DevLan/vispubs huggingface dataset:
Visual Cues for Logical Reasoning about Text Enhance Metacognitive Sensitivity
Authors: Antonia Schlieder, Jan Rummel, Filip Sadlo
Published at EuroVis, 2026

An important part of critical thinking is analyzing the logical structure of inferences made in text, such as checking whether if–then rules appearing in legal regulations or scientific arguments are applied correctly. Metacognitive sensitivity, defined as feeling confident about one’s correct answers and uncertain about incorrect ones, is an indispensable component of such assessments because it supports the reliable identification of unstated assumptions and flawed conclusions. In this paper, we explore how visual cues can foster metacognitive sensitivity during the different phases of reasoning in logical inference tasks. We propose a visual cueing framework based on findings from deductive reasoning psychology and multimedia learning. The cues highlight premises and conclusions of problems stated in natural language, and visualize the extracted sequences of conditional statements as diagrams to guide viewers’ attention to common invalid inferences. Results from an empirical study (N = 164) demonstrate that users exhibit significantly greater metacognitive sensitivity with such visual cues than without.
Visual Identification and Comparison of Higher Order Properties in State Transition Sequences
Authors: Tobias Mertz, Steven Lamarr Reynolds-Ringer, Jörn Kohlhammer
Published at EuroVis, 2026

The analysis of state transition sequences is a prevalent research topic in many domains. In this context, we introduce the term higher-order property to describe characteristics of the analyzed data set that span beyond the local neighborhood of a single state. For the analysis of such properties, we elaborate why sequence diagrams are generally preferred over node-link diagrams in the literature. Consequently, we provide an overview of existing adaptations to node-link diagrams to trade-off support for first-order property analysis in favor of higher-order property analysis, potentially combining the strengths of both visualization types. To better understand the impact of this tradeoff, we present a comparative study of static sequence diagrams and spline-based node-link diagrams for five perception tasks on small-scale synthetic state transition sequence data. We focus on static stimuli of small-scale data sets to model the post-filtering perceptual process rather than an end-to-end analytical workflow. The study confirmed hypotheses regarding the superior user performance with the sequence diagram for the perception of higher-order properties. To demonstrate the relevance of higher-order property analysis for real-world problems, we present an application scenario from the cybersecurity domain. Based on the results of our study, we apply a sequence diagram-based prototype to this application scenario.
Visualization Guidelines for the Control Room: An Evaluation of Visual Alert Encoding with Domain Experts
Authors: Peter Baudains, Andrew Isaac Meso, Grazia Todeschini, Rita Borgo
Published at EuroVis, 2026

Visualization is routinely used in control rooms to maintain situational awareness of complex sociotechnical systems. While control room design often incorporates human factors engineering to identify the information presented, it is not unusual for control room visual encodings to rely closely on operator experience and historical encodings, which may be contrary to recommended guidelines from the visualization literature. Such approaches may contribute to the development of sub-optimal operator mental models of the systems they monitor.
In this article, we describe a design study in which we assessed the control room context from a visualization perspective and identified a set of 15 visualization guidelines, matching each of these to the domain requirements of the control room. Inspired by these guidelines, we design a visual encoding for alerts occurring on a power system schematic single line diagram, which is commonly used to identify and respond to system alerts arising from faults and conditions that require operator attention in power system control rooms.
We report on the results of an experiment conducted with both domain experts and non-experts. Our results show support for designs that follow our visualization guidelines via faster response times. Accuracy is improved in cases where information is directly encoded by our designs. We also find increased accuracy for domain experts, but expert response times were biased towards more familiar visual encodings in comparison to non-experts. This emphasizes the importance of established mental maps for control room operators and the need for careful change management in updating control room visualizations.
Visualizing Image Segmentation Network Behavior Through the Lens of Scale Space Analysis
Authors: Annika Christina Mikliss, Thomas Schultz 0001
Published at EuroVis, 2026

