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.
Cori Space spent her freshman year of high school in Madison, Alabama, trying to keep up with her coursework while managing her celiac disease and other health problems that her school wouldn’t accommodate. For her sophomore year, her family opted for the Alabama Destinations Career Academy, a public, virtual school that promised a jump-start on […] The post Online career training is growing – does it work? appeared first on The Hechinger Report .
A new class action lawsuit accuses Oura of overstating its smart ring’s sleep tracking accuracy, arguing the device relies on AI-generated estimates rather than the clinical measurements needed for true sleep staging. The case is reigniting questions about how much precision consumers should expect from AI-powered health wearables. The post Why Oura’s Sleep-Tracking Lawsuit Is Really About Trust appeared first on MedCity News .
On this episode of This Week with EdSurge, unpacking new federal edtech guidance for schools.
Educators must shift their focus from preventing cognitive offloading to preserving the critical thinking skills AI threatens to replace.
arXiv:2608.23566v2 Announce Type: cross Abstract: Group-based reinforcement learning methods such as GRPO for large language models avoid training a critic by sampling multiple responses for each prompt. A reliable critic could instead estimate token-level advantages from one response, but standard critic-based training recipes are often unstable. We study this instability and develop **Best Practice Critic Optimization (BPCO)**, a recipe that combines DPPO, value predictions bounded to the reward range, Monte Carlo value targets, unnormalized policy advantages, and length-adaptive generalized advantage estimation. Because the critic is used only during training, BPCO can also condition it on reward-defining information, such as a reference answer or grading rubric, that is hidden from the policy. Controlled experiments isolate the effect of each design choice. Across mathematical reasoning tasks with models ranging from 1.5B parameters to 30B-A3B mixtures of experts, BPCO improves a s
arXiv:2608.23023v2 Announce Type: new Abstract: An LLM router picks which model should answer each query. The appeal is that models fail on different questions. Whatever single model is best overall still gets some wrong, and another model in the pool gets many of those right. Getting that choice right every time is the ceiling, and a router is an attempt to approach it. However, recent work reports that routers do not get close. Across 21 routing methods on five benchmarks, sharply different designs land within a fraction of a point of each other, and all of them stay far below that ceiling. Learned routers often fail to beat simply always calling the strongest model. We ask what those missed questions have in common. We set fourteen models to answer all 294 questions, with 7 task types across 3 languages: Korean, English and Hindi. We ran the whole matrix twice, changing nothing, but 5.37% of the 4,116 model-question pairs came out scored differently anyway. Run-to-run movement like
arXiv:2608.22872v2 Announce Type: new Abstract: Speech-based applications pass spoken queries through automatic speech recognition (ASR) before any retrieval module, so ASR errors enter the pipeline as a fixed upstream constraint. We empirically test whether two extensions to standard retrieval-augmented generation (RAG), entity-graph linking and iterative reformulation, absorb or amplify these errors. Using four English accents synthesized through neural TTS, we evaluate four RAG configurations on three multi-hop QA benchmarks (HotpotQA, 2WikiMultiHopQA and MuSiQue) against a clean-text oracle. Although the structurally richer configurations generally retain higher absolute F1 under ASR input, both extensions amplify the error: the F1 gap from clean text to the highest-WER accent is 36-67% larger under their combination than under naive dense retrieval, on all three benchmarks. The dominant failure mode is corruption of one or more query entities, accounting for 87-96% of degradation
arXiv:2608.23000v2 Announce Type: replace-cross Abstract: Fully online embodied learning requires synaptic adaptation to acquire new behaviors while preserving previously learned dynamics during ongoing interaction. We extend the Predictive-Coding-inspired Variational Recurrent Neural Network (PV-RNN) to continuously adapt its synaptic weights and propose Free-Energy-Gated Plasticity (FEGP), which regulates the effective learning rate according to variational free energy. In real-time physical human-robot interaction, a randomly initialized network acquired three cyclic motor patterns without offline pretraining, replay, or task-boundary signals, with all three patterns emerging in autonomous rollouts. Controlled experiments over ten randomized teaching streams and five network initializations per stream showed that FEGP substantially improved repertoire coverage and retention of previously acquired patterns after they left the recent observation window. Neither a constant learning rat
arXiv:2602.11684v2 Announce Type: replace-cross Abstract: As Large Language Models increasingly power role-playing applications, simulating patients has become a valuable tool for training counselors and scaling therapeutic assessment. However, prior work remains fragmented: existing approaches rely on incompatible, non-standardized profiles, prompts, and evaluation metrics, hindering reproducibility, fair comparison, and reuse. We introduce PatientHub, a unified and modular framework that standardizes the creation, simulation, and evaluation of LLM-based patients. Our framework provides 16 patient simulators, a graph-based orchestrator for multi-turn, multi-session interactions, and a configurable LLM-as-a-judge evaluator that supports multiple rubric types. Via our command-line interface, users can generate patient profiles, run simulations, and apply rubric-driven evaluation at the turn and session level. To demonstrate PatientHub's utility, we compare several supported simulators u
arXiv:2312.17535v2 Announce Type: replace-cross Abstract: In the past two years, the outstanding performance of ChatGPT in multilingual and multitasking has led to large language models (LLMs) attracting widespread attention. However, restricted by expensive costs, many studies have to focus on the ability of only one major language. How can we quickly improve the model's capabilities in new languages without reducing its original capabilities under limited data and computing power? In this work, we focus on improving the Chinese mathematical reasoning capability based on Llama-2-13B, which is weak in Chinese mathematical reasoning. We proposed the Mathematical Chain of Thought method (Olapa-MCoT). First, we propose Similarity RRHF (SimRRHF), which adds the constraint of model optimization direction by introducing similarity loss based on RRHF. Furthermore, the novelty Incorrect Data Relearning (IDRL) method is designed, which improves the model's ability to learn difficult knowledge.
