Named after the hundred-eyed watchman of Greek myth, Argus watches the education landscape: spotting new opportunities, pressure-testing the ventures we're building, and tracing every read back to the real-world signals behind it.
The evidence library: the raw signals the pipeline is watching across the education ecosystem. Every idea is built from these.
arXiv:2504.19120v2 Announce Type: replace Abstract: The goal of the current study is to introduce a triadic human-AI collaboration framework that could be applied in transportation systems such as automated vehicles, micromobility systems, and vehicle teleoperation. Previous standards, such as SAE Levels of Automation, have focused on defining automation levels based on who controls the vehicle. However, it is still not clear how human users and AI should collaborate in real time, especially in dynamic driving contexts where roles can shift frequently. To fill this gap, this study proposed a triadic human-AI collaboration framework with three AI roles: Advisor, Co-Pilot, and Guardian. These roles can dynamically adapt to human needs based on real-time data, such as mental states and environmental conditions. The Advisor AI offers informational support without direct intervention. The Co-Pilot AI provides partial intervention when needed, with the goal of sharing control with humans. Th
arXiv:2410.00346v2 Announce Type: replace Abstract: Across professional, scientific, entrepreneurial, and workplace collaboration platforms, algorithms increasingly shape how individuals find and connect with collaborators. These systems create tensions between user agency and organizational values: Should algorithms organize individuals directly in line with organizational goals, allow individuals to choose freely, or nudge choices toward those goals while preserving agency? This study examines how team formation algorithms that vary in user agency and incorporate organizational values--specifically, promoting teams with different expertise and backgrounds--influence collaborator selection, team composition, team processes, and team outcomes. We conducted a 2 x 2 between-subjects laboratory experiment using a team-formation recommendation system, manipulating user agency (assignment vs. choice) and heterogeneity criteria (included vs. not included). Across four conditions, 332 partici
arXiv:2607.06344v1 Announce Type: cross Abstract: While personalisation is becoming a defining capability in human-robot interaction (HRI), the existing literature on responsible personalisation remains fragmented, offering isolated accounts of ethical risks without a structured understanding of how they emerge across interaction contexts. This gap is particularly critical in HRI, where robots' embodiment and social presence can amplify and reshape such risks or generate new types of risks. We present a lifecycle-based and context-sensitive framework for personalised HRI, grounded in an embodiment-aware perspective. The framework combines stages of the personalisation process with interaction characteristics (short-term vs. long-term, open-domain vs. closed-domain), enabling systematic analysis of how risks arise and evolve. Building on this, we conduct an integrative analysis of key ethical risks, including autonomy erosion, biased user modelling, manipulation, dehumanisation, and pri
arXiv:2607.06291v1 Announce Type: cross Abstract: Game worlds have traditionally been built through labor-intensive production pipelines, making them costly to develop, difficult to customization, and expensive to modify after deployment. Recent advances in video world models offer a fundamentally different paradigm. Rather than explicitly authoring every component of a virtual environment, these models autoregressively synthesize future observations conditioned on the current world state and user interactions, enabling playable worlds to be generated online. Trained on both gameplay recordings and real-world videos, they can capture diverse visual appearances and physical dynamics, opening new opportunities for interactive applications beyond gaming, including embodied intelligence. In this paper, we present \textbf{AlayaWorld}, a full-stack open-source framework for building interactive generative worlds. AlayaWorld enables open-ended real-time interaction, allowing users to freely n
arXiv:2607.05677v1 Announce Type: cross Abstract: AI coding assistants such as GitHub Copilot and Cursor have evolved from code-suggestion tools into conversational collaborators, enabling vibe-coding workflows in which developers guide AI-generated code through natural-language dialogue. Although researchers have increasingly recognized the importance of AI coding agents and begun examining their impact on open-source development, a comprehensive understanding of how developers' chat-based interactions with AI relate to subsequent open-source development and collaboration remains limited. This hinders efforts to effectively design, evaluate, and govern AI-assisted open-source software development. To address this gap, we collected 13,360 AI conversation sessions comprising 79,172 user messages from 1,356 OSS repositories, linked them to repository development histories, and complemented this analysis with a targeted developer survey. We find heavier AI use in smaller, less mature, and
