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:2603.22787v2 Announce Type: replace Abstract: Shared autonomy combines human user and AI copilot actions to control complex systems such as robotic arms. When a task is challenging, requires high dimensional control, or is subject to corruption, shared autonomy can significantly increase task performance by using a trained copilot to effectively correct user actions in a manner consistent with the user's goals. To significantly improve the performance of shared autonomy, we introduce Diffusion Sequence Copilots (DiSCo): a method of shared autonomy with diffusion policy that plans action sequences consistent with past user actions. DiSCo seeds and inpaints the diffusion process with user-provided actions with hyperparameters to balance conformity to expert actions, alignment with user intent, and perceived responsiveness. We demonstrate that DiSCo substantially improves task performance in simulated driving and robotic arm tasks. Project website: https://sites.google.com/view/disc
arXiv:2510.04465v3 Announce Type: replace Abstract: LLM agents require personal information for personalization in order to effectively act on users' behalf, but this raises privacy concerns that can discourage data sharing, limiting both the autonomy levels at which agents can operate and the effectiveness of personalization. Yet the expanded design space of agent autonomy also presents opportunities to shape these effects, which remain underexplored. We conducted a $3\times3$ between-subjects experiment ($N=450$) to study how agent autonomy level influences personalization's effects on users' privacy concerns, trust, and willingness to use, as well as the underlying psychological processes. We find that risk-contingent autonomy, where the agent delegates control back to users upon detecting potential privacy leakage, improves users' perceived control. This in turn attenuates personalization's adverse effects: privacy concerns rise less and trust declines less. Our results suggest tha
arXiv:2505.07069v4 Announce Type: replace Abstract: Group awareness--the ability to perceive the activities of collaborators in a shared space--is a vital mechanism to support effective coordination and joint data analysis in collaborative visualization. We introduce collaborative attention-aware visualizations (CAAVs) that track, record, and revisualize the collective attention of multiple users over time. We implement this concept in HeedVision, a standards-compliant WebXR system built with React Three Fiber that runs on modern AR/VR headsets, and complement it with proof-of-concept implementations covering the remaining three quadrants of our design space--varying presentation (embedded vs. separated) and situatedness (world space vs. camera space). Through a mixed-methods exploratory study where pairs of co-located analysts performed visual search tasks in a shared immersive AR environment, we investigate how attention revisualization affects collaborative coordination in immersive
arXiv:2608.09917v1 Announce Type: cross Abstract: TikTok's global growth has made it a prime platform for both entertainment and political discourse, prompting increased social science research. However, this rapidly evolving research field faces a fundamental reproducibility crisis. TikTok's opaque algorithmic systems hinder researchers from drawing meaningful empirical inferences, while the lack of standardized data collection methods compounds these challenges. This study addresses these methodological gaps by systematically comparing three data collection tools - the official TikTok Research API, Pyktok, and Apify. We evaluated five endpoints: User, Hashtag, Keyword, Comment, and Related Video. Results show substantial cross-tool differences, especially for hashtag and keyword searches. The Research API uses back-end API calls, whereas Apify and Pyktok rely on front-end web scraping, producing systematic differences in the time periods and popularity levels represented in retrieved
arXiv:2608.09658v1 Announce Type: cross Abstract: Human-Robot Collaboration (HRC) plays a vital role in dynamic, high mix, low volume industrial scenarios such as remanufacturing, which frequently face workcell rearrangements. Traditional setups are constrained by power and data cabling, restricting modularity and reconfigurations, while the selection of commercial wireless devices suitable for real-time perception and safe collaboration are limited in availability. This paper presents a highly flexible, wireless, 5G-based system that serves as a versatile experimental testbed for applications including remanufacturing, operator training, and user studies. To eliminate infrastructure barriers, the workcell integrates a novel battery-powered, multi-sensor platform prototype. Additionally, to support operator safety and system adaptability across environmental shifts, the system integrates a computer vision module for object detection and pose estimation, further augmented for robust han
arXiv:2608.09421v1 Announce Type: cross Abstract: Wearable human activity recognition (HAR) remains challenging due to the computational and energy constraints of deep learning models on resource-limited devices. Existing lightweight approaches often rely on recurrent architectures (e.g., GRU and LSTM), limiting parallelism and increasing inference latency. We propose LITEWAY, a modality-agnostic, fully convolutional framework for multichannel sensor time series that replaces recurrent temporal modeling with structured convolutional decomposition. LITEWAY combines lightweight convolutional blocks, strided temporal processing, and convolution-attention pooling to efficiently capture temporal dependencies while reducing computational complexity. We evaluate LITEWAY on 16 HAR datasets against TinyHAR, TinierHAR, and MLP-HAR. LITEWAY achieves competitive macro F1 while reducing model size by 4.06x-9.52x (Light) and 3.87x-9.07x (Full) compared with TinyHAR and TinierHAR. Deployment experime