Deep neural networks are widely used for image segmentation, also in sensitive applications such as medical imaging or autonomous driving. However, few explainable AI methods are available that help developers understand such networks beyond classification. We demonstrate that studying how segmentation networks behave in scale space, i.e., when increasingly blurring their input, conveys relevant insight into whether detections require features such as sharp edges or high-frequency textures, and what priors have been learned implicitly. We introduce a visual analytics framework that supports a systematic qualitative and quantitative investigation of how segmentations evolve across scale space. In particular, summary visualizations for individual images support the formation of hypotheses that are subsequently formalized via customizable feature representations and evaluated on larger sets of test images using interactive embeddings and quantitative plots. On a cardiac MRI dataset, we demonstrate that this framework reveals important differences between a convolutional and a transformer-based neural network that, at first glance, appear to produce similar results. We confirm the practical relevance of those insights on out-of-distribution images from an MR scanner from which no images were included in the training data. Beyond this primary analysis, we provide extended results on the Adverse Conditions Dataset with Correspondences (ACDC) for semantic driving scene understanding.
Volume Data Reconstruction and Uncertainty Quantification in an Implicit Neural Representation via Multi-Task Learning
Authors: Weiyang Liu, Huijie Zhang, Zhaohan Lv, Yiming Lin
Published at EuroVis, 2026

Implicit neural representations (INRs) have become a common surrogate representation for scientific data due to their continuous representation and flexibility. Since INRs are lossy representations that introduce errors in data reconstruction, it is crucial to convey the uncertainty of the results to scientists to prevent them from being misled. However, current uncertainty quantification schemes generally face the challenge of compromising data reconstruction accuracy while maintaining the compactness of INRs. To address this issue, we formulate data reconstruction and uncertainty quantification as a multi-task learning (MTL) problem and treat uncertainty quantification as an auxiliary task to the reconstruction task. Unlike conventional auxiliary tasks which aim to improve the performance of the primary task, our objective is to obtain highly credible uncertainty estimates while minimizing the negative impact on the primary reconstruction task. To this end, we address this MTL problem with a Multi-gate Mixture-of-Experts (MMoE) architecture that partitions the network into multiple sub-networks and employs distinct gates to weight their outputs for each task. Furthermore, we propose a virtual expert mechanism to mitigate the trade-off between model flexibility and sub-network performance. To ensure that both tasks are fully optimized, we propose a loss weighting method combining dynamic and static weights. The dynamic weights adapt based on task convergence, while the static weights scale the losses and balance their relative importance. We comprehensively evaluated existing uncertainty quantification methods and compared them with our method. The results indicate that our method achieves higher reconstruction accuracy and more reliable uncertainty than existing methods.
When the Chain Breaks: Interactive Diagnosis of LLM Chain-of-Thought Reasoning Errors
Authors: Shiwei Chen, Niruthikka Sritharan, Xiaolin Wen, Chenxi Zhang, Xingbo Wang 0001, Yong Wang 0021
Published at EuroVis, 2026

Current Large Language Models (LLMs), especially Large Reasoning Models, can generate Chain-of-Thought (CoT) reasoning traces to illustrate how they produce final outputs, thereby facilitating trust calibration for users. However, these CoT reasoning traces are usually lengthy and tedious, and can contain various issues, such as logical and factual errors, which make it difficult for users to interpret the reasoning traces efficiently and accurately. To address these challenges, we develop an error detection pipeline that combines external fact-checking with symbolic formal logical validation to identify errors at the step level. Building on this pipeline, we propose ReasonDiag, an interactive visualization system for diagnosing CoT reasoning traces. ReasonDiag provides 1) an integrated arc diagram to show reasoning-step distributions and error-propagation patterns, and 2) a hierarchical node-link diagram to visualize high-level reasoning flows and premise dependencies. We evaluate ReasonDiag through a technical evaluation for the error detection pipeline, two case studies, and user interviews with 16 participants. The results indicate that ReasonDiag helps users effectively understand CoT reasoning traces, identify erroneous steps, and determine their root causes.
"Alone and Adrift in Analytics" - Insights from Long-term Involvements with stroke Clinicians when Using Care Quality Monitoring Systems
Authors: Hamzah Ziadeh, Mathias Sand Kristensen, Lina Palaiodimou, Michal A. Karlinski, Terry Quinn, Robert Mikulík, Hendrik Knoche
Published at CHI, 2026

