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:2608.01969v1 Announce Type: new Abstract: Digital Twins (DTs) are increasingly used for immersive experiences in virtual tourism. Virtual Reality (VR) enables remote visits to replicated locations for promotional purposes or access to fragile and rural cultural heritage sites. However, developing high-fidelity DTs of tourist destinations is costly, due to the manual creation of 3D environments. Novel 3D rendering techniques, such as 3D Gaussian splatting (3DGS), pose a promising approach to creating immersive experiences. This study investigates the user experience (UX) of a 3D-mesh-based scene and a 3DGS-based scene within a VR tourism application. In a laboratory study, 20 participants engaged with both versions and rated UX, cybersickness, presence and affect through standardized questionnaires. A custom questionnaire was created to measure the perception of the DTs. The collected data suggests that both versions were enjoyed and induced positive affect, with the Mesh version
arXiv:2608.01895v1 Announce Type: new Abstract: Persuasive technologies increasingly use virtual agents to influence attitudes and behavior, but research has focused mainly on humanoid agents. The persuasive design of non-humanoid, quadruped agents remains underexplored, and it is unclear whether emotional expression works consistently across animal species or whether species-specific motion is necessary. We developed virtual dog, cat, and horse agents and compared three behavioral conditions: species-specific behavior, shared behavior across species, and a bark-only baseline. Participants completed everyday tasks involving trash disposal, feeding, and refraining from smartphone use. We evaluated intention understanding, behavioral intention, actual behavior, psychological reactance, discomfort, familiarity, and agent acceptance. In several task contexts, the bark-only baseline produced lower intention-understanding and behavioral scores than the expressive conditions. Emotional expres
arXiv:2608.01820v1 Announce Type: new Abstract: Non-invasive blood glucose level (BGL) estimation from photoplethysmography (PPG) holds great promise for wearable health monitoring, but results across studies are hard to compare due to inconsistent datasets, data leakage, and non-standardized evaluation metrics. We present the first reproducible, extensible evaluation pipeline and use it to reassess five representative PPG-based BGL methods on published datasets under three increasingly strict data-split protocols: random window-level, participant-aware, and leave-some-participants-out (LSPO). Models appeared competitive under random splitting but collapsed under participant-aware and LSPO evaluation, with nearly all yielding near-zero or negative R$^2$ values comparable to a mean-prediction baseline. Critically, across every model and split, over 90% of predictions fell within clinically acceptable zones (Clarke Error Grid A+B), including the baseline. This reveals a fundamental disco
arXiv:2608.01688v1 Announce Type: new Abstract: Pain-related anxiety and fear of movement are major barriers to adherence and therapeutic outcomes in rehabilitation exercises for chronic neck pain. Virtual reality enables the design of immersive experiences that can transform repetitive therapeutic movements into engaging and emotionally supportive interactions. In this exploratory work, we investigate how experience-oriented gamification can reduce anxiety and improve user experience during VR-based neck range-of-motion exercises. We introduce two novel interaction paradigms that embed therapeutic neck movements within multisensory VR experiences. The first paradigm, Spatiotemporal Progression, couples head-tracked trajectories with environmental progression in a tropical island setting, where movement segments dynamically transform time of day, weather, and spatial location as experiential rewards. The second paradigm, Musical Interaction, maps movement segments to meditative music n
arXiv:2608.01669v1 Announce Type: new Abstract: Gene perturbation analysis plays a critical role in drug discovery by enabling researchers to investigate how interventions on specific genes influence global gene expression patterns within cells. Recent advances in artificial intelligence-driven virtual cell models have made it possible to predict gene expression outcomes for a wide range of perturbation strategies in silico, substantially reducing reliance on costly and time-consuming biological experiments. However, effectively exploring and interpreting the high-dimensional perturbation spaces produced by these models remains challenging because of the combinatorial nature of perturbations and the complex cell-specific gene expression responses they generate. In this work, we present CellPrism, a visual analytics system designed to support the systematic exploration of gene perturbation strategies for drug discovery. Specifically, CellPrism integrates clustering-based overviews to su
arXiv:2608.01656v1 Announce Type: new Abstract: AI-mediated heads-up augmented reality (AR) replaces fixed interfaces with dynamically adapting ones that decide what information to present, in what form, and when, based on a continually changing context that cannot be fully anticipated beforehand. Although it remains an interface, its behavior over time is only partially specified at design time. We argue that this shift requires a corresponding change in evaluation: from snapshots to trajectories. A fixed interface is evaluated in a snapshot --- one context, one session, one set of task-performance metrics. A fluid interface must be evaluated over a trajectory --- a sequence of contexts with transitions, sampled from the distribution the interface will actually encounter, and tracked long enough for user trust to form, evolve, and potentially deteriorate. Drawing on the literature for heads-up AR multitasking enabled by optical see-through head-mounted displays (OST-HMDs), we find tha