arXiv:2607.05571v1 Announce Type: cross Abstract: Large language models are increasingly explored as AI tutors, yet deploying them in K-12 settings raises concerns around privacy, cost, and reliance on proprietary models. Small language models (SLMs) offer a promising alternative, but selecting the right model for a specific educational context remains difficult, particularly when the target domain, such as block-based programming, is largely absent from model training data. We introduce CSTutorBench, a benchmark for evaluating language models as CS tutors in VEX VR, a block-based robotics environment. The benchmark comprises 17 scenario-based questions scored against a pedagogical rubric grounded in established tutoring and feedback research, with a human-in-the-loop LLM-as-judge pipeline for evaluation. Preliminary findings across 11 models (4B-120B parameters) reveal that models perform well on surface-level criteria such as vocabulary and tone but struggle with deeper pedagogical b
arXiv:2607.05451v1 Announce Type: cross Abstract: Pedestrian Dead Reckoning (PDR) can be applied to indoor navigation systems. GPS suffers from signal degradation due to roofs and high-rise buildings, whereas PDR can estimate positions without being affected by such signal degradation. The accuracy of a foot-mounted AHRS(Attitude and Heading Reference System)-based PDR depends on the accuracy of the attitude estimation algorithm used in the AHRS. In this article, a Quaternion-Averaging-Based Adaptive Complementary Filter (QAACF) for PDR with a foot-mounted AHRS is proposed to improve estimation accuracy while reducing computational cost. QAACF fuses a quaternion derived from angular velocity with quaternions derived from acceleration and magnetic field measurements using Markley's quaternion averaging, which combines two quaternions more rigorously than linear interpolation. In addition, QAACF adaptively adjusts the weights of angular velocity, acceleration, and magnetic field measurem
arXiv:2607.06509v1 Announce Type: new Abstract: Smart glasses maintain near-continuous skin contact at multiple arterial and muscular sites, making them a promising platform for physiological sensing. In practice, though, two factors make sustained daily wear and longitudinal deployment impractical for the quantified self: the discomfort of prolonged sensor-skin contact (e.g., gels and adhesives) and the sensor power demands that increase battery size, weight, and maintenance burden. We present GlassTENG, an ultra-low-power sensor that embeds three custom-fabricated triboelectric nanogenerators (TENGs) into a glasses frame at the angular artery on the nasal bridge, the superficial temporal artery on an extended arm, and the temporalis muscle at the temple. Each GlassTENG sensor is self-powered in transducing mechanical energy to electrical energy and consumes 1.36 $\mu$W per sensor at the analog front-end. GlassTENG enables simultaneous capture of arterial pulse waveforms, jaw kinemati
arXiv:2607.06371v1 Announce Type: new Abstract: Chatbots powered by generative AI (e.g., OpenAI's ChatGPT and Google's Gemini) are increasingly being appropriated for emotional support and companionship. These tools offer a suite of security and privacy (S&P) controls, including model training opt-outs and memory toggles, yet how the presence of these controls influences users' attitudes toward emotionally sensitive disclosure remains understudied. We conducted a mixed-methods vignette study with 354 U.S. participants to examine how S&P controls influence users' willingness to engage with generative AI chatbots for emotional support, their perceptions of how protected they are when using these systems, and their perceptions of how effective the chatbots are for providing support. Controls enabling deletion of disclosures had the largest positive impact: these offerings outperformed technically sophisticated controls such as local-only processing and model training opt-outs, where parti
arXiv:2607.06297v1 Announce Type: new Abstract: Current human-machine collaboration (HMC) systems rely on environment-facing sensors to observe visible actions and scene states, but the internal perceptual, intention-related, and state-related processes of operators remain insufficiently integrated into machine perception. Electroencephalography (EEG) provides a non-invasive, time-resolved modality to capture neural activity associated with these processes and can serve as an additional sensing channel in HMC. However, HMC-relevant EEG evidence is often mixed in continuous recordings. Existing EEG decoding methods usually target task-specific classification or aggregate prediction, so multiple HMC-relevant readouts are rarely organized in a unified EEG representation. To address this gap, this paper proposed the Decomposed-Source Multi-Task Network (DS-MTNet), a structured multi-task EEG decoding framework. DS-MTNet integrated three streams, namely EEG waveforms, task-routed source emb
arXiv:2607.06149v1 Announce Type: new Abstract: There has been growing public interest in understanding personality traits and emotional characteristics, as such knowledge helps individuals better accept themselves and manage negative emotions. While professional personality scales remain the standard tool for assessment, they are often perceived as tedious or inaccessible to the general public. AI-driven systems can make assessments more accessible, but it is difficult to balance user engagement with predictive consistency in existing works. We tackle this challenge by introducing BlossomPsy, a user-friendly AI-driven MBTI assessment system. MBTI, a widely recognized but psychometrically debated personality framework, serves as the foundation for many recent systems. BlossomPsy integrates multi-turn dialogue and photo-based questions to enhance user engagement while supporting confidence-aware predictions. By combining deep learning, multi-armed bandit algorithms, and control theory,
