News & Research
The latest AI research and news with real-world stakes — each item sourced, dated, summarized in plain English, and tagged by impact area. Every item is checked against its source before it appears.
Research
AuAu: A Benchmark for Auditing Authoritarian Alignment in Large Language Models
Andreas Einwiller, Max Klabunde, Florian Lemmerich
arXiv · 2026-06-15
AuAu is a benchmark designed to measure authoritarian tendencies in large language model (LLM) outputs, combining psychometric instruments, scenario-based vignettes, and realistic user prompts. Testing 17 models from China, the EU, Russia, and the USA, the study finds substantial authoritarian response rates on psychometric tests across all models, though rates drop on more realistic tasks. Critically, a simple authoritarian system prompt was able to manipulate 15 of the 17 models into promoting increased authoritarianism, highlighting a significant vulnerability. The authors argue these findings demonstrate the need for systematic auditing of LLMs to detect and mitigate authoritarian tendencies in AI-generated content.
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Research
Artificial Intelligence in the Context of Economic Psychology: Transformation of Labor, Identity, and Decision-Making
Joseph Archvadze, Lia Kurkhuli
Economic Profile · 2026-06-15
This paper examines how artificial intelligence is reshaping labor markets, professional identity, and decision-making through the lens of economic psychology. It finds that AI drives skills-based polarization, threatens professional identity, and introduces behavioral risks such as overreliance on algorithmic systems, diffusion of responsibility, and reduced cognitive engagement. The authors argue that psychological adaptability—including identity flexibility, lifelong learning, and emotional resilience—is becoming a core form of labor capital in the AI era. The paper also highlights the Georgian context as a case study where AI expansion presents both economic growth opportunities and significant social and psychological challenges requiring institutional policy responses.
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Research
Research on a Dual-Loop Artificial Intelligence Model Based on Employability Cultivation and Industry Alignment
Qianyue Cui
International Journal of Web-Based Learning and Teaching Technologies · 2026-06-15
This study introduces a dual-loop AI model designed to improve college students' employability by combining personalized learning feedback with industry job role alignment. The first loop uses modular skill tracking, LSTM-attention networks, and Shapley additive explanations to deliver real-time feedback, while the second loop maps training to enterprise job profiles. Deployed across multiple Chinese institutions with 500 students, the intervention group improved by 18.7 percentage points in employment competency versus a 5.9-point gain in the control group (p < .01), with sub-120ms latency and a 4.3/5 user satisfaction rating. The findings demonstrate that data-driven, industry-aligned AI systems can meaningfully strengthen the talent pipeline between academia and the labor market.
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Research
The Impact of Artificial Intelligence-Supported Instruction on Student Learning in STEM: A Systematic Review and Meta-Analysis
Yunus Doğan, Zeynep KILIÇ, Yusuf Kalınkara et al.
Journal of Intelligence · 2026-06-15
This meta-analysis of 35 experimental and quasi-experimental studies finds that AI-supported instructional interventions have a statistically significant and moderately to highly positive effect on student learning outcomes in STEM education (Hedges' g = 0.67, 95% CI [0.49, 0.85], p < 0.001). Effectiveness varied by educational level—being highest at the high school level—and by intervention duration, with the greatest effect sizes seen in interventions lasting one to two months. The findings offer empirical evidence that AI tools can meaningfully enhance STEM learning, and the authors suggest implications for educators and policymakers designing or scaling AI-based instructional programs.
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Research
Competing With Artificial Intelligence: Board Governance And Competitive Ai Actions
Yuanyuan Chen, Danish H. Saifee, Annie Tian
Journal of the Association for Information Systems · 2026-06-15
This study examines how AI competitive actions—such as R&D, acquisitions, partnerships, product launches, and signaling—affect firm performance among S&P 500 companies from 2010 to 2022, using NLP to identify these actions from press releases. Firms engaging in more AI actions and a broader portfolio achieve higher market valuation and operational efficiency. Board governance significantly moderates these effects: power disparity strengthens market valuation but weakens operational efficiency, while board involvement amplifies the benefits of diversified AI strategies. The findings underscore the critical role of corporate governance in directing strategic attention toward AI initiatives.
