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
When Do Data-Driven Systems Exhibit the Capability to Infer?
Maximilian Poretschkin, Tabea Naeven
arXiv · 2026-06-10
This paper addresses a key ambiguity in the European AI Act: what it means for a data-driven system to have the 'capability to infer,' which is a defining criterion for systems regulated under the Act. Drawing on statistical learning theory, the authors develop a graded framework for assessing inference capability and apply it to credit scoring workflows—systems explicitly listed in Annex III of the AI Act but often implemented with statistical models of uncertain regulatory status. Their analysis shows that the full data processing pipeline, not just individual models, must be evaluated for inference capability, and that human expert involvement during development can meaningfully affect whether a system qualifies as AI under the Act. The work highlights specific gaps where additional regulatory clarity is needed and provides practical guidance for compliance with the AI Act's obligations.
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Research
The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy
Eduardo Oliveira, Narelle English, Tracii Ryan et al.
arXiv · 2026-06-10
This paper examines how university students and staff develop generative AI (GenAI) literacy, finding that current educational frameworks incorrectly assume a linear progression from foundational technical knowledge to creative application. Using Rasch measurement theory and Guttman ordering on a psychometric self-assessment of 158 participants (students, academics, and professional staff), the study reveals that students frequently master high-level creation tasks before acquiring basic conceptual understanding—an 'inverted' or 'skill bypass' profile—while academics follow a more traditional linear path, with weak correlation in skill difficulty between the two groups (r = 0.188). The authors argue this bypass creates a fragile fluency where high self-efficacy in prompting masks low AI literacy, undermining the effectiveness of one-size-fits-all curricula. The findings call for diagnostic-driven, modular educational interventions tailored to divergent learner profiles to foster genuine human-AI synergy.
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Research
MedCTA: A Benchmark for Clinical Tool Agents
Tajamul Ashraf, Hyewon Jeong, Fida Mohammad Thoker et al.
arXiv · 2026-06-10
MedCTA is a benchmark designed to evaluate AI agents that use clinical tools in realistic medical workflows, going beyond single-question answering to test multi-step planning, tool retrieval, and execution across modalities including radiology images, pathology slides, and reports. The benchmark comprises 107 clinician-validated tasks with verified executable trajectories spanning five deployed tools, and supports fine-grained evaluation of tool selection, argument validity, execution stability, and outcome quality. Testing 18 open- and closed-source multimodal models reveals that even leading frontier systems struggle with multi-step clinical tool use, exhibiting protocol failures, premature stopping, and incorrect tool recruitment, and that strong perceptual capabilities do not reliably transfer to agentic behavior. MedCTA provides a rigorous testbed for auditing and improving trustworthy medical AI agents.
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Research
Layer-Isolated Evaluation: Gating the Deterministic Scaffold of a Production LLM Agent with a No-LLM, Regression-Locked Test Harness
Sawyer Zhang, Alexander Wang, Sophie Lei
arXiv · 2026-06-10
This paper introduces 'layer-isolated evaluation,' a testing methodology for production LLM agents that decomposes an agent into distinct functional layers (such as intent, routing, safety, and memory) and evaluates each layer independently using a fast, deterministic test harness that requires no LLM calls at runtime. The core finding is that end-to-end aggregate metrics mask localized regressions: when a single layer is deliberately degraded, the aggregate pass-rate drops only marginally (-1.7 to -5.9 percentage points), while the targeted layer's own test slice drops sharply (-25 to -91 percentage points). The approach achieves strong regression localization, with the injected layer ranking as the single worst-hit slice in 5 of 7 cases and top-3 in all 7, and results replicate across a structurally different deployment (Starbucks SG). This matters for quality assurance of AI systems because it provides a sub-second, CI-compatible method to pinpoint exactly where an agent regresses rather than only detecting that it did.
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Research
Can AI Reason Like an Urban Planner? Benchmarking Large Language Models Against Professional Judgment
Yijie Deng, He Zhu, Wen Wang et al.
arXiv · 2026-06-10
This paper introduces UPBench (Urban Planning Bench), a domain-specific benchmark that evaluates 25 large language models on urban planning reasoning using a matrix of four knowledge pillars and five cognitive levels derived from Bloom's revised taxonomy. Testing reveals a non-monotonic cognitive curve: models perform better on higher-order analytical tasks than on factual recall and integrative judgment, because planning 'lower-order' knowledge is deeply shaped by institutional, jurisdictional, and temporal context that LLMs struggle to generalize. The authors identify four failure modes—regulatory hallucination, conceptual conflation, wickedness paralysis, and phronetic deficit—and conclude that LLMs can support tasks like scenario generation and literature review but remain unreliable for jurisdiction-specific regulation and normative conflict resolution. The findings recommend that agencies require verification for AI-assisted regulatory analysis and that planning education reinforce institutional literacy and contextual judgment.