arXiv:2608.09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks. However, their reliability under uncertainty remains poorly understood which raises critical concerns for deployment in high-stakes clinical settings. In such environments, incorrect predictions are inherently risky, but confident incorrect predictions can be particularly harmful as they may mislead clinical decision-making. In this paper, we conduct a systematic behavioral analysis of LLMs under clinical information uncertainty. We propose an evaluation framework based on the MedMCQA dataset consisting of two complementary uncertainty settings. First, we introduce linguistic uncertainty cues through prompt modifications to simulate ambiguous clinical contexts. Second, we construct an answer removal setting, wherein the correct option is deliberately excluded mandating the model to recognize insufficient information
arXiv:2608.08947v1 Announce Type: cross Abstract: Current hazard detection systems in autonomous driving may develop mesa objectives, learned internal goals that achieve high training performance through spurious correlations rather than genuine hazard recognition. We investigate whether human gaze patterns, captured via webcam-based eye tracking (WebGazer.js), can serve as privileged information to constrain mesa-objective formation. We collected 137,663 frame-level gaze samples synchronized with hazard annotations across 388 real dashcam clips, then test this hypothesis across two calibration protocols (9-point/45-click and 11-point/440-click), two model architectures (Random Forest and causal Transformer), and five random seeds per experiment with paired t-tests. No experiment yields a statistically significant improvement from gaze (p = 0.919, 0.578, and 0.667 respectively). A geometric analysis reveals the root cause: WebGazer's reported error (~130-257 px depending on configurati
arXiv:2608.08884v1 Announce Type: cross Abstract: As collaborative robots have entered domains such as manufacturing, agriculture, and healthcare, programming or adapting robot behavior typically requires robotic expertise that most end users lack. Natural language lowers this barrier. Recent advancements in large language models (LLMs) have made it feasible to translate natural language into robot task plans. However, language-based task specification suffers from semantic ambiguity, and generative models lack transparency for how language instructions become robot actions, making it difficult for users to validate the plan before execution. To address these issues, we introduce SHRIMP, a system that allows users to automatically generate a hierarchical robot primitive plan using natural language and iteratively revise their plan through re-prompting and explicit correction. At each revision, SHRIMP allows users to validate their plan in simulation, and once satisfied, execute it on t
arXiv:2608.08860v1 Announce Type: cross Abstract: Robotic neural-thread placement requires regulating the insertion-tool tip relative to tissue that moves with cardiac and respiratory pulsation. This paper develops a preview-based relative-motion controller that estimates latency-delayed periodic surface motion, predicts it over a short horizon, and uses offset-free model predictive control to regulate relative placement while limiting actuator effort and lateral relative velocity. In MuJoCo, the 1-DOF controller achieves 12.0\um\ free-space and 1.9\um\ contact RMS relative-placement error, versus 18.3/176.8\um\ for delayed-feedback impedance and 286.1/275.5\um\ for lab-frame PD, at the cost of higher peak contact force (3.43 versus 2.00~mN) since offset-free tracking drives the tip fully to the commanded depth rather than yielding against the tissue. In 3 DOF, coupled preview reduces contact lateral shear from 1.34 to 0.50~mm/s with 2.1\um\ lateral RMS error. A feasibility-restored oc
arXiv:2608.08617v1 Announce Type: cross Abstract: Group discussion-based teaching is widely used to foster collaborative learning, yet teachers in physical classrooms often struggle to simultaneously monitor multiple groups and quickly diagnose a target group before intervening. Existing visual analytics tools primarily support post-hoc analysis on desktop, providing limited support for in-situ walk-around teaching. To address this gap, we present MobileGroupVis, a mobile visual analytics system for in-situ analysis of classroom group discussions. MobileGroupVis integrates multi-group monitoring, single-group diagnosis, and instructional intervention into a concise analytical workflow tailored for small-screen touch interaction. The system is powered by a lightweight streaming analysis pipeline that converts group audio into structured discussion data and further extracts interaction patterns, topic progression, and topic deviation through a dialogue analysis module. To enable both gla
arXiv:2608.08362v1 Announce Type: cross Abstract: Recent Text-To-Speech (TTS) systems have achieved strong naturalness and zero-shot voice cloning performance, but fine-grained control of expressive speech at the word or phoneme level remains challenging. We propose CtrlSpeech, a controllable, expressive TTS framework with coarse-to-fine control. Built on the DiTAR architecture, CtrlSpeech combines global speaker conditioning with phone-aligned pitch, loudness, and duration signals, enabling localized prosodic control while preserving the target speaker's timbre. This design allows users to adjust expressive attributes at a fine temporal granularity, making speech refinement more flexible and controllable. Experimental results show that CtrlSpeech achieves competitive zero-shot TTS performance and improves controllability over expressive attributes, demonstrating its effectiveness for flexible and practical expressive speech synthesis.
