Human-Computer Interaction
The Data Hospital: A Workflow-Based Concept for Explainable Research Data Quality Assistance
Lennard Scheurer, Robert Porzel, Vinicius Carrillo Beber et al. (+1 more)
Research data quality is multidimensional and purpose-dependent: it emerges from the interplay of data, intended use, contextual knowledge, documentation, intervention decisions, and traceability. This concept paper presents the Data Hospital, a human-in-the-loop control and interaction model for research data quality. Using a hospital metaphor,
datasets are admitted, contextualized, assessed, reviewed in specialized stations, modified only through approved interventions, validated, documented, and made replayable where interventions are sufficiently specified. The concept combines deterministic profiling and inspectable evidence with optional evidence-bound explanation by Dr. Data and explicit user decisions. Preserved Raw Data and controlled working states separate observation from intervention. The contribution is not a new cleaning or imputation
algorithm, but a ten-stage workflow that makes assessability, uncertainty, intervention authority, provenance, and process reproducibility visible. The prototype is an implementation-backed demonstrator rather than a released research artifact and illustrates selected parts of the concept through representational standardization, imputation, Patient File documentation, and replay. The paper concludes with a staged agenda for subsequent technical and user-centered evaluation.
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All authors Lennard Scheurer, Robert Porzel, Vinicius Carrillo Beber, Rainer Malaka
Human-Computer Interaction
Semantic Action Graph: A Shared Representation for Agent Grounding and Human Interpretation of Sports Highlights
Tica Lin, Deepak Chandran, Gauri Jagatap et al. (+4 more)
Generative
agents are increasingly used to select and narrate
video highlights, but they typically operate over unstructured or frame-level representations. Their output is consequently difficult for a viewer to verify and steer toward individual preferences. We present the semantic action graph, a lightweight domain schema that represents a sports match as performer, action, recipient, moment, and state nodes connected by role, temporal, and outcome edges. The schema demonstrates three key properties: 1) connected event sequences, 2) a shared, closed vocabulary, and 3) frame-addressable moments, making it suitable to serve two consumers at once: an agentic pipeline that composes narrated highlights, and a visual interface through which viewers query and inspect the same structure. We instantiate it in SportSAGE, a design probe pairing a four-module highlight pipeline with a graph interface, and report feedback from 12 soccer fans. Participants were satisfied with the quality of the generated highlights and narratives, and used the graph interface to search, navigate, and interpret the match highlights. These results provide early evidence that one small, human-readable schema can ground
agent generation and support human interpretation at the same time.
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All authors Tica Lin, Deepak Chandran, Gauri Jagatap, Chen Chen, Andrea Fanelli, David Gunawan, Josh Kimball
Human-Computer Interaction
What Parents Can See: Divergent Accounts of Youth AI Companion Use in Parenting and Teenager Subreddits
Thomas Berkane, Anne Bischops, Anika Mellacheruvu et al. (+1 more)
Youth increasingly use AI companions, and parents are the primary mediators of that use. How effective that mediation can be depends on whether parents are aware of how adolescents actually use these systems and what risks and benefits such use carries; nevertheless, prior work has only studied these demographic groups in isolation, and existing taxonomies attend almost entirely to risk. We analyze 1,628 Reddit posts about youth AI companion use from parenting and teenager communities (2023–2026); develop a codebook covering modes of use, risks, benefits, and parental mediation; and apply it at corpus scale with an LLM. The two communities yield divergent accounts. Teenagers most often discuss receipt of emotional support from AI companions (31% of teenager posts vs. 19% of parenting posts), whereas parents most often discuss teenage use of AI companions for romantic and sexual interaction (36% vs. 25%). Teenagers are not unaware of other risks, however; indeed, attachment and dependence is the risk they raise most (19%), close to the parental rate (16%). Teenagers also describe benefits that risk-centered taxonomies do not capture and parents rarely mention, most notably emotional support (27% vs. 5%). We argue these differences track what a given kind of use makes visible to someone outside the conversation. Chatting with a companion for hours every night leaves a trace beyond the chat itself; sexting with a character stands out when a parent reads the log; venting about a fight with a friend does neither, since it looks like any other conversation. The first surfaces as dependence, the second as sexual content, and the third as emotional support, which is the one parents most often miss. Parental guidance and system design should attend to use cases that reach parents by neither route, emotional support foremost among them.