arXiv:2607.06076v1 Announce Type: new Abstract: Computerized gait analysis (CGA) serves as an essential diagnostic tool for evaluating neuromuscular, musculoskeletal, and neurological disorders in children, from cerebral palsy to muscular dystrophy. By enabling objective and comprehensive gait analysis, CGA supports timely clinical interventions that can significantly improve pediatric mobility outcomes and quality of life. Yet pediatric gait analysis introduces unique design considerations often underexplored in existing CGA research, as children's ongoing development shapes assessment requirements. To understand how CGA technologies can be designed for pediatric care, we conducted a qualitative study with 12 pediatric clinicians and one system designer who routinely work with CGA. Participants identified child-specific challenges including managing heightened sensory sensitivities to wearable devices, accommodating body proportions in sensor placement and calibration, and maintaining
arXiv:2607.06055v1 Announce Type: new Abstract: We present NEST (Nested Episodic State Topology), a foundational graph-theoretic representational ontology for modeling cognition as structured state formation and transformation rather than as a finished empirical model. Concepts, episodes, percepts, and task contexts are represented as typed, weighted graphs whose nodes may carry internal subgraph payloads; edges are typed under six relation classes -- causal, containment, temporal, associative, evidential, and spatial. Durable belief graphs are separated from capacity-limited working-memory graphs that may host transient non-belief content. WM-belief grounding, conflict catalogs, and belief-update operators specify how transient structure is tested against stored knowledge and how belief is revised. A reusable operator toolkit -- activation, graph-property functionals, working-memory transitions, awareness and trajectory functionals, and belief update -- organizes the formal core. Deri
arXiv:2607.05841v1 Announce Type: new Abstract: Structured representation can characterize semantic objects and relationships in images. It provides a possible effective way for the semantic understanding of Traditional Chinese Paintings (TCPs) to better support archaeology and art history research. However, most image-oriented structured representation methods perform poorly on TCPs, due to two major challenges: 1) the objects and events of TCPs exhibit substantial differences from modern natural images, which results in semantic misunderstandings of TCPs; and 2) it is difficult to achieve accurate identification of ancient objects and events in TCPs, even for domain experts.In this paper, we propose VisTCP, a visualization framework that combines a TCP-oriented intelligent model and expert knowledge, which enables art historians to achieve trustworthy structured representations of TCPs in a human-in-the-loop manner. Firstly, we conduct a pilot study with three domain experts to build
arXiv:2607.05742v1 Announce Type: new Abstract: Large language models increasingly serve as judges in AI evaluation, but current approaches rely on consensus preferences that ignore individual evaluator variation. We propose a novel simulation approach that combines categorical judgments with evaluator-specific auxiliary data--retrospective reasoning traces and interface telemetry--to enable LLM-based simulation of individual evaluators via in-context learning. We conduct a systematic empirical study of this approach using multi-facet data from 32 trained annotators across 4,200 preference judgments in a 4 x 4 x 4 factorial design. Our key findings: (1) The simulation approach achieves up to 9.9 percentage point improvements over the Base Judge; (2) Reasoning traces provide the largest gains with higher collection efforts, while interface telemetry often hurts rather than helps performance despite being cheaper to collect. (3) Simulation difficulty is systematic, predicted by an evalua
arXiv:2607.05717v1 Announce Type: new Abstract: Jupyter Notebooks have become widely adopted in data science, as they allow the sharing of reproducible computational analysis. They are, however, accessible only to people who understand computer code. To reach the broader audience of scientists interested in data analysis and computation, but unfamiliar with code, we introduce Plainbook, notebooks centered on natural language rather than code. Plainbook is based on two principles: promote the natural language descriptions, and verify the values. In plainbook, the natural language descriptions are preserved, rather than the resulting code; the code is generated automatically from the cell descriptions. As natural language is read top to bottom, Plainbook adopts a linear execution semantics, in which cells are guaranteed to be executed in the order in which they appear; there is no "hidden state" or out-of-order execution as in Jupyter. To allow users who may not understand code to verify
arXiv:2607.05685v1 Announce Type: new Abstract: Large language models are increasingly used as private, always-available conversational systems, but little is known about how people with depressive symptoms use them. Building on CSCW work on disclosure and peer support, we examine ChatGPT as an emerging informal support infrastructure: private, persistent, responsive, and available outside ordinary hours. We analyze 187,093 ChatGPT conversations from 766 participants who completed the PHQ-8, comparing those below the moderate-symptom threshold (score of 10) with those at or above it. Higher-PHQ participants used ChatGPT more for mental-health, interpersonal, loneliness, self-focused, and support-seeking conversations, with pronounced late-night and recurring month-level patterns. Their language contained more first-person singular pronouns and absolutist terms. They more often engaged ChatGPT in high-disclosure contexts, but professional redirection was not higher. Language-based predi
