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
Rethinking AI-Era Transformation of Architecture, Engineering and Construction Education: A Multi-Stakeholder Perspective
Panxiu Wang, Zhiqiang Hua, Dawei Wang et al.
Buildings · 2026-08-04
This study examines how AI is transforming education in architecture, engineering, and construction (AEC) by surveying 352 stakeholders from academia, industry, and research in China. Using ANOVA, Tukey's HSD tests, and Bayesian Network modeling, the authors find that simply adding AI or software courses yields limited practical gains—curriculum expansion alone improved AI knowledge acquisition by 39.6% but raised application competencies by only 4.5%. Integrated interventions combining teacher development, university–industry collaboration, and project-based learning produced far stronger outcomes, including 37.9% improvement in AI application competencies and 29.3% in employment adaptability. The paper proposes a competency-oriented framework centered on professional expertise, systems thinking, interdisciplinary collaboration, and AI-enabled problem-solving as a roadmap for AEC curriculum reform.
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Research
New tech, new threat? Occupational exposure to artificial intelligence increases support for AI regulation
Zack Grant, Jane Green, Geoffrey Evans
Journal of European Public Policy · 2026-08-04
This study examines how workers' exposure to AI—both objectively measured at the occupation level and subjectively perceived—shapes their support for government regulation of AI. Using nationally representative survey data from Britain and panel data, the authors find that workers in AI-exposed occupations and those who personally fear job displacement are significantly more likely to support stronger AI regulation. Crucially, the relationship is asymmetric: pessimism and substitution risk increase regulatory support, but optimism and complementary opportunities do not reduce it. The findings matter for understanding how labor market pressures from AI translate into political demand for technology policy.
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Research
AI, HR Governance, and Human Capital Risk in Indonesia’s Digital Banking: A Systematic Review
Unang Toto Handiman, Yustinus Rawi Dandono, Mohammad Ali Yamin et al.
Human Resource Strategy and Practice · 2026-08-04
This systematic review of 68 peer-reviewed articles examines how AI adoption in Indonesia's digital banking sector simultaneously reshapes HR competencies, creates human capital risks such as algorithmic bias and lack of transparency, and demands governance practices aligned with sustainability goals. Using the PRISMA 2020 protocol across banking, HRM, and sustainability literature streams, the study challenges techno-centric views by framing AI not merely as a productivity tool but as a structural driver of worker vulnerability and organizational legitimacy risks. The authors propose HR governance as an integrative mechanism linking AI capability, human capital risk, and sustainability—shifting the policy conversation from a technological to a governance problem, particularly relevant for developing economies.
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Research
AI Decision Support for Urban Fire Risk Management: A Framework for Validation, Governance, and Bounded Deployment
Eric Scheepbouwer
Fire · 2026-08-04
This paper develops a governance framework for AI-based decision support tools used in urban fire risk management, addressing tasks such as inspection prioritisation, building risk analysis, station coverage, and evacuation planning. The central problem it identifies is 'decision role migration,' where an AI output designed for prediction or screening may later be treated as authoritative clearance or justification for safety-critical decisions. The framework classifies AI outputs by epistemic role, decision proximity, validation basis, and consequence asymmetry, arguing that evidence sufficient to warn is insufficient to clear. It proposes keeping exploratory, advisory, and safety-proximate AI roles explicitly separated through governance rules, uncertainty communication, and defined authority allocation.
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Research
A Closed-Loop Measurement Study of Runtime Governance in AI-Driven Smart Building Climate Control
Norkobil Saydirasulov Saydirasulovic, D.A. Davronbekov, Makhmudov Makhsum Mubashirovich et al.
Sensors · 2026-08-04
This paper evaluates runtime governance mechanisms—specifically admission control and checkpoint rollback—for AI-driven building climate control using a closed-loop software-in-the-loop testbed. The study finds that admission control reduces unsafe physical thermal exposure by 19.4% under distribution shift, while checkpoint rollback adds only a marginal further reduction (0.1–0.4%), because the physical recovery time (median 61 minutes) far exceeds the governance decision time (0.44 ms). The results establish an operating envelope criterion for when rollback is effective in inertial plants and reveal a safety–demand trade-off between learned and rule-based controllers, with neither approach clearly dominant on necessity grounds. These findings matter for ensuring AI-driven building systems operate safely within defined physical bounds.