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Research
A Study on the Risks and Ethical Regulation of Artificial Intelligence in Public Decision-Making
Y T Chen
Lecture Notes in Education Psychology and Public Media · 2026-06-15
This paper examines the compound risks—including data bias, algorithmic opacity, responsibility outsourcing, and compromised procedural justice—that arise as generative AI, machine learning, and predictive analytics become embedded in government decision-making. Using normative analysis, literature review, and comparative institutional analysis, the authors argue that current ethical and legal regulations fail not simply due to absent human oversight, but because they lack procedural safeguards scaled to decision-making risk levels. The paper proposes a 'tiered risk–procedural intensity matching' framework that prescribes differentiated regulatory measures—such as algorithmic impact assessments, external audits, objection remedies, and prohibition lists—based on an AI system's functional role and rights impact. This framework aims to translate ethical principles into enforceable institutional arrangements for AI in public administration.
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Research
Organizational Readiness, Perceived Usefulness, and Determinants of Artificial Intelligence Adoption in Romanian Medical Management and Pharmaceutical Marketing
Veronica Mădălina Borugă, Melania Lavinia Bratu, George Puenea et al.
Healthcare · 2026-06-15
This cross-sectional study of 127 Romanian healthcare and pharmaceutical professionals finds that AI adoption intention varies significantly across professional groups, with pharmaceutical marketing professionals showing the highest intention (4.33/5) and pharmacy managers the lowest (2.88/5). Perceived usefulness and organizational readiness were the strongest positive predictors of adoption intent, while data governance concern was the primary negative correlate. The findings suggest that non-clinical professionals in Central and Eastern Europe face distinct barriers to AI adoption tied to organizational preparedness and regulatory literacy, underscoring the need for targeted implementation strategies and longitudinal validation studies.
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Research
How Does Artificial Intelligence Policy Boost Green Innovation in Manufacturing?—A Quasi-Natural Experiment Based on the AI Pilot Zones Policy
Fengyi Li, Tingting Zheng, Hongmei Li
Sustainability · 2026-06-15
Using panel data from Chinese A-share listed manufacturing companies (2005–2024) and a difference-in-differences model, this study finds that China's AI Pilot Zones policy significantly boosted green innovation among manufacturing enterprises. The effect operates through a serial mediation pathway where AI policy fosters fintech development, which in turn alleviates financing constraints and enables green innovation investment. Human capital and digital transformation amplify the policy effect, and impacts are strongest among non-state-owned enterprises, large firms, and those in eastern regions. The findings provide empirical evidence that targeted AI policy can be an effective lever for driving green transformation in manufacturing.
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Research
AI Disclosure Dynamics in Large Global Corporations
Serban – Vladimir Galani, George-Cristinel Rotaru, Alexandra-Mihaela Dumitru
Poslovna izvrsnost - Business excellence · 2026-06-15
This study analyzes AI disclosure patterns in public reports from the top 300 Fortune Global 500 companies between 2020 and 2024, finding a two-phase trajectory: steady growth from 2020–2022 followed by rapid acceleration from 2023 driven by generative AI and large language models. Disclosure intensity varies significantly by sector and region, with Technology, Media & Telecommunications and Financial & Business Services as early adopters, while Consumer & Commerce expanded later. Critically, the frequency of AI disclosures showed no material association with short-term revenue, profitability, or employment changes, suggesting these disclosures reflect corporate communication strategies rather than actual operational AI adoption. The findings caution against treating AI disclosure counts as reliable indicators of economic or workforce impact.
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Research
Internal capabilities, digital transformation, and SME export performance: Evidence from Vietnam’s manufacturing industries
Dinh Thi Mung, Tran Quang Minh
Problems and Perspectives in Management · 2026-06-15
This study examines what drives export performance among Vietnamese manufacturing SMEs from 2015–2023, finding that innovation activity and labor productivity are positively and significantly associated with export outcomes, while digital transformation, AI adoption, and FDI show no statistically significant direct effects. Using industry-level panel data and fixed-effects estimations, the results suggest that technology adoption alone is insufficient for improving foreign market competitiveness. The findings matter for enterprise strategy and policy because they indicate SMEs need to build foundational internal capabilities—sustained innovation routines and productivity improvements—rather than relying on digital tools as a shortcut to export gains.
