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:2607.01214v1 Announce Type: new Abstract: Statistical visualization is usually treated as a visual medium, but data can also be touched. Three dimensional printed tactile graphs let blind and low vision students feel distributions, trace trends, and explore relationships through direct haptic interaction. Yet classroom scale use remains limited because producing each graph in CAD software requires specialized skill and hours of manual work. We address this bottleneck as a software problem through a three layer reusable pipeline in about 1500 lines of JavaScript. The first layer derives tactile design parameters automatically from plate dimensions using tactile perception research. The second provides shared chart scaffolding and five modular builders for scatter, bar, histogram, line, and box plots. The optional third layer uses a multi-modal large language model to extract structured chart specifications from uploaded images, with mandatory teacher review before print generation
arXiv:2607.00989v1 Announce Type: new Abstract: Semantic trajectory analysis has recently emerged as an approach for modeling human movement by capturing implicit patterns and behaviors through semantic information (e.g., visitors' profiles and goals) beyond raw spatial paths to better understand why people move in certain ways. However, analyzing semantic trajectories in real-world scenarios remains challenging, as collecting high-quality data is costly and often lacks rich semantic information. Meanwhile, existing simulation tools require substantial technical expertise, which makes them difficult for practitioners to adopt. To address these limitations, the paper proposes ${SenseWalk}$, an interactive system that supports simulating semantic trajectories by LLM-powered agents. We develop a simulation workflow that combines LLMs and the social force model to balance physical plausibility and semantic coherence. A user-friendly interface is designed to facilitate users in customizing
arXiv:2607.00979v1 Announce Type: new Abstract: This study examined students' experiences with mixed-reality applications and physical toolkits in Engineering Mechanics to inform design guidelines for educational tools. In a user study with 24 participants, we compared classroom instruction alone, classroom instruction with a mixed-reality application, and classroom instruction with physical toolkits. Thematic analysis of participant feedback revealed that learners' workflows and engagement with fundamental mechanics problems varied across instructional modalities. Participants valued multimodal and interactive experiences that combined visualization with hands-on interaction, while reporting challenges with complex or unclear visualizations. These insights support the human-centered design of mixed-reality and physical tools for engineering education.
arXiv:2607.00969v1 Announce Type: new Abstract: Visual analytics (VA) plays an increasingly important role in supporting machine learning (ML) workflows. In the field of visualization, such approaches and techniques are referred to as VIS4ML. While ML models are mostly learned automatically, the corresponding ML workflows receive a variety of human inputs, such as data labelling, feature engineering, model architecture designing, hyper-parameter tuning, and so on. In this work, we surveyed over 200 VIS4ML papers to gain an understanding of how humans inject their knowledge into ML workflows through interactive visualization. We collected a corpus of VIS4ML papers from the IEEE VIS conferences in the past decade. We developed a coding scheme to facilitate the literature research from four perspectives: characteristics of ML, visualization, interaction, and actions. The analysis of the coded dataset allows us to observe different pathways that transfer human knowledge to ML workflows via
arXiv:2607.00542v1 Announce Type: new Abstract: The intersection of AI, healthcare, and visualization is evolving rapidly, posing challenges that cut across disciplinary boundaries and resist easy resolution. The Visual Analytics in Healthcare workshop (VAHC), co-located every other year at the IEEE VIS conference and the AMIA (American Medical Informatics Association) annual conference, has served as a forum to connect the visualization and medical informatics community since 2010. In 2025, to celebrate the 16th edition, we used the workshop as an opportunity to consolidate the community's collective experience (and expertise) and identify Grand Challenges where the field should prioritize going forward. We combined thematic coding of the 15 accepted VAHC workshop papers with structured group discussions among more than 40 participants, organized around three major themes: "Technical innovation vs. clinical reality", "Human-centered and scalable VAHC", and "From foundations to actiona