Care quality improvement systems (CQIS) allow hospitals to monitor and improve their care by analysing performance data. However, many CQIS fail at helping clinicians improve as they see decreased use and stagnating care quality. In this paper, we investigate the use of one CQIS through ten qualitative activities carried out across five years. We synthesize and present our results regarding specific problems various stakeholders face, exact data visualization tasks that clinicians struggle with, and concrete solutions described by clinicians. Our research proposes steps that designers can take towards integrating data analysis interfaces and automating tasks in CQIS to assist with concrete data visualization problems identified by the 74 clinicians who participated in our study.
"Hey Dashboard!": Supporting Voice, Text, and Pointing Modalities in Dashboard Onboarding using Large Language Models
Authors: Vaishali Dhanoa, Gabriela Molina León, Eve E. Hoggan, Eduard Gröller, Marc Streit, Niklas Elmqvist
Published at CHI, 2026

Visualization dashboards are regularly used for data exploration and analysis, but their complex interactions and interlinked views often require time-consuming onboarding sessions from dashboard authors. Preparing these onboarding materials is labor-intensive and requires manual updates when dashboards change. Recent advances in multimodal interaction powered by large language models (LLMs) provide ways to support self-guided onboarding. We present Diana (Dashboard Interactive Assistant for Navigation and Analysis), a multimodal dashboard assistant that helps users for navigation and guided analysis through chat, audio, and mouse-based interactions. Users can choose any interaction modality or a combination of them to onboard themselves on the dashboard. Each modality highlights relevant dashboard features to support user orientation. Unlike typical LLM systems that rely solely on text-based chat, Diana combines multiple modalities to provide explanations directly in the dashboard interface. We conducted a comparative qualitative user study to understand the use of different modalities for different types of onboarding tasks and their complexities.
A Critical Reflection on the Values and Assumptions in Data Visualization
Authors: Shehryar Saharan, Ibrahim Al Hazwani, Miriah Meyer, Laura A. Garrison
Published at CHI, 2026

Visualization has matured into an established research field, producing widely adopted tools, design frameworks, and empirical foundations. As the field has grown, ideas from outside computer science have increasingly entered visualization discourse, questioning the fundamental values and assumptions on which visualization research stands. In this short position paper, we examine a set of values that we see underlying the seminal works of Jacques Bertin, John Tukey, Leland Wilkinson, Colin Ware, and Tamara Munzner. We articulate three prominent values in these texts — universality, objectivity, and efficiency — and examine how these values permeate visualization tools, curricula, and research practices. We situate these values within a broader set of critiques that call for more diverse priorities and viewpoints. By articulating these tensions, we call for our community to embrace a more pluralistic range of values to shape our future visualization tools and guidelines.
A Multiliteracy Model for Interactive Visualization Literacy: Definitions, Literacies, and Steps for Future Research
Authors: Gabriela Molina León, Benjamin Bach, Matheus Valentim, Niklas Elmqvist
Published at CHI, 2026

This paper presents a theoretical model for interactive visualization literacy to describe how people use interactive data visualizations and systems. Literacies have become an important concept in describing modern life skills, with visualization literacy generally referring to the use and interpretation of data visualizations. However, prior work on visualization literacy overlooks interaction and its associated challenges, despite it being an intrinsic aspect of using visualizations. Based on existing theoretical frameworks, we derive a two-dimensional model that combines four well-known literacies with five novel ones. We found evidence for our model through analyzing existing visualization systems as well as through observations from an exploratory study involving such systems. We conclude by outlining steps towards measuring, evaluating, designing for, and teaching interactive visualization literacy.
A Paradigm for Creative Ownership
Authors: Tejaswi Polimetla, Katy Ilonka Gero, Elena L. Glassman
Published at CHI, 2026

