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
Measuring Digital Labour Market Transitions with a Digital Semantic Score: An AI-Based Methodology Applied to the Dutch Labour Market
Sadegh Shahmohammadi, Xavier Pinho, Mairi Bowdler et al.
arXiv · 2026-08-25
This paper develops an AI-based methodology to measure the digitalisation of the Dutch labour market using millions of job profiles. It combines embedding-based similarity search and large language model classification to map job information to standardised ESCO occupations, and introduces a 'Digital Semantic Score' that quantifies how strongly job titles and skills are associated with digital concepts using cosine similarity against digital and non-digital anchor groups. The findings show that digitalisation is unevenly distributed—most prominent in managerial, professional, and ICT roles but growing in hybrid business, marketing, and automation positions—and that career transitions toward digital work are pathway-dependent. The framework is designed to identify emerging skill needs, support reskilling strategies, and inform policy responses to skills mismatches and labour shortages in the Netherlands.
- Workforce
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Research
Constraint-Guided Enterprise Data Mapping with Large Language Models
Sebastian Monka, Pramod Anantharam, Thien Vo Minh et al.
arXiv · 2026-08-25
This paper proposes Constraint-Guided Mapping (CGM), a neuro-symbolic method for automating enterprise data entity alignment that combines schema-grounded structural constraints with large language model (LLM) ranking. The key finding is that hard admissibility constraints reduce the candidate space by roughly 480x without losing ground-truth matches, and this constraint gate — not the LLM — drives the primary performance gain (F1 from 0.08 to 0.66). Crucially, a small model paired with constraints matches a frontier LLM used alone at roughly 28x lower cost, and the approach generalizes across seven enterprise data sources while reducing expert effort by roughly 7x compared to spreadsheet workflows. This matters for enterprise data integration teams facing scaling challenges as schemas and data providers evolve.
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Research
'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection
Fawzia Zehra, Kara-Isitt, Sonal Khosla et al.
arXiv · 2026-08-25
This paper investigates whether Urdu's near-total absence from LLM safety benchmarks leads to measurable gaps in hate speech detection. The authors tested five major LLMs (GPT-4o, Claude Sonnet 4.5, Gemini 2.5 Flash, Qwen-2.5, and Llama-3.1) across six datasets covering Nastaliq Urdu, Roman Urdu, English, and code-switched Urdu-English, finding label instability between original-script and English-translation classification ranging from 15.9% to 31.6%. A key metric, the 'Missed-in-Urdu' rate—content flagged as harmful in English translation but passed as safe in the original Urdu script—ranged from 2.4% to 9.9% (median 4.3%), with smaller open-weight models showing substantially higher instability than frontier closed models. The findings demonstrate that 246 million Urdu speakers face uneven safety assurance from current LLMs, and a full enumeration of 205 papers across nine ALW/WOAH workshop editions confirms zero dedicated Urdu papers, underscoring a systemic gap in content moderation research and deployment.
- Quality assurance
- AI policy
Research
MemUse: Moving Memory Evaluation from Direct QA to Natural Integration in Long-Term Human-AI Conversation
Ryuichi Sumida, Koji Inoue, Tatsuya Kawahara
arXiv · 2026-08-25
This paper challenges how memory in conversational AI is typically evaluated, showing that standard 'Direct QA' benchmarks—which test whether a model can recall a specific fact when directly asked—do not correlate with real user satisfaction. In a 4-month deployment with 40 users across 1,872 sessions and 7 memory conditions, Direct QA accuracy ranged from 19.7% to 70.1% while satisfaction remained flat across conditions. The authors introduce MemUse, a new evaluation framework using real user-cued memory moments scored on 'natural integration'—whether the model spontaneously and appropriately weaves prior context into responses—and find a 71-point gap between Direct QA scores and actual conversational use of those facts. Natural integration, unlike Direct QA, is meaningfully associated with user satisfaction, suggesting current benchmarks miss the capability that actually matters in long-term human-AI conversation.
