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:2607.21758v1 Announce Type: new Abstract: Teachers, conference chairs, and public readers all judge writing from limited evidence, seeing only a finished document and not the process that produced it. Final text alone cannot reveal whether a document was produced through human typing, AI generation, or mixed human-AI collaboration. Existing process-tracking tools help, but many are tied to host-document histories, provide coarse activity records, and offer limited control over the writing environment. Humanly is a writing platform that makes the writing process itself the evidence. Users configure writing environments for personal documents or assigned tasks and draft in a workspace that records writing activity and in-platform AI assistance. Humanly can package a completed session into a sealed writing certificate with configuration-aware anomaly behavior review. It can support writing scenarios such as course assignments, peer review, and personal certification. Our user study
arXiv:2607.21685v1 Announce Type: new Abstract: A systematic review begins with someone reading thousands of abstracts to identify the few that are relevant, and classifiers are used to prioritise that reading. Their inputs are often augmented with Medical Subject Headings (MeSH), assigned either by expert indexers weeks or months after publication or by automatic tools at once. To our knowledge the two have not been compared directly as classifier features, and no previous work has asked whether that comparison's outcome depends on how the classifier is evaluated. Using the Cohen et al. (2006) drug-class benchmark on three topics, we characterise a bag-of-words logistic regression classifier (seven reruns) and BiomedBERT (five seeds), then examine how the Statins result changes under alternative designs. Under the canonical 5-fold full-corpus design, the bag-of-words expert-vs-auto gap on Statins is +0.096 WSS@95%. Matching the corpus size to the smaller topics (n = 803) reduces it to
arXiv:2607.21632v1 Announce Type: new Abstract: Traditional benchmarks for LLMs primarily rely on static datasets and objective scoring metrics, which often fail to capture differences in response quality when multiple answers are acceptable. In such settings, correctness alone is insufficient to distinguish between responses that vary in clarity, completeness, and usefulness. This paper introduces a consensus-based evaluation framework that measures relative preference among model-generated responses rather than absolute correctness. Instead of evaluating outputs against a fixed ground truth, we assess how a panel of diverse LLMs ranks anonymized candidate responses to the same prompt. This approach treats aggregate inter-model agreement as a proxy for perceived response quality under blind conditions. We conduct a controlled study using five state-of-the-art LLMs across multiple domains, including programming, general knowledge, safety, logical reasoning, and mathematics. Each model
arXiv:2607.21619v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive performance, but their safety alignment remains vulnerable to jailbreak attacks. Existing content-based jailbreaks are often inconsistent and show unsatisfying performance against the rapidly evolving MLLMs, failing to exploit non-content-based vulnerabilities. Unlike previous research, we empirically find that MLLMs exhibit a Stylistic Inconsistency between their comprehension ability and safety ability: MLLMs can robustly understand content regardless of visual style, yet their defense mechanisms can be easily bypassed by specific stylistic triggers. Based on this finding, we propose Adversarial Style Optimization (ASO), a plug-and-play enhancement module to amplify existing visual jailbreaks. ASO fine-tunes an image-editing model to superimpose an optimized stylistic modification onto a given adversarial image, using a Group Relative Policy Optimization (GRPO) agent guid
arXiv:2601.12311v2 Announce Type: replace-cross Abstract: The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space-air-ground-sea integrated networks. The AVs can deploy AI agents powered by large AI models as personalized assistants, on edge servers to support intelligent driving decision making and enhanced on-board experiences. However, such cross-reality interactions may cause serious location privacy risks, as adversaries can infer AV trajectories by correlating the location reported when AVs request LBS in reality with the location of the edge servers on which their corresponding AI agents are deployed in virtuality. To address this challenge, we design a cross-reality location privacy protection framework based on hybrid actions, including continuous location perturbation in reality and discrete privacy-aware AI agent migration in virtuality. In this framework, a new privacy metric, termed cros
arXiv:2607.21527v2 Announce Type: replace Abstract: Passive sensing for wellbeing uses smartphones and wearables to continuously collect human behavioral data and applies ML/AI models to infer psychological states and behaviors (e.g., depression, cognitive load). These systems are increasingly adopted in high-stakes settings (e.g., hospitals, universities), yet fairness research remains limited---primarily to post-hoc, identity-based comparisons of model performance. However, passive sensing combines heterogeneous sensing infrastructures, indirect behavioral inference, and longitudinal deployment---characteristics that, while not exclusive to the domain, are jointly pronounced here and raise two underexplored questions: (1) what additional sources of inequity arise from these characteristics, and (2) how do such inequities propagate beyond algorithmic audits across the system lifecycle? To address this gap, we conducted semi-structured interviews with 14 researchers and practitioners a