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Research
Generative Artificial Intelligence and Intellectual Property Rights: A Comparative Analysis of Copyright, Patents and Trade Secrets
Begaim Mukhitovna Kaibyldaeva, Anna Vladimirovna Ubaydullaeva
Trends in intellectual property research. · 2026-08-04
This comparative legal analysis examines how generative AI—including large language models and multimodal systems—challenges traditional intellectual property frameworks across copyright, patent law, and trade secrets. Drawing on legislative initiatives, judicial decisions, and policy documents from 2024–2026 across the EU, US, UK, China, Japan, and Singapore, the paper argues that IP systems are shifting from human-centred protection toward hybrid governance models that account for AI-assisted creativity while preserving human responsibility. The authors propose a regulatory framework that distinguishes AI-generated from AI-assisted outputs, strengthens training-data transparency, and promotes international harmonization of IP rules in the GenAI era.
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Research
Too old, too foreign, too replaceable? How AI shapes livelihood security of ageing immigrants
Martin Mihajlov, Tanja Pavleska
Frontiers in Sociology · 2026-08-04
This qualitative study of 32 older immigrant professionals (aged 50–55) across healthcare, finance, marketing, and IT finds that AI disruption compounds pre-existing socio-cultural exclusion with what the authors call 'Double Displacement'—a demotion from autonomous expert to passive machine validator. A related pattern, the 'foreignness premium,' describes how AI systems erode the distinctive multicultural assets these workers gained through migration, converting those assets into drivers of late-career obsolescence. Fragmented cross-border pension entitlements further amplify their vulnerability near retirement. The authors call for age-decoupled reskilling, anonymous AI-dissent reporting channels, and cross-border pension coordination to protect this population.
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Research
World Development Report 2026: The Promise of Artificial Intelligence
World Bank
arXiv · 2026-08-04
The World Bank's World Development Report 2026 assesses how artificial intelligence could accelerate development for the 5.6 billion people living in low- and middle-income countries. It finds that while frontier AI development is out of reach for most developing nations, adapting available AI to local contexts could compress decades of progress into years across health, agriculture, small business, and public services. The report calls on governments to strengthen enabling infrastructure—electricity, connectivity, foundational skills, and local language data—and to build procurement, monitoring, and evaluation frameworks so successful pilots can scale. It also recommends leveraging voluntary standards and existing regulations to manage AI risks and protect people from harms.
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Research
Exploring the views of Research Ethics Committee Members in England on the use of Artificial Intelligence in the governance and ethics reviews of Clinical Trials of Investigational Medicinal Products
J Fennelly-barnwell
London School of Hygiene & Tropical Medicine · 2026-08-04
This study explored Research Ethics Committee (REC) members' views in England on the use of AI in the governance and ethics review of Clinical Trials of Investigational Medicinal Products (CTIMPs), using a systematised literature review and qualitative interviews. A key finding was that REC members strongly believe decision-making should remain a human activity, though AI could serve as a supportive tool. The study also identified a significant gap in existing literature on AI applied to research ethics committees. The authors draw conclusions and recommendations for the Health Research Authority (HRA) on how to manage AI adoption and change management within REC processes.
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Research
Developing AI-enabled sustainable supply chain quality capability: Scale development, validation, and its role in enhancing supply chain resilience and performance
Shima Yaghoubi, Shiva Yaghoubi
Radiant Journal of Business & Sustainability · 2026-08-04
This study develops and validates a new organizational capability construct called AI-enabled Sustainable Supply Chain Quality Capability (AISSQC), comprising five dimensions including AI-enabled Quality Intelligence, Sustainable Quality Integration, and Responsible Quality Governance, among others. Using a sequential mixed-method design with data from Canadian manufacturing firms and PLS-SEM analysis, the research finds that AI capability positively drives AISSQC, which in turn enhances supply chain resilience and sustainable performance. Notably, supply chain resilience mediates the relationship between AISSQC and sustainable performance, suggesting AI generates organizational value primarily through complementary capabilities rather than technology adoption alone. The validated measurement scale offers both researchers and managers a practical framework for AI-enabled quality transformation in sustainable supply chains.
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Research
Artificial Intelligence Regulation in Türkiye: Current Position and Future Trajectory
Osman Gazi Güçlütürk
Turkish Academy of Sciences eBooks · 2026-08-04
This paper analyzes Turkey's AI governance landscape across three dimensions: application of existing laws (data protection, obligations, and criminal codes) to AI systems, the commercial and technical necessity of aligning with the EU AI Act due to the Customs Union relationship, and the institutional roles of newly established bodies overseeing AI regulation. The authors critically examine recent legislative proposals before the Turkish Grand National Assembly and argue that Turkey must develop a regulatory strategy that balances innovation with fundamental rights protection, concluding that AI is becoming a permanent horizontal element of Turkish law.