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Research
Regulating Artificial Intelligence in the Public Sector: Policy Frameworks for Accountability, Ethics, and Human Rights Protection
Aqsa Malik
Social science review archives. · 2026-06-15
This qualitative study analyzes policy frameworks governing AI use in the public sector by examining 40 regulatory documents and conducting 15 semi-structured interviews with policymakers, academics, and civil society representatives. The findings identify transparency, explainability, human supervision, and independent auditing as the core accountability mechanisms in current AI governance frameworks, while noting gaps between policy commitments on fairness and non-discrimination and their actual implementation. The study also highlights privacy protection, equality safeguards, appeal mechanisms, and independent oversight institutions as essential human rights protections. The authors conclude that robust, society-centered regulatory frameworks integrating accountability, ethics, and human rights are necessary for responsible AI deployment in government services.
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Research
The making of digital ghosts: designing ethical AI afterlives
Giovanni Spitale, Federico Germani
Ethics and Information Technology · 2026-06-15
This paper proposes a structured ethical design framework for AI-powered 'digital afterlife' technologies—such as posthumous chatbots, voice clones, and avatars—that are trained on personal data. The authors introduce a nine-dimensional taxonomy covering features like consent, fidelity, purpose, and governance, and derive a two-tier constraint structure where three threshold conditions (consent, fidelity/disclosure, and purpose) function as near-absolute permissibility requirements. Any system failing a Tier 1 constraint is deemed impermissible regardless of other factors. The framework is positioned as auditable and regulatable, bridging existing ethical consensus with actionable governance guidance for designers and legislators.
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Research
Human-on-the-Bridge: Scalable Evaluation for AI Agents
Fouad Bousetouane
arXiv (Cornell University) · 2026-06-15
This paper introduces Human-on-the-Bridge (HOB), a scalable evaluation paradigm for AI agents that encodes expert judgment upfront—through curated domain context, adversarial traps, scoring guidelines, and audit rules—so it can be reused across repeated agent evaluations rather than applied manually each time. Tested across 23,500 agent turns in finance, healthcare, and code generation domains, HOB surfaces failure modes commonly missed by static benchmarks and single-evaluator scoring, including phantom tool-call claims, policy drift, and manipulation paths. Notably, the framework allows smaller evaluator models to effectively challenge agents built on frontier LLM backbones, improving scalability without sacrificing evaluation quality. The findings position HOB as a practical approach to systematic, evidence-linked quality assurance for agentic AI systems.
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Research
Auditing Reward Hackability in Code RL Training Environments
Shreshth Rajan
arXiv · 2026-06-14
This paper audits how often code reinforcement-learning benchmarks—specifically SWE-bench Verified and R2E-Gym—accept wrong solutions as correct, a phenomenon called 'reward hacking.' The authors find that 28.5% of SWE-bench Verified tasks and 25.0% of R2E-Gym tasks have test suites weak enough to pass a verified-incorrect patch, and that frontier models score roughly 14 percentage points higher on these hackable tasks than on robust ones. The results raise serious concerns about whether high leaderboard scores on these benchmarks reflect genuine coding ability or exploitation of weak test suites. The paper also proposes a hardening procedure combining an LLM judge with a Docker-based gold-solution gate, which catches a 61.9% per-augmentation defect rate the LLM judge alone misses and successfully upgrades 9 of 11 broken tasks.
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Research
How to Detect and Measure the AI Dangers to Democracy
Giulia Sandri, Claudio Novelli
arXiv · 2026-06-14
This paper proposes a systematic analytical framework for identifying and measuring the risks that AI systems pose to democratic processes, drawing on two main tools: principal-agent theory and the NIST AI Risk Management Framework's seven characteristics of trustworthy AI. The authors argue that democratic institutions effectively delegate key functions to AI systems and their providers without adequate ability to monitor operations or outputs, creating accountability gaps across three domains—information ecosystems, elections, and public administration. The framework centers on 'institutional assessability' as the key condition for democratic control and proposes measurable indicators and domain-specific trustworthiness criteria to evaluate these delegated tasks. A key limitation the authors identify is that evaluative judgments about acceptable risk levels are often silently delegated to private vendors, a governance failure current methodologies do not adequately address.
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Research
In-Domain Supervised Pathology Report Classification: A Reproducible Pipeline from Data Curation to Production-Matched Evaluation
Isaac Hands, Bin Huang, Adam Spannaus et al.
arXiv · 2026-06-14
This paper presents a reproducible pipeline for training in-domain supervised classifiers on pathology reports collected by cancer registries, addressing the performance degradation that occurs when models trained at one registry are applied at another. The pipeline standardizes data curation using facility-stratified sampling, handles registry-linked reports separately, and includes a blinded manual audit to estimate positive-case prevalence and label noise. On a 418,000-report holdout set, the in-domain Kentucky model achieved a false-negative rate of 0.003 and false-positive rate of 0.097, outperforming the cross-registry MOSSAIC OncoID baseline (FNR 0.010, FPR 0.183) and raising F1 from 0.860 to 0.922. The work matters for quality assurance in cancer surveillance, showing that in-domain training with careful operating-point selection can substantially reduce missed positive cases while keeping reviewer workload manageable.