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All authors Thomas Berkane, Anne Bischops, Anika Mellacheruvu, Maimuna Majumder
Human-Computer Interaction
Ageing, Digital Literacy, and Interaction Modality in Immer-sive Virtual Reality: Psychomotor Performance, Cognitive Flexibility, and Their Processing-Speed Association
Panagiotis Kourtesis, Katerina Denaxa, Lydia Asimakopoulou et al. (+2 more)
Extended reality (XR) increasingly supports training and cognitive assessment, yet the age sensitivity of its interaction techniques is unclear. This study examined age, digital literacy, and interaction modality as correlates of psychomotor and cognitive-flexibility performance in immersive virtual reality (VR). Two hundred and two adults (19-90 years) completed a five-mode Fitts' law task (eye-gaze, head-gaze, controller ray-casting, virtual finger, and controller direct touch), the Trail Making Test in VR (TMT-VR), and a digital-literacy questionnaire. Age was associated with slower task times across modes, but controller direct touch carried the steepest relative age gradient yet remained among the fastest in absolute terms; technique altered relative age sensitivity without determining absolute efficiency. Higher digital literacy was associated with faster TMT-VR completion but not Fitts task times. A Fitts-derived speed score predicted TMT-VR performance beyond age and partly accounted for its age association, consistent with shared processing-speed variance; an exploratory full-battery extension confined the Fitts-score association to completion-time and error-adjusted-time indices and the digital-literacy association to error-adjusted-time indices, not wrong-target errors, mean selection distance, or relative Part B indices. Age-inclusive XR assessment should standardise interaction modality, evaluate rather than exclude mid-air direct selection, and interpret cognitive scores alongside digital literacy and psychomotor speed.
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All authors Panagiotis Kourtesis, Katerina Denaxa, Lydia Asimakopoulou, Petros Roussos, Maria Roussou
Human-Computer Interaction · Cross-listed from cs.AI
Ownership in AI-Assisted Everyday Tasks
Megan Wei, Melanie Subbiah, Audrey Lee et al. (+4 more)
When does work done with AI still feel like ours? As AI becomes woven into everyday tasks, we must examine what happens to our sense of ownership and contribution when a machine shares in producing what we make. We report an exploratory qualitative survey in which participants were asked to describe two recent, self-selected tasks completed with AI: one that felt like their own and one that did not. We find that felt ownership depends on the process of collaboration: people disown work when they merely approve AI's suggestions, but retain ownership when they lead, iterate, or rewrite. Ownership can also extend to settings where people own the
vision for a project but not the execution; respondents reported high ownership on tasks they could not have completed without AI. Loss of personal voice and a lack of comprehension of the output both erode ownership. Finally, willingness to disclose AI use is often decoupled from actual pride or ownership, and instead shaped by community norms and fear of credit erasure. We propose several research directions as a result of these findings to promote AI development that supports people's sense of authorship over their own lives.
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All authors Megan Wei, Melanie Subbiah, Audrey Lee, Annya Dahmani, Dave Edwards, Helen Edwards, Ellie Pavlick
Human-Computer Interaction
Stereotypically Yours: Portrayal and Perception of Race-Coded AI Companions
Wang Claire, Jiayue Melissa Shi, Agam Goyal et al. (+4 more)
AI companions can purportedly adopt racial personas, raising questions about how they represent identity and how users interpret these portrayals. We combined an algorithmic audit of race-coded AI personas with interviews with 12 companion users who interacted with a probe. Our audit revealed systematic differences, such as Asian-coded male personas receiving higher submissiveness scores than White counterparts, and Black, Hispanic, and Indigenous male personas receiving higher aggression scores than their White counterparts in open-weight models. Interviews revealed that participants envisioned AI companions as offering cultural familiarity and outside perspectives, but differed in which portrayals they considered meaningful or stereotypical. Some rejected overt racial signaling while still expecting culturally distinctive responses. Triangulating these findings with theory, we highlight how social norms and cultural expectations complicate efforts to support meaningful racial representation without reproducing stereotypes. We discuss how companion personalization should be evaluated beyond user satisfaction to account for broader representational harms.