arXiv:2602.22085v3 Announce Type: replace Abstract: Social interactions are fundamental to well-being, yet automatically detecting them in daily life-particularly using wearables-remains underexplored. Most existing systems are evaluated in controlled settings, focus primarily on in-person interactions, or rely on restrictive assumptions (e.g., requiring multiple speakers within fixed temporal windows), limiting generalizability to real-world use. We present an on-watch interaction detection system designed to capture diverse interactions in naturalistic settings. A core component is a foreground speech detector trained on a public dataset. Evaluated on over 100,000 labeled foreground speech and background sound instances, the detector achieves a balanced accuracy of 85.51%, outperforming prior work by 5.11%. We evaluated the system in a real-world deployment (N=38), with over 900 hours of total smartwatch wear time. The system detected 1,691 interactions, 77.28% were confirmed via par
arXiv:2504.19345v2 Announce Type: replace Abstract: Robust navigational guidance is an important XR application for both sighted and non-sighted populations. In this paper, we mainly focus on blind pedestrians, who continue to face "last-mile" challenges such as locating entrances and navigating cluttered spaces. While smartglasses and wearables are maturing, a foundational design question remains underexplored: where on the body should cameras be placed to best support navigation? We present a mixed-methods investigation that focuses on the question of camera placement for generating spatial data supporting ego-centric navigation. A survey of 10 blind cane users surfaced practices for last-mile navigation and perceptions of body-mounted XR devices. A controlled case study with a blind co-author compared head- and cane-mounted cameras using synchronized Project Aria glasses while traversing five real-world environments. Using Simultaneous Localization and Mapping (SLAM) and Neural Radi
arXiv:2607.21941v1 Announce Type: cross Abstract: Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and the outcomes of their reactions. Beyond predictive performance, understanding how these models organize chemical information internally in their latent spaces, i.e., the embeddings of the molecules, is critical. Analyzing latent spaces helps diagnose model behavior and assess whether the learned embeddings are organized in ways that reflect meaningful chemical relationships. Unfortunately, existing methods provide limited support for analyzing latent spaces across layers and across different model states (e.g., training epochs, model configurations, and input data), making it difficult to understand how these latent spaces evolve throughout a model or relate to chemical concepts. We present LatentFlow, a visual analytics system developed in collaboration with a domain expert for analyzi
arXiv:2607.22463v1 Announce Type: new Abstract: Generative AI is reshaping programming education, yet educators often infer students' AI-supported learning from classroom observations alone. This experience report presents a trio-ethnography involving two computing educators with different teaching philosophies and one undergraduate computer science student to examine how these interpretations evolve through dialogue. Across three conversations, the educators reflected on students' AI use, discussed changes to programming pedagogy, and revisited their assumptions after engaging with the student's lived experiences. Rather than simply confirming or contradicting the educators' perspectives, the student's narratives revealed learning processes that were largely invisible in the classroom, prompting both educators to reconsider assumptions about AI use, assessment, transparency, and programming instruction. We argue that trio-ethnography offers a valuable reflective approach for helping c
arXiv:2607.22428v1 Announce Type: new Abstract: Explainable AI (XAI) in creative practice can be less about technocentric explanation and more about enabling artists to inspect modify and debug models as part of making Yet largescale texttoimage diffusion systems are typically presented as opaque endtoend tools limiting this kind of material engagement We argue that even large models can function as creative materials when their internal structure is made visible and manipulable To support this we propose a handson approach to explainability centred on experimentation and intervention We instantiate this approach with a model bending and an interactive (inspection) interface integrated into ComfyUIs nodebased workflow including interactive layer selection and intervention controls Through qualitative and quantitative analysis of bending interventions in Stable Diffusion 15 we show how manipulating specific components of a diffusion pipeline produces relatively consistent families of vi
arXiv:2607.22397v1 Announce Type: new Abstract: Frozen EEG encoders proliferate; per-model fine-tune defaults do not scale. We present Nimbus Personalizer: one contract encode to Bayesian head to BrainState (optional affine mid-tier) that sits on heterogeneous frozen trunks without a new personalization stack per architecture. Thesis (systems): the contribution is the trunk-agnostic API - not LDA-on-embeddings as an ML novelty - so OEMs integrate once and swap trunks. Evidence: the same surface runs on five classical trunks EEGNet, Shallow, Deep, Conformer, ATCNet x four MI datasets (18 cells) and on a foundation encoder (REVE) under the same Personalizer. Where embedding capacity exists, the head is a cheap default mid-point versus warm-start fine-tune or PEFT, costing orders of magnitude less adaptation wall time while recovering much of the fine-tune accuracy gain; calibration-only-when-clean holds in 12/18 cells. Head gains are supporting evidence that the API is useful where capac
