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Changing the composition of feedback and asking annotators for probabilities reduces systematic under-detection of rare events, and applying a simple recalibration step to aggregated probabilities produces better-calibrated labels that materially improve CNN performance out of sample.
high rct arxiv
Managing Cognitive Bias in Human Labeling Operations for Rare-Event AI: Evidence from a Field Experiment · Gunnar P. Epping, Andrew Caplin, Erik Duhaime, William R. Holmes, Daniel Martin,... · Mar 12, 2026
Providing three retrieved passages lets humans check AI extractions nearly as accurately as reading the full source while saving time; by contrast, LLM-generated explanations are faster but encourage over-reliance and make users less likely to spot errors, particularly on complex answers.
medium rct arxiv
To Believe or Not To Believe: Comparing Supporting Information Tools to Aid Human Judgments of AI Veracity · Jessica Irons, Patrick Cooper, Necva Bolucu, Roelien Timmer, Huichen Yang, Chang... · Mar 12, 2026
Digital–real integration and higher-quality productive capacity reinforce one another across Chinese provinces, but gains are locally concentrated: progress in one province tends to crowd out development in neighbors, producing negative spatial spillovers.
medium quasi_experimental openalex
Spatial Interplay Between Digital–Real Integration and New Quality Productive Forces: Evidence from China · Xiao Li, Nan Liu · Mar 12, 2026
Europe’s legal frameworks lag behind AI‑amplified online harms against children, leaving regulatory gaps that enable cross‑border hosting of abuse and weak private incentives to prevent harm; robust, technology‑aware, rights‑preserving, and internationally coordinated rules — alongside enforcement and clear platform obligations — are needed to align market incentives and protect young users.
low descriptive openalex
Navigating Digital Safety for Minors in Europe · Jutta Sonja Oberlin, Sarah von Hoyningen-Huene · Mar 12, 2026
AI adoption in Chinese sports firms stabilises supplier relationships but destabilises customer ones, driven chiefly by talent acquisition rather than logistics gains. The net effects vary by firm type and profitability, implying AI returns depend heavily on firm context and workforce strategies.
medium quasi_experimental openalex
Can Artificial Intelligence Enhance the Stability of Supply Chain Systems for Sports Enterprises? Insights from Systems Theory and Supply Chain Management Theory · Zhaoyang Zhao, Biao Wang, X. He, Jing Huang · Mar 12, 2026
Researchers using machine learning on corporate disclosures rely mostly on hand-crafted sentiment indices and conventional supervised models rather than embedding-based or end-to-end deep learning, while studies are concentrated geographically and lack common benchmarks — a fragmentation that constrains robust conclusions about how disclosure tone predicts firm financial outcomes.
n/a review_meta openalex
Machine Learning for Sentiment-Based Corporate Disclosure Analytics: A Systematic Review of Data, Sentiment Representations, and Predictive Models · Ramon Abilio, Guilherme Palermo Coelho, Ana Estela Antunes da Silva · Mar 12, 2026
Viewing LLM teams through the lens of distributed systems exposes the core trade-offs—coordination, redundancy and fault tolerance—that determine whether multiple models beat a single agent; this conceptual framework offers a principled way to choose team size and structure without pure trial-and-error.
n/a theoretical openalex
Language Model Teams as Distributed Systems · Elizabeth Mieczkowski, Katherine M. Collins, Ilia Sucholutsky, Natalia Vélez, Th... · Mar 12, 2026
Hybrid work expands flexibility but fragments workplace social capital, threatening visibility and career pathways for remote employees; deliberate leadership and inclusive policies are essential to preserve productivity and equity.
n/a theoretical openalex
The Sociology of Remote Work and Organisational Culture: How the Shift to Hybrid and Remote Work Reshapes Workplace Norms, Power Dynamics, and Social Cohesion · Adebisi A., Balogun O. · Mar 12, 2026
The China–ASEAN FTA cut regional trade-policy uncertainty and unexpectedly boosted China’s agricultural imports from third countries by lowering marginal import costs and creating complementary supply channels; the windfall accrued mainly to medium and large firms, with micro and small firms left behind.
