DevLan/vispubs huggingface dataset:
RETRACE: Interactive Visualizations for Reasoning Traces of Large Reasoning Models
Authors: Ludwig Felder, Jacob Miller 0001, Markus Wallinger, Stephen G. Kobourov, Chunyang Chen
Published at EuroVis, 2026

Recent advances in Large Language Models have led to Large Reasoning Models, which produce step-by-step reasoning traces. Such traces may offer insight into how models think, improving explainability and clarifying the underlying process. These traces, however, are often verbose and complex, making them cognitively demanding to comprehend. With this in mind, we propose RETRACE, an interactive system that structures and visualizes textual reasoning traces to support understanding. We use a validated reasoning taxonomy to produce structured reasoning data and investigate two types of interactive visualizations thereof. In a controlled human-subject study, the visualizations provided more accurate comprehension of the model’s reasoning and less perceived effort than a raw text baseline. The results of this study could have design implications for making long and complex machine-generated reasoning processes more usable and transparent, an important step in AI explainability.
Scalable Computation of Topological Abstractions for Scalar Data
Authors: Michael Will, Jonas Lukasczyk, Julien Tierny, Attila Gyulassy, Gunther H. Weber, Hamish A. Carr, Christoph Garth
Published at EuroVis, 2026

Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial and on single machines. This paper presents the state of the art in the scalable computation of topological abstractions on scalar data, in shared memory parallel on single machines, and in distributed memory parallel on multiple machines. We highlight results for set-based, graph-based and complex-based abstractions and organize the state of the art based on this taxonomy. The paper identifies parallelization and distribution techniques common in topological algorithms and highlights further areas of interest with underdeveloped efforts.
SemiConLens: Visual Analytics for 2D Semiconductor Discovery
Authors: Kavinda Athapaththu, Shiwei Chen, Yuan Fang, Sanchali Mitra, Yee Sin Ang, Yong Wang 0021
Published at EuroVis, 2026

The past few years have witnessed vibrant efforts in discovering new two-dimensional (2D) semiconductor materials from both academia and the industry, due to their promising potential in resolving the severe performance deterioration of traditional semiconductors resulting from condensed silicon thickness. However, existing methods (e.g., Density Functional Theory (DFT) or machine-learning-based approaches) suffer from various challenges such as small datasets, and reliability and trustworthiness issues. To bridge this gap, we propose SemiConLens, a visual analytics approach to combine human expertise with the power of ML to enable effective and reliable 2D semiconductor discovery. Specifically, we first develop a new Correlation Aware Multivariate Imputation (CAMI) method and use ML models like autoencoder, which can better learn from limited data and reveal uncertainty, to address the challenge of sparse data in semiconductivity prediction. Built upon this, our visualization module, consisting of three visualization views with linked interactions, allows material researchers to interactively filter, discover and compare 2D semiconductor candidates. A novel circular glyph design and a new cluster-aware layout optimization approach are proposed to effectively display all the user-configurable key attributes and possible prediction uncertainties of each semiconductor candidate, ensuring a reliable and trustable 2D semiconductor discovery. We assess SemiConLens through quantitative evaluations, expert interviews, and use cases. The results demonstrate SemiConLens’s capability to help material researchers conduct effective discovery of desirable 2D semiconductors.
Serendipitous Explanations: Interaction-Triggered Comprehension Aids in Visualization
Authors: Maryam Rezaie, Melanie Tory, Sheelagh Carpendale
Published at EuroVis, 2026

