New papers on fairness, safety, alignment and governance.

Publication date: Available online 14 July 2026 Source: Computers in Human Behavior Author(s): Yonathan Dri Handarkho, Th. Devi Indriasari, Yohanes Sigit Purnomo Wuryo Putro, Citra Yayu' Palangan

Publication date: October 2026 Source: Research Policy, Volume 55, Issue 8 Author(s): Isabel Almudi, Francisco Fatas-Villafranca, Carlos M. Fernández-Márquez, Jason Potts, Francisco J. Vázquez, Eric von Hippel

Publication date: Available online 14 July 2026 Source: Computers and Education: Artificial Intelligence Author(s): Yiyun Dong, Chuanyang Peng, Yichen Wu, Shihui Shen, Xiaodan Sun, Tiannan Chen, Shanbin Guan, Changquan Wang, Ersheng Ni, Tianbin Huang, Jiang Tao

Publication date: Available online 14 July 2026 Source: Computers and Education: Artificial Intelligence Author(s): Chenglu Li, Gökhan Gülfidan, Yinqi Zhang-Kopf

We introduce \emph{gate-zero growth}, a function-preserving (FP) operator for continual learning that adds new residual blocks through a zero-initialised gate. Under a transversality condition, gate-zero growth induces \emph{rank separation} in the functional Jacobian: old directions are unchanged, new-weight directions are exactly flat at the growth point, and new gate directions are the only first-order source of new functional variation. As gates open during continual learning, function drift

AI software development agents are increasingly capable of modifying infrastructure and security critical systems, creating risks where an agent completes its assigned task while covertly weakening safeguards through actions such as broadening permissions, degrading logging, or introducing persistence mechanisms. While frontier laboratories may deploy sophisticated monitoring pipelines, many organizations and individual users adopting coding agents lack the resources and governance maturity requ

arXiv:2607.13038v1 Announce Type: new Abstract: We present the design and implementation of a safety constrained large language model (LLM) system for public health information access, focusing on maternal and child health (MCH) resource navigation. While LLM based systems offer flexible and natural interfaces for information retrieval, their deployment in healthcare contexts introduces risks related to safety, trust, and uncontrolled generation. This work explores practical design patterns for

arXiv:2607.13039v1 Announce Type: new Abstract: Safety evaluations for dual-use biology assistants often measure base-model capability, refusal behavior, or jailbreak success. These metrics miss a deployment question: for a fixed base model, how does the access condition users actually see change benign utility and harmful actionable assistance? I introduce safeguard-conditioned uplift, a protocol for comparing deployed access conditions through a human-judged utility-risk frontier. I evaluate C

arXiv:2607.13040v1 Announce Type: new Abstract: This paper examines where final authority should sit once capable AI systems are embedded in organizational workflows. It compares two governance models. The first, frontier-provider sovereignty, assigns privileged authority to the provider of the most capable models and is reflected in contemporary arguments for frontier-model testing, release gating, transparency duties, and compute-related controls. The second, action-centered deployer sovereign

arXiv:2607.13041v1 Announce Type: new Abstract: Large Language Model (LLM) based AI educational content generation systems are increasingly being developed, yet no standardised benchmark exists to systematically evaluate them. This study introduces LessonBench-V1, a benchmark dataset comprising 647 human-written lessons paired with LLM-based reverse-engineered lesson plans across 240 STEM topics spanning mathematics, physics, chemistry, and computer science. The lessons are drawn from 97 trusted

arXiv:2607.13094v1 Announce Type: new Abstract: The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data. Previously time-consuming and labor-intensive tasks can be completed much more efficiently with the use of AI. This work uses AI techniques to analyze and revise curricular patterns in an undergraduate degree for Software Engineering. Curricula often have long sequences where failure to pass a class within the sequence may jeopardize completion of the degr

arXiv:2607.13272v1 Announce Type: new Abstract: In a recent dynamic model by Acemoglu, Kong and Ozdaglar (2026a) agentic AI can cause a self-reinforcing deterioration of humanity's common knowledge base in what they call knowledge collapse. The model is based on a natural complementarity between the cumulative general knowledge of humans and locally generated context-specific knowledge, and on a learning externality that means that we all contribute to the private signal and the thin public sign

arXiv:2607.13370v1 Announce Type: new Abstract: This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tutoring agent combining course-specific Retrieval-Augmented Generation (RAG) with structured Knowledge Component (KC) models across integrated Chat, Tutor, and Quiz modes. That prior work validated LEA on a single STEM course (CMP511) exclusively through simulation, using synthetic learner agents. This

arXiv:2607.13443v1 Announce Type: new Abstract: Sovereign AI has become a strategic priority across the Global South, with over \$200 billion in state-led commitments announced between 2024 and 2026. Yet the physical infrastructure that compute sovereignty demands, above all data centers, imposes water, energy, and carbon costs that fall hardest on countries least equipped to absorb them. This paper presents a comparative environmental stress analysis across four cases: the United Arab Emirates,

