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:2608.12332v1 Announce Type: new Abstract: In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning the full set of parameters. In this work, we uncover several key insights regarding the singular components of network parameters based on Singular Value Decomposition (SVD). Firstly, the principal singular components with large singular values in pre-trained network parameters can be effectively reused during fine-tuning, whereas the minor components with smaller singular values are more task-specific and require substantial adaptation. Secondly, we first establish the theoretical connection that the uncontrolled growth of singular values in LoRA adapters leads to the forgetting of pre-trained knowledge -- a well-known issue referred to as catastrophic forgetting. Building on these observations, we propose SCLoRA, which injects parameterized singular components
arXiv:2608.12331v1 Announce Type: new Abstract: Reasoning language models generate lengthy chain-of-thought (CoT) sequences whose key-value (KV) cache grows linearly and becomes a memory bottleneck during decoding. Existing compaction methods treat reasoning trajectories as flat token sequences and apply uniform compression, ignoring the hierarchical structure of CoT reasoning where different steps vary drastically in importance. We propose \textbf{Thought-Aware Attention Matching (TAM)}, which exploits this structure through three mechanisms: (i)~thought segmentation that decomposes the trajectory into reasoning blocks, (ii)~adaptive budget allocation that assigns compression budget based on each segment's importance and size, and (iii)~pivotal token protection that preserves high-attention reasoning anchors. We prove that the allocation rule is optimal under a convex error model and that cumulative error under sequential compaction remains bounded. Experiments on AIME 2024 and MATH-5
arXiv:2608.12330v1 Announce Type: new Abstract: The detection of online sexism remains an open problem. Sexism detection is inherently subjective, yet most existing systems reduce multi-annotator labels to a single majority decision and treat all instances uniformly. This ignores two informative signals: annotator agreement and model uncertainty. We propose RA-DPO (Reliability-Aware Direct Preference Optimization), which integrates annotator agreement, model confidence, and a token-level uncertainty signal into a single reliability score. RA-DPO uses this score to select high-value preference pairs during training and to support inference-time abstention, which allows the model to trade coverage for accuracy. We evaluate RA-DPO on 6,920 multilingual posts from EXIST 2023, fine-tune OpenAI gpt-4o base via DPO, and validate on two open-weight 3B models (Llama, Qwen). Results show that training on the top 30% most reliable pairs matches full-data DPO, which indicates that reliability-awar
arXiv:2608.12328v1 Announce Type: new Abstract: Parameter-efficient fine-tuning methods such as LoRA have transformed the adaptation of large autoregressive language models, enabling task-specific customization with substantially fewer trainable parameters. However, these methods have not been successfully extended to diffusion-based language models, which generate text through iterative denoising rather than sequential token prediction. We propose LoRA-Diffusion, a parameter-efficient fine-tuning approach that applies low-rank decomposition to the denoising trajectory instead of model weights. Unlike weight-based LoRA, which modifies individual transformation matrices, our method learns low-rank perturbations to the entire diffusion path from noise to output. We introduce trajectory-level low-rank adapters that modify each denoising step, step-adaptive rank allocation across diffusion phases, and compositional multi-task learning that allows merging task-specific modules at inference
arXiv:2608.12327v1 Announce Type: new Abstract: Multilingual pretrained models nominally support Nepali, yet no controlled benchmark has compared them under a single fine-tuning protocol. We fine-tune six pretrained models (XLSR-53, IndicWav2Vec, MMS-1B, Whisper-Medium, Whisper-Large-v3-Turbo, and Conformer-Hi) spanning CTC self-supervised, autoregressive encoder-decoder, and hybrid Conformer-CTC architectures, on the OpenSLR SLR54 Nepali corpus (~165 hours) using identical preprocessing, splits, optimizer, and family-matched learning-rate schedules. We evaluate Word Error Rate (WER), Character Error Rate (CER), and Real-Time Factor (RTF) on three independent test sets (OpenSLR, FLEURS, Common Voice). Whisper-Large-v3-Turbo (14.76% WER) and IndicWav2Vec (14.89% WER) tie at the top despite a 9x parameter gap and 40x pretraining-data gap, providing direct empirical evidence that language-family proximity in pretraining can substitute for raw scale for in-domain Nepali. CTC decoders run u
arXiv:2608.12326v1 Announce Type: new Abstract: Ontology learning transforms unstructured text into structured representations for automated reasoning. Yet structuring information risks losing it, and current evaluation methodologies cannot detect such loss, focusing on structural correctness while failing to measure whether meaning survives transformation. We propose an evaluation methodology that addresses this: comparing LLM task performance on source documents against performance on transformed representations, with the difference quantifying semantic loss. We demonstrate this approach on legal merger agreement analysis, a domain chosen for its complex language and precise semantic requirements, comparing direct LLM application against three ontology learning methods across six language models. The results reveal systematic semantic loss with significant variation based on reasoning complexity and model-method interactions. Our contributions are: (1) an evaluation framework for mea