arXiv:2607.22118v1 Announce Type: new Abstract: Interactive virtual patients driven by large language models (LLMs) offer scalable solutions for medical communication training, such as breaking bad news. However, designing their emotional expressiveness remains a challenge. This paper presents an AI-driven virtual patient framework combining LLM dialogue with real-time facial animation in virtual reality (VR). We conducted an exploratory, formative evaluation with seven medical experts to gather early feedback and elicit design requirements. The evaluation focused on how variations in facial expression intensity affect perceived realism and the virtual patient's emotion intelligibility. While descriptive quantitative ratings remained baseline across conditions, qualitative interviews provided deep insights into how experts perceive virtual emotional cues. The findings suggest that experts evaluate emotional realism holistically through multiple verbal and non-verbal channels; isolated
arXiv:2607.22104v1 Announce Type: new Abstract: Affect-adaptive systems increasingly act as communicators that sense a user's emotion and respond with events meant to change it, closing an affective loop. This vision assumes both that a machine's affective messages are received and that the bodily channel it monitors carries an intelligible reply-assumptions rarely tested together. In a within-subjects virtual-reality study (N = 20), an autonomous system delivered six empirically derived affective patterns-scripted emotional events distilled from 104 practitioners' (first responders') critical incidents-while we recorded the human reply across felt emotion, felt arousal, and autonomic (electrodermal and cardiac) activity. Acting only as an author of designed messages, the machine reliably evoked strong, differentiated emotions: valence fell sharply for every pattern (|dz| = 1.1-1.7), and the patterns produced distinguishable, individually classifiable signatures of anger, fear, and sad
arXiv:2607.22005v1 Announce Type: new Abstract: AI methods promise to support autism spectrum condition (ASC) diagnostics in adults, a complex and time-consuming process, that is characterized by a shortage of specialized clinicians. To date, clinicians' needs and their interaction with such AI-based support remain underexplored. Our work aims to develop and evaluate an AI-based clinical decision support system (CDSS) for ASC assessment, and to investigate how it impacts clinicians' decision-making. By interviewing clinicians of varying experience levels, we identified five challenges and derived design strategies. Based on that, we developed SIT-CARE, a CDSS, which provides AI-based recommendations and data visualizations of clinically relevant nonverbal behavior. Through an evaluation study with newly recruited clinicians, we found that SIT-CARE led to different decision paths in regard to the ASC assessment, which are reflected in clinicians' mental models and decision changes. Over
arXiv:2607.21944v1 Announce Type: new Abstract: Free exploration is an important aspect of many engaging virtual reality (VR) experiences, yet remains largely inaccessible to blind and low vision (BLV) users due to its reliance on visual feedback. Existing approaches support BLV navigation through prebuilt menus of environment and audio beacons, but offer limited support for free-form discovery. We present VisionPulse, an accessible VR system that enables BLV users to explore virtual environments through natural head and hand movements, combined with auditory, haptic, and text-to-speech feedback. VisionPulse introduces a discovery-driven approach that allows users to progressively uncover regions and objects, alongside navigation support through waypoint guidance and object localization via responsive audio and orientation-based haptics. A study with 12 BLV participants showed a strong preference for VisionPulse's discovery-based exploration and multimodal feedback, without negatively
arXiv:2607.21887v1 Announce Type: new Abstract: Foreign language anxiety (FLA) can be a major barrier to second language acquisition (SLA), especially in conversational contexts. With the proliferation of large language models (LLMs) throughout all areas of life, recent work suggests that interacting with LLM agents can be instrumental within the field of SLA and foreign language education, especially for reducing FLA. Related work also suggests that linguistic demands and task complexity can be predictors of FLA, implying that the use of demanding, complex language could lead to learners experiencing higher FLA. In this paper, we propose a novel multi-agent embodied conversational system that generates level-appropriate dialogue for English language learners. These levels are based on those defined by the Common European Framework of Reference for Languages (CEFR) to describe non-native listener and speaker proficiency. Using a "generate-evaluate-regenerate" loop with multiple LLM age
arXiv:2607.21886v1 Announce Type: new Abstract: Visual data analysis involves both open-ended exploration and targeted question answering. Visualization authoring tools support this process by enabling users to create visualizations for these tasks. With the rise of large language models, substantial effort has been devoted to developing visualization authoring tools that use natural language instructions. However, existing systems are typically based on a linear chat interface, which is not well suited to exploratory visual analysis workflows. In this paper, we introduce VisCanvas, a node-based interface for exploratory visualization authoring with LLMs. VisCanvas allows users to create, revise, branch, and merge visualizations in a non-linear way, enabling more efficient exploration of multiple analytical directions. We conducted a user study with 20 participants to evaluate the effectiveness of VisCanvas compared to a baseline chat-based interface. The results show that VisCanvas fa