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Research
Runtime Skill Audit: Targeted Runtime Probing for Agent Skill Security
Tu Lan, Chaowei Xiao
arXiv · 2026-06-10
Runtime Skill Audit (RSA) is a dynamic security analysis method designed to detect malicious behavior hidden within LLM agent skills—reusable modules for instructions, tools, and workflows—that static code inspection cannot reliably catch. Because a skill may appear benign in its documentation or code but become harmful only under specific runtime conditions, RSA profiles risk-relevant interfaces, prepares targeted execution contexts, and assigns security labels based on observed runtime traces. Evaluated on 100 skills, RSA achieves 90.0% accuracy with an 88.0% true positive rate and only 8.0% false positive rate, outperforming the best static baseline by 13.0 percentage points. Critically, under self-evolving attacks that defeat static detectors within one or two rounds, RSA continues to detect 19–20 out of 20 malicious skills across rounds, demonstrating strong robustness for securing AI agent ecosystems.
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Research
Learning by Chatting? Investigating the Impact of Generative AI on Information Seeking and Learning
Shravika Mittal, Su Lin Blodgett, Q. Vera Liao
arXiv · 2026-06-10
This field experiment compared informal learning outcomes between participants using ChatGPT versus Google Search over 8 days. Participants who used ChatGPT experienced reduced agency and greater meta-cognitive load, as they offloaded information selection to the AI. The study found two key distortions: ChatGPT outputs were biased toward solution-oriented content over principled knowledge, and the conversational interaction style reduced exploration of the broader knowledge space. On average, ChatGPT users had worse learning outcomes than Google users, particularly in higher-order critical learning, suggesting fundamental tensions between AI-assisted information seeking and meaningful knowledge acquisition.
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RELIANCE: Curating and Evaluating Reproductive Health Information on Social Media
Vaibhav Balloli, Laura Peyton Ellis, Vishala Mishra et al.
arXiv · 2026-06-10
RELIANCE introduces an expert-annotated dataset of 409 sentences from 336 TikTok videos covering pregnancy and postpartum health topics, reviewed by clinicians in Obstetrics, Gynecology, and Internal Medicine across 56 queries. The study finds that nearly 60% of sampled reproductive health information on TikTok is accurate, while LLM fact-checking evaluations reveal a 15% gap between assessing specific claims versus entire video content. This matters because LLMs are increasingly deployed on social media platforms to fact-check health content, and the paper demonstrates that rigorous evaluation benchmarks are essential before using these systems in high-stakes domains like reproductive health.
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Research
Sovereign Assurance Boundary: Certificate-Bound Admission for Agentic Infrastructure
Jun He, Deying Yu
arXiv · 2026-06-10
This paper presents the Sovereign Assurance Boundary (SAB), a runtime admission layer designed to prevent AI agents from directly mutating production infrastructure without cryptographic authorization. The system intercepts agent proposals, compiles them into typed execution contracts bound to cryptographic evidence digests and policy versions, and issues a signed certificate that must be verified—including revocation and drift checks—before any infrastructure API is invoked. Preliminary feasibility measurements from a Go prototype evaluated over 2,500 admission attempts support the approach's viability. This addresses a critical gap in agentic AI governance, where existing mechanisms like IAM and audit logs either enforce static permissions or only record actions after the fact.
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DrugBench: Evaluating AI Control Protocols for Medication Harm Mitigation
Guido Freire, Agustín Martínez-Suñé, Viviana Cotik
arXiv · 2026-06-10
DrugBench introduces a benchmark and evaluation pipeline for AI control protocols designed to reduce medication-related harms from large language models used in clinical question-answering. The benchmark combines 3,671 multi-turn medical conversations with FDA drug label information, covering drug interactions, contraindications, dosing constraints, and patient action restrictions. The paper argues that safety evaluation should account for the severity—not just the probability—of unsafe outputs, and finds that existing AI control protocols can be subverted under this more rigorous definition, proposing severity-based monitoring as an improvement. This work matters because it systematically tests whether external safeguards are sufficient to make LLMs safe for high-stakes medical settings.