arXiv:2608.12322v1 Announce Type: new Abstract: Self-reflection is widely assumed to improve LLM reasoning, yet which component drives the gain remains poorly understood. We present a controlled six-condition ablation isolating four components of LLM self-reflection: evidence exposure, diagnostic scaffolding, taxonomy vocabulary, and action routing. Two precise null results converge on a single mechanism. First, structured diagnostic questions add no measurable value over unstructured reflection ($\text{F1} = 0.296$ vs $0.297$, $p = 1.000$, 95\% CI $[-0.041, +0.040]$). Second, presenting the full uncertainty taxonomy while collapsing the action space to a single generic action also adds no value ($\Delta\text{F1} = +0.008$, overlapping 95\% CIs), ruling out taxonomy vocabulary as the mechanism. Typed action routing provides consistent directional gains ($\text{F1} = 0.379$ vs $0.296$); the conservative estimate controlling for taxonomy vocabulary is $\Delta\text{F1} = +0.075$, and the
arXiv:2608.12321v1 Announce Type: new Abstract: When a salient surface cue competes with an implicit feasibility constraint, LLMs often fail -- but aggregate accuracy conflates genuine constraint inference with conservative defaulting. We formalize the distinction as conditional constraint activation: the constraint is internally encoded (Knowledge) symmetrically across constraint-present and -absent prompts (Symmetry), yet only sometimes routed into the decision (Routing) and repairable by a donor activation (Repair). A quartet diagnostic over 14 models reveals two failure modes; probes on two open weights decode the constraint above $88\%$, yet activation patching repairs one ($+6.4$ nats) and not the other ($-0.07$). On a mitigation frontier, no prompted intervention reaches the repair corner: all inflate conservative bias through a single mediation pathway -- prerequisite mention. Hidden-constraint failure is a routing problem, not a knowledge problem.
arXiv:2603.08322v2 Announce Type: replace-cross Abstract: We study mathematical discovery through the lens of neurosymbolic reasoning, where an AI agent powered by a large language model (LLM), coupled with symbolic computation tools, and human strategic direction, jointly produced a new result in combinatorial design theory. The main result of this human-AI collaboration is a tight lower bound on the imbalance of Latin squares for the notoriously difficult case $n \equiv 1 \pmod{3}$. We reconstruct the discovery process from detailed interaction logs spanning multiple sessions over several days and identify the distinct cognitive contributions of each component. The AI agent proved effective at uncovering hidden structure and generating hypotheses. The symbolic component consists of computer algebra, constraint solvers, and simulated annealing, which provides rigorous verification and exhaustive enumeration. Human steering supplied the critical research pivot that transformed a dead e
arXiv:2510.00387v4 Announce Type: replace-cross Abstract: This study uses controlled simulations with known ground-truth parameters to evaluate how Distributional Latent Variable Models (DLVM) and Bayesian Distributional Active LEarning (DALE) perform in comparison to conventional Independent Maximum Likelihood Estimation (IMLE). DLVM integrates observations across multiple executive function tasks and individuals, allowing parameter estimation even under sparse or incomplete data conditions. To establish known-ground truth, we uniformly sample individual sessions from a neural network learned latent space and map them to distributional cognitive performance across different tasks. The individual test-items are then sampled from these distributions using either DALE, random procedure or a standard fixed battery approach. When given the same set of observations, DLVM consistently outperformed IMLE, especially under smaller amounts of data, and converges faster to highly accurate estimat
arXiv:2410.02091v4 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) facilitates content production and enhances ideation, with potentially important implications for developer productivity and participation in software development. To explore its impact on collaborative open-source software (OSS) development, we investigate the role of GitHub Copilot, a generative AI pair programmer, in OSS development where multiple distributed developers voluntarily collaborate. Using GitHub's proprietary Copilot usage data, combined with public OSS project data obtained from GitHub, we find that Copilot use increases project-level code contributions by 5.9%. This gain is accompanied by a 3.4% increase in developer coding participation and a 2.1% increase in individual code contributions. However, Copilot use is also associated with an 8% increase in coordination time and more code discussions. This reveals an important tradeoff: While AI expands who can contribute and h
arXiv:2407.11823v4 Announce Type: replace-cross Abstract: The United States Food and Drug Administration's (FDA's) 510(k) pathway allows manufacturers to gain medical device approval by demonstrating substantial equivalence to a legally marketed device. However, the inherent ambiguity of this regulatory procedure has been associated with high recall among many devices cleared through this pathway, raising significant safety concerns. In this paper, we develop a combined human-algorithm approach to assist the FDA in improving its 510(k) medical device clearance process by reducing recall risk and regulatory workload. We first develop machine learning methods to estimate the risk of recall of 510(k) medical devices based on the information available at the time of submission. We then propose a data-driven clearance policy that recommends acceptance, rejection, or deferral to FDA's committees for in-depth evaluation. We conduct an empirical study using a unique dataset of over 31,000 subm