- Quality assurance
Research
Evaluating and Preventing Security Smells in AI-Generated Ansible Code
Pandu Ranga Reddy Konala, Vimal Kumar, David Bainbridge et al.
arXiv · 2026-08-25
This paper evaluates 16 AI models generating Infrastructure as Code (Ansible roles) for Apache Tomcat v10 and MongoDB v7, finding that without security guidance all 16 models produced code containing security smells that fail CIS benchmark compliance and underperform human developers. The authors introduce a prompt-engineering approach that integrates Ansible best practices and CIS benchmarks via an extended CO-STAR framework, enabling security smell prevention during code synthesis rather than after deployment. When applied, 4 of 16 models generate compliant code, with the top model achieving 95%-100% CIS compliance—a fourfold improvement over the 23%-43% compliance achieved by human developers—and overall code quality improving by 19%-49%. The findings matter for enterprise and quality-assurance contexts because insecure infrastructure code propagates vulnerabilities to deployed systems, and the approach requires no model retraining, making it immediately adoptable via system prompts.
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Research
The Gold Rush in AI4Math: Where Are We Now?
Jiashun Jin, Zheng Tracy Ke, Bingcheng Sui
arXiv · 2026-08-25
This paper provides an empirical snapshot of how AI is being used in mathematical research by analyzing all 32,944 arXiv mathematics submissions posted between March 1 and August 20, 2026. Of these, 3,575 explicitly disclosed AI use, and 1,712 involved substantive mathematical contributions — with disclosed substantive use rising sharply from 1.39% of submissions in March to 14.09% by late August. The study finds that AI-assisted math is geographically concentrated (the U.S. and China account for roughly two-thirds of weighted author contributions), unevenly distributed across subfields, and already being applied to open problems, with 71% of 717 named open-problem records labeled as fully resolved. These findings have direct relevance to science policy and research workforce dynamics, signaling a rapid but uneven early adoption of AI tools in formal mathematical practice.
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Research
MC-CXR: A Multi-Context Chest X-ray Benchmark for Context-Induced Disruption in Vision-Language Models
Junhyeok Lee, Songsoo Kim, Kyu Sung Choi
arXiv · 2026-08-25
MC-CXR is a new benchmark designed to test whether vision-language models (VLMs) maintain correct chest X-ray diagnoses when presented with conflicting contextual information such as retrieved reports or prior imaging. The benchmark includes 240 cases expanded to 2,522 instances and evaluates ten VLMs using two metrics: switch-to-wrong rate and context-aligned error rate. Results show that misleading text causes models to abandon correct image-based decisions at rates of 45.6–78.1%, while misleading visual context causes switches at 35.7–61.7%, and that 74.6% of text-induced switches align with the misleading label versus only 17.6% for visual context—a 57-point gap. These findings reveal a critical reliability gap in VLMs deployed in clinical radiology pipelines, where plausible but incorrect contextual information can substantially distort model outputs.
- Quality assurance
Research
AgentWorld: Personality-Aware Reliability Evaluation for Agentic Information Retrieval
Gunja Agarwal, Arup Kumar Das, Arun Menon et al.
arXiv · 2026-08-25
AgentWorld is a simulation framework for evaluating AI agents used in information retrieval tasks, addressing limitations of existing benchmarks that rely on scripted, uniform user interactions. The framework introduces personality-diverse user populations modeled on the Big Five (OCEAN) traits, a consistency metric with fault classification and partial-credit scoring, and an adversarial Risk Analyser that stress-tests agent trajectories using Monte-Carlo rollouts and Shapley attribution. Experiments across multiple agent types reveal that personality variation exposes failure modes invisible to uniform testing—including a 0.27-point quality gap and pass rates ranging from 50% to 100% on the same task across personas—while adversarial analysis shows tool and infrastructure-layer attacks dominate brittleness (46% system, 38% action via Shapley). The framework matters because it provides a more realistic and rigorous standard for measuring AI agent reliability before deployment.
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Research
What Guides the Agent? Adjudicating Unauthorized Behavior via Localizing Behavior-Guiding Instructions
Yichao Gao, Yumo Zhang, Yunhao Yao et al.
arXiv · 2026-08-25
This paper presents Attnlocate, a runtime defense framework for LLM-based agents that identifies which parts of an agent's context window are actually driving its tool-calling decisions. By treating the localization of 'behavior-guiding instructions' as an object detection problem over the attention matrix, Attnlocate can flag when untrusted external data—such as injected prompts or poisoned tool outputs—is covertly steering agent behavior. Evaluated across ten agent configurations spanning five LLM families, the system achieves a mean IoU of 0.743, an AUROC of 0.956, and a 0.934 true-positive rate at a 0.067 false-positive rate. The results matter for enterprise and policy contexts because they provide a principled, low-overhead way to enforce authority boundaries in deployed AI agents without requiring model retraining.