As generative AI tools become embedded in creative practice, questions of ownership in co-creative contexts are pressing. Yet studies of human-AI collaboration often invoke "ownership" without definition: sometimes conflating it with other concepts, and other times leaving interpretation to participants. This inconsistency makes findings difficult to compare across or even within studies. We introduce a framework of creative ownership comprising three dimensions - Person, Process, and System - each with three subdimensions, offering a shared language for both system design and HCI research. In semi-structured interviews with 21 creative professionals, we found that participants' initial references to ownership (e.g., embodiment, control, concept) were fully encompassed by the framework, demonstrating its coverage. Once introduced, however, they also articulated and prioritized the remaining subdimensions, underscoring how the framework expands reflection and enables richer insights. Our contributions include 1) the framework, 2) a web-based visualization tool, and 3) empirical findings on its utility.
A Scoping Review and Guidelines on Privacy Policy's Visualization from an HCI Perspective
Authors: Shuning Zhang, Eve He, Sixing Tao, Yuting Yang, Ying Ma, Ailei Wang, Xin Yi 0001, Hewu Li
Published at CHI, 2026

Privacy Policies are a cornerstone of informed consent, yet a persistent gap exists between their legal intent and practical efficacy. Despite decades of research proposing various visualizations, user comprehension remains low, and designs rarely see widespread adoption. To understand this landscape and chart a path forward, we synthesized 65 top-tier papers using a framework adapted from user-centered design lifecycles. Our analysis presented four findings of the field's evolution: (1) trade-off between information load and decision efficacy, which shows a shift from augmenting disclosures to cognitive load management, (2) co-evolutionary dynamic of design and automation, revealing that designs such as context-awareness drove automation needs, while LLM breakthroughs enable the semantic interpretation required to realize those designs, (3) tension between generality and specificity, highlighting the divergence between standardized solutions and the increasing necessity for specialized interaction in IoT and immersive environments, and (4) balancing stakeholder opinions, where visualization efficacy is constrained by the interplay of regulatory mandates, developer capabilities and provider incentives.
A Scoping Survey on Augmented Display Systems
Authors: Muhammad Raza, Vachiraporn Ketsoi, Derek Reilly
Published at CHI, 2026

Augmented displays (ADs), where AR extends the workspace of a physical display, are gaining momentum across productivity, visual analytics, and collaborative scenarios; yet, the field lacks a unified conceptual foundation. We present the first scoping survey on ADs, synthesizing 62 papers (2010-2024), including one earlier paper from 2005. From the corpus, we derive the AR-Display Spatial Integration Framework, capturing recurring patterns across AD systems along four core dimensions: extension type, AR content type, placement, and layout. We map existing systems across the framework to identify design patterns and translate them into recommendations. We further consolidate insights on development and evaluation practices, followed by a discussion on using the framework, AD applications, cross-cutting factors, and managing the complexity of AD systems. Our survey also outlines research gaps for advancing the field, particularly in the design space of AR-Display integration and the broader support and use of AD.
AI-generated AR Reassembly Guidance from Disassembly Videos to Scaffold Everyday Repair
Authors: Wenjing Deng, Zhihao Yao 0004, Xinhui Kang, Qirui Sun, Xintong Wu, Sisi He, Chenzhuo Xiang, Haipeng Mi
Published at CHI, 2026