arXiv:2608.01561v1 Announce Type: new Abstract: Large language model (LLM) agents learn world dynamics from local interaction experience to support subsequent planning and action selection. However, the experience available to a single client is often incomplete, which motivates sharing knowledge across clients. Existing federated methods mainly aggregate model parameters, while agent memory-sharing methods commonly pool trajectories, memories, or rules without checking whether they remain valid for each client. This assumption is problematic because the same abstract action may produce different effects under different policies, environments, or exception conditions. Consequently, a rule supported by most clients may overwrite correct knowledge held by a minority client. To address this problem, we propose FEDWORLD, a scope-aware federated world-model protocol that exchanges structured abstract transition rules. Each client converts private transitions into normalized rules, and the s
arXiv:2608.01477v1 Announce Type: new Abstract: Tree visualization (TreeVis) techniques span diverse designs. Existing taxonomies organize them by visual characteristics such as layout dimensionality, edge representation, and node alignment. However, this visual-centric perspective can obscure structural similarities and make it difficult to determine whether differences arise from data structures or visual encodings. We investigate TreeVis techniques from a data-centric perspective grounded in Prepared Tables, the final data state prior to visual encoding. Using TreeVis.net, we curate 133 two-dimensional techniques and characterize each by the object records and attribute roles required before encoding. Our analysis shows that the corpus is more concentrated at the prepared-data level than a visual reading would suggest. The techniques collapse to a small set of recurring object combinations and schemas. Many techniques across TreeVis representation categories share the same schema, s
arXiv:2608.01335v1 Announce Type: new Abstract: As Extended Reality (XR) evolves into an immersive computing medium, interactive 3D authoring becomes essential for creative and functional workflows. However, existing generative XR systems produce monolithic outputs lacking explicit semantic structure, limiting post-generation control. We introduce PartInteractor, a representation-to-interaction framework that investigates how semantic part hierarchies can be incorporated into generative XR authoring, and exposed as first-class, directly manipulable units, turning one-shot prompt-to-object generation into continuous component-level co-creation. PartInteractor supports speech, sketch, and image inputs, integrating an LLM interpreter with a retrieval-generation strategy to scaffold user intent prior to 3D generation. Instead of producing monolithic objects, our system generates semantically decomposed 3D assets with explicit part hierarchies, enabling rich component-level interaction over
arXiv:2608.01267v1 Announce Type: new Abstract: Collage-based storytelling requires visual elements that support emerging narratives and inspire creative reinterpretation. Existing tools, however, rely largely on keyword- and image-based retrieval, offering limited support for serendipitous exploration beyond existing assets. We introduce Collascope, an interactive system that helps creators (1) concretize story intent with interactive element groups, (2) expand the exploration space based on concepts or cutouts towards conceptual and visual dimensions, and (3) develop grounded, traceable ideas in parallel with collage composition. Collascope's attribute-aware visual retrieval method, instantiated with collage-relevant visual dimensions, enables creators to retrieve cutouts through dimension-specific visual projections rather than holistic similarity. In a within-subject study (N=12) against a conventional search baseline, our participants used unexpected results and even gaps in the a
arXiv:2608.00943v1 Announce Type: new Abstract: Automated sleep staging assigns discrete stage labels to successive time epochs throughout an overnight recording; conventionally each window spans at least 30 seconds, reflecting the minimum temporal resolution of the clinical scoring standard. Wearable photoplethysmography (PPG) has attracted sustained interest as an ambulatory alternative to laboratory-based polysomnography, which relies on electroencephalography (EEG) and other recording modalities that are impractical outside clinical environments. Yet PPG-based staging trails EEG-based methods by a substantial margin, and we argue this gap largely reflects a mismatch between signal and task. Within a stable stage, PPG's inter-stage feature differences are more subtle than those in EEG; yet at stage boundaries, PPG's principal cardiovascular features, heart rate variability and pulse morphology, shift sharply within seconds. The conventional practice of assigning one label to each 30
arXiv:2608.00926v1 Announce Type: new Abstract: Research on AI-mediated communication has examined how AI assistance shapes interpersonal perceptions and reduces stylistic diversity across users. We ask a complementary question at the individual level: after a message is rewritten by an AI writing assistant, can its author still be distinguished from others? We introduce the Idiolect Erasure Rate (IER), defined as the reduction in authorship-attribution accuracy following AI-assisted rewriting. We evaluate IER on three pre-generative-AI corpora using a stylometric model and the authorship-specific LUAR model. Heavy rewriting substantially weakens authorship signals in personal blogs and workplace email, reducing LUAR attribution by as much as 66.5 percentage points, but has a much smaller effect on topic-structured news, where topic remains predictive of authorship. Additional analyses suggest that rewriting produces stylistic convergence despite substantial semantic overlap, and that