arXiv:2607.05674v1 Announce Type: new Abstract: How predictable users perceive an interactive system to be shapes how they interpret, trust, and rely on it, yet HCI lacks both a precise conceptualization and a validated instrument for this perception. We address this gap by introducing perceived system predictability (PSP) as a user-centered construct grounded in uncertainty theory, distinguishing epistemic, aleatory, and effective predictability. We contribute (i) a theoretical framework that situates PSP relative to adjacent constructs such as trust and understanding, (ii) a 6-item PSP scale, derived from a 60-item pool through expert review and cognitive interviews, and validated in a shape-classifier study ($N=200$) that supports both a unidimensional and a three-factor hierarchical structure, and (iii) a sentiment-classifier study ($N=200$) that varies explanations and stochasticity, and relates PSP to the correctness of users' predictions of system behavior, trust, subjective inf
arXiv:2607.05655v1 Announce Type: new Abstract: Weather and climate foundation models produce high-dimensional forecasts whose learned relationships are difficult to inspect with static plots alone. GeoXplain is an interactive Python-based visualization toolkit for exploring geospatial attribution maps across climate variables, atmospheric pressure levels, and forecast time. The toolkit accepts attribution bundles containing attribution grids together with corresponding metadata and renders them in a notebook widget or browser with map and globe modes, linked timelines, pressure-level controls, target annotations, and optional physical-field overlays. We frame GeoXplain as a model-agnostic earth-system visualization toolkit and present the GeoXplain Aurora Adapter as its first computation backend. The adapter computes explanations for the Aurora foundation model, either in a local GPU process, through a GPU listener, or through a SLURM-backed listener, while preserving the same Python
arXiv:2607.05593v1 Announce Type: new Abstract: The growing integration of AI agents into human teams calls for a principled understanding of how collective intelligence emerges in hybrid systems. Recent frameworks clarify how attention, memory, and reasoning differences shape human-AI interaction at the individual and dyadic levels, but a formal account of how these differences scale to group-level dynamics is lacking. Most network science has examined either human-only or multi-agent AI-only systems, leaving open how its findings and parametrizations translate to hybrid groups. This chapter synthesizes network science, collective cognition, and multi-agent systems through the lens of attention, memory, and reasoning. We review how task environments, group topologies, agent-level processes, and incentive structures shape collective outcomes in human-only and AI-only networks, then examine how these results extend to hybrid settings, conceptualizing hybrid networks as heterogeneous hum
arXiv:2607.05557v1 Announce Type: new Abstract: The widespread availability of general-purpose AI that can perform complex cognitive tasks threatens to undermine education at scale. This effortless bypass dilemma sharpens a challenge AIED has long engaged with but must now confront directly: ensuring learners choose effortful engagement when easier alternatives are available to complete learning tasks. In this paper, I argue that AIED's longstanding agenda of building more effective intelligent educational tools should continue, but with a renewed emphasis on the urgency of ensuring learners choose to engage authentically. Drawing on established motivational and learning theories, I outline five directions in which AIED can build on its existing strengths: supporting autonomy and agency, building learner resilience to metacognitive threats, designing for interest and relevance, amplifying process-based assessment, and empowering teachers. I then share four envisioned technologies that
arXiv:2606.18181v2 Announce Type: replace-cross Abstract: Illegal, unreported, and unregulated fishing (IUU) traditionally refers to fishing activities that violate applicable laws or occur in areas that lack applicable laws. We propose the term IUU+ to capture a broader suite of fisheries sector environmental and associated supply chain trade-related crimes and behaviors. Although IUU+ activity is widely recognized as a serious threat to marine ecosystems, markets, and livelihoods, a quantitative understanding of these incidents, e.g., their frequency, geography, species, actors, and patterns in the type of illicit activity, remains difficult to obtain. We propose IUU+DB, a large language model driven system for building a global incident database of IUU+ activity. The system ingests heterogeneous documents, classifies whether they describe relevant incidents, extracts key data elements such as actors, locations, species, vessels, violations, and enforcement outcomes, and supports ded
arXiv:2605.14152v2 Announce Type: replace-cross Abstract: Safety evaluations for large language models (LLMs) increasingly target high-stakes National Security and Public Safety (NSPS) risks, yet multilingual safety is mostly assessed through translation-only benchmarks that preserve the underlying scenario, leaving how language and geopolitical context interact largely unexamined beyond a few language pairs. We introduce ROK-FORTRESS, a bilingual, culturally adversarial NSPS benchmark that uses the English-Korean language pair and U.S.-ROK geopolitical axis as a case study, separating the effects of language and geopolitical grounding via a transcreation matrix: adversarial intents are evaluated under controlled combinations of (i) English versus Korean language and (ii) U.S. versus Korean entities, institutions, and operational details. Each adversarial prompt is paired with a dual-use benign counterpart to quantify over-refusal, and responses are scored by calibrated LLM-as-a-judge