medium quasi_experimental openalex
How regional trade policy uncertainty affects agricultural imports from third countries: evidence from the China-ASEAN free trade area · Cheng Qin, Bao Wang, Xueyi Wang, Jiye Hu · Mar 12, 2026
Autonomous, multi-stage cyber agents could democratize top-tier offensive operations and reshape the cyber-economy, raising strategic escalation and systemic-risk concerns; governments and industry must prioritize monitoring, defensive investment, and governance to manage widened externalities and market disruptions.
low descriptive openalex
Highly Autonomous Cyber-Capable Agents: Anticipating Capabilities, Tactics, and Strategic Implications · Jam Kraprayoon, Shaun Kai Ern Ee, Brianna Rosen, Yohan Matthew, Aditya Kumar Sin... · Mar 12, 2026
China’s green data‑centre pilots lower firms’ energy intensity by boosting ESG performance and green spending, with the biggest gains in resource‑based cities, competitive industries and mature firms led by managers combining green and digital vision. As successive pilot rounds roll out the aggregate payoff first rises and then fades, implying early concentrated benefits and later dilution.
medium quasi_experimental openalex
How Does Urban Green Data Center Policy Empower Corporate Energy Utilization Efficiency? Evidence from China · Guoliang Wang, Haoxuan Sheng, Yue Lu · Mar 12, 2026
AI could sharply speed and standardize administrative decisions in Vietnam, but entrenched civil‑law protections around discretion, reason‑giving and review mean straightforward automation is legally fraught; a phased, risk‑based rollout with mandated oversight and procedural redesign is needed to unlock benefits without eroding rule‑of‑law safeguards.
low descriptive openalex
ARTIFICIAL INTELLIGENCE AND ADMINISTRATIVE GOVERNANCE: A CRITICAL ANALYSIS OF TECHNOLOGICAL INTEGRATION IN VIETNAM'S LEGAL FRAMEWORK · Hoang Thanh Hanh, Vu Minh Chau, Tran Van Hoy · Mar 12, 2026
High-quality LLM suggestions markedly improve caseworkers’ SNAP accuracy—by about 27 percentage points—while incorrect suggestions substantially reduce accuracy; gains level off above roughly 80% chatbot accuracy, revealing an 'AI underreliance plateau' that complicates real-world deployment.
high rct arxiv
LLMs in social services: How does chatbot accuracy affect human accuracy? · Jennah Gosciak, Eric Giannella, Zhaowen Guo, Michael Chen, Allison Koenecke · Mar 11, 2026
Making readers' trails visible — by logging navigation, highlights, links and notes — would expose previously invisible interpretive labor and create new signals for personalization and model training. But turning reading traces into a data asset risks privacy harms and market concentration unless accompanied by careful governance and empirical validation.
n/a descriptive arxiv
Chasing RATs: Tracing Reading for and as Creative Activity · Sophia Liu, Shm Garanganao Almeda · Mar 11, 2026
Boson Sampling Born Machines can be trained on classical hardware yet retain quantum sampling hardness, enabling a 'train-classically, deploy-quantumly' model; modest architectural additions (ancillas and postprocessing) restore full expressivity while keeping training classically tractable.
n/a theoretical arxiv
Universality of Classically Trainable, Quantum-Deployed Boson-Sampling Generative Models · Andrii Kurkin, Ulysse Chabaud, Zoltán Kolarovszki, Bence Bakó, Zoltán Zimborás, ... · Mar 11, 2026
Task-aware capability and risk signals constructed from human pairwise comparisons let users predict LLM performance more accurately and turn delegation into a visible, auditable choice; surfacing these primitives could reduce information asymmetry and enable risk-priced routing, auditing, and new supervisory roles.
medium descriptive arxiv
Task-Aware Delegation Cues for LLM Agents · Xingrui Gu · Mar 11, 2026
How you apply PRF matters more than where it comes from: across 13 low-resource retrieval tasks, the feedback model drives retrieval gains more than feedback source; LLM-generated feedback is the cheapest high-return option unless a high-quality first-stage retriever supplies strong candidate documents, in which case corpus-derived feedback outperforms.