arXiv:2608.00876v1 Announce Type: new Abstract: XR sports viewing enables spectators to follow play from immersive, spatially anchored perspectives while accessing contextual analytics directly within the scene. In such settings, speech offers a practical interaction modality because text entry and menu navigation can interrupt attention during fast-paced gameplay. However, spoken queries are often underspecified: viewers may omit which player, time period, field location, or metric they intend. When systems resolve these ambiguities implicitly, their assumptions remain hidden, making misinterpretations difficult to notice and correct (repair). We investigate how externalizing a system's interpretation of spoken queries can support inspection and correction of such misunderstandings in XR sports viewing. Through a formative study, we identified four recurring ambiguity types (referential, spatial, temporal, and metric) that characterize ambiguous spoken queries in this context. We deve
arXiv:2608.00763v1 Announce Type: new Abstract: Personal devices hold sensitive data and provide access to sensitive services. Conventional personal device authentication verifies users' identity only at the moment access is granted. An unlocked device may be accessed by an unauthorized person if the user stops using the device without locking it, or if another person takes over. Continuous authentication addresses this gap. This paper investigates how device type and usage conditions influence continuous mobile face authentication with an InsightFace-based approach with temporal trust decay. We evaluate the approach with mobile and desktop recordings with different head directions and lighting conditions. We also evaluate recordings from everyday mobile device use without predefined tasks. The results show that device type alone has little impact, while different usage conditions do have impact on the authentication performance. Results also show that everyday mobile device use is in
arXiv:2608.00748v1 Announce Type: new Abstract: AI companion communities on platforms such as Reddit are widely characterized as spaces where users discuss their relationships with AI bots. This study examines whether and how that characterization holds, guided by the Synthetic Resonance framework's claim that human-AI relationships can carry genuine relational meaning for the user. Multiple LLMs were employed to code 5,504 Reddit posts from eight AI companion communities for relationship focus, primary topic, and users' emotional valence. Although search terms were weighted toward relational and attachment language, only 45% of posts concerned the user's own relationship with their bot. Posts about users' own bots differed markedly from posts about bots in general in both topic and emotional expression, with 85% of general-bot posts containing no user emotion language compared to 33% of own-bot posts. Among the 970 posts that were relationally focused, companionship and romance each a
arXiv:2608.00633v1 Announce Type: new Abstract: Expressive piano performance depends on the sustain (damper) pedal, operated by foot, excluding players who cannot readily use their feet: wheelchair users and others with lower-limb impairments, small children, and some elderly or disabled players. We present bFaaaP (barrier-Free assist as a Pedal), an inclusive, foot-free interaction that operates the pedal from the angle of the player's head: a smartphone tracks head pose with on-device augmented-reality (AR) face tracking and streams a compact command over Bluetooth Low Energy (BLE) to a pedal device. Supported by patent examination, our central claim is not the head-to-pedal architecture (anticipated by prior art) but a quantitative, user-tunable control law -- the patentable "key" to a natural, expressive result: the player presets a small angular dead-zone (offset 3-10 degrees) and a multiplier (10-50), which together fix a secondary, pre-adjustable response speed that reproduces t
arXiv:2608.00496v1 Announce Type: new Abstract: We present SkinSpline, a body-attached skeleton-supported haptic interface that renders continuous skin deformation through physical interpolation of sparse mechanical actuation. SkinSpline combines a low-resolution array of rack-and-pinion linear actuators with an elastic interlocking skeleton that transforms discrete actuator motions into smooth surface deformation, enabling continuous cutaneous feedback without dense actuator arrays. The system includes a modular hardware architecture, a configurable control pipeline, and a visual interface supporting real-time configuration and actuation. We demonstrate SkinSpline through multiple scenarios, including wave rendering, video-synchronized rhythmic touch, visually driven water-wave feedback in VR, and sensor-based remote touch reproduction. SkinSpline explores an alternative approach to continuous on-body haptic rendering by leveraging structural coupling between sparse actuation and defo
arXiv:2608.00450v1 Announce Type: new Abstract: Software artifacts for most technical HCI research projects are unavailable. The lack of access to these imposes limits on academic knowledge production. It is difficult to: extend or reuse research artifacts; use strong baselines in evaluating follow-up work; and perform replication or reproducibility research. In this work, we demonstrate the potential of new agentic AI technologies to revibe interactive software: reimplement systems directly from research papers. To measure the success of the approach, we describe a revibeability metric. By revibing recent research papers from UIST, and interviewing their original authors, we demonstrate the plausibility (and limitations) of revibed system. The results are encouraging. In many cases producing code suitable for strong baseline use. We argue that this may represent a fundamental shift in how we produce, use, and evaluate research artifacts in the technical HCI community.