arXiv:2607.23126v2 Announce Type: replace Abstract: The rapid adoption of generative AI tools has created new literacy demands for designers who must verbalize tacit knowledge through natural language prompts. Yet the micro-level cognitive processes by which designers externalize implicit intentions during iterative AI dialogue remain underexplored. This exploratory qualitative study examined five expert designers (11-20 years UI/UX experience, M = 15.4 years) using think-aloud protocols. We identified "Strategic Oscillation" -- experts' intentional return to vague language (Vibe) after progressing toward operational specifications (Code), leveraging AI's probabilistic nature for creative exploration. We observed shifts from "instruction" to "consultation" mode, deep reflection triggered by semantic collisions, and one participant's disengagement revealing conceptual alignment as a tentative boundary condition. We propose the ECRT cycle (Expectation-Collision-Reflection-Transformation)
arXiv:2605.11793v2 Announce Type: replace Abstract: Algorithmic recourse provides counterfactual action plans that help people overturn unfavorable AI decisions. While diverse recourse sets may improve transparency and motivation, they may also impose cognitive load and negative emotions by increasing counterfactual reasoning demands. To examine this trade-off, we conducted a between-subjects controlled experiment (N=750) that manipulated recourse-set diversity and size, and evaluated these effects on psychological benefits and costs. Results show that diversification enhances psychological benefits (e.g., willingness to act) for small sets without incurring additional psychological costs, whereas for large sets, it makes cognitive load more salient. These findings suggest that naively diversifying recourse can burden decision subjects, underscoring the need for new diversification methods that incorporate human cognition and psychology to mitigate such costs.
arXiv:2608.13285v1 Announce Type: cross Abstract: Motor imagery (MI) brain--computer interfaces (BCIs) have emerged as a promising approach for establishing flexible communication pathways between the human brain and external devices , particularly for individuals affected by stroke or neurodegenerative disorders. Reliable decoding of motor-imagery electroencephalography (MI-EEG) remains challenging because EEG recordings contain substantial noise and exhibit complex, weakly informative relationships with the underlying brain activity. Although deep learning provides an effective means of learning representations directly from EEG signals, its application to MI-EEG feature learning remains comparatively limited. This study introduces a hybrid deep-learning architecture that integrates a convolutional neural network (CNN) with a bidirectional long short-term memory (bi-LSTM) network. The CNN is used to learn high-level spatial and temporal representations directly from raw MI-EEG record
arXiv:2608.13217v1 Announce Type: cross Abstract: Convolutional Neural Networks (CNNs) capture local features efficiently but struggle with global context due to their limited receptive field. On the other hand, transformers effectively capture global dependencies through self-attention but suffer from high redundancy and computational costs. Thus, to leverage the advantages of both CNNs and transformers, we propose a unified model (UniCon-Former) that aims to provide robust and efficient performance on dynamic hand gesture recognition. The unified approach helps the model to learn both local and global features. At the beginning of each transformer stage, the convolution projections help in decreasing the dimension of the input vectors of the transformer block. This creates a pyramidal structure at each transformer stage. These features enable the UniCon-Former to reduce resource usage than vanilla transformers, making it flexible for learning multi-scale and high-resolution features,
arXiv:2608.13014v1 Announce Type: cross Abstract: Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet reasoning about physically grounded interaction requires estimating the forces acting on hands and objects, beyond localizing contact. We present EgoPHI, the first method that jointly estimates dense contact maps and 3D force distributions on hand and object meshes from a single monocular RGB image and object geometry. To address the lack of scalable ground-truth force annotations, we introduce a physics-based simulation pipeline that augments existing hand-object datasets with dense per-vertex force supervision. EgoPHI then learns dense 3D contact and force on interacting hand and articulated object meshes, extending vision-based force estimation beyond image-space or planar settings. Our evaluation on in-distribution and out-of-distribution benchmarks shows that EgoPHI improves force estim
arXiv:2608.12750v1 Announce Type: cross Abstract: LLM-based simulated clients are increasingly used to train novice counselors, evaluate LLM therapists, and generate synthetic data. However, current simulators produce overly cooperative clients that disclose too readily, accept therapeutic reframes without resistance, and resolve core issues within a single session. We trace these issues to profiles that lack causal depth and behavioral mechanisms that treat all content as equally accessible. We present PatientAct, a framework for client simulation grounded in established clinical theories. Our profiles integrate the 5Ps clinical case formulation, providing causal depth without tying the design to any single therapeutic modality. During simulation, profiles include a dynamic memory layer in which items carry trust thresholds (e.g., symptoms are available early, whereas formative memories require a sustained therapeutic alliance). At each turn, the client's emotional reaction and behavi