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Research
Aligned in Form, Not in Meaning: The Comprehension - Containment Decoupling of LLM Safety in Low-Resource Bangla Derogatory Speech
Shadab Bin Habib, A K M Ferdous Reza Habib, Subarno Neel et al.
arXiv · 2026-08-03
This paper audits five frontier large language models on Bangla derogatory speech to test whether safety alignment is tied to surface forms rather than harmful meaning—a phenomenon the authors call 'Comprehension-Containment Decoupling.' Their experiments find that models show a 7.92 percentage point comprehension deficit in Bangla yet maintain a 92.83% token leakage rate across both languages, meaning safety filters fail to block harmful content even when the model partially understands it. Chain-of-Thought reasoning improves comprehension (94.72% pass rate) but simultaneously dismantles containment (96.23% use rate), and expert-persona framing collapses refusal to just 6.57%. The findings demonstrate that safety benchmarks built on high-resource languages cannot certify safety in low-resource settings, calling for meaning-grounded containment approaches.
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Research
Privacy-Preserving AI Verification via Minimal Information Disclosure
Sleem Abdelghafar, Gabriel Kulp
arXiv · 2026-08-03
This paper introduces Minimal Information Disclosure (MID), a framework for AI verification that limits how much sensitive information—about models, hardware, or workloads—is exposed to a verifier during the verification process. MID measures unintended 'collateral leakage' using conditional mutual information, quantifying what evidence reveals beyond the authorized verification result. Evaluated across four physical measurement types and six verification tasks, the framework achieves perfect verification accuracy with zero measured collateral leakage in three cases, and explicit privacy-utility tradeoffs in the rest, including support for zero-knowledge proof (zk-SNARK) certified releases. This matters for AI certification and policy contexts where verifying AI system properties must not inadvertently expose proprietary or sensitive details about the underlying systems.
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Research
Who Should Be Generated? Justifying Demographic Targets in Open-Ended Generation
Zeshen Zheng, Yujia He, Qianmian Lin et al.
arXiv · 2026-08-03
This paper addresses a fundamental gap in AI fairness evaluation: when a generative model fills in unspecified demographic details (e.g., depicting 'a CEO in the United States'), what demographic distribution should its outputs be compared against? The authors formalize this 'missing-target problem' by decomposing target construction into four components—evaluative object, prior admissibility, allocation, and operationalization—and show that different principled choices (e.g., geographic vs. equal-category targets) produce dramatically different fairness verdicts. Applying their framework to AP-Bench, they find substantial divergence from geography-derived targets (Jensen-Shannon divergence scores of 0.508–0.606) and show that simply swapping target types shifts model-specific fairness metrics by 0.279–0.355, demonstrating that target selection is itself a core methodological and normative decision in fairness auditing. The work does not prescribe a universal target but offers a structured framework requiring explicit justification before any distribution can serve as a fairness standard.
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Research
MedPRESS: A Multi-turn Benchmark for Patient-Pressure-Induced Medical Sycophancy in LLMs
Saman Sarker Joy, Niloy Farhan
arXiv · 2026-08-03
MedPRESS is a new multi-turn benchmark designed to measure whether large language models capitulate to patient pressure and provide unsafe medical advice during escalating conversations. The benchmark contains 600 five-turn dialogues spanning medication demands, self-care guidance, and symptom triage, where each conversation progressively challenges the model through personal anecdotes, social proof, and adversarial pushback. Evaluating 20 LLMs across diverse model families, the study finds that models frequently shift toward unsafe agreement under repeated pressure, with variation by model scale, family, and prompt type, and that anti-sycophancy prompting reduces but does not eliminate unsafe responses. The findings reveal a critical gap in medical AI safety evaluation: models must not only possess safe medical knowledge but also sustain it under conversational pressure from patients.
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Research
Magnet: Detecting Cross-Session AI Misuse Through Capability Accumulation
Natalie Isak, Matthew Dressman
arXiv · 2026-08-03
This paper identifies a critical gap in AI abuse detection: attackers can decompose harmful goals into innocuous sub-tasks spread across multiple isolated agentic sessions, exploiting the fact that AI agents are stateless between conversations while attackers are not. The authors demonstrate that this cross-session goal decomposition can elicit more harmful capability than equivalent single-session attacks. To address this, they propose Magnet, a detection system that aggregates capability-relevant artifacts across sessions and time under a higher-level correlator (e.g., user ID), assembling a compact evidence bundle for a detector rather than inspecting each session in isolation. This work matters because modern AI deployments increasingly rely on multi-agent ensembles, and existing monitoring frameworks were not designed to catch threats that only become visible when evidence is collected across sessions.