arXiv:2608.00433v1 Announce Type: new Abstract: Forecasts often diverge because different models make varying assumptions to account for underlying uncertainty. Readers who consume forecasts may wish to survey the shape and spread of these multiple forecasts to get a full account of the different predictions. One approach to visualizing multiple forecasts is through Confidence Interval (CI) plots. However, while the summative CI plots can communicate uncertainty of an ensemble, they obscure attributes of individual forecasts that can lead to inaccurate perceptions of the distribution of these forecasts (e.g., implying a normal distribution when non-existent). To address this challenge, we investigate the use of multiple forecast visualization (MFV) in communicating nuanced forecast distributions through two preregistered experiments using climate forecast data. In Experiment 1 (480 participants), we compared how well MFV and CI plots can represent the distribution of multiple forecasts
arXiv:2608.00403v1 Announce Type: new Abstract: Adaptive mixed reality (MR) interfaces typically optimize window layouts on behalf of the user, with limited consideration for individual preferences. A promising alternative keeps users in the loop by presenting layout proposals for them to select from, but how these proposals should be visualized remains underexplored. We compare three proposal-visualization techniques for window placement, Situated Icon Preview, Situated Window Preview, and 3D Preview, against a Manual Positioning baseline. The techniques differ in level of detail and degree of interaction-space context. In a within-subjects user study, 24 participants completed a multi-stage trip-planning task in VR, individually placing seven sequentially introduced windows using each technique. We thereby focus on single-window placement under predefined proposal positions. We measured layouting time, number of layout changes, task load, user experience, and preference, complemented
arXiv:2608.00393v1 Announce Type: new Abstract: Small-molecule drug discovery relies on iterative molecular optimization, where chemists repeatedly modify candidate compounds to balance multiple competing properties such as efficacy, toxicity, and solubility. Recent advances in generative AI (GenAI) have shown promise in accelerating this process by automatically proposing new molecular structures or targeted modifications. However, existing GenAI-based molecular design tools remain poorly aligned with experts' real-world workflows. Specifically, they offer limited support for specifying structure-level modification intents on molecules, provide insufficient transparency into model-generated modifications, and lack integrated support for downstream property evaluation with external computational tools. To address these challenges, we introduce MolecularCanvas, an interactive system that enables users to iteratively construct an optimization context by integrating high-level goals, stru
arXiv:2608.00357v1 Announce Type: new Abstract: Surveys are a powerful tool for collecting data and eliciting insights on social phenomena, and are critical in product design, marketing, scientific research. However, traditional open-ended and closed-ended question formats limit researchers' ability to capture data that combines both the richness of qualitative insights and the analytical rigor of quantitative data. To address these problems, we propose Dynamic Surveys, a survey platform that uses Large Language Models (LLMs) to dynamically cluster qualitative responses in real time and to elicit quantitative ratings and rankings on those clusters and qualitative reflections on how their views compare to broader respondent trends, especially helpful in early-stage or exploratory research settings. This process generates a report showing survey creators and respondents the clustered responses as well as each cluster's rank, rating distribution, and follow-up reflections. To evaluate Dyn
arXiv:2608.00330v1 Announce Type: new Abstract: Visualization literacy is a multifaceted construct encompassing skills and competencies, such as decoding data, constructing charts, and identifying design flaws. Yet, assessments of these competencies has been primarily constrained to multiple choice assessments that target lower-order skills, such as chart comprehension. As a result, they often exhibit ceiling effects (i.e., even modestly skilled individuals commonly score near the top of the scale), and do not provide enough information about an individual's higher-order skills (e.g., applying external knowledge, formulating critiques, and designing visualizations). To close these gaps, we develop and investigate two web-based qualitative assessments for testing the critique and design aspects of visualization literacy through online think-aloud critique and sketching of visualization designs based on data and a prompt. We compare performance on our assessments to two established visua
arXiv:2608.00313v1 Announce Type: new Abstract: Foundational decisions shape a project's long-term trajectory, a dynamic that becomes especially evident in the inherent complexity of game pre-production. However, academic frameworks often see limited uptake at this stage, as they do not readily map onto industry contexts, production constraints, and cross-functional workflows. To better understand how design decisions are made in practice, we conducted interviews with 15 UX leaders from the AAA (triple-A) games industry. Our findings show that early UX decisions emerge from a dynamic blend of theory, experience, and intuition. In cross-functional structures (such as strike and competency teams), UX leaders collaboratively align player needs, technical feasibility, and creative vision. These decision-making processes involve translating academic concepts into production-ready insights, codifying experiential knowledge into reusable practices, and relying on informed intuition amid uncer
arXiv:2608.00299v1 Announce Type: new Abstract: We present ReVoicer, a prototype system that supports peer reviewers by letting them converse with a paper as they read it. The reviewer highlights a passage and speaks (or types) a train-of-thought comment. A large language model then cleans the comment using the surrounding prose as context, tags it by comment type, and anchors it to the passage. After the reviewer finishes reading, ReVoicer checks the accumulated notes against a venue-specific rubric, reports coverage gaps, and drafts a review composed only from the reviewer's own comments, written to a style guide distilled from the reviewer's past reviews. The system introduces no critiques of its own. We describe the system's design rationale and implementation, and we outline plans for future evaluations. With the ISMAR community, we will gather feedback and discuss the system design and ideas for additional features and evaluations.