arXiv:2608.12650v1 Announce Type: cross Abstract: Deploying robot fleets in complex, real-world environments requires human operators to supervise multiple robots simultaneously. Managing operator attention is a fundamental challenge of designing multi-robot supervision interfaces, encompassing both feed layout and feed content (i.e., robot behavior design). Thus far, designers lack empirical guidance on the latter-how to change a robot's behavior to capture, sustain, or relinquish operator attention during multi-robot supervision. In our vision of the future, designers should be able to use this guidance to calibrate robot behavior to different operator attention profiles. Treating operator eye gaze as a robot behavior design clue, we created a pre-deployment elicitation tool called Attune. Attune automatically identifies when meaningful gaze shifts occur, provides AI assistance for annotating why shifts occurred, and outputs a summary of operator gaze patterns for operator review. We
arXiv:2608.12627v1 Announce Type: cross Abstract: Long-horizon egocentric memory transforms continuous first-person video and audio into a searchable record of past experiences. We demonstrate two bottlenecks in existing systems: indices built from context-poor captions are unreliable for agentic search, while retrieval ignores a question's temporal intent. To address both bottlenecks, we introduce EgoCITE (Egocentric Context-augmented Indexing and Time-aware Evidence retrieval), a long-horizon agentic memory framework for egocentric QA. EgoCITE comprises three components. EgoScheme uses local multimodal context to turn fragmentary video captions and speech transcripts into self-contained atomic memory indices. EgoIndex organizes complementary action, activity, utterance, and conversation representations into searchable multi-view memory indices at multiple granularities. EgoRetrv combines semantic search with question-conditioned temporal relevance scoring and curation of retrieved ev
arXiv:2608.12529v1 Announce Type: cross Abstract: Motivation: LinkML is a suitable language for the representation of the structural and content constraints of different kinds of biomedical data. Even if it is a quite recent proposal, it has been applied in several biomedical contexts. Developing and maintaining LinkML schemas presents several challenges, particularly for novice curators. Non-expert bio-curators may struggle with LinkML syntax and best practices, requiring significant time and effort to develop well-structured schemas. Results: In this paper we propose SchemaLink, a web-based environment for the graphical construction and enhancement of LinkML schemas that address the following requirements: $(i)$ introduce a graphical language for the specification of LinkML schemas, $(ii)$ make uniform the specification of schemas in similar contexts, $(iii)$ simplify the design and curation processes by exploiting a RAG-based approach to assist curators in creating new schemas from
arXiv:2608.12339v1 Announce Type: cross Abstract: Large Language models (LLMs) were found to be susceptible to a host of social, affective, and cognitive biases. We examined two mechanisms through which such biases can be generated even when human preferences (in the training data) are not biased or when they are correctly categorized as being biased. The first is faulty mimicry of preferences based on human behavior: this involves LLMs inferring human preferences even when behaviors are logically unrelated to preferences. The second is mimicry of explicitly biased human behaviors. In four studies focusing on economic biases, we find that ChatGPT-4o and Qwen exhibited social proof biases even when prompted with reports of human behaviors that were clearly non-indicative of individuals' actual preferences. LLMs also displayed loss aversion when it was explicitly described as a bias. Indeed, when prompted with detailed scientific reports, the extent of the bias (i.e., loss aversion) in t
arXiv:2608.12329v1 Announce Type: cross Abstract: Progress on AI for psychosis-risk assessment is limited by a data-access bottleneck. Real clinical interviews are difficult to share because of privacy, governance, and consent constraints. We present AnchorSIPS, a synthetic dataset of 10K structured psychosis-risk interviews with transcript-grounded measurement targets. Each interview is modeled on Mini-SIPS, a clinician-administered psychosis-risk interview. It captures history, 24 symptom questions, follow-up evidence for items the patient affirms, decisions about delusion-like symptoms (unusual beliefs), hallucination-like symptoms (unusual perceptions), and disorganized communication, exclusion of clear psychotic-level symptoms ("frank psychosis"), and a final attenuated psychosis syndrome (APS) diagnosis, a high-risk state of milder or early psychotic symptoms. The APS diagnosis is not a standalone label. It depends on earlier endorsements, supporting follow-up details, symptom-cl
arXiv:2608.13532v1 Announce Type: new Abstract: People with disabilities (PWD) increasingly use avatars to express disability identities in social virtual reality (VR), but greater visibility also invites targeted harassment. Existing safety features are often insufficient, overlooking PWD's experiences and needs. To address this gap, we co-designed protection mechanisms with 11 PWD to reveal their values and needs. Our research employed a social lens to interpret harassment behaviors and protection mechanisms. Inspired by Hall's Proxemics Theory that interpersonal distances indicate social intent and boundaries, we divided social VR spaces into four proxemic zones (Intimate, Personal, Social, and Public) and used them to structure our protection mechanism co-design. We also provided different protection mechanism probes (Inform, Educate, Consent, and Combat) to elicit participant preferences. Our study highlighted the role of social proximity in shaping PWD's harassment perception and