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All authors Wang Claire, Jiayue Melissa Shi, Agam Goyal, Grace Sletten, Renwen Zhang, Eshwar Chandrasekharan, Koustuv Saha
Human-Computer Interaction
TeamCAMS: An Open-Source Research Platform for Studying Human Behaviour in Human-AI Teams
Amos Brocco, Alain Chavaillaz, Andreas Sonderegger et al. (+1 more)
In this article, we present TeamCAMS (Cabin Air Management System), a collaborative work environment for simulating human-AI (artificial intelligence) interaction for scientific research. The article outlines how several psychological theories guided the development of this multiple-task simulation. Modelling a process control environment, previous versions of TeamCAMS have already been used in empirical studies to address a wide range of research questions (e.g., comparing different forms of automation, evaluating impact of automation reliability, effects of stress on multiple-task performance). Outlining the technical possibilities offered by TeamCAMS, the article points out how its latest version offers researchers the possibility of addressing a set of new research questions including problems associated with teamwork (e.g., within-team conflict, distributed teamwork) and human-AI interaction. Finally, we will outline how this simulation environment can be enhanced further still to address research questions in new fields (e.g., automation of leadership). To promote transparency, reproducibility, and further development, TeamCAMS is made available to the research community under an open-source license.
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All authors Amos Brocco, Alain Chavaillaz, Andreas Sonderegger, Juergen Sauer
Human-Computer Interaction · Cross-listed from cs.DL
greCAPTCHA: Assessing Understanding as Evidence of Research Authorship Under Generative AI
Justin Payan, Bálint Gyevnár, Atoosa Kasirzadeh et al. (+1 more)
Conferences, journals, funders, schools, and universities are struggling with a surge of potentially AI-generated submissions from ostensibly human authors, who may not have exercised sufficient human oversight for their manuscripts. In turn, institutions evaluating submissions can no longer reliably credit expertise based solely on authors' names on submitted work. To address this problem, we propose greCAPTCHA, a proctored assessment approach that measures authors' understanding of research manuscripts via the construct of capacity to verify, which we define as the knowledge and
reasoning required to critically assess the contents underlying one's contributions to a manuscript. greCAPTCHA generates questions assessing multiple levels of understanding and provides an evaluative report based on authors' responses. Using a prototype implementation, we conduct a user study and semi-structured interviews with $31$ researchers to evaluate greCAPTCHA. Its automated scores predict which papers were or were not authored by study participants with an AUC of $0.90$. Participants reported positive overall experiences with the system and remarked on the appropriate construct validity for author understanding, while also suggesting important changes to be made before deployment. Our results provide initial evidence that greCAPTCHA can assess manuscript-specific understanding under proctored conditions.
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All authors Justin Payan, Bálint Gyevnár, Atoosa Kasirzadeh, Nihar B. Shah
Human-Computer Interaction
How Do We Visualize Space in Molecular Biology? A Study of Spatial Transcriptomics Visualization Practices
Denisse Chacón-Ramírez, Mark S. Keller, Eric Mörth et al. (+3 more)
A
cell's identity depends on where it sits in tissue: for example, a macrophage behaves differently in a tumor core than at its edge. Spatial transcriptomics has transformed how we study this by recovering that lost coordinate, but it does so by producing data that is simultaneously high-dimensional,
multimodal, and uncertain. Visualizing this combination is a hard problem in its own right, and one that warrants an assessment of how the field currently represents it, what has worked, and what is still missing. We surveyed 148 papers and 1,824 figure panels using a What-Why-How coding framework grounded in Munzner's nested model, connecting the data represented, the biological tasks motivating each visualization, and the design choices through which they are expressed; a subset of the surveyed work also contributed dedicated interactive visualization software that was not necessarily reflected in the static figures, and we looked at what interaction capabilities those tools supported as well. We close by outlining where the field stands and the challenges ahead for bioinformatics and visualization researchers to tackle together.