arXiv:2607.00533v1 Announce Type: new Abstract: As AI takes on more software work, the line between human and AI effort is shifting. Where developers draw that line around AI autonomy bears on how we design tools and roles that preserve meaningful work. Drawing on cognitive appraisal theory, work design, and automation research, we conducted a mixed-methods study of 448 professional developers at Microsoft to investigate their accepted levels of AI autonomy across software engineering work. Most developers accepted AI producing work under their oversight, although accepted autonomy varied substantively across tasks and individuals. Acceptance was lowest for identity-defining, human-facing, and design-oriented work, and higher among developers with more AI experience and risk tolerance. Task accountability was associated with lower odds of allowing AI to act on developers' behalf, whereas task identity was associated with lower odds of granting AI decision-making autonomy. Task demands
arXiv:2607.00518v1 Announce Type: new Abstract: Conventional Mixed Reality (MR) workspaces are frequently organized in cockpit-like layouts, where multiple floating windows surround the user. While this configuration facilitates access to digital content, it often induces occlusion, reducing understanding of the physical environment and limiting access to real-world objects. To overcome this challenge, we present the Contour-Adaptive Mixed Environment Overlays (CAMEO), a contour-adaptive MR interface that drapes virtual windows onto physical surfaces. This design integrates digital content with nearby items, thereby improving users' visual access to background objects and supporting interaction with them. We evaluate CAMEO in two controlled studies. The first demonstrates that draping reduces hand-movement detours relative to flat mid-air surfaces, enabling more direct interaction with nearby items. The second shows that controlled window deformation does not significantly impair text
arXiv:2607.00445v1 Announce Type: new Abstract: Proactive assistance with large language models (LLMs) has received growing attention in the human computer interaction (HCI) community. However, most past work on proactive LLMs' assistance has focused on adult users and task-oriented settings, leaving open how such systems could support children, whose interests and needs are often expressed through gaze and other nonverbal behaviors rather than explicit requests. In this study, we focus on two key challenges of proactive assistance in children's picture exploration: when to provide assistance and what assistance to provide based on children's nonverbal behaviors. To address these challenges, we introduce Ollie, a gaze-informed proactive artificial intelligence (AI) assistant that offers short narrative descriptions based on where a child is looking. Ollie uses children's gaze to estimate their attention, identify their current visual focus, and select a related picture region for the L
arXiv:2607.00420v1 Announce Type: new Abstract: Organizations increasingly deploy autonomous artificial intelligence (AI) systems for operational decisions, such as inventory replenishment. Yet fully granting override rights can degrade performance due to human bias and noise, while prohibiting them may overlook valuable private information. This raises a key question: How should override rights be structured to improve human supervision of autonomous AI? Methodology/results: We propose a constrained override policy that limits overrides per decision episode to enable selective filtering that prioritizes high-value overrides. We tested it through a randomized field experiment with 553 workers at a major Chinese smart vending machine retailer that manages more than 59,000 machines and 4,000 SKUs. Workers were assigned to no overrides, free overrides, or a two-per-machine limit on downward overrides. Free overrides reduce inventory by 1.95% but also cut sales by 1.19%. Constrained overri
arXiv:2607.00395v1 Announce Type: new Abstract: As generative AI is increasingly used by children and adolescents, there is a growing need for risk evaluation frameworks that account for child-specific harms. However, most existing safety evaluation frameworks focus on general user populations, often overlooking risks unique to younger users. To address this gap, we propose an evaluation framework that integrates expert-guided risk factors with real-world AI incident data for child safety. The framework identifies hazard categories from expert guidelines and AI incident databases and uses this information to construct a synthetic test set for model evaluation. Particularly, we apply the framework to the education domain and evaluate three Llama Guard models on their ability to detect unsafe user prompts. Our results show that current Llama Guard models struggle to identify education-related unsafe user prompts. We conclude by discussing how future work can extend the evaluation to addi