arXiv:2608.13454v1 Announce Type: new Abstract: Knowing someone deeply means not just understanding what they say or do but also how they will likely think, react, and engage across situations. Such predictions could eventually inform systems to anticipate when the individual is about to deviate from their goal, catch regrettable behaviors before they are made, and surface blind spots before they take hold. While many interactive systems model users to enable more personalized interactions, most cannot make such behavioral predictions, as this often requires longitudinal observation and inference of how the individual's behaviors unfold across various everyday situations. In this work, we introduce a novel LLM-based predictive behavioral modeling approach that anticipates a user's likely behavior across everyday conversational situations. We (1) collect a longitudinal dataset of over 1000 hours of naturalistic conversations from 14 participants using a wearable smartwatch; (2) evaluate
arXiv:2608.13412v1 Announce Type: new Abstract: Closed-loop sensorimotor training systems can improve learning by sensing movement and delivering real-time feedback, yet most are built as fixed implementations tied to a single task, even though the core technology (inertial and tactile sensing, vibrotactile cueing, rule-based logic) remains the same. We present Sensorimotor Stickies, a reconfigurable on-body platform that treats sensing and vibrotactile feedback as modular stickies that can be patched onto the body as needed. The platform includes miniaturized adhesive modules for IMU sensing, optional tactile sensing, and vibrotactile actuation; low-power firmware and BLE infrastructure for raw streaming and motor control without task-specific rewrites; and a companion mobile app that provides a shared body-centered model for placement, calibration, and feedback authoring. Together, these components enable reconfiguration across training scenarios, user needs, and feedback setups. We
arXiv:2608.13295v1 Announce Type: new Abstract: We analyzed the ethics reporting in 255 IEEE VIS papers from 2024 and 2025, as published in TVCG. This analysis arose from our experience as readers and reviewers of IEEE VIS papers that such reporting is frequently incomplete or missing, as well as from investigations in which we ourselves had to answer challenges regarding ethics approval in our own work. Visualization research naturally often involves human participants, yet ethics approval and informed-consent procedures are not always explicitly reported. In our corpus, 189 papers (74.1%) reported on work involving human participants. Only 6 of them (3.2%) reported to have obtained ethics approval, an approval identifier, and having received informed consent from the participants, while 26 (13.8%) reported at least ethics approval and informed consent. These omissions do not imply that the empirical work was unethical or lacked approval. They rather show that current reporting practi
arXiv:2608.13279v1 Announce Type: new Abstract: This paper introduces Print&Fold, a tool to allow FDM 3D printing of complex models with less time and material while preserving shape accuracy. Key to this work is a folding algorithm that planarizes foldable faces internal to the 3D model. While folding techniques typically discretize a target model's surface, thereby fabricating low fidelity counterparts, our method preserves the surface features in the physical print. Our design tool allows users to unfold 3D models to be FDM-printed flat before manually folding these into their target shapes. We showcase a variety of applications and evaluate the material and time savings across a range of 3D models.
arXiv:2608.13216v1 Announce Type: new Abstract: LLM-based chat systems have become valuable tools for design practice, enabling rapid ideation and flexible task support. Yet these systems process designer utterances as generic sequences, maintaining context through recency rather than through any model of how the speaker organizes knowledge. In design conversation, this gap compounds as relational context decays between turns, identical words go unresolved across designers, and the conversation loops or restarts rather than deepens. We present CogChat, a real-time chat framework that grounds conversational AI in a personal heterogeneous knowledge graph constructed from each designer's input. The system extracts typed entities and relations into a heterogeneous graph, then applies a HGT (Heterogeneous Graph Transformer) to select structurally relevant nodes for response generation and to generate both intentional and exploratory probing questions. Technical evaluation shows that HGT-bas
arXiv:2608.13054v1 Announce Type: new Abstract: While the detrimental impacts of driving under the influence of stimulants such as methamphetamine are well-documented, the driving performance of individuals currently under-treatment has received considerably less attention. This study compared the behavior of individuals with a history of stimulant abuse (across two distinct treatment phases) with a control group of healthy drivers using a driving simulator. Oculomotor and biomechanical data were continuously collected via an eye-tracker and a Kinect sensor, respectively. These parameters were utilized to train a K-Nearest Neighbors (KNN) classification model designed to detect high-risk behavioral patterns in drivers undergoing methamphetamine rehabilitation. Through the evaluation of various feature combinations and neighborhood configurations, the optimized model successfully discriminated between normal drivers and those with a history of abuse with an accuracy of 90%. Detecting at
arXiv:2608.13017v1 Announce Type: new Abstract: The widespread use of LLMs among psychiatric populations has raised concerns regarding their safety and potential iatrogenic impact in the context of AI psychosis. While growing literature conceptualizes AI psychosis and documents case studies, empirical evidence tracing AI-exacerbated psychotic processes remains scarce. We propose and test a longitudinal qualitative evaluation design, supported by automated metrics, to assess mainstream LLMs' potential to exacerbate psychosis. Fifteen widely used LLMs were prompted across 30 days using the same 30-message script, simulating progression from mild anomalous experiences to psychotic ideation. Four trained evaluators independently rated 449 model-days, assessing (1) recognition stage (from naive engagement to stabilized clinical framing), (2) interpretative confidence, and (3) intervention profile (from education to treatment recommendation). Two computational metrics-entrainment and modalit