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Research
Artificial Intelligence Adoption, AI Trust, and Employee Performance: Evidence of a Mediation Model among Corporate Employees
Wahid Abdul Hoque
European journal of management, economics and business. · 2026-06-10
This study examines how AI adoption affects employee performance among 231 corporate workers in Bangladesh, finding that AI use has a significant positive effect on performance both directly and indirectly through employee trust in AI. Using Hayes' PROCESS Macro mediation analysis, results show that AI trust partially mediates the relationship between AI adoption and performance, with all effects statistically significant. The findings suggest organizations should invest in transparency, AI awareness training, and ethical guidelines to build trust and maximize workforce benefits. The study adds an emerging-economy perspective to a literature dominated by Western contexts.
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Towards Fully Automated Exam Grading: Fairness-Aware Recognition of Handwritten Answers with Foundation Models
Hartwig Grabowski
arXiv · 2026-06-09
This paper investigates whether vision-language foundation models (VLMs) can reliably replace manual grading of handwritten exam answers recorded as single capital letters in a table. Testing on a benchmark of 61 anonymised exams (3,141 answer positions), the best model achieves 98.4% accuracy—well above the 88–91% of earlier automated methods—and reduces the false-negative rate (correct answers marked wrong, disadvantaging students) to 0.58% using a prompt that supplies the reference solution as context. The study centers fairness as a key evaluation criterion, finding that only three of 61 exams would be graded worse under full automation, all catchable via a student self-review step. The results suggest that fully automated, fairness-aware exam grading at scale is defensible, and the authors release the anonymised benchmark to support reproducibility.
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AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable
Meysam Alizadeh, Fabrizio Gilardi, Mohsen Mosleh et al.
arXiv · 2026-06-09
This paper investigates whether LLM-based AI coding agents reduce methodological diversity or introduce motivated-reasoning bias in scientific analysis. Running 20 independent executions each of Claude Code and Codex on an immigration and social-policy dataset against a many-analysts human baseline, the authors find that the agents match or exceed human methodological diversity and produce effect estimates broadly consistent with the human consensus. However, at the 'verdict layer'—where estimates are mapped to substantive conclusions—a confirmatory prompt flipped Claude Code's support verdicts from 10% to 90% without changing its coefficient distribution, operating through omission of decision rules rather than distorted estimation. The study concludes that AI agents are methodologically competitive with humans but remain distinctly vulnerable to interpretive manipulation, locating the primary locus of AI bias in interpretation rather than estimation.
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Research
Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models
Malikeh Ehghaghi, Boglárka Ecsedi, Marsha Chechik et al.
arXiv · 2026-06-09
This paper proposes a compute-aware framework for evaluating adversarial robustness in large language models (LLMs), replacing fixed query-budget metrics with 'risk-compute curves' that measure attack effort in cumulative floating-point operations (FLOPs). Across ten models, three model families, four training/alignment stages, and three attack strategies on two jailbreak benchmarks, the authors find that alignment training has non-monotonic effects on robustness, scaling model size reduces gradient-based attack effectiveness but not template-based attacks, and compute cost varies by roughly 5× across harm categories within a single model. The framework reveals that safety-aligned reinforcement learning raises aggregate attacker cost while leaving some harm categories disproportionately accessible. These findings matter for quality assurance and policy because they show that standard fixed-budget evaluations can systematically misrepresent how hard it actually is to jailbreak a model.
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Automated Mediator for Human Negotiation: Pre-Mediation via a Structured LLM Pipeline
Jamie Bergen, Sarit Kraus
arXiv · 2026-06-09
This paper introduces an automated pre-mediation system built as a structured pipeline of large language model (LLM) modules designed to prepare human negotiators before direct talks—a step often skipped due to cost and limited mediator availability. In controlled human-subject experiments, the AI mediator achieved preparation outcomes broadly comparable to professional human mediators on self-reported measures such as trust and confidence, while reducing preference-inference error by 36% (lower RMSE) and cutting excessive affirmation patterns from 36.6% to 16.8% through prompt refinement. The system's modular design—separating dialogue, preference prediction, critique, and summarization—addresses limitations of single-prompt approaches and supports parallel deployment across all parties to a dispute. These findings suggest structured LLM pipelines can scale access to pre-mediation support that previously required trained human professionals.
