DevLan/vispubs huggingface dataset:
SemTabla: A Human-in-the-Loop Framework for Semantic Enrichment and Validation of Data Tables
Authors: Zhuochen Jin, Yingjie Mi, Yehang Zhu, Yichen Yao, Chongyang Yu, Ke Xu
Published at CHI, 2026

Data tables are widely used to record critical information, enabling decision-makers to derive insights through table question answering (Table QA). However, the metadata from table schemas alone often fail to capture the underlying business semantics embedded in the tabular data, leading to reasoning errors. Existing automated approaches to semantic enrichment face challenges in insufficient data utilization, narrow feature coverage, and limited interpretability. To overcome these limitations, we propose SemTabla, an interactive system that employs a human-in-the-loop mechanism to extract comprehensive and interpretable semantics from tabular data. Our key contributions include: (1) a hierarchical framework for extracting semantic attributes; (2) a novel sampling method that identifies critical but rare row instances; and (3) an interactive interface that supports visualization, validation, and refinement of the extracted table semantics. A user study confirmed the system's usability, and quantitative experiments demonstrate that the extracted semantics significantly enhance the reasoning capabilities of large language models.
SoundWeAR: Co-Designing AR Sound Cues to Support Outdoor Awareness for DHH Individuals
Authors: Anna Surovkova, Tianze Xie, Xinan Yang, Seungwoo Je
Published at CHI, 2026

For Deaf and Hard of Hearing (DHH) individuals, limited access to sound cues in outdoor environments can reduce situational awareness, making it challenging to notice events and respond to potential dangers. To address this, we investigated the needs for sound awareness and preferences of DHH individuals for visualizing environmental sounds using AR glasses. We conducted four participatory design workshops with DHH participants, social workers, and designers to explore sound awareness needs and co-design ideal visual representations. Based on our insights, we conducted interviews with 15 DHH participants to select their preferred visualizations. The most voted designs were implemented in prototype, which eight DHH participants evaluated in outdoor environment. Results demonstrate that visualizing sound cues through AR can enhance situational awareness and increase sense of safety and confidence among DHH individuals while walking outdoors. Our findings provide design suggestions for translating auditory information into accessible visual representations for DHH users.
Sprout: Using a Visual Metaphor to Support Customizable and Collaborative Health Tracking
Authors: Pape Sow Traore, Shirin Amouei, Mira Ram, Elizabeth L. Murnane
Published at CHI, 2026

Self-tracking tools can support health awareness and behavior change, though sustaining engagement remains difficult. Prior work has explored qualitative visualization, customization, and collaborative features to promote engagement; but little is known about how these strategies interact when combined. We present Sprout, a mobile application that integrates qualitative, customizable, and collaborative health tracking using a garden metaphor. Sprout allows users to choose what they track, customize how data is visually encoded, and participate in anonymous communities where collective progress unlocks shared features. In a 2-week field study with N=22 participants, users reported that qualitative displays worked best as a complement to quantitative tools, customization mostly happened during app setup, social features were the most engaging though collaboration produced both motivation and frustration, and anonymity protected privacy but limited social connection. Our findings show how multiple design strategies coexist in one system, sometimes competing and sometimes aligning in supporting users' tracking needs.
Studying the Separability of Visual Channel Pairs in Symbol Maps
Authors: Poorna Talkad Sukumar, Maurizio Porfiri, Oded Nov
Published at CHI, 2026

Visualizations often encode multivariate data by mapping attributes to distinct visual channels such as color, size, or shape. The effectiveness of these encodings depends on separability—the extent to which channels can be perceived independently. Yet systematic evidence for separability, especially in map-based contexts, is lacking. We present a crowdsourced experiment that evaluates the separability of four channel pairs—color (ordered) × shape, color (ordered) × size, size × shape, and size × orientation—in the context of bivariate symbol maps. Both accuracy and speed analyses show that color × shape is the most separable and size × orientation the least separable, while size × color and size × shape do not differ. Separability also proved asymmetric—performance depended on which channel encoded the task-relevant variable, with color and shape outperforming size, and square shape especially difficult to discriminate. Our findings advance the empirical understanding of visual separability, with implications for multivariate map design.
SVATA: A Spatial Visual Attention Tracking and Analysis Platform for Embodied Cognition Research
Authors: Xuchao Ren, Jing Huang, Siyuan Feng, Yi Wei, Jiangtao Gong, Sai Ma
Published at CHI, 2026