arXiv:2308.06783v2 Announce Type: replace-cross Abstract: Virtual Reality (VR) applications couple software behavior with head-mounted displays, body movement, spatial interaction, and multimodal feedback. Consequently, familiar software-quality problems can have distinct consequences in VR, yet empirical knowledge of the quality concerns expressed by VR users remains fragmented. We analyze 1,656,968 public reviews of 28,745 VR applications across seven app stores. Our semiautomatic workflow uses five unsupervised methods to generate candidate aspects and multi-round human open and focused coding to construct and refine a hierarchical model of user-perceived VR software quality. The resulting model comprises 12 quality attributes and ten influencing factors. It distinguishes conventional attributes whose consequences change in VR from attributes closely tied to VR, including multisensory perception, user-friendly interaction mechanisms, immersivity, and comfort and safety. The influenc
arXiv:2507.16013v2 Announce Type: replace Abstract: The EU AI Act places teachers in charge of using high-risk AI safely in their classes, which requires them to assess AI-generated outputs. Feedback is one of the most consequential of these outputs, yet little is known about pre-service teachers perceptions of AI-generated feedback. In a randomised experiment, 273 pre-service teachers each received one of 30 written feedback messages on a mathematics learning goal, produced under identical instructions by an expert, a peer, or a large language model (LLM). Without knowing the source, the participants judged who had written the message, rated six feedback perception subscales, and revised the learning goal. Source judgements were inaccurate (peer 46%, expert 40%, LLM 36%) and followed message length, not coded feedback quality. LLM feedback received more positive evaluations when ascribed to a human source. Ratings did not differ between feedback ascribed to experts and to peers. Relat
arXiv:2608.24730v1 Announce Type: cross Abstract: Emotion preference learning uses pairwise comparisons between candidate descriptions to align multimodal large language models (MLLMs) with human judgments of open-ended emotion descriptions and to train reward models that capture human emotional preferences. However, conventional pairwise supervision is often sparse, typically providing only a single negative description for each positive description, and therefore offers limited coverage of the diverse ways in which an emotion description can be incorrect. In particular, models may be insufficiently exposed to semantically fluent but emotionally inconsistent descriptions. Beyond this data-level limitation, relying on a single MLLM judge introduces a distinct model-level concern: its judgments can be affected by model-specific biases when interpreting fine-grained or ambiguous multimodal emotional cues. To address these limitations, we propose Error-Augmented Preference Optimization (E
arXiv:2608.24669v1 Announce Type: cross Abstract: As mobile phone adoption has surged, so have scams involving these devices. One such scam, known as SMiShing (or smishing) after Short Message Service (SMS), involves fraudsters sending phishing links via mobile texts. Despite the prevalence of SMiShing, there is a lack of data on who is most vulnerable to these attacks. Prior research on phishing (its email counterpart) suggests that susceptibility may vary by demographic and contextual factors. In two large-scale surveys, we use a previously published simulation method to collect data from representative samples of U.S. adult mobile phone users. Our findings indicate that younger individuals and college students are particularly vulnerable. Participants struggled to correctly identify legitimate messages, with the second study providing comparisons of financial message variants. Researchers, regulators, and telecoms can help users by creating mobile-specific interventions for under-24
arXiv:2608.24535v1 Announce Type: cross Abstract: Data visualizations are widely used for communicating information, but they are also vulnerable to intentional manipulations that induce misleading interpretations. Existing methods focus on locating tampered regions or recovering hidden information, without explaining how the visualization has been manipulated or why the resulting changes may mislead viewers. We propose \textbf{VizAnchor}, a framework for visualization manipulation understanding through dual-anchor evidence construction and VLM-based reasoning. In the first stage, VizAnchor constructs a semantic anchor to recover authentic chart information and a spatial anchor to localize tampered regions. In the second stage, three specialized agents decode the manipulation. The misleader grounding agent analyzes a four-panel visual prompt to predict the misleader information. The chart narrative reconstruction agent takes the original and tampered charts as inputs and reconstructs t
arXiv:2608.24340v1 Announce Type: cross Abstract: The prediction of student engagement from the online tutoring videos is difficult because engagement is a multidimensional construct comprising distinct behavioral, emotional, and cognitive states. A reliable prediction requires bringing together different types of behavioral signals as well as expressive cues. Through our analysis of the CASED dataset, it is clear that engagement prediction gets even harder due to the high inter-person variability as well as the subjectivity of the engagement annotation. To tackle these challenges, we develop a multimodal framework that integrates the implicit spatiotemporal features extracted from pretrained video, audio, and image encoders along with structured behavioral modalities like head pose, gaze, facial action units, emotion, and wavelet-based audio features. We integrate these modalities via a Perceiver IO latent bottleneck. Moreover, student and instructor personalities are modeled as varia