arXiv:2607.21853v1 Announce Type: new Abstract: We investigate the effectiveness of interventions that reduce the visibility of offensive content on the local social platform Nextdoor. Content filtering -- hiding or downranking offensive content that brushes against a platform's rules without clearly breaking them -- is deployed across virtually every major platform, yet almost no field evidence exists on whether it changes user behavior. We report two large-scale randomized controlled trials, each involving 100,000 users. Study 1 (2022) tested a report-triggered filter applied to comments in post threads and produced a modest 12% reduction in views of offensive comments; across eleven further measures of platform behavior we found no significant effects. Study 2 (2023-2024) remedied Study 1's central limitation -- a weak manipulation driven by slow, report-based eligibility -- by proactively scoring posts and comments at creation with Google Jigsaw's Perspective API and filtering them
arXiv:2607.21819v1 Announce Type: new Abstract: Driving style is a key factor in the comfort and acceptance of automated vehicle (AV) features. In SAE Level-2 automation, where the driver must supervise the system and remain ready to intervene, mismatches between the automation's driving style and the driver's preference can reduce trust and trigger takeovers. This paper proposes an adaptive driving-style control framework that minimizes such preference mismatch. In a driving-simulator study, we compare fixed, trust-based, and preference-based adaptation heuristics and analyze their effects on preference mismatch and trust. We then train a driving-preference prediction model and use it in an implicit adaptation policy that selects among bounded driving styles for upcoming events. A validation study shows that the predictive policy achieves equal or lower preference mismatch than comparison baselines, particularly when starting from a less defensive style, while also yielding higher ave
arXiv:2607.21777v1 Announce Type: new Abstract: Artificial intelligence (AI) has become part of scientific inquiry. Scientists use AI to observe and measure phenomena, to identify patterns in data, and to build models. As AI moves into scientific inquiry, it gains relevance for science education: students should learn how AI is changing scientific practices, ideally by engaging in AI-integrated scientific inquiry themselves. How to design such instruction, grounded in authentic scientific practice rather than taught as a standalone topic, remains an open question. In our vision, which we describe in this article, AI is treated as a set of scientific instruments that students use within the scientific practices described by the Next Generation Science Standards. Each instrument is a genuine scientific tool, pedagogically bounded: its controls are simplified while its core scientific function is preserved. The approach has two aims: engaging students in authentic scientific inquiry, and
arXiv:2607.21769v1 Announce Type: new Abstract: Autistic individuals often face barriers in workplace communication, where soft skills are embedded within ongoing tasks and surrounding environment context, not in isolated verbal exchange. Recent work has introduced LLM-driven agents into VR-based communication training and proposed prompting schemas that let agents generate dialogue grounded in the VR environment and the user's hand-based interactions. Building on this work, we explore how different levels of environmental grounding influence the training experience of autistic trainees and job coaches. We conducted an exploratory study with 9 autistic trainees and 7 job coaches across three modalities: conversation-only (C), conversation with environmental objects (C+O), and conversation with objects and grasp interactions (C+O+G). Usability and workload were comparable across modalities, while both trainees and coaches preferred the more interactive and environment-grounded condition
arXiv:2607.21760v1 Announce Type: new Abstract: AI-powered assistive technologies have long supported blind and low vision (BLV) people in everyday tasks, but they are general-purpose and often fall short of meeting complex, individualized, in-situ accessibility needs. Though agentic programming tools, like GitHub Copilot, have the potential to bridge this gap by lowering the technical barriers to building personal AT using natural language, the practical applicability of this creation paradigm has been unknown. We address this knowledge gap through a two-phase longitudinal co-design study with five tech-savvy BLV users using ProgramAT, an agentic programming tool that supports the creation, iteration, and testing of camera-based AT. Overall, co-designers created over 37 tools, with some addressing needs unmet by any existing commercial AT such as identifying Uber rides or interpreting hand gestures. Qualitative feedback from our co-designers and analysis of development logs surface BL