Understanding spatial visual attention is important for embodied cognition research, yet practical platforms for 3D attention analysis remain limited. We present SVATA—Spatial Visual Attention Tracking and Analysis, an open-source platform that supports an end-to-end workflow for collecting, analyzing, and visualizing world-referenced gaze-and-movement data within a 3D spatial context. SVATA maps multimodal signals (gaze, head, position) onto reconstructed geometry and computes a physiologically informed Average Focus Weight (\(AFW/m^{2}\)) metric as a proxy for overt visual focus. This representation supports structured analysis and multidimensional visualization of spatial viewing patterns. We evaluated SVATA through an in-the-wild museum deployment with 78 visitors and an expert study with seven prospective users; the results suggest its feasibility and perceived utility for analyzing spatial viewing behavior in embodied cognition research.
Taking a Walk on the Wild Side: Effects of Walking in Synchrony with Pitch-Altered Footstep Sounds on Body Perception in Outside the Lab Contexts
Authors: Amar D'Adamo, Karunya Srinivasan, Laia Turmo Vidal, Daniel De La Prida, Luis Antonio Azpicueta-Ruiz, Aleksander Väljamäe, Ana Tajadura-Jiménez
Published at CHI, 2026

The 'Footsteps Illusion' shows that pitch-altering footstep sounds in real-time affects body perception, gait, and emotion: high versus low frequencies evoke a lighter versus heavier body. We tested whether this illusion extends beyond the lab, where environmental factors matter. Using a mixed-methods approach, twenty-eight participants used a minimal setup to synchronize with three prerecorded pitch-altered footstep soundtracks: Control, High-Frequency, and Low-Frequency. In Experiment 1, they walked a fixed path, with gait recordings, questionnaires, and body visualizations. In Experiment 2, they walked freely across campus routes with High/Low-Frequency soundtracks, followed by post-walk interviews using a novel spatial mapping tool linking body sensations to context. Results replicate the illusion outdoors, showing synchronization with prerecorded sounds as a viable alternative to real-time feedback, and revealing that contextual factors modulate illusion effects. We contribute insights and novel tools (prototype, spatial mapping method, and footstep soundbase) for studying multisensory body perception in everyday contexts.
Talking Inspiration: A Discourse Analysis of Data Visualization Podcasts
Authors: Ali Baigelenov, Prakash Shukla 0001, Phuong Bui, Paul Parsons
Published at CHI, 2026

Data visualization practitioners routinely invoke inspiration, yet we know little about how it is constructed in public conversations. We conduct a discourse analysis of 31 episodes from five popular data visualization podcasts. Podcasts are public-facing and inherently performative: guests manage impressions, articulate values, and model "good practice" for broad audiences. We use this performative setting to examine how legitimacy, identity, and practice are negotiated in community talk. We show that "inspiration talk" is operative rather than ornamental: speakers legitimize what counts, who counts, and how work proceeds. Our analysis surfaces four adjustable evaluation criteria by which inspiration is judged—novelty, authority, authenticity, and affect—and three operative metaphors that license different practices—spark, muscle, and resource bank. We argue that treating inspiration as a boundary object helps explain why these frames coexist across contexts. Findings provide a vocabulary for examining how inspiration is mobilized in visualization practice, with implications for evaluation, pedagogy, and the design of galleries and repositories that surface inspirational examples.
Tattered Teddies and Pentagram Charms: How People Use Touchable Comfort Objects and What This Means for Designing Affective Haptic Systems
Authors: Preeti Vyas, Bereket Guta, Angel Bao, Rúbia Reis Guerra, Mara Solen, Noor Naila Imtinan Himam, Andero Uusberg, Karon E. MacLean
Published at CHI, 2026

