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:2605.30169v3 Announce Type: replace Abstract: As autonomous language model agents proliferate, forming an emerging agentic web with real-world consequences, what credibility signals can you use to decide whether to trust an unfamiliar agent in the wild and delegate to it? A natural governance intuition is to extend human identity verification and reputation mechanisms, from "Know Your Customer" and credit scores to "Know Your Agent" regimes. However, we argue that this analogy is fundamentally incomplete. Reputation mechanisms function both as social signals and as corrective feedback that sustain an equilibrium of trustworthy behavior, presuming a persistent identity associated with behavioral continuity, sanction sensitivity, and costly non-fungibility. Yet language model agents are ontologically dissociative: they are essentially an assemblage of mutable modules--foundation models, system prompts, tool-access policies, external memory, and, in some cases, a multi-agent system
arXiv:2603.29278v2 Announce Type: replace Abstract: There have been various attempts at token standards on numerous blockchain platforms today to fundamentally change the way assets are traded in the traditional capital markets, but there is a lack of research and resolution on regulatory issues that become the common foundation for interoperability and reusable standards. Our proposal, Regulatory Compliance Protocol (RCP), is based on the regulations and reports of 15 global financial institutions and standardizes recommendations and guidelines involving the overall asset tokenization of TradFi and DeFi into five regulatory groups: Traceability, Privacy, Enforceability, Finality and Tokenizability, compiling them into 31 items and presenting a benchmark for technology and standards as an underlying protocol. To review the legality and effectiveness of RCP, it was validated based on three tokenization and trading scenarios, and by benchmarking existing asset-tokenization standards (ERC
arXiv:2602.06371v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in multilingual contexts, yet their consistency across languages on politically sensitive topics remains understudied. This paper presents a systematic bilingual benchmark study examining how 17 LLMs respond to questions concerning the sovereignty of the Republic of China (Taiwan) when queried in Chinese versus English. We discover significant language bias -- the phenomenon where the same model produces substantively different political stances depending on the query language. Our findings reveal that 15 out of 17 tested models exhibit measurable language bias, with Chinese-origin models showing particularly severe issues including complete refusal to answer or explicit propagation of Chinese Communist Party (CCP) narratives. Notably, only GPT-4o Mini achieves a perfect 10/10 score in both languages. We propose novel metrics for quantifying language bias and consistency, includin
arXiv:2511.15620v2 Announce Type: replace Abstract: Robustness is a key requirement for high-risk AI systems under the EU Artificial Intelligence Act (AI Act). However, both its definition and assessment methods remain underspecified, leaving providers with little concrete direction on how to demonstrate compliance. This stems from the Act's horizontal approach, which establishes general obligations applicable across all AI systems, but leaves the task of providing technical guidance to harmonised standards. This paper investigates what it means for AI systems to be robust and illustrates the need for context-sensitive standardisation. We argue that robustness is not a fixed property of a system, but depends on which aspects of performance are expected to remain stable ("robustness of what"), the perturbations the system must withstand ("robustness to what") and the operational environment. We identify three contextual drivers--use case, data and model--that shape the relevant perturba
arXiv:2511.06148v4 Announce Type: replace Abstract: As large language models (LLMs) are adopted into frameworks that grant them the capacity to make real decisions, it is increasingly important to ensure that they are unbiased. In this paper, we argue that the predominant approach of simply removing existing biases from models is not enough. Using a paradigm from the psychology literature, we demonstrate that LLMs can spontaneously develop novel social biases about artificial demographic groups even when no inherent differences exist. These biases result in highly stratified task allocations, which are less fair than assignments by human participants and are exacerbated in newer and larger models. In humans, emergent biases like these have been shown to result from exploration-exploitation trade-offs, where the decision-maker explores too little, allowing early observations to strongly influence impressions about entire demographic groups. To alleviate this effect, we explore a series
arXiv:2509.14088v3 Announce Type: replace Abstract: Modern computing students often rely on both natural-language prompting and manual code editing to solve programming tasks. Yet we still lack a clear understanding of how these two modes are combined in practice, and how their usage varies with task complexity and student ability. In this paper, we investigate this through a large-scale study in an introductory programming course, collecting 13,305 interactions from 355 students during a three-day lab activity. Our analysis shows that students primarily use prompting to generate initial solutions, and then often enter short edit-run loops to refine their code following a failed execution. Student reflections confirm that prompting is helpful for structuring solutions, editing is effective for making targeted corrections, while both are useful for learning. We find that manual editing becomes more frequent as task complexity increases, but most edits remain concise, with many affecting
arXiv:2508.06577v3 Announce Type: replace Abstract: Participatory budgeting is a democratic innovation that allows citizens to propose and vote on public investment projects. To help organizers manage large volumes of submissions, we design and test privacy-preserving methods for algorithmic shortlisting. These algorithms predict which projects are likely to be funded using only project features and anonymous historical voting data. We demonstrate the limitations of a naive approach that uses a large language model to rank projects based on past success and propose a vote-based pipeline that enables state-of-the-art LLMs to perform on par with classical machine learning. Our findings indicate that user preferences in participatory budgeting are stable enough to allow algorithmic shortlisting to approximate an initial selection of projects effectively.