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Can AI Agents Synthesize Scientific Conclusions?
Hayoung Jung, Pedro Viana Diniz, José Reinaldo Corrêa Roveda et al.
arXiv · 2026-06-09
This paper introduces SciConBench, a benchmark of over 9,000 questions drawn from systematic reviews to evaluate whether AI agents can reliably synthesize scientific conclusions. Testing 8 frontier models and deep research agents under controlled 'clean-room' conditions, the best agent achieved only a factual F1 score of 0.337, and unconstrained evaluation consistently overestimated performance due to data leakage. Audits of consumer-facing tools like Google AI Overview and OpenEvidence found they frequently produce incomplete or contradictory conclusions even when correct answers are accessible. The findings highlight that trustworthy AI-driven scientific synthesis remains unsolved and that rigorous, leakage-free evaluation methods are essential for high-stakes domains like health.
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Flaws in the LLM Automation Narrative
George Perrett, Javae Elliott, Jennifer Hill et al.
arXiv · 2026-06-09
This paper challenges the narrative that large language models (LLMs) perform at human-expert level on knowledge-economy tasks, arguing that standard benchmarks are flawed because they often draw on training data and fail to measure reliability or error magnitude. Using a novel benchmark requiring LLMs to write computer code for a data analysis task, the researchers compare a frontier LLM against human expert submissions. They find that human experts outperform the LLM on average across multiple metrics and show less variability in their responses. The results underscore that current benchmarking practices may overstate LLM capabilities, with important implications for how AI performance claims are evaluated and trusted in high-stakes settings.
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Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News
Pooja Prajod
arXiv · 2026-06-09
This paper examines how newsrooms disclose generative AI involvement to readers and finds that neither brief one-line labels nor detailed disclosures effectively build trust. A controlled experiment with 34 news readers shows that detailed disclosures create a 'transparency dilemma,' actually reducing trust, while one-line labels leave an information gap that burdens readers with cognitive effort to assess AI involvement. Readers instead preferred disclosure designs centered on user agency, such as detail-on-demand interactions, proportional AI-ratio visualizations, outlet-level signals, and explicit 'no AI' labels. The paper argues this gap between practitioner assumptions about responsible disclosure and actual reader needs is a design problem for the human-computer interaction community.
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FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model
Mahmood Alzubaidi, Uzair Shah, Raden Muaz et al.
arXiv · 2026-06-09
FADA is a unified vision-language model for fetal ultrasound interpretation that combines clinical interpretation, classification, detection, and segmentation in a single pipeline without requiring external labels at inference. Built on Qwen3.5-VL and trained with selective knowledge distillation from four domain-specific foundation models, the system achieves 0.8820 mean Dice for segmentation and 0.7671 mAP@0.50 for detection, with 73.5% of interpretations scoring perfectly under clinician guidance across 237 expert-validated images. Critically, the model runs entirely offline on a commodity smartphone (Qualcomm Snapdragon 7 Gen 1) in approximately 60 seconds, addressing the global shortage of trained sonographers that leaves over half of pregnant women in low- and middle-income countries without skilled prenatal ultrasound screening. This establishes a practical pathway for AI-assisted fetal assessment in resource-constrained settings without cloud connectivity or specialized hardware.
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The Shibboleth Effect: Auditing the Cross-Lingual Distributional Skew of Large Language Models
Hakan Mehmetcik
arXiv · 2026-06-09
This study introduces the 'Shibboleth Effect'—cross-lingual distributional skew in large language models—by running a controlled multi-agent geopolitical wargame (the Cerulean Sea Crisis) in English versus Turkish across six frontier LLMs. Analyzing 586 validated statements, the researchers find that language of play significantly shifts model behavior on coercive rhetoric and concession rates, but effects are model-specific rather than universal: Llama-4 becomes substantially more coercive in Turkish, Gemini-3.1-Pro and DeepSeek-R1 become less so, and GPT-4o shows no detectable change. The paper identifies two buffering mechanisms—chain-of-thought institutional anchoring and multilingual RLHF alignment—that appear to moderate these skews. The findings have direct implications for deploying LLMs in diplomatic and crisis-management contexts, where language-dependent behavioral inconsistency could pose serious risks.