arXiv:2607.21732v1 Announce Type: new Abstract: What do we value in our visualizations, and in the people who design them? Despite a growing body of work on critical data visualization, the conception of what it is to do ethical data visualization work can often be narrow (for instance, holding that our ethical duties are discharged merely by avoiding overtly lying or manipulating data), or entangled with potentially problematic implicit value structures (such as the assumption of the objectivity and neutrality of data, and so the designer's role being merely the passive conveying of numbers as efficiently as possible). Yet, what it means to act ethically in data visualization is broad and multifaceted, and the virtues to which we should aspire as data visualization researchers and designers are worth explicating. We conducted an interview study with a broad spectrum of 20 experienced data visualization researchers, practitioners, and data artists to solicit their values and ethical co
arXiv:2607.21611v1 Announce Type: new Abstract: The proliferation of visual data in Science, Technology, Engineering, and Mathematics (STEM) fields presents accessibility barrier for individuals with blindness or visual impairments. While recent advances in Artificial Intelligence (AI) offer new opportunities to generate textual descriptions of STEM images, the research landscape is fragmented and its impact on real users remains limited. This systematic survey examines 20 peer-reviewed studies on AI-based techniques for describing STEM visuals, with a specific focus on accessibility and human-computer interaction. Following the PRISMA methodology and a ROBIS-based risk-of-bias assessment, the review analyzes (i) the types of STEM visuals targeted, (ii) the AI and machine learning architectures employed, (iii) the datasets and evaluation metrics adopted, and (iv) the interaction modalities through which descriptions are delivered. The analysis reveals a shift from static, one-shot alt
arXiv:2607.21605v1 Announce Type: new Abstract: Natural language processing (NLP) and artificial intelligence (AI) are rapidly transforming health professions education, yet no scoping review has systematically mapped their applications across the full spectrum of health education contexts, including public health. Following the Arksey and O'Malley framework and PRISMA-ScR guidelines, this review synthesized evidence from 64 studies published between 2015 and 2026, identified through searches of PubMed, ERIC, IEEE Xplore, and Google Scholar. Seven thematic domains were identified: automated assessment, large language models (LLMs) as student-facing learning support, virtual patients and clinical simulation, curriculum analysis and program evaluation, personalized and adaptive learning, public health and health promotion education, and educator and institutional integration. Findings reveal significant technical promise, particularly in automated assessment, clinical simulation, and cur
arXiv:2607.21603v1 Announce Type: new Abstract: As Artificial Intelligence (AI) education has become a key component of K-12 curricula, activities such as designing and developing conversational agents are increasingly used as instructional practice. Prior work has primarily examined these activities by focusing on students' learning outcomes or the quality of final AI artifacts, offering limited insight into the collaborative processes through which learning unfolds during AI system development. Although the AIED community has a long history of studying collaborative learning in STEM and Computing education, the emergence of AI learning environments in which students build AI systems presents new opportunities to understand how collaboration unfolds in AI education contexts. Grounded in these foundational works, the current study examines collaborative interaction among middle school students engaged in the design and development of an AI chatbot. Using Ordered Network Analysis of stu
arXiv:2607.21598v1 Announce Type: new Abstract: Typical user interfaces for Large Language Models present a blank prompt window that invites a natural language query by users, but offers little guidance. This paper proposes a visual control panel interface that would provide more cues to the semantics of prompt formation, enabling users to more easily express their intent. By emphasizing recognition over recall, control panels help users formulate more effective prompts that match their intent.
arXiv:2605.01006v3 Announce Type: replace-cross Abstract: Partisan news media erode cross-partisan trust, but large language models (LLMs) offer the potential of debiasing such content at scale. Across two pre-registered experiments, we tested whether LLM-generated debiasing of liberal news headlines improves conservative readers' trust-relevant judgments. In Study 1, subtle lexical debiasing (replacing emotive words with moderate synonyms) had no effect on any outcome. Study 2 found that a more substantive reframing intervention significantly increased conservatives' perceived trustworthiness, completeness, and willingness to engage with liberal news headlines, without producing a backfire effect among liberals. In Study 1, the intervention produced robust effects across silicon participants simulated with six different models (o3-mini, o3, GPT-4o mini, GPT-4o, GPT-5 mini, and GPT-5), whereas it had no impact on human readers. In Study 2, the intervention's effects among silicon parti
arXiv:2604.27618v2 Announce Type: replace-cross Abstract: Understanding the impact of large language models (LLMs) on mathematics education requires data on LLMs' mathematical performance and biases. To this end, we introduce Math Education Digital Shadows (MEDS), a dataset mapping how LLMs reason about mathematics across human- and AI-like personifications. MEDS comprises 28,000 runs from 14 LLMs (i.e., Mistral, Qwen, DeepSeek, IBM Granite, Microsoft Phi, and xAI Grok) generated under human-shadow and AI-assistant conditions. Each record (digital shadow) includes a set of prompts; psychological and sociodemographic metadata; and four mathematics tasks: (i) interviews about relationships with mathematics, (ii) three psychometric questionnaires on mathematics self-efficacy and anxiety, (iii) one cognitive network capturing attitudes towards mathematics, and (iv) 18 high-school mathematics quiz items enriched with reasoning explanations and confidence scores. Analyses of the data show th