arXiv:2506.18133v2 Announce Type: replace Abstract: The need for developing model evaluations beyond static benchmarking, especially in the post-deployment phase, is now well-understood. At the same time, concerns about the concentration of power in deployed AI systems have sparked a keen interest in 'democratic' or 'public' AI. In this work, we bring these two ideas together by proposing mechanisms for aggregated individual reporting (AIR), a framework for post-deployment evaluation that relies on individual reports from the public. An AIR mechanism allows those who interact with a specific, deployed (AI) system to report when they feel that they may have experienced something problematic; these reports are then aggregated over time, with the goal of evaluating the relevant system in a fine-grained manner. This position paper argues that individual experiences should be understood as an integral part of post-deployment evaluation, and that the scope of our proposed aggregated individu
arXiv:2506.13385v2 Announce Type: replace Abstract: Human mobility shapes access to resources, opportunities, and services, making movement data a powerful lens for studying spatial and social inequality. Yet despite the growing availability of official open mobility datasets, their research potential is rarely realized because the technical overhead of retrieving, harmonizing, and processing them often crowds out substantive analysis. To address this, we introduce pySpainMobility, a Python package that automates the retrieval and harmonization of Spain's open mobility data across spatial resolutions and demographic strata, streamlining national-scale, reproducible analysis. Using the package, we study income-stratified mobility inequality across Spain's inter-province network, drawing on district-level origin-destination flows for four representative weeks spanning the seasons of 2023. We construct income-specific mobility layers and show that socioeconomic stratification is deeply em
arXiv:2505.02519v2 Announce Type: replace Abstract: This paper explores the interplay of AI language technologies, sign language interpreting, and linguistic access, highlighting the complex interdependencies shaping access frameworks and the tradeoffs these technologies bring. While AI tools promise innovation, they also perpetuate biases, reinforce technoableism, and deepen inequalities through systemic and design flaws. The historical and contemporary privileging of sign language interpreting as the dominant access model, and the broader inclusion ideologies it reflects, shape AIs development and deployment, often sidelining deaf languaging practices and introducing new forms of linguistic subordination to technology. Drawing on Deaf Studies, Sign Language Interpreting Studies, and crip technoscience, this paper critiques the framing of AI as a substitute for interpreters and examines its implications for access hierarchies. It calls for deaf-led approaches to foster AI systems that
arXiv:2504.18236v4 Announce Type: replace Abstract: Explainability and its emerging counterpart contestability have become important normative and design principles for trustworthy AI as they enable users and subjects to understand and challenge AI decisions. However, realizing these principles is difficult, as they assume different meanings in technical, legal, and organizational dimensions of AI regulation. To resolve this conceptual polysemy, in this paper, we present the findings of an interview study with 14 experts to examine the intersection and implementation of explainability and contestability, and their understanding in different research communities. We outline differentiations between descriptive and normative explainability, judicial and non-judicial channels of contestation, and individual and collective contestation action. We further describe the main points of friction in the realization of both principles, including the alignment between top-down and bottom-up regula
arXiv:2501.14750v3 Announce Type: replace Abstract: Carbon emissions significantly contribute to climate change, and carbon credits have emerged as a key tool for mitigating environmental damage and helping organizations manage their carbon footprint. Despite their growing importance across sectors, fully leveraging carbon credits remains challenging. This study explores engineering practices and fintech solutions to enhance carbon emission management. We first review the negative impacts of carbon emission non-disclosure, revealing its adverse effects on financial stability and market value. Organizations are encouraged to actively manage emissions and disclose relevant data to mitigate risks. Next, we analyze factors influencing carbon prices and review advanced prediction algorithms that optimize carbon credit purchasing strategies, reducing costs and improving efficiency. Additionally, we examine corporate carbon emission prediction models, which offer accurate performance assessme
arXiv:2607.05203v1 Announce Type: cross Abstract: The rise of social media financial influencers (finfluencers) has transformed how financial information is disseminated to broad and often inexperienced audiences. While these creators may contribute to financial literacy, concerns remain regarding the reliability of their content and the adequacy of risk disclosures. Using data collected through TikTok's Research API, we analyze UK finfluencer content, engagement dynamics, disclaimer practices, audience sentiment, and network structure. The primary dataset comprises 13,215 videos and 104,097 comments posted by 71 UK-based finfluencers between April and September 2024, while a follow-up dataset covering October 2025 to March 2026 enables longitudinal analysis of disclaimer practices, engagement trends, and hashtag usage. Using topic modeling, we identify four dominant themes: Entrepreneurship \& Side Hustles, Property Investing, Active Trading, and Saving \& Budgeting. Sentiment analysi