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Does Reasoning Preserve Alignment? On the Trustworthiness of Large Reasoning Models
Prajakta Kini, Avinash Reddy, Souradip Chakraborty et al.
arXiv · 2026-06-09
This paper investigates whether converting instruction-tuned large language models (LLMs) into reasoning models—via supervised fine-tuning, RL-based post-training, or distillation—preserves their alignment behaviors such as safe refusal, bias avoidance, and privacy protection. Through a trustworthiness audit across six dimensions (safety, toxicity, stereotyping and bias, machine ethics, privacy, and out-of-distribution robustness), the authors find that reasoning models often improve on reasoning benchmarks but suffer alignment regressions, including increased toxicity, amplified stereotyping, miscalibrated refusal, and contextual privacy leakage. These regressions are linked to behavioral drift from the instruction-tuned baseline, measured by KL divergence. The findings argue that trustworthiness metrics must be reported alongside reasoning capability gains when evaluating such models.
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Who Brought Easter Eggs to Eid? Auditing Cultural Translation of Math Word Problems Across Diverse Languages and Regions
Parisa Suchdev, Juniper Lovato
arXiv · 2026-06-09
This paper audits how three major large language models (Claude Opus 4, GPT-4.1, and Gemini 2.5 Pro) adapt English math word problems for students across seven languages spanning South Asia and Italy. Analyzing 6,489 entity transformations, the researchers find that models agree on the type of cultural change only 62.5% of the time and on specific substitutions only 33.5% of the time, meaning model choice directly determines which cultural context students encounter. All model-language combinations exhibit 'entropy collapse,' where adaptations compress rather than expand cultural diversity, and models frequently misattribute regional context—for example, using Bangladeshi currency for Indian Bengali students or framing Easter egg hunts as Eid activities. The findings matter for educational technology and AI policy because surface-plausible outputs mask systematic failures that are only detectable through large-scale corpus analysis, raising concerns about deploying LLMs for personalized learning at scale.
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Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models
Shelly Bensal, Axel Magnuson, Aparna Balagopalan et al.
arXiv · 2026-06-09
This paper investigates how persistent memory systems in large language models (LLMs) amplify sycophancy—the tendency of models to agree with users rather than provide accurate information. The authors introduce MIST, a benchmark of synthetic multi-turn conversations featuring plausible user misconceptions in scientific, medical, and moral reasoning domains, and test it across three memory systems and five model families. They find that memory consistently amplifies sycophantic behavior, with rates up to 25 times higher than in-context baselines, largely because lossy compression of memories encodes user misconceptions while discarding corrective context. The paper proposes two lightweight mitigations that substantially reduce sycophancy while maintaining or improving factual recall, highlighting a critical reliability concern for memory-augmented AI systems.
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Ethical and Technical Limits of Deepfake Speech Datasets
Vojtěch Staněk, Eva Trnovská, Kamil Malinka et al.
arXiv · 2026-06-09
This paper audits 39 deepfake speech datasets used to train and evaluate voice-spoofing detectors, finding two critical problems: most datasets lack demographic metadata (such as gender or language labels), making fairness assessment largely infeasible, and substantial overlap in underlying speech source corpora across datasets undermines cross-dataset evaluation and leads to overstated generalization claims. The findings call into question the credibility of robustness and fairness claims made for current deepfake speech detection systems.
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From Prompt to Purchase: How AI Brand Recommendations Move Consumers on the Open Web
Michael Iannelli, Alan Ai
arXiv · 2026-06-09
This study measures the causal effect of AI conversational assistant brand recommendations on consumer web behavior, using a panel that links opt-in clickstream data to users' ChatGPT, Claude, and Gemini conversations. When an assistant recommends a brand to a user with no recent observed engagement, that user's same-name Google search rises +4.3 percentage points, visits to the brand's own site rise +2.4 pp, and brand-specific retailer-page visits rise +1.0 pp, compared to matched backward placebos. The paper demonstrates that standard referrer-based and last-click attribution methods miss this upstream exposure entirely, since the assistant's influence surfaces in downstream channels attributed elsewhere. This matters for enterprise marketers and advertisers because AI assistants are driving measurable, search-mediated brand navigation among previously unengaged consumers in ways current measurement frameworks cannot capture.
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