medium other arxiv
A Systematic Study of Pseudo-Relevance Feedback with LLMs · Nour Jedidi, Jimmy Lin · Mar 11, 2026
Randomized 'human uplift' trials remain useful but are routinely compromised by frontier AI's nonstationarity, shifting baselines, diverse and evolving users, and spillovers; experts recommend adaptive designs, continuous post-deployment monitoring, mixed methods, and conservative interpretation rather than reliance on single trials for high‑stakes decisions.
medium descriptive arxiv
RCTs & Human Uplift Studies: Methodological Challenges and Practical Solutions for Frontier AI Evaluation · Patricia Paskov, Kevin Wei, Shen Zhou Hong, Dan Bateyko, Xavier Roberts-Gaal, Ca... · Mar 11, 2026
AI tools automate routine software-engineering work—speeding delivery and improving defect management—yet benefits vary widely by team, domain and data maturity, and require new skills, governance and quality-assurance practices.
low descriptive arxiv
Artificial Intelligence as a Catalyst for Innovation in Software Engineering · Carlos Alberto Fernández-y-Fernández, Jorge R. Aguilar-Cisneros · Mar 11, 2026
A unified Bayesian-surrogate framework cuts costly quantum-chemistry evaluations by about tenfold without losing accuracy, speeding up minima and saddle-point searches on potential-energy surfaces. The method—implemented in Rust and combining derivative-aware GPs, inverse-distance kernels, optimal-transport sampling, and trust-region controls—lowers compute and capital costs for computational discovery workflows.
medium other arxiv
Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches · Rohit Goswami · Mar 11, 2026
A simulation-trained 'summary network' can learn finite-sample estimators and even mimic iterative solvers, offering fast amortized inference for parametric models; however, evidence comes from simulations only, so real-data validity and uncertainty quantification remain open challenges.
low other arxiv
ForwardFlow: Simulation only statistical inference using deep learning · Stefan Böhringer · Mar 11, 2026
Evaluation metrics push image generators toward brighter, exaggerated colors; a new 1.3M‑image benchmark, a learned color‑fidelity metric, and a training‑free refinement method restore authentic color and let developers improve photorealism without costly retraining.
n/a descriptive arxiv
Too Vivid to Be Real? Benchmarking and Calibrating Generative Color Fidelity · Zhengyao Fang, Zexi Jia, Yijia Zhong, Pengcheng Luo, Jinchao Zhang, Guangming Lu... · Mar 11, 2026
Feeding MCMC-drawn parameter posterior samples into deterministic neural surrogates reproduces model uncertainty at a fraction of the computational cost and without training on implausible parameterizations. The architecture-agnostic method includes theoretical bounds tying performance loss to distribution mismatch, giving practitioners a practical diagnostic for retraining or reweighting.
n/a other arxiv
MCMC Informed Neural Emulators for Uncertainty Quantification in Dynamical Systems · Heikki Haario, Zhi-Song Liu, Martin Simon, Hendrik Weichel · Mar 11, 2026
A lightweight GNN trained via federated learning across orbital planes—coordinated through HAPS—improves beam-selection accuracy and stability in LEO satellite networks, cutting the need to centralize raw channel data. The approach promises lower operational overheads and easier on-board deployment, though real-world deployment and full cost savings remain to be demonstrated.
medium other arxiv
Federated Learning-driven Beam Management in LEO 6G Non-Terrestrial Networks · Maria Lamprini Bartsioka, Ioannis A. Bartsiokas, Athanasios D. Panagopoulos, Dim... · Mar 11, 2026
Adaptive RL-driven contact control raises robotic vial-scraping performance by roughly 11% versus fixed-force policies after sim-to-real transfer; gains are shown on five material setups but rely on simplified material models and unspecified sensing details.
medium descriptive arxiv
Learning Adaptive Force Control for Contact-Rich Sample Scraping with Heterogeneous Materials · Cenk Cetin, Shreyas Pouli, Gabriella Pizzuto · Mar 11, 2026