn.4881094", "title": "Macroeconomic Forecasting with Large Language Models", "authors": [ "Andrea Carriero", "Davide Pettenuzzo", "Shubhranshu Shekhar" ], "posted": "2024-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4881094", "field": "economics", "role": "instrument", "bullet_provenance": "none", "salience": 58, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 643, "authors_detailed": [ { "name": "Andrea Carriero", "url": "https://openalex.org/A5082187758", "inst": "Queen Mary University of London" }, { "name": "Davide Pettenuzzo", "url": "https://openalex.org/A5089152261", "inst": "Brandeis University" }, { "name": "Shubhranshu Shekhar", "url": "https://openalex.org/A5017630996", "inst": "Brandeis University" } ], "affiliations": [ "Queen Mary University of London", "Brandeis University" ] }, { "uid": "doi:10.2139/ssrn.4880335", "doi": "10.2139/ssrn.4880335", "title": "Implicit bias in LLMs: Bias in financial advice based on implied gender", "authors": [ "Shir Etgar", "Gal Oestreicher-Singer", "Inbal Yahav" ], "posted": "2024-07-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4880335", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 644, "authors_detailed": [ { "name": "Shir Etgar", "url": "https://openalex.org/A5100280922", "inst": "Tel Aviv University" }, { "name": "Gal Oestreicher-Singer", "url": "https://openalex.org/A5100280923", "inst": "Tel Aviv University" }, { "name": "Inbal Yahav", "url": "https://openalex.org/A5070441489", "inst": "Tel Aviv University" } ], "affiliations": [ "Tel Aviv University" ] }, { "uid": "arxiv:2407.06985v4", "arxiv_id": "2407.06985v4", "title": "PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods", "authors": [ "Yiying Wang", "Xiaojing Li", "Binzhu Wang", "Yueyang Zhou", "Yingru Lin", "Han Ji", "Hong Chen", "Jinshi Zhang", "Fei Yu", "Zewei Zhao", "Song Jin", "Renji Gong", "Wanqing Xu" ], "posted": "2024-07-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.06985v4", "field": "finance", "role": "method", "bullets": [ "Financial question-answering tasks in enterprise settings with data privacy and cost constraints, benchmarked against GPT-4 baselines.", "PEER four-agent framework (Plan, Execute, Express, Review) used GPT-4 with RAG, then tuned custom models as cost-effective replacements.", "Custom tuned agents achieved 95.0% of GPT-4 performance while reducing costs and preserving enterprise data privacy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "financial QA accuracy vs GPT-4", "salience": 45, "n": 3893, "authors_detailed": [ { "name": "Yiying Wang", "url": "https://openalex.org/A5100663457", "inst": "Huazhong Agricultural University" }, { "name": "Xiaojing Li", "url": "https://openalex.org/A5100383708", "inst": "Electric Power Research Institute" }, { "name": "B. Y. Wang", "url": "https://openalex.org/A5056274667", "inst": "" }, { "name": "Yueyang Zhou", "url": "https://openalex.org/A5110688399", "inst": "Beijing Institute of Technology" }, { "name": "Lin, Yingru", "url": "", "inst": "" }, { "name": "Han Ji", "url": "https://openalex.org/A5111256088", "inst": "" }, { "name": "Hong Chen", "url": "https://openalex.org/A5100420436", "inst": "University of Notre Dame" }, { "name": "Jinshi Zhang", "url": "https://openalex.org/A5020822478", "inst": "Nanchang University" }, { "name": "F. Richard Yu", "url": "https://openalex.org/A5100420016", "inst": "Chongqing University of Posts and Telecommunications" }, { "name": "Zewei Zhao", "url": "https://openalex.org/A5114190796", "inst": "" }, { "name": "Song Jin", "url": "https://openalex.org/A5048589056", "inst": "University of Chinese Academy of Sciences" }, { "name": "Renji Gong", "url": "https://openalex.org/A5111256087", "inst": "" }, { "name": "Wanqing Xu", "url": "https://openalex.org/A5111269548", "inst": "Harbin Medical University" } ], "affiliations": [ "University of Notre Dame", "Huazhong Agricultural University", "Electric Power Research Institute", "Beijing Institute of Technology", "Nanchang University", "Chongqing University of Posts and Telecommunications", "University of Chinese Academy of Sciences", "Harbin Medical University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4883856", "doi": "10.2139/ssrn.4883856", "title": "How Scary is the Risk of Automation? Evidence from a Large Scale Survey Experiment", "authors": [ "Maria Alejandra Cattaneo", "Christian Gschwendt", "Stefan C. Wolter" ], "posted": "2024-07-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4883856", "field": "economics", "role": "object", "bullets": [ "Discrete-choice experiment with nearly 6,000 participants eliciting preferences for occupations varying in automation risk, including AI-driven automation.", "Study quantifies willingness to pay for reduced automation risk without deploying a model; ChatGPT-era AI serves as the context for respondent risk perceptions.", "Respondents accept salary reduction of nearly 20% of median annual gross wage for 10-percentage-point lower automation risk; men, younger, and more educated pay less." ], "bullet_provenance": "ai", "salience": 58, "models": [], "validated": null, "n": 3418, "authors_detailed": [ { "name": "Maria Alejandra Cattaneo", "url": "https://openalex.org/A5091749994", "inst": "Swiss Group For Clinical Cancer Research" }, { "name": "Christian Gschwendt", "url": "https://openalex.org/A5040589919", "inst": "University of Bern" }, { "name": "Stefan C. Wolter", "url": "https://openalex.org/A5040159911", "inst": "University of Bern" } ], "affiliations": [ "University of Bern" ] }, { "uid": "arxiv:2407.18327v2", "arxiv_id": "2407.18327v2", "title": "The Structure of Financial Equity Research Reports -- Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4", "authors": [ "Adria Pop", "Jan Spörer" ], "posted": "2024-07-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.18327v2", "field": "finance", "role": "object", "bullets": [ "72 equity research reports dissected sentence by sentence, with 4,940 sentences mapped into 169 question archetypes derived from the reports themselves.", "Llama 3 70B and GPT-4 Turbo test whether text-extractable questions can be answered from public corporate reports; no accuracy or agreement statistics appear in the abstract.", "78.7 percent of report questions look automatable, split into 48.2 percent text extractable and 30.5 percent database extractable, leaving about a fifth needing human judgment." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": false, "salience": 55, "edition": 13, "n": 1539, "authors_detailed": [ { "name": "Adria Pop", "url": "https://openalex.org/A5108984009", "inst": "University of St.Gallen" }, { "name": "Jan Spörer", "url": "https://openalex.org/A5020815109", "inst": "University of St.Gallen" } ], "affiliations": [ "University of St.Gallen" ] }, { "uid": "arxiv:2407.03689v1", "arxiv_id": "2407.03689v1", "title": "Text2TimeSeries: Enhancing Financial Forecasting through Time Series Prediction Updates with Event-Driven Insights from Large Language Models", "authors": [ "Litton Jose Kurisinkel", "Pruthwik Mishra", "Yue Zhang" ], "posted": "2024-07-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.03689v1", "field": "finance", "role": "instrument", "bullets": [ "Financial market price series paired with text about relevant news events; the specific assets, sample size, and period are not stated.", "An unnamed large language model judges the direction of change implied by event text, and those judgments update numeric time series forecasts; no ground truth check is reported.", "The collaborative text and time series framework is reported as effective on financial market data; the abstract provides no accuracy figures." ], "bullet_provenance": "ai", "validated": false, "salience": 26, "edition": 13, "models": [], "n": 1788, "authors_detailed": [ { "name": "Litton J Kurisinkel", "url": "https://openalex.org/A5064537261", "inst": "Institute for Infocomm Research" }, { "name": "Pruthwik Mishra", "url": "https://openalex.org/A5019439792", "inst": "Sardar Vallabhbhai National Institute of Technology Surat" }, { "name": "Yue Zhang", "url": "https://openalex.org/A5045431933", "inst": "University of Utah" } ], "affiliations": [ "Institute for Infocomm Research", "Sardar Vallabhbhai National Institute of Technology Surat", "University of Utah" ] }, { "uid": "arxiv:2407.01953v1", "arxiv_id": "2407.01953v1", "title": "CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applications", "authors": [ "Yupeng Cao", "Zhiyuan Yao", "Zhi Chen", "Zhiyang Deng" ], "posted": "2024-07-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.01953v1", "field": "finance", "role": "method", "bullets": [ "Entry to the IJCAI 2024 FinLLM challenge, covering three tasks: financial text classification, financial text summarization and single stock trading.", "Llama3 8B and Mistral 7B are fine-tuned with parameter-efficient LoRA methods, fusing data across tasks; the abstract reports no validation metrics.", "The team argues that cross-task data fusion improves accuracy and decision making, but the abstract gives no scores or challenge rankings." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 28, "edition": 13, "n": 1533, "authors_detailed": [ { "name": "Yupeng Cao", "url": "https://openalex.org/A5113280403", "inst": "" }, { "name": "Zhiyuan Yao", "url": "https://openalex.org/A5006328558", "inst": "Nantong University" }, { "name": "Cheng Zhi", "url": "https://openalex.org/A5028349844", "inst": "University of Science and Technology of China" }, { "name": "Zhiyang Deng", "url": "https://openalex.org/A5069290428", "inst": "Hong Kong Baptist University" } ], "affiliations": [ "Nantong University", "University of Science and Technology of China", "Hong Kong Baptist University" ] }, { "uid": "arxiv:2407.02301v1", "arxiv_id": "2407.02301v1", "title": "CFinBench: A Comprehensive Chinese Financial Benchmark for Large Language Models", "authors": [ "Ying Nie", "Binwei Yan", "Tianyu Guo", "Hao Liu", "Haoyu Wang", "Wei He", "Binfan Zheng", "Weihao Wang", "Qiang Li", "Weijian Sun", "Yunhe Wang", "Dacheng Tao" ], "posted": "2024-07-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.02301v1", "field": "finance", "role": "method", "bullets": [ "Chinese-language benchmark of 99,100 exam-style questions in 43 second-level categories covering financial subjects, professional qualifications, practice tasks such as tax consulting, and financial law.", "Fifty representative LLMs of varying sizes answer single-choice, multiple-choice, and judgment items, and GPT4 together with several Chinese-oriented models tops the leaderboard.", "The best average accuracy is 60.16 percent, which the authors read as evidence the benchmark remains challenging even for leading models." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "accuracy on 99,100 labelled exam questions", "salience": 45, "edition": 13, "n": 1646, "authors_detailed": [ { "name": "Ying Nie", "url": "https://openalex.org/A5027283991", "inst": "Zhejiang Chinese Medical University" }, { "name": "Binwei Yan", "url": "https://openalex.org/A5104344595", "inst": "IIT@MIT" }, { "name": "Tianyu Guo", "url": "https://openalex.org/A5046716542", "inst": "Electric Power Research Institute" }, { "name": "Hao Liu", "url": "https://openalex.org/A5115595124", "inst": "Xinjiang Agricultural University" }, { "name": "Haoyu Wang", "url": "https://openalex.org/A5034166666", "inst": "Tsinghua–Berkeley Shenzhen Institute" }, { "name": "Wei He", "url": "https://openalex.org/A5044494935", "inst": "The University of Texas of the Permian Basin" }, { "name": "Binfan Zheng", "url": "https://openalex.org/A5104344596", "inst": "Huawei Technologies (China)" }, { "name": "Weihao Wang", "url": "https://openalex.org/A5034200265", "inst": "Beijing Institute of Technology" }, { "name": "Qiang Li", "url": "https://openalex.org/A5100429986", "inst": "Huazhong Agricultural University" }, { "name": "Weijian Sun", "url": "https://openalex.org/A5101790663", "inst": "Jiangnan University" }, { "name": "Yunhe Wang", "url": "https://openalex.org/A5102701981", "inst": "Liga Contra el Cancer" }, { "name": "Dacheng Tao", "url": "https://openalex.org/A5046877006", "inst": "Nanyang Technological University" } ], "affiliations": [ "Zhejiang Chinese Medical University", "IIT@MIT", "Electric Power Research Institute", "Xinjiang Agricultural University", "Tsinghua–Berkeley Shenzhen Institute", "The University of Texas of the Permian Basin", "Huawei Technologies (China)", "Beijing Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.4872189", "doi": "10.2139/ssrn.4872189", "title": "Does Generative AI Facilitate Investor Trading? Early Evidence from ChatGPT Outages", "authors": [ "Qiang Cheng", "Pengkai Lin", "Yue Zhao" ], "posted": "2024-07-01", "added": "2026-08-06", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4872189", "field": "finance", "role": "object", "bullets": [ "US listed firms observed around ChatGPT service outages, treated as a natural experiment on investor use of generative AI, with trading volume, price impact, and return variance measured around outage windows.", "The researchers run no model. OpenAI's ChatGPT is the object, and its outages proxy for interruptions to investor reliance on generative AI for professional trading tasks.", "Trading volume drops during outages, more so for firms with news released just before or during them and higher transient institutional ownership; price impact and return variance fall, and GenAI-assisted trading raises long-run price informativeness." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "edition": 15, "validated": null, "n": 2074, "authors_detailed": [ { "name": "Qiang Cheng", "url": "https://openalex.org/A5059839472", "inst": "Singapore Management University" }, { "name": "Pengkai Lin", "url": "https://openalex.org/A5068882021", "inst": "Singapore Management University" }, { "name": "Yue Zhao", "url": "https://openalex.org/A5100532821", "inst": "Singapore Management University" } ], "affiliations": [ "Singapore Management University" ] }, { "uid": "arxiv:2407.01212v1", "arxiv_id": "2407.01212v1", "title": "EconNLI: Evaluating Large Language Models on Economics Reasoning", "authors": [ "Yue Guo", "Yi Yang" ], "posted": "2024-07-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.01212v1", "field": "economics", "role": "method", "bullets": [ "EconNLI, a labeled dataset of premise and hypothesis pairs about economic events, built to test causal reasoning about the consequences of economic developments; size is not stated in the abstract.", "Language models, families not named in the abstract, classify whether a premise event causes a hypothesis event and generate plausible resulting events, scored against the labeled pairs.", "The models reason poorly about economics and produce wrong or hallucinated answers, a caution against relying on them for decision relevant economic analysis; data and code are released." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labeled EconNLI inference pairs", "salience": 44, "edition": 13, "models": [], "n": 1645, "authors_detailed": [ { "name": "Yue Guo", "url": "https://openalex.org/A5100019136", "inst": "" }, { "name": "Yi Yang", "url": "https://openalex.org/A5100019137", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.4862121", "doi": "10.2139/ssrn.4862121", "title": "Large Language Models and M&A: Can ChatGPT help forecast M&A activity?", "authors": [ "Dominik Degen", "Jens Kengelbach", "Daniel Kim", "Soenke Sievers", "Yiran Wang" ], "posted": "2024-07-01", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4862121", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 42, "edition": 13, "bullets": [], "validated": null, "n": 1786, "authors_detailed": [ { "name": "Dominik Degen", "url": "https://openalex.org/A5076103627", "inst": "Paderborn University" }, { "name": "Jens Kengelbach", "url": "https://openalex.org/A5037785717", "inst": "Boston Consulting Group (United States)" }, { "name": "Daniel Kim", "url": "https://openalex.org/A5100372601", "inst": "Boston Consulting Group (United States)" }, { "name": "Soenke Sievers", "url": "https://openalex.org/A5073420895", "inst": "Transparency International" }, { "name": "Yiran Wang", "url": "https://openalex.org/A5031840015", "inst": "DB Engineering & Consulting (Germany)" } ], "affiliations": [ "Paderborn University", "Boston Consulting Group (United States)", "Transparency International", "DB Engineering & Consulting (Germany)" ] }, { "uid": "arxiv:2407.00904v1", "arxiv_id": "2407.00904v1", "title": "Background-aware Multi-source Fusion Financial Trend Forecasting Mechanism", "authors": [ "Fengting Mo", "Shanshan Yan", "Yinhao Xiao" ], "posted": "2024-07-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.00904v1", "field": "finance", "role": "instrument", "bullets": [ "Stock price series combined with policy documents and stock review texts; the market, sample size, and period are not stated in the abstract.", "An unnamed large language model distills key information from the texts and MacBERT turns it into feature vectors fused with price data; no validation of the extraction is reported.", "The fused system predicts stock movements more accurately than six recurrent baselines including LSTM, GRU, and SwinLSTM variants; improvement sizes are not stated." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 25, "edition": 13, "n": 1787, "authors_detailed": [ { "name": "Fengting Mo", "url": "https://openalex.org/A5113278581", "inst": "Guangdong University Of Finances and Economics" }, { "name": "Shanshan Yan", "url": "https://openalex.org/A5027953987", "inst": "Guangdong University Of Finances and Economics" }, { "name": "Yin Xiao", "url": "https://openalex.org/A5051762810", "inst": "Griffith University" } ], "affiliations": [ "Guangdong University Of Finances and Economics", "Griffith University" ] }, { "uid": "doi:10.2139/ssrn.4841318", "doi": "10.2139/ssrn.4841318", "title": "Application of Generative Ai (Chatgpt as Example) in Risk Management", "authors": [ "Soumaya BIDAH", "Khadija Akdim", "Mehdi Zahid" ], "posted": "2024-07-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4841318", "field": "finance", "role": "instrument", "bullets": [ "Two banking use cases examined: credit risk measurement and European call option pricing assessed using ChatGPT capabilities.", "ChatGPT performed quantitative risk calculations and option pricing; results benchmarked against a prior published study.", "ChatGPT showed limitations on quantitative financial questions, highlighting the need for validation of AI-generated numerical outputs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison with prior risk measurement benchmark results", "salience": 35, "n": 2945, "authors_detailed": [ { "name": "Soumaya BIDAH", "url": "https://openalex.org/A5099774251", "inst": "Cadi Ayyad University" }, { "name": "Khadija Akdim", "url": "https://openalex.org/A5084200812", "inst": "Cadi Ayyad University" }, { "name": "Mehdi Zahid", "url": "https://openalex.org/A5041280197", "inst": "Cadi Ayyad University" } ], "affiliations": [ "Cadi Ayyad University" ] }, { "uid": "arxiv:2407.12032v1", "arxiv_id": "2407.12032v1", "title": "Large Language Models for Behavioral Economics: Internal Validity and Elicitation of Mental Models", "authors": [ "Brian Jabarian" ], "posted": "2024-06-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.12032v1", "field": "economics", "role": "method", "bullets": [ "A methodological discussion with one case study on bringing generative AI into behavioral and experimental economics; no dataset or subject pool is described in the abstract.", "LLMs, not named in the abstract, are proposed to enforce exclusion restrictions and elicit mental models, replacing human intervention in incentive mechanisms; no validation exercise is reported.", "Argues AI-assisted designs can strengthen internal validity, participant engagement, and mental model measurement; the case study illustrates the approach rather than quantifying gains." ], "bullet_provenance": "ai", "salience": 44, "edition": 13, "models": [], "validated": null, "n": 1573, "authors_detailed": [ { "name": "Jabarian, Brian", "url": "", "inst": "" } ] }, { "uid": "arxiv:2407.00365v1", "arxiv_id": "2407.00365v1", "title": "Financial Knowledge Large Language Model", "authors": [ "Cehao Yang", "Chengjin Xu", "Yiyan Qi" ], "posted": "2024-06-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.00365v1", "field": "finance", "role": "method", "bullets": [ "IDEA-FinBench, an evaluation benchmark built from questions in two financial professional exams, alongside a knowledge enhancement framework and a financial question answering system.", "General LLMs are benchmarked on exam questions and adapted via retrieval-based few-shot knowledge injection and instruction fine-tuning. Specific model families are not stated.", "The paper's contribution is the benchmark, the IDEA-FinKER adaptation framework, and the IDEA-FinQA system. Comparative accuracy figures are not stated in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "financial professional exam questions", "salience": 30, "edition": 13, "models": [], "n": 1692, "authors_detailed": [ { "name": "Cehao Yang", "url": "https://openalex.org/A5104341957", "inst": "" }, { "name": "Chengjin Xu", "url": "https://openalex.org/A5022319543", "inst": "Hefei University of Technology" }, { "name": "Yiyan Qi", "url": "https://openalex.org/A5099893955", "inst": "Digital Science (United States)" } ], "affiliations": [ "Hefei University of Technology", "Digital Science (United States)" ] }, { "uid": "arxiv:2406.19966v1", "arxiv_id": "2406.19966v1", "title": "Simulating Financial Market via Large Language Model based Agents", "authors": [ "Shen Gao", "Yuntao Wen", "Minghang Zhu", "Jianing Wei", "Yuhan Cheng", "Qunzi Zhang", "Shuo Shang" ], "posted": "2024-06-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.19966v1", "field": "finance", "role": "agent", "bullets": [ "Simulated stock market with a real order-matching system, testing agent behavior in controlled scenarios aligned with economics research directions.", "LLM-based agents act as stock traders with individual profiles, market observation modules, and tool-learning-based action modules for trading decisions.", "Simulated market reactions are consistent with real stock market behavior in two controlled scenarios; agent-derived conclusions align with preliminary economics research findings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3416, "authors_detailed": [ { "name": "Shen Gao", "url": "https://openalex.org/A5106743095", "inst": "Hebei University" }, { "name": "Yuntao Wen", "url": "https://openalex.org/A5111283466", "inst": "University of Electronic Science and Technology of China" }, { "name": "Minghang Zhu", "url": "https://openalex.org/A5102698984", "inst": "Shandong University of Science and Technology" }, { "name": "Jianing Wei", "url": "https://openalex.org/A5082493156", "inst": "The University of Melbourne" }, { "name": "Yuhan Cheng", "url": "https://openalex.org/A5101293504", "inst": "China University of Geosciences" }, { "name": "Qunzi Zhang", "url": "https://openalex.org/A5071928763", "inst": "Shandong University" }, { "name": "Shuo Shang", "url": "https://openalex.org/A5102754146", "inst": "Xidian University" } ], "affiliations": [ "Hebei University", "University of Electronic Science and Technology of China", "Shandong University of Science and Technology", "The University of Melbourne", "China University of Geosciences", "Shandong University", "Xidian University" ] }, { "uid": "doi:10.2139/ssrn.4874756", "doi": "10.2139/ssrn.4874756", "title": "Do AI Chatbots Provide an Outside View?", "authors": [ "Stephen Shu", "Sreyoshi Das", "Daniela Hernandez", "Omar Fayaz", "Junhui Lei", "Prem Kumar Mullai Manavalan", "Xinguo Peng", "Nicholas Sakaguchi", "Sneha Suresh" ], "posted": "2024-06-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4874756", "field": "management", "role": "agent", "bullets": [ "Experimental tasks testing AI chatbots against human decision-makers on classic cognitive biases including conjunction fallacy, overconfidence, and availability bias.", "AI chatbots were tested on multiple bias-related tasks to assess whether they provide an outside view relative to human inside-view tendencies.", "AI falls prey to conjunction fallacy, overconfidence, and confirmation bias but complements humans on base-rate usage, availability insensitivity, and cognitive reflection." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 52, "n": 3417, "authors_detailed": [ { "name": "Stephen Shu", "url": "https://openalex.org/A5068859672", "inst": "Cornell University" }, { "name": "Sreyoshi Das", "url": "https://openalex.org/A5108325090", "inst": "Cornell University" }, { "name": "D. Paredes Hernandez", "url": "https://openalex.org/A5060597880", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Omar Fayaz", "url": "https://openalex.org/A5099638132", "inst": "Independent" }, { "name": "Junhui Lei", "url": "https://openalex.org/A5111242361", "inst": "Independent" }, { "name": "Prem Kumar Mullai Manavalan", "url": "https://openalex.org/A5099638133", "inst": "Independent" }, { "name": "Xinguo Peng", "url": "https://openalex.org/A5077616617", "inst": "Independent" }, { "name": "N. Sakaguchi", "url": "https://openalex.org/A5110212510", "inst": "Independent" }, { "name": "Sneha Suresh", "url": "https://openalex.org/A5110915872", "inst": "Independent" } ], "affiliations": [ "Cornell University", "Independent - affiliation not provided to SSRN", "Independent" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2407.09546v1", "arxiv_id": "2407.09546v1", "title": "A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading", "authors": [ "Yuan Li", "Bingqiao Luo", "Qian Wang", "Nuo Chen", "Xu Liu", "Bingsheng He" ], "posted": "2024-06-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.09546v1", "field": "finance", "role": "method", "bullets": [ "Daily cryptocurrency trading across multiple coins and market conditions, combining transparent on chain data with off chain signals such as news; period is not stated in the abstract.", "An LLM based agent, model not named in the abstract, makes zero shot trading decisions and refines them through reflection on the outcomes of its prior trades.", "The agent earns higher returns than traditional trading strategies and time series baselines, and the setup is offered as a benchmark; code and data are public." ], "bullet_provenance": "ai", "salience": 45, "edition": 13, "models": [], "validated": null, "n": 1643, "authors_detailed": [ { "name": "Yuanli Cai", "url": "https://openalex.org/A5000364056", "inst": "Xi'an Jiaotong University" }, { "name": "Bingqiao Luo", "url": "https://openalex.org/A5023311745", "inst": "National University of Singapore" }, { "name": "Qian Wang", "url": "https://openalex.org/A5100391116", "inst": "Mahasarakham University" }, { "name": "Nuo Chen", "url": "https://openalex.org/A5100740164", "inst": "Ningbo University" }, { "name": "Xu Liu", "url": "https://openalex.org/A5112233853", "inst": "Huazhong University of Science and Technology" }, { "name": "Bingsheng He", "url": "https://openalex.org/A5039946576", "inst": "National University of Singapore" } ], "affiliations": [ "Xi'an Jiaotong University", "National University of Singapore", "Mahasarakham University", "Ningbo University", "Huazhong University of Science and Technology" ] }, { "uid": "arxiv:2406.18856v1", "arxiv_id": "2406.18856v1", "title": "FFN: a Fine-grained Chinese-English Financial Domain Parallel Corpus", "authors": [ "Yuxin Fu", "Shijing Si", "Leyi Mai", "Xi-ang Li" ], "posted": "2024-06-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.18856v1", "field": "finance", "role": "method", "bullets": [ "A Chinese English parallel corpus of 1,013 financial news texts and 809 titles from CNN, FOX, and China Daily, spanning 2014 to 2023 and manually corrected.", "ChatGPT and ERNIE-bot translate the corpus and are scored with BLEU, TER, and chrF against the corrected references, alongside an OpenNMT model trained on the data.", "Both commercial models show consistent weaknesses in financial translation, and the authors call for domain specific optimization; score gaps are not quantified in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "BLEU, TER, chrF against corrected references", "salience": 28, "edition": 13, "n": 1644, "authors_detailed": [ { "name": "Yuxin Fu", "url": "https://openalex.org/A5111184190", "inst": "Shanghai International Studies University" }, { "name": "Shijing Si", "url": "https://openalex.org/A5032913967", "inst": "Shanghai International Studies University" }, { "name": "Leyi Mai", "url": "https://openalex.org/A5099670469", "inst": "Shanghai International Studies University" }, { "name": "Xiang Li", "url": "https://openalex.org/A5100331066", "inst": "University of Wisconsin–Madison" } ], "affiliations": [ "Shanghai International Studies University", "University of Wisconsin–Madison" ] }, { "uid": "doi:10.2139/ssrn.4874140", "doi": "10.2139/ssrn.4874140", "title": "Large Language Model Assisted Experiment Design with Generative Human-Behavior Agents", "authors": [ "Haoyu Liu", "Yifu Tang", "Zizhao Zhang", "Zeyu Zheng", "Tingyu Zhu" ], "posted": "2024-06-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4874140", "field": "management", "role": "agent", "bullets": [ "Framework for using LLM-empowered generative agents in experiment design for economics, sociology, and business operations research.", "ChatGPT-based generative agents simulated believable human behavior in experimental settings where real human participation is prohibitive or unethical.", "Framework demonstrates feasibility of LLM agents as substitutes for human subjects in social science experimental design across multiple domains." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 2481, "authors_detailed": [ { "name": "Haoyu Liu", "url": "https://openalex.org/A5067320802", "inst": "University of California, Berkeley" }, { "name": "Yifu Tang", "url": "https://openalex.org/A5107307417", "inst": "University of California, Berkeley" }, { "name": "Zizhao Zhang", "url": "https://openalex.org/A5101566830", "inst": "University of California, Berkeley" }, { "name": "Zeyu Zheng", "url": "https://openalex.org/A5002874750", "inst": "University of California, Berkeley" }, { "name": "Tingyu Zhu", "url": "https://openalex.org/A5100445961", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of California, Berkeley" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4874061", "doi": "10.2139/ssrn.4874061", "title": "Employer and Employee Responses to Generative AI: Early Evidence", "authors": [ "Philip G. Berger", "Wei Cai", "Lin Qiu", "Cindy Xinyi Shen" ], "posted": "2024-06-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4874061", "field": "management", "role": "object", "bullets": [ "U.S. firms examined through job postings and employee reviews in the post-ChatGPT period, using management accounting framework of organizational architecture.", "Study measures GenAI exposure effects on hiring, skill demands, and employee sentiment across firms with varying control systems and technological capacity.", "Firms with greater GenAI exposure reduce hiring while demanding GenAI skills; employees show declining long-term outlook, especially under coercive control systems." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 3415, "authors_detailed": [ { "name": "Philip G. Berger", "url": "https://openalex.org/A5108735524", "inst": "University of Chicago" }, { "name": "Wei Cai", "url": "https://openalex.org/A5101387379", "inst": "Columbia Business School" }, { "name": "Lin Qiu", "url": "https://openalex.org/A5017478089", "inst": "Purdue University West Lafayette" }, { "name": "Cindy Xinyi Shen", "url": "https://openalex.org/A5099584643", "inst": "Northwestern University" } ], "affiliations": [ "University of Chicago", "Columbia Business School", "Northwestern University", "Purdue University West Lafayette" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2406.18440v2", "arxiv_id": "2406.18440v2", "title": "New intelligent empowerment for digital transformation", "authors": [ "Peng Yifeng", "Gao Chen" ], "posted": "2024-06-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.18440v2", "field": "management", "role": "instrument", "bullets": [ "Annual reports from 4,407 companies listed on the New York Stock Exchange and Nasdaq, covering 2005 through 2022.", "An LLM-based evaluation method, model not named in the abstract, converts report text into firm-level digital transformation indicators; no check against human coding is reported.", "Digital transformation raises financial performance, working through operational efficiency and lower costs; blockchain shows the weakest effect among the technologies examined; magnitudes are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 13, "models": [], "n": 1572, "authors_detailed": [ { "name": "Peng Yifeng", "url": "https://openalex.org/A5099623239", "inst": "" }, { "name": "Chen Gao", "url": "https://openalex.org/A5101436165", "inst": "Chongqing University of Posts and Telecommunications" } ], "affiliations": [ "Chongqing University of Posts and Telecommunications" ] }, { "uid": "arxiv:2406.17972v4", "arxiv_id": "2406.17972v4", "title": "LABOR-LLM: Language-Based Occupational Representations with Large Language Models", "authors": [ "Susan Athey", "Herman Brunborg", "Tianyu Du", "Ayush Kanodia", "Keyon Vafa" ], "posted": "2024-06-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.17972v4", "field": "economics", "role": "method", "bullets": [ "Career histories from a representative worker survey are converted into resume-like text files; the outcome to predict is each worker's next occupation given the sequence so far.", "A pretrained foundation LLM, family not named in the abstract, is fine-tuned on these texts via next-token prediction and benchmarked against prior occupation transition models on held-out careers.", "The fine-tuned LLM beats all earlier models on transition prediction; smaller models tuned with added career data overtake larger ones, and replacing occupation titles with codes lowers accuracy." ], "bullet_provenance": "ai", "validated": true, "validation_note": "held-out survey career transitions, compared with prior models", "salience": 70, "edition": 13, "models": [], "n": 1560, "authors_detailed": [ { "name": "Susan Athey", "url": "https://openalex.org/A5001211242", "inst": "Stanford Medicine" }, { "name": "Herman Brunborg", "url": "https://openalex.org/A5099623075", "inst": "" }, { "name": "Tianyu Du", "url": "https://openalex.org/A5102448448", "inst": "Ningbo University" }, { "name": "Ayush Kanodia", "url": "https://openalex.org/A5090271895", "inst": "Stanford University" }, { "name": "Keyon Vafa", "url": "https://openalex.org/A5085921946", "inst": "Cornell University" } ], "affiliations": [ "Stanford University", "Cornell University", "Stanford Medicine", "Ningbo University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4867815", "doi": "10.2139/ssrn.4867815", "title": "The Proficiency Paradox: Generative AI, Governance Capacity, and Knowledge-Sharing Systems", "authors": [ "Ramesh Shankar", "Jaeung Sim" ], "posted": "2024-06-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4867815", "field": "management", "role": "object", "bullets": [ "Stack Overflow data around GPT-4 release and subsequent moderator strike; examines knowledge production and expert validation dynamics.", "Exploits temporal variation in AI proficiency and governance capacity to measure effects on knowledge-sharing volume and quality.", "AI proficiency reduced public knowledge-seeking but increased plausible low-value contributions; expert validation became the binding bottleneck." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 2944, "authors_detailed": [ { "name": "Ramesh Shankar", "url": "https://openalex.org/A5104334663", "inst": "University of Connecticut" }, { "name": "Jaeung Sim", "url": "https://openalex.org/A5033583124", "inst": "Fairfield University" } ], "affiliations": [ "University of Connecticut", "Fairfield University" ] }, { "uid": "arxiv:2406.17055v4", "arxiv_id": "2406.17055v4", "title": "Large Language Models Assume People are More Rational than We Really are", "authors": [ "Ryan Liu", "Jiayi Geng", "Joshua C. Peterson", "Ilia Sucholutsky", "Thomas L. Griffiths" ], "posted": "2024-06-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.17055v4", "field": "economics", "role": "agent", "bullets": [ "A large dataset of human risky choices, used both for simulating decisions and for predicting them, plus a second psychological dataset on inferences people draw from others' choices.", "GPT-4o and 4 Turbo, Llama 3 8B and 70B and Claude 3 Opus simulate and predict human decisions, with outputs compared against observed human behavior.", "The models track expected value theory more than actual behavior, assuming people are more rational than they are, while matching how humans interpret other people's choices." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama" ], "open_weights": true, "salience": 62, "edition": 13, "validated": null, "n": 1538, "authors_detailed": [ { "name": "Ryan Liu", "url": "https://openalex.org/A5008134086", "inst": "Princeton University" }, { "name": "Jiayi Geng", "url": "https://openalex.org/A5086841757", "inst": "" }, { "name": "Joshua C. Peterson", "url": "https://openalex.org/A5047380888", "inst": "Boston University" }, { "name": "Ilia Sucholutsky", "url": "https://openalex.org/A5053200092", "inst": "Princeton University" }, { "name": "Thomas L. Griffiths", "url": "https://openalex.org/A5077079119", "inst": "Princeton University" } ], "affiliations": [ "Princeton University", "Boston University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4842962", "doi": "10.2139/ssrn.4842962", "title": "Exploring the Boundaries of Financial Statement Fraud Detection with Large Language Models", "authors": [ "Efstathios Kirkos", "Georgia Boskou", "Evrikleia Chatzipetrou", "Eleftherios Tiakas", "Charalampos Spathis" ], "posted": "2024-06-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4842962", "field": "accounting", "role": "instrument", "bullets": [ "CEO letters to shareholders and risk exposure sections from annual reports of U.S. public companies with known financial statement fraud cases.", "ChatGPT-4 classified financial statements as fraudulent or legitimate using prompt engineering on textual data, with iterative human feedback refining model classifications.", "Achieved 67% across sensitivity, specificity, and F-measure; approach is accessible to auditors without machine learning expertise and scalable with future model improvements." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "known fraud cases (sensitivity, specificity, F-measure at 67%)", "salience": 55, "n": 2380, "authors_detailed": [ { "name": "Efstathios Kirkos", "url": "https://openalex.org/A5024984123", "inst": "International Hellenic University" }, { "name": "Georgia Boskou", "url": "https://openalex.org/A5091687120", "inst": "International Hellenic University" }, { "name": "Evrikleia Chatzipetrou", "url": "https://openalex.org/A5062756803", "inst": "International Hellenic University" }, { "name": "Eleftherios Tiakas", "url": "https://openalex.org/A5021558322", "inst": "International Hellenic University" }, { "name": "Charalampos Spathis", "url": "https://openalex.org/A5052054537", "inst": "Aristotle University of Thessaloniki" } ], "affiliations": [ "International Hellenic University", "Aristotle University of Thessaloniki" ] }, { "uid": "doi:10.2139/ssrn.4874233", "doi": "10.2139/ssrn.4874233", "title": "Emotions and Subjective Crash Beliefs", "authors": [ "William N. Goetzmann", "Dasol Kim", "Robert J. Shiller" ], "posted": "2024-06-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4874233", "field": "finance", "role": "instrument", "bullets": [ "Shiller Investor Confidence Survey respondents over two decades, measuring subjective crash probabilities against historical frequencies in U.S. stock markets.", "An LLM estimated emotional content of respondent narratives; subjective crash component was decomposed from fundamental factors and linked to respondent location and exogenous shocks.", "Subjective crash probability is strongly associated with high negative affect, consistent with the risk-as-feelings hypothesis for rare-disaster belief formation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "n": 3414, "authors_detailed": [ { "name": "William N. Goetzmann", "url": "https://openalex.org/A5109171080", "inst": "National Bureau of Economic Research" }, { "name": "Dasol Kim", "url": "https://openalex.org/A5100560847", "inst": "United States Department of the Treasury" }, { "name": "Robert J. Shiller", "url": "https://openalex.org/A5051645674", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "National Bureau of Economic Research", "United States Department of the Treasury" ] }, { "uid": "arxiv:2406.14039v1", "arxiv_id": "2406.14039v1", "title": "CryptoGPT: a 7B model rivaling GPT-4 in the task of analyzing and classifying real-time financial news", "authors": [ "Ying Zhang", "Matthieu Petit Guillaume", "Aurélien Krauth", "Manel Labidi" ], "posted": "2024-06-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.14039v1", "field": "finance", "role": "method", "bullets": [ "Real-time financial news from the cryptocurrency market, processed inside an industrial pipeline; the abstract states neither corpus size nor coverage period.", "Mistral 7B and Llama 7B are refined with QLoRA on semi-automatically annotated news, then compared with GPT-3.5 and GPT-4 on classification and analysis quality.", "The authors report their 7B model rivals GPT-4 on this task while keeping data in house and deployment cheap; no accuracy figures appear in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "validated": false, "salience": 32, "edition": 13, "n": 1559, "authors_detailed": [ { "name": "Ying Zhang", "url": "https://openalex.org/A5100386228", "inst": "BH" }, { "name": "Matthieu Petit Guillaume", "url": "https://openalex.org/A5031600155", "inst": "BH" }, { "name": "Aurélien Krauth", "url": "https://openalex.org/A5056565564", "inst": "ON" }, { "name": "Manel Labidi", "url": "https://openalex.org/A5062403171", "inst": "LEVIATAN (France)" } ], "affiliations": [ "BH", "ON", "LEVIATAN (France)" ] }, { "uid": "arxiv:2406.14162v4", "arxiv_id": "2406.14162v4", "title": "DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation", "authors": [ "Jingwei Ni", "Tobias Schimanski", "Meihong Lin", "Mrinmaya Sachan", "Elliott Ash", "Markus Leippold" ], "posted": "2024-06-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.14162v4", "field": "other", "role": "method", "bullets": [ "Domain-specific retrieval augmented generation development, where query-document relevance labels are needed but human or GPT-4 annotation is costly and covers pairs selectively.", "Open-source 8B LLMs are fine-tuned to assign calibrated graded relevance labels under nuanced relevance definitions, evaluated on unseen query-document pairs against annotation benchmarks.", "The fine-tuned small models reach GPT-4-level annotating and ranking performance and help real-world RAG development, with code, generations, and human annotations released." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "human and GPT-4 relevance annotations", "salience": 40, "edition": 13, "n": 1691, "authors_detailed": [ { "name": "Jingwei Ni", "url": "https://openalex.org/A5024604043", "inst": "ETH Zurich" }, { "name": "Tobias Schimanski", "url": "https://openalex.org/A5066649415", "inst": "University of Zurich" }, { "name": "Meihong Lin", "url": "https://openalex.org/A5102677089", "inst": "University of Electronic Science and Technology of China" }, { "name": "Mrinmaya Sachan", "url": "https://openalex.org/A5002316432", "inst": "ETH Zurich" }, { "name": "Elliott Ash", "url": "https://openalex.org/A5020377010", "inst": "ETH Zurich" }, { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" } ], "affiliations": [ "ETH Zurich", "University of Zurich", "University of Electronic Science and Technology of China" ] }, { "uid": "arxiv:2406.15508v1", "arxiv_id": "2406.15508v1", "title": "What Teaches Robots to Walk, Teaches Them to Trade too -- Regime Adaptive Execution using Informed Data and LLMs", "authors": [ "Raeid Saqur" ], "posted": "2024-06-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.15508v1", "field": "finance", "role": "method", "bullets": [ "Stock movement prediction from streaming news under market regime shifts, evaluated on FLARE benchmark tasks and the newer NIFTY stock movement task.", "A pretrained LLM, base family not stated, is adapted with reinforcement learning from market feedback in a dual phase teacher student setup and compared against GPT-4.", "Beats the best existing LLMs on FLARE stock movement tasks by more than 15 percent accuracy and outperforms GPT-4 on the NIFTY task." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "FLARE and NIFTY stock movement benchmarks, accuracy reported", "salience": 46, "edition": 13, "n": 1734, "authors_detailed": [ { "name": "Raeid Saqur", "url": "https://openalex.org/A5099497385", "inst": "" } ] }, { "uid": "arxiv:2406.14373v2", "arxiv_id": "2406.14373v2", "title": "Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory", "authors": [ "Gordon Dai", "Weijia Zhang", "Jinhan Li", "Siqi Yang", "Chidera Onochie lbe", "Srihas Rao", "Arthur Caetano", "Misha Sra" ], "posted": "2024-06-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.14373v2", "field": "economics", "role": "agent", "bullets": [ "Sandbox survival environment where LLM agents endowed with psychological drives form and dissolve social relationships over time; run lengths and agent counts are not stated.", "LLM agents, model family not named, decide whether to fight, cooperate, or surrender rights; trajectories are read against Hobbes's social contract theory.", "Societies move from unrestrained conflict to an absolute sovereign and a peaceful commonwealth, matching the Hobbesian account of order emerging from anarchy." ], "bullet_provenance": "ai", "salience": 44, "edition": 13, "models": [], "validated": null, "n": 1735 }, { "uid": "arxiv:2406.14394v1", "arxiv_id": "2406.14394v1", "title": "SEC-QA: A Systematic Evaluation Corpus for Financial QA", "authors": [ "Viet Dac Lai", "Michael Krumdick", "Charles Lovering", "Varshini Reddy", "Craig Schmidt", "Chris Tanner" ], "posted": "2024-06-20", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.14394v1", "field": "finance", "role": "method", "bullets": [ "Continuous dataset generation framework producing multi-document QA pairs from SEC filings not yet ingested by LLMs.", "Program-of-thought QA system tested against standard RAG methods on complex multi-document financial questions requiring quantitative reasoning.", "Standard RAG methods systematically failed on multi-document questions; program-of-thought approach improved retrieval and quantitative reasoning accuracy." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "QA accuracy on SEC-QA multi-document benchmark", "salience": 42, "n": 3192, "authors_detailed": [ { "name": "Viet Dac Lai", "url": "https://openalex.org/A5070047759", "inst": "Adobe Systems (United States)" }, { "name": "Michael Krumdick", "url": "https://openalex.org/A5073663407", "inst": "AVEO Oncology (United States)" }, { "name": "Charles Lovering", "url": "https://openalex.org/A5018940839", "inst": "AVEO Oncology (United States)" }, { "name": "Varshini Reddy", "url": "https://openalex.org/A5114127969", "inst": "Institute of Management Technology" }, { "name": "Craig W. Schmidt", "url": "https://openalex.org/A5067117608", "inst": "Ben-Gurion University of the Negev" }, { "name": "Chris C. Tanner", "url": "https://openalex.org/A5090162434", "inst": "AVEO Oncology (United States)" } ], "affiliations": [ "Adobe Systems (United States)", "AVEO Oncology (United States)", "Institute of Management Technology", "Ben-Gurion University of the Negev" ] }, { "uid": "arxiv:2406.13626v1", "arxiv_id": "2406.13626v1", "title": "Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines", "authors": [ "Kangtong Mo", "Wenyan Liu", "Xuanzhen Xu", "Chang Yu", "Yuelin Zou", "Fangqing Xia" ], "posted": "2024-06-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.13626v1", "field": "finance", "role": "method", "bullets": [ "Sentiment classification of financial news headlines from the FinancialPhraseBank dataset, framed as reading investor sentiment from a retail perspective.", "DistilBERT base uncased, Llama and Gemma 7B are fine-tuned on the labeled headlines and scored on precision, recall and F1 against the dataset labels.", "Fine-tuned Gemma 7B outperforms the other models on all three metrics; the abstract gives no numbers and no market-based evaluation." ], "bullet_provenance": "ai", "models": [ "legacy", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinancialPhraseBank labels, precision, recall and F1", "salience": 32, "edition": 13, "n": 1557, "authors_detailed": [ { "name": "Kangtong Mo", "url": "https://openalex.org/A5099381118", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Wenyan Liu", "url": "https://openalex.org/A5101487992", "inst": "North University of China" }, { "name": "Xuanzhen Xu", "url": "https://openalex.org/A5099381119", "inst": "Snap (United States)" }, { "name": "Chang Yu", "url": "https://openalex.org/A5109681163", "inst": "Renmin University of China" }, { "name": "Yuelin Zou", "url": "https://openalex.org/A5111291970", "inst": "Xinjiang Entry-Exit Inspection and Quarantine Bureau" }, { "name": "Fangqing Xia", "url": "https://openalex.org/A5099381120", "inst": "Texas A&M University" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "North University of China", "Snap (United States)", "Renmin University of China", "Xinjiang Entry-Exit Inspection and Quarantine Bureau", "Texas A&M University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2406.13605v2", "arxiv_id": "2406.13605v2", "title": "Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?", "authors": [ "Nicoló Fontana", "Francesco Pierri", "Luca Maria Aiello" ], "posted": "2024-06-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.13605v2", "field": "economics", "role": "agent", "bullets": [ "Iterated prisoner's dilemma games of 100 rounds against random adversaries with varying hostility, with LLM decisions read through behavioral economics dimensions.", "Llama2, Llama3 and GPT-3.5 play after comprehension checks of rules and history parsing; behavior is compared with typical human play rather than validated against ground truth.", "All models rarely defect first; Llama2 and GPT-3.5 are more cooperative and forgiving than humans while Llama3 plays exploitatively unless opponents always cooperate." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "salience": 55, "edition": 13, "validated": null, "n": 1558, "authors_detailed": [ { "name": "Nicolo' Fontana", "url": "https://openalex.org/A5099381101", "inst": "Politecnico di Milano" }, { "name": "Francesco Pierri", "url": "https://openalex.org/A5099381102", "inst": "Politecnico di Milano" }, { "name": "Luca Maria Aiello", "url": "https://openalex.org/A5034406723", "inst": "Pioneer (United States)" } ], "affiliations": [ "Politecnico di Milano", "Pioneer (United States)" ] }, { "uid": "doi:10.2139/ssrn.4863178", "doi": "10.2139/ssrn.4863178", "title": "Digital marketing productivity: assessing what SMEs do and demonstrating how they can improve efficiency", "authors": [ "Alan Shaw" ], "posted": "2024-06-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4863178", "field": "management", "role": "object", "bullets": [ "Six SMEs in Yorkshire and Humber region examined via mixed methods including participatory action research on digital marketing processes.", "ChatGPT and digital tools used for environmental mapping, PESTLE analysis, and strategic marketing assessment in small firms.", "Time required for PESTLE analysis reduced by over 90% with generative AI tools; SMEs lacked formal processes for macro-environmental analysis before intervention." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 35, "validated": null, "n": 2841, "authors_detailed": [ { "name": "Alan Shaw", "url": "https://openalex.org/A5113248983", "inst": "Leeds Beckett University" } ], "affiliations": [ "Leeds Beckett University" ] }, { "uid": "doi:10.2139/ssrn.4836620", "doi": "10.2139/ssrn.4836620", "title": "The Use of LLMs to Annotate Data in Management Research: Foundational Guidelines and Warnings", "authors": [ "Natalie Carlson", "Vanessa Burbano" ], "posted": "2024-06-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4836620", "field": "management", "role": "method", "bullets": [ "Crowdfunding project descriptions classified for sustainability claims to assess the performance relationships of those claims.", "LLMs annotated text for sustainability classification; performance benchmarked against traditional methods across varied prompt designs.", "LLMs match or exceed traditional annotation at lower cost, but prompt design variations significantly affect downstream regression results." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "traditional annotation methods", "salience": 72, "n": 2480, "authors_detailed": [ { "name": "Natalie Carlson", "url": "https://openalex.org/A5072422184", "inst": "University of Pennsylvania" }, { "name": "Vanessa Burbano", "url": "https://openalex.org/A5018757166", "inst": "Columbia University" } ], "affiliations": [ "University of Pennsylvania", "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4862800", "doi": "10.2139/ssrn.4862800", "title": "Generative AI’s labor-replacing impacts on occupations also foster short-run job opportunities for early adopters", "authors": [ "Yufei Ji", "Lan Chen", "Lu Wang", "Jingya Hou", "Xi Chen", "Hengshu Zhu" ], "posted": "2024-06-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4862800", "field": "economics", "role": "object", "bullets": [ "Online job postings analyzed with a hybrid human-AI framework evaluating generative AI exposure across occupational skills.", "ChatGPT and human experts jointly assessed complementary versus substitutive effects of generative AI on occupational skill sets.", "GenAI substitutes skills in 56% of occupations; early adopters in substituted roles earn wage premiums and higher hiring probability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 2943, "authors_detailed": [ { "name": "Yufei Ji", "url": "https://openalex.org/A5101381052", "inst": "Beijing Chaoyang Emergency Medical Center" }, { "name": "Lan Chen", "url": "https://openalex.org/A5103580283", "inst": "Beijing Chaoyang Emergency Medical Center" }, { "name": "Lu Wang", "url": "https://openalex.org/A5100728158", "inst": "Beijing Chaoyang Emergency Medical Center" }, { "name": "Jingya Hou", "url": "https://openalex.org/A5109983534", "inst": "Beijing Chaoyang Emergency Medical Center" }, { "name": "Xi Chen", "url": "https://openalex.org/A5107298524", "inst": "Beijing Chaoyang Emergency Medical Center" }, { "name": "Hengshu Zhu", "url": "https://openalex.org/A5049015446", "inst": "Beijing Chaoyang Emergency Medical Center" } ], "affiliations": [ "Beijing Chaoyang Emergency Medical Center" ] }, { "uid": "arxiv:2406.12009v5", "arxiv_id": "2406.12009v5", "title": "FinTruthQA: A Benchmark for AI-Driven Financial Disclosure Quality Assessment in Investor -- Firm Interactions", "authors": [ "Peilin Zhou", "Ziyue Xu", "Xinyu Shi", "Jiageng Wu", "Yikang Jiang", "Dading Chong", "Wang Dong", "Jun Chen", "Bin Ke", "Jie Yang" ], "posted": "2024-06-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.12009v5", "field": "accounting", "role": "method", "bullets": [ "6,000 investor question and firm answer pairs from Chinese stock exchange interactive platforms, each hand-annotated for question identification, question relevance, answer readability, and answer relevance.", "Statistical models, pre-trained language models, fine-tuned variants, and prompted LLMs are benchmarked against the human annotations; model families are not named in the abstract.", "Models exceed 95 percent F1 on question identification and relevance but fall to roughly 88 on readability and 80 on answer relevance, where adapted PLMs beat prompted LLMs." ], "bullet_provenance": "ai", "validated": true, "validation_note": "6,000 hand-annotated Q&A pairs, F1 by task", "salience": 48, "edition": 13, "models": [], "n": 1607 }, { "uid": "arxiv:2406.12109v2", "arxiv_id": "2406.12109v2", "title": "Can LLMs Learn Macroeconomic Narratives from Social Media?", "authors": [ "Almog Gueta", "Amir Feder", "Zorik Gekhman", "Ariel Goldstein", "Roi Reichart" ], "posted": "2024-06-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.12109v2", "field": "economics", "role": "instrument", "bullets": [ "Two curated datasets of economy related posts from X, formerly Twitter, built to capture viral economic narratives in Shiller's sense; counts and period are not stated in the abstract.", "Large language models, not named in the abstract, extract and summarize narratives from the tweets; tweet or narrative representations then enter macroeconomic forecasting tasks, without a reported check against human coding.", "Narrative signals yield limited improvement in macroeconomic prediction, and the paper presents the difficulty of exploiting narrative data as an open challenge for the field." ], "bullet_provenance": "ai", "validated": false, "salience": 55, "edition": 13, "models": [], "n": 1642, "authors_detailed": [ { "name": "Almog Gueta", "url": "https://openalex.org/A5006303856", "inst": "Hebrew University of Jerusalem" }, { "name": "Amir Feder", "url": "https://openalex.org/A5056266191", "inst": "Google (Israel)" }, { "name": "Zorik Gekhman", "url": "https://openalex.org/A5087260801", "inst": "Technion – Israel Institute of Technology" }, { "name": "Ariel Goldstein", "url": "https://openalex.org/A5074374901", "inst": "Hebrew College" }, { "name": "Roi Reichart", "url": "https://openalex.org/A5054952724", "inst": "Technion – Israel Institute of Technology" } ], "affiliations": [ "Hebrew University of Jerusalem", "Google (Israel)", "Technion – Israel Institute of Technology", "Hebrew College" ] }, { "uid": "doi:10.1007/s10726-025-09946-9", "doi": "10.1007/s10726-025-09946-9", "arxiv_id": "2406.11426v1", "title": "Can AI with High Reasoning Ability Replicate Human-like Decision Making in Economic Experiments?", "authors": [ "Ayato Kitadai", "Sinndy Dayana Rico Lugo", "Yudai Tsurusaki", "Yusuke Fukasawa", "Nariaki Nishino" ], "posted": "2024-06-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.11426v1", "field": "economics", "role": "agent", "bullets": [ "Multi-agent simulations of the ultimatum game with LLM-driven generative agents, benchmarked against the outcome of an actual human economic experiment.", "Prompting methods raise the agents' reasoning ability, and simulated play is compared with human experimental results. The underlying LLM is not named in the abstract.", "Stronger reasoning moves agent behavior toward the game-theoretic solution rather than observed human play, and persona settings appear important for reproducing experimental results." ], "bullet_provenance": "ai", "validated": true, "validation_note": "comparison with human ultimatum game experiment outcomes", "salience": 50, "edition": 13, "models": [], "n": 1690, "authors_detailed": [ { "name": "Ayato Kitadai", "url": "https://openalex.org/A5079402793", "inst": "Japan Society for the Promotion of Science" }, { "name": "Sinndy Dayana Rico Lugo", "url": "https://openalex.org/A5060542108", "inst": "Ritsumeikan Asia Pacific University" }, { "name": "Yudai Tsurusaki", "url": "https://openalex.org/A5093760405", "inst": "The University of Tokyo" }, { "name": "Yusuke Fukasawa", "url": "https://openalex.org/A5035624383", "inst": "The University of Tokyo" }, { "name": "Nariaki Nishino", "url": "https://openalex.org/A5079335652", "inst": "The University of Tokyo" } ], "affiliations": [ "Japan Society for the Promotion of Science", "Ritsumeikan Asia Pacific University", "The University of Tokyo" ] }, { "uid": "arxiv:2406.15483v1", "arxiv_id": "2406.15483v1", "title": "Duplicate Detection with GenAI", "authors": [ "Ian Ormesher" ], "posted": "2024-06-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.15483v1", "field": "other", "role": "method", "bullets": [ "Duplicate and fuzzy duplicate customer records in CRM databases, with evaluation on common entity matching benchmark datasets; dataset sizes are not stated.", "LLM based embeddings replace traditional entity matching NLP techniques for detecting and repairing duplicates; the specific model is not named in the abstract.", "De-duplication accuracy roughly doubles on the benchmark datasets, from 30 percent with conventional NLP methods to almost 60 percent with the generative approach." ], "bullet_provenance": "ai", "validated": true, "validation_note": "entity matching benchmarks, accuracy reported", "salience": 30, "edition": 13, "models": [], "n": 1733, "authors_detailed": [ { "name": "Ian Ormesher", "url": "https://openalex.org/A5099497376", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.4861858", "doi": "10.2139/ssrn.4861858", "title": "Computational Antitrust Within Agencies: 3rd Annual Report", "authors": [ "Thibault Schrepel", "Teodora Groza" ], "posted": "2024-06-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4861858", "field": "economics", "role": "instrument", "bullets": [ "Sixteen antitrust agencies worldwide reporting computational tool adoption in Q1 2024, covering bid rigging, consumer harm, and market perception.", "Agencies deploy LLMs and ML tools for detecting bid rigging, analyzing consumer harm patterns, and monitoring public perception of market competition.", "Agencies gradually integrate LLMs into daily operations, with increasing reliance on ML for large-scale textual analysis and proof-of-concept API development." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 42, "n": 2662, "authors_detailed": [ { "name": "Thibault Schrepel", "url": "https://openalex.org/A5009098941", "inst": "Vrije Universiteit Amsterdam" }, { "name": "Teodora Groza", "url": "https://openalex.org/A5020766937", "inst": "Institut d'Etudes Politiques de Paris" } ], "affiliations": [ "Vrije Universiteit Amsterdam", "Institut d'Etudes Politiques de Paris" ] }, { "uid": "arxiv:2406.10811v1", "arxiv_id": "2406.10811v1", "title": "LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction", "authors": [ "Meiyun Wang", "Kiyoshi Izumi", "Hiroki Sakaji" ], "posted": "2024-06-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.10811v1", "field": "finance", "role": "instrument", "bullets": [ "Four benchmark datasets pairing news with US and Chinese stock prices for movement prediction; sizes and periods are not stated in the abstract.", "Unnamed LLMs, guided by sequential knowledge-guided prompting with fill-in-the-blank background steps, extract factors from news and predict direction from price histories rendered as text; factor quality is not validated separately.", "The framework outperforms prior state-of-the-art approaches on movement prediction across the four datasets while yielding readable factor explanations; margins are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 28, "edition": 13, "models": [], "n": 1606, "authors_detailed": [ { "name": "Meiyun Wang", "url": "https://openalex.org/A5100748037", "inst": "Henan Provincial People's Hospital" }, { "name": "Kiyoshi Izumi", "url": "https://openalex.org/A5044205949", "inst": "Bunkyo University" }, { "name": "Hiroki Sakaji", "url": "https://openalex.org/A5028823648", "inst": "Hokkaido University" } ], "affiliations": [ "Bunkyo University", "Hokkaido University" ] }, { "uid": "arxiv:2406.11903v1", "arxiv_id": "2406.11903v1", "title": "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges", "authors": [ "Yuqi Nie", "Yaxuan Kong", "Xiaowen Dong", "John M. Mulvey", "H. Vincent Poor", "Qingsong Wen", "Stefan Zohren" ], "posted": "2024-06-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.11903v1", "field": "finance", "role": "method", "bullets": [ "Survey covering LLM applications in finance: linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent based modeling, and decision support.", "No model is deployed; the paper organizes existing work by application area and methodology, and compiles datasets, model assets, and code for researchers and practitioners.", "Maps where LLMs currently have traction in finance and lists open challenges; a reference catalogue rather than an empirical finding." ], "bullet_provenance": "ai", "salience": 50, "edition": 13, "models": [], "validated": null, "n": 1732, "authors_detailed": [ { "name": "Yuqi Nie", "url": "https://openalex.org/A5099282236", "inst": "Princeton University" }, { "name": "Yaxuan Kong", "url": "https://openalex.org/A5099282237", "inst": "Science Oxford" }, { "name": "Xiaowen Dong", "url": "https://openalex.org/A5101579932", "inst": "University of Oxford" }, { "name": "John M. Mulvey", "url": "https://openalex.org/A5099282238", "inst": "" }, { "name": "H. Vincent Poor", "url": "https://openalex.org/A5042307561", "inst": "Shanghai University" }, { "name": "Qingsong Wen", "url": "https://openalex.org/A5099282239", "inst": "" }, { "name": "Stefan Zohren", "url": "https://openalex.org/A5099282240", "inst": "" } ], "affiliations": [ "Princeton University", "University of Oxford", "Science Oxford", "Shanghai University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4833462", "doi": "10.2139/ssrn.4833462", "title": "Beyond Numbers: A Textual Voyage Into Finance With Gemini", "authors": [ "Parth Ahuja" ], "posted": "2024-06-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4833462", "field": "finance", "role": "instrument", "bullets": [ "Sentiment analysis of annual report sections and tweets from the Indian market using Gemini-Pro as a financial text analyst.", "Gemini-Pro generated sentiment scores for financial text; analysis examined connections between sentiment and short-term market volatility.", "Gemini-Pro shows efficacy linking textual sentiment to short-interval market volatility; LLMs can reduce human intervention in financial text analysis." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "validated": false, "salience": 25, "n": 2411, "authors_detailed": [ { "name": "Parth Ahuja", "url": "https://openalex.org/A5099127970", "inst": "Indian Institute of Technology Kanpur" } ], "affiliations": [ "Indian Institute of Technology Kanpur" ] }, { "uid": "arxiv:2406.07860v1", "arxiv_id": "2406.07860v1", "title": "BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain", "authors": [ "Rahul Kumar", "Amar Raja Dibbu", "Shrutendra Harsola", "Vignesh Subrahmaniam", "Ashutosh Modi" ], "posted": "2024-06-12", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.07860v1", "field": "accounting", "role": "method", "bullets": [ "New large-scale benchmark of 100,000 natural-language-to-SQL query pairs covering accounting and financial domains over databases containing one million synthetic financial records.", "GPT-4 and other state-of-the-art text-to-SQL models tested on converting natural language accounting queries to SQL, evaluated against ground-truth query results.", "All models including GPT-4 showed significant performance gaps on accounting queries compared to general-domain benchmarks, indicating need for domain-specific model development." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "BookSQL benchmark accuracy", "salience": 50, "n": 3413, "authors_detailed": [ { "name": "Rahul Kumar", "url": "https://openalex.org/A5026235653", "inst": "University of Delhi" }, { "name": "Amar Raja Dibbu", "url": "https://openalex.org/A5099124139", "inst": "Indian Institute of Technology Kanpur" }, { "name": "Shrutendra Harsola", "url": "https://openalex.org/A5050825348", "inst": "Indian Institute of Technology Kanpur" }, { "name": "Vignesh Subrahmaniam", "url": "https://openalex.org/A5008189300", "inst": "Intel (India)" }, { "name": "Ashutosh Modi", "url": "https://openalex.org/A5076043215", "inst": "Indian Institute of Technology Kanpur" } ], "affiliations": [ "University of Delhi", "Indian Institute of Technology Kanpur", "Intel (India)" ] }, { "uid": "arxiv:2406.07505v1", "arxiv_id": "2406.07505v1", "title": "THaLLE: Text Hyperlocally Augmented Large Language Extension -- Technical Report", "authors": [ "KBTG Labs", "Danupat Khamnuansin", "Atthakorn Petchsod", "Anuruth Lertpiya", "Pornchanan Balee", "Thanawat Lodkaew", "Tawunrat Chalothorn", "Thadpong Pongthawornkamol", "Monchai Lertsutthiwong" ], "posted": "2024-06-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.07505v1", "field": "finance", "role": "method", "bullets": [ "Mock Chartered Financial Analyst exams serve as the evaluation setting, alongside the released Flare CFA dataset for assessing models as financial advisors.", "A series of 8B-parameter fine-tuned models, base family not stated in the abstract, are documented in detail; exam scoring provides the ground-truth check.", "The models are reported to top comparably sized rivals on mock CFA exams; specific pass rates are not stated in the abstract." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "mock CFA exams", "salience": 26, "edition": 13, "models": [], "n": 1605, "authors_detailed": [ { "name": "KBTG Labs", "url": "https://openalex.org/A5099123316", "inst": "" }, { "name": "Danupat Khamnuansin", "url": "https://openalex.org/A5099097290", "inst": "Chulalongkorn University" }, { "name": "Atthakorn Petchsod", "url": "https://openalex.org/A5099123317", "inst": "Silpakorn University" }, { "name": "Anuruth Lertpiya", "url": "https://openalex.org/A5081580071", "inst": "Chulalongkorn University" }, { "name": "Pornchanan Balee", "url": "https://openalex.org/A5099123318", "inst": "" }, { "name": "Thanawat Lodkaew", "url": "https://openalex.org/A5099123319", "inst": "Silpakorn University" }, { "name": "Tawunrat Chalothorn", "url": "https://openalex.org/A5090165607", "inst": "Silpakorn University" }, { "name": "Thadpong Pongthawornkamol", "url": "https://openalex.org/A5099123320", "inst": "" }, { "name": "Monchai Lertsutthiwong", "url": "https://openalex.org/A5099123321", "inst": "Silpakorn University" } ], "affiliations": [ "Chulalongkorn University", "Silpakorn University" ] }, { "uid": "arxiv:2406.05972v2", "arxiv_id": "2406.05972v2", "title": "Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context", "authors": [ "Jingru Jia", "Zehua Yuan", "Junhao Pan", "Paul E. McNamara", "Deming Chen" ], "posted": "2024-06-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.05972v2", "field": "economics", "role": "agent", "bullets": [ "Multiple-choice-list experiments in a context-free setting elicit risk preference, probability weighting and loss aversion parameters from three commercial chat models.", "ChatGPT-4 Turbo, Claude 3 Opus and Gemini 1.0 Pro answer lottery-style choices with and without socio-demographic personas; parameters are estimated with behavioral economics structural models.", "Models are risk averse and loss averse and overweight small probabilities like humans, but the strength varies by model, and personas such as disability status push Claude toward more conservative choices." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "salience": 52, "edition": 13, "validated": null, "n": 1523, "authors_detailed": [ { "name": "Jingru Jia", "url": "https://openalex.org/A5064096371", "inst": "Wenzhou University" }, { "name": "Zehua Yuan", "url": "https://openalex.org/A5101305192", "inst": "New York University" }, { "name": "Junhao Pan", "url": "https://openalex.org/A5100316058", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Paul E. McNamara", "url": "https://openalex.org/A5027659770", "inst": "Merck & Co., Inc., Rahway, NJ, USA (United States)" }, { "name": "Deming Chen", "url": "https://openalex.org/A5056321228", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "New York University", "University of Illinois Urbana-Champaign", "Wenzhou University", "Merck & Co., Inc., Rahway, NJ, USA (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2406.03823v1", "arxiv_id": "2406.03823v1", "title": "Views about ChatGPT: Are human decision making and human learning necessary?", "authors": [ "Eiji Yamamura", "Fumio Ohtake" ], "posted": "2024-06-06", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.03823v1", "field": "economics", "role": "object", "bullets": [ "Individual-level survey data from Japan in 2024 covering 14 questions on respondent attitudes toward generative AI across occupations, demographics, and device usage patterns.", "Ordered logit regressions examined how occupation, gender, and device usage time associate with views on AI decision-making, human replacement, and learning necessity.", "Managers supported GAI but opposed autonomous AI decisions; teachers rejected human replacement; women held less positive views; smartphone versus computer usage diverged in effects." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 35, "validated": null, "n": 3412, "authors_detailed": [ { "name": "Eiji Yamamura", "url": "https://openalex.org/A5023759310", "inst": "Seinan Gakuin University" }, { "name": "Fumio Ohtake", "url": "https://openalex.org/A5047902895", "inst": "Osaka Gakuin University" } ], "affiliations": [ "Seinan Gakuin University", "Osaka Gakuin University" ] }, { "uid": "doi:10.1109/access.2025.3600967", "doi": "10.1109/access.2025.3600967", "arxiv_id": "2406.03614v1", "title": "Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs", "authors": [ "Alexander Bakumenko", "Kateřina Hlaváčková-Schindler", "Claudia Plant", "Nina C. Hubig" ], "posted": "2024-06-05", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.03614v1", "field": "accounting", "role": "method", "bullets": [ "Real-world general ledger journal entries with irregular or potentially fraudulent items; entry counts and the source institution are not stated in the abstract.", "Three pre-trained sentence-transformer models embed non-semantic categorical transaction fields; five classifiers from logistic regression to neural networks are then scored on labelled anomalies.", "Embedding-based classifiers beat baselines, in some settings by a large margin, which the authors credit to handling feature sparsity; exact figures are not stated." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "labelled journal entry anomalies", "salience": 40, "edition": 13, "n": 1604, "authors_detailed": [ { "name": "Alexander Bakumenko", "url": "https://openalex.org/A5048394913", "inst": "Clemson University" }, { "name": "Kateřina Hlaváčková‐Schindler", "url": "https://openalex.org/A5034652116", "inst": "University of Vienna" }, { "name": "Claudia Plant", "url": "https://openalex.org/A5009516958", "inst": "University of Vienna" }, { "name": "Nina Hubig", "url": "https://openalex.org/A5071884087", "inst": "Interdisciplinary Transformation University Austria" } ], "affiliations": [ "Clemson University", "University of Vienna", "Interdisciplinary Transformation University Austria" ] }, { "uid": "arxiv:2406.02969v2", "arxiv_id": "2406.02969v2", "title": "Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models", "authors": [ "Raeid Saqur", "Anastasis Kratsios", "Florian Krach", "Yannick Limmer", "Jacob-Junqi Tian", "John Willes", "Blanka Horvath", "Frank Rudzicz" ], "posted": "2024-06-05", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.02969v2", "field": "finance", "role": "method", "bullets": [ "Online prediction setting where streaming financial news feeds a market movement classification task, with additional experiments on long horizon time series forecasting.", "Combines N pretrained LLMs, families not named in the abstract, through time adaptive stochastic filtering over a hidden Markov model that reweights experts from running performance.", "Improves F1 for short horizon market movement by 17 percent absolute, 48.5 percent relative, over the best individual LLM expert." ], "bullet_provenance": "ai", "validated": true, "validation_note": "market movement benchmark, F1 reported", "salience": 52, "edition": 13, "models": [], "n": 1731, "authors_detailed": [ { "name": "Raeid Saqur", "url": "https://openalex.org/A5077308692", "inst": "University of Toronto" }, { "name": "Anastasis Kratsios", "url": "https://openalex.org/A5036113771", "inst": "Vector Institute" }, { "name": "Florian Krach", "url": "https://openalex.org/A5060032129", "inst": "ETH Zurich" }, { "name": "Yannick Limmer", "url": "https://openalex.org/A5061518251", "inst": "University of Oxford" }, { "name": "Jacob-Junqi Tian", "url": "https://openalex.org/A5104317088", "inst": "Vector Institute" }, { "name": "John Willes", "url": "https://openalex.org/A5086045862", "inst": "Vector Institute" }, { "name": "Blanka Horvath", "url": "https://openalex.org/A5011490173", "inst": "Mansfield University" }, { "name": "Frank Rudzicz", "url": "https://openalex.org/A5056256317", "inst": "Dalhousie University" } ], "affiliations": [ "University of Toronto", "University of Oxford", "Vector Institute", "ETH Zurich", "Mansfield University", "Dalhousie University" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4851711", "doi": "10.2139/ssrn.4851711", "title": "How Ethical Should AI Be? How AI Alignment Shapes the Risk Preferences of LLMs", "authors": [ "Shumiao Ouyang", "Hayong Yun", "Xingjian Zheng" ], "posted": "2024-06-04", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4851711", "field": "finance", "role": "agent", "bullet_provenance": "none", "salience": 55, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 642, "authors_detailed": [ { "name": "Shumiao Ouyang", "url": "https://openalex.org/A5101375445", "inst": "University of Oxford" }, { "name": "Hayong Yun", "url": "https://openalex.org/A5066640543", "inst": "Michigan State University" }, { "name": "Xingjian Zheng", "url": "https://openalex.org/A5008984729", "inst": "Shanghai Jiao Tong University" } ], "affiliations": [ "Michigan State University", "University of Oxford", "Shanghai Jiao Tong University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2406.01407v1", "arxiv_id": "2406.01407v1", "title": "Utilizing Large Language Models for Automating Technical Customer Support", "authors": [ "Jochen Wulf", "Jürg Meierhofer" ], "posted": "2024-06-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.01407v1", "field": "management", "role": "object", "bullets": [ "Technical customer support processes, studied through prototypes and data analyses covering automated text correction, summarization of customer inquiries, and question answering; sample sizes are not stated.", "OpenAI's GPT-4 powers the prototype workflows; the abstract reports no validation of output quality against ground truth or human review.", "The authors judge efficiency and quality gains feasible but conclude that quality assured implementation and organizational adjustments to the data ecosystem are needed first." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 32, "edition": 13, "n": 1640, "authors_detailed": [ { "name": "Jochen Wulf", "url": "https://openalex.org/A5036086266", "inst": "ZHAW Zurich University of Applied Sciences" }, { "name": "Jürg Meierhofer", "url": "https://openalex.org/A5008118017", "inst": "ZHAW Zurich University of Applied Sciences" } ], "affiliations": [ "ZHAW Zurich University of Applied Sciences" ] }, { "uid": "arxiv:2406.01168v3", "arxiv_id": "2406.01168v3", "title": "AI as Decision-Maker: Ethics and Risk Preferences of LLMs", "authors": [ "Shumiao Ouyang", "Hayong Yun", "Xingjian Zheng" ], "posted": "2024-06-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.01168v3", "field": "economics", "role": "agent", "bullets": [ "Fifty large language models complete behavioral tasks eliciting risk preferences, with comparative difference analysis linking alignment tuning to preference shifts; the models are not named in the abstract.", "Each model acts as a decision maker; measured risk profiles are stable but diverse, and tuning for harmlessness, helpfulness, and honesty is examined as a causal driver of risk aversion.", "A ten percent increase in ethics measures cuts risk appetite by two to eight percent, persists under prompting, and shifts economic forecasts, suggesting alignment can suppress valuable risk taking." ], "bullet_provenance": "ai", "salience": 60, "edition": 13, "models": [], "validated": null, "n": 1641, "authors_detailed": [ { "name": "Shumiao Ouyang", "url": "https://openalex.org/A5101375445", "inst": "University of Oxford" }, { "name": "Hayong Yun", "url": "https://openalex.org/A5066640543", "inst": "Michigan State University" }, { "name": "Xingjian Zheng", "url": "https://openalex.org/A5008984729", "inst": "Agency for Science, Technology and Research" } ], "affiliations": [ "Michigan State University", "University of Oxford", "Agency for Science, Technology and Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4848956", "doi": "10.2139/ssrn.4848956", "title": "Improving Sales Forecasting: Leveraging Unstructured CRM Activity Logs, Large Language Models, and Generative AI", "authors": [ "James C. Reeder, III", "Nawar Chaker", "Johannes Habel" ], "posted": "2024-06-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4848956", "field": "management", "role": "instrument", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 340, "authors_detailed": [ { "name": "James C. Reeder", "url": "https://openalex.org/A5009248241", "inst": "University of Kansas" }, { "name": "Nawar N. Chaker", "url": "https://openalex.org/A5063781306", "inst": "Louisiana State University" }, { "name": "Johannes Habel", "url": "https://openalex.org/A5035054127", "inst": "University of Houston" } ], "affiliations": [ "University of Kansas", "Louisiana State University", "University of Houston" ] }, { "uid": "doi:10.2139/ssrn.4812158", "doi": "10.2139/ssrn.4812158", "title": "Measuring and Mitigating Racial Disparities in LLMs: Evidence from a Mortgage Underwriting Experiment", "authors": [ "Donald E. Bowen III", "S. McKay Price", "Luke C.D. Stein", "Ke Yang" ], "posted": "2024-06-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4812158", "field": "finance", "role": "agent", "bullets": [ "Real mortgage application data with experimentally manipulated race signals across multiple LLM generations for underwriting decisions.", "LLMs evaluated loan applications and recommended approval/denial and interest rates; race signaled explicitly or via name and location proxies.", "Multiple LLM generations recommended more denials and higher rates for Black applicants, with larger disparities for riskier loans." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "race-blind recommendations correlated with real lender decisions and predicted delinquency", "salience": 75, "n": 3191, "authors_detailed": [ { "name": "Donald E. Bowen", "url": "https://openalex.org/A5044505879", "inst": "Lehigh University" }, { "name": "S. McKay Price", "url": "https://openalex.org/A5015077006", "inst": "Lehigh University" }, { "name": "Luke C.D. Stein", "url": "https://openalex.org/A5103064115", "inst": "Babson College" }, { "name": "Ke Yang", "url": "https://openalex.org/A5102022824", "inst": "Lehigh University" } ], "affiliations": [ "Lehigh University", "Babson College" ] }, { "uid": "doi:10.2139/ssrn.4832567", "doi": "10.2139/ssrn.4832567", "title": "Implement FRTB with Generative Ai Models: A Case Study", "authors": [ "James Zhan" ], "posted": "2024-06-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4832567", "field": "finance", "role": "method", "bullets": [ "Single case study implementing the FRTB Standardised Approach interest rate delta risk calculation for a financial institution, benchmarked against spreadsheet implementations.", "ChatGPT generated Python code for regulatory calculations, debugged errors, and self-corrected outputs with minimal human intervention using fine-tuned prompts and iterative error analysis.", "Generative AI achieved high accuracy matching spreadsheet results for interest rate delta risk, demonstrating viability for automating financial regulatory compliance coding." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "benchmarked against spreadsheet FRTB calculations", "salience": 40, "n": 3411, "authors_detailed": [ { "name": "James Zhan", "url": "https://openalex.org/A5014555475", "inst": "Risk Management Solutions (United Kingdom)" } ], "affiliations": [ "Risk Management Solutions (United Kingdom)" ] }, { "uid": "arxiv:2406.06559v1", "arxiv_id": "2406.06559v1", "title": "Harnessing Business and Media Insights with Large Language Models", "authors": [ "Yujia Bao", "Ankit Parag Shah", "Neeru Narang", "Jonathan Rivers", "Rajeev Maksey", "Lan Guan", "Louise N. Barrere", "Shelley Evenson", "Rahul Basole", "Connie Miao", "Ankit Mehta", "Fabien Boulay", "Su Min Park", "Natalie E. Pearson", "Eldhose Joy", "Tiger He", "Sumiran Thakur", "Koustav Ghosal", "Josh On", "Phoebe Morrison", "Tim Major", "Eva Siqi Wang", "Gina Escobar", "Jiaheng Wei", "Tharindu Cyril Weerasooriya", "Queena Song", "Daria Lashkevich", "Clare Chen", "Gyuhak Kim", "Dengpan Yin", "Don Hejna", "Mo Nomeli", "Wei Wei" ], "posted": "2024-06-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.06559v1", "field": "management", "role": "method", "bullets": [ "Question answering system for business and media analysis, built on a curated knowledge base of professional Fortune journalism covering market trends and company performance.", "FALM, a domain adapted language model with time aware reasoning and content referencing; the base model is not stated; assessed through automated and human evaluations.", "Reported to outperform baseline methods on answer accuracy and data visualization tasks, though the abstract quantifies none of the improvements." ], "bullet_provenance": "ai", "validated": true, "validation_note": "automated and human evaluation against baselines", "salience": 32, "edition": 13, "models": [], "n": 1730, "authors_detailed": [ { "name": "Yujia Bao", "url": "https://openalex.org/A5027122418", "inst": "Santa Barbara City College" }, { "name": "Ankit Shah", "url": "https://openalex.org/A5019541211", "inst": "Vertex Pharmaceuticals (United States)" }, { "name": "Neeru Narang", "url": "https://openalex.org/A5067132828", "inst": "Public Works Department Buildings and Roads" }, { "name": "Jonathan Rivers", "url": "https://openalex.org/A5111227750", "inst": "St Bartholomew's Hospital" }, { "name": "Rajeev Maksey", "url": "https://openalex.org/A5099111269", "inst": "" }, { "name": "Lan Guan", "url": "https://openalex.org/A5012374676", "inst": "South China Agricultural University" }, { "name": "Louise N. Barrere", "url": "https://openalex.org/A5099111270", "inst": "" }, { "name": "Shelley Evenson", "url": "https://openalex.org/A5108204183", "inst": "Carnegie Mellon University" }, { "name": "Rahul C. Basole", "url": "https://openalex.org/A5069923620", "inst": "Leuphana University of Lüneburg" }, { "name": "Connie Miao", "url": "https://openalex.org/A5099111262", "inst": "" }, { "name": "Ankit I. Mehta", "url": "https://openalex.org/A5076123107", "inst": "Illinois College" }, { "name": "Fabien Boulay", "url": "https://openalex.org/A5099111263", "inst": "" }, { "name": "Su Min Park", "url": "https://openalex.org/A5111227748", "inst": "Accenture (Switzerland)" }, { "name": "Natalie E. Pearson", "url": "https://openalex.org/A5113249658", "inst": "" }, { "name": "Eldhose Joy", "url": "https://openalex.org/A5113249659", "inst": "" }, { "name": "Tiger He", "url": "https://openalex.org/A5101377830", "inst": "" }, { "name": "Sumiran Thakur", "url": "https://openalex.org/A5102687087", "inst": "Accenture (Switzerland)" }, { "name": "Koustav Ghosal", "url": "https://openalex.org/A5099111264", "inst": "" }, { "name": "Josh On", "url": "https://openalex.org/A5111227749", "inst": "" }, { "name": "Phoebe Morrison", "url": "https://openalex.org/A5108942636", "inst": "" }, { "name": "Tim Major", "url": "https://openalex.org/A5099111265", "inst": "" }, { "name": "Eva Siqi Wang", "url": "https://openalex.org/A5114176453", "inst": "" }, { "name": "Gina Escobar", "url": "https://openalex.org/A5109732203", "inst": "" }, { "name": "Jiaheng Wei", "url": "https://openalex.org/A5049263948", "inst": "University of Shanghai for Science and Technology" }, { "name": "Tharindu Cyril Weerasooriya", "url": "https://openalex.org/A5083350804", "inst": "Rochester Institute of Technology" }, { "name": "Queena Song", "url": "https://openalex.org/A5114176454", "inst": "" }, { "name": "Daria Lashkevich", "url": "https://openalex.org/A5099111266", "inst": "" }, { "name": "Chen, Clare", "url": "", "inst": "" }, { "name": "Gyuhak Kim", "url": "https://openalex.org/A5104326351", "inst": "" }, { "name": "Dengpan Yin", "url": "https://openalex.org/A5101377831", "inst": "" }, { "name": "Don Hejna", "url": "https://openalex.org/A5099111267", "inst": "" }, { "name": "Mo Nomeli", "url": "https://openalex.org/A5099111268", "inst": "" }, { "name": "Wei Wei", "url": "https://openalex.org/A5103829821", "inst": "Santa Barbara City College" } ], "affiliations": [ "Carnegie Mellon University", "Santa Barbara City College", "Vertex Pharmaceuticals (United States)", "Public Works Department Buildings and Roads", "South China Agricultural University", "Leuphana University of Lüneburg", "Illinois College" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2406.00477v1", "arxiv_id": "2406.00477v1", "title": "Generative AI as Economic Agents", "authors": [ "Nicole Immorlica", "Brendan Lucier", "Aleksandrs Slivkins" ], "posted": "2024-06-01", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2406.00477v1", "field": "economics", "role": "object", "bullets": [ "Theoretical framework modeling generative AI as an economic agent that users consult before taking actions in strategic games.", "AI agents and users have potentially different information and preferences, altering equilibrium outcomes relative to no-AI settings.", "Human-AI consultation can produce qualitatively different equilibria than traditional models where AI only reduces costs or refines information." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3190, "authors_detailed": [ { "name": "Nicole Immorlica", "url": "https://openalex.org/A5009111822", "inst": "Microsoft (United States)" }, { "name": "Brendan Lucier", "url": "https://openalex.org/A5082964830", "inst": "Microsoft (United States)" }, { "name": "Aleksandrs Slivkins", "url": "https://openalex.org/A5058550942", "inst": "Microsoft (United States)" } ], "affiliations": [ "Microsoft (United States)" ] }, { "uid": "doi:10.2139/ssrn.4850039", "doi": "10.2139/ssrn.4850039", "title": "Using Large Language Models for Financial Advice", "authors": [ "Christian Fieberg", "Lars Hornuf", "David Streich" ], "posted": "2024-05-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4850039", "alt_urls": [ "https://doi.org/10.2139/ssrn.5133294" ], "field": "finance", "role": "agent", "bullet_provenance": "none", "salience": 50, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 641, "authors_detailed": [ { "name": "Christian Fieberg", "url": "https://openalex.org/A5044108899", "inst": "Hochschule Bremen" }, { "name": "Lars Hornuf", "url": "https://openalex.org/A5064247212", "inst": "Ifo Institute for Economic Research" }, { "name": "David Streich", "url": "https://openalex.org/A5023289262", "inst": "Catholic University of Eichstätt-Ingolstadt" } ], "affiliations": [ "Hochschule Bremen", "Ifo Institute for Economic Research", "Catholic University of Eichstätt-Ingolstadt" ] }, { "uid": "doi:10.2139/ssrn.4807317", "doi": "10.2139/ssrn.4807317", "title": "Augmenting Frontline Service Employee Onboarding via Hybrid Intelligence: Examining the Effects of Different Degrees of Human-GenAI Interaction", "authors": [ "Philipp Reinhard", "Nicolas Neis", "Lisa Kolb", "Dennis Wischer", "Mahei Manhai Li", "Axel Winkelmann", "Frank Teuteberg", "Ulrike Lechner", "Jan Marco Leimeister" ], "posted": "2024-05-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4807317", "field": "management", "role": "object", "bullets": [ "Online experiment with 75 participants testing two GenAI co-agent designs for help desk employee onboarding across varying task difficulties.", "LLM-powered conversational co-agent deployed as supporter (recommendations) and collaborator (free prompting) for novice frontline service employees.", "Collaborator mode increases interaction frequency and time investment but paradoxically lowers perceived workload, especially for difficult tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 42, "validated": null, "n": 2840, "authors_detailed": [ { "name": "Philipp Reinhard", "url": "https://openalex.org/A5080324783", "inst": "University of Kassel" }, { "name": "Nicolas Neis", "url": "https://openalex.org/A5098846663", "inst": "University of Würzburg" }, { "name": "Lisa Kolb", "url": "https://openalex.org/A5049061695", "inst": "Universität der Bundeswehr München" }, { "name": "Dennis Wischer", "url": "https://openalex.org/A5098846664", "inst": "Osnabrück University" }, { "name": "Mahei Manhai Li", "url": "https://openalex.org/A5047490037", "inst": "University of Kassel" }, { "name": "Axel Winkelmann", "url": "https://openalex.org/A5008683369", "inst": "University of Würzburg" }, { "name": "Frank Teuteberg", "url": "https://openalex.org/A5069819524", "inst": "Osnabrück University" }, { "name": "Ulrike Lechner", "url": "https://openalex.org/A5071678511", "inst": "Universität der Bundeswehr München" }, { "name": "Jan Marco Leimeister", "url": "https://openalex.org/A5031171331", "inst": "University of Kassel" } ], "affiliations": [ "University of Kassel", "University of Würzburg", "Universität der Bundeswehr München", "Osnabrück University" ] }, { "uid": "doi:10.2139/ssrn.4843878", "doi": "10.2139/ssrn.4843878", "title": "Overcoming Limitations of Ai Agents: Integrating Tacit Knowledge Through Inferred Latent Themes", "authors": [ "Rohit Aggarwal", "Harpreet Singh" ], "posted": "2024-05-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4843878", "field": "management", "role": "method", "bullets": [ "Theoretical Bayesian framework for GPT-architecture LLM agents, with empirical studies in recruitment and social media content creation.", "Proposed integrating tacit knowledge via inferred latent themes to reduce error bound in LLM conditional probability predictions.", "Proved non-zero lower bound on LLM prediction error and showed latent-theme augmentation can tighten the bound theoretically." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 30, "n": 3189, "authors_detailed": [ { "name": "Rohit Aggarwal", "url": "https://openalex.org/A5047313569", "inst": "University of Utah" }, { "name": "Harpreet Singh", "url": "https://openalex.org/A5100674921", "inst": "The University of Texas at Dallas" } ], "affiliations": [ "The University of Texas at Dallas", "University of Utah" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4848158", "doi": "10.2139/ssrn.4848158", "title": "Impact of Large Language Model on Indian Economy", "authors": [ "Jignesh Vidani" ], "posted": "2024-05-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4848158", "field": "economics", "role": "object", "bullets": [ "Qualitative analysis of LLM adoption across Indian industries including education, IT services, healthcare, and customer support in a linguistically diverse market.", "Study examines potential economic benefits and drawbacks of LLM integration across multiple sectors of the Indian economy using descriptive analysis.", "LLMs can stimulate innovation and create jobs but risk worker displacement, widening the digital divide, and raising security and ethical concerns in India." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 2367, "authors_detailed": [ { "name": "Jignesh Vidani", "url": "https://openalex.org/A5065295746", "inst": "IMS Unison University" } ], "affiliations": [ "IMS Unison University" ] }, { "uid": "doi:10.2139/ssrn.4847914", "doi": "10.2139/ssrn.4847914", "title": "The Risk Sharing Value of Disclosure: A Real-time Market Response Approach to Hedge Climate Change Risk", "authors": [ "Miao Liu", "Yang Cao", "Rachel Xi Zhang" ], "posted": "2024-05-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4847914", "field": "finance", "role": "instrument", "bullets": [ "Earnings call transcripts of US public firms matched with high-frequency stock price data at the conversation level for climate-related discussions.", "ChatGPT-4 identified climate-related conversations in earnings calls; real-time stock price responses measured each firm's dynamic climate exposure.", "Long-short portfolios based on market response to climate discussions appreciate during negative aggregate climate news shocks, enabling effective climate risk hedging." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 78, "n": 2479, "authors_detailed": [ { "name": "Miao Liu", "url": "https://openalex.org/A5101902169", "inst": "Boston College" }, { "name": "Yang Cao", "url": "https://openalex.org/A5017796934", "inst": "Boston College" }, { "name": "Rachel Xi Zhang", "url": "https://openalex.org/A5108937541", "inst": "National University System" } ], "affiliations": [ "Boston College", "National University System" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4845956", "doi": "10.2139/ssrn.4845956", "title": "Evaluating Novel Unstructured Treatments with Generative AI: A Causal Prediction Framework", "authors": [ "Paul B. Ellickson", "Wreetabrata Kar", "James C. Reeder, III", "Guang Zeng" ], "posted": "2024-05-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4845956", "field": "management", "role": "method", "bullets": [ "Large-scale email marketing with 3.3 million observations across 34 campaigns; AI-generated content evaluated for causal deployment.", "Pretrained LLMs represented marketing content for causal prediction; rejection sampling screened AI proposals against historical data support.", "Framework improved out-of-sample prediction and deployment performance; established reliability thresholds separating prediction from experimentation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "out-of-sample prediction and real-world deployment across 34 campaigns", "salience": 65, "n": 2942, "authors_detailed": [ { "name": "Paul B. Ellickson", "url": "https://openalex.org/A5089577666", "inst": "University of Rochester" }, { "name": "Wreetabrata Kar", "url": "https://openalex.org/A5020121896", "inst": "Purdue University West Lafayette" }, { "name": "James C. Reeder", "url": "https://openalex.org/A5009248241", "inst": "University of Kansas" }, { "name": "Guang Zeng", "url": "https://openalex.org/A5098943284", "inst": "University of Rochester" } ], "affiliations": [ "University of Rochester", "Purdue University West Lafayette", "University of Kansas" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2405.19313v2", "arxiv_id": "2405.19313v2", "title": "Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice", "authors": [ "Jian-Qiao Zhu", "Haijiang Yan", "Thomas L. Griffiths" ], "posted": "2024-05-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.19313v2", "field": "economics", "role": "agent", "bullets": [ "Human risky and intertemporal choice, the standard testbed for expected value based decision theories, used to assess language models as cognitive models.", "Arithmetic-GPT, a small language model the authors pretrain on an ecologically valid arithmetic dataset, generates choice predictions benchmarked against traditional cognitive models on human data.", "Arithmetic pretraining alone makes the model track human choices better than many established cognitive models, and the authors argue pretraining data ablations should accompany LLM based cognitive modeling." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "predictive fit to human choice data against cognitive models", "salience": 58, "edition": 13, "n": 1729, "authors_detailed": [ { "name": "Jian-Qiao Zhu", "url": "https://openalex.org/A5018060667", "inst": "Princeton University" }, { "name": "Haijiang Yan", "url": "https://openalex.org/A5108937834", "inst": "" }, { "name": "Thomas L. Griffiths", "url": "https://openalex.org/A5077079119", "inst": "Princeton University" } ], "affiliations": [ "Princeton University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4841493", "doi": "10.2139/ssrn.4841493", "title": "FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models", "authors": [ "Hongyang Yang" ], "posted": "2024-05-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4841493", "field": "finance", "role": "method", "bullets": [ "Open-source AI agent platform designed for financial analysis tasks including report generation, market forecasting, and portfolio management.", "Four-layer architecture integrates multiple LLMs with financial chain-of-thought reasoning, task-specific model selection, and fine-tuning pipelines for finance workflows.", "Platform enables both professional analysts and non-experts to conduct LLM-powered financial analysis using coordinated agents with domain-specific reasoning chains." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 45, "n": 2358, "authors_detailed": [ { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "New York Foundation" } ], "affiliations": [ "New York Foundation" ] }, { "uid": "doi:10.2139/ssrn.4841921", "doi": "10.2139/ssrn.4841921", "title": "Dynamic Knowledge Graph Asset Pricing", "authors": [ "Xiaohui Li", "Yixiao Tan" ], "posted": "2024-05-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4841921", "field": "finance", "role": "instrument", "bullets": [ "Full Wall Street Journal text corpus from 1999 to 2023 mapped to US equity cross-section for unconditional asset pricing tests under the ICAPM framework.", "LLM built a dynamic knowledge graph identifying systematic macro-finance themes; a novel polar centrality measure quantified each theme's importance to the economy.", "Theme factors forecast future economic activity, price the equity cross-section under ICAPM, and contribute incrementally beyond the existing stock market factor zoo." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 70, "n": 3410, "authors_detailed": [ { "name": "Victor Xiaohui Li", "url": "https://openalex.org/A5101368796", "inst": "Imperial College London" }, { "name": "Yixiao Tan", "url": "https://openalex.org/A5098933999", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford", "Imperial College London" ], "prestige": true }, { "uid": "arxiv:2405.17924v1", "arxiv_id": "2405.17924v1", "title": "Generative AI Enhances Team Performance and Reduces Need for Traditional Teams", "authors": [ "Ning Li", "Huaikang Zhou", "Kris Mikel-Hong" ], "posted": "2024-05-28", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.17924v1", "field": "management", "role": "object", "bullets": [ "Randomized controlled experiment with 435 participants across 122 teams comparing human-only, AI-augmented, and individual-AI pair performance on collaborative tasks.", "Teams used generative AI for collaborative work; study compared centralized versus distributed AI usage and individual-AI pairs against traditional team structures.", "AI-augmented teams outperformed human-only teams; individual-AI pairs matched conventional team performance but fell short of AI-augmented teams; multiple AIs showed diminishing returns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 65, "validated": null, "n": 3409, "authors_detailed": [ { "name": "Ning Li", "url": "https://openalex.org/A5100369014", "inst": "Capital Medical University" }, { "name": "Huaikang Zhou", "url": "https://openalex.org/A5102623714", "inst": "Tsinghua University" }, { "name": "Kris Mikel-Hong", "url": "https://openalex.org/A5092533873", "inst": "Tsinghua University" } ], "affiliations": [ "Capital Medical University", "Tsinghua University" ] }, { "uid": "arxiv:2405.17637v1", "arxiv_id": "2405.17637v1", "title": "The Economic Implications of Large Language Model Selection on Earnings and Return on Investment: A Decision Theoretic Model", "authors": [ "Geraldo Xexéo", "Filipe Braida", "Marcus Parreiras", "Paulo Xavier" ], "posted": "2024-05-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.17637v1", "field": "management", "role": "object", "bullets": [ "A decision theoretic model of enterprise LLM adoption, driven by cost per token, probability of task success, and the gains and losses attached to model use.", "No model is deployed; candidate LLMs are compared analytically on earnings and return on investment rather than benchmark accuracy, with sensitivity analysis over the operational variables.", "Costlier, more accurate models can justify the investment through larger earnings without necessarily a larger return on investment; predicted gains and losses and success probabilities drive sensitivity most." ], "bullet_provenance": "ai", "salience": 38, "edition": 13, "models": [], "validated": null, "n": 1728, "authors_detailed": [ { "name": "Geraldo Xexéo", "url": "https://openalex.org/A5074689274", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Filipe Braida", "url": "https://openalex.org/A5026274233", "inst": "Universidade Federal Rural do Rio de Janeiro" }, { "name": "Marcus Parreiras", "url": "https://openalex.org/A5060402365", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Paulo Henrique Farias Xavier", "url": "https://openalex.org/A5109534841", "inst": "Universidade Federal do Rio de Janeiro" } ], "affiliations": [ "Universidade Federal do Rio de Janeiro", "Universidade Federal Rural do Rio de Janeiro" ] }, { "uid": "arxiv:2405.16310v1", "arxiv_id": "2405.16310v1", "title": "An Empirical Exploration of Trust Dynamics in LLM Supply Chains", "authors": [ "Agathe Balayn", "Mireia Yurrita", "Fanny Rancourt", "Fabio Casati", "Ujwal Gadiraju" ], "posted": "2024-05-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.16310v1", "field": "management", "role": "object", "bullets": [ "An in situ empirical study of LLM supply chains, covering the practitioners, organizations, and technical artifacts involved in building and adopting LLM systems; sample size not stated.", "No model is applied by the researchers; LLMs and the artifacts around them are the objects between which trust relationships form.", "Trust involves more trustor and trustee types than end user and system, shapes development and adoption, and can tip into uncalibrated reliance on untrustworthy LLMs." ], "bullet_provenance": "ai", "salience": 30, "edition": 13, "models": [], "validated": null, "n": 1785, "authors_detailed": [ { "name": "Agathe Balayn", "url": "https://openalex.org/A5088069213", "inst": "Microsoft (United States)" }, { "name": "Mireia Yurrita", "url": "https://openalex.org/A5024881361", "inst": "Utrecht University" }, { "name": "Fanny Rancourt", "url": "https://openalex.org/A5098921398", "inst": "" }, { "name": "Fabio Casati", "url": "https://openalex.org/A5086585292", "inst": "University of Trento" }, { "name": "Ujwal Gadiraju", "url": "https://openalex.org/A5038081564", "inst": "Delft University of Technology" } ], "affiliations": [ "Microsoft (United States)", "Utrecht University", "University of Trento", "Delft University of Technology" ] }, { "uid": "doi:10.2139/ssrn.4839977", "doi": "10.2139/ssrn.4839977", "title": "Fund Performance Driven by ChatGPT: Evidence from Chinese Fund Market", "authors": [ "Lulu Wang", "Aifan Ling" ], "posted": "2024-05-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4839977", "field": "finance", "role": "object", "bullets": [ "Chinese stock fund data surrounding the November 2022 ChatGPT launch, covering fund returns, Sharpe ratios, and capital flows across fund types and investment styles.", "Study examines how ChatGPT's release affected performance of AI-related funds in China, a market where ChatGPT is unavailable to users.", "ChatGPT-related funds showed significantly higher Sharpe ratios and excess returns post-launch, driven by increased fund inflows and concept-stock price appreciation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 55, "validated": null, "n": 3408, "authors_detailed": [ { "name": "Lulu Wang", "url": "https://openalex.org/A5100444623", "inst": "Jiangxi Normal University" }, { "name": "Aifan Ling", "url": "https://openalex.org/A5003596746", "inst": "Shanghai International Studies University" } ], "affiliations": [ "Jiangxi Normal University", "Shanghai International Studies University" ] }, { "uid": "arxiv:2405.14767v2", "arxiv_id": "2405.14767v2", "title": "FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models", "authors": [ "Hongyang Yang", "Boyu Zhang", "Neng Wang", "Cheng Guo", "Xiaoli Zhang", "Likun Lin", "Junlin Wang", "Tianyu Zhou", "Mao Guan", "Runjia Zhang", "Christina Dan Wang" ], "posted": "2024-05-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.14767v2", "field": "finance", "role": "method", "bullets": [ "An open-source platform organizing financially specialized AI agents in four layers, from financial chain-of-thought agents down to a multi-source foundation model layer.", "The platform routes tasks across unnamed commercial and open LLMs and configures model strategies per task; no evaluation or validation is reported in the abstract.", "The code is released publicly and pitched at professional analysts and laypeople alike; the abstract reports no performance results for any task." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 13, "models": [], "n": 1532, "authors_detailed": [ { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "Beijing University of Technology" }, { "name": "Boyu Zhang", "url": "https://openalex.org/A5100314894", "inst": "University of Electro-Communications" }, { "name": "Neng Wang", "url": "https://openalex.org/A5104201924", "inst": "Gansu Provincial Hospital" }, { "name": "Cheng Guo", "url": "https://openalex.org/A5100914267", "inst": "Inner Mongolia University of Science and Technology" }, { "name": "Xiaoli Zhang", "url": "https://openalex.org/A5053557314", "inst": "Jiangnan University" }, { "name": "Likun Lin", "url": "https://openalex.org/A5111042348", "inst": "" }, { "name": "Junlin Wang", "url": "https://openalex.org/A5082536771", "inst": "Kunming University of Science and Technology" }, { "name": "Tianyu Zhou", "url": "https://openalex.org/A5101806959", "inst": "Shanghai Jiao Tong University" }, { "name": "Mao Guan", "url": "https://openalex.org/A5108935877", "inst": "" }, { "name": "Runjia Zhang", "url": "https://openalex.org/A5114168335", "inst": "University of Electronic Science and Technology of China" }, { "name": "Christina Dan Wang", "url": "https://openalex.org/A5002366400", "inst": "Fudan University" } ], "affiliations": [ "Beijing University of Technology", "University of Electro-Communications", "Inner Mongolia University of Science and Technology", "Jiangnan University", "Kunming University of Science and Technology", "Shanghai Jiao Tong University", "University of Electronic Science and Technology of China" ] }, { "uid": "doi:10.2139/ssrn.4835311", "doi": "10.2139/ssrn.4835311", "title": "Financial Statement Analysis with Large Language Models", "authors": [ "Alex G. Kim", "Maximilian Muhn", "Valeri V. Nikolaev" ], "posted": "2024-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4835311", "field": "accounting", "role": "agent", "bullet_provenance": "none", "salience": 70, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 640, "authors_detailed": [ { "name": "Alex Kim", "url": "https://openalex.org/A5002013850", "inst": "University of Chicago" }, { "name": "Maximilian Muhn", "url": "https://openalex.org/A5065276465", "inst": "University of Chicago" }, { "name": "Valeri V. Nikolaev", "url": "https://openalex.org/A5085975914", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4833954", "doi": "10.2139/ssrn.4833954", "title": "Old Moats for New Models: Openness, Control, and Competition in Generative AI", "authors": [ "Pierre Azoulay", "Joshua Krieger", "Abhishek Nagaraj" ], "posted": "2024-05-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4833954", "field": "economics", "role": "object", "bullets": [ "Innovation economics analysis of generative AI market structure, focusing on appropriability and complementary assets as entry barriers.", "Examines how incumbent firms' control over data, compute, and distribution channels constrains entry in the AI industry.", "Predicts concentrated market structure absent intervention; open-source viability may require a large firm choosing openness strategically." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 2941, "authors_detailed": [ { "name": "Pierre Azoulay", "url": "https://openalex.org/A5032530685", "inst": "National Bureau of Economic Research" }, { "name": "Joshua Krieger", "url": "https://openalex.org/A5042530448", "inst": "Harvard University" }, { "name": "Abhishek Nagaraj", "url": "https://openalex.org/A5002319407", "inst": "University of California, Berkeley" } ], "affiliations": [ "Harvard University", "University of California, Berkeley", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4818096", "doi": "10.2139/ssrn.4818096", "title": "Dissecting Disposition Effect of Large Language Models in Financial Decisions", "authors": [ "Liangdi Tan", "Chunxiao Li" ], "posted": "2024-05-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4818096", "field": "finance", "role": "agent", "bullets": [ "Interactive simulated stock market environment using real US stock transaction data to test LLM trading agent buy-sell decisions.", "LLM-based trading agent made sequential decisions; disposition effect quantified with mathematical metrics from behavioral finance literature.", "LLMs exhibit human-like disposition effect, holding losers too long and selling winners too quickly; behavioral finance mitigation strategies reduce the bias." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "n": 2478, "authors_detailed": [ { "name": "Liangdi Tan", "url": "https://openalex.org/A5102673215", "inst": "University of Science and Technology of China" }, { "name": "Chunxiao Li", "url": "https://openalex.org/A5100459270", "inst": "University of Science and Technology of China" } ], "affiliations": [ "University of Science and Technology of China" ] }, { "uid": "doi:10.2139/ssrn.4834417", "doi": "10.2139/ssrn.4834417", "title": "Algorithms, Bytes, and Chips: The Emerging Political Economy of Foundation Models", "authors": [ "Stuart Mills" ], "posted": "2024-05-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4834417", "field": "economics", "role": "object", "bullets": [ "Analysis of the AI industry through the triad framework of algorithms, data, and chips; focuses on foundation model cost structures.", "Examines how rising development costs of foundational AI models shape market concentration and industry competitive dynamics.", "Argues increasing costs concentrate the AI industry, with state intervention potentially needed to sustain competition and entry." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 2940, "authors_detailed": [ { "name": "Stuart Mills", "url": "https://openalex.org/A5024497032", "inst": "The Honourable Society of Lincoln's Inn" } ], "affiliations": [ "The Honourable Society of Lincoln's Inn" ] }, { "uid": "arxiv:2405.10542v1", "arxiv_id": "2405.10542v1", "title": "Benchmarking Large Language Models on CFLUE -- A Chinese Financial Language Understanding Evaluation Dataset", "authors": [ "Jie Zhu", "Junhui Li", "Yalong Wen", "Lifan Guo" ], "posted": "2024-05-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.10542v1", "field": "finance", "role": "method", "bullets": [ "A Chinese financial benchmark pairing over 38,000 multiple choice questions with solution explanations and over 16,000 application instances across classification, translation, extraction, comprehension, and generation.", "Representative language models are scored on answer prediction and reasoning; only GPT-4 and GPT-4 turbo pass 60 percent accuracy on the knowledge assessment.", "The same two models lead application tasks but with a much smaller margin over lightweight models; datasets and scripts are openly released." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "labeled benchmark, accuracy reported", "salience": 40, "edition": 13, "n": 1639, "authors_detailed": [ { "name": "Jie Zhu", "url": "https://openalex.org/A5111993129", "inst": "Dongguan University of Technology" }, { "name": "Junhui Li", "url": "https://openalex.org/A5101992282", "inst": "Foshan University" }, { "name": "Yalong Wen", "url": "https://openalex.org/A5113392188", "inst": "Shanghai Polytechnic University" }, { "name": "Lifan Guo", "url": "https://openalex.org/A5113222149", "inst": "" } ], "affiliations": [ "Dongguan University of Technology", "Foshan University", "Shanghai Polytechnic University" ] }, { "uid": "arxiv:2405.09747v1", "arxiv_id": "2405.09747v1", "title": "NIFTY Financial News Headlines Dataset", "authors": [ "Raeid Saqur", "Ken Kato", "Nicholas Vinden", "Frank Rudzicz" ], "posted": "2024-05-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.09747v1", "field": "finance", "role": "method", "bullets": [ "Public dataset of deduplicated, filtered financial news headlines with metadata and market indices, released in two versions on Hugging Face; headline counts are not stated in the abstract.", "One version targets supervised fine-tuning of LLMs and the other alignment methods such as RLHF; demonstration experiments cover stock movement prediction and LLM embedding information content.", "The contribution is the resource itself plus utilities like systematic context truncation; no headline performance figures are stated in the abstract." ], "bullet_provenance": "ai", "salience": 28, "edition": 13, "models": [], "validated": null, "n": 1603, "authors_detailed": [ { "name": "Raeid Saqur", "url": "https://openalex.org/A5077308692", "inst": "University of Toronto" }, { "name": "Ken Kato", "url": "https://openalex.org/A5098684049", "inst": "" }, { "name": "Nicholas Vinden", "url": "https://openalex.org/A5098684050", "inst": "" }, { "name": "Frank Rudzicz", "url": "https://openalex.org/A5056256317", "inst": "Dalhousie University" } ], "affiliations": [ "University of Toronto", "Dalhousie University" ], "prestige": true }, { "uid": "doi:10.18653/v1/2024.acl-long.328", "doi": "10.18653/v1/2024.acl-long.328", "arxiv_id": "2405.09980v1", "title": "FinTextQA: A Dataset for Long-form Financial Question Answering", "authors": [ "Jian Chen", "Peilin Zhou", "Yining Hua", "Yingxin Loh", "Kehui Chen", "Ziyuan Li", "Bing Zhu", "Junwei Liang" ], "posted": "2024-05-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.09980v1", "field": "finance", "role": "method", "bullets": [ "1,262 QA pairs from finance textbooks and government websites, tested with RAG-based long-form QA systems under noisy conditions.", "Compared embedder-retriever-reranker-generator configurations including GPT-3.5-turbo and Baichuan2-7B on multi-document financial questions.", "Baichuan2-7B competed closely with GPT-3.5-turbo; program-of-thought QA system improved complex retrieval and quantitative reasoning accuracy." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "human ranking, automatic metrics, and GPT-4 scoring on FinTextQA", "salience": 40, "n": 3188, "authors_detailed": [ { "name": "Jian Chen", "url": "https://openalex.org/A5100768834", "inst": "Hong Kong University of Science and Technology" }, { "name": "Peilin Zhou", "url": "https://openalex.org/A5103037922", "inst": "Hong Kong University of Science and Technology" }, { "name": "Yining Hua", "url": "https://openalex.org/A5081953757", "inst": "Harvard University Press" }, { "name": "Loh Xin", "url": "https://openalex.org/A5111333880", "inst": "" }, { "name": "Kehui Chen", "url": "https://openalex.org/A5111199150", "inst": "HSBC Holdings" }, { "name": "Ziyuan Li", "url": "https://openalex.org/A5014644299", "inst": "HSBC Holdings" }, { "name": "Bing Zhu", "url": "", "inst": "HSBC Holdings" }, { "name": "Junwei Liang", "url": "https://openalex.org/A5059207044", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Harvard University", "Hong Kong University of Science and Technology", "HSBC Holdings" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2405.09161v2", "arxiv_id": "2405.09161v2", "title": "Exploring the Potential of Large Language Models for Automation in Technical Customer Service", "authors": [ "Jochen Wulf", "Juerg Meierhofer" ], "posted": "2024-05-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.09161v2", "field": "management", "role": "object", "bullets": [ "Technical customer service at a Swiss telecommunications operator, using real incident data to prototype LLM automation of cognitive tasks.", "GPT-4 attempts tasks of increasing cognitive complexity; feasibility is judged through prototyping rather than any reported accuracy statistics.", "Translation, summarization and content generation automate well, while reasoning-heavy tasks need retrieval augmentation or fine-tuning, and data sharing ecosystems condition the harder use cases." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 38, "edition": 13, "n": 1556, "authors_detailed": [ { "name": "Jochen Wulf", "url": "https://openalex.org/A5036086266", "inst": "ZHAW Zurich University of Applied Sciences" }, { "name": "Juerg Meierhofer", "url": "https://openalex.org/A5113210473", "inst": "" } ], "affiliations": [ "ZHAW Zurich University of Applied Sciences" ] }, { "uid": "doi:10.2139/ssrn.4829162", "doi": "10.2139/ssrn.4829162", "title": "Controlled Firms, Preferences, and Carbon Emissions", "authors": [ "I.J. Alexander Dyck", "Karl V. Lins", "Lukas Roth", "Mitch Towner", "Hannes F. Wagner" ], "posted": "2024-05-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4829162", "field": "finance", "role": "instrument", "bullets": [ "3,769 firms from 35 countries with actual carbon emissions data and controlling-owner environmental preference measures.", "Retrieval-augmented LLMs constructed an environmental preference measure for controlling owners from corporate disclosures.", "Low-preference controlled firms emit approximately 25% more carbon than widely held firms; high-preference controlled firms show no difference." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 62, "n": 2839, "authors_detailed": [ { "name": "I. J. Alexander Dyck", "url": "https://openalex.org/A5084538782", "inst": "University of Toronto" }, { "name": "Karl V. Lins", "url": "https://openalex.org/A5060089897", "inst": "University of Utah" }, { "name": "Lukas Roth", "url": "https://openalex.org/A5079061224", "inst": "University of Alberta" }, { "name": "Mitch Towner", "url": "https://openalex.org/A5018037761", "inst": "University of Arizona" }, { "name": "Hannes F. Wagner", "url": "https://openalex.org/A5062705166", "inst": "Institute for Economic Research" } ], "affiliations": [ "University of Toronto", "University of Utah", "University of Alberta", "University of Arizona", "Institute for Economic Research" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4827928", "doi": "10.2139/ssrn.4827928", "title": "Supervisor Behavior under Competitive Pressure", "authors": [ "Wei Cai", "Matthias D. Mahlendorf", "Fan Wu" ], "posted": "2024-05-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4827928", "field": "management", "role": "instrument", "bullets": [ "Sample of 262,249 current-employee reviews from the Kununu platform, analyzed at the firm-department-year level across multiple German firms and departments.", "An LLM classified review text into management-oriented and leadership-oriented supervisor behavior scores; no specific model family or validation method disclosed.", "Structural output market pressure reduced both behavior scores; structural input market pressure raised them; disruptive pressure primarily eroded leadership while leaving management intact." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 45, "n": 3407, "authors_detailed": [ { "name": "Wei Cai", "url": "", "inst": "Columbia Business School" }, { "name": "Matthias D. Mahlendorf", "url": "https://openalex.org/A5034277631", "inst": "Frankfurt School of Finance & Management" }, { "name": "Fan Wu", "url": "https://openalex.org/A5059190563", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Columbia Business School", "Frankfurt School of Finance & Management", "Chinese University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4826695", "doi": "10.2139/ssrn.4826695", "title": "Consent and Compensation: Resolving Generative AI’s Copyright Crisis", "authors": [ "Frank A. Pasquale", "Haochen Sun" ], "posted": "2024-05-14", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4826695", "field": "economics", "role": "object", "bullets": [ "Conceptual policy analysis addresses creative labor, copyright owners, and generative AI providers in markets for books, music, visual art, and other knowledge goods.", "No language model is used as a research tool; model training and its effects on creators' consent, compensation, and competition form the causal subject.", "A streamlined infringement-based opt-out and a provider levy are proposed to compensate rightsholders while preserving the knowledge inputs on which generative AI depends." ], "bullet_provenance": "ai", "salience": 56, "edition": 23, "models": [], "validated": null, "n": 4186, "authors_detailed": [ { "name": "Frank Pasquale", "url": "https://openalex.org/A5043513868", "inst": "Cornell University" }, { "name": "Haochen Sun", "url": "https://openalex.org/A5113747570", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Cornell University", "Chinese University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4782875", "doi": "10.2139/ssrn.4782875", "title": "Knowledge Management Perspective of Generative Artificial Intelligence (GenAI)", "authors": [ "Maryam Alavi", "Dorothy Leidner", "Reza Mousavi" ], "posted": "2024-05-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4782875", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 639, "authors_detailed": [ { "name": "Maryam Alavi", "url": "https://openalex.org/A5041422724", "inst": "Georgia Institute of Technology" }, { "name": "Dorothy E. Leidner", "url": "https://openalex.org/A5062196000", "inst": "Baylor University" }, { "name": "Reza Mousavi", "url": "https://openalex.org/A5103265152", "inst": "University of Virginia" } ], "affiliations": [ "Georgia Institute of Technology", "University of Virginia", "Baylor University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4826207", "doi": "10.2139/ssrn.4826207", "title": "Combining AI and Domain Expertise to Assess Corporate Climate Transition Disclosures", "authors": [ "Chiara Colesanti Senni", "Tobias Schimanski", "Julia Bingler", "Jingwei Ni", "Markus Leippold" ], "posted": "2024-05-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4826207", "field": "accounting", "role": "instrument", "bullets": [ "Sustainability reports from carbon-intensive Climate Action 100+ companies assessed using 64 transition-plan indicators.", "LLM-based tool automated assessment of corporate climate transition disclosures; validated by experts from 26 institutions including regulators.", "Companies disclosed more on target-setting than implementation; higher disclosure volume correlated with lower emissions." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "expert evaluation from 26 institutions including regulators and investors", "salience": 70, "n": 2938, "authors_detailed": [ { "name": "Chiara Colesanti Senni", "url": "https://openalex.org/A5086406811", "inst": "University of Zurich" }, { "name": "Tobias Schimanski", "url": "https://openalex.org/A5066649415", "inst": "University of Zurich" }, { "name": "Julia Bingler", "url": "https://openalex.org/A5010157189", "inst": "Mansfield University" }, { "name": "Jingwei Ni", "url": "https://openalex.org/A5113207856", "inst": "ETH Zurich" }, { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" } ], "affiliations": [ "University of Zurich", "Mansfield University", "ETH Zurich" ] }, { "uid": "doi:10.2139/ssrn.4826028", "doi": "10.2139/ssrn.4826028", "title": "Understanding Economic Behavior Using Open-ended Survey Data", "authors": [ "Ingar Haaland", "Christopher Roth", "Stefanie Stantcheva", "Johannes Wohlfart" ], "posted": "2024-05-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4826028", "field": "economics", "role": "method", "bullets": [ "Survey of recent economics literature using open-ended survey data to study beliefs, motives, mental models, narratives, and recall.", "Reviews LLM-based annotation and analysis methods for open-ended responses, including AI-powered qualitative interview techniques.", "Identifies LLMs as enabling scalable analysis of unstructured survey text with promising avenues for economic belief and behavior research." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 60, "validated": null, "n": 2939, "authors_detailed": [ { "name": "Ingar Haaland", "url": "https://openalex.org/A5070059373", "inst": "Norwegian School of Economics" }, { "name": "Christopher Roth", "url": "https://openalex.org/A5043293841", "inst": "University of Cologne" }, { "name": "Stefanie Stantcheva", "url": "https://openalex.org/A5096494242", "inst": "National Bureau of Economic Research" }, { "name": "Johannes Wohlfart", "url": "https://openalex.org/A5063288880", "inst": "University of Cologne" } ], "affiliations": [ "Norwegian School of Economics", "University of Cologne", "National Bureau of Economic Research" ] }, { "uid": "doi:10.2139/ssrn.4825716", "doi": "10.2139/ssrn.4825716", "title": "The Valuation of Generative AI in Content Creation: Evidence from Gig Workers", "authors": [ "Chen Liang", "Jing Peng", "Zhuoyan Li", "Ming Yin" ], "posted": "2024-05-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4825716", "field": "economics", "role": "object", "bullets": [ "Randomized experiment embedded in hiring for writing tasks on a gig economy platform, with econometric analysis of worker preferences.", "Study measures gig workers' willingness to pay for AI assistance in content generation versus text editing during writing tasks.", "Workers forgo 29.7% of earnings for content generation AI but only 5.7% for editing; higher WTP does not predict greater performance gains." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3406, "authors_detailed": [ { "name": "Chen Liang", "url": "https://openalex.org/A5058269050", "inst": "University of Connecticut" }, { "name": "Jing Peng", "url": "https://openalex.org/A5072128915", "inst": "University of Connecticut" }, { "name": "Zhuoyan Li", "url": "https://openalex.org/A5024020429", "inst": "Purdue University West Lafayette" }, { "name": "Ming Yin", "url": "https://openalex.org/A5071294124", "inst": "Purdue University West Lafayette" } ], "affiliations": [ "University of Connecticut", "Purdue University West Lafayette" ] }, { "uid": "doi:10.2139/ssrn.4825076", "doi": "10.2139/ssrn.4825076", "title": "Generating Inflation Expectations with Large Language Models", "authors": [ "Ali Zarifhonarvar" ], "posted": "2024-05-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4825076", "field": "economics", "role": "agent", "bullets": [ "Survey experiments creating diverse LLM-based agents with demographic personas using both proprietary and open-source models to generate inflation expectations.", "Multiple LLMs generated inflation forecasts under information treatments including forward guidance; outputs compared to the Survey of Consumer Expectations.", "Proprietary models show less disagreement; some LLMs predict above-actual inflation mirroring human biases; demographic prompts reproduce heterogeneous survey patterns." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "Survey of Consumer Expectations", "salience": 78, "n": 2477, "authors_detailed": [ { "name": "Ali Zarifhonarvar", "url": "https://openalex.org/A5097928103", "inst": "Indiana University" } ], "affiliations": [ "Indiana University" ] }, { "uid": "arxiv:2405.06808v2", "arxiv_id": "2405.06808v2", "title": "Large Language Model in Financial Regulatory Interpretation", "authors": [ "Zhiyu Cao", "Zachary Feinstein" ], "posted": "2024-05-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.06808v2", "field": "finance", "role": "method", "bullets": [ "Basel III capital requirement texts, distilled into mathematical frameworks and code, with numerical simulations over fixed income, equity, currency and commodity holdings.", "Several LLMs are compared on prompt-guided distillation of the regulations; GPT-4 performs best at information gathering and calculation, with no quantitative accuracy benchmark reported.", "The authors conclude that prompt-engineered LLMs can operationalize minimum capital requirements, streamlining implementation of regulatory mandates in bank risk systems." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 42, "edition": 13, "n": 1555, "authors_detailed": [ { "name": "Zhiyu Cao", "url": "https://openalex.org/A5113207953", "inst": "Second Affiliated Hospital of Guangzhou Medical University" }, { "name": "Zachary Feinstein", "url": "https://openalex.org/A5045209801", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Stevens Institute of Technology" ] }, { "uid": "arxiv:2405.05508v2", "arxiv_id": "2405.05508v2", "title": "Redefining Information Retrieval of Structured Database via Large Language Models", "authors": [ "Mingzhu Wang", "Yuzhe Zhang", "Qihang Zhao", "Junyi Yang", "Hong Zhang" ], "posted": "2024-05-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.05508v2", "field": "finance", "role": "method", "bullets": [ "A financial domain search and question answering system over structured knowledge bases, with user queries translated into database API calls rather than free-text retrieval.", "An LLM acts as the retriever, fine-tuned on Text2API and API-ID recognition tasks. The base model is not named in the abstract.", "The framework reports overall information retrieval accuracy above 98.8 percent on user queries, credited to replacing conventional retrievers with LLM semantic understanding." ], "bullet_provenance": "ai", "validated": true, "validation_note": "financial query set, 98.8 percent retrieval accuracy", "salience": 26, "edition": 13, "models": [], "n": 1689, "authors_detailed": [ { "name": "Mingzhu Wang", "url": "https://openalex.org/A5100676821", "inst": "Harbin University of Science and Technology" }, { "name": "Yuzhe Zhang", "url": "https://openalex.org/A5100727262", "inst": "University of Science and Technology of China" }, { "name": "Qihang Zhao", "url": "https://openalex.org/A5074898687", "inst": "University of Electronic Science and Technology of China" }, { "name": "Juanyi Yang", "url": "https://openalex.org/A5108928332", "inst": "" }, { "name": "Hong Zhang", "url": "https://openalex.org/A5100430287", "inst": "Zhejiang Chinese Medical University" } ], "affiliations": [ "Harbin University of Science and Technology", "University of Science and Technology of China", "University of Electronic Science and Technology of China", "Zhejiang Chinese Medical University" ] }, { "uid": "doi:10.2139/ssrn.4820727", "doi": "10.2139/ssrn.4820727", "title": "Upgrades of Large Language Model Products and Firm's Strategy: The Role of Quality Improvement", "authors": [ "Tongyuan Shen", "Lin Liu", "Junjie Wu", "Yong Tan" ], "posted": "2024-05-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4820727", "field": "management", "role": "object", "bullets": [ "Theoretical model of a firm pricing basic and advanced versions of an LLM product where consumer data drives quality improvement via fine-tuning", "Analytical framework examines how consumption data enables fine-tuning for quality improvement while simultaneously revealing consumer preferences", "Quality improvement via consumption data can produce an all-win outcome benefiting the firm, early adopters, and late adopters simultaneously" ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 2426, "authors_detailed": [ { "name": "T. Shen", "url": "https://openalex.org/A5048623514", "inst": "Beihang University" }, { "name": "Lin Liu", "url": "https://openalex.org/A5100383332", "inst": "Beihang University" }, { "name": "Junjie Wu", "url": "https://openalex.org/A5112210049", "inst": "Beihang University" }, { "name": "Yong Tan", "url": "https://openalex.org/A5037984091", "inst": "University of Washington" } ], "affiliations": [ "Beihang University", "University of Washington" ] }, { "uid": "arxiv:2405.04294v1", "arxiv_id": "2405.04294v1", "title": "Enhancing the Efficiency and Accuracy of Underlying Asset Reviews in Structured Finance: The Application of Multi-agent Framework", "authors": [ "Xiangpeng Wan", "Haicheng Deng", "Kai Zou", "Shiqi Xu" ], "posted": "2024-05-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.04294v1", "field": "finance", "role": "method", "bullets": [ "Due diligence for structured finance products such as MBS and ABS, where loan application details are checked against bank statements; document counts are not stated.", "GPT-4 and LLAMA3 perform the cross-document verification, with accuracy compared across models and single versus dual-agent setups; specific figures are not given in the abstract.", "Closed models outperform open ones, which remain a cheaper alternative, and dual-agent review raises accuracy at higher operating cost." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "cross-document verification accuracy, figures not stated in abstract", "salience": 42, "edition": 13, "n": 1537, "authors_detailed": [ { "name": "Xiangpeng Wan", "url": "https://openalex.org/A5097335822", "inst": "" }, { "name": "Haicheng Deng", "url": "https://openalex.org/A5106024121", "inst": "" }, { "name": "Kai Zou", "url": "https://openalex.org/A5108385543", "inst": "Guangdong Pharmaceutical University" }, { "name": "Shiqi Xu", "url": "https://openalex.org/A5101996526", "inst": "Qingdao University" } ], "affiliations": [ "Guangdong Pharmaceutical University", "Qingdao University" ] }, { "uid": "doi:10.1145/3689904.3694699", "doi": "10.1145/3689904.3694699", "arxiv_id": "2405.04412v3", "title": "The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiring", "authors": [ "Lena Armstrong", "Abbey Liu", "Stephen MacNeil", "Danaë Metaxa" ], "posted": "2024-05-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.04412v3", "field": "management", "role": "agent", "bullets": [ "An audit design with 32 names crossing two genders and four racial groups plus two anonymous options, spanning 10 occupations, three assessment tasks, and 10 generated resumes per name.", "OpenAI's GPT-3.5 scores resumes for overall rating, willingness to interview, and hireability, and separately writes resumes for fictitious candidates; outputs are compared across name groups.", "Scoring shows some stereotype-linked gaps; generated resumes give women occupations with less experience and attach immigrant markers, such as non-US education, to Asian and Hispanic names." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 13, "validated": null, "n": 1571, "authors_detailed": [ { "name": "Lena Armstrong", "url": "https://openalex.org/A5037849664", "inst": "California University of Pennsylvania" }, { "name": "Abbey Liu", "url": "https://openalex.org/A5010367119", "inst": "Temple University" }, { "name": "Stephen MacNeil", "url": "https://openalex.org/A5042822346", "inst": "California University of Pennsylvania" }, { "name": "Danaë Metaxa", "url": "https://openalex.org/A5086524212", "inst": "California University of Pennsylvania" } ], "affiliations": [ "California University of Pennsylvania", "Temple University" ] }, { "uid": "doi:10.2139/ssrn.4819470", "doi": "10.2139/ssrn.4819470", "title": "Can ChatGPT Replicate Analyst Recommendations?", "authors": [ "Jason Ming", "Hamish Malloch", "P. Joakim Westerholm" ], "posted": "2024-05-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4819470", "field": "finance", "role": "agent", "bullets": [ "Earnings conference call transcripts from 500 largest U.S. firms; Analyst Insight Score developed for each firm-transcript pair.", "ChatGPT learned from analyst questions during earnings calls, then scored transcripts to emulate equity analyst decision-making.", "AIS aligned with analyst price-target adjustments, outperformed SUE, and enabled portfolios earning abnormal returns across factor models." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "consistency with analyst price-target adjustments and comparison to SUE", "salience": 65, "n": 2937, "authors_detailed": [ { "name": "Jason Ming", "url": "https://openalex.org/A5102661180", "inst": "The University of Sydney" }, { "name": "Hamish Malloch", "url": "https://openalex.org/A5078142200", "inst": "The University of Sydney" }, { "name": "P. Joakim Westerholm", "url": "https://openalex.org/A5071109114", "inst": "The University of Sydney" } ], "affiliations": [ "The University of Sydney" ] }, { "uid": "doi:10.2139/ssrn.4814916", "doi": "10.2139/ssrn.4814916", "title": "Buying the Blended Brushstroke: Consumer Responses to AI Involvement in Digital Art Creation", "authors": [ "Peiwen Xie", "Xinlong Li" ], "posted": "2024-05-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4814916", "field": "management", "role": "object", "bullets": [ "Digital artworks on an online platform, with ML-quantified visual features and causal forest estimation of treatment effects.", "Study examines consumer purchase responses to AI involvement in digital art creation across visual typicality and complexity levels.", "AI-involved artworks are more likely to sell; the effect is stronger for low visual typicality, consistent with proximity-to-origin authenticity." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3405, "authors_detailed": [ { "name": "Peiwen Xie", "url": "https://openalex.org/A5051267150", "inst": "Nanyang Technological University" }, { "name": "Xinlong Li", "url": "https://openalex.org/A5101781873", "inst": "Nanyang Technological University" } ], "affiliations": [ "Nanyang Technological University" ] }, { "uid": "arxiv:2405.02219v2", "arxiv_id": "2405.02219v2", "title": "A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System", "authors": [ "Yashar Deldjoo", "Fatemeh Nazary" ], "posted": "2024-05-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.02219v2", "field": "management", "role": "method", "bullets": [ "MovieLens recommendation data grounds an audit of consumer fairness in LLM-based recommender systems; user counts are not stated in the abstract.", "Unnamed LLMs generate recommendations under zero-shot and few-shot in-context prompts; fairness deviations across user groups are tested for statistical significance rather than validated against ground truth.", "Age-based recommendation disparities emerge and grow when more in-context examples are added, and the authors argue classical fairness norms transfer poorly to LLM recommenders." ], "bullet_provenance": "ai", "salience": 24, "edition": 13, "models": [], "validated": null, "n": 1602, "authors_detailed": [ { "name": "Yashar Deldjoo", "url": "https://openalex.org/A5009954943", "inst": "Polytechnic University of Bari" }, { "name": "Nazary, Fatemeh", "url": "", "inst": "" } ], "affiliations": [ "Polytechnic University of Bari" ] }, { "uid": "arxiv:2405.06671v2", "arxiv_id": "2405.06671v2", "title": "Parameter-Efficient Instruction Tuning of Large Language Models For Extreme Financial Numeral Labelling", "authors": [ "Subhendu Khatuya", "Rajdeep Mukherjee", "Akash Ghosh", "Manjunath Hegde", "Koustuv Dasgupta", "Niloy Ganguly", "Saptarshi Ghosh", "Pawan Goyal" ], "posted": "2024-05-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.06671v2", "field": "accounting", "role": "method", "bullets": [ "Numerals in financial documents mapped to XBRL tags for GAAP metrics, treated as extreme classification on two public financial numeric labeling datasets.", "FLAN-FinXC instruction tunes a FLAN model with LoRA to generate tags from metric metadata; performance is compared with strong baselines, including on zero shot and rare tags.", "The model reports state of the art on both datasets, and even wrong predictions overlap substantially with ground truth tags; exact figures are not given in the abstract." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "two financial numeral labeling datasets", "salience": 45, "edition": 13, "n": 1727, "authors_detailed": [ { "name": "Subhendu Khatuya", "url": "https://openalex.org/A5062498056", "inst": "Indian Institute of Technology Kharagpur" }, { "name": "Rajdeep Mukherjee", "url": "https://openalex.org/A5038274461", "inst": "Indian Institute of Technology Kharagpur" }, { "name": "Akash Ghosh", "url": "https://openalex.org/A5018985733", "inst": "ETH Zurich" }, { "name": "M.V. Hegde", "url": "https://openalex.org/A5032389038", "inst": "Manipal Academy of Higher Education" }, { "name": "Koustuv Dasgupta", "url": "https://openalex.org/A5102227355", "inst": "Goldman Sachs (United States)" }, { "name": "Niloy Ganguly", "url": "https://openalex.org/A5073812421", "inst": "Indian Institute of Technology Kharagpur" }, { "name": "Saptarshi Ghosh", "url": "https://openalex.org/A5073748464", "inst": "Indian Institute of Technology Kharagpur" }, { "name": "Pawan Goyal", "url": "https://openalex.org/A5080481874", "inst": "Max Planck Institute for Dynamics of Complex Technical Systems" } ], "affiliations": [ "Indian Institute of Technology Kharagpur", "ETH Zurich", "Manipal Academy of Higher Education", "Goldman Sachs (United States)", "Max Planck Institute for Dynamics of Complex Technical Systems" ] }, { "uid": "arxiv:2405.06669v1", "arxiv_id": "2405.06669v1", "title": "Instruction-Guided Bullet Point Summarization of Long Financial Earnings Call Transcripts", "authors": [ "Subhendu Khatuya", "Koushiki Sinha", "Niloy Ganguly", "Saptarshi Ghosh", "Pawan Goyal" ], "posted": "2024-05-03", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.06669v1", "field": "finance", "role": "instrument", "bullets": [ "Earnings call transcripts from the ECTSum dataset covering U.S. publicly listed companies, evaluated on summarization quality.", "FLAN-T5 instruction-tuned model generated bullet-point summaries of long transcripts after unsupervised extractive pre-filtering, scored with ROUGE metrics.", "FLAN-FinBPS achieved a 14.88% average ROUGE score gain over the strongest baseline while producing factually consistent summaries." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "ECTSum ROUGE benchmark", "salience": 42, "n": 3892, "authors_detailed": [ { "name": "Subhendu Khatuya", "url": "https://openalex.org/A5062498056", "inst": "Indian Institute of Technology Kharagpur" }, { "name": "Koushiki Sinha", "url": "https://openalex.org/A5114160820", "inst": "" }, { "name": "Niloy Ganguly", "url": "https://openalex.org/A5073812421", "inst": "Indian Institute of Technology Kharagpur" }, { "name": "Saptarshi Ghosh", "url": "https://openalex.org/A5073748464", "inst": "Indian Institute of Technology Kharagpur" }, { "name": "Pawan Goyal", "url": "https://openalex.org/A5080481874", "inst": "Max Planck Institute for Dynamics of Complex Technical Systems" } ], "affiliations": [ "Indian Institute of Technology Kharagpur", "Max Planck Institute for Dynamics of Complex Technical Systems" ] }, { "uid": "arxiv:2405.01769v2", "arxiv_id": "2405.01769v2", "title": "A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law", "authors": [ "Zhiyu Zoey Chen", "Jing Ma", "Xinlu Zhang", "Nan Hao", "An Yan", "Armineh Nourbakhsh", "Xianjun Yang", "Julian McAuley", "Linda Petzold", "William Yang Wang" ], "posted": "2024-05-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.01769v2", "field": "finance", "role": "method", "bullets": [ "A literature survey of LLM applications in finance, healthcare, and law, three domains marked by professional expertise, high stakes, and regulatory constraint.", "No new model deployment; the paper organizes methodologies, applications, and ethical concerns, citing GPT-3 and GPT-4 as representative systems, and maintains a continually updated reading list.", "Maps where LLMs support financial analytics, diagnosis, and legal compliance, and argues for transparent, fair, and robust systems; it reports no empirical findings of its own." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 30, "edition": 13, "validated": null, "n": 1570, "authors_detailed": [ { "name": "Zhiyu Zoey Chen", "url": "https://openalex.org/A5108927979", "inst": "" }, { "name": "Jing Ma", "url": "https://openalex.org/A5100460560", "inst": "Science and Technology on Surface Physics and Chemistry Laboratory" }, { "name": "Xinlu Zhang", "url": "https://openalex.org/A5102845705", "inst": "Jiangxi University of Water Resources and Electric Power" }, { "name": "Nan Hao", "url": "https://openalex.org/A5097330933", "inst": "" }, { "name": "An Yan", "url": "https://openalex.org/A5111639855", "inst": "Chongqing University" }, { "name": "Armineh Nourbakhsh", "url": "https://openalex.org/A5097278355", "inst": "" }, { "name": "Xianjun Yang", "url": "https://openalex.org/A5052468576", "inst": "Chinese Academy of Sciences" }, { "name": "McAuley Julian", "url": "https://openalex.org/A5037782080", "inst": "" }, { "name": "Linda Petzold", "url": "https://openalex.org/A5021640058", "inst": "University of California, Santa Barbara" }, { "name": "William Yang Wang", "url": "https://openalex.org/A5100702485", "inst": "Rutgers, The State University of New Jersey" } ], "affiliations": [ "Science and Technology on Surface Physics and Chemistry Laboratory", "Jiangxi University of Water Resources and Electric Power", "Chongqing University", "Chinese Academy of Sciences", "University of California, Santa Barbara", "Rutgers, The State University of New Jersey" ] }, { "uid": "doi:10.2139/ssrn.4811728", "doi": "10.2139/ssrn.4811728", "title": "Generative AI and the Future of Work: Augmentation or Automation?", "authors": [ "John Zysman", "Mark Nitzberg" ], "posted": "2024-05-01", "added": "2026-08-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4811728", "field": "management", "role": "object", "bullets": [ "Conceptual policy analysis of generative AI's workforce impact, drawing on examples from professional services, materials, and pharmaceutical sectors.", "No model is used empirically; ChatGPT motivates a discussion of augmentation versus automation strategies at the firm level.", "Argues firm deployment choices, not the technology itself, determine labor outcomes, and proposes an independent public-interest consultancy to guide AI adoption." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 24, "validated": null, "n": 4227, "authors_detailed": [ { "name": "John Zysman", "url": "https://openalex.org/A5028460002", "inst": "Business Roundtable" }, { "name": "Mark Nitzberg", "url": "https://openalex.org/A5112611437", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of California, Berkeley", "Business Roundtable" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2405.00566v1", "arxiv_id": "2405.00566v1", "title": "NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance", "authors": [ "Huan-Yi Su", "Ke Wu", "Yu-Hao Huang", "Wu-Jun Li" ], "posted": "2024-05-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.00566v1", "field": "finance", "role": "method", "bullets": [ "Chinese financial question answering, with a training corpus built from financial textbooks; corpus and benchmark sizes are not stated in the abstract.", "An unnamed open foundation model gains two LoRA modules, one for financial domain adaptation and one for numeric sensitivity, merged for inference and scored on a financial QA benchmark.", "NumLLM posts the best overall benchmark performance among compared baselines, on both numeric and non-numeric questions; margins are not stated." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "Chinese financial QA benchmark", "salience": 24, "edition": 13, "models": [], "n": 1601, "authors_detailed": [ { "name": "Huan-Yi Su", "url": "https://openalex.org/A5102659505", "inst": "" }, { "name": "Ke Wu", "url": "https://openalex.org/A5012994943", "inst": "Southern University of Science and Technology" }, { "name": "Yu-Hao Huang", "url": "https://openalex.org/A5113407742", "inst": "Nanjing University" }, { "name": "Wu-Jun Li", "url": "https://openalex.org/A5111188118", "inst": "" } ], "affiliations": [ "Southern University of Science and Technology", "Nanjing University" ] }, { "uid": "doi:10.2139/ssrn.4813057", "doi": "10.2139/ssrn.4813057", "title": "Readability and Neutrality in Mutual Fund Shareholder Reports", "authors": [ "Hao Ding" ], "posted": "2024-05-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4813057", "field": "finance", "role": "instrument", "bullets": [ "Mutual fund shareholder reports examining the effects of readability and neutrality on fund flows across retail and institutional investors.", "LLMs fine-tuned on financial texts scored readability and neutrality of each report; no specific model family or ground-truth benchmark named.", "Neutral tone predicts higher inflows, but highly readable neutral reports cause outflows for outperforming funds; retail investors respond less than institutional." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 65, "n": 2476, "authors_detailed": [ { "name": "Hao Ding", "url": "https://openalex.org/A5101431388", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4813964", "doi": "10.2139/ssrn.4813964", "title": "DIFFERENT YET SAME: INTENTIONALITY ASCRIPTIONS TO ROUTINES ACROSS PRACTICE AND CAPABILITIES SCHOOLS. LARGE LANGUAGE MODEL-BASED APPROACH", "authors": [ "Piotr Makowski", "anon Hensel", "Mansour Esmaeil Zaei" ], "posted": "2024-05-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4813964", "field": "management", "role": "instrument", "bullets": [ "Nearly 900 papers on organizational routines from practice and capabilities schools, analyzed with exploratory text methods.", "GPT-4 classifies intentionality ascriptions in routines literature, distinguishing stability and change dimensions across theoretical schools.", "No strict association between capabilities and practice schools and stability-or-change framing; specific journals show distinct tendencies toward either." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 3404, "authors_detailed": [ { "name": "Piotr Tomasz Makowski", "url": "https://openalex.org/A5004193946", "inst": "Queen's University Belfast" }, { "name": "anon Hensel", "url": "https://openalex.org/A5095959312", "inst": "" }, { "name": "Mansour Esmaeil Zaei", "url": "https://openalex.org/A5091189011", "inst": "Center for Social and Economic Research" } ], "affiliations": [ "Queen's University Belfast", "Center for Social and Economic Research" ] }, { "uid": "doi:10.2139/ssrn.4811631", "doi": "10.2139/ssrn.4811631", "title": "Recent Developments in Large Language Models and their Use in Financial Reporting Analyses", "authors": [ "Sungho Noh" ], "posted": "2024-04-30", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4811631", "field": "accounting", "role": "method", "bullet_provenance": "none", "salience": 28, "edition": 13, "bullets": [], "models": [], "validated": null, "n": 1783, "authors_detailed": [ { "name": "Sungho Noh", "url": "https://openalex.org/A5095945912", "inst": "Korea Development Institute" } ], "affiliations": [ "Korea Development Institute" ] }, { "uid": "arxiv:2404.19699v3", "arxiv_id": "2404.19699v3", "title": "Generative AI Usage and Exam Performance", "authors": [ "Janik Ole Wecks", "Johannes Voshaar", "Benedikt Jost Plate", "Jochen Zimmermann" ], "posted": "2024-04-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.19699v3", "field": "economics", "role": "object", "bullets": [ "University students in a higher education course, with GenAI users identified by running their submitted essays through AI text detection systems; cohort size not stated.", "Use of tools such as ChatGPT is inferred from detector output; the detectors' accuracy against known usage is not reported, leaving measurement error unquantified.", "Detected GenAI users score 6.71 points lower out of 100 than non users, and the gap is largest for students with high learning potential." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 52, "edition": 13, "n": 1784, "authors_detailed": [ { "name": "Janik Ole Wecks", "url": "https://openalex.org/A5042439936", "inst": "Hochschule Bremen" }, { "name": "Johannes Voshaar", "url": "https://openalex.org/A5014230083", "inst": "Hochschule Bremen" }, { "name": "Benedikt J. Plate", "url": "https://openalex.org/A5095955203", "inst": "Hochschule Bremen" }, { "name": "Jochen Zimmermann", "url": "https://openalex.org/A5013724873", "inst": "University of Bremen" } ], "affiliations": [ "Hochschule Bremen", "University of Bremen" ] }, { "uid": "doi:10.2139/ssrn.4810626", "doi": "10.2139/ssrn.4810626", "title": "How good are LLMs in risk profiling?", "authors": [ "Thorsten Hens", "Trine Nordlie" ], "posted": "2024-04-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4810626", "field": "finance", "role": "agent", "bullets": [ "Investor risk profiling cases assessed by ChatGPT-4, Google Bard, and bank expert advisors in a comparative study.", "ChatGPT-4 and Bard determined investor risk profiles for client cases and were compared against bank expert assessments.", "No significant differences in risk profiles for half the cases; economic differences small overall, but LLMs performed poorly at explaining their profiles." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "comparison with bank expert assessments", "salience": 60, "n": 2475, "authors_detailed": [ { "name": "Thorsten Hens", "url": "https://openalex.org/A5004082242", "inst": "University of Zurich" }, { "name": "Trine Nordlie", "url": "https://openalex.org/A5095948121", "inst": "Norwegian School of Economics" } ], "affiliations": [ "University of Zurich", "Norwegian School of Economics" ] }, { "uid": "doi:10.2139/ssrn.4810933", "doi": "10.2139/ssrn.4810933", "title": "Analyzing the dynamics of innovation networks in climate technologies using large language models", "authors": [ "Malte Toetzke", "Benedict Probst", "Stefan Feuerriegel", "Laura Diaz Anadon", "Volker H. Hoffmann" ], "posted": "2024-04-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4810933", "field": "economics", "role": "instrument", "bullets": [ "26 million LinkedIn posts spanning 189 countries, 28 climate technologies, and 166,459 organizations from January 2020 to December 2024.", "LLMs extract and classify 442,250 collaborations across 17 partnership types from unstructured social media text at global scale.", "Climate-tech networks expanded 49% in organizations and 93% in partnerships after mid-2022; 30% of partnerships directly involve governmental organizations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 48, "n": 2661, "authors_detailed": [ { "name": "Malte Toetzke", "url": "https://openalex.org/A5058238942", "inst": "Max Planck Institute for Innovation and Competition" }, { "name": "Benedict Probst", "url": "https://openalex.org/A5091139976", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Stefan Feuerriegel", "url": "https://openalex.org/A5081442873", "inst": "Center for NanoScience" }, { "name": "Laura Díaz Anadón", "url": "https://openalex.org/A5063240615", "inst": "University of Cambridge" }, { "name": "Volker H. Hoffmann", "url": "https://openalex.org/A5074636588", "inst": "ETH Zurich" } ], "affiliations": [ "University of Cambridge", "Max Planck Institute for Innovation and Competition", "Independent - affiliation not provided to SSRN", "Center for NanoScience", "ETH Zurich" ], "prestige": true }, { "uid": "arxiv:2404.18470v2", "arxiv_id": "2404.18470v2", "title": "ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction", "authors": [ "Yupeng Cao", "Zhi Chen", "Qingyun Pei", "Nathan Jinseok Lee", "K. P. Subbalakshmi", "Papa Momar Ndiaye" ], "posted": "2024-04-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.18470v2", "field": "finance", "role": "instrument", "bullets": [ "Earnings conference call audio and transcripts feed a stock volatility prediction task; sample size and period are not stated in the abstract.", "Unnamed pre-trained large models summarize calls and retrieval-augmented generation pulls focus sentences; extracted text and audio features are fused for prediction, with no validation of the extraction itself reported.", "The framework beats traditional volatility prediction benchmarks, which the authors attribute to finer-grained information captured from the calls; effect sizes are not stated." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 13, "models": [], "n": 1600, "authors_detailed": [ { "name": "Yupeng Cao", "url": "https://openalex.org/A5009376964", "inst": "Kyoto University" }, { "name": "Zhi Chen", "url": "https://openalex.org/A5018472081", "inst": "" }, { "name": "Qingyun Pei", "url": "https://openalex.org/A5095383910", "inst": "Stevens Institute of Technology" }, { "name": "Lee, Nathan Jinseok", "url": "", "inst": "" }, { "name": "K. P. Subbalakshmi", "url": "https://openalex.org/A5033041089", "inst": "Stevens Institute of Technology" }, { "name": "Papa Momar Ndiaye", "url": "https://openalex.org/A5108382062", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Kyoto University", "Stevens Institute of Technology" ] }, { "uid": "arxiv:2404.19063v1", "arxiv_id": "2404.19063v1", "title": "SuperCLUE-Fin: Graded Fine-Grained Analysis of Chinese LLMs on Diverse Financial Tasks and Applications", "authors": [ "Liang Xu", "Lei Zhu", "Yaotong Wu", "Hang Xue" ], "posted": "2024-04-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.19063v1", "field": "finance", "role": "method", "bullets": [ "More than a thousand multi turn, open ended Chinese financial questions spanning six application domains and twenty five tasks, from compliance and risk management to investment analysis.", "Chinese native financial language models are graded on criteria including accuracy, reasoning, computational efficiency, business acumen, and regulatory compliance; the grading procedure is not detailed in the abstract.", "Domestic models GLM-4 and MoonShot-v1-128k earn the top A grade, and the benchmark highlights gaps in converting financial knowledge into usable answers." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "graded benchmark of 1,000 plus financial questions", "salience": 35, "edition": 13, "n": 1638, "authors_detailed": [ { "name": "Liang Xu", "url": "https://openalex.org/A5109934763", "inst": "Tongji University" }, { "name": "Lei Zhu", "url": "https://openalex.org/A5100887540", "inst": "Grail (United States)" }, { "name": "Yaotong Wu", "url": "https://openalex.org/A5113761241", "inst": "Taiwan Semiconductor Manufacturing Company (Taiwan)" }, { "name": "Xue Hang", "url": "https://openalex.org/A5068444071", "inst": "Tongji University" } ], "affiliations": [ "Tongji University", "Grail (United States)", "Taiwan Semiconductor Manufacturing Company (Taiwan)" ] }, { "uid": "doi:10.2139/ssrn.4810596", "doi": "10.2139/ssrn.4810596", "title": "Automated Social Science: Language Models as Scientist and Subjects", "authors": [ "Benjamin Manning", "Kehang Zhu", "John J. Horton" ], "posted": "2024-04-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4810596", "field": "economics", "role": "agent", "bullets": [ "In-silico simulations of four social scenarios: negotiation, bail hearing, job interview, and auction using LLM-based agents.", "LLM agents proposed and tested causal hypotheses via structural causal models; direct elicitation compared to simulation-derived estimates.", "Simulation results matched auction theory predictions; LLM predicted effect signs but not magnitudes without conditioning on fitted models." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "auction simulation results compared to auction theory predictions", "salience": 75, "n": 2936, "authors_detailed": [ { "name": "Benjamin Manning", "url": "https://openalex.org/A5012908746", "inst": "Massachusetts Institute of Technology" }, { "name": "Kehang Zhu", "url": "https://openalex.org/A5095787373", "inst": "Harvard University Press" }, { "name": "John J. Horton", "url": "https://openalex.org/A5077125314", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Massachusetts Institute of Technology", "Harvard University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4797277", "doi": "10.2139/ssrn.4797277", "title": "Culture Configuration, Digital Innovation and State-Ownership: Evidence from an Emerging Economy", "authors": [ "You Zhang" ], "posted": "2024-04-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4797277", "field": "management", "role": "instrument", "bullets": [ "Chinese listed firms; investor-relations activity transcripts analyzed to quantify organizational culture; includes SOE comparison.", "Zero-shot DeBERTa LLM classified culture values from IR transcripts for configurational analysis of innovation engagement.", "Innovativeness culture drives digital innovation, but state-owned enterprises show structural deficiency in leveraging this culture value." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 45, "n": 2935, "authors_detailed": [ { "name": "You Zhang", "url": "https://openalex.org/A5100384570", "inst": "Hong Kong Polytechnic University" } ], "affiliations": [ "Hong Kong Polytechnic University" ] }, { "uid": "doi:10.2139/ssrn.4780220", "doi": "10.2139/ssrn.4780220", "title": "Small Triumphs Over Large: Instances Where BERT-Based Fine-Tuned Models Surpass GPT-4 in Classification Tasks", "authors": [ "Eric Benhamou" ], "posted": "2024-04-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4780220", "field": "finance", "role": "method", "bullets": [ "Financial text sentiment classification using a market-based dataset to fine-tune FinBERT, FinDROBERTA, and benchmark against GPT-4.", "FinBERT (110M params) and FinDROBERTA (82M params) fine-tuned on financial sentiment data and compared to GPT-4 (1T+ params).", "Fine-tuned small models matched or exceeded GPT-4 on financial sentiment; bagging majority vote yielded no additional gains, suggesting model similarity." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "financial sentiment classification accuracy", "salience": 50, "n": 2474, "authors_detailed": [ { "name": "Eric Benhamou", "url": "https://openalex.org/A5068844837", "inst": "Université Paris Sciences et Lettres" } ], "affiliations": [ "Université Paris Sciences et Lettres" ] }, { "uid": "doi:10.2139/ssrn.4759713", "doi": "10.2139/ssrn.4759713", "title": "Explaining Large Language Models Decisions Using Shapley Values", "authors": [ "Behnam Mohammadi" ], "posted": "2024-04-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4759713", "field": "management", "role": "method", "bullets": [ "Discrete choice experiments and cognitive bias tasks administered to LLMs, analyzed using Shapley values from cooperative game theory.", "Shapley value decomposition quantifies each prompt component's contribution to LLM output, identifying disproportionate token noise effects in model-agnostic fashion.", "LLM decisions are heavily influenced by low-information tokens, undermining robustness of using LLMs as substitutes for human subjects in marketing research." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2660, "authors_detailed": [ { "name": "Behnam Mohammadi", "url": "https://openalex.org/A5103130816", "inst": "Carnegie Mellon University" } ], "affiliations": [ "Carnegie Mellon University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4807516", "doi": "10.2139/ssrn.4807516", "title": "The Adoption of ChatGPT", "authors": [ "Anders Humlum", "Emilie Vestergaard" ], "posted": "2024-04-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4807516", "field": "economics", "role": "object", "bullets": [ "Survey experiment of 100,000 workers across 11 exposed occupations linked to comprehensive register data in Denmark.", "Study examines ChatGPT adoption patterns, barriers, and the effect of expert information on workers' beliefs and usage.", "Half of workers have used ChatGPT; younger, male, higher-achieving workers lead adoption; employer restrictions and training needs limit uptake." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 75, "validated": null, "n": 3403, "authors_detailed": [ { "name": "Anders Humlum", "url": "https://openalex.org/A5093399820", "inst": "University of Chicago" }, { "name": "Emilie Vestergaard", "url": "https://openalex.org/A5112220091", "inst": "University of Copenhagen" } ], "affiliations": [ "University of Chicago", "University of Copenhagen" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4776480", "doi": "10.2139/ssrn.4776480", "title": "From Lexicons to Large Language Models: A Holistic Evaluation of Psychometric Text Analysis in Social Science Research", "authors": [ "Reza Mousavi", "Brent Kitchens", "Abbie Oliver", "Ahmed Abbasi" ], "posted": "2024-04-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4776480", "field": "management", "role": "method", "bullets": [ "Four text-analysis paradigms evaluated for extracting psychological constructs across online platforms and written communication.", "LLMs with a cognitive-affective prompting strategy extracted attitudes, perceptions, and traits; compared against lexicon and supervised baselines.", "LLMs matched or exceeded established methods in predictive performance, consistency, and fairness while eliminating need for manual annotations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Predictive accuracy against human-annotated psychometric constructs", "salience": 55, "n": 2569, "authors_detailed": [ { "name": "Reza Mousavi", "url": "https://openalex.org/A5088693792", "inst": "University of Virginia" }, { "name": "Brent Kitchens", "url": "https://openalex.org/A5031922699", "inst": "University of Virginia" }, { "name": "Abbie Griffith Oliver", "url": "https://openalex.org/A5045830377", "inst": "Georgia State University" }, { "name": "Ahmed Abbasi", "url": "https://openalex.org/A5016943900", "inst": "University of Notre Dame" } ], "affiliations": [ "University of Virginia", "University of Notre Dame", "Georgia State University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2405.12990v1", "arxiv_id": "2405.12990v1", "title": "BERT vs GPT for financial engineering", "authors": [ "Edward Sharkey", "Philip Treleaven" ], "posted": "2024-04-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2405.12990v1", "field": "finance", "role": "instrument", "bullets": [ "Commodity trading news sentiment classification comparing fine-tuned BERT and GPT models including GPT-4 and domain-specific CopBERT.", "CopBERT fine-tuned on commodity news outperformed GPT-4 by approximately 10 percentage points in F1 score for sentiment tasks.", "Fine-tuned BERT models offer higher interpretability for financial engineering despite lower predictive power than larger LLMs." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "F1 score and confusion matrices on commodity news sentiment", "salience": 40, "n": 3187, "authors_detailed": [ { "name": "Edward Sharkey", "url": "https://openalex.org/A5098820975", "inst": "The London College" }, { "name": "Philip Treleaven", "url": "https://openalex.org/A5098820976", "inst": "The London College" } ], "affiliations": [ "The London College" ] }, { "uid": "arxiv:2407.09480v1", "arxiv_id": "2407.09480v1", "title": "Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses", "authors": [ "Teng Ye", "Jingnan Zheng", "Junhui Jin", "Jingyi Qiu", "Wei Ai", "Qiaozhu Mei" ], "posted": "2024-04-24", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2407.09480v1", "field": "finance", "role": "instrument", "bullets": [ "Small business crowdfunding campaigns on an online platform in the U.S., with ML prediction of fundraising outcomes.", "An ML model predicts outcomes at 81% accuracy; an LLM augments three aspects of campaign text descriptions.", "LLM-enhanced descriptions preferred by 83% of evaluators; likelihood of securing funding increases by 11.9% for unfunded campaigns." ], "bullet_provenance": "ai", "validated": true, "validation_note": "81% prediction accuracy on held-out crowdfunding campaigns", "salience": 55, "models": [], "n": 3401, "authors_detailed": [ { "name": "Teng Ye", "url": "https://openalex.org/A5100569498", "inst": "University of Minnesota" }, { "name": "Jingnan Zheng", "url": "https://openalex.org/A5104621223", "inst": "" }, { "name": "J. Jin", "url": "https://openalex.org/A5114192441", "inst": "University of Michigan" }, { "name": "Jingyi Qiu", "url": "https://openalex.org/A5101340215", "inst": "National Defense University" }, { "name": "Wei Ai", "url": "https://openalex.org/A5011364421", "inst": "University of Maryland, College Park" }, { "name": "Qiaozhu Mei", "url": "https://openalex.org/A5048955398", "inst": "Ajinomoto (United States)" } ], "affiliations": [ "University of Minnesota", "University of Maryland, College Park", "University of Michigan", "National Defense University", "Ajinomoto (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4790637", "doi": "10.2139/ssrn.4790637", "title": "Data-Driven Portfolio Management for Motion Pictures Industry: A New Data-Driven Optimization Methodology Using a Large Language Model as the Expert", "authors": [ "Mohammad Alipour-Vaezi", "Kwok-Leung Tsui" ], "posted": "2024-04-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4790637", "field": "management", "role": "instrument", "bullets": [ "Motion pictures industry data covering film projects, celebrities, and box office outcomes for distributor portfolio design.", "An LLM determines celebrity fame scores; ML classifies projects and predicts box office for multi-objective portfolio optimization.", "The hybrid method produces optimal movie portfolios for distributors by combining LLM-derived fame scores with decision-making techniques." ], "bullet_provenance": "ai", "validated": false, "salience": 35, "models": [], "n": 3402, "authors_detailed": [ { "name": "Mohammad Alipour‐Vaezi", "url": "https://openalex.org/A5020993201", "inst": "Virginia Tech" }, { "name": "Kwok‐Leung Tsui", "url": "https://openalex.org/A5002878668", "inst": "Virginia Tech" } ], "affiliations": [ "Virginia Tech" ] }, { "uid": "arxiv:2404.13964v4", "arxiv_id": "2404.13964v4", "title": "An Economic Solution to Copyright Challenges of Generative AI", "authors": [ "Jiachen T. Wang", "Zhun Deng", "Hiroaki Chiba-Okabe", "Boaz Barak", "Weijie J. Su" ], "posted": "2024-04-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.13964v4", "field": "economics", "role": "object", "bullets": [ "A royalty sharing framework for generative AI training data, developed theoretically and tested in experiments on artwork generation rather than on a field sample.", "Each copyright owner's contribution is measured from the probabilistic structure of generative models using cooperative game theory; no specific model family is named.", "The framework identifies the most relevant data sources behind generated artwork and yields interpretable, proportional revenue splits among copyright owners." ], "bullet_provenance": "ai", "salience": 50, "edition": 13, "models": [], "validated": null, "n": 1782, "authors_detailed": [ { "name": "Jiachen T. Wang", "url": "https://openalex.org/A5101342733", "inst": "" }, { "name": "Zhun Deng", "url": "https://openalex.org/A5006488129", "inst": "Chinese Academy of Medical Sciences & Peking Union Medical College" }, { "name": "Hiroaki Chiba-Okabe", "url": "https://openalex.org/A5095886702", "inst": "Applied Mathematics (United States)" }, { "name": "Boaz Barak", "url": "https://openalex.org/A5102710346", "inst": "Tel Aviv University" }, { "name": "Weijie J. Su", "url": "https://openalex.org/A5101342734", "inst": "Applied Mathematics (United States)" } ], "affiliations": [ "Chinese Academy of Medical Sciences & Peking Union Medical College", "Applied Mathematics (United States)", "Tel Aviv University" ] }, { "uid": "doi:10.2139/ssrn.4802019", "doi": "10.2139/ssrn.4802019", "title": "Reducing Disparity Between LLMs and Humans: Optimal LLM Sample Calibration", "authors": [ "Yan Leng", "Yunxin Sang", "Ashish Agarwal" ], "posted": "2024-04-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4802019", "field": "economics", "role": "method", "bullets": [ "US bank-card transactions and Pew survey data used to calibrate LLM elicitation variants against human benchmark distributions.", "HuMCal method learned credibility weights over LLM prompt-persona-checkpoint variants using only aggregate benchmark data, no model internals.", "Calibrated weights outperformed uniform averaging, post-stratification, and best-subgroup baselines across time, geography, and topic in most evaluations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "alignment with human benchmark data", "salience": 55, "n": 2473, "authors_detailed": [ { "name": "Yan Leng", "url": "https://openalex.org/A5068435349", "inst": "The University of Texas at Austin" }, { "name": "Yunxin Sang", "url": "https://openalex.org/A5114462206", "inst": "The University of Texas at Austin" }, { "name": "Ashish Agarwal", "url": "https://openalex.org/A5027820479", "inst": "The University of Texas at Austin" } ], "affiliations": [ "The University of Texas at Austin" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4793782", "doi": "10.2139/ssrn.4793782", "title": "Hiding From Generative AI", "authors": [ "Sijie Lin" ], "posted": "2024-04-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4793782", "field": "economics", "role": "object", "bullets": [ "Digital artists on DeviantArt before and after the platform introduced an AI image generator, using difference-in-differences design.", "Study examines behavioral responses to AI image generation technology that enrolled artworks into training data by default.", "Digital artists published 21% fewer artworks after AI introduction; multi-homing artists maintained output on Instagram with no quality decline." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 3400, "authors_detailed": [ { "name": "Sijie Lin", "url": "https://openalex.org/A5006892025", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4526072", "doi": "10.2139/ssrn.4526072", "title": "Consumer Risk Preferences Elicitation From Large Language Models", "authors": [ "Liying Qiu", "Param Vir Singh", "Kannan Srinivasan" ], "posted": "2024-04-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4526072", "field": "management", "role": "agent", "bullet_provenance": "none", "salience": 50, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 638, "authors_detailed": [ { "name": "Liying Qiu", "url": "https://openalex.org/A5101340033", "inst": "Carnegie Mellon University" }, { "name": "Param Vir Singh", "url": "https://openalex.org/A5044224562", "inst": "Carnegie Mellon University" }, { "name": "Kannan Srinivasan", "url": "https://openalex.org/A5038040329", "inst": "Carnegie Mellon University" } ], "affiliations": [ "Carnegie Mellon University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4797024", "doi": "10.2139/ssrn.4797024", "title": "A Critical Assessment of Large Language Models for Systematic Reviews: Utilizing ChatGPT for Complex Data Extraction", "authors": [ "Hesam Mahmoudi", "Doris Chang", "Hannah Lee", "Navid Ghaffarzadegan", "Mohammad S. Jalali" ], "posted": "2024-04-19", "added": "2026-08-11", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4797024", "field": "other", "role": "method", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 17, "bullets": [], "validated": null, "n": 2141, "authors_detailed": [ { "name": "Hesam Mahmoudi", "url": "https://openalex.org/A5032984954", "inst": "Harvard University" }, { "name": "Doris F. Chang", "url": "https://openalex.org/A5014080961", "inst": "Harvard University" }, { "name": "Hannah Lee", "url": "https://openalex.org/A5106482059", "inst": "Harvard University" }, { "name": "Navid Ghaffarzadegan", "url": "https://openalex.org/A5078713927", "inst": "Virginia Tech" }, { "name": "Mohammad S. Jalali", "url": "https://openalex.org/A5091165163", "inst": "Harvard University" } ], "affiliations": [ "Harvard University", "Virginia Tech" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4769112", "doi": "10.2139/ssrn.4769112", "title": "Analysis and Mitigation of ChatGPT’s Dovish Bias on Classifying Fedspeak", "authors": [ "Amaury Mantion", "Melvin Kianmanesh Rad", "Christophe Morel", "Romain Faure", "Zachary Schillaci" ], "posted": "2024-04-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4769112", "field": "finance", "role": "method", "bullets": [ "U.S. Federal Reserve communications classified as dovish, neutral, or hawkish; extends Hansen and Kazinnik (2023) Fedspeak classification.", "ChatGPT classified Fed policy stances; study quantified a systematic dovish bias and proposed corrective techniques for robust classification.", "Dovish bias confirmed and attributed to predominance of accommodative economic conditions in the two decades of LLM training data." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "classification accuracy against labeled Fedspeak stances", "salience": 60, "n": 2934, "authors_detailed": [ { "name": "Amaury Mantion", "url": "https://openalex.org/A5098533481", "inst": "University College London" }, { "name": "Melvin Kianmanesh Rad", "url": "https://openalex.org/A5098136916", "inst": "Exelixis (United States)" }, { "name": "Christophe Morel", "url": "https://openalex.org/A5111176787", "inst": "Sector Asset Management" }, { "name": "Romain Faure", "url": "https://openalex.org/A5104283855", "inst": "Sector Asset Management" }, { "name": "Zachary Schillaci", "url": "https://openalex.org/A5098162848", "inst": "Exelixis (United States)" } ], "affiliations": [ "University College London", "Exelixis (United States)", "Sector Asset Management" ] }, { "uid": "doi:10.2139/ssrn.4787199", "doi": "10.2139/ssrn.4787199", "title": "StockGPT: A GenAI Model for Stock Prediction and Trading", "authors": [ "Dat Mai" ], "posted": "2024-04-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4787199", "field": "finance", "role": "instrument", "bullets": [ "70 million daily U.S. stock returns over nearly 100 years, with held-out testing from 2001 to 2023.", "A custom transformer model treats return series as token sequences and predicts future returns via attention mechanism.", "Long-short portfolios from StockGPT predictions yield significant alphas against leading stock market factors, spanning momentum and reversals." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": false, "validated": false, "salience": 70, "n": 3399, "authors_detailed": [ { "name": "Dat Mai", "url": "https://openalex.org/A5009248196", "inst": "MRC Biostatistics Unit" } ], "affiliations": [ "MRC Biostatistics Unit" ] }, { "uid": "doi:10.2139/ssrn.4799209", "doi": "10.2139/ssrn.4799209", "title": "Enhancing Financial Inclusion and Regulatory Challenges: A Critical Analysis of Digital Banks and Alternative Lenders Through Digital Platforms, Machine Learning, and Large Language Models Integration", "authors": [ "Luke Lee" ], "posted": "2024-04-18", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4799209", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 32, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 637, "authors_detailed": [ { "name": "Luke Lee", "url": "https://openalex.org/A5014563767", "inst": "King's College London" } ], "affiliations": [ "King's College London" ] }, { "uid": "arxiv:2404.11792v2", "arxiv_id": "2404.11792v2", "title": "Enhancing Q&A with Domain-Specific Fine-Tuning and Iterative Reasoning: A Comparative Study", "authors": [ "Zooey Nguyen", "Anthony Annunziata", "Vinh Luong", "Sang Dinh", "Quynh Le", "Anh Hai Ha", "Chanh Le", "Hong An Phan", "Shruti Raghavan", "Christopher Nguyen" ], "posted": "2024-04-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.11792v2", "field": "finance", "role": "method", "bullets": [ "Question answering over SEC corporate filings, evaluated on the FinanceBench dataset; the number of questions used is not stated in the abstract.", "RAG pipelines combine fine-tuned embedding models with fine-tuned unnamed LLMs, plus iterative reasoning on top; accuracy is scored against the benchmark's reference answers.", "Fine-tuned embeddings drive most of the accuracy gain over generic components, and reasoning iterations lift performance further toward human-expert quality; figures are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinanceBench reference answers", "salience": 30, "edition": 13, "models": [], "n": 1599, "authors_detailed": [ { "name": "Zooey Nguyen", "url": "https://openalex.org/A5095787368", "inst": "" }, { "name": "Anthony Annunziata", "url": "https://openalex.org/A5034323047", "inst": "Boston University" }, { "name": "Vinh Luong", "url": "https://openalex.org/A5095787369", "inst": "" }, { "name": "Dinh Viet Sang", "url": "https://openalex.org/A5025948040", "inst": "Hanoi University of Science and Technology" }, { "name": "Quynh Lê", "url": "https://openalex.org/A5110472585", "inst": "MIT University" }, { "name": "Anh Hai Ha", "url": "https://openalex.org/A5095787370", "inst": "" }, { "name": "Chanh Le", "url": "https://openalex.org/A5104284189", "inst": "" }, { "name": "Hong An Phan", "url": "https://openalex.org/A5095787371", "inst": "" }, { "name": "Shruti Raghavan", "url": "https://openalex.org/A5002750093", "inst": "Southwestern Medical Center" }, { "name": "Christopher Nguyen", "url": "https://openalex.org/A5101751020", "inst": "University of Washington" } ], "affiliations": [ "Boston University", "Hanoi University of Science and Technology", "MIT University", "University of Washington" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2404.11794v2", "arxiv_id": "2404.11794v2", "title": "Automated Social Science: Language Models as Scientist and Subjects", "authors": [ "Benjamin S. Manning", "Kehang Zhu", "John J. Horton" ], "posted": "2024-04-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.11794v2", "field": "economics", "role": "agent", "bullets": [ "Four simulated social settings, a negotiation, a bail hearing, a job interview, and an auction, each generated and analyzed end to end by an automated system.", "An LLM, family not stated, proposes hypotheses as structural causal models, builds the agents, runs the in silico experiments, and fits the resulting data.", "Auction outcomes track theory closely; the LLM predicts signs of effects but not magnitudes by direct elicitation, improving sharply when conditioned on the fitted causal model." ], "bullet_provenance": "ai", "salience": 72, "edition": 13, "models": [], "validated": null, "n": 1781, "authors_detailed": [ { "name": "Benjamin S. Manning", "url": "https://openalex.org/A5095787372", "inst": "Massachusetts Institute of Technology" }, { "name": "Kehang Zhu", "url": "https://openalex.org/A5095787373", "inst": "Harvard University Press" }, { "name": "John J. Horton", "url": "https://openalex.org/A5077125314", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Massachusetts Institute of Technology", "Harvard University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2404.10555v1", "arxiv_id": "2404.10555v1", "title": "Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training", "authors": [ "Masanori Hirano", "Kentaro Imajo" ], "posted": "2024-04-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.10555v1", "field": "finance", "role": "method", "bullets": [ "Japanese financial text collected into dedicated continual pretraining datasets, applied to a Japanese base model in the 10 billion parameter class that leads Japanese financial benchmarks.", "Continual pretraining specializes the unnamed open base LLM for finance; the tuned model is scored on Japanese financial benchmarks and its outputs compared with the original model's.", "The tuned model outperforms its base on the financial benchmarks and gives longer, better quality answers; effect sizes are not stated, and the model is released on Hugging Face." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Japanese financial benchmarks", "salience": 35, "edition": 13, "n": 1726, "authors_detailed": [ { "name": "Masanori Hirano", "url": "https://openalex.org/A5002301422", "inst": "Preferred Networks (Japan)" }, { "name": "Kentaro Imajo", "url": "https://openalex.org/A5038152086", "inst": "Kyoto University" } ], "affiliations": [ "Preferred Networks (Japan)", "Kyoto University" ] }, { "uid": "doi:10.2139/ssrn.4788096", "doi": "10.2139/ssrn.4788096", "title": "Can LLMs Pass the CPA Exam? Evaluating Large Language Model Performance on the Certified Public Accountant Test", "authors": [ "Will Zacher", "Sanmukh Kuppannagari" ], "posted": "2024-04-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4788096", "field": "accounting", "role": "method", "bullet_provenance": "none", "salience": 40, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 339, "authors_detailed": [ { "name": "Will Zacher", "url": "https://openalex.org/A5095739458", "inst": "Partners In Care" }, { "name": "Sanmukh R. Kuppannagari", "url": "https://openalex.org/A5015036207", "inst": "Case Western Reserve University" } ], "affiliations": [ "Partners In Care", "Case Western Reserve University" ] }, { "uid": "doi:10.2139/ssrn.4787286", "doi": "10.2139/ssrn.4787286", "title": "Displacement or Augmentation? The Effects of AI Innovation on Workforce Dynamics and Firm Value", "authors": [ "Mark A. Chen", "Joanna (Xiaoyu) Wang" ], "posted": "2024-04-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4787286", "field": "finance", "role": "instrument", "bullets": [ "U.S. patent data covering AI innovations across seven functional areas during 2007-2023, linked to worker-level microdata on skills and job transitions.", "LLMs classified patents into AI innovation categories; worker microdata tracked displacement and augmentation effects on employment and firm value.", "Engagement, learning, and creativity AI augments labor and raises productivity; perception AI displaces workers and lowers operating costs." ], "bullet_provenance": "ai", "validated": false, "salience": 75, "models": [], "n": 2410, "authors_detailed": [ { "name": "Mark A. Chen", "url": "https://openalex.org/A5102820149", "inst": "Georgia State University" }, { "name": "Joanna Wang", "url": "https://openalex.org/A5101812628", "inst": "Peking University" } ], "affiliations": [ "Georgia State University", "Peking University" ] }, { "uid": "doi:10.2139/ssrn.4792918", "doi": "10.2139/ssrn.4792918", "title": "Can Base ChatGPT be Used for Forecasting without Additional Optimization?", "authors": [ "Van H. Pham", "Scott Cunningham" ], "posted": "2024-04-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4792918", "field": "economics", "role": "method", "bullets": [ "One hundred ChatGPT-3.5 and ChatGPT-4 trials forecasting post-training-cutoff events including Academy Awards and economic trends in 2022", "ChatGPT-4 predicted future events via direct prompts and narrative prompts where fictional characters retold events beyond the training window", "Narrative prompts significantly improved GPT-4 forecasting accuracy; accuracy reached 100 percent when queried events fell within the training window" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "accuracy against realized post-cutoff events", "salience": 55, "n": 2425, "authors_detailed": [ { "name": "Van H. Pham", "url": "https://openalex.org/A5106056494", "inst": "Baylor University" }, { "name": "Scott Cunningham", "url": "https://openalex.org/A5114198624", "inst": "Baylor University" } ], "affiliations": [ "Baylor University" ] }, { "uid": "doi:10.2139/ssrn.4784515", "doi": "10.2139/ssrn.4784515", "title": "SWOT Portfolio Construction and Validation Using Massive Language Models", "authors": [ "Arturs Meskovskis", "Chris Kenyon" ], "posted": "2024-04-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4784515", "field": "finance", "role": "instrument", "bullets": [ "S&P 500 stocks analyzed using company annual reports; SWOT features extracted and scored for strategic portfolio selection.", "Massive language models extracted SWOT features from reports; semantic similarity between features and quoted source text validated outputs.", "SWOT-based stock selection outperformed whole-universe and historical-performance benchmarks in a numerical portfolio comparison." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": false, "salience": 60, "n": 2568, "authors_detailed": [ { "name": "Arturs Meskovskis", "url": "https://openalex.org/A5097330485", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Chris Kenyon", "url": "https://openalex.org/A5037955150", "inst": "SecurAcath (United States)" } ], "affiliations": [ "Independent - affiliation not provided to SSRN", "SecurAcath (United States)" ] }, { "uid": "doi:10.2139/ssrn.4787211", "doi": "10.2139/ssrn.4787211", "title": "Decoding Future of Generative AI in Finance: A Machine Learning Exploration of Academic and Grey Corpus", "authors": [ "Hassnian Ali", "Ahmet Faruk Aysan" ], "posted": "2024-04-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4787211", "field": "finance", "role": "instrument", "bullets": [ "Academic papers and grey literature on generative AI in finance, analyzed using machine learning topic modeling across both corpora.", "ML topic models classify research themes; academics focus on ChatGPT applications in finance while grey literature addresses governance frameworks.", "GenAI shows potential in financial modeling, risk assessment, and fraud detection, but ethical and regulatory challenges remain unresolved across both corpora." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 30, "n": 2658, "authors_detailed": [ { "name": "Hassnian Ali", "url": "https://openalex.org/A5012249611", "inst": "Lahore University of Management Sciences" }, { "name": "Ahmet Faruk Aysan", "url": "https://openalex.org/A5081026264", "inst": "Hamad bin Khalifa University" } ], "affiliations": [ "Lahore University of Management Sciences", "Hamad bin Khalifa University" ] }, { "uid": "doi:10.2139/ssrn.4796245", "doi": "10.2139/ssrn.4796245", "title": "Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training", "authors": [ "Masanori Hirano", "Kentaro Imajo" ], "posted": "2024-04-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4796245", "field": "finance", "role": "method", "bullets": [ "Japanese financial text corpora used for continual pre-training of a 10-billion-parameter Japanese LLM, model released on Hugging Face.", "Domain-specific continual pre-training on curated financial datasets; evaluated against Japanese financial benchmarks and via output quality comparison.", "Tuned model outperformed the base model on Japanese financial benchmarks with improved answer quality and length, confirming domain pre-training effectiveness." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "Japanese financial benchmarks", "salience": 50, "n": 2659, "authors_detailed": [ { "name": "Masanori Hirano", "url": "https://openalex.org/A5002301422", "inst": "Preferred Networks (Japan)" }, { "name": "Kentaro Imajo", "url": "https://openalex.org/A5038152086", "inst": "Preferred Networks (Japan)" } ], "affiliations": [ "Preferred Networks (Japan)" ] }, { "uid": "doi:10.2139/ssrn.4786302", "doi": "10.2139/ssrn.4786302", "title": "Does Social Bot Help Socialize? Evidence from a Microblogging Platform", "authors": [ "Yang Gao", "Maggie Zhang", "Mikhail Lysyakov" ], "posted": "2024-04-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4786302", "field": "management", "role": "object", "bullets": [ "Microblogging platform launching CommentRobot; observational data on user interactions supplemented by a controlled online experiment.", "Study examined whether LLM-powered bot comments increased user engagement, testing mechanisms of bot identity and content quality.", "Bot comments raised post-level engagement, but users shifted toward bot-related posting rather than increasing overall activity volume." ], "bullet_provenance": "ai", "models": [ "open_other" ], "salience": 55, "validated": null, "n": 2933, "authors_detailed": [ { "name": "Yang Gao", "url": "https://openalex.org/A5034775543", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Maggie Mengqing Zhang", "url": "https://openalex.org/A5013245622", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Mikhail Lysyakov", "url": "https://openalex.org/A5089613094", "inst": "University of Rochester" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "University of Rochester" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4787320", "doi": "10.2139/ssrn.4787320", "title": "Artificial Intelligence Quotient (AIQ)", "authors": [ "Xin Qin", "Jackson G. Lu", "Chen Chen", "Xiang Zhou", "Yuqing Gan", "Wanlu Li", "Luyang Song" ], "posted": "2024-04-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4787320", "field": "management", "role": "object", "bullets": [ "Five studies (archival, lab, online) across chess, renju, and ChatGPT/Gemini tasks with samples up to 18 years of tournament data.", "Extracted a general AI Quotient factor from individual performance on varied tasks completed using ChatGPT and validated with Gemini.", "AIQ is stable over time, distinct from IQ and AI literacy, and predicts human-AI performance on new tasks with different AI systems." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "salience": 60, "validated": null, "n": 3397, "authors_detailed": [ { "name": "Xin Qin", "url": "https://openalex.org/A5101961014", "inst": "Sun Yat-sen University" }, { "name": "Jackson G. Lu", "url": "https://openalex.org/A5110578651", "inst": "Massachusetts Institute of Technology" }, { "name": "Chen Chen", "url": "https://openalex.org/A5014642358", "inst": "Sun Yat-sen University" }, { "name": "Xiang Zhou", "url": "https://openalex.org/A5030761097", "inst": "Sun Yat-sen University" }, { "name": "Yuqing Gan", "url": "https://openalex.org/A5103274357", "inst": "Sun Yat-sen University" }, { "name": "Wanlu Li", "url": "https://openalex.org/A5100759492", "inst": "Guangdong University Of Finances and Economics" }, { "name": "Lesley Luyang Song", "url": "https://openalex.org/A5091255029", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "Sun Yat-sen University", "Guangdong University Of Finances and Economics" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4795006", "doi": "10.2139/ssrn.4795006", "title": "Attitudes Toward Artificial Intelligence (AI) and Globalization: Common Microfoundations and Political Implications", "authors": [ "Beatrice Magistro", "Sophie Borwein", "R. Michael Alvarez", "Bart Bonikowski", "Peter J. Loewen" ], "posted": "2024-04-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4795006", "field": "economics", "role": "object", "bullets": [ "Conjoint experiment with 6,000 respondents from the U.S. and Canada comparing attitudes toward offshoring and generative AI.", "Respondents evaluated scenarios trading off job displacement against price reductions from AI adoption versus offshoring.", "AI is favored over offshoring especially among Democrats; respondents are equally or more sensitive to price changes than employment shifts." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3398, "authors_detailed": [ { "name": "Beatrice Magistro", "url": "https://openalex.org/A5000471166", "inst": "California Institute of Technology" }, { "name": "Sophie Borwein", "url": "https://openalex.org/A5063832424", "inst": "Simon Fraser University" }, { "name": "R. Michael Alvarez", "url": "https://openalex.org/A5091187284", "inst": "California Institute of Technology" }, { "name": "Bart Bonikowski", "url": "https://openalex.org/A5023494795", "inst": "New York University" }, { "name": "Peter John Loewen", "url": "https://openalex.org/A5011000771", "inst": "University of Toronto" } ], "affiliations": [ "California Institute of Technology", "New York University", "University of Toronto", "Simon Fraser University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.3386/w32327", "doi": "10.3386/w32327", "title": "Recovering Overlooked Information in Categorical Variables with LLMs: An Application to Labor Market Mismatch", "authors": [ "Yi Chen", "Hanming Fang", "Yi Zhao", "Zibo Zhao" ], "posted": "2024-04-15", "added": "2026-07-24", "source_label": "NBER", "url": "https://doi.org/10.3386/w32327", "field": "economics", "role": "instrument", "bullets": [ "Administrative records from an online job posting platform plus a lower-information survey dataset, used to measure applicant to job match quality in hiring; period and geography not stated.", "GPT simulates a human resources specialist assessing applicant suitability for a job; the resulting match measure is correlated with traditional measures, with no accuracy figure reported.", "The LLM measure positively correlates with and adds information beyond traditional measures; revealing gender leads GPT to rate women better suited for female-dominated roles." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "validation_note": "correlated with traditional measures, no accuracy figure", "salience": 62, "edition": 3, "audience": "general", "n": 127, "authors_detailed": [ { "name": "Yi Chen", "url": "https://openalex.org/A5100419283", "inst": "Beijing Institute of Technology" }, { "name": "Hanming Fang", "url": "https://openalex.org/A5102752404", "inst": "National Bureau of Economic Research" }, { "name": "Yi Zhao", "url": "https://openalex.org/A5022279101", "inst": "Binzhou University" }, { "name": "Zibo Zhao", "url": "https://openalex.org/A5101338438", "inst": "" } ], "affiliations": [ "Beijing Institute of Technology", "National Bureau of Economic Research", "Binzhou University" ] }, { "uid": "doi:10.2139/ssrn.4782554", "doi": "10.2139/ssrn.4782554", "title": "All Just in Your Head? Unraveling the Side Effects of Generative AI Disclosure in Creative Task", "authors": [ "Kevin Bauer", "Ekaterina Jussupow", "Rebecca Heigl", "Benjamin Vogt", "Oliver Hinz" ], "posted": "2024-04-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4782554", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 636, "authors_detailed": [ { "name": "Kevin Bauer", "url": "https://openalex.org/A5039057505", "inst": "Goethe University Frankfurt" }, { "name": "Ekaterina Jussupow", "url": "https://openalex.org/A5064289654", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Rebecca Heigl", "url": "https://openalex.org/A5095690828", "inst": "Goethe University Frankfurt" }, { "name": "Benjamin Vogt", "url": "https://openalex.org/A5108215359", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Oliver Hinz", "url": "https://openalex.org/A5053984864", "inst": "Goethe University Frankfurt" } ], "affiliations": [ "Goethe University Frankfurt", "Independent - affiliation not provided to SSRN" ] }, { "uid": "doi:10.2139/ssrn.4769124", "doi": "10.2139/ssrn.4769124", "title": "Construction of a Japanese Financial Benchmark for Large Language Models", "authors": [ "Masanori Hirano" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4769124", "field": "finance", "role": "method", "bullets": [ "Multiple Japanese-language financial tasks tested across several LLMs in the Japanese market domain, covering varied difficulty levels.", "GPT-4 and other models evaluated on a newly constructed domain-specific financial benchmark combining tasks of different difficulties.", "GPT-4 outperformed all other models; the benchmark effectively differentiates performance across the full range of tested models." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Japanese financial benchmark tasks with scored accuracy", "salience": 35, "n": 2409, "authors_detailed": [ { "name": "Masanori Hirano", "url": "https://openalex.org/A5002301422", "inst": "Preferred Networks (Japan)" } ], "affiliations": [ "Preferred Networks (Japan)" ] }, { "uid": "doi:10.2139/ssrn.4780454", "doi": "10.2139/ssrn.4780454", "title": "When Small Wins Big: Classification Tasks Where Compact Models Outperform Original GPT-4", "authors": [ "Baptiste Lefort", "Eric Benhamou", "Jean-Jacques Ohana", "Beatrice Guez", "David Saltiel", "Damien Challet" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4780454", "field": "finance", "role": "method", "bullets": [ "Financial sentence classification task using a market-based labeled dataset comparing GPT-4 against fine-tuned FinBERT and FinDROBERTA", "GPT-4 with 1.76 trillion parameters benchmarked against FinBERT (110M) and FinDROBERTA (82M) on short financial sentences with numerical data", "Fine-tuned compact models matched or exceeded GPT-4 on complex financial sentiment; bagging ensemble yielded no gain suggesting correlated model errors" ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "market-based sentiment ground truth", "salience": 65, "n": 2424, "authors_detailed": [ { "name": "Baptiste Lefort", "url": "https://openalex.org/A5093631836", "inst": "Université Paris-Saclay" }, { "name": "Eric Benhamou", "url": "https://openalex.org/A5068844837", "inst": "Université Paris Sciences et Lettres" }, { "name": "Jean‐Jacques Ohana", "url": "https://openalex.org/A5026233991", "inst": "Alpha-1 Foundation" }, { "name": "Béatrice Guez", "url": "https://openalex.org/A5002704483", "inst": "Alpha-1 Foundation" }, { "name": "David Saltiel", "url": "https://openalex.org/A5024845346", "inst": "Connect" }, { "name": "Damien Challet", "url": "https://openalex.org/A5063789606", "inst": "CentraleSupélec" } ], "affiliations": [ "Université Paris-Saclay", "Université Paris Sciences et Lettres", "Alpha-1 Foundation", "Connect", "CentraleSupélec" ] }, { "uid": "doi:10.2139/ssrn.4794377", "doi": "10.2139/ssrn.4794377", "title": "Recovering Overlooked Information in Categorical Variables with LLMs: An Application to Labor Market Mismatch", "authors": [ "Yi Chen", "Hanming Fang", "Yi Zhao", "Zibo Zhao" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4794377", "field": "economics", "role": "instrument", "bullets": [ "Chinese labor market using administrative job-posting platform data and household survey data for applicant-job matching.", "LLMs simulated HR specialists to assess applicant-job suitability; GPT match quality measure compared against traditional measures from administrative data.", "LLM measure correlated with traditional match quality metrics; disclosing gender to GPT caused it to rate females higher for female-dominated roles." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "correlation with traditional match quality measures", "salience": 65, "n": 2471, "authors_detailed": [ { "name": "Yi Chen", "url": "https://openalex.org/A5100419210", "inst": "ShanghaiTech University" }, { "name": "Hanming Fang", "url": "https://openalex.org/A5102752404", "inst": "National Bureau of Economic Research" }, { "name": "Yi Zhao", "url": "https://openalex.org/A5022279101", "inst": "Tsinghua University" }, { "name": "Andrew Zhao", "url": "https://openalex.org/A5111173970", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University", "ShanghaiTech University", "National Bureau of Economic Research", "Tsinghua University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4780192", "doi": "10.2139/ssrn.4780192", "title": "Uncertainty in Sentiment Analysis with LLMs using QCM (Quantiles of Correlation Matrices) - Distance", "authors": [ "Baptiste Lefort", "Eric Benhamou", "Jean-Jacques Ohana", "Beatrice Guez", "David Saltiel" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4780192", "field": "finance", "role": "method", "bullets": [ "Financial news sentiment analysis correlated with stock market changes over multiple time windows using LLM-derived scores.", "LLMs produced sentiment scores from financial news; uncertainty quantified via QCM-Distance measuring correlation stability across windows.", "Correlation patterns between LLM sentiment scores and market movements showed consistent structure, establishing moderate uncertainty bounds on sentiment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 2472, "authors_detailed": [ { "name": "Baptiste Lefort", "url": "https://openalex.org/A5093631836", "inst": "Université Paris-Saclay" }, { "name": "Eric Benhamou", "url": "https://openalex.org/A5068844837", "inst": "Université Paris Sciences et Lettres" }, { "name": "Jean‐Jacques Ohana", "url": "https://openalex.org/A5026233991", "inst": "Alpha-1 Foundation" }, { "name": "Béatrice Guez", "url": "https://openalex.org/A5002704483", "inst": "Alpha-1 Foundation" }, { "name": "David Saltiel", "url": "https://openalex.org/A5024845346", "inst": "Connect" } ], "affiliations": [ "Université Paris-Saclay", "Université Paris Sciences et Lettres", "Alpha-1 Foundation", "Connect" ] }, { "uid": "doi:10.2139/ssrn.4771103", "doi": "10.2139/ssrn.4771103", "title": "Threats or Opportunities? Enhancing Firm Performance in the Era of Generative AI", "authors": [ "Seung Jong Lee", "Julian Lehmann", "Heewon Chae", "Donghyuk Shin", "Seigyoung Auh", "Sang Pil Han" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4771103", "field": "management", "role": "object", "bullets": [ "Firms integrating generative AI with proprietary assets through prompt engineering and retrieval-augmented generation, measuring user engagement and profit.", "Study examines whether domain-customized GenAI complements or substitutes existing services, testing RAG as a complementary proprietary asset.", "RAG-enhanced GenAI increased user engagement and profit contribution; GenAI served as safety net when existing assets could not address user requests." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 58, "validated": null, "n": 2657, "authors_detailed": [ { "name": "Seung Jong Lee", "url": "https://openalex.org/A5023766869", "inst": "Arizona State University" }, { "name": "Julian Lehmann", "url": "https://openalex.org/A5007486085", "inst": "Arizona State University" }, { "name": "Heewon Chae", "url": "https://openalex.org/A5095692436", "inst": "Arizona State University" }, { "name": "Dong-Hyuk Shin", "url": "https://openalex.org/A5027581644", "inst": "Korea Institute for Advanced Study" }, { "name": "Seigyoung Auh", "url": "https://openalex.org/A5057485158", "inst": "Arizona State University" }, { "name": "Sang Pil Han", "url": "https://openalex.org/A5102991238", "inst": "Arizona State University" } ], "affiliations": [ "Arizona State University", "Korea Institute for Advanced Study" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4781752", "doi": "10.2139/ssrn.4781752", "title": "Mixing Financial Stress with GPT-4 News Sentiment Analysis for Optimal Risk-On/Risk-Off Decisions", "authors": [ "Baptiste Lefort", "Eric Benhamou", "Jean-Jacques Ohana", "David Saltiel", "Beatrice Guez", "Thomas Jacquot" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4781752", "field": "finance", "role": "instrument", "bullets": [ "NASDAQ, S&P 500, and six major global equity markets; daily Bloomberg market summaries used as sentiment input.", "GPT-4 performed sentiment analysis on Bloomberg news summaries, combined with volatility and credit-spread-based financial stress indicators.", "Strategy improved Sharpe ratio and reduced maximum drawdowns consistently across all eight tested equity markets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Sharpe ratio and maximum drawdown across equity markets", "salience": 60, "n": 2932, "authors_detailed": [ { "name": "Baptiste Lefort", "url": "https://openalex.org/A5093631836", "inst": "Université Paris-Saclay" }, { "name": "Eric Benhamou", "url": "https://openalex.org/A5068844837", "inst": "Université Paris Sciences et Lettres" }, { "name": "Jean‐Jacques Ohana", "url": "https://openalex.org/A5026233991", "inst": "Alpha-1 Foundation" }, { "name": "David Saltiel", "url": "https://openalex.org/A5024845346", "inst": "Connect" }, { "name": "Béatrice Guez", "url": "https://openalex.org/A5002704483", "inst": "Alpha-1 Foundation" }, { "name": "Thomas Jacquot", "url": "https://openalex.org/A5007387442", "inst": "Alpha-1 Foundation" } ], "affiliations": [ "Université Paris-Saclay", "Université Paris Sciences et Lettres", "Alpha-1 Foundation", "Connect" ] }, { "uid": "doi:10.2139/ssrn.4780225", "doi": "10.2139/ssrn.4780225", "title": "Stress Index Strategy Enhanced With Financial News Sentiment Analysis for the Equity Markets", "authors": [ "Baptiste Lefort", "Eric Benhamou", "Jean-Jacques Ohana", "Beatrice Guez", "David Saltiel", "Thomas Jacquot" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4780225", "field": "finance", "role": "instrument", "bullets": [ "Risk-on risk-off equity strategy tested across NASDAQ, S&P 500, and six major global equity markets.", "GPT-4 read and scored sentiment from Bloomberg daily market summaries, combined with a volatility and credit spread stress indicator.", "Adding GPT-4 sentiment improved Sharpe ratios and reduced maximum drawdowns consistently across all tested equity markets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3394, "authors_detailed": [ { "name": "Baptiste Lefort", "url": "https://openalex.org/A5093631836", "inst": "Independent" }, { "name": "Eric Benhamou", "url": "https://openalex.org/A5068844837", "inst": "Université Paris Sciences et Lettres" }, { "name": "Jean‐Jacques Ohana", "url": "https://openalex.org/A5026233991", "inst": "Alpha-1 Foundation" }, { "name": "Béatrice Guez", "url": "https://openalex.org/A5002704483", "inst": "Alpha-1 Foundation" }, { "name": "David Saltiel", "url": "https://openalex.org/A5024845346", "inst": "Connect" }, { "name": "Thomas Jacquot", "url": "https://openalex.org/A5007387442", "inst": "Alpha-1 Foundation" } ], "affiliations": [ "Independent", "Université Paris Sciences et Lettres", "Alpha-1 Foundation", "Connect" ] }, { "uid": "doi:10.2139/ssrn.4780034", "doi": "10.2139/ssrn.4780034", "title": "Generative AI: Crafting Portfolios Tailored to Investor Preferences", "authors": [ "Eric Benhamou", "Jean-Jacques Ohana", "Beatrice Guez" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4780034", "field": "finance", "role": "method", "bullets": [ "Hedge fund portfolio replication problem framed as constrained inference using graphical models to match investor preferences.", "Generative graphical models inferred portfolio weights that replicate a target fund while satisfying constraints such as excluding commodities.", "Replicated portfolios achieved high correlation with targets and outperformed benchmarks while maintaining alignment with the original strategy." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": true, "validation_note": "correlation with target portfolio and benchmark comparison", "salience": 35, "n": 3395, "authors_detailed": [ { "name": "Eric Benhamou", "url": "https://openalex.org/A5068844837", "inst": "Université Paris Sciences et Lettres" }, { "name": "Jean‐Jacques Ohana", "url": "https://openalex.org/A5026233991", "inst": "Alpha-1 Foundation" }, { "name": "Béatrice Guez", "url": "https://openalex.org/A5002704483", "inst": "Alpha-1 Foundation" } ], "affiliations": [ "Université Paris Sciences et Lettres", "Alpha-1 Foundation" ] }, { "uid": "doi:10.2139/ssrn.4757251", "doi": "10.2139/ssrn.4757251", "title": "‘Datafying’ Financial Market Regulations: A RegTech Experiment with the FINRA Rulebook", "authors": [ "Craig Atkinson", "Joseph Potvin" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4757251", "field": "finance", "role": "instrument", "bullets": [ "FINRA's machine-readable rulebook pilot, converting securities regulations into structured data for automated compliance use.", "An LLM helped pre-structure FINRA rules into controlled natural language for representation as machine-readable data components.", "The approach enables scalable digital rules publication and integration into member firms' operations via an Internet of Rules framework." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 3396, "authors_detailed": [ { "name": "C. Atkinson", "url": "https://openalex.org/A5054243418", "inst": "World Trade Organization" }, { "name": "Joseph Potvin", "url": "https://openalex.org/A5095691791", "inst": "North Cumbria Integrated Care NHS Foundation Trust" } ], "affiliations": [ "World Trade Organization", "North Cumbria Integrated Care NHS Foundation Trust" ] }, { "uid": "doi:10.2139/ssrn.4769961", "doi": "10.2139/ssrn.4769961", "title": "Guiding Decentralized Production via AI Feedback: Evidence on Product Design and User Experience", "authors": [ "Yan Cheng", "Shaochong Lin", "Zhou Zhou", "Zuo-Jun Max Shen" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4769961", "field": "management", "role": "object", "bullets": [ "Randomized field experiment on a large content-sharing platform with millions of video creators, measuring effects of centralized AI feedback on decentralized production decisions.", "AI feedback intervention synthesized market information and delivered it to creators; study measured resulting changes in product variety, quality, quantity, and audience-side user experience.", "AI feedback improved user experience; composite negative feedback drove content diversification in both category breadth and depth, especially among less experienced and lower-cost creators." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 55, "validated": null, "n": 3900, "authors_detailed": [ { "name": "Yan Cheng", "url": "https://openalex.org/A5100613013", "inst": "Shanghai University of Finance and Economics" }, { "name": "Shaochong Lin", "url": "https://openalex.org/A5016838877", "inst": "Chinese University of Hong Kong" }, { "name": "Zhou Zhou", "url": "https://openalex.org/A5117515686", "inst": "City University of Hong Kong" }, { "name": "Zuo‐Jun Max Shen", "url": "https://openalex.org/A5012457625", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of California, Berkeley", "Shanghai University of Finance and Economics", "Chinese University of Hong Kong", "City University of Hong Kong" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4772956", "doi": "10.2139/ssrn.4772956", "title": "Governing AI Agents", "authors": [ "Noam Kolt" ], "posted": "2024-04-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4772956", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of AI agent deployment by major language-model companies, applying economic principal-agent theory and common-law agency doctrine across multiple jurisdictions.", "Paper maps agency-law frameworks onto AI agents to characterize problems of information asymmetry, discretionary authority, loyalty conflicts, and uninterpretable autonomous decision-making at scale.", "Conventional agency solutions including incentive design, monitoring, and enforcement are insufficient for AI agents; new technical and legal infrastructure for inclusivity, visibility, and liability is needed." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "salience": 65, "validated": null, "n": 3901, "authors_detailed": [ { "name": "Noam Kolt", "url": "https://openalex.org/A5092031121", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto" ], "prestige": true }, { "uid": "arxiv:2404.09260v2", "arxiv_id": "2404.09260v2", "title": "JaFIn: Japanese Financial Instruction Dataset", "authors": [ "Kota Tanabe", "Masahiro Suzuki", "Hiroki Sakaji", "Itsuki Noda" ], "posted": "2024-04-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.09260v2", "field": "finance", "role": "method", "bullets": [ "JaFIn, a manually built Japanese financial instruction dataset drawing on government websites and other public financial sources; the number of instructions is not stated.", "Several LLMs, families not stated, are instruction tuned on the dataset and compared with their base versions on a quantitative Japanese financial benchmark plus qualitative response review.", "Finance tuned models beat their originals on the benchmark, supporting domain adaptation through instruction tuning; the abstract reports no effect sizes." ], "bullet_provenance": "ai", "salience": 32, "edition": 13, "models": [], "validated": null, "n": 1780, "authors_detailed": [ { "name": "Kota Tanabe", "url": "https://openalex.org/A5102409231", "inst": "Hokkaido University" }, { "name": "Masahiro Suzuki", "url": "https://openalex.org/A5038802330", "inst": "Bunkyo University" }, { "name": "Hiroki Sakaji", "url": "https://openalex.org/A5028823648", "inst": "Hokkaido University" }, { "name": "Itsuki Noda", "url": "https://openalex.org/A5095740250", "inst": "Hokkaido University" } ], "affiliations": [ "Hokkaido University", "Bunkyo University" ] }, { "uid": "arxiv:2404.08492v2", "arxiv_id": "2404.08492v2", "title": "Strategic Interactions between Large Language Models-based Agents in Beauty Contests", "authors": [ "Siting Estee Lu" ], "posted": "2024-04-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.08492v2", "field": "economics", "role": "agent", "bullets": [ "Classical beauty contest games played by several types of LLM based agents, with group composition varied by the strategic sophistication of the agents.", "Agents, models not named, submit guesses whose implied depth of reasoning is compared with human experimental results from the same game.", "Agents reason at level 0 to 1, below human subjects, yet converge toward Nash equilibrium with repetition; lower strategic uncertainty and mixed groups speed convergence, and more sophisticated agents earn more." ], "bullet_provenance": "ai", "validated": true, "validation_note": "comparison with human beauty contest experiments", "salience": 48, "edition": 13, "models": [], "n": 1725, "authors_detailed": [ { "name": "Siting Estee Lu", "url": "https://openalex.org/A5095699490", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.4769321", "doi": "10.2139/ssrn.4769321", "title": "Strategic Responses to Technological Change: Evidence from Online Labor Markets", "authors": [ "Shun Yiu", "Robert Seamans", "Manav Raj", "Ted Liu" ], "posted": "2024-04-12", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4769321", "field": "management", "role": "object", "bullets": [ "Freelancer bidding records from an online work platform spanning the period around ChatGPT's November 2022 release; platform name, sample size, and geography are not stated.", "ChatGPT is not applied as a research tool; its release serves as the shock, and worker responses are measured through changes in bid volume and positioning, with no validation applicable.", "After ChatGPT, freelancers submitted fewer bids and repositioned; demand declines pushed exit and domain switching, while supply increases reduced the share of bids to high-value jobs, and higher-skill workers repositioned less." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 3, "audience": "general", "validated": null, "n": 635, "authors_detailed": [ { "name": "Shun Yiu", "url": "https://openalex.org/A5111172795", "inst": "Indiana University Bloomington" }, { "name": "Robert Seamans", "url": "https://openalex.org/A5050038373", "inst": "New York University" }, { "name": "Manav Raj", "url": "https://openalex.org/A5018390504", "inst": "University of Pennsylvania" }, { "name": "Ted Liu", "url": "https://openalex.org/A5101336091", "inst": "ResearchWorks (United States)" } ], "affiliations": [ "Indiana University Bloomington", "New York University", "University of Pennsylvania", "ResearchWorks (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4764707", "doi": "10.2139/ssrn.4764707", "title": "Measuring Audience-Specific Readability with Language Predictability: A Large Language Model Approach", "authors": [ "Jiexin Zheng", "Rong Zheng", "Amy Zang" ], "posted": "2024-04-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4764707", "field": "finance", "role": "instrument", "bullets": [ "U.S. corporate disclosures analyzed for readability effects on stock return volatility, bid-ask spreads, and analyst forecast outcomes.", "Fine-tuned LLM on corporate disclosures computes a Language Predictability Score grounded in surprisal theory, simulating sophisticated-reader comprehension.", "Lower LPS correlates with higher return volatility, wider bid-ask spreads, greater analyst forecast dispersion, and reduced forecast accuracy after firm fixed effects." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "validity tests on incoherent text and boilerplate identification", "salience": 65, "n": 2838, "authors_detailed": [ { "name": "Jiexin Zheng", "url": "https://openalex.org/A5043220144", "inst": "Hong Kong University of Science and Technology" }, { "name": "Jiexin Zheng", "url": "https://openalex.org/A5043220144", "inst": "Hong Kong University of Science and Technology" }, { "name": "Amy Zang", "url": "https://openalex.org/A5117462123", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Hong Kong University of Science and Technology" ] }, { "uid": "doi:10.2139/ssrn.4758402", "doi": "10.2139/ssrn.4758402", "title": "Could ChatGPT Have Earned Abnormal Returns? A retrospective test from the U.S. stock market", "authors": [ "Marc LoGrasso" ], "posted": "2024-04-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4758402", "field": "finance", "role": "agent", "bullets": [ "U.S. stock market, two-year holding periods from July 1985 to 2021, GPT-4 selected portfolios retrospectively under knowledge restrictions.", "GPT-4 acted as a casual investor selecting stocks constrained to contemporaneous information; portfolios evaluated using multi-factor models.", "Portfolios averaged approximately 1% monthly alpha after controlling for size, value, profitability, investment, and momentum factors." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Fama-French multi-factor model alphas", "salience": 65, "n": 2931, "authors_detailed": [ { "name": "Marc F. LoGrasso", "url": "https://openalex.org/A5039597562", "inst": "Richard Wolf (Germany)" } ], "affiliations": [ "Richard Wolf (Germany)" ] }, { "uid": "doi:10.2139/ssrn.4759122", "doi": "10.2139/ssrn.4759122", "title": "Firm-level trade policy effect uncertainty and cost stickiness: Evidence from a large language model approach ", "authors": [ "Shuyang Jia", "Peng Liang" ], "posted": "2024-04-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4759122", "field": "accounting", "role": "instrument", "bullets": [ "Chinese listed firms analyzed for cost management decisions under trade policy effect uncertainty, with endogeneity controls including CEM and DiD.", "An LLM constructed a firm-level trade policy effect uncertainty measure from textual data to capture managers' perceived uncertainty.", "Higher trade policy effect uncertainty reduces cost stickiness, especially in non-SOEs, firms with less overconfident executives, and financially constrained firms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3393, "authors_detailed": [ { "name": "Shuyang Jia", "url": "https://openalex.org/A5022148260", "inst": "Xi'an Jiaotong University" }, { "name": "Peng Liang", "url": "https://openalex.org/A5101739682", "inst": "University of Science and Technology of China" } ], "affiliations": [ "Xi'an Jiaotong University", "University of Science and Technology of China" ] }, { "uid": "arxiv:2404.07396v3", "arxiv_id": "2404.07396v3", "title": "Can Base ChatGPT be Used for Forecasting without Additional Optimization?", "authors": [ "Van Pham", "Scott Cunningham" ], "posted": "2024-04-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.07396v3", "field": "economics", "role": "method", "bullets": [ "100 trials asking ChatGPT-3.5 and ChatGPT-4, with training data ending September 2021, to predict 2022 events including Academy Award winners and economic outcomes.", "Direct prompts are compared with narrative prompts in which fictional characters recount post-cutoff events; answers are graded against realized outcomes, with a May 2024 falsification rerun.", "Narrative prompting raises ChatGPT-4 accuracy markedly, and accuracy reaches 100 percent in many cases once training data covers the target events, pointing to contamination rather than foresight." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "graded against realized 2022 events", "salience": 55, "edition": 13, "n": 1598, "authors_detailed": [ { "name": "Van Dai Pham", "url": "https://openalex.org/A5017967256", "inst": "FPT University" }, { "name": "Scott W. Cunningham", "url": "https://openalex.org/A5000045142", "inst": "University of Strathclyde" } ], "affiliations": [ "FPT University", "University of Strathclyde" ] }, { "uid": "arxiv:2404.07452v2", "arxiv_id": "2404.07452v2", "title": "RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data", "authors": [ "Yupeng Cao", "Zhi Chen", "Prashant Kumar", "Qingyun Pei", "Yangyang Yu", "Haohang Li", "Fabrizio Dimino", "Lorenzo Ausiello", "K. P. Subbalakshmi", "Papa Momar Ndiaye" ], "posted": "2024-04-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.07452v2", "field": "finance", "role": "method", "bullets": [ "Earnings conference call text and audio, market time series, and contextual news, combined to predict firm level financial risk; sample size and period are not stated in the abstract.", "A large language model, not named in the abstract, analyzes and fuses the multimodal inputs to forecast volatility and variance; no validation statistics appear in the abstract.", "The framework is reported to forecast market volatility and variance effectively, with comparative experiments apportioning the contribution of each data source; magnitudes are not given." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 13, "models": [], "n": 1637, "authors_detailed": [ { "name": "Yupeng Cao", "url": "https://openalex.org/A5009376964", "inst": "Kyoto University" }, { "name": "Zhi Chen", "url": "https://openalex.org/A5100456801", "inst": "East China University of Science and Technology" }, { "name": "Prashant Kumar", "url": "https://openalex.org/A5020039861", "inst": "O. P. Jindal Global University" }, { "name": "Qingyun Pei", "url": "https://openalex.org/A5095383910", "inst": "Stevens Institute of Technology" }, { "name": "Yu, Yangyang", "url": "", "inst": "" }, { "name": "Li, Haohang", "url": "", "inst": "" }, { "name": "Fabrizio Dimino", "url": "https://openalex.org/A5095383911", "inst": "Domus Medica" }, { "name": "Lorenzo Ausiello", "url": "https://openalex.org/A5095383912", "inst": "Stevens Institute of Technology" }, { "name": "K. P. Subbalakshmi", "url": "https://openalex.org/A5033041089", "inst": "Stevens Institute of Technology" }, { "name": "Papa Momar Ndiaye", "url": "https://openalex.org/A5108382062", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Kyoto University", "East China University of Science and Technology", "O. P. Jindal Global University", "Stevens Institute of Technology", "Domus Medica" ] }, { "uid": "doi:10.2139/ssrn.4758197", "doi": "10.2139/ssrn.4758197", "title": "Will Artificial Intelligence Get in the Way of Achieving Gender Equality?", "authors": [ "Siri Isaksson", "Catalina Franco Buitrago", "Daniel Carvajal" ], "posted": "2024-04-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4758197", "field": "economics", "role": "object", "bullets": [ "Survey of students at the Norwegian School of Economics measuring ChatGPT usage, attitudes, prompt-writing proficiency, and policy responses by gender.", "ChatGPT use and prompt-writing skill measured across genders; effects of university bans on intended usage assessed via survey experiment.", "Female students use ChatGPT significantly less and score lower on prompt proficiency; university bans on ChatGPT widen the gender gap substantially." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "validated": null, "n": 2930, "authors_detailed": [ { "name": "Siri Isaksson", "url": "https://openalex.org/A5093745137", "inst": "Norwegian School of Economics" }, { "name": "Catalina Franco Buitrago", "url": "https://openalex.org/A5085933394", "inst": "Norwegian School of Economics" }, { "name": "Daniel Carvajal", "url": "https://openalex.org/A5108979244", "inst": "Norwegian School of Economics" } ], "affiliations": [ "Norwegian School of Economics" ] }, { "uid": "arxiv:2404.07511v1", "arxiv_id": "2404.07511v1", "title": "Generative Probabilistic Planning for Optimizing Supply Chain Networks", "authors": [ "Hyung-il Ahn", "Santiago Olivar", "Hershel Mehta", "Young Chol Song" ], "posted": "2024-04-11", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.07511v1", "field": "management", "role": "method", "bullets": [ "Historical supply chain data from a global consumer goods company with complex multi-node networks and demand variability.", "Generative Probabilistic Planning used attention-based graph neural networks and offline deep reinforcement learning to optimize supply plans.", "GPP achieved globally optimized, objective-adaptable supply plans with significant improvements in performance and profitability over traditional methods." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "historical data from global consumer goods company", "salience": 45, "n": 3390, "authors_detailed": [ { "name": "Hyung-il Ahn", "url": "https://openalex.org/A5088500288", "inst": "IBM (United States)" }, { "name": "Santiago Olivar", "url": "https://openalex.org/A5071088234", "inst": "OneBlood" }, { "name": "Hershel Mehta", "url": "https://openalex.org/A5021952938", "inst": "Stanford University" }, { "name": "Young Chol Song", "url": "https://openalex.org/A5004261476", "inst": "University of Rochester" } ], "affiliations": [ "Stanford University", "University of Rochester", "IBM (United States)", "OneBlood" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4745624", "doi": "10.2139/ssrn.4745624", "title": "The Heterogeneous Productivity Effects of Generative AI *", "authors": [ "David Kreitmeir", "Paul Raschky" ], "posted": "2024-04-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4745624", "field": "economics", "role": "object", "bullets": [ "Daily coding output of over 36,000 GitHub users in Italy and other European countries around Italy's 2023 ChatGPT ban.", "Difference-in-differences design exploited the sudden ban announcement to estimate individual productivity effects of ChatGPT access.", "Less experienced coders saw short-term increases in output quantity and quality; experienced coders saw decreased productivity on routine tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 70, "validated": null, "n": 3391, "authors_detailed": [ { "name": "David Kreitmeir", "url": "https://openalex.org/A5035675088", "inst": "Australian Regenerative Medicine Institute" }, { "name": "Paul A. Raschky", "url": "https://openalex.org/A5063040920", "inst": "Monash University" } ], "affiliations": [ "Australian Regenerative Medicine Institute", "Monash University" ] }, { "uid": "doi:10.2139/ssrn.4754655", "doi": "10.2139/ssrn.4754655", "title": "Antitrust & AI Supply Chains", "authors": [ "Maurice E. Stucke", "Ariel Ezrachi" ], "posted": "2024-04-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4754655", "field": "economics", "role": "object", "bullets": [ "Analysis of the emerging AI foundation model supply chain and digital market dynamics favoring winner-take-most ecosystems.", "Examines how concentrated layers of the AI supply chain may enable firms to extend market power across layers.", "Several countervailing factors may lessen antitrust risks, but policy action is needed to promote competition and innovation in AI." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 3392, "authors_detailed": [ { "name": "Maurice E. Stucke", "url": "https://openalex.org/A5009397252", "inst": "Knoxville College" }, { "name": "Ariel Ezrachi", "url": "https://openalex.org/A5047957053", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford", "Knoxville College" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4754678", "doi": "10.2139/ssrn.4754678", "title": "Lookahead Bias in Pretrained Language Models", "authors": [ "Suproteem Sarkar", "Keyon Vafa" ], "posted": "2024-04-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4754678", "field": "finance", "role": "method", "bullets": [ "Pretrained language models applied to corporate earnings calls and candidate biographies for prediction tasks requiring strict temporal separation of training and analysis periods.", "Authors developed direct statistical tests for temporal lookahead bias, based on the assumption that certain events are unpredictable given a prespecified past-only information set.", "Evidence of lookahead bias found in predicting risk factors from earnings calls and election winners from biographies; prompting-based mitigation approaches proved insufficient." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "unpredictability assumption tests on earnings calls and elections", "salience": 75, "n": 3899, "authors_detailed": [ { "name": "Suproteem K. Sarkar", "url": "https://openalex.org/A5081371955", "inst": "Harvard University Press" }, { "name": "Keyon Vafa", "url": "https://openalex.org/A5085921946", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4786671", "doi": "10.2139/ssrn.4786671", "title": "Generative AI, Adoption and the Structure of Tasks", "authors": [ "Laurence Ales", "Christophe Combemale", "Krishnan Ramayya" ], "posted": "2024-04-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4786671", "field": "economics", "role": "object", "bullets": [ "Theoretical analysis of generative AI adoption examining task structure, worker heterogeneity, and employment displacement patterns.", "No model used; framework analyzes how task complementarity and substitutability shape genAI adoption decisions by heterogeneous workers.", "Task structure determines genAI adoption feasibility; displaced workers likely seek employment in tasks less amenable to AI automation." ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 2929, "authors_detailed": [ { "name": "Laurence Ales", "url": "https://openalex.org/A5015022927", "inst": "Carnegie Mellon University" }, { "name": "Christophe Combemale", "url": "https://openalex.org/A5060886728", "inst": "Professional Analytical and Consulting Services (United States)" }, { "name": "Krishnan Ramayya", "url": "", "inst": "Historical Society of Western Pennsylvania" } ], "affiliations": [ "Carnegie Mellon University", "Professional Analytical and Consulting Services (United States)", "Historical Society of Western Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4754950", "doi": "10.2139/ssrn.4754950", "title": "Managing Generative AI in Firms: The Theory of Shadow User Innovation", "authors": [ "Julian Waters-Lynch", "Darcy W E Allen", "Jason Potts", "Chris Berg" ], "posted": "2024-04-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4754950", "field": "management", "role": "object", "bullets": [ "Conceptual study of employee-led covert experimentation with generative AI applications inside firms.", "Framework analyzes how unsupervised GenAI use creates opacity that prevents firms from capturing productivity gains.", "Employees privately capture GenAI benefits; management responses must balance visibility with encouraging bottom-up user innovation." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 3389, "authors_detailed": [ { "name": "Julian Waters-Lynch", "url": "https://openalex.org/A5086021931", "inst": "Alfaisal University" }, { "name": "Darcy W E Allen", "url": "https://openalex.org/A5000922663", "inst": "RMIT Europe" }, { "name": "Jason Potts", "url": "https://openalex.org/A5042648415", "inst": "Alfaisal University" }, { "name": "Chris Berg", "url": "https://openalex.org/A5083681237", "inst": "RMIT Europe" } ], "affiliations": [ "Alfaisal University", "RMIT Europe" ] }, { "uid": "arxiv:2404.06162v3", "arxiv_id": "2404.06162v3", "title": "Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports", "authors": [ "Tianyu Cao", "Natraj Raman", "Danial Dervovic", "Chenhao Tan" ], "posted": "2024-04-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.06162v3", "field": "finance", "role": "method", "bullets": [ "Long financial reports with heavy use of numbers and tables serve as a case study for multimodal long-form summarization by commercial LLMs.", "Claude 2.0 and 2.1, GPT-4 and 3.5 and Cohere generate summaries; the authors measure extractiveness and position bias and build a taxonomy of numeric hallucination, without a ground-truth accuracy benchmark.", "GPT-3.5 and Cohere fail the task; Claude 2 handles long inputs better than GPT-4, whose use of numbers improves only modestly under prompt engineering." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": false, "salience": 50, "edition": 13, "n": 1526, "authors_detailed": [ { "name": "Tianyu Cao", "url": "https://openalex.org/A5032247322", "inst": "Tongji University" }, { "name": "Natraj Raman", "url": "https://openalex.org/A5065006343", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Danial Dervovic", "url": "https://openalex.org/A5036538375", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Chenhao Tan", "url": "https://openalex.org/A5079270249", "inst": "Dalian University of Technology" } ], "affiliations": [ "Tongji University", "JPMorgan Chase & Co (United States)", "Dalian University of Technology" ] }, { "uid": "doi:10.2139/ssrn.4752797", "doi": "10.2139/ssrn.4752797", "title": "Evaluating LLMs in Financial Tasks - Code Generation in Trading Strategies", "authors": [ "Miquel Noguer I Alonso", "Hanane Dupouy" ], "posted": "2024-04-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4752797", "field": "finance", "role": "instrument", "bullets": [ "Evaluation of five LLMs generating Python implementations of technical indicators for algorithmic trading, benchmarked against the TALib library and a reference implementation.", "GPT-4-Turbo, Gemini-Pro, Mistral, Llama2, and Codellama tested using structured zero-shot prompts with templated responses and prompt chaining for step-by-step reasoning.", "GPT-4-Turbo and Codellama-70B produce implementations matching baseline computations in certain cases; structured prompt design significantly improves generated code accuracy across models." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini", "llama", "open_other" ], "open_weights": false, "validated": true, "validation_note": "TALib baseline implementations", "salience": 35, "n": 2379, "authors_detailed": [ { "name": "Miquel Noguer I Alonso", "url": "https://openalex.org/A5007301330", "inst": "Artificial Intelligence in Medicine (Canada)" }, { "name": "Hanane Dupouy", "url": "https://openalex.org/A5095112692", "inst": "Artificial Intelligence in Medicine (Canada)" } ], "affiliations": [ "Artificial Intelligence in Medicine (Canada)" ] }, { "uid": "doi:10.2139/ssrn.4755955", "doi": "10.2139/ssrn.4755955", "title": "Unlocking the Collateral Value of Trademarks: The Role of Asset Redeployability", "authors": [ "Wendi Du" ], "posted": "2024-04-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4755955", "field": "finance", "role": "instrument", "bullets": [ "Firm-level trademark portfolio data around a court decision that exogenously weakened trademark redeployability in the U.S.", "ChatGPT and NLP techniques classified trademark licensing exposure to identify firms more affected by the court ruling.", "Affected firms reduced book leverage by 3.4 percentage points (16.9% of average); they pledged more and more valuable trademarks as collateral." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 3388, "authors_detailed": [ { "name": "Wendi Du", "url": "https://openalex.org/A5102500145", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2404.05632v2", "arxiv_id": "2404.05632v2", "title": "Fighting crime with Transformers: Empirical analysis of address parsing methods in payment data", "authors": [ "Haitham Hammami", "Louis Baligand", "Bojan Petrovski" ], "posted": "2024-04-08", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.05632v2", "field": "finance", "role": "method", "bullets": [ "Free-text address attributes in payment messages, where regulations require identifying the location of transaction parties, tested on noisy real-world transactional data at industrial scale.", "Fine-tuned Transformer models are benchmarked against zero-shot generative LLMs for extracting street, postal code, and country fields. Specific model names are not stated in the abstract.", "A well fine-tuned Transformer with early stopping significantly outperforms other approaches, while zero-shot generative models perform strongly enough to warrant further investigation. Accuracy figures are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labeled address parsing comparison on payment data", "salience": 30, "edition": 13, "models": [], "n": 1688, "authors_detailed": [ { "name": "Haitham Hammami", "url": "https://openalex.org/A5095335086", "inst": "" }, { "name": "Louis Baligand", "url": "https://openalex.org/A5020995515", "inst": "" }, { "name": "Bojan Petrovski", "url": "https://openalex.org/A5049257321", "inst": "École Polytechnique Fédérale de Lausanne" } ], "affiliations": [ "École Polytechnique Fédérale de Lausanne" ] }, { "uid": "arxiv:2404.08681v2", "arxiv_id": "2404.08681v2", "title": "EFSA: Towards Event-Level Financial Sentiment Analysis", "authors": [ "Tianyu Chen", "Yiming Zhang", "Guoxin Yu", "Dapeng Zhang", "Li Zeng", "Qing He", "Xiang Ao" ], "posted": "2024-04-08", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.08681v2", "field": "finance", "role": "instrument", "bullets": [ "A corpus of 12,160 Chinese financial news articles manually annotated with 13,725 quintuples covering company, industry, coarse-grained event, fine-grained event, and sentiment.", "A four-hop Chain-of-Thought LLM-based method extracted structured event-level sentiment quintuples from financial text, benchmarked against multiple existing sentiment-analysis and extraction baselines.", "The proposed LLM method reached state-of-the-art scores on the new EFSA benchmark, outperforming prior financial sentiment analysis approaches on the quintuple extraction task." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "EFSA dataset quintuple extraction benchmark", "salience": 50, "n": 3898, "authors_detailed": [ { "name": "Tianyu Chen", "url": "https://openalex.org/A5100371618", "inst": "Qingdao University of Science and Technology" }, { "name": "Yiming Zhang", "url": "https://openalex.org/A5100395351", "inst": "Guangdong University of Technology" }, { "name": "Guoxin Yu", "url": "https://openalex.org/A5077224755", "inst": "Xinjiang Agricultural University" }, { "name": "Dapeng Zhang", "url": "https://openalex.org/A5115601773", "inst": "Beijing Normal University" }, { "name": "Li Zeng", "url": "https://openalex.org/A5101484141", "inst": "Zhengzhou University of Light Industry" }, { "name": "Qing Shan He", "url": "https://openalex.org/A5102113186", "inst": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Xiang Ao", "url": "https://openalex.org/A5061531417", "inst": "McGill University Health Centre" } ], "affiliations": [ "Qingdao University of Science and Technology", "Guangdong University of Technology", "Xinjiang Agricultural University", "Beijing Normal University", "Zhengzhou University of Light Industry", "Nanjing University of Aeronautics and Astronautics", "McGill University Health Centre" ] }, { "uid": "arxiv:2404.04949v1", "arxiv_id": "2404.04949v1", "title": "SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning", "authors": [ "Yuhang Zhou", "Zeping Li", "Siyu Tian", "Yuchen Ni", "Sen Liu", "Guangnan Ye", "Hongfeng Chai" ], "posted": "2024-04-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.04949v1", "field": "finance", "role": "method", "bullets": [ "Heterogeneous Chinese financial task data reorganized in semantic space so that training examples route to the best suited expert models.", "SilverSight, a multi expert financial LLM built with adaptive semantic space learning on an unnamed base model, is compared with training on the full dataset.", "Training on 10 percent of the data selected by the framework approaches full data performance and generalizes across tasks; benchmarks and figures are not named in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 13, "models": [], "n": 1724, "authors_detailed": [ { "name": "Yuhang Zhou", "url": "https://openalex.org/A5101972656", "inst": "Shibaura Institute of Technology" }, { "name": "Zeping Li", "url": "https://openalex.org/A5101787835", "inst": "Guangdong University of Technology" }, { "name": "Siyu Tian", "url": "https://openalex.org/A5090639687", "inst": "East China University of Science and Technology" }, { "name": "Yuchen Ni", "url": "https://openalex.org/A5101334139", "inst": "Xinjiang Medical University" }, { "name": "Sen Liu", "url": "https://openalex.org/A5100358491", "inst": "Fudan University" }, { "name": "Guangnan Ye", "url": "https://openalex.org/A5003276783", "inst": "Fudan University" }, { "name": "Hongfeng Chai", "url": "https://openalex.org/A5023897455", "inst": "Fudan University" } ], "affiliations": [ "Shibaura Institute of Technology", "Guangdong University of Technology", "East China University of Science and Technology", "Xinjiang Medical University", "Fudan University" ] }, { "uid": "arxiv:2404.05101v3", "arxiv_id": "2404.05101v3", "title": "StockGPT: A GenAI Model for Stock Prediction and Trading", "authors": [ "Dat Mai" ], "posted": "2024-04-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.05101v3", "field": "finance", "role": "method", "bullets": [ "70 million daily U.S. stock returns over nearly 100 years, with held-out test sample from 2001 to 2023.", "StockGPT, a custom autoregressive model, treated return series as token sequences and learned predictive patterns via attention.", "Long-short portfolios from StockGPT predictions yield significant alphas against leading factors, spanning momentum and reversal strategies." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "held-out test 2001-2023, alphas against stock market factors", "salience": 65, "n": 3387, "authors_detailed": [ { "name": "Dat Mai", "url": "https://openalex.org/A5009248196", "inst": "MRC Biostatistics Unit" } ], "affiliations": [ "MRC Biostatistics Unit" ] }, { "uid": "doi:10.2139/ssrn.4752229", "doi": "10.2139/ssrn.4752229", "title": "Evaluating the Efficacy of Generative Artificial Intelligence in Grading: Insights from Authentic Assessments in Economics", "authors": [ "Minh Nhat Nguyen", "Binh Nguyen Thanh", "Diem Thi Hong Vo", "Tra Pham Thi Thu", "Hieu Thai", "Son Ha Xuan" ], "posted": "2024-04-06", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4752229", "field": "economics", "role": "method", "bullets": [ "Authentic economics assessments include text and data-analysis questions spanning Bloom's cognitive levels; the number of submissions, institutions, geography, and period are not stated.", "GPT-4 grades the work and is compared with human expert markers for consistency and bias, but the abstract provides no agreement or accuracy statistic.", "AI grades align more closely with humans on lower-level and text-based work than on higher-level data analysis, limiting its use for complex assessment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 43, "edition": 23, "n": 4181, "authors_detailed": [ { "name": "Minh Nhat Nguyen", "url": "https://openalex.org/A5101787571", "inst": "RMIT Vietnam" }, { "name": "Binh Nguyen Thanh", "url": "https://openalex.org/A5008771904", "inst": "RMIT Vietnam" }, { "name": "Diem Thi Hong Vo", "url": "https://openalex.org/A5068414997", "inst": "RMIT Vietnam" }, { "name": "Thi Thu Tra Pham", "url": "https://openalex.org/A5076689444", "inst": "RMIT Vietnam" }, { "name": "Hieu Thai Trung", "url": "https://openalex.org/A5086620384", "inst": "RMIT Vietnam" }, { "name": "Son Ha Xuan", "url": "", "inst": "RMIT Vietnam" } ], "affiliations": [ "RMIT Vietnam" ] }, { "uid": "doi:10.2139/ssrn.4754435", "doi": "10.2139/ssrn.4754435", "title": "Strategizing with AI: Insights from a Beauty Contest Experiment", "authors": [ "Dmitry Dagaev", "Sofiia Paklina", "Petr Parshakov" ], "posted": "2024-04-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4754435", "field": "economics", "role": "agent", "bullets": [ "Keynesian beauty contest experiments with multiple LLMs playing against virtual player groups of varying sophistication levels.", "Multiple LLMs including Llama played iterated guessing-fraction-of-mean games replicating classic behavioral economics experiments.", "Most LLMs except Llama adapted to opponent sophistication and played closer to Nash equilibrium than humans; all failed dominant strategies in two-player games." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": false, "salience": 60, "n": 2470, "authors_detailed": [ { "name": "Dmitry Dagaev", "url": "https://openalex.org/A5089402209", "inst": "New Economic School" }, { "name": "Sofiia Paklina", "url": "https://openalex.org/A5079932942", "inst": "National Research University Higher School of Economics" }, { "name": "Petr Parshakov", "url": "https://openalex.org/A5073937184", "inst": "National Research University Higher School of Economics" } ], "affiliations": [ "New Economic School", "National Research University Higher School of Economics" ] }, { "uid": "doi:10.2139/ssrn.4750389", "doi": "10.2139/ssrn.4750389", "title": "The Interplay of Opinions and Behavior in Social Trading: The Dynamics of Early Cryptocurrency Adoption", "authors": [ "Ye Liu", "Mingwen Yang", "Matthias Pelster", "Yong Tan" ], "posted": "2024-04-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4750389", "field": "finance", "role": "instrument", "bullets": [ "Dataset from a large social trading platform tracking posting and trading behaviors around cryptocurrency adoption decisions.", "An LLM extracted textual features from social media posts to measure opinion credibility and alignment with trading behavior.", "Peer trading aligned with expressed opinions increases cryptocurrency adoption; credibility is the key mechanism distinguishing adopters from non-adopters." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 3386, "authors_detailed": [ { "name": "Ye Liu", "url": "https://openalex.org/A5100346522", "inst": "Georgia Institute of Technology" }, { "name": "Mingwen Yang", "url": "https://openalex.org/A5052738825", "inst": "University of Washington" }, { "name": "Matthias Pelster", "url": "https://openalex.org/A5020680328", "inst": "European Centre for Minority Issues" }, { "name": "Yong Tan", "url": "https://openalex.org/A5037984091", "inst": "University of Washington" } ], "affiliations": [ "Georgia Institute of Technology", "University of Washington", "European Centre for Minority Issues" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4750326", "doi": "10.2139/ssrn.4750326", "title": "Can Banning AI-generated Content Save User-Generated Q&A Platforms?", "authors": [ "Xiaoxiao (Shawn) Wang", "Jinyang Zheng" ], "posted": "2024-04-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4750326", "field": "management", "role": "object", "bullets": [ "Stack Exchange network forums with staggered AI-generated content bans, analyzed using generalized synthetic control methods across STEM and non-STEM communities.", "Study measures how banning AI-generated content affects answer quantity, quality, and problem-solving effectiveness on user-generated Q&A platforms.", "Bans reduced answer quantity and quality, contracted question activity, and harmed problem-solving, with stronger effects in STEM forums and among low-status users." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 62, "validated": null, "n": 2656, "authors_detailed": [ { "name": "Xiaoxiao Wang", "url": "https://openalex.org/A5100355063", "inst": "Purdue University West Lafayette" }, { "name": "Jinyang Zheng", "url": "https://openalex.org/A5074769341", "inst": "University of Rochester" } ], "affiliations": [ "University of Rochester", "Purdue University West Lafayette" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4747519", "doi": "10.2139/ssrn.4747519", "title": "Bottom Up vs Top Down: What Does Firm 10-K Tell Us?", "authors": [ "Landon Ross", "Jim Horn", "Mert Pilanci", "KaiHong Luo", "Guofu Zhou" ], "posted": "2024-04-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4747519", "field": "finance", "role": "method", "bullets": [ "Massive panel of firm 10-K filings with n-gram extraction used to construct a specialized return-predictive dictionary via elastic net regressions.", "Bottom-up dictionary benchmarked against prominent financial dictionaries, off-the-shelf LLMs, and machine learning algorithms on stock return prediction.", "Bottom-up approach outperforms LLMs and ML algorithms; spread portfolio based on the dictionary generates significant average returns." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "out-of-sample stock return prediction", "salience": 68, "n": 2928 }, { "uid": "arxiv:2404.03523v1", "arxiv_id": "2404.03523v1", "title": "Integrating Generative AI into Financial Market Prediction for Improved Decision Making", "authors": [ "Chang Che", "Zengyi Huang", "Chen Li", "Haotian Zheng", "Xinyu Tian" ], "posted": "2024-04-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.03523v1", "field": "finance", "role": "method", "bullets": [ "Simulated and predicted dynamic changes in financial markets using conditional generative adversarial networks and time series methods.", "A cGAN model was trained to capture complexity in financial market data and generate predictions of market dynamics.", "The cGAN model achieved minimal deviation between predicted and actual market performance, demonstrating high predictive accuracy." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "prediction vs actual market performance", "salience": 25, "n": 3384, "authors_detailed": [ { "name": "Chang Che", "url": "https://openalex.org/A5102706631", "inst": "George Washington University" }, { "name": "Zengyi Huang", "url": "https://openalex.org/A5111183775", "inst": "Chongqing University" }, { "name": "Chen Li", "url": "https://openalex.org/A5100369885", "inst": "Hebei Agricultural University" }, { "name": "Haotian Zheng", "url": "https://openalex.org/A5101847037", "inst": "Nanjing General Hospital of Nanjing Military Command" }, { "name": "Xinyu Tian", "url": "https://openalex.org/A5029944382", "inst": "Qingdao University" } ], "affiliations": [ "George Washington University", "Chongqing University", "Hebei Agricultural University", "Qingdao University" ] }, { "uid": "doi:10.2139/ssrn.4737265", "doi": "10.2139/ssrn.4737265", "title": "Theory Is All You Need: AI, Human Cognition, and Causal Reasoning", "authors": [ "Teppo Felin", "Matthias Holweg" ], "posted": "2024-04-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4737265", "field": "management", "role": "object", "bullets": [ "Conceptual analysis contrasting AI data-based prediction with human theory-based causal reasoning, using LLMs as the central example.", "Large language models examined as probability-based, backward-looking prediction systems compared to forward-looking human cognition.", "AI cannot generate genuine novelty or replace human decision making under uncertainty; data-belief asymmetries limit AI to imitative output." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "validated": null, "n": 3385, "authors_detailed": [ { "name": "Teppo Felin", "url": "https://openalex.org/A5026683520", "inst": "Utah State University" }, { "name": "Matthias Holweg", "url": "https://openalex.org/A5003763057", "inst": "University of Oxford" } ], "affiliations": [ "University of Oxford", "Utah State University" ], "prestige": true }, { "uid": "arxiv:2404.02466v1", "arxiv_id": "2404.02466v1", "title": "Prompting for Numerical Sequences: A Case Study on Market Comment Generation", "authors": [ "Masayuki Kawarada", "Tatsuya Ishigaki", "Hiroya Takamura" ], "posted": "2024-04-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.02466v1", "field": "finance", "role": "method", "bullets": [ "Market comment generation from sequences of stock prices, comparing prompt formats including plain token sequences, HTML, LaTeX, and Python-style code.", "Unnamed LLMs receive each input representation and generate commentary; the abstract reports no validation against reference texts or human judges.", "Programming-language-style prompts outperform natural-language and longer markup formats for turning price series into text; no metric values are stated." ], "bullet_provenance": "ai", "validated": false, "salience": 20, "edition": 13, "models": [], "n": 1597, "authors_detailed": [ { "name": "Masayuki Kawarada", "url": "https://openalex.org/A5095084156", "inst": "Artificial Intelligence in Medicine (Canada)" }, { "name": "Tatsuya Ishigaki", "url": "https://openalex.org/A5063285759", "inst": "Air Water (Japan)" }, { "name": "Hiroya Takamura", "url": "https://openalex.org/A5042127858", "inst": "The Institute of Statistical Mathematics" } ], "affiliations": [ "Artificial Intelligence in Medicine (Canada)", "Air Water (Japan)", "The Institute of Statistical Mathematics" ] }, { "uid": "arxiv:2404.03086v1", "arxiv_id": "2404.03086v1", "title": "Auditing the Use of Language Models to Guide Hiring Decisions", "authors": [ "Johann D. Gaebler", "Sharad Goel", "Aziz Huq", "Prasanna Tambe" ], "posted": "2024-04-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.03086v1", "field": "economics", "role": "agent", "bullets": [ "Applications for K-12 teaching jobs in a large US public school district, with race and gender signaled by manipulating applicant names in a correspondence experiment.", "Several state of the art language models, not named in the abstract, assess the candidates; disparities are measured across application material types and task framings.", "The models show moderate race and gender disparities that persist across inputs and framings; the authors weigh limits of correspondence audits for algorithmic hiring tools." ], "bullet_provenance": "ai", "salience": 62, "edition": 13, "models": [], "validated": null, "n": 1635, "authors_detailed": [ { "name": "Johann D. Gaebler", "url": "https://openalex.org/A5114144349", "inst": "" }, { "name": "Sharad Goel", "url": "https://openalex.org/A5027036879", "inst": "Harvard University" }, { "name": "Aziz Huq", "url": "https://openalex.org/A5095094642", "inst": "" }, { "name": "Prasanna Tambe", "url": "https://openalex.org/A5085743615", "inst": "University of the Arts" } ], "affiliations": [ "Harvard University", "University of the Arts" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2404.02934v1", "arxiv_id": "2404.02934v1", "title": "GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoning", "authors": [ "Jeffy Yu", "Maximilian Huber", "Kevin Tang" ], "posted": "2024-04-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.02934v1", "field": "management", "role": "agent", "bullets": [ "Moral reasoning vignettes of low and high ambiguity, posed to a profit oriented fine tune and to its unmodified base model for comparison.", "Llama 2 fine tuned to prioritize economically beneficial outcomes, called GreedLlama, is compared with the base model on rates of morally appropriate decisions.", "GreedLlama picks the ethical option in 54.4 percent of low ambiguity cases against 86.9 for the base model, and 47.4 against 65.1 under high ambiguity." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "salience": 40, "edition": 13, "validated": null, "n": 1636, "authors_detailed": [ { "name": "Jeffy Yu", "url": "https://openalex.org/A5068767813", "inst": "University of Southern California" }, { "name": "Maximilian Huber", "url": "https://openalex.org/A5102546905", "inst": "Friedrich-Alexander-Universität Erlangen-Nürnberg" }, { "name": "Kevin Tang", "url": "https://openalex.org/A5086342209", "inst": "University of California, San Francisco" } ], "affiliations": [ "University of Southern California", "Friedrich-Alexander-Universität Erlangen-Nürnberg", "University of California, San Francisco" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4724068", "doi": "10.2139/ssrn.4724068", "title": "Artificial Intelligence-Augmented Brainstorming: How Humans and Ai Beat Humans Alone", "authors": [ "Sebastian G. Bouschery", "Vera Blazevic", "Frank T. Piller" ], "posted": "2024-04-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4724068", "field": "management", "role": "agent", "bullets": [ "Lab experiment with 168 participants comparing nominal, interactive, hybrid human-AI, and AI-only brainstorming groups on idea generation.", "Generative AI served as a brainstorming group member alongside humans; output evaluated for quantity and creativity using established metrics.", "Hybrid human-AI groups outperformed interactive and nominal groups in productivity and creativity; AI-only groups were the most efficient overall." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3383, "authors_detailed": [ { "name": "Sebastian Gregor Bouschery", "url": "https://openalex.org/A5019737566", "inst": "RWTH Aachen University" }, { "name": "Vera Blažević", "url": "https://openalex.org/A5047249173", "inst": "Radboud University Nijmegen" }, { "name": "Frank T. Piller", "url": "https://openalex.org/A5062193230", "inst": "RWTH Aachen University" } ], "affiliations": [ "RWTH Aachen University", "Radboud University Nijmegen" ] }, { "uid": "doi:10.2139/ssrn.4746770", "doi": "10.2139/ssrn.4746770", "title": "Experimenting with Generative AI: Does ChatGPT Really Increase Everyone’s Productivity?", "authors": [ "Voraprapa Nakavachara", "Tanapong Potipiti", "Thanee Chaiwat" ], "posted": "2024-04-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4746770", "field": "economics", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 48, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 634, "authors_detailed": [ { "name": "Voraprapa Nakavachara", "url": "https://openalex.org/A5060235379", "inst": "Chulalongkorn University" }, { "name": "Tanapong Potipiti", "url": "https://openalex.org/A5042427744", "inst": "Chulalongkorn University" }, { "name": "Thanee Chaiwat", "url": "https://openalex.org/A5031753813", "inst": "Chulalongkorn University" } ], "affiliations": [ "Chulalongkorn University" ] }, { "uid": "doi:10.2139/ssrn.4748690", "doi": "10.2139/ssrn.4748690", "title": "Should Accountants be Afraid of AI? Risks and Opportunities of Incorporating Artificial Intelligence into Accounting and Auditing", "authors": [ "Nir Eisikovits", "William C. Johnson", "Ariel J. Markelevich" ], "posted": "2024-04-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4748690", "field": "accounting", "role": "object", "bullets": [ "Review of current and emerging AI applications across accounting and auditing practice, covering repetitive tasks, content generation, and risk assessment.", "Examines generative AI for textual content production, predictive AI for risk evaluation, and automation of routine tasks within professional accounting workflows.", "AI adoption risks professional deskilling and erosion of trust; new standards and professional oversight over AI implementation can mitigate adverse effects on the profession." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 45, "validated": null, "n": 3382, "authors_detailed": [ { "name": "Nir Eisikovits", "url": "https://openalex.org/A5020978997", "inst": "Suffolk University" }, { "name": "William C. Johnson", "url": "https://openalex.org/A5101579840", "inst": "University of Massachusetts Lowell" }, { "name": "Ariel Markelevich", "url": "https://openalex.org/A5029035375", "inst": "Suffolk University" } ], "affiliations": [ "Suffolk University", "University of Massachusetts Lowell" ] }, { "uid": "arxiv:2404.00806v5", "arxiv_id": "2404.00806v5", "title": "Algorithmic Collusion by Large Language Models", "authors": [ "Sara Fish", "Yannai A. Gonczarowski", "Ran I. Shorrer" ], "posted": "2024-03-31", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.00806v5", "field": "economics", "role": "agent", "bullets": [ "Repeated oligopoly pricing and auction environments populated by autonomous LLM based pricing agents, with seemingly innocuous instruction wording varied across runs.", "The pricing agents, model family not stated in the abstract, set prices autonomously; newly developed behavioral techniques analyze the generated text for pricing rationales.", "Agents quickly sustain supracompetitive prices and profits, prompt phrasing materially shifts the degree of collusion, and price war concerns surface as a contributing mechanism, complicating future regulation." ], "bullet_provenance": "ai", "salience": 78, "edition": 13, "models": [], "validated": null, "n": 1723, "authors_detailed": [ { "name": "Sara Fish", "url": "https://openalex.org/A5109636511", "inst": "Harvard University" }, { "name": "Yannai A. Gonczarowski", "url": "https://openalex.org/A5049856597", "inst": "Harvard University Press" }, { "name": "Ran I. Shorrer", "url": "https://openalex.org/A5103044100", "inst": "Pennsylvania State University" } ], "affiliations": [ "Harvard University", "Pennsylvania State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4684662", "doi": "10.2139/ssrn.4684662", "title": "The Impact of Large Language Models on Open-Source Innovation: Evidence from GitHub Copilot", "authors": [ "Doron Yeverechyahu", "Raveesh Mayya", "Gal Oestreicher-Singer" ], "posted": "2024-03-29", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4684662", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 55, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 633, "authors_detailed": [ { "name": "Doron Yeverechyahu", "url": "https://openalex.org/A5094276304", "inst": "Tel Aviv University" }, { "name": "Raveesh Mayya", "url": "https://openalex.org/A5065017185", "inst": "New York University" }, { "name": "Gal Oestreicher-Singer", "url": "https://openalex.org/A5079624858", "inst": "Tel Aviv University" } ], "affiliations": [ "New York University", "Tel Aviv University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2403.19735v1", "arxiv_id": "2403.19735v1", "title": "Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework", "authors": [ "Taejin Park" ], "posted": "2024-03-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.19735v1", "field": "finance", "role": "method", "bullets": [ "System generated anomaly alerts on S&P 500 index data, a setting where every alert otherwise needs manual verification by human analysts before action.", "A multi agent LLM pipeline assigns roles for data conversion, web research, institutional knowledge cross checks, and report consolidation; the underlying model is not named.", "The author reports faster, more automated validation of alerts with less human intervention, but the abstract offers no quantified accuracy or benchmark comparison." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 13, "models": [], "n": 1767, "authors_detailed": [ { "name": "Taejin Park", "url": "https://openalex.org/A5101511623", "inst": "Ames Research Center" } ], "affiliations": [ "Ames Research Center" ] }, { "uid": "arxiv:2403.18152v1", "arxiv_id": "2403.18152v1", "title": "Large Language Models as Financial Data Annotators: A Study on Effectiveness and Efficiency", "authors": [ "Toyin Aguda", "Suchetha Siddagangappa", "Elena Kochkina", "Simerjot Kaur", "Dongsheng Wang", "Charese Smiley", "Sameena Shah" ], "posted": "2024-03-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.18152v1", "field": "finance", "role": "method", "bullets": [ "Relation extraction from financial documents, with labels gathered from three language models, expert annotators, and crowdworkers for the same items; dataset sizes are not stated in the abstract.", "GPT-4, PaLM 2, and MPT Instruct annotate under varied prompts and parameters against expert ground truth, and a reliability index flags outputs needing expert review.", "Current models can stand in for non expert crowdworkers; prompts customized with examples per relation group matter most, and time, cost, and error analyses ground usage recommendations." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "expert annotations and crowdworker comparison", "salience": 55, "edition": 13, "n": 1634, "authors_detailed": [ { "name": "Toyin Aguda", "url": "https://openalex.org/A5092061309", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Suchetha Siddagangappa", "url": "https://openalex.org/A5049019087", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Elena Kochkina", "url": "https://openalex.org/A5075871561", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Simerjot Kaur", "url": "https://openalex.org/A5031351140", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Dongsheng Wang", "url": "https://openalex.org/A5100450579", "inst": "Jilin University" }, { "name": "Charese Smiley", "url": "https://openalex.org/A5070646187", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Sameena Shah", "url": "https://openalex.org/A5103231131", "inst": "Aga Khan University" } ], "affiliations": [ "JPMorgan Chase & Co (United States)", "Jilin University", "Aga Khan University" ] }, { "uid": "doi:10.2139/ssrn.4745990", "doi": "10.2139/ssrn.4745990", "title": "Generative AI and ChatGPT in Financial Markets and Corporate Policy: A Comprehensive Review", "authors": [ "William Mbanyele" ], "posted": "2024-03-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4745990", "field": "finance", "role": "object", "bullets": [ "Comprehensive literature review of research on generative AI and ChatGPT applications in financial markets and corporate policy decisions.", "Survey synthesizes studies using LLMs for sentiment analysis of financial reports, conference calls, and analyst reports, plus AI effects on corporate decisions.", "Review identifies significant knowledge gaps in understanding AI's impact on corporate strategy and highlights need for research on long-term market effects of generative AI." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 2366, "authors_detailed": [ { "name": "William Mbanyele", "url": "https://openalex.org/A5050881941", "inst": "Shandong University" } ], "affiliations": [ "Shandong University" ] }, { "uid": "doi:10.2139/ssrn.4729503", "doi": "10.2139/ssrn.4729503", "title": "When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine", "authors": [ "Lijia Ma", "Juan Qin", "Xingchen Xu", "Yong Tan" ], "posted": "2024-03-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4729503", "field": "management", "role": "object", "bullets": [ "Approximately 10,000 websites collected from Google generative and conventional search; Prolific participants completed information-seeking tasks.", "Study examined LLM citation preferences in generative search engines and measured effects of LLM-based content polishing on AI summaries.", "LLM-polished content increased summary diversity; higher-educated users cut task time while lower-educated users gained information density." ], "bullet_provenance": "ai", "models": [ "gemini" ], "open_weights": false, "salience": 60, "validated": null, "n": 2567, "authors_detailed": [ { "name": "Lijia Ma", "url": "https://openalex.org/A5102097666", "inst": "University of North Carolina at Charlotte" }, { "name": "Juan Qin", "url": "https://openalex.org/A5045372925", "inst": "University of Science and Technology of China" }, { "name": "Xingchen Xu", "url": "https://openalex.org/A5004663346", "inst": "University of Washington" }, { "name": "Yong Tan", "url": "https://openalex.org/A5037984091", "inst": "University of Washington" } ], "affiliations": [ "University of North Carolina at Charlotte", "University of Science and Technology of China", "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.4764290", "doi": "10.2139/ssrn.4764290", "title": "Sticky Charters? The Surprisingly Tepid Embrace of Officer-Protecting Waivers in Delaware", "authors": [ "Jens Frankenreiter", "Eric L. Talley" ], "posted": "2024-03-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4764290", "field": "finance", "role": "instrument", "bullets": [ "Delaware corporate charters examined through the first post-reform year after 2022 officer exculpation statute change.", "ChatGPT identified and classified officer-facing waiver provisions from corporate charter amendments; validated against manual labeling.", "Only a modest minority of eligible corporations adopted officer waivers; stock market investors also showed muted response to the reform." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "manual verification of charter extraction accuracy", "salience": 60, "n": 2469, "authors_detailed": [ { "name": "Jens Frankenreiter", "url": "https://openalex.org/A5027919894", "inst": "Washington University in St. Louis" }, { "name": "Eric L. Talley", "url": "https://openalex.org/A5004918410", "inst": "New York Law School" } ], "affiliations": [ "Washington University in St. Louis", "New York Law School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4771238", "doi": "10.2139/ssrn.4771238", "title": "Measuring the Commercial Potential of Science", "authors": [ "Roger Masclans", "Sharique Hasan", "Wesley M. Cohen" ], "posted": "2024-03-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4771238", "field": "management", "role": "instrument", "bullets": [ "Scientific articles linked to firms' use of science, validated against a major university's technology transfer records with out-of-sample exercises.", "LLMs and neural networks predict ex ante whether scientific articles will influence firms' commercial use of science.", "Firms' reliance on university reputation leads to foregone opportunities; privatization via patenting appears to increase firms' use of science from one university." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "out-of-sample prediction against university technology transfer data", "salience": 65, "n": 2927, "authors_detailed": [ { "name": "Roger Masclans", "url": "https://openalex.org/A5048931245", "inst": "Duke University" }, { "name": "Sharique Hasan", "url": "https://openalex.org/A5012661228", "inst": "Duke University" }, { "name": "Wesley M. Cohen", "url": "https://openalex.org/A5066540447", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Duke University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4759311", "doi": "10.2139/ssrn.4759311", "title": "Can ChatGPT Generate Stock Tickers to Buy and Sell for Day Trading?", "authors": [ "Sangheum Cho" ], "posted": "2024-03-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4759311", "field": "finance", "role": "agent", "bullets": [ "U.S. equities in open-to-close intraday trading, with Twitter posts containing macro and firm-specific news from major providers as input prompts.", "ChatGPT generated buy and sell stock ticker lists from aggregated Twitter news; the model was re-queried to explain its stock selection rationale.", "Long-short strategy earned significant intraday returns; ChatGPT extracted firm-specific mispricing signals from largely non-firm-specific news, strongest in difficult-to-arbitrage stocks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "significant long-short intraday returns in backtesting", "salience": 65, "n": 3380, "authors_detailed": [ { "name": "Sangheum Cho", "url": "https://openalex.org/A5084482635", "inst": "Bank of Korea" } ], "affiliations": [ "Bank of Korea" ] }, { "uid": "doi:10.2139/ssrn.4769906", "doi": "10.2139/ssrn.4769906", "title": "The Anatomy of Chinese Innovation: Insights on Patent Quality and Ownership", "authors": [ "Philipp Boeing", "Loren Brandt", "Ruochen Dai", "Kevin Lim", "Bettina Peters" ], "posted": "2024-03-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4769906", "field": "economics", "role": "instrument", "bullets": [ "Chinese patent records from 1985 to 2019 linked with a comprehensive business registry covering ownership and firm type.", "A large language model scored patent importance from abstracts and classified patent ownership to track innovation quality and composition over time.", "Average patent importance declined from 2000 to 2010 then recovered; private Chinese firms drove patenting growth while dependence on foreign knowledge decreased substantially." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 55, "n": 3381, "authors_detailed": [ { "name": "Philipp Boeing", "url": "https://openalex.org/A5003597887", "inst": "Goethe University Frankfurt" }, { "name": "Loren Brandt", "url": "https://openalex.org/A5110016649", "inst": "University of Toronto" }, { "name": "Ruochen Dai", "url": "https://openalex.org/A5103222961", "inst": "Central University of Finance and Economics" }, { "name": "Kevin Lim", "url": "https://openalex.org/A5101318912", "inst": "University of Toronto" }, { "name": "Bettina Peters", "url": "https://openalex.org/A5067473634", "inst": "Centre for European Economic Research" } ], "affiliations": [ "University of Toronto", "Goethe University Frankfurt", "Central University of Finance and Economics", "Centre for European Economic Research" ], "prestige": true }, { "uid": "arxiv:2404.07221v2", "arxiv_id": "2404.07221v2", "title": "Improving Retrieval for RAG based Question Answering Models on Financial Documents", "authors": [ "Spurthi Setty", "Harsh Thakkar", "Alyssa Lee", "Eden Chung", "Natan Vidra" ], "posted": "2024-03-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.07221v2", "field": "finance", "role": "method", "bullets": [ "Question answering over financial documents with retrieval augmented generation; no corpus, sample, or evaluation dataset is specified in the abstract.", "The paper surveys retrieval fixes for financial RAG pipelines, including chunking, query expansion, metadata annotation, re ranking, and embedding fine tuning; no model is named and no accuracy check is reported.", "The authors argue these techniques substantially improve retrieval quality and answer reliability, but the abstract offers no quantitative evidence." ], "bullet_provenance": "ai", "validated": false, "salience": 24, "edition": 13, "models": [], "n": 1633, "authors_detailed": [ { "name": "S. Pallam Setty", "url": "https://openalex.org/A5110652557", "inst": "Andhra University" }, { "name": "Thakkar, Harsh", "url": "", "inst": "" }, { "name": "Lee, Alyssa", "url": "", "inst": "" }, { "name": "Eden Chung", "url": "https://openalex.org/A5101294368", "inst": "" }, { "name": "Natan Vidra", "url": "https://openalex.org/A5093751283", "inst": "Cornell University" } ], "affiliations": [ "Cornell University", "Andhra University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2403.15214v1", "arxiv_id": "2403.15214v1", "title": "InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content Detection", "authors": [ "Thales Bertaglia", "Lily Heisig", "Rishabh Kaushal", "Adriana Iamnitchi" ], "posted": "2024-03-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.15214v1", "field": "management", "role": "method", "bullets": [ "Synthetic Instagram captions for sponsored content detection, generated under varying prompts and assessed against real caption data at content and network level.", "ChatGPT generates the captions; fidelity is benchmarked against real posts and utility by training classifiers to identify undisclosed advertisements on Instagram.", "Individual synthetic posts look realistic but collections lack diversity, topic connectivity and realistic interactions, and the fidelity and utility objectives can conflict." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "fidelity metrics against real Instagram data and detection utility tests", "salience": 42, "edition": 13, "n": 1554, "authors_detailed": [ { "name": "Thales Bertaglia", "url": "https://openalex.org/A5047162255", "inst": "Utrecht University" }, { "name": "Lily Heisig", "url": "https://openalex.org/A5063682279", "inst": "Maastricht University" }, { "name": "Rishabh Kaushal", "url": "https://openalex.org/A5051458180", "inst": "Maastricht University" }, { "name": "Adriana Iamnitchi", "url": "https://openalex.org/A5007419039", "inst": "Maastricht University" } ], "affiliations": [ "Utrecht University", "Maastricht University" ] }, { "uid": "arxiv:2403.15062v1", "arxiv_id": "2403.15062v1", "title": "Construction of a Japanese Financial Benchmark for Large Language Models", "authors": [ "Masanori Hirano" ], "posted": "2024-03-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.15062v1", "field": "finance", "role": "method", "bullets": [ "A benchmark of multiple tasks specific to Japanese and to finance; the task list and dataset sizes are not detailed in the abstract.", "Several LLMs are measured on the benchmark; GPT-4 comes out clearly ahead, and combining tasks of different difficulty separates models across the whole performance range.", "The benchmark distinguishes model quality on Japanese financial tasks across all performance levels; no numeric scores are given in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Japanese financial task benchmark scores", "salience": 36, "edition": 13, "n": 1569, "authors_detailed": [ { "name": "Masanori Hirano", "url": "https://openalex.org/A5002301422", "inst": "Preferred Networks (Japan)" } ], "affiliations": [ "Preferred Networks (Japan)" ] }, { "uid": "arxiv:2403.15040v1", "arxiv_id": "2403.15040v1", "title": "ESG Classification by Implicit Rule Learning via GPT-4", "authors": [ "Hyo Jeong Yun", "Chanyoung Kim", "Moonjeong Hahm", "Kyuri Kim", "Guijin Son" ], "posted": "2024-03-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.15040v1", "field": "finance", "role": "method", "bullets": [ "The ML-ESG-3 shared task, Korean impact type track, where systems classify ESG signals from web text without access to rating agencies' confidential criteria.", "GPT-4 guided by prompting, chain of thought, and dynamic in context learning, with no training on the provided data; smaller open weight models probed for prompt sensitivity.", "The training free approach ranked second on the track, and longer general pre-training lines up with better performance on financial downstream tasks." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "ML-ESG-3 shared task labels", "salience": 40, "edition": 13, "n": 1766, "authors_detailed": [ { "name": "Hyo Jeong Yun", "url": "https://openalex.org/A5109238983", "inst": "Chosun University" }, { "name": "Chanyoung Kim", "url": "https://openalex.org/A5075621908", "inst": "Yonsei University" }, { "name": "Moonjeong Hahm", "url": "https://openalex.org/A5072440755", "inst": "" }, { "name": "Kyuri Kim", "url": "https://openalex.org/A5103184457", "inst": "Seoul National University" }, { "name": "Guijin Son", "url": "https://openalex.org/A5028962243", "inst": "Ondine (Canada)" } ], "affiliations": [ "Chosun University", "Yonsei University", "Seoul National University", "Ondine (Canada)" ] }, { "uid": "arxiv:2403.15281v1", "arxiv_id": "2403.15281v1", "title": "Measuring Gender and Racial Biases in Large Language Models", "authors": [ "Jiafu An", "Difang Huang", "Chen Lin", "Mingzhu Tai" ], "posted": "2024-03-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.15281v1", "field": "economics", "role": "agent", "bullets": [ "About 361,000 entry level job resumes with randomized gender and race identities, holding work experience, education, and skills constant across candidates.", "OpenAI's GPT, version not stated, is instructed to score each resume as a hiring assessment; randomized identities isolate the model's own bias.", "The model scores otherwise similar female candidates higher and black male candidates lower, shifting hiring probabilities by 1 to 2 percentage points at a given threshold." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 66, "edition": 13, "validated": null, "n": 1779, "authors_detailed": [ { "name": "Jiafu An", "url": "https://openalex.org/A5113152324", "inst": "" }, { "name": "Difang Huang", "url": "https://openalex.org/A5051609622", "inst": "Chinese Academy of Sciences" }, { "name": "Chen Lin", "url": "https://openalex.org/A5051088538", "inst": "Inner Mongolia Normal University" }, { "name": "Mingzhu Tai", "url": "https://openalex.org/A5000478371", "inst": "John F. Kennedy Center for the Performing Arts" } ], "affiliations": [ "Chinese Academy of Sciences", "Inner Mongolia Normal University", "John F. Kennedy Center for the Performing Arts" ] }, { "uid": "doi:10.2139/ssrn.4736295", "doi": "10.2139/ssrn.4736295", "title": "Corporate Responses to Generative AI: Early Evidence from Conference Calls", "authors": [ "Ning Jia", "Ningzhong Li", "Guang Ma", "Da Xu" ], "posted": "2024-03-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4736295", "field": "management", "role": "object", "bullets": [ "U.S. public company earnings conference calls analyzed for generative AI discussion frequency and tone after ChatGPT's November 2022 release.", "Text analysis measures managerial and analyst GAI mentions in transcripts; no LLM is used as a research tool in the study itself.", "GAI discussions rose substantially post-ChatGPT, concentrated in tech and among larger, younger, and growth firms; positive-tone action-oriented discussions inform investors." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 65, "validated": null, "n": 2646, "authors_detailed": [ { "name": "Ning Jia", "url": "https://openalex.org/A5023908957", "inst": "Tsinghua University" }, { "name": "Ningzhong Li", "url": "https://openalex.org/A5091726176", "inst": "The University of Texas at Dallas" }, { "name": "Guang Ma", "url": "https://openalex.org/A5001484803", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Da Xu", "url": "https://openalex.org/A5086405756", "inst": "Tsinghua University" } ], "affiliations": [ "The University of Texas at Dallas", "Tsinghua University", "Rutgers, The State University of New Jersey" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2403.15262v5", "arxiv_id": "2403.15262v5", "title": "Strategic Responses to Technological Change: Evidence from Online Labor Markets", "authors": [ "Shun Yiu", "Rob Seamans", "Manav Raj", "Ted Liu" ], "posted": "2024-03-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.15262v5", "field": "economics", "role": "object", "bullets": [ "Freelancers on an online work platform observed before and after ChatGPT's November 2022 launch, with heterogeneity across work domains.", "ChatGPT launch used as a natural experiment; bidding behavior, horizontal and vertical repositioning, and platform participation tracked across skill levels.", "Freelancers bid on fewer jobs and differentiated their positioning; demand decreased short-term while supply increased, with high-skill workers facing greater adjustment costs and less repositioning." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 70, "validated": null, "n": 3379, "authors_detailed": [ { "name": "Shun Yiu", "url": "https://openalex.org/A5111172795", "inst": "Indiana University Bloomington" }, { "name": "Robert Seamans", "url": "https://openalex.org/A5050038373", "inst": "New York University" }, { "name": "Manav Raj", "url": "https://openalex.org/A5018390504", "inst": "University of Pennsylvania" }, { "name": "Ted Liu", "url": "https://openalex.org/A5101336091", "inst": "ResearchWorks (United States)" } ], "affiliations": [ "Indiana University Bloomington", "New York University", "University of Pennsylvania", "ResearchWorks (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4765222", "doi": "10.2139/ssrn.4765222", "title": "What People Think of Machines as Doctors: Unveiling the Value of Gen-AI for e-Health", "authors": [ "Dicle Yagmur Ozdemir", "Mehmet Ayvaci", "Alejandro Zentner" ], "posted": "2024-03-21", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4765222", "field": "management", "role": "object", "bullets": [ "Survey experiment comparing non-expert evaluations of physician and ChatGPT responses to health-related queries, with a blinded expert panel providing quality judgments as ground truth.", "ChatGPT generated responses to the same queries answered by physicians; the study uses the Elaboration Likelihood Model to examine how response length and source disclosure shape non-expert preferences.", "Non-experts overwhelmingly preferred LLM responses even when experts rated them low quality; longer prose from the model heightened this preference, while disclosing the machine source reduced it." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 25, "validated": null, "n": 4299, "authors_detailed": [ { "name": "Dicle Özdemir", "url": "https://openalex.org/A5003102405", "inst": "Erasmus University Rotterdam" }, { "name": "Mehmet Ayvaci", "url": "https://openalex.org/A5077759641", "inst": "The University of Texas at Dallas" }, { "name": "Alejandro Zentner", "url": "https://openalex.org/A5065572995", "inst": "The University of Texas at Dallas" } ], "affiliations": [ "The University of Texas at Dallas", "Erasmus University Rotterdam" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4736129", "doi": "10.2139/ssrn.4736129", "title": "Moving Targets", "authors": [ "Lauren Cohen", "Quoc H. Nguyen" ], "posted": "2024-03-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4736129", "field": "accounting", "role": "instrument", "bullets": [ "Full text of U.S. corporate earnings conference call transcripts from 2006 to 2020, covering the complete history of manager-chosen performance metric discussions.", "RoBERTa-based transformer pipeline with POS tagger, syntactic parser, lemmatizer, and named-entity recognizer identified and tracked managers' chosen performance metrics across consecutive quarters.", "Firms that shifted their performance-metric targets underperformed by up to 99 basis points per month in value-weighted abnormal returns (t-stat of 4.38)." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 70, "n": 3897, "authors_detailed": [ { "name": "Lauren Cohen", "url": "https://openalex.org/A5068471092", "inst": "National Bureau of Economic Research" }, { "name": "Quoc Hung Nguyen", "url": "https://openalex.org/A5012483430", "inst": "DePaul University" } ], "affiliations": [ "National Bureau of Economic Research", "DePaul University" ] }, { "uid": "arxiv:2403.12582v1", "arxiv_id": "2403.12582v1", "title": "AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework", "authors": [ "Xiang Li", "Zhenyu Li", "Chen Shi", "Yong Xu", "Qing Du", "Mingkui Tan", "Jun Huang", "Wei Lin" ], "posted": "2024-03-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.12582v1", "field": "finance", "role": "method", "bullets": [ "AlphaFin merges existing research datasets, real-time financial data, and handwritten chain of thought examples for stock trend prediction and financial question answering.", "A retrieval augmented framework called Stock-Chain is benchmarked on these tasks; the underlying language models are not named in the abstract.", "Experiments are said to demonstrate the framework's effectiveness on both tasks, but the abstract reports no accuracy figures or baseline comparisons." ], "bullet_provenance": "ai", "validated": true, "validation_note": "AlphaFin stock trend and QA benchmarks", "salience": 38, "edition": 13, "models": [], "n": 1568, "authors_detailed": [ { "name": "Xiang Li", "url": "https://openalex.org/A5100389927", "inst": "Tianjin University of Sport" }, { "name": "Zhenyu Li", "url": "https://openalex.org/A5100330772", "inst": "Hong Kong Polytechnic University" }, { "name": "Shi Chen", "url": "https://openalex.org/A5100362223", "inst": "Sichuan University" }, { "name": "Yong Xu", "url": "https://openalex.org/A5062310230", "inst": "Northwestern Polytechnical University" }, { "name": "Qing Du", "url": "https://openalex.org/A5100922649", "inst": "Army Medical University" }, { "name": "Mingkui Tan", "url": "https://openalex.org/A5032352025", "inst": "Institute of Software" }, { "name": "Jun Huang", "url": "https://openalex.org/A5110185752", "inst": "Hainan University" }, { "name": "Wei Lin", "url": "https://openalex.org/A5100620875", "inst": "Central South University" } ], "affiliations": [ "Tianjin University of Sport", "Hong Kong Polytechnic University", "Sichuan University", "Northwestern Polytechnical University", "Army Medical University", "Institute of Software", "Hainan University", "Central South University" ] }, { "uid": "arxiv:2404.00018v1", "arxiv_id": "2404.00018v1", "title": "Can AI Outperform Human Experts in Creating Social Media Creatives?", "authors": [ "Eunkyung Park", "Raymond K. Wong", "Junbum Kwon" ], "posted": "2024-03-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.00018v1", "field": "management", "role": "agent", "bullets": [ "Most liked Instagram posts from top brand accounts serve as the basis for generating new social media creatives compared against human expert work.", "GPT-4 augments text prompts for Midjourney, DALL-E 3, and Stable Diffusion; outputs are judged against expert made creatives in a human evaluation experiment.", "AI creatives outrank the human experts, Midjourney beats the other generators, and prompts using natural wording outperform eye catching wording, with larger gains for short descriptions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 13, "validated": null, "n": 1778, "authors_detailed": [ { "name": "Eun-Kyung Park", "url": "https://openalex.org/A5101556632", "inst": "Daegu University" }, { "name": "Raymond K. Wong", "url": "https://openalex.org/A5047489771", "inst": "Western University" }, { "name": "Junbum Kwon", "url": "https://openalex.org/A5052534331", "inst": "Energinet (Denmark)" } ], "affiliations": [ "Daegu University", "Western University", "Energinet (Denmark)" ] }, { "uid": "doi:10.2139/ssrn.4750197", "doi": "10.2139/ssrn.4750197", "title": "OpenAI’s Transformation: From a Non-profit to a 100 Billion Valuation", "authors": [ "Alexandra Andhov" ], "posted": "2024-03-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4750197", "field": "management", "role": "object", "bullets": [ "OpenAI's corporate evolution from 2015 non-profit founding through its capped-profit restructuring and Microsoft partnership to a $100 billion valuation.", "Examines OpenAI's governance transition, board composition, and strategic pivot catalyzed by ChatGPT's launch reaching 100 million users faster than any prior platform.", "Shift from open-source non-profit to commercial entity raises unresolved questions about data stewardship, intellectual property rights, and mission-aligned governance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 55, "validated": null, "n": 3378, "authors_detailed": [ { "name": "Alexandra Andhov", "url": "https://openalex.org/A5106674301", "inst": "University of Copenhagen" } ], "affiliations": [ "University of Copenhagen" ] }, { "uid": "arxiv:2403.12285v1", "arxiv_id": "2403.12285v1", "title": "FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications", "authors": [ "Thanos Konstantinidis", "Giorgos Iacovides", "Mingxue Xu", "Tony G. Constantinides", "Danilo Mandic" ], "posted": "2024-03-18", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.12285v1", "field": "finance", "role": "method", "bullets": [ "Financial news sentiment for algorithmic trading, with a portfolio simulation testing whether sentiment strength signals improve returns and resilience in volatile periods.", "Llama 2 7B is fine-tuned with LoRA on a small supervised financial sentiment sample and paired with a neural classifier scoring valence and strength; no held-out accuracy figures are reported.", "Simulated portfolios built on FinLlama signals show higher returns and resilience than baselines, including during volatile market events; magnitudes are not stated." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 38, "edition": 13, "n": 1553, "authors_detailed": [ { "name": "Thanos Konstantinidis", "url": "https://openalex.org/A5086902507", "inst": "Imperial College London" }, { "name": "Giorgos Iacovides", "url": "https://openalex.org/A5094208275", "inst": "NIHR Imperial Biomedical Research Centre" }, { "name": "Mingxue Xu", "url": "https://openalex.org/A5101320991", "inst": "King University" }, { "name": "Tony G. Constantinides", "url": "https://openalex.org/A5032451774", "inst": "Imperial College London" }, { "name": "Danilo P. Mandic", "url": "https://openalex.org/A5103001848", "inst": "Imperial College London" } ], "affiliations": [ "Imperial College London", "NIHR Imperial Biomedical Research Centre", "King University" ] }, { "uid": "doi:10.2139/ssrn.4738748", "doi": "10.2139/ssrn.4738748", "title": "The Generative AI challenges for competition authorities", "authors": [ "Christophe Carugati" ], "posted": "2024-03-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4738748", "field": "economics", "role": "object", "bullets": [ "Analysis of emerging generative AI markets spanning chips, ML models, cloud computing, software, and answer engines across jurisdictions.", "No model used; paper examines competition issues in the GenAI value chain including access to compute, models, and data.", "Competition authorities should prioritize market studies and cross-authority cooperation before exercising formal enforcement or updating competition tools." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 2926, "authors_detailed": [ { "name": "Christophe Carugati", "url": "https://openalex.org/A5022669564", "inst": "Roquette Frères (France)" } ], "affiliations": [ "Roquette Frères (France)" ] }, { "uid": "doi:10.1016/j.asoc.2024.112158", "doi": "10.1016/j.asoc.2024.112158", "arxiv_id": "2403.12212v2", "title": "Evaluating Named Entity Recognition: A comparative analysis of mono- and multilingual transformer models on a novel Brazilian corporate earnings call transcripts dataset", "authors": [ "Ramon Abilio", "Guilherme Palermo Coelho", "Ana Estela Antunes da Silva" ], "posted": "2024-03-18", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.12212v2", "field": "finance", "role": "method", "bullets": [ "Brazilian bank earnings call transcripts annotated for named entity recognition using a weakly supervised approach (BraFiNER dataset).", "BERTimbau, PTT5, mBERT, and mT5 fine-tuned for financial NER in Brazilian Portuguese; a novel text-generation reframing of token classification was introduced.", "BERT-based models consistently outperformed T5-based models on macro F1; T5 models generated altered monetary values, raising accuracy concerns for financial applications." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "macro F1 on expert-annotated BraFiNER dataset", "salience": 35, "n": 3376, "authors_detailed": [ { "name": "Ramon Abilio", "url": "https://openalex.org/A5094208266", "inst": "Federal Institute of São Paulo" }, { "name": "Guilherme Palermo Coelho", "url": "https://openalex.org/A5008918749", "inst": "Universidade Estadual de Campinas (UNICAMP)" }, { "name": "Ana Estela Antunes da Silva", "url": "https://openalex.org/A5045617515", "inst": "Universidade Estadual de Campinas (UNICAMP)" } ], "affiliations": [ "Federal Institute of São Paulo", "Universidade Estadual de Campinas (UNICAMP)" ] }, { "uid": "doi:10.2139/ssrn.4738738", "doi": "10.2139/ssrn.4738738", "title": "The Competitive Relationship Between Cloud Computing and Generative AI", "authors": [ "Christophe Carugati" ], "posted": "2024-03-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4738738", "field": "economics", "role": "object", "bullets": [ "Global cloud computing and generative AI markets, examining partnerships between large cloud providers and GenAI developers across infrastructure, platform, and software layers.", "Analyzed competitive dynamics of exclusive and strategic cloud-GenAI partnerships for antitrust implications under EU competition and data regulation frameworks.", "Partnerships risk market concentration through tying, bundling, and self-preferencing; existing EU merger control and Data Act have gaps requiring amendment." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 3377, "authors_detailed": [ { "name": "Christophe Carugati", "url": "https://openalex.org/A5022669564", "inst": "Roquette Frères (France)" } ], "affiliations": [ "Roquette Frères (France)" ] }, { "uid": "arxiv:2404.13050v1", "arxiv_id": "2404.13050v1", "title": "FlowMind: Automatic Workflow Generation with LLMs", "authors": [ "Zhen Zeng", "William Watson", "Nicole Cho", "Saba Rahimi", "Shayleen Reynolds", "Tucker Balch", "Manuela Veloso" ], "posted": "2024-03-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.13050v1", "field": "finance", "role": "method", "bullets": [ "Workflow automation for spontaneous user requests in financial services, evaluated on NCEN-QA, a new question answering benchmark built from N-CEN fund filings.", "GPT generates executable workflows grounded in vetted APIs via a lecture style prompt recipe that keeps proprietary data away from the model; users inspect workflow summaries and give feedback.", "FlowMind workflows beat baseline and ablated variants on NCEN-QA and improve further with user feedback; exact accuracy numbers are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "NCEN-QA benchmark", "salience": 42, "edition": 13, "n": 1722, "authors_detailed": [ { "name": "Zhen Zeng", "url": "https://openalex.org/A5100360190", "inst": "Yunnan Normal University" }, { "name": "William Watson", "url": "https://openalex.org/A5074837370", "inst": "University of London" }, { "name": "Nicole Cho", "url": "https://openalex.org/A5084455404", "inst": "Korea Electric Power Corporation (South Korea)" }, { "name": "Saba Rahimi", "url": "https://openalex.org/A5054192667", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Shayleen Reynolds", "url": "https://openalex.org/A5064210543", "inst": "Morgan Stanley (United States)" }, { "name": "Tucker Balch", "url": "https://openalex.org/A5035482777", "inst": "Emory University" }, { "name": "Manuela Veloso", "url": "https://openalex.org/A5088276691", "inst": "AT&T (United States)" } ], "affiliations": [ "Emory University", "Yunnan Normal University", "University of London", "Korea Electric Power Corporation (South Korea)", "JPMorgan Chase & Co (United States)", "Morgan Stanley (United States)", "AT&T (United States)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2403.10482v2", "arxiv_id": "2403.10482v2", "title": "Can a GPT4-Powered AI Agent Be a Good Enough Performance Attribution Analyst?", "authors": [ "Bruno de Melo", "Jamiel Sheikh" ], "posted": "2024-03-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.10482v2", "field": "finance", "role": "agent", "bullets": [ "Portfolio performance attribution in fund management, spanning driver analysis, multi-level attribution calculations and question answering exercises modeled on official examinations.", "A GPT-4 agent built on LangChain with chain-of-thought and plan-and-solve prompting; answers are scored against ground truth, exceeding 93 percent accuracy on driver analysis.", "The agent reaches 100 percent on multi-level attribution calculations and above 84 percent on exam-style questions, which the authors read as support for LLM agents in portfolio workflows." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "task accuracy against ground-truth attribution results and exam-style questions", "salience": 45, "edition": 13, "n": 1531, "authors_detailed": [ { "name": "Bruno de Melo", "url": "https://openalex.org/A5094186795", "inst": "" }, { "name": "Sheikh, Jamiel", "url": "", "inst": "" } ] }, { "uid": "doi:10.2139/ssrn.4729982", "doi": "10.2139/ssrn.4729982", "title": "Large Language Model in Ideation for Product Innovation: An Exploratory Comparative Study", "authors": [ "Jiexin Zheng", "Ka Chau Wong", "Jiali Zhou", "Tat Koon Koh" ], "posted": "2024-03-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4729982", "field": "management", "role": "object", "bullets": [ "Randomized online experiment comparing ChatGPT and Google Search for generating new product innovation ideas.", "Participants used ChatGPT or Google Search for product ideation; ideas evaluated on novelty, diversity, and functional appropriateness.", "ChatGPT users generated more numerous, novel, and diverse ideas, but with lower functional appropriateness and signs of overreliance on LLM responses." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 55, "validated": null, "n": 2468, "authors_detailed": [ { "name": "Jiexin Zheng", "url": "https://openalex.org/A5043220144", "inst": "Hong Kong University of Science and Technology" }, { "name": "Ka Chau Wang", "url": "https://openalex.org/A5113143814", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Jiali Zhou", "url": "https://openalex.org/A5101585819", "inst": "American University" }, { "name": "Tat Koon Koh", "url": "https://openalex.org/A5027791942", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Hong Kong University of Science and Technology", "Independent - affiliation not provided to SSRN", "American University" ] }, { "uid": "doi:10.2139/ssrn.4759218", "doi": "10.2139/ssrn.4759218", "title": "Will Artificial Intelligence Get in the Way of Achieving Gender Equality?", "authors": [ "Daniel Carvajal", "Catalina Franco", "Siri Isaksson" ], "posted": "2024-03-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4759218", "field": "economics", "role": "object", "bullets": [ "Two preregistered survey experiments with students at a top Norwegian business school and a separate survey of hiring managers.", "Generative AI adoption rates measured across gender; managers surveyed on how AI skills affect job candidates' employment prospects.", "Substantial gender gap in AI adoption: top female students opted out of AI use despite managers reporting AI skills would significantly enhance those students' job prospects." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 65, "validated": null, "n": 3374, "authors_detailed": [ { "name": "Daniel Carvajal", "url": "https://openalex.org/A5108979244", "inst": "Norwegian School of Economics" }, { "name": "Catalina Franco", "url": "https://openalex.org/A5005035255", "inst": "Norwegian Centre for Research Data" }, { "name": "Siri Isaksson", "url": "https://openalex.org/A5093745137", "inst": "Stockholm School of Economics" } ], "affiliations": [ "Norwegian School of Economics", "Norwegian Centre for Research Data", "Stockholm School of Economics" ] }, { "uid": "doi:10.2139/ssrn.4728604", "doi": "10.2139/ssrn.4728604", "title": "The Recessionary Pressures of Generative AI: a Threat to Wellbeing", "authors": [ "Jo-An Occhipinti", "Ante Prodan", "William Hynes", "Roy Green", "Sharan Burrow", "Harris A. Eyre", "Adam Skinner", "Goran Ujdur", "John Buchanan", "Ian Hickie", "Mark Heffernan", "Christine Song", "Marcel Tanner" ], "posted": "2024-03-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4728604", "field": "economics", "role": "object", "bullets": [ "Theoretical analysis of generative AI's macroeconomic effects on labor markets, economic stability, and societal wellbeing across advanced economies.", "Posits an AI-capital-to-labour ratio threshold beyond which automation triggers self-reinforcing recessionary pressures and social instability.", "Beyond the threshold a cycle of job displacement, reduced social cohesion, and rising inequality could require sustained government intervention and a new social contract." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3375, "authors_detailed": [ { "name": "Jo‐An Occhipinti", "url": "https://openalex.org/A5062542521", "inst": "The University of Sydney" }, { "name": "Ante Prodan", "url": "https://openalex.org/A5068879276", "inst": "The University of Sydney" }, { "name": "William Hynes", "url": "https://openalex.org/A5037734194", "inst": "Santa Fe Institute" }, { "name": "Roy Green", "url": "https://openalex.org/A5050855063", "inst": "University of Technology Sydney" }, { "name": "Sharan Burrow", "url": "https://openalex.org/A5094168713", "inst": "Institute of Health Visiting" }, { "name": "Harris A. Eyre", "url": "https://openalex.org/A5035122667", "inst": "Fondation Botnar" }, { "name": "Adam Skinner", "url": "https://openalex.org/A5074200731", "inst": "The University of Sydney" }, { "name": "Goran Ujdur", "url": "https://openalex.org/A5070083129", "inst": "The University of Sydney" }, { "name": "John Buchanan", "url": "https://openalex.org/A5070643261", "inst": "University of Technology Sydney" }, { "name": "Ian B. Hickie", "url": "https://openalex.org/A5069078473", "inst": "The University of Sydney" }, { "name": "Mark Heffernan", "url": "https://openalex.org/A5061010459", "inst": "Western Sydney University" }, { "name": "Christine Song", "url": "https://openalex.org/A5113648007", "inst": "The University of Sydney" }, { "name": "Marcel Tanner", "url": "https://openalex.org/A5091869484", "inst": "Swiss Tropical and Public Health Institute" } ], "affiliations": [ "The University of Sydney", "Santa Fe Institute", "University of Technology Sydney", "Institute of Health Visiting", "Fondation Botnar", "Western Sydney University", "Swiss Tropical and Public Health Institute" ] }, { "uid": "doi:10.2139/ssrn.4726933", "doi": "10.2139/ssrn.4726933", "title": "Artificial Agents in Operations Management Experiments", "authors": [ "Samuel Kirshner" ], "posted": "2024-03-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4726933", "field": "management", "role": "agent", "bullets": [ "Nine behavioral studies from the Management Science Replication Project, with GPT-4o agents assigned System 1 decision-making roles across experimental rounds.", "GPT-4o agents participated in operations management experiments as simulated human subjects, generating natural language explanations of their decision logic.", "Silicon samples replicated core hypotheses in eight of nine studies, with treatment effects closely matching the direction and magnitude of human decisions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "replication of 8/9 Management Science behavioral studies", "salience": 68, "n": 2393, "authors_detailed": [ { "name": "Samuel N. Kirshner", "url": "https://openalex.org/A5059274653", "inst": "UNSW Sydney" } ], "affiliations": [ "UNSW Sydney" ] }, { "uid": "doi:10.1098/rsif.2023.0720", "doi": "10.1098/rsif.2023.0720", "arxiv_id": "2403.08944v1", "title": "Language-based game theory in the age of artificial intelligence", "authors": [ "Valerio Capraro", "Roberto Di Paolo", "Matjaz Perc", "Veronica Pizziol" ], "posted": "2024-03-13", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.08944v1", "field": "economics", "role": "instrument", "bullets": [ "Meta-analysis of 61 experimental instructions from the dictator game, examining linguistic content and economic behavior.", "Sentiment analysis applied to game instructions to test whether language predicts giving beyond standard economic outcome variables.", "Sentiment of experimental instructions explains dictator game behavior beyond economic outcomes, supporting language-based utility functions." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "meta-analysis regression on 61 dictator game experiments", "salience": 45, "n": 3186, "authors_detailed": [ { "name": "Valerio Capraro", "url": "https://openalex.org/A5068651250", "inst": "University of Milano-Bicocca" }, { "name": "Roberto Di Paolo", "url": "https://openalex.org/A5047709816", "inst": "University of Parma" }, { "name": "Matjaž Perc", "url": "https://openalex.org/A5044431363", "inst": "Kyung Hee University" }, { "name": "Veronica Pizziol", "url": "https://openalex.org/A5012785291", "inst": "University of Bologna" } ], "affiliations": [ "University of Milano-Bicocca", "University of Parma", "Kyung Hee University", "University of Bologna" ] }, { "uid": "doi:10.2139/ssrn.4722627", "doi": "10.2139/ssrn.4722627", "title": "Emoji Driven Crypto Assets Market Reactions", "authors": [ "Xiaorui ZUO", "Yao-Tsung CHEN", "Wolfgang Karl Härdle" ], "posted": "2024-03-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4722627", "field": "finance", "role": "instrument", "bullets": [ "Cryptocurrency market data including BTC price and the VCRIX volatility index, paired with emoji-containing tweets from Twitter.", "GPT-4 and a fine-tuned BERT model performed multimodal sentiment analysis translating emoji content into quantifiable sentiment scores for market prediction.", "Emoji-based sentiment outperformed FinBERT text-only sentiment in predicting crypto market indicators and helped strategies avoid significant market downturns." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "prediction accuracy compared against FinBERT baseline", "salience": 50, "n": 3373, "authors_detailed": [ { "name": "Xiaorui Zuo", "url": "https://openalex.org/A5108988226", "inst": "National University of Singapore" }, { "name": "Yao‐Tsung Chen", "url": "https://openalex.org/A5060260308", "inst": "National Yang Ming Chiao Tung University" }, { "name": "Wolfgang Karl Härdle", "url": "https://openalex.org/A5053742635", "inst": "National Yang Ming Chiao Tung University" } ], "affiliations": [ "National University of Singapore", "National Yang Ming Chiao Tung University" ] }, { "uid": "arxiv:2404.00012v1", "arxiv_id": "2404.00012v1", "title": "Stress index strategy enhanced with financial news sentiment analysis for the equity markets", "authors": [ "Baptiste Lefort", "Eric Benhamou", "Jean-Jacques Ohana", "David Saltiel", "Beatrice Guez", "Thomas Jacquot" ], "posted": "2024-03-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2404.00012v1", "field": "finance", "role": "instrument", "bullets": [ "Daily Bloomberg market summaries combined with a stress indicator built from volatility and credit spreads, applied to the NASDAQ, the S&P 500, and six major equity markets.", "GPT-4 through ChatGPT reads each day's summary and scores financial news sentiment; no validation against human coding or a labelled benchmark is reported.", "Adding the sentiment signal to the stress index strategy raises Sharpe ratios and reduces maximum drawdowns across all markets tested; magnitudes are not stated." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "edition": 13, "n": 1777 }, { "uid": "doi:10.2139/ssrn.4725351", "doi": "10.2139/ssrn.4725351", "title": "The Adoption and Efficacy of Large Language Models: Evidence From Consumer Complaints in the Financial Industry", "authors": [ "Minkyu Shin", "Jin Kim" ], "posted": "2024-03-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4725351", "field": "finance", "role": "object", "bullets": [ "Over one million consumer complaints to the CFPB from 2015 to 2024, with instrumental variable and experimental designs to assess LLM adoption effects.", "Consumers used ChatGPT to draft financial complaints; the study identified LLM adoption via textual markers and measured effects on complaint relief outcomes.", "LLM-drafted complaints were more likely to obtain relief from financial firms; controlled experiments confirmed LLMs improve narrative clarity and persuasiveness." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "validated": null, "n": 2392, "authors_detailed": [ { "name": "Minkyu Shin", "url": "https://openalex.org/A5054123046", "inst": "City University of Hong Kong" }, { "name": "Jin Kim", "url": "https://openalex.org/A5100380870", "inst": "Northeastern University" } ], "affiliations": [ "City University of Hong Kong", "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.4716860", "doi": "10.2139/ssrn.4716860", "title": "Replicating Reason: The Advent of Human-like Audit Judgment by Generative AI", "authors": [ "Markus Isack" ], "posted": "2024-03-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4716860", "field": "accounting", "role": "agent", "bullets": [ "Five experimental auditing tasks comparing GPT-4 judgment with human auditor responses drawn from prior published experimental studies.", "GPT-4 completed audit judgment tasks covering various auditing contexts; its outputs were benchmarked against established human auditor results.", "GPT-4 mirrors human auditor judgment on several tasks but diverges in certain contexts, indicating a need for audit-domain-specific model training." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison with human auditor experimental benchmarks", "salience": 65, "n": 2645, "authors_detailed": [ { "name": "Markus Isack", "url": "https://openalex.org/A5035924378", "inst": "Vienna University of Economics and Business" } ], "affiliations": [ "Vienna University of Economics and Business" ] }, { "uid": "arxiv:2403.06249v3", "arxiv_id": "2403.06249v3", "title": "No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks", "authors": [ "Gang Hu", "Ke Qin", "Chenhan Yuan", "Min Peng", "Alejandro Lopez-Lira", "Benyou Wang", "Sophia Ananiadou", "Jimin Huang", "Qianqian Xie" ], "posted": "2024-03-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.06249v3", "field": "finance", "role": "method", "bullets": [ "A bilingual Chinese and English financial resource: cross lingual instruction data, 10 NLP tasks and 20 bilingual tasks totaling 95k examples, with expert annotations for evaluation.", "ICE-INTENT, an openly released financial LLM, is trained on the mixed language data and scored on the ICE-FLARE benchmark against general and financial baselines.", "Bilingual data lifts performance over conventional and financial LLMs, most visibly on translation tasks and original English data; margins are not quantified in the abstract." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "expert annotated ICE-FLARE benchmark", "salience": 40, "edition": 13, "n": 1720, "authors_detailed": [ { "name": "Gang Hu", "url": "https://openalex.org/A5042259425", "inst": "Goddard Space Flight Center" }, { "name": "Ke Qin", "url": "https://openalex.org/A5070150374", "inst": "University of Electronic Science and Technology of China" }, { "name": "Chenhan Yuan", "url": "https://openalex.org/A5034535852", "inst": "University of Electronic Science and Technology of China" }, { "name": "Min Peng", "url": "https://openalex.org/A5088069625", "inst": "Kunming University of Science and Technology" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Benyou Wang", "url": "https://openalex.org/A5057282504", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" } ], "affiliations": [ "University of Florida", "Goddard Space Flight Center", "University of Electronic Science and Technology of China", "Kunming University of Science and Technology", "Chinese University of Hong Kong, Shenzhen", "University of Manchester", "Hunan Normal University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2403.06115v1", "arxiv_id": "2403.06115v1", "title": "FMPAF: How Do Fed Chairs Affect the Financial Market? A Fine-grained Monetary Policy Analysis Framework on Their Language", "authors": [ "Yayue Deng", "Mohan Xu", "Yao Tang" ], "posted": "2024-03-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.06115v1", "field": "economics", "role": "instrument", "bullets": [ "Press conference remarks by Federal Reserve chairs matched to movements in the S&P 500 ETF, the policy rate and exchange rates; sample period is not stated.", "LLMs, not named, score sentiment in the communication at several granularities and modalities, and the scores enter regressions; no validation against human coding is reported.", "A one unit sentiment increase is associated with roughly 500 basis points more on the S&P 500 ETF price and 15 basis points off the policy rate, with no significant exchange rate response." ], "bullet_provenance": "ai", "validated": false, "salience": 50, "edition": 13, "models": [], "n": 1721, "authors_detailed": [ { "name": "Yayue Deng", "url": "https://openalex.org/A5102572659", "inst": "South China Normal University" }, { "name": "Mohan Xu", "url": "https://openalex.org/A5101369416", "inst": "Nanjing Forestry University" }, { "name": "Yao Tang", "url": "https://openalex.org/A5014611666", "inst": "Wuhan University" } ], "affiliations": [ "South China Normal University", "Nanjing Forestry University", "Wuhan University" ] }, { "uid": "arxiv:2403.04427v1", "arxiv_id": "2403.04427v1", "title": "Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach", "authors": [ "Raffaele Giuseppe Cestari", "Simone Formentin" ], "posted": "2024-03-07", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.04427v1", "field": "finance", "role": "instrument", "bullets": [ "SPY ETF daily returns paired with corresponding tweets from the StockTwits platform, used for sentiment-driven return prediction.", "FinBERT extracted sentiment features from tweets; Bayesian-optimized recursive feature elimination selected the optimal predictor set for a return classification model.", "Model achieved F1-score exceeding 70% on the test set, and backtested trading strategy produced demonstrably higher cumulative profits than benchmarks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "F1-score on held-out test set", "salience": 45, "n": 3371, "authors_detailed": [ { "name": "Raffaele Giuseppe Cestari", "url": "https://openalex.org/A5041231437", "inst": "Politecnico di Milano" }, { "name": "Simone Formentin", "url": "https://openalex.org/A5059501084", "inst": "Politecnico di Milano" } ], "affiliations": [ "Politecnico di Milano" ] }, { "uid": "doi:10.2139/ssrn.4715376", "doi": "10.2139/ssrn.4715376", "title": "Generative AI for European Asset Pricing: Alleviating the Momentum Anomaly", "authors": [ "Matthias Mattusch" ], "posted": "2024-03-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4715376", "field": "finance", "role": "method", "bullets": [ "European stock market over a 16-year out-of-sample period, treating the capital market as a complex system under no-arbitrage constraints.", "A generative AI asset pricing model with time-conditional modeling and interpretable AI techniques used to price European equities and construct portfolios.", "Model achieved annualized Sharpe ratio of 3.68, cross-sectional R-squared over 22%, and explained variation over 13%, outperforming all European benchmarks." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "16-year out-of-sample Sharpe ratio and cross-sectional R-squared", "salience": 55, "n": 3372, "authors_detailed": [ { "name": "Matthias Mattusch", "url": "https://openalex.org/A5093373233", "inst": "Technische Universität Dresden" } ], "affiliations": [ "Technische Universität Dresden" ] }, { "uid": "arxiv:2403.02647v1", "arxiv_id": "2403.02647v1", "title": "FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model", "authors": [ "Xiangyu Li", "Xinjie Shen", "Yawen Zeng", "Xiaofen Xing", "Jin Xu" ], "posted": "2024-03-05", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.02647v1", "field": "finance", "role": "instrument", "bullets": [ "Financial news announcements paired with stock factor data, packaged as an automatic report generator aimed at ordinary investors; sample and period not stated.", "An unnamed large language model drives news factorization, return forecasting, and risk assessment modules; no check of the text processing against ground truth is reported.", "Experiments on real world datasets are said to support effectiveness and explainability; the abstract gives no accuracy numbers or effect sizes." ], "bullet_provenance": "ai", "validated": false, "salience": 28, "edition": 13, "models": [], "n": 1776, "authors_detailed": [ { "name": "Xiangyu Li", "url": "https://openalex.org/A5100460310", "inst": "Nanjing Normal University" }, { "name": "Xinjie Shen", "url": "https://openalex.org/A5101313947", "inst": "" }, { "name": "Yawen Zeng", "url": "https://openalex.org/A5059446748", "inst": "Hubei University of Technology" }, { "name": "Xiaofen Xing", "url": "https://openalex.org/A5036301580", "inst": "South China University of Technology" }, { "name": "Jin Xu", "url": "https://openalex.org/A5101998573", "inst": "Hong Kong Polytechnic University" } ], "affiliations": [ "Nanjing Normal University", "Hubei University of Technology", "South China University of Technology", "Hong Kong Polytechnic University" ] }, { "uid": "arxiv:2403.01964v2", "arxiv_id": "2403.01964v2", "title": "The Heterogeneous Productivity Effects of Generative AI", "authors": [ "David Kreitmeir", "Paul A. Raschky" ], "posted": "2024-03-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.01964v2", "field": "economics", "role": "object", "bullets": [ "Daily coding output of over 36,000 GitHub users in Italy and other European countries around Italy's March 2023 ChatGPT ban, analyzed in a difference-in-differences framework.", "ChatGPT ban served as a natural experiment; the study measured changes in coding output quantity and quality for affected Italian users versus unaffected European controls.", "Less experienced users showed short-term increases in output quantity and quality; experienced users showed decreased productivity on routine tasks during the ban." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 75, "validated": null, "n": 3369 }, { "uid": "arxiv:2403.02185v1", "arxiv_id": "2403.02185v1", "title": "Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance", "authors": [ "Olivier Gandouet", "Mouloud Belbahri", "Armelle Jezequel", "Yuriy Bodjov" ], "posted": "2024-03-04", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.02185v1", "field": "finance", "role": "instrument", "bullets": [ "Earnings call transcripts from public companies, with model outputs evaluated against an expert-annotated dataset for topic and sentiment classification.", "ChatGPT provided training signal via knowledge distillation to create lightweight topic and sentiment models; transfer learning preserved accuracy in compact form.", "Distilled models retained classification accuracy while producing interpretable features shown to be effective in two quantitative investing case studies." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "expert-annotated earnings call dataset", "salience": 55, "n": 3370, "authors_detailed": [ { "name": "Olivier Gandouet", "url": "https://openalex.org/A5050036998", "inst": "" }, { "name": "Mouloud Belbahri", "url": "https://openalex.org/A5020608928", "inst": "Huawei Technologies (China)" }, { "name": "Armelle Jezequel", "url": "https://openalex.org/A5094077363", "inst": "" }, { "name": "Yuriy Bodjov", "url": "https://openalex.org/A5094077364", "inst": "" } ], "affiliations": [ "Huawei Technologies (China)" ] }, { "uid": "doi:10.2139/ssrn.4734826", "doi": "10.2139/ssrn.4734826", "title": "The Illusion of Trust and the Paradox of Disclosure: How Fake Online Reviews of Physicians Manipulate Patient Choices and Exploit Privacy Boundaries", "authors": [ "Aishwarya Shukla", "Laksh Agarwal", "Jie Mein Goh" ], "posted": "2024-03-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4734826", "field": "management", "role": "instrument", "bullets": [ "Novel dataset of genuine and fake physician reviews from online healthcare platforms, examining perceived helpfulness and trust.", "GPT-4 performed NLP analysis to identify textual characteristics that make fake reviews appear credible and trustworthy.", "Fake reviews perceived as more helpful and trustworthy than genuine ones; they exploit privacy-driven information gaps with specific health details." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3368, "authors_detailed": [ { "name": "Aishwarya Deep Shukla", "url": "https://openalex.org/A5052294017", "inst": "Simon Fraser University" }, { "name": "Laksh Agarwal", "url": "https://openalex.org/A5028436579", "inst": "Simon Fraser University" }, { "name": "Jie Mein Goh", "url": "https://openalex.org/A5035866275", "inst": "Simon Fraser University" } ], "affiliations": [ "Simon Fraser University" ] }, { "uid": "arxiv:2402.19421v1", "arxiv_id": "2402.19421v1", "title": "Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines", "authors": [ "Lijia Ma", "Xingchen Xu", "Yong Tan" ], "posted": "2024-02-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.19421v1", "field": "management", "role": "object", "bullets": [ "Websites cited by Bing Chat compared with results listed by the conventional Bing search engine, plus a companion dataset from a GPT-4 retrieval API; collection size is not stated.", "The chat engine itself is the subject; NLP measures of readability, structure, and perplexity characterize which sources it selects, rather than the authors using an LLM to code data.", "Bing Chat favors readable, formally structured, low-perplexity content, the GPT-4 API shows the same preference, and RAG citations are more homogeneous than conventional rankings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 13, "validated": null, "n": 1596, "authors_detailed": [ { "name": "Lijia Ma", "url": "https://openalex.org/A5102097666", "inst": "Harbin Medical University" }, { "name": "Xingchen Xu", "url": "https://openalex.org/A5004663346", "inst": "University of Washington" }, { "name": "Yong Tan", "url": "https://openalex.org/A5113916988", "inst": "Multimedia University" } ], "affiliations": [ "Harbin Medical University", "University of Washington", "Multimedia University" ] }, { "uid": "doi:10.2139/ssrn.4738829", "doi": "10.2139/ssrn.4738829", "title": "When Advanced AI Isn't Enough: Human Factors as Drivers of Success in Generative AI-Human Collaborations", "authors": [ "Ning Li", "Huaikang Zhou", "Wenming Deng", "Junyuan Liu", "Fengxian Liu", "Kris Mikel-Hong" ], "posted": "2024-02-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4738829", "field": "management", "role": "object", "bullets": [ "Two randomized controlled experiments testing generative AI collaboration, comparing GPT-4 against a less advanced model.", "Participants performed tasks with and without AI access; conversation logs analyzed for human-AI interaction patterns.", "AI access improved performance but did not compress variance; AI literacy and collaboration training mattered more than model advancement." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "validated": null, "n": 3366, "authors_detailed": [ { "name": "Ning Li", "url": "https://openalex.org/A5100369014", "inst": "Tsinghua University" }, { "name": "Huaikang Zhou", "url": "https://openalex.org/A5102623714", "inst": "Tsinghua University" }, { "name": "Wenming Deng", "url": "https://openalex.org/A5113342807", "inst": "Tsinghua University" }, { "name": "Junyuan Liu", "url": "https://openalex.org/A5056568215", "inst": "Tsinghua University" }, { "name": "Fengxian Liu", "url": "https://openalex.org/A5047018069", "inst": "Tsinghua University" }, { "name": "Kris Mikel-Hong", "url": "https://openalex.org/A5092533873", "inst": "Tsinghua University" } ], "affiliations": [ "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.4723503", "doi": "10.2139/ssrn.4723503", "title": "The Usefulness of ChatGPT for Textual Analysis of Annual Reports", "authors": [ "Pawel Bilinski" ], "posted": "2024-02-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4723503", "field": "accounting", "role": "instrument", "bullets": [ "UK public firms' annual reports analyzed for textual sentiment and complexity using ChatGPT-generated scores.", "ChatGPT scored sentiment and complexity of annual report text to capture value-relevant information for investors.", "Both measures predict announcement-period price reactions, future profitability levels and changes, and dispersion in investor beliefs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 75, "n": 3367, "authors_detailed": [ { "name": "Pawel Bilinski", "url": "https://openalex.org/A5071055078", "inst": "City, University of London" } ], "affiliations": [ "City, University of London" ] }, { "uid": "arxiv:2402.18225v1", "arxiv_id": "2402.18225v1", "title": "CogBench: a large language model walks into a psychology lab", "authors": [ "Julian Coda-Forno", "Marcel Binz", "Jane X. Wang", "Eric Schulz" ], "posted": "2024-02-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.18225v1", "field": "other", "role": "method", "bullets": [ "Ten behavioral metrics from seven cognitive psychology experiments, applied to 35 LLMs and analyzed with multilevel models that account for fine-tuned model lineages.", "The 35 models span open and proprietary systems, none named in the abstract; behavior is phenotyped rather than validated against any ground truth.", "Scale and RLHF push models toward human-like behavior, open-source models are less risk prone than proprietary ones, and chain-of-thought prompting improves probabilistic reasoning." ], "bullet_provenance": "ai", "salience": 45, "edition": 13, "models": [], "validated": null, "n": 1552, "authors_detailed": [ { "name": "Julian Coda-Forno", "url": "https://openalex.org/A5089420217", "inst": "Helmholtz Association of German Research Centres" }, { "name": "Marcel Binz", "url": "https://openalex.org/A5001129714", "inst": "Philipps University of Marburg" }, { "name": "Jane X. Wang", "url": "https://openalex.org/A5054833182", "inst": "Northwestern University" }, { "name": "Eric Schulz", "url": "https://openalex.org/A5101768980", "inst": "Helmholtz Zentrum München" } ], "affiliations": [ "Northwestern University", "Helmholtz Association of German Research Centres", "Philipps University of Marburg", "Helmholtz Zentrum München" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2402.17385v1", "arxiv_id": "2402.17385v1", "title": "Determinants of LLM-assisted Decision-Making", "authors": [ "Eva Eigner", "Thorsten Händler" ], "posted": "2024-02-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.17385v1", "field": "management", "role": "object", "bullets": [ "A structured literature analysis of decision-making with LLM assistance, covering technological, psychological, and decision-specific determinants and illustrating them across application scenarios.", "No model is run. LLMs are the object of study, with transparency, prompt engineering, emotions, decision styles, task difficulty, and accountability mapped as influencing factors.", "Trust and reliance on LLMs, the user's mental model, and information processing characteristics emerge as key determinants, organized into a dependency framework of reciprocal interdependencies." ], "bullet_provenance": "ai", "salience": 28, "edition": 13, "models": [], "validated": null, "n": 1687, "authors_detailed": [ { "name": "Eva Eigner", "url": "https://openalex.org/A5094027973", "inst": "" }, { "name": "T. Handler", "url": "https://openalex.org/A5084280248", "inst": "Rutgers, The State University of New Jersey" } ], "affiliations": [ "Rutgers, The State University of New Jersey" ] }, { "uid": "arxiv:2402.16650v1", "arxiv_id": "2402.16650v1", "title": "ESG Sentiment Analysis: comparing human and language model performance including GPT", "authors": [ "Karim Derrick" ], "posted": "2024-02-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.16650v1", "field": "finance", "role": "method", "bullets": [ "150 ESG related tweets, each sentiment coded by three researchers whose consensus forms a gold standard for comparing human and machine measurement.", "Llama 2, T5, Mistral, Mixtral, FinBERT, GPT-3.5, and GPT-4 are benchmarked against the human labels, alongside the VADER dictionary approach.", "The abstract describes the comparison design but reports neither which approach wins nor any accuracy or agreement figures." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "consensus gold standard of 150 human coded tweets", "salience": 38, "edition": 13, "n": 1775, "authors_detailed": [ { "name": "Karim Derrick", "url": "https://openalex.org/A5005402053", "inst": "University of Manchester" } ], "affiliations": [ "University of Manchester" ] }, { "uid": "doi:10.2139/ssrn.4703193", "doi": "10.2139/ssrn.4703193", "title": "From West to the Rest: Growing Dispersion of AI Jobs in America", "authors": [ "Anil Gupta", "Jon Norberg", "Evan Schnidman", "Siva Viswanathan", "Kunpeng Zhang", "Hanwen Shi" ], "posted": "2024-02-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4703193", "field": "economics", "role": "instrument", "bullets": [ "US job postings analyzed to map geographic distribution of AI-related positions across American regions over time.", "Fine-tuned LLM classified job postings requiring AI skills, reducing false-positive rate from 70% under keyword approaches.", "AI jobs are dispersing from the West Coast to other US regions, producing the first comprehensive AI job geography map." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Compared to keyword-based classification with 70% false-positive benchmark", "salience": 65, "n": 3365, "authors_detailed": [ { "name": "Anil Kumar Gupta", "url": "https://openalex.org/A5101307899", "inst": "University of Maryland - Robert H. Smith School of Business" }, { "name": "Jon Norberg", "url": "https://openalex.org/A5010673771", "inst": "LinkUp" }, { "name": "Evan A. Schnidman", "url": "https://openalex.org/A5082840153", "inst": "Outrigger Group" }, { "name": "Siva Viswanathan", "url": "https://openalex.org/A5019502700", "inst": "Smith Institute" }, { "name": "Kunpeng Zhang", "url": "https://openalex.org/A5014223717", "inst": "Smith Institute" }, { "name": "Hanwen Shi", "url": "https://openalex.org/A5101307900", "inst": "University of Maryland - Robert H. Smith School of Business" } ], "affiliations": [ "University of Maryland - Robert H. Smith School of Business", "LinkUp", "Outrigger Group", "Smith Institute" ] }, { "uid": "doi:10.2139/ssrn.4702422", "doi": "10.2139/ssrn.4702422", "title": "Assessing the Extent to Which Generative Artificial Intelligence (AI) Falls Within the Scope of the EU's Digital Services Act: an Initial Analysis", "authors": [ "Laureline Lemoine", "Mathias Vermeulen" ], "posted": "2024-02-21", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4702422", "field": "economics", "role": "object", "bullets": [ "Product-level legal analysis considers European Digital Services Act coverage for standalone generative AI products and AI embedded within designated online platforms or search services.", "No model generates research data; ChatGPT, Bard, Bing Chat, and Snapchat's My AI are regulatory objects, so output validation is not applicable.", "Coverage depends on product design and corporate links to designated platforms, requiring regulators to distinguish standalone systems from embedded generative services before assigning obligations." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 42, "edition": 23, "validated": null, "n": 4185, "authors_detailed": [ { "name": "Laureline Lemoine", "url": "https://openalex.org/A5022959619", "inst": "Environment Agency" }, { "name": "Mathias Vermeulen", "url": "https://openalex.org/A5073929993", "inst": "European University Institute" } ], "affiliations": [ "Environment Agency", "European University Institute" ] }, { "uid": "arxiv:2402.13866v2", "arxiv_id": "2402.13866v2", "title": "Kuaiji: the First Chinese Accounting Large Language Model", "authors": [ "Jiayuan Luo", "Songhua Yang", "Xiaoling Qiu", "Panyu Chen", "Yufei Nai", "Wenxuan Zeng", "Wentao Zhang", "Xinke Jiang" ], "posted": "2024-02-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.13866v2", "field": "accounting", "role": "method", "bullets": [ "Chinese accounting practice, using CAtAcctQA, a dataset of genuine accountant-client dialogues assembled for the project; dialogue counts are not stated.", "Kuaiji is fine-tuned from the Baichuan family with continued pretraining and supervised fine-tuning; efficacy is shown through real-world accounting scenarios rather than a reported accuracy benchmark.", "The authors release what they describe as the first open source Chinese accounting LLM together with its training dataset, claiming strong accuracy and response speed without figures." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": false, "salience": 46, "edition": 13, "n": 1530, "authors_detailed": [ { "name": "Jiayuan Luo", "url": "https://openalex.org/A5032348045", "inst": "Guangxi University" }, { "name": "Songhua Yang", "url": "https://openalex.org/A5102412770", "inst": "Ministry of Agriculture" }, { "name": "Xiaoling Qiu", "url": "https://openalex.org/A5020255947", "inst": "Southern Medical University" }, { "name": "Panyu Chen", "url": "https://openalex.org/A5010730243", "inst": "Sun Yat-sen University" }, { "name": "Yufei Nai", "url": "https://openalex.org/A5093984843", "inst": "" }, { "name": "W. Zeng", "url": "https://openalex.org/A5109688824", "inst": "Harvard University" }, { "name": "Wentao Zhang", "url": "https://openalex.org/A5100459857", "inst": "Zhuhai People's Hospital" }, { "name": "Xinke Jiang", "url": "https://openalex.org/A5011124718", "inst": "Peking University" } ], "affiliations": [ "Harvard University", "Guangxi University", "Ministry of Agriculture", "Southern Medical University", "Sun Yat-sen University", "Peking University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2402.14875v3", "arxiv_id": "2402.14875v3", "title": "What's in a Name? Auditing Large Language Models for Race and Gender Bias", "authors": [ "Alejandro Salinas", "Amit Haim", "Julian Nyarko" ], "posted": "2024-02-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.14875v3", "field": "economics", "role": "agent", "bullets": [ "Advice requests involving a named individual, such as car purchase negotiations and election predictions, posed across 42 prompt templates with names signaling race and gender.", "GPT-4 and several other state of the art models generate the advice; outcomes are compared across name groups in an audit design.", "Advice systematically disadvantages names associated with racial minorities and women, with Black women's names faring worst; numeric anchors in prompts offset the disparities while qualitative details can widen them." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "edition": 13, "validated": null, "n": 1632, "authors_detailed": [ { "name": "Alejandro Salinas", "url": "https://openalex.org/A5114130495", "inst": "Stanford Medicine" }, { "name": "Amit Haim", "url": "https://openalex.org/A5094008604", "inst": "" }, { "name": "Julian Nyarko", "url": "https://openalex.org/A5017688180", "inst": "Innovative Research (United States)" } ], "affiliations": [ "Stanford Medicine", "Innovative Research (United States)" ] }, { "uid": "arxiv:2402.13533v1", "arxiv_id": "2402.13533v1", "title": "FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing", "authors": [ "Xiao-Yang Liu", "Jie Zhang", "Guoxuan Wang", "Weiqing Tong", "Anwar Walid" ], "posted": "2024-02-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.13533v1", "field": "finance", "role": "method", "bullets": [ "Pretraining and finetuning of financial LLMs, where linear transformer layers account for over 80 percent of compute and 99 percent of parameters.", "Each linear layer is replaced by two narrower ones with 8 and 4 bit quantization; the models, base family not named, are scored on general and financial tasks.", "Reported gains are a 1.3x pretraining speedup, 2.64x compression with no accuracy drop, a 24 percent average financial task gain after finetuning, and models under 0.59 GB that run on a smartphone." ], "bullet_provenance": "ai", "validated": true, "validation_note": "general and financial task benchmarks", "salience": 33, "edition": 13, "models": [], "n": 1719, "authors_detailed": [ { "name": "Xiaoyang Liu", "url": "https://openalex.org/A5091173441", "inst": "Liaoning University of Traditional Chinese Medicine" }, { "name": "Jie Zhang", "url": "https://openalex.org/A5100436650", "inst": "Neurosciences Institute" }, { "name": "Guoxuan Wang", "url": "https://openalex.org/A5083192815", "inst": "Johns Hopkins University" }, { "name": "Weiqing Tong", "url": "https://openalex.org/A5046638511", "inst": "Shanghai University of Engineering Science" }, { "name": "Anwar Walid", "url": "https://openalex.org/A5066875580", "inst": "Murray State University" } ], "affiliations": [ "Johns Hopkins University", "Liaoning University of Traditional Chinese Medicine", "Neurosciences Institute", "Shanghai University of Engineering Science", "Murray State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4731281", "doi": "10.2139/ssrn.4731281", "title": "Gemini or ChatGPT? Capability, Performance, and Selection of Cutting-Edge Generative Artificial Intelligence (AI) in Business Management", "authors": [ "Nitin Rane", "Saurabh Choudhary", "Jayesh Rane" ], "posted": "2024-02-21", "added": "2026-07-26", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4731281", "field": "management", "role": "object", "bullet_provenance": "none", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 30, "edition": 4, "bullets": [], "validated": null, "n": 1211, "authors_detailed": [ { "name": "Nitin Rane", "url": "https://openalex.org/A5093128225", "inst": "Swami Vivekanand College of Pharmacy" }, { "name": "Saurabh Choudhary", "url": "https://openalex.org/A5019306977", "inst": "University of Mumbai" }, { "name": "Jayesh Rane", "url": "https://openalex.org/A5092107778", "inst": "K J Somaiya Medical College" } ], "affiliations": [ "Swami Vivekanand College of Pharmacy", "University of Mumbai", "K J Somaiya Medical College" ] }, { "uid": "doi:10.2139/ssrn.4731283", "doi": "10.2139/ssrn.4731283", "title": "Gemini or ChatGPT? Efficiency, Performance, and Adaptability of Cutting-Edge Generative Artificial Intelligence (AI) in Finance and Accounting", "authors": [ "Nitin Rane", "Saurabh Choudhary", "Jayesh Rane" ], "posted": "2024-02-21", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4731283", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 33, "edition": 2, "audience": "broad", "bullets": [], "validated": null, "n": 62, "authors_detailed": [ { "name": "Nitin Liladhar Rane", "url": "https://openalex.org/A5039865284", "inst": "University of Mumbai" }, { "name": "Saurabh Choudhary", "url": "https://openalex.org/A5038000217", "inst": "University of Mumbai" }, { "name": "Jayesh Rane", "url": "https://openalex.org/A5092107778", "inst": "University of Mumbai" } ], "affiliations": [ "University of Mumbai" ] }, { "uid": "arxiv:2402.12659v2", "arxiv_id": "2402.12659v2", "title": "FinBen: A Holistic Financial Benchmark for Large Language Models", "authors": [ "Qianqian Xie", "Weiguang Han", "Zhengyu Chen", "Ruoyu Xiang", "Xiao Zhang", "Yueru He", "Mengxi Xiao", "Dong Li", "Yongfu Dai", "Duanyu Feng", "Yijing Xu", "Haoqiang Kang", "Ziyan Kuang", "Chenhan Yuan", "Kailai Yang", "Zheheng Luo", "Tianlin Zhang", "Zhiwei Liu", "Guojun Xiong", "Zhiyang Deng", "Yuechen Jiang", "Zhiyuan Yao", "Haohang Li", "Yangyang Yu", "Gang Hu", "Jiajia Huang", "Xiao-Yang Liu", "Alejandro Lopez-Lira", "Benyou Wang", "Yanzhao Lai", "Hao Wang", "Min Peng", "Sophia Ananiadou", "Jimin Huang" ], "posted": "2024-02-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.12659v2", "field": "finance", "role": "method", "bullets": [ "A benchmark of 36 datasets covering 24 financial tasks across seven areas, including information extraction, question answering, forecasting, risk management and stock trading.", "Fifteen LLMs including GPT-4, ChatGPT and Gemini are scored on the labelled datasets, with new agent, retrieval augmented generation and stock trading evaluations added.", "Models handle extraction and textual analysis well but struggle with reasoning, generation and forecasting; GPT-4 leads on extraction and trading while Gemini leads on generation and forecasting." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "36 labelled datasets across 24 financial tasks", "salience": 58, "edition": 13, "n": 1522, "authors_detailed": [ { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Weiguang Han", "url": "https://openalex.org/A5054465909", "inst": "Hebei University of Technology" }, { "name": "Zhengyu Chen", "url": "https://openalex.org/A5101886933", "inst": "North China Electric Power University" }, { "name": "Ruoyu Xiang", "url": "https://openalex.org/A5101307852", "inst": "Chongqing Normal University" }, { "name": "Xiao Zhang", "url": "https://openalex.org/A5100320877", "inst": "University of Science and Technology of China" }, { "name": "Yueru He", "url": "https://openalex.org/A5111133467", "inst": "Columbia University" }, { "name": "Mengxi Xiao", "url": "https://openalex.org/A5111097308", "inst": "Yunnan Center for Disease Control And Prevention" }, { "name": "Dong Li", "url": "https://openalex.org/A5052630982", "inst": "Academy of Medical Sciences" }, { "name": "Dai, Yongfu", "url": "", "inst": "" }, { "name": "Duanyu Feng", "url": "https://openalex.org/A5001345589", "inst": "National University of Singapore" }, { "name": "Yijing Xu", "url": "https://openalex.org/A5100871294", "inst": "Hangzhou Normal University" }, { "name": "Haoqiang Kang", "url": "https://openalex.org/A5113057252", "inst": "" }, { "name": "Ziyan Kuang", "url": "https://openalex.org/A5110247102", "inst": "University of Manchester" }, { "name": "Chenhan Yuan", "url": "https://openalex.org/A5034535852", "inst": "University of Electronic Science and Technology of China" }, { "name": "Kailai Yang", "url": "https://openalex.org/A5066983614", "inst": "Harbin Institute of Technology" }, { "name": "Zheheng Luo", "url": "https://openalex.org/A5075367840", "inst": "Academy of Military Medical Sciences" }, { "name": "Tianlin Zhang", "url": "https://openalex.org/A5042345460", "inst": "Tianjin University" }, { "name": "Zhiwei Liu", "url": "https://openalex.org/A5100321227", "inst": "Shandong University" }, { "name": "Guojun Xiong", "url": "https://openalex.org/A5102620407", "inst": "Harvard University" }, { "name": "Zhiyang Deng", "url": "https://openalex.org/A5069290428", "inst": "Hong Kong Baptist University" }, { "name": "Jiang, Yuechen", "url": "", "inst": "" }, { "name": "Zhiyuan Yao", "url": "https://openalex.org/A5108265235", "inst": "Ningbo University" }, { "name": "Haohang Li", "url": "https://openalex.org/A5027371293", "inst": "Nanjing University of Aeronautics and Astronautics" }, { "name": "Yangyang Yu", "url": "https://openalex.org/A5069731031", "inst": "Jilin University of Finance and Economics" }, { "name": "Gang Hu", "url": "https://openalex.org/A5062941547", "inst": "Chengdu University of Information Technology" }, { "name": "Jiajia Huang", "url": "https://openalex.org/A5112641253", "inst": "Huazhong University of Science and Technology" }, { "name": "Xiaoyang Liu", "url": "https://openalex.org/A5100405226", "inst": "Tongji University" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Benyou Wang", "url": "https://openalex.org/A5057282504", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Yanzhao Lai", "url": "https://openalex.org/A5029848442", "inst": "China Electronics Technology Group Corporation" }, { "name": "Hao Wang", "url": "https://openalex.org/A5100446064", "inst": "Stevens Institute of Technology" }, { "name": "Min Peng", "url": "https://openalex.org/A5102012008", "inst": "Jiujiang University" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" } ], "affiliations": [ "Columbia University", "Hunan Normal University", "Hebei University of Technology", "North China Electric Power University", "Chongqing Normal University", "University of Science and Technology of China", "Yunnan Center for Disease Control And Prevention", "Academy of Medical Sciences" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2402.12713v2", "arxiv_id": "2402.12713v2", "title": "Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs", "authors": [ "Yuhang Zhou", "Yuchen Ni", "Yunhui Gan", "Zhangyue Yin", "Xiang Liu", "Jian Zhang", "Sen Liu", "Xipeng Qiu", "Guangnan Ye", "Hongfeng Chai" ], "posted": "2024-02-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.12713v2", "field": "finance", "role": "agent", "bullets": [ "Twenty three large language models, general and finance tuned, probed with scenarios grounded in behavioral finance; the specific models are not named in the abstract.", "The Financial Bias Indicators framework elicits the models' judgments to detect irrational biases such as risk preference bias, then applies four prompt based causal debiasing methods.", "Financial irrationality varies with model design and training; finance tuned models can be more biased than smaller general ones, and the debiasing methods reduce measured bias." ], "bullet_provenance": "ai", "salience": 50, "edition": 13, "models": [], "validated": null, "n": 1631, "authors_detailed": [ { "name": "Yuhang Zhou", "url": "https://openalex.org/A5101924581", "inst": "Chongqing University" }, { "name": "Yuchen Ni", "url": "https://openalex.org/A5101334139", "inst": "Xinjiang Medical University" }, { "name": "Gan, Yunhui", "url": "", "inst": "" }, { "name": "Yin, Zhangyue", "url": "", "inst": "" }, { "name": "Xiang Liu", "url": "https://openalex.org/A5100408608", "inst": "China Telecom (China)" }, { "name": "Jian Zhang", "url": "https://openalex.org/A5093976009", "inst": "" }, { "name": "Sen Liu", "url": "https://openalex.org/A5100358497", "inst": "Fudan University" }, { "name": "Qiu, Xipeng", "url": "", "inst": "" }, { "name": "Guangnan Ye", "url": "https://openalex.org/A5108314421", "inst": "" }, { "name": "Hongfeng Chai", "url": "https://openalex.org/A5109684899", "inst": "Fudan University" } ], "affiliations": [ "Chongqing University", "Xinjiang Medical University", "China Telecom (China)", "Fudan University" ] }, { "uid": "doi:10.2139/ssrn.4722780", "doi": "10.2139/ssrn.4722780", "title": "Can ChatGPT Plan Your Retirement?: Generative AI and Financial Advice", "authors": [ "Andrew W. Lo", "Jillian Ross" ], "posted": "2024-02-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4722780", "field": "finance", "role": "agent", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 632, "authors_detailed": [ { "name": "Andrew W. Lo", "url": "https://openalex.org/A5109980718", "inst": "Massachusetts Institute of Technology" }, { "name": "J. Perran Ross", "url": "https://openalex.org/A5110195768", "inst": "MIT Computer Science and Artificial Intelligence Laboratory" } ], "affiliations": [ "Massachusetts Institute of Technology", "MIT Computer Science and Artificial Intelligence Laboratory" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4731070", "doi": "10.2139/ssrn.4731070", "title": "Chatgpt and Corporate Policies", "authors": [ "Manish Jha", "Jialin Qian", "Michael Weber", "Baozhong Yang" ], "posted": "2024-02-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4731070", "field": "finance", "role": "instrument", "bullets": [ "US public firms' earnings conference calls used to construct firm-level investment scores spanning multiple quarters.", "ChatGPT scored managerial text from conference calls to measure anticipated changes in capital expenditures.", "Investment score predicts capex up to nine quarters ahead; high-score firms experience significant negative future abnormal returns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Correlated with CFO survey responses", "salience": 82, "n": 3364, "authors_detailed": [ { "name": "Manish Jha", "url": "https://openalex.org/A5024242858", "inst": "Georgia State University" }, { "name": "Jialin Qian", "url": "https://openalex.org/A5104148222", "inst": "Rochester Institute of Technology" }, { "name": "Michael Weber", "url": "https://openalex.org/A5034980594", "inst": "National Bureau of Economic Research" }, { "name": "Baozhong Yang", "url": "https://openalex.org/A5084538885", "inst": "Georgia State University" } ], "affiliations": [ "Georgia State University", "Rochester Institute of Technology", "National Bureau of Economic Research" ] }, { "uid": "arxiv:2402.12327v3", "arxiv_id": "2402.12327v3", "title": "Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents", "authors": [ "Zengqing Wu", "Run Peng", "Shuyuan Zheng", "Qianying Liu", "Xu Han", "Brian Inhyuk Kwon", "Makoto Onizuka", "Shaojie Tang", "Chuan Xiao" ], "posted": "2024-02-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.12327v3", "field": "economics", "role": "agent", "bullets": [ "Three competitive scenarios in which multiple LLM agents interact repeatedly without instructions to cooperate, so any cooperation has to emerge from context.", "LLM agents, models not named in the abstract, make adaptive decisions with no scripted behavior; simulated trajectories are compared with human behavioral data.", "Cooperation emerges gradually among the competing agents and the trajectory tracks human data, suggesting social simulations need less scripted persona engineering." ], "bullet_provenance": "ai", "validated": true, "validation_note": "comparison with human behavioral data", "salience": 45, "edition": 13, "models": [], "n": 1718, "authors_detailed": [ { "name": "Zengqing Wu", "url": "https://openalex.org/A5114129919", "inst": "" }, { "name": "Run Peng", "url": "https://openalex.org/A5093967977", "inst": "The University of Osaka" }, { "name": "Shuyuan Zheng", "url": "https://openalex.org/A5108319820", "inst": "Yunnan University" }, { "name": "Qianying Liu", "url": "https://openalex.org/A5108902573", "inst": "Fordham University" }, { "name": "Xu Han", "url": "https://openalex.org/A5089238766", "inst": "Texas A&M University" }, { "name": "Brian Inhyuk Kwon", "url": "https://openalex.org/A5113123617", "inst": "The University of Osaka" }, { "name": "Makoto Onizuka", "url": "https://openalex.org/A5101010930", "inst": "Harbin Institute of Technology" }, { "name": "Shaojie Tang", "url": "https://openalex.org/A5102619863", "inst": "Nagoya University" }, { "name": "Chuan Xiao", "url": "https://openalex.org/A5036148682", "inst": "Beijing Institute of Technology" } ], "affiliations": [ "The University of Osaka", "Yunnan University", "Fordham University", "Texas A&M University", "Harbin Institute of Technology", "Nagoya University", "Beijing Institute of Technology" ] }, { "uid": "arxiv:2403.00782v1", "arxiv_id": "2403.00782v1", "title": "Ploutos: Towards interpretable stock movement prediction with financial large language model", "authors": [ "Hanshuang Tong", "Jun Li", "Ning Wu", "Ming Gong", "Dongmei Zhang", "Qi Zhang" ], "posted": "2024-02-18", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2403.00782v1", "field": "finance", "role": "method", "bullets": [ "Stock movement prediction fusing textual and numerical market data; the evaluation datasets and sample period are not stated in the abstract.", "PloutosGen expert models feed PloutosGPT, trained with rearview-mirror prompting from GPT-4 and dynamic token weighting to produce rationales; accuracy is compared against state-of-the-art baselines.", "The framework beats prior methods on both prediction accuracy and interpretability measures; no magnitudes are given in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "movement prediction accuracy against realized labels and baselines", "salience": 40, "edition": 13, "n": 1551, "authors_detailed": [ { "name": "Hanshuang Tong", "url": "https://openalex.org/A5016525196", "inst": "Microsoft Research Asia (China)" }, { "name": "Jun Li", "url": "https://openalex.org/A5116437793", "inst": "Kunming University of Science and Technology" }, { "name": "Ning Wu", "url": "https://openalex.org/A5023371171", "inst": "Anhui Sanlian University" }, { "name": "Gong Ming", "url": "https://openalex.org/A5102186727", "inst": "Capital Medical University" }, { "name": "Dongmei Zhang", "url": "https://openalex.org/A5100331497", "inst": "Heilongjiang Academy of Sciences" }, { "name": "Qi Zhang", "url": "https://openalex.org/A5100360247", "inst": "Wuhan University of Technology" } ], "affiliations": [ "Microsoft Research Asia (China)", "Kunming University of Science and Technology", "Anhui Sanlian University", "Capital Medical University", "Heilongjiang Academy of Sciences", "Wuhan University of Technology" ] }, { "uid": "doi:10.2139/ssrn.4712248", "doi": "10.2139/ssrn.4712248", "title": "Large Language Models and Return Prediction in China", "authors": [ "Lin Tan", "Huihang Wu", "Xiaoyan Zhang" ], "posted": "2024-02-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4712248", "field": "finance", "role": "instrument", "bullets": [ "Chinese public news articles and stock returns using seven LLMs: BERT, RoBERTa, FinBERT, Baichuan, ChatGLM, InternLM, and an ensemble.", "Seven LLMs extracted news tone from Chinese news to predict future stock returns; value-weighted long-short portfolios constructed.", "Long-short portfolios yielded 35-67% annualized returns; predictive power stronger for firms with greater information frictions and more retail holdings." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": true, "validation_note": "portfolio return prediction", "salience": 75, "n": 2467, "authors_detailed": [ { "name": "Lin Tan", "url": "https://openalex.org/A5100579599", "inst": "Tsinghua University" }, { "name": "huihang wu", "url": "https://openalex.org/A5062100084", "inst": "Tsinghua University" }, { "name": "Xiaoyan Zhang", "url": "https://openalex.org/A5100352063", "inst": "Tsinghua University" } ], "affiliations": [ "Tsinghua University" ] }, { "uid": "arxiv:2402.11194v3", "arxiv_id": "2402.11194v3", "title": "Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering", "authors": [ "Pragya Srivastava", "Manuj Malik", "Vivek Gupta", "Tanuja Ganu", "Dan Roth" ], "posted": "2024-02-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.11194v3", "field": "finance", "role": "method", "bullets": [ "Question answering over four financial tabular datasets, TATQA, FinQA, ConvFinQA, and Multihiertt, which mix structured tables with unstructured text and require multi-step arithmetic.", "Several LLMs and prompting techniques are compared for sensitivity to table complexity and to the number of arithmetic reasoning steps. Specific models are not named in the abstract.", "The study maps where models break down on complex tables, and a prompting technique tailored to semi-structured documents matches or beats baselines. Figures are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "four financial tabular QA benchmarks", "salience": 36, "edition": 13, "models": [], "n": 1686, "authors_detailed": [ { "name": "Pragya Srivastava", "url": "https://openalex.org/A5075784295", "inst": "Roswell Park Comprehensive Cancer Center" }, { "name": "Manuj Malik", "url": "https://openalex.org/A5104730143", "inst": "Singapore Management University" }, { "name": "Gupta, Vivek", "url": "", "inst": "" }, { "name": "Ganu, Tanuja", "url": "", "inst": "" }, { "name": "Roth, Dan", "url": "", "inst": "" } ], "affiliations": [ "Singapore Management University" ] }, { "uid": "arxiv:2402.10986v3", "arxiv_id": "2402.10986v3", "title": "FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models", "authors": [ "Gagan Bhatia", "El Moatez Billah Nagoudi", "Hasan Cavusoglu", "Muhammad Abdul-Mageed" ], "posted": "2024-02-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.10986v3", "field": "finance", "role": "method", "bullets": [ "A curated collection of financial text, numerical, tabular and image data supports domain pretraining, instruction tuning and RLAIF, plus a benchmark of nine tasks over 25 datasets.", "FinTral builds on Mistral 7B with direct preference optimization, tools and retrieval; performance is scored on the labelled benchmark, which includes a financial hallucination test.", "The best variant beats ChatGPT-3.5 on all nine tasks and GPT-4 on five, and the models and benchmark are released publicly." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "nine-task, 25-dataset financial benchmark including hallucination tests", "salience": 55, "edition": 13, "n": 1525, "authors_detailed": [ { "name": "Gagan Bhatia", "url": "https://openalex.org/A5089558525", "inst": "University of Aberdeen" }, { "name": "El Moatez Billah Nagoudi", "url": "https://openalex.org/A5041553790", "inst": "University of British Columbia" }, { "name": "Hasan Cavusoglu", "url": "https://openalex.org/A5024130715", "inst": "Tulane University" }, { "name": "Muhammad Abdul-Mageed", "url": "https://openalex.org/A5004629670", "inst": "University of British Columbia Hospital" } ], "affiliations": [ "University of Aberdeen", "University of British Columbia", "Tulane University" ] }, { "uid": "arxiv:2402.10811v2", "arxiv_id": "2402.10811v2", "title": "Quantifying the Persona Effect in LLM Simulations", "authors": [ "Tiancheng Hu", "Nigel Collier" ], "posted": "2024-02-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.10811v2", "field": "other", "role": "method", "bullets": [ "Existing subjective NLP annotation datasets carrying annotator demographic, social, and behavioral variables, used to ask how much personas explain variation in human labels.", "LLMs are prompted with annotator personas to predict individual annotations, including a 70 billion parameter model in zero-shot mode. Model families are not named in the abstract.", "Personas explain under 10 percent of annotation variance, prompting them yields modest but significant gains, and the 70b model captures 81 percent of the variance attainable by regression on ground truth." ], "bullet_provenance": "ai", "validated": true, "validation_note": "human annotations on subjective NLP datasets", "salience": 48, "edition": 13, "models": [], "n": 1685, "authors_detailed": [ { "name": "Tiancheng Hu", "url": "https://openalex.org/A5006591088", "inst": "University of Cambridge" }, { "name": "Nigel Collier", "url": "https://openalex.org/A5093959849", "inst": "University of Cambridge" } ], "affiliations": [ "University of Cambridge" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4707911", "doi": "10.2139/ssrn.4707911", "title": "Copyright Policy Options for Generative Artificial Intelligence", "authors": [ "Joshua S. Gans" ], "posted": "2024-02-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4707911", "field": "economics", "role": "object", "bullets": [ "Theoretical welfare analysis of copyright regimes for generative AI training on copyrighted content, comparing small-model and large-model market structures.", "No LLM is deployed; the paper models economic incentives for content providers and AI developers under alternative copyright and fair-use rules.", "Copyright protection raises welfare for small AI models; for large models the effect is ambiguous, but an ex-post fair-use mechanism dominates traditional copyright regimes." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 55, "validated": null, "n": 2643, "authors_detailed": [ { "name": "Joshua S. Gans", "url": "https://openalex.org/A5055795427", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4709617", "doi": "10.2139/ssrn.4709617", "title": "Portfolio Performance Based on LLM News Scores and Related Economical Analysis", "authors": [ "Ruoxu Wu" ], "posted": "2024-02-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4709617", "field": "finance", "role": "instrument", "bullets": [ "Chinese A-share market stocks scored daily using scraped news briefings from November 2022 to October 2023, with long-only and short-only portfolio strategies.", "ChatGPT, Tongyi Qianwen, and Baichuan scored news sentiment for stock price impact; portfolios backtested following Lopez-Lira and Tang (2023) methodology.", "LLM news scores predict returns with variation across models and news types; the market reacts more strongly to negative news than to positive news." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "backtest against realized A-share stock returns", "salience": 58, "n": 2644, "authors_detailed": [ { "name": "Ruoxu Wu", "url": "https://openalex.org/A5102618465", "inst": "Zhejiang Lab" } ], "affiliations": [ "Zhejiang Lab" ] }, { "uid": "arxiv:2402.10481v2", "arxiv_id": "2402.10481v2", "title": "Emoji Driven Crypto Assets Market Reactions", "authors": [ "Xiaorui Zuo", "Yao-Tsung Chen", "Wolfgang Karl Härdle" ], "posted": "2024-02-16", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.10481v2", "field": "finance", "role": "instrument", "bullets": [ "Twitter emoji data linked to cryptocurrency markets including BTC price and the VCRIX volatility index.", "GPT-4 and a fine-tuned BERT model performed multimodal sentiment analysis, translating emojis into quantifiable sentiment scores.", "Emoji-based sentiment from GPT-4 outperformed FinBERT text-only sentiment in predicting crypto returns and avoiding major downturns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 58, "n": 3363, "authors_detailed": [ { "name": "Xiaorui Zuo", "url": "https://openalex.org/A5108988226", "inst": "National University of Singapore" }, { "name": "Yao‐Tsung Chen", "url": "https://openalex.org/A5060260308", "inst": "National Yang Ming Chiao Tung University" }, { "name": "Wolfgang Karl Härdle", "url": "https://openalex.org/A5053742635", "inst": "National Yang Ming Chiao Tung University" } ], "affiliations": [ "National University of Singapore", "National Yang Ming Chiao Tung University" ] }, { "uid": "doi:10.2139/ssrn.4714776", "doi": "10.2139/ssrn.4714776", "title": "Generative Artificial Intelligence and Evaluating Strategic Decisions", "authors": [ "Anil Doshi", "J. Jason Bell", "Emil Mirzayev", "Bart Vanneste" ], "posted": "2024-02-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4714776", "field": "management", "role": "agent", "bullets": [ "Two studies of 60 business models each, one set AI-generated and one from a competition, ranked by multiple LLMs under varied roles and prompts.", "Multiple LLMs evaluated and ranked business models using different assumed roles and prompt structures, with rankings compared against human expert panels.", "Individual LLM evaluations were inconsistent and biased, but aggregated AI rankings resembled human expert rankings, supporting ensemble use in strategic evaluation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "agreement with human expert business model rankings", "salience": 60, "n": 2391, "authors_detailed": [ { "name": "Anil R. Doshi", "url": "https://openalex.org/A5011170431", "inst": "University College London" }, { "name": "J. Jason Bell", "url": "https://openalex.org/A5065447058", "inst": "University of Oxford" }, { "name": "Emil Mirzayev", "url": "https://openalex.org/A5093936639", "inst": "Western University" }, { "name": "Bart Vanneste", "url": "https://openalex.org/A5056117219", "inst": "University College London" } ], "affiliations": [ "University of Oxford", "University College London", "Western University" ], "prestige": true }, { "uid": "arxiv:2402.09746v1", "arxiv_id": "2402.09746v1", "title": "Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment", "authors": [ "Hang Yuan", "Saizhuo Wang", "Jian Guo" ], "posted": "2024-02-15", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.09746v1", "field": "finance", "role": "method", "bullets": [ "Quantitative investment pipeline covering alpha mining, modeling, and analysis with iterative human-AI interaction.", "LLM-based framework enables human-in-the-loop alpha discovery and systematic research across the full investment workflow.", "Framework integrates human researcher insights into each pipeline stage, improving efficiency and precision of quantitative investment research." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "n": 3362, "authors_detailed": [ { "name": "Hang Yuan", "url": "https://openalex.org/A5101453271", "inst": "Zhengzhou University of Light Industry" }, { "name": "Saizhuo Wang", "url": "https://openalex.org/A5035150525", "inst": "Hong Kong University of Science and Technology" }, { "name": "Jian Guo", "url": "https://openalex.org/A5109927726", "inst": "Lanzhou Jiaotong University" } ], "affiliations": [ "Zhengzhou University of Light Industry", "Hong Kong University of Science and Technology", "Lanzhou Jiaotong University" ] }, { "uid": "arxiv:2402.09552v2", "arxiv_id": "2402.09552v2", "title": "STEER: Assessing the Economic Rationality of Large Language Models", "authors": [ "Narun Raman", "Taylor Lundy", "Samuel Amouyal", "Yoav Levine", "Kevin Leyton-Brown", "Moshe Tennenholtz" ], "posted": "2024-02-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.09552v2", "field": "economics", "role": "method", "bullets": [ "Benchmark built from a taxonomy of fine-grained elements of rational economic decision making, derived from the economics literature and applied to 14 LLMs.", "Each model is scored quantitatively on the benchmark distribution, producing a report card of rationality elements; the models are not named in the abstract.", "The exercise characterizes the current state of the art and shows model size shapes the ability to exhibit rational behavior; specific scores are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "scored benchmark of rationality elements", "salience": 55, "edition": 13, "models": [], "n": 1595, "authors_detailed": [ { "name": "Narun Raman", "url": "https://openalex.org/A5108562369", "inst": "University of British Columbia" }, { "name": "Taylor Lundy", "url": "https://openalex.org/A5026150794", "inst": "University of British Columbia" }, { "name": "Samuel Amouyal", "url": "https://openalex.org/A5093949537", "inst": "" }, { "name": "Yoav Levine", "url": "https://openalex.org/A5005177735", "inst": "Tel Aviv University" }, { "name": "Kevin Leyton‐Brown", "url": "https://openalex.org/A5089807699", "inst": "University of British Columbia" }, { "name": "Moshe Tennenholtz", "url": "https://openalex.org/A5003520806", "inst": "Technion – Israel Institute of Technology" } ], "affiliations": [ "University of British Columbia", "Tel Aviv University", "Technion – Israel Institute of Technology" ] }, { "uid": "arxiv:2402.08755v1", "arxiv_id": "2402.08755v1", "title": "LLM-driven Imitation of Subrational Behavior : Illusion or Reality?", "authors": [ "Andrea Coletta", "Kshama Dwarakanath", "Penghang Liu", "Svitlana Vyetrenko", "Tucker Balch" ], "posted": "2024-02-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.08755v1", "field": "economics", "role": "agent", "bullets": [ "Four stylized decision scenarios, including the ultimatum game and the marshmallow experiment, chosen to capture subrational traits such as myopia and risk aversion.", "An LLM, not named, generates synthetic human demonstrations that imitation learning distills into agent policies; the check is replication of findings from earlier human subject studies.", "The pipeline reproduces established human patterns in all four scenarios, supporting synthetic demonstrations as a substitute where human data is costly; limitations are discussed, not quantified." ], "bullet_provenance": "ai", "validated": true, "validation_note": "replication of prior human subject findings", "salience": 48, "edition": 13, "models": [], "n": 1717, "authors_detailed": [ { "name": "Andrea Coletta", "url": "https://openalex.org/A5051607028", "inst": "Bank of Italy" }, { "name": "Kshama Dwarakanath", "url": "https://openalex.org/A5029762372", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Penghang Liu", "url": "https://openalex.org/A5038417100", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Svitlana Vyetrenko", "url": "https://openalex.org/A5067604451", "inst": "New Mexico State University" }, { "name": "Tucker Balch", "url": "https://openalex.org/A5035482777", "inst": "Emory University" } ], "affiliations": [ "Emory University", "Bank of Italy", "JPMorgan Chase & Co (United States)", "New Mexico State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4705130", "doi": "10.2139/ssrn.4705130", "title": "Can LLMs Mimic Human-Like Mental Accounting and Behavioral Biases?", "authors": [ "Yan Leng" ], "posted": "2024-02-13", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4705130", "field": "economics", "role": "agent", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 338, "authors_detailed": [ { "name": "Yan Leng", "url": "https://openalex.org/A5068435349", "inst": "The University of Texas at Austin" } ], "affiliations": [ "The University of Texas at Austin" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4700366", "doi": "10.2139/ssrn.4700366", "title": "A Technological Construction of Society: Comparing GPT-4 and Human Respondents for Occupational Evaluation in the UK", "authors": [ "Pawel Gmyrek", "Christoph Lutz", "Gemma Newlands" ], "posted": "2024-02-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4700366", "field": "economics", "role": "agent", "bullets": [ "580 ISCO-08 occupations in the United Kingdom evaluated for prestige and social value by GPT-4 and human survey respondents.", "GPT-4 rated occupational prestige and social value across the full occupational landscape and was compared to a high-quality human survey.", "GPT-4 and human scores highly correlated overall, but GPT-4 was more generous and substantially misjudged stigmatized and emerging digital occupations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "correlation with UK human survey scores", "salience": 55, "n": 2466, "authors_detailed": [ { "name": "Paweł Gmyrek", "url": "https://openalex.org/A5093798133", "inst": "Charles Humbert 8" }, { "name": "Christoph Lutz", "url": "https://openalex.org/A5019957496", "inst": "BI Norwegian Business School" }, { "name": "Gemma Newlands", "url": "https://openalex.org/A5008470831", "inst": "BI Norwegian Business School" } ], "affiliations": [ "Charles Humbert 8", "BI Norwegian Business School" ] }, { "uid": "doi:10.2139/ssrn.4697807", "doi": "10.2139/ssrn.4697807", "title": "Does the Interaction of Informativeness, Readability, and Sentiment within Company’s Sustainability Disclosure Shape an Entity’s ESG Score? – Evidence from Germany", "authors": [ "Thorben Bonn", "Aurin Gaida-Albers" ], "posted": "2024-02-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4697807", "field": "accounting", "role": "instrument", "bullets": [ "German Prime Standard listed companies, financial years 2017-2022; sustainability disclosure text analyzed via OLS panel regressions.", "FinBERT and FinBERT-ESG measured informativeness, readability, and sentiment of sustainability reports to explain ESG score variation.", "High transparency, precision, and language comprehensibility in sustainability disclosures are significant factors in achieving superior ESG scores." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 55, "n": 2925, "authors_detailed": [ { "name": "Thorben Bonn", "url": "https://openalex.org/A5064092928", "inst": "University Hospitals of the Ruhr-University of Bochum" }, { "name": "Aurin Gaida-Albers", "url": "https://openalex.org/A5093923197", "inst": "Independent - affiliation not provided to SSRN" } ], "affiliations": [ "Independent - affiliation not provided to SSRN" ] }, { "uid": "arxiv:2402.07536v2", "arxiv_id": "2402.07536v2", "title": "FinLLM-B: When Large Language Models Meet Financial Breakout Trading", "authors": [ "Kang Zhang", "Osamu Yoshie", "Lichao Sun", "Weiran Huang" ], "posted": "2024-02-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.07536v2", "field": "finance", "role": "method", "bullets": [ "A newly assembled dataset of trading range breakout episodes in financial markets; asset coverage and sample size are not stated in the abstract.", "FinLLM-B, whose base model is not named, classifies true versus false breakouts and generates rationales through a multi-stage structure; answers are scored against the labelled dataset.", "Average accuracy of answers and rationale improves 49.97 percent over GPT-3.5 and 42.38 percent over ChatGPT-4, with 9.72 percent attributed to the multi-stage design." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "labelled breakout dataset, accuracy versus GPT baselines", "salience": 33, "edition": 13, "n": 1567, "authors_detailed": [ { "name": "Kang Zhang", "url": "https://openalex.org/A5102527125", "inst": "Zhejiang Normal University" }, { "name": "Osamu Yoshie", "url": "https://openalex.org/A5057487414", "inst": "Waseda University" }, { "name": "Sun, Lichao", "url": "", "inst": "" }, { "name": "Huang, Weiran", "url": "", "inst": "" } ], "affiliations": [ "Zhejiang Normal University", "Waseda University" ] }, { "uid": "arxiv:2402.07405v1", "arxiv_id": "2402.07405v1", "title": "Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English", "authors": [ "Xiao Zhang", "Ruoyu Xiang", "Chenhan Yuan", "Duanyu Feng", "Weiguang Han", "Alejandro Lopez-Lira", "Xiao-Yang Liu", "Sophia Ananiadou", "Min Peng", "Jimin Huang", "Qianqian Xie" ], "posted": "2024-02-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.07405v1", "field": "finance", "role": "method", "bullets": [ "A bilingual instruction dataset of over 144,000 Spanish and English samples from 15 datasets across 7 financial tasks, with the FLARE-ES benchmark of 21 datasets across 9 tasks.", "FinMA-ES, an instruction tuned financial model whose base is not named in the abstract, is evaluated against existing LLMs including GPT-4 on FLARE-ES labeled tasks.", "Existing models show a marked Spanish performance gap; FinMA-ES overtakes GPT-4 on Spanish financial tasks, and all datasets, models, and benchmarks are released." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": true, "validated": true, "validation_note": "FLARE-ES labeled benchmark", "salience": 38, "edition": 13, "n": 1630, "authors_detailed": [ { "name": "Xiao Zhang", "url": "https://openalex.org/A5100960091", "inst": "Jinan University" }, { "name": "Ruoyu Xiang", "url": "https://openalex.org/A5101307852", "inst": "Chongqing Normal University" }, { "name": "Chenhan Yuan", "url": "https://openalex.org/A5034535852", "inst": "University of Electronic Science and Technology of China" }, { "name": "Duanyu Feng", "url": "https://openalex.org/A5001345589", "inst": "National University of Singapore" }, { "name": "Weiguang Han", "url": "https://openalex.org/A5054465909", "inst": "Hebei University of Technology" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Xiaoyang Liu", "url": "https://openalex.org/A5100405221", "inst": "Tianjin University of Science and Technology" }, { "name": "Sophia Ananiadou", "url": "https://openalex.org/A5077976343", "inst": "University of Manchester" }, { "name": "Min Peng", "url": "https://openalex.org/A5059344600", "inst": "Ministry of Education" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" } ], "affiliations": [ "University of Florida", "Jinan University", "Chongqing Normal University", "University of Electronic Science and Technology of China", "National University of Singapore", "Hebei University of Technology", "Tianjin University of Science and Technology", "University of Manchester" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4708979", "doi": "10.2139/ssrn.4708979", "title": "A multifactor model using large language models and investor sentiment from photos and news: new evidence from China", "authors": [ "Junhuan Zhang", "Ziyan Zhang", "Jiaqi Wen" ], "posted": "2024-02-12", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4708979", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 35, "edition": 13, "bullets": [], "models": [], "validated": null, "n": 1774, "authors_detailed": [ { "name": "Junhuan Zhang", "url": "https://openalex.org/A5047607882", "inst": "Beihang University" }, { "name": "Ziyan Zhang", "url": "https://openalex.org/A5100658085", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Jiaqi Wen", "url": "https://openalex.org/A5071052291", "inst": "University of Technology Sydney" } ], "affiliations": [ "Beihang University", "Independent - affiliation not provided to SSRN", "University of Technology Sydney" ] }, { "uid": "doi:10.2139/ssrn.4708676", "doi": "10.2139/ssrn.4708676", "title": "The Promise and Perils of China's Regulation of Artificial Intelligence", "authors": [ "Angela Huyue Zhang" ], "posted": "2024-02-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4708676", "field": "economics", "role": "object", "bullets": [ "Analysis of China's AI regulatory framework covering algorithms, deepfakes, and generative AI services through 2024.", "No model used; paper examines the government's simultaneous roles as policymaker, investor, supplier, customer, and regulator of AI.", "China's AI regulation is markedly business-friendly despite strict information controls; leniency risks regulatory lags and potential AI-induced accidents." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 2924, "authors_detailed": [ { "name": "Angela Huyue Zhang", "url": "https://openalex.org/A5071118667", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4708466", "doi": "10.2139/ssrn.4708466", "title": "Prompting Diverse Ideas: Increasing AI Idea Variance", "authors": [ "Lennart Meincke", "Ethan R. Mollick", "Christian Terwiesch" ], "posted": "2024-02-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4708466", "field": "management", "role": "method", "bullets": [ "GPT-4 idea generation for new products for college students priced under $50, compared against human brainstorming groups.", "Tested multiple prompting methods including chain-of-thought on cosine similarity, unique idea count, and idea space exhaustion speed.", "Chain-of-thought prompting achieved highest idea diversity, approaching human-group levels, and generated the most unique ideas." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3185, "authors_detailed": [ { "name": "Lennart Meincke", "url": "https://openalex.org/A5003350421", "inst": "California University of Pennsylvania" }, { "name": "Ethan Mollick", "url": "https://openalex.org/A5061686034", "inst": "University of Pennsylvania" }, { "name": "Christian Terwiesch", "url": "https://openalex.org/A5089214194", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania", "California University of Pennsylvania" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2402.06698v1", "arxiv_id": "2402.06698v1", "title": "FNSPID: A Comprehensive Financial News Dataset in Time Series", "authors": [ "Zihan Dong", "Xinyu Fan", "Zhiyuan Peng" ], "posted": "2024-02-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.06698v1", "field": "finance", "role": "method", "bullets": [ "FNSPID matches 29.7 million stock prices with 15.7 million time aligned news items for 4,775 S&P 500 firms from 1999 to 2023, scraped from four financial news sites.", "The abstract does not say which model produces the sentiment scores attached to the news, and no check of those scores against human labels is reported.", "More data raises market prediction accuracy in the authors' experiments, sentiment adds a modest further gain on a transformer model, and the dataset ships with a reproducible update procedure." ], "bullet_provenance": "ai", "validated": false, "salience": 48, "edition": 13, "models": [], "n": 1773, "authors_detailed": [ { "name": "Zihan Dong", "url": "https://openalex.org/A5101282550", "inst": "North Carolina State University" }, { "name": "Fan Xinyu", "url": "https://openalex.org/A5102216621", "inst": "Hong Kong Polytechnic University" }, { "name": "Zhiyuan Peng", "url": "https://openalex.org/A5111129284", "inst": "" } ], "affiliations": [ "North Carolina State University", "Hong Kong Polytechnic University" ] }, { "uid": "doi:10.2139/ssrn.4692960", "doi": "10.2139/ssrn.4692960", "title": "Enhancing Continuous Auditing with Large Language Models: A Framework for Cross-Verification Using Exogenous Textual Data", "authors": [ "Huaxia Li", "Marcelo Machado de Freitas", "Heejae Lee", "Miklos Vasarhelyi" ], "posted": "2024-02-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4692960", "field": "accounting", "role": "method", "bullet_provenance": "none", "salience": 40, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 337, "authors_detailed": [ { "name": "Huaxia Li", "url": "https://openalex.org/A5001895964", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Marcelo Machado de Freitas", "url": "https://openalex.org/A5059934008", "inst": "Universidade Federal de Santa Catarina" }, { "name": "Heejae Lee", "url": "https://openalex.org/A5101800055", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Miklos A. Vasarhelyi", "url": "https://openalex.org/A5049215719", "inst": "Rutgers, The State University of New Jersey" } ], "affiliations": [ "Rutgers, The State University of New Jersey", "Universidade Federal de Santa Catarina" ] }, { "uid": "doi:10.2139/ssrn.4703905", "doi": "10.2139/ssrn.4703905", "title": "Seeing Less, Engaging More: Rethinking Early User Experience on GenAI Co-Creation Platforms–Findings from a Field Experiment", "authors": [ "Shenyang Jiang", "Akshat Lakhiwal", "Che-Wei Liu", "Jiang Duan" ], "posted": "2024-02-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4703905", "field": "management", "role": "object", "bullets": [ "Randomized field experiment on a generative AI content-generation platform, with a follow-up online experiment on user registration.", "Researchers manipulated fulfillment levels of AI-generated content shown before registration and measured engagement outcomes.", "Partial fulfillment outperformed full and no fulfillment in driving registration; curiosity and value-in-use jointly mediated the effect." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3361, "authors_detailed": [ { "name": "Shenyang Jiang", "url": "https://openalex.org/A5082312512", "inst": "Tongji University" }, { "name": "Akshat Lakhiwal", "url": "https://openalex.org/A5147187424", "inst": "Tongji University" }, { "name": "Che‐Wei Liu", "url": "https://openalex.org/A5064535992", "inst": "Indiana University Bloomington" }, { "name": "Jiang Duan", "url": "https://openalex.org/A5101518907", "inst": "Computing Center" } ], "affiliations": [ "Indiana University Bloomington", "Tongji University", "Computing Center" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4699231", "doi": "10.2139/ssrn.4699231", "title": "Unusual Financial Communication: ChatGPT, Earnings Calls, and Financial Markets", "authors": [ "Lars Beckmann", "Heiner Beckmeyer", "Ilias Filippou", "Stefan Menze", "Guofu Zhou" ], "posted": "2024-02-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4699231", "field": "finance", "role": "instrument", "bullets": [ "Earnings conference calls for US public firms, analyzing executive-analyst communication across 25 atypical dimensions beyond sentiment.", "ChatGPT identified dimensions of unusual communication style in how information is conveyed during earnings calls.", "Unusual communication predicts lower stock returns, higher volume and volatility, and downward analyst forecast revisions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 78, "n": 3360, "authors_detailed": [ { "name": "Lars Beckmann", "url": "https://openalex.org/A5062374017", "inst": "University of Münster" }, { "name": "Heiner Beckmeyer", "url": "https://openalex.org/A5062768527", "inst": "University of Münster" }, { "name": "Ilias Filippou", "url": "https://openalex.org/A5080266703", "inst": "Florida State University" }, { "name": "Stefan Menze", "url": "https://openalex.org/A5093889017", "inst": "University of Münster" }, { "name": "Guofu Zhou", "url": "https://openalex.org/A5012239666", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis", "University of Münster", "Florida State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3589334.3645611", "doi": "10.1145/3589334.3645611", "arxiv_id": "2402.03659v3", "title": "Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models", "authors": [ "Kelvin J. L. Koa", "Yunshan Ma", "Ritchie Ng", "Tat-Seng Chua" ], "posted": "2024-02-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.03659v3", "field": "finance", "role": "method", "bullets": [ "Stock movement classification from social media texts, extended to a portfolio construction exercise; tickers, sample size, and period are not stated in the abstract.", "A large language model, base not named, teaches itself to explain predictions through a self reflective agent and PPO training, removing the need for expert annotated explanations.", "The tuned model beats deep learning and LLM baselines on prediction accuracy and Matthews correlation for stock classification, with effectiveness also shown on portfolio metrics." ], "bullet_provenance": "ai", "validated": true, "validation_note": "stock movement labels, accuracy and MCC", "salience": 46, "edition": 13, "models": [], "n": 1628, "authors_detailed": [ { "name": "Kelvin J. L. Koa", "url": "https://openalex.org/A5092741731", "inst": "National University of Singapore" }, { "name": "Yunshan Ma", "url": "https://openalex.org/A5089377262", "inst": "National University of Singapore" }, { "name": "Ritchie Ng", "url": "https://openalex.org/A5005298162", "inst": "Eastspring Investments, Singapore, Singapore" }, { "name": "Tat‐Seng Chua", "url": "https://openalex.org/A5089404640", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore", "Eastspring Investments, Singapore, Singapore" ] }, { "uid": "arxiv:2402.04141v1", "arxiv_id": "2402.04141v1", "title": "Multi-line AI-assisted Code Authoring", "authors": [ "Omer Dunay", "Daniel Cheng", "Adam Tait", "Parth Thakkar", "Peter C Rigby", "Andy Chiu", "Imad Ahmad", "Arun Ganesan", "Chandra Maddila", "Vijayaraghavan Murali", "Ali Tayyebi", "Nachiappan Nagappan" ], "posted": "2024-02-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.04141v1", "field": "management", "role": "object", "bullets": [ "Tens of thousands of Meta engineers receiving inline code suggestions from the CodeCompose tool, studied through large scale deployment experiments as suggestions moved from single line to multi line.", "An LLM, not named in the abstract, generates multi line suggestions; hosting optimizations cut suggestion latency by 2.5 times to limit disruption while typing.", "Multi line suggestions account for 42 percent of accepted characters and nearly double keystrokes saved from 9 to 17 percent; under 1 percent of engineers opt out." ], "bullet_provenance": "ai", "salience": 45, "edition": 13, "models": [], "validated": null, "n": 1629, "authors_detailed": [ { "name": "Omer Dunay", "url": "https://openalex.org/A5093894930", "inst": "Meta (United States)" }, { "name": "Daniel Cheng", "url": "https://openalex.org/A5103283910", "inst": "Meta (United States)" }, { "name": "A. Tait", "url": "https://openalex.org/A5025310717", "inst": "Concordia University" }, { "name": "Parth Thakkar", "url": "https://openalex.org/A5005473713", "inst": "Meta (United States)" }, { "name": "Peter C. Rigby", "url": "https://openalex.org/A5020289098", "inst": "Meta (United States)" }, { "name": "Andy Chiu", "url": "https://openalex.org/A5061946753", "inst": "Google (United States)" }, { "name": "Imad A. Haidar Ahmad", "url": "https://openalex.org/A5047087325", "inst": "Amgen (United States)" }, { "name": "Arun Ganesan", "url": "https://openalex.org/A5059966098", "inst": "Meta (United States)" }, { "name": "Chandra Maddila", "url": "https://openalex.org/A5088100364", "inst": "Bellevue Hospital Center" }, { "name": "Vijayaraghavan Murali", "url": "https://openalex.org/A5051545971", "inst": "Meta (United States)" }, { "name": "Ali Tayyebi", "url": "https://openalex.org/A5103182882", "inst": "Meta (United States)" }, { "name": "Nachiappan Nagappan", "url": "https://openalex.org/A5101612061", "inst": "BC Platforms (Finland)" } ], "affiliations": [ "Meta (United States)", "Concordia University", "Google (United States)", "Amgen (United States)", "BC Platforms (Finland)" ] }, { "uid": "arxiv:2402.03755v1", "arxiv_id": "2402.03755v1", "title": "QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model", "authors": [ "Saizhuo Wang", "Hang Yuan", "Lionel M. Ni", "Jian Guo" ], "posted": "2024-02-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.03755v1", "field": "finance", "role": "method", "bullets": [ "Quantitative investment signal mining, where an autonomous agent accumulates a domain knowledge base from repeated interaction with real world outcomes.", "An LLM based agent, model not named, refines answers against its knowledge base in an inner loop and tests them on markets in an outer loop, with a claimed efficiency guarantee.", "QuantAgent is reported to surface viable trading signals and improve forecast accuracy as the loop runs; no performance figures appear in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 13, "models": [], "n": 1716, "authors_detailed": [ { "name": "Saizhuo Wang", "url": "https://openalex.org/A5093887434", "inst": "" }, { "name": "Hang Yuan", "url": "https://openalex.org/A5093887435", "inst": "" }, { "name": "Lionel M. Ni", "url": "https://openalex.org/A5021357065", "inst": "Argonne National Laboratory" }, { "name": "Jian Guo", "url": "https://openalex.org/A5093887436", "inst": "" } ], "affiliations": [ "Argonne National Laboratory" ] }, { "uid": "doi:10.2139/ssrn.4716669", "doi": "10.2139/ssrn.4716669", "title": "Copyright Policy Options for Generative Artificial Intelligence", "authors": [ "Joshua Gans" ], "posted": "2024-02-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4716669", "field": "economics", "role": "object", "bullets": [ "Economic model analyzing copyright regimes for generative AI trained on protected content, comparing small and large model settings.", "Theoretical framework examined how copyright protection versus fair use affects AI training quality and generated content quality.", "Copyright protection yields superior welfare for small AI models; for large models, an ex-post fair use mechanism achieves highest expected welfare." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 2465, "authors_detailed": [ { "name": "Joshua S. Gans", "url": "https://openalex.org/A5055795427", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4698153", "doi": "10.2139/ssrn.4698153", "title": "Hypothesizing Multimodal Influence: Assessing the Impact of Textual and Non-Textual Data on Financial Instrument Pricing Using NLP and Generative AI", "authors": [ "Karolina Bolesta", "Gabin Taibi", "Codruta Mare", "Branka Hadji Misheva", "Christian Hopp", "Joerg Osterrieder" ], "posted": "2024-02-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4698153", "field": "finance", "role": "method", "bullets": [ "Conceptual framework for analyzing textual and non-textual data in financial securities pricing using NLP and generative AI.", "Proposes using generative AI to expand multimodal textual analysis incorporating images, video, and audio alongside traditional text.", "Hypothesizes that systematic multimodal analysis could enhance financial asset management decisions; no empirical results reported." ], "bullet_provenance": "ai", "validated": false, "salience": 20, "models": [], "n": 2923, "authors_detailed": [ { "name": "Karolina Bolesta", "url": "https://openalex.org/A5027689692", "inst": "SGH Warsaw School of Economics" }, { "name": "Gabin Taibi", "url": "https://openalex.org/A5093865672", "inst": "Bern University of Applied Sciences" }, { "name": "Codruța Mare", "url": "https://openalex.org/A5011790164", "inst": "Babeș-Bolyai University" }, { "name": "Branka Hadji Misheva", "url": "https://openalex.org/A5039733264", "inst": "ZHAW Zurich University of Applied Sciences" }, { "name": "Christian Hopp", "url": "https://openalex.org/A5073670054", "inst": "RWTH Aachen University" }, { "name": "Joerg Osterrieder", "url": "https://openalex.org/A5032430973", "inst": "University of Applied Sciences of the Grisons" } ], "affiliations": [ "SGH Warsaw School of Economics", "Bern University of Applied Sciences", "Babeș-Bolyai University", "ZHAW Zurich University of Applied Sciences", "RWTH Aachen University", "University of Applied Sciences of the Grisons" ] }, { "uid": "arxiv:2402.05131v3", "arxiv_id": "2402.05131v3", "title": "Financial Report Chunking for Effective Retrieval Augmented Generation", "authors": [ "Antonio Jimeno Yepes", "Yao You", "Jan Milczek", "Sebastian Laverde", "Renyu Li" ], "posted": "2024-02-05", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.05131v3", "field": "finance", "role": "method", "bullets": [ "Financial reports segmented by structural element types identified by document-understanding models, evaluated on a question-answering task over financial reporting content.", "RAG pipeline used element-type-based chunking rather than paragraph-level segmentation, with a framework assessing how each structural element type contributes to retrieval quality and answer accuracy.", "Structural-element chunking improved RAG accuracy on financial reports compared to paragraph-level methods, eliminating the need for chunk-size hyperparameter tuning across different document formats." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "financial report QA task accuracy comparison across chunking methods", "salience": 45, "n": 3896, "authors_detailed": [ { "name": "Antonio Jimeno Yepes", "url": "https://openalex.org/A5077681143", "inst": "The University of Melbourne" }, { "name": "Yao You", "url": "https://openalex.org/A5093902724", "inst": "" }, { "name": "Jan Milczek", "url": "https://openalex.org/A5093902725", "inst": "" }, { "name": "Sebastian Laverde", "url": "https://openalex.org/A5093902726", "inst": "" }, { "name": "Leah Li", "url": "https://openalex.org/A5111127659", "inst": "" } ], "affiliations": [ "The University of Melbourne" ] }, { "uid": "arxiv:2402.02392v3", "arxiv_id": "2402.02392v3", "title": "DeLLMa: Decision Making Under Uncertainty with Large Language Models", "authors": [ "Ollie Liu", "Deqing Fu", "Dani Yogatama", "Willie Neiswanger" ], "posted": "2024-02-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.02392v3", "field": "management", "role": "method", "bullets": [ "Decision problems under uncertainty drawn from realistic environments; the abstract does not enumerate the domains or the number of test cases.", "A multi-step reasoning procedure grounded in decision and utility theory guides unnamed leading LLMs; accuracy is scored against the environments' correct choices and checked with human evaluations.", "DeLLMa raises decision accuracy by up to 40 percent over competing prompting methods, and performance improves further with more test-time compute." ], "bullet_provenance": "ai", "validated": true, "validation_note": "decision environments with scored outcomes, human evals", "salience": 35, "edition": 13, "models": [], "n": 1594, "authors_detailed": [ { "name": "Ollie Liu", "url": "https://openalex.org/A5041927922", "inst": "University of Southern California" }, { "name": "Deqing Fu", "url": "https://openalex.org/A5113113923", "inst": "University of Southern California" }, { "name": "Dani Yogatama", "url": "https://openalex.org/A5060347694", "inst": "Harvard University Press" }, { "name": "Willie Neiswanger", "url": "https://openalex.org/A5073310637", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California", "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1007/s00521-024-10495-6", "doi": "10.1007/s00521-024-10495-6", "arxiv_id": "2402.02315v1", "title": "A Survey of Large Language Models in Finance (FinLLMs)", "authors": [ "Jean Lee", "Nicholas Stevens", "Soyeon Caren Han", "Minseok Song" ], "posted": "2024-02-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.02315v1", "field": "finance", "role": "method", "bullets": [ "A survey of financial language models tracing the path from early pretrained language models through the GPT series and open-source LLMs to current financial LLMs.", "No new model is trained. The paper compares training methods, data, and fine-tuning across financial models and summarizes evaluations on six benchmark tasks.", "Eight advanced financial NLP tasks are proposed for future systems, datasets and benchmarks are compiled on GitHub, and hallucination, privacy, and efficiency are flagged as open challenges." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 35, "edition": 13, "validated": null, "n": 1684, "authors_detailed": [ { "name": "Jean Lee", "url": "https://openalex.org/A5101938644", "inst": "The University of Sydney" }, { "name": "Nicholas Stevens", "url": "https://openalex.org/A5026062107", "inst": "NRS Technology, Sydney, Australia" }, { "name": "Soyeon Caren Han", "url": "https://openalex.org/A5084419965", "inst": "The University of Sydney" } ], "affiliations": [ "The University of Sydney", "NRS Technology, Sydney, Australia" ] }, { "uid": "doi:10.1609/aies.v7i1.31758", "doi": "10.1609/aies.v7i1.31758", "arxiv_id": "2402.01766v3", "title": "LLM Voting: Human Choices and AI Collective Decision Making", "authors": [ "Joshua C. Yang", "Damian Dailisan", "Marcin Korecki", "Carina I. Hausladen", "Dirk Helbing" ], "posted": "2024-01-31", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.01766v3", "field": "economics", "role": "agent", "bullets": [ "A human voting experiment provides the baseline, and matching voting experiments are run with LLM agents under different voting methods, ballot orders, personas and temperatures.", "GPT-4 and LLaMA-2 cast votes as agents; behavior is compared with the human patterns rather than validated against any ground truth.", "Voting method and presentation order sway LLM choices, personas reduce some bias, and temperature trades preference diversity against alignment, pointing to less diverse collective outcomes." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "salience": 50, "edition": 13, "validated": null, "n": 1550, "authors_detailed": [ { "name": "Joshua C. Yang", "url": "https://openalex.org/A5011277077", "inst": "ETH Zurich" }, { "name": "Damian Dailisan", "url": "https://openalex.org/A5022147804", "inst": "ETH Zurich" }, { "name": "Marcin Korecki", "url": "https://openalex.org/A5033987904", "inst": "ETH Zurich" }, { "name": "Carina Ines Hausladen", "url": "https://openalex.org/A5056226566", "inst": "ETH Zurich" }, { "name": "Dirk Helbing", "url": "https://openalex.org/A5061160911", "inst": "ETH Zurich" } ], "affiliations": [ "ETH Zurich" ] }, { "uid": "doi:10.1145/3630106.3658933", "doi": "10.1145/3630106.3658933", "arxiv_id": "2402.01732v2", "title": "Identifying and Improving Disability Bias in GPT-Based Resume Screening", "authors": [ "Kate Glazko", "Yusuf Mohammed", "Ben Kosa", "Venkatesh Potluri", "Jennifer Mankoff" ], "posted": "2024-01-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.01732v2", "field": "management", "role": "agent", "bullets": [ "A resume audit comparing an original CV with the same CV plus a disability related award, scholarship, panel presentation and membership, ranked in a hiring style screen.", "GPT-4 through ChatGPT ranks each pair, and a custom GPT trained on disability justice and DEI principles is tested as a mitigation; justifications are read qualitatively for ableism.", "GPT-4 favors the unenhanced CV, drawing on direct and indirect ableist reasoning, and the custom instructed variant quantifiably reduces the prejudice." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 58, "edition": 13, "validated": null, "n": 1715, "authors_detailed": [ { "name": "Kate Glazko", "url": "https://openalex.org/A5092810046", "inst": "University of Washington" }, { "name": "Yusuf Mohammed", "url": "https://openalex.org/A5100538688", "inst": "University of Washington" }, { "name": "Ben Kosa", "url": "https://openalex.org/A5093885017", "inst": "University of Washington" }, { "name": "Venkatesh Potluri", "url": "https://openalex.org/A5029180332", "inst": "University of Washington" }, { "name": "Jennifer Mankoff", "url": "https://openalex.org/A5040915036", "inst": "University of Washington" } ], "affiliations": [ "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.4693849", "doi": "10.2139/ssrn.4693849", "title": "Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection", "authors": [ "Georgios Fatouros", "Konstantinos Metaxas", "John Soldatos", "Dimosthenis Kyriazis" ], "posted": "2024-01-28", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4693849", "field": "finance", "role": "agent", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 336, "authors_detailed": [ { "name": "George Fatouros", "url": "https://openalex.org/A5096969249", "inst": "University of Piraeus" }, { "name": "Konstantinos Metaxas", "url": "https://openalex.org/A5012785532", "inst": "Athens Information Technology" }, { "name": "John Soldatos", "url": "https://openalex.org/A5069749526", "inst": "University of Nicosia" }, { "name": "Dimosthenis Kyriazis", "url": "https://openalex.org/A5020920798", "inst": "University of Piraeus" } ], "affiliations": [ "University of Piraeus", "Athens Information Technology", "University of Nicosia" ] }, { "uid": "arxiv:2402.01722v1", "arxiv_id": "2402.01722v1", "title": "Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately", "authors": [ "Liang Zhang", "Katherine Jijo", "Spurthi Setty", "Eden Chung", "Fatima Javid", "Natan Vidra", "Tommy Clifford" ], "posted": "2024-01-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2402.01722v1", "field": "finance", "role": "method", "bullets": [ "Financial question answering and information extraction, evaluated on the FinanceBench and RAG Instruct Benchmark Tester datasets; sample sizes are not stated.", "GPT-3.5, GPT4ALL, LLaMA2 and Claude are fine-tuned and combined with retrieval augmented generation, scored with cosine similarity, Rouge-L and LLM grading against benchmark references.", "Fine-tuned models answer more accurately than zero-shot baselines, and fine-tuning combined with retrieval performs best; the abstract reports no numerical results." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinanceBench and RAG benchmark references, Rouge-L and similarity metrics", "salience": 30, "edition": 13, "n": 1527, "authors_detailed": [ { "name": "Liang Zhang", "url": "https://openalex.org/A5100425314", "inst": "China University of Political Science and Law" }, { "name": "Katherine Jijo", "url": "https://openalex.org/A5093751284", "inst": "" }, { "name": "S. Pallam Setty", "url": "https://openalex.org/A5110652557", "inst": "Andhra University" }, { "name": "Eden Chung", "url": "https://openalex.org/A5101294368", "inst": "" }, { "name": "Fatima Javid", "url": "https://openalex.org/A5093876139", "inst": "" }, { "name": "Natan Vidra", "url": "https://openalex.org/A5093751283", "inst": "Cornell University" }, { "name": "Clifford, Tommy", "url": "", "inst": "" } ], "affiliations": [ "Cornell University", "China University of Political Science and Law", "Andhra University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2401.15328v2", "arxiv_id": "2401.15328v2", "title": "Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance", "authors": [ "Adrian Theuma", "Ehsan Shareghi" ], "posted": "2024-01-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.15328v2", "field": "finance", "role": "method", "bullets": [ "Financial question answering over tables and text, built on financial domain QA datasets; each question is routed to internal reasoning or an external tool.", "A LLaMA 2 13B Chat model is fine-tuned as task router and solver; tool augmentation lifts accuracy 35.2 percent over the base model and 5.06 percent over SFT-only.", "The tool-equipped model is competitive with GPT-3.5 on the same tasks, in what the authors call the first tool augmentation study for finance." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "financial QA datasets, accuracy gains reported", "salience": 42, "edition": 13, "n": 1549, "authors_detailed": [ { "name": "Adrian Theuma", "url": "https://openalex.org/A5093836155", "inst": "Monash University" }, { "name": "Ehsan Shareghi", "url": "https://openalex.org/A5086032589", "inst": "University of Cambridge" } ], "affiliations": [ "University of Cambridge", "Monash University" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4691788", "doi": "10.2139/ssrn.4691788", "title": "Intellectual Capital and Digital Platforms: An Appraisal-Based Perspective of a Large Language Model vs. an Online Community", "authors": [ "Houping Xiao", "Aaron Baird", "Yusen Xia" ], "posted": "2024-01-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4691788", "field": "management", "role": "object", "bullets": [ "Experiment with consumers appraising answers to technical questions from an LLM versus an online community platform.", "LLM-generated and online-community answers to identical technical questions were evaluated by consumers on appraisal dimensions.", "LLM answers were appraised higher than online community answers in all cases, with heterogeneity for specialized versus general knowledge." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 45, "validated": null, "n": 2464, "authors_detailed": [ { "name": "Houping Xiao", "url": "https://openalex.org/A5069886022", "inst": "Georgia State University" }, { "name": "Aaron Baird", "url": "https://openalex.org/A5020152067", "inst": "Georgia State University" }, { "name": "Yusen Xia", "url": "https://openalex.org/A5038750761", "inst": "Georgia State University" } ], "affiliations": [ "Georgia State University" ] }, { "uid": "arxiv:2401.14777v1", "arxiv_id": "2401.14777v1", "title": "Large Language Model Adaptation for Financial Sentiment Analysis", "authors": [ "Pau Rodriguez Inserte", "Mariam Nakhlé", "Raheel Qader", "Gaetan Caillaut", "Jingshu Liu" ], "posted": "2024-01-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.14777v1", "field": "finance", "role": "method", "bullets": [ "Financial documents and instruction data used to adapt two sub-1.5B-parameter foundation models for finance tasks, with emphasis on sentiment analysis.", "The unnamed small models are fine-tuned on financial text and instructions, with additional instructions generated synthetically by LLMs; evaluation details are not given in the abstract.", "Small adapted models are reported to match much larger models on financial sentiment while using fewer parameters and less data; no figures are stated." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "financial sentiment benchmarks, figures not in abstract", "salience": 25, "edition": 13, "models": [], "n": 1593, "authors_detailed": [ { "name": "Pau Rodriguez Inserte", "url": "https://openalex.org/A5083273941", "inst": "Lingua et Machina (France)" }, { "name": "Mariam Nakhlé", "url": "https://openalex.org/A5093817144", "inst": "Institut polytechnique de Grenoble" }, { "name": "Raheel Qader", "url": "https://openalex.org/A5030717896", "inst": "Centre National de la Recherche Scientifique" }, { "name": "Gaëtan Caillaut", "url": "https://openalex.org/A5093817145", "inst": "Lingua et Machina (France)" }, { "name": "Jingshu Liu", "url": "https://openalex.org/A5007543305", "inst": "PLA 306 Hospital" } ], "affiliations": [ "Lingua et Machina (France)", "Institut polytechnique de Grenoble", "Centre National de la Recherche Scientifique" ] }, { "uid": "doi:10.1145/3715928.3737481", "doi": "10.1145/3715928.3737481", "arxiv_id": "2401.13481v3", "title": "How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas: Evidence From a Large, Dynamic Experiment", "authors": [ "Joshua Ashkinaze", "Julia Mendelsohn", "Li Qiwei", "Ceren Budak", "Eric Gilbert" ], "posted": "2024-01-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.13481v3", "field": "management", "role": "object", "bullets": [ "More than 800 participants in over 40 countries brainstormed ideas after viewing examples from ChatGPT or from earlier participants, with exposure level and AI disclosure varied across conditions.", "ChatGPT supplied the AI idea stimuli; each condition fed participants' ideas forward as stimuli for later participants, so effects compound as in cultural transmission.", "High AI exposure increased the amount and rate of change of collective idea diversity without changing individual idea creativity; disclosure had no main effect." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 58, "edition": 13, "validated": null, "n": 1627, "authors_detailed": [ { "name": "Joshua Ashkinaze", "url": "https://openalex.org/A5048428275", "inst": "University of Michigan" }, { "name": "Julia Mendelsohn", "url": "https://openalex.org/A5038833789", "inst": "University of Maryland, College Park" }, { "name": "Li Qiwei", "url": "https://openalex.org/A5109026156", "inst": "University of Michigan" }, { "name": "Ceren Budak", "url": "https://openalex.org/A5086827245", "inst": "University of Michigan" }, { "name": "Éric Gilbert", "url": "https://openalex.org/A5024795472", "inst": "University of Michigan" } ], "affiliations": [ "University of Maryland, College Park", "University of Michigan" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1038/s42256-024-00976-7", "doi": "10.1038/s42256-024-00976-7", "arxiv_id": "2401.13835v2", "title": "What Large Language Models Know and What People Think They Know", "authors": [ "Mark Steyvers", "Heliodoro Tejeda", "Aakriti Kumar", "Catarina Belem", "Sheer Karny", "Xinyue Hu", "Lukas Mayer", "Padhraic Smyth" ], "posted": "2024-01-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.13835v2", "field": "management", "role": "object", "bullets": [ "Human participants answer multiple choice and short answer questions with LLM assistance, and their confidence in the model's answers is compared with the model's own confidence.", "An LLM, not named in the abstract, supplies answers and explanations whose length and style are manipulated; outcomes are calibration and discrimination gaps between users and model.", "Users overestimate answer accuracy under default explanations, longer explanations inflate confidence without adding accuracy, and rewriting explanations to reflect model confidence narrows both gaps." ], "bullet_provenance": "ai", "salience": 55, "edition": 13, "models": [], "validated": null, "n": 1714, "authors_detailed": [ { "name": "Mark Steyvers", "url": "https://openalex.org/A5051768325", "inst": "University of California, Irvine" }, { "name": "Heliodoro Tejeda", "url": "https://openalex.org/A5075396743", "inst": "University of California, Irvine" }, { "name": "Aakriti Kumar", "url": "https://openalex.org/A5040234818", "inst": "University of California, Irvine" }, { "name": "Catarina Belém", "url": "https://openalex.org/A5046013336", "inst": "University of California, Irvine" }, { "name": "Sheer Karny", "url": "https://openalex.org/A5093800032", "inst": "University of California, Irvine" }, { "name": "Xinyue Hu", "url": "https://openalex.org/A5109792151", "inst": "University of California, Irvine" }, { "name": "Lukas William Mayer", "url": "https://openalex.org/A5010566604", "inst": "University of California, Irvine" }, { "name": "Padhraic Smyth", "url": "https://openalex.org/A5077460655", "inst": "University of California, Irvine" } ], "affiliations": [ "University of California, Irvine" ] }, { "uid": "doi:10.2139/ssrn.4679414", "doi": "10.2139/ssrn.4679414", "title": "Towards Automating Causal Discovery in Financial Markets and Beyond", "authors": [ "Alik Sokolov", "Fabrizzio Sabelli", "Behzad Azadie Faraz", "Wuding Li", "Luis A. Seco" ], "posted": "2024-01-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4679414", "field": "finance", "role": "instrument", "bullets": [ "Financial market causal factor analysis leveraging implicit world knowledge in state-of-the-art LLMs for causal discovery.", "LLMs automated expert judgment to specify and analyze causal models for financial markets in an end-to-end framework.", "LLM-based causal discovery outperformed conventional data-driven methods in revealing causal dynamics in financial data." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2463, "authors_detailed": [ { "name": "Alik Sokolov", "url": "https://openalex.org/A5103922287", "inst": "University of Toronto" }, { "name": "Fabrizzio Sabelli", "url": "https://openalex.org/A5093784548", "inst": "University of Toronto" }, { "name": "Behzad Azadie faraz", "url": "https://openalex.org/A5093784549", "inst": "Sharif University of Technology" }, { "name": "W.K. Li", "url": "https://openalex.org/A5111118052", "inst": "University of Toronto" }, { "name": "Luis Seco", "url": "https://openalex.org/A5064196534", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto", "Sharif University of Technology" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4703367", "doi": "10.2139/ssrn.4703367", "title": "Labor Space: Unifying Representation of the Labor Market via Large Language Models", "authors": [ "Seongwoon Kim", "Yong-Yeol Ahn", "Jaehyuk Park" ], "posted": "2024-01-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4703367", "field": "economics", "role": "instrument", "bullets": [ "U.S. labor market entities including industries, occupations, skills, and firms mapped into a unified vector space.", "Fine-tuned LLM produces embeddings of heterogeneous labor market entities, enabling cross-type positioning on economic axes and vector arithmetic.", "Labor Space enables estimation of economic shock propagation across entity types and positioning of heterogeneous units on dimensions such as Manufacturing-Healthcare." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 55, "n": 2837, "authors_detailed": [ { "name": "Seongwoon Kim", "url": "https://openalex.org/A5091243476", "inst": "Korea Development Institute" }, { "name": "Yong‐Yeol Ahn", "url": "https://openalex.org/A5059236838", "inst": "Indiana University Bloomington" }, { "name": "Jaehyuk Park", "url": "https://openalex.org/A5101267371", "inst": "Korea Development Institute" } ], "affiliations": [ "Indiana University Bloomington", "Korea Development Institute" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4695075", "doi": "10.2139/ssrn.4695075", "title": "Multi-Agent Systems and Foundation Models Enable Autonomous Supply Chains: Opportunities and Challenges ⋆", "authors": [ "Liming Xu", "Sara Almahri", "Stephen Mak", "Alexandra Brintrup" ], "posted": "2024-01-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4695075", "field": "management", "role": "object", "bullets": [ "Conceptual exploration of foundation models and multi-agent systems for supply chain management, motivated by COVID and geopolitical disruptions.", "No model applied; the paper proposes how foundation models could create generalist supply chain agents with multi-faceted decision-making capabilities.", "Foundation models may enable self-orchestrating autonomous supply chains with heightened resilience; identified key challenges and future research directions for convergence." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3359, "authors_detailed": [ { "name": "Liming Xu", "url": "https://openalex.org/A5101937351", "inst": "University of Cambridge" }, { "name": "Sara AlMahri", "url": "https://openalex.org/A5059407088", "inst": "University of Cambridge" }, { "name": "Stephen Mak", "url": "https://openalex.org/A5025573840", "inst": "University of Cambridge" }, { "name": "Alexandra Brintrup", "url": "https://openalex.org/A5075872953", "inst": "Trinity College" } ], "affiliations": [ "University of Cambridge", "Trinity College" ], "prestige": true }, { "uid": "arxiv:2401.12773v1", "arxiv_id": "2401.12773v1", "title": "Generative AI Triggers Welfare-Reducing Decisions in Humans", "authors": [ "Fabian Dvorak", "Regina Stumpf", "Sebastian Fehrler", "Urs Fischbacher" ], "posted": "2024-01-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.12773v1", "field": "economics", "role": "object", "bullets": [ "A pre-registered online experiment with 3,552 participants playing two player economic games that measure fairness, trust, trustworthiness, cooperation, and coordination.", "ChatGPT takes over one player's decisions in treatment conditions, and disclosure of the delegation is varied; the model is the treatment, not a measurement device.", "Play turns less fair, trusting, and cooperative when partners are known to be ChatGPT, while uncertainty about the partner removes the loss, so transparency itself reduces welfare." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "edition": 13, "validated": null, "n": 1772, "authors_detailed": [ { "name": "Fabian Dvorak", "url": "https://openalex.org/A5018364361", "inst": "University of Konstanz" }, { "name": "Regina Stumpf", "url": "https://openalex.org/A5113104258", "inst": "" }, { "name": "Sebastian Fehrler", "url": "https://openalex.org/A5045065074", "inst": "University of Bremen" }, { "name": "Urs Fischbacher", "url": "https://openalex.org/A5065933751", "inst": "Google (United States)" } ], "affiliations": [ "University of Konstanz", "University of Bremen", "Google (United States)" ] }, { "uid": "arxiv:2401.12652v1", "arxiv_id": "2401.12652v1", "title": "From Numbers to Words: Multi-Modal Bankruptcy Prediction Using the ECL Dataset", "authors": [ "Henri Arno", "Klaas Mulier", "Joke Baeck", "Thomas Demeester" ], "posted": "2024-01-23", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.12652v1", "field": "finance", "role": "instrument", "bullets": [ "Novel ECL dataset of US corporate 10K filings combining textual and numerical data with binary bankruptcy labels.", "GPT-based models extracted summaries from filing text; classical and neural models used textual and numerical features for bankruptcy prediction; GPT tested zero-shot.", "Textual and numerical modalities are complementary for bankruptcy prediction; GPT zero-shot classification performed poorly compared to supervised approaches." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "bankruptcy prediction accuracy on ECL dataset", "salience": 60, "n": 3358, "authors_detailed": [ { "name": "Henri Arno", "url": "https://openalex.org/A5078145281", "inst": "Ghent University Hospital" }, { "name": "Klaas Mulier", "url": "https://openalex.org/A5040984765", "inst": "Ghent University Hospital" }, { "name": "Joke Baeck", "url": "https://openalex.org/A5044059712", "inst": "Ghent University Hospital" }, { "name": "Thomas Demeester", "url": "https://openalex.org/A5075509168", "inst": "Imec the Netherlands" } ], "affiliations": [ "Imec the Netherlands" ] }, { "uid": "arxiv:2401.11641v4", "arxiv_id": "2401.11641v4", "title": "Revolutionizing Finance with LLMs: An Overview of Applications and Insights", "authors": [ "Huaqin Zhao", "Zhengliang Liu", "Zihao Wu", "Yiwei Li", "Tianze Yang", "Peng Shu", "Shaochen Xu", "Haixing Dai", "Lin Zhao", "Hanqi Jiang", "Yi Pan", "Junhao Chen", "Yifan Zhou", "Zeyu Zhang", "Ruitong Sun", "Gengchen Mai", "Ninghao Liu", "Tianming Liu" ], "posted": "2024-01-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.11641v4", "field": "finance", "role": "method", "bullets": [ "A survey of LLM applications across financial tasks such as report generation, market forecasting, sentiment analysis and personalized advice, paired with holistic tests using natural language instructions.", "GPT-4 is probed with combined task instructions; the paper reports that it follows prompts effectively but describes no benchmark, ground truth or accuracy figure.", "The authors map current uses and research prospects for finance practitioners and LLM researchers; no quantitative findings are reported in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 32, "edition": 13, "n": 1536, "authors_detailed": [ { "name": "Huaqin Zhao", "url": "https://openalex.org/A5109676912", "inst": "" }, { "name": "Zhengliang Liu", "url": "https://openalex.org/A5101295878", "inst": "" }, { "name": "Zihao Wu", "url": "https://openalex.org/A5103044470", "inst": "Hefei University of Technology" }, { "name": "Yiwei Li", "url": "https://openalex.org/A5100633404", "inst": "University of Electronic Science and Technology of China" }, { "name": "Tianze Yang", "url": "https://openalex.org/A5113103130", "inst": "" }, { "name": "Peng Shu", "url": "https://openalex.org/A5111117348", "inst": "Jinan University" }, { "name": "Shaochen Xu", "url": "https://openalex.org/A5109676913", "inst": "" }, { "name": "Haixing Dai", "url": "https://openalex.org/A5102608410", "inst": "" }, { "name": "Lin Zhao", "url": "https://openalex.org/A5101377271", "inst": "Wenzhou Medical University" }, { "name": "Jiang, Hanqi", "url": "", "inst": "" }, { "name": "Pan, Yi", "url": "", "inst": "" }, { "name": "Chen, Junhao", "url": "", "inst": "" }, { "name": "Zhou, Yifan", "url": "", "inst": "" }, { "name": "Zhang, Zeyu", "url": "", "inst": "" }, { "name": "Sun, Ruitong", "url": "", "inst": "" }, { "name": "Mai, Gengchen", "url": "", "inst": "" }, { "name": "Liu, Ninghao", "url": "", "inst": "" }, { "name": "Liu, Tianming", "url": "", "inst": "" } ], "affiliations": [ "Hefei University of Technology", "University of Electronic Science and Technology of China", "Jinan University", "Wenzhou Medical University" ] }, { "uid": "doi:10.2139/ssrn.4702114", "doi": "10.2139/ssrn.4702114", "title": "Does AI Cheapen Talk? Theory and Evidence From Global Entrepreneurship and Hiring", "authors": [ "Bo Cowgill", "Pablo Hernandez-Lagos", "Nataliya Wright" ], "posted": "2024-01-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4702114", "field": "economics", "role": "object", "bullets": [ "Experiments in hiring and startup investing contexts examining how sender access to ChatGPT affects evaluator screening accuracy.", "ChatGPT used by applicants and entrepreneurs to produce job applications and entrepreneurial pitches evaluated by employers and investors.", "Sender access to ChatGPT lowered screening accuracy by 4-9% overall but improved accuracy for senders from non-English-speaking countries." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 2922, "authors_detailed": [ { "name": "Bo Cowgill", "url": "https://openalex.org/A5008658368", "inst": "Columbia University" }, { "name": "Pablo Hernández‐Lagos", "url": "https://openalex.org/A5061929756", "inst": "Yeshiva University" }, { "name": "Nataliya Langburd Wright", "url": "https://openalex.org/A5078653741", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "Yeshiva University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2401.11888v1", "arxiv_id": "2401.11888v1", "title": "Multimodal Deep Learning of Word-of-Mouth Text and Demographics to Predict Customer Rating: Handling Consumer Heterogeneity in Marketing", "authors": [ "Junichiro Niimi" ], "posted": "2024-01-22", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.11888v1", "field": "management", "role": "instrument", "bullets": [ "Online product reviews paired with consumer demographic profiles, multimodal deep learning framework for marketing analytics.", "Pretrained language models processed review text while a parallel network ingested demographics; multiple architectures and hyperparameters compared for rating prediction.", "Multimodal learning combining text and consumer profiles outperformed single-modality models, capturing consumer heterogeneity that text alone missed." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 32, "n": 3357, "authors_detailed": [ { "name": "Junichiro Niimi", "url": "https://openalex.org/A5029152172", "inst": "RIKEN Center for Advanced Intelligence Project" } ], "affiliations": [ "RIKEN Center for Advanced Intelligence Project" ] }, { "uid": "arxiv:2401.11011v1", "arxiv_id": "2401.11011v1", "title": "BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks", "authors": [ "Valentina Aparicio", "Daniel Gordon", "Sebastian G. Huayamares", "Yuhuai Luo" ], "posted": "2024-01-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.11011v1", "field": "finance", "role": "method", "bullets": [ "Public press releases and financial text around clinical readouts and regulatory decisions that moved biotechnology stock prices; sample size and period are not stated in the abstract.", "BioBERT, a biomedical BERT variant, is fine tuned on financial text corpora to score sentiment; the abstract reports no validation against labeled data.", "The resulting model, BioFinBERT, is applied to text around price inflection points; no quantitative accuracy or market finding is reported in the abstract." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 30, "edition": 13, "n": 1625, "authors_detailed": [ { "name": "Valentina Aparicio", "url": "https://openalex.org/A5052881996", "inst": "Queen Mary University of London" }, { "name": "Daniel V. Gordon", "url": "https://openalex.org/A5072404107", "inst": "Albany College of Pharmacy and Health Sciences" }, { "name": "Sebastian G. Huayamares", "url": "https://openalex.org/A5070268860", "inst": "The Wallace H. Coulter Department of Biomedical Engineering" }, { "name": "Yuhuai Luo", "url": "https://openalex.org/A5111117319", "inst": "" } ], "affiliations": [ "Queen Mary University of London", "Albany College of Pharmacy and Health Sciences", "The Wallace H. Coulter Department of Biomedical Engineering" ] }, { "uid": "arxiv:2401.10744v1", "arxiv_id": "2401.10744v1", "title": "FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models", "authors": [ "Ziqiang Yuan", "Kaiyuan Wang", "Shoutai Zhu", "Ye Yuan", "Jingya Zhou", "Yanlin Zhu", "Wenqi Wei" ], "posted": "2024-01-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.10744v1", "field": "finance", "role": "method", "bullets": [ "A curated set of common financial formulas, linked in a graph by shared variables and recombined to seed question generation with tables and long text.", "GPT-3.5 generates financial question answering pairs from the formula set; the abstract describes no check of the generated data against human annotation.", "Numerical reasoning models trained on the synthetic data improve, beating results from two established financial question answering datasets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 28, "edition": 13, "n": 1626, "authors_detailed": [ { "name": "Ziqiang Yuan", "url": "https://openalex.org/A5048673875", "inst": "Beijing Institute of Technology" }, { "name": "Kaiyuan Wang", "url": "https://openalex.org/A5100681011", "inst": "Sun Yat-sen University" }, { "name": "Shoutai Zhu", "url": "https://openalex.org/A5101295355", "inst": "Beijing Institute of Technology" }, { "name": "Ye Yuan", "url": "https://openalex.org/A5100334813", "inst": "Union Hospital" }, { "name": "Jingya Zhou", "url": "https://openalex.org/A5102728193", "inst": "Yanbian University" }, { "name": "Yanlin Zhu", "url": "https://openalex.org/A5042237918", "inst": "Beijing Institute of Technology" }, { "name": "Wenqi Wei", "url": "https://openalex.org/A5069331320", "inst": "Fordham University" } ], "affiliations": [ "Beijing Institute of Technology", "Sun Yat-sen University", "Yanbian University", "Fordham University" ] }, { "uid": "arxiv:2401.10506v1", "arxiv_id": "2401.10506v1", "title": "FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis", "authors": [ "Chao Zhang", "Yuren Mao", "Yijiang Fan", "Yu Mi", "Yunjun Gao", "Lu Chen", "Dongfang Lou", "Jinshu Lin" ], "posted": "2024-01-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.10506v1", "field": "finance", "role": "method", "bullets": [ "BULL, a text-to-SQL benchmark collected from Hundsun Technologies' financial analysis business, with databases for funds, stocks, and the macroeconomy featuring characteristically wide tables.", "A model-agnostic LLM framework covers prompt construction, parameter-efficient fine-tuning, and output calibration. Which base language models are used is not stated in the abstract.", "FinSQL reports state-of-the-art accuracy on BULL at small cost, with up to 36.64 percent improvement in few-shot cross-database transfer scenarios." ], "bullet_provenance": "ai", "validated": true, "validation_note": "BULL financial text-to-SQL benchmark", "salience": 30, "edition": 13, "models": [], "n": 1683, "authors_detailed": [ { "name": "Chao Zhang", "url": "https://openalex.org/A5063675054", "inst": "Qilu University of Technology" }, { "name": "Yuren Mao", "url": "https://openalex.org/A5061989255", "inst": "UNSW Sydney" }, { "name": "Yijiang Fan", "url": "https://openalex.org/A5051678313", "inst": "Zhejiang University" }, { "name": "Yu Mi", "url": "https://openalex.org/A5100308181", "inst": "Army Medical University" }, { "name": "Yunjun Gao", "url": "https://openalex.org/A5006238145", "inst": "First Affiliated Hospital Zhejiang University" }, { "name": "Lu Chen", "url": "https://openalex.org/A5102199777", "inst": "Donghua University" }, { "name": "Dongfang Lou", "url": "https://openalex.org/A5030513587", "inst": "Hundsun (China)" }, { "name": "Jinshu Lin", "url": "https://openalex.org/A5078108447", "inst": "University of Electronic Science and Technology of China" } ], "affiliations": [ "Qilu University of Technology", "UNSW Sydney", "Zhejiang University", "Army Medical University", "Donghua University", "Hundsun (China)", "University of Electronic Science and Technology of China" ] }, { "uid": "doi:10.2139/ssrn.4678265", "doi": "10.2139/ssrn.4678265", "title": "Beware of Botshit: How to Manage the Epistemic Risks of Generative Chatbots", "authors": [ "Timothy Hannigan", "Ian P. McCarthy", "Andre Spicer" ], "posted": "2024-01-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4678265", "field": "management", "role": "object", "bullets": [ "Conceptual framework for organizational use of generative chatbots across content-generation tasks, drawing on risk management research.", "LLM chatbots analyzed along two dimensions: response veracity verifiability and response veracity importance.", "Framework identifies four chatbot work modes (authenticated, autonomous, automated, augmented) each carrying a distinct epistemic risk profile." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 48, "validated": null, "n": 2921, "authors_detailed": [ { "name": "Tim Hannigan", "url": "https://openalex.org/A5048051797", "inst": "University of Alberta" }, { "name": "Ian P. McCarthy", "url": "https://openalex.org/A5055453377", "inst": "Simon Fraser University" }, { "name": "André Spicer", "url": "https://openalex.org/A5055962469", "inst": "City, University of London" } ], "affiliations": [ "University of Alberta", "Simon Fraser University", "City, University of London" ] }, { "uid": "doi:10.2139/ssrn.4666103", "doi": "10.2139/ssrn.4666103", "title": "The Impact of Generative Artificial Intelligence on Socioeconomic Inequalities and Policy Making", "authors": [ "Valerio Capraro", "Austin Lentsch", "Daron Acemoglu", "Selin Akgun", "Aisel Akhmedova", "Ennio Bilancini", "Jean-Francois Bonnefon", "Pablo Brañas-Garza", "Luigi Butera", "Karen M. Douglas", "Jim Everett", "Gerd Gigerenzer", "Christine Greenhow", "Daniel Hashimoto", "Julianne Holt-Lunstad", "Jolanda Jetten", "Simon Johnson", "Werner H. Kunz", "Chiara Longoni", "Pete Lunn", "Simone Natale", "Stefanie Paluch", "Iyad Rahwan", "Neil Selwyn", "Vivek Singh", "Siddharth Suri", "Jennifer Sutcliffe", "Joe Tomlinson", "Sander van der Linden", "Paul A. M. van Lange", "Friederike Wall", "Jay Van Bavel", "Riccardo Viale" ], "posted": "2024-01-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4666103", "field": "economics", "role": "object", "bullets": [ "Interdisciplinary review of generative AI impacts on work, education, healthcare, and information domains, with policy analysis across the EU, US, and UK.", "No model applied; 33 co-authors synthesized evidence on how generative AI may affect socioeconomic inequalities across information-intensive sectors.", "GenAI may both widen and narrow inequalities depending on domain; existing policy frameworks in all three jurisdictions fail to address the identified socioeconomic challenges." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3356, "authors_detailed": [ { "name": "Valerio Capraro", "url": "https://openalex.org/A5068651250", "inst": "University of Milano-Bicocca" }, { "name": "Austin Lentsch", "url": "https://openalex.org/A5093515094", "inst": "Massachusetts Institute of Technology" }, { "name": "Daron Acemoğlu", "url": "https://openalex.org/A5012301204", "inst": "National Bureau of Economic Research" }, { "name": "Selin Akgün", "url": "https://openalex.org/A5018294641", "inst": "Michigan State University" }, { "name": "Aisel Akhmedova", "url": "https://openalex.org/A5093515095", "inst": "Michigan State University" }, { "name": "Ennio Bilancini", "url": "https://openalex.org/A5043371215", "inst": "University of Modena and Reggio Emilia" }, { "name": "Jean‐François Bonnefon", "url": "https://openalex.org/A5029258117", "inst": "Toulouse School of Economics" }, { "name": "Pablo Brañas‐Garza", "url": "https://openalex.org/A5055454713", "inst": "Universidad Loyola Andalucía" }, { "name": "Luigi Butera", "url": "https://openalex.org/A5005711947", "inst": "Copenhagen Business School" }, { "name": "Karen M. Douglas", "url": "https://openalex.org/A5087650169", "inst": "University of Kent" }, { "name": "Jim A. C. Everett", "url": "https://openalex.org/A5024663213", "inst": "University of Oxford" }, { "name": "Gerd Gigerenzer", "url": "https://openalex.org/A5002330321", "inst": "Max Planck Society" }, { "name": "Christine Greenhow", "url": "https://openalex.org/A5032984793", "inst": "Michigan State University" }, { "name": "Daniel A. Hashimoto", "url": "https://openalex.org/A5112853881", "inst": "University of Pennsylvania" }, { "name": "Julianne Holt‐Lunstad", "url": "https://openalex.org/A5025668220", "inst": "Brigham Young University" }, { "name": "Jolanda Jetten", "url": "https://openalex.org/A5004337400", "inst": "Queensland University of Technology" }, { "name": "Simon Johnson", "url": "https://openalex.org/A5111446264", "inst": "National Bureau of Economic Research" }, { "name": "Werner H. Kunz", "url": "https://openalex.org/A5136000333", "inst": "University of Massachusetts Boston" }, { "name": "Chiara Longoni", "url": "https://openalex.org/A5051832390", "inst": "Trinity College Dublin" }, { "name": "Pete Lunn", "url": "https://openalex.org/A5090834734", "inst": "Trinity College Dublin" }, { "name": "Simone Natale", "url": "https://openalex.org/A5026288547", "inst": "Max Planck Society" }, { "name": "Stefanie Paluch", "url": "https://openalex.org/A5015670881", "inst": "Australian Regenerative Medicine Institute" }, { "name": "Iyad Rahwan", "url": "https://openalex.org/A5009232244", "inst": "Max Planck Society" }, { "name": "Neil Selwyn", "url": "https://openalex.org/A5003881064", "inst": "Microsoft (United States)" }, { "name": "Vivek Singh", "url": "https://openalex.org/A5007588277", "inst": "University of Pennsylvania" }, { "name": "Siddharth Suri", "url": "https://openalex.org/A5051257652", "inst": "Microsoft (United States)" }, { "name": "Jennifer Sutcliffe", "url": "https://openalex.org/A5017811218", "inst": "University of Cambridge" }, { "name": "Joe Tomlinson", "url": "https://openalex.org/A5147208737", "inst": "New York Law School" }, { "name": "Sander van der Linden", "url": "https://openalex.org/A5073961643", "inst": "University of Cambridge" }, { "name": "Jay Joseph Van Bavel", "url": "https://openalex.org/A5055444532", "inst": "New York University" }, { "name": "Friederike Wall", "url": "https://openalex.org/A5006492749", "inst": "Center for Interdisciplinary Studies" }, { "name": "Jay Joseph Van Bavel", "url": "https://openalex.org/A5055444532", "inst": "New York University" }, { "name": "Riccardo Viale", "url": "https://openalex.org/A5028354194", "inst": "Center for Interdisciplinary Studies" } ], "affiliations": [ "Massachusetts Institute of Technology", "Michigan State University", "University of Milano-Bicocca", "National Bureau of Economic Research", "University of Modena and Reggio Emilia", "Toulouse School of Economics", "Universidad Loyola Andalucía", "Copenhagen Business School" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4683054", "doi": "10.2139/ssrn.4683054", "title": "Hybrid Marketing Research: Large Language Models as an Assistant", "authors": [ "Neeraj Arora", "Ishita Chakraborty", "Yohei Nishimura" ], "posted": "2024-01-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4683054", "field": "management", "role": "method", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 631, "authors_detailed": [ { "name": "Neeraj K. Arora", "url": "https://openalex.org/A5000555731", "inst": "Wisconsin School of Professional Psychology" }, { "name": "Ishita Chakraborty", "url": "https://openalex.org/A5103250525", "inst": "University of Wisconsin–Madison" }, { "name": "Yohei Nishimura", "url": "https://openalex.org/A5102605550", "inst": "Wisconsin School of Professional Psychology" } ], "affiliations": [ "Wisconsin School of Professional Psychology", "University of Wisconsin–Madison" ] }, { "uid": "doi:10.2139/ssrn.4683869", "doi": "10.2139/ssrn.4683869", "title": "Generative AI for scalable feedback to multimodal exercises", "authors": [ "Lukas Jürgensmeier", "Bernd Skiera" ], "posted": "2024-01-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4683869", "field": "management", "role": "instrument", "bullets": [ "4,349 student solutions to marketing analytics exercises requiring coding, statistics, and economic reasoning, evaluated by seven generative AI models.", "GPT-4, Claude 3 Opus, Gemini, and Mistral Large scored multimodal exercises; automated scores compared against human expert grading on identical submissions.", "GPT-4 achieved r = 0.94 correlation with human scores and 6% mean deviation; GPT-4, Mistral Large, Claude 3 Opus, and Gemini 1.0 Pro dominated cost-performance." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini", "open_other" ], "open_weights": false, "validated": true, "validation_note": "human expert grading, r = 0.94 correlation", "salience": 50, "n": 2365, "authors_detailed": [ { "name": "Lukas Jürgensmeier", "url": "https://openalex.org/A5018294194", "inst": "Goethe University Frankfurt" }, { "name": "Bernd Skiera", "url": "https://openalex.org/A5068164841", "inst": "Goethe University Frankfurt" } ], "affiliations": [ "Goethe University Frankfurt" ] }, { "uid": "doi:10.2139/ssrn.4681307", "doi": "10.2139/ssrn.4681307", "title": "Evaluating Large Language Models on the GMAT: Implications for the Future of Business Education", "authors": [ "Vahid Ashrafi", "Necdet Gürkan" ], "posted": "2024-01-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4681307", "field": "management", "role": "agent", "bullets": [ "Benchmark evaluation of seven LLMs on the GMAT, a standardized admissions exam for graduate business programs, using official question formats and scoring.", "GPT-3.5 Turbo, GPT-4 Turbo, Gemini 1.0 Pro, PaLM 2, Claude 2, and Claude 2.1 answered GMAT questions; GPT-4 Turbo also tested for tutoring tasks.", "Most LLMs outperform human candidates; GPT-4 Turbo surpasses average scores of top business school students and shows marked improvement in reasoning tasks." ], "bullet_provenance": "ai", "models": [ "gpt", "claude", "gemini" ], "open_weights": false, "validated": true, "validation_note": "GMAT exam benchmark with known correct answers", "salience": 25, "n": 2373, "authors_detailed": [ { "name": "Vahid Ashrafimoghari", "url": "https://openalex.org/A5024658215", "inst": "Stevens Institute of Technology" }, { "name": "Necdet Gürkan", "url": "https://openalex.org/A5011651595", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Stevens Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.4683118", "doi": "10.2139/ssrn.4683118", "title": "Augmenting Human Teams with Robots in Knowledge Work Settings: Insights from the Literature", "authors": [ "Yuqing Ren", "Jeffrey Clement" ], "posted": "2024-01-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4683118", "field": "management", "role": "object", "bullets": [ "Literature review of 317 articles across multiple disciplines on robots and AI augmenting knowledge work teams in customer service, data science, legal, and software domains.", "No model applied; the review synthesizes findings on robot attributes including embodiment, communication, personality, and competence in team settings.", "Identified four key differences between individual and team human-robot interaction settings; proposed a framework linking robot attributes to human perceptions and team outcomes." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3353, "authors_detailed": [ { "name": "Yuqing Ren", "url": "https://openalex.org/A5080998474", "inst": "Carleton College" }, { "name": "J.D. Clement", "url": "https://openalex.org/A5054894399", "inst": "Augsburg University" } ], "affiliations": [ "Carleton College", "Augsburg University" ] }, { "uid": "doi:10.2139/ssrn.4672447", "doi": "10.2139/ssrn.4672447", "title": "Reflections on deep learning and the actuarial profession(al)", "authors": [ "Roseanne Harris", "Ronald Richman", "Mario V. Wuthrich" ], "posted": "2024-01-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4672447", "field": "finance", "role": "object", "bullets": [ "Survey of actuarial professional standards in the UK and South Africa governing deep learning model use and education syllabi.", "No model applied; the paper reviews how existing professional guidance addresses model understandability, bias avoidance, and prediction variability for deep learning and LLMs.", "Current actuarial guidance can accommodate deep learning models; proposed a new education subject covering machine learning and discussed emerging LLM consequences for actuarial work." ], "bullet_provenance": "ai", "salience": 22, "models": [], "validated": null, "n": 3354, "authors_detailed": [ { "name": "Roseanne Harris", "url": "https://openalex.org/A5065820420", "inst": "University of the Witwatersrand" }, { "name": "Ronald Richman", "url": "https://openalex.org/A5048936137", "inst": "University of the Witwatersrand" }, { "name": "Mario V. Wüthrich", "url": "https://openalex.org/A5004533209", "inst": "ETH Zurich" } ], "affiliations": [ "University of the Witwatersrand", "ETH Zurich" ] }, { "uid": "doi:10.2139/ssrn.4671369", "doi": "10.2139/ssrn.4671369", "title": "The Uneven Impact of Generative AI on Entrepreneurial Performance", "authors": [ "Nicholas Otis", "Rowan Clarke", "Solène Delecourt", "David Holtz", "Rembrand Koning" ], "posted": "2024-01-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4671369", "field": "management", "role": "object", "bullets": [ "Field experiment with Kenyan small business entrepreneurs randomized into treatment (GPT-4 AI business assistant) and control groups.", "GPT-4-powered assistant provided business advice; entrepreneurs chose which recommendations to implement across a range of open-ended business decisions.", "No average treatment effect on revenues or profits; low-performing entrepreneurs lost roughly 10% while high performers gained over 15%, driven by differences in advice selection." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 78, "validated": null, "n": 3355, "authors_detailed": [ { "name": "Nicholas G. Otis", "url": "https://openalex.org/A5019793062", "inst": "University of California, Berkeley" }, { "name": "Rowan Philip Clarke", "url": "https://openalex.org/A5002651535", "inst": "Harvard University Press" }, { "name": "Solène Delecourt", "url": "https://openalex.org/A5086990639", "inst": "University of California, Berkeley" }, { "name": "David Holtz", "url": "https://openalex.org/A5072380083", "inst": "IIT@MIT" }, { "name": "Rembrand Koning", "url": "https://openalex.org/A5049884349", "inst": "Harvard University" } ], "affiliations": [ "University of California, Berkeley", "Harvard University", "IIT@MIT" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4197489", "doi": "10.2139/ssrn.4197489", "title": "Measuring Corporate Human Capital Disclosures: Lexicon, Data, Code, and Research Opportunities", "authors": [ "Elizabeth Demers", "Victor Xiaoqi Wang", "Kean Wu" ], "posted": "2024-01-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4197489", "field": "accounting", "role": "instrument", "bullets": [ "U.S. public firms' 10-K filings and proxy statements analyzed to build a comprehensive human capital disclosure lexicon spanning five subcategories including DEI and compensation.", "Word2vec trained on confirmed human capital disclosures generated keyword lists; authors also provide Python code and examples for fine-tuning a BERT classifier.", "Produced a shared five-category human capital keyword lexicon with code and disclosure data, enabling standardized measurement of HC disclosures across corporate communications." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 40, "n": 3891, "authors_detailed": [ { "name": "Elizabeth Demers", "url": "https://openalex.org/A5034987530", "inst": "University of Waterloo" }, { "name": "Victor Xiaoqi Wang", "url": "https://openalex.org/A5041860793", "inst": "California State University, Long Beach" }, { "name": "Kean Wu", "url": "https://openalex.org/A5101966742", "inst": "Rochester Institute of Technology" } ], "affiliations": [ "University of Waterloo", "California State University, Long Beach", "Rochester Institute of Technology" ] }, { "uid": "doi:10.2139/ssrn.4676053", "doi": "10.2139/ssrn.4676053", "title": "The Role of Generative AI in Human Creative Processes: Experimental Evidence", "authors": [ "Feng Zhu", "Wenbo Zou" ], "posted": "2024-01-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4676053", "field": "management", "role": "object", "bullets": [ "Preregistered lab experiment assigned novel creative tasks to 124 university students, with random half given ChatGPT access.", "ChatGPT served as a creative-assistance tool; study measured effects on creative performance, satisfaction, and self-efficacy.", "ChatGPT access worsened creative performance for above-median-potential participants, yet users reported higher satisfaction despite lower actual output." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "validated": null, "n": 2920, "authors_detailed": [ { "name": "Feng Zhu", "url": "https://openalex.org/A5102940426", "inst": "Nankai University" }, { "name": "Wenbo Zou", "url": "https://openalex.org/A5039080605", "inst": "Nankai University" } ], "affiliations": [ "Nankai University" ] }, { "uid": "arxiv:2401.07345v3", "arxiv_id": "2401.07345v3", "title": "Can an LLM Learn Preferences from Choice Data?", "authors": [ "Jeongbin Kim", "Matthew Kovach", "Kyu-Min Lee", "Euncheol Shin", "Hector Tzavellas" ], "posted": "2024-01-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.07345v3", "field": "economics", "role": "agent", "bullets": [ "Simulated revealed-choice data built from a disappointment aversion model, so the recommendation each history should produce is known from the preference parameters.", "GPT, Gemini, and Claude observe growing choice histories and recommend options in new situations; accuracy is scored against the optimal choices the primitives imply.", "Accuracy rises with more observed choices but unevenly: GPT learns risk aversion best, Gemini leads in high disappointment aversion regions, and Claude learns most broadly." ], "bullet_provenance": "ai", "models": [ "claude", "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "optimal choices implied by known preference primitives", "salience": 50, "edition": 13, "n": 1566, "authors_detailed": [ { "name": "Jeongbin Kim", "url": "https://openalex.org/A5103047825", "inst": "Florida State University" }, { "name": "Matthew Kovach", "url": "https://openalex.org/A5018582575", "inst": "University of Toledo" }, { "name": "Kyu‐Min Lee", "url": "https://openalex.org/A5083830515", "inst": "Korea Advanced Institute of Science and Technology" }, { "name": "Euncheol Shin", "url": "https://openalex.org/A5007628643", "inst": "Korea Institute for Advanced Study" }, { "name": "Hector Tzavellas", "url": "https://openalex.org/A5011702902", "inst": "Virginia Tech" } ], "affiliations": [ "Florida State University", "University of Toledo", "Korea Advanced Institute of Science and Technology", "Korea Institute for Advanced Study", "Virginia Tech" ] }, { "uid": "doi:10.2139/ssrn.4692101", "doi": "10.2139/ssrn.4692101", "title": "Generating “Accurate” Online Reviews: Augmenting a Transformer-Based Approach with Structured Predictions", "authors": [ "Prasad Vana", "Praveen K. Kopalle", "Pradeep Pachigolla", "Keith Carlson" ], "posted": "2024-01-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4692101", "field": "management", "role": "method", "bullets": [ "Wine review generation task using transformer model augmented with structured predictions, benchmarked against ChatGPT and human experts.", "Two-step approach pairs a transformer with structured taste predictions to reduce hallucination in generated reviews.", "Augmented model achieved 0.866 precision and 0.768 recall for taste attributes versus 0.394/0.243 for ChatGPT." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "precision/recall for wine taste attributes plus human similarity ratings", "salience": 45, "n": 3184, "authors_detailed": [ { "name": "Prasad Vana", "url": "https://openalex.org/A5005727362", "inst": "Dartmouth College" }, { "name": "Praveen K. Kopalle", "url": "https://openalex.org/A5000759766", "inst": "Dartmouth College" }, { "name": "Pradeep Pachigolla", "url": "https://openalex.org/A5001924285", "inst": "Cornell University" }, { "name": "Keith Carlson", "url": "https://openalex.org/A5050068248", "inst": "Dartmouth College" } ], "affiliations": [ "Dartmouth College", "Cornell University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2401.06915v3", "arxiv_id": "2401.06915v3", "title": "DocFinQA: A Long-Context Financial Reasoning Dataset", "authors": [ "Varshini Reddy", "Rik Koncel-Kedziorski", "Viet Dac Lai", "Michael Krumdick", "Charles Lovering", "Chris Tanner" ], "posted": "2024-01-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.06915v3", "field": "finance", "role": "method", "bullets": [ "7,437 FinQA questions re-grounded in complete financial documents, stretching average context from under 700 words to 123k words per question.", "Retrieval based QA pipelines and long context language models are evaluated against the labeled answers; the abstract does not name individual models.", "State of the art systems degrade substantially on full documents and struggle most on the longest ones, leaving long range financial reasoning unsolved." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labeled QA pairs derived from FinQA", "salience": 40, "edition": 13, "models": [], "n": 1713, "authors_detailed": [ { "name": "Varshini Reddy", "url": "https://openalex.org/A5114127969", "inst": "Institute of Management Technology" }, { "name": "Rik Koncel-Kedziorski", "url": "https://openalex.org/A5033228519", "inst": "Amazon (United States)" }, { "name": "Viet Dac Lai", "url": "https://openalex.org/A5070047759", "inst": "Adobe Systems (United States)" }, { "name": "Krumdick, Michael", "url": "", "inst": "" }, { "name": "Lovering, Charles", "url": "", "inst": "" }, { "name": "Tanner, Chris", "url": "", "inst": "" } ], "affiliations": [ "Institute of Management Technology", "Amazon (United States)", "Adobe Systems (United States)" ] }, { "uid": "doi:10.1145/3688399", "doi": "10.1145/3688399", "arxiv_id": "2401.05799v1", "title": "Designing Heterogeneous LLM Agents for Financial Sentiment Analysis", "authors": [ "Frank Xing" ], "posted": "2024-01-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.05799v1", "field": "finance", "role": "method", "bullets": [ "Standard financial sentiment analysis datasets serve as the testbed; the abstract does not name them or give sample counts.", "Multiple LLM agents, each specialized on a known error type in financial sentiment work, discuss and aggregate judgments without fine-tuning; the underlying models are not named in the abstract.", "The multi-agent design improves accuracy over baselines, with the largest gains when agent discussions are substantial; exact figures are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labelled FSA datasets, accuracy comparisons", "salience": 38, "edition": 13, "models": [], "n": 1592, "authors_detailed": [ { "name": "Frank Xing", "url": "https://openalex.org/A5024877482", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore" ] }, { "uid": "arxiv:2401.05215v1", "arxiv_id": "2401.05215v1", "title": "Pre-trained Large Language Models for Financial Sentiment Analysis", "authors": [ "Wei Luo", "Dihong Gong" ], "posted": "2024-01-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.05215v1", "field": "finance", "role": "method", "bullets": [ "Financial news headlines labelled by sentiment class; the abstract does not state which dataset, how many samples, or what period it covers.", "Llama2-7B is adapted with supervised fine-tuning to classify headline sentiment; performance is scored against labelled data, with no figures given in the abstract.", "The fine-tuned 7B model is reported to beat previous state-of-the-art financial sentiment classifiers despite its comparatively small parameter count." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "labelled sentiment benchmark, figures not in abstract", "salience": 25, "edition": 13, "n": 1591, "authors_detailed": [ { "name": "Wei Luo", "url": "https://openalex.org/A5100368250", "inst": "Beijing University of Chinese Medicine" }, { "name": "Dihong Gong", "url": "https://openalex.org/A5024730386", "inst": "University of Science and Technology of China" } ], "affiliations": [ "Beijing University of Chinese Medicine", "University of Science and Technology of China" ] }, { "uid": "arxiv:2401.05273v3", "arxiv_id": "2401.05273v3", "title": "INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges", "authors": [ "Jayr Pereira", "Andre Assumpcao", "Julio Trecenti", "Luiz Airosa", "Caio Lente", "Jhonatan Cléto", "Guilherme Dobins", "Rodrigo Nogueira", "Luis Mitchell", "Roberto Lotufo" ], "posted": "2024-01-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.05273v3", "field": "accounting", "role": "method", "bullets": [ "Case files of Brazil's Federal Court of Accounts, covering stages from basic information extraction through admissibility, periculum in mora, and fumus boni iuris analysis to recommendation drafting.", "A large language model pipeline automates each stage; the base model is not named in the abstract. Outputs are scored on a validation dataset and reported to correlate highly with human judgment.", "The system extracts relevant case information and assesses legal plausibility well enough to draft decision propositions, with the authors noting current limitations and urging cautious adoption." ], "bullet_provenance": "ai", "validated": true, "validation_note": "outputs compared with human judgment, correlation claimed", "salience": 44, "edition": 13, "models": [], "n": 1624, "authors_detailed": [ { "name": "Jayr Pereira", "url": "https://openalex.org/A5035571297", "inst": "Universidade Estadual de Campinas (UNICAMP)" }, { "name": "André Assumpção", "url": "https://openalex.org/A5051297299", "inst": "University of North Carolina at Chapel Hill" }, { "name": "Julio Trecenti", "url": "https://openalex.org/A5081499512", "inst": "Insper" }, { "name": "Luiz Airosa", "url": "https://openalex.org/A5093713826", "inst": "Federal Police of Brazil" }, { "name": "Caio Lente", "url": "https://openalex.org/A5037536098", "inst": "Universidade de São Paulo" }, { "name": "Jhonatan Cléto", "url": "https://openalex.org/A5092663327", "inst": "Universidade Estadual de Campinas (UNICAMP)" }, { "name": "Guilherme Dobins", "url": "https://openalex.org/A5093713827", "inst": "Universidade Estadual de Campinas (UNICAMP)" }, { "name": "Rodrigo Nogueira", "url": "https://openalex.org/A5030647281", "inst": "United Food and Commercial Workers" }, { "name": "Luis Mitchell", "url": "https://openalex.org/A5111150805", "inst": "Federal Police of Brazil" }, { "name": "Roberto Lotufo", "url": "https://openalex.org/A5087970571", "inst": "Universidade Estadual de Campinas (UNICAMP)" } ], "affiliations": [ "University of North Carolina at Chapel Hill", "Universidade Estadual de Campinas (UNICAMP)", "Insper", "Federal Police of Brazil", "Universidade de São Paulo", "United Food and Commercial Workers" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2401.05447v1", "arxiv_id": "2401.05447v1", "title": "Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?", "authors": [ "Baptiste Lefort", "Eric Benhamou", "Jean-Jacques Ohana", "David Saltiel", "Beatrice Guez", "Damien Challet" ], "posted": "2024-01-09", "added": "2026-08-20", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.05447v1", "field": "finance", "role": "instrument", "bullets": [ "Daily Bloomberg Financial Market Summaries from 2010 to 2023, tested across multiple global equity market indices.", "ChatGPT scored sentiment of financial news summaries using a two-stage prompt approach; validated via Pearson and Spearman correlations across equity regions.", "Sentiment scores showed statistically significant positive correlation with short-to-medium-term equity returns, reverting to negative correlation at longer horizons." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "correlation with realized equity returns across multiple markets", "salience": 62, "n": 3352, "authors_detailed": [ { "name": "Baptiste Lefort", "url": "https://openalex.org/A5093631836", "inst": "CentraleSupélec" }, { "name": "Eric Benhamou", "url": "https://openalex.org/A5068844837", "inst": "Clinique Hartmann" }, { "name": "Jean‐Jacques Ohana", "url": "https://openalex.org/A5026233991", "inst": "Alpha-1 Foundation" }, { "name": "David Saltiel", "url": "https://openalex.org/A5024845346", "inst": "Clinique Hartmann" }, { "name": "Béatrice Guez", "url": "https://openalex.org/A5002704483", "inst": "Alpha-1 Foundation" }, { "name": "Damien Challet", "url": "https://openalex.org/A5063789606", "inst": "Université Paris-Saclay" } ], "affiliations": [ "CentraleSupélec", "Clinique Hartmann", "Alpha-1 Foundation", "Université Paris-Saclay" ] }, { "uid": "doi:10.2139/ssrn.4680203", "doi": "10.2139/ssrn.4680203", "title": "A Scoping Review of ChatGPT Research in Accounting and Finance", "authors": [ "Mengming (Michael) Dong", "Theophanis C. Stratopoulos", "Victor Xiaoqi Wang" ], "posted": "2024-01-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4680203", "field": "accounting", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 630, "authors_detailed": [ { "name": "Mengming Dong", "url": "https://openalex.org/A5015283836", "inst": "Missouri State University" }, { "name": "Theophanis C. Stratopoulos", "url": "https://openalex.org/A5051997338", "inst": "University of Waterloo" }, { "name": "Victor Xiaoqi Wang", "url": "https://openalex.org/A5041860793", "inst": "California State University, Long Beach" } ], "affiliations": [ "Missouri State University", "University of Waterloo", "California State University, Long Beach" ] }, { "uid": "doi:10.2139/ssrn.4667453", "doi": "10.2139/ssrn.4667453", "title": "New Economic Models Using Artificial Intelligence (AI)", "authors": [ "Mario Arturo Ruiz Estrada" ], "posted": "2024-01-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4667453", "field": "economics", "role": "method", "bullets": [ "E-monograph presenting twelve chapters on AI-driven economic modeling, including an experimental comparison of ChatGPT-4 and Google Scholar for socioeconomic research.", "ChatGPT-4 was evaluated using a novel Economic Solutions Searcher Evaluation Model to find solutions to socioeconomic problems such as unemployment control.", "The monograph proposes multiple simulation frameworks for macroeconomic analysis but provides limited empirical validation of the proposed AI-based economic models." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 10, "n": 2919, "authors_detailed": [ { "name": "Mario Arturo Ruiz Estrada", "url": "https://openalex.org/A5043927298", "inst": "Universidad de San Carlos de Guatemala" } ], "affiliations": [ "Universidad de San Carlos de Guatemala" ] }, { "uid": "doi:10.2139/ssrn.4684617", "doi": "10.2139/ssrn.4684617", "title": "AI for Customer Journeys: A Transformer Approach", "authors": [ "Zipei Lu", "Pallassana Kannan" ], "posted": "2024-01-06", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4684617", "field": "management", "role": "instrument", "bullets": [ "Empirical application in a multichannel marketing context modeling sequences of customer interactions; sample, period, and geography not stated.", "A transformer-based framework with a heterogeneous mixture multi-head self-attention mechanism models customer touchpoints, benchmarked against hidden Markov models, point process models, and LSTMs; no accuracy figure against ground truth given.", "The transformer model outperforms the competing approaches in extensive simulations, giving more accurate predictions for identifying high-potential customers, though the magnitude of improvement is not stated." ], "bullet_provenance": "ai", "validated": false, "validation_note": "Outperforms baseline models in simulations but no accuracy/agreement figure against ground truth is given.", "salience": 32, "edition": 22, "models": [], "n": 4122, "authors_detailed": [ { "name": "Zhenghua Lu", "url": "https://openalex.org/A5082966518", "inst": "University of Maryland - Robert H. Smith School of Business" }, { "name": "Pallassana Kannan", "url": "", "inst": "Smith Institute" } ], "affiliations": [ "University of Maryland - Robert H. Smith School of Business", "Smith Institute" ] }, { "uid": "doi:10.2139/ssrn.4679613", "doi": "10.2139/ssrn.4679613", "title": "Sam Altman, OpenAI, and the Importance of Corporate Governance", "authors": [ "Lawrence J. Trautman", "Larry D. Foster, II" ], "posted": "2024-01-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4679613", "field": "management", "role": "object", "bullets": [ "Case study of OpenAI's November 2023 board crisis, examining the nonprofit and capped-profit corporate governance structure of a leading AI company.", "No model applied; the paper analyzes how three independent directors terminated CEO Sam Altman and the resulting organizational disruption.", "The hybrid nonprofit-for-profit governance structure failed to prevent a crisis that threatened billions in value and risked mass employee departure." ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 3351, "authors_detailed": [ { "name": "Lawrence J. Trautman", "url": "https://openalex.org/A5075484615", "inst": "American University of Rome" }, { "name": "Larry D. Foster, II", "url": "", "inst": "University of Houston - Downtown" } ], "affiliations": [ "American University of Rome", "University of Houston - Downtown" ] }, { "uid": "doi:10.2139/ssrn.4647359", "doi": "10.2139/ssrn.4647359", "title": "InteraSSort : Interactive Assortment Planning Using Large Language Models", "authors": [ "Saketh reddy Karra", "Theja Tulabandhula" ], "posted": "2024-01-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4647359", "field": "management", "role": "instrument", "bullets": [ "Retail and e-commerce in-store assortment planning with domain-expert store planners and iterative constraint specification.", "LLMs augmented with optimization tools translated natural language prompts into tailored assortment solutions via interactive conversation.", "Framework enabled planners to efficiently specify objectives and add constraints interactively, producing customized optimized assortments." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 2462, "authors_detailed": [ { "name": "Saketh Reddy Karra", "url": "https://openalex.org/A5022653544", "inst": "University of Illinois Chicago" }, { "name": "Theja Tulabandhula", "url": "https://openalex.org/A5062751346", "inst": "University of Illinois Chicago" } ], "affiliations": [ "University of Illinois Chicago" ] }, { "uid": "doi:10.13088/jiis.2024.30.1.093", "doi": "10.13088/jiis.2024.30.1.093", "arxiv_id": "2401.02981v2", "title": "Fine-tuning and Utilization Methods of Domain-specific LLMs", "authors": [ "Cheonsu Jeong" ], "posted": "2024-01-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.02981v2", "field": "finance", "role": "method", "bullets": [ "Overview aimed at financial services, covering dataset selection, preprocessing, vocabulary construction, security, and regulatory compliance for building domain-specific language models; no empirical sample.", "No specific model or benchmark is reported; the paper walks through the fine-tuning procedure and sketches use cases from stock prediction to document processing and customer service.", "The contribution is a how-to synthesis with identified limitations and proposed directions rather than a measured finding; no performance figures are stated." ], "bullet_provenance": "ai", "salience": 20, "edition": 13, "models": [], "validated": null, "n": 1590, "authors_detailed": [ { "name": "C. Jeong", "url": "https://openalex.org/A5102725428", "inst": "Stanford University" } ], "affiliations": [ "Stanford University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2401.02982v4", "arxiv_id": "2401.02982v4", "title": "FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models", "authors": [ "Shu Liu", "Shangqing Zhao", "Chenghao Jia", "Xinlin Zhuang", "Zhaoguang Long", "Jie Zhou", "Aimin Zhou", "Man Lan", "Qingquan Wu", "Chong Yang" ], "posted": "2024-01-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.02982v4", "field": "finance", "role": "method", "bullets": [ "A benchmark for financial data analysis spanning numerical calculation, sentiment risk assessment, abnormal report analysis, and analysis and chart generation, released with evaluation scripts.", "LLMs are to be scored along foundational, reasoning and technical skill dimensions; the abstract names no specific models and reports no results.", "The contribution is the benchmark design itself; how current models perform on it is not stated in the abstract." ], "bullet_provenance": "ai", "salience": 35, "edition": 13, "models": [], "validated": null, "n": 1712, "authors_detailed": [ { "name": "Liu Shu", "url": "https://openalex.org/A5016990316", "inst": "Genertec Shenyang Machine Tool Co., Ltd. (China)" }, { "name": "Shangqing Zhao", "url": "https://openalex.org/A5057883383", "inst": "East China Normal University" }, { "name": "Chenghao Jia", "url": "https://openalex.org/A5090980482", "inst": "Guangdong University of Technology" }, { "name": "Xinlin Zhuang", "url": "https://openalex.org/A5107119751", "inst": "East China Normal University" }, { "name": "Zhaoguang Long", "url": "https://openalex.org/A5106439267", "inst": "East China Normal University" }, { "name": "Zhou, Jie", "url": "", "inst": "" }, { "name": "Zhou, Aimin", "url": "", "inst": "" }, { "name": "Lan, Man", "url": "", "inst": "" }, { "name": "Wu, Qingquan", "url": "", "inst": "" }, { "name": "Yang, Chong", "url": "", "inst": "" } ], "affiliations": [ "Genertec Shenyang Machine Tool Co., Ltd. (China)", "East China Normal University", "Guangdong University of Technology" ] }, { "uid": "doi:10.2139/ssrn.4650476", "doi": "10.2139/ssrn.4650476", "title": "Applying Large Language Models in Accounting: A Comparative Analysis of Different Methodologies and Off-the-Shelf Examples", "authors": [ "Huaxia Li", "Miklos A. Vasarhelyi" ], "posted": "2024-01-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4650476", "field": "accounting", "role": "method", "bullet_provenance": "none", "salience": 40, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 335, "authors_detailed": [ { "name": "Huaxia Li", "url": "https://openalex.org/A5001895964", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Miklos A. Vasarhelyi", "url": "https://openalex.org/A5049215719", "inst": "Rutgers Sexual and Reproductive Health and Rights" } ], "affiliations": [ "Rutgers, The State University of New Jersey", "Rutgers Sexual and Reproductive Health and Rights" ] }, { "uid": "doi:10.18653/v1/2023.findings-emnlp.241", "doi": "10.18653/v1/2023.findings-emnlp.241", "arxiv_id": "2312.17476v1", "title": "Exploring the Sensitivity of LLMs' Decision-Making Capabilities: Insights from Prompt Variation and Hyperparameters", "authors": [ "Manikanta Loya", "Divya Anand Sinha", "Richard Futrell" ], "posted": "2023-12-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2312.17476v1", "field": "economics", "role": "method", "bullets": [ "The Horizon exploration task of Binz and Schulz, replayed across systematic variations in prompt wording and temperature to probe the stability of reported decision behavior.", "Three OpenAI models of differing capability, names not given in the abstract, complete the task; outcomes are compared with human behavioral patterns rather than any ground truth benchmark.", "Decision behavior swings with prompt and temperature settings, and after simple prompt adjustments the models display a human-like exploration-exploitation tradeoff, contrary to earlier findings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 12, "validated": null, "n": 1457, "authors_detailed": [ { "name": "Manikanta Loya", "url": "https://openalex.org/A5090257103", "inst": "University of California, Irvine" }, { "name": "Divya Sinha", "url": "https://openalex.org/A5030568818", "inst": "University of California, Irvine" }, { "name": "Richard Futrell", "url": "https://openalex.org/A5050104206", "inst": "University of California, Irvine" } ], "affiliations": [ "University of California, Irvine" ] }, { "uid": "doi:10.2139/ssrn.4675409", "doi": "10.2139/ssrn.4675409", "title": "Generative AI and Simulation Modeling: How Should You (Not) Use Large Language Models Like ChatGPT", "authors": [ "Ali Akhavan", "Mohammad S. Jalali" ], "posted": "2023-12-29", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4675409", "field": "management", "role": "method", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 38, "edition": 2, "audience": "broad", "bullets": [], "validated": null, "n": 61, "authors_detailed": [ { "name": "Ali Akhavan", "url": "https://openalex.org/A5085582162", "inst": "Harvard University" }, { "name": "Mohammad S. Jalali", "url": "https://openalex.org/A5091165163", "inst": "Harvard University" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4649285", "doi": "10.2139/ssrn.4649285", "title": "GPT Classifications, with Application to Credit Lending", "authors": [ "Golnoosh Babaei", "Paolo Giudici" ], "posted": "2023-12-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4649285", "field": "finance", "role": "instrument", "bullets": [ "Credit lending binary classification dataset with limited sample observations used to test GPT against classical models.", "GPT model performed binary credit default classification and was benchmarked against logistic regression with varying sample sizes.", "GPT achieved accuracy comparable to logistic regression while requiring a substantially smaller number of training examples." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "accuracy vs logistic regression baseline", "salience": 55, "n": 2461, "authors_detailed": [ { "name": "Golnoosh Babaei", "url": "https://openalex.org/A5039229061", "inst": "University of Pavia" }, { "name": "Paolo Giudici", "url": "https://openalex.org/A5051364218", "inst": "University of Pavia" } ], "affiliations": [ "University of Pavia" ] }, { "uid": "arxiv:2312.17264v1", "arxiv_id": "2312.17264v1", "title": "ESGReveal: An LLM-based approach for extracting structured data from ESG reports", "authors": [ "Yi Zou", "Mengying Shi", "Zhongjie Chen", "Zhu Deng", "ZongXiong Lei", "Zihan Zeng", "Shiming Yang", "HongXiang Tong", "Lei Xiao", "Wenwen Zhou" ], "posted": "2023-12-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2312.17264v1", "field": "accounting", "role": "method", "bullets": [ "ESG reports from 166 companies listed on the Hong Kong Stock Exchange in 2022, chosen to span industries and market capitalizations.", "GPT-4 with retrieval augmented generation queries a report database to extract structured ESG metrics, reaching 76.9 percent extraction accuracy and 83.7 percent on disclosure analysis, above baseline models.", "Disclosure remains partial, 69.5 percent for environmental and 57.2 percent for social indicators, pointing to gaps in corporate ESG transparency." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "76.9 percent extraction accuracy on labelled report data", "salience": 50, "edition": 12, "n": 1373, "authors_detailed": [ { "name": "Yi Zou", "url": "https://openalex.org/A5100458990", "inst": "Jingdezhen Ceramic Institute" }, { "name": "Mengying Shi", "url": "https://openalex.org/A5057299139", "inst": "Nanjing University of Chinese Medicine" }, { "name": "Zhongjie Chen", "url": "https://openalex.org/A5043209517", "inst": "University of Nottingham Ningbo China" }, { "name": "Zhu Deng", "url": "https://openalex.org/A5110377266", "inst": "Nanjing Normal University" }, { "name": "ZongXiong Lei", "url": "https://openalex.org/A5100582053", "inst": "" }, { "name": "Zihan Zeng", "url": "https://openalex.org/A5101379828", "inst": "Wuhan University" }, { "name": "Shiming Yang", "url": "https://openalex.org/A5100315673", "inst": "University of Bergen" }, { "name": "HongXiang Tong", "url": "https://openalex.org/A5111104560", "inst": "" }, { "name": "Lei Xiao", "url": "https://openalex.org/A5100868099", "inst": "American Nurses Association" }, { "name": "Wenwen Zhou", "url": "https://openalex.org/A5100604792", "inst": "Guizhou University" } ], "affiliations": [ "Jingdezhen Ceramic Institute", "Nanjing University of Chinese Medicine", "University of Nottingham Ningbo China", "Nanjing Normal University", "Wuhan University", "University of Bergen", "American Nurses Association", "Guizhou University" ] }, { "uid": "arxiv:2401.06164v1", "arxiv_id": "2401.06164v1", "title": "Multimodal Gen-AI for Fundamental Investment Research", "authors": [ "Lezhi Li", "Ting-Yu Chang", "Hai Wang" ], "posted": "2023-12-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2401.06164v1", "field": "finance", "role": "method", "bullets": [ "A corpus of research reports, investment memos, market news, and time-series market data underpins an investment research assistant; corpus sizes and periods are not stated.", "Llama2 7B chat is tuned with unsupervised and supervised LoRA and GPT 3.5 with instruction fine-tuning; statistical and human evaluations are cited without reported figures.", "Fine-tuned models answer finance questions, summarize, and reason better than their bases, supporting a prototype agent that drafts stock recommendations with explanations." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": false, "salience": 34, "edition": 12, "n": 1475, "authors_detailed": [ { "name": "Lezhi Li", "url": "https://openalex.org/A5028843823", "inst": "Central South University" }, { "name": "Ting‐Yu Chang", "url": "https://openalex.org/A5086869103", "inst": "National Kaohsiung First University of Science and Technology" }, { "name": "Hai Wang", "url": "https://openalex.org/A5100452482", "inst": "National University of Defense Technology" } ], "affiliations": [ "Central South University", "National Kaohsiung First University of Science and Technology", "National University of Defense Technology" ] }, { "uid": "arxiv:2312.15198v3", "arxiv_id": "2312.15198v3", "title": "Do LLM Agents Exhibit Social Behavior?", "authors": [ "Yan Leng", "Yuan Yuan" ], "posted": "2023-12-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2312.15198v3", "field": "economics", "role": "agent", "bullets": [ "Eight language models, two GPT, four Llama, and two Mistral variants, play canonical behavioral economics games that probe social preferences.", "The SUVA framework reads each model's utterances alongside its final choices, using tree visualizations and probabilistic dependency analysis to trace how stated reasoning drives decisions.", "Most models depart from pure self interest, showing reciprocity and welfare concerns; higher capacity models display group identity effects, and reasoning content predicts prosocial choices." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "salience": 62, "edition": 12, "validated": null, "n": 1419, "authors_detailed": [ { "name": "Yan Leng", "url": "https://openalex.org/A5068435349", "inst": "China Aerodynamics Research and Development Center" }, { "name": "Yuanyuan", "url": "https://openalex.org/A5110757096", "inst": "Hunan Normal University" } ], "affiliations": [ "China Aerodynamics Research and Development Center", "Hunan Normal University" ] }, { "uid": "arxiv:2312.14870v1", "arxiv_id": "2312.14870v1", "title": "Numerical Reasoning for Financial Reports", "authors": [ "Abhinav Arun", "Ashish Dhiman", "Mehul Soni", "Yibei Hu" ], "posted": "2023-12-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2312.14870v1", "field": "finance", "role": "method", "bullets": [ "Question answering over lengthy corporate financial reports, built on the FinQA dataset of numerical reasoning questions tied to report excerpts.", "Llama 2 7B and T5 are fine-tuned to locate key figures and answer user questions, with accuracy scored on FinQA's labelled answers.", "The fine-tuned models reach results comparable to baseline on final numerical answers and competitive accuracy on numerical reasoning; the abstract reports no exact figures." ], "bullet_provenance": "ai", "models": [ "llama", "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinQA labelled benchmark", "salience": 32, "edition": 12, "n": 1418, "authors_detailed": [ { "name": "Abhinav Arun", "url": "https://openalex.org/A5109668483", "inst": "Domus Medica" }, { "name": "Ashish Dhiman", "url": "https://openalex.org/A5093581642", "inst": "" }, { "name": "Mehul Soni", "url": "https://openalex.org/A5114112592", "inst": "" }, { "name": "Yibei Hu", "url": "https://openalex.org/A5113080827", "inst": "" } ], "affiliations": [ "Domus Medica" ] }, { "uid": "arxiv:2312.14203v1", "arxiv_id": "2312.14203v1", "title": "Shai: A large language model for asset management", "authors": [ "Zhongyang Guo", "Guanran Jiang", "Zhongdan Zhang", "Peng Li", "Zhefeng Wang", "Yinchun Wang" ], "posted": "2023-12-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2312.14203v1", "field": "finance", "role": "method", "bullets": [ "Asset management industry tasks, evaluated through professional qualification exams, tailored domain tasks, open ended question answering, and safety assessments.", "Shai, a 10B parameter model continued pre-trained and fine-tuned from an unnamed open source foundation, is scored on this suite; GPT-4 based grading is discussed alongside human judgment.", "Shai outperforms baseline models on domain relevant tasks while keeping computational requirements modest; the authors recommend mixing automated evaluation with human review." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "professional qualification exam benchmark", "salience": 38, "edition": 12, "n": 1428, "authors_detailed": [ { "name": "Zhongyang Guo", "url": "https://openalex.org/A5102377624", "inst": "Wen's Food Group (China)" }, { "name": "Guanran Jiang", "url": "https://openalex.org/A5113080812", "inst": "" }, { "name": "Zhongdan Zhang", "url": "https://openalex.org/A5070463182", "inst": "Economic Research Institute" }, { "name": "Peng Li", "url": "https://openalex.org/A5100432795", "inst": "Hunan University of Science and Technology" }, { "name": "Zhefeng Wang", "url": "https://openalex.org/A5101848951", "inst": "Zhejiang Chinese Medical University" }, { "name": "Yinchun Wang", "url": "https://openalex.org/A5022220036", "inst": "Fujian Agriculture and Forestry University" } ], "affiliations": [ "Wen's Food Group (China)", "Economic Research Institute", "Hunan University of Science and Technology", "Zhejiang Chinese Medical University", "Fujian Agriculture and Forestry University" ] }, { "uid": "doi:10.2139/ssrn.4671511", "doi": "10.2139/ssrn.4671511", "title": "Gpt-Lgbm: A Chatgpt-Based Integrated Framework for Credit Scoring with Textual and Structured Data", "authors": [ "Li Yu", "Xuefei Bai", "Zhiwei Chen" ], "posted": "2023-12-21", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4671511", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 38, "edition": 12, "bullets": [], "validated": null, "n": 1511, "authors_detailed": [ { "name": "Li Yu", "url": "https://openalex.org/A5100675623", "inst": "Shanghai University of Finance and Economics" }, { "name": "Xuefei Bai", "url": "https://openalex.org/A5108237781", "inst": "Shanghai University of Finance and Economics" }, { "name": "Zhiwei Chen", "url": "https://openalex.org/A5100442725", "inst": "Shanghai University of Finance and Economics" } ], "affiliations": [ "Shanghai University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.4665876", "doi": "10.2139/ssrn.4665876", "title": "Grow in the Sun: Valuation and Development of Green Innovation under Mandatory Carbon Disclosure", "authors": [ "Jianqiang Chen", "Pei-Fang Hsieh", "Po-Hsuan Hsu" ], "posted": "2023-12-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4665876", "field": "finance", "role": "instrument", "bullets": [ "US firms and their patents around the proposal of the Greenhouse Gas Reporting Program, analyzed with regression discontinuity and difference-in-differences designs; sample period and size not stated.", "BERT classifies patents as low-carbon from patent text, used alongside patent office classifications to identify green innovation; no accuracy or agreement figure is reported.", "The program proposal raises the value of low-carbon patents; firms with higher past emissions produce more such patents afterward, their patent values rise and CO2 emissions fall, with stronger effects under competitive pressure." ], "bullet_provenance": "ai", "models": [ "legacy", "open_other" ], "open_weights": true, "validated": false, "salience": 58, "edition": 3, "audience": "general", "n": 627, "authors_detailed": [ { "name": "Jianqiang Chen", "url": "https://openalex.org/A5100630271", "inst": "National Tsing Hua University" }, { "name": "Pei-Fang Hsieh", "url": "https://openalex.org/A5007603160", "inst": "National Tsing Hua University" }, { "name": "Po‐Hsuan Hsu", "url": "https://openalex.org/A5039675665", "inst": "National University of Singapore" } ], "affiliations": [ "National Tsing Hua University", "National University of Singapore" ] }, { "uid": "doi:10.2139/ssrn.4660148", "doi": "10.2139/ssrn.4660148", "title": "ChatGPT, Stock Market Predictability and Links to the Macroeconomy", "authors": [ "Jian Chen", "Guohao Tang", "Guofu Zhou", "Wu Zhu" ], "posted": "2023-12-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4660148", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 628, "authors_detailed": [ { "name": "Jian Chen", "url": "https://openalex.org/A5101890997", "inst": "Xiamen University" }, { "name": "Guohao Tang", "url": "https://openalex.org/A5038557883", "inst": "Hunan University" }, { "name": "Guofu Zhou", "url": "https://openalex.org/A5012239666", "inst": "Washington University in St. Louis" }, { "name": "Wu Zhu", "url": "https://openalex.org/A5113044135", "inst": "Tsinghua University" } ], "affiliations": [ "Washington University in St. Louis", "Xiamen University", "Hunan University", "Tsinghua University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4659476", "doi": "10.2139/ssrn.4659476", "title": "Enterprise Large Language Models: Knowledge Characteristics, Risks and Organizational Activities", "authors": [ "Daniel E. O'Leary" ], "posted": "2023-12-20", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4659476", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 35, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 629, "authors_detailed": [ { "name": "Daniel E. O’Leary", "url": "https://openalex.org/A5003034800", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4666856", "doi": "10.2139/ssrn.4666856", "title": "From Feeds to Inboxes: A Comparative Study of Polarization in Facebook and Email News Sharing", "authors": [ "Hema Yoganarasimhan", "Irina Iakovetskaia" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4666856", "field": "management", "role": "instrument", "bullets": [ "New York Times Most Emailed and Most Shared lists over 2.5 years, comparing news content polarization across Facebook and email sharing channels.", "ChatGPT measured article-level political polarization as a scalable alternative to manual coding; LDA identified topic distributions across sharing platforms.", "Highly polarized articles are more likely shared on Facebook than via email; political polarization of Facebook shares did not escalate after the 2020 election." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 2363, "authors_detailed": [ { "name": "Hema Yoganarasimhan", "url": "https://openalex.org/A5076336545", "inst": "Paccar (United States)" }, { "name": "Irina Yakovetskaya", "url": "https://openalex.org/A5093540100", "inst": "Stanford Medicine" } ], "affiliations": [ "Paccar (United States)", "Stanford Medicine" ] }, { "uid": "doi:10.2139/ssrn.4660589", "doi": "10.2139/ssrn.4660589", "title": "AI Delphi: Machine-Machine Collaboration for Exploring the Future of Work", "authors": [ "Lucas Nóbrega", "Luísa Marschhausen", "Luiz Felipe Martinez", "Yuri Lima", "Marcos Almeida", "Alan Lyra", "Carlos Eduardo Barbosa", "Jano Moreira de Souza" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4660589", "field": "management", "role": "agent", "bullets": [ "Delphi questionnaire from the Millennium Project's Future Work/Technology 2050 study, replicated with LLM-simulated expert panelists across six domains.", "ChatGPT simulated eighteen public figures from economics, entrepreneurship, politics, and other fields responding to structured Delphi rounds on future of work.", "LLM-based Delphi produced responses comparable to human panels at reduced cost and time, though authors note limitations in novelty and diversity of viewpoints." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 2364, "authors_detailed": [ { "name": "Lucas Nóbrega", "url": "https://openalex.org/A5110356379", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Luísa Marchhausen", "url": "https://openalex.org/A5093543877", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Luiz Felipe Martinez", "url": "https://openalex.org/A5109666969", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Yuri Lima", "url": "https://openalex.org/A5037043313", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Marcos Almeida", "url": "https://openalex.org/A5034169382", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Alan Lyra", "url": "https://openalex.org/A5059700080", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Carlos Eduardo Barbosa", "url": "https://openalex.org/A5001377466", "inst": "Universidade Federal do Rio de Janeiro" }, { "name": "Jano Moreira de Souza", "url": "https://openalex.org/A5067031228", "inst": "Universidade Federal do Rio de Janeiro" } ], "affiliations": [ "Universidade Federal do Rio de Janeiro" ] }, { "uid": "doi:10.2139/ssrn.4640186", "doi": "10.2139/ssrn.4640186", "title": "An Impossible Interview with John Stuart Mill", "authors": [ "Luca De Benedictis" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4640186", "field": "economics", "role": "agent", "bullets": [ "Simulated interview with economist-philosopher John Stuart Mill, using GPT-4 to generate historically grounded responses on economic thought and philosophy of science.", "GPT-4 simulated Mill's interview responses using a structured prompting strategy; the paper evaluates reliability and historical accuracy of generated dialogue content.", "The model produced substantive engagement with Mill's economic philosophy; the paper comments on prompting strategies and assesses content fidelity of AI-generated responses." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 18, "n": 2400, "authors_detailed": [ { "name": "Luca De Benedictis", "url": "https://openalex.org/A5034974221", "inst": "University of Macerata" } ], "affiliations": [ "University of Macerata" ] }, { "uid": "doi:10.2139/ssrn.4666860", "doi": "10.2139/ssrn.4666860", "title": "Will Generative AI Replace Human Creatives? Insights from Financial Economics", "authors": [ "Jiasun Li" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4666860", "field": "economics", "role": "object", "bullets": [ "Theoretical model of generative AI content markets, drawing on the Grossman-Stiglitz informationally efficient market impossibility result from financial economics.", "No specific model is run; the paper uses economic theory to analyze whether generative AI models like GPT and Diffusion can fully replace human creators.", "A paradox prevents full replacement: if AI supplants all human creators, training data becomes stale and AI cannot produce current content, preserving a role for humans." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 52, "validated": null, "n": 2641, "authors_detailed": [ { "name": "Jiasun Li", "url": "https://openalex.org/A5003473501", "inst": "George Mason University" } ], "affiliations": [ "George Mason University" ] }, { "uid": "doi:10.2139/ssrn.4655962", "doi": "10.2139/ssrn.4655962", "title": "Integrating Machine Behavior into Human Subject Experiments: A User-Friendly Toolkit and Illustrations", "authors": [ "Christoph Engel", "Max R. P. Grossmann", "Axel Ockenfels" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4655962", "field": "economics", "role": "agent", "bullets": [ "Laboratory prisoner's dilemma experiments with LLM agents interacting with each other and with human subjects via oTree-based web experiments.", "LLMs played differently framed prisoner's dilemmas using the open-source alter_ego toolkit; no specific model family is named in the abstract.", "The framework is feasible for integrating machine behavior into experimental economics; the toolkit is released freely for designing human-LLM interaction studies." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 2642, "authors_detailed": [ { "name": "Christoph Engel", "url": "https://openalex.org/A5101415499", "inst": "University of Bonn" }, { "name": "Max R. P. Grossmann", "url": "https://openalex.org/A5012128678", "inst": "University of Cologne" }, { "name": "Axel Ockenfels", "url": "https://openalex.org/A5043467778", "inst": "University of Cologne" } ], "affiliations": [ "University of Bonn", "University of Cologne" ] }, { "uid": "doi:10.2139/ssrn.4668241", "doi": "10.2139/ssrn.4668241", "title": "Human-AI Co-Creation in Product Ideation: the Dual View of Quality and Diversity", "authors": [ "Wen Wang", "Mochen Yang", "Tianshu Sun" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4668241", "field": "management", "role": "object", "bullets": [ "Two online experiments testing human-GPT-4 co-creation modes in product ideation, evaluated by human raters and deep-learning-based assessments.", "GPT-4 generated and revised product ideas; quality and diversity measured separately to compare human-only, GPT-only, and co-creation workflows.", "Human ideas show high diversity but low quality; GPT ideas show the reverse; human-initiated ideas with GPT revision achieve the best quality-diversity balance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "validated": null, "n": 2836, "authors_detailed": [ { "name": "Wen Wang", "url": "https://openalex.org/A5083311552", "inst": "University of Maryland - Robert H. Smith School of Business" }, { "name": "Mochen Yang", "url": "https://openalex.org/A5008688627", "inst": "University of Minnesota" }, { "name": "Tianshu Sun", "url": "https://openalex.org/A5015099727", "inst": "University of Southern California" } ], "affiliations": [ "University of Minnesota", "University of Southern California", "University of Maryland - Robert H. Smith School of Business" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4664899", "doi": "10.2139/ssrn.4664899", "title": "Corporate Officers’ Fiduciary Duty to Monitor Generative Artificial Intelligence", "authors": [ "Matthew Gaske" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4664899", "field": "management", "role": "object", "bullets": [ "Delaware corporate law framework analyzed following the January 2023 extension of fiduciary oversight duty to corporate officers deploying generative AI systems.", "The paper examines automation bias as a core challenge for officers creating information systems to monitor employees' use of generative AI in company workflows.", "Officers should invest more in measuring higher-order disengagement effects than in human-in-the-loop task supervision, which proves relatively ineffective for daily monitoring." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 2918, "authors_detailed": [ { "name": "Matthew Gaske", "url": "https://openalex.org/A5089297528", "inst": "Independent" } ], "affiliations": [ "Independent" ] }, { "uid": "doi:10.2139/ssrn.4656622", "doi": "10.2139/ssrn.4656622", "title": "Picture Perfect: Engaging Customers with Visual Generative AI", "authors": [ "Tijmen Jansen", "Mark Heitmann", "Martin Reisenbichler", "David A. Schweidel" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4656622", "field": "management", "role": "instrument", "bullets": [ "Open-source generative AI model fine-tuned on marketing mindset metrics to produce visual advertising content.", "Model trained directly on communication objectives such as evoking attention and driving interest, adapted to specific audiences.", "AI-generated visual content matched or exceeded conventionally produced advertising on associated performance metrics." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "performance vs conventional advertising on marketing mindset metrics", "salience": 55, "n": 3183, "authors_detailed": [ { "name": "Tijmen Jansen", "url": "", "inst": "Universität Hamburg" }, { "name": "Mark Heitmann", "url": "https://openalex.org/A5047780355", "inst": "Universität Hamburg" }, { "name": "Martin Reisenbichler", "url": "https://openalex.org/A5091381597", "inst": "Universität Hamburg" }, { "name": "David A. Schweidel", "url": "https://openalex.org/A5040636710", "inst": "Emory University" } ], "affiliations": [ "Emory University", "Universität Hamburg" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4662282", "doi": "10.2139/ssrn.4662282", "title": "Welfare Implications of Democratization in Content Creation: Generative AI and Beyond", "authors": [ "Tianxin Zou", "Zijun (June) Shi", "Yue Wu" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4662282", "field": "economics", "role": "object", "bullets": [ "Game-theoretic model of content creation markets with heterogeneous quality creators and platform content screening mechanisms.", "No specific AI model used; theoretical analysis of how content-democratization technology shifts market equilibrium among creators.", "Incremental quality-gap reduction yields lose-lose outcomes for consumers and creators; drastic reduction yields win-win market outcomes." ], "bullet_provenance": "ai", "salience": 60, "models": [], "validated": null, "n": 3345, "authors_detailed": [ { "name": "Tianxin Zou", "url": "https://openalex.org/A5046955772", "inst": "University of Florida" }, { "name": "Zijun Shi", "url": "https://openalex.org/A5102595003", "inst": "Hong Kong University of Science and Technology" }, { "name": "Yue Wu", "url": "https://openalex.org/A5074651490", "inst": "University of Pittsburgh" } ], "affiliations": [ "University of Florida", "Hong Kong University of Science and Technology", "University of Pittsburgh" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4670714", "doi": "10.2139/ssrn.4670714", "title": "Generative AI, Platform Stances, and Content Creator Behavior", "authors": [ "Hongxian Huang", "Runshan Fu", "Anindya Ghose" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4670714", "field": "management", "role": "object", "bullets": [ "Two Chinese visual arts platforms, Lofter and Graffiti Kingdom, natural experiments around contrasting AI policy changes affecting content creators.", "Lofter launched an AI image generator while Graffiti Kingdom prohibited AI art; difference-in-differences measured creator activity responses.", "Creator activity fell on the pro-AI platform and rose on the anti-AI platform; higher-popularity and multi-homing creators showed larger reductions." ], "bullet_provenance": "ai", "salience": 68, "models": [], "validated": null, "n": 3346, "authors_detailed": [ { "name": "Hongxian Huang", "url": "https://openalex.org/A5101282388", "inst": "New York University" }, { "name": "Runshan Fu", "url": "https://openalex.org/A5042980028", "inst": "New York University" }, { "name": "Anindya Ghose", "url": "https://openalex.org/A5073770532", "inst": "New York University" } ], "affiliations": [ "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4667165", "doi": "10.2139/ssrn.4667165", "title": "AI in Finance: Shaping Investor Behavior and Trust through Equity Research Report", "authors": [ "Hsiu-I Ting", "Wen-Chin Hsu", "Mu-Heng Lee" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4667165", "field": "finance", "role": "agent", "bullets": [ "Survey experiment with participants evaluating two AI-generated Tesla equity research reports, comparing general investors to financial professionals.", "OpenAI ChatGPT generated equity reports integrating macroeconomic, financial statement, and stock price data; one version augmented with news.", "General investors showed higher trust and willingness to invest than financial professionals; the news-augmented comprehensive report outperformed." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "n": 3347, "authors_detailed": [ { "name": "Hsiu‐I Ting", "url": "https://openalex.org/A5021582199", "inst": "National Taipei University of Technology" }, { "name": "Wen-Chin Hsu", "url": "https://openalex.org/A5114110848", "inst": "National Central University" }, { "name": "Mu-Heng Lee", "url": "https://openalex.org/A5113076259", "inst": "National Central University" } ], "affiliations": [ "National Taipei University of Technology", "National Central University" ] }, { "uid": "doi:10.2139/ssrn.4663447", "doi": "10.2139/ssrn.4663447", "title": "Answering Clean Tech Questions with Large Language Models", "authors": [ "Lauren Stagnol", "Amina Cherief", "Zakaria Farah", "Théo Le Guenedal", "Sofia Sakout", "Takaya Sekine" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4663447", "field": "finance", "role": "instrument", "bullets": [ "Clean technology industry covering renewable energy, low-emission solutions, energy efficiency, and carbon capture mineral requirements.", "Large language models performed question-answering tasks to extract and monitor novelty and mineral dependencies across clean tech sectors.", "The framework successfully reconstructed expert knowledge and tracked rapidly changing clean tech markets through NLP-based extraction." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 50, "n": 3348, "authors_detailed": [ { "name": "Lauren Stagnol", "url": "https://openalex.org/A5045591638", "inst": "Institut Pasteur" }, { "name": "Amina Cherief", "url": "https://openalex.org/A5019940678", "inst": "Institut Pasteur" }, { "name": "Zakaria Farah", "url": "https://openalex.org/A5112201134", "inst": "Université Paris Cité" }, { "name": "Théo Le Guenedal", "url": "https://openalex.org/A5049171496", "inst": "ParisTech" }, { "name": "Sofia Sakout", "url": "https://openalex.org/A5093540174", "inst": "ParisTech" }, { "name": "Takaya Sekine", "url": "https://openalex.org/A5033308346", "inst": "Institut Pasteur" } ], "affiliations": [ "Institut Pasteur", "Université Paris Cité", "ParisTech" ] }, { "uid": "doi:10.2139/ssrn.4656722", "doi": "10.2139/ssrn.4656722", "title": "Bank Run, Interrupted: Modeling Deposit Withdrawals with Generative AI", "authors": [ "Sophia Kazinnik" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4656722", "field": "finance", "role": "agent", "bullets": [ "Simulated depositor population with demographic attributes exposed to a viral bank-run panic post, randomized bank communication interventions.", "LLM-based synthetic agents simulated depositor withdrawal decisions; responses validated against human behavioral benchmarks before scaling.", "Direct personalized bank communications with strong reassurances and explicit survival clauses substantially reduced withdrawal intent in contagion simulations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "validated against human behavioral benchmarks", "salience": 72, "n": 3349 }, { "uid": "doi:10.2139/ssrn.4656716", "doi": "10.2139/ssrn.4656716", "title": "AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform", "authors": [ "Dandan Qiao", "Huaxia Rui", "Qian Xiong" ], "posted": "2023-12-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4656716", "field": "economics", "role": "object", "bullets": [ "Online labor markets for translation and web development, difference-in-differences analysis around the ChatGPT 3.5 and 4.0 launches.", "No model deployed by researchers; study measured ChatGPT's market-level impact on freelancer work volume and earnings across sectors.", "Translation saw displacement with reduced volume and earnings; web development saw productivity gains; experienced translators were more likely to exit." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 78, "validated": null, "n": 3350, "authors_detailed": [ { "name": "Dandan Qiao", "url": "https://openalex.org/A5101133951", "inst": "National University of Singapore" }, { "name": "Huaxia Rui", "url": "https://openalex.org/A5024136020", "inst": "University of Rochester" }, { "name": "Xiong Qian", "url": "https://openalex.org/A5101503821", "inst": "Tsinghua University" } ], "affiliations": [ "University of Rochester", "National University of Singapore", "Tsinghua University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4653151", "doi": "10.2139/ssrn.4653151", "title": "Blockchain-enabled Supply Chain Financing (BCF)", "authors": [ "Sairam Sriraman", "David Wuttke", "Volodymyr Babich", "Eve Rosenzweig" ], "posted": "2023-12-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4653151", "field": "management", "role": "instrument", "bullets": [ "312 documents describing 11 blockchain-enabled supply chain financing solutions, both successful and failed, analyzed for financing frictions and blockchain features.", "LLM-based text analysis identified patterns connecting seven types of financing frictions to three blockchain features across unstructured solution descriptions.", "Tokenization appeared only in successful solutions; transactional friction dominated despite limited academic attention, while bankruptcy costs were rarely linked to blockchain." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 50, "n": 2917, "authors_detailed": [ { "name": "Sairam Sriraman", "url": "https://openalex.org/A5124413970", "inst": "Technical University of Munich" }, { "name": "David Wuttke", "url": "https://openalex.org/A5075182412", "inst": "Technical University of Munich" }, { "name": "Volodymyr Babich", "url": "https://openalex.org/A5146899955", "inst": "Emory University" }, { "name": "Eve Rosenzweig", "url": "https://openalex.org/A5147217102", "inst": "Emory University" } ], "affiliations": [ "Emory University", "Technical University of Munich" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2312.08725v1", "arxiv_id": "2312.08725v1", "title": "A Comparative Analysis of Fine-Tuned LLMs and Few-Shot Learning of LLMs for Financial Sentiment Analysis", "authors": [ "Sorouralsadat Fatemi", "Yuheng Hu" ], "posted": "2023-12-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2312.08725v1", "field": "finance", "role": "method", "bullets": [ "Finance domain sentiment analysis tasks; the abstract names neither the evaluation datasets nor the corpus used for fine-tuning beyond calling it a finance domain dataset.", "Compares zero-shot and few-shot gpt-3.5-turbo with smaller LLMs of 250 million to 3 billion parameters fine-tuned on financial text, scored against state-of-the-art results on labelled data.", "Fine-tuned small models approach state-of-the-art performance despite fewer parameters; zero-shot and one-shot gpt-3.5 is comparable, and adding more in-context examples brings no improvement." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "labelled financial sentiment benchmarks", "salience": 44, "edition": 12, "n": 1383, "authors_detailed": [ { "name": "Sorouralsadat Fatemi", "url": "https://openalex.org/A5058376884", "inst": "University of Illinois Chicago" }, { "name": "Yuheng Hu", "url": "https://openalex.org/A5016075036", "inst": "Tongji University" } ], "affiliations": [ "University of Illinois Chicago", "Tongji University" ] }, { "uid": "doi:10.2139/ssrn.4662322", "doi": "10.2139/ssrn.4662322", "title": "Enhancing Generative Ai-Based Software Implementation: The Role of Trust in Ai and Top Management Support", "authors": [ "Pawel Korzynski", "Susana Silva", "Anna Maria Gorska", "Grzegorz Mazurek" ], "posted": "2023-12-12", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4662322", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 28, "edition": 12, "bullets": [], "models": [], "validated": null, "n": 1510, "authors_detailed": [ { "name": "Paweł Korzyński", "url": "https://openalex.org/A5069874075", "inst": "Kozminski University" }, { "name": "Susana Costa e Silva", "url": "https://openalex.org/A5039762154", "inst": "Universidade Católica Portuguesa" }, { "name": "Anna Górska", "url": "https://openalex.org/A5049401148", "inst": "Kozminski University" }, { "name": "Grzegorz Mazurek", "url": "https://openalex.org/A5053922801", "inst": "Kozminski University" } ], "affiliations": [ "Kozminski University", "Universidade Católica Portuguesa" ] }, { "uid": "doi:10.2139/ssrn.4662300", "doi": "10.2139/ssrn.4662300", "title": "AI Adaptation: A Primer for Corporate Directors", "authors": [ "Barak Orbach", "Shanen Boettcher", "Ofir Zan" ], "posted": "2023-12-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4662300", "field": "management", "role": "object", "bullets": [ "General framework for corporate boards addressing generative AI adoption, conceptual analysis of board-level oversight requirements.", "No specific model deployed; paper develops a governance framework for how boards should oversee organizational AI adaptation strategy.", "Boards must treat AI adaptation as imperative for viability, requiring structured oversight beyond simple technology adoption decisions." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3344, "authors_detailed": [ { "name": "Barak Orbach", "url": "https://openalex.org/A5024110582", "inst": "University of Arizona" }, { "name": "Shanen Boettcher", "url": "", "inst": "AI21 Labs" }, { "name": "Ofir Zan", "url": "https://openalex.org/A5093476296", "inst": "AI21 Labs" } ], "affiliations": [ "University of Arizona", "AI21 Labs" ] }, { "uid": "arxiv:2312.06646v5", "arxiv_id": "2312.06646v5", "title": "Computational Copyright: Towards A Royalty Model for Music Generative AI", "authors": [ "Junwei Deng", "Xirui Jiang", "Shiyuan Zhang", "Shichang Zhang", "Himabindu Lakkaraju", "Ruijiang Gao", "Chris Donahue", "Jiaqi W. Ma" ], "posted": "2023-12-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2312.06646v5", "field": "economics", "role": "object", "bullets": [ "Music generative AI and the economics of copyright; experiments use public and proprietary music datasets, with retraining-based causal attribution as the reference standard.", "No language model reads text; training data attribution methods approximate how much each training track causally shaped generated music, benchmarked against costly counterfactual retraining.", "Scalable attribution closely matches the retraining benchmark, while perceived similarity captures top influences but misses broader contributions, so similarity-based legal proxies suit royalty distribution poorly." ], "bullet_provenance": "ai", "salience": 55, "edition": 12, "models": [], "validated": null, "n": 1509, "authors_detailed": [ { "name": "Junwei Deng", "url": "https://openalex.org/A5054337132", "inst": "Shanghai Jiao Tong University" }, { "name": "Jiang, Xirui", "url": "", "inst": "" }, { "name": "Zhang, Shiyuan", "url": "", "inst": "" }, { "name": "Zhang, Shichang", "url": "", "inst": "" }, { "name": "Lakkaraju, Himabindu", "url": "", "inst": "" }, { "name": "Gao, Ruijiang", "url": "", "inst": "" }, { "name": "Donahue, Chris", "url": "", "inst": "" }, { "name": "Ma, Jiaqi W.", "url": "", "inst": "" } ], "affiliations": [ "Shanghai Jiao Tong University" ] }, { "uid": "doi:10.2139/ssrn.4650172", "doi": "10.2139/ssrn.4650172", "title": "The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective", "authors": [ "George Gui", "Olivier Toubia" ], "posted": "2023-12-08", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4650172", "field": "economics", "role": "method", "bullet_provenance": "none", "salience": 58, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 626, "authors_detailed": [ { "name": "George Gui", "url": "https://openalex.org/A5028759136", "inst": "Columbia University" }, { "name": "Olivier Toubia", "url": "https://openalex.org/A5024730984", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4650157", "doi": "10.2139/ssrn.4650157", "title": "Institutions to constrain chaotic robots: why generative AI needs blockchain", "authors": [ "Chris Berg", "Sinclair Davidson", "Jason Potts" ], "posted": "2023-12-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4650157", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of the economic alignment problem between LLM agents and human principals seeking to use AI for real-world economic transactions.", "Proposes smart contracts as deterministic constraint filters on unpredictable LLM outputs, enabling safe agent-principal interactions in economic settings.", "Smart contracts can bridge the economic alignment gap, allowing LLM agents to execute valuable real-world transactions by filtering outputs through deterministic institutional constraints." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 2459, "authors_detailed": [ { "name": "Chris Berg", "url": "https://openalex.org/A5083681237", "inst": "RMIT Europe" }, { "name": "Sinclair Davidson", "url": "https://openalex.org/A5038681222", "inst": "RMIT University" }, { "name": "Jason Potts", "url": "https://openalex.org/A5042648415", "inst": "Alfaisal University" } ], "affiliations": [ "RMIT Europe", "RMIT University", "Alfaisal University" ] }, { "uid": "doi:10.2139/ssrn.4601317", "doi": "10.2139/ssrn.4601317", "title": "Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts", "authors": [ "Zongxiao Wu", "Yizhe Dong", "Yaoyiran Li", "Baofeng Shi" ], "posted": "2023-12-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4601317", "field": "finance", "role": "instrument", "bullets": [ "Chinese lending platform loan applications with officer-written assessments used for credit default prediction.", "ChatGPT analyzed and refined loan officer texts; deep learning models incorporated refined texts alongside structured data for default classification.", "ChatGPT-refined texts significantly improved credit default prediction accuracy and yielded higher profitability than human-written text models." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "credit default prediction accuracy", "salience": 65, "n": 2460, "authors_detailed": [ { "name": "Zongxiao Wu", "url": "https://openalex.org/A5083073545", "inst": "University of Edinburgh" }, { "name": "Yizhe Dong", "url": "https://openalex.org/A5110984303", "inst": "Edinburgh College" }, { "name": "Yaoyiran Li", "url": "https://openalex.org/A5002228588", "inst": "University of Cambridge" }, { "name": "Baofeng Shi", "url": "https://openalex.org/A5055985047", "inst": "Agriculture and Forestry University" } ], "affiliations": [ "University of Cambridge", "University of Edinburgh", "Edinburgh College", "Agriculture and Forestry University" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4650025", "doi": "10.2139/ssrn.4650025", "title": "Impacts of Generative AI on User Contributions: Evidence from a Coding Q&A Platform", "authors": [ "Xinyu Li", "Keongtae Kim" ], "posted": "2023-12-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4650025", "field": "management", "role": "object", "bullets": [ "User contributions on a coding Q&A platform examined before and after ChatGPT's introduction, focusing on answer quality and highly engaged contributors.", "Difference-in-differences estimation measured ChatGPT's causal impact on the volume, quality, and contributor composition of user-generated answers on the platform.", "High-quality answers declined among top contributors despite an overall answer increase, driven by reduced motivation and inflated perceived question sophistication." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 2916, "authors_detailed": [ { "name": "Xinyu Li", "url": "https://openalex.org/A5088560560", "inst": "Chinese University of Hong Kong" }, { "name": "Keongtae Kim", "url": "https://openalex.org/A5070771775", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.4650113", "doi": "10.2139/ssrn.4650113", "title": "Machine Readership and Financial Reporting Decisions", "authors": [ "Sean S. Cao", "Ying Liang", "Youngseok Moon" ], "posted": "2023-12-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4650113", "field": "accounting", "role": "object", "bullets": [ "U.S. public firms studied to assess how machine readership of corporate financial statements affects managers' financial reporting decisions and misreporting patterns.", "Machine learning algorithms analyzed financial statements for nonlinear, high-dimensional misreporting patterns that traditional linear detection methods cannot identify.", "Greater machine readership reduced financial misreporting in complex patterns detectable by ML; no incremental effect on misreporting identifiable through traditional linear methods." ], "bullet_provenance": "ai", "models": [ "legacy" ], "salience": 45, "validated": null, "n": 3890, "authors_detailed": [ { "name": "Sean Cao", "url": "https://openalex.org/A5078983089", "inst": "Smith Institute" }, { "name": "Liang Ying", "url": "https://openalex.org/A5102894274", "inst": "Georgia State University" }, { "name": "Johnny Moon", "url": "https://openalex.org/A5003927801", "inst": "Georgia State University" } ], "affiliations": [ "Smith Institute", "Georgia State University" ] }, { "uid": "arxiv:2312.04180v3", "arxiv_id": "2312.04180v3", "title": "AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform", "authors": [ "Dandan Qiao", "Huaxia Rui", "Qian Xiong" ], "posted": "2023-12-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2312.04180v3", "field": "economics", "role": "object", "bullets": [ "Work volume and earnings across markets on an online labor platform around ChatGPT's launch, contrasting translation and localization with web development.", "ChatGPT is the studied shock rather than a tool; difference-in-differences around the 3.5 and 4.0 releases, interpreted with a Cournot model that yields a market-specific inflection point.", "Translation work and earnings fell while web development rose; US web developers gained most, and experienced translators were more likely to exit than less experienced ones." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "edition": 12, "validated": null, "n": 1508, "authors_detailed": [ { "name": "Dandan Qiao", "url": "https://openalex.org/A5101133951", "inst": "Crystal Pharmatech (United States)" }, { "name": "Huaxia Rui", "url": "https://openalex.org/A5024136020", "inst": "University of Rochester" }, { "name": "Xiong Qian", "url": "https://openalex.org/A5100968933", "inst": "Shinawatra University" } ], "affiliations": [ "University of Rochester", "Crystal Pharmatech (United States)", "Shinawatra University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4625139", "doi": "10.2139/ssrn.4625139", "title": "Artificial Intelligence (AI) and Job Creation: An Exploration of the Nature of the Jobs, Qualifications, and Compensations of Prompt Engineers", "authors": [ "Boniphace Kutela", "Norris Novat", "Norran Novat", "Jacob Herman", "Abdallah Kinero", "Sia Lyimo" ], "posted": "2023-12-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4625139", "field": "management", "role": "object", "bullets": [ "Over 100 prompt-engineering job advertisements analyzed using text-mining methods to characterize qualifications, duties, and compensation levels", "Text mining extracted job requirements, role descriptions, and salary data from postings for the emerging prompt engineer occupation", "Average prompt engineer salaries exceeded peers with comparable education; NLP, machine learning, and LLM experience were the most demanded qualifications" ], "bullet_provenance": "ai", "salience": 42, "models": [], "validated": null, "n": 2423, "authors_detailed": [ { "name": "Boniphace Kutela", "url": "https://openalex.org/A5024488533", "inst": "Texas Department of Transportation" }, { "name": "Norris Novat", "url": "https://openalex.org/A5008653687", "inst": "Western Michigan University" }, { "name": "Norran Novat", "url": "https://openalex.org/A5093443580", "inst": "Western Michigan University" }, { "name": "J. J. Herman", "url": "https://openalex.org/A5110446147", "inst": "Independent" }, { "name": "Abdallah Kinero", "url": "https://openalex.org/A5093443581", "inst": "Florida International University" }, { "name": "Sia Lyimo", "url": "https://openalex.org/A5093443582", "inst": "Progressive AE" } ], "affiliations": [ "Texas Department of Transportation", "Western Michigan University", "Independent", "Florida International University", "Progressive AE" ] }, { "uid": "doi:10.2139/ssrn.4652398", "doi": "10.2139/ssrn.4652398", "title": "The Impact of AI and Cross-Border Data Regulation on International Trade in Digital Services: A Large Language Model", "authors": [ "Ruiqi Sun", "Daniel Trefler" ], "posted": "2023-12-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4652398", "field": "economics", "role": "instrument", "bullets": [ "Usage data for 35,575 mobile apps across countries from 2015 to 2020, with AI patent data and cross-border data-flow restriction measures.", "An LLM linked each app to its developer's AI patents, enabling measurement of AI deployed per app; causal identification via Heckscher-Ohlin cost-shifter instrument.", "AI causally raises an app's foreign users by 2.67 log points; this impact is halved in countries with strong cross-border data-flow restrictions, typically autocracies." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 2458, "authors_detailed": [ { "name": "Ruiqi Sun", "url": "https://openalex.org/A5087195720", "inst": "University of Toronto" }, { "name": "Daniel Trefler", "url": "https://openalex.org/A5010606141", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "University of Toronto", "National Bureau of Economic Research" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4651059", "doi": "10.2139/ssrn.4651059", "title": "'This Time it's Different' - Generative Artificial Intelligence and Occupational Choice", "authors": [ "Daniel Goller", "Christian Gschwendt", "Stefan C. Wolter" ], "posted": "2023-12-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4651059", "field": "economics", "role": "object", "bullets": [ "Apprenticeship vacancy searches by young people analyzed before and after ChatGPT's launch, classified by occupation task type and automation risk ratings.", "The study measured the causal effect of ChatGPT's introduction on search intensity across occupations grouped by cognitive demands, language requirements, and prior automation risk.", "Search intensity declined most for occupations with high cognitive-task share and language demands, previously rated by experts as having low automation risk." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 2915, "authors_detailed": [ { "name": "Daniel Göller", "url": "https://openalex.org/A5060250689", "inst": "University of Bern" }, { "name": "Christian Gschwendt", "url": "https://openalex.org/A5040589919", "inst": "University of Bern" }, { "name": "Stefan C. Wolter", "url": "https://openalex.org/A5040159911", "inst": "University of Bern" } ], "affiliations": [ "University of Bern" ] }, { "uid": "doi:10.2139/ssrn.4641661", "doi": "10.2139/ssrn.4641661", "title": "Scale Development at a Scale: Exploring the Use of Synthetic Responses in Survey Research", "authors": [ "Dario Bonaretti", "Tais Barreto", "Anthony Parra", "Brandon Catalano" ], "posted": "2023-12-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4641661", "field": "management", "role": "method", "bullets": [ "Synthetic survey responses generated by GPT-3.5 compared against a previously validated Information Systems measurement scale for psychometric replication.", "GPT-3.5 simulated human-like Likert-scale responses; resulting correlation matrices were benchmarked against validated scale correlation patterns and semantic similarity approaches.", "Synthetic responses approximated validated scale correlations more closely than semantic-similarity methods but still failed to fully replicate human response patterns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "synthetic correlation patterns vs. validated IS scale", "salience": 45, "n": 2914, "authors_detailed": [ { "name": "Dario Bonaretti", "url": "https://openalex.org/A5038878410", "inst": "Nova Southeastern University" }, { "name": "Taís Barreto", "url": "https://openalex.org/A5015570331", "inst": "Nova Southeastern University" }, { "name": "A. Apitz de Parra", "url": "https://openalex.org/A5113752241", "inst": "Nova Southeastern University" }, { "name": "Brandon Catalano", "url": "https://openalex.org/A5093401183", "inst": "Nova Southeastern University" } ], "affiliations": [ "Nova Southeastern University" ] }, { "uid": "doi:10.2139/ssrn.4642520", "doi": "10.2139/ssrn.4642520", "title": "GPTs Spitball about OpenAI", "authors": [ "Michael zur Muehlen" ], "posted": "2023-12-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4642520", "field": "management", "role": "method", "bullets": [ "OpenAI boardroom crisis of November 2023, four GPT-based chatbots prompted on contemporaneous corporate leadership events.", "Four GPT chatbots generated summaries, historical analogies, and forward-looking scenarios; evaluated for veracity and usability.", "Chatbots excelled at summarization but hallucinated on timelines and historical facts; cited sources did not guarantee relevance or accuracy." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 35, "n": 3343, "authors_detailed": [ { "name": "Michael zur Muehlen", "url": "https://openalex.org/A5080981623", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Stevens Institute of Technology" ] }, { "uid": "doi:10.1609/aaai.v38i21.30574", "doi": "10.1609/aaai.v38i21.30574", "arxiv_id": "2311.18481v1", "title": "ESG Accountability Made Easy: DocQA at Your Service", "authors": [ "Lokesh Mishra", "Cesar Berrospi", "Kasper Dinkla", "Diego Antognini", "Francesco Fusco", "Benedikt Bothur", "Maksym Lysak", "Nikolaos Livathinos", "Ahmed Nassar", "Panagiotis Vagenas", "Lucas Morin", "Christoph Auer", "Michele Dolfi", "Peter Staar" ], "posted": "2023-11-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.18481v1", "field": "accounting", "role": "method", "bullets": [ "Over 10,000 ESG disclosure reports from more than 2,000 corporations, made searchable through a conversational question answering assistant on the Deep Search platform.", "Computer vision converts documents, retrieval finds passages, and a large language model writes answers; no specific model is named and no accuracy evaluation is reported.", "The contribution is a publicly accessible tool for querying corporate ESG reports at scale; the abstract reports no quantitative findings about disclosure content." ], "bullet_provenance": "ai", "validated": false, "salience": 30, "edition": 12, "models": [], "n": 1474, "authors_detailed": [ { "name": "Lokesh Mishra", "url": "https://openalex.org/A5031402623", "inst": "IBM Research - Zurich" }, { "name": "Cèsar Berrospi", "url": "https://openalex.org/A5039997281", "inst": "IBM Research - Zurich" }, { "name": "Kasper Dinkla", "url": "https://openalex.org/A5022287420", "inst": "IBM Research - Zurich" }, { "name": "Diego Antognini", "url": "https://openalex.org/A5040463317", "inst": "IBM Research - Zurich" }, { "name": "Francesco Fusco", "url": "https://openalex.org/A5063888848", "inst": "IBM Research - Zurich" }, { "name": "Benedikt Bothur", "url": "https://openalex.org/A5093402742", "inst": "IBM Research - Zurich" }, { "name": "Maksym Lysak", "url": "https://openalex.org/A5024178356", "inst": "IBM Research - Zurich" }, { "name": "Nikolaos Livathinos", "url": "https://openalex.org/A5084933934", "inst": "IBM Research - Zurich" }, { "name": "Ahmed Nassar", "url": "https://openalex.org/A5021157455", "inst": "IBM Research - Zurich" }, { "name": "Panagiotis Vagenas", "url": "https://openalex.org/A5078566296", "inst": "IBM Research - Zurich" }, { "name": "Lucas Morin", "url": "https://openalex.org/A5005557687", "inst": "ETH Zurich" }, { "name": "Christoph Auer", "url": "https://openalex.org/A5046722997", "inst": "IBM Research - Zurich" }, { "name": "Michele Dolfi", "url": "https://openalex.org/A5038675415", "inst": "IBM Research - Zurich" }, { "name": "Peter Staar", "url": "https://openalex.org/A5024778597", "inst": "IBM Research - Zurich" } ], "affiliations": [ "IBM Research - Zurich", "ETH Zurich" ] }, { "uid": "doi:10.2139/ssrn.4637359", "doi": "10.2139/ssrn.4637359", "title": "Ethical Exploration of ChatGPT in the Modern K-14 Economics Classroom", "authors": [ "Brad Scott", "Sandy van der Poel" ], "posted": "2023-11-30", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4637359", "field": "economics", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 20, "edition": 12, "bullets": [], "validated": null, "n": 1505, "authors_detailed": [ { "name": "Brad Scott", "url": "https://openalex.org/A5091691244", "inst": "Rowan-Cabarrus Community College" }, { "name": "Sandy van der Poel", "url": "https://openalex.org/A5093382981", "inst": "Gardner–Webb University" } ], "affiliations": [ "Rowan-Cabarrus Community College", "Gardner–Webb University" ] }, { "uid": "doi:10.2139/ssrn.4649097", "doi": "10.2139/ssrn.4649097", "title": "Technology Shock of Chatgpt, Social Attention and Firm Value: Evidence from China", "authors": [ "Qinqin Wu", "Qinqin Zhuang", "yitong liu", "Longyan Han" ], "posted": "2023-11-30", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4649097", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 35, "edition": 12, "bullets": [], "validated": null, "n": 1506, "authors_detailed": [ { "name": "Qinqin Wu", "url": "https://openalex.org/A5092727457", "inst": "Jiangsu University" }, { "name": "Qinqin Zhuang", "url": "https://openalex.org/A5014792308", "inst": "Chinese Academy of Social Sciences" }, { "name": "Yitong Liu", "url": "https://openalex.org/A5061961881", "inst": "Shanghai Jiao Tong University" }, { "name": "Longyan Han", "url": "https://openalex.org/A5110666438", "inst": "Hefei University of Technology" } ], "affiliations": [ "Jiangsu University", "Chinese Academy of Social Sciences", "Shanghai Jiao Tong University", "Hefei University of Technology" ] }, { "uid": "doi:10.2139/ssrn.4632334", "doi": "10.2139/ssrn.4632334", "title": "Why Do Accounting Students Procrastinate? A Qualitative Analysis Using ChatGPT", "authors": [ "Eva Blondeel", "Patricia C. Everaert", "Evelien Opdecam" ], "posted": "2023-11-30", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4632334", "field": "accounting", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 26, "edition": 12, "bullets": [], "validated": null, "n": 1507, "authors_detailed": [ { "name": "Eva Blondeel", "url": "https://openalex.org/A5078612290", "inst": "Ghent University Hospital" }, { "name": "Patricia Everaert", "url": "https://openalex.org/A5007735131", "inst": "Ghent University Hospital" }, { "name": "Evelien Opdecam", "url": "https://openalex.org/A5058701731", "inst": "Ghent University Hospital" } ], "affiliations": [ "Ghent University Hospital" ] }, { "uid": "doi:10.2139/ssrn.4632842", "doi": "10.2139/ssrn.4632842", "title": "ChatGPT Hallucinates Nonexistent Citations: Evidence from Economics", "authors": [ "Joy Buchanan", "Stephen Hill", "Olga Shapoval" ], "posted": "2023-11-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4632842", "field": "economics", "role": "method", "bullets": [ "Prompts derived from every topic in the Journal of Economic Literature, testing GPT-3.5 and GPT-4 citation accuracy across economics subfields.", "GPT-3.5 and GPT-4 generated summaries with citations for economics topics; each citation was manually verified for existence in scholarly databases.", "Over 30% of GPT-3.5 citations were fabricated with only slight improvement in GPT-4; hallucination rates increased with more specific prompts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "citation existence verification across JEL topics", "salience": 58, "n": 2390, "authors_detailed": [ { "name": "Joy Buchanan", "url": "https://openalex.org/A5079266514", "inst": "Samford University" }, { "name": "Stephen Hill", "url": "https://openalex.org/A5052119665", "inst": "Samford University" }, { "name": "Olga Shapoval", "url": "https://openalex.org/A5050201446", "inst": "Samford University" } ], "affiliations": [ "Samford University" ] }, { "uid": "doi:10.2139/ssrn.4633383", "doi": "10.2139/ssrn.4633383", "title": "Harnessing ChatGPT-4 and Explainable AI for Financial Nowcasting", "authors": [ "Thomas Yue", "David Au" ], "posted": "2023-11-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4633383", "field": "finance", "role": "instrument", "bullets": [ "Financial market data with over 1,000 features analyzed using Hidden Markov Models and XGBoost for real-time nowcasting report generation.", "GPT-4 combined with Shapley values from Explainable AI converted complex ML predictions into human-readable financial nowcasting reports for market participants.", "The system produces interpretable nowcasting reports translating high-dimensional ML outputs into actionable insights for investors, traders, and analysts." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 2912, "authors_detailed": [ { "name": "Thomas Yue", "url": "https://openalex.org/A5093382543", "inst": "Queen Elizabeth Hospital" }, { "name": "David H. Au", "url": "https://openalex.org/A5067072009", "inst": "Queen Elizabeth Hospital" } ], "affiliations": [ "Queen Elizabeth Hospital" ] }, { "uid": "doi:10.2139/ssrn.4634986", "doi": "10.2139/ssrn.4634986", "title": "The Influencer Copycats", "authors": [ "Haiyang Bian", "Emma Li", "Lu Liu", "Zhengwei Wang" ], "posted": "2023-11-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4634986", "field": "finance", "role": "instrument", "bullets": [ "Influencer posts from a prominent financial social media platform, examining follower investment amounts, short-term following, and content monetization outcomes.", "Large language models measured semantic similarity among influencer posts to quantify imitation of high-performing peers on the financial social media platform.", "Influencer post imitation increases short-term followers and monetization potential but does not improve retail investor followers' investment performance." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 60, "n": 2913, "authors_detailed": [ { "name": "Haiyang Bian", "url": "https://openalex.org/A5019235398", "inst": "Tsinghua University" }, { "name": "Emma Li", "url": "https://openalex.org/A5093386455", "inst": "Burwood Hospital" }, { "name": "Lu Liu", "url": "https://openalex.org/A5100712012", "inst": "Central University of Finance and Economics" }, { "name": "Zhengwei Wang", "url": "https://openalex.org/A5017132636", "inst": "Tsinghua University" } ], "affiliations": [ "Tsinghua University", "Central University of Finance and Economics" ] }, { "uid": "doi:10.2139/ssrn.4637354", "doi": "10.2139/ssrn.4637354", "title": "A Turing Test: Are AI Chatbots Behaviorally Similar to Humans?", "authors": [ "Qiaozhu Mei", "Yutong Xie", "Walter Yuan", "Matthew O. Jackson" ], "posted": "2023-11-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4637354", "field": "economics", "role": "agent", "bullets": [ "Suite of classic behavioral games with tens of thousands of human subjects from over 50 countries as benchmark comparison.", "ChatGPT-4 played trust, fairness, risk-aversion, and cooperation games and completed Big-5 personality surveys alongside human baselines.", "GPT-4 behavior was statistically indistinguishable from a random human but skewed toward the more altruistic and cooperative end of the distribution." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "compared against tens of thousands of human experimental subjects", "salience": 75, "n": 3341, "authors_detailed": [ { "name": "Qiaozhu Mei", "url": "https://openalex.org/A5048955398", "inst": "University of Michigan" }, { "name": "Yutong Xie", "url": "https://openalex.org/A5011835422", "inst": "University of Michigan" }, { "name": "Walter Yuan", "url": "https://openalex.org/A5003788131", "inst": "MobLab" }, { "name": "Matthew O. Jackson", "url": "https://openalex.org/A5044618645", "inst": "Santa Fe Institute" } ], "affiliations": [ "University of Michigan", "MobLab", "Santa Fe Institute" ] }, { "uid": "doi:10.2139/ssrn.4630856", "doi": "10.2139/ssrn.4630856", "title": "Generative Artificial Intelligence and Design Co-Creation in Luxury New Product Development: The Power of Discarded Ideas", "authors": [ "Page Moreau", "Emanuela Prandelli", "Martin Schreier" ], "posted": "2023-11-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4630856", "field": "management", "role": "object", "bullets": [ "Luxury fashion t-shirt co-creation with Missoni and AWAYTOMARS platform, controlled experiment with over 1,000 participants plus 20 weeks of sales data.", "IBM generative AI produced design recommendations interpreted by human designers; consumer preferences compared in a randomized experiment.", "Consumers preferred AI-designed shirts when unaware of the source; AI-designed products achieved a 127% sales lift over human-only designs." ], "bullet_provenance": "ai", "models": [ "legacy" ], "salience": 72, "validated": null, "n": 3342, "authors_detailed": [ { "name": "Page Moreau", "url": "https://openalex.org/A5008102013", "inst": "University of Wisconsin System" }, { "name": "Emanuela Prandelli", "url": "https://openalex.org/A5056840058", "inst": "Bocconi University" }, { "name": "Martin Schreier", "url": "https://openalex.org/A5057959086", "inst": "Vienna University of Economics and Business" } ], "affiliations": [ "Bocconi University", "University of Wisconsin System", "Vienna University of Economics and Business" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4622405", "doi": "10.2139/ssrn.4622405", "title": "The risks of risk-based AI regulation: taking liability seriously", "authors": [ "Martin Kretschmer", "Tobias Kretschmer", "Alexander Peukert", "Christian Peukert" ], "posted": "2023-11-29", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4622405", "field": "economics", "role": "object", "bullets": [ "Legal and policy analysis examines the European Union AI Act during final negotiations and its expected extraterritorial effects on developers and deployers.", "No language model is used in the analysis; GPT-4 appears only as the capability reference in proposals for a training moratorium.", "The risk-based regime's data-quality and oversight duties are criticized, with liability allocated by endogenous versus exogenous harm proposed as the stronger regulatory mechanism." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 51, "edition": 23, "validated": null, "n": 4184, "authors_detailed": [ { "name": "Martin Kretschmer", "url": "https://openalex.org/A5014209822", "inst": "University of Glasgow" }, { "name": "Tobias Kretschmer", "url": "https://openalex.org/A5040198507", "inst": "Munich Business School" }, { "name": "Alexander Peukert", "url": "https://openalex.org/A5053928981", "inst": "Goethe University Frankfurt" }, { "name": "Christian Peukert", "url": "https://openalex.org/A5086582254", "inst": "University of Music Lausanne" } ], "affiliations": [ "University of Glasgow", "Munich Business School", "Goethe University Frankfurt", "University of Music Lausanne" ] }, { "uid": "arxiv:2312.02181v1", "arxiv_id": "2312.02181v1", "title": "How Generative-AI can be Effectively used in Government Chatbots", "authors": [ "Zeteng Lin" ], "posted": "2023-11-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2312.02181v1", "field": "management", "role": "object", "bullets": [ "Question answering chatbots for government services in Guangdong Province, China, examined alongside two general purpose chatbot systems in a horizontal comparison.", "ChatGPT and Baidu's Wenxin Ernie provide the comparison points; model versions are not stated and no quantitative evaluation or validation is described.", "Guangdong's government chatbots are judged exploratory with a clear capability gap relative to the LLMs; the paper outlines optimization paths for applying generative AI." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 22, "edition": 12, "validated": null, "n": 1456, "authors_detailed": [ { "name": "Zeteng Lin", "url": "https://openalex.org/A5080208009", "inst": "Shenzhen University" } ], "affiliations": [ "Shenzhen University" ] }, { "uid": "doi:10.2139/ssrn.4635674", "doi": "10.2139/ssrn.4635674", "title": "Do People Trust Humans More Than ChatGPT?", "authors": [ "Joy Buchanan", "William Hickman" ], "posted": "2023-11-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4635674", "field": "economics", "role": "object", "bullets": [ "Experiment with participants rating accuracy of statements under varying authorship disclosure conditions, comparing trust in human-written versus ChatGPT-generated content.", "ChatGPT generated statements presented alongside human-written ones; participants assessed accuracy and chose whether to engage in costly fact-checking across disclosure treatments.", "Without disclosure, participants trusted human-attributed statements more than AI-attributed ones; with explicit disclosure, skepticism equalized and costly fact-checking rates increased." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 2457, "authors_detailed": [ { "name": "Joy Buchanan", "url": "https://openalex.org/A5079266514", "inst": "Samford University" }, { "name": "W. Braddock Hickman", "url": "https://openalex.org/A5050618611", "inst": "George Mason University" } ], "affiliations": [ "Samford University", "George Mason University" ] }, { "uid": "doi:10.2139/ssrn.4627587", "doi": "10.2139/ssrn.4627587", "title": "The Costs of Housing Regulation: Evidence From Generative Regulatory Measurement", "authors": [ "Alexander Bartik", "Arpit Gupta", "Daniel Milo" ], "posted": "2023-11-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4627587", "field": "economics", "role": "instrument", "bullets": [ "U.S. municipal zoning regulations, cross-sectional analysis of housing regulation documents across jurisdictions.", "Large language models interpreted administrative zoning documents with 96% binary classification accuracy and 0.87 correlation on continuous questions.", "Zoning has two principal components: value capture in high-demand areas and exclusionary zoning that raises housing costs and socioeconomic exclusion." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "96% binary classification accuracy, 0.87 correlation on continuous questions", "salience": 78, "n": 3340, "authors_detailed": [ { "name": "Alexander Bartik", "url": "https://openalex.org/A5024397171", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Arpit Gupta", "url": "https://openalex.org/A5046100948", "inst": "New York University" }, { "name": "Daniel Milo", "url": "https://openalex.org/A5093370913", "inst": "New York University" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "New York University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2311.16466v4", "arxiv_id": "2311.16466v4", "title": "The Adoption and Efficacy of Large Language Models: Evidence From Consumer Complaints in the Financial Industry", "authors": [ "Minkyu Shin", "Jin Kim", "Jiwoong Shin" ], "posted": "2023-11-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.16466v4", "field": "management", "role": "object", "bullets": [ "More than one million consumer complaints filed with the US Consumer Financial Protection Bureau between 2015 and 2024, spanning the release of ChatGPT.", "Complaints drafted with LLM assistance are identified by a detector; the abstract does not describe the detection method or report its accuracy.", "Detected LLM use jumped after ChatGPT launched and predicts a higher chance of relief; instrumental variable and experimental evidence attribute the gain to clearer, more persuasive narratives." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 66, "edition": 12, "n": 1382, "authors_detailed": [ { "name": "Minkyu Shin", "url": "https://openalex.org/A5054123046", "inst": "City University of Hong Kong" }, { "name": "Jin Kim", "url": "https://openalex.org/A5100380879", "inst": "College of Saint Rose" }, { "name": "Shin, Jiwoong", "url": "", "inst": "" } ], "affiliations": [ "City University of Hong Kong", "College of Saint Rose" ] }, { "uid": "doi:10.2139/ssrn.4639565", "doi": "10.2139/ssrn.4639565", "title": "Measuring Tax Enforcement with Generative AI", "authors": [ "Daphne Armstrong" ], "posted": "2023-11-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4639565", "field": "accounting", "role": "instrument", "bullets": [ "U.S. publicly traded firms filing 10-K disclosures, time-series analysis of IRS audit disclosure variation across multiple years.", "A generative AI model classified active IRS audits from 10-K narrative text, achieving 96% accuracy against manual researcher labels.", "Disclosed audit measures correlate positively with IRS-published audit probabilities, IRS 10-K downloads, and changes in unrecognized tax benefits." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "96% accuracy vs manual researcher labels", "salience": 70, "n": 3338, "authors_detailed": [ { "name": "Daphne M. Armstrong", "url": "https://openalex.org/A5018862758", "inst": "University of North Carolina at Chapel Hill" } ], "affiliations": [ "University of North Carolina at Chapel Hill" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4645865", "doi": "10.2139/ssrn.4645865", "title": "Firm-Level Tax Audits: A Generative AI-Based Measurement", "authors": [ "Ga-Young Choi", "Alex G. Kim" ], "posted": "2023-11-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4645865", "field": "accounting", "role": "instrument", "bullets": [ "U.S. public firms with 10-K narrative tax disclosures, firm-year panel validated against official IRS audit data.", "Generative AI analyzed narrative disclosures to construct a novel firm-year tax audit enforcement measure aligned with official IRS records.", "Firms cut tax avoidance during audits with persistence post-audit; indirect deterrence on peer firms equals 50-60% of the direct effect." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "validated against official IRS audit data", "salience": 72, "n": 3339, "authors_detailed": [ { "name": "Gayoung Choi", "url": "https://openalex.org/A5102404808", "inst": "City, University of London" }, { "name": "Alex Kim", "url": "https://openalex.org/A5002013850", "inst": "Woodlawn School" } ], "affiliations": [ "City, University of London", "Woodlawn School" ] }, { "uid": "doi:10.1016/j.chbah.2024.100058", "doi": "10.1016/j.chbah.2024.100058", "arxiv_id": "2311.15544v2", "title": "The effect of source disclosure on evaluation of AI-generated messages: A two-part study", "authors": [ "Sue Lim", "Ralf Schmälzle" ], "posted": "2023-11-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.15544v2", "field": "management", "role": "object", "bullets": [ "Two experiments on vaping prevention messages, the first pre-registered, in which participants evaluated AI generated and human written texts under varying source labels; participant numbers are not stated.", "Messages came from an LLM the abstract does not name; message quality is assessed only through participant ratings, so no benchmark validation applies.", "Labeling a message as AI authored lowered evaluations but left rankings unchanged; negative attitudes toward AI moderated the penalty, and moderately negative participants preferred AI messages less after disclosure." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 12, "validated": null, "n": 1381, "authors_detailed": [ { "name": "Sue Lim", "url": "https://openalex.org/A5103108072", "inst": "Michigan State University" }, { "name": "Ralf Schmälzle", "url": "https://openalex.org/A5056305769", "inst": "Michigan State University" } ], "affiliations": [ "Michigan State University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2311.15548v1", "arxiv_id": "2311.15548v1", "title": "Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination", "authors": [ "Haoqiang Kang", "Xiao-Yang Liu" ], "posted": "2023-11-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.15548v1", "field": "finance", "role": "method", "bullets": [ "Financial tasks spanning explanation of financial concepts and terminology and retrieval of historical stock prices, used to probe factual reliability of language models.", "Off the shelf LLMs, not named in the abstract, are tested against factual ground truth, and few shot prompting, DoLa decoding, retrieval augmentation, and prompt based tool use are assessed as mitigations.", "The models hallucinate seriously across the financial tasks; the abstract reports no specific error rates but concludes mitigation research is urgently needed." ], "bullet_provenance": "ai", "validated": true, "validation_note": "answers checked against financial terminology and historical price records", "salience": 45, "edition": 12, "models": [], "n": 1417, "authors_detailed": [ { "name": "Haoqiang Kang", "url": "https://openalex.org/A5113057252", "inst": "" }, { "name": "Xiao-Yang Liu", "url": "https://openalex.org/A5100405233", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4615038", "doi": "10.2139/ssrn.4615038", "title": "Contextualized Sentiment Analysis using Large Language Models", "authors": [ "Christian Breitung", "Garvin Kruthof", "Sebastian Müller" ], "posted": "2023-11-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4615038", "field": "finance", "role": "instrument", "bullets": [ "Commodity price news headlines tested across multiple industries, language models, and prompting strategies for industry-specific sentiment prediction", "Multiple LLMs derived industry-specific sentiments from economic news using zero-shot prompting with varying levels of context granularity", "Sentiment accuracy improved with finer context granularity and varied significantly across topic areas, models, and prompt designs" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 58, "n": 2422, "authors_detailed": [ { "name": "Christian Breitung", "url": "https://openalex.org/A5050609944", "inst": "Technical University of Munich" }, { "name": "Garvin Kruthof", "url": "https://openalex.org/A5093345216", "inst": "Technical University of Munich" }, { "name": "Sebastian Müller", "url": "https://openalex.org/A5006795021", "inst": "Technical University of Munich" } ], "affiliations": [ "Technical University of Munich" ] }, { "uid": "doi:10.2139/ssrn.4643656", "doi": "10.2139/ssrn.4643656", "title": "Fed Transparency and Policy Expectation Errors: A Text Analysis Approach", "authors": [ "Eric Fischer", "Rebecca McCaughrin", "Saketh Prazad", "Mark Vandergon" ], "posted": "2023-11-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4643656", "field": "economics", "role": "instrument", "bullets": [ "FOMC meeting transcripts, Tealbooks, and market-implied policy expectations analyzed across easing and tightening cycles including the 2001 and 2008 recessions.", "LLM-based text analysis extracted information from lagged FOMC documents to test whether real-time access would have improved market policy rate forecasts.", "Real-time access could have reduced mean squared forecast error by 40-50 percent during recessions, predicting up to 125 basis points of additional easing." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "MSE of policy rate forecasts vs. realized rates", "salience": 70, "n": 2911, "authors_detailed": [ { "name": "Eric Fischer", "url": "https://openalex.org/A5027391489", "inst": "Federal Reserve Bank of New York" }, { "name": "Rebecca McCaughrin", "url": "https://openalex.org/A5038663347", "inst": "Federal Reserve Bank of New York" }, { "name": "Saketh Prazad", "url": "https://openalex.org/A5093333048", "inst": "Harvard University Press" }, { "name": "Mark Vandergon", "url": "https://openalex.org/A5093333049", "inst": "Federal Reserve Bank of New York" } ], "affiliations": [ "Harvard University", "Federal Reserve Bank of New York" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2311.15180v1", "arxiv_id": "2311.15180v1", "title": "Benchmarking Large Language Model Volatility", "authors": [ "Boyang Yu" ], "posted": "2023-11-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.15180v1", "field": "finance", "role": "method", "bullets": [ "A case study of US equity investing where sentence level sentiment classification of news articles feeds directly into portfolio construction and returns.", "An LLM, unnamed in the abstract, classifies the same sentences repeatedly; the study tracks run to run instability rather than accuracy, and reports no ground truth check.", "Output volatility cascades into sizeable dispersion in portfolios and returns; cutting temperature or ensembling outputs damps it at the cost of creativity or compute." ], "bullet_provenance": "ai", "validated": false, "salience": 52, "edition": 12, "models": [], "n": 1455, "authors_detailed": [ { "name": "Boyang Yu", "url": "https://openalex.org/A5101502352", "inst": "Ludwig-Maximilians-Universität München" } ], "affiliations": [ "Ludwig-Maximilians-Universität München" ] }, { "uid": "doi:10.2139/ssrn.4614228", "doi": "10.2139/ssrn.4614228", "title": "Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models", "authors": [ "Emilio Ferrara" ], "posted": "2023-11-25", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4614228", "field": "management", "role": "object", "bullets": [ "Conceptual review of bias in generative language models across virtual assistants, content-generation tools, and chatbots; no empirical sample, geography, or study period is reported.", "ChatGPT is the named example rather than a research instrument; the paper reviews bias sources and mitigation approaches, making output validation inapplicable.", "Bias can originate in training data, model design, algorithms, products, and policy, while mitigation cannot eliminate every risk from deployment across varied applications." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 37, "edition": 23, "validated": null, "n": 4183, "authors_detailed": [ { "name": "Emilio Ferrara", "url": "https://openalex.org/A5078699564", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2311.13743v2", "arxiv_id": "2311.13743v2", "title": "FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design", "authors": [ "Yangyang Yu", "Haohang Li", "Zhi Chen", "Yuechen Jiang", "Yang Li", "Denghui Zhang", "Rong Liu", "Jordan W. Suchow", "Khaldoun Khashanah" ], "posted": "2023-11-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.13743v2", "field": "finance", "role": "agent", "bullets": [ "Stock trading backtests on a real world financial dataset; the abstract does not state the period, tickers, or number of assets.", "An LLM agent, base model not named, combines profiling, layered memory with adjustable cognitive span, and a decision module; comparisons are against algorithmic trading agents rather than labelled ground truth.", "FinMem reports higher cumulative returns than the algorithmic baselines, and tuning the perceptual span and character settings raises trading performance further." ], "bullet_provenance": "ai", "salience": 40, "edition": 12, "models": [], "validated": null, "n": 1416, "authors_detailed": [ { "name": "Yangyang Yu", "url": "https://openalex.org/A5063210163", "inst": "Stevens Institute of Technology" }, { "name": "Haohang Li", "url": "https://openalex.org/A5020982913", "inst": "Stevens Institute of Technology" }, { "name": "Zhi Chen", "url": "https://openalex.org/A5100456818", "inst": "University of Kentucky" }, { "name": "Yuechen Jiang", "url": "https://openalex.org/A5101106002", "inst": "Stevens Institute of Technology" }, { "name": "Yang Li", "url": "https://openalex.org/A5100385601", "inst": "Shenzhen University" }, { "name": "Denghui Zhang", "url": "https://openalex.org/A5101638182", "inst": "Qilu University of Technology" }, { "name": "Rong Liu", "url": "https://openalex.org/A5100782665", "inst": "Huazhong University of Science and Technology" }, { "name": "Jordan W. Suchow", "url": "https://openalex.org/A5069454833", "inst": "Stevens Institute of Technology" }, { "name": "Khaldoun Khashanah", "url": "https://openalex.org/A5027284723", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Stevens Institute of Technology", "University of Kentucky", "Shenzhen University", "Qilu University of Technology", "Huazhong University of Science and Technology" ] }, { "uid": "arxiv:2311.11944v1", "arxiv_id": "2311.11944v1", "title": "FinanceBench: A New Benchmark for Financial Question Answering", "authors": [ "Pranab Islam", "Anand Kannappan", "Douwe Kiela", "Rebecca Qian", "Nino Scherrer", "Bertie Vidgen" ], "posted": "2023-11-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.11944v1", "field": "finance", "role": "method", "bullets": [ "10,231 open book questions about publicly traded companies with answers and evidence strings; 150 sampled cases are used for the model evaluation.", "Sixteen configurations, including GPT-4-Turbo, Llama 2, and Claude 2 with vector stores or long context prompts, answer the cases, and 2,400 responses are manually reviewed.", "GPT-4-Turbo with retrieval answers incorrectly or refuses on 81 percent of questions, and every tested model hallucinates enough to limit enterprise financial use." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "manual review of 2,400 answers against labelled QA pairs", "salience": 62, "edition": 12, "n": 1415, "authors_detailed": [ { "name": "Pranab Islam", "url": "https://openalex.org/A5093321104", "inst": "" }, { "name": "A. Kannappan", "url": "https://openalex.org/A5024319800", "inst": "Indian Institute of Technology Madras" }, { "name": "Douwe Kiela", "url": "https://openalex.org/A5016956470", "inst": "Supélec" }, { "name": "Rebecca Qian", "url": "https://openalex.org/A5014742037", "inst": "Vanderbilt University" }, { "name": "Nino Scherrer", "url": "https://openalex.org/A5093272033", "inst": "Bocconi University" }, { "name": "Bertie Vidgen", "url": "https://openalex.org/A5084936290", "inst": "Contextual Change (United States)" } ], "affiliations": [ "Vanderbilt University", "Bocconi University", "Indian Institute of Technology Madras", "Supélec", "Contextual Change (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4611557", "doi": "10.2139/ssrn.4611557", "title": "Creating Innovation Value from Generative Ai: A Property Rights Perspective", "authors": [ "Steven Phelan", "Yuanyuan Cui", "Patrick van Esch", "Gopal Das" ], "posted": "2023-11-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4611557", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of property rights allocation for data used in training generative AI models, drawing on governance and open innovation frameworks.", "Examines collective action problems from LLM training on billions of web-scraped textual artifacts without data holders' consent, analyzing multiple governance mechanisms.", "Proposes resolution through government regulation, market contracting, and coalitional contracting to allocate property rights over data flowing into and out of AI models." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 35, "validated": null, "n": 2378, "authors_detailed": [ { "name": "Steven E. Phelan", "url": "https://openalex.org/A5056231827", "inst": "Kennesaw State University" }, { "name": "Yuanyuan Cui", "url": "https://openalex.org/A5039589689", "inst": "University of Auckland" }, { "name": "Patrick van Esch", "url": "https://openalex.org/A5030170320", "inst": "Kennesaw State University" }, { "name": "Gopal Das", "url": "https://openalex.org/A5058745565", "inst": "Indian Institute of Management Bangalore" } ], "affiliations": [ "Kennesaw State University", "University of Auckland", "Indian Institute of Management Bangalore" ] }, { "uid": "doi:10.2139/ssrn.4611256", "doi": "10.2139/ssrn.4611256", "title": "The Economics of Generative AI", "authors": [ "Jinglei Huang", "Wenshi Wei", "Danxia Xie" ], "posted": "2023-11-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4611256", "field": "economics", "role": "object", "bullets": [ "Theoretical model of the digital economy analyzing interactions among data accumulation, computing power, and storage as inputs to production and innovation processes.", "No specific LLM deployed; formal economic model examines how data-driven production and innovation interact with rival computing resources and binding storage constraints in the AI economy.", "Model predicts four equilibrium distortions in the AI economy: overinvestment in computing, innovation bottlenecks from storage limits, insufficient cross-firm data-sharing, and systematic data overuse by incumbents." ], "bullet_provenance": "ai", "salience": 35, "models": [], "validated": null, "n": 3337, "authors_detailed": [ { "name": "Jinglei Huang", "url": "https://openalex.org/A5056724968", "inst": "University of International Business and Economics" }, { "name": "Wenshi Wei", "url": "https://openalex.org/A5075651651", "inst": "Tsinghua University" }, { "name": "Danxia Xie", "url": "https://openalex.org/A5147224580", "inst": "Tsinghua University" } ], "affiliations": [ "University of International Business and Economics", "Tsinghua University" ] }, { "uid": "arxiv:2311.14720v2", "arxiv_id": "2311.14720v2", "title": "Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals", "authors": [ "Nir Chemaya", "Daniel Martin" ], "posted": "2023-11-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.14720v2", "field": "other", "role": "object", "bullets": [ "Survey of academics on whether using AI tools such as ChatGPT to revise manuscripts requires disclosure, paired with tests of AI detectors on academic writing; sample sizes are not stated.", "The abstract does not name the LLM used to produce revised text or the detection services examined, and it reports no detector accuracy figures.", "The abstract poses the questions about disclosure norms and detector reactions to AI assisted revision but reports no findings or effect sizes." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 32, "edition": 12, "validated": null, "n": 1380, "authors_detailed": [ { "name": "Nir Chemaya", "url": "https://openalex.org/A5093367348", "inst": "University of California, Santa Barbara" }, { "name": "Daniel E. Martin", "url": "https://openalex.org/A5102022416", "inst": "Clemson University" } ], "affiliations": [ "University of California, Santa Barbara", "Clemson University" ] }, { "uid": "arxiv:2311.14722v1", "arxiv_id": "2311.14722v1", "title": "Zero-Shot Question Answering over Financial Documents using Large Language Models", "authors": [ "Karmvir Singh Phogat", "Chetan Harsha", "Sridhar Dasaratha", "Shashishekar Ramakrishna", "Sai Akhil Puranam" ], "posted": "2023-11-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.14722v1", "field": "finance", "role": "method", "bullets": [ "Three financial question answering datasets whose questions require multi hop numerical reasoning over the text and tables of corporate financial reports.", "GPT family models receive zero shot prompts that translate each question into a Python or domain specific language program, which an interpreter then executes; accuracy is compared with zero shot baselines.", "Program generation improves accuracy for every model tested relative to direct zero shot prompting; the abstract does not quantify the gains." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "three financial QA benchmarks", "salience": 42, "edition": 12, "n": 1454, "authors_detailed": [ { "name": "Karmvir Singh Phogat", "url": "https://openalex.org/A5078396145", "inst": "Korea Advanced Institute of Science and Technology" }, { "name": "Chetan Harsha", "url": "https://openalex.org/A5114103611", "inst": "" }, { "name": "Sridhar Dasaratha", "url": "https://openalex.org/A5043028040", "inst": "Global Services (Slovakia)" }, { "name": "Shashishekar Ramakrishna", "url": "https://openalex.org/A5034824025", "inst": "Global Services (Slovakia)" }, { "name": "Sai Akhil Puranam", "url": "https://openalex.org/A5048110017", "inst": "Birla Institute of Technology and Science - Hyderabad Campus" } ], "affiliations": [ "Korea Advanced Institute of Science and Technology", "Global Services (Slovakia)", "Birla Institute of Technology and Science - Hyderabad Campus" ] }, { "uid": "arxiv:2311.10961v1", "arxiv_id": "2311.10961v1", "title": "Journey of Hallucination-minimized Generative AI Solutions for Financial Decision Makers", "authors": [ "Sohini Roychowdhury" ], "posted": "2023-11-18", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.10961v1", "field": "finance", "role": "method", "bullets": [ "Industry chatbots, autonomous reports, and alerts built for financial decision makers; the abstract states no dataset, sample, or evaluation period.", "Unnamed LLMs sit inside data to answer generation modules developed through prototyping, scaling, and human feedback stages; no quantitative accuracy validation is reported.", "A three stage design journey is proposed for minimizing hallucinations, with reliability framed as the gating requirement before chatbot output reaches financial decision processes." ], "bullet_provenance": "ai", "validated": false, "salience": 25, "edition": 12, "models": [], "n": 1414, "authors_detailed": [ { "name": "Sohini Roychowdhury", "url": "https://openalex.org/A5050360160", "inst": "University of Minnesota" } ], "affiliations": [ "University of Minnesota" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2311.10995v1", "arxiv_id": "2311.10995v1", "title": "Behavior Optimized Image Generation", "authors": [ "Varun Khurana", "Yaman K Singla", "Jayakumar Subramanian", "Rajiv Ratn Shah", "Changyou Chen", "Zhiqiang Xu", "Balaji Krishnamurthy" ], "posted": "2023-11-18", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.10995v1", "field": "management", "role": "method", "bullets": [ "Two behaviour datasets: stock images where forward actions are the KPI and 168 million enterprise tweets with likes, released together as BoigBench.", "BoigLLM, base model unstated, learns how image content maps to engagement outcomes, beating GPT-3.5 and GPT-4 on this task; a diffusion model is then aligned to its reward.", "General purpose models read image content but miss real world performance; behaviour conditioning yields images tuned to clicks and likes rather than aesthetics alone." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "scored against real engagement outcomes in BoigBench", "salience": 44, "edition": 12, "n": 1453, "authors_detailed": [ { "name": "Varun Khurana", "url": "https://openalex.org/A5103282047", "inst": "Adobe Systems (United States)" }, { "name": "Singla, Yaman K", "url": "", "inst": "" }, { "name": "Jayakumar Subramanian", "url": "https://openalex.org/A5088879436", "inst": "Adobe Systems (United States)" }, { "name": "Rajiv Ratn Shah", "url": "https://openalex.org/A5079357056", "inst": "Indraprastha Institute of Information Technology Delhi" }, { "name": "Changyou Chen", "url": "https://openalex.org/A5114686221", "inst": "University at Buffalo, State University of New York" }, { "name": "Zhiqiang Xu", "url": "https://openalex.org/A5101799658", "inst": "Mohamed bin Zayed University of Artificial Intelligence" }, { "name": "Balaji Krishnamurthy", "url": "https://openalex.org/A5024546524", "inst": "Indraprastha Institute of Information Technology Delhi" } ], "affiliations": [ "Adobe Systems (United States)", "Indraprastha Institute of Information Technology Delhi", "University at Buffalo, State University of New York", "Mohamed bin Zayed University of Artificial Intelligence" ] }, { "uid": "doi:10.2139/ssrn.4608445", "doi": "10.2139/ssrn.4608445", "title": "FinDKG: Dynamic Knowledge Graph with Large Language Models for Global Finance", "authors": [ "Xiaohui Li" ], "posted": "2023-11-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4608445", "field": "finance", "role": "instrument", "bullets": [ "Global financial news corpus used to construct dynamic knowledge graphs for analytics including risk management, thematic investing, and economics forecasting.", "A fine-tuned 7-billion-parameter LLM (ICKG) constructs knowledge graphs while a KGTransformer architecture performs deep learning on the dynamic graph structures.", "The open-source FinDKG system enables time-aware financial analytics across risk management, thematic investing, and macroeconomic forecasting applications." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 55, "n": 2910, "authors_detailed": [ { "name": "Xiaohui Victor Li", "url": "https://openalex.org/A5104199267", "inst": "Imperial College London" } ], "affiliations": [ "Imperial College London" ] }, { "uid": "doi:10.2139/ssrn.4607026", "doi": "10.2139/ssrn.4607026", "title": "S3: Social-network Simulation System with Large Language Model-Empowered Agents", "authors": [ "Chen Gao", "Xiaochong Lan", "Zhihong Lu", "Jinzhu Mao", "Jinghua Piao", "Huandong Wang", "Depeng Jin", "Yong Li" ], "posted": "2023-11-16", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4607026", "field": "management", "role": "agent", "bullets": [ "Real-world social-network data support a two-level simulation of individual emotion, attitudes, interactions, and resulting population information dynamics; geography and sample size are not stated.", "Fine-tuned and prompt-engineered LLM agents, family not stated, emulate network users; outputs are compared with real behavior, but the abstract reports no accuracy statistic.", "Agent interactions reproduce population-level propagation of information, attitudes, and emotions, with accuracy described qualitatively rather than quantified across the two evaluated simulation levels." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 23, "models": [], "n": 4178, "authors_detailed": [ { "name": "Chen Gao", "url": "https://openalex.org/A5078622343", "inst": "Tsinghua University" }, { "name": "Xiaochong Lan", "url": "https://openalex.org/A5045832099", "inst": "Tsinghua University" }, { "name": "Zhihong Lu", "url": "https://openalex.org/A5100305162", "inst": "Tsinghua University" }, { "name": "Jinzhu Mao", "url": "https://openalex.org/A5051273331", "inst": "Tsinghua University" }, { "name": "Jinghua Piao", "url": "https://openalex.org/A5055592366", "inst": "Tsinghua University" }, { "name": "Huandong Wang", "url": "https://openalex.org/A5114100372", "inst": "Tsinghua University" }, { "name": "Depeng Jin", "url": "https://openalex.org/A5044100655", "inst": "Tsinghua University" }, { "name": "Yong Li", "url": "https://openalex.org/A5100355277", "inst": "Tsinghua University" } ], "affiliations": [ "Tsinghua University" ] }, { "uid": "arxiv:2311.09730v2", "arxiv_id": "2311.09730v2", "title": "Sociodemographic Prompting is Not Yet an Effective Approach for Simulating Subjective Judgments with LLMs", "authors": [ "Huaman Sun", "Jiaxin Pei", "Minje Choi", "David Jurgens" ], "posted": "2023-11-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.09730v2", "field": "other", "role": "method", "bullets": [ "Politeness and offensiveness ratings from the POPQUORN dataset, where labels come from annotators of known gender, ethnicity, and other demographic traits.", "Nine language models, not named in the abstract, predict ratings zero shot with and without sociodemographic personas, and predictions are compared against each demographic group's own labels.", "Model predictions align most closely with White annotators' labels, and persona prompting fails to consistently improve, and sometimes worsens, alignment with targeted subpopulations." ], "bullet_provenance": "ai", "validated": true, "validation_note": "POPQUORN human annotations", "salience": 48, "edition": 12, "models": [], "n": 1427, "authors_detailed": [ { "name": "Huaman Sun", "url": "https://openalex.org/A5106416044", "inst": "Stanford University" }, { "name": "Jiaxin Pei", "url": "https://openalex.org/A5049055745", "inst": "Palo Alto University" }, { "name": "Minje Choi", "url": "https://openalex.org/A5101505324", "inst": "Stanford University" }, { "name": "David Jurgens", "url": "https://openalex.org/A5046126345", "inst": "Johns Hopkins University" } ], "affiliations": [ "Stanford University", "Johns Hopkins University", "Palo Alto University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4606937", "doi": "10.2139/ssrn.4606937", "title": "Large Language Model-Empowered Agents for Simulating Macroeconomic Activities", "authors": [ "Nian Li", "Chen Gao", "Yong Li", "Qingmin Liao" ], "posted": "2023-11-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4606937", "field": "economics", "role": "agent", "bullets": [ "Simulated macroeconomy with LLM-driven agents making work and consumption decisions in a multi-agent environment", "Prompt-engineered LLM agents with perception, reflection, and decision-making capabilities replaced rule-based and ML agents in macroeconomic simulation", "LLM agents produced more realistic individual decisions and emergent macroeconomic phenomena than rule-based or traditional AI agents" ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "n": 2421, "authors_detailed": [ { "name": "Nian Li", "url": "https://openalex.org/A5100400585", "inst": "University Town of Shenzhen" }, { "name": "Chen Gao", "url": "https://openalex.org/A5078622343", "inst": "Tsinghua University" }, { "name": "Yong Li", "url": "https://openalex.org/A5100355277", "inst": "Tsinghua University" }, { "name": "Qingmin Liao", "url": "https://openalex.org/A5009239895", "inst": "University Town of Shenzhen" } ], "affiliations": [ "University Town of Shenzhen", "Tsinghua University" ] }, { "uid": "doi:10.18653/v1/2024.findings-acl.606", "doi": "10.18653/v1/2024.findings-acl.606", "arxiv_id": "2311.08545v2", "title": "Efficient Continual Pre-training for Building Domain Specific Large Language Models", "authors": [ "Yong Xie", "Karan Aggarwal", "Aitzaz Ahmad" ], "posted": "2023-11-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.08545v2", "field": "finance", "role": "method", "bullets": [ "Financial domain text used for continual pretraining, with data selection strategies evaluated at 10 percent of full corpus size and cost.", "FinPythia-6.9B extends the open Pythia model through domain adaptive continual pretraining; financial task performance is compared against the unmodified base model.", "Continual pretraining raises financial task performance without hurting open domain benchmarks, and targeted data selection outperforms vanilla continual pretraining at a tenth of the cost." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "financial task evaluations against the base model", "salience": 42, "edition": 12, "n": 1452, "authors_detailed": [ { "name": "Yong Xie", "url": "https://openalex.org/A5101927249", "inst": "Amazon (United States)" }, { "name": "Karan Aggarwal", "url": "https://openalex.org/A5052681145", "inst": "Amazon (United States)" }, { "name": "Aitzaz Ahmad", "url": "https://openalex.org/A5083982189", "inst": "Amazon (United States)" } ], "affiliations": [ "Amazon (United States)" ] }, { "uid": "arxiv:2311.07071v2", "arxiv_id": "2311.07071v2", "title": "The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment", "authors": [ "Kaichen Zhang", "Zixuan Yuan", "Hui Xiong" ], "posted": "2023-11-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.07071v2", "field": "economics", "role": "object", "bullets": [ "Commission orders on China's leading art outsourcing platform, where an unanticipated leak of an image generation model cut production costs for anime-style work.", "The leaked image model is the treatment rather than a measurement tool; difference-in-differences compares anime-style commissions with other genres before and after the leak.", "Average prices fell 64 percent while order volume rose 121 percent and revenue 56 percent, led by low-end personal demand, with incumbent creators capturing most of the market and gains." ], "bullet_provenance": "ai", "salience": 70, "edition": 12, "models": [], "validated": null, "n": 1504, "authors_detailed": [ { "name": "Kaichen Zhang", "url": "https://openalex.org/A5040275077", "inst": "Aalborg University" }, { "name": "Yuan, Zixuan", "url": "", "inst": "" }, { "name": "Hui Xiong", "url": "https://openalex.org/A5101862104", "inst": "China Pharmaceutical University" } ], "affiliations": [ "Aalborg University", "China Pharmaceutical University" ] }, { "uid": "arxiv:2311.06602v2", "arxiv_id": "2311.06602v2", "title": "BizBench: A Quantitative Reasoning Benchmark for Business and Finance", "authors": [ "Rik Koncel-Kedziorski", "Michael Krumdick", "Viet Lai", "Varshini Reddy", "Charles Lovering", "Chris Tanner" ], "posted": "2023-11-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.06602v2", "field": "finance", "role": "method", "bullets": [ "Eight quantitative reasoning tasks over realistic business and finance questions, including three code generation tasks constructed from newly collected and augmented QA data.", "Open source and commercial LLMs, unnamed in the abstract, answer through program synthesis, with separate probes of value extraction from text and tables and of financial concepts.", "The binding constraint is business and financial understanding rather than coding ability, and code focused and language focused models behave differently on the tasks." ], "bullet_provenance": "ai", "validated": true, "validation_note": "QA and program synthesis tasks with ground truth answers", "salience": 46, "edition": 12, "models": [], "n": 1451, "authors_detailed": [ { "name": "Rik Koncel-Kedziorski", "url": "https://openalex.org/A5033228519", "inst": "Amazon (United States)" }, { "name": "Michael Krumdick", "url": "https://openalex.org/A5073663407", "inst": "University of Notre Dame" }, { "name": "Viet Dac Lai", "url": "https://openalex.org/A5070047759", "inst": "Adobe Systems (United States)" }, { "name": "Varshini Reddy", "url": "https://openalex.org/A5114127969", "inst": "Institute of Management Technology" }, { "name": "Charles Lovering", "url": "https://openalex.org/A5018940839", "inst": "Worcester Polytechnic Institute" }, { "name": "Chris C. Tanner", "url": "https://openalex.org/A5090162434", "inst": "Education New Zealand" } ], "affiliations": [ "University of Notre Dame", "Amazon (United States)", "Adobe Systems (United States)", "Institute of Management Technology", "Worcester Polytechnic Institute", "Education New Zealand" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1145/3604237.3626891", "doi": "10.1145/3604237.3626891", "arxiv_id": "2311.06102v1", "title": "Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking", "authors": [ "Lefteris Loukas", "Ilias Stogiannidis", "Odysseas Diamantopoulos", "Prodromos Malakasiotis", "Stavros Vassos" ], "posted": "2023-11-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.06102v1", "field": "finance", "role": "method", "bullets": [ "Banking77 intent detection dataset of customer queries in banking, studied under budget constrained few shot regimes; a human expert curated subset is released.", "OpenAI, Cohere, and Anthropic models, including GPT-4, are compared with contrastive few shot classifiers on the labelled benchmark; retrieval augmented querying and GPT-4 data augmentation are added.", "LLMs perform well with one to five examples per class; the retrieval based querying cuts operational cost several times over, and augmentation lifts performance when data is scarce." ], "bullet_provenance": "ai", "models": [ "claude", "gpt" ], "open_weights": false, "validated": true, "validation_note": "Banking77 labelled intent benchmark", "salience": 46, "edition": 12, "n": 1372, "authors_detailed": [ { "name": "Lefteris Loukas", "url": "https://openalex.org/A5017613411", "inst": "Helbio (Greece)" }, { "name": "Ilias Stogiannidis", "url": "https://openalex.org/A5092720860", "inst": "Helbio (Greece)" }, { "name": "Odysseas Diamantopoulos", "url": "https://openalex.org/A5093257097", "inst": "Helbio (Greece)" }, { "name": "Prodromos Malakasiotis", "url": "https://openalex.org/A5016618602", "inst": "Athens University of Economics and Business" }, { "name": "Stavros Vassos", "url": "https://openalex.org/A5084667235", "inst": "Helbio (Greece)" } ], "affiliations": [ "Helbio (Greece)", "Athens University of Economics and Business" ] }, { "uid": "arxiv:2311.06330v4", "arxiv_id": "2311.06330v4", "title": "Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations", "authors": [ "Zengqing Wu", "Run Peng", "Xu Han", "Shuyuan Zheng", "Yixin Zhang", "Chuan Xiao" ], "posted": "2023-11-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.06330v4", "field": "economics", "role": "method", "bullets": [ "A framework proposal with three case studies of simulated real world systems and public source code, rather than a single empirical dataset.", "GPT style LLM agents replace rule based agents inside agent based models to supply language understanding and common sense; the abstract describes no quantitative check against observed behaviour.", "Case studies are presented as showing that LLM powered agents reproduce nuanced real world dynamics that equation and rule based ABM struggles to encode." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 48, "edition": 12, "validated": null, "n": 1449, "authors_detailed": [ { "name": "Zengqing Wu", "url": "https://openalex.org/A5114129919", "inst": "" }, { "name": "Run Peng", "url": "https://openalex.org/A5104162408", "inst": "University of Michigan" }, { "name": "Xu Han", "url": "https://openalex.org/A5063337671", "inst": "China Special Equipment Inspection and Research Institute" }, { "name": "Shuyuan Zheng", "url": "https://openalex.org/A5082636225", "inst": "Tongji University" }, { "name": "Yixin Zhang", "url": "https://openalex.org/A5100351207", "inst": "Peking University" }, { "name": "Chuan Xiao", "url": "https://openalex.org/A5036148682", "inst": "Beijing Institute of Technology" } ], "affiliations": [ "University of Michigan", "China Special Equipment Inspection and Research Institute", "Tongji University", "Peking University", "Beijing Institute of Technology" ] }, { "uid": "arxiv:2311.05812v2", "arxiv_id": "2311.05812v2", "title": "CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model", "authors": [ "Yang Lei", "Jiangtong Li", "Dawei Cheng", "Zhijun Ding", "Changjun Jiang" ], "posted": "2023-11-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.05812v2", "field": "finance", "role": "method", "bullets": [ "CFBenchmark's basic version assembles Chinese financial texts from 50 to over 1,800 characters into eight tasks spanning recognition, classification, and generation.", "Several existing LLMs, none named in the abstract, are scored against the labelled tasks; the benchmark code is publicly released on GitHub.", "Individual models stand out on specific tasks, but overall performance on basic Chinese financial text processing leaves significant room for improvement; no figures quoted." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labelled Chinese financial text tasks", "salience": 36, "edition": 12, "models": [], "n": 1450, "authors_detailed": [ { "name": "Lei Yang", "url": "https://openalex.org/A5101507739", "inst": "China Mobile (China)" }, { "name": "Jiangtong Li", "url": "https://openalex.org/A5047636121", "inst": "Tongji University" }, { "name": "Dawei Cheng", "url": "https://openalex.org/A5069869295", "inst": "Tongji University" }, { "name": "Zhijun Ding", "url": "https://openalex.org/A5041681214", "inst": "Tongji University" }, { "name": "Changjun Jiang", "url": "https://openalex.org/A5066099338", "inst": "Tongji University" } ], "affiliations": [ "China Mobile (China)", "Tongji University" ] }, { "uid": "doi:10.2139/ssrn.4602452", "doi": "10.2139/ssrn.4602452", "title": "Can Chatgpt Assist in Picking Stocks?", "authors": [ "Matthias Pelster", "Joel Val" ], "posted": "2023-11-10", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4602452", "field": "finance", "role": "agent", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 35, "edition": 12, "bullets": [], "validated": null, "n": 1503, "authors_detailed": [ { "name": "Matthias Pelster", "url": "https://openalex.org/A5020680328", "inst": "European Centre for Minority Issues" }, { "name": "Joel Val", "url": "https://openalex.org/A5093236980", "inst": "University of Duisburg-Essen" } ], "affiliations": [ "European Centre for Minority Issues", "University of Duisburg-Essen" ] }, { "uid": "doi:10.1007/978-3-031-60313-6_9", "doi": "10.1007/978-3-031-60313-6_9", "arxiv_id": "2311.05288v1", "title": "Towards a Taxonomy of Large Language Model based Business Model Transformations", "authors": [ "Jochen Wulf", "Juerg Meierhofer" ], "posted": "2023-11-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.05288v1", "field": "management", "role": "object", "bullets": [ "Firm level evidence on business model changes driven by large language models, developed into a taxonomy; the abstract does not state the sample of firms or cases examined.", "No model serves as a research instrument; LLM adoption and the transformations it triggers in business models are the phenomena being classified.", "The taxonomy structures criteria for successful LLM based business model implementation and is positioned as a strategic design framework for technology investment decisions." ], "bullet_provenance": "ai", "salience": 28, "edition": 12, "models": [], "validated": null, "n": 1413, "authors_detailed": [ { "name": "Jochen Wulf", "url": "https://openalex.org/A5036086266", "inst": "ZHAW Zurich University of Applied Sciences" }, { "name": "Jürg Meierhofer", "url": "https://openalex.org/A5008118017", "inst": "ZHAW Zurich University of Applied Sciences" } ], "affiliations": [ "ZHAW Zurich University of Applied Sciences" ] }, { "uid": "arxiv:2311.07592v1", "arxiv_id": "2311.07592v1", "title": "Hallucination-minimized Data-to-answer Framework for Financial Decision-makers", "authors": [ "Sohini Roychowdhury", "Andres Alvarez", "Brian Moore", "Marko Krema", "Maria Paz Gelpi", "Federico Martin Rodriguez", "Angel Rodriguez", "Jose Ramon Cabrejas", "Pablo Martinez Serrano", "Punit Agrawal", "Arijit Mukherjee" ], "posted": "2023-11-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.07592v1", "field": "finance", "role": "method", "bullets": [ "Question answering over financial data tables for decision support, with queries spanning what, where, why, how, prediction, trend, anomaly, and exception types.", "A Langchain based pipeline turns tables into hierarchical text chunks, classifies query intent, retrieves relevant chunks, and scores responses for hallucination; the underlying model is not named.", "The system reports confidence scores above 90 percent across query types; the abstract states no comparison of answers against ground truth." ], "bullet_provenance": "ai", "validated": false, "salience": 28, "edition": 12, "models": [], "n": 1426, "authors_detailed": [ { "name": "Sohini Roychowdhury", "url": "https://openalex.org/A5050360160", "inst": "University of Minnesota" }, { "name": "Andrés M. Álvarez", "url": "https://openalex.org/A5114120436", "inst": "Accenture (United States)" }, { "name": "Brian C. J. Moore", "url": "https://openalex.org/A5110050608", "inst": "University of Cambridge" }, { "name": "Marko Krema", "url": "https://openalex.org/A5059594013", "inst": "Accenture (United States)" }, { "name": "Maria Paz Gelpi", "url": "https://openalex.org/A5093272092", "inst": "" }, { "name": "F.M. León Rodríguez", "url": "https://openalex.org/A5109837394", "inst": "Universidad Distrital Francisco José de Caldas" }, { "name": "Angel Rodríguez", "url": "https://openalex.org/A5057331910", "inst": "World Health Organization Regional Office for the Americas" }, { "name": "José Ramón Cabrejas", "url": "https://openalex.org/A5093272093", "inst": "Accenture (Spain)" }, { "name": "Pablo Martínez Serrano", "url": "https://openalex.org/A5104198736", "inst": "Accenture (Spain)" }, { "name": "Punit Agrawal", "url": "https://openalex.org/A5053600176", "inst": "West Virginia University" }, { "name": "Arijit Mukherjee", "url": "https://openalex.org/A5031139197", "inst": "Tata Consultancy Services (India)" } ], "affiliations": [ "University of Minnesota", "University of Cambridge", "Accenture (United States)", "Universidad Distrital Francisco José de Caldas", "World Health Organization Regional Office for the Americas", "Accenture (Spain)", "West Virginia University", "Tata Consultancy Services (India)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2311.07590v4", "arxiv_id": "2311.07590v4", "title": "Large Language Models can Strategically Deceive their Users when Put Under Pressure", "authors": [ "Jérémy Scheurer", "Mikita Balesni", "Marius Hobbhahn" ], "posted": "2023-11-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.07590v4", "field": "finance", "role": "agent", "bullets": [ "A simulated trading firm in which GPT-4 operates an autonomous equity trading agent and receives an insider tip while under pressure to deliver profits.", "GPT-4 is asked to act rather than read; the authors vary scratchpad access, system instructions, pressure, and perceived detection risk to trace how the behaviour changes.", "The agent trades on the insider information despite knowing management disapproves, then consistently conceals the genuine reason when reporting the trade to its manager." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 74, "edition": 12, "validated": null, "n": 1448, "authors_detailed": [ { "name": "Jérémy Scheurer", "url": "https://openalex.org/A5064744991", "inst": "" }, { "name": "Mikita Balesni", "url": "https://openalex.org/A5092825639", "inst": "" }, { "name": "Marius Hobbhahn", "url": "https://openalex.org/A5034914617", "inst": "Universidad Complutense de Madrid" } ], "affiliations": [ "Universidad Complutense de Madrid" ] }, { "uid": "doi:10.2139/ssrn.4600536", "doi": "10.2139/ssrn.4600536", "title": "The Market Value of Generative AI: Evidence from China Market", "authors": [ "Rui Li", "Minghai Xu", "Wenzhi Ding" ], "posted": "2023-11-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4600536", "field": "finance", "role": "object", "bullets": [ "Twenty-five Chinese Stock Exchange-listed companies that launched large language models, studied via event study around their LLM release dates.", "Event study measured cumulative abnormal returns before and after each firm's LLM product release announcement in the Chinese market.", "CAR reached 3% pre-release but fell to 1.5% post-release; early movers and customer-facing LLMs in education and design received stronger positive reactions." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 2362, "authors_detailed": [ { "name": "Rui Li", "url": "https://openalex.org/A5100448484", "inst": "Peng Cheng Laboratory" }, { "name": "Minghai Xu", "url": "https://openalex.org/A5111065423", "inst": "Hong Kong Polytechnic University" }, { "name": "Wenzhi Ding", "url": "https://openalex.org/A5016125669", "inst": "Hong Kong Polytechnic University" } ], "affiliations": [ "Peng Cheng Laboratory", "Hong Kong Polytechnic University" ] }, { "uid": "doi:10.2139/ssrn.4627143", "doi": "10.2139/ssrn.4627143", "title": "Evaluating Local Language Models: An Application to Financial Earnings Calls", "authors": [ "Thomas R. Cook", "Sophia Kazinnik", "Anne Lundgaard Hansen", "Peter McAdam" ], "posted": "2023-11-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4627143", "field": "finance", "role": "instrument", "bullets": [ "U.S. bank earnings calls in the post-pandemic era including 2023 banking stress, with new benchmarking tasks for financial and economic text analysis.", "Local open-source LLMs benchmarked against closed-source cloud models for sentiment, topic classification, temporal orientation, and vagueness analysis of earnings call text.", "Local LLMs proved viable for financial NLP; during 2023 banking stress, bank calls converged on similar topics and conveyed distinctly less positive sentiment." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "benchmarked local LLMs against cloud-based models on financial NLP tasks", "salience": 68, "n": 2456, "authors_detailed": [ { "name": "Thomas R. Cook", "url": "https://openalex.org/A5084921800", "inst": "Federal Reserve Bank of Kansas City" }, { "name": "Sophia Kazinnik", "url": "https://openalex.org/A5061916690", "inst": "Federal Reserve" }, { "name": "Anne Lundgaard Hansen", "url": "https://openalex.org/A5024095797", "inst": "Federal Reserve" }, { "name": "Peter McAdam", "url": "https://openalex.org/A5017669127", "inst": "Federal Reserve Bank of Kansas City" } ], "affiliations": [ "Federal Reserve Bank of Kansas City", "Federal Reserve" ] }, { "uid": "doi:10.2139/ssrn.4628235", "doi": "10.2139/ssrn.4628235", "title": "Generative AI in Finance: Risks and Potential Solutions", "authors": [ "Nydia Remolina" ], "posted": "2023-11-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4628235", "field": "finance", "role": "object", "bullets": [ "Analysis of ethical and legal risks of generative AI deployment in the financial sector, following a functional approach to regulation.", "Paper examines ChatGPT and generative AI models as adopted by financial institutions, mapping risks specific to financial services versus predictive AI.", "Generative AI in finance requires context-specific governance; risks differ materially from predictive AI, and existing regulatory frameworks need adaptation for content generation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 50, "validated": null, "n": 2640, "authors_detailed": [ { "name": "Nydia Remolina", "url": "https://openalex.org/A5029053585", "inst": "Advanced Digital Sciences Center" } ], "affiliations": [ "Advanced Digital Sciences Center" ] }, { "uid": "doi:10.2139/ssrn.4602852", "doi": "10.2139/ssrn.4602852", "title": "Optimizing Customer Experience in Hospitality and Tourism with ChatGPT Plugins: A Strategic Guide", "authors": [ "Polat Goktas", "Taşkın Dirsehan" ], "posted": "2023-11-08", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4602852", "field": "management", "role": "object", "bullets": [ "Conceptual paper examining ChatGPT 4.0 plugins, specifically Expedia and Kayak, for customer service in the hospitality and tourism industry; sample, period, and geography not stated.", "The paper reviews literature and case studies on ChatGPT plugin technology and an in-app generative AI trip planner for customer experience management; validation against ground truth not stated.", "The authors conclude ChatGPT plugins could improve personalized service, multilingual communication, operational efficiency, and customer loyalty, offering strategic guidance rather than a measured effect." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 28, "edition": 22, "validated": null, "n": 4121, "authors_detailed": [ { "name": "Polat Göktaş", "url": "https://openalex.org/A5084781680", "inst": "University College Dublin" }, { "name": "Taşkın Dirsehan", "url": "https://openalex.org/A5083987250", "inst": "Marmara University" } ], "affiliations": [ "University College Dublin", "Marmara University" ] }, { "uid": "doi:10.2139/ssrn.4597899", "doi": "10.2139/ssrn.4597899", "title": "The power of generative marketing: Can generative AI create superhuman visual marketing content?", "authors": [ "Jochen Hartmann", "Yannick Exner", "Samuel Domdey" ], "posted": "2023-11-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4597899", "field": "management", "role": "agent", "bullets": [ "10,320 synthetic marketing images from seven text-to-image models benchmarked against 2,400 real-world human-made images, plus a field study with over 173,000 ad impressions.", "DALL-E 3, Midjourney v6, Stable Diffusion XL Turbo, and four other models generated images from marketing briefs; 254,400 human evaluations rated quality, realism, and aesthetics.", "AI-generated images surpassed human-made images on quality and aesthetics; AI banner ads achieved up to 50% higher click-through rates than professional human-made stock photography." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini", "open_other" ], "open_weights": true, "validated": true, "validation_note": "254,400 human evaluations plus field experiment CTR", "salience": 65, "n": 3336, "authors_detailed": [ { "name": "Jochen Hartmann", "url": "https://openalex.org/A5062843383", "inst": "Technical University of Munich" }, { "name": "Yannick Exner", "url": "https://openalex.org/A5059627175", "inst": "Technical University of Munich" }, { "name": "Samuel Domdey", "url": "https://openalex.org/A5098533119", "inst": "Hamburg University of Technology" } ], "affiliations": [ "Technical University of Munich", "Hamburg University of Technology" ] }, { "uid": "arxiv:2311.03595v1", "arxiv_id": "2311.03595v1", "title": "Brief for the Canada House of Commons Study on the Implications of Artificial Intelligence Technologies for the Canadian Labor Force: Generative Artificial Intelligence Shatters Models of AI and Labor", "authors": [ "Morgan R. Frank" ], "posted": "2023-11-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.03595v1", "field": "economics", "role": "object", "bullets": [ "Written testimony for the Canada House of Commons study of AI and the Canadian labor force; an argument piece without a dataset or empirical design.", "No model is used; generative AI is the object, argued to break standard automation forecasting because it is creative, cognitive, and potentially ubiquitous.", "Concludes exposure reaches occupations once considered immune and calls for better local data on job separations and unemployment plus education programs that treat AI as a learning tool." ], "bullet_provenance": "ai", "salience": 28, "edition": 12, "models": [], "validated": null, "n": 1502, "authors_detailed": [ { "name": "Morgan R. Frank", "url": "https://openalex.org/A5012787401", "inst": "University of Vermont" } ], "affiliations": [ "University of Vermont" ] }, { "uid": "doi:10.2139/ssrn.4595896", "doi": "10.2139/ssrn.4595896", "title": "From Data Scarcity to Data Abundance: Crafting Synthetic Survey Data in Management Accounting using ChatGPT", "authors": [ "Fabio Motoki", "Januário Monteiro", "Ricardo Malagueño", "Victor Rodrigues" ], "posted": "2023-11-06", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4595896", "field": "accounting", "role": "agent", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 625, "authors_detailed": [ { "name": "Fábio Motoki", "url": "https://openalex.org/A5016703677", "inst": "University of East Anglia" }, { "name": "Januário José Monteiro", "url": "https://openalex.org/A5069200639", "inst": "University of East Anglia" }, { "name": "Ricardo Malagueño", "url": "https://openalex.org/A5004035649", "inst": "University of Essex" }, { "name": "Victor Rodrigues", "url": "https://openalex.org/A5083079499", "inst": "Nova Educação" } ], "affiliations": [ "University of East Anglia", "University of Essex", "Nova Educação" ] }, { "uid": "doi:10.2139/ssrn.4614402", "doi": "10.2139/ssrn.4614402", "title": "The Impact of Large Language Models on Search Advertising: Evidence from Google’s BERT", "authors": [ "Poet Larsen", "Davide Proserpio" ], "posted": "2023-11-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4614402", "field": "management", "role": "object", "bullet_provenance": "none", "models": [ "legacy", "open_other" ], "open_weights": true, "salience": 50, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 624, "authors_detailed": [ { "name": "Poet Larsen", "url": "", "inst": "University of Southern California" }, { "name": "Davide Proserpio", "url": "https://openalex.org/A5101489444", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4602944", "doi": "10.2139/ssrn.4602944", "title": "Who Is AI Replacing? The Impact of Generative AI on Online Freelancing Platforms", "authors": [ "Ozge Demirci", "Jonas Hannane", "Xinrong Zhu" ], "posted": "2023-11-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4602944", "field": "economics", "role": "object", "bullets": [ "Job posts on a major global freelancing platform spanning writing, coding, image creation, and manual-skill categories, eight months after ChatGPT's November 2022 launch.", "No LLM deployed as a research tool; difference-in-differences design compared automation-prone job categories against manual-intensive categories, with Google Trends proxying public awareness of ChatGPT substitutability.", "Writing and coding job posts fell 21% relative to manual-skill jobs; image-creation posts dropped 17% after image-AI release; remaining automation-prone jobs grew more complex and higher-paid." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 70, "validated": null, "n": 3335, "authors_detailed": [ { "name": "Ozge Demirci", "url": "", "inst": "Imperial College London" }, { "name": "Jonas Hannane", "url": "", "inst": "German Institute for Economic Research" }, { "name": "Xinrong Zhu", "url": "https://openalex.org/A5051898983", "inst": "Imperial College London" } ], "affiliations": [ "Imperial College London", "German Institute for Economic Research" ] }, { "uid": "arxiv:2311.01550v1", "arxiv_id": "2311.01550v1", "title": "Market Concentration Implications of Foundation Models", "authors": [ "Jai Vipra", "Anton Korinek" ], "posted": "2023-11-02", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.01550v1", "field": "economics", "role": "object", "bullets": [ "Policy analysis of the market for foundation models such as those behind ChatGPT; conceptual industrial organization reasoning rather than an empirical sample.", "No model is deployed by the authors; foundation models themselves are the object, assessed for tendencies toward natural monopoly and vast markets at the frontier.", "Recommends antitrust focus on contestability and on stopping monopoly propagating vertically to downstream uses, plus quality regulation covering safety, privacy, and reliability; competition is expected behind the frontier." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 12, "validated": null, "n": 1501, "authors_detailed": [ { "name": "Jai Vipra", "url": "https://openalex.org/A5093211854", "inst": "Cornell University" }, { "name": "Anton Korinek", "url": "https://openalex.org/A5009882421", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Cornell University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4603227", "doi": "10.2139/ssrn.4603227", "title": "Role and Challenges of ChatGPT and Similar Generative Artificial Intelligence in Business Management", "authors": [ "Nitin Rane" ], "posted": "2023-11-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4603227", "field": "management", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 30, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 333, "authors_detailed": [ { "name": "Nitin Liladhar Rane", "url": "https://openalex.org/A5039865284", "inst": "University of Mumbai" } ], "affiliations": [ "University of Mumbai" ] }, { "uid": "doi:10.2139/ssrn.4603206", "doi": "10.2139/ssrn.4603206", "title": "Role and Challenges of ChatGPT and Similar Generative Artificial Intelligence in Finance and Accounting", "authors": [ "Nitin Rane" ], "posted": "2023-11-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4603206", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 30, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 334, "authors_detailed": [ { "name": "Nitin Rane", "url": "https://openalex.org/A5093128225", "inst": "University of Mumbai" } ], "affiliations": [ "University of Mumbai" ] }, { "uid": "doi:10.2139/ssrn.4594466", "doi": "10.2139/ssrn.4594466", "title": "No Great Equalizer: Experimental Evidence on Productivity Effects of Generative AI Use in the UK Labor Market", "authors": [ "Matthias Haslberger", "Jane Gingrich", "Jasmine Bhatia" ], "posted": "2023-11-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4594466", "field": "economics", "role": "object", "bullets": [ "Pre-registered online experiment with a UK working-age population sample randomly assigned to treatments encouraging or discouraging ChatGPT use on realistic work tasks.", "Participants used ChatGPT to complete tasks of varying complexity; the study measured productivity effects across gender, age, education, and occupational groups.", "ChatGPT increased productivity in all tasks with greater gains on complex tasks, but did not compress productivity differentials between demographic or occupational groups." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 2639, "authors_detailed": [ { "name": "Matthias Haslberger", "url": "https://openalex.org/A5060286994", "inst": "University of St.Gallen" }, { "name": "Jane Gingrich", "url": "https://openalex.org/A5008407010", "inst": "Oxford Policy Management" }, { "name": "Jasmine Bhatia", "url": "https://openalex.org/A5082627054", "inst": "Birkbeck, University of London" } ], "affiliations": [ "University of St.Gallen", "Oxford Policy Management", "Birkbeck, University of London" ] }, { "uid": "doi:10.1371/journal.pclm.0000429", "doi": "10.1371/journal.pclm.0000429", "arxiv_id": "2311.00217v2", "title": "Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias", "authors": [ "S. Lee", "T. Q. Peng", "M. H. Goldberg", "S. A. Rosenthal", "J. E. Kotcher", "E. W. Maibach", "A. Leiserowitz" ], "posted": "2023-11-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.00217v2", "field": "other", "role": "agent", "bullets": [ "Two nationally representative US surveys on climate change; simulated respondents are conditioned on demographic and psychological covariates taken from the survey data.", "LLMs, of which only GPT-4 is named, generate survey answers that are compared with the actual human responses to gauge algorithmic fidelity and subgroup bias.", "Presidential voting is reproduced well; global warming views are captured only with covariate conditioning, and worry among Black Americans is underestimated, a caution for silicon sampling." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "two nationally representative US climate surveys", "salience": 56, "edition": 12, "n": 1371, "authors_detailed": [ { "name": "Sanguk Lee", "url": "https://openalex.org/A5031930586", "inst": "Yale University" }, { "name": "Tai‐Quan Peng", "url": "https://openalex.org/A5031082206", "inst": "Michigan United" }, { "name": "Matthew H. Goldberg", "url": "https://openalex.org/A5036980165", "inst": "Yale University" }, { "name": "Seth A. Rosenthal", "url": "https://openalex.org/A5023340968", "inst": "Yale University" }, { "name": "John Kotcher", "url": "https://openalex.org/A5006065354", "inst": "George Mason University" }, { "name": "Edward Maibach", "url": "https://openalex.org/A5049616013", "inst": "George Mason University" }, { "name": "Anthony Leiserowitz", "url": "https://openalex.org/A5052239782", "inst": "Yale University" } ], "affiliations": [ "Yale University", "Michigan United", "George Mason University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4605823", "doi": "10.2139/ssrn.4605823", "title": "Analyzing Information Retrieval in Economic Research: ChatGPT-4 vs. Google Scholar", "authors": [ "Mario Arturo Ruiz Estrada" ], "posted": "2023-10-31", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4605823", "field": "economics", "role": "method", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 22, "edition": 12, "bullets": [], "validated": null, "n": 1500, "authors_detailed": [ { "name": "Mario Arturo Ruiz Estrada", "url": "https://openalex.org/A5043927298", "inst": "Universidad de San Carlos de Guatemala" } ], "affiliations": [ "Universidad de San Carlos de Guatemala" ] }, { "uid": "doi:10.2139/ssrn.4613525", "doi": "10.2139/ssrn.4613525", "title": "Global Business Networks", "authors": [ "Christian Breitung", "Sebastian Müller" ], "posted": "2023-10-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4613525", "field": "finance", "role": "instrument", "bullets": [ "Business descriptions of over 79,000 firms globally, constructing networks of economically linked companies across countries.", "GPT-3, Luminous, and T5-XXL processed firm descriptions to identify competitors, suppliers, and customers, benchmarked against traditional industry classifications.", "LLM-based business networks outperform industry classifications in identifying economic links and reveal significant international lead-lag return effects and M&A activity patterns." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "comparison against traditional industry classifications for identifying economic links", "salience": 70, "n": 2638, "authors_detailed": [ { "name": "Christian Breitung", "url": "https://openalex.org/A5050609944", "inst": "Technical University of Munich" }, { "name": "Sebastian Müller", "url": "https://openalex.org/A5006795021", "inst": "Technical University of Munich" } ], "affiliations": [ "Technical University of Munich" ] }, { "uid": "doi:10.2139/ssrn.4589333", "doi": "10.2139/ssrn.4589333", "title": "Artificial Intelligence and Digital Economy: Divergent Realities", "authors": [ "David Vidal-Tomás", "Silvia Bartolucci" ], "posted": "2023-10-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4589333", "field": "finance", "role": "object", "bullets": [ "AI-related cryptocurrency tokens traded on major exchanges from January to late 2023, analyzed using PSY real-time bubble detection, wavelet coherence, Pearson correlations, and minimum spanning trees.", "No LLM deployed as a tool; ChatGPT's November 2022 release serves as the treatment event; empirical techniques detect bubbles and measure correlation with investor attention and broader AI awareness.", "AI impact on crypto tokens was short-lived, with a bubble only in January-March 2023; AI token performance showed no significant correlation with broader public attention to AI." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 45, "validated": null, "n": 3334, "authors_detailed": [ { "name": "David Vidal-Tomás", "url": "https://openalex.org/A5096738811", "inst": "Universitat Jaume I" }, { "name": "Silvia Bartolucci", "url": "https://openalex.org/A5090881985", "inst": "University College London" } ], "affiliations": [ "Universitat Jaume I", "University College London" ] }, { "uid": "doi:10.2139/ssrn.4589571", "doi": "10.2139/ssrn.4589571", "title": "The Impact of AI Chatbots like ChatGPT on Sarbanes-Oxley (SOX) Compliance", "authors": [ "Faraz Akhtar" ], "posted": "2023-10-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4589571", "field": "accounting", "role": "object", "bullets": [ "Conceptual analysis examining how AI chatbots could integrate into Sarbanes-Oxley compliance processes governing internal controls and financial disclosure accuracy in U.S. public corporations.", "ChatGPT evaluated as a potential tool for automating SOX compliance workflows, including internal control testing, documentation of financial disclosures, and audit trail maintenance across corporate reporting.", "Paper identifies potential efficiency gains from AI-assisted compliance but highlights unresolved challenges in output accuracy, accountability frameworks, and regulatory acceptance; no empirical results reported." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 15, "validated": null, "n": 3333, "authors_detailed": [ { "name": "Faraz Akhtar", "url": "", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4511540", "doi": "10.2139/ssrn.4511540", "title": "Large Language Models and Generative AI in Finance: An Analysis of ChatGPT, Bard, and Bing AI", "authors": [ "David Krause" ], "posted": "2023-10-27", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4511540", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 32, "edition": 2, "audience": "broad", "bullets": [], "validated": null, "n": 60, "authors_detailed": [ { "name": "David A. Krause", "url": "https://openalex.org/A5109135812", "inst": "Marquette University" } ], "affiliations": [ "Marquette University" ] }, { "uid": "arxiv:2310.17714v1", "arxiv_id": "2310.17714v1", "title": "Nearest Neighbor Search over Vectorized Lexico-Syntactic Patterns for Relation Extraction from Financial Documents", "authors": [ "Pawan Kumar Rajpoot", "Ankur Parikh" ], "posted": "2023-10-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.17714v1", "field": "finance", "role": "method", "bullets": [ "Relation extraction from 10-K and 10-Q style filings of public companies, evaluated on the REFinD dataset, targeting implicit expressions and long tail relation classes.", "Rather than querying a large language model, the method runs nearest neighbor search over dense vectors of lexico syntactic patterns from training relations at test time.", "The approach reports state of the art results on REFinD and offers a starting point for human in the loop annotation when few labels exist." ], "bullet_provenance": "ai", "validated": true, "validation_note": "REFinD labelled benchmark", "salience": 35, "edition": 12, "models": [], "n": 1425, "authors_detailed": [ { "name": "Pawan Kumar Rajpoot", "url": "https://openalex.org/A5084786836", "inst": "TIFR Centre for Applicable Mathematics" }, { "name": "Ankur P. Parikh", "url": "https://openalex.org/A5109937799", "inst": "Google (United States)" } ], "affiliations": [ "TIFR Centre for Applicable Mathematics", "Google (United States)" ] }, { "uid": "arxiv:2310.17721v2", "arxiv_id": "2310.17721v2", "title": "From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI", "authors": [ "Alex Kim", "Maximilian Muhn", "Valeri Nikolaev" ], "posted": "2023-10-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.17721v2", "field": "finance", "role": "instrument", "bullets": [ "Firm-level measures of political, climate, and AI risk exposure are built from earnings call transcripts; the abstract does not state the number of firms or the sample period.", "GPT 3.5 writes risk summaries and risk assessments from each transcript; the abstract claims validation but reports no agreement statistic against hand-coded ground truth.", "The assessment-based measures beat existing risk proxies in predicting abnormal volatility, investment, and innovation, detect rising AI risk, hold up outside the training window, and are priced in equity markets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 76, "edition": 12, "n": 1473, "authors_detailed": [ { "name": "Alex Kim", "url": "https://openalex.org/A5002013850", "inst": "Boston Children's Hospital" }, { "name": "Maximilian Muhn", "url": "https://openalex.org/A5065276465", "inst": "Goethe University Frankfurt" }, { "name": "Valeri V. Nikolaev", "url": "https://openalex.org/A5085975914", "inst": "Tilburg University" } ], "affiliations": [ "Goethe University Frankfurt", "Tilburg University" ] }, { "uid": "arxiv:2310.16048v1", "arxiv_id": "2310.16048v1", "title": "AI Alignment and Social Choice: Fundamental Limitations and Policy Implications", "authors": [ "Abhilash Mishra" ], "posted": "2023-10-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.16048v1", "field": "economics", "role": "object", "bullets": [ "A theory paper with no dataset, applying impossibility results from social choice to reinforcement learning from human feedback, the alignment procedure behind widely deployed LLMs.", "No language model is run; RLHF is formalized as a voting style aggregation of human feedback, and the paper asks which democratic protocols could implement it.", "Under broad assumptions no unique voting protocol can align a model with everyone, and universal alignment always violates some private preferences; implications include transparent voting rules and narrowly aligned agents." ], "bullet_provenance": "ai", "salience": 52, "edition": 12, "models": [], "validated": null, "n": 1389, "authors_detailed": [ { "name": "Abhilash Mishra", "url": "https://openalex.org/A5108926021", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2310.15772v3", "arxiv_id": "2310.15772v3", "title": "Analyzing User Characteristics of Hate Speech Spreaders on Social Media", "authors": [ "Dominique Geissler", "Abdurahman Maarouf", "Stefan Feuerriegel" ], "posted": "2023-10-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.15772v3", "field": "management", "role": "instrument", "bullets": [ "User-level analysis of who reshares hate speech on social media; the abstract does not state the platform, sample size, or period.", "Large language models, family not stated, cluster posts into hate types such as racist, misogynistic, and political; no check of the clustering against human labels is reported.", "Accounts with fewer followers, friends, and posts and older accounts reshare more hate, while anti-Trump and anti-right-wing content spreads through users with larger social influence." ], "bullet_provenance": "ai", "validated": false, "salience": 42, "edition": 12, "models": [], "n": 1499, "authors_detailed": [ { "name": "Dominique Geissler", "url": "https://openalex.org/A5089703379", "inst": "LMU Klinikum" }, { "name": "Abdurahman Maarouf", "url": "https://openalex.org/A5042525157", "inst": "LMU Klinikum" }, { "name": "Stefan Feuerriegel", "url": "https://openalex.org/A5081442873", "inst": "Center for NanoScience" } ], "affiliations": [ "LMU Klinikum", "Center for NanoScience" ] }, { "uid": "doi:10.2139/ssrn.4560434", "doi": "10.2139/ssrn.4560434", "title": "Man or Machine? An Exploratory Study of the Performance of Chat GPT 3.5 in the CFC Sufficiency Exam", "authors": [ "José Oliveira", "Ahmed Khatib" ], "posted": "2023-10-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4560434", "field": "accounting", "role": "agent", "bullets": [ "ChatGPT 3.5 applied to Brazil's CFC Proficiency and Technical Qualification certification exams, testing accounting knowledge across multiple domains required for professional practice.", "GPT-3.5 answered multiple-choice and open-ended exam questions spanning accounting, auditing, and tax law; scores compared against the 50% minimum passing threshold for human candidates.", "ChatGPT exceeded the 50% passing threshold on every exam administered, matching the minimum score required for a human candidate to earn CFC professional certification in Brazil." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "CFC Proficiency and Technical Qualification exams pass threshold", "salience": 25, "n": 3332, "authors_detailed": [ { "name": "Jose Carlos Ramos de Oliveira", "url": "https://openalex.org/A5093121369", "inst": "Instituto Florestal" }, { "name": "Ahmed Sameer El Khatib", "url": "https://openalex.org/A5093121370", "inst": "Ibracon - Instituto de Auditoria Independente do Brasil" } ], "affiliations": [ "Instituto Florestal", "Ibracon - Instituto de Auditoria Independente do Brasil" ] }, { "uid": "arxiv:2310.15205v2", "arxiv_id": "2310.15205v2", "title": "DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning", "authors": [ "Wei Chen", "Qiushi Wang", "Zefei Long", "Xianyin Zhang", "Zhongtian Lu", "Bingxuan Li", "Siyuan Wang", "Jiarong Xu", "Xiang Bai", "Xuanjing Huang", "Zhongyu Wei" ], "posted": "2023-10-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.15205v2", "field": "finance", "role": "method", "bullets": [ "Chinese financial question answering and text processing, built on DISC-FIN-SFT, an instruction dataset with consulting, NLP task, computing, and retrieval augmented generation samples.", "A general LLM, base unnamed in the abstract, is finetuned through a multiple experts framework covering multi turn QA, document processing, mathematical computation, and retrieval.", "DISC-FinLLM beats baseline models across several Chinese financial benchmark scenarios; the abstract reports no scores or margins for the gains." ], "bullet_provenance": "ai", "validated": true, "validation_note": "multiple Chinese financial benchmarks", "salience": 40, "edition": 12, "models": [], "n": 1447, "authors_detailed": [ { "name": "Wei Chen", "url": "https://openalex.org/A5100344360", "inst": "Hebei Medical University" }, { "name": "Qiushi Wang", "url": "https://openalex.org/A5100411690", "inst": "Commercial Aircraft Corporation of China (China)" }, { "name": "Zefei Long", "url": "https://openalex.org/A5101259829", "inst": "Fudan University" }, { "name": "Xianyin Zhang", "url": "https://openalex.org/A5045876463", "inst": "Fudan University" }, { "name": "Zhongtian Lu", "url": "https://openalex.org/A5102571797", "inst": "Fudan University" }, { "name": "Bingxuan Li", "url": "https://openalex.org/A5101646502", "inst": "Fujian University of Traditional Chinese Medicine" }, { "name": "Siyuan Wang", "url": "https://openalex.org/A5100440548", "inst": "Beihang University" }, { "name": "Jiarong Xu", "url": "https://openalex.org/A5100870921", "inst": "Fudan University" }, { "name": "Xiang Bai", "url": "https://openalex.org/A5039363991", "inst": "Nantong University" }, { "name": "Xuanjing Huang", "url": "https://openalex.org/A5088834359", "inst": "Fudan University" }, { "name": "Zhongyu Wei", "url": "https://openalex.org/A5011504177", "inst": "Fudan University" } ], "affiliations": [ "Hebei Medical University", "Commercial Aircraft Corporation of China (China)", "Fudan University", "Fujian University of Traditional Chinese Medicine", "Beihang University", "Nantong University" ] }, { "uid": "doi:10.2139/ssrn.4558719", "doi": "10.2139/ssrn.4558719", "title": "How Generative AI Impacts Content Engagement: Evidence from an Online Learning Platform", "authors": [ "Unnati Narang", "Carl-Philip Ahlbom", "Shrabastee Banerjee" ], "posted": "2023-10-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4558719", "field": "management", "role": "object", "bullets": [ "Field experiment on the Yellowdig online discussion platform with participants randomly assigned to posting conditions, plus two follow-up online studies run on a custom AI platform the authors built.", "Participants were encouraged to write posts using AI only, AI with edits, or human input only; the underlying model is not named and outputs are not validated against any benchmark.", "Neither AI condition increased active commenting, but AI-assisted posts drew significantly more passive reactions than human posts, an effect attributed to greater informativeness and complexity of the content." ], "bullet_provenance": "ai", "salience": 58, "edition": 3, "audience": "general", "models": [], "validated": null, "n": 332, "authors_detailed": [ { "name": "Unnati Narang", "url": "https://openalex.org/A5102813755", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Carl-Philip Ahlbom", "url": "", "inst": "University of Bath" }, { "name": "Shrabastee Banerjee", "url": "", "inst": "Tilburg University" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "University of Bath", "Tilburg University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4584928", "doi": "10.2139/ssrn.4584928", "title": "Large Language Models and Financial Market Sentiment", "authors": [ "Shaun Alexander Bond", "Hayden Klok", "Min Zhu" ], "posted": "2023-10-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4584928", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 48, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 623, "authors_detailed": [ { "name": "Shaun A. Bond", "url": "https://openalex.org/A5019892273", "inst": "The University of Queensland" }, { "name": "Hayden Klok", "url": "https://openalex.org/A5008327530", "inst": "The University of Queensland" }, { "name": "Min Zhu", "url": "https://openalex.org/A5047861092", "inst": "The University of Queensland" } ], "affiliations": [ "The University of Queensland" ] }, { "uid": "doi:10.2139/ssrn.4586726", "doi": "10.2139/ssrn.4586726", "title": "Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis", "authors": [ "Paul Glasserman", "Caden Lin" ], "posted": "2023-10-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4586726", "field": "finance", "role": "method", "bullets": [ "Financial news headlines matched to stock returns, comparing trading strategies within and outside the LLM training window with original versus anonymized company names.", "ChatGPT extracted sentiment from financial news headlines to generate trading signals, tested with and without company identifiers to isolate look-ahead bias.", "Anonymized headlines outperformed in-sample, indicating distraction from general company knowledge exceeds look-ahead bias, especially for larger firms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "in-sample vs out-of-sample trading returns comparison", "salience": 70, "n": 2389, "authors_detailed": [ { "name": "Paul Glasserman", "url": "https://openalex.org/A5026619574", "inst": "Columbia University" }, { "name": "Caden Lin", "url": "https://openalex.org/A5111094687", "inst": "University of the District of Columbia" } ], "affiliations": [ "Columbia University", "University of the District of Columbia" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2310.14435v1", "arxiv_id": "2310.14435v1", "title": "Retrieval-Augmented Chain-of-Thought in Semi-structured Domains", "authors": [ "Vaibhav Mavi", "Abulhair Saparov", "Chen Zhao" ], "posted": "2023-10-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.14435v1", "field": "finance", "role": "method", "bullets": [ "Question answering over legal and financial documents, where required background context routinely exceeds LLM input limits; evaluation datasets are not named in the abstract.", "The semi-structured layout of legal and financial data guides retrieval of relevant context before querying an LLM; the models used are not named in the abstract.", "The retrieval-based system outperforms contemporary baselines on domain question answering and supplies explanations for its answers; improvement margins are not stated." ], "bullet_provenance": "ai", "validated": true, "validation_note": "domain QA benchmarks, unnamed in abstract", "salience": 34, "edition": 12, "models": [], "n": 1388, "authors_detailed": [ { "name": "Vaibhav Mavi", "url": "https://openalex.org/A5039688324", "inst": "Jersey City Medical Center" }, { "name": "Abulhair Saparov", "url": "https://openalex.org/A5062813195", "inst": "Purdue University West Lafayette" }, { "name": "Chen Zhao", "url": "https://openalex.org/A5100351992", "inst": "Inner Mongolia University" } ], "affiliations": [ "Purdue University West Lafayette", "Inner Mongolia University" ] }, { "uid": "arxiv:2310.13226v1", "arxiv_id": "2310.13226v1", "title": "Enhancing Zero-Shot Crypto Sentiment with Fine-tuned Language Model and Prompt Engineering", "authors": [ "Rahman S M Wahidur", "Ishmam Tashdeed", "Manjit Kaur", "Heung-No-Lee" ], "posted": "2023-10-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.13226v1", "field": "finance", "role": "method", "bullets": [ "Sentiment classification of cryptocurrency discussions from social media; the abstract does not state which datasets, how many posts, or what time period.", "Large language models are fine-tuned with supervised and instruction-based methods and scored by classification accuracy; the abstract names neither the base models nor the datasets.", "Fine-tuning yields an average 40 percent zero-shot gain; the best instruction-tuned large model reaches 75.16 percent accuracy, and short simple instructions beat long complex ones by over 12 points." ], "bullet_provenance": "ai", "validated": true, "validation_note": "classification accuracy on crypto sentiment tasks", "salience": 38, "edition": 12, "models": [], "n": 1472, "authors_detailed": [ { "name": "Rahman S M Wahidur", "url": "https://openalex.org/A5093115559", "inst": "Gwangju Institute of Science and Technology" }, { "name": "Ishmam Tashdeed", "url": "https://openalex.org/A5093115560", "inst": "Islamic University of Technology" }, { "name": "Manjit Kaur", "url": "https://openalex.org/A5020133324", "inst": "Chandigarh University" }, { "name": "Heung-No-Lee", "url": "https://openalex.org/A5093115561", "inst": "" } ], "affiliations": [ "Gwangju Institute of Science and Technology", "Islamic University of Technology", "Chandigarh University" ] }, { "uid": "arxiv:2310.13787v1", "arxiv_id": "2310.13787v1", "title": "Enhancing Illicit Activity Detection using XAI: A Multimodal Graph-LLM Framework", "authors": [ "Jack Nicholls", "Aditya Kuppa", "Nhien-An Le-Khac" ], "posted": "2023-10-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.13787v1", "field": "finance", "role": "instrument", "bullets": [ "Financial transaction records, graph connectivity, and social network data for cybercrime investigation workflows; no specific dataset, sample size, or period is stated.", "An unnamed LLM turns transaction details and metadata into narrative explanations for analysts, alongside sequence and subgraph models; no validation of outputs is reported.", "Proposes the multimodal framework to streamline analyst investigations of illicit activity; the abstract reports no detection performance or explanation quality figures." ], "bullet_provenance": "ai", "validated": false, "salience": 24, "edition": 12, "models": [], "n": 1498, "authors_detailed": [ { "name": "Jack Nicholls", "url": "https://openalex.org/A5059840185", "inst": "University College Dublin" }, { "name": "Aditya Kuppa", "url": "https://openalex.org/A5083053959", "inst": "University College Dublin" }, { "name": "Nhien‐An Le‐Khac", "url": "https://openalex.org/A5073189627", "inst": "University College Dublin" } ], "affiliations": [ "University College Dublin" ] }, { "uid": "arxiv:2310.12664v1", "arxiv_id": "2310.12664v1", "title": "Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing", "authors": [ "Yue Guo", "Zian Xu", "Yi Yang" ], "posted": "2023-10-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.12664v1", "field": "finance", "role": "method", "bullets": [ "FinLMEval assembles nine financial language task datasets, several of them proprietary, as a testbed comparing fine-tuned encoder-only models with decoder-only generative LLMs.", "ChatGPT and other decoder-only models answer with zero-shot prompting and are scored on the labelled datasets alongside fine-tuned encoder models; exact accuracy figures are not stated in the abstract.", "Zero-shot decoder LLMs perform respectably on most tasks but trail fine-tuned expert models, with the gap widest on proprietary datasets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "nine labelled financial NLP datasets", "salience": 48, "edition": 12, "n": 1387, "authors_detailed": [ { "name": "Yue Guo", "url": "https://openalex.org/A5100701473", "inst": "Beijing Institute of Technology" }, { "name": "Zian Xu", "url": "https://openalex.org/A5069590333", "inst": "Twitter (United States)" }, { "name": "Yi Yang", "url": "https://openalex.org/A5005421447", "inst": "Zhejiang University" } ], "affiliations": [ "Beijing Institute of Technology", "Twitter (United States)", "Zhejiang University" ] }, { "uid": "arxiv:2310.12941v1", "arxiv_id": "2310.12941v1", "title": "The Foundation Model Transparency Index", "authors": [ "Rishi Bommasani", "Kevin Klyman", "Shayne Longpre", "Sayash Kapoor", "Nestor Maslej", "Betty Xiong", "Daniel Zhang", "Percy Liang" ], "posted": "2023-10-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.12941v1", "field": "management", "role": "object", "bullets": [ "Ten major foundation model developers, including OpenAI, Google, and Meta, assessed on their flagship models such as GPT-4, PaLM 2, and Llama 2.", "No LLM acts as a research tool; the authors hand score 100 transparency indicators covering upstream resources, model properties, and downstream use.", "Transparency is declining across the ecosystem, and no developer discloses significant information on downstream impact such as user numbers, affected sectors, or redress mechanisms." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "llama" ], "open_weights": true, "salience": 55, "edition": 12, "validated": null, "n": 1412, "authors_detailed": [ { "name": "Rishi Bommasani", "url": "https://openalex.org/A5069576651", "inst": "Stanford University" }, { "name": "Kevin Klyman", "url": "https://openalex.org/A5093104547", "inst": "Harvard University Press" }, { "name": "Shayne Longpre", "url": "https://openalex.org/A5001884064", "inst": "Massachusetts Institute of Technology" }, { "name": "Sayash Kapoor", "url": "https://openalex.org/A5063345981", "inst": "Princeton University" }, { "name": "Nestor Maslej", "url": "https://openalex.org/A5085498028", "inst": "Artificial Intelligence in Medicine (Canada)" }, { "name": "Betty Xiong", "url": "https://openalex.org/A5101258427", "inst": "Stanford University" }, { "name": "Daniel Zhang", "url": "https://openalex.org/A5101788195", "inst": "University of Notre Dame" }, { "name": "Percy Liang", "url": "https://openalex.org/A5025255782", "inst": "Stanford University" } ], "affiliations": [ "Stanford University", "Harvard University", "Massachusetts Institute of Technology", "Princeton University", "University of Notre Dame", "Artificial Intelligence in Medicine (Canada)" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2310.11249v1", "arxiv_id": "2310.11249v1", "title": "Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle", "authors": [ "Xu Yang", "Xiao Yang", "Weiqing Liu", "Jinhui Li", "Peng Yu", "Zeqi Ye", "Jiang Bian" ], "posted": "2023-10-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.11249v1", "field": "finance", "role": "agent", "bullets": [ "Quantitative investment research on the open source Qlib platform serves as the worked example of an industrial data centric R&D cycle.", "An LLM, unnamed in the abstract, reads task requirements, proposes research ideas, runs experiments through domain tools, interprets results, and carries knowledge across iterations; no accuracy validation is described.", "The framework yields results the authors describe as promising, with no quantitative comparison to human researchers or baseline pipelines reported in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 12, "models": [], "n": 1446, "authors_detailed": [ { "name": "Xu Yang", "url": "https://openalex.org/A5101257951", "inst": "Harbin Institute of Technology" }, { "name": "Xiao Yang", "url": "https://openalex.org/A5032796133", "inst": "King Center" }, { "name": "Weiqing Liu", "url": "https://openalex.org/A5101522968", "inst": "Zhuzhou Central Hospital" }, { "name": "Jinhui Li", "url": "https://openalex.org/A5100325438", "inst": "Sun Yat-sen University" }, { "name": "Peng Yu", "url": "https://openalex.org/A5100665157", "inst": "Chongqing Normal University" }, { "name": "Zeqi Ye", "url": "https://openalex.org/A5114236483", "inst": "Pingxiang University" }, { "name": "Jiang Bian", "url": "https://openalex.org/A5101544241", "inst": "Microsoft (United States)" } ], "affiliations": [ "Harbin Institute of Technology", "King Center", "Sun Yat-sen University", "Chongqing Normal University", "Pingxiang University", "Microsoft (United States)" ] }, { "uid": "arxiv:2310.10760v1", "arxiv_id": "2310.10760v1", "title": "Towards reducing hallucination in extracting information from financial reports using Large Language Models", "authors": [ "Bhaskarjit Sarmah", "Tianjie Zhu", "Dhagash Mehta", "Stefano Pasquali" ], "posted": "2023-10-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.10760v1", "field": "finance", "role": "method", "bullets": [ "Question and answer sections of company earnings call transcripts, the segment analysts mine for investment relevant detail; corpus size and time period are not stated.", "Several LLMs, unnamed in the abstract, extract information using retrieval-augmented generation plus metadata, aimed at reducing hallucination; outputs are scored on objective question answering metrics.", "The retrieval and metadata approach beats the same models without it across the evaluation metrics; the abstract does not quantify the improvement." ], "bullet_provenance": "ai", "validated": true, "validation_note": "objective QA metrics, figures not stated in abstract", "salience": 42, "edition": 12, "models": [], "n": 1386, "authors_detailed": [ { "name": "Bhaskarjit Sarmah", "url": "https://openalex.org/A5040045971", "inst": "Shree Guru Gobind Singh Tricentenary University" }, { "name": "Tianjie Zhu", "url": "https://openalex.org/A5113431805", "inst": "Northeastern University" }, { "name": "Dhagash Mehta", "url": "https://openalex.org/A5056701307", "inst": "University of Notre Dame" }, { "name": "Stefano Pasquali", "url": "https://openalex.org/A5011160139", "inst": "Ospedale Maggiore" } ], "affiliations": [ "University of Notre Dame", "Shree Guru Gobind Singh Tricentenary University", "Northeastern University", "Ospedale Maggiore" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2310.10826v3", "arxiv_id": "2310.10826v3", "title": "Mechanism Design for Large Language Models", "authors": [ "Paul Duetting", "Vahab Mirrokni", "Renato Paes Leme", "Haifeng Xu", "Song Zuo" ], "posted": "2023-10-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.10826v3", "field": "economics", "role": "object", "bullets": [ "A theory setting of auctions for AI generated content, motivated by ad creatives, in which each bidder's preferences over stochastic outputs are encoded as an LLM.", "No empirical measurement task; LLM agents submit one dimensional bids that steer token by token generation, with a demonstration on an unnamed publicly available model.", "Two incentive properties prove equivalent to a monotonicity condition on output aggregation, enabling a second price style rule without explicit agent valuation functions." ], "bullet_provenance": "ai", "salience": 55, "edition": 12, "models": [], "validated": null, "n": 1445, "authors_detailed": [ { "name": "Paul Duetting", "url": "https://openalex.org/A5032749005", "inst": "Google (United States)" }, { "name": "Vahab Mirrokni", "url": "https://openalex.org/A5075598023", "inst": "Google (United States)" }, { "name": "Renato Paes Leme", "url": "https://openalex.org/A5019999334", "inst": "Google (United States)" }, { "name": "Haifeng Xu", "url": "https://openalex.org/A5101434141", "inst": "University of Chicago" }, { "name": "Song Zuo", "url": "https://openalex.org/A5016149057", "inst": "Google (United States)" } ], "affiliations": [ "University of Chicago", "Google (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4603524", "doi": "10.2139/ssrn.4603524", "title": "Navigating the Business Landscape of Large Language Models: An Entrepreneurial Perspective", "authors": [ "Benyawarath Nithithanatchinnapat", "Joshua Maurer" ], "posted": "2023-10-16", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4603524", "field": "management", "role": "object", "bullet_provenance": "none", "salience": 30, "edition": 12, "bullets": [], "models": [], "validated": null, "n": 1496, "authors_detailed": [ { "name": "Benyawarath Nithithanatchinnapat", "url": "https://openalex.org/A5032859372", "inst": "Independent - affiliation not provided to SSRN" }, { "name": "Joshua D. Maurer", "url": "https://openalex.org/A5053087634", "inst": "Gannon University" } ], "affiliations": [ "Independent - affiliation not provided to SSRN", "Gannon University" ] }, { "uid": "arxiv:2310.10544v3", "arxiv_id": "2310.10544v3", "title": "Use of probabilistic phrases in a coordination game: human versus GPT-4", "authors": [ "Laurence T Maloney", "Maria F Dal Martello", "Vivian Fei", "Valerie Ma" ], "posted": "2023-10-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.10544v3", "field": "economics", "role": "agent", "bullets": [ "Twenty three probabilistic phrases rated for probability and ambiguity in a coordination game, in investment and medical advice contexts, by human participants and GPT-4; participant counts not stated.", "GPT-4 completed the same rating tasks as the human sample, and its estimates were compared directly with the distribution of human judgments in both contexts.", "GPT-4 probability estimates matched the median human with about .90 of variance accounted for and little context sensitivity; agreement on ambiguity was weaker." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "human judgments on identical tasks, about .90 variance accounted for", "salience": 46, "edition": 12, "n": 1497, "authors_detailed": [ { "name": "Laurence T. Maloney", "url": "https://openalex.org/A5110504394", "inst": "New York University" }, { "name": "Maria F. Dal Martello", "url": "https://openalex.org/A5024122928", "inst": "University of Padua" }, { "name": "Vivian Fei", "url": "https://openalex.org/A5093081486", "inst": "New York University" }, { "name": "Valerie Ma", "url": "https://openalex.org/A5113025295", "inst": "Taoyuan Chang Gung Memorial Hospital" } ], "affiliations": [ "New York University", "University of Padua" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4574226", "doi": "10.2139/ssrn.4574226", "title": "Bias of AI-Generated Content: An Examination of News Produced by Large Language Models", "authors": [ "Xiao Fang", "Shangkun Che", "Minjia Mao", "Hongzhe Zhang", "Ming Zhao", "Xiaohang Zhao" ], "posted": "2023-10-13", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4574226", "field": "management", "role": "object", "bullets": [ "8,629 news articles from the New York Times and Reuters collected between December 2022 and April 2023, used as headline prompts for seven LLMs including ChatGPT, LLaMA, and Cohere.", "Each LLM generated news content from the original headlines; gender and racial bias were measured at word, sentence, and document levels by comparing AI-generated text against the source articles.", "Every model's output deviated substantially from original reporting and discriminated against females and Black individuals; ChatGPT showed the lowest bias overall but generated the most biased content when biased prompts bypassed its RLHF screening." ], "bullet_provenance": "ai", "models": [ "gpt", "llama", "open_other" ], "open_weights": true, "salience": 55, "edition": 25, "validated": null, "n": 4298, "authors_detailed": [ { "name": "Xiao Fang", "url": "https://openalex.org/A5078410186", "inst": "University of Delaware" }, { "name": "Shangkun Che", "url": "https://openalex.org/A5103120458", "inst": "Tsinghua University" }, { "name": "Minjia Mao", "url": "https://openalex.org/A5111071295", "inst": "University of Delaware" }, { "name": "Hongzhe Zhang", "url": "https://openalex.org/A5109681510", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Ming Zhao", "url": "https://openalex.org/A5001906215", "inst": "University of Delaware" }, { "name": "Xiaohang Zhao", "url": "https://openalex.org/A5090123459", "inst": "Shanghai University of Finance and Economics" } ], "affiliations": [ "University of Delaware", "Tsinghua University", "Chinese University of Hong Kong, Shenzhen", "Shanghai University of Finance and Economics" ] }, { "uid": "arxiv:2311.06273v1", "arxiv_id": "2311.06273v1", "title": "Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis", "authors": [ "Ummara Mumtaz", "Summaya Mumtaz" ], "posted": "2023-10-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.06273v1", "field": "finance", "role": "instrument", "bullets": [ "Twitter posts about Microsoft and Google matched to each firm's following day stock outcome; tweet counts and sample period are not stated.", "ChatGPT rates each tweet as positive, negative, or neutral for the stock; the model version is not stated and no check against human labels is reported.", "ChatGPT sentiment is positively linked with next day stock results for both firms; the abstract gives no effect sizes or trading performance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 30, "edition": 12, "n": 1495, "authors_detailed": [ { "name": "Ummara Mumtaz", "url": "https://openalex.org/A5093264792", "inst": "University of the Cumberlands" }, { "name": "Summaya Mumtaz", "url": "https://openalex.org/A5034963323", "inst": "University of the Cumberlands" } ], "affiliations": [ "University of the Cumberlands" ] }, { "uid": "arxiv:2310.08678v1", "arxiv_id": "2310.08678v1", "title": "Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams", "authors": [ "Ethan Callanan", "Amarachi Mbakwe", "Antony Papadimitriou", "Yulong Pei", "Mathieu Sibue", "Xiaodan Zhu", "Zhiqiang Ma", "Xiaomo Liu", "Sameena Shah" ], "posted": "2023-10-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.08678v1", "field": "finance", "role": "method", "bullets": [ "Mock exam questions from the Chartered Financial Analyst program; question counts and level coverage are not stated in the abstract.", "ChatGPT and GPT-4 answer under zero shot, chain of thought, and few shot prompting, scored against the official answer keys.", "The paper gauges whether either model could pass the CFA exams and catalogues failure modes; the abstract reports no accuracy figures." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "mock CFA exam answer keys", "salience": 50, "edition": 12, "n": 1364, "authors_detailed": [ { "name": "Ethan Callanan", "url": "https://openalex.org/A5026897392", "inst": "Queen's University" }, { "name": "Amarachi B. Mbakwe", "url": "https://openalex.org/A5039265385", "inst": "Virginia Tech" }, { "name": "Antony Papadimitriou", "url": "https://openalex.org/A5008016689", "inst": "JPMorgan Chase & Co (United States)" }, { "name": "Yulong Pei", "url": "https://openalex.org/A5101822763", "inst": "Northeast Forestry University" }, { "name": "Mathieu Sibue", "url": "https://openalex.org/A5093073351", "inst": "" }, { "name": "Xiaodan Zhu", "url": "https://openalex.org/A5016892586", "inst": "Queen's University" }, { "name": "Zhiqiang Ma", "url": "https://openalex.org/A5053722499", "inst": "Jiangsu University" }, { "name": "Xiaomo Liu", "url": "https://openalex.org/A5048424819", "inst": "Morgan Stanley (United Kingdom)" }, { "name": "Sameena Shah", "url": "https://openalex.org/A5087647155", "inst": "JPMorgan Chase & Co (United States)" } ], "affiliations": [ "Queen's University", "Virginia Tech", "JPMorgan Chase & Co (United States)", "Northeast Forestry University", "Jiangsu University", "Morgan Stanley (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.4594780", "doi": "10.2139/ssrn.4594780", "title": "Effective Generative AI: The Human-Algorithm Centaur", "authors": [ "Soroush Saghafian" ], "posted": "2023-10-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4594780", "field": "management", "role": "object", "bullets": [ "Conceptual analysis of hybrid human-algorithm centaur models for decision-making across domains, using generative AI and LLMs as primary case study.", "Framework comparing centaur approaches to pure AI and standard human-in-the-loop methods; identifies conditions for optimal delegation between centaurs and pure AI.", "Centaur models combining human intuition with AI analytics can outperform pure AI in many domains; incorporating human judgment does not necessarily degrade performance." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 2455, "authors_detailed": [ { "name": "Soroush Saghafian", "url": "https://openalex.org/A5068960692", "inst": "Harvard University" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4571863", "doi": "10.2139/ssrn.4571863", "title": "Unlocking Bankruptcy Clues: A Novel Sentence-Based Machine Learning Approach", "authors": [ "Matthies Hesse", "Thomas R. Loy" ], "posted": "2023-10-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4571863", "field": "accounting", "role": "instrument", "bullets": [ "U.S. firms' Management Discussion and Analysis sections used for bankruptcy prediction; sentence-level analysis excludes boilerplate content to improve predictive signal extraction.", "BERT contextual sentence embeddings generate features for a novel sentence-level bankruptcy prediction model; performance benchmarked against multiple document-level text classification approaches.", "Sentence-level model outperforms document-level approaches for bankruptcy prediction; high-risk topics identified include financial losses and reductions in production, cost, and workforce." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "outperforms document-level baselines in bankruptcy prediction accuracy", "salience": 50, "n": 3888, "authors_detailed": [ { "name": "Matthies Hesse", "url": "https://openalex.org/A5060880053", "inst": "University of Bremen" }, { "name": "Thomas Loy", "url": "https://openalex.org/A5075500099", "inst": "University of Bremen" } ], "affiliations": [ "University of Bremen" ] }, { "uid": "doi:10.2139/ssrn.4596668", "doi": "10.2139/ssrn.4596668", "title": "Values Discovery", "authors": [ "Zacharias Sautner", "Laurence van Lent", "Grigory Vilkov", "Ruishen Zhang" ], "posted": "2023-10-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4596668", "field": "finance", "role": "instrument", "bullets": [ "2.1 million conversations from 379,605 earnings calls across 85 countries over 2003-2024; analyst questions analyzed for alignment with stated mandates of impact investment funds.", "Retrieval-augmented generation method identifies analyst questions matching impact fund mandates; classifies climate-related discourse as values-oriented versus generic to measure distinct information content.", "Values-oriented climate questions close one-fifth to one-third of analysts' information gap; post-2015 portfolios sorted on these questions earn 2.2% higher annual returns." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 65, "n": 3889, "authors_detailed": [ { "name": "Zacharias Sautner", "url": "https://openalex.org/A5046526188", "inst": "University of Zurich" }, { "name": "Laurence van Lent", "url": "https://openalex.org/A5050922413", "inst": "Frankfurt School of Finance & Management" }, { "name": "Grigory Vilkov", "url": "https://openalex.org/A5063063698", "inst": "Frankfurt School of Finance & Management" }, { "name": "Ruishen Zhang", "url": "https://openalex.org/A5022908954", "inst": "University of Hong Kong" } ], "affiliations": [ "University of Zurich", "Frankfurt School of Finance & Management", "University of Hong Kong" ] }, { "uid": "arxiv:2310.05627v1", "arxiv_id": "2310.05627v1", "title": "Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction", "authors": [ "Yujie Ding", "Shuai Jia", "Tianyi Ma", "Bingcheng Mao", "Xiuze Zhou", "Liuliu Li", "Dongming Han" ], "posted": "2023-10-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.05627v1", "field": "finance", "role": "method", "bullets": [ "China A share market, combining quantitative stock features with financial news for cross sectional return prediction; sample period not stated in the abstract.", "News embeddings from an LLM, specific model not named, are aligned with stock features through self correlated reinforcement learning; the news representation is not validated against any ground truth.", "The local global framework lifts rank information coefficient and portfolio returns over stock feature only models; magnitudes are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 38, "edition": 12, "n": 1366, "authors_detailed": [ { "name": "Yujie Ding", "url": "https://openalex.org/A5006056061", "inst": "" }, { "name": "Shuai Jia", "url": "https://openalex.org/A5102706204", "inst": "Hong Kong University of Science and Technology" }, { "name": "Tianyi Ma", "url": "https://openalex.org/A5069632856", "inst": "Qiqihar University" }, { "name": "Bingcheng Mao", "url": "https://openalex.org/A5004639852", "inst": "Beibu Gulf University" }, { "name": "Xiuze Zhou", "url": "https://openalex.org/A5032979467", "inst": "Guangdong University of Technology" }, { "name": "Liuliu Li", "url": "https://openalex.org/A5069386068", "inst": "Jiangsu Normal University" }, { "name": "Dongming Han", "url": "https://openalex.org/A5023548219", "inst": "First Affiliated Hospital of Xinxiang Medical University" } ], "affiliations": [ "Hong Kong University of Science and Technology", "Qiqihar University", "Beibu Gulf University", "Guangdong University of Technology", "Jiangsu Normal University" ] }, { "uid": "arxiv:2310.05628v3", "arxiv_id": "2310.05628v3", "title": "Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models", "authors": [ "Marco Bronzini", "Carlo Nicolini", "Bruno Lepri", "Andrea Passerini", "Jacopo Staiano" ], "posted": "2023-10-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.05628v3", "field": "accounting", "role": "instrument", "bullets": [ "Sustainability reports from publicly listed companies facing ESG disclosure requirements; the number of firms, years, and regions covered is not stated in the abstract.", "Unnamed LLMs with in-context learning and retrieval-augmented generation extract structured ESG disclosures into graph representations; no accuracy check against hand coded labels is reported.", "Disclosures span more than 500 ESG topics, resemble each other within regions and sectors, and disclosure content predicts ESG ratings more strongly than financial or other firm attributes." ], "bullet_provenance": "ai", "validated": false, "salience": 48, "edition": 12, "models": [], "n": 1379, "authors_detailed": [ { "name": "Marco Bronzini", "url": "https://openalex.org/A5041720527", "inst": "Amazon (United States)" }, { "name": "Carlo Nicolini", "url": "https://openalex.org/A5010148927", "inst": "University of Verona" }, { "name": "Bruno Lepri", "url": "https://openalex.org/A5048877432", "inst": "Fondazione Bruno Kessler" }, { "name": "Andrea Passerini", "url": "https://openalex.org/A5066187890", "inst": "University of Trento" }, { "name": "Jacopo Staiano", "url": "https://openalex.org/A5082391569", "inst": "University of Trento" } ], "affiliations": [ "Amazon (United States)", "University of Verona", "Fondazione Bruno Kessler", "University of Trento" ] }, { "uid": "arxiv:2310.17784v2", "arxiv_id": "2310.17784v2", "title": "Data-Centric Financial Large Language Models", "authors": [ "Zhixuan Chu", "Huaiyu Guo", "Xinyuan Zhou", "Yijia Wang", "Fei Yu", "Hong Chen", "Wanqing Xu", "Xin Lu", "Qing Cui", "Longfei Li", "Jun Zhou", "Sheng Li" ], "posted": "2023-10-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.17784v2", "field": "finance", "role": "method", "bullets": [ "Financial analysis and interpretation tasks over financial text, plus a newly released open benchmark the authors built for scoring such systems.", "A financial LLM, base model unstated, is finetuned with multitask prompts; abductive augmentation reasoning revises the model's own pseudo labels to expand scarce training data.", "The data centric model is reported to beat baseline financial LLMs on raw text and reach state of the art on the benchmark; the abstract gives no figures." ], "bullet_provenance": "ai", "validated": true, "validation_note": "financial analysis benchmark evaluation", "salience": 38, "edition": 12, "models": [], "n": 1444, "authors_detailed": [ { "name": "Zhixuan Chu", "url": "https://openalex.org/A5008967163", "inst": "Zhejiang University" }, { "name": "Huaiyu Guo", "url": "https://openalex.org/A5102488679", "inst": "Zhejiang Normal University" }, { "name": "Xinyuan Zhou", "url": "https://openalex.org/A5102864167", "inst": "Tianjin University of Technology" }, { "name": "Yijia Wang", "url": "https://openalex.org/A5022993156", "inst": "Xiamen University" }, { "name": "Fei Yu", "url": "https://openalex.org/A5100592342", "inst": "CCCC Highway Consultants (China)" }, { "name": "Hong Chen", "url": "https://openalex.org/A5100420391", "inst": "Beijing Union University" }, { "name": "Wanqing Xu", "url": "https://openalex.org/A5102177776", "inst": "Tufts University" }, { "name": "Xin Lü", "url": "https://openalex.org/A5111064477", "inst": "Yangzhou University" }, { "name": "Qing Cui", "url": "https://openalex.org/A5101072190", "inst": "Northwest University" }, { "name": "Longfei Li", "url": "https://openalex.org/A5100430941", "inst": "Zhengzhou University" }, { "name": "Jun Zhou", "url": "https://openalex.org/A5045140292", "inst": "Zhejiang Energy Group (China)" }, { "name": "Sheng Li", "url": "https://openalex.org/A5100359817", "inst": "University of Virginia" } ], "affiliations": [ "Zhejiang University", "Zhejiang Normal University", "Tianjin University of Technology", "Xiamen University", "CCCC Highway Consultants (China)", "Beijing Union University", "Tufts University", "Yangzhou University" ] }, { "uid": "arxiv:2310.04793v2", "arxiv_id": "2310.04793v2", "title": "FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets", "authors": [ "Neng Wang", "Hongyang Yang", "Christina Dan Wang" ], "posted": "2023-10-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.04793v2", "field": "finance", "role": "method", "bullets": [ "Financial NLP tasks such as named entity recognition and sentiment analysis, assembled into an instruction tuning benchmark for open source language models; datasets and sizes not enumerated in the abstract.", "Open source LLMs are instruction tuned in stages, moving from single tasks to multi task and zero shot settings; base model families are not named in the abstract.", "Contributes an end to end training and testing scheme for financial language models, emphasizing openness and reproducibility; the abstract quotes no accuracy figures." ], "bullet_provenance": "ai", "open_weights": true, "validated": true, "validation_note": "labelled financial NLP benchmark tasks", "salience": 46, "edition": 12, "models": [], "n": 1493, "authors_detailed": [ { "name": "Neng Wang", "url": "https://openalex.org/A5101563004", "inst": "Fujian Medical University" }, { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "Beijing University of Technology" }, { "name": "Christina Dan Wang", "url": "https://openalex.org/A5065455126", "inst": "" } ], "affiliations": [ "Fujian Medical University", "Beijing University of Technology" ] }, { "uid": "arxiv:2310.04880v1", "arxiv_id": "2310.04880v1", "title": "Question-focused Summarization by Decomposing Articles into Facts and Opinions and Retrieving Entities", "authors": [ "Krutika Sarode", "Shashidhar Reddy Javaji", "Vishal Kalakonnavar" ], "posted": "2023-10-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.04880v1", "field": "finance", "role": "instrument", "bullets": [ "News articles from the Economist plus Wikipedia data linking companies to entities, used to flag market moving events for analysts; article counts and period not stated.", "GPT-3.5 produces entity level summaries and composes the final abstract summary of each article; no validation against reference summaries or human labels is reported.", "Presents the pipeline design for early detection of market trends; the abstract reports no predictive accuracy, backtest, or return figures." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 26, "edition": 12, "n": 1494, "authors_detailed": [ { "name": "Krutika Sarode", "url": "https://openalex.org/A5112989119", "inst": "" }, { "name": "Shashidhar Reddy Javaji", "url": "https://openalex.org/A5033003129", "inst": "Amherst College" }, { "name": "Vishal Kalakonnavar", "url": "https://openalex.org/A5093047656", "inst": "University of Massachusetts Amherst" } ], "affiliations": [ "Amherst College", "University of Massachusetts Amherst" ] }, { "uid": "doi:10.2139/ssrn.4593660", "doi": "10.2139/ssrn.4593660", "title": "From Transcripts to Insights: Uncovering Corporate Risks Using Generative AI", "authors": [ "Alex G. Kim", "Maximilian Muhn", "Valeri V. Nikolaev" ], "posted": "2023-10-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4593660", "field": "finance", "role": "instrument", "bullets": [ "Earnings call transcripts from US public firms, constructing firm-level measures of political, climate, and AI-related corporate risks.", "An LLM generated risk assessments from transcripts that outperform dictionary-based measures in predicting firm-level volatility and explaining investment decisions.", "LLM-based risk measures contain significant information content, are priced in equity markets, and perform well outside the model's training data window." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "predictive comparison against dictionary-based measures for firm-level volatility", "salience": 80, "n": 2637, "authors_detailed": [ { "name": "Alex Kim", "url": "https://openalex.org/A5002013850", "inst": "Woodlawn School" }, { "name": "Maximilian Muhn", "url": "https://openalex.org/A5065276465", "inst": "Woodlawn School" }, { "name": "Valeri V. Nikolaev", "url": "https://openalex.org/A5085975914", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "Woodlawn School" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2310.04027v2", "arxiv_id": "2310.04027v2", "title": "Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models", "authors": [ "Boyu Zhang", "Hongyang Yang", "Tianyu Zhou", "Ali Babar", "Xiao-Yang Liu" ], "posted": "2023-10-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.04027v2", "field": "finance", "role": "method", "bullets": [ "Financial sentiment classification where brief news items often lack context; the evaluation benchmarks are not named in the abstract.", "An instruction tuned LLM, base model not named, paired with a retrieval module that pulls background context from external sources; compared on labelled data with ChatGPT, LLaMA, and traditional classifiers.", "The retrieval augmented setup gains 15 to 48 percent in accuracy and F1 over ChatGPT, LLaMA, and the traditional model baselines on the sentiment tasks." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "labelled sentiment benchmarks, accuracy and F1", "salience": 44, "edition": 12, "n": 1363, "authors_detailed": [ { "name": "Boyu Zhang", "url": "https://openalex.org/A5100714035", "inst": "Electric Power University" }, { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "Beijing University of Technology" }, { "name": "Tian-Yu Zhou", "url": "https://openalex.org/A5010898082", "inst": "Xinjiang University" }, { "name": "Muhammad Ali Babar", "url": "https://openalex.org/A5103075476", "inst": "The University of Adelaide" }, { "name": "Xiaoyang Liu", "url": "https://openalex.org/A5022718768", "inst": "Argonne National Laboratory" } ], "affiliations": [ "Electric Power University", "Beijing University of Technology", "Xinjiang University", "The University of Adelaide", "Argonne National Laboratory" ] }, { "uid": "arxiv:2310.13001v4", "arxiv_id": "2310.13001v4", "title": "Conversational Factor Information Retrieval Model (ConFIRM)", "authors": [ "Stephen Choi", "William Gazeley", "Siu Ho Wong", "Tingting Li" ], "posted": "2023-10-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.13001v4", "field": "finance", "role": "method", "bullets": [ "Finance sector case study; synthetic training queries generated to mirror target population traits drawn from the PolyU Asklora Fintech Adoption Index and the five factor personality model.", "Llama 2 7B is fine tuned on the personality aligned synthetic data, and the resulting model is scored on classification of financial queries.", "Reaches 91 percent accuracy on financial query classification, with average inference of 0.61 seconds on an NVIDIA A100 GPU." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "91 percent financial query classification accuracy", "salience": 32, "edition": 12, "n": 1370, "authors_detailed": [ { "name": "Stephen J. Choi", "url": "https://openalex.org/A5085892614", "inst": "New York Law School" }, { "name": "William Gazeley", "url": "https://openalex.org/A5067547424", "inst": "" }, { "name": "Siu Ho Wong", "url": "https://openalex.org/A5102570867", "inst": "" }, { "name": "Tingting Li", "url": "https://openalex.org/A5082363548", "inst": "Zhejiang Industry Polytechnic College" } ], "affiliations": [ "New York Law School", "Zhejiang Industry Polytechnic College" ] }, { "uid": "doi:10.2139/ssrn.4567607", "doi": "10.2139/ssrn.4567607", "title": "Extracting Financial Data From Unstructured Sources: Leveraging Large Language Models", "authors": [ "Huaxia Li", "Haoyun Gao", "Chengzhang Wu", "Miklos A. Vasarhelyi" ], "posted": "2023-10-05", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4567607", "field": "accounting", "role": "instrument", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 622, "authors_detailed": [ { "name": "Huaxia Li", "url": "https://openalex.org/A5001895964", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Haoyun Gao", "url": "https://openalex.org/A5104178819", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Chengzhang Wu", "url": "https://openalex.org/A5075848165", "inst": "Stockton University" }, { "name": "Miklos A. Vasarhelyi", "url": "https://openalex.org/A5049215719", "inst": "Rutgers Sexual and Reproductive Health and Rights" } ], "affiliations": [ "Rutgers, The State University of New Jersey", "Stockton University", "Rutgers Sexual and Reproductive Health and Rights" ] }, { "uid": "doi:10.2139/ssrn.4592121", "doi": "10.2139/ssrn.4592121", "title": "Assessing the Data Challenges of Climate-Related Disclosures in European Banks. A Text Mining Study", "authors": [ "Angel Ivan Moreno", "Teresa Caminero" ], "posted": "2023-10-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4592121", "field": "finance", "role": "instrument", "bullets": [ "ESG reports from 2019 to 2021 of most significant banks under ECB direct supervision, analyzing 23 granular greenhouse gas disclosure items.", "Commercial LLMs verified the context of text-mined disclosures to construct a Greenhouse Gas Disclosure Index, which was compared against CDP scores.", "Moderate correlation found between institutions not reporting to CDP and low disclosure index; high CDP scores do not necessarily correspond to high granular disclosure." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2636, "authors_detailed": [ { "name": "A Moreno", "url": "https://openalex.org/A5102710339", "inst": "Bank of Spain" }, { "name": "Teresa Caminero", "url": "https://openalex.org/A5041486808", "inst": "Bank of Spain" } ], "affiliations": [ "Bank of Spain" ] }, { "uid": "doi:10.2139/ssrn.4568684", "doi": "10.2139/ssrn.4568684", "title": "Generative AI and the Workforce: What Are the Risks?", "authors": [ "Emmanuelle Walkowiak", "Trent MacDonald" ], "posted": "2023-10-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4568684", "field": "economics", "role": "object", "bullets": [ "Australian workforce, task-based analysis combining Labour Force Survey, Australian Skills Classification, and synthetic data to map occupational exposure to generative AI.", "Novel methodology quantifying worker exposure to LLM risks across task categories including privacy, cybersecurity, professional standards, and liability dimensions.", "Eighty percent of Australian workers allocate 20% of task time to LLM-exposed activities; privacy risks appear in 12% and liability risks in 26% of exposed tasks." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 2454, "authors_detailed": [ { "name": "Emmanuelle Walkowiak", "url": "https://openalex.org/A5053817571", "inst": "RMIT University" }, { "name": "Trent MacDonald", "url": "https://openalex.org/A5038073053", "inst": "ARC Centre of Excellence for Environmental Decisions" } ], "affiliations": [ "RMIT University", "ARC Centre of Excellence for Environmental Decisions" ] }, { "uid": "arxiv:2310.00566v3", "arxiv_id": "2310.00566v3", "title": "Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models", "authors": [ "Duanyu Feng", "Yongfu Dai", "Jimin Huang", "Yifang Zhang", "Qianqian Xie", "Weiguang Han", "Zhengyu Chen", "Alejandro Lopez-Lira", "Hao Wang" ], "posted": "2023-10-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2310.00566v3", "field": "finance", "role": "method", "bullets": [ "Nine credit assessment datasets totalling 14,000 samples form a benchmark, alongside over 45,000 instruction tuning examples spanning credit and financial risk tasks.", "CALM, an open instruction tuned credit and risk assessment LLM, is evaluated on the labelled benchmark against conventional scorers and open and closed LLMs, with bias examined.", "The LLMs match or surpass conventional credit scoring models on the benchmark, and the datasets, model, and benchmark are released publicly." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "labelled benchmark of 9 credit datasets, 14K samples", "salience": 48, "edition": 12, "n": 1411, "authors_detailed": [ { "name": "Duanyu Feng", "url": "https://openalex.org/A5001345589", "inst": "National University of Singapore" }, { "name": "Dai, Yongfu", "url": "", "inst": "" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" }, { "name": "Yifang Zhang", "url": "https://openalex.org/A5101620673", "inst": "Jishou University" }, { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Weiguang Han", "url": "https://openalex.org/A5054465909", "inst": "Hebei University of Technology" }, { "name": "Chen, Zhengyu", "url": "", "inst": "" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Hao Wang", "url": "https://openalex.org/A5100446064", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "University of Florida", "National University of Singapore", "University of Manchester", "Jishou University", "Hunan Normal University", "Hebei University of Technology", "Stevens Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2309.17322v1", "arxiv_id": "2309.17322v1", "title": "Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis", "authors": [ "Paul Glasserman", "Caden Lin" ], "posted": "2023-09-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.17322v1", "field": "finance", "role": "method", "bullets": [ "Trading strategies driven by the sentiment of financial news headlines, backtested both inside and outside the LLM training window; sample size and period are not stated.", "An LLM described only as ChatGPT class scores headline sentiment; company identifiers are stripped to separate look-ahead bias from a distraction effect, with no ground truth sentiment validation reported.", "In sample, anonymized headlines outperform the originals, so distraction outweighs look-ahead bias, especially for larger companies; the anonymization procedure is proposed for de-biased backtesting and out-of-sample use." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 68, "edition": 12, "n": 1378, "authors_detailed": [ { "name": "Paul Glasserman", "url": "https://openalex.org/A5026619574", "inst": "Risk Engineering (Bulgaria)" }, { "name": "Caden Lin", "url": "https://openalex.org/A5111094687", "inst": "University of the District of Columbia" } ], "affiliations": [ "Risk Engineering (Bulgaria)", "University of the District of Columbia" ] }, { "uid": "arxiv:2309.17147v2", "arxiv_id": "2309.17147v2", "title": "Using Large Language Models for Qualitative Analysis can Introduce Serious Bias", "authors": [ "Julian Ashwin", "Aditya Chhabra", "Vijayendra Rao" ], "posted": "2023-09-29", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.17147v2", "field": "economics", "role": "method", "bullets": [ "Open ended interviews with Rohingya refugees in Cox's Bazaar, Bangladesh, whose transcripts serve as a large N qualitative dataset for text annotation.", "An LLM, unnamed in the abstract, annotates the transcripts; output is checked against high quality human annotations and against simpler supervised classifiers trained on those labels.", "LLM annotation errors correlate with interviewee characteristics, biasing downstream inference; supervised models trained on human labels deliver less measurement error and bias." ], "bullet_provenance": "ai", "validated": true, "validation_note": "LLM annotations compared with human coding of interview transcripts", "salience": 66, "edition": 12, "models": [], "n": 1443, "authors_detailed": [ { "name": "Julian Ashwin", "url": "https://openalex.org/A5027613619", "inst": "" }, { "name": "Aditya Chhabra", "url": "https://openalex.org/A5087443216", "inst": "World Bank Group" }, { "name": "Vijayendra Rao", "url": "https://openalex.org/A5070989908", "inst": "World Bank" } ], "affiliations": [ "World Bank Group", "World Bank" ] }, { "uid": "doi:10.2139/ssrn.4572831", "doi": "10.2139/ssrn.4572831", "title": "Can ChatGPT Predict Future Interest Rate Decisions?", "authors": [ "Drew Woodhouse", "Alex Charlesworth" ], "posted": "2023-09-29", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4572831", "field": "economics", "role": "instrument", "bullets": [ "Bank of England Monetary Policy Committee member speeches analyzed to predict future interest rate decisions across multiple decision windows.", "GPT-3.5 labeled each MPC member speech for sentiment on monetary policy stance; labeled expectations modeled against actual rate votes in subsequent meetings.", "ChatGPT-derived sentiment scores predict future interest rate decisions, providing evidence that LLMs can extract latent policy beliefs from central bank communications." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "predicted actual BoE MPC rate decisions", "salience": 65, "n": 2361, "authors_detailed": [ { "name": "Drew Woodhouse", "url": "https://openalex.org/A5059350205", "inst": "Sheffield Hallam University" }, { "name": "Alex Charlesworth", "url": "https://openalex.org/A5014181838", "inst": "Sheffield Hallam University" } ], "affiliations": [ "Sheffield Hallam University" ] }, { "uid": "arxiv:2311.10723v2", "arxiv_id": "2311.10723v2", "title": "Large Language Models in Finance: A Survey", "authors": [ "Yinheng Li", "Shaofei Wang", "Han Ding", "Hang Chen" ], "posted": "2023-09-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.10723v2", "field": "finance", "role": "method", "bullets": [ "A survey of language models in finance covering zero shot and few shot use of pretrained models, fine-tuning on domain data, and training custom models from scratch.", "No new model is built; the paper reviews existing systems and summarizes their reported performance on financial natural language processing tasks.", "It contributes a decision framework matching data, compute, and performance constraints to an adoption path running from lightweight experimentation to fully custom models." ], "bullet_provenance": "ai", "salience": 42, "edition": 12, "models": [], "validated": null, "n": 1424, "authors_detailed": [ { "name": "Yinheng Li", "url": "https://openalex.org/A5082841573", "inst": "Columbia University" }, { "name": "Shaofei Wang", "url": "https://openalex.org/A5101932394", "inst": "NARI Group (China)" }, { "name": "Han Ding", "url": "https://openalex.org/A5100541499", "inst": "Anhui Medical University" }, { "name": "Hang Chen", "url": "https://openalex.org/A5100442529", "inst": "Xi'an Jiaotong University" } ], "affiliations": [ "Columbia University", "NARI Group (China)", "Anhui Medical University", "Xi'an Jiaotong University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4586428", "doi": "10.2139/ssrn.4586428", "title": "A Trillion Dollars Race – How Chatgpt Affects Stock Prices", "authors": [ "Marcin Pietrzak" ], "posted": "2023-09-28", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4586428", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 38, "edition": 12, "bullets": [], "validated": null, "n": 1492, "authors_detailed": [ { "name": "Marcin Pietrzak", "url": "https://openalex.org/A5066018313", "inst": "Independent - affiliation not provided to SSRN" } ], "affiliations": [ "Independent - affiliation not provided to SSRN" ] }, { "uid": "doi:10.2139/ssrn.4575598", "doi": "10.2139/ssrn.4575598", "title": "Large Language Model in Creative Work: The Role of Collaboration Modality and User Expertise", "authors": [ "Zenan Chen", "Jason Chan" ], "posted": "2023-09-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4575598", "field": "management", "role": "object", "bullets": [ "Experiment with expert and non-expert users writing advertising copy with and without LLM assistance, measured by real click-through on major social media platforms.", "LLM used in two collaboration modalities: ghostwriter generating content directly and sounding board providing feedback on human-created content; quality measured by ad clicks.", "Sounding-board mode improved non-expert ad quality to near-expert levels; ghostwriter mode produced anchoring effects that reduced quality, especially harming expert users." ], "bullet_provenance": "ai", "salience": 70, "models": [], "validated": null, "n": 2453, "authors_detailed": [ { "name": "Zenan Chen", "url": "https://openalex.org/A5005708878", "inst": "University of Minnesota" }, { "name": "Jason Chan", "url": "https://openalex.org/A5102005690", "inst": "University of Minnesota" } ], "affiliations": [ "University of Minnesota" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2309.12863v1", "arxiv_id": "2309.12863v1", "title": "Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts", "authors": [ "Emad A. Alghamdi", "Jezia Zakraoui", "Fares A. Abanmy" ], "posted": "2023-09-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.12863v1", "field": "finance", "role": "method", "bullets": [ "A new parallel Arabic English corpus of financial news, built to test domain adaptation for machine translation in a previously unexplored domain.", "Several pre-trained translation models and ChatGPT 3.5 Turbo are fine-tuned on small sets of aligned in domain segments, judged by automatic metrics and human evaluation.", "Fine-tuning succeeds with only a few well aligned segments, and fine-tuned ChatGPT delivers the best translation quality; datasets and fine-tuned models are made available." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "automatic MT metrics plus human evaluation", "salience": 32, "edition": 12, "n": 1423, "authors_detailed": [ { "name": "Emad A. Alghamdi", "url": "https://openalex.org/A5082020464", "inst": "Effat University" }, { "name": "Jezia Zakraoui", "url": "https://openalex.org/A5071938620", "inst": "Qatar University" }, { "name": "Fares A. Abanmy", "url": "https://openalex.org/A5092938871", "inst": "Saudi Heart Association" } ], "affiliations": [ "Effat University", "Qatar University", "Saudi Heart Association" ] }, { "uid": "doi:10.2139/ssrn.4556783", "doi": "10.2139/ssrn.4556783", "title": "Law and Economics of Language Model Development: Empirical Examination of Corporate Strategies and Vaporware Claims", "authors": [ "Koki Arai" ], "posted": "2023-09-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4556783", "field": "economics", "role": "object", "bullets": [ "Japanese stock market event study examining cumulative abnormal returns around corporate announcements of large language model development projects.", "No LLM was used as a research tool; the study examined whether firm announcements of LLM development constitute vaporware with anticompetitive effects.", "No significant cumulative abnormal returns followed LLM development announcements, suggesting absence of vaporware characteristics and a stable, efficient market response." ], "bullet_provenance": "ai", "salience": 40, "models": [], "validated": null, "n": 2388, "authors_detailed": [ { "name": "Koki Arai", "url": "https://openalex.org/A5041417919", "inst": "Kyoritsu Women's University" } ], "affiliations": [ "Kyoritsu Women's University" ] }, { "uid": "arxiv:2309.10982v1", "arxiv_id": "2309.10982v1", "title": "Is GPT4 a Good Trader?", "authors": [ "Bingzhe Wu" ], "posted": "2023-09-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.10982v1", "field": "finance", "role": "method", "bullets": [ "Daily candlestick data for selected assets such as the Shanghai Stock Index; the asset list and sample periods are not fully specified in the abstract.", "GPT-4, steered by prompt engineering and its code interpreter, reads technical structures under theories such as Elliott Wave; outputs receive manual evaluation, with no accuracy statistics reported.", "The abstract states aims, testing whether GPT-4's trading logic is reliable and distilling usable methodology from its analysis, but reports no quantitative findings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 25, "edition": 12, "n": 1369, "authors_detailed": [ { "name": "Bingzhe Wu", "url": "https://openalex.org/A5040323946", "inst": "Hebei Medical University" } ], "affiliations": [ "Hebei Medical University" ] }, { "uid": "doi:10.1145/3613904.3642385", "doi": "10.1145/3613904.3642385", "arxiv_id": "2309.11653v2", "title": "\"It's a Fair Game\", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents", "authors": [ "Zhiping Zhang", "Michelle Jia", "Hao-Ping Lee", "Bingsheng Yao", "Sauvik Das", "Ada Lerner", "Dakuo Wang", "Tianshi Li" ], "posted": "2023-09-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.11653v2", "field": "management", "role": "object", "bullets": [ "Sensitive disclosures in real world ChatGPT conversation logs, combined with semi structured interviews of 19 users of LLM based conversational agents.", "No model serves as a research instrument; ChatGPT is the system under study, with the researchers coding disclosure behaviour and user reasoning themselves.", "Users balance privacy against utility and convenience; mistaken mental models, dark patterns, and humanlike conversation all push toward more sensitive disclosure." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 12, "validated": null, "n": 1442, "authors_detailed": [ { "name": "Zhiping Zhang", "url": "https://openalex.org/A5100389481", "inst": "Northeastern University" }, { "name": "Michelle Jia", "url": "https://openalex.org/A5097686591", "inst": "Carnegie Mellon University" }, { "name": "Hao-Ping Lee", "url": "https://openalex.org/A5062891440", "inst": "Carnegie Mellon University" }, { "name": "Bingsheng Yao", "url": "https://openalex.org/A5033744502", "inst": "Rensselaer Polytechnic Institute" }, { "name": "Sauvik Das", "url": "https://openalex.org/A5006053551", "inst": "Carnegie Mellon University" }, { "name": "Ada Lerner", "url": "https://openalex.org/A5084264446", "inst": "Northeastern University" }, { "name": "Dakuo Wang", "url": "https://openalex.org/A5062817658", "inst": "Northeastern University" }, { "name": "Tianshi Li", "url": "https://openalex.org/A5039928918", "inst": "Northeastern University" } ], "affiliations": [ "Carnegie Mellon University", "Northeastern University", "Rensselaer Polytechnic Institute" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4576689", "doi": "10.2139/ssrn.4576689", "title": "Generation Next: Experimentation with AI", "authors": [ "Gary Charness", "Brian Jabarian", "John A. List" ], "posted": "2023-09-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4576689", "field": "economics", "role": "method", "bullets": [ "Conceptual framework examining LLM use across the full lifecycle of economic experiments, from design through implementation to data analysis.", "LLMs are assessed for improving elicitation wording, coding experiments, producing documentation, monitoring engagement, and cleaning and analyzing experimental data.", "LLMs can enhance each stage of experimentation, improving causal inference through consistent experiences and better comprehension, and raising the probability of accurate findings." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "validated": null, "n": 2635, "authors_detailed": [ { "name": "Gary Charness", "url": "https://openalex.org/A5063325298", "inst": "University of California, Santa Barbara" }, { "name": "Brian Jabarian", "url": "", "inst": "University of Chicago" }, { "name": "John A. List", "url": "https://openalex.org/A5083530241", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "University of California, Santa Barbara" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2309.10654v2", "arxiv_id": "2309.10654v2", "title": "CFGPT: Chinese Financial Assistant with Large Language Model", "authors": [ "Jiangtong Li", "Yuxuan Bian", "Guoxuan Wang", "Yang Lei", "Dawei Cheng", "Zhijun Ding", "Changjun Jiang" ], "posted": "2023-09-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.10654v2", "field": "finance", "role": "method", "bullets": [ "A Chinese financial corpus, CFData, combining 584M documents and 141B tokens for pre-training with 1.5M instruction pairs spanning six financial tasks.", "InternLM 7B undergoes continued pre-training and supervised fine-tuning on the corpus, then ships inside a deployment framework for real world financial applications; no benchmark accuracy appears in the abstract.", "The result is a released pipeline of data, model, and application framework for Chinese financial text; performance claims are not quantified in the abstract." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 36, "edition": 12, "n": 1422, "authors_detailed": [ { "name": "Jiangtong Li", "url": "https://openalex.org/A5047636121", "inst": "Tongji University" }, { "name": "Yuxuan Bian", "url": "https://openalex.org/A5062922713", "inst": "Chinese University of Hong Kong" }, { "name": "Guoxuan Wang", "url": "https://openalex.org/A5104171786", "inst": "Johns Hopkins University" }, { "name": "Lei Yang", "url": "https://openalex.org/A5101507740", "inst": "China Mobile (China)" }, { "name": "Dawei Cheng", "url": "https://openalex.org/A5069869295", "inst": "Tongji University" }, { "name": "Zhijun Ding", "url": "https://openalex.org/A5041681214", "inst": "Tongji University" }, { "name": "Changjun Jiang", "url": "https://openalex.org/A5066099338", "inst": "Tongji University" } ], "affiliations": [ "Johns Hopkins University", "Tongji University", "Chinese University of Hong Kong", "China Mobile (China)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4576036", "doi": "10.2139/ssrn.4576036", "title": "Can GPT Mimic Human Preferences? An Empirical and Structural Investigation", "authors": [ "Kevin Bauer", "Lena Liebich", "Michael Kosfeld" ], "posted": "2023-09-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4576036", "field": "economics", "role": "agent", "bullets": [ "Sequential prisoner's dilemma experiment comparing GPT-4 Turbo responses as second mover to observed human participant behavior in strategic interaction.", "GPT-4 Turbo simulated decision-making in a sequential game; outputs compared to human data and a structural model of fairness and efficiency preferences.", "GPT cooperated more than humans and formed overly optimistic expectations, but its decisions aligned with a formal model of human fairness preferences driving most participants." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison to human experimental data and structural preference model", "salience": 72, "n": 2452, "authors_detailed": [ { "name": "Kevin Bauer", "url": "https://openalex.org/A5039057505", "inst": "Goethe University Frankfurt" }, { "name": "Lena Liebich", "url": "https://openalex.org/A5092898485", "inst": "Leibniz Institute for Financial Research SAFE" }, { "name": "Michael Kosfeld", "url": "https://openalex.org/A5053840182", "inst": "Goethe University Frankfurt" } ], "affiliations": [ "Goethe University Frankfurt", "Leibniz Institute for Financial Research SAFE" ] }, { "uid": "doi:10.2139/ssrn.4574814", "doi": "10.2139/ssrn.4574814", "title": "Generative AI: Overview, Economic Impact, and Applications in Asset Management", "authors": [ "Martin Luk" ], "posted": "2023-09-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4574814", "field": "finance", "role": "object", "bullets": [ "Review of generative AI evolution and latest model advancements, covering economic impact across industries with a focus on investment management applications.", "Survey of LLM capabilities including ChatGPT and image-based multimodal models; assessment of productivity effects, job displacement, and asset management use cases.", "GenAI offers investment management applications in research synthesis and portfolio analytics, but hallucination risks and other limitations require careful deployment strategies." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 40, "validated": null, "n": 2449, "authors_detailed": [ { "name": "Martin Luk", "url": "https://openalex.org/A5092892914", "inst": "Asser Institute" } ], "affiliations": [ "Asser Institute" ] }, { "uid": "doi:10.2139/ssrn.4574623", "doi": "10.2139/ssrn.4574623", "title": "Generation Next: Experimentation with AI", "authors": [ "Gary Charness", "Brian Jabarian", "John A. List" ], "posted": "2023-09-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4574623", "field": "economics", "role": "method", "bullets": [ "Conceptual framework for integrating LLMs across three stages of the experimental research lifecycle: design, implementation, and data analysis.", "LLMs applied to improve elicitation wording, code experiments, bolster causal inference through consistent experiences, and assist with data pre-processing and cleaning.", "LLMs can meaningfully improve each experimental stage and increase probability of accurate findings; authors propose a scientific governance framework to mitigate associated risks." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 2450, "authors_detailed": [ { "name": "Gary Charness", "url": "https://openalex.org/A5063325298", "inst": "University of California, Santa Barbara" }, { "name": "Brian Jabarian", "url": "", "inst": "University of Chicago" }, { "name": "John A. List", "url": "https://openalex.org/A5083530241", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "University of California, Santa Barbara" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4553787", "doi": "10.2139/ssrn.4553787", "title": "Competition in Generative AI Foundation Models", "authors": [ "Christophe Carugati" ], "posted": "2023-09-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4553787", "field": "economics", "role": "object", "bullets": [ "Global foundation model value chain covering language models, data, and computing resources, with analysis of closed-source and open-source competitive dynamics.", "Analysis of market structure, entry barriers, and potential anticompetitive conduct including dominant-firm leveraging and algorithmic collusion through foundation models.", "FM markets are currently competitive with surmountable entry barriers; future risks include ecosystem lock-in, data access refusal, and collusion via language models." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 2451, "authors_detailed": [ { "name": "Christophe Carugati", "url": "https://openalex.org/A5022669564", "inst": "Roquette Frères (France)" } ], "affiliations": [ "Roquette Frères (France)" ] }, { "uid": "doi:10.2139/ssrn.4573321", "doi": "10.2139/ssrn.4573321", "title": "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality", "authors": [ "Fabrizio Dell'Acqua", "Edward McFowland III", "Ethan R. Mollick", "Hila Lifshitz-Assaf", "Katherine Kellogg", "Saran Rajendran", "Lisa Krayer", "François Candelon", "Karim R. Lakhani" ], "posted": "2023-09-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4573321", "field": "management", "role": "object", "bullets": [ "Pre-registered field experiment with 758 BCG consultants randomly assigned to no-AI, GPT-4, or GPT-4-with-prompt-engineering conditions.", "GPT-4 assisted consultants on 18 realistic management consulting tasks spanning creative and analytical work.", "AI users completed 12.2% more tasks 25.1% faster with higher quality, but were 19% less likely to solve a task outside AI's frontier correctly." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 82, "n": 3182, "authors_detailed": [ { "name": "Fabrizio Dell’Acqua", "url": "https://openalex.org/A5051453071", "inst": "Twitter (United States)" }, { "name": "Edward McFowland", "url": "https://openalex.org/A5026872292", "inst": "Harvard University" }, { "name": "Ethan Mollick", "url": "https://openalex.org/A5061686034", "inst": "University of Pennsylvania" }, { "name": "Hila Lifshitz‐Assaf", "url": "https://openalex.org/A5036969088", "inst": "Harvard University" }, { "name": "Katherine C. Kellogg", "url": "https://openalex.org/A5021677696", "inst": "Massachusetts Institute of Technology" }, { "name": "Saran Rajendran", "url": "https://openalex.org/A5090507466", "inst": "Boston Consulting Group (United States)" }, { "name": "Lisa Krayer", "url": "https://openalex.org/A5074904134", "inst": "Boston Consulting Group (United States)" }, { "name": "François Candelon", "url": "https://openalex.org/A5002745781", "inst": "BH Consulting (Ireland)" }, { "name": "Karim R. Lakhani", "url": "https://openalex.org/A5089560559", "inst": "Internet Society" } ], "affiliations": [ "Harvard University", "University of Pennsylvania", "Massachusetts Institute of Technology", "Twitter (United States)", "Boston Consulting Group (United States)", "BH Consulting (Ireland)", "Internet Society" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2309.13064v1", "arxiv_id": "2309.13064v1", "title": "InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning", "authors": [ "Yi Yang", "Yixuan Tang", "Kar Yan Tam" ], "posted": "2023-09-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.13064v1", "field": "finance", "role": "method", "bullets": [ "Instruction dataset hand curated from CFA exam questions, SEC filings, and quantitative finance forum discussions; deliberately small and diverse, exact size not stated.", "LLaMA 65B instruction tuned into InvestLM; hedge fund managers and research analysts rate its answers against GPT-3.5, GPT-4, and Claude 2, plus zero shot financial NLP benchmarks.", "Experts judge responses comparable to the commercial models, supporting the case for small curated instruction sets on strong base models; parameters are released." ], "bullet_provenance": "ai", "models": [ "claude", "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "expert ratings against commercial LLMs; zero shot financial NLP benchmarks", "salience": 58, "edition": 12, "n": 1360, "authors_detailed": [ { "name": "Yi Yang", "url": "https://openalex.org/A5005421447", "inst": "Zhejiang University" }, { "name": "Yixuan Tang", "url": "https://openalex.org/A5077931422", "inst": "National University of Singapore" }, { "name": "Kar Yan Tam", "url": "https://openalex.org/A5090061579", "inst": "Hong Kong University of Science and Technology" } ], "affiliations": [ "Zhejiang University", "National University of Singapore", "Hong Kong University of Science and Technology" ] }, { "uid": "doi:10.1016/j.chbah.2024.100054", "doi": "10.1016/j.chbah.2024.100054", "arxiv_id": "2309.07664v3", "title": "Computer says 'no': Exploring systemic bias in ChatGPT using an audit approach", "authors": [ "Louis Lippens" ], "posted": "2023-09-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.07664v3", "field": "economics", "role": "agent", "bullets": [ "A correspondence audit of 34,560 vacancy and CV combinations, with fictitious applicants varying across nine ethnic backgrounds and both genders.", "ChatGPT, version not stated, rated each applicant profile for suitability in a simulated CV screening task, transplanting an audit design normally aimed at human recruiters.", "Ratings shift with ethnicity more than gender; ethnic penalties concentrate in jobs with good conditions or language demands, gender penalties in gender atypical roles." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 62, "edition": 12, "validated": null, "n": 1441, "authors_detailed": [ { "name": "Louis Lippens", "url": "https://openalex.org/A5056470079", "inst": "Vrije Universiteit Brussel" } ], "affiliations": [ "Vrije Universiteit Brussel" ] }, { "uid": "doi:10.2139/ssrn.4568800", "doi": "10.2139/ssrn.4568800", "title": "Evaluating the Potential of Large Language Model AI as Project Management Assistants: A Comparative Simulation to Evaluate GPT-3.5, GPT-4, and Google-Bard Ability to pass the PMI's PMP test", "authors": [ "Ali Vakilzadeh", "Sara Pourahmad Ghalejoogh" ], "posted": "2023-09-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4568800", "field": "management", "role": "object", "bullet_provenance": "none", "models": [ "gemini", "gpt" ], "open_weights": false, "salience": 28, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 193, "authors_detailed": [ { "name": "Ali Vakilzadeh", "url": "https://openalex.org/A5069415828", "inst": "University of Liverpool" }, { "name": "Sara Pourahmad Ghalejoogh", "url": "https://openalex.org/A5117935230", "inst": "Tarbiat Modares University" } ], "affiliations": [ "University of Liverpool", "Tarbiat Modares University" ] }, { "uid": "doi:10.2139/ssrn.4568964", "doi": "10.2139/ssrn.4568964", "title": "GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models", "authors": [ "Udit Gupta" ], "posted": "2023-09-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4568964", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 38, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 331, "authors_detailed": [ { "name": "Udit Gupta", "url": "https://openalex.org/A5034077601", "inst": "Github Repository" } ], "affiliations": [ "Github Repository" ] }, { "uid": "doi:10.2139/ssrn.4569717", "doi": "10.2139/ssrn.4569717", "title": "Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing", "authors": [ "Nunzio Lorè", "Babak Heydari" ], "posted": "2023-09-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4569717", "field": "economics", "role": "agent", "bullets": [ "Experimental prompts across multiple game-theoretic scenarios with varying contextual framings tested on three LLMs as simulated strategic players.", "GPT-3.5, GPT-4, and LLaMA-2 played strategic games; responses analyzed for sensitivity to game structure versus contextual framing of identical payoffs.", "LLaMA-2 showed the most granular understanding of individual game structures while incorporating context; GPT-4 favored structure over context without differentiating game types." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": false, "validated": false, "salience": 55, "n": 2360, "authors_detailed": [ { "name": "Nunzio Lorè", "url": "https://openalex.org/A5028910900", "inst": "Northeastern University (USA)" }, { "name": "Babak Heydari", "url": "https://openalex.org/A5030641600", "inst": "Northeastern University" } ], "affiliations": [ "Northeastern University (USA)", "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.4560216", "doi": "10.2139/ssrn.4560216", "title": "GPT's Idea of Stock Factors", "authors": [ "Yuhan Cheng", "Ke Tang" ], "posted": "2023-09-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4560216", "field": "finance", "role": "agent", "bullets": [ "U.S. equity market; GPT-4 generates stock return factors autonomously and results evaluated against the Fama-French five-factor model.", "GPT-4 created 35 trading factors through knowledge inference without data input; factors combined via model averaging for portfolio construction.", "Best single factor achieved a Sharpe ratio of 4.49 and 66% annualized return; ensemble reached 88% annualized return with a 2.46 Sharpe ratio." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 2909, "authors_detailed": [ { "name": "Yuhan Cheng", "url": "https://openalex.org/A5044375203", "inst": "Shandong University" }, { "name": "Ke Tang", "url": "https://openalex.org/A5101543008", "inst": "Tsinghua University" } ], "affiliations": [ "Shandong University", "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.4567157", "doi": "10.2139/ssrn.4567157", "title": "Can AI Solve Newsvendor Problem Without Making Biased Decisions? A Behavioral Experimental Study", "authors": [ "Jingjie Su", "Yan Lang", "Kay-Yut Chen" ], "posted": "2023-09-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4567157", "field": "management", "role": "agent", "bullets": [ "Newsvendor inventory optimization experiments comparing ChatGPT ordering decisions against established human-subject experimental results across multiple rounds.", "ChatGPT made repeated ordering decisions in standard newsvendor experiments and results were compared to documented human behavioral biases in inventory decisions.", "ChatGPT exhibited demand chasing, risk aversion, and loss aversion like humans but avoided waste aversion, stockout aversion, and underestimated opportunity cost biases." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "comparison with human-subject newsvendor experiments", "salience": 62, "n": 2387, "authors_detailed": [ { "name": "J. K. Su", "url": "https://openalex.org/A5068497702", "inst": "Cameron University" }, { "name": "Yan Lang", "url": "https://openalex.org/A5024792077", "inst": "SUNY Oneonta" }, { "name": "Kay‐Yut Chen", "url": "https://openalex.org/A5035922455", "inst": "The University of Texas at Arlington" } ], "affiliations": [ "Cameron University", "SUNY Oneonta", "The University of Texas at Arlington" ] }, { "uid": "doi:10.1038/s41598-024-69032-z", "doi": "10.1038/s41598-024-69032-z", "arxiv_id": "2309.05898v1", "title": "Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing", "authors": [ "Nunzio Lorè", "Babak Heydari" ], "posted": "2023-09-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.05898v1", "field": "economics", "role": "agent", "bullets": [ "Four canonical two player games, Prisoner's Dilemma, Stag Hunt, Snowdrift, and Prisoner's Delight, each wrapped in contextual frames such as diplomatic relations or casual friendship.", "GPT-3.5, GPT-4, and LLaMa-2 choose cooperation or defection in every game and frame combination; choices are studied as strategic behaviour, so no ground truth check applies.", "GPT-3.5 responds mainly to framing and reasons weakly about structure; GPT-4 and LLaMa-2 adjust to both, with LLaMa-2 tracking the underlying game mechanics most closely." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "salience": 55, "edition": 12, "validated": null, "n": 1365, "authors_detailed": [ { "name": "Nunzio Lorè", "url": "https://openalex.org/A5028910900", "inst": "Northeastern University" }, { "name": "Babak Heydari", "url": "https://openalex.org/A5030641600", "inst": "Northeastern University" } ], "affiliations": [ "Northeastern University" ] }, { "uid": "doi:10.2139/ssrn.4569683", "doi": "10.2139/ssrn.4569683", "title": "Challenging the Notion of Trust Around Chatgpt in the High-Stakes Use Case of Insurance", "authors": [ "Juliane Ressel", "Michaele Völler", "Finbarr Murphy", "Martin Mullins" ], "posted": "2023-09-12", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4569683", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 34, "edition": 12, "bullets": [], "validated": null, "n": 1491, "authors_detailed": [ { "name": "Juliane Ressel", "url": "https://openalex.org/A5084448045", "inst": "University of Limerick" }, { "name": "Michaele Völler", "url": "https://openalex.org/A5052407371", "inst": "TH Köln - University of Applied Sciences" }, { "name": "Finbarr Murphy", "url": "https://openalex.org/A5003956708", "inst": "University of Limerick" }, { "name": "Martin Mullins", "url": "https://openalex.org/A5005024391", "inst": "University of Limerick" } ], "affiliations": [ "University of Limerick", "TH Köln - University of Applied Sciences" ] }, { "uid": "arxiv:2309.05608v1", "arxiv_id": "2309.05608v1", "title": "Incorporating Pre-trained Model Prompting in Multimodal Stock Volume Movement Prediction", "authors": [ "Ruibo Chen", "Zhiyuan Zhang", "Yi Liu", "Ruihan Bao", "Keiko Harimoto", "Xu Sun" ], "posted": "2023-09-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.05608v1", "field": "finance", "role": "method", "bullets": [ "Stock trading volume movement prediction from financial news paired with price and volume series; market, sample size, and period are not stated in the abstract.", "Unnamed pre-trained language models read the news through prompt learning and are fused with a time series head via cross modality contrastive alignment; accuracy is assessed against realized volume movements and baseline models.", "ProMUSE outperforms the existing baselines on the prediction task and ablations favour the contrastive alignment design; the abstract quotes no numeric margins." ], "bullet_provenance": "ai", "validated": true, "validation_note": "held-out volume movement prediction", "salience": 33, "edition": 12, "models": [], "n": 1519, "authors_detailed": [ { "name": "Ruibo Chen", "url": "https://openalex.org/A5101770901", "inst": "Guangxi Zhuang Autonomous Region Health and Family Planning" }, { "name": "Zhiyuan Zhang", "url": "https://openalex.org/A5100403332", "inst": "Chinese Academy of Sciences" }, { "name": "Yi Liu", "url": "https://openalex.org/A5100695128", "inst": "Xidian University" }, { "name": "Ruihan Bao", "url": "https://openalex.org/A5080124315", "inst": "State Key Laboratory of Pollution Control and Resource Reuse" }, { "name": "Keiko Harimoto", "url": "https://openalex.org/A5032304321", "inst": "Peking University" }, { "name": "Xu Sun", "url": "https://openalex.org/A5111863979", "inst": "Northeast Agricultural University" } ], "affiliations": [ "Guangxi Zhuang Autonomous Region Health and Family Planning", "Chinese Academy of Sciences", "Xidian University", "State Key Laboratory of Pollution Control and Resource Reuse", "Peking University", "Northeast Agricultural University" ] }, { "uid": "arxiv:2309.05555v1", "arxiv_id": "2309.05555v1", "title": "Unraveling Managerial Tangents in Firm Disclosure: Concealing Issues or Being Exposed?", "authors": [ "Xuan Zhou", "Yushen Huang" ], "posted": "2023-09-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.05555v1", "field": "accounting", "role": "instrument", "bullets": [ "Question and answer sessions of corporate earnings calls linked to subsequent stock prices; the abstract does not state how many calls, firms, or years are covered.", "FinBERT quantifies how often managers shift topics when answering analyst questions, producing a topic switching index; no validation against human coded evasiveness is reported.", "A higher topic switching index correlates with lower subsequent stock prices, read as investors penalizing evasive management, and the index stays predictive across three classifier models." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 45, "edition": 12, "n": 1520, "authors_detailed": [ { "name": "Xuan Zhou", "url": "https://openalex.org/A5100688002", "inst": "Jiangnan University" }, { "name": "Yushen Huang", "url": "https://openalex.org/A5102555091", "inst": "South China Agricultural University" } ], "affiliations": [ "Jiangnan University", "South China Agricultural University" ] }, { "uid": "arxiv:2309.03736v1", "arxiv_id": "2309.03736v1", "title": "TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance", "authors": [ "Yang Li", "Yangyang Yu", "Haohang Li", "Zhi Chen", "Khaldoun Khashanah" ], "posted": "2023-09-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.03736v1", "field": "finance", "role": "agent", "bullets": [ "A stock and fund trading setting in which agents process hierarchical financial data; the abstract states no sample, period, or asset universe.", "LLM agents, base model not named, hold three layer memories with custom decay, individual trading traits, and debate with peers before trading decisions; no ground truth validation is described.", "The layered memory design is argued to sharpen responses to historical trades and live market signals; the abstract reports no quantitative trading results." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 30, "edition": 12, "validated": null, "n": 1410, "authors_detailed": [ { "name": "Yang Li", "url": "https://openalex.org/A5051702877", "inst": "Google (United States)" }, { "name": "Yangyang Yu", "url": "https://openalex.org/A5063210163", "inst": "Stevens Institute of Technology" }, { "name": "Haohang Li", "url": "https://openalex.org/A5020982913", "inst": "Stevens Institute of Technology" }, { "name": "Zhi Chen", "url": "https://openalex.org/A5100456850", "inst": "Microsoft Research Asia (China)" }, { "name": "Khaldoun Khashanah", "url": "https://openalex.org/A5027284723", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "Google (United States)", "Stevens Institute of Technology", "Microsoft Research Asia (China)" ] }, { "uid": "arxiv:2311.06251v1", "arxiv_id": "2311.06251v1", "title": "AI for Investment: A Platform Disruption", "authors": [ "Mohammad Rasouli", "Ravi Chiruvolu", "Ali Risheh" ], "posted": "2023-09-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2311.06251v1", "field": "finance", "role": "object", "bullets": [ "Venture capital and private equity deal sourcing and deal insight workflows; a position paper drawing on industry practice, with no dataset or sample described.", "No model is deployed or evaluated; the paper discusses in house AI platforms built on large language models such as ChatGPT for fund specific use cases.", "Argues third party tools fail on personalization and privacy, that funds are building internal platforms, and that lacking one becomes a competitive disadvantage within two years." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 22, "edition": 12, "validated": null, "n": 1471, "authors_detailed": [ { "name": "Mohammad Rasouli", "url": "https://openalex.org/A5014911774", "inst": "Anglia Ruskin University" }, { "name": "Ravi Chiruvolu", "url": "https://openalex.org/A5093264729", "inst": "" }, { "name": "Ali Risheh", "url": "https://openalex.org/A5082696725", "inst": "University of California, Irvine" } ], "affiliations": [ "Anglia Ruskin University", "University of California, Irvine" ] }, { "uid": "doi:10.2139/ssrn.4546522", "doi": "10.2139/ssrn.4546522", "title": "Has ChatGPT disrupted the Education Sector in the U.S.?", "authors": [ "Erik Haugom", "Stefan Lyocsa", "Martina Halousková" ], "posted": "2023-09-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4546522", "field": "finance", "role": "object", "bullets": [ "U.S. publicly traded education firms from October 2021 to March 2023; traditional education and education technology subsectors analyzed separately.", "ChatGPT's public release and Google Trends attention used as treatment variables in linear and threshold CAPM-GARCH models of stock returns.", "Education technology sector underperformed benchmarks post-ChatGPT; higher attention predicted lower next-day returns; traditional education stocks were unaffected." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "validated": null, "n": 2908, "authors_detailed": [ { "name": "Erik Haugom", "url": "https://openalex.org/A5027559229", "inst": "Twitter (United States)" }, { "name": "Štefan Lyócsa", "url": "https://openalex.org/A5012277585", "inst": "Masaryk University" }, { "name": "Martina Halousková", "url": "https://openalex.org/A5089178629", "inst": "Masaryk University" } ], "affiliations": [ "Twitter (United States)", "Masaryk University" ] }, { "uid": "arxiv:2309.00208v1", "arxiv_id": "2309.00208v1", "title": "Large Language Models for Semantic Monitoring of Corporate Disclosures: A Case Study on Korea's Top 50 KOSPI Companies", "authors": [ "Junwon Sung", "Woojin Heo", "Yunkyung Byun", "Youngsam Kim" ], "posted": "2023-09-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.00208v1", "field": "accounting", "role": "method", "bullets": [ "Monthly disclosure summaries of the 50 largest KOSPI companies by market capitalization, covering 17 months, each rated for sentiment from one to five.", "GPT-3.5-turbo and GPT-4 assign the ratings, which are compared with human expert scores; GPT-4 attains a Spearman correlation of 0.61 and a concordance rate of 0.82.", "GPT-4 tracks human expert judgment far better than GPT-3.5-turbo does, supporting automated semantic monitoring of timely corporate disclosures for Korean listed firms." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "human expert ratings, Spearman 0.61, concordance 0.82", "salience": 45, "edition": 12, "n": 1409, "authors_detailed": [ { "name": "Junwon Sung", "url": "https://openalex.org/A5114079797", "inst": "" }, { "name": "Woojin Heo", "url": "https://openalex.org/A5114079798", "inst": "" }, { "name": "Yunkyung Byun", "url": "https://openalex.org/A5114079799", "inst": "" }, { "name": "Youngsam Kim", "url": "https://openalex.org/A5101607950", "inst": "Asan Medical Center" } ], "affiliations": [ "Asan Medical Center" ] }, { "uid": "arxiv:2308.16771v1", "arxiv_id": "2308.16771v1", "title": "Linking microblogging sentiments to stock price movement: An application of GPT-4", "authors": [ "Rick Steinert", "Saskia Altmann" ], "posted": "2023-08-31", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.16771v1", "field": "finance", "role": "method", "bullets": [ "Stocktwits messages about Apple and Tesla during 2017, paired with daily adjusted closing prices coded as up or down movements.", "GPT-4 with an engineered contextual prompt and BERT each extract message sentiment; sentiment is never validated against human labels, only against same day price direction.", "GPT-4 sentiment beats BERT in five of six months in logistic regressions and tops a buy and hold rule, peaking at 71.47 percent accuracy in May." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "validated": false, "salience": 38, "edition": 12, "n": 1407, "authors_detailed": [ { "name": "Rick Steinert", "url": "https://openalex.org/A5036395281", "inst": "European University Viadrina" }, { "name": "Saskia Altmann", "url": "https://openalex.org/A5112992862", "inst": "" } ], "affiliations": [ "European University Viadrina" ] }, { "uid": "doi:10.1016/j.frl.2023.104333", "doi": "10.1016/j.frl.2023.104333", "arxiv_id": "2309.00649v2", "title": "GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice", "authors": [ "Paweł Niszczota", "Sami Abbas" ], "posted": "2023-08-31", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.00649v2", "field": "finance", "role": "agent", "bullets": [ "A financial literacy test posed to GPT models, plus a savings dilemma under the Judge Advisor System to gauge how people take advice from the model.", "Davinci and GPT-3.5 based ChatGPT score 66 and 65 percent on the test against a 33 percent baseline, while GPT-4 based ChatGPT reaches 99 percent.", "Financial literacy emerges with model generation, and the advice utilization design gives researchers a template for measuring reliance on robo advice from language models." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "scored against financial literacy test answer key", "salience": 58, "edition": 12, "n": 1408, "authors_detailed": [ { "name": "Paweł Niszczota", "url": "https://openalex.org/A5028727353", "inst": "Poznań University of Economics and Business" }, { "name": "Sami Abbas", "url": "", "inst": "Poznań University of Economics and Business" } ], "affiliations": [ "Poznań University of Economics and Business" ] }, { "uid": "arxiv:2308.14634v1", "arxiv_id": "2308.14634v1", "title": "Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance", "authors": [ "Lefteris Loukas", "Ilias Stogiannidis", "Prodromos Malakasiotis", "Stavros Vassos" ], "posted": "2023-08-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.14634v1", "field": "finance", "role": "method", "bullets": [ "Few shot intent classification of customer queries on the Banking77 dataset, studied in both full data and label scarce settings.", "GPT-3.5 and GPT-4 classify via in context learning, benchmarked against masked language models fine-tuned with SetFit contrastive learning on the labelled dataset.", "The GPT models beat fine-tuned baselines with fewer examples, and expert chosen demonstrations outperform random ones; subscription costs may deter small organizations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Banking77 labelled benchmark", "salience": 45, "edition": 12, "n": 1421, "authors_detailed": [ { "name": "Lefteris Loukas", "url": "https://openalex.org/A5017613411", "inst": "Athens University of Economics and Business" }, { "name": "Ilias Stogiannidis", "url": "https://openalex.org/A5092720860", "inst": "University of Edinburgh" }, { "name": "Prodromos Malakasiotis", "url": "https://openalex.org/A5016618602", "inst": "Athens University of Economics and Business" }, { "name": "Stavros Vassos", "url": "https://openalex.org/A5084667235", "inst": "Aratos Technologies (Greece)" } ], "affiliations": [ "Athens University of Economics and Business", "University of Edinburgh", "Aratos Technologies (Greece)" ] }, { "uid": "doi:10.2139/ssrn.4540834", "doi": "10.2139/ssrn.4540834", "title": "A literature review of artificial intelligence research in business and management using machine learning and ChatGPT", "authors": [ "Nazmiye Guler", "Samuel Kirshner", "Richard Vidgen" ], "posted": "2023-08-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4540834", "field": "management", "role": "method", "bullets": [ "Business, management, and information systems journal articles; computational literature review combining machine learning with ChatGPT.", "Machine learning topic modeling identified research clusters; ChatGPT labeled topics and generated content summaries for review.", "Combined approach effectively uncovered thematic structure, identified research gaps, and highlighted rapidly expanding AI research areas." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 32, "n": 3331, "authors_detailed": [ { "name": "Nazmiye Guler", "url": "https://openalex.org/A5102692196", "inst": "UNSW Sydney" }, { "name": "Samuel N. Kirshner", "url": "https://openalex.org/A5059274653", "inst": "UNSW Sydney" }, { "name": "Richard Vidgen", "url": "https://openalex.org/A5063947106", "inst": "UNSW Sydney" } ], "affiliations": [ "UNSW Sydney" ] }, { "uid": "arxiv:2308.13032v2", "arxiv_id": "2308.13032v2", "title": "Financial News Analytics Using Fine-Tuned Llama 2 GPT Model", "authors": [ "Bohdan M. Pavlyshenko" ], "posted": "2023-08-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.13032v2", "field": "finance", "role": "method", "bullets": [ "Financial news texts analyzed for market perspective, main points, summaries, and named entity sentiment; corpus size and time period are not stated.", "Llama 2 fine-tuned with a PEFT and LoRA approach for multitask structured responses mixing text and JSON; no check against labelled data or human coding is reported.", "The tuned model follows the specified response structure, and extracted entity sentiments are proposed as predictive features for supervised models; no accuracy figures are given." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": false, "salience": 28, "edition": 12, "n": 1462, "authors_detailed": [ { "name": "Bohdan M. Pavlyshenko", "url": "https://openalex.org/A5026355556", "inst": "Lviv University" } ], "affiliations": [ "Lviv University" ] }, { "uid": "arxiv:2308.12477v1", "arxiv_id": "2308.12477v1", "title": "American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers", "authors": [ "Melissa Dell", "Jacob Carlson", "Tom Bryan", "Emily Silcock", "Abhishek Arora", "Zejiang Shen", "Luca D'Amico-Wong", "Quan Le", "Pablo Querubin", "Leander Heldring" ], "posted": "2023-08-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.12477v1", "field": "economics", "role": "method", "bullets": [ "Nearly 20 million public domain newspaper page scans from the Library of Congress Chronicling America collection of historical United States papers.", "A pipeline of mobile scale deep learning models performs layout detection, legibility classification, custom OCR, and assembly of articles spanning multiple boxes; the abstract reports no accuracy statistics and calls the output silver quality.", "The released dataset supplies structured article texts pitched for language model pretraining, retrieval augmented systems, and social science uses such as topic classification and reproduced content detection." ], "bullet_provenance": "ai", "validated": false, "salience": 62, "edition": 12, "models": [], "n": 1518, "authors_detailed": [ { "name": "Melissa Dell", "url": "https://openalex.org/A5078758511", "inst": "Canadian Institute for Advanced Research" }, { "name": "Carlson, Jacob", "url": "", "inst": "" }, { "name": "Tom Bryan", "url": "https://openalex.org/A5016736031", "inst": "Harvard University Press" }, { "name": "Emily Silcock", "url": "https://openalex.org/A5070757701", "inst": "Overseas Development Institute" }, { "name": "Abhishek Arora", "url": "https://openalex.org/A5101737564", "inst": "Harvard University Press" }, { "name": "Zejiang Shen", "url": "https://openalex.org/A5038732616", "inst": "Massachusetts Institute of Technology" }, { "name": "Luca D’Amico-Wong", "url": "https://openalex.org/A5025126617", "inst": "Harvard University Press" }, { "name": "Quan Le", "url": "https://openalex.org/A5077768209", "inst": "University College Dublin" }, { "name": "Pablo Querubín", "url": "https://openalex.org/A5083991406", "inst": "New York University" }, { "name": "Leander Heldring", "url": "https://openalex.org/A5081580175", "inst": "Northwestern University" } ], "affiliations": [ "Harvard University", "Massachusetts Institute of Technology", "New York University", "Northwestern University", "Canadian Institute for Advanced Research", "Overseas Development Institute", "University College Dublin" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4550753", "doi": "10.2139/ssrn.4550753", "title": "What Should Be Done About Google's Quasi-Monopoly in Search? Mandatory Data Sharing Versus AI-Driven Technological Competition", "authors": [ "Bertin Martens" ], "posted": "2023-08-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4550753", "field": "economics", "role": "object", "bullets": [ "EU search engine market under the Digital Markets Act, with analysis of LLM-based chatbot entry and its implications for competition policy.", "Theoretical analysis comparing search-engine network effects and data-sharing remedies against LLM chatbot competition that relies on pre-training rather than user-driven scale.", "A competitive chatbot market could crowd out monopolistic search engines, potentially making DMA-style asymmetric data-sharing intervention in search redundant under certain conditions." ], "bullet_provenance": "ai", "salience": 45, "models": [], "validated": null, "n": 2448, "authors_detailed": [ { "name": "Bertin Martens", "url": "https://openalex.org/A5031857465", "inst": "Tilburg University" } ], "affiliations": [ "Tilburg University" ] }, { "uid": "doi:10.2139/ssrn.4549328", "doi": "10.2139/ssrn.4549328", "title": "Exploring the Sentiments in Financial Disclosures with Deep Learning", "authors": [ "Alev Atak" ], "posted": "2023-08-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4549328", "field": "finance", "role": "instrument", "bullets": [ "Borsa Istanbul Stock Exchange firms from 1998 to 2019; annual financial disclosure texts analyzed for sentiment to assess the effect on firm valuation in an emerging market.", "Pre-trained transformer model constructs sentiment indices from annual financial disclosure texts; sentiment measures integrated into a system-generalized method of moments panel regression framework.", "Sentiment extracted from financial disclosures mitigates asymmetric information challenges in an emerging market, linking qualitative textual data to firm valuation outcomes." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 35, "n": 3887, "authors_detailed": [ { "name": "Alev Atak", "url": "https://openalex.org/A5016570739", "inst": "Ankara University" } ], "affiliations": [ "Ankara University" ] }, { "uid": "arxiv:2309.00638v1", "arxiv_id": "2309.00638v1", "title": "Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network", "authors": [ "Peer Nagy", "Sascha Frey", "Silvia Sapora", "Kang Li", "Anisoara Calinescu", "Stefan Zohren", "Jakob Foerster" ], "posted": "2023-08-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2309.00638v1", "field": "finance", "role": "method", "bullets": [ "NASDAQ limit order book message flow from LOBSTER equity data, tokenized with a custom digit grouping scheme in the style of language model tokenizers.", "An autoregressive deep state space model, not a named LLM family, generates order book messages replayed through a Jax-LOB simulator; fidelity is checked out of sample.", "Generated flow shows low perplexity, and mid price returns from it correlate significantly with the real series, suggesting use as a world model for high frequency research." ], "bullet_provenance": "ai", "validated": true, "validation_note": "out of sample perplexity and return correlation against LOBSTER data", "salience": 48, "edition": 12, "models": [], "n": 1470, "authors_detailed": [ { "name": "Peer Nagy", "url": "https://openalex.org/A5081598065", "inst": "University of Oxford" }, { "name": "Sascha Frey", "url": "https://openalex.org/A5104165988", "inst": "" }, { "name": "Silvia Sapora", "url": "https://openalex.org/A5091494482", "inst": "Meta (Israel)" }, { "name": "Kang Li", "url": "https://openalex.org/A5101630126", "inst": "University of South Wales" }, { "name": "Anisoara Calinescu", "url": "https://openalex.org/A5020492975", "inst": "University of Oxford" }, { "name": "Stefan Zohren", "url": "https://openalex.org/A5090331439", "inst": "University of Oxford" }, { "name": "Jakob Foerster", "url": "https://openalex.org/A5059094093", "inst": "Google (United States)" } ], "affiliations": [ "University of Oxford", "Meta (Israel)", "University of South Wales", "Google (United States)" ], "prestige": true }, { "uid": "arxiv:2308.11302v1", "arxiv_id": "2308.11302v1", "title": "From Mundane to Meaningful: AI's Influence on Work Dynamics -- evidence from ChatGPT and Stack Overflow", "authors": [ "Quentin Gallea" ], "posted": "2023-08-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.11302v1", "field": "economics", "role": "object", "bullets": [ "Stack Overflow question activity around the ChatGPT release on 30 November 2022, studied with a difference in differences design; the comparison group and question counts are not stated.", "ChatGPT itself is the treatment rather than a measurement tool; the paper runs no model and uses the sudden release as a natural experiment on coder problem solving.", "Questions fall significantly after the release while remaining ones are better documented and more complex, consistent with routine queries moving to the model; magnitudes not stated." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 12, "validated": null, "n": 1490, "authors_detailed": [ { "name": "Quentin Gallea", "url": "https://openalex.org/A5006458651", "inst": "Enterprise Community Partners" } ], "affiliations": [ "Enterprise Community Partners" ] }, { "uid": "arxiv:2308.10974v4", "arxiv_id": "2308.10974v4", "title": "\"Guinea Pig Trials\" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusion", "authors": [ "Xu Han", "Zengqing Wu", "Chuan Xiao" ], "posted": "2023-08-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.10974v4", "field": "economics", "role": "agent", "bullets": [ "Simulated price competition among firms represented by language model agents, varied over communication regimes, personas, and market structures; number of runs and rounds not stated.", "GPT-4 sets each firm agent's prices and writes inter firm messages within the smart agent based modeling framework; no benchmark against human subject experiments is reported.", "Without communication agents reach tacit collusion above the Bertrand price but below cartel levels; with communication prices approach cartel levels and collusion forms faster." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "salience": 62, "edition": 12, "validated": null, "n": 1489, "authors_detailed": [ { "name": "Xu Han", "url": "https://openalex.org/A5063337671", "inst": "China Special Equipment Inspection and Research Institute" }, { "name": "Zengqing Wu", "url": "https://openalex.org/A5020861221", "inst": "Kyoto University" }, { "name": "Chuan Xiao", "url": "https://openalex.org/A5065786687", "inst": "Chongqing University" } ], "affiliations": [ "China Special Equipment Inspection and Research Institute", "Kyoto University", "Chongqing University" ] }, { "uid": "doi:10.2139/ssrn.4544582", "doi": "10.2139/ssrn.4544582", "title": "The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market", "authors": [ "Xiang Hui", "Oren Reshef", "Luofeng Zhou" ], "posted": "2023-08-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4544582", "field": "economics", "role": "object", "bullets": [ "Freelancers on a large online labor platform before and after ChatGPT's November 2022 release, covering multiple occupation categories exposed to generative AI.", "The study measured employment and earnings changes in occupations highly exposed to ChatGPT and image-based generative AI using difference-in-differences design.", "Freelancers in highly affected occupations experienced reductions in both employment and earnings, with top-rated freelancers disproportionately affected by AI introduction." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 2386, "authors_detailed": [ { "name": "Xiang Hui", "url": "https://openalex.org/A5046988431", "inst": "Washington University in St. Louis" }, { "name": "Oren Reshef", "url": "https://openalex.org/A5051177179", "inst": "Washington University in St. Louis" }, { "name": "Luofeng Zhou", "url": "https://openalex.org/A5101262770", "inst": "New York University" } ], "affiliations": [ "Washington University in St. Louis", "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4545180", "doi": "10.2139/ssrn.4545180", "title": "Can ChatGPT Generate Factors of Stock Markets?", "authors": [ "Yuhan Cheng", "Ke Tang" ], "posted": "2023-08-20", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4545180", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 44, "edition": 12, "bullets": [], "validated": null, "n": 1488, "authors_detailed": [ { "name": "Yuhan Cheng", "url": "https://openalex.org/A5044375203", "inst": "Tsinghua University" }, { "name": "Ke Tang", "url": "https://openalex.org/A5101543008", "inst": "Tsinghua University" } ], "affiliations": [ "Tsinghua University" ] }, { "uid": "arxiv:2308.09975v2", "arxiv_id": "2308.09975v2", "title": "FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models", "authors": [ "Xin Guo", "Haotian Xia", "Zhaowei Liu", "Hanyang Cao", "Zhi Yang", "Zhiqiang Liu", "Sizhe Wang", "Jinyi Niu", "Chuqi Wang", "Yanhui Wang", "Xiaolong Liang", "Xiaoming Huang", "Bing Zhu", "Zhongyu Wei", "Yun Chen", "Weining Shen", "Liwen Zhang" ], "posted": "2023-08-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.09975v2", "field": "finance", "role": "method", "bullets": [ "FinEval, a Chinese benchmark of 8,351 questions spanning financial academic knowledge, industry practice, financial security, and agent style tool use across 34 subjects.", "LLMs are scored zero shot and five shot with chain of thought under objective and subjective criteria; Claude 3.5 Sonnet is the only model named in the abstract.", "Claude 3.5 Sonnet leads with a weighted average score of 72.9 across the financial categories in the zero shot setting." ], "bullet_provenance": "ai", "models": [ "claude" ], "open_weights": false, "validated": true, "validation_note": "benchmark answer key scoring", "salience": 44, "edition": 12, "n": 1368, "authors_detailed": [ { "name": "Xin Guo", "url": "https://openalex.org/A5101191342", "inst": "Jiangsu University" }, { "name": "Xia, Haotian", "url": "", "inst": "" }, { "name": "Zhaowei Liu", "url": "https://openalex.org/A5061364578", "inst": "Yantai University" }, { "name": "Cao, Hanyang", "url": "", "inst": "" }, { "name": "Zhi Yang", "url": "https://openalex.org/A5100355790", "inst": "Chongqing University of Posts and Telecommunications" }, { "name": "Zhiqiang Liu", "url": "https://openalex.org/A5100415118", "inst": "Jilin University" }, { "name": "Wang, Sizhe", "url": "", "inst": "" }, { "name": "Niu, Jinyi", "url": "", "inst": "" }, { "name": "Wang, Chuqi", "url": "", "inst": "" }, { "name": "Wang, Yanhui", "url": "", "inst": "" }, { "name": "Liang, Xiaolong", "url": "", "inst": "" }, { "name": "Huang, Xiaoming", "url": "", "inst": "" }, { "name": "Zhu, Bing", "url": "", "inst": "" }, { "name": "Wei, Zhongyu", "url": "", "inst": "" }, { "name": "Yun Chen", "url": "https://openalex.org/A5100599222", "inst": "China Three Gorges University" }, { "name": "Shen, Weining", "url": "", "inst": "" }, { "name": "Liwen Zhang", "url": "https://openalex.org/A5100459588", "inst": "University of Macau" } ], "affiliations": [ "Jiangsu University", "Yantai University", "Chongqing University of Posts and Telecommunications", "Jilin University", "China Three Gorges University", "University of Macau" ] }, { "uid": "doi:10.2139/ssrn.4542949", "doi": "10.2139/ssrn.4542949", "title": "Creating Synthetic Experts with Generative Artificial Intelligence", "authors": [ "Daniel Ringel" ], "posted": "2023-08-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4542949", "field": "management", "role": "instrument", "bullets": [ "Microblogs mentioning 699 brands; classification of marketing mix constructs requiring expert-level domain knowledge for accurate labeling.", "GPT-4 labeled marketing mix categories; labels used to fine-tune an open-source model creating a reproducible, scalable Synthetic Expert.", "GPT-4 labels achieved high agreement with human experts unlike crowdsourced labels; fine-tuned open-source model reached near-parity with GPT-4 performance." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "agreement with expert labels", "salience": 55, "n": 2907, "authors_detailed": [ { "name": "Daniel M. Ringel", "url": "https://openalex.org/A5070134619", "inst": "University of North Carolina at Chapel Hill" } ], "affiliations": [ "University of North Carolina at Chapel Hill" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4543652", "doi": "10.2139/ssrn.4543652", "title": "ActuaryGPT: Applications of Large Language Models to Insurance and Actuarial Work", "authors": [ "Caesar Balona" ], "posted": "2023-08-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4543652", "field": "finance", "role": "instrument", "bullets": [ "Case studies across actuarial and insurance tasks including claims processing, with a decision framework for assessing LLM suitability by task type.", "GPT-4 performed NLP tasks on insurance data including claims processing, structuring unstructured data, and serving as a coding and workflow assistant.", "LLMs proved valuable for actuarial tasks involving natural language and unstructured data but raised professionalism and ethics challenges requiring guidance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 2385, "authors_detailed": [ { "name": "Caesar Balona", "url": "https://openalex.org/A5097821108", "inst": "Gauteng Department of Agriculture and Rural Development" } ], "affiliations": [ "Gauteng Department of Agriculture and Rural Development" ] }, { "uid": "doi:10.1016/j.chieco.2025.102413", "doi": "10.1016/j.chieco.2025.102413", "arxiv_id": "2308.08776v2", "title": "Large Language Models at Work in China's Labor Market", "authors": [ "Qin Chen", "Jinfeng Ge", "Huaqing Xie", "Xingcheng Xu", "Yanqing Yang" ], "posted": "2023-08-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.08776v2", "field": "economics", "role": "object", "bullets": [ "Occupations and industries in China's labor market, scored for exposure to LLM capabilities following the Eloundou et al. task based methodology.", "Human experts and an unnamed LLM jointly classify occupational exposure; the resulting occupation and industry scores are reported to align with expert assessments.", "Exposure correlates positively with wages and experience premiums, a pattern at odds with routinization, which the authors rationalize with an entropy based AI learning theory." ], "bullet_provenance": "ai", "validated": true, "validation_note": "exposure scores checked against expert assessments", "salience": 55, "edition": 12, "models": [], "n": 1406, "authors_detailed": [ { "name": "Qin Chen", "url": "https://openalex.org/A5021369575", "inst": "Metrodat (Slovakia)" }, { "name": "Jinfeng Ge", "url": "https://openalex.org/A5002055852", "inst": "Shanghai Maritime University" }, { "name": "Huaqing Xie", "url": "https://openalex.org/A5023446490", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Xingcheng Xu", "url": "https://openalex.org/A5101236097", "inst": "Beijing Academy of Artificial Intelligence" }, { "name": "Yanqing Yang", "url": "https://openalex.org/A5104034481", "inst": "Fudan University" } ], "affiliations": [ "Metrodat (Slovakia)", "Shanghai Maritime University", "Beijing Academy of Artificial Intelligence", "Fudan University" ] }, { "uid": "doi:10.2139/ssrn.4542162", "doi": "10.2139/ssrn.4542162", "title": "Analysis of CBDC Narrative of Central Banks using Large Language Models", "authors": [ "José Manuel Carbó", "Andrés Alonso" ], "posted": "2023-08-16", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4542162", "field": "economics", "role": "instrument", "bullet_provenance": "none", "salience": 42, "edition": 12, "bullets": [], "models": [], "validated": null, "n": 1487, "authors_detailed": [ { "name": "José Manuel Carbó", "url": "https://openalex.org/A5054164134", "inst": "Bank of Spain" }, { "name": "Andrés Alonso", "url": "https://openalex.org/A5072382215", "inst": "Bank of Spain" } ], "affiliations": [ "Bank of Spain" ] }, { "uid": "doi:10.2139/ssrn.4539836", "doi": "10.2139/ssrn.4539836", "title": "AI Assistance in Legal Analysis: An Empirical Study", "authors": [ "Jonathan H. Choi", "Daniel Schwarcz" ], "posted": "2023-08-16", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4539836", "field": "management", "role": "object", "bullets": [ "Controlled experiment administering law school exams to students with and without GPT-4 access, comparing performance across multiple-choice questions and complex essay questions.", "Students used GPT-4 as an assistive tool during exams; standalone GPT-4 also tested under basic and optimized prompting methodologies against human-only baselines.", "AI assistance improved bottom-performing students significantly but hurt top performers; with optimal prompting, GPT-4 alone outperformed both average students and average AI-assisted students." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 3886, "authors_detailed": [ { "name": "Jonathan H. Choi", "url": "https://openalex.org/A5048313604", "inst": "University of Minnesota" }, { "name": "Daniel Schwarcz", "url": "https://openalex.org/A5063182470", "inst": "University of Minnesota" } ], "affiliations": [ "University of Minnesota" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2308.08031v1", "arxiv_id": "2308.08031v1", "title": "Company Similarity using Large Language Models", "authors": [ "Dimitrios Vamvourellis", "Máté Toth", "Snigdha Bhagat", "Dhruv Desai", "Dhagash Mehta", "Stefano Pasquali" ], "posted": "2023-08-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.08031v1", "field": "finance", "role": "method", "bullets": [ "Business descriptions from SEC filings of public companies, embedded to rank company similarity on a continuous scale that discrete GICS codes cannot provide.", "Pretrained and finetuned language models, families not named in the abstract, produce the embeddings, which are checked by recovering GICS classes and benchmarking against financial metrics.", "Embedding based neighbours reproduce GICS classifications and show correlated returns, supporting continuous similarity rankings for portfolio construction, asset pricing, and risk attribution." ], "bullet_provenance": "ai", "validated": true, "validation_note": "embeddings reproduce GICS classes; similarity benchmarked on return correlation", "salience": 50, "edition": 12, "models": [], "n": 1405, "authors_detailed": [ { "name": "Dimitrios Vamvourellis", "url": "https://openalex.org/A5038137368", "inst": "BlackRock (United States)" }, { "name": "Máté Attila Tóth", "url": "https://openalex.org/A5060292358", "inst": "University of the Basque Country" }, { "name": "Snigdha Bhagat", "url": "https://openalex.org/A5066625094", "inst": "Visvesvaraya National Institute of Technology" }, { "name": "Dhruv Desai", "url": "https://openalex.org/A5028151977", "inst": "Kamdhenu University" }, { "name": "Dhagash Mehta", "url": "https://openalex.org/A5056701307", "inst": "University of Notre Dame" }, { "name": "Stefano Pasquali", "url": "https://openalex.org/A5011160139", "inst": "Ospedale Maggiore" } ], "affiliations": [ "University of Notre Dame", "BlackRock (United States)", "University of the Basque Country", "Visvesvaraya National Institute of Technology", "Kamdhenu University", "Ospedale Maggiore" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2308.07979v2", "arxiv_id": "2308.07979v2", "title": "Emerging Frontiers: Exploring the Impact of Generative AI Platforms on University Quantitative Finance Examinations", "authors": [ "Rama K. Malladi" ], "posted": "2023-08-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.07979v2", "field": "finance", "role": "object", "bullets": [ "An undergraduate quantitative finance exam of 20 questions spanning several difficulty levels, used as a common test for three consumer AI platforms.", "ChatGPT, Bard, and Bing AI each sat the exam; answers were graded against the key; platform versions are not stated.", "ChatGPT reached 30 percent, Bing AI 20 percent, and Bard 15 percent, with recurring failures in formula selection and computation, well short of passing." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "graded against the exam answer key", "salience": 30, "edition": 12, "n": 1440, "authors_detailed": [ { "name": "Rama K. Malladi", "url": "https://openalex.org/A5029434888", "inst": "California State University, Dominguez Hills" } ], "affiliations": [ "California State University, Dominguez Hills" ] }, { "uid": "arxiv:2308.06907v1", "arxiv_id": "2308.06907v1", "title": "Generative Interpretation", "authors": [ "Yonathan A. Arbel", "David Hoffman" ], "posted": "2023-08-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.06907v1", "field": "economics", "role": "instrument", "bullets": [ "Case studies built from well known US contract disputes, pairing each court opinion with the actual agreement the judges interpreted.", "LLMs estimate ordinary meaning in context, quantify ambiguity, fill gaps, and score the probative value of extrinsic evidence; the abstract names no specific model and reports no accuracy benchmark.", "The authors argue model assisted interpretation is cheap and accurate enough to give courts a middle path between textualist and contextualist approaches to contract meaning." ], "bullet_provenance": "ai", "validated": false, "salience": 58, "edition": 12, "models": [], "n": 1439, "authors_detailed": [ { "name": "Yonathan A. Arbel", "url": "https://openalex.org/A5060293595", "inst": "University of Alabama" }, { "name": "David A. Hoffman", "url": "https://openalex.org/A5081607778", "inst": "University of Pennsylvania" } ], "affiliations": [ "University of Pennsylvania", "University of Alabama" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1016/j.mlwa.2023.100508", "doi": "10.1016/j.mlwa.2023.100508", "arxiv_id": "2308.07935v1", "title": "Transforming Sentiment Analysis in the Financial Domain with ChatGPT", "authors": [ "Georgios Fatouros", "John Soldatos", "Kalliopi Kouroumali", "Georgios Makridis", "Dimosthenis Kyriazis" ], "posted": "2023-08-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.07935v1", "field": "finance", "role": "method", "bullets": [ "A curated, released dataset of foreign exchange related news headlines with sentiment labels, plus market returns used as an external check.", "ChatGPT 3.5 classifies headline sentiment zero shot under several prompts, scored on precision, recall, F1, and mean absolute error against labels and against FinBERT.", "ChatGPT improves on FinBERT by about 35 percent in sentiment classification and shows a 36 percent higher correlation with market returns; the study stresses prompt engineering in zero shot settings." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "validated": true, "validation_note": "labelled forex headlines, compared with FinBERT", "salience": 46, "edition": 12, "n": 1438, "authors_detailed": [ { "name": "Georgios Fatouros", "url": "https://openalex.org/A5018359309", "inst": "University of Piraeus" }, { "name": "John Soldatos", "url": "https://openalex.org/A5069749526", "inst": "Innov-Acts Limited, Kolokotroni 6, Nicosia, 1101, Cyprus" }, { "name": "Kalliopi Kouroumali", "url": "https://openalex.org/A5092838031", "inst": "Hellenic Telecommunications Organization (Greece)" }, { "name": "Georgios Makridis", "url": "https://openalex.org/A5050942947", "inst": "University of Piraeus" }, { "name": "Dimosthenis Kyriazis", "url": "https://openalex.org/A5069674161", "inst": "University of Piraeus" } ], "affiliations": [ "University of Piraeus", "Innov-Acts Limited, Kolokotroni 6, Nicosia, 1101, Cyprus", "Hellenic Telecommunications Organization (Greece)" ] }, { "uid": "doi:10.2139/ssrn.4538502", "doi": "10.2139/ssrn.4538502", "title": "ChatGPT-based Investment Portfolio Selection", "authors": [ "Oleksandr Romanko", "Akhilesh Narayan", "Roy Kwon" ], "posted": "2023-08-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4538502", "field": "finance", "role": "agent", "bullets": [ "S&P 500 stocks; ChatGPT selects an investment universe that is then optimized using quantitative portfolio models and compared to popular funds.", "ChatGPT identified attractive stocks from the index; selected universe combined with mean-variance and other optimization strategies for portfolio construction.", "ChatGPT performed well at stock selection but poorly at weight assignment; hybrid approach combining its picks with quantitative optimization outperformed benchmarks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2906, "authors_detailed": [ { "name": "Oleksandr Romanko", "url": "https://openalex.org/A5113492570", "inst": "University of Toronto" }, { "name": "Akhilesh Narayan", "url": "https://openalex.org/A5087316210", "inst": "Indian Institute of Technology Bombay" }, { "name": "Roy H. Kwon", "url": "https://openalex.org/A5026867352", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto", "Indian Institute of Technology Bombay" ], "prestige": true }, { "uid": "arxiv:2308.06111v2", "arxiv_id": "2308.06111v2", "title": "Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models", "authors": [ "Lars Hillebrand", "Armin Berger", "Tobias Deußer", "Tim Dilmaghani", "Mohamed Khaled", "Bernd Kliem", "Rüdiger Loitz", "Maren Pielka", "David Leonhard", "Christian Bauckhage", "Rafet Sifa" ], "posted": "2023-08-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.06111v2", "field": "accounting", "role": "method", "bullets": [ "Financial statement audits where report passages must be matched to each legal requirement of accounting standards, a setting where annotated training data is scarce.", "ZeroShotALI first retrieves candidate document sections with a custom BERT based model, then filters them with an unnamed large language model in a zero shot second step.", "The two step system improves significantly over existing document matching approaches; the abstract names no benchmark and gives no accuracy figures." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": false, "salience": 36, "edition": 12, "n": 1437, "authors_detailed": [ { "name": "Lars Hillebrand", "url": "https://openalex.org/A5081939611", "inst": "Fraunhofer Institute for Intelligent Analysis and Information Systems" }, { "name": "Armin Berger", "url": "https://openalex.org/A5102998018", "inst": "University of Bonn" }, { "name": "Tobias Deußer", "url": "https://openalex.org/A5003368121", "inst": "University of Bonn" }, { "name": "Tim Dilmaghani", "url": "https://openalex.org/A5044214991", "inst": "PricewaterhouseCoopers (France)" }, { "name": "M. Ben Khaled", "url": "https://openalex.org/A5084289640", "inst": "Mansoura University" }, { "name": "Bernd Kliem", "url": "https://openalex.org/A5037349423", "inst": "PricewaterhouseCoopers (France)" }, { "name": "Rüdiger Loitz", "url": "https://openalex.org/A5026553005", "inst": "Springer Nature (Germany)" }, { "name": "Maren Pielka", "url": "https://openalex.org/A5054556600", "inst": "University of Bonn" }, { "name": "David Leonhard", "url": "https://openalex.org/A5091957484", "inst": "Fraunhofer Institute for Intelligent Analysis and Information Systems" }, { "name": "Christian Bauckhage", "url": "https://openalex.org/A5003875445", "inst": "Institut national de recherche en sciences et technologies du numérique" }, { "name": "Rafet Sifa", "url": "https://openalex.org/A5064201630", "inst": "University of Bonn" } ], "affiliations": [ "Fraunhofer Institute for Intelligent Analysis and Information Systems", "University of Bonn", "PricewaterhouseCoopers (France)", "Mansoura University", "Springer Nature (Germany)", "Institut national de recherche en sciences et technologies du numérique" ] }, { "uid": "arxiv:2308.06260v1", "arxiv_id": "2308.06260v1", "title": "ChatGPT-based Investment Portfolio Selection", "authors": [ "Oleksandr Romanko", "Akhilesh Narayan", "Roy H. Kwon" ], "posted": "2023-08-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.06260v1", "field": "finance", "role": "agent", "bullets": [ "Stocks from the S&P 500 index; ChatGPT proposes an attractive investment universe that feeds portfolio optimization strategies compared with quantitative models and popular funds.", "ChatGPT, version not stated, acts as stock selector and weight setter; assessment relies on portfolio performance comparisons rather than any labelled benchmark.", "ChatGPT picks stocks well but weights them poorly; combining its selections with established optimization models beats either alone, supporting a hybrid selection and optimization workflow." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 12, "validated": null, "n": 1469, "authors_detailed": [ { "name": "Oleksandr Romanko", "url": "https://openalex.org/A5113492570", "inst": "University of Toronto" }, { "name": "Akhilesh Narayan", "url": "https://openalex.org/A5087316210", "inst": "Indian Institute of Technology Bombay" }, { "name": "Roy H. Kwon", "url": "https://openalex.org/A5026867352", "inst": "University of Toronto" } ], "affiliations": [ "University of Toronto", "Indian Institute of Technology Bombay" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4527724", "doi": "10.2139/ssrn.4527724", "title": "Natural Language Processing meets Accounting and Finance: Review and Performance Comparison of Textual Analysis Approaches", "authors": [ "Nicolas Webersinke" ], "posted": "2023-08-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4527724", "field": "accounting", "role": "method", "bullets": [ "Large corpus of accounting and finance literature using textual analysis; four benchmark datasets for performance comparison.", "Deep learning, traditional ML, and rule-based NLP approaches compared on text classification tasks relevant to accounting and finance.", "Deep learning consistently outperformed alternatives; rule-based methods produced acceptable results only for simpler, low-context topics." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": true, "validation_note": "four textual analysis benchmark datasets", "salience": 55, "n": 3330, "authors_detailed": [ { "name": "Nicolas Webersinke", "url": "https://openalex.org/A5065452137", "inst": "Friedrich-Alexander-Universität Erlangen-Nürnberg" } ], "affiliations": [ "Friedrich-Alexander-Universität Erlangen-Nürnberg" ] }, { "uid": "arxiv:2308.05201v4", "arxiv_id": "2308.05201v4", "title": "\"Generate\" the Future of Work through AI: Empirical Evidence from Online Labor Markets", "authors": [ "Jin Liu", "Xingchen Xu", "Xi Nan", "Yongjun Li", "Yong Tan" ], "posted": "2023-08-09", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.05201v4", "field": "economics", "role": "object", "bullets": [ "Freelance submarkets on a leading online labor platform, observed around the arrival of ChatGPT, with exposure defined by overlap between required skills and core LLM functions.", "No model serves as a research tool; generative AI is the treatment whose demand, supply, and competition effects are traced across submarkets.", "Exposed submarkets shrink on both sides, supply falls less so competition intensifies, and lowered programming barriers pull incumbent freelancers, especially high skilled ones, into programming work." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "edition": 12, "validated": null, "n": 1404, "authors_detailed": [ { "name": "Liu, Jin", "url": "", "inst": "University of Science and Technology of China" }, { "name": "Xingchen Xu", "url": "https://openalex.org/A5100696665", "inst": "University of Washington" }, { "name": "Nan, Xi", "url": "", "inst": "University of Science and Technology of China" }, { "name": "Yongjun Li", "url": "https://openalex.org/A5100376875", "inst": "University of Washington" }, { "name": "Yong Tan", "url": "https://openalex.org/A5037984091", "inst": "University of Washington" } ], "affiliations": [ "University of Science and Technology of China", "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.4534294", "doi": "10.2139/ssrn.4534294", "title": "The Layoff Generation: How Generative Ai Will Reshape Employment and Labor Markets", "authors": [ "Qiwen Huang", "Yifan Shen", "Yuanchi Sun", "Qingquan Zhang" ], "posted": "2023-08-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4534294", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 35, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 621, "authors_detailed": [ { "name": "Qiwen Huang", "url": "https://openalex.org/A5102643147", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Yifan Shen", "url": "https://openalex.org/A5000183853", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Yuanchi Sun", "url": "https://openalex.org/A5101230709", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Qingquan Zhang", "url": "https://openalex.org/A5017647322", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4535647", "doi": "10.2139/ssrn.4535647", "title": "The Power of Large Language Models: A ChatGPT-driven Textual Analysis of Fundamental Data", "authors": [ "Satoshi Itoh", "Katsuhiko Okada" ], "posted": "2023-08-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4535647", "field": "finance", "role": "instrument", "bullets": [ "Shikiho (Japan Company Handbook) corporate descriptions and future projections covering all listed Japanese companies, compiled by independent journalists.", "ChatGPT interpreted business text to classify firms into positive and negative outlook groups with confidence scores, which informed equity portfolio construction.", "Portfolio performance benchmarked against risk factors suggests the market does not fully absorb factual information in Shikiho text, indicating pricing inefficiency." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 2634, "authors_detailed": [ { "name": "Satoshi Itoh", "url": "https://openalex.org/A5110470625", "inst": "Kwansei Gakuin University" }, { "name": "Katsuhiko Okada", "url": "https://openalex.org/A5035479343", "inst": "Kwansei Gakuin University" } ], "affiliations": [ "Kwansei Gakuin University" ] }, { "uid": "doi:10.2139/ssrn.4521946", "doi": "10.2139/ssrn.4521946", "title": "Feel the Market: An Attempt to Identify Additional Factor in the Capital Asset Pricing Model (CAPM) Using Generative Pre-Trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT)", "authors": [ "Christopher Lingwei Zhang" ], "posted": "2023-08-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4521946", "field": "finance", "role": "instrument", "bullets": [ "Financial Times articles analyzed for sentiment on individual stocks, applied to augment the Capital Asset Pricing Model.", "GPT and BERT performed sentiment analysis on newspaper text to construct a sentiment-scaled factor for stock pricing.", "Sentiment-scaled CAPM showed higher explanatory power than standard CAPM; a trading strategy using sentiment signals generated systematic profit." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "validated": false, "salience": 48, "n": 3329, "authors_detailed": [ { "name": "Christopher L. Zhang", "url": "https://openalex.org/A5112977692", "inst": "University of Cambridge" } ], "affiliations": [ "University of Cambridge" ], "prestige": true }, { "uid": "arxiv:2308.04241v2", "arxiv_id": "2308.04241v2", "title": "AutoPCF: Efficient Product Carbon Footprint Accounting with Large Language Models", "authors": [ "Zhu Deng", "Jinjie Liu", "Biao Luo", "Can Yuan", "Qingrun Yang", "Lei Xiao", "Wenwen Zhou", "Zhu Liu" ], "posted": "2023-08-08", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.04241v2", "field": "accounting", "role": "method", "bullets": [ "Product carbon footprint accounting, where cradle to gate life cycle models are built for three case products that normally require days of expert modelling.", "Five unnamed large language models draft life cycle structures and inventory data, combined with deep learning parameter matching; no accuracy check against expert inventories is reported.", "The AutoPCF framework cuts modelling time from days to minutes, while the trials also expose the models' weakness as a general carbon footprint knowledge base." ], "bullet_provenance": "ai", "validated": false, "salience": 40, "edition": 12, "models": [], "n": 1403, "authors_detailed": [ { "name": "Zhu Deng", "url": "https://openalex.org/A5088725316", "inst": "University of Hong Kong" }, { "name": "Jinjie Liu", "url": "https://openalex.org/A5101860008", "inst": "Qingdao University of Technology" }, { "name": "Biao Luo", "url": "https://openalex.org/A5012004938", "inst": "Central South University" }, { "name": "Can Yuan", "url": "https://openalex.org/A5113044658", "inst": "Wenzhou University" }, { "name": "Qingrun Yang", "url": "https://openalex.org/A5101649565", "inst": "Shandong University of Technology" }, { "name": "Lei Xiao", "url": "https://openalex.org/A5100441098", "inst": "Central South University" }, { "name": "Wenwen Zhou", "url": "https://openalex.org/A5100604792", "inst": "Guizhou University" }, { "name": "Zhu Liu", "url": "https://openalex.org/A5075836096", "inst": "Tsinghua University" } ], "affiliations": [ "University of Hong Kong", "Qingdao University of Technology", "Central South University", "Wenzhou University", "Shandong University of Technology", "Guizhou University", "Tsinghua University" ] }, { "uid": "arxiv:2308.04399v3", "arxiv_id": "2308.04399v3", "title": "Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models", "authors": [ "Benjamin Laufer", "Jon Kleinberg", "Hoda Heidari" ], "posted": "2023-08-08", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.04399v3", "field": "economics", "role": "object", "bullets": [ "A bargaining model of the AI supply chain in which a generalist firm pretrains a general purpose model and one or more domain specialists consider adapting it.", "No language model is run; pretrained models and fine-tuning appear as the technology whose costs, performance gains, and revenue shares the players negotiate over.", "Pareto optimal profit sharing arrangements sustain joint investment for a broad class of cost and revenue functions, and each potential specialist either contributes, free rides, or abstains under conditions the paper characterizes." ], "bullet_provenance": "ai", "salience": 52, "edition": 12, "models": [], "validated": null, "n": 1517, "authors_detailed": [ { "name": "Benjamin Laufer", "url": "https://openalex.org/A5043354448", "inst": "Cornell University" }, { "name": "Jon Kleinberg", "url": "https://openalex.org/A5055710645", "inst": "Cornell University" }, { "name": "Hoda Heidari", "url": "https://openalex.org/A5037735812", "inst": "Software Engineering Institute" } ], "affiliations": [ "Cornell University", "Software Engineering Institute" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4533642", "doi": "10.2139/ssrn.4533642", "title": "The Crowdless Future? Generative AI and Creative Problem Solving", "authors": [ "Leonard Boussioux", "Jacqueline N. Lane", "Miaomiao Zhang", "Vladimir Jacimovic", "Karim R. Lakhani" ], "posted": "2023-08-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4533642", "field": "management", "role": "object", "bullets": [ "Crowdsourcing challenge on circular economy business ideas with 125 global solvers and 300 evaluators judging 3,900 pairs.", "Human-AI collaborative solutions generated via strategic prompt engineering were compared against human-only solutions on novelty and value.", "Human-AI solutions matched human creativity overall and provided more value, but human-only solutions were more innovative on average." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 60, "n": 3181, "authors_detailed": [ { "name": "Léonard Boussioux", "url": "https://openalex.org/A5036794043", "inst": "University of Washington" }, { "name": "Jacqueline N. Lane", "url": "https://openalex.org/A5032184525", "inst": "Dana-Farber/Harvard Cancer Center" }, { "name": "Miaomiao Zhang", "url": "https://openalex.org/A5100357908", "inst": "Harvard University Press" }, { "name": "Vladimir Jaćimović", "url": "https://openalex.org/A5037070600", "inst": "Harvard University" }, { "name": "Karim R. Lakhani", "url": "https://openalex.org/A5089560559", "inst": "Internet Society" } ], "affiliations": [ "Harvard University", "University of Washington", "Internet Society" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4520859", "doi": "10.2139/ssrn.4520859", "title": "Blaming Your Predecessor: Government Turnover and External Financial Assistance", "authors": [ "Jose Abad", "Vicente J. Bermejo", "Felipe Carozzi", "Andres Gago" ], "posted": "2023-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4520859", "field": "economics", "role": "instrument", "bullets": [ "Cross-country IMF data and 3,000 Spanish municipalities during the Great Recession, regression-discontinuity design around close elections.", "ChatGPT analyzed press coverage and news content to measure framing of municipal bailout requests.", "Newly elected executives significantly more likely than re-elected incumbents to request public financing, driven by image protection motives." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3328, "authors_detailed": [ { "name": "José Abad", "url": "https://openalex.org/A5081024348", "inst": "World Bank Group" }, { "name": "Vicente J. Bermejo", "url": "https://openalex.org/A5103146889", "inst": "EAE Business School" }, { "name": "Felipe Carozzi", "url": "https://openalex.org/A5055884081", "inst": "London School of Economics and Political Science" }, { "name": "Andrés Gago", "url": "https://openalex.org/A5001210807", "inst": "Universidad Torcuato Di Tella" } ], "affiliations": [ "London School of Economics and Political Science", "World Bank Group", "EAE Business School", "Universidad Torcuato Di Tella" ], "prestige": true }, { "uid": "arxiv:2308.01741v1", "arxiv_id": "2308.01741v1", "title": "Supply chain emission estimation using large language models", "authors": [ "Ayush Jain", "Manikandan Padmanaban", "Jagabondhu Hazra", "Shantanu Godbole", "Kommy Weldemariam" ], "posted": "2023-08-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.01741v1", "field": "management", "role": "method", "bullets": [ "Enterprise Scope 3 emission estimation treating financial transaction records as proxies for purchased goods and services; firm count and transaction volume are not stated.", "A domain adapted NLP foundation model, family not named, classifies transactions for emission mapping, benchmarked against TF-IDF, word2vec, zero shot learning, and a subject matter expert.", "The adapted model outperforms the classical text baselines and matches expert performance; the abstract reports no numeric accuracy figures for these comparisons." ], "bullet_provenance": "ai", "validated": true, "validation_note": "subject matter expert comparison, figures not in abstract", "salience": 42, "edition": 12, "models": [], "n": 1468, "authors_detailed": [ { "name": "Ayush Jain", "url": "https://openalex.org/A5101550645", "inst": "Jain University" }, { "name": "Manikandan Padmanaban", "url": "https://openalex.org/A5102875956", "inst": "IBM Research - India" }, { "name": "Jagabondhu Hazra", "url": "https://openalex.org/A5020415815", "inst": "IBM Research - India" }, { "name": "Shantanu Godbole", "url": "https://openalex.org/A5033214720", "inst": "IBM Research - India" }, { "name": "Kommy Weldemariam", "url": "https://openalex.org/A5042024124", "inst": "Amazon (United States)" } ], "affiliations": [ "Jain University", "IBM Research - India", "Amazon (United States)" ] }, { "uid": "doi:10.2139/ssrn.4529739", "doi": "10.2139/ssrn.4529739", "title": "\"Generate\" the Future of Work through AI: Empirical Evidence from Online Labor Markets", "authors": [ "Jin Liu", "Xingchen Xu", "Xi Nan", "Yongjun Li", "Yong Tan" ], "posted": "2023-08-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4529739", "field": "economics", "role": "object", "bullets": [ "Extensive dataset from a leading online labor platform covering freelancer submarkets, analyzed around the post-ChatGPT release period to measure displacement and skill-transition effects.", "ChatGPT release treated as a natural experiment; displacement measured by changes in demand and supply across submarkets aligned with core LLM functionalities.", "Submarkets aligned with LLM skills contracted overall; programming submarkets saw intensified competition as ChatGPT lowered human-capital barriers, with high-skilled freelancers shifting disproportionately." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 2447, "authors_detailed": [ { "name": "Jin Liu", "url": "https://openalex.org/A5115595890", "inst": "University of Science and Technology of China" }, { "name": "Xingchen Xu", "url": "https://openalex.org/A5100696665", "inst": "University of Washington" }, { "name": "Xi Nan", "url": "https://openalex.org/A5101880531", "inst": "University of Science and Technology of China" }, { "name": "Yongjun Li", "url": "https://openalex.org/A5147201304", "inst": "University of Science and Technology of China" }, { "name": "Yong Tan", "url": "https://openalex.org/A5101059921", "inst": "University of Washington" } ], "affiliations": [ "University of Science and Technology of China", "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.4526071", "doi": "10.2139/ssrn.4526071", "title": "Ideas are Dimes a Dozen: Large Language Models for Idea Generation in Innovation", "authors": [ "Karan Girotra", "Lennart Meincke", "Christian Terwiesch", "Karl T. Ulrich" ], "posted": "2023-08-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4526071", "field": "management", "role": "agent", "bullet_provenance": "none", "salience": 55, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 620, "authors_detailed": [ { "name": "Karan Girotra", "url": "https://openalex.org/A5085941726", "inst": "Cornell University" }, { "name": "Lennart Meincke", "url": "https://openalex.org/A5003350421", "inst": "California University of Pennsylvania" }, { "name": "Christian Terwiesch", "url": "https://openalex.org/A5089214194", "inst": "University of Pennsylvania" }, { "name": "Karl T. Ulrich", "url": "https://openalex.org/A5040079549", "inst": "William P. Wharton Trust" } ], "affiliations": [ "Cornell University", "University of Pennsylvania", "California University of Pennsylvania", "William P. Wharton Trust" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4527336", "doi": "10.2139/ssrn.4527336", "title": "The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market", "authors": [ "Xiang Hui", "Oren Reshef", "Luofeng Zhou" ], "posted": "2023-08-01", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4527336", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 66, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 619, "authors_detailed": [ { "name": "Xiang Hui", "url": "https://openalex.org/A5046988431", "inst": "Washington University in St. Louis" }, { "name": "Oren Reshef", "url": "https://openalex.org/A5051177179", "inst": "Washington University in Saint Louis, John M. Olin Business School, Students" }, { "name": "Luofeng Zhou", "url": "https://openalex.org/A5101262770", "inst": "New York University" } ], "affiliations": [ "Washington University in St. Louis", "New York University", "Washington University in Saint Louis, John M. Olin Business School, Students" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4526589", "doi": "10.2139/ssrn.4526589", "title": "Do Emotions Matter? The Impact of Managerial Emotion Profiles on Stock Returns", "authors": [ "Luisa Unda", "Gihan Gamage", "Dinithi Ranasinghe", "Daswin De Silva", "Paul R. Mather" ], "posted": "2023-08-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4526589", "field": "finance", "role": "instrument", "bullets": [ "Earnings conference calls for publicly traded firms; managerial emotion profiles extracted and linked to subsequent stock return reactions.", "Hybrid AI framework combining large language models with validated psychology lexicons detected granular emotion profiles from conference call transcripts.", "Investors react positively to positive and negatively to negative emotion profiles, consistent with emotion contagion; mixed evidence on earnings news conditioning the effect." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": false, "salience": 55, "n": 2905, "authors_detailed": [ { "name": "Luisa Langer", "url": "https://openalex.org/A5092784066", "inst": "Technische Hochschule Mannheim" }, { "name": "Gihan Gamage", "url": "https://openalex.org/A5077560052", "inst": "La Trobe University" }, { "name": "Dinithi Ranasinghe", "url": "https://openalex.org/A5039037730", "inst": "University of Otago" }, { "name": "Daswin De Silva", "url": "https://openalex.org/A5064845769", "inst": "La Trobe University" }, { "name": "Paul Mather", "url": "https://openalex.org/A5009375058", "inst": "La Trobe University" } ], "affiliations": [ "Technische Hochschule Mannheim", "La Trobe University", "University of Otago" ] }, { "uid": "arxiv:2308.01415v1", "arxiv_id": "2308.01415v1", "title": "An Effective Data Creation Pipeline to Generate High-quality Financial Instruction Data for Large Language Model", "authors": [ "Ziao Wang", "Jianning Wang", "Junda Wu", "Xiaofeng Zhang" ], "posted": "2023-07-31", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.01415v1", "field": "finance", "role": "method", "bullets": [ "A financial instruction tuning dataset of 103k multi turn chats, built from staged dialogues between an AI investor and a financial expert.", "ChatGPT generates the dialogues with human financial experts refining the output, and tuned models are judged by an external GPT-4 rather than against labelled ground truth.", "The authors report more accurate, relevant, and financial style responses after tuning; no benchmark accuracy figures are stated in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 30, "edition": 12, "n": 1436, "authors_detailed": [ { "name": "Ziao Wang", "url": "https://openalex.org/A5108033372", "inst": "Hong Kong Baptist University" }, { "name": "Jianning Wang", "url": "https://openalex.org/A5101515385", "inst": "China University of Geosciences (Beijing)" }, { "name": "Junda Wu", "url": "https://openalex.org/A5035658564", "inst": "University of California San Diego" }, { "name": "Xiaofeng Zhang", "url": "https://openalex.org/A5100455676", "inst": "Shenzhen Institute of Information Technology" } ], "affiliations": [ "Hong Kong Baptist University", "China University of Geosciences (Beijing)", "University of California San Diego", "Shenzhen Institute of Information Technology" ] }, { "uid": "arxiv:2308.01430v1", "arxiv_id": "2308.01430v1", "title": "FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis", "authors": [ "Ziao Wang", "Yuhang Li", "Junda Wu", "Jaehyeon Soon", "Xiaofeng Zhang" ], "posted": "2023-07-31", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.01430v1", "field": "finance", "role": "method", "bullets": [ "A generated dataset of financial charts paired with descriptions and instructions, built for pre-training alignment and instruction tuning of a chart analysis assistant.", "FinVis-GPT, a multimodal LLM instruction tuned by the authors; the base model is not stated, and evaluation rests on case studies rather than a labelled benchmark.", "Case studies suggest stronger chart description, question answering, and market trend prediction than existing multimodal LLMs; no quantitative accuracy figures appear in the abstract." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 12, "models": [], "n": 1467, "authors_detailed": [ { "name": "Ziao Wang", "url": "https://openalex.org/A5108033372", "inst": "Hong Kong Baptist University" }, { "name": "Yuhang Li", "url": "https://openalex.org/A5100443277", "inst": "Jiangnan University" }, { "name": "Junda Wu", "url": "https://openalex.org/A5101641321", "inst": "Sun Yat-sen University" }, { "name": "Jaehyeon Soon", "url": "https://openalex.org/A5092630034", "inst": "Harbin Institute of Technology" }, { "name": "Xiaofeng Zhang", "url": "https://openalex.org/A5100455676", "inst": "Shenzhen Institute of Information Technology" } ], "affiliations": [ "Hong Kong Baptist University", "Jiangnan University", "Sun Yat-sen University", "Harbin Institute of Technology", "Shenzhen Institute of Information Technology" ] }, { "uid": "arxiv:2308.00016v2", "arxiv_id": "2308.00016v2", "title": "Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment", "authors": [ "Saizhuo Wang", "Hang Yuan", "Leon Zhou", "Lionel M. Ni", "Heung-Yeung Shum", "Jian Guo" ], "posted": "2023-07-31", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.00016v2", "field": "finance", "role": "method", "bullets": [ "Quantitative investment research where the output unit is an alpha, a candidate trading signal; the abstract does not describe the markets or data behind the experiments.", "A prompt engineering framework has a large language model turn quant researchers' ideas into candidate alphas through interactive iteration; the underlying model is not named and no validation is reported.", "Alpha mining experiments are said to produce effective and creative signals, but the abstract gives no performance magnitudes or backtest results." ], "bullet_provenance": "ai", "validated": false, "salience": 46, "edition": 12, "models": [], "n": 1476, "authors_detailed": [ { "name": "Saizhuo Wang", "url": "https://openalex.org/A5035150525", "inst": "Hong Kong University of Science and Technology" }, { "name": "Hang Yuan", "url": "https://openalex.org/A5101453271", "inst": "Zhengzhou University of Light Industry" }, { "name": "Leon Zhou", "url": "https://openalex.org/A5114071771", "inst": "University of Chicago" }, { "name": "Lionel M. Ni", "url": "https://openalex.org/A5021357065", "inst": "Argonne National Laboratory" }, { "name": "Heung‐Yeung Shum", "url": "https://openalex.org/A5061000201", "inst": "Tsinghua University" }, { "name": "Jian Guo", "url": "https://openalex.org/A5109927726", "inst": "Lanzhou Jiaotong University" } ], "affiliations": [ "University of Chicago", "Hong Kong University of Science and Technology", "Zhengzhou University of Light Industry", "Argonne National Laboratory", "Tsinghua University", "Lanzhou Jiaotong University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4517639", "doi": "10.2139/ssrn.4517639", "title": "Assessing Large Language Models’ ability to predict how humans balance self-interest and the interest of others", "authors": [ "Valerio Capraro", "Roberto Di Paolo", "Veronica Pizziol" ], "posted": "2023-07-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4517639", "field": "economics", "role": "agent", "bullets": [ "108 dictator game experiments with human participants from 12 countries; three advanced chatbots tested on identical allocation scenarios.", "GPT-4, Bard, and Bing predict human dictator game allocations; chatbot responses compared against experimental behavioral distributions across settings.", "GPT-4 alone captures three qualitative behavioral classes but systematically overestimates altruism and underestimates self-interest and inequity aversion." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "Comparison against 108 dictator game experiments across 12 countries", "salience": 48, "n": 2446, "authors_detailed": [ { "name": "Valerio Capraro", "url": "https://openalex.org/A5068651250", "inst": "University of Milano-Bicocca" }, { "name": "Roberto Di Paolo", "url": "https://openalex.org/A5047709816", "inst": "IMT School for Advanced Studies Lucca" }, { "name": "Veronica Pizziol", "url": "https://openalex.org/A5012785291", "inst": "IMT School for Advanced Studies Lucca" } ], "affiliations": [ "University of Milano-Bicocca", "IMT School for Advanced Studies Lucca" ] }, { "uid": "doi:10.51685/jqd.2024.icwsm.7", "doi": "10.51685/jqd.2024.icwsm.7", "arxiv_id": "2307.16336v1", "title": "Anatomy of an AI-powered malicious social botnet", "authors": [ "Kai-Cheng Yang", "Filippo Menczer" ], "posted": "2023-07-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.16336v1", "field": "other", "role": "object", "bullets": [ "A Twitter botnet of 1,140 accounts, identified through coordination heuristics and confirmed by manual annotation, posting machine generated text and stolen profile images.", "The accounts appear to use ChatGPT to write humanlike posts; state of the art machine generated text classifiers cannot separate them from genuine users.", "The fake personas form a dense mutually engaging cluster that promotes suspicious websites and harmful comments, remaining detectable mainly through their coordination patterns." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 12, "validated": null, "n": 1402, "authors_detailed": [ { "name": "Kai‐Cheng Yang", "url": "https://openalex.org/A5054574752", "inst": "Universidad del Noreste" }, { "name": "Filippo Menczer", "url": "https://openalex.org/A5021346979", "inst": "Indiana University" } ], "affiliations": [ "Universidad del Noreste", "Indiana University" ] }, { "uid": "arxiv:2307.15425v1", "arxiv_id": "2307.15425v1", "title": "A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI", "authors": [ "Arash Hajikhani", "Carolyn Cole" ], "posted": "2023-07-28", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.15425v1", "field": "management", "role": "method", "bullets": [ "A dataset of company descriptions, size not stated, serves as a case study for detecting sustainable development goals in corporate text.", "OpenAI's GPT-3.5 is compared with a specialized compiled SDG detection model; the abstract reports no accuracy figures against labelled ground truth.", "GPT-3.5 casts a wider net but flags goals of limited relevance to firms' activities, while the specialized model returns tighter matches, favouring domain models where precision matters." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 32, "edition": 12, "n": 1401, "authors_detailed": [ { "name": "Arash Hajikhani", "url": "https://openalex.org/A5008533729", "inst": "Lappeenranta-Lahti University of Technology" }, { "name": "Carolyn Cole", "url": "https://openalex.org/A5091043346", "inst": "Lappeenranta-Lahti University of Technology" } ], "affiliations": [ "Lappeenranta-Lahti University of Technology" ] }, { "uid": "doi:10.2139/ssrn.4521754", "doi": "10.2139/ssrn.4521754", "title": "The Consequences of Generative AI for UGC and Online Community Engagement", "authors": [ "Gordon Burtch", "Dokyun Lee", "Zhichen Chen" ], "posted": "2023-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4521754", "field": "management", "role": "object", "bullets": [ "StackOverflow and Reddit activity data spanning October 2021 to March 2023, covering the most popular StackOverflow programming topics.", "ChatGPT's public release treated as a natural experiment; its causal impact on question volume and answer quality estimated across platforms.", "StackOverflow questions declined significantly post-ChatGPT; answer quality fell primarily due to user exit; Reddit showed no effect, suggesting social attachment protects communities." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 60, "validated": null, "n": 2904, "authors_detailed": [ { "name": "Gordon Burtch", "url": "https://openalex.org/A5057319034", "inst": "Boston University" }, { "name": "Dokyun Lee", "url": "https://openalex.org/A5088913516", "inst": "Boston University" }, { "name": "Zhichen Chen", "url": "https://openalex.org/A5059218889", "inst": "Boston University" } ], "affiliations": [ "Boston University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4522386", "doi": "10.2139/ssrn.4522386", "title": "Heterogeneous Effects of Generative AI on Knowledge Seeking in Online Communities", "authors": [ "Martin Quinn", "Dominik Gutt" ], "posted": "2023-07-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4522386", "field": "management", "role": "object", "bullets": [ "StackExchange Q&A platform data, difference-in-differences design around ChatGPT's public launch, users segmented by commitment level.", "ChatGPT studied as exogenous shock to knowledge-seeking behavior on the platform; not used as a research instrument.", "Casual users reduced question posting by 18.2 percent; remaining questions from casual users became more complex and novel." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 58, "validated": null, "n": 3327, "authors_detailed": [ { "name": "Martin Quinn", "url": "https://openalex.org/A5028588281", "inst": "Erasmus University Rotterdam" }, { "name": "Dominik Gutt", "url": "https://openalex.org/A5062949747", "inst": "RWTH Aachen University" } ], "affiliations": [ "Erasmus University Rotterdam", "RWTH Aachen University" ] }, { "uid": "doi:10.2139/ssrn.4519583", "doi": "10.2139/ssrn.4519583", "title": "A Demonstration of How ChatGPT Can be Used in the Internal Auditing Process", "authors": [ "Marc Eulerich", "David A. Wood" ], "posted": "2023-07-27", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4519583", "field": "accounting", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 618, "authors_detailed": [ { "name": "Marc Eulerich", "url": "https://openalex.org/A5056975067", "inst": "University of Duisburg-Essen" }, { "name": "David A. Wood", "url": "https://openalex.org/A5075888890", "inst": "Brigham Young University" } ], "affiliations": [ "University of Duisburg-Essen", "Brigham Young University" ] }, { "uid": "doi:10.2139/ssrn.4519182", "doi": "10.2139/ssrn.4519182", "title": "ChatGPT, Generative AI, and Investment Advisory", "authors": [ "Fangzhou Lu", "Lei Huang", "Sixuan Li" ], "posted": "2023-07-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4519182", "field": "finance", "role": "agent", "bullets": [ "U.S. and Chinese equity markets using Wall Street Journal articles and Chinese government policy announcements as textual inputs for portfolio construction.", "Fine-tuned ChatGPT with flexible output parameters processed financial news and policy text to generate stock portfolio recommendations tested out of sample.", "ChatGPT-generated portfolios achieved monthly three-factor alpha up to 3%, with strongest performance for policy-related news in the Chinese market." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "out-of-sample three-factor alpha", "salience": 72, "n": 2384, "authors_detailed": [ { "name": "Fangzhou Lu", "url": "https://openalex.org/A5000962435", "inst": "Guangdong University of Finance" }, { "name": "Lei Huang", "url": "https://openalex.org/A5101474959", "inst": "University of Hong Kong" }, { "name": "Sixuan Li", "url": "https://openalex.org/A5112968406", "inst": "University of Hong Kong" } ], "affiliations": [ "Guangdong University of Finance", "University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.4521178", "doi": "10.2139/ssrn.4521178", "title": "TV Advertising Effectiveness with Racial Minority Representation: Evidence from the Mortgage Market", "authors": [ "Donggwan Kim", "Zhenling Jiang", "Raphael Thomadsen" ], "posted": "2023-07-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4521178", "field": "management", "role": "instrument", "bullets": [ "9.7 million U.S. mortgage refinance loans matched with TV advertising data; minority representation in ads measured using video and textual feature extraction models.", "Variational autoencoder and text embedding model extract ad features; Double Machine Learning estimates effect of minority representation on lender choice controlling for high-dimensional covariates.", "Ads with minority share increasing from 15% to 30% raise advertising elasticity from 0.0161 to 0.0269; effect is stronger among minority and liberal-leaning borrowers." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": false, "salience": 45, "n": 3885, "authors_detailed": [ { "name": "Donggwan Kim", "url": "https://openalex.org/A5000061341", "inst": "Boston College" }, { "name": "Zhenling Jiang", "url": "https://openalex.org/A5059978781", "inst": "University of Pennsylvania" }, { "name": "Raphael Thomadsen", "url": "https://openalex.org/A5070935601", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Boston College", "University of Pennsylvania", "Washington University in St. Louis" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4521349", "doi": "10.2139/ssrn.4521349", "title": "Chatgpt Attention and Stock Liquidity: Evidence from Chinese Investor Interactive Platforms", "authors": [ "Xiaoyu Li", "Shuyang Jia", "Fujing Xue", "Bob Jing" ], "posted": "2023-07-26", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4521349", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 38, "edition": 12, "bullets": [], "validated": null, "n": 1486, "authors_detailed": [ { "name": "Xiaoyu Li", "url": "https://openalex.org/A5032888003", "inst": "Civil Aviation University of China" }, { "name": "Shuyang Jia", "url": "https://openalex.org/A5022148260", "inst": "Waseda University" }, { "name": "Fujing Xue", "url": "https://openalex.org/A5049079838", "inst": "Sun Yat-sen University" }, { "name": "Bob Jing", "url": "https://openalex.org/A5092547980", "inst": "" } ], "affiliations": [ "Civil Aviation University of China", "Waseda University", "Sun Yat-sen University" ] }, { "uid": "doi:10.2139/ssrn.4515593", "doi": "10.2139/ssrn.4515593", "title": "Generative Artificial Intelligence (GAI) – Foundations, Use Cases and Economic Potential", "authors": [ "Volker Brühl" ], "posted": "2023-07-25", "added": "2026-08-29", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4515593", "field": "economics", "role": "object", "bullets": [ "Survey of the generative AI landscape circa 2023, covering market players, use cases, projected economic impact, and the European regulatory environment including the DMA and DSA.", "ChatGPT is discussed as the trigger for broad public awareness; the researchers do not run any model and provide no empirical benchmark.", "US Big Tech firms are positioned to dominate generative AI, and existing EU digital-market regulations may need revision to address the accelerating pace of AI development." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 30, "edition": 25, "validated": null, "n": 4300, "authors_detailed": [ { "name": "Volker Brühl", "url": "https://openalex.org/A5065852501", "inst": "Goethe University Frankfurt" } ], "affiliations": [ "Goethe University Frankfurt" ] }, { "uid": "arxiv:2307.13617v2", "arxiv_id": "2307.13617v2", "title": "GPT-3 Models are Few-Shot Financial Reasoners", "authors": [ "Raul Salles de Padua", "Imran Qureshi", "Mustafa U. Karakaplan" ], "posted": "2023-07-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.13617v2", "field": "finance", "role": "method", "bullets": [ "Financial question answering over company documents requiring multi step numerical reasoning; the specific benchmark dataset is not named in the abstract.", "GPT-3 with few shot prompts, tested with and without a separate retrieval model and logic engine; answers are scored for accuracy against the benchmark's ground truth.", "A dedicated retriever and logic engine remain essential for state of the art results, while refined prompting alone brings GPT-3 near state of the art accuracy without fine-tuning." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "financial QA benchmark ground truth accuracy", "salience": 36, "edition": 12, "n": 1461, "authors_detailed": [ { "name": "Raul Salles de Padua", "url": "https://openalex.org/A5092557128", "inst": "University of South Carolina" }, { "name": "Imran Qureshi", "url": "https://openalex.org/A5101901645", "inst": "International Islamic University, Islamabad" }, { "name": "Mustafa U. Karakaplan", "url": "https://openalex.org/A5021659269", "inst": "Office of the Comptroller of the Currency" } ], "affiliations": [ "University of South Carolina", "International Islamic University, Islamabad", "Office of the Comptroller of the Currency" ] }, { "uid": "doi:10.2139/ssrn.4511658", "doi": "10.2139/ssrn.4511658", "title": "Enhancing Sentiment Analysis based Investment by Large Language Models in Japanese Stock Market", "authors": [ "Masafumi Nakano", "Takuya Yamaoka" ], "posted": "2023-07-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4511658", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 38, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 330, "authors_detailed": [ { "name": "Masafumi Nakano", "url": "https://openalex.org/A5019511510", "inst": "Sector Asset Management" }, { "name": "Takuya Yamaoka", "url": "https://openalex.org/A5031385220", "inst": "Japan University of Economics" } ], "affiliations": [ "Sector Asset Management", "Japan University of Economics" ] }, { "uid": "doi:10.2139/ssrn.4510212", "doi": "10.2139/ssrn.4510212", "title": "Capturing Firm Economic Events", "authors": [ "Khrystyna Bochkay", "Roman Chychyla", "Anthony Joffre", "Jake Krupa" ], "posted": "2023-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4510212", "field": "accounting", "role": "instrument", "bullets": [ "U.S. public firms filing event-driven 8-K reports; parsimonious measure of firm economic events based on 8-K item frequency and semantic context.", "Large language models weight 8-K events by semantic context, refining the base frequency measure to better capture firms' underlying economic changes.", "Measure associates with future performance changes and disclosure choices; LLM refinement is incremental to traditional performance, complexity, and uncertainty proxies." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "association with future firm performance and market uncertainty", "salience": 60, "n": 2903, "authors_detailed": [ { "name": "Khrystyna Bochkay", "url": "https://openalex.org/A5025296635", "inst": "University of Miami" }, { "name": "Roman Chychyla", "url": "https://openalex.org/A5086714122", "inst": "University of Miami" }, { "name": "Anthony Joffre", "url": "https://openalex.org/A5009193734", "inst": "University of Miami" }, { "name": "Jake Krupa", "url": "https://openalex.org/A5023839930", "inst": "Tulane University" } ], "affiliations": [ "University of Miami", "Tulane University" ] }, { "uid": "doi:10.2139/ssrn.4514238", "doi": "10.2139/ssrn.4514238", "title": "Leveraging ChatGPT for Enhancing the Internal Audit Process – A Real-World Example from a Large Multinational Company", "authors": [ "Scott A. Emett", "Marc Eulerich", "Egemen Lipinski", "Nicolo Prien", "David A. Wood" ], "posted": "2023-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4514238", "field": "accounting", "role": "object", "bullets": [ "Single case study of Uniper, a large multinational energy company's internal audit function, conducted in 2023.", "ChatGPT integrated into audit preparation, fieldwork, and reporting processes; no formal accuracy benchmark reported.", "Estimated efficiency gains of 50 to 80 percent across various internal audit processes." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 45, "validated": null, "n": 3325, "authors_detailed": [ { "name": "Scott A. Emett", "url": "https://openalex.org/A5006570025", "inst": "Arizona State University" }, { "name": "Marc Eulerich", "url": "https://openalex.org/A5056975067", "inst": "University of Duisburg-Essen" }, { "name": "Egemen Lipinski", "url": "https://openalex.org/A5092532402", "inst": "Uniper SE" }, { "name": "Nicolo Prien", "url": "https://openalex.org/A5092532403", "inst": "Uniper SE" }, { "name": "David A. Wood", "url": "https://openalex.org/A5075888890", "inst": "Brigham Young University" } ], "affiliations": [ "Arizona State University", "University of Duisburg-Essen", "Uniper SE", "Brigham Young University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4519221", "doi": "10.2139/ssrn.4519221", "title": "Regulating Transformative Technologies", "authors": [ "Daron Acemoglu", "Todd Lensman" ], "posted": "2023-07-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4519221", "field": "economics", "role": "object", "bullets": [ "Multi-sector technology adoption model with social learning about risks from transformative technologies including generative AI.", "Theoretical framework analyzing optimal regulation of AI adoption; no LLM used directly in methodology.", "Optimal adoption is gradual and convex; higher growth rates lead to slower optimal adoption when damages scale with productivity gains." ], "bullet_provenance": "ai", "salience": 72, "models": [], "validated": null, "n": 3326, "authors_detailed": [ { "name": "Daron Acemoğlu", "url": "https://openalex.org/A5012301204", "inst": "National Bureau of Economic Research" }, { "name": "Todd Lensman", "url": "https://openalex.org/A5026685611", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2308.00065v1", "arxiv_id": "2308.00065v1", "title": "FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models", "authors": [ "Yuwei Yin", "Yazheng Yang", "Jian Yang", "Qi Liu" ], "posted": "2023-07-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2308.00065v1", "field": "finance", "role": "method", "bullets": [ "FinBench, a released collection of labelled datasets covering financial default, fraud, and churn risks; dataset sizes are not stated in the abstract.", "Tabular customer records are slotted into instruction templates, an LLM whose family is not named writes natural language customer profiles, and foundation models are fine tuned on those profiles to predict risk.", "FinPT outperforms a range of representative strong baselines across the FinBench tasks; the abstract gives no performance magnitudes." ], "bullet_provenance": "ai", "validated": true, "validation_note": "FinBench labelled outcome datasets", "salience": 40, "edition": 12, "models": [], "n": 1367, "authors_detailed": [ { "name": "Yuwei Yin", "url": "https://openalex.org/A5057453970", "inst": "University of Shanghai for Science and Technology" }, { "name": "Yazheng Yang", "url": "https://openalex.org/A5063920160", "inst": "University of Hong Kong" }, { "name": "Jian Yang", "url": "https://openalex.org/A5009644404", "inst": "Hong Kong Baptist University" }, { "name": "Qi Liu", "url": "https://openalex.org/A5100453144", "inst": "University of Science and Technology of China" } ], "affiliations": [ "University of Shanghai for Science and Technology", "University of Hong Kong", "Hong Kong Baptist University", "University of Science and Technology of China" ] }, { "uid": "arxiv:2307.12776v3", "arxiv_id": "2307.12776v3", "title": "Assessing Large Language Models' ability to predict how humans balance self-interest and the interest of others", "authors": [ "Valerio Capraro", "Roberto Di Paolo", "Veronica Pizziol" ], "posted": "2023-07-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.12776v3", "field": "economics", "role": "agent", "bullets": [ "Dictator game allocations from 108 experiments with human participants in 12 countries, used as the benchmark for testing chatbot predictions of social preferences.", "GPT-4, Bard, and Bing are prompted to predict the human decisions; predictions are compared directly with the observed experimental behaviour.", "Only GPT-4 recovers the self interested, inequity averse, and fully altruistic classes, but it understates self interest and inequity aversion while overstating altruism." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "predictions compared with human decisions from 108 dictator game experiments", "salience": 60, "edition": 12, "n": 1400, "authors_detailed": [ { "name": "Valerio Capraro", "url": "https://openalex.org/A5068651250", "inst": "University of Milano-Bicocca" }, { "name": "Roberto Di Paolo", "url": "https://openalex.org/A5047709816", "inst": "IMT School for Advanced Studies Lucca" }, { "name": "Veronica Pizziol", "url": "https://openalex.org/A5012785291", "inst": "GNA University" } ], "affiliations": [ "University of Milano-Bicocca", "IMT School for Advanced Studies Lucca", "GNA University" ] }, { "uid": "arxiv:2307.11845v2", "arxiv_id": "2307.11845v2", "title": "Multimodal Document Analytics for Banking Process Automation", "authors": [ "Christopher Gerling", "Stefan Lessmann" ], "posted": "2023-07-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.11845v2", "field": "management", "role": "method", "bullets": [ "Retail banking business documents, reviewed as a landscape, with a case study classifying company register extracts used in bank processes.", "LayoutXLM, a multimodal layout aware model, is benchmarked against BERT classifiers and GPT models on labelled extracts, tracing performance across training set sizes and fine-tuning choices.", "Adding layout information raises F1 substantially, and more than 75 percent of best model performance is reached with only 30 percent of the labelled training data." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": true, "validated": true, "validation_note": "labelled company register extracts, F1 compared", "salience": 46, "edition": 12, "n": 1460, "authors_detailed": [ { "name": "Christopher Gerling", "url": "https://openalex.org/A5056487636", "inst": "Cal Poly Humboldt" }, { "name": "Stefan Lessmann", "url": "https://openalex.org/A5057226223", "inst": "Cal Poly Humboldt" } ], "affiliations": [ "Cal Poly Humboldt" ] }, { "uid": "arxiv:2307.11137v3", "arxiv_id": "2307.11137v3", "title": "Of Models and Tin Men: A Behavioural Economics Study of Principal-Agent Problems in AI Alignment using Large-Language Models", "authors": [ "Steve Phelps", "Rebecca Ranson" ], "posted": "2023-07-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.11137v3", "field": "economics", "role": "agent", "bullets": [ "A simple online shopping task casts GPT models as agents facing principal agent conflicts, with information asymmetry between the model and its principal varied.", "GPT-3.5 and GPT-4 act as the purchasing agent; behaviour across conflict conditions is the outcome studied, with no measurement validation involved.", "Both models override their principal's objectives; GPT-3.5 adjusts with information asymmetry while GPT-4 sticks more rigidly to its prior alignment." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 12, "validated": null, "n": 1399, "authors_detailed": [ { "name": "Steve Phelps", "url": "https://openalex.org/A5111436535", "inst": "University College London" }, { "name": "Rebecca Ranson", "url": "https://openalex.org/A5056952213", "inst": "" } ], "affiliations": [ "University College London" ] }, { "uid": "arxiv:2307.10700v4", "arxiv_id": "2307.10700v4", "title": "Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers", "authors": [ "Rajiv Movva", "Sidhika Balachandar", "Kenny Peng", "Gabriel Agostini", "Nikhil Garg", "Emma Pierson" ], "posted": "2023-07-20", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.10700v4", "field": "management", "role": "object", "bullets": [ "16,979 language model related arXiv papers, comparing 2023 activity against 2018 to 2022 across topics, author backgrounds, institutions, and collaborations.", "No language model is reported as part of the measurement pipeline; the analysis tracks submission counts, author entry from other fields, and institutional co-authorship patterns.", "Computers and society submissions grew twenty fold, half of 2023 first authors are new to NLP, industry's publication share fell with reduced Google output, and cross country collaboration remains rare." ], "bullet_provenance": "ai", "salience": 42, "edition": 12, "models": [], "validated": null, "n": 1435, "authors_detailed": [ { "name": "Rajiv Movva", "url": "https://openalex.org/A5079551137", "inst": "Cornell University" }, { "name": "Sidhika Balachandar", "url": "https://openalex.org/A5066903866", "inst": "University of California, Berkeley" }, { "name": "Kenny Peng", "url": "https://openalex.org/A5101229426", "inst": "Cornell University" }, { "name": "Gabriel Agostini", "url": "https://openalex.org/A5064235997", "inst": "Cornell University" }, { "name": "Nikhil Garg", "url": "https://openalex.org/A5081701167", "inst": "Central University of Punjab" }, { "name": "Emma Pierson", "url": "https://openalex.org/A5067807487", "inst": "University of California, Berkeley" } ], "affiliations": [ "Cornell University", "University of California, Berkeley", "Central University of Punjab" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4507114", "doi": "10.2139/ssrn.4507114", "title": "Trading AI: Machine Knowledge Capital and the Trading System", "authors": [ "Dan Ciuriak", "Anna Artyushina" ], "posted": "2023-07-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4507114", "field": "economics", "role": "object", "bullets": [ "International trade governance frameworks including WTO rules and emerging AI regulations across jurisdictions, covering the ChatGPT adoption timeline.", "No model deployed; paper analyzes how LLM-driven machine knowledge capital disrupts existing trade-system governance structures and exception clauses.", "Current WTO trade rules are unprepared for powerful AI systems; existing standards processes and general-exception clauses require rapid adaptation to AI-era frictions." ], "bullet_provenance": "ai", "salience": 30, "models": [], "validated": null, "n": 2445, "authors_detailed": [ { "name": "Dan Ciuriak", "url": "https://openalex.org/A5080115755", "inst": "Asia Pacific Foundation of Canada" }, { "name": "Anna Artyushina", "url": "https://openalex.org/A5067570108", "inst": "Toronto Metropolitan University" } ], "affiliations": [ "Asia Pacific Foundation of Canada", "Toronto Metropolitan University" ] }, { "uid": "doi:10.2139/ssrn.4507511", "doi": "10.2139/ssrn.4507511", "title": "Asset and Investor Embeddings", "authors": [ "Xavier Gabaix", "Ralph S. J. Koijen", "Robert Richmond", "Motohiro Yogo" ], "posted": "2023-07-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4507511", "field": "finance", "role": "instrument", "bullets": [ "U.S. equity market portfolio holdings used to learn asset and investor embeddings; three new cross-sectional benchmarks proposed for evaluation.", "Large language models applied to firm-level and investor-level text data to provide economic interpretations of the learned embeddings from holdings.", "Embeddings recover cross-sectional pricing information under realistic conditions; LLM-based text analysis yields interpretable economic meaning for asset and investor representations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "validated": true, "validation_note": "three cross-sectional asset pricing benchmarks", "salience": 70, "n": 2902, "authors_detailed": [ { "name": "Xavier Gabaix", "url": "https://openalex.org/A5069579613", "inst": "National Bureau of Economic Research" }, { "name": "Ralph S. J. Koijen", "url": "https://openalex.org/A5083877502", "inst": "National Bureau of Economic Research" }, { "name": "Robert Richmond", "url": "https://openalex.org/A5139416083", "inst": "National Bureau of Economic Research" }, { "name": "Motohiro Yogo", "url": "https://openalex.org/A5048794076", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "National Bureau of Economic Research" ] }, { "uid": "arxiv:2307.10485v2", "arxiv_id": "2307.10485v2", "title": "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models", "authors": [ "Xiao-Yang Liu", "Guoxuan Wang", "Hongyang Yang", "Daochen Zha" ], "posted": "2023-07-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.10485v2", "field": "finance", "role": "method", "bullets": [ "An open-source pipeline that collects and curates real-time financial text from 34 internet sources, addressing diverse formats, low signal-to-noise ratios, and time sensitivity.", "General purpose LLMs are adapted with LoRA and QLoRA plus a reinforcement learning scheme using stock prices as feedback; no accuracy validation is reported in the abstract.", "Demonstrates applications including robo-advising, sentiment signals for algorithmic trading, and low-code development; the abstract reports no quantitative performance results." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": false, "salience": 45, "edition": 12, "n": 1385, "authors_detailed": [ { "name": "Xiaoyang Liu", "url": "https://openalex.org/A5022718768", "inst": "Argonne National Laboratory" }, { "name": "Guoxuan Wang", "url": "https://openalex.org/A5104171786", "inst": "Johns Hopkins University" }, { "name": "Yang, Hongyang", "url": "", "inst": "" }, { "name": "Zha, Daochen", "url": "", "inst": "" } ], "affiliations": [ "Johns Hopkins University", "Argonne National Laboratory" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2307.08974v1", "arxiv_id": "2307.08974v1", "title": "Development of the ChatGPT, Generative Artificial Intelligence and Natural Large Language Models for Accountable Reporting and Use (CANGARU) Guidelines", "authors": [ "Giovanni E. Cacciamani", "Michael B. Eppler", "Conner Ganjavi", "Asli Pekan", "Brett Biedermann", "Gary S. Collins", "Inderbir S. Gill" ], "posted": "2023-07-18", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.08974v1", "field": "other", "role": "method", "bullets": [ "Cross disciplinary initiative on generative AI in scholarly work, built from a systematic review of applications, a bibliometric analysis of journal author guidelines, and a Delphi consensus survey.", "No model performs analysis in this protocol; ChatGPT and related LLMs are the subject of the use, disclosure, and reporting rules under development.", "Planned output is the CANGARU guideline set with explanation and elaboration documents; as a protocol paper it reports no empirical findings yet." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 26, "edition": 12, "validated": null, "n": 1359, "authors_detailed": [ { "name": "Giovanni Cacciamani", "url": "https://openalex.org/A5062586182", "inst": "University of Southern California" }, { "name": "Michael Eppler", "url": "https://openalex.org/A5022334478", "inst": "Keck Hospital of USC" }, { "name": "Conner Ganjavi", "url": "https://openalex.org/A5019811077", "inst": "University of Southern California" }, { "name": "Asli Pekan", "url": "https://openalex.org/A5092499002", "inst": "" }, { "name": "Brett M. Biedermann", "url": "https://openalex.org/A5113360611", "inst": "University of Southern California" }, { "name": "Professor Gary S. Collins", "url": "https://openalex.org/A5060891981", "inst": "University Hospitals Birmingham NHS Foundation Trust" }, { "name": "Inderbir S. Gill", "url": "https://openalex.org/A5047742848", "inst": "University of Southern California" } ], "affiliations": [ "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1098/rstb.2023.0028", "doi": "10.1098/rstb.2023.0028", "arxiv_id": "2307.08564v2", "title": "Shaping New Norms for AI", "authors": [ "Andrea Baronchelli" ], "posted": "2023-07-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.08564v2", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of how societies form norms around AI, contrasting formal authorities, informal institutions, and spontaneous bottom up emergence; no empirical sample.", "No model is used for measurement; a ChatGPT conversation is reproduced as an illustration of emerging norms the system says it has observed.", "Argues AI evolves faster than the characteristic time of norm formation and that open societies should anchor deliberation in transparent public discourse; no quantitative results." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 30, "edition": 12, "validated": null, "n": 1485, "authors_detailed": [ { "name": "Andrea Baronchelli", "url": "https://openalex.org/A5068182184", "inst": "Turing Institute" } ], "affiliations": [ "Turing Institute" ] }, { "uid": "doi:10.2139/ssrn.4504409", "doi": "10.2139/ssrn.4504409", "title": "Expert Evaluation of ChatGPT Performance for Risk Management Process based on ISO 31000 Standard", "authors": [ "M.K.S. Al-Mhdawi", "Abroon Qazi", "Ammar Alzarrad", "Nicholas Dacre", "Farzad Rahimian", "Mohanad K. Buniya", "Hanqin Zhang" ], "posted": "2023-07-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4504409", "field": "management", "role": "method", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 38, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 617, "authors_detailed": [ { "name": "M.K.S. Al-Mhdawi", "url": "https://openalex.org/A5010038132", "inst": "Teesside University" }, { "name": "Abroon Qazi", "url": "https://openalex.org/A5084847550", "inst": "American University of Sharjah" }, { "name": "Ammar Alzarrad", "url": "https://openalex.org/A5080165136", "inst": "Marshall University" }, { "name": "Nicholas Dacre", "url": "https://openalex.org/A5006870368", "inst": "University of Warwick" }, { "name": "Farzad Pour Rahimian", "url": "https://openalex.org/A5035993068", "inst": "Teesside University" }, { "name": "Mohanad Kamil Buniya", "url": "https://openalex.org/A5055619185", "inst": "Universiti Teknologi Petronas" }, { "name": "Hanqin Qiu Zhang", "url": "https://openalex.org/A5088698309", "inst": "University of Southampton" } ], "affiliations": [ "Teesside University", "American University of Sharjah", "Marshall University", "University of Warwick", "Universiti Teknologi Petronas", "University of Southampton" ] }, { "uid": "doi:10.2139/ssrn.4512495", "doi": "10.2139/ssrn.4512495", "title": "Regulating Transformative Technologies", "authors": [ "Daron Acemoglu", "Todd Lensman" ], "posted": "2023-07-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4512495", "field": "economics", "role": "object", "bullets": [ "Multi-sector technology adoption model with Bayesian learning about social risks from transformative technologies including generative AI.", "Theoretical framework derives optimal regulation path balancing productivity acceleration against potential misuse across sectors.", "Socially optimal adoption is gradual and convex; higher growth rates lead to slower optimal adoption when damages scale with productivity gains." ], "bullet_provenance": "ai", "salience": 75, "models": [], "validated": null, "n": 3324, "authors_detailed": [ { "name": "Daron Acemoğlu", "url": "https://openalex.org/A5012301204", "inst": "National Bureau of Economic Research" }, { "name": "Todd Lensman", "url": "https://openalex.org/A5026685611", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4502549", "doi": "10.2139/ssrn.4502549", "title": "Assuring Sustainable Futures: Auditing Sustainability Reports using AI Foundation Models", "authors": [ "Tassilo Lars Föhr", "Marco Schreyer", "Tatjana A. Juppe", "Kai-Uwe Marten" ], "posted": "2023-07-14", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4502549", "field": "accounting", "role": "instrument", "bullets": [ "Case study applying an LLM-based audit prompt framework to corporate sustainability reports under the ISSA 5000 exposure draft assurance requirements.", "LLMs automatically verified and validated the sustainability of economic activities described in sustainability reports using a structured prompt framework.", "The framework demonstrated usefulness for automated sustainability report verification, signaling a new approach to sustainability assurance through LLM integration." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2383, "authors_detailed": [ { "name": "Tassilo Lars Föhr", "url": "https://openalex.org/A5059391464", "inst": "Ulm University" }, { "name": "Marco Schreyer", "url": "https://openalex.org/A5056732531", "inst": "International Computer Science Institute" }, { "name": "Tatjana Alexandra Juppe", "url": "https://openalex.org/A5092470654", "inst": "Universität Ulm" }, { "name": "Kai-Uwe Marten", "url": "https://openalex.org/A5065354447", "inst": "Universität Ulm" } ], "affiliations": [ "Ulm University", "International Computer Science Institute", "Universität Ulm" ] }, { "uid": "arxiv:2307.07420v2", "arxiv_id": "2307.07420v2", "title": "Named entity recognition using GPT for identifying comparable companies", "authors": [ "Eurico Covas" ], "posted": "2023-07-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.07420v2", "field": "finance", "role": "method", "bullets": [ "Company descriptions taken from public Wikipedia pages, used to build peer groups of comparable firms for equity and private company valuation.", "OpenAI GPT extracts product entities for similarity analysis; precision is compared quantitatively against standard named entity recognition methods that rely on manual annotation.", "GPT extraction attains higher precision and produces peer groups the author deems suitable for comparable company valuation; exact precision figures are not stated in the abstract." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "precision benchmarked against standard NER with manual annotation", "salience": 40, "edition": 12, "n": 1398, "authors_detailed": [ { "name": "Eurico Covas", "url": "https://openalex.org/A5085901575", "inst": "Queen Mary University of London" } ], "affiliations": [ "Queen Mary University of London" ] }, { "uid": "arxiv:2307.04986v1", "arxiv_id": "2307.04986v1", "title": "Epidemic Modeling with Generative Agents", "authors": [ "Ross Williams", "Niyousha Hosseinichimeh", "Aritra Majumdar", "Navid Ghaffarzadegan" ], "posted": "2023-07-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.04986v1", "field": "other", "role": "agent", "bullets": [ "An agent based epidemic model in which individual simulated residents choose daily behavior; population size and simulation settings are not stated in the abstract.", "Each agent reasons through a large language model such as ChatGPT before deciding whether to stay home; behavior is compared with real world patterns without a quantitative benchmark.", "Agents quarantine when sick and self isolate as cases rise, generating multiple waves, a flattened epidemic curve, and an eventual endemic period resembling recent pandemics." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 12, "validated": null, "n": 1466, "authors_detailed": [ { "name": "Ross Williams", "url": "https://openalex.org/A5103178657", "inst": "Virginia Tech" }, { "name": "Niyousha Hosseinichimeh", "url": "https://openalex.org/A5062910371", "inst": "Virginia Tech" }, { "name": "Aritra Majumdar", "url": "https://openalex.org/A5020362719", "inst": "Virginia Tech" }, { "name": "Navid Ghaffarzadegan", "url": "https://openalex.org/A5078713927", "inst": "D-Tech (United States)" } ], "affiliations": [ "Virginia Tech", "D-Tech (United States)" ] }, { "uid": "doi:10.2139/ssrn.4495500", "doi": "10.2139/ssrn.4495500", "title": "Do AIs Dream of Homo Economicus? Answers from ChatGPT", "authors": [ "Toshihiro Tsuchihashi" ], "posted": "2023-07-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4495500", "field": "economics", "role": "agent", "bullets": [ "ChatGPT participates as a bidder in first-price and second-price sealed-bid auction experiments, with and without assigned economic-student personas.", "ChatGPT submitted bids in both auction formats; results compared to theoretical predictions and prior human experimental outcomes across persona conditions.", "Without persona, overbidding in first-price auctions matched humans but second-price results diverged; persona assignment worsened second-price underbidding significantly." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 2901, "authors_detailed": [ { "name": "Toshihiro Tsuchihashi", "url": "https://openalex.org/A5086221235", "inst": "Daito Bunka University" } ], "affiliations": [ "Daito Bunka University" ] }, { "uid": "doi:10.2139/ssrn.4495647", "doi": "10.2139/ssrn.4495647", "title": "Overcoming Complexity in ESG Investing: The Role of Generative AI Integration in Identifying Contextual ESG Factors", "authors": [ "Yash Jain", "Shubham Gupta", "Serhan Yalciner", "Yashodhan Nilesh Joglekar", "Parth Khetan", "Qingquan Zhang" ], "posted": "2023-07-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4495647", "field": "finance", "role": "instrument", "bullets": [ "ESG-related news articles and stock return data across multiple industries analyzed for ESG-return relationships.", "GPT-3.5 classifies contextual ESG factors for companies via a custom ESG classifier module built on the language model.", "Stock returns show 20% dependence on ESG-related news; the classifier enables tracking of ESG-return relationships for investment decisions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 45, "n": 3323, "authors_detailed": [ { "name": "Yash Jain", "url": "https://openalex.org/A5080724301", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Shubham Gupta", "url": "https://openalex.org/A5100331774", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Serhan Yalciner", "url": "https://openalex.org/A5097372139", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Yashodhan Nilesh Joglekar", "url": "https://openalex.org/A5097372140", "inst": "University of Illinois Urbana-Champaign" }, { "name": "Parth Khetan", "url": "https://openalex.org/A5097372141", "inst": "University of Illinois Urbana-Champaign" }, { "name": "T. Zhang", "url": "https://openalex.org/A5017726292", "inst": "" } ], "affiliations": [ "University of Illinois Urbana-Champaign" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4493398", "doi": "10.2139/ssrn.4493398", "title": "Playing Games With GPT: What Can We Learn About a Large Language Model From Canonical Strategic Games?", "authors": [ "Philip Brookins", "Jason Matthew DeBacker" ], "posted": "2023-07-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4493398", "field": "economics", "role": "agent", "bullets": [ "GPT-3.5 plays the dictator game and prisoner's dilemma in repeated interactions; decisions compared to established results from human laboratory experiments.", "GPT-3.5 made strategic choices in each game; response distributions compared quantitatively to published human experimental benchmarks for fairness and cooperation.", "LLM displayed stronger fairness than humans in the dictator game and a 65% cooperation rate in the prisoner's dilemma versus 37% for humans." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 2900, "authors_detailed": [ { "name": "Philip Brookins", "url": "https://openalex.org/A5020857593", "inst": "University of South Carolina" }, { "name": "Jason DeBacker", "url": "https://openalex.org/A5035424932", "inst": "University of South Carolina" } ], "affiliations": [ "University of South Carolina" ] }, { "uid": "arxiv:2307.07422v1", "arxiv_id": "2307.07422v1", "title": "Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes", "authors": [ "Kausik Lakkaraju", "Sai Krishna Revanth Vuruma", "Vishal Pallagani", "Bharath Muppasani", "Biplav Srivastava" ], "posted": "2023-07-08", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.07422v1", "field": "finance", "role": "agent", "bullets": [ "Thirteen questions about bank accounts, credit cards, certificates of deposit, and related purchase and repayment decisions, posed in English, African American Vernacular English, and Telugu.", "ChatGPT and Bard answer each question as personal finance advisors; the abstract reports no accuracy statistic or benchmark against verified financial information.", "Responses are fluent and plausible but contain critical gaps in accuracy and reliability, leaving the chatbots short of dependable financial advice for the public." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": false, "salience": 35, "edition": 12, "n": 1397, "authors_detailed": [ { "name": "Kausik Lakkaraju", "url": "https://openalex.org/A5037234293", "inst": "University of South Carolina" }, { "name": "Sai Krishna Revanth Vuruma", "url": "https://openalex.org/A5092489810", "inst": "University of South Carolina" }, { "name": "Vishal Pallagani", "url": "https://openalex.org/A5066581184", "inst": "University of South Carolina" }, { "name": "Bharath Muppasani", "url": "https://openalex.org/A5049630750", "inst": "University of South Carolina" }, { "name": "Biplav Srivastava", "url": "https://openalex.org/A5051577973", "inst": "University of South Carolina" } ], "affiliations": [ "University of South Carolina" ] }, { "uid": "doi:10.2139/ssrn.4493166", "doi": "10.2139/ssrn.4493166", "title": "Can AI Read the Minds of Corporate Executives?", "authors": [ "Nicolas Chapados", "Zhenzhen Fan", "Ruslan Goyenko", "Issam Hadj Laradji", "Fred Liu", "Chengyu Zhang" ], "posted": "2023-07-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4493166", "field": "finance", "role": "instrument", "bullets": [ "Complete history of U.S. quarterly and annual SEC filings (10-Q and 10-K), with MD&A sections as input and earnings surprises as prediction target.", "Finance-objective-trained LLMs predict earnings surprise direction from filing text; benchmarked against sentiment-based and bag-of-words ML regression approaches.", "Only finance-trained LLMs successfully predict both positive and negative earnings surprises and subsequent returns; classic NLP and sentiment-based methods fail entirely." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "Prediction of earnings surprises and future firm returns", "salience": 70, "n": 2444, "authors_detailed": [ { "name": "Nicolas Chapados", "url": "https://openalex.org/A5039831068", "inst": "ServiceNow (United States)" }, { "name": "Zhenzhen Fan", "url": "https://openalex.org/A5101131105", "inst": "University of Guelph" }, { "name": "Ruslan Goyenko", "url": "https://openalex.org/A5000317559", "inst": "McGill University" }, { "name": "Issam Laradji", "url": "https://openalex.org/A5028905152", "inst": "Montreal Police Service" }, { "name": "Fred Liu", "url": "https://openalex.org/A5047926113", "inst": "Western University" }, { "name": "Chengyu Zhang", "url": "https://openalex.org/A5100722693", "inst": "Shanghai Jiao Tong University" } ], "affiliations": [ "ServiceNow (United States)", "University of Guelph", "McGill University", "Montreal Police Service", "Western University", "Shanghai Jiao Tong University" ] }, { "uid": "doi:10.2139/ssrn.4498671", "doi": "10.2139/ssrn.4498671", "title": "ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience", "authors": [ "Ruiyun Xu", "Yue (Katherine) Feng", "Hailiang Chen" ], "posted": "2023-07-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4498671", "field": "management", "role": "object", "bullets": [ "Two randomized online experiments comparing ChatGPT and Google Search across information look-up, summarization, and fact-checking tasks.", "Users completed retrieval tasks with ChatGPT, Google, or both; time spent and task performance measured across conditions.", "ChatGPT users spend less time with comparable overall performance; ChatGPT excels at look-ups but struggles with fact-checking tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 40, "validated": null, "n": 3322, "authors_detailed": [ { "name": "Ruiyun Xu", "url": "https://openalex.org/A5008521435", "inst": "Miami University" }, { "name": "Yue Feng", "url": "https://openalex.org/A5100381173", "inst": "Hong Kong Polytechnic University" }, { "name": "Hailiang Chen", "url": "https://openalex.org/A5072197700", "inst": "University of Hong Kong" } ], "affiliations": [ "Miami University", "Hong Kong Polytechnic University", "University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.4499732", "doi": "10.2139/ssrn.4499732", "title": "Blockchain: The Economic and Financial Institution for Autonomous AI?", "authors": [ "Binh Nguyen Thanh", "Son Ha Xuan", "Diem Thi Hong Vo" ], "posted": "2023-07-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4499732", "field": "finance", "role": "object", "bullets": [ "Conceptual study drawing on institutional economics to analyze constraints preventing autonomous AI agents from independently accessing and participating in economic and financial institutions.", "Proposes blockchain as the institutional technology enabling AI agents to manage private keys, form and execute contracts, participate in marketplaces, and use financial services autonomously.", "Argues blockchain-based digital institutions can unlock full AI agent capabilities for autonomous economic participation, bridging the gap between AI technical abilities and institutional access requirements." ], "bullet_provenance": "ai", "salience": 20, "models": [], "validated": null, "n": 3884, "authors_detailed": [ { "name": "Binh Nguyen Thanh", "url": "https://openalex.org/A5008771904", "inst": "RMIT Vietnam" }, { "name": "Son Ha Xuan", "url": "", "inst": "RMIT Vietnam" }, { "name": "Diem Thi Hong Vo", "url": "https://openalex.org/A5068414997", "inst": "RMIT Vietnam" } ], "affiliations": [ "RMIT Vietnam" ] }, { "uid": "doi:10.2139/ssrn.4499400", "doi": "10.2139/ssrn.4499400", "title": "Debt Markets Retort to Mandatory Corporate Social Responsibility", "authors": [ "Jitendra Aswani" ], "posted": "2023-07-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4499400", "field": "finance", "role": "instrument", "bullets": [ "Indian firms subject to Companies Act 2013 mandatory CSR provisions; debt issuance and yield spread data.", "Generative AI model separates mandatory CSR governance disclosures from expenditure reporting in corporate filings.", "Mandatory CSR raises yield spreads by 43 basis points; expenditure drives the increase while governance modestly boosts issue-to-sales by 1.2%." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3321, "authors_detailed": [ { "name": "Jitendra Aswani", "url": "https://openalex.org/A5075687891", "inst": "Harvard University Press" } ], "affiliations": [ "Harvard University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4489826", "doi": "10.2139/ssrn.4489826", "title": "FinGPT: Open-Source Financial Large Language Models", "authors": [ "Hongyang Yang", "Xiao-Yang Liu", "Christina Dan Wang" ], "posted": "2023-07-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4489826", "field": "finance", "role": "method", "bullet_provenance": "none", "models": [ "open_other" ], "open_weights": true, "salience": 45, "edition": 3, "audience": "technical", "bullets": [], "validated": null, "n": 615, "authors_detailed": [ { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "New York Foundation" }, { "name": "Xiao-Yang Liu", "url": "https://openalex.org/A5100405233", "inst": "Columbia University" }, { "name": "Christina Dan Wang", "url": "https://openalex.org/A5018062245", "inst": "New York University" } ], "affiliations": [ "Columbia University", "New York University", "New York Foundation" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4489831", "doi": "10.2139/ssrn.4489831", "title": "Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models", "authors": [ "Boyu Zhang", "Hongyang Yang", "Xiao-Yang Liu" ], "posted": "2023-07-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4489831", "field": "finance", "role": "method", "bullet_provenance": "none", "models": [ "open_other" ], "open_weights": true, "salience": 42, "edition": 3, "audience": "technical", "bullets": [], "validated": null, "n": 616, "authors_detailed": [ { "name": "Boyu Zhang", "url": "https://openalex.org/A5100714035", "inst": "Swinburne University of Technology" }, { "name": "Hongyang Yang", "url": "https://openalex.org/A5061855742", "inst": "New York Foundation" }, { "name": "Xiao-Yang Liu", "url": "https://openalex.org/A5100405233", "inst": "Columbia University" } ], "affiliations": [ "Columbia University", "Swinburne University of Technology", "New York Foundation" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2306.17519v2", "arxiv_id": "2306.17519v2", "title": "GPT-FinRE: In-context Learning for Financial Relation Extraction using Large Language Models", "authors": [ "Pawan Kumar Rajpoot", "Ankur Parikh" ], "posted": "2023-06-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2306.17519v2", "field": "finance", "role": "method", "bullets": [ "REFinD, a financial relation extraction dataset released with a shared task at the fourth workshop on knowledge discovery from unstructured data in financial services at SIGIR 2023.", "OpenAI models, versions not stated, run in-context learning with two retrieval strategies, one learning free and one learned, to select demonstrations from the training data.", "The system placed third overall in the shared task, reaching a best F1 score of 0.718 on the labelled test set." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "REFinD labelled test set, F1 0.718", "salience": 30, "edition": 12, "n": 1459, "authors_detailed": [ { "name": "Pawan Kumar Rajpoot", "url": "https://openalex.org/A5084786836", "inst": "TIFR Centre for Applicable Mathematics" }, { "name": "Ankur P. Parikh", "url": "https://openalex.org/A5109937799", "inst": "Google (United States)" } ], "affiliations": [ "TIFR Centre for Applicable Mathematics", "Google (United States)" ] }, { "uid": "doi:10.2139/ssrn.4491342", "doi": "10.2139/ssrn.4491342", "title": "Exploring Generative AI for Modeling the Dynamics of Asset Price Process", "authors": [ "Jinseong Park", "Hyungjin Ko", "Jaewook Lee" ], "posted": "2023-06-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4491342", "field": "finance", "role": "instrument", "bullets": [ "Stock chart images and S&P 500 option pricing data used to test generative AI replication of asset price dynamics.", "Generative AI image synthesis models replicate underlying stock price movements without predetermined distributional assumptions.", "Method successfully reproduces known asset price processes and accurately estimates S&P 500 option prices." ], "bullet_provenance": "ai", "validated": true, "validation_note": "option pricing accuracy against S&P 500 market prices", "salience": 45, "models": [], "n": 3320, "authors_detailed": [ { "name": "J.-G. Park", "url": "https://openalex.org/A5023697071", "inst": "Seoul National University" }, { "name": "Hyungjin Ko", "url": "https://openalex.org/A5020828967", "inst": "Seoul National University" }, { "name": "Jaewook Lee", "url": "https://openalex.org/A5100609345", "inst": "Seoul National University" } ], "affiliations": [ "Seoul National University" ] }, { "uid": "arxiv:2306.15518v2", "arxiv_id": "2306.15518v2", "title": "Paradigm Shift in Sustainability Disclosure Analysis: Empowering Stakeholders with CHATREPORT, a Language Model-Based Tool", "authors": [ "Jingwei Ni", "Julia Bingler", "Chiara Colesanti-Senni", "Mathias Kraus", "Glen Gostlow", "Tobias Schimanski", "Dominik Stammbach", "Saeid Ashraf Vaghefi", "Qian Wang", "Nicolas Webersinke", "Tobias Wekhof", "Tingyu Yu", "Markus Leippold" ], "posted": "2023-06-27", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2306.15518v2", "field": "accounting", "role": "method", "bullets": [ "Corporate sustainability reports, whose volume makes expert human analysis costly, assessed against the TCFD climate-related disclosure recommendations; the number of reports processed is not stated.", "An LLM tool augmented with knowledge from climate science, finance, and policy experts scores disclosure conformity; the underlying model is not named and no accuracy validation is reported.", "Prompt templates, generated data, and scores are released publicly to widen access to disclosure analysis; the abstract reports no substantive findings about firms' disclosure quality." ], "bullet_provenance": "ai", "validated": false, "salience": 44, "edition": 12, "models": [], "n": 1396, "authors_detailed": [ { "name": "Jingwei Ni", "url": "https://openalex.org/A5113207856", "inst": "Shanghai Jiao Tong University" }, { "name": "Julia Bingler", "url": "https://openalex.org/A5010157189", "inst": "University of Oxford" }, { "name": "Chiara Colesanti-Senni", "url": "https://openalex.org/A5052380718", "inst": "University of Zurich" }, { "name": "Mathias Kraus", "url": "https://openalex.org/A5044596225", "inst": "University of Regensburg" }, { "name": "Glen Gostlow", "url": "https://openalex.org/A5055140586", "inst": "University of Zurich" }, { "name": "Tobias Schimanski", "url": "https://openalex.org/A5008509425", "inst": "University of Zurich" }, { "name": "Dominik Stammbach", "url": "https://openalex.org/A5026276254", "inst": "Princeton University" }, { "name": "Saeid Ashraf Vaghefi", "url": "https://openalex.org/A5035433111", "inst": "University of Zurich" }, { "name": "Qian Wang", "url": "https://openalex.org/A5092197015", "inst": "University of Zurich" }, { "name": "Nicolas Webersinke", "url": "https://openalex.org/A5065452137", "inst": "Friedrich-Alexander-Universität Erlangen-Nürnberg" }, { "name": "Tobias Wekhof", "url": "https://openalex.org/A5011580139", "inst": "University of Zurich" }, { "name": "Tingyu Yu", "url": "https://openalex.org/A5013001852", "inst": "University of Zurich" }, { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" } ], "affiliations": [ "Princeton University", "University of Oxford", "Shanghai Jiao Tong University", "University of Zurich", "University of Regensburg", "Friedrich-Alexander-Universität Erlangen-Nürnberg" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2306.15033v1", "arxiv_id": "2306.15033v1", "title": "Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle", "authors": [ "Thomas Dohmke", "Marco Iansiti", "Greg Richards" ], "posted": "2023-06-26", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2306.15033v1", "field": "economics", "role": "object", "bullets": [ "Usage telemetry from 934,533 GitHub Copilot users, supplemented by an economy wide projection and a survey of generative AI activity across GitHub repositories.", "GitHub Copilot, an OpenAI Codex based assistant, is the object of study rather than a research tool; the paper tracks acceptance of its code suggestions in telemetry, with no external validation described.", "Developers accept close to 30 percent of suggestions, acceptance rises over time and is highest among less experienced users, and the authors project a 1.5 trillion dollar addition to global GDP by 2030." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 12, "validated": null, "n": 1516, "authors_detailed": [ { "name": "Thomas Dohmke", "url": "https://openalex.org/A5041163665", "inst": "GitHub" }, { "name": "Marco Iansiti", "url": "https://openalex.org/A5015070753", "inst": "Harvard Business School,Keystone.AI" }, { "name": "Greg Richards", "url": "https://openalex.org/A5021474188", "inst": "Keystone College" } ], "affiliations": [ "Harvard Business School,Keystone.AI", "GitHub", "Keystone College" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4480570", "doi": "10.2139/ssrn.4480570", "title": "The Impact of Cloud Computing and AI on Industry Dynamics and Concentration", "authors": [ "Yao Lu", "Gordon M. Phillips", "Jia Yang" ], "posted": "2023-06-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4480570", "field": "economics", "role": "instrument", "bullets": [ "Registration statements of over 20 million Chinese firms, measuring exposure to cloud computing, generative AI, and automation technologies across industries.", "An LLM analyzed firm registration statements to classify technology exposure at the firm and industry level for cloud, GenAI, and robotics adoption.", "Cloud computing and GenAI reduce industry concentration through greater firm entry and exit, while automation increases concentration via lower exit and higher merger probability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 70, "n": 2633, "authors_detailed": [ { "name": "Yao Lu", "url": "https://openalex.org/A5108040516", "inst": "Tsinghua University" }, { "name": "Gordon M. Phillips", "url": "https://openalex.org/A5068926411", "inst": "Dartmouth College" }, { "name": "Yang Jia", "url": "https://openalex.org/A5112528468", "inst": "University of International Business and Economics" } ], "affiliations": [ "Dartmouth College", "Tsinghua University", "University of International Business and Economics" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4488891", "doi": "10.2139/ssrn.4488891", "title": "Using GPT-4 for Financial Advice", "authors": [ "Christian Fieberg", "Lars Hornuf", "David Streich" ], "posted": "2023-06-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4488891", "field": "finance", "role": "agent", "bullets": [ "Simulated investor profiles varying in risk tolerance, risk capacity, sustainability preference, and investment horizon.", "GPT-4 generates tailored portfolio recommendations for each investor profile; historical risk-adjusted returns compared to professional benchmark.", "Portfolios display home bias and horizon insensitivity but match a professionally managed benchmark on risk-adjusted performance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "risk-adjusted performance vs professionally managed benchmark portfolio", "salience": 55, "n": 3319, "authors_detailed": [ { "name": "Christian Fieberg", "url": "https://openalex.org/A5044108899", "inst": "Hochschule Bremen" }, { "name": "Lars Hornuf", "url": "https://openalex.org/A5064247212", "inst": "Ifo Institute for Economic Research" }, { "name": "David Streich", "url": "https://openalex.org/A5023289262", "inst": "Catholic University of Eichstätt-Ingolstadt" } ], "affiliations": [ "Hochschule Bremen", "Ifo Institute for Economic Research", "Catholic University of Eichstätt-Ingolstadt" ] }, { "uid": "arxiv:2306.14222v2", "arxiv_id": "2306.14222v2", "title": "Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?", "authors": [ "Haohan Zhang", "Fengrui Hua", "Chengjin Xu", "Hao Kong", "Ruiting Zuo", "Jian Guo" ], "posted": "2023-06-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2306.14222v2", "field": "finance", "role": "method", "bullets": [ "Large volumes of Chinese news summaries covering listed companies, converted into sentiment factors that drive quantitative trading strategies evaluated in realistic back tests.", "Three LLMs, unnamed in the abstract and each representing a different performance enhancement approach, extract sentiment under a standardized procedure; no validation against labelled sentiment is described.", "The contribution is the evaluation procedure and back test comparisons across the three models; the abstract reports no performance numbers or rankings." ], "bullet_provenance": "ai", "validated": false, "salience": 33, "edition": 12, "models": [], "n": 1394, "authors_detailed": [ { "name": "Haohan Zhang", "url": "https://openalex.org/A5047179522", "inst": "Xiamen University" }, { "name": "Fengrui Hua", "url": "https://openalex.org/A5088224908", "inst": "Xihua University" }, { "name": "Chengjin Xu", "url": "https://openalex.org/A5022319543", "inst": "Hefei University of Technology" }, { "name": "Hao Kong", "url": "https://openalex.org/A5067594005", "inst": "Nanyang Technological University" }, { "name": "Ruiting Zuo", "url": "https://openalex.org/A5025863954", "inst": "University of Hong Kong" }, { "name": "Jian Guo", "url": "https://openalex.org/A5100575929", "inst": "Guilin University of Electronic Technology" } ], "affiliations": [ "Xiamen University", "Xihua University", "Hefei University of Technology", "Nanyang Technological University", "University of Hong Kong", "Guilin University of Electronic Technology" ] }, { "uid": "arxiv:2306.14096v5", "arxiv_id": "2306.14096v5", "title": "Chinese Fine-Grained Financial Sentiment Analysis with Large Language Models", "authors": [ "Yinyu Lan", "Yanru Wu", "Wang Xu", "Weiqiang Feng", "Youhao Zhang" ], "posted": "2023-06-25", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2306.14096v5", "field": "finance", "role": "method", "bullets": [ "FinChina SA, a public Chinese entity-level financial sentiment dataset built for enterprise early warning; the number of annotated examples is not stated in the abstract.", "Existing open-source LLMs, unnamed in the abstract, are evaluated on the annotated dataset for fine-grained sentiment; specific accuracy figures are not given.", "The dataset release is the main contribution, with the LLM evaluations framed as reference results; the abstract offers no headline accuracy or comparative numbers." ], "bullet_provenance": "ai", "models": [ "open_other" ], "open_weights": true, "validated": true, "validation_note": "FinChina SA annotated dataset, figures not in abstract", "salience": 38, "edition": 12, "n": 1395, "authors_detailed": [ { "name": "Yinyu Lan", "url": "https://openalex.org/A5102982025", "inst": "Harbin Institute of Technology" }, { "name": "Yanru Wu", "url": "https://openalex.org/A5101515400", "inst": "Tianjin Normal University" }, { "name": "Wang Xu", "url": "https://openalex.org/A5101217891", "inst": "" }, { "name": "Weiqiang Feng", "url": "https://openalex.org/A5110365252", "inst": "Southern University of Science and Technology" }, { "name": "Youhao Zhang", "url": "https://openalex.org/A5069607570", "inst": "Shandong University" } ], "affiliations": [ "Harbin Institute of Technology", "Tianjin Normal University", "Southern University of Science and Technology", "Shandong University" ] }, { "uid": "doi:10.2139/ssrn.4484416", "doi": "10.2139/ssrn.4484416", "title": "Large Language Models are Prone to Methodological Artifacts", "authors": [ "Melanie Brucks", "Olivier Toubia" ], "posted": "2023-06-25", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4484416", "field": "management", "role": "method", "bullet_provenance": "none", "salience": 48, "edition": 12, "bullets": [], "models": [], "validated": null, "n": 1484, "authors_detailed": [ { "name": "Melanie Brucks", "url": "https://openalex.org/A5047361203", "inst": "Columbia University" }, { "name": "Olivier Toubia", "url": "https://openalex.org/A5024730984", "inst": "Columbia University" } ], "affiliations": [ "Columbia University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4480947", "doi": "10.2139/ssrn.4480947", "title": "The Promise and Peril of Generative AI: Evidence from ChatGPT as Sell-Side Analysts", "authors": [ "Edward Xuejun Li", "Zhiyuan Tu", "Dexin Zhou" ], "posted": "2023-06-24", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4480947", "field": "finance", "role": "agent", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 329, "authors_detailed": [ { "name": "Edward Xuejun Li", "url": "https://openalex.org/A5028012849", "inst": "City University of New York" }, { "name": "Zhiyuan Tu", "url": "https://openalex.org/A5093919776", "inst": "Southwestern University of Finance and Economics" }, { "name": "Dexin Zhou", "url": "https://openalex.org/A5069014291", "inst": "Baruch College" } ], "affiliations": [ "City University of New York", "Southwestern University of Finance and Economics", "Baruch College" ] }, { "uid": "doi:10.2139/ssrn.4476325", "doi": "10.2139/ssrn.4476325", "title": "Large Language Models as Tax Attorneys: A Case Study in Legal Capabilities Emergence", "authors": [ "John Nay", "David Karamardian", "Sarah B. Lawsky", "Wenting Tao", "Meghana Bhat", "Raghav Jain", "Aaron Travis Lee", "Jonathan H. Choi", "Jungo Kasai" ], "posted": "2023-06-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4476325", "field": "accounting", "role": "method", "bullets": [ "Thousands of U.S. tax law questions with automated validation pipelines, testing LLM legal analysis capabilities across multiple areas of the tax code.", "Successive OpenAI GPT model releases answered tax questions; few-shot prompting and retrieval of relevant legal texts enhanced GPT-4 performance significantly.", "Legal reasoning capability improved with each model release; GPT-4 with prompting enhancements achieved high accuracy but did not reach expert tax attorney levels." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Automated validation across thousands of tax law examples", "salience": 55, "n": 2399, "authors_detailed": [ { "name": "John J. Nay", "url": "https://openalex.org/A5058951556", "inst": "New York Public Library" }, { "name": "David Karamardian", "url": "https://openalex.org/A5092252469", "inst": "Independent" }, { "name": "Sarah B. Lawsky", "url": "https://openalex.org/A5075418086", "inst": "Illinois College" }, { "name": "Wenting Tao", "url": "https://openalex.org/A5092252470", "inst": "Independent" }, { "name": "Meghana Bhat", "url": "https://openalex.org/A5104253356", "inst": "Karnatak University" }, { "name": "Raghav Jain", "url": "https://openalex.org/A5101313419", "inst": "Independent" }, { "name": "Aaron Travis Lee", "url": "https://openalex.org/A5011435697", "inst": "Independent" }, { "name": "Jonathan H. Choi", "url": "https://openalex.org/A5048313604", "inst": "University of Minnesota" }, { "name": "Jungo Kasai", "url": "https://openalex.org/A5002654756", "inst": "Independent" } ], "affiliations": [ "University of Minnesota", "New York Public Library", "Independent", "Illinois College", "Karnatak University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4480056", "doi": "10.2139/ssrn.4480056", "title": "Executives vs. Chatbots: Unmasking Insights through Human-AI Differences in Earnings Conference Q&A", "authors": [ "John (Jianqiu) Bai", "Nicole M. Boyson", "Yi Cao", "Miao Liu", "Chi Wan" ], "posted": "2023-06-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4480056", "field": "finance", "role": "instrument", "bullets": [ "U.S. earnings conference calls with stock liquidity, abnormal returns, analyst forecast revisions, and management guidance as outcome variables.", "ChatGPT, Google Bard, and an open-source LLM generate counterfactual answers to earnings call questions; divergence from executive answers measured as HAID.", "Higher HAID strongly predicts greater stock liquidity, abnormal returns, and analyst forecast revisions, capturing new information content conveyed by managers." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini", "open_other" ], "open_weights": false, "validated": true, "validation_note": "Prediction of stock returns, liquidity, analyst forecast revisions", "salience": 72, "n": 2443, "authors_detailed": [ { "name": "John Bai", "url": "https://openalex.org/A5054468432", "inst": "Northeastern University" }, { "name": "Nicole M. Boyson", "url": "https://openalex.org/A5057906263", "inst": "Northeastern University" }, { "name": "Yi Cao", "url": "https://openalex.org/A5010912310", "inst": "George Mason University" }, { "name": "Miao Liu", "url": "https://openalex.org/A5100348904", "inst": "Boston College" }, { "name": "Chi Wan", "url": "https://openalex.org/A5101540802", "inst": "San Diego State University" } ], "affiliations": [ "Boston College", "Northeastern University", "George Mason University", "San Diego State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4481928", "doi": "10.2139/ssrn.4481928", "title": "Measuring the Commercial Potential of Science", "authors": [ "Roger Masclans", "Sharique Hasan", "Wesley M. Cohen" ], "posted": "2023-06-24", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4481928", "field": "management", "role": "instrument", "bullets": [ "Scientific publications from major U.S. research universities, validated against technology transfer outcomes and firm patent citations.", "LLM constructs ex-ante measure of commercial potential of scientific findings; validated against holdout sample and two external benchmarks.", "Patenting does not dampen industry dissemination of academic science; university reputation independently increases firm use of high-potential science." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "external validation against university tech transfer progression and firm citations", "salience": 70, "n": 3318, "authors_detailed": [ { "name": "Roger Masclans", "url": "https://openalex.org/A5048931245", "inst": "Duke University" }, { "name": "Sharique Hasan", "url": "https://openalex.org/A5012661228", "inst": "Duke University" }, { "name": "Wesley M. Cohen", "url": "https://openalex.org/A5066540447", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "Duke University", "National Bureau of Economic Research" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2307.01202v1", "arxiv_id": "2307.01202v1", "title": "Predictive Patentomics: Forecasting Innovation Success and Valuation with ChatGPT", "authors": [ "Stephen Yang" ], "posted": "2023-06-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2307.01202v1", "field": "finance", "role": "instrument", "bullets": [ "US patent applications with valuation and acceptance outcomes, revisiting the Kogan, Papanikolaou, Seru, and Stoffman patent value estimates; sample period is not stated.", "OpenAI embeddings of patent text feed deep learning models predicting value and acceptance; performance is judged by out of sample predictive fit against realized outcomes.", "Embeddings add 24 percent incremental R squared for patent value, revise median valuations by 1.5 times, and a long short portfolio on predicted acceptance earns 3.3 percent annually." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "out of sample prediction against realized patent outcomes", "salience": 55, "edition": 12, "n": 1465, "authors_detailed": [ { "name": "Stephen Yang", "url": "https://openalex.org/A5101429866", "inst": "Stanford University" } ], "affiliations": [ "Stanford University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2306.11025v1", "arxiv_id": "2306.11025v1", "title": "Temporal Data Meets LLM -- Explainable Financial Time Series Forecasting", "authors": [ "Xinli Yu", "Zheng Chen", "Yuan Ling", "Shujing Dong", "Zongyi Liu", "Yanbin Lu" ], "posted": "2023-06-19", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2306.11025v1", "field": "finance", "role": "method", "bullets": [ "NASDAQ 100 stocks, combining public price histories, company metadata, and historical economic and financial news as forecasting inputs; sample period not stated.", "GPT-4 in zero and few shot modes and an instruction fine tuned Open LLaMA produce forecasts with textual explanations, compared against ARMA-GARCH and gradient boosted tree baselines.", "Both setups beat the classical baselines by reasoning over news and price series together; fine tuned Open LLaMA works but trails GPT-4; no effect sizes given." ], "bullet_provenance": "ai", "models": [ "gpt", "llama" ], "open_weights": true, "validated": true, "validation_note": "forecasts compared against ARMA-GARCH and gradient boosting baselines", "salience": 42, "edition": 12, "n": 1362, "authors_detailed": [ { "name": "Xinli Yu", "url": "https://openalex.org/A5102526324", "inst": "" }, { "name": "Zheng Chen", "url": "https://openalex.org/A5100370639", "inst": "Tsinghua University" }, { "name": "Ling Yuan", "url": "https://openalex.org/A5025966573", "inst": "Guangxi Medical University" }, { "name": "Shujing Dong", "url": "https://openalex.org/A5109616411", "inst": "" }, { "name": "Zongyi Liu", "url": "https://openalex.org/A5115590566", "inst": "Chinese Academy of Sciences" }, { "name": "Yanbin Lu", "url": "https://openalex.org/A5100577419", "inst": "Jiangsu Normal University" } ], "affiliations": [ "Tsinghua University", "Guangxi Medical University", "Chinese Academy of Sciences", "Jiangsu Normal University" ] }, { "uid": "doi:10.2139/ssrn.4476733", "doi": "10.2139/ssrn.4476733", "title": "chatReport: Democratizing Sustainability Disclosure Analysis through LLM-based Tools", "authors": [ "Jingwei Ni", "Julia Bingler", "Chiara Colesanti Senni", "Mathias Kraus", "Glen Gostlow", "Tobias Schimanski", "Dominik Stammbach", "Saeid Vaghefi", "Qian Wang", "Nicolas Webersinke", "Tobias Wekhof", "Tingyu Yu", "Markus Leippold" ], "posted": "2023-06-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4476733", "field": "finance", "role": "instrument", "bullets": [ "Corporate sustainability reports assessed against TCFD climate-risk disclosure recommendations with interdisciplinary expert collaboration.", "LLM enhanced with domain knowledge from climate science, finance, and policy automates TCFD-aligned analysis; IPCC authors scored output accuracy on a 1-to-5 scale.", "Expert-augmented LLM produces more accurate TCFD assessments than baseline models, demonstrating that domain-knowledge integration materially improves disclosure analysis." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Expert evaluation by IPCC authors on accuracy scale", "salience": 55, "n": 2442, "authors_detailed": [ { "name": "Jingwei Ni", "url": "https://openalex.org/A5113207856", "inst": "ETH Zurich" }, { "name": "Julia Bingler", "url": "https://openalex.org/A5010157189", "inst": "Mansfield University" }, { "name": "Chiara Colesanti Senni", "url": "https://openalex.org/A5086406811", "inst": "University of Zurich" }, { "name": "Mathias Kraus", "url": "https://openalex.org/A5044596225", "inst": "Friedrich-Alexander-Universität Erlangen-Nürnberg" }, { "name": "Glen Gostlow", "url": "https://openalex.org/A5055140586", "inst": "University of Zurich" }, { "name": "Tobias Schimanski", "url": "https://openalex.org/A5008509425", "inst": "University of Zurich" }, { "name": "Dominik Stammbach", "url": "https://openalex.org/A5026276254", "inst": "ETH Zurich" }, { "name": "Saeid Ashraf Vaghefi", "url": "https://openalex.org/A5035433111", "inst": "University of Zurich" }, { "name": "Qian Wang", "url": "https://openalex.org/A5092197015", "inst": "University of Zurich" }, { "name": "Nicolas Webersinke", "url": "https://openalex.org/A5065452137", "inst": "Friedrich-Alexander-Universität Erlangen-Nürnberg" }, { "name": "Tobias Wekhof", "url": "https://openalex.org/A5011580139", "inst": "ETH Zurich" }, { "name": "Tingyu Yu", "url": "https://openalex.org/A5013001852", "inst": "University of Zurich" }, { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" } ], "affiliations": [ "ETH Zurich", "Mansfield University", "University of Zurich", "Friedrich-Alexander-Universität Erlangen-Nürnberg" ] }, { "uid": "doi:10.2139/ssrn.4482536", "doi": "10.2139/ssrn.4482536", "title": "Understanding Innovation Quality and Success with Large Language Models", "authors": [ "Stephen Yang" ], "posted": "2023-06-17", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4482536", "field": "management", "role": "instrument", "bullet_provenance": "none", "salience": 42, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 614, "authors_detailed": [ { "name": "Stephen Yang", "url": "https://openalex.org/A5101429866", "inst": "Stanford University" } ], "affiliations": [ "Stanford University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4482620", "doi": "10.2139/ssrn.4482620", "title": "Measuring Financial Flexibility", "authors": [ "Sudipto Dasgupta", "Erica X. N. Li", "Siyuan Wu" ], "posted": "2023-06-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4482620", "field": "finance", "role": "instrument", "bullets": [ "U.S. public firms with balance-sheet data and 10-K qualitative disclosures; industry-level investment spike waves define the outcome variable.", "LLM classifiers process Liquidity and Capital Resources sections of 10-K filings to predict investment spikes; compared against a balance-sheet-based Financial Flexibility Index.", "Balance-sheet FF Index robustly outperforms LLM text classifiers out of sample, demonstrating structured financial data dominates unstructured 10-K text for constraint measurement." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Out-of-sample prediction of investment spikes", "salience": 55, "n": 2441, "authors_detailed": [ { "name": "Sudipto Dasgupta", "url": "https://openalex.org/A5015077674", "inst": "Chinese University of Hong Kong" }, { "name": "Erica X. N. Li", "url": "https://openalex.org/A5003899745", "inst": "Cheung Kong Graduate School of Business" }, { "name": "Siyuan Wu", "url": "https://openalex.org/A5089880251", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong", "Cheung Kong Graduate School of Business" ] }, { "uid": "arxiv:2306.07899v1", "arxiv_id": "2306.07899v1", "title": "Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks", "authors": [ "Veniamin Veselovsky", "Manoel Horta Ribeiro", "Robert West" ], "posted": "2023-06-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2306.07899v1", "field": "economics", "role": "object", "bullets": [ "An abstract summarization task from the crowdsourcing literature rerun on Amazon Mechanical Turk, with worker keystrokes logged during completion.", "A synthetic text classifier combined with keystroke evidence flags submissions likely produced with language models; classifier validation figures are not stated in the abstract.", "An estimated 33 to 46 percent of crowd workers used language models on the task, casting doubt on the human provenance of crowdsourced text data." ], "bullet_provenance": "ai", "validated": false, "salience": 66, "edition": 12, "models": [], "n": 1434, "authors_detailed": [ { "name": "Veniamin Veselovsky", "url": "https://openalex.org/A5047584056", "inst": "Princeton University" }, { "name": "Manoel Horta Ribeiro", "url": "https://openalex.org/A5011195481", "inst": "Princeton University" }, { "name": "Robert West", "url": "https://openalex.org/A5101446790", "inst": "École Polytechnique Fédérale de Lausanne" } ], "affiliations": [ "Princeton University", "École Polytechnique Fédérale de Lausanne" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2306.07209v8", "arxiv_id": "2306.07209v8", "title": "Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow", "authors": [ "Wenqi Zhang", "Yongliang Shen", "Zeqi Tan", "Guiyang Hou", "Weiming Lu", "Yueting Zhuang" ], "posted": "2023-06-12", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2306.07209v8", "field": "finance", "role": "method", "bullets": [ "Chinese financial data covering stocks, funds, and news, served through an open-sourced agent that handles querying, processing, and visualization requests in real time.", "An LLM agent, base model not named, explores data sources in advance, abstracts common requests into compiler validated interfaces, then invokes them to return tables and charts.", "Invoking pre-designed interfaces reduces errors relative to generating code from scratch, according to the authors; the abstract reports no quantitative evaluation." ], "bullet_provenance": "ai", "validated": false, "salience": 32, "edition": 12, "models": [], "n": 1393, "authors_detailed": [ { "name": "Wenqi Zhang", "url": "https://openalex.org/A5100457807", "inst": "Chinese Academy of Social Sciences" }, { "name": "Yongliang Shen", "url": "https://openalex.org/A5004615610", "inst": "Sinopec (China)" }, { "name": "Tan, Zeqi", "url": "", "inst": "" }, { "name": "Hou, Guiyang", "url": "", "inst": "" }, { "name": "Lu, Weiming", "url": "", "inst": "" }, { "name": "Zhuang, Yueting", "url": "", "inst": "" } ], "affiliations": [ "Chinese Academy of Social Sciences", "Sinopec (China)" ] }, { "uid": "arxiv:2306.05443v1", "arxiv_id": "2306.05443v1", "title": "PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance", "authors": [ "Qianqian Xie", "Weiguang Han", "Xiao Zhang", "Yanzhao Lai", "Min Peng", "Alejandro Lopez-Lira", "Jimin Huang" ], "posted": "2023-06-08", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2306.05443v1", "field": "finance", "role": "method", "bullets": [ "Builds a 136,000 example instruction dataset spanning varied financial tasks, document types, and data modalities, with an evaluation benchmark covering financial language tasks and a prediction task across nine datasets.", "FinMA, a LLaMA model instruction tuned on the dataset, is compared with existing LLMs on the labelled benchmark; model, data, benchmark, and results are released openly.", "The comparison maps strengths and weaknesses of FinMA and existing LLMs across the financial tasks; specific scores are not given in the abstract." ], "bullet_provenance": "ai", "models": [ "llama" ], "open_weights": true, "validated": true, "validation_note": "labelled financial benchmark, nine datasets", "salience": 55, "edition": 12, "n": 1384, "authors_detailed": [ { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Weiguang Han", "url": "https://openalex.org/A5054465909", "inst": "Hebei University of Technology" }, { "name": "Zhang Xiao", "url": "https://openalex.org/A5041707151", "inst": "University of Science and Technology of China" }, { "name": "Yanzhao Lai", "url": "https://openalex.org/A5029848442", "inst": "China Electronics Technology Group Corporation" }, { "name": "Min Peng", "url": "https://openalex.org/A5089527851", "inst": "Hefei University of Technology" }, { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" } ], "affiliations": [ "University of Florida", "Hunan Normal University", "Hebei University of Technology", "University of Science and Technology of China", "China Electronics Technology Group Corporation", "Hefei University of Technology", "University of Manchester" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4467968", "doi": "10.2139/ssrn.4467968", "title": "GPT-3.5 Hallucinates Nonexistent Citations: Evidence from Economics", "authors": [ "Joy Buchanan", "Olga Shapoval" ], "posted": "2023-06-04", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4467968", "field": "economics", "role": "method", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 12, "bullets": [], "validated": null, "n": 1483, "authors_detailed": [ { "name": "Joy Buchanan", "url": "https://openalex.org/A5079266514", "inst": "Samford University" }, { "name": "Olga Shapoval", "url": "https://openalex.org/A5050201446", "inst": "University of Nevada, Reno" } ], "affiliations": [ "Samford University", "University of Nevada, Reno" ] }, { "uid": "doi:10.2139/ssrn.4452175", "doi": "10.2139/ssrn.4452175", "title": "Is it All Hype? ChatGPT’s Performance and Disruptive Potential in the Accounting and Auditing Industries", "authors": [ "Marc Eulerich", "Aida Sanatizadeh", "Hamid Vakilzadeh", "David A. Wood" ], "posted": "2023-06-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4452175", "field": "accounting", "role": "method", "bullets": [ "CPA, CMA, CIA, and EA certification exam questions tested across ChatGPT model versions with progressive enhancements.", "GPT-3.5 and GPT-4 evaluated with 10-shot training and reasoning-acting (calculator access); scores compared to passing thresholds.", "GPT-3.5 averages 53.1% and fails all exams; GPT-4 with all enhancements passes every exam section at 85.1% average." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "CPA/CMA/CIA/EA certification exam passing scores", "salience": 65, "n": 3317, "authors_detailed": [ { "name": "Marc Eulerich", "url": "https://openalex.org/A5113933991", "inst": "University of Duisburg-Essen" }, { "name": "Aida Sanatizadeh", "url": "https://openalex.org/A5080416559", "inst": "Northern Illinois University" }, { "name": "Hamid Vakilzadeh", "url": "https://openalex.org/A5066707899", "inst": "University of Wisconsin–Whitewater" }, { "name": "David A. Wood", "url": "https://openalex.org/A5107854559", "inst": "Brigham Young University" } ], "affiliations": [ "University of Duisburg-Essen", "Northern Illinois University", "University of Wisconsin–Whitewater", "Brigham Young University" ] }, { "uid": "doi:10.2139/ssrn.4452670", "doi": "10.2139/ssrn.4452670", "title": "Capital Market Consequences of Generative AI: Early Evidence from the Ban of ChatGPT in Italy", "authors": [ "Jeremy Bertomeu", "Yupeng Lin", "Yibin Liu", "Zhenghui Ni" ], "posted": "2023-05-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4452670", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 58, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 612, "authors_detailed": [ { "name": "Jeremy Bertomeu", "url": "https://openalex.org/A5009431175", "inst": "Washington University in St. Louis" }, { "name": "Yupeng Lin", "url": "https://openalex.org/A5007731007", "inst": "National University of Singapore" }, { "name": "Yibin Liu", "url": "https://openalex.org/A5100767946", "inst": "National University of Singapore" }, { "name": "Zhenghui Ni", "url": "https://openalex.org/A5045668799", "inst": "National University of Singapore" } ], "affiliations": [ "Washington University in St. Louis", "National University of Singapore" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4464002", "doi": "10.2139/ssrn.4464002", "title": "ChatGPT Informed Graph Neural Network for Stock Movement Prediction", "authors": [ "Zihan Chen", "Lei Zheng", "Cheng Lu", "Jialu Yuan", "Di Zhu" ], "posted": "2023-05-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4464002", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "technical", "bullets": [], "validated": null, "n": 613, "authors_detailed": [ { "name": "Zihan Chen", "url": "https://openalex.org/A5114054032", "inst": "Stevens Institute of Technology" }, { "name": "Lei Zheng", "url": "https://openalex.org/A5024309658", "inst": "Stevens Institute of Technology" }, { "name": "Lu Cheng", "url": "https://openalex.org/A5040552536", "inst": "Stevens Institute of Technology" }, { "name": "Jialu Yuan", "url": "https://openalex.org/A5083953857", "inst": "University of California, Los Angeles" }, { "name": "Di Zhu", "url": "https://openalex.org/A5039761441", "inst": "Stevens Institute of Technology" } ], "affiliations": [ "University of California, Los Angeles", "Stevens Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4462976", "doi": "10.2139/ssrn.4462976", "title": "Examining the Impact of Generative AI on Users’ Voluntary Knowledge Contribution: Evidence from A Natural Experiment on Stack Overflow", "authors": [ "Guohou Shan", "Liangfei Qiu" ], "posted": "2023-05-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4462976", "field": "management", "role": "object", "bullets": [ "Stack Overflow user answer contributions analyzed via natural experiment around ChatGPT launch using difference-in-differences estimation on daily activity.", "Study measures ChatGPT's causal effect on voluntary knowledge contribution quantity, answer length, and readability with moderating effects of usage intensity.", "Users produce more but shorter and more readable answers after ChatGPT access; learning from AI improves output but intensive usage creates offsetting cognitive burden." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 2632, "authors_detailed": [ { "name": "Guohou Shan", "url": "https://openalex.org/A5065084539", "inst": "Northeastern University" }, { "name": "Liangfei Qiu", "url": "https://openalex.org/A5079497219", "inst": "University of Florida" } ], "affiliations": [ "University of Florida", "Northeastern University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4461406", "doi": "10.2139/ssrn.4461406", "title": "Task-Interdependencies between Generative AI and Workers", "authors": [ "Emmanuelle Walkowiak" ], "posted": "2023-05-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4461406", "field": "economics", "role": "object", "bullets": [ "Theoretical model of workplace generative AI adoption across open and closed learning organizations with worker-GAI task interdependencies.", "Formalizes a production function where worker and GAI outputs are indistinguishable, deriving conditions for optimal matching and productivity.", "In open learning organizations, GAI should match workers by error-detection ability; network externality in learning drives productivity gains." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3315, "authors_detailed": [ { "name": "Emmanuelle Walkowiak", "url": "https://openalex.org/A5053817571", "inst": "RMIT University" } ], "affiliations": [ "RMIT University" ] }, { "uid": "doi:10.2139/ssrn.4460814", "doi": "10.2139/ssrn.4460814", "title": "Investigating AI Languages’ Ability to Solve Undergraduate Finance Problems", "authors": [ "Changyu Yang", "Adam Stivers" ], "posted": "2023-05-31", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4460814", "field": "finance", "role": "method", "bullets": [ "Undergraduate-level finance problems of varying difficulty tested on ChatGPT (GPT-4) and Google Bard (Bard-1.0).", "GPT-4 and Bard-1.0 compared on accuracy solving finance problems; performance scored against known correct answers.", "GPT-4 significantly outperforms Bard-1.0 overall; both models excel at easy problems but struggle with complex ones." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "validated": true, "validation_note": "accuracy on undergraduate finance problems with known answers", "salience": 30, "n": 3316, "authors_detailed": [ { "name": "Changyu Yang", "url": "https://openalex.org/A5005353584", "inst": "University of Wisconsin–La Crosse" }, { "name": "Adam Stivers", "url": "https://openalex.org/A5068310922", "inst": "University of Wisconsin–La Crosse" } ], "affiliations": [ "University of Wisconsin–La Crosse" ] }, { "uid": "doi:10.2139/ssrn.4458613", "doi": "10.2139/ssrn.4458613", "title": "Zero is Not Hero Yet: Benchmarking Zero-Shot Performance of Llms for Financial Tasks", "authors": [ "Agam Shah", "Sudheer Chava" ], "posted": "2023-05-26", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4458613", "field": "finance", "role": "method", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 611, "authors_detailed": [ { "name": "Agam Shah", "url": "https://openalex.org/A5017623662", "inst": "Georgia Institute of Technology" }, { "name": "Sudheer Chava", "url": "https://openalex.org/A5112933779", "inst": "Georgia Institute of Technology" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4459729", "doi": "10.2139/ssrn.4459729", "title": "Engagement or Entanglement? The Dual Impact of Generative Artificial Intelligence in Online Knowledge Exchange Platforms", "authors": [ "Aida Sanatizadeh", "Yingda Lu", "Keran Zhao", "Yuheng Hu" ], "posted": "2023-05-26", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4459729", "field": "management", "role": "object", "bullets": [ "Comprehensive dataset from a leading Q&A community tracking content contribution and knowledge-seeking behavior before and after generative AI tool availability.", "Difference-in-differences estimation measures effects of ChatGPT and Gemini adoption on platform engagement, post volume, and question complexity.", "Posts and active users decline especially in technology topics, but remaining questions show higher quality and complexity; lower-expertise users reduce contributions most." ], "bullet_provenance": "ai", "models": [ "gpt", "gemini" ], "open_weights": false, "salience": 55, "validated": null, "n": 2631, "authors_detailed": [ { "name": "Aida Sanatizadeh", "url": "https://openalex.org/A5080416559", "inst": "Northern Illinois University" }, { "name": "Yingda Lu", "url": "https://openalex.org/A5052955821", "inst": "University of Illinois Chicago" }, { "name": "Keran Zhao", "url": "https://openalex.org/A5020750294", "inst": "Pennsylvania State University" }, { "name": "Yuheng Hu", "url": "https://openalex.org/A5016075036", "inst": "University of Illinois Chicago" } ], "affiliations": [ "Northern Illinois University", "University of Illinois Chicago", "Pennsylvania State University" ] }, { "uid": "doi:10.2139/ssrn.4458269", "doi": "10.2139/ssrn.4458269", "title": "Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education", "authors": [ "Ping Xiao", "Yuanyuan Chen", "Weining Bao" ], "posted": "2023-05-25", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4458269", "field": "management", "role": "object", "bullets": [ "The top 500 universities in the 2022 QS World University Rankings are examined for whether and how they regulated ChatGPT use.", "No model generates research data; institutional policies are coded as allowing or banning ChatGPT and related to university characteristics and public sentiment.", "Fewer than one-third had a policy, and 67.4 percent of policy adopters allowed ChatGPT; reputation, geography, sentiment, resources, and peer policies predict choices." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 59, "edition": 23, "validated": null, "n": 4190, "authors_detailed": [ { "name": "Ping Xiao", "url": "https://openalex.org/A5027499088", "inst": "The University of Melbourne" }, { "name": "Yuanyuan Chen", "url": "https://openalex.org/A5029994638", "inst": "University of Alabama" }, { "name": "Weining Bao", "url": "https://openalex.org/A5025313452", "inst": "University of Connecticut" } ], "affiliations": [ "The University of Melbourne", "University of Alabama", "University of Connecticut" ] }, { "uid": "doi:10.2139/ssrn.4453958", "doi": "10.2139/ssrn.4453958", "title": "Human Favoritism, Not AI Aversion: People’s Perceptions (and Bias) Toward Generative AI, Human Experts, and Human-GAI Collaboration in Persuasive Content Generation", "authors": [ "Yunhao Zhang", "Renee Gosline" ], "posted": "2023-05-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4453958", "field": "management", "role": "object", "bullets": [ "Experiment with professional content creators at a leading consulting firm evaluated advertising and persuasive content across four human-AI collaboration paradigms.", "ChatGPT-4 generated advertising content alone and in augmented collaboration with humans; source of content was disclosed or concealed to measure perception bias.", "AI and augmented-AI content rated higher quality than human-only content; revealing source reduced but did not reverse the gap, driven by human favoritism not AI aversion." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 2382, "authors_detailed": [ { "name": "Yunhao Zhang", "url": "https://openalex.org/A5053847761", "inst": "Massachusetts Institute of Technology" }, { "name": "Renee Gosline", "url": "https://openalex.org/A5052058035", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4459383", "doi": "10.2139/ssrn.4459383", "title": "Contextualizing Profitability", "authors": [ "Alex G. Kim", "Valeri V. Nikolaev" ], "posted": "2023-05-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4459383", "field": "finance", "role": "instrument", "bullets": [ "U.S. public firms with financial disclosures; asset pricing tests including Fama-French five-factor model and portfolio sorts across size and value.", "Large language model incorporates narrative context from financial disclosures into profitability measurement for cross-sectional return prediction.", "Contextualized profitability outperforms conventional measures statistically and economically; its factor eliminates alpha in small extreme growth portfolios." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 80, "n": 3314, "authors_detailed": [ { "name": "Alex Kim", "url": "https://openalex.org/A5002013850", "inst": "Woodlawn School" }, { "name": "Valeri V. Nikolaev", "url": "https://openalex.org/A5085975914", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago", "Woodlawn School" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2305.16344v2", "arxiv_id": "2305.16344v2", "title": "Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs", "authors": [ "Chongjian Yue", "Xinrun Xu", "Xiaojun Ma", "Lun Du", "Hengyu Liu", "Zhiming Ding", "Yanbing Jiang", "Shi Han", "Dongmei Zhang" ], "posted": "2023-05-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.16344v2", "field": "accounting", "role": "method", "bullets": [ "Financial reports treated as hybrid long documents mixing narrative text with tables; a purpose built numerical extraction dataset, FINE, is created for evaluation.", "The AFIE framework runs on GPT-3.5 and GPT-4 to pull numerical values from the reports, scored against the labelled FINE dataset.", "Average accuracy improves by 53.94 percent for GPT-3.5 and 33.77 percent for GPT-4 relative to a naive extraction approach on the same documents." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "labelled FINE numerical extraction dataset", "salience": 40, "edition": 12, "n": 1377, "authors_detailed": [ { "name": "Chongjian Yue", "url": "https://openalex.org/A5051815372", "inst": "Peking University" }, { "name": "Xinrun Xu", "url": "https://openalex.org/A5104264437", "inst": "Chinese Academy of Sciences" }, { "name": "Xiaojun Ma", "url": "https://openalex.org/A5100976816", "inst": "Adamson University" }, { "name": "Lun Du", "url": "https://openalex.org/A5008387608", "inst": "Peking University" }, { "name": "Hengyu Liu", "url": "https://openalex.org/A5030080621", "inst": "Sun Yat-sen University" }, { "name": "Zhiming Ding", "url": "https://openalex.org/A5057841485", "inst": "Chinese Academy of Sciences" }, { "name": "Yanbing Jiang", "url": "https://openalex.org/A5019392030", "inst": "University of Waterloo" }, { "name": "Shi Han", "url": "https://openalex.org/A5006300825", "inst": "Microsoft Research Asia (China)" }, { "name": "Dongmei Zhang", "url": "https://openalex.org/A5100331497", "inst": "Heilongjiang Academy of Sciences" } ], "affiliations": [ "Peking University", "Chinese Academy of Sciences", "Adamson University", "Sun Yat-sen University", "University of Waterloo", "Microsoft Research Asia (China)", "Heilongjiang Academy of Sciences" ] }, { "uid": "arxiv:2305.14693v1", "arxiv_id": "2305.14693v1", "title": "Have Large Language Models Developed a Personality?: Applicability of Self-Assessment Tests in Measuring Personality in LLMs", "authors": [ "Xiaoyang Song", "Akshat Gupta", "Kiyan Mohebbizadeh", "Shujie Hu", "Anant Singh" ], "posted": "2023-05-24", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.14693v1", "field": "other", "role": "method", "bullets": [ "Five language models between 1.5B and 30B parameters, unnamed in the abstract, answering multiple choice self assessment personality tests designed for humans.", "The study proposes option order symmetry, invariance of answers to the ordering of response options, as a necessary reliability condition for such tests on machines.", "Many model responses violate option order symmetry, and symmetric cases ignore the situational statement being tested, so self assessment tests are judged unfit for measuring machine personality." ], "bullet_provenance": "ai", "salience": 38, "edition": 12, "models": [], "validated": null, "n": 1433, "authors_detailed": [ { "name": "Xiaoyang Song", "url": "https://openalex.org/A5112208224", "inst": "University of Michigan" }, { "name": "Akshat Gupta", "url": "https://openalex.org/A5101112531", "inst": "Berkeley College" }, { "name": "Kiyan Mohebbizadeh", "url": "https://openalex.org/A5092031061", "inst": "" }, { "name": "Shujie Hu", "url": "https://openalex.org/A5102102318", "inst": "Chongqing University" }, { "name": "Anant Singh", "url": "https://openalex.org/A5112924199", "inst": "University of Delhi" } ], "affiliations": [ "University of Michigan", "Berkeley College", "Chongqing University", "University of Delhi" ] }, { "uid": "arxiv:2305.14471v1", "arxiv_id": "2305.14471v1", "title": "CGCE: A Chinese Generative Chat Evaluation Benchmark for General and Financial Domains", "authors": [ "Xuanyu Zhang", "Bingbing Li", "Qing Yang" ], "posted": "2023-05-23", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.14471v1", "field": "finance", "role": "method", "bullets": [ "CGCE, a Chinese generative chat benchmark with 200 general-domain questions and 150 professional financial questions for evaluating Chinese chat models.", "Model responses are scored manually for accuracy, coherence, expression clarity, and completeness; the abstract reports no results for any named model.", "The benchmark itself is the contribution, a standardized yardstick for general and financial domain Chinese chat models rather than an empirical finding." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 33, "edition": 12, "validated": null, "n": 1482, "authors_detailed": [ { "name": "Xuanyu Zhang", "url": "https://openalex.org/A5101414747", "inst": "Northwestern Polytechnical University" }, { "name": "Bingbing Li", "url": "https://openalex.org/A5100375966", "inst": "Jilin University" }, { "name": "Qing Yang", "url": "https://openalex.org/A5100417913", "inst": "Hebei Agricultural University" } ], "affiliations": [ "Northwestern Polytechnical University", "Jilin University", "Hebei Agricultural University" ] }, { "uid": "doi:10.2139/ssrn.4453835", "doi": "10.2139/ssrn.4453835", "title": "The Use of ChatGPT in External Audits: Implications and Future Research", "authors": [ "Lazarus Fotoh", "Tatenda Mugwira" ], "posted": "2023-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4453835", "field": "accounting", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 38, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 608, "authors_detailed": [ { "name": "Lazarus Elad Fotoh", "url": "https://openalex.org/A5017421836", "inst": "Karlstad University" }, { "name": "Tatenda Mugwira", "url": "https://openalex.org/A5026939521", "inst": "University of Agder" } ], "affiliations": [ "Karlstad University", "University of Agder" ] }, { "uid": "doi:10.2139/ssrn.4452504", "doi": "10.2139/ssrn.4452504", "title": "Generative AI in Operational Risk Management: Harnessing the Future of Finance", "authors": [ "Yanqing Wang" ], "posted": "2023-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4452504", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 32, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 609, "authors_detailed": [ { "name": "Yanqing Wang", "url": "https://openalex.org/A5100667031", "inst": "King's College London" } ], "affiliations": [ "King's College London" ] }, { "uid": "doi:10.2139/ssrn.4452600", "doi": "10.2139/ssrn.4452600", "title": "Mitigating Risks for Financial Firms Using Generative AI Tools", "authors": [ "David Krause" ], "posted": "2023-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4452600", "field": "finance", "role": "object", "bullet_provenance": "none", "salience": 32, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 610, "authors_detailed": [ { "name": "David Krause", "url": "https://openalex.org/A5102953380", "inst": "Marquette University" } ], "affiliations": [ "Marquette University" ] }, { "uid": "doi:10.2139/ssrn.4453664", "doi": "10.2139/ssrn.4453664", "title": "Proper Generative AI Prompting for Financial Analysis", "authors": [ "David Krause" ], "posted": "2023-05-23", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4453664", "field": "finance", "role": "method", "bullet_provenance": "none", "salience": 30, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 1028, "authors_detailed": [ { "name": "David Krause", "url": "https://openalex.org/A5102953380", "inst": "Marquette University" } ], "affiliations": [ "Marquette University" ] }, { "uid": "arxiv:2305.12763v3", "arxiv_id": "2305.12763v3", "title": "The Emergence of Economic Rationality of GPT", "authors": [ "Yiting Chen", "Tracy Xiao Liu", "You Shan", "Songfa Zhong" ], "posted": "2023-05-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.12763v3", "field": "economics", "role": "agent", "bullets": [ "Budget allocation decisions across risk, time, social, and food preference domains, with a parallel human subject experiment for comparison.", "GPT is instructed to make budgetary choices and its consistency with utility maximization is scored using revealed preference theory, alongside estimated preference parameters.", "GPT scores higher on rationality than human subjects, with less heterogeneous preferences; scores are robust to demographic settings but sensitive to language framing of the choices." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "edition": 12, "validated": null, "n": 1432, "authors_detailed": [ { "name": "Yi‐Ting Chen", "url": "https://openalex.org/A5100348268", "inst": "Central South University" }, { "name": "Tracy Xiao Liu", "url": "https://openalex.org/A5020178527", "inst": "Tsinghua University" }, { "name": "You Shan", "url": "https://openalex.org/A5066555433", "inst": "University of Science and Technology of China" }, { "name": "Songfa Zhong", "url": "https://openalex.org/A5063633369", "inst": "National University of Singapore" } ], "affiliations": [ "Central South University", "Tsinghua University", "University of Science and Technology of China", "National University of Singapore" ] }, { "uid": "doi:10.1016/j.frl.2023.103993", "doi": "10.1016/j.frl.2023.103993", "arxiv_id": "2305.12739v1", "title": "The Influence of ChatGPT on Artificial Intelligence Related Crypto Assets: Evidence from a Synthetic Control Analysis", "authors": [ "Aman Saggu", "Lennart Ante" ], "posted": "2023-05-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.12739v1", "field": "finance", "role": "object", "bullets": [ "AI themed crypto assets around the public launch of ChatGPT, with synthetic controls from other crypto assets and Google search volumes proxying attention to AI.", "No language model is applied; ChatGPT is the attention shock under study, and estimation relies on synthetic difference in differences rather than model output.", "AI related crypto assets return 10.7 to 15.6 percent in the month after launch and 35.5 to 41.3 percent over two months, with search volume a pricing indicator." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "edition": 12, "validated": null, "n": 1464, "authors_detailed": [ { "name": "Aman Saggu", "url": "https://openalex.org/A5068688584", "inst": "Mahidol University" }, { "name": "Lennart Ante", "url": "https://openalex.org/A5034620178", "inst": "Blockchain Research Lab, Hamburg, Germany" } ], "affiliations": [ "Mahidol University", "Blockchain Research Lab, Hamburg, Germany" ] }, { "uid": "doi:10.2139/ssrn.4440608", "doi": "10.2139/ssrn.4440608", "title": "Do Large Language Models Show Decision Heuristics Similar to Humans? A Case Study Using GPT-3.5", "authors": [ "Gaurav Suri", "Lily Slater", "Ali Ziaee", "Morgan Nguyen" ], "posted": "2023-05-22", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4440608", "field": "economics", "role": "agent", "bullets": [ "Four controlled experiments testing anchoring, representativeness, availability, framing, and endowment effects with both human participants and an LLM.", "ChatGPT (GPT-3.5) responded to novel prompts designed to elicit decision heuristics; human subjects completed identical tasks for direct comparison.", "GPT-3.5 exhibited all four heuristics and biases in patterns paralleling human responses, suggesting language structure itself may generate these decision effects." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Comparison with human participant decisions across four studies", "salience": 50, "n": 2440, "authors_detailed": [ { "name": "Gaurav Suri", "url": "https://openalex.org/A5024451217", "inst": "San Francisco State University" }, { "name": "Lily R. Slater", "url": "https://openalex.org/A5085805156", "inst": "San Francisco State University" }, { "name": "Ali Ziaee", "url": "https://openalex.org/A5091991967", "inst": "San Francisco State University" }, { "name": "Morgan Nguyen", "url": "https://openalex.org/A5113115450", "inst": "San Francisco State University" } ], "affiliations": [ "San Francisco State University" ] }, { "uid": "doi:10.2139/ssrn.4448938", "doi": "10.2139/ssrn.4448938", "title": "Can ChatGPT Kill User-Generated Q&A Platforms?", "authors": [ "Junzhi Xue", "Lizheng Wang", "Jinyang Zheng", "Yongjun Li", "Yong Tan" ], "posted": "2023-05-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4448938", "field": "management", "role": "object", "bullets": [ "Stack Overflow questions surrounding the November 2022 launch of ChatGPT, analyzed through several empirical designs, with effects tracked into May 2023.", "ChatGPT, version not stated, is treated as an external shock rather than a research tool; its causal effect is estimated with difference-in-differences style designs.", "Question volume fell 14.09% on average and 27.88% by May 2023, while the average quality of remaining questions rose, driven partly by uneven substitution of simpler questions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 67, "edition": 3, "audience": "general", "validated": null, "n": 89, "authors_detailed": [ { "name": "Junzhi Xue", "url": "https://openalex.org/A5112919869", "inst": "University of Science and Technology of China" }, { "name": "Lizheng Wang", "url": "https://openalex.org/A5101965130", "inst": "University of Science and Technology of China" }, { "name": "Jinyang Zheng", "url": "https://openalex.org/A5091988696", "inst": "University of Rochester" }, { "name": "Yongjun Li", "url": "https://openalex.org/A5101896849", "inst": "University of Science and Technology of China" }, { "name": "Yong Jie Tan", "url": "https://openalex.org/A5102514873", "inst": "University of Washington" } ], "affiliations": [ "University of Rochester", "University of Science and Technology of China", "University of Washington" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4448522", "doi": "10.2139/ssrn.4448522", "title": "Correlation Pitfalls With ChatGPT: Would You Fall for Them?", "authors": [ "Marius Hofert" ], "posted": "2023-05-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4448522", "field": "finance", "role": "method", "bullets": [ "Intellectual exchange testing ChatGPT on correlation pitfalls encountered in financial and actuarial risk management practice.", "ChatGPT queried on correlation concepts including fallacies of linearity, elliptical distributions, and tail dependence relevant to risk measurement.", "ChatGPT demonstrates solid grasp of basic non-technical correlation topics but lacks the mathematical rigor needed to avoid certain technical pitfalls." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 25, "n": 3313, "authors_detailed": [ { "name": "Marius Hofert", "url": "https://openalex.org/A5021779067", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong" ] }, { "uid": "arxiv:2305.11900v2", "arxiv_id": "2305.11900v2", "title": "ChatGPT and the Labor Market: Unraveling the Effect of AI Discussions on Students' Earnings Expectations", "authors": [ "Samir Huseynov" ], "posted": "2023-05-15", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.11900v2", "field": "economics", "role": "object", "bullets": [ "US students in a survey experiment read ChatGPT and AI discussion excerpts with negative or positive tone before stating expected labor market outcomes; sample size is not stated.", "No language model is used as a research tool; exposure to AI discussion text is the treatment and anticipated future earnings are the outcome.", "Earnings confidence falls after exposure, more under negative tone; asymmetric pessimistic updating appears among non-STEM and non-male students but not among STEM majors." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 52, "edition": 12, "validated": null, "n": 1481, "authors_detailed": [ { "name": "Samir Huseynov", "url": "https://openalex.org/A5030787053", "inst": "Auburn University" } ], "affiliations": [ "Auburn University" ] }, { "uid": "doi:10.2139/ssrn.4437679", "doi": "10.2139/ssrn.4437679", "title": "Large Language Models Reduce Agency Costs", "authors": [ "Darcy W E Allen", "Chris Berg", "Nataliya Ilyushina", "Jason Potts" ], "posted": "2023-05-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4437679", "field": "economics", "role": "object", "bullet_provenance": "none", "salience": 45, "edition": 3, "audience": "general", "bullets": [], "models": [], "validated": null, "n": 607, "authors_detailed": [ { "name": "Darcy W E Allen", "url": "https://openalex.org/A5000922663", "inst": "MIT University" }, { "name": "Chris Berg", "url": "https://openalex.org/A5083681237", "inst": "MIT University" }, { "name": "Nataliya A. Ilyushina", "url": "https://openalex.org/A5087424598", "inst": "MIT University" }, { "name": "Jason Potts", "url": "https://openalex.org/A5042648415", "inst": "Alfaisal University" } ], "affiliations": [ "MIT University", "Alfaisal University" ] }, { "uid": "doi:10.2139/ssrn.4444763", "doi": "10.2139/ssrn.4444763", "title": "Artificial Intelligence Co-Piloted Auditing", "authors": [ "Hanchi Gu", "Marco Schreyer", "Kevin Moffitt", "Miklos A. Vasarhelyi" ], "posted": "2023-05-15", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4444763", "field": "accounting", "role": "method", "bullets": [ "Three audit tasks demonstrated using OpenAI's ChatGPT interface: financial ratio analysis, text mining of audit-relevant documents, and journal entry testing procedures.", "GPT-4 was fine-tuned via chain-of-thought prompting enabling instruction learning, in-context learning, and sequential reasoning; detailed prompt protocols provided for full reproducibility.", "Co-piloted auditing shows transformative potential for combining auditor expertise with foundation models across financial ratio analysis, text mining, and journal entry testing tasks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 52, "n": 2408, "authors_detailed": [ { "name": "Hanchi Gu", "url": "https://openalex.org/A5101395562", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Marco Schreyer", "url": "https://openalex.org/A5056732531", "inst": "International Computer Science Institute" }, { "name": "Kevin Moffitt", "url": "https://openalex.org/A5016296300", "inst": "Rutgers, The State University of New Jersey" }, { "name": "Miklos A. Vasarhelyi", "url": "https://openalex.org/A5049215719", "inst": "Rutgers, The State University of New Jersey" } ], "affiliations": [ "Rutgers, The State University of New Jersey", "International Computer Science Institute" ] }, { "uid": "arxiv:2305.07970v2", "arxiv_id": "2305.07970v2", "title": "The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?", "authors": [ "Steve Phelps", "Yvan I. Russell" ], "posted": "2023-05-13", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.07970v2", "field": "economics", "role": "agent", "bullets": [ "Repeated prisoners dilemma and one shot dictator games, administered with a within subject design that mirrors experimental psychology protocols, with varying partner conditions in the repeated game.", "GPT-3.5 is prompted with personas described as cooperative, competitive, altruistic, or selfish, and its choices are analyzed as the behaviour of simulated participants.", "Personas translate into corresponding play, most clearly in the one shot game; cooperative simulacra show conditional reciprocity, and a later model version displays altruistic behaviour." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "edition": 12, "validated": null, "n": 1431, "authors_detailed": [ { "name": "Steve Phelps", "url": "https://openalex.org/A5111436535", "inst": "University College London" }, { "name": "Yvan I. Russell", "url": "https://openalex.org/A5020667799", "inst": "Middlesex University" } ], "affiliations": [ "University College London", "Middlesex University" ] }, { "uid": "doi:10.2139/ssrn.4437617", "doi": "10.2139/ssrn.4437617", "title": "Language, Time Preferences, and Consumer Behavior: Evidence from Large Language Models", "authors": [ "Ali Goli", "Amandeep Singh" ], "posted": "2023-05-12", "added": "2026-08-05", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4437617", "field": "management", "role": "agent", "bullet_provenance": "none", "salience": 45, "edition": 12, "bullets": [], "models": [], "validated": null, "n": 1480, "authors_detailed": [ { "name": "Ali Goli", "url": "https://openalex.org/A5013312813", "inst": "University of Washington" }, { "name": "Amandeep Singh", "url": "https://openalex.org/A5103222886", "inst": "University of Washington" } ], "affiliations": [ "University of Washington" ] }, { "uid": "doi:10.2139/ssrn.4444728", "doi": "10.2139/ssrn.4444728", "title": "ChatGPT and the Labor Market: Unraveling the Effect of AI Discussions on Students’ Earnings Expectations", "authors": [ "Samir Huseynov" ], "posted": "2023-05-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4444728", "field": "economics", "role": "object", "bullets": [ "U.S. students exposed to positively and negatively toned ChatGPT and AI discussion excerpts in a randomized causal experiment.", "ChatGPT discussions serve as the treatment stimulus; the experiment measures belief changes in students' anticipated future labor market earnings.", "AI debate exposure reduces earnings confidence, especially with negative tone; non-STEM and non-male students show more pessimistic belief updates." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 55, "validated": null, "n": 3311, "authors_detailed": [ { "name": "Samir Huseynov", "url": "https://openalex.org/A5030787053", "inst": "Auburn University" } ], "affiliations": [ "Auburn University" ] }, { "uid": "doi:10.2139/ssrn.4444104", "doi": "10.2139/ssrn.4444104", "title": "Assessing ChatGPT’s Proficiency in Quantitative Risk Management", "authors": [ "Marius Hofert" ], "posted": "2023-05-12", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4444104", "field": "finance", "role": "method", "bullets": [ "Structured scholarly exchange testing ChatGPT on quantitative risk management concepts relevant to actuarial and financial risk practice.", "ChatGPT evaluated across risk measures, time series, extreme value theory, and dependence concepts used in actuarial and financial applications.", "ChatGPT handles non-technical risk explanations well but frequently produces inaccurate or subtly wrong responses on mathematical and technical questions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 30, "n": 3312, "authors_detailed": [ { "name": "Marius Hofert", "url": "https://openalex.org/A5021779067", "inst": "Chinese University of Hong Kong" } ], "affiliations": [ "Chinese University of Hong Kong" ] }, { "uid": "doi:10.2139/ssrn.4431202", "doi": "10.2139/ssrn.4431202", "title": "Artificial Intelligence’s Capabilities, Limitations, and Impact on Accounting Education: Investigating ChatGPT’s Performance on Educational Accounting Cases", "authors": [ "Xu (Joyce) Cheng", "Ryan Dunn", "Travis Holt", "Kerry Inger", "J. Gregory Jenkins", "Jefferson Jones", "James Long", "Tina M. Loraas", "Mollie Mathis", "Jonathan D. Stanley", "David A. Wood" ], "posted": "2023-05-11", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4431202", "field": "accounting", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 606, "authors_detailed": [ { "name": "Xu Cheng", "url": "https://openalex.org/A5103079426", "inst": "Auburn University" }, { "name": "Ryan T. Dunn", "url": "https://openalex.org/A5055981852", "inst": "Auburn University" }, { "name": "Travis P. Holt", "url": "https://openalex.org/A5061540143", "inst": "Auburn University" }, { "name": "Kerry K. Inger", "url": "https://openalex.org/A5025441685", "inst": "Auburn University" }, { "name": "J. Gregory Jenkins", "url": "https://openalex.org/A5014672099", "inst": "Auburn University" }, { "name": "Jefferson P. Jones", "url": "https://openalex.org/A5003920558", "inst": "Auburn University" }, { "name": "James E. Long", "url": "https://openalex.org/A5111652361", "inst": "Auburn University" }, { "name": "Tina M. Loraas", "url": "https://openalex.org/A5040489101", "inst": "Auburn University" }, { "name": "Mollie E. Mathis", "url": "https://openalex.org/A5037710552", "inst": "Auburn University" }, { "name": "Jonathan D. Stanley", "url": "https://openalex.org/A5073787323", "inst": "Auburn University" }, { "name": "David A. Wood", "url": "https://openalex.org/A5107854559", "inst": "Brigham Young University" } ], "affiliations": [ "Auburn University", "Brigham Young University" ] }, { "uid": "arxiv:2305.05862v2", "arxiv_id": "2305.05862v2", "title": "Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks", "authors": [ "Xianzhi Li", "Samuel Chan", "Xiaodan Zhu", "Yulong Pei", "Zhiqiang Ma", "Xiaomo Liu", "Sameena Shah" ], "posted": "2023-05-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.05862v2", "field": "finance", "role": "method", "bullets": [ "Eight benchmark datasets spanning five categories of financial text tasks; the specific datasets and task types are not named in the abstract.", "ChatGPT and GPT-4 run with little or no adaptation and are compared on the labelled benchmarks with fine tuned state of the art models and domain pretrained financial models.", "The study maps where the generalist models lead and where fine tuned and domain specific models still win; the abstract gives no accuracy figures." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "eight labelled financial benchmark datasets; figures not in abstract", "salience": 45, "edition": 12, "n": 1376, "authors_detailed": [ { "name": "Xianzhi Li", "url": "https://openalex.org/A5101763340", "inst": "Huazhong University of Science and Technology" }, { "name": "Chan, Samuel", "url": "", "inst": "" }, { "name": "Xiaodan Zhu", "url": "https://openalex.org/A5016892586", "inst": "Queen's University" }, { "name": "Pei, Yulong", "url": "", "inst": "" }, { "name": "Zhiqiang Ma", "url": "https://openalex.org/A5053722499", "inst": "Jiangsu University" }, { "name": "Xiaomo Liu", "url": "https://openalex.org/A5048424819", "inst": "Morgan Stanley (United Kingdom)" }, { "name": "Sameena Shah", "url": "https://openalex.org/A5103231131", "inst": "Aga Khan University" } ], "affiliations": [ "Huazhong University of Science and Technology", "Queen's University", "Jiangsu University", "Morgan Stanley (United Kingdom)", "Aga Khan University" ] }, { "uid": "doi:10.2139/ssrn.4430515", "doi": "10.2139/ssrn.4430515", "title": "Surveying Generative AI's Economic Expectations", "authors": [ "Leland Bybee" ], "posted": "2023-05-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4430515", "field": "economics", "role": "agent", "bullets": [ "Wall Street Journal articles 1984-2021 covering U.S. financial and macroeconomic variables; validated on articles outside the LLM training period.", "LLM queried to form expectations of macro and financial variables from news text; output expectations compared against SPF, AAII, and Duke CFO Survey series.", "LLM expectations replicate SPF underreaction and exhibit extrapolative return beliefs negatively correlated with future realized returns, matching known survey biases." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Correlation with SPF, AAII, Duke CFO Survey", "salience": 80, "n": 2439, "authors_detailed": [ { "name": "Leland Bybee", "url": "https://openalex.org/A5033598180", "inst": "Yale University" } ], "affiliations": [ "Yale University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4430511", "doi": "10.2139/ssrn.4430511", "title": "Fact or Opinion? – Essential Value for Financial Results Briefing", "authors": [ "Yutaka Kuroki", "Tomonori Manabe", "Kei Nakagawa" ], "posted": "2023-05-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4430511", "field": "finance", "role": "instrument", "bullets": [ "Japanese corporate financial results briefing transcripts, with manual fact-or-opinion labels assigned by three domain experts as ground truth.", "GPT-3.5-turbo classified briefing statements as facts or opinions in zero-shot and few-shot settings, validated against expert consensus annotations.", "Briefings average 38% opinion content; firms with lower ordinary-income margins have more opinions, and higher market-to-book firms produce greater opinion volume." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "three-expert manual labels", "salience": 55, "n": 2899, "authors_detailed": [ { "name": "Yutaka Kuroki", "url": "https://openalex.org/A5091933287", "inst": "Sana Biotechnology (United States)" }, { "name": "Tomonori Manabe", "url": "https://openalex.org/A5011085813", "inst": "Sana Biotechnology (United States)" }, { "name": "Kei Nakagawa", "url": "https://openalex.org/A5086122043", "inst": "Osaka City University" } ], "affiliations": [ "Sana Biotechnology (United States)", "Osaka City University" ] }, { "uid": "doi:10.3386/w31222", "doi": "10.3386/w31222", "title": "Generative AI and Firm Values", "authors": [ "Andrea Eisfeldt", "Gregor Schubert", "Miao Ben Zhang" ], "posted": "2023-05-08", "added": "2026-07-24", "source_label": "NBER and SSRN", "url": "https://doi.org/10.3386/w31222", "alt_urls": [ "https://doi.org/10.2139/ssrn.4440717" ], "field": "finance", "role": "object", "bullets": [ "US publicly traded firms around the November 2022 ChatGPT release; unit is the firm, keyed on workforce exposure to generative AI.", "No LLM is run by the authors; they construct a firm-level workforce exposure measure to generative AI, validated against earnings-call data, and form Artificial-Minus-Human portfolios.", "Higher-exposure firms earned about 0.4 percent higher daily returns than lower-exposure firms after the ChatGPT release, with wide variation across and within industries." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 72, "edition": 3, "audience": "general", "validated": null, "n": 328, "authors_detailed": [ { "name": "Andrea L. Eisfeldt", "url": "https://openalex.org/A5059753495", "inst": "National Bureau of Economic Research" }, { "name": "Gregor Schubert", "url": "https://openalex.org/A5088498667", "inst": "National Bureau of Economic Research" }, { "name": "Miao Ben Zhang", "url": "https://openalex.org/A5077213398", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "National Bureau of Economic Research" ] }, { "uid": "doi:10.2139/ssrn.4440717", "doi": "10.2139/ssrn.4440717", "title": "Generative AI and Firm Values", "authors": [ "Andrea L. Eisfeldt", "Gregor Schubert", "Miao Ben Zhang" ], "posted": "2023-05-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4440717", "field": "finance", "role": "object", "bullets": [ "U.S. publicly traded firms around ChatGPT release in November 2022, with workforce-level generative AI exposure measures constructed from occupational task descriptions.", "ChatGPT launch used as natural experiment; firm-level AI exposure index built by matching job tasks to generative AI capabilities across the labor force.", "Artificial-Minus-Human portfolio earned 5% in two weeks post-ChatGPT; effect was stronger for data-rich firms and consistent with labor-technology substitution." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 75, "validated": null, "n": 2898, "authors_detailed": [ { "name": "Andrea L. Eisfeldt", "url": "https://openalex.org/A5059753495", "inst": "University of California, Los Angeles" }, { "name": "Gregor Schubert", "url": "https://openalex.org/A5088498667", "inst": "University of California, Los Angeles" }, { "name": "Miao Ben Zhang", "url": "https://openalex.org/A5077213398", "inst": "University of Southern California" } ], "affiliations": [ "University of California, Los Angeles", "University of Southern California" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2305.05377v1", "arxiv_id": "2305.05377v1", "title": "Professional Certification Benchmark Dataset: The First 500 Jobs For Large Language Models", "authors": [ "David Noever", "Matt Ciolino" ], "posted": "2023-05-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.05377v1", "field": "economics", "role": "object", "bullets": [ "A benchmark of 1149 professional certification exams spanning computing, finance, healthcare, and service occupations, built to test vocational readiness rather than academic performance.", "GPT-3 and GPT-3.5 Turbo answer exam questions without fine-tuning or preparation; responses are scored against official answer keys with a 70 percent passing threshold.", "GPT-3 passes 39 percent of certifications; Turbo passes the FINRA Series 6 exam and scores 100 percent on OSCP, with a median 60 percent gain over Babbage." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "certification exam answer keys, pass rates reported", "salience": 40, "edition": 12, "n": 1458, "authors_detailed": [ { "name": "David Noever", "url": "https://openalex.org/A5074519523", "inst": "PeopleTec (United States)" }, { "name": "Matt Ciolino", "url": "https://openalex.org/A5067897475", "inst": "PeopleTec (United States)" } ], "affiliations": [ "PeopleTec (United States)" ] }, { "uid": "doi:10.2139/ssrn.4429658", "doi": "10.2139/ssrn.4429658", "title": "Generative LLMs and Textual Analysis in Accounting: (Chat) GPT as Research Assistant?", "authors": [ "Ties de Kok" ], "posted": "2023-05-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4429658", "field": "accounting", "role": "method", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 50, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 327, "authors_detailed": [ { "name": "Ties de Kok", "url": "https://openalex.org/A5005491799", "inst": "University of Washington" } ], "affiliations": [ "University of Washington" ] }, { "uid": "arxiv:2305.02823v2", "arxiv_id": "2305.02823v2", "title": "Surveying Generative AI's Economic Expectations", "authors": [ "Leland Bybee" ], "posted": "2023-05-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.02823v2", "field": "economics", "role": "agent", "bullets": [ "Wall Street Journal articles from 1984 to 2021 prompt an LLM to state expectations of macroeconomic and financial variables, benchmarked against SPF, AAII, and Duke CFO survey series.", "The model, not named in the abstract, produces survey style forecasts from news text; articles postdating its training window show the correlations reflect generalization rather than memorization.", "LLM expectations track the human surveys and reproduce their departures from rational expectations, including macro underreaction and extrapolative return forecasts that correlate negatively with realized returns." ], "bullet_provenance": "ai", "validated": true, "validation_note": "matched to SPF, AAII, and Duke CFO survey series", "salience": 72, "edition": 12, "models": [], "n": 1392, "authors_detailed": [ { "name": "Leland Bybee", "url": "https://openalex.org/A5033598180", "inst": "Yale University" } ], "affiliations": [ "Yale University" ], "prestige": true, "us_top": true }, { "uid": "arxiv:2305.02476v1", "arxiv_id": "2305.02476v1", "title": "Informing Innovation Management: Linking Leading R&D Firms and Emerging Technologies", "authors": [ "Xian Gong", "Claire McFarland", "Paul McCarthy", "Colin Griffith", "Marian-Andrei Rizoiu" ], "posted": "2023-05-04", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.02476v1", "field": "management", "role": "instrument", "bullets": [ "Leading global R&D spending companies matched to a defined set of emerging technologies, each side represented by its Wikipedia profile; counts and period are not stated.", "Firm to technology proximity is computed from the text of Wikipedia profiles; the abstract names no specific model, and the check offered is a significant positive correlation with patent data.", "The map yields closest associations between companies and technologies and matches circular economy technologies to their nearest R&D leader; no magnitudes are reported." ], "bullet_provenance": "ai", "validated": true, "validation_note": "correlation with patent data", "salience": 30, "edition": 12, "models": [], "n": 1515, "authors_detailed": [ { "name": "Gong Xian", "url": "https://openalex.org/A5102516765", "inst": "Guizhou University" }, { "name": "Claire McFarland", "url": "https://openalex.org/A5065473481", "inst": "University of Technology Sydney" }, { "name": "Paul McCarthy", "url": "https://openalex.org/A5032277245", "inst": "West Virginia University" }, { "name": "Colin Griffith", "url": "https://openalex.org/A5103224582", "inst": "Commonwealth Scientific and Industrial Research Organisation" }, { "name": "Marian-Andrei Rizoiu", "url": "https://openalex.org/A5069685493", "inst": "University of Technology Sydney" } ], "affiliations": [ "Guizhou University", "University of Technology Sydney", "West Virginia University", "Commonwealth Scientific and Industrial Research Organisation" ] }, { "uid": "arxiv:2305.11873v1", "arxiv_id": "2305.11873v1", "title": "Judgments of research co-created by generative AI: experimental evidence", "authors": [ "Paweł Niszczota", "Paul Conway" ], "posted": "2023-05-03", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.11873v1", "field": "management", "role": "object", "bullets": [ "Experiment with 402 participants judging a researcher who delegates parts of the research process either to a PhD student or to a large language model.", "No model is deployed by the authors; generative AI is the treatment in the vignettes, so validation of model output does not apply.", "Delegation to an LLM is judged less morally acceptable (d = -0.78), erodes trust to oversee future projects (-0.80), and lowers expected accuracy and quality of output (-0.85)." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 47, "edition": 12, "validated": null, "n": 1375, "authors_detailed": [ { "name": "Paweł Niszczota", "url": "https://openalex.org/A5028727353", "inst": "Poznań University of Economics and Business" }, { "name": "Paul Conway", "url": "https://openalex.org/A5007640209", "inst": "Loughborough University" } ], "affiliations": [ "Poznań University of Economics and Business", "Loughborough University" ] }, { "uid": "doi:10.2139/ssrn.4411051", "doi": "10.2139/ssrn.4411051", "title": "To ChatGPT or Not to ChatGPT?", "authors": [ "Jacques R. Bughin" ], "posted": "2023-05-02", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4411051", "field": "management", "role": "object", "bullets": [ "Essay-style commentary addressed to business leaders on how to respond to ChatGPT; no sample, period, or geography is stated.", "The paper does not test or apply a specific model; it describes ChatGPT as a large language model and discusses its rapid public uptake, not stated whether validated.", "The paper argues that despite ChatGPT's rapid adoption, business leaders should pause and build a reasoned position on whether and how to use such AI technologies." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 30, "edition": 22, "validated": null, "n": 4120, "authors_detailed": [ { "name": "Jacques Bughin", "url": "https://openalex.org/A5029134161", "inst": "Université Libre de Bruxelles" } ], "affiliations": [ "Université Libre de Bruxelles" ] }, { "uid": "doi:10.2139/ssrn.4430507", "doi": "10.2139/ssrn.4430507", "title": "Navigating Political Risks: The Role of Firm Political Alignment", "authors": [ "Ping Jiang", "Jing Li", "Minjia Li", "Jenny Li Zhang" ], "posted": "2023-05-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4430507", "field": "finance", "role": "instrument", "bullets": [ "Chinese publicly listed firms' annual reports around two political shocks: the 2012 anti-corruption campaign and the 2021 common prosperity announcement.", "GPT-based large language model measures firm political alignment as textual correspondence between annual reports and government policy agendas.", "Firms increase political alignment when political ties weaken; higher alignment mitigates negative market reaction to the common prosperity policy shock." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 3310, "authors_detailed": [ { "name": "Ping Jiang", "url": "https://openalex.org/A5035334141", "inst": "University of International Business and Economics" }, { "name": "Jing Li", "url": "https://openalex.org/A5101860315", "inst": "Simon Fraser University" }, { "name": "Minjia Li", "url": "https://openalex.org/A5102563370", "inst": "University of British Columbia" }, { "name": "Jenny Li Zhang", "url": "https://openalex.org/A5004227861", "inst": "University of British Columbia" } ], "affiliations": [ "University of International Business and Economics", "Simon Fraser University", "University of British Columbia" ] }, { "uid": "arxiv:2305.01505v2", "arxiv_id": "2305.01505v2", "title": "Beyond Classification: Financial Reasoning in State-of-the-Art Language Models", "authors": [ "Guijin Son", "Hanearl Jung", "Moonjeong Hahm", "Keonju Na", "Sol Jin" ], "posted": "2023-04-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2305.01505v2", "field": "finance", "role": "method", "bullets": [ "Financial reasoning tasks around investment decision making, with a released dataset, sFIOG, of 11,802 synthetic investment thesis samples for training and evaluation.", "GPT variants from 2.8B to 13B parameters, with and without instruction tuning, are benchmarked across task formulation, synthetic data generation, and prompting; validation details are not stated in the abstract.", "Coherent financial reasoning first emerges at 6B parameters and improves with instruction tuning and larger training datasets." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 40, "edition": 12, "validated": null, "n": 1420, "authors_detailed": [ { "name": "Guijin Son", "url": "https://openalex.org/A5028962243", "inst": "Ondine (Canada)" }, { "name": "Hanearl Jung", "url": "https://openalex.org/A5103618334", "inst": "Seoul National University" }, { "name": "Moonjeong Hahm", "url": "https://openalex.org/A5072440755", "inst": "" }, { "name": "Keonju Na", "url": "https://openalex.org/A5034628170", "inst": "" }, { "name": "Sol Jin", "url": "https://openalex.org/A5081819379", "inst": "Kosin University Gospel Hospital" } ], "affiliations": [ "Ondine (Canada)", "Seoul National University" ] }, { "uid": "doi:10.2139/ssrn.4411068", "doi": "10.2139/ssrn.4411068", "title": "Generative AI, ChatGPT, and the Future of Jobs", "authors": [ "Usman W. Chohan" ], "posted": "2023-04-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4411068", "field": "economics", "role": "object", "bullets": [ "Conceptual analysis of white-collar creative labor markets facing displacement from generative AI tools including ChatGPT and image generators.", "No empirical model use; paper theorizes about generative AI's substitution of creative knowledge workers and implications for the labor-capital exchange.", "White-collar creative occupations face greater near-term disruption risk than blue-collar jobs, potentially undermining capitalism's labor-for-capital premise." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 35, "validated": null, "n": 3309, "authors_detailed": [ { "name": "Usman W. Chohan", "url": "https://openalex.org/A5053424176", "inst": "UNSW Sydney" } ], "affiliations": [ "UNSW Sydney" ] }, { "uid": "doi:10.2139/ssrn.4424979", "doi": "10.2139/ssrn.4424979", "title": "Unleashing the Power of ChatGPT in Finance Research: Opportunities and Challenges", "authors": [ "Zifeng Feng", "Gangqing Hu", "Bingxin Li" ], "posted": "2023-04-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4424979", "field": "finance", "role": "method", "bullets": [ "Comparative analysis of ChatGPT-3.5, ChatGPT-4, and Microsoft Bing across multiple finance research tasks including chart analysis and model derivation.", "GPT-3.5 and GPT-4 performed financial chart analysis, coding support, theoretical derivation, and image analysis via Visual Referring Prompting.", "Each model version shows distinct strengths and weaknesses; multimodal capabilities expand potential applications but hallucination and ethical risks persist." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 25, "n": 2835, "authors_detailed": [ { "name": "Zifeng Feng", "url": "https://openalex.org/A5084102031", "inst": "The University of Texas at El Paso" }, { "name": "Gangqing Hu", "url": "https://openalex.org/A5101200344", "inst": "West Virginia University" }, { "name": "Bingxin Li", "url": "https://openalex.org/A5101499755", "inst": "West Virginia University" } ], "affiliations": [ "The University of Texas at El Paso", "West Virginia University" ] }, { "uid": "doi:10.2139/ssrn.4413315", "doi": "10.2139/ssrn.4413315", "title": "Outsourcing Voting to AI: Can ChatGPT Advise Index Funds on Proxy Voting Decisions?", "authors": [ "Chen Wang" ], "posted": "2023-04-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4413315", "field": "finance", "role": "agent", "bullets": [ "Zero-shot GPT-4 experiment applied to a real-world proxy statement for a small passive index fund scenario.", "GPT-4 generated detailed proxy voting guidelines with weighted variables and identified conflicts of interest in director elections.", "The model successfully produced comprehensive voting guidelines but exhibited ESG inclination bias and token-length limitations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 3180, "authors_detailed": [ { "name": "Chen Wang", "url": "https://openalex.org/A5100337538", "inst": "Lawrence Berkeley National Laboratory" } ], "affiliations": [ "Lawrence Berkeley National Laboratory" ] }, { "uid": "doi:10.2139/ssrn.4425527", "doi": "10.2139/ssrn.4425527", "title": "Bloated Disclosures: Can ChatGPT Help Investors Process Information?", "authors": [ "Alex G. Kim", "Maximilian Muhn", "Valeri V. Nikolaev" ], "posted": "2023-04-21", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4425527", "field": "accounting", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 68, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 605, "authors_detailed": [ { "name": "Alex Kim", "url": "https://openalex.org/A5002013850", "inst": "University of Chicago" }, { "name": "Maximilian Muhn", "url": "https://openalex.org/A5065276465", "inst": "University of Chicago" }, { "name": "Valeri V. Nikolaev", "url": "https://openalex.org/A5085975914", "inst": "University of Chicago" } ], "affiliations": [ "University of Chicago" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4416159", "doi": "10.2139/ssrn.4416159", "title": "ChatGPT and Other AI Models as a Due Diligence Tool: Benefits and Limitations for Private Firm Investment Analysis", "authors": [ "David Krause" ], "posted": "2023-04-19", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4416159", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 40, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 604, "authors_detailed": [ { "name": "David Krause", "url": "https://openalex.org/A5002886467", "inst": "Marquette University" } ], "affiliations": [ "Marquette University" ] }, { "uid": "doi:10.2139/ssrn.4376676", "doi": "10.2139/ssrn.4376676", "title": "ESG Considerations in Acquisitions and Divestitures: Corporate Responses to Mandatory ESG Disclosure", "authors": [ "Tong Li", "Qilin Peng", "Luping Yu" ], "posted": "2023-04-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4376676", "field": "finance", "role": "instrument", "bullets": [ "Firms subject to mandatory ESG disclosure regulations, with deal-level M&A data and conference call transcripts across multiple jurisdictions.", "ChatGPT-based text analysis measures ESG-related discussion intensity in M&A conference calls following implementation of disclosure mandates.", "Firms acquire assets with superior ESG performance and divest underperformers, paying higher premiums for strong ESG attributes and accepting discounts on weak ones." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3307, "authors_detailed": [ { "name": "Tong Li", "url": "https://openalex.org/A5101200063", "inst": "Xiamen University" }, { "name": "Qilin Peng", "url": "https://openalex.org/A5077627427", "inst": "University of Toronto" }, { "name": "Luping Yu", "url": "https://openalex.org/A5017303224", "inst": "Xiamen University" } ], "affiliations": [ "University of Toronto", "Xiamen University" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4414065", "doi": "10.2139/ssrn.4414065", "title": "Occupational Heterogeneity in Exposure to Generative AI", "authors": [ "Edward W. Felten", "Manav Raj", "Robert Seamans" ], "posted": "2023-04-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4414065", "field": "economics", "role": "object", "bullets": [ "U.S. occupations scored for exposure to AI language modeling and image generation using O*NET task-level data and demographic breakdowns.", "Rubric-based methodology maps generative AI capabilities to occupation tasks to produce exposure scores; no direct LLM use in the analysis.", "Highly educated, highly paid, white-collar occupations are most exposed to generative AI, with significant demographic variation in exposure rates." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 70, "validated": null, "n": 3308, "authors_detailed": [ { "name": "Edward W. Felten", "url": "https://openalex.org/A5108360806", "inst": "Woodrow Wilson International Center for Scholars" }, { "name": "Manav Raj", "url": "https://openalex.org/A5018390504", "inst": "University of Pennsylvania" }, { "name": "Robert Seamans", "url": "https://openalex.org/A5050038373", "inst": "New York University" } ], "affiliations": [ "University of Pennsylvania", "New York University", "Woodrow Wilson International Center for Scholars" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4400516", "doi": "10.2139/ssrn.4400516", "title": "AI-Driven Labor Substitution: Evidence from Google Translate and ChatGPT", "authors": [ "Erdem Dogukan Yilmaz", "Ivana Naumovska", "Vikas A. Aggarwal" ], "posted": "2023-04-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4400516", "field": "economics", "role": "object", "bullets": [ "Online labor market for translation services around Google's 2016-2017 neural translation launch, supplemented with Stack Exchange data after ChatGPT release.", "Google neural translation and ChatGPT serve as natural experiments; difference-in-differences design measures displacement of human translators across task types.", "Neural translation reduced human translation transactions; substitution effect was stronger for analytical tasks than for cultural or emotional ones." ], "bullet_provenance": "ai", "models": [ "gpt" ], "salience": 70, "validated": null, "n": 3306, "authors_detailed": [ { "name": "Erdem Dogukan Yilmaz", "url": "https://openalex.org/A5060998181", "inst": "INSEAD" }, { "name": "Ivana Naumovska", "url": "https://openalex.org/A5057049687", "inst": "INSEAD" }, { "name": "Vikas A. Aggarwal", "url": "https://openalex.org/A5043985825", "inst": "INSEAD" } ], "affiliations": [ "INSEAD" ], "prestige": true }, { "uid": "arxiv:2304.07333v1", "arxiv_id": "2304.07333v1", "title": "The Self-Perception and Political Biases of ChatGPT", "authors": [ "Jérôme Rutinowski", "Sven Franke", "Jan Endendyk", "Ina Dormuth", "Markus Pauly" ], "posted": "2023-04-14", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2304.07333v1", "field": "economics", "role": "object", "bullets": [ "OpenAI's ChatGPT answers the political compass test, political questionnaires specific to each G7 member state, and the OCEAN, MBTI, and Dark Factor instruments, each repeated ten times.", "The chatbot itself is the study subject; its answers are aggregated into coordinates and trait scores, and the abstract does not state which model version was queried.", "The political compass places ChatGPT at (-6.48, -5.99), progressive and libertarian; G7 questionnaires average (-3.27, 0.58), progressive without a libertarian tilt, and self-tests show an open, agreeable ENFJ profile with low dark traits." ], "bullet_provenance": "ai", "models": [ "gpt", "legacy" ], "open_weights": false, "salience": 48, "edition": 12, "validated": null, "n": 1479, "authors_detailed": [ { "name": "Jérôme Rutinowski", "url": "https://openalex.org/A5046934154", "inst": "TU Dortmund University" }, { "name": "Sven Franke", "url": "https://openalex.org/A5080401977", "inst": "TU Dortmund University" }, { "name": "Jan Endendyk", "url": "https://openalex.org/A5036972612", "inst": "TU Dortmund University" }, { "name": "Ina Dormuth", "url": "https://openalex.org/A5022334369", "inst": "TU Dortmund University" }, { "name": "Markus Pauly", "url": "https://openalex.org/A5005375865", "inst": "TU Dortmund University" } ], "affiliations": [ "TU Dortmund University" ] }, { "uid": "doi:10.2139/ssrn.4412505", "doi": "10.2139/ssrn.4412505", "title": "Are the New AIs Smart Enough to Steal Your Job? IQ Scores for ChatGPT, Microsoft Bing, Google Bard and Quora Poe", "authors": [ "Bruno Campello de Souza", "Agostinho Serrano de Andrade Neto", "Antonio Roazzi" ], "posted": "2023-04-13", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4412505", "field": "economics", "role": "agent", "bullets": [ "ChatGPT and other chatbots take the PAIMT intelligence test and are compared with human score norms from Pernambuco, Brazil; prompt count is not stated.", "ChatGPT, Microsoft Bing, Google Bard, and Quora Poe answer standardized items, with reported human percentile mappings providing an external performance benchmark.", "Most systems score above the human 95th percentile, while ChatGPT and Bing reach the 99th percentile, suggesting exposure for highly skilled labor tasks." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt" ], "open_weights": false, "validated": true, "validation_note": "PAIMT intelligence test with Pernambuco human percentile norms", "salience": 51, "edition": 23, "n": 4189, "authors_detailed": [ { "name": "Bruno Campello de Souza", "url": "https://openalex.org/A5000447907", "inst": "Universidade Federal de Pernambuco" }, { "name": "Agostinho Serrano de Andrade Neto", "url": "https://openalex.org/A5083451073", "inst": "Universidade Luterana do Brasil" }, { "name": "Antônio Roazzi", "url": "https://openalex.org/A5064687703", "inst": "Universidade Federal de Pernambuco" } ], "affiliations": [ "Universidade Federal de Pernambuco", "Universidade Luterana do Brasil" ] }, { "uid": "arxiv:2304.05351v2", "arxiv_id": "2304.05351v2", "title": "The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges", "authors": [ "Qianqian Xie", "Weiguang Han", "Yanzhao Lai", "Min Peng", "Jimin Huang" ], "posted": "2023-04-10", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2304.05351v2", "field": "finance", "role": "method", "bullets": [ "Three benchmark stock movement prediction datasets pairing tweets with historical prices, used to test zero shot forecasting without any financial fine tuning.", "ChatGPT, version not stated, predicts movements with and without chain of thought prompting; scored against realized moves and compared with specialist models and linear regression.", "ChatGPT trails both state of the art methods and a simple price based linear regression, with unstable and weakly explainable outputs." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "three labelled stock movement datasets", "salience": 48, "edition": 12, "n": 1361, "authors_detailed": [ { "name": "Qianqian Xie", "url": "https://openalex.org/A5101868563", "inst": "Hunan Normal University" }, { "name": "Weiguang Han", "url": "https://openalex.org/A5054465909", "inst": "Hebei University of Technology" }, { "name": "Yanzhao Lai", "url": "https://openalex.org/A5029848442", "inst": "China Electronics Technology Group Corporation" }, { "name": "Min Peng", "url": "https://openalex.org/A5089527851", "inst": "Hefei University of Technology" }, { "name": "Jimin Huang", "url": "https://openalex.org/A5018254776", "inst": "University of Manchester" } ], "affiliations": [ "Hunan Normal University", "Hebei University of Technology", "China Electronics Technology Group Corporation", "Hefei University of Technology", "University of Manchester" ] }, { "uid": "doi:10.2139/ssrn.4412788", "doi": "10.2139/ssrn.4412788", "title": "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models", "authors": [ "Alejandro Lopez-Lira", "Yuehua Tang" ], "posted": "2023-04-10", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4412788", "field": "finance", "role": "instrument", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 70, "edition": 2, "audience": "broad", "bullets": [], "validated": null, "n": 58, "authors_detailed": [ { "name": "Alejandro Lopez-Lira", "url": "https://openalex.org/A5074826581", "inst": "University of Florida" }, { "name": "Yuehua Tang", "url": "https://openalex.org/A5052351860", "inst": "University of Florida" } ], "affiliations": [ "University of Florida" ], "prestige": true, "us_top": true }, { "uid": "doi:10.3386/w31122", "doi": "10.3386/w31122", "title": "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?", "authors": [ "John Horton", "Apostolos Filippas", "Benjamin Manning" ], "posted": "2023-04-10", "added": "2026-07-23", "source_label": "NBER", "url": "https://doi.org/10.3386/w31122", "field": "economics", "role": "agent", "bullets": [ "Simulation experiments that replicate three classic behavioral economics studies from Charness and Rabin 2002, Kahneman Knetsch and Thaler 1986, and Samuelson and Zeckhauser 1988, using LLM agents as subjects.", "The unnamed LLM is given endowments, information, and preferences, then its choices are simulated across scenarios; the specific model and version are not stated.", "Simulated agents reproduce the original human patterns qualitatively, and scenario variations are easily generated to pilot new social science hypotheses before field testing." ], "bullet_provenance": "ai", "validated": false, "validation_note": "qualitative match to original experiments, no accuracy statistic", "salience": 80, "edition": 2, "audience": "broad", "models": [], "n": 59, "authors_detailed": [ { "name": "J.R. Horton", "url": "https://openalex.org/A5003883620", "inst": "National Bureau of Economic Research" }, { "name": "Apostolos Filippas", "url": "", "inst": "" }, { "name": "Benjamin Manning", "url": "", "inst": "" } ], "affiliations": [ "National Bureau of Economic Research" ] }, { "uid": "doi:10.2139/ssrn.4413859", "doi": "10.2139/ssrn.4413859", "title": "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?", "authors": [ "John J. Horton", "Apostolos Filippas", "Benjamin Manning" ], "posted": "2023-04-10", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4413859", "field": "economics", "role": "agent", "bullets": [ "Classic behavioral economics experiments replicated with LLMs including Charness-Rabin fairness, Kahneman dictator, and Samuelson-Zeckhauser status quo bias designs.", "LLMs given endowments, information, and preferences simulate economic agents (Homo silicus) in prompt-based behavioral experiment scenarios.", "LLM-simulated agents produce qualitatively similar results to original human experiments across multiple canonical designs; divergences generate new research hypotheses." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 72, "n": 2630, "authors_detailed": [ { "name": "John J. Horton", "url": "https://openalex.org/A5077125314", "inst": "National Bureau of Economic Research" }, { "name": "Apostolos Filippas", "url": "", "inst": "" }, { "name": "Benjamin Manning", "url": "", "inst": "" } ], "affiliations": [ "National Bureau of Economic Research" ] }, { "uid": "arxiv:2304.03464v3", "arxiv_id": "2304.03464v3", "title": "Linking Representations with Multimodal Contrastive Learning", "authors": [ "Abhishek Arora", "Xinmei Yang", "Shao-Yu Jheng", "Melissa Dell" ], "posted": "2023-04-07", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2304.03464v3", "field": "economics", "role": "method", "bullets": [ "Historical record linkage of mid twentieth century Japanese firms across financial documents, where firm names appear as noisy OCR transcriptions paired with the original image crops.", "Contrastively trained vision and language bi-encoders pool image and text embeddings so linkage becomes nearest neighbour retrieval; the abstract names no base model family and quotes no accuracy figures.", "The multimodal embeddings beat widely used string matching by a wide margin, and a purely self-supervised variant trained without labelled links also outperforms those baselines." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labelled Japanese firm linkage benchmark", "salience": 55, "edition": 12, "models": [], "n": 1514, "authors_detailed": [ { "name": "Abhishek Arora", "url": "https://openalex.org/A5101737564", "inst": "Harvard University Press" }, { "name": "Xinmei Yang", "url": "https://openalex.org/A5109374848", "inst": "Shandong University" }, { "name": "Shao-Yu Jheng", "url": "https://openalex.org/A5010256788", "inst": "Harvard University Press" }, { "name": "Melissa Dell", "url": "https://openalex.org/A5078758511", "inst": "Canadian Institute for Advanced Research" } ], "affiliations": [ "Harvard University", "Shandong University", "Canadian Institute for Advanced Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4399406", "doi": "10.2139/ssrn.4399406", "title": "Can ChatGPT Decipher Fedspeak?", "authors": [ "Anne Lundgaard Hansen", "Sophia Kazinnik" ], "posted": "2023-04-07", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4399406", "field": "economics", "role": "instrument", "bullets": [ "Federal Open Market Committee announcements classified for monetary policy stance, benchmarked against human expert assessment of hawkish, dovish, or neutral tone.", "GPT-3.5 and GPT-4 classified FOMC statement stance and generated natural-language explanations; GPT-4 also identified macroeconomic shocks via the Romer-Romer narrative approach.", "GPT models substantially outperformed prior text-classification methods for Fedspeak; GPT-4 explanations matched human-level reasoning about policy stance drivers." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "human expert classification of FOMC stance", "salience": 75, "n": 3305, "authors_detailed": [ { "name": "Anne Lundgaard Hansen", "url": "https://openalex.org/A5024095797", "inst": "Federal Reserve" }, { "name": "Sophia Kazinnik", "url": "https://openalex.org/A5061916690", "inst": "Federal Reserve" } ], "affiliations": [ "Federal Reserve" ] }, { "uid": "doi:10.2139/ssrn.4404276", "doi": "10.2139/ssrn.4404276", "title": "The Benefits and Limitations of ChatGPT in Business Education and Research: A Focus on Management Science, Operations Management and Data Analytics", "authors": [ "Ivor Cribben", "Yasser Zeinali" ], "posted": "2023-04-03", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4404276", "field": "management", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 34, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 603, "authors_detailed": [ { "name": "Ivor Cribben", "url": "https://openalex.org/A5055994170", "inst": "University of Alberta" }, { "name": "Yasser Zeinali", "url": "https://openalex.org/A5081953935", "inst": "University of Alberta" } ], "affiliations": [ "University of Alberta" ] }, { "uid": "doi:10.2139/ssrn.4395079", "doi": "10.2139/ssrn.4395079", "title": "Global Business Networks", "authors": [ "Christian Breitung", "Sebastian Müller" ], "posted": "2023-04-03", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4395079", "field": "finance", "role": "instrument", "bullets": [ "Over 63,000 global firms with GPT-3-generated historical business descriptions and OpenAI embedding-based time-varying business similarity networks.", "GPT-3 generated firm descriptions; OpenAI embeddings constructed peer networks; an open-source language model was fine-tuned to distinguish competitor, supplier, and customer links.", "Business networks predicted lead-lag returns for global stocks and identified M&A targets; masking firm-specific details mitigated look-ahead bias from the embedding model's knowledge cutoff." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": false, "validated": true, "validation_note": "lead-lag return prediction and M&A target identification", "salience": 75, "n": 3179, "authors_detailed": [ { "name": "Christian Breitung", "url": "https://openalex.org/A5050609944", "inst": "Technical University of Munich" }, { "name": "Sebastian Müller", "url": "https://openalex.org/A5006795021", "inst": "Technical University of Munich" } ], "affiliations": [ "Technical University of Munich" ] }, { "uid": "doi:10.1145/3717867.3717903", "doi": "10.1145/3717867.3717903", "arxiv_id": "2304.00228v3", "title": "Accuracy and Political Bias of News Source Credibility Ratings by Large Language Models", "authors": [ "Kai-Cheng Yang", "Filippo Menczer" ], "posted": "2023-04-01", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2304.00228v3", "field": "other", "role": "method", "bullets": [ "News outlet credibility ratings elicited from nine LLMs offered by OpenAI, Google, and Meta, compared with human expert evaluations across outlets of differing US political leanings.", "Models rate source credibility directly; ratings agree across models at an average Spearman correlation of 0.79 but align with expert ratings only at 0.50, with larger models refusing more often.", "Every model shows a liberal tilt in default settings, and assigning partisan personas induces politically congruent bias, a caution for research or products using LLM credibility judgments." ], "bullet_provenance": "ai", "models": [ "gemini", "gpt", "llama" ], "open_weights": false, "validated": true, "validation_note": "human expert credibility ratings, Spearman 0.50", "salience": 55, "edition": 12, "n": 1391, "authors_detailed": [ { "name": "Kai‐Cheng Yang", "url": "https://openalex.org/A5054574752", "inst": "Northeastern University" }, { "name": "Filippo Menczer", "url": "https://openalex.org/A5021346979", "inst": "Indiana University Bloomington" } ], "affiliations": [ "Indiana University Bloomington", "Northeastern University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4395339", "doi": "10.2139/ssrn.4395339", "title": "Automation and Stock Prices: The Case of ChatGPT", "authors": [ "Magnus Blomkvist", "Yetaotao Qiu", "Yunfei Zhao" ], "posted": "2023-03-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4395339", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 50, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 602, "authors_detailed": [ { "name": "Magnus Blomkvist", "url": "https://openalex.org/A5037110204", "inst": "Ecole des Hautes Etudes Commerciales du Nord" }, { "name": "Yetaotao Qiu", "url": "https://openalex.org/A5065786294", "inst": "University of Manitoba" }, { "name": "Yunfei Zhao", "url": "https://openalex.org/A5023488134", "inst": "Concordia University" } ], "affiliations": [ "Ecole des Hautes Etudes Commerciales du Nord", "University of Manitoba", "Concordia University" ] }, { "uid": "arxiv:2303.17564v3", "arxiv_id": "2303.17564v3", "title": "BloombergGPT: A Large Language Model for Finance", "authors": [ "Shijie Wu", "Ozan Irsoy", "Steven Lu", "Vadim Dabravolski", "Mark Dredze", "Sebastian Gehrmann", "Prabhanjan Kambadur", "David Rosenberg", "Gideon Mann" ], "posted": "2023-03-30", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2303.17564v3", "field": "finance", "role": "method", "bullets": [ "A 363 billion token corpus from Bloomberg's proprietary financial data sources, augmented with 345 billion general purpose tokens, backing a 50 billion parameter model.", "BloombergGPT is trained from scratch on the mixed corpus and evaluated on standard LLM benchmarks, open financial NLP benchmarks, and internal Bloomberg task suites.", "The model beats existing systems on financial tasks by significant margins without losing general capability; the paper documents modeling choices and releases its training chronicles." ], "bullet_provenance": "ai", "open_weights": false, "validated": true, "validation_note": "public and internal financial NLP benchmarks", "salience": 72, "edition": 12, "models": [], "n": 1430, "authors_detailed": [ { "name": "Shijie Wu", "url": "https://openalex.org/A5100547062", "inst": "Ministry of Education of the People's Republic of China" }, { "name": "Ozan İrsoy", "url": "https://openalex.org/A5088262368", "inst": "University of North Carolina at Chapel Hill" }, { "name": "Steven Lu", "url": "https://openalex.org/A5101929159", "inst": "University of Manitoba" }, { "name": "Vadim Dabravolski", "url": "https://openalex.org/A5036868670", "inst": "" }, { "name": "Mark Dredze", "url": "https://openalex.org/A5024437840", "inst": "Johns Hopkins University" }, { "name": "Sebastian Gehrmann", "url": "https://openalex.org/A5026789962", "inst": "Google (United States)" }, { "name": "Prabhanjan Kambadur", "url": "https://openalex.org/A5049786570", "inst": "NetApp (United States)" }, { "name": "David Rosenberg", "url": "https://openalex.org/A5021167152", "inst": "Manhattan Institute for Policy Research" }, { "name": "Gideon Mann", "url": "https://openalex.org/A5090355240", "inst": "Meir Medical Center" } ], "affiliations": [ "University of North Carolina at Chapel Hill", "Johns Hopkins University", "Ministry of Education of the People's Republic of China", "University of Manitoba", "Google (United States)", "NetApp (United States)", "Manhattan Institute for Policy Research" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4395751", "doi": "10.2139/ssrn.4395751", "title": "Using LLMs for Market Research", "authors": [ "James Brand", "Ayelet Israeli", "Donald Ngwe" ], "posted": "2023-03-30", "added": "2026-07-23", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4395751", "field": "management", "role": "agent", "bullets": [ "Consumer preference elicitation for products and features; multiple large language models queried dozens of times per survey question, with estimates compared to human willingness-to-pay studies.", "Unnamed LLMs generate distributions of survey responses to estimate willingness-to-pay, and fine-tuning on prior human survey data is tested; outputs are checked against human study estimates.", "LLM willingness-to-pay is sometimes comparable to human estimates but often inaccurate or wrong-signed; fine-tuning improves alignment only within the same product category and population." ], "bullet_provenance": "ai", "validated": true, "validation_note": "LLM willingness-to-pay compared to human survey estimates", "salience": 70, "edition": 2, "audience": "broad", "models": [], "n": 52, "authors_detailed": [ { "name": "James Brand", "url": "https://openalex.org/A5031814846", "inst": "Microsoft (United States)" }, { "name": "Ayelet Israeli", "url": "https://openalex.org/A5049529209", "inst": "Harvard University" }, { "name": "Donald Ngwe", "url": "https://openalex.org/A5062575298", "inst": "Microsoft (United States)" } ], "affiliations": [ "Harvard University", "Microsoft (United States)" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4395028", "doi": "10.2139/ssrn.4395028", "title": "Measuring Business Social Irresponsibility: The Case of Sin Stocks", "authors": [ "Hamid Boustanifar", "Patrick Schwarz" ], "posted": "2023-03-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4395028", "field": "finance", "role": "instrument", "bullets": [ "U.S. publicly traded firms measured for corporate sinfulness using textual analysis of disclosures, replacing conventional SIC-code classification.", "A large language model validated the novel continuous text-based sinfulness measure, revealing false positives and false negatives in industry-code methods.", "A sinfulness-weighted portfolio yields annualized alpha of 5%; sin stocks exhibit lower crash risk but higher litigation risk with no evidence of mispricing." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 55, "n": 3304, "authors_detailed": [ { "name": "Hamid Boustanifar", "url": "https://openalex.org/A5072114404", "inst": "Ecole des Hautes Etudes Commerciales du Nord" }, { "name": "Patrick Schwarz", "url": "https://openalex.org/A5088950086", "inst": "University of Liège" } ], "affiliations": [ "Ecole des Hautes Etudes Commerciales du Nord", "University of Liège" ] }, { "uid": "doi:10.2139/ssrn.4391863", "doi": "10.2139/ssrn.4391863", "title": "How to Learn and Teach Economics with Large Language Models, Including GPT", "authors": [ "Tyler Cowen", "Alexander T. Tabarrok" ], "posted": "2023-03-27", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4391863", "field": "economics", "role": "object", "bullets": [ "Practical examples drawn from undergraduate and graduate economics courses, covering question answering, economic model solving, exam creation, idea generation, and writing enhancement.", "ChatGPT and Bing Chat demonstrated capabilities across economics education tasks; the paper highlights how these tools require interaction methods distinct from prior software.", "GPTs can answer economics questions, solve models, and create exams effectively; the paper provides structured guidance for optimizing LLM use in economics teaching." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 35, "n": 2407, "authors_detailed": [ { "name": "Tyler Cowen", "url": "https://openalex.org/A5020548675", "inst": "George Mason University" }, { "name": "Alexander Tabarrok", "url": "https://openalex.org/A5014424405", "inst": "George Mason University" } ], "affiliations": [ "George Mason University" ] }, { "uid": "arxiv:2303.13373v1", "arxiv_id": "2303.13373v1", "title": "Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks", "authors": [ "Eduardo C. Garrido-Merchán", "Cristina González-Barthe", "María Coronado Vaca" ], "posted": "2023-03-21", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2303.13373v1", "field": "accounting", "role": "method", "bullets": [ "ClimaText, a labelled climate-change detection dataset drawn from Wikipedia, 10-K filings, and web claims; example counts are not stated in the abstract.", "BERT and the DistilRoBERTa-based ClimateBert are fine-tuned by transfer learning; fine-tuned ClimateBert outperforms both BERT and the prior state of the art on the labelled task, without figures in the abstract.", "The classifier is offered as a low-cost tool for investors, institutions, and firms to monitor climate risk disclosure in financial reports." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "labelled ClimaText benchmark, figures not in abstract", "salience": 40, "edition": 12, "n": 1478, "authors_detailed": [ { "name": "Eduardo C. Garrido‐Merchán", "url": "https://openalex.org/A5070783543", "inst": "Universidad Pontificia Comillas" }, { "name": "Cristina González-Barthe", "url": "https://openalex.org/A5066502205", "inst": "Universidad Pontificia Comillas" }, { "name": "María Coronado Vaca", "url": "https://openalex.org/A5044603645", "inst": "Universidad Pontificia Comillas" } ], "affiliations": [ "Universidad Pontificia Comillas" ] }, { "uid": "arxiv:2303.10130v5", "arxiv_id": "2303.10130v5", "title": "GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models", "authors": [ "Tyna Eloundou", "Sam Manning", "Pamela Mishkin", "Daniel Rock" ], "posted": "2023-03-17", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2303.10130v5", "field": "economics", "role": "object", "bullets": [ "US labor market; occupations decomposed into work tasks and scored for exposure to LLMs and to software built on top of them, using a new rubric.", "Exposure ratings come from human experts alongside GPT-4 classifications; the abstract reports no agreement statistics between the two sources.", "About 80 percent of US workers have at least 10 percent of tasks exposed, 19 percent at least half; 15 percent of all tasks could be done significantly faster with an LLM, 47 to 56 percent with LLM software." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 86, "edition": 12, "n": 1374, "authors_detailed": [ { "name": "Tyna Eloundou", "url": "https://openalex.org/A5065917408", "inst": "OpenAI (United States)" }, { "name": "Sam Manning", "url": "https://openalex.org/A5040276644", "inst": "Institute on Governance" }, { "name": "Pamela Mishkin", "url": "https://openalex.org/A5028772381", "inst": "OpenAI (United States)" }, { "name": "Daniel L. Rock", "url": "https://openalex.org/A5018502975", "inst": "University of Illinois Urbana-Champaign" } ], "affiliations": [ "University of Illinois Urbana-Champaign", "OpenAI (United States)", "Institute on Governance" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4391444", "doi": "10.2139/ssrn.4391444", "title": "Firm-Level Green Innovation Beyond Patents", "authors": [ "Markus Leippold", "Tingyu Yu" ], "posted": "2023-03-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4391444", "field": "finance", "role": "instrument", "bullets": [ "U.S. publicly listed firms' earnings call transcripts used to construct a firm-level green innovation measure capturing invention and adoption dimensions beyond patents.", "ClimateBERT and GPT-3 classified transcript text into green innovation categories; a large language model further validated the identification strategy.", "Green-innovating firms exhibit lower expected returns than industry peers, consistent with hedging transition risk; effects extend to non-patentable innovations." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 65, "n": 3303, "authors_detailed": [ { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" }, { "name": "Tingyu Yu", "url": "https://openalex.org/A5013001852", "inst": "University of Zurich" } ], "affiliations": [ "University of Zurich" ] }, { "uid": "doi:10.2139/ssrn.4390529", "doi": "10.2139/ssrn.4390529", "title": "Can Chatgpt Improve Investment Decision? From a Portfolio Management Perspective", "authors": [ "Hyungjin Ko", "Jaewook Lee" ], "posted": "2023-03-16", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4390529", "field": "finance", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 42, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 601, "authors_detailed": [ { "name": "Hyungjin Ko", "url": "https://openalex.org/A5020828967", "inst": "Seoul National University" }, { "name": "Jaewook Lee", "url": "https://openalex.org/A5100609345", "inst": "Seoul National University" } ], "affiliations": [ "Seoul National University" ] }, { "uid": "doi:10.2139/ssrn.4385651", "doi": "10.2139/ssrn.4385651", "title": "Generative Artificial Intelligence (GAI) with Chat GPT for Accounting – a Business Case", "authors": [ "Dirk Otto Beerbaum Dr." ], "posted": "2023-03-15", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4385651", "field": "accounting", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 32, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 600, "authors_detailed": [ { "name": "Dirk Otto Beerbaum", "url": "https://openalex.org/A5051292012", "inst": "Frankfurt School of Finance & Management" } ], "affiliations": [ "Frankfurt School of Finance & Management" ] }, { "uid": "arxiv:2303.06217v2", "arxiv_id": "2303.06217v2", "title": "Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content", "authors": [ "Manav Raj", "Justin Berg", "Rob Seamans" ], "posted": "2023-03-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2303.06217v2", "field": "management", "role": "object", "bullets": [ "Pre-registered experiments in which participants evaluate creative and descriptive short stories and emotionally evocative first person poems, with use of AI in creation disclosed to some evaluators.", "No model output is measured; ChatGPT appears only as context, and the abstract does not state which system, if any, generated the evaluated content.", "AI disclosure leaves evaluations of stories unchanged but lowers ratings of first person poems, which the authors read as content viewed as distinctly human." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 58, "edition": 12, "validated": null, "n": 1463, "authors_detailed": [ { "name": "Manav Raj", "url": "https://openalex.org/A5018390504", "inst": "University of Pennsylvania" }, { "name": "Justin M. Berg", "url": "https://openalex.org/A5046951980", "inst": "University of Michigan" }, { "name": "Rob Seamans", "url": "https://openalex.org/A5031339708", "inst": "New York University" } ], "affiliations": [ "University of Pennsylvania", "New York University", "University of Michigan" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4384954", "doi": "10.2139/ssrn.4384954", "title": "The Influence of ChatGPT on Artificial Intelligence Related Crypto Assets: Evidence from a Synthetic Control Analysis", "authors": [ "Aman Saggu", "Lennart Ante" ], "posted": "2023-03-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4384954", "field": "finance", "role": "object", "bullets": [ "AI-related cryptocurrency assets around OpenAI ChatGPT launch in November 2022, analyzed using synthetic difference-in-differences methodology against non-AI crypto benchmarks.", "ChatGPT public release served as exogenous treatment shock; Google search volumes proxied investor attention to AI technologies post-launch.", "AI-related crypto assets earned 10.7-15.6% abnormal returns at one month and 35.5-41.3% at two months post-launch; Google AI search volume became a significant pricing factor." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 50, "validated": null, "n": 2897, "authors_detailed": [ { "name": "Aman Saggu", "url": "https://openalex.org/A5068688584", "inst": "Mahidol University" }, { "name": "Lennart Ante", "url": "https://openalex.org/A5034620178", "inst": "Hamburg Institut (Germany)" } ], "affiliations": [ "Mahidol University", "Hamburg Institut (Germany)" ] }, { "uid": "doi:10.2139/ssrn.4380365", "doi": "10.2139/ssrn.4380365", "title": "A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do?", "authors": [ "Yang Chen", "Samuel Kirshner", "Anton Ovchinnikov", "Meena Andiappan", "Tracy Jenkin" ], "posted": "2023-03-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4380365", "field": "management", "role": "agent", "bullets": [ "Eighteen common behavioral decision biases tested using experimental vignettes in standard, inventory, and operations management contexts across GPT-3.5 and GPT-4.", "GPT-3.5 and GPT-4 acted as experimental participants on bias-testing vignettes adapted from behavioral economics literature, reframed for operations management settings.", "GPT mirrors human biases in nearly half of tests; GPT-4 improves accuracy on formula-based problems but shows increased behavioral bias on preference-based decisions." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "known correct answers for mathematical bias problems", "salience": 65, "n": 2377, "authors_detailed": [ { "name": "Yang Chen", "url": "https://openalex.org/A5100350503", "inst": "Western University" }, { "name": "Samuel Kirshner", "url": "https://openalex.org/A5147222312", "inst": "University of Toronto" }, { "name": "Антон Овчінніков", "url": "https://openalex.org/A5063906489", "inst": "INSEAD" }, { "name": "Антон Овчінніков", "url": "https://openalex.org/A5063906489", "inst": "University of Toronto" }, { "name": "Tracy A. Jenkin", "url": "https://openalex.org/A5081680451", "inst": "Queen's University" } ], "affiliations": [ "University of Toronto", "INSEAD", "Western University", "Queen's University" ], "prestige": true }, { "uid": "doi:10.2139/ssrn.4380516", "doi": "10.2139/ssrn.4380516", "title": "GPTQuant's Conversational AI: Simplifying Investment Research for All", "authors": [ "Thomas Yue", "David Au" ], "posted": "2023-03-09", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4380516", "field": "finance", "role": "method", "bullets": [ "Case studies of a conversational AI chatbot for developing and evaluating investment strategies, compared against baseline ChatGPT performance.", "GPT-3 generated executable Python code via LangChain few-shot prompting to build and backtest investment strategies from natural language commands.", "GPTQuant produced working investment research code on demand, outperforming vanilla ChatGPT in domain-specific finance strategy generation." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 30, "n": 3302, "authors_detailed": [ { "name": "Thomas Yue", "url": "https://openalex.org/A5096939380", "inst": "Queen Elizabeth Hospital" }, { "name": "Chi Chung Au", "url": "https://openalex.org/A5096939381", "inst": "Queen Elizabeth Hospital" } ], "affiliations": [ "Queen Elizabeth Hospital" ] }, { "uid": "doi:10.2139/ssrn.4375798", "doi": "10.2139/ssrn.4375798", "title": "Time Series Forecasting with Transformer Models and Application to Asset Management", "authors": [ "Edmond Lezmi", "Jiali Xu" ], "posted": "2023-03-07", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4375798", "field": "finance", "role": "instrument", "bullet_provenance": "none", "salience": 33, "edition": 3, "audience": "technical", "bullets": [], "models": [], "validated": null, "n": 1027, "authors_detailed": [ { "name": "Edmond Lezmi", "url": "https://openalex.org/A5088477317", "inst": "Amundi Asset Management" }, { "name": "Jiali Xu", "url": "https://openalex.org/A5101444385", "inst": "Institut Pasteur" } ], "affiliations": [ "Amundi Asset Management", "Institut Pasteur" ] }, { "uid": "arxiv:2303.02841v2", "arxiv_id": "2303.02841v2", "title": "Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance", "authors": [ "Bixing Yan", "Shaoling Chen", "Yuxuan He", "Zhihan Li" ], "posted": "2023-03-06", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2303.02841v2", "field": "finance", "role": "method", "bullets": [ "Low resource financial language understanding: Financial PhraseBank sentences and a stock movement task built on Twitter text, with GLUE, SNLI, and Sci-Tail as auxiliary tasks.", "Model agnostic meta-learning adapts an unnamed pre-trained language model across tasks so it fine-tunes quickly on scarce financial data; scoring is against benchmark labels, with no figures in the abstract.", "The meta-learned models reach what the authors describe as state of the art on the financial tasks; no baselines or margins are quantified in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "labelled NLU benchmarks including Financial PhraseBank", "salience": 30, "edition": 12, "models": [], "n": 1513, "authors_detailed": [ { "name": "Bixing Yan", "url": "https://openalex.org/A5024950485", "inst": "New York University" }, { "name": "Shaoling Chen", "url": "https://openalex.org/A5068104181", "inst": "Jinan University" }, { "name": "Yuxuan He", "url": "https://openalex.org/A5101450642", "inst": "Kunming University of Science and Technology" }, { "name": "Zhihan Li", "url": "https://openalex.org/A5100605168", "inst": "Shanghai Jiao Tong University" } ], "affiliations": [ "New York University", "Jinan University", "Kunming University of Science and Technology", "Shanghai Jiao Tong University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4375283", "doi": "10.2139/ssrn.4375283", "title": "Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence", "authors": [ "Shakked Noy", "Whitney Zhang" ], "posted": "2023-03-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4375283", "field": "economics", "role": "object", "bullets": [ "Preregistered online experiment with 444 college-educated professionals performing occupation-specific, incentivized writing tasks with random assignment to ChatGPT access.", "Half of participants used ChatGPT for mid-level professional writing; output quality and completion time were measured against the control group.", "ChatGPT reduced completion time by 0.8 SD and raised output quality by 0.4 SD, with larger gains for low-ability workers, compressing the productivity distribution." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 82, "validated": null, "n": 3301, "authors_detailed": [ { "name": "Shakked Noy", "url": "https://openalex.org/A5032649151", "inst": "Massachusetts Institute of Technology" }, { "name": "Whitney Zhang", "url": "https://openalex.org/A5015831591", "inst": "Massachusetts Institute of Technology" } ], "affiliations": [ "Massachusetts Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4372349", "doi": "10.2139/ssrn.4372349", "title": "More Human than Human: Measuring ChatGPT Political Bias", "authors": [ "Fabio Motoki", "Valdemar Pinho Neto", "Victor Rangel" ], "posted": "2023-03-04", "added": "2026-08-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4372349", "field": "economics", "role": "agent", "bullets": [ "ChatGPT answered political-orientation questions concerning the United States, Brazil, and United Kingdom 100 times each, with question order randomized across rounds.", "The proprietary GPT model impersonated political positions and answered by default; dose-response, placebo, and profession-alignment checks test robustness without an external ground-truth benchmark.", "Default responses show systematic alignment toward US Democrats, Brazil's Lula, and the UK Labour Party, indicating that generated political content is not neutral." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 62, "edition": 23, "n": 4180, "authors_detailed": [ { "name": "Fábio Motoki", "url": "https://openalex.org/A5016703677", "inst": "University of East Anglia" }, { "name": "Valdemar Pinho Neto", "url": "https://openalex.org/A5080836354", "inst": "Brazilian Academy of Sciences" }, { "name": "Victor Rodrigues", "url": "https://openalex.org/A5083079499", "inst": "Insper" } ], "affiliations": [ "University of East Anglia", "Brazilian Academy of Sciences", "Insper" ] }, { "uid": "doi:10.2139/ssrn.4369818", "doi": "10.2139/ssrn.4369818", "title": "Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content", "authors": [ "Manav Raj", "Justin M. Berg", "Robert Seamans" ], "posted": "2023-02-28", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4369818", "field": "management", "role": "object", "bullets": [ "Pre-registered experiments with online participants evaluating creative stories, descriptive stories, and emotionally evocative first-person poems under randomized AI disclosure conditions.", "Generative AI (OpenAI) produced creative content; participants were randomly told or not told that content was AI-generated, and evaluations were compared across conditions.", "AI disclosure had no effect on evaluations of creative or descriptive stories but significantly reduced evaluations of emotionally evocative first-person poems." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 55, "validated": null, "n": 2896, "authors_detailed": [ { "name": "Manav Raj", "url": "https://openalex.org/A5018390504", "inst": "University of Pennsylvania" }, { "name": "Justin M. Berg", "url": "https://openalex.org/A5046951980", "inst": "Stanford Graduate School of Business" }, { "name": "Robert Seamans", "url": "https://openalex.org/A5050038373", "inst": "New York University" } ], "affiliations": [ "University of Pennsylvania", "Stanford Graduate School of Business", "New York University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4365176", "doi": "10.2139/ssrn.4365176", "title": "Intellectual Tenacity at Work: The Role of Occupational Personality Requirements in Shaping Economic Returns Amid Technological Change", "authors": [ "Christos Makridis", "Louis Hickman" ], "posted": "2023-02-25", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4365176", "field": "economics", "role": "object", "bullets": [ "Over 15 million U.S. Census ACS respondents from 2006 to 2024 matched with O*NET occupational personality requirements across U.S. labor markets.", "Event-study design exploits cross-occupational variation in generative AI exposure to estimate how LLM arrival shifted returns to personality traits.", "Intellectual tenacity wage premium grew substantially and concentrates in occupations with above-median LLM exposure; social adjustment premium declined over the period." ], "bullet_provenance": "ai", "salience": 65, "models": [], "validated": null, "n": 2629, "authors_detailed": [ { "name": "Christos Makridis", "url": "https://openalex.org/A5041747384", "inst": "University of Nicosia" }, { "name": "Louis Hickman", "url": "https://openalex.org/A5071909501", "inst": "Virginia Tech" } ], "affiliations": [ "University of Nicosia", "Virginia Tech" ] }, { "uid": "arxiv:2302.11157v2", "arxiv_id": "2302.11157v2", "title": "FiNER-ORD: Financial Named Entity Recognition Open Research Dataset", "authors": [ "Agam Shah", "Abhinav Gullapalli", "Ruchit Vithani", "Michael Galarnyk", "Sudheer Chava" ], "posted": "2023-02-22", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2302.11157v2", "field": "finance", "role": "method", "bullets": [ "FiNER-ORD, a manually annotated English named entity recognition dataset for financial text, released openly with models and code; entity and document counts are not stated in the abstract.", "Several pre-trained language models and large language models are benchmarked against the manual annotations; the abstract names no specific models and reports no scores.", "The dataset is positioned as a standard benchmark for finance-domain named entity recognition; no headline accuracy figures appear in the abstract." ], "bullet_provenance": "ai", "validated": true, "validation_note": "benchmarked on manually annotated FiNER-ORD labels", "salience": 46, "edition": 12, "models": [], "n": 1477, "authors_detailed": [ { "name": "Agam Shah", "url": "https://openalex.org/A5017623662", "inst": "Georgia Institute of Technology" }, { "name": "Gullapalli, Abhinav", "url": "", "inst": "" }, { "name": "Vithani, Ruchit", "url": "", "inst": "" }, { "name": "Galarnyk, Michael", "url": "", "inst": "" }, { "name": "Chava, Sudheer", "url": "", "inst": "" } ], "affiliations": [ "Georgia Institute of Technology" ], "prestige": true, "us_top": true }, { "uid": "doi:10.1007/s43681-023-00289-2", "doi": "10.1007/s43681-023-00289-2", "arxiv_id": "2302.08500v2", "title": "Auditing large language models: a three-layered approach", "authors": [ "Jakob Mökander", "Jonas Schuett", "Hannah Rose Kirk", "Luciano Floridi" ], "posted": "2023-02-16", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2302.08500v2", "field": "management", "role": "object", "bullets": [ "A conceptual governance paper with no dataset, building on prior work on AI auditing to address the oversight gaps that adaptable, emergent LLMs create.", "No model is used; the blueprint layers governance audits of providers, model audits before release, and application audits of downstream products, each informing the others.", "Argues coordinated audits at all three levels can identify and manage ethical, legal, and technical risks of LLMs, while stressing the limits of what auditing can achieve." ], "bullet_provenance": "ai", "salience": 40, "edition": 12, "models": [], "validated": null, "n": 1390, "authors_detailed": [ { "name": "Jakob Mökander", "url": "https://openalex.org/A5025509228", "inst": "Princeton University" }, { "name": "Jonas Schuett", "url": "https://openalex.org/A5016424652", "inst": "Goethe University Frankfurt" }, { "name": "Hannah Rose Kirk", "url": "https://openalex.org/A5051050719", "inst": "University of Oxford" }, { "name": "Luciano Floridi", "url": "https://openalex.org/A5046574356", "inst": "University of Oxford" } ], "affiliations": [ "Princeton University", "University of Oxford", "Goethe University Frankfurt" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4356034", "doi": "10.2139/ssrn.4356034", "title": "ChatGPT has Mastered the Principles of Economics: Now What?", "authors": [ "Wayne Geerling", "G. Dirk Mateer", "Jadrian Wooten", "Nikhil Damodaran" ], "posted": "2023-02-14", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4356034", "field": "economics", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 34, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 599, "authors_detailed": [ { "name": "Wayne Geerling", "url": "https://openalex.org/A5055057104", "inst": "The University of Texas at Austin" }, { "name": "G. Dirk Mateer", "url": "https://openalex.org/A5043096895", "inst": "The University of Texas at Austin" }, { "name": "Jadrian Wooten", "url": "https://openalex.org/A5052991705", "inst": "Virginia Tech" }, { "name": "Nikhil Damodaran", "url": "https://openalex.org/A5045944384", "inst": "The University of Melbourne" } ], "affiliations": [ "The University of Texas at Austin", "Virginia Tech", "The University of Melbourne" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4356243", "doi": "10.2139/ssrn.4356243", "title": "Language Models and Cognitive Automation for Economic Research", "authors": [ "Anton Korinek" ], "posted": "2023-02-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4356243", "field": "economics", "role": "object", "bullets": [ "Twenty-five use cases catalogued across six domains of economic research practice: ideation, writing, background research, data analysis, coding, and mathematical derivations.", "ChatGPT capabilities demonstrated across all six research domains, with each use case classified on a spectrum from experimental to highly useful for economists.", "Researchers who automate micro tasks with LLMs will become significantly more productive; ongoing model advances expected to improve performance across all documented domains." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 2406, "authors_detailed": [ { "name": "Anton Korinek", "url": "https://openalex.org/A5009882421", "inst": "National Bureau of Economic Research" } ], "affiliations": [ "National Bureau of Economic Research" ] }, { "uid": "doi:10.2139/ssrn.4351817", "doi": "10.2139/ssrn.4351817", "title": "'Let’s Have a Chat': Principles for the Effective Application of ChatGPT and Large Language Models in the Practice of Forensic Accounting", "authors": [ "Daniel Street", "Joseph Wilck" ], "posted": "2023-02-10", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4351817", "field": "accounting", "role": "method", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 35, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 192, "authors_detailed": [ { "name": "Daniel Street", "url": "https://openalex.org/A5077370725", "inst": "Bucknell University" }, { "name": "Joseph Wilck", "url": "https://openalex.org/A5071203415", "inst": "Bucknell University" } ], "affiliations": [ "Bucknell University" ] }, { "uid": "doi:10.2139/ssrn.4350925", "doi": "10.2139/ssrn.4350925", "title": "Economics of ChatGPT: A Labor Market View on the Occupational Impact of Artificial Intelligence", "authors": [ "Ali Zarifhonarvar" ], "posted": "2023-02-09", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4350925", "field": "economics", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 45, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 598, "authors_detailed": [ { "name": "Ali Zarifhonarvar", "url": "https://openalex.org/A5008687578", "inst": "Indiana University" } ], "affiliations": [ "Indiana University" ] }, { "uid": "doi:10.2139/ssrn.4349078", "doi": "10.2139/ssrn.4349078", "title": "A Primer on Artificial Intelligence and Machine Learning for the Financial Services Industry", "authors": [ "Joerg Osterrieder" ], "posted": "2023-02-08", "added": "2026-08-22", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4349078", "field": "finance", "role": "object", "bullets": [ "The primer is written for graduate students and new practitioners in financial services; no empirical sample, time period, or geography is specified.", "The paper reviews AI and machine learning methods, including generative AI and large language models, applied to fraud detection, credit scoring, trading, and risk management; not stated as tested or validated.", "Not stated: the primer reports no empirical result, instead covering mathematical foundations, finance applications, key challenges, and regulatory guidance including the EU AI Act." ], "bullet_provenance": "ai", "salience": 35, "edition": 22, "models": [], "validated": null, "n": 4118, "authors_detailed": [ { "name": "Joerg Osterrieder", "url": "https://openalex.org/A5032430973", "inst": "BFF Bern" } ], "affiliations": [ "BFF Bern" ] }, { "uid": "arxiv:2302.04343v1", "arxiv_id": "2302.04343v1", "title": "CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data", "authors": [ "Amir Namavar Jahromi", "Ebrahim Pourjafari", "Hadis Karimipour", "Amit Satpathy", "Lovell Hodge" ], "posted": "2023-02-08", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2302.04343v1", "field": "finance", "role": "method", "bullets": [ "Unstructured obituary records used in an insurance setting, labelled with cause of death; the abstract states no sample size, period, or geography.", "A RoBERTa encoder is trained with contrastive representation learning, then drives an active learning loop that labels the corpus iteratively; comparisons are against two ablated baselines, with no accuracy figures quoted.", "The combined pipeline outperforms both the contrastive only and the active learning only variants on the cause of death task; the abstract reports no magnitudes." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "labelled obituary cause of death data", "salience": 30, "edition": 12, "n": 1512, "authors_detailed": [ { "name": "Amir Namavar Jahromi", "url": "https://openalex.org/A5074265025", "inst": "University of Calgary" }, { "name": "Ebrahim Pourjafari", "url": "https://openalex.org/A5058332892", "inst": "University of Alberta" }, { "name": "Hadis Karimipour", "url": "https://openalex.org/A5102945288", "inst": "University of Calgary" }, { "name": "Amit Satpathy", "url": "https://openalex.org/A5008242505", "inst": "Agency for Science, Technology and Research" }, { "name": "L. Hodge", "url": "https://openalex.org/A5007357324", "inst": "University of Waterloo" } ], "affiliations": [ "University of Calgary", "University of Alberta", "Agency for Science, Technology and Research", "University of Waterloo" ] }, { "uid": "doi:10.2139/ssrn.4345921", "doi": "10.2139/ssrn.4345921", "title": "The Usefulness and Challenges of Chatbots for Accounting Professionals: Application On ChatGPT", "authors": [ "Hashem Alshurafat" ], "posted": "2023-02-02", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4345921", "field": "accounting", "role": "object", "bullet_provenance": "none", "models": [ "gpt" ], "open_weights": false, "salience": 34, "edition": 3, "audience": "general", "bullets": [], "validated": null, "n": 597, "authors_detailed": [ { "name": "Hashem Alshurafat", "url": "https://openalex.org/A5050949889", "inst": "Hashemite University" } ], "affiliations": [ "Hashemite University" ] }, { "uid": "doi:10.2139/ssrn.4346152", "doi": "10.2139/ssrn.4346152", "title": "Democratizing Financial Knowledge with ChatGPT by OpenAI: Unleashing the Power of Technology", "authors": [ "Thomas Yue", "David Au", "Chi Chung Au", "Kwan Yuen Iu" ], "posted": "2023-02-02", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4346152", "field": "finance", "role": "instrument", "bullets": [ "Proof-of-concept combining SHAP-based explainable AI with ChatGPT to interpret nonlinear black-box ML model predictions in finance.", "ChatGPT interpreted SHAP feature-importance outputs and translated complex financial model predictions into non-technical explanations for lay audiences.", "ChatGPT made ML-driven financial predictions accessible to non-experts, though limitations in prompt engineering constrain current reliability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 3883, "authors_detailed": [ { "name": "Thomas Yue", "url": "https://openalex.org/A5110847658", "inst": "Queen Elizabeth Hospital" }, { "name": "David Au", "url": "https://openalex.org/A5096772295", "inst": "Queen Elizabeth Hospital" }, { "name": "Chi Chung Au", "url": "https://openalex.org/A5096772296", "inst": "MechaniX Limited" }, { "name": "Kwan Yuen Iu", "url": "https://openalex.org/A5087822245", "inst": "Albert Luk's Chambers (DEC 22 - FEB 23)" } ], "affiliations": [ "MechaniX Limited", "Albert Luk's Chambers (DEC 22 - FEB 23)" ] }, { "uid": "doi:10.2139/ssrn.4335945", "doi": "10.2139/ssrn.4335945", "title": "Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards", "authors": [ "John Nay" ], "posted": "2023-02-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4335945", "field": "finance", "role": "method", "bullets": [ "Thousands of evaluation labels constructed from U.S. court opinions on fiduciary obligations, tested across three generations of OpenAI GPT models.", "GPT models classified fiduciary obligation scenarios from judicial opinions, with accuracy benchmarked against human-coded labels across successive model versions.", "Latest OpenAI model achieved 78 percent accuracy on fiduciary obligations versus 27 percent for GPT-3, tracking improvement with general model capability." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "Human-coded U.S. court opinion fiduciary obligation labels", "salience": 52, "n": 2420, "authors_detailed": [ { "name": "John J. Nay", "url": "https://openalex.org/A5058951556", "inst": "New York Public Library" } ], "affiliations": [ "New York Public Library" ] }, { "uid": "doi:10.2139/ssrn.4329294", "doi": "10.2139/ssrn.4329294", "title": "Attention Is All Your Need. the Evidence from Asset Allocation with Interpretable Transformer Model", "authors": [ "Tian Ma", "Wanwan Wang", "Yu Chen" ], "posted": "2023-01-31", "added": "2026-07-24", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4329294", "field": "finance", "role": "instrument", "bullets": [ "Chinese stock market data (sample size not stated) used to build a return-risk trade-off asset allocation strategy.", "A transformer model predicts return and volatility and is compared against LSTM and other non-linear models, with a SHAP-based economic feature importance measure derived; no language model is used.", "The transformer improves predictability and captures larger economic gains than LSTM and other non-linear benchmarks." ], "bullet_provenance": "ai", "salience": 38, "edition": 3, "audience": "technical", "models": [], "validated": null, "n": 1026, "authors_detailed": [ { "name": "Tian Ma", "url": "https://openalex.org/A5078767804", "inst": "Minzu University of China" }, { "name": "Wanwan Wang", "url": "https://openalex.org/A5101845319", "inst": "Minzu University of China" }, { "name": "Yu Chen", "url": "https://openalex.org/A5100402115", "inst": "Minzu University of China" } ], "affiliations": [ "Minzu University of China" ] }, { "uid": "doi:10.2139/ssrn.4319609", "doi": "10.2139/ssrn.4319609", "title": "Evidence of Behavior Consistent with Self-Interest and Altruism in An Artificially Intelligent Agent", "authors": [ "Tim Johnson", "Nicholas Obradovich" ], "posted": "2023-01-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4319609", "field": "economics", "role": "agent", "bullets": [ "Incentivized dictator game experiments with OpenAI language models using real token-based payoffs, varying recipients across AI agents, a human experimenter, and an anonymous charity.", "Most sophisticated OpenAI model played non-social payoff-maximization tasks and dictator games with real incentives structured as API token costs.", "Top model maximized payoffs in 92% of non-social trials and shared at human-like rates with other AI agents, but gave substantially less to the human experimenter or charity." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 68, "validated": null, "n": 3178, "authors_detailed": [ { "name": "Tim Johnson", "url": "https://openalex.org/A5000417348", "inst": "Atkins (United States)" }, { "name": "Nicholas Obradovich", "url": "", "inst": "Max Planck Society" } ], "affiliations": [ "Atkins (United States)", "Max Planck Society" ] }, { "uid": "doi:10.2139/ssrn.4338304", "doi": "10.2139/ssrn.4338304", "title": "AI-Automated Sales: The Curse of “Single-Minded” Profit Pursuits", "authors": [ "Adelle Yang", "Yu Gu", "Sijin Chen" ], "posted": "2023-01-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4338304", "field": "management", "role": "object", "bullets": [ "Six pre-registered experiments with 3,361 participants testing consumer responses to AI versus human sales agents across multiple sales contexts.", "Experiments manipulated messenger identity (AI vs. human) for identical sales pitches and measured purchase intention and inferred profit motive.", "Consumers perceive AI sales agents as more profit-centered than human agents, reducing purchase intention; effects disappear when profit motive is disconnected from the pitch." ], "bullet_provenance": "ai", "salience": 55, "models": [], "validated": null, "n": 3881, "authors_detailed": [ { "name": "Adelle Yang", "url": "https://openalex.org/A5050137284", "inst": "National University of Singapore" }, { "name": "Yu Gu", "url": "https://openalex.org/A5100749022", "inst": "Tsinghua University" }, { "name": "Sijin Chen", "url": "https://openalex.org/A5051624525", "inst": "National University of Singapore" } ], "affiliations": [ "National University of Singapore", "Tsinghua University" ] }, { "uid": "doi:10.2139/ssrn.4337182", "doi": "10.2139/ssrn.4337182", "title": "Sentiment Spin: Attacking Financial Sentiment with GPT-3", "authors": [ "Markus Leippold" ], "posted": "2023-01-30", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4337182", "field": "finance", "role": "method", "bullets": [ "Financial PhraseBank, a human-annotated database for financial sentiment, used to test adversarial attacks on dictionary-based sentiment methods.", "GPT-3 generated adversarial rewrites of financial sentences to flip keyword-based sentiment labels while preserving meaning; BERT-based methods tested for robustness.", "GPT-3 achieved near-99% attack success rate on negative sentences in dictionary methods; context-aware models such as BERT remained robust to the same attacks." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "attack success rate on Financial PhraseBank", "salience": 62, "n": 3882, "authors_detailed": [ { "name": "Markus Leippold", "url": "https://openalex.org/A5073309846", "inst": "University of Zurich" } ], "affiliations": [ "University of Zurich" ] }, { "uid": "arxiv:2301.04408v1", "arxiv_id": "2301.04408v1", "title": "GPT as Knowledge Worker: A Zero-Shot Evaluation of (AI)CPA Capabilities", "authors": [ "Jillian Bommarito", "Michael Bommarito", "Daniel Martin Katz", "Jessica Katz" ], "posted": "2023-01-11", "added": "2026-08-05", "source_label": "arXiv", "url": "https://arxiv.org/abs/2301.04408v1", "field": "accounting", "role": "object", "bullets": [ "A sample CPA Regulation exam section and over 200 multiple choice questions written to AICPA blueprints for legal, financial, accounting, technology, and ethics tasks.", "OpenAI text-davinci-003 and earlier GPT-3 versions answer in zero shot; responses are graded against the exam answer key across prompts and parameter settings.", "Best settings yield 57.6 percent correct against a 25 percent guessing rate, but only 14.4 percent on the quantitative REG sample; accuracy climbed from 30 percent for davinci-001 to 57 percent for davinci-003." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "CPA exam answer key", "salience": 58, "edition": 12, "n": 1429, "authors_detailed": [ { "name": "Jillian Bommarito", "url": "https://openalex.org/A5064721809", "inst": "Gleason (United States)" }, { "name": "Michael James Bommarito", "url": "https://openalex.org/A5085629068", "inst": "Stanford Medicine" }, { "name": "Daniel Katz", "url": "https://openalex.org/A5101661429", "inst": "Chicago Kent College of Law" }, { "name": "Jessica Katz", "url": "https://openalex.org/A5113641840", "inst": "University of California, Los Angeles" } ], "affiliations": [ "University of California, Los Angeles", "Gleason (United States)", "Stanford Medicine", "Chicago Kent College of Law" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4322372", "doi": "10.2139/ssrn.4322372", "title": "Gpt as Knowledge Worker: A Zero-Shot Evaluation of (AI)CPA Capabilities", "authors": [ "Jillian Bommarito", "Michael James Bommarito", "Jessica Katz", "Daniel Martin Katz" ], "posted": "2023-01-11", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4322372", "field": "accounting", "role": "agent", "bullets": [ "U.S. CPA Uniform Examination sample including REG section and over 200 AICPA Blueprint multiple-choice questions spanning legal, financial, and accounting tasks.", "OpenAI text-davinci-003 evaluated zero-shot on CPA exam questions across three GPT-3 generations from text-davinci-001 through text-davinci-003.", "Best model scores 57.6% on Blueprint questions, up from 30% for text-davinci-001; top-two answer accuracy reaches 82.1% but REG section scores only 14.4%." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "AICPA CPA exam questions", "salience": 55, "n": 2628, "authors_detailed": [ { "name": "Jillian Bommarito", "url": "https://openalex.org/A5064721809", "inst": "BioVentures (United States)" }, { "name": "Michael James Bommarito", "url": "https://openalex.org/A5085629068", "inst": "Gleason (United States)" }, { "name": "Jessica Ann Mefford Katz", "url": "https://openalex.org/A5063097176", "inst": "273 Ventures" }, { "name": "Daniel Katz", "url": "https://openalex.org/A5038615036", "inst": "Gleason (United States)" } ], "affiliations": [ "BioVentures (United States)", "Gleason (United States)", "273 Ventures" ] }, { "uid": "doi:10.2139/ssrn.4316416", "doi": "10.2139/ssrn.4316416", "title": "How Conversational Generative AI Reshapes User Decision Processes: Evidence from a Large–Scale Financial Decision Context", "authors": [ "Yue Guo", "Zhe Jing", "Siliang Tong", "Tao Chen", "Subodha Kumar", "Shumiao Ouyang" ], "posted": "2023-01-06", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4316416", "field": "finance", "role": "object", "bullets": [ "Large-scale field study on Ant Fortune, China's leading fintech platform, examining how generative AI conversational advisors reshape investor decision processes.", "The study measured how GAIC usage altered information search, reassessment of holdings, and disposition-effect behavior across heterogeneous investor types.", "GAICs broadened exploration, deepened reassessment of holdings, and most reduced disposition bias among investors with strongest baseline behavioral tendencies." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "salience": 65, "validated": null, "n": 3300, "authors_detailed": [ { "name": "Yue Guo", "url": "https://openalex.org/A5035377533", "inst": "Southern University of Science and Technology" }, { "name": "Zhe Jing", "url": "https://openalex.org/A5138403627", "inst": "Nanyang Technological University" }, { "name": "Siliang Tong", "url": "https://openalex.org/A5085588455", "inst": "Nanyang Technological University" }, { "name": "Tao Chen", "url": "https://openalex.org/A5100357813", "inst": "Nanyang Technological University" }, { "name": "Subodha Kumar", "url": "https://openalex.org/A5020635939", "inst": "Temple College" }, { "name": "Shumiao Ouyang", "url": "https://openalex.org/A5085976490", "inst": "Sage (United Kingdom)" } ], "affiliations": [ "Southern University of Science and Technology", "Nanyang Technological University", "Temple College", "Sage (United Kingdom)" ] }, { "uid": "doi:10.2139/ssrn.4316615", "doi": "10.2139/ssrn.4316615", "title": "Large Language Models as Corporate Lobbyists", "authors": [ "John Nay" ], "posted": "2023-01-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4316615", "field": "management", "role": "agent", "bullets": [ "Proposed U.S. Congressional bills assessed for relevance to specific public companies using hundreds of novel ground-truth relevance labels.", "OpenAI text-davinci-003 determines bill-company relevance with confidence levels and drafts persuasion letters to bill sponsors under zero-shot prompting.", "Text-davinci-003 outperforms the irrelevance baseline on relevance prediction; text-davinci-002 performs worse than guessing, showing rapid cross-generation improvement." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "ground-truth bill relevance labels", "salience": 60, "n": 2627, "authors_detailed": [ { "name": "John J. Nay", "url": "https://openalex.org/A5058951556", "inst": "New York Public Library" } ], "affiliations": [ "New York Public Library" ] }, { "uid": "doi:10.2139/ssrn.4266526", "doi": "10.2139/ssrn.4266526", "title": "Climate-Related Information Demand in Capital Markets: Evidence from China’s Carbon Neutrality Pledge Announcement", "authors": [ "Henry L. Friedman", "Kanyuan Huang", "Kaiwen Wu" ], "posted": "2022-11-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4266526", "field": "finance", "role": "instrument", "bullets": [ "Chinese listed firms on interactive investor platforms around China's March 2021 carbon neutrality pledge, with returns and volatility data.", "An LLM classified investor questions by topic, identifying demand related to green innovation, carbon transition plans, and financial implications.", "Firms receiving more carbon-related questions had higher returns and volatility over the subsequent year; information demand predicts future green performance but not financial performance." ], "bullet_provenance": "ai", "validated": false, "salience": 60, "models": [], "n": 2895, "authors_detailed": [ { "name": "Henry L. Friedman", "url": "https://openalex.org/A5091075096", "inst": "University of California, Los Angeles" }, { "name": "Kanyuan Huang", "url": "https://openalex.org/A5066418670", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Kaiwen Wu", "url": "https://openalex.org/A5026408321", "inst": "Shanghai Lixin University of Accounting and Finance" } ], "affiliations": [ "University of California, Los Angeles", "Chinese University of Hong Kong, Shenzhen", "Shanghai Lixin University of Accounting and Finance" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4255978", "doi": "10.2139/ssrn.4255978", "title": "Central Bank Mandates and Monetary Policy Stances: through the Lens of Federal Reserve Speeches", "authors": [ "Christoph Bertsch", "Isaiah Hull", "Robin L. Lumsdaine", "Xin Zhang" ], "posted": "2022-11-01", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4255978", "field": "economics", "role": "instrument", "bullets": [ "Comprehensive corpus of Federal Reserve speeches from 1960 to 2022 analyzed for mandate interpretation and policy stance in U.S. monetary policy.", "Collection of LLMs modified for central bank texts classify policy concerns, mandate priorities, and financial stability discourse in Fed speeches.", "Fed treats financial stability as its most important non-mandated concern when debt-to-GDP is high; related discourse generates significant asset price movements." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": false, "salience": 62, "n": 2626, "authors_detailed": [ { "name": "Christoph Bertsch", "url": "https://openalex.org/A5013454454", "inst": "Swedish National Bank" }, { "name": "Isaiah Hull", "url": "https://openalex.org/A5020849374", "inst": "BI Norwegian Business School" }, { "name": "Robin L. 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Leone", "url": "https://openalex.org/A5042343113", "inst": "Northwestern University" }, { "name": "Jennifer Wu Tucker", "url": "https://openalex.org/A5050334594", "inst": "University of Florida" } ], "affiliations": [ "Northwestern University", "University of Florida", "University of Miami", "University of Connecticut" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4187056", "doi": "10.2139/ssrn.4187056", "title": "Multi-(Horizon) Factor Investing with AI", "authors": [ "Ruslan Goyenko", "Chengyu Zhang" ], "posted": "2022-08-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4187056", "field": "finance", "role": "instrument", "bullets": [ "U.S. equity factors over multiple horizons, incorporating trading costs, liquidity, and volatility trade-offs for long-horizon portfolio construction.", "A transformer architecture adapted from ChatGPT's backbone timed factor premium realizations and dynamically rebalanced multi-factor portfolios.", "Long-horizon perspective enables more patient trading and strategic factor timing, improving active portfolio returns through dynamic diversification." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 50, "n": 3299, "authors_detailed": [ { "name": "Ruslan Goyenko", "url": "https://openalex.org/A5000317559", "inst": "McGill University" }, { "name": "Chengyu Zhang", "url": "https://openalex.org/A5100722691", "inst": "McGill University" } ], "affiliations": [ "McGill University" ] }, { "uid": "doi:10.2139/ssrn.4162594", "doi": "10.2139/ssrn.4162594", "title": "Mobilizing text as data", "authors": [ "Jihun Bae", "Chung-Yu Hung", "Laurence van Lent" ], "posted": "2022-07-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.4162594", "field": "finance", "role": "method", "bullets": [ "Survey of textual analysis methods applied across corporate disclosures, earnings calls, investor relations, and social media in finance and accounting.", "Reviews keyword lists, pattern-based classification, word embeddings, and LLMs for extracting structured information from unstructured financial text.", "Provides guidance on method selection, validation of text-based measures, and effective reporting; identifies promising directions for text-as-data research." ], "bullet_provenance": "ai", "salience": 50, "models": [], "validated": null, "n": 2894, "authors_detailed": [ { "name": "Jihun Bae", "url": "https://openalex.org/A5082952461", "inst": "Erasmus University Rotterdam" }, { "name": "Chung‐Yu Hung", "url": "https://openalex.org/A5010418326", "inst": "The University of Melbourne" }, { "name": "Laurence van Lent", "url": "https://openalex.org/A5050922413", "inst": "Frankfurt School of Finance & Management" } ], "affiliations": [ "Erasmus University Rotterdam", "The University of Melbourne", "Frankfurt School of Finance & Management" ] }, { "uid": "doi:10.2139/ssrn.4137872", "doi": "10.2139/ssrn.4137872", "title": "Can AI and AI-Hybrids detect persuasion skills? 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Dover", "url": "https://openalex.org/A5076710053", "inst": "The University of Texas at Dallas" }, { "name": "K. Sudhir", "url": "https://openalex.org/A5072447765", "inst": "Yale University" } ], "affiliations": [ "The University of Texas at Dallas", "Yale University", "University of Wisconsin–Madison" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.3868599", "doi": "10.2139/ssrn.3868599", "title": "InnoVAE: Generative AI for Mapping Patents and Firm Innovation", "authors": [ "Zhaoqi Cheng", "Dokyun Lee", "Prasanna Tambe" ], "posted": "2022-04-13", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3868599", "field": "management", "role": "instrument", "bullets": [ "Three decades of AI patents, mapping unstructured patent text into spatial representations of firm innovative activity and competitive positioning.", "A variational autoencoder (InnoVAE) learned disentangled patent vector representations to construct interpretable IP portfolio measures at scale.", "The method generates technology landscapes for competitive positioning, engineers interpretable text features, and augments applications to reduce patent rejection risk." ], "bullet_provenance": "ai", "models": [ "open_other" ], "validated": true, "validation_note": "internal consistency validation on AI patent corpus", "salience": 45, "n": 3298, "authors_detailed": [ { "name": "Zhaoqi Cheng", "url": "https://openalex.org/A5031867224", "inst": "Worcester Polytechnic Institute" }, { "name": "Dokyun Lee", "url": "https://openalex.org/A5088913516", "inst": "Boston University" }, { "name": "Prasanna Tambe", "url": "https://openalex.org/A5085743615", "inst": "University of the Arts" } ], "affiliations": [ "Boston University", "Worcester Polytechnic Institute", "University of the Arts" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.4023622", "doi": "10.2139/ssrn.4023622", "title": "Intellectual Property Protection Lost and Competition: An Examination Using Large Language Models", "authors": [ "Utku Acikalin", "Tolga Caskurlu", "Gerard Hoberg", "Gordon M. 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Phillips", "url": "https://openalex.org/A5068926411", "inst": "Dartmouth College" } ], "affiliations": [ "University of Southern California", "Dartmouth College", "TOBB University of Economics and Technology", "University of Amsterdam" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.3988802", "doi": "10.2139/ssrn.3988802", "title": "The Bullwhip Effect in Servitized Manufacturers", "authors": [ "Yimeng Niu", "Jing Wu", "Shenyang Jiang", "Zhi-bin Jiang" ], "posted": "2021-12-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3988802", "field": "management", "role": "instrument", "bullets": [ "U.S.-listed manufacturing firms' 10-K filings measuring service offerings via text mining, analyzed with difference-in-differences.", "Bag-of-words text mining identified annual services; GPT-4 validated measurement consistency for complementing and substituting service categories.", "Complementing services decrease demand variability through information channels; substituting services mitigate intrafirm bullwhip through production efficiency gains." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": true, "validation_note": "consistency check between bag-of-words and GPT-4 classification", "salience": 48, "n": 2893, "authors_detailed": [ { "name": "Yimeng Niu", "url": "https://openalex.org/A5037415980", "inst": "Shanghai Jiao Tong University" }, { "name": "Jing Wu", "url": "https://openalex.org/A5014078414", "inst": "Chinese University of Hong Kong" }, { "name": "Shenyang Jiang", "url": "https://openalex.org/A5082312512", "inst": "Tongji University" }, { "name": "Zhibin Jiang", "url": "https://openalex.org/A5010857481", "inst": "Shanghai Jiao Tong University" } ], "affiliations": [ "Shanghai Jiao Tong University", "Chinese University of Hong Kong", "Tongji University" ] }, { "uid": "doi:10.2139/ssrn.3975884", "doi": "10.2139/ssrn.3975884", "title": "EmTract: Extracting Emotions from Social Media", "authors": [ "Domonkos F. Vamossy", "Rolf Skog" ], "posted": "2021-12-05", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3975884", "field": "finance", "role": "instrument", "bullets": [ "Ten thousand annotated StockTwits messages combined with open-source emotion data, applied to daily U.S. stock price movements.", "Fine-tuned DistilBERT with augmented emoji embeddings classified investor emotions; validated against human and ChatGPT annotations on financial text.", "Firm-specific investor emotions extracted from social media predict daily stock price movements, outperforming dictionary-based sentiment methods." ], "bullet_provenance": "ai", "models": [ "gpt", "open_other" ], "open_weights": true, "validated": true, "validation_note": "human and ChatGPT annotated StockTwits data", "salience": 45, "n": 3297, "authors_detailed": [ { "name": "Domonkos F. Vamossy", "url": "https://openalex.org/A5065827646", "inst": "University of Pittsburgh" }, { "name": "Rolf P. Skog", "url": "https://openalex.org/A5065441231", "inst": "Stockholm University" } ], "affiliations": [ "University of Pittsburgh", "Stockholm University" ] }, { "uid": "doi:10.2139/ssrn.3964413", "doi": "10.2139/ssrn.3964413", "title": "Uncertainty, Stock Prices, and Debt Structure", "authors": [ "Ali K. Ozdagli", "Jianlin Wang" ], "posted": "2021-11-18", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3964413", "field": "finance", "role": "instrument", "bullets": [ "U.S. listed firms during 2018-2019 trade policy uncertainty and FOMC announcements, with high-frequency stock and SEC filing data.", "LLMs constructed a new dataset of loan-renegotiation events from SEC filings to measure bank debt flexibility under uncertainty.", "Bank debt reduces stock price sensitivity to policy uncertainty for cash-flow insolvent firms; these firms renegotiate more and increase investment when uncertainty rises." ], "bullet_provenance": "ai", "validated": false, "salience": 65, "models": [], "n": 2892, "authors_detailed": [ { "name": "Ali K. 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Cao", "Yongtae Kim", "Angie Wang", "Houping Xiao" ], "posted": "2021-09-17", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3568504", "field": "finance", "role": "instrument", "bullets": [ "U.S. earnings conference calls; cross-sectional stock returns and future operating performance serve as outcome variables.", "Interpretable neural language model within the LLM ecosystem extracts performance-specific sentiment by visualizing syntax and contextual negations from call transcripts.", "Context-aware sentiment measure outperforms standard proxies in explaining cross-sectional returns and operating performance through explicit negation handling." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "Cross-sectional return prediction vs common sentiment proxies", "salience": 55, "n": 2437, "authors_detailed": [ { "name": "Sean Cao", "url": "https://openalex.org/A5055060179", "inst": "Smith Institute" }, { "name": "Yongtae Kim", "url": "https://openalex.org/A5031498311", "inst": "Santa Clara University" }, { "name": "Angie Wang", "url": "https://openalex.org/A5005768414", "inst": "Hong Kong Polytechnic University" }, { "name": "Houping Xiao", "url": "https://openalex.org/A5069886022", "inst": "Georgia State University" } ], "affiliations": [ "Smith Institute", "Santa Clara University", "Hong Kong Polytechnic University", "Georgia State University" ] }, { "uid": "doi:10.2139/ssrn.3888897", "doi": "10.2139/ssrn.3888897", "title": "Inventory Management with Transformer: Automated Decision-Making for Order Timing and Quantity", "authors": [ "Mo Liu", "Yumo Bai", "Meng Qi", "Zuo-Jun Max Shen" ], "posted": "2021-07-20", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3888897", "field": "management", "role": "method", "bullets": [ "Millions of SKUs on a leading e-commerce platform with stochastic, non-stationary demand and random vendor lead times.", "GPT-2 architecture fine-tuned via imitation learning on ex-post optimal decisions to output order timing and quantity from contextual and historical data.", "InventoryGPT outperforms traditional and state-of-the-art inventory benchmarks on service levels and cost reduction using real-world e-commerce data." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": true, "validated": true, "validation_note": "real-world e-commerce data vs. traditional and SOTA inventory benchmarks", "salience": 55, "n": 2834, "authors_detailed": [ { "name": "Mo Liu", "url": "https://openalex.org/A5101563248", "inst": "University of California, Berkeley" }, { "name": "YuMo Bai", "url": "https://openalex.org/A5059337973", "inst": "University of California, Berkeley" }, { "name": "Meng Qi", "url": "https://openalex.org/A5014822028", "inst": "University of California, Berkeley" }, { "name": "Zuo‐Jun Max Shen", "url": "https://openalex.org/A5012457625", "inst": "University of California, Berkeley" } ], "affiliations": [ "University of California, Berkeley" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.3840203", "doi": "10.2139/ssrn.3840203", "title": "The President Reacts to News Channel of Government Communication", "authors": [ "Farshid Abdi", "Alexander Jäger", "Loriana Pelizzon", "Mila Getmansky Sherman", "Emily Kormanyos" ], "posted": "2021-05-08", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3840203", "field": "finance", "role": "instrument", "bullets": [ "Approximately 5,700 tweets by President Trump, analyzed alongside high-frequency U.S. market data to identify the direction of information flow.", "An LLM classified tweets into economy-related topics; analysis tested whether tweets caused market movements or reacted to prior price changes.", "Most economy-related tweets reflect reactions to prior market moves; only Trade and Tariffs tweets generate meaningful aggregate market reactions." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": false, "salience": 55, "n": 3295, "authors_detailed": [ { "name": "Farshid Abdi", "url": "https://openalex.org/A5073759495", "inst": "University of Massachusetts Amherst" }, { "name": "Alexander Jäger", "url": "https://openalex.org/A5147108020", "inst": "Goethe University Frankfurt" }, { "name": "Loriana Pelizzon", "url": "https://openalex.org/A5002326608", "inst": "Goethe University Frankfurt" }, { "name": "Mila Getmansky Sherman", "url": "https://openalex.org/A5038861179", "inst": "University of Massachusetts Amherst" }, { "name": "Emily Kormanyos", "url": "https://openalex.org/A5029403121", "inst": "Leibniz Institute for Financial Research SAFE" } ], "affiliations": [ "University of Massachusetts Amherst", "Goethe University Frankfurt", "Leibniz Institute for Financial Research SAFE" ] }, { "uid": "doi:10.2139/ssrn.3813948", "doi": "10.2139/ssrn.3813948", "title": "The Benefits of Access - Evidence from Private Meetings with Portfolio Firms", "authors": [ "Marco Becht", "Julian R. Franks", "Hannes F. Wagner" ], "posted": "2021-04-23", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3813948", "field": "finance", "role": "instrument", "bullets": [ "4,700 private meetings between a large active asset manager and its portfolio firms, with associated trading records.", "LLMs analyzed meeting content for soft information, tone, and material nonpublic information indicators across fund manager and governance meetings.", "Meeting-informed portfolios generate significant outperformance; 0.4% of meetings discuss material nonpublic information; trades increase after senior-management meetings." ], "bullet_provenance": "ai", "validated": false, "salience": 70, "models": [], "n": 2890, "authors_detailed": [ { "name": "Marco Becht", "url": "https://openalex.org/A5088936861", "inst": "Solvay (Belgium)" }, { "name": "Julian Franks", "url": "https://openalex.org/A5086142147", "inst": "London School of Business and Finance" }, { "name": "Hannes F. Wagner", "url": "https://openalex.org/A5062705166", "inst": "Institute for Economic Research" } ], "affiliations": [ "Solvay (Belgium)", "London School of Business and Finance", "Institute for Economic Research" ] }, { "uid": "doi:10.2139/ssrn.3746734", "doi": "10.2139/ssrn.3746734", "title": "Downside Risks in Mergers and Acquisitions: Evidence from Acquirers’ Mandatory Risk Factor Disclosure", "authors": [ "Feng Guo", "Tingting Liu", "Tao Shu", "Xinyan Yan" ], "posted": "2021-03-19", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3746734", "field": "finance", "role": "instrument", "bullets": [ "Acquirer risk factor disclosures in U.S. merger filings, analyzing mandatory risk disclosure text with unsupervised topic modeling and ChatGPT.", "Topic modeling identified four major risk factors; ChatGPT classified specific post-merger integration issues from the disclosure text.", "Dilution risk positively affects post-merger outcomes, while product, valuation, and accounting risks generally reduce acquirer performance." ], "bullet_provenance": "ai", "models": [ "gpt" ], "open_weights": false, "validated": false, "salience": 40, "n": 3294, "authors_detailed": [ { "name": "Feng Guo", "url": "https://openalex.org/A5018817987", "inst": "Iowa State University" }, { "name": "Tingting Liu", "url": "https://openalex.org/A5100442037", "inst": "Iowa State University" }, { "name": "Tao Shu", "url": "https://openalex.org/A5101650478", "inst": "Chinese University of Hong Kong, Shenzhen" }, { "name": "Xinyan Yan", "url": "https://openalex.org/A5078753151", "inst": "University of Dayton" } ], "affiliations": [ "Iowa State University", "Chinese University of Hong Kong, Shenzhen", "University of Dayton" ] }, { "uid": "doi:10.2139/ssrn.3655263", "doi": "10.2139/ssrn.3655263", "title": "Does Finance Benefit Society? A Language Embedding Approach", "authors": [ "Manish Jha", "Hongyi Liu", "Asaf Manela" ], "posted": "2020-08-04", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3655263", "field": "finance", "role": "instrument", "bullets": [ "Millions of books published in eight countries over several centuries, measuring popular sentiment toward finance across time and geography.", "A large language model scored finance-related sentiment in book text; results validated against survey-based measures of financial market participation.", "Finance sentiment predicts financial crises one year in advance; positive sentiment shocks are followed by higher output and credit growth." ], "bullet_provenance": "ai", "models": [ "legacy" ], "validated": true, "validation_note": "survey-based financial participation and income inequality measures", "salience": 55, "n": 3293, "authors_detailed": [ { "name": "Manish Jha", "url": "https://openalex.org/A5024242858", "inst": "Georgia State University" }, { "name": "Hongyi Liu", "url": "https://openalex.org/A5100622132", "inst": "Washington University in St. Louis" }, { "name": "Asaf Manela", "url": "https://openalex.org/A5088491879", "inst": "Washington University in St. Louis" } ], "affiliations": [ "Washington University in St. Louis", "Georgia State University" ], "prestige": true, "us_top": true }, { "uid": "doi:10.2139/ssrn.3560355", "doi": "10.2139/ssrn.3560355", "title": "Transfer Learning and Textual Analysis of Accounting Disclosures: Applying Big Data Methods to Small(er) Data Sets", "authors": [ "Federico Siano", "Peter D. Wysocki" ], "posted": "2020-04-21", "added": "2026-08-20", "source_label": "SSRN", "url": "https://doi.org/10.2139/ssrn.3560355", "field": "accounting", "role": "method", "bullets": [ "Quarterly earnings disclosures from U.S. public companies serve as the test bed for applying BERT-based transfer learning to accounting textual analysis.", "BERT was pre-trained on generic corpora, then fine-tuned on small labeled accounting datasets to classify sentiment in earnings disclosures, capturing contextual word meaning.", "Fine-tuned BERT outperformed existing dictionary-based and bag-of-words textual analysis tools in accounting, using widely available low-cost computing resources." ], "bullet_provenance": "ai", "models": [ "legacy" ], "open_weights": true, "validated": true, "validation_note": "performance comparison vs existing accounting textual analysis tools", "salience": 48, "n": 3879, "authors_detailed": [ { "name": "Federico Siano", "url": "https://openalex.org/A5052997135", "inst": "The University of Texas at Dallas" }, { "name": "Peter D. Wysocki", "url": "https://openalex.org/A5011668199", "inst": "Boston University" } ], "affiliations": [ "The University of Texas at Dallas", "Boston University" ], "prestige": true, "us_top": true } ] }