Repair is a valuable yet challenging activity, especially when product manuals are missing or outdated. Augmented Reality (AR) has been widely explored for repair tasks, but most systems rely on CAD models or pre-constructed assets, which escalate authoring costs and constrain scalability. We introduce RePairAR, a system that leverages multimodal large language models (MLLMs) to generate interactive AR reassembly guidance derived directly from user-recorded egocentric disassembly videos. RePairAR deduces step-part-relation structures, reverses these for reassembly planning, and delivers the guidance through mixed-media AR visualizations. In a user study with repair novices, RePairAR significantly reduced perceived temporal demand compared to traditional how-to videos. Both media improved self-efficacy, with RePairAR providing greater gains. Follow-up interviews revealed the mechanisms behind these effects. We contribute a validated MLLM-driven pipeline and highlight design implications for scalable, situated support in everyday repair practices.
AIDED: Augmenting Interior Design with Human Experience Data for Designer-AI Co-Design
Authors: Yang Chen Lin, Chen-Ying Chien, Kai-Hsin Hou, Hung-Yu Chen, Po-Chih Kuo
Published at CHI, 2026

Interior design often struggles to capture the subtleties of client experiences, leaving gaps between what clients feel and what designers can act upon. We present AIDED, a designer–AI co-design workflow that integrates multimodal client data into generative AI (GAI) design processes. In a within-subjects study with twelve professional designers, we compared four modalities: baseline briefs, gaze heatmaps, questionnaires visualizations, and AI-predicted overlays. Results show that questionnaire data were trusted, creativity-enhancing, and satisfying; gaze heatmaps increased cognitive load; and AI-predicted overlays improved GAI communication but required natural language mediation to earn trust. Interviews confirmed that an authenticity–interpretability trade-off is central to balancing client voices with professional control. Our contributions are: (1) a system that incorporates experiential client signals into GAI design workflows, (2) empirical evidence of how different modalities affect design outcomes, and (3) implications for future AI tools that support human–data interaction in creative practice.
Anu.js: Accelerating Web-based Immersive Analytics
Authors: David Saffo, Benjamin Lee 0001, Feiyu Lu 0001, Cheng Yao Wang, Blair MacIntyre
Published at CHI, 2026

We present Anu.js, a toolkit for web-based immersive analytics (IA). The IA design space is vast, multi-faceted, and everchanging, challenging development in the absence of robust authoring support. The web is a popular platform for visualization applications, research, and teaching, and by leveraging the benefits of web technologies and adopting imperative authoring paradigms we can achieve the necessary expressiveness, compatibility, and ergonomics to support IA research and development. Anu.js adapts D3's data-binding model to 3D contexts, granting fine-grained control over the creation, representation, animation, performance, and interaction of 3D scene-graphs. Additionally, Anu.js offers declarative prefabs to support common visualization elements and interactions, and synergizes with popular visualization libraries which allows developers to leverage these proven utilities. We demonstrate Anu.js's potential through our diverse example gallery, expert evaluation, and potential future applications. Through this, Anu.js empowers developers in accelerating the creation of novel and bespoke visualizations for immersive web-based applications.
Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetrics
Authors: Yinghao Zhu, Dehao Sui, Zixiang Wang, Xuning Hu, Lei Gu, Yifan Qi, Tianchen Wu, Ling Wang, Yuan Wei, Wen Tang 0001, Zhihan Cui, Yasha Wang, Lequan Yu, Ewen M. Harrison, Junyi Gao, Liantao Ma
Published at CHI, 2026

Clinician skepticism toward opaque AI hinders adoption in high-stakes healthcare. We present AICare, an interactive and interpretable AI copilot for collaborative clinical decision-making. By analyzing longitudinal electronic health records, AICare grounds dynamic risk predictions in scrutable visualizations and LLM-driven diagnostic recommendations. Through a within-subjects counterbalanced study with 16 clinicians across nephrology and obstetrics, we comprehensively evaluated AICare using objective measures (task completion time and error rate), subjective assessments (NASA-TLX, SUS, and confidence ratings), and semi-structured interviews. Our findings indicate AICare's reduced cognitive workload. Beyond performance metrics, qualitative analysis reveals that trust is actively constructed through verification, with interaction strategies diverging by expertise: junior clinicians used the system as cognitive scaffolding to structure their analysis, while experts engaged in adversarial verification to challenge the AI's logic. This work offers design implications for creating AI systems that function as transparent partners, accommodating diverse reasoning styles to augment rather than replace clinical judgment.