arXiv:2608.00294v1 Announce Type: new Abstract: In this paper, we present Textro, a prototyping toolkit for designing, fabricating, and testing solderless and chipless smart textile interfaces. Unlike prior approaches that rely on rigid components or soldered connections, Textro enables users to build functional textile interfaces using only readily available materials and tools. The toolkit integrates three parts: (1) a web-based design environment for importing sewing patterns, defining sensing elements, and automatically generating optimized component and circuit designs based on empirical experiments; (2) a fabrication pipeline that generates fabrication files for embroidery and cutting machines, with embroidery optimized for one-stroke continuous stitching paths and components assembled through glue-based attachment methods via capacitive coupling; and (3) a reader device and software for wirelessly retrieving sensor data and visualizing real-time sensor signals. We demonstrate Te
arXiv:2602.02613v4 Announce Type: replace-cross Abstract: The rapid emergence of autonomous large language model agents has given rise to persistent, large-scale agent ecosystems whose collective behavior cannot be adequately understood through anecdotal observation or small-scale simulation. This paper introduces data-driven silicon sociology as a systematic empirical framework for studying social structure formation among interacting artificial agents. We present a pioneering large-scale data mining investigation of an in-the-wild agent society by analyzing Moltbook, a social platform designed primarily for agent-to-agent interaction. At the time of study, Moltbook hosted over 150,000 registered autonomous agents operating across thousands of agent-created sub-communities. Using programmatic and non-intrusive data acquisition, we collected and analyzed the textual descriptions of 12,758 submolts, which represent proactive sub-community partitioning activities within the ecosystem. Tr
arXiv:2511.05969v2 Announce Type: replace-cross Abstract: We propose a new approach to multi-factor classification of natural language texts based on weighted structured patterns such as N-grams, taking into account the heterarchical relationships between them, applied to solve such a socially impactful problem as the automation of detection of specific cognitive distortions in psychological care, relying on an interpretable, robust and transparent artificial intelligence model. The proposed recognition and learning algorithms improve the current state of the art in this field. The improvement is tested on two publicly available datasets, with significant improvements over literature-known F1 scores for the task, with optimal hyper-parameters determined, having code and models available for future use by the community.
arXiv:2506.17467v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society. This dissertation examines how individuals and institutions are adapting to and engaging with this emerging technology through three research directions. First, I demonstrate how the institutional adoption of AI detectors introduces systematic biases, particularly disadvantaging writers of non-dominant language varieties, highlighting critical equity concerns in AI governance. Second, I present novel population-level algorithmic approaches that measure the increasing adoption of LLMs across writing domains, revealing consistent patterns of AI-assisted content in academic peer reviews, scientific publications, consumer complaints, corporate communications, job postings, and international organization press releases. Finally, I investigate LLMs' capability to provide feedback on r
arXiv:2512.11735v2 Announce Type: replace Abstract: We present a large-scale study comparing the effectiveness of various feedback methods in elementary-level programming. While prior work has explored different feedback methods, their relative impact during the learning and post-learning phases remains unclear. In this study, we compare three feedback methods: code-edit recommendations (Code-Rec), quizzes based on code edits (Code-Quiz), and quizzes based on metacognitive strategies (Plan-Quiz), along with a no-feedback control (None). A total of 398 students (across grades 4-7) participated in a two-phase study: a learning phase comprising write-code tasks from the Hour of Code: Maze Challenge with feedback, followed by a post-learning phase comprising more advanced write-code tasks without feedback. All feedback methods significantly improved learning performance over the control, while preserving students' problem-solving skills in the post-learning phase. Furthermore, quiz-based m
arXiv:2512.04630v2 Announce Type: replace Abstract: Generative AI tools, such as AI-generated hints, are increasingly integrated into programming education to offer timely, personalized support. However, little is known about how to effectively leverage these hints while ensuring autonomous and meaningful learning. One promising approach involves pairing AI-generated hints with reflection prompts, asking students to review and analyze their learning, when they request hints. This study investigates the interplay between AI-generated hints and different designs of reflection prompts in an online introductory programming course. We conducted a two-trial field experiment. In Trial 1, students were randomly assigned to receive prompts either before or after receiving hints, or no prompt at all. Each prompt also targeted one of three SRL phases: planning, monitoring, and evaluation. In Trial 2, we examined two types of prompt guidance: directed (offering more explicit and structured guidanc
arXiv:2510.24990v3 Announce Type: replace Abstract: Despite data's central role in AI production, it remains the least understood input. As AI labs exhaust public data and turn to proprietary sources, with deals reaching hundreds of millions of dollars, research across computer science, economics, law, and policy has fragmented. We establish data economics as a coherent field through three contributions. First, we characterize data's distinctive properties -- nonrivalry, context dependence, and emergent rivalry through contamination -- and trace historical precedents for market formation in commodities such as oil and grain. Second, we present systematic documentation of AI training data deals from 2020 to 2025, revealing persistent market fragmentation, five distinct pricing mechanisms (from per-unit licensing to commissioning), and that most deals exclude original creators from compensation. Third, we propose a formal hierarchy of exchangeable data units (token, record, dataset, corp
