Papers (1699)

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Treat AI governance as a strategic board-level function rather than a technical compliance task: embedding AI oversight in enterprise risk management can reduce deployment risk and speed adoption, turning trustworthy AI practices into a source of competitive advantage.

n/a theoretical Full text openalex

Artificial Intelligence Governance In Corporate Strategy: Ethical Risk, Regulatory Compliance, And Competitive Advantage · Dr. Yemi Martins · Mar 19, 2026

Large language models subtly but systematically change what people mean when they write: heavy LLM users produce nearly 70% more neutral answers and report less creative, less ‘in‑their‑voice’ prose. When asked to revise human essays or write peer reviews, LLMs frequently alter semantics and give reviews that weight clarity/significance less and score papers roughly one point higher on average.

medium quasi_experimental Full text arxiv

How LLMs Distort Our Written Language · Marwa Abdulhai, Isadora White, Yanming Wan, Ibrahim Qureshi, Joel Leibo, Max Kle... · Mar 18, 2026

A test-driven agent workflow that surfaces which tests to run cuts AI-induced regressions by about 70% and raises bug-resolution rates from 24% to 32% on a standard coding benchmark; merely instructing agents to follow TDD increased regressions, while an autonomous improvement loop achieved 60% resolution with zero regressions on a small subset.

medium quasi_experimental Full text arxiv

TDAD: Test-Driven Agentic Development - Reducing Code Regressions in AI Coding Agents via Graph-Based Impact Analysis · Pepe Alonso · Mar 18, 2026

Allowing everyone to game AI decision-support systems can entrench inequality: simultaneous recourse shifts selection thresholds so early winners set the benchmark and direct improvement, amplifying initial disparities and creating persistent performance gaps.

n/a theoretical Full text arxiv

Actionable Recourse in Competitive Environments: A Dynamic Game of Endogenous Selection · Ya-Ting Yang, Quanyan Zhu · Mar 18, 2026

Tax AI only when it threatens cognitive jobs: a theoretical model finds governments should begin taxing AI once its capabilities make cognitive workers consider moving into manual roles, because that threshold marks the point where taxing AI improves labor allocation without unduly distorting capital and growth incentives.

n/a theoretical Full text arxiv

Workers' Incentives and the Optimal Taxation of AI · Jakub Growiec, Klaus Prettner, Maciej Szkróbka · Mar 18, 2026

An AI career coach modestly boosts short-term goal progress compared with no support, but not versus structured written reflection; its chief added value appears to be raising users' sense of accountability, not deeper changes in goal alignment.

medium rct Full text arxiv

AI-Assisted Goal Setting Improves Goal Progress Through Social Accountability · Michel Schimpf, Julian Voigt, Thomas Bohné · Mar 18, 2026

A Governed Memory layer stitches together agent-specific memories into a governed shared store, eliminating cross-entity leakage and enforcement failures while cutting context tokens and maintaining retrieval accuracy; controlled tests and a public benchmark show high recall, strong governance routing, and no quality penalty from schema enforcement.

medium descriptive Full text arxiv

Governed Memory: A Production Architecture for Multi-Agent Workflows · Hamed Taheri · Mar 18, 2026

A multi‑agent LLM sandbox that learns cross‑category consumer preferences and uses mean‑field interactions improves simulated product choice and purchase forecasts, offering a promising platform for scalable market simulation—though its claims rest on simulated environments and depend heavily on the training transaction data.

low descriptive Full text arxiv

MALLES: A Multi-agent LLMs-based Economic Sandbox with Consumer Preference Alignment · Yusen Wu, Yiran Liu, Xiaotie Deng · Mar 18, 2026

Anonymized LLM trading agents produce robust backtest returns (Sharpe ~1.40) and negative controls indicate signals are not just memorized tickers; however, the alpha is sensitive to market regime and limited to short historical windows, raising questions about long-term live performance.

medium quasi_experimental Full text arxiv

Can Blindfolded LLMs Still Trade? An Anonymization-First Framework for Portfolio Optimization · Joohyoung Jeon, Hongchul Lee · Mar 18, 2026

AI is speeding early-stage drug discovery—improving hit-finding, property prediction and protein modelling—but it has not yet delivered a fully AI-originated approved drug; data fragmentation, model limitations and biological uncertainty mean human-led validation and costly clinical testing still determine ultimate success.

medium review_meta Summary semantic_scholar

Has AI Reshaped Drug Discovery, or Is There Still a Long Way to Go? · K. Shree Harini, D. Ezhilarasan · Fetched Mar 18, 2026

AI is speeding up drug discovery and clinical development—cutting timelines and reducing some failure modes—but the economic upside will accrue unevenly because performance depends on proprietary data, heavy upfront costs and regulatory uncertainty.

medium review_meta Summary semantic_scholar

From Algorithm to Medicine: AI in the Discovery and Development of New Drugs · A. Lopes, C. F. Rodrigues, Francisco A. M. Silva · Fetched Mar 18, 2026

AI is already shaving time off early-stage drug discovery but has not moved the needle on Phase II/III success—real value accrues to firms that couple AI with high-quality data, experiments and organizational change.

low review_meta Summary semantic_scholar

Learning from the successes and failures of early artificial intelligence (AI) adoption for drug discovery in Big BioPharma · M. Braddock, Krzysztof Jeziorski · Fetched Mar 18, 2026

AI promises faster, cheaper drug discovery but gains are conditional: large firms adopt via partnerships, cultural transformation, or productionized tools while startups exploit pre‑trained models and cloud services, yet durable democratized discovery hinges on interoperable data, robust validation, skilled teams and regulatory clarity.