arXiv:2607.00239v1 Announce Type: new Abstract: In 3D visualizations of natural phenomena, improving aesthetics can provide measurable benefits, but often involves transformations that affect how the data is perceived. As a growing range of tools - including AI-based methods - make visual design and modification more accessible, it is increasingly important to understand trade offs and concerns when making these changes. We conducted an expert survey (N=95) with visualization researchers, practitioners, and domain scientists, investigating reactions to fifteen alterations spanning presentation-level adjustments (e.g., lighting, camera position) and data-level modifications (e.g., removing errors, filling gaps), applied by both humans and AI systems. Results show differences in perceived acceptability are driven by the transformation's meaning, regardless of whether it operates at the presentation or data level. Additionally, certain modifications were consistently judged as more permis
arXiv:2607.00103v1 Announce Type: new Abstract: Although textual framing in data visualizations is known to influence comprehension, recall, and perceptions of bias, its effects on viewers' emotional responses remain underexplored. Drawing on two widely studied framing strategies in political communication, we examine how episodic framing (foregrounding a specific event) versus thematic framing (foregrounding broader trends) affects emotional and attitudinal responses to visualizations. We conducted a preregistered, between-subjects online experiment (N = 800) in which participants viewed identical visualizations of U.S. mass shooting data that varied only in textual framing: a thematic title, a thematic title with annotation, or an episodic title paired with the same annotation. Results show that episodic framing elicited significantly more negative emotional valence than both thematic conditions. In contrast, adding an annotation to a thematic title did not alter emotional impact. Wh
arXiv:2604.11430v2 Announce Type: replace-cross Abstract: AI agents that pay for resources via the x402 protocol embed payment metadata - resource URLs, descriptions, and reason strings - in every HTTP payment request. This metadata is transmitted to the payment server and to the centralised facilitator API before any on-chain settlement occurs; neither party is typically bound by a data processing agreement. We present presidio-hardened-x402, the first open-source middleware that intercepts x402 payment requests before transmission to detect and redact personally identifiable information (PII), enforce declarative spending policies, and block duplicate replay attempts. To evaluate the PII filter, we construct a labeled synthetic corpus of 2,000 x402 metadata triples spanning seven use-case categories, and run a 42-configuration precision/recall sweep across two detection modes (regex, NLP) and five confidence thresholds. The recommended configuration (mode=nlp, min_score=0.4, all enti
arXiv:2511.06160v2 Announce Type: replace-cross Abstract: While recent safety guardrails effectively suppress overtly biased outputs, subtler forms of social bias emerge during complex logical reasoning tasks that evade current evaluation benchmarks. To fill this gap, we introduce a new evaluation framework, PRIME (Puzzle Reasoning for Implicit Biases in Model Evaluation), that uses logic grid puzzles to systematically probe the influence of social stereotypes on logical reasoning and decision making in LLMs. Our use of logic puzzles enables automatic generation and verification, as well as variability in complexity and biased settings. PRIME includes stereotypical, anti-stereotypical, and neutral puzzle variants generated from a shared puzzle structure, allowing for controlled and fine-grained comparisons. We evaluate multiple model families across puzzle sizes and test the effectiveness of prompt-based mitigation strategies. Focusing our experiments on gender stereotypes, our finding
arXiv:2601.06038v2 Announce Type: replace Abstract: Methodological innovations have become increasingly critical in the humanities and social sciences (HSS) as researchers confront complex, nonlinear, and rapidly evolving socio-environmental systems. On the other hand, Early Career Researchers (ECRs) continue to face intensified publication pressure, limited resources, and persistent methodological barriers. Employing the GITT-VT analytical paradigm--which integrates worldviews from quantum physics, mathematical logic, and information theory--this study examines the seven-year evolution of the Bayesian Mindsponge Framework (BMF) analytics and the bayesvl R software (hereafter referred to collectively as BMF analytics) and evaluates their contributions to strengthening ECRs' capacity for rigorous and innovative research. Since 2019, the bayesvl R package and BMF analytics have supported more than 160 authors from 22 countries in producing 112 peer-reviewed publications spanning both qua