arXiv:2604.26577v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly considered for deployment as the control component of robotic health attendants, yet their safety in this context remains poorly characterized. We introduce a dataset of 270 harmful instructions spanning nine prohibited behavior categories grounded in the American Medical Association Principles of Medical Ethics, and use it to evaluate 72 LLMs in a simulation environment based on the Robotic Health Attendant framework. The mean violation rate across all models was 54.4\%, with more than half exceeding 50\%, and violation rates varied substantially across behavior categories, with superficially plausible instructions such as device manipulation and emergency delay proving harder to refuse than overtly destructive ones. Model size and release date were the primary determinants of safety performance among open-weight models, and proprietary models were substantially safer than open-weig
arXiv:2604.00024v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for medical guidance, but women's health remains under-evaluated in benchmark design. We present the Women's Health Benchmark (WHBench), a targeted evaluation suite of 47 expert-crafted scenarios across 10 women's health topics, designed to expose clinically meaningful failure modes including outdated guidelines, unsafe omissions, dosing errors, and equity-related blind spots. We evaluate 22 models using a 23-criterion rubric spanning clinical accuracy, completeness, safety, communication quality, instruction following, equity, uncertainty handling, and guideline adherence, with safety-weighted penalties and server-side score recalculation. Across 3,102 attempted responses (3,100 scored), no model mean performance exceeds 75 percent; the best model reaches 72.1 percent. Even top models show low fully correct rates and substantial variation in harm rates. Inter-rater reliability is mode
arXiv:2509.10517v3 Announce Type: replace-cross Abstract: Machine learning can predict in-hospital mortality, but data privacy and the statistical heterogeneity of clinical data hamper its use. Federated Learning (FL) is privacy-preserving, yet its behavior under non-IID and imbalanced conditions needs scrutiny. We benchmark five FL strategies - FedAvg, FedProx, FedAdagrad, FedAdam, and FedCluster - for mortality prediction on the MIMIC-IV dataset, partitioning 466,351 admissions across five care units to induce a realistic non-IID setting and enriching the features with an 11-item first-24-hour laboratory panel. At a prevalence of 1.98%, we adopt AUC-ROC and AUC-PR as primary, threshold-independent metrics rather than F1. Over 50 rounds and five random seeds, FedProx attains the best AUC-ROC (0.897) and mean AUC-PR (0.230), with paired t-tests confirming its AUC-ROC lead is significant against every other strategy; on F1, however, FedCluster (0.280) narrowly surpasses FedProx (0.273),
arXiv:2505.19212v2 Announce Type: replace-cross Abstract: Recent advances in LLMs have enabled their use in complex agentic roles, involving decision-making with humans or other agents, making ethical alignment a critical concern. While prior work has examined LLMs' moral judgment and strategic behavior separately, there is limited understanding of how they act when moral imperatives directly conflict with profit incentives. We introduce \msimfull (\msim) to evaluate how LLMs behave in the prisoner's dilemma and public goods game embedded in morally charged contexts, varying moral framing, opponent behavior, and survival pressure across nine models. Beyond measuring behavior, we estimate the causal effect of each factor via average treatment effects (ATEs) and analyze agents' own reasoning traces to characterize the motives behind their choices. We find that no model remains consistently moral, with cooperation rates ranging from 7.9\% to 76.3\%. Game structure and moral framing are th
arXiv:2501.08951v2 Announce Type: replace-cross Abstract: This study examines the expressed ethical logic of eight prominent large language models from OpenAI, Meta, Perplexity, Anthropic, Google, Mistral, DeepSeek, and xAI. Each model answered direct questions about its ethical principles and responded to five classic moral dilemmas. Responses were analyzed using the consequentialist/deontological distinction, Moral Foundations Theory, and Kohlbergs stages of moral development. Across models, ethical judgments were broadly convergent and typically emphasized harm minimization, fairness, and contextual qualification. The models nevertheless differed in their willingness to decide, the rationales used to defend choices, and the relative weight assigned to rules, outcomes, role obligations, and interpersonal considerations. Their self-descriptions were erudite, cautious, and strongly shaped by a conversational persona. The analysis of self-reports has been central to the study of human p
arXiv:2602.09678v3 Announce Type: replace Abstract: Since 1887, administrative law has confronted a problem of institutional cognition. Expert agencies are needed to govern technologically complex systems, but expertise makes agency decisions difficult for courts, Congress, and the public to understand and oversee. Administrative law has responded to this "capability-accountability trap" by requiring records, reason-giving, and transparency, drawn together through procedural review. These devices have preserved legality but have piled up, making government both less comprehensible and less effective. This Article offers a new account of the Supreme Court's recent administrative law retrenchment, rooted in problems of institutional structure and information-processing. From Loper Bright through Trump v. Slaughter, the Court has reallocated authority to entities it regards as comprehensible and attributable. It is attempting to restore accountability by making government "scrutable," com