arXiv:2608.24189v1 Announce Type: cross Abstract: Memory systems for conversational LLMs are conventionally evaluated by direct, fact-seeking questions about prior dialogue (Direct QA): can the model recall fact X from a prior conversation? We tested whether higher Direct QA accuracy correlates with higher user satisfaction in a 4-month deployment (40 users, 1,872 sessions, 7 memory conditions). Existing-benchmark Direct QA varies from 19.7% to 70.1% across the 7 conditions, but satisfaction does not change. We hypothesize that existing benchmarks and user satisfaction are tracking different capabilities: benchmarks measure elicited retrieval (recall when asked), while conversation requires natural integration (detecting relevance and naturally weaving prior context into a response). To examine this, we introduce MemUse, a set of real user-cued memory moments drawn from the deployment, scored by an integration-aware judgment of the natural conversational response. Holding the model and
arXiv:2608.24169v1 Announce Type: cross Abstract: 3D geometry editing is a critical yet labor-intensive part of the graphics pipeline, requiring artists to translate creative intent into precise operations in complex professional software. Large language models (LLMs) have shown promise for script-based 3D creation, but script generation is less suited to perception-driven editing of arbitrary existing meshes, where execution must remain visually grounded and untouched regions should be preserved. We present a \emph{visual-centric}, training-free multi-agent system that edits existing 3D meshes directly in Blender by emulating the iterative workflow of human artists. Rather than generating scripts or regenerating geometry, our system operates through the Blender GUI: multimodal LLM agents observe the viewport, reason about the current mesh state, and execute localized edits through simulated user interactions. Experiments on a curated benchmark provide initial evidence that this agenti
arXiv:2608.23994v1 Announce Type: cross Abstract: Human-robot teaching focuses on enabling nontechnical experts to customize robots according to their needs after deployment. With recent advances in machine learning, human-robot teaching is no longer confined to offline learning where the data gathering step from a human teacher is separated from when the robot learns. Instead, more recent approaches for human-robot teaching focus on coupling human teaching with robot learning. This coupling impacts the structure, timing, and content of the teaching and learning interaction. However, it is currently unclear how such coupling dynamics affect humanrobot teaching effectiveness and human perceptions towards the teaching process. Informed by human learning theories, in this paper we propose a new scale for classifying human-robot teaching interactions according to coupling dynamics present between the human teacher and robot learner. We apply this scale to a subset of the human-robot teachi
arXiv:2608.23780v1 Announce Type: cross Abstract: LLMs are being used increasingly to measure aspects of student discourse (e.g. talk moves, collaboration, equity of voice) at scale. Typically, LLM-based measures of student talk use transcriptions of classroom conversations that only include verbal contributions, which de-contextualize student language. Common practices for validating these measures include comparing outputs against expert annotations by adults, using held out evaluation sets and F1 scores. We argue that these approaches are insufficient to ensure that such measures are meaningful and equitable for teaching and learning, particularly for racially and linguistically marginalized youth. In order to center the youth whose talk is being analyzed, re-contextualizing these classroom conversations and engaging youth in the research process is necessary. Sharing epistemic authority with youth, ultimately, centers their point of view and adds crucial nuance to the analysis of t
arXiv:2608.23642v1 Announce Type: cross Abstract: AI agents pose significant risks as they are granted increasing autonomy. A commonly proposed solution is human oversight and keeping a ''human in the loop'', but this is not a simple solution: Not only do current approaches to AI agent design impede effective human oversight, but the cognitive capacities required for it are also themselves degraded by extended use of AI systems. This position paper argues that current approaches to the development and deployment of AI agent systems do not support effective human oversight -- they contribute to its degradation. To address this, a top priority in the advancement of AI agents should be supporting the situated goals and cognitive requirements of effective human oversight, treating the human needs of overseers at the same level of importance as AI agent capability. To put this idea into practice, we connect work on automation and human-computer interaction to AI agent processes, outlining d