arXiv:2607.00854v1 Announce Type: cross Abstract: Recent advances in AI have heightened scholars' and policy makers' concern with social influence and behavioral contagion in online communities. We conduct a field experiment on Reddit to investigate the extent to which online users are susceptible to positive behavioral stimuli from other users and artificial agents. We let apparent human and bot accounts give symbolic awards to users with one of four rationales: praising the recipient's logical argument, emotional sensitivity, or moral integrity, or explaining that the award resulted from a random draw in a lottery. We evaluate how the different rationales for the award affect the recipients' subsequent behavior on the platform in terms of volume, impact, and content, as well as the further behavioral contagion to other users. We find that awards do not increase user activity and downstream impact, and awards from bots with the lottery rationale can in fact reduce them. Nevertheless,
arXiv:2607.00523v1 Announce Type: cross Abstract: Artificial intelligence (AI) is becoming ubiquitous, and across domains, increasingly autonomous systems are carrying out tasks which raise significant ethical and legal challenges which demonstrate a need for strong human-machine teams rooted in trust. In this article, I argue that within highly impactful areas (such as medicine or warfighting) there are grounds for us initially treating autonomous and opaque systems as relevantly analogous to dogs (or other animals with which we have close relationships). Under this analogy, humans making use of these systems are not to be viewed as "users" or "deployers" of these systems, but instead take the role of "handlers". This recasting of roles shifts the way we view humans, AI-enabled and autonomous systems, and the relations between them, and moreover clarifies the clear and traceable lines of responsibility humans have for the outcomes brought about when using these systems. In developing
arXiv:2607.00403v1 Announce Type: cross Abstract: Large language models are increasingly used by participants on crowdsourcing platforms when responding to surveys, potentially undermining the validity of collected data. Our study aims to quantify the prevalence of this behavior and investigate methods to detect and prevent it. In a series of surveys (N = 250), we examined conditions such as platform choice, survey length, requests not to use AI, and disabling copy-paste functionality. We were able to identify distinct characteristics of LLM-assisted responses and found that their frequency varied widely, from under 10% on Prolific to over 80% on Mechanical Turk. Mitigation measures reduced LLM usage but did not necessarily improve data quality. No participants employed browser-use agents at the time of our survey, but we report on our own detection experiments. We recommend that researchers actively screen survey responses for LLM usage by recording and analyzing keystroke data and cr
arXiv:2607.00280v1 Announce Type: cross Abstract: Airbnb is a community based on connection and belonging -- many hosts on Airbnb are everyday people who share their worlds to provide guests with the feeling of connection and being at home; Airbnb strives to connect people and places. Among our efforts to connect guests and hosts, we provide tools to enable hosts to set competitive prices, which helps improve affordability for guests while helping hosts get more bookings. We also personalize the guest experience to show them the listings that match their needs. To help inform these efforts, we combine economic modeling and causal inference techniques to understand how guests book stays based on the prices hosts set, among other factors, and how that preference varies across different guests and listings. Such understanding helps us identify opportunities for Airbnb to support the marketplace and better connect guests and hosts. For example, understanding how much guests respond to diff
arXiv:2607.00220v1 Announce Type: cross Abstract: Artificial intelligence (AI) systems are routinely modified after deployment through retraining and changes in their environments. These transformations raise a metaphysical question: under what conditions does an AI system remain the same system over time or across deployments? Earlier work formulates synchronic and diachronic identity propositionally, by relating identity within a fixed AI system type to equality of trustworthiness levels. Such criteria specify when identity statements are true, but leave implicit the structure of the states compared, the transformations connecting them, and the temporal organization of persistence. We develop a category-theoretic formalization of AI identity. An AI system type is specified by a datum consisting of a techno-function, a trustworthiness profile, and a trustworthiness-level function. Profile-relative states are connected by admissible lifecycle paths, which are restricted to trustworthin
