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Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

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arXiv2026

Location-Independent Robot-Assisted Finishing Using Digital Twins and Extended Reality

This paper presents a cyber-physical system (CPS) for location-independent programming, supervision, training, and teleoperation of a Robot-Assisted Finishing (RAF) system used to post-process metal additive-manufactured (AM) components. A digital twin (DT) built in Unity is delivered to the operator as a WebGL application that supports both desktop and immersive modes through WebXR-compatible devices. Moreover, it exchanges robot state and pose commands with a collaborative robot through a Message Queuing Telemetry Transport (MQTT) broker. The DT enforces kinematic and collision constraints before a pose is released to the physical robot, and augments the virtual component with a color map of the surface topography that supports operator decisions on part repositioning or process termination. The architecture was validated on a specially designed physical RAF system. A steady-state joint synchronization error of 0.12 deg and a mean round-trip latency of 563 ms were measured, which is adequate for supervisory programming and intermittent teleoperation.

cs.HC↗

arXiv2026

Performance of Clinical AI System and Physicians and Frontier Language Models in primary care diagnostics

Clinical AI evaluation should encompass diagnosis and management after adaptive information gathering. We compared Doctorina, eight physicians and four standalone frontier language models in 150 synthetic Polish-language primary-care consultations. Doctorina achieved 82.0% Top-1 concordance versus 57.0% for physicians (difference, 25.0 percentage points; 95% confidence interval, 17.7-32.7) and 97.3% versus 85.0% primary-or-reference-differential concordance. Across 149 case pairs, normalized workup and treatment scores were 89.4 versus 66.9 and 83.7 versus 61.2. Doctorina had the highest diagnostic point estimates among all six groups; Kimi K3 ranked next, while Claude Opus 5 led the closely spaced management estimates of Opus, Doctorina and Kimi. A second Doctorina execution reproduced the advantages over physicians across all outcomes. Doctorina's advantage over physicians therefore extended from primary-diagnosis selection to higher-rated diagnostic workup and initial treatment after adaptive consultation.

cs.CL↗

arXiv2026

Endorsement Without New Evidence: How Sequential Voting Inflates Mandates in Online Community Governance

Online communities often treat large support margins in public elections as strong mandates. We argue that such margins can overstate the independent scrutiny behind a decision. Using 198,275 free-text rationales from Wikipedia admin elections, we introduce vote-text divergence, a measure that flags a decisive vote paired with a thin, deferential rationale. Divergence rises as voters arrive later, even after controlling for voter and election fixed effects. The pattern is consistent with information saturation: once prior text is accounted for, arrival order no longer predicts divergence, while accumulated prior evidence does. The effect is strongest among peripheral voters in the co-voting network. Yet divergence does not predict worse post-promotion outcomes, such as administrative activity or survival. Public tallies can therefore weaken the scrutiny signal even while selecting capable administrators: a margin may appear to reflect more consensus and support than it actually contains.

cs.SI↗

arXiv2026

Ephemeral Feeds and Enduring Rituals: RushTok and the Formation of Event-Based Algorithmic Communities

Each August, TikTok's For You page turns the University of Alabama's sorority recruitment into RushTok. We examine RushTok as an event-based algorithmic community: a collective assembled around a bounded offline ritual and sustained by recommendation. Using a mixed-methods survey (n=71) and a reflexive account of creator outreach, we ask who participates, how, and with what stakes. Findings show an ambiguous and entertainment based throughline; many called it a community (51/71) but few claimed membership (11/71). Affiliation centered on creators rather than shared practices, with parasocial attention clustering around a small set of potential new members (PNMs) and returning figures. Higher content exposure tracked with self-identification as a community member; those members commented, followed creators, and engaged across videos. Attempts to interview creators were met with silence or refusals, reflecting community boundary-work despite viral visibility. We outline implications for platform governance, including time-bounded context, graduated visibility, and aftercare.

cs.HC↗

arXiv2026

Where Does the Human End? Creative Agency with Generative AI across Five Years of Chinese Digital Painting