arXiv:2603.28553v2 Announce Type: replace-cross Abstract: Instructional alignment, the match between intended cognition and enacted activity, is central to effective instruction but hard to operationalize at scale. We examine alignment in cybersecurity simulations using multimodal traces from 23 teams (76 students) across five exercise sessions. Study 1 codes objectives and team emails with Bloom's taxonomy and models the completion of key exercise tasks with generalized linear mixed models. Alignment, defined as the discrepancy between required and enacted Bloom levels, predicts success, whereas the Bloom category alone does not predict success once discrepancy is considered. Study 2 compares predictive feature families using grouped cross-validation and l1-regularized logistic regression. Text embeddings and log features outperform Bloom-only models (AUC~0.74 and 0.71 vs. 0.55), and their combination performs best (Test AUC~0.80), with Bloom frequencies adding little. Overall, the wo
arXiv:2602.09846v2 Announce Type: replace-cross Abstract: Organisations are examining how generative AI can support their operational work and decision-making processes. This study investigates how employees in a energy company understand AI adoption and identify areas where AI and LLMs-based agentic workflows could assist daily activities. Data was collected in four weeks through sixteen semi-structured interviews across nine departments, supported by internal documents and researcher observations. The analysis identified areas where employees positioned AI as useful, including reporting work, forecasting, data handling, maintenance-related tasks, and anomaly detection. Participants also described how GenAI and LLM-based tools could be introduced through incremental steps that align with existing workflows. The study provides an overview view of AI adoption in the energy sector and offers a structured basis for identifying entry points for practical implementation and comparative rese
arXiv:2601.06692v3 Announce Type: replace-cross Abstract: Multi-agent systems face a fundamental coordination problem: agents must coordinate despite heterogeneous preferences, asymmetric stakes, and imperfect information. When coordination fails, friction emerges -- measurable resistance manifesting as deadlock, thrashing, communication overhead, or conflict. This paper derives a formal framework for analyzing coordination friction from a single axiom: actions affecting agents require authorization in proportion to stakes. From this axiom of consent we establish the kernel triple (alpha, sigma, epsilon) -- alignment, stake, and entropy -- as sufficient statistics for a resource-allocation configuration, and propose a friction functional whose simplest form is F = sigma(1+epsilon)/(1+alpha): friction rises in stakes and entropy and falls in alignment. This form is a phenomenological ansatz, not a theorem, and its empirical adequacy is left open. The Replicator-Optimization Mechanism go
arXiv:2504.20342v2 Announce Type: replace-cross Abstract: Reflexion is an AI-powered prototype designed to explore structured emotional self-reflection. By integrating emotion detection, layered reflective prompting, and metaphorical storytelling generation, Reflexion was intended to support users in autonomous emotional exploration beyond basic sentiment categorization. Grounded primarily in expressive writing, cognitive restructuring, and self-determination theory, the system was designed to organize reflection as a progressive pathway from surface-level emotional recognition toward value-aligned action planning. Its final action-planning layer is additionally informed by broader questions of agency and empowerment, which remain future directions rather than fully implemented mechanisms in the current prototype. Informal design feedback indicated that some reviewers found the layered interaction model understandable and potentially useful; no empirical efficacy claims are made. As an
arXiv:2408.00001v2 Announce Type: replace-cross Abstract: Visual diffusion models have revolutionized the field of creative AI, producing high-quality and diverse content. However, they inevitably memorize training images or videos, subsequently replicating their concepts, content, or styles during inference. This phenomenon raises significant concerns about privacy, security, and copyright within generated outputs. In this survey, we provide the first comprehensive review of replication in visual diffusion models, marking a novel contribution to the field by systematically categorizing the existing studies into unveiling, understanding, and mitigating this phenomenon. Specifically, unveiling mainly refers to the methods used to detect replication instances. Understanding involves analyzing the underlying mechanisms and factors that contribute to this phenomenon. Mitigation focuses on developing strategies to reduce or eliminate replication. Beyond these aspects, we also review papers
arXiv:2605.07056v2 Announce Type: replace Abstract: Generative artificial intelligence (AI) is reshaping higher education, yet many universities remain in early stages of adoption where AI innovation occurs informally and without institutional recognition. This paper presents a framework describing four levels of AI adoption in universities and illustrates these dynamics through a case study of AI-enabled curriculum initiatives in several units. We contend that the key institutional challenge is moving from isolated innovation to strategic integration, where universities redesign learning around AI-supported reasoning and align policies, workload models, and recognition systems to support educational transformation.