arXiv:2607.00002v1 Announce Type: cross Abstract: Moral cognition has traditionally been modeled as adherence to fixed ethical theories--deontology, consequentialism, virtue ethics--implemented as static rules or value functions. We propose Bounded Morality, a formal framework for analyzing the computational demands of moral problems faced by finite agents. Extending Herbert Simon's notion of bounded rationality, we formalize moral situations along two orthogonal dimensions: moral breadth, the scope of entities treated as morally relevant, and moral depth, the inferential integration required to evaluate their interactions. Limited resources impose an unavoidable tradeoff between these dimensions, defining a feasible space of moral computation. Within this space, ethical theories correspond to locally efficient strategies adapted to different demand regimes rather than competing accounts of moral truth. The framework yields a formal notion of moral regret and moral progress under const
arXiv:2607.00001v1 Announce Type: cross Abstract: Most approaches to AI alignment treat human preferences as fixed targets to be inferred and optimized. This assumption conflicts with extensive empirical evidence showing that preferences are layered, dynamic, and constructed through interaction--particularly with adaptive technologies. As AI systems become more persistent, personalized, and socially embedded, they increasingly participate in shaping what people attend to, value, and endorse over time. We introduce Constructive Alignment, a paradigm that reframes alignment as a control problem over evolving human preference trajectories rather than static preference satisfaction. Drawing on behavioral economics, psychology, and constructivist social theory, we model preferences as layered state variables that evolve under interaction with AI systems. We formalize this view using a control-theoretic framework in which system actions and interaction design jointly influence both world sta
arXiv:2607.01113v1 Announce Type: new Abstract: Corporate sponsorship is increasingly prevalent at computer science conferences. However, a quantitative understanding of this phenomenon has yet to be established, let alone insights into the interplay between academic conferences and sponsoring corporations, or how to leverage it. To fill these gaps, this study first explores the landscape of corporate sponsorship across a wide range of high-profile computer science conferences, shedding light on its evolution over a 25-year period from 2000 to 2024. The complex and expansive relationships between these conferences and their corporate sponsors are then systematically organized into a network for structural analysis and conference evaluation. Specifically, after modularity optimization, the network's topological properties are analyzed to identify key conferences and corporations that shape the overall structure, connectivity, and functionality. More importantly, this study makes the fir
arXiv:2607.00941v1 Announce Type: new Abstract: Agentic AI systems generate runtime records, logs, traces, and audit artefacts, but the existence or integrity of such records does not by itself establish that legally operative oversight findings can be recovered from them. This technical report defines an evidentiary-adequacy criterion for a bounded class of determinations: binary findings of fact about specific events and their relations, such as whether protected data crossed a boundary, whether a human could intervene, whether an information barrier held, or whether delegated authority was valid at the moment of use. The criterion states that a runtime record can answer such a determination only if it carries both a typing that maps recorded events to the legally operative category and the relation, such as provenance, authority, derivation, or temporal validity, on which the determination's truth depends. The claim is one of necessity, not sufficiency. The report instantiates the c
arXiv:2607.00641v1 Announce Type: new Abstract: Advances in generative AI are rapidly increasing the quality and commercial value of generated music, and this progress depends on large catalogs of creators' recordings. This raises a central question for platform design: how should creators be compensated when their work is used to train generative AI models that in turn produce commercial outputs? We develop a framework for fairly compensating creators in generative-music markets, where each creator's payment depends on a data-attribution score estimating their contribution to model outputs. Compared to past compensation frameworks, our framework has two unique considerations: (1) attribution is traced to entire creator catalogs, not individual songs, and (2) the informativeness (signal-to-noise ratio) of the attribution score is an input to the payment mechanism. The framework yields a closed-form payment rule per creator and measures the welfare cost of inaccurate attribution for bot
arXiv:2607.00437v1 Announce Type: new Abstract: In the current era of great-power competition and the diffusion of emerging disruptive technologies on the battlefield, NATO's approach to coordinating the development, adoption, and standardization of new technologies is changing from its practices during the Cold War, but the nature of these technologies poses additional challenges for the alliance.