arXiv:2608.24789v1 Announce Type: new Abstract: Color discrimination is a fundamental aspect of visualization as it influences how people interpret visual encodings. Many visualization guidelines are informed by perceptual studies, yet relatively few have been replicated. Acknowledging that the interaction between human perception, visual tasks, and display technology can change over time, we replicate two crowdsourced color discrimination studies conducted 10 years earlier. Specifically, we replicated a visualization-focused color discrimination task (N=144) and a more general perceptual discrimination task (N=394). In both studies, our results reproduced the original perceptual effects. We further use the replication to investigate whether color-related practice influences color discrimination. Specifically, we extended our replication studies by adding questions about participants' engagement with color practices. We then examined whether diverse color-related practices (e.g., artis
arXiv:2608.24555v1 Announce Type: new Abstract: Prehospital stroke assessment aims to accurately identify stroke symptoms and make rapid decisions through standardized procedures within an extremely narrow time window, thereby saving valuable time for subsequent treatment. In clinical practice, FAST-based scales are widely used for prehospital stroke assessment by issuing instructions that guide subjects to perform specific actions to screen facial, arm, and speech functions. However, in home and community settings, non-clinical users often encounter challenges such as inaccurate descriptions, incomplete symptom observation, and difficult operational procedures, which may lead to inaccurate or biased assessment results. To address these challenges, this paper presents StrokeGuard: a multi-agent guided system designed for prehospital stroke assessment that makes mobile FAST screening more standardized and executable. Specifically, to overcome the limitations of traditional single-agent
arXiv:2608.24367v1 Announce Type: new Abstract: This chapter examines AI-media objects - creative and artistic outputs generated through generative artificial intelligence in the form of text-to-X tools - in relation to three avant-garde movements of the twentieth century: Dadaism, Surrealism, and Conceptual Art. Drawing on Lewis Carroll's Through the Looking-Glass as an early precursor to these three movements and to anti-rationalist aesthetics, and on three case studies in AI-generated conceptual architecture - Matias del Campo's "Deep House" and Hassan Ragab's "Post-Pharaonic Architecture" and "A State of Decay" - the chapter develops the concept of latent-surrealism. Latent-surrealism includes a set of aesthetic and methodological conditions inherent to creative AI-media objects. These include the use of readymade datasets reassembled through collage-like processes, the absurd as an aesthetic quality of machine hallucinations, and the decoupling of craft from artistic value. The ch
arXiv:2608.24329v1 Announce Type: new Abstract: On social media, we often encounter short-form video ads that employ cinematic editing techniques to evoke an emotional response. While AI tools are beginning to generate such cinematic ads automatically, we lack a fine-grained framework for evaluating these ads. In this paper, we first characterize social media video ad formats and identify cinematic ads as a recurring format in our corpus. We then analyze the duration, shot structure, audio and text elements, and editing techniques of cinematic ads to inform a two-stage generation pipeline in which an LLM first generates a shot plan and a video generation model renders the video. Using this pipeline, we generated 70 cinematic ads for 35 real brands and recruited professional video editors to critique their editing choices. From their critiques, we derive six dimensions of editing quality: narrative progression, audiovisual coordination and sound design, visual composition and graphics,
arXiv:2608.24297v1 Announce Type: new Abstract: LLMs are becoming part of everyday life, including for older adults (OAs). OAs often learn digital technologies with younger family members, who have traditionally served as "warm experts" providing trusted and personalized operational help. LLMs expand this role: family supporters may also help OAs judge appropriate uses, consider what information to disclose, assess the credibility of outputs, and decide when AI-generated advice is safe to act on. We conducted a formative qualitative study with six OAs and seven younger adults (YAs), using semi-structured interviews and scenario-based think-aloud activities. OA participants described using LLMs to lighten their recurring reliance on family, while preserving family as a selectively invoked support channel. However, because LLMs rarely produced visible operational breakdowns, YAs had limited signals for when support was actually needed. Instead, YAs relied on OAs' partial disclosures and
arXiv:2608.24224v1 Announce Type: new Abstract: Effective human-AI interaction requires systems that dynamically adapt to a user's behavior and evolving understanding. When users interact with Large Language Models (LLMs), these models typically respond to prompts without sensing the user's immediate reactions. This lack of communicative synchrony can lead to information overload or leave confusion unresolved in real time. In this paper, we introduce Aura, a framework that enables LLM systems to dynamically modulate output based on a user's evolving emotions. Aura's Perception Module continuously estimates the user's emotional state from facial expressions. Our Policy Module then selects interventions through a probabilistic belief model. Finally, Aura's Generation Module uses parameter-efficient Low-Rank Adaptation (LoRA) adapters to produce contextually tailored responses mid-turn during response generation. We evaluated Aura in a within-subjects user study (N=20) on information-seek