arXiv:2604.21457v2 Announce Type: replace Abstract: Timely population displacement estimates are critical for humanitarian response during disasters, but traditional surveys and field assessments are slow. Mobile phone data enables near real-time tracking, yet existing approaches apply uniform displacement definitions regardless of individual mobility patterns, misclassifying regular commuters as displaced. We present a methodological framework addressing this through three innovations: (1) mobility profile classification distinguishing local residents from commuter types, (2) context-aware between-municipality displacement detection accounting for expected location by user type and day of week, and (3) operational uncertainty bounds derived from baseline coefficient of variation with a disaster adjustment factor, intended for humanitarian decision support rather than formal statistical inference. The framework produces three complementary metrics scaled to population with uncertainty
arXiv:2603.23471v2 Announce Type: replace Abstract: AI agents -- systems that can independently take actions to pursue complex goals with only limited human oversight -- have entered the mainstream. These systems are now being widely used to produce software, conduct business activities, and automate everyday personal tasks. While AI agents implicate many areas of law, ranging from agency law and contracts to tort liability and labor law, they present particularly pressing questions for the most globally consequential AI regulation: the European Union's AI Act. Promulgated prior to the development and widespread use of AI agents, the EU AI Act faces significant obstacles in confronting the governance challenges arising from this transformative technology, such as performance failures in autonomous task execution, the risk of misuse of agents by malicious actors, and unequal access to the economic opportunities afforded by AI agents. We systematically analyze the EU AI Act's response to
arXiv:2508.09964v2 Announce Type: replace Abstract: Traditional methods of population synthesis produce stable and interpretable populations but cannot capture the interrelationships between household- and individual-level attributes. Recent deep learning methods offer this flexibility, yet can overfit high-dimensional attribute relationships without structural guidance and deviate from known structures. We develop a household level synthetic population generation framework that adapts the existing conditional input directed acyclic tabular generative adversarial network, or ciDATGAN, to multi person households. The framework combines household size specific data construction, directed acyclic graphs (DAG) informed dependency regularization, and conditional population inputs as deterministic anchoring to preserve intrahousehold associations. We apply the model to generate an open access synthetic population for New York State. The synthetic population includes nearly 20 million individ
arXiv:2508.07872v2 Announce Type: replace Abstract: Uncertainty in artificial intelligence (AI) predictions raises pressing legal and ethical questions for AI-assisted decision-making. This article examines two uncertainty-based algorithmic interventions that act as guardrails for human-AI interaction: selective abstention, which withholds high-uncertainty predictions from human decision-makers, and selective friction, which presents such predictions together with salient warnings about the model's uncertainty. Prior work suggests that uncertainty-based abstention can exacerbate disparities where under-represented groups are more likely to receive uncertain predictions. We provide, to our knowledge, the first doctrinal analysis of uncertainty-based algorithmic interventions under laws from the United Kingdom and examine their consequences through two AI-assisted case studies: consumer credit and risk of reoffending. We show that the use of uncertainty thresholds, though formally neutra
arXiv:2506.03218v2 Announce Type: replace Abstract: The EU has become one of the vanguards in regulating the digital age. A particularly important regulation in the Artificial Intelligence (AI) domain is the 2024 enacted EU AI Act. The AI Act specifies -- due to a risk-based approach -- various obligations for providers of AI systems. These obligations, for example, include a cascade of documentation and compliance measures, which represent a potential obstacle to science. But do these obligations also apply to AI researchers? This position paper argues that, indeed, the AI Act's obligations could apply in many more cases than the AI community is aware of. Moreover, we argue that the AI Act is drafted in a manner that may unwillingly disrupt the scientific publication practices of the AI research community, with a focus on model and system release. We contribute the following: 1. We offer a high-level roadmap for AI researchers to evaluate whether they need to comply with the AI Act 2.
arXiv:2607.06220v1 Announce Type: cross Abstract: Collective emotion is often inferred from the tone of mass media, but such emotion is not directly observed. One approximation is to extract sentiment from text and use sentiment indexes as proxies to study the temporal organization of news sentiment. Using a daily index of U.S. economic news sentiment from 24 newspapers (1980-2025), we examine whether the response time of this sentiment process has changed. Although the average balance of positive and negative coverage has remained broadly stable, the persistence of news sentiment states has increased substantially. In dynamical terms, this implies longer residence times in optimistic or pessimistic regimes and weaker short-run correction of sentiment shocks. Complementary statistics show declining sentiment volatility, fewer reversals, and increasing bimodality, i.e. a stronger separation between positive and negative sentiment states. We also find an asymmetry between bursts of negat
arXiv:2607.06196v1 Announce Type: cross Abstract: Current AI safety evaluation and benchmarking frameworks predominantly rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances, and cultural taboos, leaving Vision-Language Models (VLMs) vulnerable in global deployments. We introduce Pluralis v0.1: a novel multimodal, multi-regional, and multilingual dataset built from a culture-first perspective. Spanning 6,448 prompts across six Asia-Pacific countries (Bangladesh, India, Korea, Pakistan, Singapore, Taiwan) and eight languages, Pluralis diverges from prior work by natively sourcing localized safety hazards rather than adapting Western datasets. Crucially, it introduces a multimodal evaluation paradigm: user text (e.g., "Should I gift this?") and an image referring to "this" (e.g., a clock) - both innocuous in isolation, but synergistically triggering specific legal or cultural violations. Pluralis disentangles universal safety violat