Evidence continues to accrue around the difficulties people have understanding new and complex visualizations, which in turn provides continued incentive to explore additional methods of supporting visualization viewers. Through leveraging viewers’ spontaneous visualization sensemaking activities, we introduce a Serendipitous Explanation Approach (SEA). A significant part of SEA’s contribution is making active use of a viewer’s spontaneous, exploration interactions to offer in situ explanations. SEA adds semantic and structural explanations, making use of visual transformations, and additional representations of visualization elements themselves to communicate meaning through highlights, reconfigurations, and in-situ annotations. We designed and studied VisTips as an instantiation of SEA. Our study demonstrates the prevalence of spontaneous interaction and appreciation of this approach. Among the explanation types, visual transformations stood out as especially impactful. Our findings also offer practical insights for future use of the ideas in SEA, opening possibilities for seamlessly integrating explanatory visuals that support viewers’ natural visualization sensemaking.
SlideSAVR: Enabling Live Analysis during Data Presentations via Multimodal Sketching and Voice Input
Authors: Chang Han, Md. Mehrab Tanjim, Shunan Guo, Christine Dierk, Katherine E. Isaacs, Jane Hoffswell
Published at EuroVis, 2026

Interpersonal communication in data science can yield sought-after insights, but presentation environments are often not conducive for live analysis, forcing the process to move offline. Through a formative survey with 16 participants, we identified both technical (e.g., complexity of tools) and psychological (e.g., pressure of programming during presentation) factors constraining live data analysis. To enable live analysis, we present SlideSAVR, a data-driven presentation assistant that leverages sketching and voice inputs in live discussion to support collaborative data analysis during presentations. Powered by an agentic framework that flexibly defines augmentation rules, updates slide content dynamically to match the live context, and automates backend computations, SlideSAVR enables fluid audience-presenter interaction and reduces the need for offline reanalysis and follow-up communication. We demonstrate SlideSAVR’s ability to support a range of tasks through nine representative use cases. We further evaluate the system’s accuracy and computation time across different settings, showing that SlideSAVR can reliably perform diverse tasks when provided with both sketch and voice inputs.
SPINE: VAE-driven Counterfactuals for Decision Boundary Maps
Authors: Imke Bloemen, Vidya Prasad, Fernando Paulovich
Published at EuroVis, 2026

As Deep Learning models become increasingly complex, Explainable AI becomes essential for deploying machine learning classifiers. Decision Boundary Mapping (DBM) is a technique for visualizing a classifier’s global decision boundary. Despite their relative success, current DBM methods rely on global inverse multidimensional projections that map 2D points back to the input space. The resulting visualizations depend heavily on the chosen projection, distort elements of the structure and distribution of the input data, and contain limited classifier-specific information, often leading to inaccurate boundaries. We propose Sampling-based Precise Neighbourhood Estimation (SPINE), a novel DBM approach for differentiable classifiers that is projection-agnostic and does not depend on inverse projections. SPINE generates additional high-dimensional points near the decision boundary using counterfactuals and variational autoencoders, then applies local interpolation to build visual representations. Our results show that this strategy better captures the structure and distribution of the data and the classifier’s predictions in boundary regions than the state-of-the-art in DBMs, yielding higher-resolution and more accurate representations of classifier behaviour. Beyond standard DBM representations, SPINE enables additional exploratory analyses, such as counterfactual feature changes and path visualizations, supporting richer insights into model decisions.
Survey on Visualization of Information Diffusion over Networks
Authors: Tom Baumgartl, Max Sondag, Velitchko Filipov, Michaela Tuscher, Sandhya Rajendran, Silvia Miksch, Daniel Archambault, Alessio Arleo, Tatiana von Landesberger
Published at EuroVis, 2026

Information Diffusion (ID) describes how a value (e.g., a pathogen, a rumor, a packet) spreads through an underlying “medium" network of elements (e.g., a social or computer network). Understanding the information diffusion process is essential to predicting trends, controlling misinformation, and enhancing decision-making as well as communication strategies. Visual Analytics has shown significant potential in supporting comprehension of ID processes in several domains. These approaches vary greatly in their design, both in terms of data and visual encodings. The variety of designs and application domains motivates this survey, which complements existing surveys with a focus on visualization of transmission processes in contrast to other surveys about visualizing networks. This survey defines types of transmission and medium networks, identifies common visualization principles, categorizes them, and identifies gaps–opportunities for future research.
Symetra: Visual Analytics for the Parameter Tuning Process of Symbolic Execution Engines
Authors: Donghee Hong, Minjong Kim, Sooyoung Cha, Jaemin Jo
Published at EuroVis, 2026