- Enterprise
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Research
WebMCP-Phalanx: Enforcing and Characterizing Trust Boundaries for Browser-Integrated LLM Agents
Lin-Fa Lee, YI-YU Chang, Kuo-Hui Yeh
arXiv · 2026-08-25
WebMCP-Phalanx proposes a dual-layer browser runtime architecture to secure LLM agents operating under the emerging W3C WebMCP standard, which allows agents to invoke tools exposed by web pages. The system addresses three identified risks—subject-attribution spoofing, uncontrolled tool lifecycles, and semantic prompt injection—by combining cryptographically protected capability credentials with a two-agent inspection model (a Quarantine Agent and a Privileged Agent). Empirical evaluation shows the browser-native ownership layer reduces revocation and overwrite attack success from 100% to 0%, while the dual-agent runtime blocks all 80 prompt-injection attempts in tool descriptions and limits tool-return attacks to 2 out of 80. A residual vulnerability under white-box adaptive attackers motivates an additional call-timing gate to delay tool invocation until all agent-visible metadata is validated.
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Research
The urban right to AI: Pluralistic co-design and governance of public space
Rashid Mushkani
arXiv · 2026-08-25
This thesis argues that cities increasingly rely on AI-generated scores, maps, and images to make decisions about public space, creating an 'epistemic, algorithmic layer' that shapes municipal perception and action. Using Montréal as an empirical base, the author combines participatory research and computer vision across ~45,000 street-view images (Street Review) and develops LIVS, a dataset of 37,710 pairwise comparisons from 30 community organizations used to fine-tune a Stable Diffusion XL model via Direct Preference Optimization. Results show that AI alignment can improve but persistent disagreement and neutrality among community groups reflect genuinely contested values rather than annotation noise. The thesis translates findings into a civic 'Right to AI' framework with concrete municipal governance mechanisms—including lifecycle governance, procurement rules, oversight, and recourse—that treat plural public values as legitimate rather than averaging them into a single objective.
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Research
More Rejective, Not More Discriminative: The Unit of Verification in Pre-Execution LLM Oversight
Yuchen Han, Cheng Yan, Wuyang Zhang
arXiv · 2026-08-25
This paper investigates how the size of the 'unit of verification'—how many actions an LLM monitor reviews in a single call before execution—affects the quality of pre-execution AI oversight. Using a novel 'twin-prefix framework' that pairs plans containing a single injected error with clean counterparts reviewed at five nested lengths, the authors find that longer review windows increase both catch rates and false rejection rates equally, making monitors more rejective but not more discriminative. Discrimination (measured by pre-registered 'informedness,' defined as catch minus false rejection) peaks at one or two actions for all tested judges, with the failure attributed largely to 'observation deprivation' in longer windows. The study argues that safety protocols should explicitly state the unit of verification and co-report results on clean (error-free) action series, not catch rates alone.
- Quality assurance
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Research
Strategic Alignment, Emergent Practice: Institutional Decoupling and the Governance of Artificial Intelligence in United Arab Emirates Newsrooms
Elsayed Darwish, Fawzia AlAli
International Journal of Computer Information Systems and Industrial Management Applications · 2026-08-25
A survey of 336 media practitioners across major UAE newsrooms finds that AI adoption is driven primarily by individual professional skills (β=0.41) and organizational culture (β=0.38), together explaining 42% of variance in adoption depth, while AI usage strongly predicts media performance (β=0.47, R²=0.39). Despite 76.7% of respondents using generative AI tools daily, only 28.6% use AI for verification and 12.5% for deepfake detection, and 38.5% report receiving no formal organizational training. The study identifies a pattern of 'productive decoupling' where personal initiative fills gaps left by uneven institutional governance, even as organizations publicly align with the UAE National AI Strategy 2031. The findings carry direct implications for media managers, regulators, and journalism educators seeking to strengthen institutional AI governance and verification practices.