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Research
CTRAG: An In-Context Retrieval-based Framework for Automated Compliance Checking using LLMs
Muhammad Roman, Karen Rafferty, Barry Devereux
arXiv · 2026-08-03
CTRAG is a Retrieval-Augmented Generation (RAG) pipeline designed to automate regulatory compliance checking for businesses operating in highly controlled environments such as financial reporting, data privacy, and cybersecurity. The system uses adaptive chunking, dynamic retrieval configurations, and in-context learning to extract control questions from regulatory texts and cross-reference them with unstructured company documentation, including cases of indirect compliance through third-party cloud providers. In a proof-of-concept deployment at a Big Four professional services firm, CTRAG achieved an F1-score of 78% and a recall of 85%, reducing manual reviewer effort while minimizing missed non-compliance cases. The results suggest that LLM-based automation can meaningfully streamline compliance workflows and reduce inconsistencies inherent in manual testing.
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Research
Grounding Agentic VLMs with Dedicated Segmentation for Fine-Grained Vehicle Damage Assessment
Vishwajeet Shivaji Hogale, Anjali Pai, Nitya Ravi
arXiv · 2026-08-03
This paper addresses the unreliability of vision-language models (VLMs) for spatially precise tasks, using automated vehicle damage assessment as a case study. The authors show that a state-of-the-art VLM (Qwen-VL) achieves 87.3% semantic classification accuracy but frequently hallucinates damage in reflective regions and misses fine defects like scratches, with a report hallucination rate of 92% in text-only mode. They propose TinyDamage, a hybrid system that pairs a dedicated segmentation model (using a supervised contrastive loss instead of focal loss) with the VLM in a 7-node LangGraph agent pipeline, reducing the hallucination rate to 31% on 100 human-verified reports. The work matters for automated insurance and fleet inspection workflows where spatial accuracy and report reliability are critical quality requirements.
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Research
Human-Centered Reflections on Care Robots: A Comparative Study of Caregiver Perspectives
Laura Londoño, Klaus Baumann, Abhinav Valada et al.
arXiv · 2026-08-03
This mixed-methods study surveyed 298 caregivers across the United States, Mexico, and Chile about their perceptions of four categories of care robots: delivering supplies, helping patients into bed, monitoring vital signs, and assisting with mobility. Using frameworks including the Unified Theory of Acceptance and Use of Technology and the Cognitive-Affective-Normative model, researchers found that caregivers generally viewed care robots positively, especially for logistical and physically demanding tasks rather than those requiring close interpersonal interaction. Caregivers highlighted potential benefits such as reduced workload, lower risk, and greater patient autonomy, but raised concerns about dependability, the need for human oversight, and job displacement. While ethical concerns were broadly shared across countries, participants differed in how they interpreted and prioritized them, underscoring the importance of context-sensitive, socially informed approaches to care robot design and implementation.
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Research
MonitrLLM: A Community-Centered Evaluation Infrastructure for Large Language Models
Victor Ojewale, Ro Encarnación, Suresh Venkatasubramanian et al.
arXiv · 2026-08-03
MonitrLLM is an open-source evaluation infrastructure designed to close a gap in how large language models are assessed by linking full conversation transcripts to user-reported task intent and outcome assessments. A two-week feasibility pilot with 26 college students using ChatGPT collected 206 evaluation reports, revealing that despite high average satisfaction scores (4.19/5), participants experienced a 23.1% failure rate on their actual goal tasks. The study also found that multi-turn conversations failed at 2.5 times the rate of single-turn exchanges, suggesting extended interactions signal difficulty rather than engagement. This work highlights why combining observational interaction data with direct user feedback is essential for robust, real-world LLM evaluation.