arXiv:2607.00140v1 Announce Type: new Abstract: As computing education expands beyond traditional programming into operational domains such as systems administration and command-line environments, existing pedagogical frameworks struggle to capture a dimension that is critical in these contexts: the real-world consequences of learner actions. Existing cognitive taxonomies classify learning objectives by mental operations but do not account for system impact, leaving a critical gap in command-line education where conceptually simple commands can have severe consequences. This work presents CogTax, a four-level cognitive taxonomy that integrates two dimensions: cognitive complexity, derived from Bloom's Revised Taxonomy, and operational impact, which distinguishes observational, reversible, structural, and administrative operations. The four progressive levels range from safe read-only inspection to advanced system management requiring integration of multiple abstract models. Then, the t
arXiv:2607.00120v1 Announce Type: new Abstract: Emotional bonds between humans and AI companions are growing, and the question of whether a person may marry an AI system will soon move from speculative fiction into law. This chapter examines whether the autonomy-centered logic that has expanded marital choice among human beings can justify extending marital status to superintelligent companions. Following a scenario-envisioning exercise informed by anticipatory ethics, I argue that granting such status leads to socially unjust outcomes, even under the generous assumption of reliable superintelligence. Marriage as a socio-legal institution does more than ratify private agreement; it creates networks of mutual obligation, joins families, and makes each partner vulnerable to the other. A relationship sustained by corporate policy and continued payments is a subscription rather than a bond tested by time. Discussing wholesale marital status is therefore the wrong frame. Law should carve ou
arXiv:2607.00019v1 Announce Type: new Abstract: This paper offers a call to action. We urge our colleagues in the research community to play a greater role in the articulation of our findings to the public. To illustrate the stakes we present a case study on the initial stages of an LLM-based machine translation application's deployment in a real-world context: a text-2-911 system advertising capabilities in 55 languages for use in emergencies in which it may be difficult to call operators directly. We identify a number of common misconceptions about technologies such as these, concluding with a set of concrete recommendations and best practices for stakeholders at every stage of the development and deployment pipeline. While the advancement of scientific research often lies in solving the "hard" problems, we argue it is often the "easy" ones -- problems for which the latest technology is often unnecessary -- that are most overlooked.
arXiv:2607.00018v1 Announce Type: new Abstract: Media coverage of armed conflict is deeply asymmetric: we document a 224$\times$ gap between the most and least covered conflict zones in English-language media across 22 countries (2020--2026). We evaluate zero-shot conflict escalation forecasting across all 22 countries on a 660-case held-out test set, comparing Llama-3.3-70B and GPT-4o against three structured baselines. The central finding is not a performance gradient but a qualitative failure: LLMs do not forecast conflict -- they categorize it. Llama predicts escalation on every under-covered case, matching the trivial Always-YES baseline to three decimals; GPT-4o predicts NO on every over-covered case, missing all five actual escalation events. A logistic regression using only eleven observation-window features with \emph{no country information} achieves F1~=~0.402, outperforming both LLMs in every measurable tier. This failure cannot be resolved at inference time: adding structur
arXiv:2607.00015v1 Announce Type: new Abstract: To improve residents' well-being in Australia's urban areas, governments have introduced policy reforms such as SEPP65, BADS, and SPP7.3 to enhance apartment design quality. These regulations require precise geometric and spatial analysis to evaluate health-related features, including daylight access, natural ventilation, privacy, and space efficiency. However, compliance checking remains challenging due to its manual, time-intensive nature. Additionally, evolving policies limit scalability for large-scale assessments across thousands of apartments. Existing automated floor plan analysis methods are fragmented and typically focus on single apartments, lacking a unified framework for multi-unit compliance checking. This article explores current advancements in automated floor plan analysis, particularly AI-driven approaches, and highlights key challenges in their practical adoption. To address these gaps, a conceptual framework is proposed
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Hey Folks, I have penned down my thoughts on EdTech 2.0. This is an idea, I am exploring pursuing as my next entrepreneurial venture. I would appreciate feedback and suggestions. Link - https://open.substack.com/pub/monkeylike/p/edtech-20 Thanks in advance. Comments URL: https://news.ycombinator.com/item?id=42985788 Points: 1 # Comments: 0
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The job market is often perceived as tough: too few jobs and too many applicants. Newly graduated college students trying to secure their first jobs in the cybersecurity industry have it tough for another reason: Employers want experience. “One of the biggest challenges in cybersecurity is that employers want people with experience, but students need somewhere to get that experience before they can land those jobs,” explains James Lanham, director of operations at Marshall University’s Institute for Cyber Security. Thanks to a partnership with Intuit, Marshall University is helping students…
More than 9 in 10 U.S. school districts say they have planned to purchase Chromebooks for their students or staff this year. So, when rumors began to buzz about the Googlebook, which brings together elements of ChromeOS and Android, many district leaders understandably had a lot of questions. Will my school’s devices eventually become obsolete? What can I do to future proof my device program? We have good news for K–12 districts that are concerned about this change: The real story is that schools have ample time and multiple options to prepare for the upcoming upgrades. By beginning to plan…
Article URL: https://metis-learn.io/ Comments URL: https://news.ycombinator.com/item?id=48719104 Points: 2 # Comments: 0