As generative AI enters creative work, practitioners must decide where AI assistance ends and human authorship begins. Human-agent interaction (HAI) research has examined AI as a tool, collaborator, consultant, and competitor. The longitudinal problem is how these roles are revised as systems become more capable, public, and economically embedded. We report a five-year interview study with 17 Chinese digital painters, based on annual semi-structured interviews from 2021 to 2025. Participants described recurring but non-uniform patterns of protective resistance, pragmatic task delegation, and, for some, reflective agency repartitioning. Early resistance protected observation, originality, signature, and ownership from AI. Later delegation placed AI in bounded tasks such as references, backgrounds, rough sketches, and client-facing drafts. By 2025, some participants built hybrid workflows around human-only zones, while others described fatigue, precarity, or difficulty locating a remaining human role. Peer norms, emotional climates, and production pressures shaped which delegations felt useful, acceptable, or exhausting. Copyright, authorship, and creative labor remained recurring limits on what participants were willing to delegate. We frame these accounts as longitudinal agency partitioning, the situated work of deciding which stages, responsibilities, values, and claims remain human in creative human-agent interaction. We discuss design implications for revisable agency-boundary controls, provenance scaffolds, and community-facing authorship norms.

cs.HC↗

arXiv2026

Echoes in the Algorithm: Analyzing the Fidelity of User Preferences Against Realized Platform Reach

What does popular content look like when platforms withhold the usual cues? On TikTok, users still form impressions about which videos are taking off even when likes and view counts are hidden, delayed, or pushed to the margins of the interface. We study this problem through TokOrNot, a web-based game in which participants compared pairs of TikTok videos and reported (i) which one they preferred and (ii) which one they believed had reached a larger audience. We benchmark these judgments against verified public view counts, which we use as a bounded proxy for realized platform reach. Across 3,513 judgments from 363 participants, participants identified the higher-reach video only modestly above chance (56.75%, 95% CI: 56.01-58.55). Preference aligned with the higher-view video at a similar rate, while preference and prediction matched in 83.48% of trials (95% CI: 83.12-85.95). Performance also varied across content categories. Taken together, these results do not suggest that users can reliably read platform success from content alone. Instead, they point to a looser and more uncertain interpretive process in which reach judgments often track personal taste or other weak heuristics when explicit popularity cues are absent. We discuss the implications for algorithmic literacy and for interface designs that reduce visible metrics without leaving users to infer reach from uneven or idiosyncratic cues alone.

cs.HC↗

arXiv2026

"It's Like Drinking from a Fire Hose": Understanding and Characterizing Video Learning Experiences for Individuals with ADHD

Video lectures have become increasingly prevalent for education and professional development, yet their static visuals, dense information, and long duration pose attentional challenges for individuals with ADHD. While adaptive learning offers opportunities towards ADHD-accessible video learning, little is known about how to suitably adapt such videos: What components in multimodal video lectures are challenging for ADHD viewers? How do these experiences surface in behavioral signals to trigger an adaptation? What presentations do they prefer? To answer these questions, we conducted an eye-tracking-based retrospective think-aloud study with 16 participants with ADHD, who watched and reflected on a curated set of video lecture segments. Our study uncovered video design elements that hindered learning and revealed participants' coping strategies along with their limitations. By jointly analyzing behavioral signals and retrospective reflections, we characterized how these experiences manifested in behavioral patterns. We further surfaced participants' practices for addressing learning needs beyond the video watching process, and derived design implications for future ADHD-friendly adaptive video learning systems.

cs.HC↗

arXiv2026

Integrating Multi-Source Feedback in Computational Design

In real-world design practice, evaluations rarely rely on a single source of judgment. Designers routinely combine expert opinions, empirical studies, and computational models, each with distinct strengths and limitations. While machine learning offers methods to integrate multiple feedback sources, these approaches remain largely inaccessible to designers without technical expertise. In this paper, we explore how to integrate multiple feedback sources, primarily through: (1) a practical approach for multi-source integration, and (2) its implementation in MUSE, a no-code tool that allows designers to combine and balance diverse sources. Our technical findings show that independent modeling of multiple evaluation sources enables exploration across heterogeneous feedback, accommodates different evaluation speeds, surfaces disagreements between sources, and supports an adaptable evaluation setup that designers can reconfigure during their process. In a visualization design study, participants navigated their own judgments alongside simulator feedback, reporting a perception of enhanced confidence and flexibility. Our results highlight the viability of multi-source integration to support computational design, offering a step toward bridging the gap between advanced optimization methods and design practice.