arXiv:2608.24157v1 Announce Type: new Abstract: Location-aware generative systems can now select historical archives and real-time contextual information based on a user's surroundings to automatically generate narratives for urban heritage walks. Yet when multiple sources jointly inform generation, existing systems provide neither a clear representation of how much content from each source actually appears in the output nor an operational means of measuring it. We introduce the Historical Grounding Ratio (HGR), defined as the proportion of claim-bearing information units in a generated narrative that are supported by historical archives. HGR turns the realized share of historical evidence in a narrative into a directly measurable design parameter. In GeoDrama, a mobile narrative system, we created three conditions that used a common retrieval procedure and comparable evidence-bundle sizes while varying the allocation of information from different sources during generation. We evaluate
arXiv:2608.24058v1 Announce Type: new Abstract: Designed artifacts are ontological, shaping, and at times limiting, what becomes possible or imaginable. One path toward mitigating such foreclosures is giving people power over how systems are designed and built. Despite decades of scholarship around systems that enable such authorship, these systems are often evaluated on whether or not they are usable, useful, or technically feasible, leaving questions of ontological boundary negotiation, unexamined. We design two open-ended probes that utilize a Wizard of Oz technique to enable the experience of training a personalized machine learning system on phenomena people define themselves. In a week-long exploratory study, participants use one of two probes in the course of their everyday lives. We identify four sites where ontological boundaries were negotiated; the boundaries of a phenomena, the subject as part of relations, what is signal and what is noise, and the objectivity of data. We o
arXiv:2608.23966v1 Announce Type: new Abstract: AI systems increasingly make joint recommendations for users with conflicting preferences. However, when reasonable aggregation rules support different actions, a further question arises: who may choose how those preferences are combined? We study this interaction-level problem as aggregation-rule authority. Using synthetic preference profiles and profiles constructed from empirical ratings, we conduct a controlled behavioral audit of three LLMs under three authority conditions: unspecified, explicitly retained by users, and delegated to the model. In cases where two witness rules supported different actions, models almost never committed when users retained authority, but committed in every delegated case. All three models executed both witness rules perfectly when directly instructed. Yet when authority was unspecified or delegated, their aggregation-consistent outcome distributions differed across models and preference settings. Togeth
arXiv:2608.23852v1 Announce Type: new Abstract: Effective color contrast in visual design is essential for content accessibility. While existing tools can identify contrast issues, they often operate in isolation from design workflows or are used as an afterthought. We present ColorA11Y, a system that supports designers in creating accessible content by providing just-in-time feedback and actionable recommendations throughout the authoring process to meet accessibility color contrast guidelines. Our system analyzes the visual properties of text and background elements and offers recommended changes, including text color adjustments, background modifications, and opacity changes. Through two user studies, we evaluate ColorA11Y's effectiveness. A user preference study (n=40) revealed varying effectiveness of different recommendations based on design context, while a qualitative study with designers (n=8) indicated a more seamless workflow experience in comparison to a baseline using a co
arXiv:2608.23820v1 Announce Type: new Abstract: On a 3D-printed tactile map, a building felt under the finger is an anonymous shape: touch alone cannot tell which footprint is which, and a spoken description cannot reliably point to one shape at one place. We present a web-based tool that lets a sighted helper click to add on-shape Braille labels to an already-generated map model, downstream of the geometry generator so that whoever knows the reader and the local Braille standard does the labeling. The tool offers click-based OpenStreetMap matching, hand-editable abbreviation that shrinks a name to fit a footprint, and print-safe dot geometry with a review step that catches anomalies before printing. We demonstrate it on five printed maps of different place types, from a downtown core to a college campus and a small dining mall. In formative sessions in which ten BLV readers compared an unlabeled print with an annotated one, four read Braille fluently, so we treat Braille as one output