arXiv:2608.08245v1 Announce Type: cross Abstract: LLM applications deployed at scale face a fundamental challenge: privacy constraints prevent direct inspection of user interactions, making it difficult to obtain any representative evaluation dataset or to track the ongoing evolution of production traffic. We present ProxyDrift, a framework that (i) identifies and measures drift between production traffic and offline evaluation sets, and (ii) constructs and refreshes those evaluation sets accordingly; all without access to raw user data. Our approach operates entirely on non-PII proxy representations: structured, multi-dimensional descriptors derived from LLM-based classification of user interactions. We introduce (1) a chance-calibrated, redundancy-aware (RA) alignment score that aggregates per-dimension drift measurements via mutual information; (2) a conditional sampler that generates synthetic proxies respecting inter-dimensional dependencies; (3) a roundtrip consistency analysis t
arXiv:2608.08227v1 Announce Type: cross Abstract: Focus particles such as "even" and "only" are central to formal semantic theories that posit structured representations over sets of alternatives. "Even" highlights unexpected or extreme alternatives, while "only" enforces exclusivity. If such scalar representations are robust and generalizable, they should give rise to consistent judgments across contexts and systems. In this work, we test whether humans and large language models (LLMs) construct stable scalar representations from sentences containing these particles. Using a dataset of approximately 100 items, participants and models were asked to make scalar judgments. Preliminary results suggest that similar outputs across humans and LLMs may arise from different underlying mechanisms.
arXiv:2608.08200v1 Announce Type: cross Abstract: Communication delay remains a central challenge in telerobotics, where it disrupts visuomotor coordination and reduces task precision. Motion scaling is an effective countermeasure to delay-induced overshoot, yet typical deployments rely on uniform gains that neglect individual and contextual variability. We propose a human-centered method that fits personalized delay-, direction-, and distance-specific scaling parameters for each participant. We conducted experiments with twenty participants who performed delayed reaching tasks in a virtual simulator. Scaling gains were computed to minimize mean overshoot in simulation in each combination of experimental conditions. Evaluation was done in simulation and on a telesurgical robot to evaluate assistance benefits. Performance was assessed across multiple delays, distances, and movement directions using overshoot, endpoint error, trajectory smoothness, economy of motion, and a composite erro
arXiv:2608.08166v1 Announce Type: cross Abstract: Research on visualizing large-scale datasets traditionally relies on empirical evaluation of scalability, determining the effectiveness of specific computation methods, algorithmic strategies, or implementations. Weak scalability, which assesses the algorithm's performance as problem size and computing resources increase, is a valuable indicator for a method's applicability at scale. However, sufficiently large data sets with increasing size are needed for weak scalability studies. To this end, it is customary to use simple scaling techniques to increase problem size by generating larger input data sets from a base data set. Nevertheless, many visualization algorithms' workload depends on factors beyond input size, such as input data complexity or output size, leading to inaccuracies in the attributed weak scalability. In this work, we highlight different common data scaling methods on multiple algorithms and data sets, recognizing that
arXiv:2608.07861v1 Announce Type: cross Abstract: Vision-language models (VLMs) are becoming a practical backend for mobile visual question answering (VQA) systems, enabling smartphones and smart glasses to answer users' questions about the physical world. Since modern VLMs remain difficult to run on mobile and edge devices, VQA systems increasingly offload inference to cloud-based VLMs. This gives mobile devices access to stronger computation, but it also makes visual input preparation a key system variable: how the image is prepared before offloading affects not only answer quality but also payload size, token cost, and system latency. Proprietary APIs expose little control over model internals or serving behavior, leaving client-side preprocessing as the main practical optimization space for downstream developers. Many such techniques have been proposed for visual offloading, yet their cost-quality impact on commercial cloud VLMs has never been studied. To fill this gap, we present
arXiv:2608.07688v1 Announce Type: cross Abstract: IT audits require auditors to judge whether heterogeneous organizational evidence satisfies semantic security and compliance controls. This judgment is difficult to automate because relevant evidence is distributed across policies, records, spreadsheets, and operational artifacts, and because audit conclusions depend on evidentiary sufficiency rather than keyword matching. We present IntelliAudit, a retrieval-grounded multi-agent system for IT audit evidence evaluation. Given a control and an evidence corpus, IntelliAudit retrieves relevant artifacts, generates an evidence-grounded assessment, challenges adverse findings, adjudicates disagreements, and produces an auditor-facing recommendation with cited evidence, rationale, missing-evidence analysis, and remediation guidance. We instantiate IntelliAudit on ISO/IEC 27001 and evaluate it across multiple simulated organizations using expert auditor review and audit-readiness user feedback
arXiv:2608.07606v1 Announce Type: cross Abstract: Despite advances in 3D ultrasound, most percutaneous cardiac interventions still rely on 2D visualization, limiting depth perception and spatial understanding. To address this challenge, we developed an Extended Reality (XR)-based platform that enables real-time six-degree-of-freedom (6-DOF) catheter tracking and visualization within a patient-specific 3D heart model. The system combines a custom machine-vision algorithm for 5-DOF catheter tracking with a 3D-printed electromechanical encoder that measures catheter roll, providing complete 6-DOF motion reconstruction. In a proof-of-concept study, 20 novice medical students navigated an intracardiac echocardiography (ICE) catheter to six anatomical targets using either immersive 3D visualization or a conventional 2D cathlab-style view. Participants in the 3D condition completed the task in 54.6 seconds and traveled 1,939 mm on average, compared with 267.5 seconds and 7,854 mm in the 2D co