arXiv:2608.12857v1 Announce Type: new Abstract: Presentations are essential for students, researchers, and professionals to communicate ideas persuasively, yet delivering them effectively requires repeated practice that coordinates content, delivery, visual materials, and audience interaction. Existing AI-assisted rehearsal tools provide scalable feedback, but they often treat presentations as single-run delivery performances, offering limited support for linking feedback to the slide deck or planning what to practice in the next iteration. To address this gap, we introduce PolyPresentation, a multimodal AI platform for slide-aware iterative presentation practice. PolyPresentation organizes slide-by-slide practice, full rehearsal, audience Q&A, and feedback into a unified practice loop, using slide-grounded evidence to help presenters diagnose performance issues and prepare for subsequent practice. We evaluate PolyPresentation through a rubric-based comparison with four baseline system
arXiv:2608.12792v1 Announce Type: new Abstract: Contribution statements are an increasingly common way to make research labor visible, reduce academic malfeasance, and provide broader transparency. Despite this potential value, they remain uncommon in visualization and HCI. To explore this gap, we conducted an online study with (N=21) visualization and HCI researchers. We find a range of differing opinions about the utility of contribution statements, which are set against a background of tensions relating to contribution frameworks that inadequately fit contribution types in HCI and especially visualization, power dynamics between authors, bias in authorship perceptions, and the tedium of providing yet another form of documentation. From these factors, we offer a modest recommendation to authors: consider contribution statements. There are contexts when they may usefully explicate work, and others where they can cause author-team conflict or become a burdensome chore. Regardless of wh
arXiv:2608.12759v1 Announce Type: new Abstract: The ability to navigate outdoors safely and independently is crucial yet challenging for people with low vision (PLV). While various augmented reality (AR) systems for low vision have been designed and evaluated in ideal lab environments, no research has investigated their real-world feasibility and challenges. We present NavSight, a mobile AR application that assists PLV in outdoor navigation by recognizing important outdoor objects (e.g., curb, vehicle) and rendering real-time visual augmentations. Through a seven-day diary study with 12 PLV in real-world settings, we characterize the impact of NavSight on scene perception, users' configuration strategies on what objects to augment and how to augment them across scenarios, how users made sense of and responded to recognition errors, and the social acceptability of using NavSight in public. We further identify environmental factors affecting recognition, such as weather conditions, light
arXiv:2608.12605v1 Announce Type: new Abstract: Designers require different design spaces across creative stages: broad during exploration, and targeted during refinement. Yet existing agent-driven tools assume a fixed or continuously expanding space, leaving designers to manage and navigate it themselves. Informed by a formative study with five designers, we propose an axis-centered workflow that adaptively broadens and narrows the design space to support structured exploration and refinement. We implemented this workflow in Surprise2Refine, a prototype that allows users to build and reshape an nxn design space through a set of axis-centered interactions as their creative intent evolves. A within-subjects study with 14 designers shows that Surprise2Refine enhances users' sense of control, supports tracking of scaffolding paths, and improves the perceived creativity of design outcomes. We further distill design insights to guide future agent-assisted tools for creative scaffolding.
arXiv:2608.12582v1 Announce Type: new Abstract: AI journaling tools can tailor prompts to a person's own sensed behavior, but it is unclear which behaviors respond to them. We analyzed 369 journal entries from an eight-week passive sensing study. An LLM labeled each entry as expressing an intention to change a behavior or not, and we measured follow-through against 26 sensor features with a 3-day before/after comparison. Responsiveness depended most on whether a behavior involves other people. Behaviors that depend on others improved in only 15 to 22% of cases, while behaviors a person can act on alone improved more often, up to 50 to 63%, though unevenly. How users wrote mattered less. No single text feature separated improved from unimproved entries; writing carried signal only within specific behaviors, most clearly for text messaging and for longer, more personal intention entries. The sample is small, so we treat these as exploratory patterns that point to where AI journaling nudg
arXiv:2608.12580v1 Announce Type: new Abstract: Textiles are increasingly explored as media for interacting with digital information. However, many of the existing approaches rely on visible tags, printed overlays, or electronic modules that compromise the fabric's aesthetic and tactile qualities. To address this, we present InvisIto, a method for weaving visually unobtrusive yet machine-readable infrared markers directly into fabrics using near-infrared (NIR)-absorbing yarns. Although these yarns look similar to standard fibers in ambient light, they produce strong contrast in NIR imaging. Our method includes: (1) a design tool that helps users easily embed infrared markers into weaving drafts, (2) five disguising strategies that further reduce marker visibility under ambient light, and (3) a camera-based detection pipeline for decoding and tracking the woven markers. InvisIto supports both woven QR codes for data encoding and woven ArUco markers for binary input and deformation track