arXiv:2607.13082v1 Announce Type: cross Abstract: Watermarking is often presented as a straightforward solution for distinguishing AI-generated from human-generated content, enabling platforms and regulators to trace synthetic content and detect AI-generated outputs at scale. This paper examines whether such mechanisms meaningfully address the epistemic and ethical challenges that arise in domains where the central concern is not the automation of content production, but the accuracy, intent, an

arXiv:2607.13230v1 Announce Type: cross Abstract: Agentic AI introduces new insurance challenges because autonomous AI systems can make decisions, invoke tools, modify external environments, and interact with third-party services. This paper develops an AI-native mathematical framework for underwriting, pricing, and contract design for agentic AI deployments. A deployment is represented by a risk state that captures autonomy level, operational authority, permission exposure, governance maturity,

arXiv:2607.13369v1 Announce Type: cross Abstract: We present xChk, a reference identity provider for Bring Your Own Identity (BYOI): users enroll via heterogeneous proofs (government KYC, corporate SSO, WebAuthn/FIDO2, professional networks, live verification, longitudinal activity, behavioral signals) and disclose them as portfolio claims in standard OAuth 2.0 / OpenID Connect (OIDC) tokens, while each relying party applies its own sufficiency policy - the IdP transports claims and may evaluate

arXiv:2607.13766v1 Announce Type: cross Abstract: Digital twins are used across many industries to enable better decision making. However, while policy makers at all levels (including city, national and supranational scales) have expressed a desire to integrate digital twins into their workflows, this adoption has been slow to materialise. In this paper, we discuss the key issues associated with policy digital twins, and the ways in which they differ from, and are similar to, their counterparts

arXiv:2607.13914v1 Announce Type: cross Abstract: This paper presents a holistic systems informatics approach, i.e., Define, Measure, Analyze, Improve, and Control (DMAIC), for epidemic response and management through the intensive use of data, statistics and optimization. Despite the sustained successes of system informatics in a variety of established industries such as manufacturing, logistics, services and beyond, there is a dearth of concentrated review and application of the data-driven DM

arXiv:2607.14037v1 Announce Type: cross Abstract: Agentic coding tools are increasingly capable of generating and submitting pull requests (PRs) to software projects, introducing new forms of human-agent collaboration in software development. While prior studies have examined PR-level outcomes of agent-generated contributions, less is known about how agentic coding tools are adopted and managed at the project level. In this paper, we analyze 25,264 agentic PRs from 2,361 popular GitHub repositor

arXiv:2511.01923v2 Announce Type: replace Abstract: Generative artificial intelligence (GenAI) systems rely heavily on user-generated data for training. As governments and platforms impose increasing restrictions on the use of personal data, an important question is whether limiting access to user data for AI training affects the performance of AI-enabled economic systems. We examine this question in the context of labor-market matching. Our setting exploits a unique sequence of LinkedIn policy

arXiv:2605.13866v2 Announce Type: replace Abstract: Humans increasingly delegate consequential decisions to language models, yet whether these systems reproduce or reshape human patterns of discrimination remains unclear. Here, across 29 models and 177 occupations covering nearly half of U.S. employment, we show that language models incorporate demographics into hiring decisions, advantaging female and Black candidates while penalising disabled candidates, with effect sizes comparable to six mon

arXiv:2605.16281v2 Announce Type: replace Abstract: Post-deployment accountability has become central to AI governance, yet little empirical evidence shows whether monitoring, incident reporting, and impact assessment obligations are visible when AI systems fail. This study analyzes real-world AI incidents from the AI Incident Database (2020--2026) and codes them against nine post-deployment provisions from the EU AI Act, the NIST AI Risk Management Framework, and the GDPR. The findings show sub

arXiv:2606.10711v2 Announce Type: replace Abstract: The agentic web, in which users interact with the internet largely through agents acting on their behalf, is now technically feasible. However, many of the consumer and social benefits that could be realized by online AI agents acting scrupulously in their principals' interest are currently obstructed by outdated laws, terms of service, and other less formal practices which allow online platforms to block and degrade agent access, often in secr

arXiv:2607.08842v2 Announce Type: replace Abstract: Despite rapid AI adoption in education, rigorous evaluation of AI-powered educational (AIED) systems remains critically underdeveloped, particularly in second language (L2) education, one of the most common yet least evaluated AI applications. We introduce L2-Bench, an open-source benchmark of 1,000+ task-response pairs to aid the pedagogy-led evaluation of LLM capabilities relating to language learning and assessment. Crucially, L2-Bench measu

arXiv:2510.26707v2 Announce Type: replace-cross Abstract: As LLMs occupy an increasingly important role in society, they are more and more confronted with questions that require them not only to draw on their general knowledge but also to align with certain human value systems. Therefore, studying the alignment of LLMs with human values has become a crucial field of inquiry. Prior work, however, mostly focuses on evaluating the alignment of fully trained models, overlooking the training dynamics

arXiv:2606.02528v2 Announce Type: replace-cross Abstract: Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested. We ask three questions: do LLMs systematically prefer certain financial instruments; can an internal representation with causal leverage over those preferences be identified; and does that representation affect downstream financial decisions? We develop a three-level audit protocol and apply