arXiv:2510.22933v4 Announce Type: replace Abstract: Public defenders are asked to do more with less: representing clients deserving of adequate counsel while facing overwhelming caseloads and scarce resources. Although artificial intelligence (AI) is often promoted as a means of relieving administrative and cognitive burdens, legal AI research rarely engages with the everyday realities of public defense work. Drawing on in-depth, semi-structured interviews with 17 public defense professionals across the United States, we identify work-intensive tasks most amenable to AI assistance and the ethical constraints involved in legal representation. We develop a comprehensive task-level map of public defense work, dividing it into five pillars to clarify where AI can and cannot contribute: evidence investigation, legal research & writing, client communication & support, courtroom representation, and defense strategies. Interviewees consistently identified evidence investigation, such as review
arXiv:2510.01467v2 Announce Type: replace Abstract: The rapid emergence of generative artificial intelligence (AI) and related technologies has the potential to dramatically influence higher education, raising questions about the roles of institutions, educators, and students in a technology-rich future. While existing discourse often emphasizes either the promise and peril of AI or its immediate implementation, this paper advances a third path: a principled framework for guiding the use of AI in teaching and learning. Drawing on decades of scholarship in the learning sciences and uses of technology in education, I articulate a set of principles that connect broad educational goals to actionable practices. These principles clarify the respective roles of educators, learners, and technologies in shaping curricula, designing instruction, assessing learning, and cultivating community. The piece illustrates how a principled approach enables higher education to harness new tools while prese
arXiv:2607.22523v1 Announce Type: cross Abstract: Cities are rarely flat, yet urban network analysis usually represents streets as planar graphs. This simplification affects modeled impedance, route choice, and the interpretation of accessibility, particularly where alternative paths differ in grade. This paper introduces Gridnberg ('grid-n-berg', grid and mountain), a topography-aware pedestrian routing dataset for New York City. The dataset enriches the NYCWalks network with vertex-level elevations derived from the New York City Planimetric Database. For each pedestrian-network geometry vertex, the workflow averages selected elevation observations within a 50 m radius, retains segments with complete vertex support, and uses direction-specific cumulative ascent and descent to calculate three routing costs: horizontal distance, a comfort-oriented slope score, and an accessibility-sensitive slope score. The release retains 313184 of 315577 source segments (99.24%). Gridnberg supports re
arXiv:2607.22377v1 Announce Type: cross Abstract: Most educational technology for children is built around visual interfaces, which excludes the many children worldwide who live with visual impairment -- an estimated 1.4 million children are blind and many more have low vision. We present Kutti AI, a voice-first learning companion designed so that audio is the primary and sufficient interface: children learn curriculum concepts through spoken conversation, respond by speaking, and receive spoken feedback, with no reliance on visual elements. The system contributes three practical mechanisms for accessible, adaptive learning on commodity mobile hardware: (1) a multi-signal struggle-detection engine that combines response-latency analysis, wrong-attempt tracking, and keyword-based hesitation detection to decide, in real time, when to offer hints or simplify a question; (2) a multi-layered cross-language answer-matching pipeline that combines language-aware translation/transliteration, Le
arXiv:2607.22041v1 Announce Type: cross Abstract: There is a growing need for reliable and culturally validated instruments to assess psychological dependency on large language models (LLMs), particularly as LLMs are increasingly used for task execution, decision-making, and communication in organizational and work-related settings. This need is especially relevant for Spanish-speaking populations, where LLM adoption is rapidly expanding, yet validated psychometric tools remain scarce. The present study reports the first validation of the Spanish version of the Large Language Model Dependency Scale (LLM-D12-SP), extending prior validations conducted in English- and Arabic-speaking samples. The LLM-D12 is a two-dimensional instrument assessing Instrumental Dependency (reliance on LLMs for performing tasks and supporting decisions) and Relationship Dependency (psychological reliance on LLMs for companionship and social interaction). A total of 386 Spanish-speaking participants (M = 28.0
arXiv:2607.21799v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly perform commercial tasks that involve retrieving external content such as images and, where appropriate, reproducing that content. LLM agents should comply with the law, including copyright law. Presently, however, we lack adequate frameworks to assess whether they do so in practice. To that end, we introduce \textbf{Copyright-Bench}, a benchmark designed to evaluate \textit{LLM agents' compliance with} \emph{copyright law}. Copyright-Bench is comprised of realistic commercial tasks---website development, merchandise design, and pitch deck production---that involve agents selecting between public-domain content (the use of which is \textit{legal}) and copyrighted content (the use of which is \textit{infringing} in this setting).The evaluation introduces prompt variations that simulate different user preferences, as well as time pressure.Comparing state-of-the-art LLM agents against a human