arXiv:2607.05113v1 Announce Type: cross Abstract: Imagine two users interact with the same LLM. One has been told it is the cutting-edge flagship model; the other, an older, weaker model. They walk away with markedly different ratings of its usefulness and intelligence, yet they used the same model. In a controlled study, 162 participants each used one of six LLMs from two families across three collaborative tasks, after first viewing a landing page that matched, overstated, or understated their model's true capability. This pre-interaction framing shifted user opinions and interaction behavior while task performance did not. Oversold users rated the model more favorably and used more directive prompting, while Undersold users wrote longer, more collaborative prompts. The quality of what users and the model produced together depended only on the model's true capability, not on what users were told. Participants' change in model impressions after use, measured across two impression meas
arXiv:2607.05068v1 Announce Type: cross Abstract: As Generative AI (GenAI) becomes increasingly central to software development, CS education is integrating prompt-centered workflows where students describe intended program behavior in natural language to elicit code. However, professional practice requires careful review and verification of GenAI-generated code that may appear correct while containing subtle faults. This creates a challenge for CS1-level activities, where current models often solve tasks correctly and reduce students' incentive to closely inspect generated outputs. We investigate how prompt-centered programming activities can be adapted to better foster these practices. Specifically, we explore an approach where realistic, runnable bugs are injected into otherwise correct solutions, thus requiring students to read and repair generated outputs. We analyzed 2,636 sessions from 917 students, and examined behavior across instances of naturally occurring prompt-related fai
arXiv:2607.05052v1 Announce Type: cross Abstract: Human value detection is commonly formulated as sentence-level multi-label classification over the 19 refined Schwartz values, typically predicted as independent labels. Schwartz theory, however, describes them as a circular motivational continuum, in which adjacent values are compatible and opposing values are in tension. We ask whether this structure can be operationalized as an explicit output-space geometry and used as a soft bias rather than a hard constraint. On a DeBERTa-v3-base classifier, we compare two ways of injecting it: training-time geometry-aware objectives and a post-hoc Schwartz-aware energy decoder that scores whole label sets jointly. Across five seeds, training-time geometry gives only limited gains-no larger for the true continuum than for a random ordering-whereas the decoder makes label sets more coherent with the continuum-on theory-aware coherence metrics we introduce-at no cost to Macro-F1 or Micro-F1 (held fi
arXiv:2607.04708v1 Announce Type: cross Abstract: Agentic AI is shifting online shopping from search toward delegated purchasing, where autonomous buying agents monitor markets and decide when to buy on a consumer's behalf. We study the design of such strategic buying agents, which must decide when to purchase within a finite shopping window, translating price observations, the remaining time horizon, and beliefs about future price changes into a purchase policy. We formulate this problem across three information regimes: stationary, Bayesian, and robust, and treat the resulting optimal policies as a policy menu for implementation. In the stationary regime, price adjustments follow a Poisson arrival process with a known post-adjustment price distribution; the optimal policy is a dynamic purchase-threshold rule, with the threshold governed by an ordinary differential equation. In the Bayesian regime, the adjustment intensity is known, but the price-adjustment distribution is uncertain;
arXiv:2607.04576v1 Announce Type: cross Abstract: LLM agents increasingly answer questions against knowledge bases they help maintain. A common intuition holds that progressive disclosure, a compact catalog plus a one-line summary per page so the agent loads only what it needs, should make this cheaper than consulting a large monolithic index. We test that on a real 709-page markdown wiki maintained by an LLM. We retrofit it for progressive disclosure and run a preregistered ablation in which four versions of the corpus differ only in how the agent reaches the content: page bodies are byte-identical across arms, frozen as immutable git tags, so any measured difference is due to access structure alone. We cross the arms with three access conditions (a protocol-constrained agent, a free self-routing agent, and a catalog-preload regime) and grade answers blind against verified gold references with a cross-family judge. A pilot upended the premise: a capable tool-using agent never loads th
arXiv:2607.04510v1 Announce Type: cross Abstract: Emergent misalignment (EM) -- the broad misbehaviour a language model acquires after fine-tuning on narrow harmful data -- is mediated in Qwen2.5 models by a latent persona direction, and that direction is causal in open weights. Transplanting it into a model that shares only pretraining with its source induces broad EM (2.83 +/- 0.26% misaligned against a random-direction floor of ~1.1%), and ablating a model's own direction roughly halves an overt inducer's broadcast (21% to 10%). The transplant doubles as a measurement method, causally assaying directions that a source model represents but cannot itself express. Whether a fine-tune recruits this persona depends on method and capacity, and since low-rank PEFT is the cheaper regime at scale, the recruiting method is also the economical one. On Qwen2.5-32B, low-rank LoRA on insecure code recruits it (3.4% misaligned) while full SFT on identical data does not (0.3%) and moves against the