arXiv:2608.23751v1 Announce Type: new Abstract: Transformed by digitization, everyday activities-paying bills, shopping, managing transportation-increasingly require older adults to navigate digital systems. To accomplish these digital activities of daily living (DADLs), older adults often rely on help that looks less like IT support-institutional, episodic, and product-oriented-and more like caregiving: relational, ongoing, and aimed at preserving their functional independence. We argue that this practice is technology caregiving and introduce a framework characterizing it along four dimensions: why support is needed, who provides it, when it occurs, and how it is delivered. Applying this framework, we then systematically review the literature on how older adults are supported in DADLs. From 3,381 unique records, 36 articles met the inclusion criteria. Findings show that technology caregiving involves burden, like traditional care, but is distinctly shaped as much by digital systems a
arXiv:2608.23727v1 Announce Type: new Abstract: Inter-annotator disagreement in real-time affect annotation is widely treated as stochastic noise. We challenge this view by modelling ordinal annotation as an implicit game-theoretic coordination process against an internalised population prior under a post-hoc majority vote. We present the Ordinal Annotation Game, a conceptual scaffold in which the mapping from individual effort to collective consensus is governed by the semantic abstraction of the target construct. We evaluate it across two experiments sharing identical interface software and a uniform sensitivity threshold: a controlled sensory tracking study and an in-the-wild engagement study. Sensory annotation yields a consensus-dominant regime where active updates reinforce agreement, whereas engagement annotation inverts into an effort-limited regime where more labelling penalises consensus. The payoff slope reverses sign under identical processing, showing that ordinal disagree
arXiv:2603.00708v2 Announce Type: replace-cross Abstract: Voting is the primary mechanism through which Decentralised Autonomous Organisations (DAOs) reach decisions. Although transparent, the voting process can be opaque: it can involve many interacting smart contracts. The nexus of the decision-making process can be relocated and the true voter demographic obfuscated. DAOs can also govern other DAOs, a process known as metagovernance. We present a method for identifying DAO-to-DAO metagovernance on the Ethereum blockchain. We focus on the links between DAOs and token contracts. We use a signature-matching algorithm to handle a variety of DAO frameworks and voting schemes. Once we establish token-to-DAO relationships, we gather and process voting data to produce a list of metagovernance relationships. We apply this algorithm to an initial set of sixteen DAOs and we extend the dataset as more DAOs are identified. We produce a metagovernance network with 61 DAOs and 72 metagovernance re
arXiv:2509.15959v2 Announce Type: replace-cross Abstract: Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque decision-making and poorly calibrated human-automation interaction remain key barriers to safe adoption. This article synthesizes 100 studies on automation transparency for Maritime Autonomous Surface Ships (MASS) spanning situation awareness (SA), human factors, interface design, and regulation. We (i) map the Guidance-Navigation-Control stack to shore-based operational modes, namely remote supervision (RSM) and remote control (RCM), and identify where human unsafe control actions (Human-UCAs) concentrate in handover and emergency loops; (ii) summarize evidence that transparency features (decision rationales, alternatives, confidence/uncertainty, and rule-compliance indicators) improve understanding and support trust calibration, though reliability and predictability often dominate trust; (i
arXiv:2512.06529v2 Announce Type: replace Abstract: Required broader coursework can secure participation without being recognised as legitimate computing knowledge. This study conceptualises this disconnect as a curricular legitimacy gap and tests an asymmetric appraisal model grounded in situated expectancy value theory. The model distinguishes curricular devaluation, judging broader coursework unnecessary or professionally irrelevant, from integrative intention, or willingness to reuse its learning. Survey data from 212 Computer Science and Engineering undergraduates in Bangladesh recruited through snowball sampling were analysed using robust structural equation modelling. Primary inference combined robust direct-path estimates with 5,000 respondent-level bootstrap resamples; alternative measurement, response-quality, and ordinal-estimator specifications were also examined. Perceived burden was positively associated with devaluation, which was negatively associated with intention. Th
arXiv:2509.25298v3 Announce Type: replace Abstract: This study investigates the shifting global dynamics of Artificial Intelligence (AI) research by analysing the trajectories of countries dominating AI publications between 2000 and 2025. Drawing on the comprehensive OpenAlex datasets and employing fractional counting to avoid double attribution in co-authored work, the research maps the relative shares of AI publications across major global players. The analysis reveals a profound restructuring of the international AI research landscape. The US and the European Union (representing EU27), once the undisputed and established leaders, have experienced a notable decline in relative dominance, with their combined share of publications falling from over 57% in 2000 to less than 25% in 2025. In contrast, China has undergone a dramatic ascent, expanding its global share of AI publications from under 5% in 2000 to nearly 36% by 2025, therefore emerging as the single most dominant contributor.