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Research
Clinical Specialty Expansion of AI-Enabled and Machine Learning–Enabled Medical Devices Authorized by the US Food and Drug Administration From 1995 to 2025: Longitudinal Content Analysis
Youn-Soo Lee, Bo-Young Youn
Journal of Medical Internet Research · 2026-08-25
This longitudinal content analysis examined all 1,430 FDA-authorized AI/ML-enabled medical devices from 1995 to 2025, finding that radiology has dominated throughout but showed its first significant decline in share during 2023–2025 (dropping from 85.5% to 77.5%), accompanied by measurable diversification across other clinical specialties. Annual authorizations surged from a mean of 2 per year in the earliest era to 264 per year in the most recent era, with 331 authorizations in 2025 alone. Start-ups and technology companies were significantly more likely than incumbent manufacturers to receive authorization in non-radiology specialties, suggesting these entrants are driving specialty expansion. The authors note that authorization does not equal adoption, and the findings have direct implications for workforce training, health-system readiness, and specialty-specific regulatory frameworks.
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Research
''You Can't Open an LLM With a Screwdriver'': The De-Democratization of Software
Zixuan Feng, Italo Santos, K Damevski et al.
arXiv (Cornell University) · 2026-08-25
This vision paper argues that generative AI broadens access to code production but concentrates control over software in the hands of those who can inspect, evaluate, integrate, maintain, and govern AI-generated artifacts. Drawing on an expert panel, the authors contend that software engineering expertise is not eliminated but shifts toward intent specification, orchestrating AI behavior, and managing deployment pipelines. The paper warns of a 'de-democratization' dynamic where the gap between access and control widens, and identifies research opportunities in education, tools, and policy to help the software engineering community maintain agency and accountability.
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Research
A Privacy-Preserving Federated Learning Framework for Collaborative Academic Certificate Fraud Detection Across Institutions
Miriam W. Kaara, Jael S. Wekesa, Michael W. Kimwele
Journal of Information and Technology · 2026-08-25
This paper proposes a federated learning framework that allows multiple academic institutions to collaboratively detect certificate fraud without sharing private data. The system combines Convolutional Neural Networks for visual forgery detection and XGBoost for metadata anomaly detection, with local models aggregated via the FedAvg algorithm. Experiments show the federated approach achieves up to 94% accuracy and an AUC of 0.97, with fewer false positives and false negatives than centralized methods. The framework addresses data privacy, regulatory compliance, and scalability concerns in academic credentialing.
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Research
Institutional transformations of the labour market under the influence of artificial intelligence
S.G. Churbanov
Vestnik Universiteta · 2026-08-25
This paper examines how AI technologies are reshaping the Russian labour market through an institutional lens, finding that AI acts as an external shock accelerating the fragmentation of employment into platform and self-employment forms. Drawing on Rosstat, OECD AI Observatory, and World Economic Forum data, the study identifies an 'institutional vacuum' in which digital work practices have outpaced regulatory frameworks, leaving non-standard workers with weakened social protection. The authors argue that AI adoption creates asymmetric economic effects—lowering transaction costs for firms while increasing individual employment risks—and that sustainable adaptation requires proactive institutional design combining social protection reform, lifelong learning, and coordinated labour market regulation.
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Research
Working with AI: A Practice-Based Study of Knowledge Sharing in a Multinational Enterprise (MNE)
Xiang Li, Bidyut Baruah
European Conference on Knowledge Management · 2026-08-25
This qualitative case study examines how employees at a multinational manufacturing enterprise (GlobalMFG) use AI in everyday knowledge-sharing practices, drawing on six semi-structured interviews with managers and knowledge workers. Findings show AI improves information access, cross-location communication, and meeting follow-up, but its effectiveness depends on employees' prompting skills and contextual judgment, with experienced colleagues remaining the preferred source for specialist knowledge. The study also reveals uneven AI implementation due to inconsistencies in tool access, system integration, and governance. The authors recommend connecting AI investment with clear governance, consistent tool access, task-specific support, and ongoing employee feedback mechanisms.
- Enterprise
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Research
AI Readiness in Organisations: A Systematic Literature Review and the TOP-L Framework Development
Maria Kretschmer, Alice Coen, Ronald Orth
European Conference on Knowledge Management · 2026-08-25
This paper conducts a systematic literature review of AI readiness in organisations, analysing 34 existing frameworks and assessment instruments to identify 372 AI readiness factors. The authors find that prior research overemphasises technical and environmental factors while underrepresenting human elements and organisational learning. They develop the TOP-L (Technology-Organisation-People-Learning) framework, which adds organisational learning as a dynamic capability to the existing socio-technical TOP framework, synthesising the 372 factors into four dimensions and 20 factor clusters. The framework is specifically designed to support practical AI readiness assessment, particularly for small and medium-sized enterprises (SMEs).