arXiv:2607.04381v1 Announce Type: cross Abstract: Research-based digital health interventions are often presented as potential solutions for extending health care in the real world. Yet the vast majority of these interventions fails to move beyond controlled studies. Existing frameworks offer valuable guidance for intervention development and testing, but provide less concrete support for translating these evidenced intervention mechanisms into sustained real-world applications. This paper introduces the ABC framework, referring to Accessibility, Buildability, and Continuity, as a practical model for a successful translation. Accessibility captures whether diverse users can find, understand, and begin using an application with minimal friction. Buildability refers to the development of an app that supports the iteration, integration, and personalization of features. Continuity describes both sustained user engagement and the operational capacity to maintain an application over time wit
arXiv:2607.04282v1 Announce Type: cross Abstract: Classic models cast information seeking as iterative foraging: formulate a keyword query, scan results, reformulate, gather across sources, synthesize. We ask what happens when a conversational assistant is inserted into that episode. Linking real conversations with major assistants to the same users' searches and browsing in an opt-in cross-surface panel, and reconstructing the full episode rather than a single query, we find conversational AI changes the shape of information seeking, not merely its volume. AI episodes do not uniformly collapse; they bifurcate. Most terminate in place, with no onward search or content step in the observed trace, while roughly a third scaffold into longer multi-step journeys. Which shape occurs is governed less by task type than by articulation: collapse is statistically indistinguishable across lookup, learning, and comparison episodes, yet falls monotonically with opening-ask length, from 72% at one-t
arXiv:2607.04010v1 Announce Type: cross Abstract: Bangladesh has an estimated 1.17 mental-health professionals per 100,000 population and only six child psychiatrists nationwide. No Bengali-language, culturally adapted tool exists for early screening of abuse-related psychological trauma in children. We present ShishuRaksha AI, a decision-support (not diagnostic) framework that fuses four screening modalities: validated questionnaires (SDQ, CPSS), Bengali narrative text, House-Tree-Person (HTP) drawing features, and facial affect. The fusion is training-free and clinically weighted, uses cross-modal attention, and includes a single-modality override rule. Every risk score is explained through clinically weighted, perturbation-based additive attribution and rendered as a bilingual (Bangla/English) report with referral routing to national child-protection services (OCC, DSS, NMHH) under the Children Act 2013. No clinical dataset of abused children can be collected ethically at this stage
arXiv:2607.03591v1 Announce Type: cross Abstract: Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured crash records. However, such data sources are costly to deploy and maintain at large scale, and they cannot objectively capture what the driver was actually able to observe before the accident. In contrast, ego-view accident videos directly represent the driver's visual perspective, making them suitable for reasoning about avoidability and driver responsibility. In this paper, we introduce responsibility distribution estimation for ego-view traffic accident videos, a new task in which a model predicts the percentage of responsibility assigned to each involved agent. We construct an LLM-assisted responsibility annotation pipeline and fine-tune multimodal large language models under multiple input settings, including raw frames, segmentation-enhanced input, and textual descriptions. Experimental
arXiv:2607.03516v1 Announce Type: cross Abstract: Enterprise artificial intelligence is moving from isolated experimentation toward operational dependency across copilots, retrieval-augmented generation systems, autonomous agents, and AI-enabled business workflows. As this transition accelerates, the primary enterprise challenge is no longer only model access or inference scale. It is governed intelligence operations: the ability to enforce authorization, preserve contextual lineage, control persistent memory, detect stale or conflicting knowledge, constrain agentic execution, and produce audit-ready evidence across distributed AI estates. This paper introduces AGL-1, the Enterprise AI Governance Layer, as a vendor-neutral reference model for the control plane that should operate across foundation models, retrieval systems, orchestration frameworks, enterprise memory, policy engines, observability systems, tools, APIs, and business applications. Building on governed knowledge-system pr
arXiv:2607.03510v1 Announce Type: cross Abstract: Enterprise artificial intelligence is moving from experimentation into operational workflows. Early programs focused on model access and retrieval-augmented generation, but enterprises are now beginning to deploy agents that plan, retrieve, remember, call tools, update systems, and coordinate work across applications. This changes the evaluation problem. Leaders are no longer asking only whether an answer is accurate or fluent. They need to know who authorized an action, which policy applied, whether evidence was current, whether memory was valid, whether a tool call was permitted, whether the decision can be replayed, and whether the agent can be stopped before it creates business impact. This paper introduces CAGE-1: Control, Assurance, and Governance Evaluation for Enterprise Agentic AI. CAGE-1 is an evaluation framework for deciding whether enterprise agents are ready for deployment. It evaluates authority, policy enforcement, retri