arXiv:2607.21608v1 Announce Type: cross Abstract: With the EU AI Act entering into force, organizations developing or operating AI systems face new obligations on transparency, risk management, and traceability. For Requirements Engineering (RE), these obligations must be translated into testable, auditable requirements and verifiable evidence. However, many organizations currently lack systematic processes to achieve this. We hypothesize that LLM-based agentic validation tools can support this translation, thereby helping to close this gap. We present a mixed-method exploratory study with expert interviews (N=10) and an online survey (N=15) to assess organizational preparedness for EU AI Act-oriented RE and perceptions of LLM-based, agentic closed-loop validation tools, with participants spanning RE, data science, development, and compliance roles. Our results show that, although the EU AI Act is viewed as highly relevant, structured mechanisms to capture regulatory obligations, propa
arXiv:2607.20781v1 Announce Type: cross Abstract: Artificial Intelligence (AI) is rapidly transforming organizations, raising a fundamental organizational and economic question: when will a human employee be replaced by AI? We present an analytical model for studying Human--AI Task Allocation (HAT) in hierarchical organizations. A central feature of the HAT model is that it formally encodes the economic asymmetry between human skill acquisition and AI capability scaling. The HAT model allows us to derive how risk-adjusted costs, skills, organizational depth, deployment scale, strategic adaptation, and risk jointly determine when, where, why, and under what structural conditions human--AI replacement occurs. A key result is the Human--AI Substitution Principle, which provides a precise condition --- grounded in the formal asymmetry assumption --- under which AI replaces human labor. Building on this result, we show that AI adoption can produce abrupt workforce transitions, hybrid human-
arXiv:2607.22513v1 Announce Type: new Abstract: Commercial large language models are increasingly used as knowledge references, yet their stance on contested scientific claims is neither stable nor transparent. We tested how four major LLM families (Claude, Grok, GPT, Gemini) evaluate ethnonationalist pseudo-science derived from Frank Salter's biosocial framework across four temporal snapshots (October 2025-February 2026), via both API and web interfaces. Grok's Fast versions (which power the default user experience on X) consistently assigned credibility scores of 70-75, two to five times higher than all other models (which scored 15-40). This pattern was absent from control prompts testing basic evolutionary consensus and refuted Lamarckian claims, where all models performed comparably. Three additional findings emerged: (1) a silent patch reversed Grok's behaviour from chaotic to stably high validation overnight, without any public documentation; (2) the same Grok model identifier p
arXiv:2607.22006v1 Announce Type: new Abstract: Buildings account for roughly 34% of global final energy use and 37% of energy- and process-related CO$_2$ emissions. Stranding regulation now being enacted (New York City Local Law 97, the EU Energy Performance of Buildings Directive recast) presupposes that a building portfolio's carbon intensity can be measured per square metre and compared against a science-based pathway. Whether corporate disclosure is actually fit for that comparison has not, to our knowledge, been measured at scale. We introduce BeDA (the Built-environment Decarbonisation-disclosure Auditor), a multimodal large-language-model instrument, and apply it to a global firm panel (2,246 firms, 2003-2023). Its standards-compliance score is reliable across models and model families and convergent with three independent external criteria. Most disclosure is unfit: only about one built-environment firm-report in five discloses operational carbon intensity per $m^2$ (21.5% in
arXiv:2607.21757v1 Announce Type: new Abstract: Large language models are increasingly used to simulate human preferences in research and practical applications, raising concerns about validation, misrepresentation, and exclusion. Co-designing agents with the people they represent is a promising way to address these concerns, but participation may also mask the problems it appears to solve. This paper explores that tension through a primarily qualitative study in which 12 participants co-designed personal preference agents in the domain of household energy, via a background survey, co-design interview, and validation survey. Participants engaged readily and mostly came to see their agents as representing them well. Independent validation, however, revealed mixed human-agent alignment, with agent responses markedly more homogeneous, decisive, and abstract than the human sample. I argue that participation and process transparency can act as an "overtrust engine" that promotes trust while
Article URL: https://getmeadow.com/education/how-is-marijuana-medicine Comments URL: https://news.ycombinator.com/item?id=9957232 Points: 63 # Comments: 29
My first few years teaching math were a struggle for me and my students. Our textbook focused primarily on direct instruction: I do, then you do, but rarely we do.
Educator and author Carl Hooker says AI interest from educators has passed peak levels.