Many people use physical objects to self-calm and regulate difficult emotions. Studies evaluating the effectiveness of Touchable Comfort Objects (TCOs) imbued with haptic expressivity, affect awareness, and adaptive support have been primarily lab-based, so we know little about underlying user needs and real-world practices. We asked 132 participants about their favorite TCO: how they use it, where it helps (or fails), and what they would value or avoid in a technology-enhanced TCO. Our survey analysis, based on a proposed data-validated dimensional space, reveals that people choose TCOs as much for emotional significance as for physical soothing, engage with them multiple times daily, and about half desire deeper support for emotion awareness, social-emotional agency, and communication facilitation. We identified four profiles (Physical Soothers, Frodo Fidgeters, Sentimental Functionalists, Nostalgic Comfort-Seekers) that capture diverse patterns. We further contribute a dataset with an interactive visualization, and describe TCO design opportunities to support researchers and designers.
The Evolving Duet of Two Modalities: A Survey on Integrating Text and Visualization for Data Communication
Authors: Xingyu Lan, Xi Li, Yixing Zhang, Mengqin Cheng, Jiazhe Wang, Siming Chen 0001
Published at CHI, 2026

Text plays a fundamental yet understudied role as a narrative device in data visualization. While existing research has extensively explored text as data input and interaction modality, its function in supporting storytelling and interpretation remains fragmented. To address this gap, this work presents a systematic review of 98 publications that provide insights into using text as narrative. We investigate how text can be utilized in visualization, analyze its functions and effects, and explore how it can be designed to facilitate data communication. Our synthesis identifies significant research gaps in this domain and proposes future directions to advance the integration of text and visualization, ultimately aiming to provide guidance for designing text that enhances narrative clarity and fosters engagement.
The Impact of Uncertainty Visualization on Trust in Thematic Maps
Authors: Varun Srivastava, Fan Lei, Alan M. MacEachren, Ross Maciejewski
Published at CHI, 2026

Thematic maps are widely used to communicate spatial patterns to non-expert audiences. Although uncertainty is inherent in thematic map data, it is rarely visualized, raising questions about how its inclusion affects trust. Prior work offers mixed perspectives: some argue that uncertainty fosters trust through transparency, while others suggest it may reduce trust by introducing confusion. Yet few empirical studies explicitly measure trust in thematic maps. We conducted a between-subjects experiment (N = 161) to evaluate how visualizing uncertainty at varying levels (low, medium, high) influences trust. We find that uncertainty visualization generally reduces trust, with greater reductions observed as uncertainty levels increase. However, maps dominated by low uncertainty do not significantly differ in trust from those with no uncertainty. Moreover, while uncertainty visualization tends to make readers question the accuracy of the data, it appears to have a weaker influence on perceptions of the mapmaker's integrity.
The Socio-Technical Energy Triad: Uncovering Tensions between Service Providers, Users, and the Energy System
Authors: Florian Güldenpfennig, Nikolas Zechner, Daniel Tîrcob, Jan Köck, Dorothee Köstlin, Oliver Hödl
Published at CHI, 2026

The digital transformation of the energy sector introduces HCI challenges that exceed traditional end-user design. While systemic perspectives are increasingly called for, empirical work on the tensions between provider constraints and domestic practices remains limited. We introduce the Socio-Technical Energy Triad, situating digital energy services at the intersection of providers, households, and the emerging energy system. Drawing on 21 provider interviews, a technology-probe deployment with 10 users, and a survey with 98 respondents, we identify a structural disconnect across the Triad. Providers struggle with fragmented data infrastructures and limited user trust, hindering data-driven optimization. For households, this system logic remains largely invisible: they focus on efficiency and resist additional cognitive labor in managing domestic energy. We argue that bridging this "Invisibility Gap" requires not just traditional visualization but the design of legible automation and the translation of system-level needs into clear, user-centered signals.
Three Modalities, Two Design Probes, One Prototype, and No Vision: Experience-Based Co-Design of a Multi-modal 3D Data Visualization Tool
Authors: Sanchita S. Kamath, Aziz N. Zeidieh, Venkatesh Potluri, Sile O'Modhrain, Kenneth Perry, Jooyoung Seo
Published at CHI, 2026