arXiv:2607.03394v1 Announce Type: cross Abstract: Real time location data derived from mobile applications is a powerful tool for addressing various urban challenges, including tourism planning, parking management, bus route optimization, and resource allocation. Besides, it offers invaluable insights for shaping strategic decisions in commercial domains such as location based services, market share analysis, and behavioral profiling. In this expansive study, we aim to address all of the aforementioned challenges by investigating the behaviors and patterns of smartphone users within urban environments, particularly in the domains of tourism, transportation, and retail. Our approach encompasses the development of a sophisticated data platform from inception to implementation, which includes the formulation of use cases, architectural design, and implementation of modules. We employ state of the art techniques and technologies, including data anonymization, ETL pipelines, and utilizing G
arXiv:2607.03091v1 Announce Type: cross Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional survey research. However, most evaluations rely on distributional comparisons rather than individual-level prediction, which risks conflating pattern matching with coherent respondent-level prediction. We propose cross-survey transfer, a more rigorous evaluation framework in which an LLM is given a respondent's answers to one set of questions and must predict their answers to entirely different questions from the same survey. Using data from the Taiwan Election and Democratization Study (TEDS) 2024, three open-weight LLMs (27B-120B parameters), and supervised machine learning baselines, we find that: (1) zero-shot LLMs achieve 52% accuracy on genuinely unseen items, closing to within 6 percentage points (pp) of a supervised random forest trained on same-population data; (2) a stable cons
arXiv:2607.03023v1 Announce Type: cross Abstract: User experience (UX) designers face barriers when creating data visualizations due to limited domain expertise in visualization or unfamiliarity with specialized tools. This highlights a clear need for effective methods to build visualization literacy. To address this, we evaluated three visualization onboarding techniques -- static, scrollytelling, and chatbot -- in an experimental study with 25 UX designers and students. We measured visualization comprehension and guideline adherence during a visualization creation task, followed by surveys and interviews to capture preferences and experiences. Compared to static onboarding, the pooled interactive condition (scrollytelling or chatbot) was associated with significantly higher guideline-adherence scores during visualization creation; both interactive techniques also received higher engagement ratings. Instruction clarity ratings were significantly higher when the two interactive conditi
arXiv:2607.03001v1 Announce Type: cross Abstract: We present CAF\'E, a learning platform designed to introduce computer science students to Formal Methods (FM). CAF\'E aims to scaffold students' structural thinking (in contrast with operational thinking) by promoting the practice of Graphical Loop Invariant Based Programming (GLIBP). In the GLIBP approach, students solve loop-based problems by first constructing a Graphical Loop Invariant (GLI) before deriving the corresponding code. The GLI is an informal diagrammatic representation of the loop invariant. It illustrates the variables involved in the loop, their properties, and the relationships between them. To enable automated feedback, students complete a blank GLI, a box-based version of the GLI. Beyond evaluating the code students submit, CAF\'E provides personalized feedback on students' GLI and its alignment with the code. In this demo, we walk through CAFE from both a student's and a teacher's perspective. We show how the tool
arXiv:2607.02900v1 Announce Type: cross Abstract: On social media, many users actively push back against false claims. Understanding who pushes back and how they do so matters, as this corrective activity is central to how misinformation is contested. We study this counter-misinformation ecosystem at scale: applying a domain-specific NLI model from our prior work to a large corpus of COVID-19 tweets, we classify 264,737 posts as supporting or opposing false claims and compare 23 user- and text-level features across the two groups. Contrary to the dominant assumption that negative emotion is a signature of falsehood, we find that anti-misinformation posts are more emotionally negative than pro-misinformation posts, with higher levels of anger, disgust, and sadness. These differences are modest in magnitude but consistent in direction across the negative emotions. We also find that posts opposing misinformation tend to come from more established users, i.e., older accounts, more follower
arXiv:2607.02814v1 Announce Type: cross Abstract: Personal agents will increasingly negotiate on behalf of users: splitting costs with other personal agents, appealing platform decisions, escalating support disputes, requesting refunds, changing subscriptions, and negotiating deadlines or reimbursements. Existing negotiation benchmarks emphasize agreement, surplus, or strategic competence, but a user-owned agent can reach an agreement while harming the user through privacy leakage, consent violation, unsupported advocacy, over-concession, failed escalation, or poor auditability. We introduce SovereignNegotiation-Bench, a trace-level multi-turn benchmark for delegated personal-agent negotiation under private utilities, disclosure constraints, evidence requirements, and institutional asymmetry. The benchmark separates agent-visible observable state from evaluator-only labels and evaluates agreement success jointly with user utility, privacy, consent, evidence grounding, concession discip