n/a commentary Full text semantic_scholar

AI as the Catalyst for a New Paradigm in Biomedical Research · R. Pajon · Fetched Mar 18, 2026

AI acts chiefly as cheaper prediction: it boosts productivity by augmenting human judgment rather than simply automating jobs, shifting value toward complementary skills, data-rich firms and new organizational practices.

n/a theoretical Summary semantic_scholar

PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery · Weize Xu, Erwin Poussi, Quan Zhong, Zehua Zeng, Christopher Zou, Xuehai Wang, Yi... · Fetched Mar 18, 2026

Decentralized Autonomous Organizations can mobilize tokenized capital and community governance to accelerate early-stage drug discovery, but regulatory uncertainty, health-data privacy and token-market incentives threaten long-run effectiveness and patient protection.

low review_meta Summary semantic_scholar

Decentralized Autonomous Organizations in the Pharmaceutical Sector: A Paradigm Shift in Research, Development, and Governance · Rajendra V. Patil, G. M. Poddar, S. M. Rajput, D. Bhadane, A. Suryavanshi, Shrav... · Fetched Mar 18, 2026

Seeing food as soft matter reframes R&D: digital twins and AI can sharply speed formulation and embed sensory knowledge, but they also risk locking in consensus and erasing cultural nuance unless paired with interdisciplinary, language-aware practices.

low theoretical Summary semantic_scholar

At the table with Wittgenstein: How language shapes taste and texture · Florian Nettesheim · Fetched Mar 18, 2026

AI monitoring and predictive models can flag risky online gambling behaviour and enable tailored interventions, but evidence is mostly retrospective and short-term; without randomized evaluations, transparency, and stronger data governance the systems remain promising prototypes rather than proven public‑policy tools.

low review_meta Full text semantic_scholar

Deep technologies and safer gambling: A systematic review. · L. G. Cardoso, Beatriz C R Barroso, G. Piccoli, Miguel Peixoto, Pedro Morgado, A... · Fetched Mar 18, 2026

Deep learning is poised to speed materials discovery and cut R&D cycle times by enabling end-to-end prediction and inverse design; realizing those gains requires calibrated, interpretable models and standardized multimodal data plus automated labs to turn predictions into validated materials.

medium review_meta Summary semantic_scholar

Machine Learning-Driven R&D of Perovskites and Spinels: From Traditional Models to Deep Learning. · Mengxue Sun, Yingquan Song, Zhengxin Chen, Lanze Xiao, Hairui Zhou, Jia Lin · Fetched Mar 18, 2026

AI for ecology has moved beyond simple automation to produce methodological advances and tangible conservation value when computer scientists and ecologists collaborate closely. To realize scientific and policy impact, projects must couple technical innovation with ecological relevance, shared data infrastructure, and long-term partnerships.

n/a review_meta Summary semantic_scholar

Towards ‘digital ecology’: Advances in integrating artificial intelligence from data generation to ecological insight · D. Tuia, Sara Beery, Blair R. Costelloe, Ruth Y. Oliver, Nicolas Lecomte · Fetched Mar 18, 2026

Finance professionals who report using AI also report sizable gains: survey respondents link AI adoption to materially higher decision‑making efficiency, operational efficiency and resilience. These results are based on cross‑sectional self‑reports, so causal claims require follow‑up with objective, longitudinal evidence.

low correlational Full text semantic_scholar

From Data to Decisions: Harnessing Artificial Intelligence for Smarter, Faster, and More Resilient Financial Systems · Islam Belhaoua, I. Hanif · Fetched Mar 18, 2026

AI can raise radiology accuracy and throughput, but benefits are conditional on real-world integration—poorly designed deployments risk automation bias, deskilling and workflow disruption that can erode clinical and economic gains.

low review_meta Full text semantic_scholar

Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation. · B. Koçak, Renato Cuocolo · Fetched Mar 18, 2026

Organizational backing and managerial mentoring determine whether AI delivers value: firms that invest in strategic support and developmental mentoring are more likely to realize productivity and innovation gains from human–AI collaboration.

n/a theoretical Summary semantic_scholar

Revolutionizing Human Resource Development: A Theoretical Framework for Enhancing Effective Human–AI Collaboration · Chieh‐Peng Lin · Fetched Mar 18, 2026

Human–AI co-learning with a metacognitive 'Cognitive Shadow' speeds decisions and reduces missed detections in simulated Arctic surveillance, while confidence estimates enable safe adjustable autonomy. The approach promises productivity gains and easier procurement adoption but remains to be validated in real-world operations.

medium quasi_experimental Summary semantic_scholar

Human Autonomy Teaming and AI Metacognition in Maritime Threat Assessment · K. Schulze, A. Gallant, Tanya S Paul, Cindy Chamberland, Daniel Lafond, Sébastie... · Fetched Mar 18, 2026

In a live capture‑the‑flag contest, autonomous agents that self‑direct prompting and tool use beat most human teams, exposing human prompting and context specification as the key bottleneck to effective human–AI collaboration.

medium descriptive Full text semantic_scholar

Understanding Human-AI Collaboration in Cybersecurity Competitions · Tingxuan Tang, N. Janis, Kalyn Asher Montague, Kevin Eykholt, Dhilung Kirat, You... · Fetched Mar 18, 2026

A Human–AI team can deliver near-expert online diagnoses while cutting required doctor involvement to roughly one-tenth; hierarchical reinforcement learning allocates human attention sparingly to preserve accuracy and lower labor per consultation.

medium descriptive Summary semantic_scholar

Hierarchical Reinforcement Learning Based Human-AI Online Diagnosis · Jiaqi Liu, Xuehan Zhao, Xin Zhang, Zhiwen Yu, Bin Guo · Fetched Mar 18, 2026

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