Three-dimensional (3D) data visualizations, such as surface plots, are vital in STEM fields from biomedical imaging to meteorology and spectroscopy, yet remain largely inaccessible to blind and low-vision (BLV) people. To address this gap, we conducted an Experience-Based Co-Design (EBCD) with BLV co-designers with expertise in non-visual data representations to create an accessible, multi-modal, web-native visualization tool. Using a multi-phase co-design methodology, our team of five BLV and one non-BLV researcher(s) participated in two iterative sessions, comparing a low-fidelity tactile probe with a high-fidelity digital prototype. This process produced a prototype with empirically grounded features, including reference sonification, stereo and volumetric audio, and configurable buffer aggregation, which our BLV co-designers validated as improving analytic accuracy and learnability. In this study, we explicitly target core analytic tasks essential for non-visual 3D data exploration: 3D orientation, landmark and peak finding, comparing local maxima versus global trends, gradient tracing, and identifying occluded or partially hidden features. Our work offers accessibility researchers and developers a co-design protocol for translating tactile knowledge to digital interfaces, concrete design guidance for future systems, and opportunities to extend accessible 3D visualization into embodied data environments.
Touch, Gesture, and Conversation: A Case Study in the Choreography of Interaction with a Data Physicalization
Authors: Laura J. Perovich, Christina Wu, Bernice E. Rogowitz, Dietmar Offenhuber
Published at CHI, 2026

Many potential benefits of data physicalizations are thought to stem from their tangible nature and their presence in a shared space, which allows for physical interaction in a social environment. Prior research has studied where observers touch data physicalizations and how these touches depend on task. However, limited research explores the moment-by-moment details of touches, gestures, and verbal dialogue and how these interactions contribute to the larger data physicalization sensemaking process. This case study offers a new data analysis from a previous study to provide fine-grained accounting of one participant's touches, gestures, and conversations around a data object. We identify four types of touches and gestures and describe relational patterns between individual interactions. This work provides a foundation for further exploring the reasons people touch or gesture with data physicalizations, connecting these efforts with gesture studies research, and identifying design implications for data physicalization.
Toward Flexible Psychiatric History-Taking and Visualization: Exploring Clinician Perspectives with Large Language Models
Authors: Yugyeong Jung, Thu Hoang Anh Vo, Hyun Seung Moon, Jae Young Choi, Hyangkyeong Oh, Ujin Lee, Eunjoo Kim, Tak Yeon Lee, Uichin Lee
Published at CHI, 2026

The initial psychiatric interview centers on patients' chief complaints, symptoms, and functional impairments, forming the basis of diagnostic impressions. In real clinical practice, however, interviews are constrained by limited time and the unpredictability of patient responses, making it difficult to secure essential information efficiently. While prior conversational agents have focused on conversationalizing validated instruments or advancing interview systems in general medical domains, little research has addressed the distinctive challenges of initial psychiatric history-taking from clinicians' perspective. We present a flexible psychiatric interviewer that dynamically adapts question flow and prioritizes clinically essential information within time constraints, with a clinical dashboard for efficient review. We evaluated the system through 1,440 simulated patient dialogues and follow-up interviews with 19 clinicians. Results show that it captures essential information within a limited time while preserving conversational flexibility and empathy, highlighting design implications for coachable and responsible AI interviewers that align with clinical practice.
Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation
Authors: Aeree Cho, Grace C. Kim, Alexander Karpekov, Seongmin Lee 0007, Alec Helbling, Benjamin Hoover, Zijie J. Wang, Minsuk Kahng, Duen Horng (Polo) Chau
Published at CHI, 2026