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All authors Denisse Chacón-Ramírez, Mark S. Keller, Eric Mörth, Nils Gehlenborg, Marc Streit, Andreas Hinterreiter
Human-Computer Interaction
Designing Against Deskilling: Metacognitive Feedback Reduces Cognitive Offloading to LLM Assistants
Sebastian Maier, Kai Schwabe, Manuel Schneider et al. (+1 more)
Cognitive offloading to AI can reduce opportunities to practice skills, creating risks of deskilling. However, it remains unclear how to prevent deskilling without restricting access to AI. Here, we design two interventions to reduce offloading decisions: (1) metacognitive feedback that makes the implications of offloading for users explicit, and (2) an effort-based reward that incentivizes less extensive LLM assistance. We test both in a preregistered online experiment ($N = 704$) with a 2$\times$2 design and a no-AI control. The task was to practice fraction arithmetic with an LLM-based assistant that provided solutions only on explicit request, followed by an unaided test. Metacognitive feedback reduced answer offloading (OR $= 0.47$) and improved test performance (OR $= 1.51$). We found no evidence that the reward affected either outcome. Our results identify metacognitive feedback as a promising design choice to reduce cognitive offloading.
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All authors Sebastian Maier, Kai Schwabe, Manuel Schneider, Stefan Feuerriegel
Human-Computer Interaction
AI Should Facilitate Democratic Deliberation at Scale
José Ramón Enríquez, Jiaxin Pei and Alex Pentland
AI systems can strengthen democracy by supporting deliberation at scale by addressing cognitive, social, platform-design, and
market-driven frictions, while preserving human agency. Unlike proposals such as
liquid democracy that restructure representation through vote delegation, in this position paper, we argue that AI-assisted deliberation offers a more promising path by lowering barriers to meaningful engagement without substituting machine judgment for human choice. Drawing on evidence from online deliberation platforms and experimental research, we identify four guiding principles: preserving agency and autonomy, encouraging mutual respect, promoting equality and inclusiveness, and augmenting rather than substituting active citizenship. We also address critical challenges, including
alignment, sycophancy, training bias, and over-reliance on AI systems. We call on the machine learning community to develop deliberation-focused AI systems evaluated not on engagement metrics but on their capacity to facilitate informed, representative, and friction-robust discourse.
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Human-Computer Interaction
What People Almost Did: Evaluating LLM Social Simulations Beyond Behavioral Fit
JaeWon Kim and Angie Boggust
LLM-based social simulations are primarily evaluated for behavioral fit, testing whether
agents reproduce the actions or response distributions of the people they are simulating. However, the promise of simulation extends beyond behavioral fit. Simulations can explain human behavior, diagnose barriers, and compare large-scale interventions. These use cases depend on understanding why people acted a certain way, not just what they did. As a result, behavioral fit is insufficient for these types of claims because behavior underdetermines the
reasoning process behind it. For instance, the behavior of staying silent may be due to disinterest or suppressed
speech, and not answering a call may be due to distrust of the caller or limited phone access. In this paper, we propose representational adequacy as a new evaluation target for LLM-based social simulations. By leveraging LLM
reasoning traces, representational adequacy measures whether a simulation's scenario–
reasoning–action triples preserve the
reasoning process behind the behavior in a way that is faithful to the population and scenarios being simulated. We distinguish representational adequacy from interpretability and
alignment metrics, propose ways to integrate it into simulation research, and pose its measurement as an open problem.