arXiv:2608.07537v1 Announce Type: cross Abstract: In this study, we propose a framework that incorporates subjective evaluations provided by a Vision-Language Model (VLM) into the fitness evaluation and selection processes of a genetic algorithm. As the target of evolution, we employ virtual soft robots with flexible morphologies and locomotion and present the VLM with sequence images representing the locomotion of two individuals. Selection is performed via pairwise comparisons based on subjective evaluation terms such as adorably and weirdly. The outcomes of these comparisons are used as selection pressure within the genetic algorithm, enabling the simultaneous evolution of morphology and locomotion. Experimental results demonstrate that subjective selection by the VLM accelerates population convergence compared to random selection, while also giving rise to distinctive morphologies and motions corresponding to each evaluation term. An auxiliary experiment with human participants fur
arXiv:2608.07480v1 Announce Type: cross Abstract: Active inference has emerged as a principled framework for modeling adaptive behavior by balancing goal-directed action with uncertainty reduction. It has been successfully applied across biological and artificial systems, including recent work on human driving. However, existing active inference models of driving have yet to address an important determinant of behavior in traffic: affective state, which significantly influences decision-making. Prior work in non-traffic domains has explored active inference agents in which emotions are represented along the axes of valence and arousal in the circumplex model. However, this work has been limited to simplified settings with discrete state spaces. In this work, we propose an expanded formulation of valence and arousal that can be extracted from a more complex active inference model of driving with continuous states. In particular, we condition affective estimates not only on the current s
arXiv:2608.00817v1 Announce Type: cross Abstract: Retrieval-augmented large language models (LLMs) promise source-linked clinical support, but their value depends on whether displayed evidence guides rather than distorts physician reliance. We developed CORA, an agentic retrieval-augmented LLM, to investigate how source-linked assistance affects physician decision-making. CORA maintained benchmark performance and achieved larger gains on cases published after the models' training-data cutoffs. In a study of 46 physicians, accuracy increased from 70.8% unaided to 82.6% with CORA. Supporting citations predicted correct answers (87.7% vs 65.5%), but citations created an important asymmetry: perceived support increased adoption of correct advice from 34% to 76.9% but when an incorrect LLM answer appeared citation-supported, physician resistance to it fell from 92% to 34.8%. These findings show that source-linked LLM assistance can improve physician accuracy while introducing a grounding-de
arXiv:2608.09719v1 Announce Type: new Abstract: Learners often perceive history as distant from themselves, which limits immersion and empathy in history learning. To bridge this gap, we introduce the "Ancestral Digital Self," an AI-generated pedagogical agent presented in prerecorded videos that mirrors the learner's facial features and vocal timbre, representing a historically situated version of the self. We developed a reproducible workflow for creating AI-generated historical learning videos and conducted a within-subjects study (N=36) comparing a Digital Self agent with a non-self pedagogical agent. The Digital Self agent enhanced experiential measures, including narrative transportation, perceived relatedness, self-other inclusion, and agent perception. However, it did not improve immediate learning outcomes: quiz scores were lower in the Digital Self condition, and Remember/Know judgments showed no reliable differences. Interviews further suggested that self-similarity increase
arXiv:2608.09715v1 Announce Type: new Abstract: Debriefing is central to effective simulation-based education. However, effective debriefing is challenged by high instructor workloads and limited engagement of observing students. A real-time annotation tool to support debriefing, called PULSE, was co-designed with nursing educators. A field study comparing three standard simulation debriefings with three debriefings using PULSE was conducted as a preliminary evaluation. Outcomes were assessed using the Debriefing Assessment for Simulation in Healthcare (DASH) student survey and a follow-up instructor interview. PULSE significantly improved overall DASH scores (t(4) = 4.03, p = 0.027, Cohen's d = 2.05). Survey findings suggested improvements in debriefing organization and depth of reflection. Interview data indicated that the student-generated annotations enhanced engagement and stimulated more interactive discussions. PULSE shows promise as a support tool for debriefing, particularly b
arXiv:2608.09698v1 Announce Type: new Abstract: Great fiction earns its verisimilitude through precise details, from how a longsword is gripped to pierce armor gaps to why a bleeding corpse cannot yet smell of decay, weaving domain expertise into the fabric of invented worlds. Current AI writing tools offer limited support for discovering and integrating unfamiliar domain knowledge into narrative. They require explicit queries that authors cannot formulate, generate finished prose that risks homogenizing voice, or assist only within the boundaries of what authors already know. We argue that AI should reveal latent knowledge gaps to writers while preserving their agency to transform discovered knowledge into authentic prose. Grounded in formative interviews with 9 fiction writers, we present VeriForge, a mixed-initiative writing system that divides cognitive labor so that the system assumes initiative over domain discovery while the author retains full initiative over narrative synthesi