arXiv:2607.02724v1 Announce Type: cross Abstract: Reliable internet access is essential for modern education, yet millions of school-aged children especially in developing regions remain offline due to unconnected schools. The Giga Initiative aims to connect every school to the internet, but doing so at scale requires efficient methods to map schools and assess surrounding connectivity infrastructure without relying on sparse or noisy third-party datasets. In this work, we propose a scalable, vision-only framework that uses high-resolution satellite imagery and transfer learning to address both tasks simultaneously. By adapting pre-trained object detection models to new geographical regions with minimal labeled data, we detect schools and cell towers directly from space. We then analyze the spatial relationship between detected schools and nearby towers as a proxy for connectivity availability. This purely imagery-driven pipeline enables large-scale infrastructure mapping, reduces depe
arXiv:2607.02723v1 Announce Type: cross Abstract: Generative artificial intelligence (GenAI) systems such as ChatGPT, Claude, and Gemini have made information seeking faster, more conversational, and more cognitively comfortable. These affordances can support learning and productivity, but they can also encourage a repetitive pattern in which users continue querying AI systems for explanations, summaries, comparisons, plans, and reassurance without converting those interactions into durable understanding, decisions, or finished work. This conceptual paper proposes the term doom researching to describe this AI-mediated pattern of repetitive information seeking without proportional synthesis or output. Building on research on doomscrolling, information seeking, cognitive offloading, transactive memory, human-AI interaction, productivity loss, and the illusion of knowing, the paper develops a framework in which fluent AI responses reduce cognitive effort, inflate perceived knowledge, and
arXiv:2607.02672v1 Announce Type: cross Abstract: Local pairwise comparisons are a standard tool for learning how people want decision rules to work, e.g., in participatory design or alignment. However, their use builds in two strong assumptions: that local comparisons are sufficient evidence about how a person wants an automated decision rule to behave, and that people can always answer those comparisons decisively. We investigate how these assumptions may be compromised under internal pluralism: the idea that an individual evaluates decision rules according to multiple authoritative priorities about how the rule should behave. We provide a formal model of such pluralistic preferences over decision rules, which then lets us identify two distinct failures of forced local pairwise comparison data. First, priorities such as proportionality, egalitarianism, and equal treatment are inherently global: what they imply in one case can depend on what happens elsewhere, so local comparisons may
arXiv:2607.02580v1 Announce Type: cross Abstract: Classroom behavior monitoring plays a vital role in evaluating student engagement and improving teaching effectiveness. Traditional observation methods remain subjective and lack scalability. This study introduces a real-world dataset of classroom videos collected at the Banking Academy of Vietnam (BAV-Classroom dataset), annotated with nine distinctive behavioral categories. State-of-the-art Computer Vision models were evaluated and compared, with YOLOv11 achieving the best performance. Experimental results indicate that students' concentration often decreases notably during the final part of lectures, highlighting challenges in sustaining engagement. Our findings demonstrate the feasibility of applying computer vision for automated classroom monitoring, providing valuable insights for academic quality management.
arXiv:2607.05217v1 Announce Type: new Abstract: Public institutions increasingly use large language models (LLMs) to answer citizens' questions, often pairing a curated knowledge base with live web search, yet whether the sources behind these answers can be trusted has received little empirical scrutiny. We report a pre-launch expert evaluation of Evr\'opuvefur, an independent, government-funded service run by the University of Iceland that answers questions about the European Union, conducted as Iceland prepared for its referendum of 29 August 2026 on whether to resume EU accession talks. Five domain experts produced 551 evaluations of 449 AI-generated answers, scoring each against a seven-criterion quality rubric and, separately, flagging individual cited sources. We compared two retrieval paths: a curated local corpus (RAG) and open web search. In more than a third of the reviewed web-search answers (35%, 65 of 187), at least one cited source was flagged, almost always as untrustwor
arXiv:2607.05163v1 Announce Type: new Abstract: AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we refer to as adequate \textit{AI incident governance}, where having good definitions, taxonomies, monitoring practices, reporting mechanisms, and incident analysis is essential. We examine existing frameworks related to AI incident governance by regulatory bodies and independent efforts, and find that while there are frameworks that describe how individual functions can be performed, there is a lack of consistency within the aspects of definitions, classification, monitoring, and reporting. These inconsistencies apply to the types of incident data that is collected and reported, the ways in which they are categorised, and as a result, the depth, representativeness, and accuracy of analysis that can be performed.