- Enterprise
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Research
Generative AI as a Knowledge Governance Mechanism: The Role of Digital Trust and Information Security
Justyna Żywiołek
European Conference on Knowledge Management · 2026-08-25
This study examines how generative AI functions as a knowledge governance mechanism across 300 firms in Poland, Germany, and Spain, using structural equation modeling and multilevel analysis. Results show that AI use significantly increases digital trust, which in turn improves decision quality, with information security maturity moderating this relationship. The findings suggest that generative AI's impact on organizational decision-making depends on governance conditions—not technology alone—making governance frameworks a critical consideration for enterprise AI adoption.
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Research
How Universities Shape Responsible AI Use? A Three-Layer Gap Model
Alina Guzik, Adrian Lis
European Conference on Knowledge Management · 2026-08-25
This paper proposes and empirically tests a four-level model of how universities influence responsible AI use, spanning institutional governance, teaching practice, attitude internalization, and future diffusion of responsible practices. Using a mixed-methods study combining Likert-scale surveys with content analysis of open-ended responses, the authors find that while institutional governance is linked to students' educational experiences and internalization predicts diffusion intentions, the link between educational experiences and actual attitude internalization is weak and statistically insignificant. This suggests that formal university education has limited effectiveness in developing responsible AI competencies in students. The findings highlight critical gaps in AI governance in higher education and call for more intentionally designed educational activities that bridge institutional policy and genuine student learning outcomes.
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Research
Exploring How the Adoption of Artificial Intelligence in Human Resource Management Enhances Organizational Performance and Efficiency
Olugbenga Adeyanju Akintola, Samson Onyeluka Chukwuedo, Imad Nawaz et al.
Journal of Business and Digital Innovation · 2026-08-25
This study examines how AI adoption in human resource management (HRM) improves organizational performance and efficiency among 452 HR personnel and employees at IT-related organizations in Lagos, Nigeria. Using path analysis, the findings show that AI adoption positively affects both organizational performance and efficiency, with HR process efficiency serving as a key mediating pathway. The research is grounded in the Technology Acceptance Model, Resource-Based View, and Dynamic Capabilities Theory, offering empirical evidence that integrating AI into HRM operations yields measurable organizational benefits.
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Research
The impact of generative artificial intelligence on innovation: Evidence from software products
Erdem Dogukan Yilmaz, Tim Meyer
Strategic Management Journal · 2026-08-25
Studying the Firefox browser add-on ecosystem around ChatGPT's release, this paper finds that generative AI tools increased new add-on creation by 34% while simultaneously reducing improvements to existing add-ons by 20%. The shift reflects a reallocation of developer effort from incremental generational innovation toward original but not necessarily more novel products. The findings suggest generative AI democratizes product creation and broadens knowledge combinations but does not inherently produce genuinely novel innovations, posing challenges for platform managers seeking to sustain quality and encourage true novelty.
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Research
Digitalization and Labor Force Restructuring in China: Empirical Evidence From a Political Economy Framework
Chengzhi Qiao
Bulletin of Economic Research · 2026-08-25
Using panel data from 31 Chinese provinces (2001–2024) and multiple econometric estimators, this study finds that digitalization significantly reduces employment for unskilled and low-skilled workers while raising demand for medium- and high-skilled workers, with medium-skilled workers benefiting most. The authors interpret these findings through a Marxist political economy lens, arguing that digitalization expands the industrial reserve army and fosters a new middle class rather than representing neutral technological progress. Heterogeneity analysis shows stronger positive effects in regions with high capital formation and consumption. The authors recommend policy responses beyond retraining, including social protection systems, digital service taxes, and labor-enhancing technologies.
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Research
Rules Without Models: Why Europe Must Build the AI It Regulates
Luciano Floridi
Philosophy & Technology · 2026-08-25
This policy paper argues that the EU's extensive AI regulatory framework (GDPR, AI Act, etc.) has left Europe legally influential but technologically subordinate, citing evidence such as Llama 4's withholding of multimodal rights from EU developers and Anthropic's model disabling under U.S. export directives. The author proposes EOSAI (European Open Source AI), a public institution producing open-source AI models deployable under EU jurisdiction, achievable through three pathways: an industrial consortium, a Member-State vehicle using national defense resources, and a European Joint Undertaking. The paper contends that a natively compliant open-source model would transform EU regulation from a compliance burden into a competitive advantage in regulated markets, making EOSAI partly self-financing.
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