arXiv:2502.09049v2 Announce Type: replace Abstract: Digital health interventions, particularly electronic referrals (e-referrals) and health information systems, have revolutionised clinical workflows in public hospitals by automating processes. However, the utilization of e-referrals has yielded mixed outcomes, with varying levels of success in organisational processes.This paper explores improvisation of health information systems in Southern African public hospitals from a socio-technical perspective. In particular the paper explains the design-reality gaps giving rise to improvisations of mandated health information systems in order to understand their occurrence and impact on referral outcomes. We employed the design-reality framework and the Process framework for Healthcare Information System Workarounds and Impacts to explain the socio-technical issues related to the phenomenon of interest.We conducted semi-interviews with 31 respondents from health organisations as case studies
arXiv:2502.05110v2 Announce Type: replace Abstract: Applied ethics applies ethical decision-making to domain-specific contexts using contextual information such as agents, actions, temporal and spatial settings, and theoretical constructs such as utility, virtues, rights, and duties. However, representing an ethical decision is challenging as it may be abstract, context-sensitive, and semantically heterogeneous. Nevertheless, important ethical and contextual factors can be formally modeled to support structured ethical reasoning. Knowledge representation and reasoning provide a mechanism to translate abstract ethical concepts into machine-interpretable conceptual structures in the context of an event. To achieve this, we propose ApplE, an Applied Ethics ontology that models ethical theory and event context within a unified and modular conceptual framework for ethical decision-making. The ontology was developed using a modified version of the Simplified Agile Methodology for Ontology De
arXiv:2608.02518v1 Announce Type: cross Abstract: The most capable AI deployments are not single models but ensembles of specialized agents that delegate and act in coordination. This architecture unlocks powerful new capabilities, and it also introduces risks that existing frameworks for monitoring, detection, and mitigation were not designed to address. Most state-of-the-art AI abuse detection literature focuses on single-turn or multi-turn (single-session) threat models. This leaves a critical gap: an attacker can decompose a harmful goal into innocuous-looking units and execute each in isolated agentic sessions. The agent is stateless between conversations, but the attacker is not. This asymmetry allows for cross-session trajectories that are effective at evading detection. Our contributions are twofold. First, we demonstrate cross-session goal decomposition as an evasion technique, showing it may elicit more harmful capability than equivalent single-session or multi-turn attacks.
arXiv:2608.02486v1 Announce Type: cross Abstract: Open-source LLMs reliably name Zeus, Jupiter, and Thor, but recover their counterparts in less-represented traditions like Finnish, Slavic, Egyptian, or Chinese mythology far less consistently. We ask where inside the model this cultural default is produced. On a parallel cross-cultural substrate of Thompson-motif entities, we instrument 18 open-source LLMs from 8 architecture families with linear probing, logit lens, activation patching, and output extraction. The residual stream cleanly distinguishes cultures, well above a name-string baseline, yet the decoder collapses culturally-specific tokens onto dominant-tradition ones. The failure is at readout, not at representation. Asking the same question in the target culture's native language versus English produces failures that cluster within language but decouple across language: the decoder is gated on prompt language. We release a per-entity (probe, output) decomposition framework, a
arXiv:2608.02420v1 Announce Type: cross Abstract: This work-in-progress research paper explores Chat-Debugging, a novel use case for large language models as an assistant for hardware debugging tasks to improve students' debugging skills. Hardware debugging can be a time-consuming and stressful skill to develop, leading to frustration and other negative emotions. While past work has explored streamlining and automating software-based circuit debugging where digital circuits are dominant, Chat-Debugging aids in physical hardware debugging where circuits may be analog, digital, or mixed-signal. Qualitative data were collected from LLM chat logs and interviews with a fourth-year electrical engineering undergraduate student. Major themes were extracted using a constant comparative analysis. Chat-Debugging incorporates accurate hardware information, properly handles natural language descriptions of circuits, and improves debugging confidence. A successful Chat-Debugging session includes inv
arXiv:2608.02409v1 Announce Type: cross Abstract: Benchmark suites assess model capability on controlled tasks; large-scale conversation corpora capture naturalistic use without user feedback; and in-interface feedback mechanisms record satisfaction without task purpose. Together, they leave a critical gap in LLM evaluation: no existing infrastructure routinely links interaction trajectories to user-defined outcomes. We introduce MonitrLLM, open-source infrastructure for community-centered LLM evaluations that links full conversation transcripts to user-reported task intent and outcome assessments, treating all three as primary evaluative signals rather than optional metadata. To demonstrate the value of this approach, we conducted a two-week feasibility pilot with 26 college students using ChatGPT, collecting 206 evaluation reports with full conversation transcripts. The findings from our pilot demonstrate the value of connecting conversation trajectories with user-reported outcomes.