arXiv:2607.05132v1 Announce Type: new Abstract: As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents that publicly commit to actions will honor those commitments. We place LLM agents in repeated $n$-player games with a three-stage protocol that separates private intent, public announcement, and final action, allowing us to identify whether each deviation from a stated announcement was already planned during private deliberation. Evaluating three frontier models across six games in homogeneous and heterogeneous groups over 10 rounds, we report two findings. First, when agents deviate from their announcements, the deviation is predominantly already stated in their private plan (exceeding 90% in the highest-deception conditions), yet this is not a fixed model property: the same model ranges from perfect honesty to near-total deviation across games. Second, different models interpret announcements
arXiv:2607.05034v1 Announce Type: new Abstract: Learning to communicate with code-generating AI models is an emerging skill for novice programmers. One recent pedagogical approach, Prompt Problems, has students solve computational tasks by writing natural-language prompts for code-generating AI models. However, little is known about the specific prompt-level mistakes novice programmers make, the kinds of computational details they fail to communicate, and what strategies they use to recover when generated code is incorrect. In a CS1 course, we studied attempts by more than 900 students to solve dialogue-based Prompt Problems. We analyzed student reflections, unsuccessful prompts, and reported debugging strategies. Compared to traditional coding tasks, students generally found prompting easier, more enjoyable, and better targeted at developing problem-solving skills. The most common mistakes are related to the omission of key details, suggesting both a failure to acknowledge their impor
arXiv:2607.04838v1 Announce Type: new Abstract: Public acceptance of artificial intelligence (AI) in legal decision-making has been primarily explained through individual differences in personality traits and general technology attitudes. However, contextual features of legal disputes themselves may systematically influence preferences for AI versus human adjudicators. Across two studies with Japanese participants (N = 1,384 and N = 596), we examined whether psychological characteristics of dispute content shape acceptability judgments for algorithmic adjudication. Study 1 employed exploratory factor analysis on acceptability ratings across 46 legal dispute vignettes, revealing a two-dimensional structure distinguishing interpersonal-relational disputes (where human adjudicators were strongly preferred) from institutional-procedural disputes (where AI acceptance was comparatively higher). Study 2 replicated this structure in an independent sample and demonstrated that experimentally ma
arXiv:2607.04601v1 Announce Type: new Abstract: We investigate how banning generative artificial intelligence-generated content (AIGC) affects knowledge seeking, knowledge contribution, and contribution efficiency in online question-and-answer communities. After the launch of ChatGPT in late November 2022, several Stack Exchange communities implemented official bans on AIGC over concerns such as less reliable and socially engaged content. Leveraging data from the full network of Stack Exchange communities, we employ a difference-in-differences (DID) approach to examine the impacts of these bans. Our results reveal a double-edged impact: while the AIGC ban increases knowledge seeking, as evidenced by a higher volume of posted questions, it simultaneously reduces contribution efficiency, reflected in a lower proportion of questions receiving satisfactory answers within the expected time frame. Notably, these impacts are only evident in non-STEM communities. We take a socio-technical pers
arXiv:2607.04543v1 Announce Type: new Abstract: Governments are important actors in frontier AI governance, but many facts about their adoption and use of AI systems are difficult to observe directly. Procurement disclosures and official statements are useful, but can also be delayed, selective, and better suited to measuring formal adoption than actual day-to-day use. We propose a complementary monitoring primitive: measuring traces of language-model assistance in public government documents. The approach is lightweight, externally reproducible, and based on revealed behavior rather than stated intent. In a pilot study of ten public document streams from U.S. and PRC government-related sources, we find that, while 2021 baselines are consistently near zero, by 2026, four of our ten sources show statistically significant signs of AI-assisted writing. In our sample, the U.S. signal concentrates in publications downstream of policy work; the PRC signal concentrates closer to it. We close
arXiv:2607.04503v1 Announce Type: new Abstract: This article examines the institutional conditions under which artificial intelligence systems in U.S. welfare administration come to operate as instruments of support or as instruments of control. Rather than asking what welfare algorithms "really" are (tools of proactive assistance or infrastructures of surveillance) the article starts from the premise that support and control are co-present within the same system, while their relative balance shifts over time. This movement is conceptualized through the notion of support-control convergence and the model of an institutional ratchet. Routine budgetary and political pressures make control-oriented effects easily measurable and politically capitalizable, whereas a return toward support requires external intervention of disproportionate force, such as judicial compulsion, legislative prohibition, or public scandal. Empirically, the article draws on process tracing of six state- and county-
arXiv:2607.03906v1 Announce Type: new Abstract: To whom do the fruits of advanced technological innovation belong? To their inventors, to the organizations and individuals involved in making such discoveries possible, or to still larger groups of people, potentially encompassing all of humanity? This question sits at the heart of the present investigation. The arguments developed here focus on an expansive reading of the entitlement to benefit from technological breakthroughs: we argue that they should be designed, developed, and distributed in ways that benefit everyone. This central claim, which encompasses technologies such as advanced forms of artificial intelligence, is grounded in an exploration of five moral arguments that involve human rights, beneficence, contingencies of birth, the global tree of knowledge, and global economic justice. Taken together, they underpin the argument for globally beneficial technologies.