arXiv:2608.09294v1 Announce Type: new Abstract: Just-In-Time Adaptive Interventions (JITAIs) increasingly rely on conversational agents to elicit user routines, yet translating fluid human dialogue into rigid schedule data remains a significant challenge. We conducted a qualitative investigation of a neurosymbolic pipeline, combining Large Language Models (LLMs) with a Neo4j knowledge graph, to map unstructured verbal narratives into actionable interventions. Through human-centric evaluation using natural-language playbacks, we identified a critical "mental-model gap," where the linear extraction of LLMs clashes with hierarchical, non-linear human storytelling, causing severe entity fragmentation. Furthermore, we articulate an "ecological mismatch," demonstrating that algorithmic schedule availability frequently ignores the user's fluctuating psychological receptivity and physical energy levels. To resolve these tensions, we propose actionable design heuristics, including routine piggy
arXiv:2608.09268v1 Announce Type: new Abstract: Visual modality has recently been explored as a way to compress textual tokens, including rendering code as images for static code understanding. We study whether this representation can serve as operational context for agentic coding, where an agent must navigate repositories, edit source files, and verify executable patches. Using SWE-bench Verified, we evaluate rendered code in repository-level repair workflows and introduce controlled agent settings to separate unguided repository exploration from more structured repair stages. Our results show a mixed picture. Rendered code consistently reduces prompt-token cost, but the savings do not increase linearly with the nominal visual compression ratio. It largely preserves end-to-end repair accuracy, but does not overcome the performance limits of the underlying model or agent architecture, and can become unstable under aggressive compression. Further analysis suggests that visual code is m
arXiv:2608.09177v1 Announce Type: new Abstract: In recent years, systems that utilize immersive space have been developed in various fields. Immersive spaces often contain considerable amounts of visual information; therefore, users often fail to obtain their desired information. Therefore, various methods have been developed to guide users toward haptic sensations. However, many of these methods have limitations in terms of the intuitive perception of haptic sensation and require practice for familiarization with haptic sensation. Fabric actuators are wearable haptic devices that combine fabric and McKibben artificial muscles to provide wearers with surface haptic sensation. These sensations can be provided to a wide area of the body with intuitive perception, instead of only to a part of the body. This paper presents a novel air pressure adjustment method for whole-body motion guidance using surface haptic sensations provided by a wearable fabric actuator. The proposed system can pro
arXiv:2608.09167v1 Announce Type: new Abstract: Hand positional guidance with intuitive perception is crucial for enhancing user interaction and task performance in immersive environments. However, conventional hand positional guidance methods, relying on tactile sensations, lack intuitiveness. Consequently, users require instruction on the relationship between the tactile sensation and target position of the guidance before using these methods. Additionally, the user needs training to become familiar with tactile sensations. This study presents a hand positional guidance system with intuitive perception that leverages McKibben-based surface tactile sensations directed to the shoulder and elbow. We developed a wearable fabric actuator that provides McKibben-based surface tactile sensations to induce six specific movements: elbow flexion, extension, shoulder abduction, adduction, horizontal abduction, and horizontal adduction. The effectiveness of the actuator was experimentally validat
arXiv:2608.09156v1 Announce Type: new Abstract: Coaching in esports continues to become more common, yet conceptual tools for coaches to analyse performance breakdowns in esports remain limited. Existing approaches lack a way to distinguish between mental errors (i.e., mistakes relate to strategy and tactics), and physical slips (i.e., motor execution). This paper introduces the Esports Performance Screening (EPS) framework, that integrates several frameworks and models from sport and computing science which can be used to analyse mental and motor performance in esports. The EPS framework organises performance into five interconnected levels: strategy, tactics, tasks, actions and operations. Across these levels, teams pursue forms of superiority that influence transitions between stability and instability during invasion-based esports competition. The framework supports two complementary modes of screening use: diagnostic during reflective review and real-time screening during live pla
arXiv:2608.09108v1 Announce Type: new Abstract: Designing effective categorical palettes requires balancing a range of factors, including perceptual distinctiveness, category count, and task effectiveness. The effectiveness of categorical encodings can vary substantially depending on the target analytical tasks; however, existing recommendation tools largely ignore task context when evaluating palette quality, resulting in inconsistent performance across tasks. We synthesize findings from a series of multi-stage user studies into a unified model of task-based effectiveness for color encodings, shape encodings, and their redundant combination across category counts and seven common scatterplot tasks. Our results show that task and palette choice jointly influence perceptual accuracy: different color and shape palettes exhibit varying levels of robustness across tasks, indicating that palette effectiveness is task-dependent. We estimate task-specific perceptual strengths for 39 colors an