The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications. However, its complexity makes it difficult for non-experts to learn. Existing resources often lack interactivity, rely on static descriptions of simplified architectures, or fail to reflect models' behavior with real data. To address this gap, we introduce Transformer Explainer, an interactive visualization tool for non-experts to learn Transformers. The tool integrates an overview illustrating the Transformer's data flow with on-demand explanations that gradually reveal mathematical details. Smooth transitions across abstraction levels highlight the interplay between high-level structures and low-level operations. Running a live GPT-2 instance directly in the browser, Transformer Explainer empowers learners to experiment with custom input and hyperparameters without setup, observing next-token predictions in real time. A 90-participant user study showed that our tool offered significant advantages in improving user understanding and engagement. Transformer Explainer has attracted over 490,000 users.
Uncertain Pointer: Situated Feedforward Visualizations for Ambiguity-Aware AR Target Selection
Authors: Ching-Yi Tsai, Nicole Tacconi, Andrew D. Wilson, Parastoo Abtahi
Published at CHI, 2026

Target disambiguation is crucial in resolving input ambiguity in augmented reality (AR), especially for queries over distant objects or cluttered scenes on the go. Yet, visual feedforward techniques that support this process remain underexplored. We present Uncertain Pointer, a systematic exploration of feedforward visualizations that annotate multiple candidate targets before user confirmation, either by adding distinct visual identities (e.g., colors) to support disambiguation or by modulating visual intensity (e.g., opacity) to convey system uncertainty. First, we construct a pointer space of 25 pointers by analyzing existing placement strategies and visual signifiers used in target visualizations across 30 years of relevant literature. We then evaluate them through two online experiments (n = 60 and 40), measuring user preference, confidence, mental ease, target visibility, and identifiability across varying object distances and sparsities. Finally, from the results, we derive design recommendations in choosing different Uncertain Pointers based on AR context and disambiguation techniques.
Virtual Minds, Real Work: LLM-Powered Preference-Based Planning through Spatial Multi-Agent-Human Collaboration
Authors: Ziyi Zhang, Xin Yi 0001, Zitong Dai, Xuewen Yu, Shuning Zhang, Bo Liu 0004, Jiuxin Cao, Hantao Zhao
Published at CHI, 2026

People frequently face preference-based planning tasks requiring balancing goals with nuanced constraints, yet even advanced LLMs demand considerable effort to produce and adjust plans reflecting complex user preferences. We present MAVIS (Multi-Agent Virtual Interactive Synergy), a multi-agent system within a virtual workspace that introduces an incremental collaboration mechanism. This mechanism automatically decomposes tasks into guidelines and sequentially introduces expert agents. Each agent proactively engages users in focused dialog to uncover implicit preferences, while successive agents add perspectives and transparently negotiate trade-offs. To mitigate textual overload, MAVIS employs spatial visualizations that externalize agents' reasoning through step-linked summaries and context-aware boards, with embodied avatars supporting natural interaction. Across studies, Study 1 showed our collaboration mechanism doubled expressed preferences and improved planning quality by 60.3% over a conventional LLM baseline. Study 2 affirmed visualization's benefits over a non-spatial baseline, while Study 3 confirmed its versatility across VR and desktop modalities and diverse tasks.
VisGuardian: A Lightweight Group-based Visual Privacy Control Technique For Smart Glasses in Home Environments
Authors: Shuning Zhang, Qucheng Zang, Yongquan 'Owen' Hu, Jiachen Du, Xueyang Wang, Yan Kong, Xinyi Fu 0003, Suranga Nanayakkara, Xin Yi 0001, Hewu Li
Published at CHI, 2026

Always-on sensing of AI applications on AR glasses makes traditional permission techniques inefficient for context-dependent private visual data within home environments. Home presents a challenging privacy context due to massive sensitive objects and the intimate nature of daily routines. We propose VisGuardian, a fine-grained content-based visual permission technique for AR glasses. VisGuardian features a group-based control mechanism that enables users to efficiently manage permissions for multiple private objects. VisGuardian detects objects using YOLO and adopts a pre-classified schema to group them. By selecting a single object, users can obscure groups of related objects based on criteria including privacy sensitivity, object category, or spatial proximity. A technical evaluation shows VisGuardian achieves mAP50 of 0.6704 with only 14.0 ms latency and a 1.7% increase in battery consumption per hour. Furthermore, a user study (N=24) comparing VisGuardian to slider-based and object-based baselines found it to be significantly faster for setting permissions and was preferred by users for its efficiency, effectiveness, and ease of use.