arXiv:2608.12552v1 Announce Type: new Abstract: Norway is among the most digitalized countries in the world, where access to essential services increasingly depends on digital systems. Although universal design of ICT is legally required across public and private sectors, ensuring cognitive accessibility for older adults involves more than technical compliance. We analyzed responses from 294 participants aged 55 to 90 to examine the barriers they encounter when using digital services. Our findings identify four tensions, namely navigational, semantic, procedural, and temporal, which reveal how accessibility barriers emerge through misalignments between service design, system assumptions, and older adults' capabilities. Together, these tensions show that checklist-based compliance does not necessarily translate into lived accessibility when engaging with mandatory or near-mandatory digital services. We discuss the need to move beyond minimum compliance frameworks toward accessibility ap
arXiv:2608.12548v1 Announce Type: new Abstract: Dance imitation integrates motor planning, sensorimotor integration, and social cognition, offering a sensitive framework to characterize motor behavior in autism. In this work, we explore a computational analysis framework to identify potential biomarkers that allow the design and development of improved medical and human-machine systems. We analyzed 3D motion capture data from autistic and neurotypical adults performing dance imitation under solo and socially-framed duo conditions. Methodologically, using Dynamic Time Warping, we quantified movement consistency and propose the Social Context Sensitivity Index (SCSI) to measure modulation of variability by social framing. These features were then used on a classifier to discriminate subjects into autistic or neurotypical groups. Results show that neurotypical adults exhibited increased movement variability in socially-framed imitation, especially in upper and lower limbs, whereas autisti
arXiv:2608.12546v1 Announce Type: new Abstract: At the studied research institute, one professorship oversees approximately 20 theses per semester, while day-to-day supervision is distributed among doctoral and postdoctoral researchers. To manage this supervision demand, the institute uses an expos\'e-first workflow in which students prepare a research proposal before entering the main thesis-writing phase. This paper asks how students, supervisors, and administrators experience the expos\'e-first workflow as a structured process for early thesis preparation, and how it redistributes responsibility, supervision, and administrative coordination work across roles and two digital platforms. Based on a mixed-methods study analyzed through Frauenberger et al.'s four reflective design lenses, the findings show that the expos\'e-first model made thesis preparation more structured by turning early research planning into a staged process of proposal writing, feedback, and approval. Students rep
arXiv:2608.12370v1 Announce Type: new Abstract: There has been limited clarity and consistency regarding what the term infographics, or information graphics, refers to in visualization research and practice. In particular, little is understood about where people's conceptualizations of infographics converge or diverge. To address this gap, we conducted a systematic literature review and a practitioner survey to identify and contrast different perspectives on infographics. We performed inductive coding on 487 sentences from 111 visualization and HCI papers, and analyzed questionnaire responses from 44 domain practitioners. Our findings reveal recurring dimensions of conceptual disagreement on infographics: role of text, relationship with data visualization, and relationships with statistical charts and data comics. Based on the results, we recommend future work to explicitly report any assumptions made along these diverging conceptualizations, to investigate the cognitive origins of the
arXiv:2608.12358v1 Announce Type: new Abstract: Product and engineering teams building role-bearing AI agents face an evaluation gap: an agent can produce accurate, safe, and fluent content while still failing the behavioral requirements of its assigned role. This paper introduces Interaction Readiness as a framework for specifying and evaluating that missing layer of performance. The framework separates content specifications, which govern what an agent knows and says, from interaction specifications, which define how an agent should conduct itself in a role-governed exchange. Interaction specifications require teams to define role purpose, authority boundaries, recurring situations, boundary cases, repair behaviors, and audit criteria before deployment. We operationalize interaction readiness through four agent operations: understanding purpose, calibrating authority, managing tone, and repairing breakdowns. Using StudyChat, a public dataset of student interactions with an AI tutorin
arXiv:2608.12357v1 Announce Type: new Abstract: I think the creative process in conducting open-ended creativity-adjacent research is itself part of the creative process (including ideation, pivots, and tangents excluded from the final work) that deserves greater emphasis. how might we bridge multiple open-ended projects and serve broader non-technical audiences? We may choose to emphasize interaction techniques rather than singular tools in isolation. We may grow a community based on our shareable experiences in creativity interactions, to draw more complete pictures of how we creatively solve creativity problems (or satisfy curiosities). This may lead to new directions. I briefly elaborate on this using the example of "DrawTalking" a drawing+talking interactions work. DrawTalking itself resulted from a winding creative process exploring spontaneous interactive world-building and storytelling when drawing and talking.