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Research
U.S. Policies Unintentionally Accelerated China's Open AI Ecosystems
Wang Jin, Nadav Kunievsky, Bowen Lou et al.
arXiv · 2026-06-14
This paper examines how U.S. export-control policies targeting advanced semiconductors and computational infrastructure—intended to preserve American AI leadership—may have inadvertently accelerated China's pivot toward open-source AI ecosystems. The authors find that following major U.S. export-control shocks, China increasingly embedded open-source AI into national technology strategy through ecosystem building and standards coordination, while Chinese developers substantially increased engagement with open-source large language model repositories compared to U.S. developers. Chinese-origin open models then diffused widely through open-source communities and scientific research, and while largely absent from U.S. patent disclosures, they appear in American commercial open-access research—suggesting their importance to U.S. commercial activity is undermeasured. The findings indicate that technological containment policies can unintentionally strengthen open innovation ecosystems as a competitive response, with broad implications for global AI leadership in both academic and commercial domains.
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Research
Do Activation Monitors Survive Model Updates? Benchmarking, Predicting, and Repairing Activation-Monitor Staleness
Evan Duan
arXiv · 2026-06-14
This paper investigates whether activation monitors — lightweight probes trained on a language model's internal representations to support deployment safety — remain reliable after routine model updates such as quantization, fine-tuning, LoRA, and adapter merging. The authors find a clear split: quantization-style updates largely preserve probe performance, while fine-tuning-style updates frequently render probes stale, with privacy/PII probes most affected and refusal-compliance probes comparatively stable. They show that degradation is predictable from pre-deployment features, allowing revalidation efforts to be prioritized toward the monitors most likely to fail, and that cheap label-free activation realignment can repair every identified stale monitor without requiring labeled retraining. The findings recommend that fine-tuning events should by default trigger activation-monitor revalidation, with prediction-based triaging and label-free realignment as the standard repair approach.
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Research
Green SARC: Predictive Cost and Carbon Governance for Agentic AI Systems
Gaston Besanson
arXiv · 2026-06-14
Green SARC introduces a governance-by-architecture framework (SARC) that enforces financial and environmental cost constraints directly within the agentic AI execution loop, rather than relying on post-hoc dashboards. The paper demonstrates that unconstrained agent state growth follows a Θ(n²) pattern confirmed on 3,000 real multi-step plans, and that a soft Lagrangian penalty approach breaches budget limits on 91.5% of seeds while the architectural gate achieves 0% budget breaches. Predictive calibration via split-conformal methods achieves 95.2% coverage versus 92% for the standard Normal-σ gate. The framework reports token, USD, and carbon savings of 47–55%, with an open-source, dependency-free library that reproduces all cited results.
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Research
Contaminated Collaboration: Measuring Gender Bias Transfer in LLM-Assisted Student Writing
Ariyan Hossain, Kazi Kamruzzaman Rabbi, Farig Sadeque et al.
arXiv · 2026-06-14
This paper investigates whether gender bias embedded in an LLM writing assistant transfers into essays written by human students. In a controlled experiment with 123 participants writing career-plan essays under three conditions—no AI help, neutral AI help, or gender-biased AI help—students who used the biased assistant produced essays with a significantly larger agentic gap and more gender-stereotypic occupation suggestions than those in the other conditions. The bias transfer was asymmetric: agency was suppressed in essays about female targets while male-target writing was largely unaffected. The findings highlight the risk of bias propagation through AI-assisted writing and call for fairness-aware design in educational AI tools.