arXiv:2608.09107v1 Announce Type: new Abstract: People routinely interleave activities while browsing the web, often simultaneously and with overlapping boundaries. Yet organizational primitives in modern browsers treat every tab uniformly, offering no structural awareness of which items serve which purpose. While task-based organization approaches exist, they typically require users to manually organize or invoke reorganization features, and quickly fall out of sync as user intents evolve. To address this, we present Ito, a mixed-initiative approach that infers user intent in real time and organizes browsing activities into dynamic collections called Flows. Ito continuously reads unfolding user context, determines moment-to-moment focus, and creates, restructures, hibernates, and awakens Flows in real time while preserving user control through a mixed-initiative loop of proposals and corrections. In a controlled lab study (N=12) and a two-week field study (N=11), results suggest that
arXiv:2608.08990v1 Announce Type: new Abstract: Audio and video have become major learning media, but learners face two persistent challenges: the time cost of consuming long-form content sequentially and the lack of scalable feedback for imitation-based skill acquisition. This dissertation proposes an AI-guided learning framework that supports three interconnected stages: Consume, Understand, and Imitate. It develops and evaluates three systems. AIxSpeed dynamically adjusts audio playback speed at the phoneme level using speech-recognition-model confidence as a proxy for listening difficulty. FastPerson generates multimodal video summaries that preserve visual and auditory information and lets learners switch between summarized and full versions by chapter. Profy learns proficiency from largely unannotated speech data and visualizes classifier-relevant regions and model-derived acoustic distances to support pronunciation practice. Technical and user evaluations show that AIxSpeed achi
arXiv:2608.08971v1 Announce Type: new Abstract: Interacting with real-world objects in AR is difficult, especially when targets are distant, cluttered, or occluded. These challenges are amplified on emerging lightweight AR glasses, which often lack binocular or large field of view on display, but also continuous inputs, such as hand or eye tracking. Proxy-based interfaces offer an alternative by allowing users to interact with virtual abstractions of physical objects that can be repositioned, reorganized, and adapted to the task and device. However, designing such interfaces is currently manual and highly device-specific. We present Generative Proxy, a method for automatically generating proxy-based interfaces from three specifications: scene, intent, and device capabilities. We formulate generation as a constrained synthesis problem that first produces valid interfaces for the target device and task, then ranks candidates using semantic and articulatory distance inspired by direct man
arXiv:2608.08938v1 Announce Type: new Abstract: This study explores user needs for the Musical Metaverse (MM) through a series of workshops with electroacoustic composers, classical musicians, and music producers. Using a design approach based on the prompt "as if by magic," participants were invited to reflect on how the MM could impact their practice in composition, performance, and education domains. While groups maintain distinct priorities based on their roles, they share interests in educational applications and creative learning environments. Key themes include preference for mixed reality over purely virtual environments, virtual space as a creative paradigm, and tensions between democratizing tools and maintaining authentic musical experiences. Education emerged as the most promising initial use case, particularly for understanding complex musical concepts. However, participants expressed skepticism about fully virtual performances, emphasizing physical connections to instrume
arXiv:2608.08882v1 Announce Type: new Abstract: AI tools that help people judge online claims are usually evaluated while the tool is present. This paper asks a different question: after using such a tool, what can the user still do on their own? I call this epistemic transfer. It refers to the effect of prior AI-assisted verification on later unassisted performance on new claims. In this paper, I make three contributions. First, I distinguish epistemic transfer from nearby outcomes such as correction effects, trust, reliance, and human--AI team performance. Second, I introduce two simple quantities for studying it: the Epistemic Transfer Effect (ETE), which compares delayed unassisted performance across conditions, and Tool-Removal Cost (TRC), which measures the immediate drop in performance when the tool is taken away. Third, I turn these ideas into a practical evaluation protocol that can be used in online experiments or field studies. The protocol combines answer-first and evidence
arXiv:2608.08876v1 Announce Type: new Abstract: A graph layout is normally a table of $N$ free coordinates. We optimise a function with a fixed number of parameters instead. This gives a drawing a sample complexity and an extensible domain. Force-directed algorithms remain the standard tools for graph drawing. The most accurate among them minimise stress in the Kamada-Kawai formulation by directly optimising the node coordinates, at a full objective cost of $O(N^2)$ in time and space. Here, we propose Fling (Field Layout via Implicit Neural Geometry), a small neural network mapping the distances of each node to a set of landmarks, positioning it in the plane by training on the layout energy. The full spring system then becomes tractable without its distance matrix, as rest lengths follow from a landmark bound in constant time per pair while a second network learns the majorisation sums from exact anchor rows, at $O(|\mathcal{A}|N)$ per step for $|\mathcal{A}|\ll N$ anchors. Unlike neur
arXiv:2608.08856v1 Announce Type: new Abstract: Older adults increasingly use health wearables, yet often cannot inspect the properties that matter for reliance. Through 31 semi-structured interviews in China, we examined how participants judged whether wearable outputs were reliable enough for everyday use. Participants relied on brand and price, visible interface activity, lived interaction experience, and comparison with bodily sensation. These cues supported conditional trust, but did not reveal sensor validity, data continuity, or failure conditions. We describe this mismatch as an observability gap and outline design directions for showing signal quality, reliability by context, human-system fit, and alert provenance.