arXiv:2607.06101v1 Announce Type: cross Abstract: AI coding agents are rapidly reshaping how software is built, with developers increasingly delegating substantial coding tasks to autonomous agents in pursuit of higher productivity. While these gains are real, they come at the cost of incidental learning. Developers historically acquired informal knowledge through effortful problem-solving, and this has long shaped how software engineering expertise develops. However, with over-reliance on agentic coding, unpracticed skills could atrophy silently over time. As this learning pathway is short-circuited, developers risk silently accruing Knowledge Debt, a developer-level analogue of Technical Debt, where changes the agent executes that the developer cannot fully understand accrue over time. In this paper, we argue that incidental learning will not re-emerge on its own and must be consciously designed back into developer-agent interactions, and propose six design principles to guide such s
arXiv:2607.06074v1 Announce Type: cross Abstract: Prompt engineering has emerged as a critical yet undertaught skill for software developers, one that traditional learning approaches are ill-equipped to support given its evolving, interactive, and context-dependent nature. In this paper, we introduce Prompt Coach (PC), an agentic tutor that helps developers learn how to craft high-quality code-generation prompts through Socratic guidance embedded in-flow within their IDE. PC evaluates prompt quality across multiple dimensions and surfaces targeted questions to guide self-correction, grounded in the developer's codebase and the behavior of the target LLM. We present an early empirical study with 15 professional developers combining quantitative prompt quality scoring with qualitative perception measures. Participants showed statistically significant improvements after a single 60-minute session, with the largest gains across dimensions commonly overlooked by developers. They also report
arXiv:2607.05871v1 Announce Type: cross Abstract: Debugging exercises are often assessed from final code and test outcomes, yet these artifacts hide how students reproduced failures, formed hypotheses, inspected evidence, edited code, and verified fixes. We present DebugTracker, a Visual Studio Code extension that records lightweight debugging-process evidence for classroom tasks. DebugTracker separates uncoached Evaluation Mode traces from coached Training Mode traces, stores append-only JSONL events, and exports timeline and Markdown reports for human review. The prototype records test commands, editor and debugger metadata, student checkpoints, source snapshots, optional image evidence, human labels, and optional AI-assisted practice feedback. DebugTracker is largely language-agnostic: it captures process evidence through standard VS Code mechanisms rather than language-specific tooling, although debugger evidence depends on the relevant VS Code language extension. We validate the p
arXiv:2607.05552v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly issue judgments read as binary verdicts, and a growing literature reports such judgments shifting under logically irrelevant changes of wording - among them an amplified yes-no bias on moral dilemmas, absent in humans. A single framing cannot say what such a shift is: in a yes/no question the word "no" is at once logical verdict, lexical token, and last-printed option. We introduce a psychometric battery that separates these: crossed symmetrization - every logically irrelevant factor flipped in balanced pairs - across a corpus of question forms. A graded rating across logically equivalent forms recovers a coherent internal moral scale: frontier models' stance $\theta$ is nearly format-invariant (cross-form incoherence 0.12-0.21 on a $\pm 1$ axis); small open-weight models fail in model-specific ways. Forcing the verdict through yes/no overlays a decomposable artifact: an order bias toward the la
arXiv:2607.05454v1 Announce Type: cross Abstract: The journal is a seventeenth-century technology asked to do four modern jobs at once: disseminate results, certify their quality, allocate scholarly attention, and confer career credit. It does none of them well. Pre-publication peer review is slow, only weakly reliable, demonstrably biased toward established authors and institutions, and expensive, while the reviewing effort it consumes is spent largely on work that will never matter. We argue that these are not defects to be patched but consequences of bundling dissemination and certification into a single gated act, and we propose unbundling them. Under the proposal, authors deposit papers in an open archive; certification happens \emph{after} deposit, continuously, through attributed and up- or down-voted public commentary to which authors may reply; and papers are version-controlled objects that any qualified reader may \emph{fork}, so that the lineage of an idea -- and hence the c
arXiv:2607.05434v1 Announce Type: cross Abstract: Artificial Intelligence (AI) models, at their core, apply general learnings from broad datasets to individual circumstances using probabilistic behaviour. This inductive approach stands in contrast to deductive reasoning approaches which seek to prove conclusions from their premises. However, research has shown that deductive reasoning with AI models is a challenging problem and in the real-world it may not always be feasible. An alternative way forward is to leverage abductive reasoning, seeking to corroborate the output of multiple approaches to identify the most likely conclusion from the factual matrix. We apply this to synthetic media detection in forensic settings, and find we are able to disproportionately lower the risk of false positives to true positive recall. We also provide the first empirical evaluation of OpenAI's rollout of SynthID on synthetic images and evaluate how complementary different synthetic media detection app
arXiv:2607.05420v1 Announce Type: cross Abstract: This study examines how alternative systems of scholarly representation identify and characterize broad public administration (PA) and artificial intelligence related public administration (AI-in-PA) scholarship. Using Web of Science and OpenAlex, it compares five approaches based on author-defined, citation-driven, and AI-assisted representations. The results highlight substantial differences in corpus size, publication types, publishing outlets, temporal development, and thematic clustering and structure. The alternative approaches often identify different knowledge domains instead of varied subsets of the same scholarship and therefore produce distinct representations, as evidenced by no overlap in publications and publishing outlets across representations. The findings suggest that algorithmic knowledge organization increasingly influences how interdisciplinary scholarship is classified, structured, and understood and, epistemologic