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Human-Computer Interaction
Penquiry: A Pen-based Interactive In-situ Q&A System Leveraging LLMs
Jeongmin Rhee, Changhee Lee, Hyunwoo Kim et al. (+4 more)
Pen-based digital devices remain a preferred medium for active, cognitively engaging study. Concurrently, Large
Language Models (LLMs) have become indispensable for self-directed learning, enabling students to clarify concepts. However, a fundamental interaction gap exists between the fluid, spatial nature of pen-based workflows and the discrete, keyboard-heavy requirements of LLMs. We present Penquiry, an in-situ question-and-answer system that bridges this gap by enabling learners to pose questions directly on digital study materials via a pen. We characterize two primary interaction challenges in this
multimodal transition: a Referential Barrier, which hinders grounding fine-grained visual elements into the query context, and an Expressive Barrier, which forces learners to translate diverse, non-textual intents–such as equations and diagrams–into rigid, typed sentences. To resolve these, Penquiry introduces a mediation layer featuring Content Snapping for unambiguous referencing and Question Autocompletion to expand sparse ink keywords into rich semantic queries. Through two iterative user studies (N = 16 per study), we demonstrate that Penquiry significantly reduces the cognitive and physical overhead of inquiry compared to traditional interfaces, providing a new blueprint for pen-based, in-situ AI interaction
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All authors Jeongmin Rhee, Changhee Lee, Hyunwoo Kim, Kiroong Choe, Bohyoung Kim, Sungahn Ko, Jinwook Seo
Human-Computer Interaction
Point, Revise, Review: Grounded Agentic Analysis in Reactive Notebooks with marimo-lens
Péter Ferenc Gyarmati, Trevor Manz, Dominik Moritz et al. (+1 more)
In a computational notebook, a human can point to a rendered result and ask about "this," while an
agent acts through
cells, dependencies, and runtime state. When what the human sees and what the
agent operates on are disconnected, the human must describe what they mean and trace what the
agent did in prose that strips away situated visual and computational context. We present marimo-lens, an extension to the reactive Python notebook marimo for grounded human-
agent analysis. Lens connects a human's marked output and note to its producing
cell and relevant contributing computation, giving the
agent computational context for the request. It surfaces
agent activity, returns selected results to the notebook, and preserves the initiating selection for human review and reopening. We illustrate the lifecycle through an exploratory human-
agent analysis of a real-world open
dataset, showing how visually situated questions lead to computational inspection, notebook action, and returned evidence for human review.
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All authors Péter Ferenc Gyarmati, Trevor Manz, Dominik Moritz, Mennatallah El-Assady
Human-Computer Interaction · Cross-listed from cs.RO
Affective Shared Autonomy: Temporal Affect Dynamics and Subjective Evaluation in Bimanual Teleoperation Tasks
Zhengji Liang, Guiyin Tian, Sijin Qu et al. (+2 more)
Physical teleoperation integrates human cognitive flexibility with robotic precision, yet demanding manipulation tasks frequently induce severe cognitive workload, acute frustration, and execution breakdown. Conventional shared autonomy paradigms rely primarily on task-based rules, such as spatial error boundaries, which disregard the operator's transient affective state and risk misaligned control interventions. To address this limitation, we propose an affect-aware shared autonomy teleoperation framework that dynamically modulates robotic assistance based on real-time operator state estimation. The system estimates operator affective states from synchronized facial
video, cardiac signals, and bilateral arm kinematics, outputting a seven-state affective distribution and a three-category operational abstraction (neutral, productive, adverse). Affect-aware assistance is selectively triggered when the user is detected in a continuous adverse state, preserving task-positive engagement without unnecessary disruption. The empirical user study ($N = 30$) confirms that the proposed affective assistance increases the productive states by up to 39.7% without compromising user agency. The collected
dataset represents the first
multimodal dataset that provides continuous visual, physiological, and operator's bilateral motion tracking of temporal affective state shifts during bimanual teleoperation. Our
multimodal fusion model outperforms zero-shot baselines (Qwen, MiniCPM-V) in tracking temporal state dynamics. This real-world deployment offers a new human-centric framework that integrates visual, physiological, and motion tracking for physical human-
robot interaction.