cs.HC↗

arXiv2026

The Mutations of Machine Speech

Algorithmic outputs now populate the digital environments through which contemporary life is organized. The role of law in facilitating and constituting (rather than merely responding to) these processes is gaining increasing traction across scholarly accounts. This inquiry traces the evolution of algorithmic outputs attending to their legal underpinnings and social implications, surfacing the mutations of machine speech. The first mutation redefined speech as data to be queried: search engines transformed the web from a space of information retrieval into an economic regime of algorithmic visibility. The second mutation reframed speech as engagement: social media platforms fused moderation with amplification, turning expression into a metric of attention, governed by corporate architectures. The third mutation emerges in conversational systems and interfaces, where generative text displaces information retrieval, bringing with it dense technolegal entanglements and profound epistemic consequences. Scholars of freedom of expression, informational privacy, and communication studies have long grappled with these dynamics, yet their implications for broader legal thought have also become urgent. This piece seeks to organize and clarify the evolving debate around algorithmic speech, making this critical but often fragmented discourse more accessible to wider legal and interdisciplinary audiences. In doing so, it bridges the gap between observing technological transformation and critically assessing the constitutive role of law within it, offering a conceptual resource for researchers, students, policymakers, and practitioners navigating and contesting this evolving landscape.

cs.CL↗

arXiv2026

To Stop or Not to Stop: Exploring the Intention-Behavior Gaps in Smartphone Usage

As smartphones become integral to daily life, researchers have sought to identify when the use becomes problematic. Previous studies have operationalized problematic smartphone usage (PSU) from either an intention or a behavior perspective. Both risk delivering interventions not welcomed by users. We propose a novel approach to operationalizing PSU as the intention-behavior gap (IBG). We collected self-reported data on intentions to stop phone usage, alongside usage behavior data, from 37 participants over two weeks. We calculated IBG, examined effects of demographic and contextual variables, and developed machine learning models to predict IBG in real time. We found that IBG was explained by gender, time, app, and input interactions, among other factors. Intention was predicted most accurately with only personal data, whereas behavior and IBG were predicted most accurately with both personal and global data. Our findings can inform the design of future intervention tools optimized for timing and adaptive intensity.

cs.HC↗

arXiv2026

StitchOver: Technical Embroidery on Seamed Fabrics

Smart textiles embed interactivity into everyday garments, supporting use cases like always-available sensing for medical applications or sports. Machine embroidery allows integrating functionalities into existing textiles. However, embroidering onto real-world textile goods remains challenging. Textile goods are rarely made of a single homogeneous substrate of fabric, and embroidery with functional materials such as conductive threads requires machines to be more tightly calibrated than for decorative embroidery. In particular, seams, which bring together different substrates, along with machine variability, cause shifts in tension and friction between the functional thread and the textile substrate that frequently lead to defects (70% of samples in our evaluation). We present a technique to reliably embroider on seamed fabric even when using functional threads. Our software tool automatically digitizes user-defined stitch patterns by introducing what we call "JumpStitches" to bypass seam interference. We evaluated our approach under varying machine states (under-tensioned, well-calibrated, and over-tensioned), and across multiple seam and pattern configurations. Our results show that the JumpStitch mechanism eliminates defects, while maintaining conductivity compared to 70% defects without JumpStitches, and even in poorly calibrated machine states continues to work well.