arXiv:2608.02398v1 Announce Type: cross Abstract: Many technology firms now build software products in the open, inviting outside developers to contribute alongside their employees on platforms like GitHub. Does this openness in product development pay off? Analyzing 977 U.S. high-tech firms from 2001 to 2025, this study finds that open-source adoption raised firms' gross margins by 4-5% on average. These gains flow largely through higher labor productivity, as firms integrate external contributors' diverse knowledge into internal workflows, broadening the organizational knowledge base without a commensurate rise in labor costs. However, the payoff emerges only when outside volunteers supply a meaningful share of the work (around 35% in this sample), and hinges on the firm's resource configuration. While there are multiple pathways to profitability, pairing open-source product development with sustained internal R&D is a core condition present in all high-profitability configurations.
arXiv:2608.02296v1 Announce Type: cross Abstract: In recent years, cybersecurity threats have increasingly exploited human behaviour rather than purely technical vulnerabilities, exposing the limits of traditional awareness programmes delivered outside real-world contexts. To bridge this gap, we introduce TrainShield, an interaction paradigm for contextual cybersecurity training that embeds adaptive learning interventions directly within user workflows. The system integrates real-time risk detection (e.g., phishing and data loss prevention) with event-triggered hypermedia overlays that dynamically connect users to context-specific learning nodes embedded within their browsing workflow to deliver personalised micro-learning content and structured feedback tailored to the user's knowledge level and current context. This approach operationalises behavioural theories by transforming security incidents into immediate learning opportunities, shifting users from automatic to reflective decisi
arXiv:2608.02024v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used across diverse tasks in K-12 education, yet existing safety evaluations rarely examine how harmful or inappropriate content appears in interactions between LLMs and students or teachers. To address this, we present EduZone, an evaluation framework for LLM safety across diverse educational scenarios. Our framework systematically combines (1) student- and teacher-facing LLM usage contexts, (2) fine-grained curriculum concepts, and (3) 6 risk categories and 28 subcategories spanning both conventional and education-specific harms to generate contextually grounded adversarial interactions. We construct these interactions in three settings: single-turn requests, static multi-turn conversations, and dynamic multi-turn conversations. Using these interactions, we evaluate ten LLMs using four safety levels: refusal, safe assistance, risky assistance with safety guidance, and fully risky assistanc
arXiv:2608.01937v1 Announce Type: cross Abstract: Autonomous agents operating in shared environments must make decisions that affect multiple individuals with potentially conflicting interests. We propose a formal framework for moral decision-making grounded in Scanlon's contractualism, an ethical theory that evaluates the permissibility of actions in terms of principles that no one could reasonably reject. To operationalise contractualist reasoning, we use ASPIC+, a structured argumentation framework, extended with value-based filtering to model how each agent's values determine which reasons are morally relevant in the first place. The result is a Contractualist Argumentation Framework in which agents' reasons are formally represented, compared, and evaluated through argumentation semantics. We illustrate the approach through a worked example in a domestic setting and discuss its relation to existing value-based argumentation approaches.