arXiv:2411.08908v2 Announce Type: replace Abstract: This study explores the intersection of technological innovation and environmental sustainability in the context of Bitcoin mining. With Bitcoin's growing adoption, concerns surrounding the energy consumption and environmental impact of mining activities have intensified. The study examines the core process of Bitcoin mining, focusing on its energy-intensive proof-of-work mechanism, and provides a detailed analysis of its ecological footprint, especially in terms of carbon emissions and electronic waste. Various models estimate that Bitcoin's energy consumption rivals that of entire nations, highlighting serious sustainability concerns. To address these issues, the paper unearths potential technological innovations, such as energy-efficient mining hardware and the integration of renewable energy sources, as viable strategies to reduce environmental impact. Additionally, the study reviews current sustainability initiatives, including e
arXiv:2608.24767v1 Announce Type: cross Abstract: As generative AI (genAI) systems become embedded in education, employment, healthcare, and creative industries, the impact and engagement among marginalized groups have become both a widespread discourse and a focus in scholarly research. As a starting point, we examine public discourse and empirical research to explore the impact of genAI systems on Black communities. We conducted a systematic literature review (SLR) of 91 empirical papers alongside a media discourse frame analysis of 28 public resources, applying Entman's framing theory to map how each corpus defines problems, attributes causes, and proposes treatments. Our SLR reveals that scholarly research concentrates heavily on technical bias detection, reducing Blackness to measurable variables rather than engaging with cultural practices, structural conditions, or Black knowledge systems. Our frame analysis reveals that public discourse attributes genAI-related harm to historic
arXiv:2608.24662v1 Announce Type: cross Abstract: Large language models (LLMs) are commonly evaluated under the assumption that their observable behavior is primarily determined by model weights, training data, alignment procedures, and user prompts. This view is incomplete. Modern inference pipelines may systematically modify the probability distribution produced by a model immediately before token selection, creating an additional layer of control between frozen weights and observed text. While controlled generation (e.g., PPLM, GeDi, DExperts, FUDGE) and text-watermarking systems (e.g., SynthID-Text) demonstrate the technical maturity of decoding- and logit-level interventions, the governance, security, and economic implications of an undisclosed inference policy remain comparatively underexplored. This paper examines the emergence of inference-time framing bias: the systematic modification of generated language toward political, ideological, institutional, or commercial frames via
arXiv:2608.24654v1 Announce Type: cross Abstract: Conversational AI systems (CAISes) continuously change through model releases, feature updates, safety interventions, and access-policy shifts, yet user perceptions are often studied as static snapshots. We conduct a long-term, large-scale analysis of Reddit discussions to examine how users perceive CAIS model release interventions across providers. By combining sentiment classification and thematic concept analysis, we show that CAIS perceptions are dynamic and intervention-sensitive. Anthropic exhibits the clearest positive release profile through Claude Code and product-model fit, OpenAI shows backlash-and-recovery dynamics around GPT-5 and GPT-5.1, Grok-3 is shaped by provider identity and political discourse, and DeepSeek-R1 combines engineering praise with concerns about censorship, access, and reliability. These findings show that model releases are not merely technical updates, but user-facing interventions that reshape sentimen
arXiv:2608.24127v1 Announce Type: cross Abstract: Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and spam calls -- 913 hours of audio and 330,956 transcribed turns from 5,780 distinct numbers -- collected over 54 days by an AI voice-agent honeypot that answered callers and kept them talking, and introduced in a companion data descriptor. We separate outright scams, which solicit sensitive information, from the larger stream of predatory but legal lead generation ("spam") that feeds them. Scam operations keep office hours (6.6x more calls per weekday than weekend day); thousands of disposable numbers run a small catalog of recycled scripts (thirty opening clusters, half the traffic in the top five); and callers solicit identity anchors -- a home address and a date of birth -- far more often than payment credentials, pressing through persistence and manufactured authority rather than overt
arXiv:2608.23979v1 Announce Type: cross Abstract: In a deliberative poll, once submissions outnumber what anyone will read, some mechanism chooses which arguments each voter sees, acquiring much of the decision; practice delegates it to opaque learned rankers, so a voter cannot recompute or contest the exposure that shaped their vote. We ask whether it can be a published rule over publicly recomputable evidence with parameters held by the voter, treating legibility as an admissibility condition on usable mechanisms, not an objective traded against accuracy. We formalise a poll over bipolar justification sets, judging a slate by reason coverage, the order it arrives in, and captured endorsement mass; we give seven checkable criteria for a civic recommender and a rule meeting them: a one-hop reversed endorsement flow parameterised by a relation-weight function. An agentic simulator records every slate at every vote, over about 17,000 seed-paired runs. Served slates fall 0.035 short of a