cs.HC↗

arXiv2026

Travel Package Booking Application with API Bot

These days we are witnessing many mobile applications based on the recommended systems, which have become a great technology which is been used by the various mobile applications according to the situation. Recommendation provided by the mobile application is a key element for the person who is traveling to several places. For any tourist information application contextual information is much needed to guide the user on his interests this can be achieved by the Context-aware computing. Which provides the user most interactive system with the suggestions provided by it based on the input from the user in a certain location, here context includes the user's mental, social, physical environments. To achieve this contextual information, we will design and implement the context-aware user interface based on the user for which we have to study the user and design a rich user interface. The final outcome for which users have the satisfaction when using context-aware functionality will be much better than non-context-aware application.

cs.HC↗

arXiv2026

Exploring patient trust in clinical advice from AI-driven LLMs like ChatGPT for self-diagnosis

Trustworthy clinical advice is crucial but can be burdensome to obtain. Limited access and financial costs may lead people to self-diagnose. However, self-diagnosis requires considerable learning and can create risks when people pursue treatment without professional guidance. Large language models (LLMs) such as GPT-4 may offer a convenient yet risky alternative because they can produce inaccurate but convincing information. We therefore ask whether patients can trust clinical advice from AI-driven LLMs. We examined this question through a think-aloud observation in which a patient used GPT-4 for self-diagnosis while a doctor assessed its responses using professional expertise. We then conducted a semi-structured interview with the patient about their trust in the system. Our results show that patients may struggle to identify errors because they lack professional medical knowledge, even when GPT-4 provides advice that a doctor can recognize as false. Patients may develop some trust because GPT-4 explains its responses and acknowledges its limitations, but this trust remains uncertain because its advice can be unreliable. The doctor also reported that checking GPT-4's responses required more effort than making a diagnosis without it. Patients tend to trust doctors because educated and authorized professionals can provide effective guidance. This trust also develops through social connection, certification, institutional accountability, and professional rules. Doctors can adapt their questions, observe patients, and use different methods when patients cannot clearly describe their symptoms. An LLM, however, depends primarily on the information provided in a prompt and may overlook details that a doctor could identify during a clinical consultation. These findings raise questions about competence, responsibility, autonomy, and safety when LLMs are used for clinical advice.

cs.HC↗

arXiv2026

Human-Robot Interaction and Perceived Irrationality: A Study of Trust Dynamics and Error Acknowledgment

As robots become increasingly integrated into various industries, understanding how humans respond to robotic failures is critical. This study systematically examines trust dynamics and system design by analyzing human reactions to robot failures. We conducted a four-stage survey to explore how trust evolves throughout human-robot interactions. The first stage collected demographic data and initial trust levels. The second stage focused on preliminary expectations and perceptions of robotic capabilities. The third stage examined interaction details, including robot precision and error acknowledgment. Finally, the fourth stage assessed post-interaction perceptions, evaluating trust dynamics, forgiveness, and willingness to recommend robotic technologies. Results indicate that trust in robotic systems significantly increased when robots acknowledged their errors or limitations. Additionally, participants showed greater willingness to suggest robots for future tasks, highlighting the importance of direct engagement in shaping trust dynamics. These findings provide valuable insights for designing more transparent, responsive, and trustworthy robotic systems. By enhancing our understanding of human-robot interaction (HRI), this study contributes to the development of robotic technologies that foster greater public acceptance and adoption.

cs.RO↗

arXiv2026

Privacy-Preserving Gaze Interaction: Reducing Re-Identification Without Degrading Utility

Gaze-based interaction is emerging as a standard input modality on consumer extended-reality (XR) devices. Yet each gaze input constitutes an involuntary biometric disclosure, as the same signal that selects a button can also identify the user who produced it. Reducing identity information without degrading interaction is difficult: most prior methods are validated on small datasets or optimized for only one of the two competing objectives. We introduce a dual-assessment framework that scores any real-time gaze transformation on two axes at once: interaction utility, measured through an offline gaze-interaction simulation and an operational spatial-accuracy metric, and privacy preservation, measured through the Rank-1 Identification Rate (IR) of a state-of-the-art re-identification adversary. We test the framework on 23 conditions: one raw baseline and 22 privacy variants from eight lightweight signal-processing families using two publicly available datasets (GazeBase and GazeBaseVR). Deterministic operators produce no significant change in identity on either dataset, because a subject-invariant mapping preserves the relative geometry a biometric embedding exploits. Identity drops sharply only when identity-uncorrelated randomness is injected per sample. Smoothing applied afterwards to recover signal quality partially restores identifiability on both datasets. The best-balanced configuration reduces the Rank-1 IR by 64.3 percentage points on GazeBase and 67.9 points on GazeBaseVR, while leaving target-selection success essentially unchanged on both datasets. Privacy-preserving gaze interaction is therefore not zero-sum. However, the reduction in identifiability is achieved by injecting randomness rather than by improving signal fidelity.