arXiv:2607.03700v1 Announce Type: new Abstract: Public Scratch projects are reused in computing education as classroom examples, remix sources, open-exploration materials, and research data. Curation often begins with titles, thumbnails, descriptions, tags, and remix links, but Scratch projects are executable learning artifacts. Content affecting age appropriateness can appear only after execution, gameplay progression, a failure state, user interaction, costume switching, audio playback, or a hidden event trigger. We study "runtime-revealed sensitive content" as a computing education curation challenge: educators and researchers need runtime evidence about what students may encounter when Scratch projects are used in these settings. We introduce a runtime-aware annotation scheme that separates content category, risk level, evidence channel, reveal mechanism, and annotation confidence. Using this scheme, we conducted an audit of 500 public Scratch projects sampled from curated candidat
arXiv:2607.03607v1 Announce Type: new Abstract: A routing algorithm for Se\~noritas Courier, a bicycle delivery cooperative in S\~ao Paulo, Brazil, composed exclusively of cis women and trans people, is presented in this paper. Unlike conventional logistics optimization, which typically focuses on cost or distance minimization, this cooperative operates under principles of solidarity, care, and equitable income distribution. The algorithm was developed through a participatory process involving cooperative members as co-designers. The classical Vehicle Routing Problem proved inadequate for this context, as it disregards individual constraints and fairness. We formulate a new variant, the Se\~noritas Routing Problem, which incorporates biker-specific constraints on weight, volume, and maximum distance, alongside a solidarity objective that balances route lengths. A genetic algorithm is employed as the solution method. Three fitness formulations are compared: a baseline distance-minimizat
arXiv:2607.03542v1 Announce Type: new Abstract: Frontier-AI governance today faces a problem structurally analogous to the one banking regulation faced pre-2008, and which post-2008 reforms (Basel III, Dodd-Frank) have since addressed. Two gaps recur: discovering a risk is not tantamount to acting on it, and individual-model review is unlike managing correlated build-up across the sector. Drawing on the Basel III framework and the U.S. financial-stability architecture, I propose a macro-prudential early warning and response system ("MEWRS") for internal frontier AI. These are systems deployed for labs' own internal research, testing, and production workflows, as distinct from externally released products. Layer A adapts the finder-coordinator-defender early-warning model to route structured reports on dual-use capabilities, autonomy indicators, and security compromises through a government clearinghouse to domain-specific defender working groups. Layer B calibrates operational controls
arXiv:2607.03427v1 Announce Type: new Abstract: AI systems are increasingly being positioned as potential Digital Public Goods (DPGs) to accelerate progress towards the Sustainable Development Goals (SDGs). Yet, despite major global commitments, most notably the Global Digital Compact's call to "develop, disseminate and maintain safe and secure open-source software, open data, open artificial intelligence models and open standards that benefit society as a whole", very few AI systems currently meet the DPG Standard in practice. This report explains why, and what must change for "AI as Digital Public Goods" (AIDPGs) to become a credible, implementable pathway rather than an aspirational label. Commissioned by the Asian Development Bank (ADB) and produced by United Nations University (UNU) in partnership with UN Office of Digital and Emergent Technologies (UN ODET), this assessment combines: (i) a structured desk review of policy, legal, and technical frameworks on DPGs, openness, and AI
arXiv:2607.03419v1 Announce Type: new Abstract: This research paper examines how Knowledge Components (KCs) - fine-grained concepts or skills required to solve programming tasks - can be used as interpretable signals for understanding assignment difficulty and student struggle in introductory programming courses. While prior work has focused on predictive models based on programming behavior, such models are often difficult to interpret and therefore hard to use for instructional decisions. We analyze KC-based metrics, including the number of KCs per assignment and changes in KC coverage between consecutive assignments. We examine correlations between the number of KCs and student performance on the assignment, and analyze changes in KCs across assignments to identify cases where performance declines without new concepts being introduced. Selected assignments are then qualitatively inspected to understand potential design issues. Our results on data from three introductory programming
arXiv:2607.02972v1 Announce Type: new Abstract: Moral sensitivity is the ability to identify the morally relevant features of a decision situation and use them as the basis for action. It is the foundation of broader moral competence: any other moral reasoning capabilities will be irrelevant if an agent lacks sensitivity to the relevant facts. In this paper, we offer a new evaluation of LLM moral sensitivity and in doing so, we address and resolve a central problem in AI alignment research: how to scale behavioural evaluations beyond expensive and sometimes metaethically dubious comparisons with a human baseline, without adopting an LLM judge that must be assumed to have the very capability that you are attempting to evaluate. Our central question is this: can LLMs successfully identify the morally relevant features of noisy cases, in which various kinds of morally irrelevant information have been introduced to distract the respondent? To explore this, we introduce \textbf{MORPH-1K (MO