arXiv:2608.23968v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly being embedded into all facets of society, from search to education, industrial, and financial applications. These systems' carbon and water footprints raise important sustainability concerns, particularly with adoption rates exceeding 80% among university students, despite limited insight into the environmental impacts of individual usage. Eco-feedback interfaces offer a promising approach to encourage more sustainable behaviors, yet their role in shaping LLM users' sustainability awareness and decision-making remains underexplored. We design and deploy the interface that visualizes latency-carbon trade-offs during live LLM interactions. We study its use with undergraduate computer science students (N=89, ages 18-24), enrolled in a computing ethics course, providing an empirical look at how a technically sophisticated and values-oriented user population responds to sustainability-aware AI i
arXiv:2608.23817v1 Announce Type: cross Abstract: SHAP and LIME are now standard tools for interpreting black-box predictions, yet their outputs can vary substantially when the input is perturbed by small amounts of noise--a problem we observed firsthand in our previous work on food security in Madagascar (Ralinirina et al., 2025). This variability raises the question of whether such explanations can be trusted at all. We address it by constructing an auditing protocol that measures two properties of any post-hoc explainer: robustness (how stable the explanation is under input perturbation) and fidelity (whether the features deemed important actually drive the model's prediction). These two quantities are combined into a single Trust Score. We run the protocol on a multi-sectoral dataset from Madagascar (83 features, 253 records, 4 malnutrition classes) using three classifiers and two explainers, plus their regularized counterparts. The results are sobering: models with AUC above 0.99
arXiv:2608.23705v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly capable of generating text that challenges human performance in domains requiring creativity, yet evaluating creativity in LLM-generated content remains a significant challenge. Here, we investigate whether current automatic evaluation methods can reliably capture human judgments of creativity. We collect human evaluations of human- and AI-generated short stories from the WritingPrompts dataset across 11 dimensions of creativity, and compare these judgments with automated objective metrics and LLM-as-a-Judge evaluations. Our experiments reveal substantial misalignment between automatic evaluations and human assessments. In particular, LLM-based judges exhibit a systematic preference for AI-generated stories, consistently favoring their stylistic characteristics over the unpredictability and other qualities of human-authored texts. Furthermore, correlation analyses show that widely used autom
arXiv:2608.23640v1 Announce Type: cross Abstract: When a large language model (LLM) is asked to write a person's life, how much of what it writes actually happened? We present a scene-level case-study audit - the first quantified audit of LLM-generated autobiography against a subject-specific ground-truth corpus that we are aware of, based on an unsystematic literature search. The subject and the author of this paper are the same person: a 366-day "page-a-day" book of first-person anecdotal entries was drafted with a conversational LLM whose documented inputs were a template, two exemplar days, and each day's quote - not her corpus - and every day was subsequently audited at the anecdote-scene level against an independent verification corpus using a four-level rubric fixed before analysis. We define the verification-failure rate as the share of days not rated VERIFIED (scene positively corroborated): 354 of 366 days fail, 96.7% (Wilson 95% CI 94.4-98.1%). Only 12 days contain a corrobo
arXiv:2608.23617v1 Announce Type: cross Abstract: We introduce adoption telemetry: a method for measuring enterprise AI adoption by computing change-management stage-progression directly from production usage signals. We contribute (1) a framework unifying pre-deployment evaluation gates, production telemetry, and change-management staging into one instrumented system; (2) NANTE, a concrete five-stage operationalization with defined telemetry thresholds, published openly so they can be tested and disproven; and (3) an open-source reference implementation that distinguishes a healthy cohort from five characteristic adoption-failure modes on synthetic populations with known ground truth. We are explicit that the thresholds are proposed constructs requiring empirical validation against real outcomes -- a research agenda we outline -- not a calibrated model.
arXiv:2608.24778v1 Announce Type: new Abstract: Cybersecurity education is facing more challenges as AI-driven attacks are becoming increasingly realistic and difficult to detect. Traditional video-based cybersecurity training in higher education often suffers from both low engagement and limited effectiveness. This dilemma motivates educators to explore innovative approaches, such as AI-powered gamification, which can deliver engaging, meaningful, and personalized learning experiences. By presenting content in a more interactive and user-friendly way, these methods have the potential to significantly improve both learner engagement and educational outcomes. This paper explores AI-powered gamification in cybersecurity education through the development of several short, mobile-friendly games. These games cover a range of topics from password security to text and phone scam recognition, incorporate multiple gamification strategies, including quiz-based, narrative-based, and simulation-ba