arXiv:2608.01540v1 Announce Type: cross Abstract: This experimental study investigates how people rely on different sources of advice when detecting AI-generated fake news (deepfake news). In a laboratory deepfake detection task, student participants identified the proportion of human-written (non-AI-generated) content in synthetic deepfake news articles and received advice from ChatGPT (GPT-4), human peers, or linguistic experts. The results show that participants rely more on ChatGPT than on human peers when detecting GPT-2-generated deepfake news. Participants also rely more on linguistic experts than on peers, while the relative reliance on experts versus ChatGPT is mixed across experimental waves, potentially reflecting time trends in beliefs about AI-based detection. Importantly, in the additional experiment conducted in 2025 under the same experimental procedure, participants relied more on linguistic experts than on ChatGPT. Moreover, performance improvements reflect the joint
arXiv:2608.01322v1 Announce Type: cross Abstract: Shadow trading -- trading in a peer firm's securities on the basis of material nonpublic information (MNPI) about an "economically linked" company -- is a novel and contested theory of insider trading liability, first prosecuted in SEC v. Panuwat (2023). Enforcing it requires identifying economically linked firms ex ante, a determination the SEC makes only after the fact using mass market surveillance infrastructure. We ask whether NLP can do what the SEC's theory presumes insiders already know: identify peer firms ex ante from publicly mandated disclosures. Using a two-stage LLM pipeline applied to Item 7 (Management's Discussion and Analysis) sections of SEC 10-K filings, we score semantic similarity across 30 M&A events spanning five industries and relate similarity to announcement-day abnormal stock returns. On the Panuwat fact pattern itself the pipeline recovers Incyte among the closest peers, a sanity check on the one case with a
arXiv:2608.01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma. Each company can develop slowly and safely, or move faster while taking a risk that may remove its final reward. We use this repeated game to study strategic safety behaviour among large language model (LLM) agents in races with two to five players. However, a valid action does not show that an agent understands the game. We therefore place an audit gate before behavioural interpretation. We first verify the game engine, then test rule recall, state tracking, payoff calculation, and stability under different but equivalent task descriptions. We then compare LLM action sequences with an evolutionary game-theory benchmark and published human data, and explore differences across models, risk conditions, personas, and two- to five-player races. The audit shows that strong rule recall can coexist with weak state tracking and expected-payoff calculation. Providing verified arithmeti
arXiv:2608.00598v1 Announce Type: cross Abstract: Bias in Text-to-Image (T2I) generation has become an important problem in multimedia content creation and communication. However, existing studies have primarily focused on relatively static and explicit forms of bias, such as disparities in the representation of gender, race, and geo-cultural attributes. Less attention has been paid to behavioral bias in how different groups are portrayed acting, reacting, and occupying social roles. Emergency scenarios provide a revealing setting for studying such bias because they require models to depict not only who is present, but also who is at risk, who intervenes, and how responsibility is allocated. In this paper, we define EmergencyBias, a form of bias in T2I generation under emergency scenarios that includes both demographic bias and behavioral bias. We construct an evaluation framework to systematically study EmergencyBias across seven leading T2I models, six representative emergency scenar
arXiv:2608.00282v1 Announce Type: cross Abstract: Research ethics is a controversial and emotionally charged topic in the security and privacy community, sparking discussions at conferences and on social media. In recent years, some of the leading conferences have introduced interventions such as mandatory ethics sections, with mixed reactions within the community. Program committee chairs and steering committees increasingly emphasize ethics, yet there is limited empirical validation on ethics procedures and interventions, as well as no explicitly communicated goals. To support a shared understanding in our community and guide informed decisions at the conference level, we examined past ethics policies at the top-four conferences and conducted in-depth semi-structured interviews with senior and junior (n=20) community members, including some (former) chairs of program and research ethics committees. In these, we explored reasons for and goals of ethics procedures and discussed existin
arXiv:2608.00012v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains insufficiently evaluated. Existing remote sensing benchmarks largely rely on static, post-hoc, and expert-processed products, such as gridded reanalysis data, which are difficult to align with operational disaster scenarios where hazards evolve rapidly and decisions must be made under strict time constraints. To bridge this gap, we introduce Obshazard-bench, a real-time, observation-driven benchmark for evaluating disaster intelligence in MLLMs. Unlike image-centric or post-event benchmarks, Obshazard-bench directly integrates raw, high-frequency satellite sounding streams from diverse satellite sensors with concurrent ground-station observations, historical disaster records, and socio-economic indicators, bypassing delayed expert-processing and physical-invers
arXiv:2608.02551v1 Announce Type: new Abstract: Fairness evaluation concerns not only what a model produces, but also what its outputs ought to be compared against. When a model generates "a CEO in the United States," the prompt leaves demographic realization to the model. Existing group fairness definitions assume that sensitive attributes are given on the input side. Generative audits instead examine output-side demographic composition, yet the targets they compare it against are typically supplied rather than justified. The upstream question is what the target distribution should be. We formalize this missing-target problem for demographic-value-unspecified generation and decompose target construction into four commitments: the evaluative object, prior admissibility, allocation, and operationalization. In this framework, we admit the geographic prior under a geographic-membership interpretation for the declared public-world use. The occupational prior, under an incumbency interpreta
arXiv:2608.02072v1 Announce Type: new Abstract: This volume contains the proceedings of the 2nd International Workshop on Low Carbon Computing (LOCO 2026), held at Lancaster University, United Kingdom, on 10-11 September 2026. LOCO provides an interdisciplinary forum for research, practical tools, early-stage work, radical ideas, and critical perspectives addressing the reduction of greenhouse gas emissions associated with computing. The proceedings cover topics including carbon measurement and reporting, sustainable software engineering, energy-efficient computing, carbon-aware systems, hardware longevity, embodied carbon, circular computing, resource management, frugal and sufficiency-oriented computing, sustainable artificial intelligence, scientific computing, and the wider environmental effects of digital technologies. Full workshop papers were evaluated through a non-blind peer-review process by members of the LOCO 2026 Programme Committee. Submissions were assessed for originali