cs.HC↗

arXiv2026

Virtual Reality in Service Design for Plastics Recycling: Two Application Cases

Plastics recycling depends on everyday sorting practices and on how recycling services are communicated and experienced. Virtual reality (VR) can present these practices and services in situated, interactive form, yet its role in service design for plastics recycling is still emerging. This paper examines how VR tools can contribute to designing plastics recycling services through two application cases that address different stages of the recycling journey. The first case, Clean Cabin Escape, is a household scale VR escape room where players collect and sort waste items into locally relevant categories, with immediate feedback that supports practice with plastics recycling decisions. The second case is a VR simulation of a plastics recycling center that represents a real planned site and is used in service design workshops where stakeholders explore layout, signage and customer paths for plastics fractions. Across the cases, we analyse how VR supported learning, engagement and shared sensemaking, and how it interacted with other service design methods such as workshops, customer path mapping and physical artefacts. The findings show that VR can make domestic sorting tasks and complex recycling centers more concrete for both citizens and professionals, but also highlight trade offs related to hardware access, onboarding effort, visual fidelity and localisation of recycling rules. The paper concludes by outlining opportunities for integrating VR into broader service design toolsets for plastics recycling and circular economy services, and by pointing to directions for future research on long term impact and inclusive design.

cs.HC↗

arXiv2026

Household Plastic Recycling: Empirical Insights and Design Explorations

This article examines household plastic recycling in Finland through two qualitative studies and four design concepts. Study 1 reports short interviews with residents about how they store, sort, and dispose of plastic packaging in their homes. The findings highlight recurring frictions: limited space, improvised storage, uncertainty about correct sorting, and difficulties with bulky or dirty items. Study 2 focuses on laundry detergent packaging as a common source of large plastic containers. Participants' purchase decisions prioritised price and cleaning performance, while expressing concern for environmental impact and confusion about materials, rinsing, and recyclability. Building on these insights, four student groups designed interactive recycling concepts that combine physical bins or bags with mobile applications. The concepts explore modular storage, sensing and compaction, playful feedback, and reward schemes to support domestic recycling routines. Together, the studies and concepts point to design opportunities at the intersection of packaging, home infrastructure, and digital services, while also raising questions about feasibility, privacy, and the cost of new devices.

cs.HC↗

arXiv2026

"It depends on where AI is used": Players' attitude patterns and evaluative logics toward different AI applications in digital games

As AI becomes increasingly embedded in digital games, players' attitudes de-pend not only on whether AI is used, but also on where and how it intervenes in gameplay. This study examines players' evaluative patterns toward eight AI application contexts, including intelligent NPCs, emergent narrative, dynamic balancing, recommendation systems, review and governance, art asset generation, co-creation gameplay, and gameplay evolution. Based on 1,856 valid open-ended responses from 310 questionnaires, we conducted thematic analysis to identify reasons for acceptance, rejection, and conditional acceptance. Results show that players welcomed AI when it enhanced immersion, personalization, novelty, efficiency, or convenience, but resisted it when it threatened creativity, emotional authenticity, autonomy, fairness, system stability, authorship, or accountability. We further identify six evaluative logics: experiential enrichment, instrumental efficiency, system reliability, agency and control, authorship and compliance, and human oversight. These preliminary findings highlight the context-sensitive nature of AI acceptance in digital games.

cs.HC↗