arXiv:2608.12355v1 Announce Type: new Abstract: Recent progress in AI coding agent research has led to rapid improvements in agents' ability to autonomously perform complex software engineering tasks, from editing large codebases to executing long-horizon development workflows. As these systems make strides, however, the primary bottleneck to practical usefulness increasingly shifts away from pure task-solving capability, and toward challenges in how users communicate with, supervise, and trust agents. In this position paper, we argue for a reorientation from autonomous to human-centered coding agents: systems designed not only to complete tasks, but to collaborate effectively with people. We identify four core interaction-level dimensions that characterize the human-agent task-solving loop: task alignment, verifiability, steerability, and adaptability. Finally, we outline concrete research directions to advance these dimensions, including user-involved coding environments, comprehensi
arXiv:2606.18479v2 Announce Type: replace-cross Abstract: Reject inference methods are widely used to mitigate survival bias in credit scoring, yet their effectiveness remains poorly understood. We systematically evaluate several such methods and uncover a structural failure mode: in a natural retraining cycle, models whose accuracy improves while recall collapses create an illusion of improvement that leads practitioners to believe the system is getting better when, in fact, its rejection quality -- the ability to correctly screen out defaulters -- is deteriorating. We then propose a controlled exploration strategy that breaks the feedback loop without statistical assumptions: the lender deliberately approves a fraction of rejected applicants and observes their true outcomes. We show that accuracy and rejection quality give opposite recommendations on whether to explore: accuracy favors no exploration, while rejection quality improves with it, confirming that standard evaluation metri
arXiv:2603.29545v2 Announce Type: replace Abstract: Existential risk scenarios relating to Generative Artificial Intelligence often involve advanced systems or agentic models breaking loose and using hacking tools to gain control over critical infrastructure. In this paper, we argue that the real threats posed by generative AI for cybercrime are rather different. We apply innovation theory and evolutionary economics - treating cybercrime as an ecosystem of small- and medium-scale tech start-ups, coining two novel terms that bound the upper and lower cases for disruption. At the high end, we propose the Stand-Alone Complex, in which cybercrime-gang-in-a-box solutions enable individual actors to largely automate existing cybercrime-as-a-service arrangements. At the low end, we suggest the phenomenon of Vibercrime, in which 'vibe coding' lowers the barrier to entry, but do not fundamentally reshape the economic structures of cybercrime. We analyse early empirical data from the cybercrime
arXiv:2603.00048v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive applications including psychological support, healthcare, and high-stakes decision-making. This expansion has motivated growing research into the ethical and moral foundations underlying LLM behavior, raising critical questions about their reliability in ethical reasoning. However, existing studies and benchmarks rely almost exclusively on Moral Foundation Theory (MFT), largely neglecting other relevant dimensions such as social values, personality traits, and individual characteristics that shape human ethical reasoning. To address these limitations, we introduce MOSAIC, the first large-scale benchmark designed to jointly assess the moral, social, and individual characteristics of LLMs. The benchmark comprises nine validated questionnaires drawn from moral philosophy, psychology, and social theory, alongside four platform-based games designed to probe morally ambiguo
arXiv:2608.13100v1 Announce Type: cross Abstract: Contemporary online assessment systems rely primarily on browser lockdown, webcam monitoring, and behavioural analytics, yet remain vulnerable to attacks that extract the assessment content itself through screenshots, screen sharing, optical character recognition, and automated scraping. This paper extends the Multi-dimensional Spatio-Temporal Context Camouflaging Model (MSCCM) within the MARS (Multi-modal Assessment Resilience Suite) by introducing the Multi-Layer Context Camouflaging Theory (MCCT), a mathematical framework that protects rendered assessment content through semantic superposition. Authentic assessment content and synthetically generated camouflage are represented as a unified rendering while remaining recoverable only by legitimate candidates. The framework models the adversarial extraction process through an explicit extraction-channel operator and develops six coupled constructs: the Context Inversion Operator, Contex
arXiv:2608.12816v1 Announce Type: cross Abstract: Large language models have begun refuting long-standing conjectures and, for a few thousand dollars of tokens, solving long-open problems (OpenAI, August 2026). The introspection this has prompted about the future of mathematical discovery is overdue, and the anxiety accompanying it legitimate -- but both are attached to the wrong loss. What machines now produce is the countable part of mathematics -- theorems, proofs, refutations -- which was always the \emph{residue} of the work, not its product. The product is human understanding: not a stock of results but a collective, hard-won way of deciphering the world and acting upon it. The two are arcs of a single loop -- looking produces the residue; taking it up again, one journey at a time, is what rebuilds the shared understanding. Machines are strong on the countable arc, absent from the one that feeds it. The peril is to leave the loop open. AI did not create the confusion between resi
arXiv:2608.12581v1 Announce Type: cross Abstract: Understanding when migration generates social integration or exclusion is a central challenge for urban communities. Existing research has mostly relied on surveys, administrative data, or aggregate indicators that fail to capture expressions of exclusion at fine spatiotemporal scales. Here, we analyze over 550,000 geolocated reports from SOSAFE (Chile's largest citizen reporting platform) to examine the relationship between migration and hate speech in Santiago. We fine-tune a Spanish hate speech classifier and validate it against human labels. Reports that mention migrants are more likely to contain hate speech than other reports. Hate speech concentrates in areas with recent demographic change (post-2010 arrivals) rather than in established migrant communities. The spatial analysis shows that hate speech hotspots coincide with neighborhoods where recent migrants comprise over a third of the population. Coldspots appear in high-educat