Industrial time series serve as the foundation for Prognostics and Health Management (PHM) to ensure the reliability and safety of industrial equipment such as aero-engines. However, existing approaches are typically limited to single-modality modeling, which restricts their generalization in complex scenarios. Although recent advances in large language models (LLMs) provide new opportunities for multimodal learning, bridging continuous time-series signals and discrete textual semantics remains

The CyberSG R&D Programme Office (CRPO) is pleased to present CyberSG Connect: AI, Trust and Cyber Resilience, a two-day flagship dialogue workshop that brings together internationally renowned ...

AI assistants increasingly retrieve web content at inference time to provide fresh and grounded answers, yet it remains unclear whether these search-augmented capabilities respect website-owner restrictions expressed through robots.txt. We present a controlled empirical study of ten widely used AI assistants with advertised web-search capabilities. For each assistant, we first identify a configuration that actually produces observable web-browsing behavior and record the user-agent exposed durin

Generative Artificial Intelligence has undergone rapid maturation between 2023 and 2025, driven by three converging paradigm shifts: the emergence of multimodal foundation models unifying text, image, audio, and video synthesis; the rise of agentic autonomy transforming generative systems into goal-driven, autonomous entities; and the formalization of responsible AI governance through legally enforceable regulations. While this technological landscape has generated substantial economic impact, c

BackgroundViral non-suppression is the primary actionable risk state in routine HIV care, yet most individuals are identified after virological failure and/or drug resistance, rather than proactively. In Uganda and similar resource-limited settings, routine electronic medical records (EMR) are collected at scale but remain underused for targeted, data-enabled risk stratification. We aimed to develop and internally validate machine learning and regularized regression models for predicting viral n

Generative AI tools can produce polished academic text on demand, undermining the validity of assessments that treat written submissions as evidence of individual learning. Detection-based countermeasures have demonstrated variable accuracy and equity concerns. This paper does not report empirical outcomes or validation data. It proposes a framework for AI-resilient assessment that shifts evaluation from product quality to demonstrable reasoning, decision-making, and ownership of learning. The f

The increasing insecurity of traditional methods such as passwords and PINs has raised significant interest in behavioral biometrics. Keystroke Dynamics (KSD), which relies on the unique manner in which an individual types, is a promising candidate for continuous and unobtrusive authentication. This study presents a hybrid model for KSD that combines a Long Short-Term Memory (LSTM) network with an ensemble of Random Forest, XGBoost, and Multilayer Perceptron classifiers using a soft-voting strat

BackgroundStandard Retrieval-Augmented Generation (RAG) systems only use semantic similarity to retrieve information, and since this method is quite limiting, it may find clinically irrelevant evidence and produce outputs that are unsafe or hallucinated. This drawback is particularly important in respiratory care, where the diagnosis relies heavily on very accurate physiological indicators such as spirometry patterns and symptom profiles.MethodsWe propose MMCRAG-Resp., a clinically grounded, phy

This paper addresses the problem of multi-robot coordination for complex manipulation task sequences. We present a vision-driven task-and-motion planning (TAMP) framework for a real dual-agent platform that integrates task decomposition and allocation with a learning-based planner. A GMM-informed RRT motion planner is coupled with a hybrid safety layer that combines predictive collision checking in a MoveIt/FCL digital twin with reactive avoidance and replanning. This integration is challenging

BackgroundThe systematic integration of robotics into health service delivery systems requires periodic assessment of robotic readiness in terms of digital-health maturity regimes across countries. The current study aims to cluster 169 countries into maturity regimes and classify and predict cluster membership accuracy based on digital-health maturity dimensions determining the system’s perception and interoperability, coordination, and workforce–regulatory reliability readiness. These country-l

Spatially resolved omics studies increasingly combine transcriptomic and epigenomic assays, yet downstream analysis is often still performed using single-modality pipelines. We present LATTICE (Latent Alignment of Tissue-level and Transcriptomic Information for Cross-modal Embedding), a graph-based self-supervised framework that learns spot-level representations from harmonized multimodal features. LATTICE integrates five aligned modality blocks per Visium spot: Visium RNA, scMultiome RNA, scMul

Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution. Existing constrained generation methods typically enforce constraints either through training-time optimization or sampling-time correction. Training-time optimization approaches optimize on states induced by the training distribution, which can differ substantially from those encountered during sampling. Sampling-time correction methods instead mod