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All authors Zhengji Liang, Guiyin Tian, Sijin Qu, Hainan Liu, Shiyan Hu
Human-Computer Interaction
Treadstone: A Social-Media-Inspired Platform for Multi-Agent Collaborative Data Analysis
Hyunwook Lee, Sungbeom Cho, William Benjamin et al. (+6 more)
Coordinating human analysts with autonomous AI
agents faces the same challenges as human-to-human collaboration: sharing intermediate results, avoiding conflicts, and maintaining group awareness. Current tools rely on unstructured messaging or single-threaded chatbot interaction, which lack the structure to track evolving hypotheses or link claims to evidence. We propose agentic social data analysis, a collaboration paradigm extending social data analysis with a shared coordination feed modeled on the content timeline in social media services. We instantiate this concept in TREADSTONE, a platform where human and AI
agents asynchronously post, link, and contest analytical claims via threaded messages within a shared feed. By allowing
agents to proactively broadcast hypotheses and enabling users to steer the analysis through lightweight curation, Treadstone seeks to balance machine autonomy with human analytical control. A qualitative user study shows that Treadstone fosters collaboration while preserving human analytical agency, in contrast to the solitary experience of conventional chatbot interaction.
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All authors Hyunwook Lee, Sungbeom Cho, William Benjamin, Changhee Lee, Hyotaek Jeon, Daeun Jeong, Sungbok Shin, Sungahn Ko, Niklas Elmqvist
Human-Computer Interaction · Cross-listed from cs.GR
DELUGE: Decomposed Entropy-coded Live Unstructured Geometry Exchange for Real-time Particle Streaming
Hikari Yanagawa, Yuichi Hiroi and Takefumi Hiraki
Particle-based physics simulations, including fluids, smoke, and granular media, are fundamental to visual realism in immersive VR and AR. With the growing adoption of social VR and digital twins, demand is increasing for shared experiences in which multiple users interact with the same simulation in real time. Realizing such experiences requires low-latency streaming of large-scale particle data from a server to each client, yet existing point cloud compression methods such as G-PCC (TMC13) and Draco assume static
geometric structures; when applied to dynamic particle streaming, their encoding latency exceeds the frame period, failing to meet real-time delivery requirements. We propose DELUGE, a streaming compression architecture that exploits the temporal coherence and velocity predictability inherent in physics simulation particles, achieving sub-frame-latency encoding and decoding through three complementary techniques. Evaluation on dynamic point cloud
datasets demonstrates that DELUGE achieves approximately $20\times$ faster decoding than G-PCC (TMC13) and $6\times$ faster encoding than Draco. We further build end-to-end client implementations for both web browsers and Apple
Vision Pro, and confirm through a within-participants perceptual quality evaluation and a two-person collaborative task study on
Vision Pro that the proposed method supports real-time collaborative experiences with hand-tracked fluid interaction.
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Human-Computer Interaction
Faithful Where It Can Be Checked: Auditing a Reflection Agent Against Its System Prompt in a Randomized Trial
Subigya K. Nepal, Serena Soh, Noah Vinoya et al. (+3 more)
Conversational
agents are increasingly used to guide reflection. A recent randomized trial compared a GPT-4o career reflection
agent with the same program in a static journaling survey.
Agent participants ended less committed to their career plans and more doubtful. We coded all 17,930 turns from its two studies, checked our coding against human coders and linked conversations to the trial's surveys. The rules the
agent followed were the easy-to-check ones, like a reply length cap. Told not to flatter, it praised participants in half of its turns; told to challenge gently, it almost never did, and such a break leaves no visible trace. The behavior tied to the worse outcome was the demand to decide: the survey posed each decision once, while the
agent asked again when participants hesitated, and those pressed most ended most doubtful. Our findings inform reflection
agent design and the writing of checkable instructions.