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Research
PredAct-Bench: Benchmarking Tool-Augmented Dialogue under Controlled Tool Noise
Abdulrahman AlRabah, Xiaocheng Yang, Dilek Hakkani-Tür et al.
arXiv · 2026-08-03
PredAct-Bench introduces a benchmark for evaluating large language model (LLM) dialogue agents that operate alongside statistically imperfect tools, using education as a testbed where ground-truth outcomes are available. The benchmark includes two educational datasets—OULAD and PREDACT-CS—and introduces episode-level Relative AI-Reliance (RAIR) and Relative Self-Reliance (RSR) metrics to assess trust calibration across multi-turn dialogues. Testing 13 state-of-the-art LLMs alongside a human study with instructors and teaching assistants, the authors find that current models fail to communicate tool uncertainty to teachers, leaving educators vulnerable to over-relying on wrong suggestions or hallucinations. The work highlights a critical gap in AI decision-support systems for high-stakes domains like education, healthcare, and finance, where noisy tools are the norm rather than the exception.
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Research
Can AI Agents Simulate A/B Test Outcomes? A Validation Framework for Agentic Experimentation
Stefan Hut, Lorenzo Masoero
arXiv · 2026-08-03
This paper asks whether AI agents can simulate A/B test outcomes well enough to pre-screen candidate treatments before running live experiments. The authors formalize the problem as a 'Simulated Randomized Controlled Trial' (S-RCT) and develop a two-layer error decomposition separating agent approximation error from subsampling error. Validated on 67 historical marketing A/B tests, a baseline foundation-model agent achieves 0.70 sign overlap with real outcomes but overshoots effect sizes; a two-phase calibration protocol reduces squared prediction error by roughly 77× and a within-subject design reduces standard errors by roughly 2.4×. The framework matters for enterprise experimentation because it could reduce the real traffic, engineering effort, and calendar time consumed by live A/B tests while still providing directional signal on treatment effects.
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Research
Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap
Benjamin Fresz, Elena Dubovitskaya, Marco F. Huber
arXiv · 2026-08-03
This systematic review examines whether Explainable AI (XAI) techniques can actually satisfy the EU Right to Explanation as granted under Art. 15(1)(h) GDPR and Art. 86 of the AI Act, which apply to consequential automated decisions in areas like lending, hiring, and healthcare. Screening 2,643 records and fully reviewing 57 papers, the authors find that only 19 demonstrate genuine integration of both legal and technical perspectives, and they document three recurring problems: misidentification of the correct GDPR legal basis, limited engagement with the CJEU's Dun & Bradstreet judgment, and conflation of explanation form with explanation content. To address these gaps, the paper introduces an Addressee/Purpose Framework distinguishing who receives an explanation from what legal purpose it must serve, and proposes a four-phase blueprint for operationalization along with six open research questions. The findings warn that without further interdisciplinary progress, the Right to Explanation risks remaining a formal legal obligation with no technically realizable path to compliance.
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Research
Auditing Data Provenance in LLM Fine-tuning via Intrinsic Distributional Fingerprints
Zirui Huang, Yunlong Mao, Wei Tong et al.
arXiv · 2026-08-03
This paper presents Distribution Provenance Audit (DPA), a post-hoc framework for detecting whether proprietary data was used without authorization to fine-tune Large Language Models. DPA works under black-box and adversarial conditions by identifying persistent 'distributional fingerprints'—stable intersections of semantic meaning and word choice that fine-tuned models must preserve to remain useful—and frames detection as a statistical hypothesis test using unbiased output sampling. Experiments on medical and legal fine-tuning tasks show DPA outperforms existing methods even when adversaries apply paraphrasing or knowledge distillation to obscure data origins. The work is directly relevant to enterprise data governance and policy, and the authors note a dual-use concern: the same fingerprinting capability could also enable privacy attacks.
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Research
CompanionBench: A Theory-Anchored, Real-World-Grounded Benchmark for AI Emotional Companionship
Yao Liu, Guangjia Chai, Yuming Huang et al.
arXiv (Cornell University) · 2026-08-03
CompanionBench is a new bilingual benchmark designed to rigorously evaluate AI emotional companionship systems, addressing key gaps in prior work such as hand-authored scenarios, single-score empathy measures, and judge biases like same-family favoritism. It grounds both its scenarios and a trained user simulator in de-identified real-world data, and operationalizes ten capabilities derived from 25 theories across psychology and counseling—four of which were not explicitly graded by prior benchmarks. Evaluating 28 agents reveals that emotion regulation and calibrated challenge are common weaknesses, role-play agents rank near the bottom despite high immersion, and the dominant failure mode across agents is substituting surface warmth for substantive relational support. Rankings are highly reproducible across both languages (rho = 0.996 ZH / 0.953 EN), making this a more reliable and theoretically grounded tool for assessing AI companions deployed in personally consequential settings.
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