arXiv:2608.08729v1 Announce Type: new Abstract: CPR training requires learners to not only understand explicit procedural targets, such as compression depth and rate, but also to internalize these targets as stable psychomotor skills. However, existing CPR training systems often rely on feedback presented outside the action space, which divides learners' attention between performing compressions and monitoring external guidance. This separation weakens the coupling between action and bodily sensation and may lead to an over-reliance on external feedback, compromising skill retention once support is removed. To address this challenge, we conducted a formative study with novice trainees and certified BLS instructors, from which we derived three design goals: embedding feedback within the task space, providing active kinesthetic guidance, and gradually fading assistance based on learning phases. Informed by these insights, we designed Kinesthetic-CPR, a stage-adaptive multimodal mixed rea
arXiv:2608.08671v1 Announce Type: new Abstract: Exploratory Data Analysis (EDA) systems extract and present data facts to summarize meaningful patterns such as trends and correlations for efficient dataset exploration. However, existing approaches rarely consider outlier detection at the level of data facts,and heterogeneous facts from different analytical scopes are often aggregated in a single view, making it difficult to define meaningful metrics and effectively analyze data fact outliers. To fill this gap, we present FOX, a novel visual analytics system for interactive data Fact Outlier eXploration. FOX organizes data facts into groups with consistent analytical scopes and computes a unified outlier score that combines distribution-based and pattern-based components. Its interface comprises an Upload Panel for data preparation and two coordinated exploration panels: the Overview Panel employs a matrix-based visualization to enable an intuitive overview of all data facts, and the Ma
arXiv:2608.08663v1 Announce Type: new Abstract: Humans converge on shared names for novel, hard-to-describe objects through repeated interaction, a process psycholinguists call lexical entrainment. Leading vision-language models fail at this: recent empirical work documents that they do not shorten references, reuse successful expressions, or maintain stable pact state across turns. We present a framework that addresses the gap by externalizing pact state into three explicit, inspectable sets of referent-object bindings ($\Gamma, \Xi, \Omega$), updated by a dynamic-semantics context-change rule. The symbolic layer sits on top of a lightweight perceptual-alignment pipeline that grounds noisy human referring expressions in crowd-sourced imagery via SIFT homographies and the Universal Quality Index. Evaluated on the Stanford Repeated Reference Game corpus (over 15{,}000 director-matcher utterances on abstract tangram stimuli), the framework places the correct target in its top-5 hypothesi
arXiv:2608.08657v1 Announce Type: new Abstract: Public health organizations regularly produce and publish data visualizations to raise awareness of critical issues, influence decision-making processes, and promote overall well-being. However, the design practices shaping these visualizations in real-world settings remain largely unexamined, limiting the research community's ability to evaluate their effectiveness, accessibility, and alignment with communication goals. To address this gap, we construct and analyze a large-scale corpus of over 4,000 real-world data visualizations drawn from more than two dozen websites associated with U.S. and international public health organizations. We evaluate salient design characteristics like chart type, visualization accessibility, use of embellishments like iconography, and design flaws. This work contributes to understanding real-world decisions in designing data visualizations and supports public health officials in improving data visualizatio
arXiv:2608.08535v1 Announce Type: new Abstract: Recorded videos of offline open classes provide good examples for early-stage teachers to learn instructional strategies, e.g., how to organize cooperative learning. However, learning by watching these videos is challenging, as these strategies are implicitly performed, and it lacks in-situ reflective support. In this paper, via a formative study (N=9), we design TeachUp to support the learning of instructional strategies from classroom teaching videos. TeachUp adopts an LLM-powered pipeline to detect nine instructional strategies in videos (precision = 63.4%), provides reflective questions and hints while watching, and generates customized practices with reflective feedback. A within-subjects study (N=16) shows that compared to a traditional video-playing and self-practicing baseline, early-stage teachers with TeachUp are more engaged in learning and perform better in applying learned strategies to new tasks. Interviews with four in-serv
arXiv:2608.08497v1 Announce Type: new Abstract: The emergence of social finance (SocialFi) transforms online communities into complex socio-economic systems. Within these spaces, collective decisions shape a "digital commons" characterized by social capital (e.g., community trust) and financial health (e.g., market liquidity). Governing such hybrid ecosystems is challenging because real-world interventions are costly and irreversible. While counterfactual simulation is essential for exploring alternative governance strategies, existing approaches fail to capture the non-linear interplay between governance rules, individual behaviors, and emergent economic outcomes. To systematically unpack this complexity, we operationalize the Institutional Analysis and Development (IAD) framework as our theoretical foundation, synthesizing prior literature with insights from formative expert interviews. Built on this framework, we present SocialFiVis, an IAD-embedded visual analytics sandbox. It intr