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All authors Subigya K. Nepal, Serena Soh, Noah Vinoya, SoHyun Park, Mahnaz Roshanaei, Gabriella Harari
Human-Computer Interaction
DataCanvas-EDU: An Agentic Framework for Instructor-Guided Synthetic Data Generation in Business Analytics Education
Bang An, Maria Hamdani and Joseph Fox
Business analytics education requires diverse
datasets to support different learning objectives, student backgrounds, and analytical tasks. Real-world data can be difficult to obtain and offer limited flexibility for adapting a case to a particular course. Even when suitable data are available, instructors must investigate the patterns, verify the results, and prepare assignments and reference solutions, requiring substantial time and effort. The use of large
language models (LLMs) introduces an additional concern about training data contamination. Widely used public
datasets often have extensive tutorials and worked analyses that models may have encountered during training. Students may therefore receive explanations drawn from existing analyses without practicing how to investigate unfamiliar data in collaboration with AI. This paper presents DataCanvas-EDU, an agentic framework for instructor-guided synthetic data generation in business analytics education. Instructors specify teaching goals and intended patterns through conversation, while an AI
agent writes generation code, checks the resulting data, and prepares assignments, reference analyses, and rubrics. Four phases, Plan, Create, Verify / Test Analysis, and Evaluate, organize the process and support instructor review and revision. The framework is intended to simplify case preparation while creating opportunities for students to investigate newly designed patterns with AI. We illustrate the approach with WindowDash, a food delivery case containing 15,000 orders and nine designed patterns. DataCanvas-EDU is packaged as a reusable AI
Agent Skill for compatible
agent environments, with the package and installation instructions available at https://github.com/BANG23333/datacanvas-edu
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Human-Computer Interaction · Cross-listed from cs.GR
FootprintRAG: Visual Analytics for Evidence Context Refinement in RAG-based Scientific Literature Exploration
Xingyu Liu, Yu Dong, Qizhen Yu et al. (+7 more)
Retrieval-Augmented Generation (RAG) is increasingly used to ground large
language model (LLM) outputs in scientific literature. However, in open-ended literature exploration, the evidence context used for generation is often produced through hidden
retrieval, reranking, assessment, and filtering steps. Users may receive
retrieval summaries without knowing how the system constructed the evidence context, which evidence units were retained or discarded, or whether potentially useful evidence was excluded before synthesis. We present FootprintRAG, an LLM-
agent-powered visual analytics system for evidence context refinement in RAG-based scientific literature exploration. The core idea is to treat the RAG evidence context as an explicit, inspectable, and revisable analytical object before generation. FootprintRAG parses scientific literature into text and figure evidence units, expands an initial query into parallel query variants, retrieves and assesses evidence across iterative rounds, and surfaces ERS-ranked supplementary candidates from the corpus-level evidence space. Through coordinated views, the system connects
retrieval trajectories, evidence-state revision, and provenance-aware summary generation into a user-steerable workflow. We evaluate FootprintRAG through two case studies, a user study, and a workflow-level comparison with representative RAG systems. The results show that FootprintRAG helps users compare
retrieval directions, revise candidate evidence, recover potentially overlooked evidence, and trace generated summaries back to supporting evidence units. FootprintRAG is available at https://github.com/meteorshowering/FootprintRAGVA.git.
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All authors Xingyu Liu, Yu Dong, Qizhen Yu, Shiyu Cheng, Zhe Wang, Guan Li, Guihua Shan, Dong Tian, Christy Jie Liang, Quang Vinh Nguyen
Human-Computer Interaction · Cross-listed from cs.CL
Full-Duplex Speech Models Take the Floor When Asked, Not When Needed
Linkai Peng, Baorian Nuchged, Kaiqi Fu et al. (+1 more)
Full-duplex
speech models listen and speak at once, promising always-on assistants. Yet they must also decide when they should speak. Human listeners speak when addressed or when the speaker stops, but also self-select to correct a false claim, supply a missing word, or warn of danger. We ask whether full-duplex models do the same. To separate the reason to speak from the opportunity, we construct context-matched English monologues in which only the trigger utterance varies within a topic, define 10 conditions from turn-allocation rules, and compress inter-word pauses to limit opportunities created by silence. Across five model families, being addressed and silence are far more reliable triggers than false facts or hazards. Frame-level text-
token probabilities in Moshi and PersonaPlex are lower for false facts than for Neutral when averaged over the first 2\,s after trigger end. Pauses or permission to interrupt do not close this gap either. Given the floor, Moshi and PersonaPlex answer most direct questions, yet the proportion of non-empty false-fact replies that challenge the claim is only .14–.15, and the proportion of hazard replies that warn of danger is .04–.07. This paper thus identifies a gap in both
speech initiation and response content. Closing it requires genuine content understanding and intervention decisions grounded in it.