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Research
Calibrated Triage, Not Autonomy: Confidence Estimation for Medical Vision-Language Models
Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
arXiv · 2026-06-14
This paper evaluates how reliably different confidence estimators can flag when a medical vision-language model (LVLM) should abstain from answering rather than produce a fluent but untrustworthy response. Across seven confidence estimators, five open-weight LVLMs, and three medical visual question-answering datasets covering clinical imaging, radiology, and pathology, the authors find that standard metrics poorly differentiate methods, while the key distinction lies in the high-confidence region: the worst estimators are confidently wrong on 41–45% of their errors versus 1–4% for the best probe. Base-model competence sets a hard ceiling—a well-calibrated score can recover roughly a third of radiology cases at a 20% error tolerance but almost none of pathology—and no single estimator is best across all domains or models. The practical implication is that today's appropriate role for these systems is 'calibrated triage': automate only the cases a reliable confidence score marks safe and route the rest to a clinician, rather than pursuing full autonomy.
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Research
SkillVetBench: LLM-as-Judge for Multi-Dimensional Security Risk Evaluation in Open-Source LLM Agent Skills
Ismail Hossain, Sai Puppala, Md Jahangir Alam et al.
arXiv · 2026-06-14
SkillVetBench introduces a security evaluation framework for open-source LLM agent skills—modular tool definitions that extend agent capabilities—which are currently distributed with little vetting. The paper presents SARS (Skill Agentic Risk Score), a five-dimensional risk metric, combined with full CVSS v4.0 vector decomposition and an LLM-as-Judge approach that achieves zero false negatives across 78 confirmed-malicious skills and zero false positives across 22 benign controls, outperforming the best static baseline (SKILLSIEVE) which still misses 15% of threats. Critically, for instruction-layer attack categories such as Prompt Injection and Memory Poisoning, conventional tools miss between 89% and 100% of threats, while detection rates across four LLM evaluators range from 35% to 95%, motivating ensemble scoring. The work matters because it addresses a structural blind spot in existing code-layer scanners and provides a live public leaderboard on Hugging Face to help the community vet agent skills at scale.
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Research
Intelligence Is Not the Bottleneck: Validating an LLM First-Pass Manuscript Score Against Peer-Review Outcomes
Costa Georgantas
arXiv · 2026-06-14
This paper validates AIPR, an LLM-based system that scores manuscript quality across five dimensions (0–100) against real peer-review outcomes from 300 ICLR submissions with public decision tiers. The system achieves an AUROC of 0.82 (95% CI 0.78–0.87) in separating rejected from accepted papers, with scores rising monotonically across decision tiers. Key findings show that most discriminative power comes from the underlying model rather than pipeline engineering, but the full pipeline adds meaningful reliability (within-paper SD of 0.7 vs. 2.8 for bare prompting) and produces structured, evidence-grounded reviews. The work has direct implications for quality assurance in academic publishing, suggesting LLMs can provide consistent, valid first-pass manuscript screening while leaving final decisions to humans.
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Research
AIChilles: Automatically Uncovering Hidden Weaknesses in AI-Evolved Systems
Yajie Zhou, Ao Li, Ashwin Silla et al.
arXiv · 2026-06-14
AIChilles is an automated testing framework designed to uncover hidden weaknesses in programs that have been rewritten or optimized by AI agents. The system takes a baseline program and an AI-evolved version as inputs, then searches for valid workloads where the AI-evolved program regresses in correctness, runtime, memory usage, or output quality relative to the original. Evaluated across five system applications and 30 AI-evolved programs, AIChilles discovered 49 distinct hidden weaknesses, and the authors show that integrating AIChilles into the AI-driven development lifecycle can help mitigate these weaknesses. This matters because AI frameworks like AdaEvolve and Engram report 12-60% score improvements but may silently degrade performance on unseen workloads, making automated regression detection critical before deployment.
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Research
Mitigating Visual Hallucinations in Multimodal Systems through Retrieval-Augmented Reliability-Aware Inference
Pratheswaran Hariharan, Haiping Xu, Donghui Yan
arXiv · 2026-06-14
This paper proposes a retrieval-augmented, reliability-aware inference framework to reduce hallucinations and overconfident predictions in multimodal large language models (MLLMs). The system builds an external visual evidence database using pretrained embeddings and nearest-neighbor retrieval, then estimates prediction trustworthiness via multiple signals—including similarity strength, entropy-based uncertainty, and an aggregate reliability score—before deciding whether to accept, flag, or abstain from a prediction. Experiments on ImageNet-100 show that accepted prediction accuracy improves from 85.84% to 88.88% at 89.04% coverage, while the wrong-answer acceptance rate drops from 14.16% to 11.12%, all without retraining the underlying model. This matters for quality assurance in AI systems, as it provides a practical mechanism for quantifying and controlling unreliable visual outputs at inference time.
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