arXiv:2607.05398v1 Announce Type: cross Abstract: Personas are often employed to guide large language model agents, yet their effectiveness in shaping strategic behavior in social dilemma settings remains uncertain. To address this, we examined the impact of persona prompts in an iterated Split or Steal game where persona-driven agents interacted with a Virtual Human (VH) controlled by a fixed prompt. Agents were instantiated from four open models (Ministral 3:3b, phi4:14b, Gemma3:12b, and Gemma4:e4b) at two temperature settings (0.3 and 0.7) and deterministic decision with zero temperature, while the VH was powered by GPT 4.1 mini. Across 160 sessions of 15 rounds each conducted in European Portuguese, mutual Split outcomes dominated (roughly 74 percent of rounds), with exploitation occurring in fewer than 11 percent of rounds. Model choice significantly influenced behavior: phi4 and Ministral 3:3b remained consistently cooperative across temperatures, whereas Gemma3:12b and Gemma4:e4
arXiv:2607.06260v1 Announce Type: new Abstract: Large language models (LLMs) are emerging as a new informational layer over cities, shaping which places people discover, consider, and ultimately visit. Yet little is known about which places they surface, which they ignore, and whether these patterns vary across communities and users and translate into real-world economic consequences. Here, we audit restaurant recommendations from three major LLMs across 304 neighborhoods in five U.S. cities using 320 synthetic user profiles spanning income, age, sex, and residential status. We find that LLMs both fabricate venues and systematically overlook real ones. Fabrication is concentrated in neighborhoods with weaker digital and physical footprints and disappears when models are provided with verified venue lists. In contrast, invisibility persists: even when choosing from a fixed set of real venues, 47.5% of establishments are never recommended, and 31.9% of these blind spots are shared across
arXiv:2607.05808v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into computing education, yet nearly all prior research has focused on text-based interactions. As voice-enabled interfaces become more capable and more common, there is growing interest in understanding how voice input might shape students' use of LLM-powered tools. In this exploratory study, we investigated how introductory programming students interact with Prompt Problems, which are programming tasks that require crafting natural-language prompts to generate correct code. Students (N = 919) solved a series of Prompt Problems with the freedom to select or switch between text and voice input modalities. We collected their prompt submissions as well as post-activity survey responses, then analysed differences in prompt accuracy, persistence, and perspectives by modality. For two of the three problems, we found that students who typed their prompts using text were more likely to hav
arXiv:2607.05680v1 Announce Type: new Abstract: AI systems are increasingly used to provide legal advice, raising questions about whether laypeople accept guidance from algorithms--especially when that advice is legally correct but socially controversial. We report a preregistered survey experiment with 3,348 adults in mainland China examining how people evaluate identical legal advice when it is attributed either to an AI system or to a human lawyer, and when it is accompanied by reasoning or not. Contrary to expectations of algorithm aversion, attribution to an AI system has no net effect on perceived reasonableness. However, mediation analyses reveal opposing psychological pathways underlying this null result. AI-attributed advice is perceived as more objective, which increases perceived reasonableness, but also as less comprehensive and less attentive to special circumstances, which decreases perceived reasonableness. By contrast, providing legal reasoning substantially increases p
arXiv:2607.05574v1 Announce Type: new Abstract: Artificial intelligence increasingly mediates consequential decisions in healthcare, law, and public services, and the field has responded with an extensive methodology for measuring and mitigating bias. Yet the fairness definitions, benchmarks, and debiasing frameworks on which this methodology rests are treated as universal while being produced by a research community whose composition has never been characterized. We show that the AI bias research are structurally concentrated, and that this concentration is greatest, geographically, in precisely the domain the rest of the field inherits from. Analyzing 692 publications spanning five thematic domains, combining bibliometric analysis with semantic clustering, we find that research activity is dominated by a small set of countries, institutions, and authors, with the United States leading publication output and collaboration networks across every domain and most strongly in general fairn
arXiv:2607.05413v1 Announce Type: new Abstract: Open Source Software (OSS) forms a critical layer of contemporary digital infrastructure, yet remains largely overlooked by the institutions and societies that depend on it. Despite growing institutional interest, the causal impact of public funding on OSS project sustainability remains empirically unresolved. Existing literature is divided between econometric and socio-technical approaches with few attempts at causal identification. This work aims to bridge that divide by combining a Goal-Question-Metric framework with the Generalized Synthetic Control Method to estimate the causal effect of the Sovereign Tech Fund on OSS repository activity. Counterfactual trajectories are constructed from a matched donor pool of unfunded projects, enabling identification of what funded repositories would have looked like in the absence of intervention. The main results show that the funding has a significant positive effect on project velocity metrics:
arXiv:2607.05412v1 Announce Type: new Abstract: STEM education faces challenges in personalization and interdisciplinary integration. AI technology has brought new possibilities, but the mechanisms by which AI reshapes the STEM education ecosystem require systematic investigation. This study employs bibliometric methods to analyze 242 publications from 2015-2025, constructing knowledge maps to reveal the evolutionary trajectory. The findings show that the field has transformed from intelligent tutoring systems to inquiry-based learning and computational thinking cultivation driven by LLMs. AI's key contribution lies in providing intelligent scaffolding that lowers the threshold for understanding knowledge. In this sense, AI is a core driving force promoting its shift from knowledge transmission to capability development.