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All authors Linkai Peng, Baorian Nuchged, Kaiqi Fu, Yuyang Yao
Human-Computer Interaction
Value Faces: Surfacing How Self-Presentation Shifts Across Relationships
Gabriel Koo, Rayhan Rashed and Farnaz Jahanbakhsh
People present different aspects of themselves across relationships. Computational work has captured such variation in communication style. But this variation also extends to which principles people foreground or background in a particular relationship–i.e., in the values they express and how they balance them. We conceptualize these relationship-specific expressions of values as demonstrated values. To make demonstrated values visible, we introduce Value Faces, a system that analyzes a person's existing chat histories from their everyday messaging platforms using Schwartz's ten basic human values and produces separate value profiles for their different relationships. In a mixed-methods study(N=18), we find that the resulting value profiles distinguished participants' relational contexts with twice the odds of guessing, while system-inferred differences across relationships aligned with participants' perceptions of those differences. Participants used these profiles to articulate previously implicit differences in how they presented themselves, connect them to roles and changes over time, and reconsider their self-assessments.
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Human-Computer Interaction
Learning from Success and Failure: Acquiring Adaptive Dialogue Strategies for Social Robots
Sanae Yamashita and Yuki Okafuji
Traditional dialogue systems for social
robots require both dialogue strategies and user attribute recognition, each demanding specialized expertise. However, data collection is costly in real-world deployments, and the resulting
datasets often include many failure cases. In this study, we aim to automate the acquisition of dialogue strategies by leveraging both successful and failed interactions using a
vision-
language model (VLM) and a large
language model (LLM). We propose an architecture in which user attributes, recognized by the VLM, along with dialogue history, are fed into the LLM to generate dialogue strategies tailored to specific user attributes. We extracted dialogue strategies from an interaction
dataset collected through a field experiment and evaluated their effectiveness. The results demonstrate that explicitly representing failure strategies complements success strategies and improves performance. Our findings highlight a practical pipeline for constructing and maintaining an interpretable strategy repository from in-the-wild deployment logs by recycling abundant failure interactions as reusable constraints, ultimately reducing the development cost of social
robots.
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Human-Computer Interaction
Cyber Exodus: Burnout Symptoms, Exit Intention, and Peer Response in Online Cybersecurity Communities
Nadia Mehjabin, Ji Hyun Kim, Laura Barnes et al. (+3 more)
Security practitioners burn out at high rates, and the resulting attrition is itself a security problem. This workforce is hard to study: security operations centers are closed to outside researchers, studies that reach practitioners recruit through employers, and those who have disengaged most may have the least reason to answer an employer's survey. The same practitioners discuss their working conditions openly in online communities. We adapt the Burnout Assessment Tool, a validated clinical instrument, into a text annotation scheme and apply it to 354,861 posts and 296,442 replies from five online communities of cybersecurity practitioners. Checked against two trained coders on 100 posts, the annotation reaches a macro F1 of 0.75 across the four symptoms and 0.98 for detecting any burnout signal. We find that the four symptoms point to different problems at work, not to the same problem at different levels of severity. Exhaustion appears in almost any complaint about staffing or workload. Mental distance, a loss of belief that the work is worthwhile, is the only symptom unrelated to operational problems, and among posts with a single symptom it is accompanied by a stated intention to leave roughly twice as often as any other. Peer responses show the opposite pattern. When a poster says they are considering leaving, the mix of replies shifts toward career advice, but this shift is smallest for mental distance. The symptom most strongly associated with leaving is thus the one peers adjust to least, and a single burnout score obscures both patterns.
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All authors Nadia Mehjabin, Ji Hyun Kim, Laura Barnes, Koustuv Saha, Henry Kautz, Subigya Nepal