arXiv:2608.08443v1 Announce Type: new Abstract: Previous studies have shown that people can develop shared symbols, partner-specific expressions, personal idioms, inside jokes, and other parts of a relational microculture. Recent work has also examined how humans and conversational AI negotiate and revise symbolic meanings. However, long-term human-AI systems still lack a clear design model for recording how a dyad-specific expression gains meaning, checking whether both sides still accept that meaning, and safely reusing the expression in later sessions. This concept-and-prototype paper introduces Private Etymology, a machine-representable relational provenance that records how a dyad-specific symbolic expression is proposed, interpreted, negotiated, repaired, reused, revised, stabilized, contested, forgotten, or retired over time. I also propose relational reuse: reactivating a dyad-specific expression in a later session without fully explaining its meaning again. The contribution is
arXiv:2608.08430v1 Announce Type: new Abstract: Virtual cells employ machine learning models to simulate and predict cellular behaviors, serving as a critical computational framework for investigating health and disease. Injecting causal graphs into virtual cells can improve the interpretability, but such graphs are usually not available in real-world applications. Recently, many methods have been proposed to construct causal graphs from data, which group genes based on their similarities to form concepts and extract their causal relationships. However, since this automatic process is unsupervised, the causal graphs usually contain errors. In this paper, we propose a human-guided causal knowledge injection method for virtual cells. We developed a gene-similarity-aware causal graph visualization supported by a hybrid optimization algorithm to help explore both the causal relationships between concepts and the similarities between genes. Based on the exploration, we further developed a c
arXiv:2608.08386v1 Announce Type: new Abstract: Modern scientific simulations generate massive volumes of data, making lossy compression essential for efficient storage and transmission. However, preserving critical quantities of interest (QoIs) under lossy compression is inherently data- and task-dependent, requiring domain scientists to navigate complex trade-offs between compression ratio and data fidelity. Exploring these trade-offs often involves large design and evaluation spaces, motivating human-in-the-loop approaches that combine interactive exploration with quantitative analysis. To address this challenge, we present FZ-VIS, an interactive framework for human-in-the-loop feature-oriented lossy compression design and visual analytics. FZ-VIS provides a web-based interface for rapidly generating and comparing compression configurations, along with integrated visualization tools for assessing reconstruction fidelity and QoI preservation through both visual inspection and quantit
arXiv:2608.08349v1 Announce Type: new Abstract: Audio dramas weave dialogue, sound effects, and music into immersive stories. Creators often adapt books into audio dramas, but this process remains labor-intensive, requiring them to interpret source material, author scripts, generate audio assets, and assemble them on a timeline. Because story elements like characters and scenes manifest across many interdependent assets, a single change can ripple into manual updates across the entire project. We present Dramarrator, an audio drama authoring tool built around object-based audio editing, where these story elements are represented as editable objects. Dramarrator extracts these objects from a book, generates linked audio assets (speech, sound effects, and music), and composes a multi-track audio drama. Edits to any object (e.g., a character's voice) automatically propagate to all dependent assets. In a user study with professionals (N=8), Dramarrator significantly lowered task load when
arXiv:2608.08333v1 Announce Type: new Abstract: Introduction: Models such as TAM, TAM2, TAM3, UTAUT, and UTAUT2 underpin a substantial share of research on technology acceptance and use in HCI and related fields. Although they differ in terms of constructs and conditions of application, their selection is often not conceptually justified, frequently driven by pragmatic considerations, with implications for theoretical consistency and cross-study comparability. Objective: To propose a decision framework that guides the selection among the five models of the TAM/UTAUT lineage based on conceptual criteria derived from their structural differences. Methods: A conceptual, artifact-oriented approach was adopted, comprising comparative analysis of the models and critical review studies, derivation of conceptual dimensions, and formulation of operational decision criteria. Applicability was demonstrated through two contrasting research scenarios. Results: The framework articulates five analyti
arXiv:2608.08274v1 Announce Type: new Abstract: Visualization design often proceeds under unresolved conditions---goals shift, data remain provisional, stakeholder needs evolve, and several plausible directions may remain available at once. Existing visualization frameworks help organize design work and articulate major decisions, yet offer limited explanation of how practitioners proceed before a path forward has become clear. Drawing on an episode-level analysis of a previously collected three-phase qualitative corpus involving eleven expert visualization practitioners, we examine situations in which the problem, representational target, or viable direction remained unsettled. We find that practitioners make such situations actionable through provisional local moves. These moves reveal patterns, distinctions, and interpretive possibilities; clarify what is tractable, viable, or worth pursuing; and sometimes reorient the work itself. The analysis shows that situated action, profession