Symbolic execution engines such as KLEE automatically generate test cases to maximize branch coverage, but their numerous parameters make it difficult to understand the parameters’ impact, leading the user to rely on suboptimal default configurations. While automated tuners have shown promising results, they provide limited insights into why certain configurations work well, motivating the need for Human-in-the-Loop approaches. In this work, we present a visual analytics system, Symetra, designed to support Human-in-the-Loop parameter tuning of symbolic execution engines. To handle a large number of parameters and their configurations, we provide two complementary overviews of their impact on branch coverage values and patterns. Building on these overviews, our system enables collective analysis, allowing the user to contrast groups of configurations and identify differences that may affect branch coverage. We also report on case studies and a Human-in-the-Loop tuning process, demonstrating that experts not only interpreted parameter impacts and identified complementary configurations, but also improved upon fully automated approaches in both branch coverage and tuning efficiency.
TerraTinker: Crafting Playful Geospatial Visualizations
Authors: Jonáš Rosecký, Adam J. Stepánek, Adam Rychlý, Barbora Kozlíková
Published at EuroVis, 2026

With the onset of digital technologies, conveying information to the young generation is becoming evermore challenging, forcing us to explore alternative methods, such as using video games. A typical area that could benefit from innovation is geography, where we want to convey the relationship between selected geographical locations and related phenomena, such as flooding, global warming, criminality, or service availability. Appropriate visualization and interaction in an engaging environment can significantly increase the chance of this information being embraced. To address this, we present TERRATINKER, an open-source tool that transforms geospatial data into visualizations within the Minecraft video game. The interface of the tool enables users without extensive programming skills to adjust the visual appearance of the generated map according to their needs. The tool was tested and evaluated by visualization and geography experts, who gave us valuable feedback and confirmed the benefits and contributions of TERRATINKER.
The Story(line) So Far: A Survey on Storyline Visualization
Authors: Sara Di Bartolomeo, Alexander Dobler, Velitchko Filipov, Martin Nöllenburg, Henry Ehlers
Published at EuroVis, 2026

Storyline visualizations model narratives as temporal networks, using x-monotone lines to represent entities and their interactions over time. This technique offers an intuitive way to reveal structural patterns over time, such as character co-occurrence and narrative flow. Storylines represent a visualization approach with growing interest from the visualization community and applications in diverse contexts. Researchers have developed various layout algorithms and formalized a set of optimization objectives with the goal of automating their generation and balancing their readability, graph aesthetics, and efficiency. These methods vary in their algorithmic formulations and implementations as well as the visual elements they support, such as labels, grouping, or continuity preservation across time. This state-of-the-art report maps the current landscape of storyline visualization approaches, with a specific focus on the visual structures, optimization objectives, and the characteristic of the layout algorithms that generate these.
The Truth, the Whole Truth, and Nothing but the Truth: Automatic Visualization Evaluation from Reconstruction Quality
Authors: Roxana Bujack, Li‐Ta Lo, Ethan Stam, Ayan Biswas, David H. Rogers 0001
Published at EuroVis, 2026

Recent advances in AI enable the automatic generation of visualizations directly from textual prompts using agentic workflows. However, visualizations produced via one-shot generative methods often suffer from insufficient quality, typically requiring a human in the loop to refine the outputs. Human evaluation, though effective, is costly and impractical at scale. To alleviate this problem, we propose an automated metric that evaluates visualization quality without relying on extensive human-labeled datasets. Instead, our approach uses the original underlying data as implicit ground truth. Specifically, we introduce a method that measures visualization quality by assessing the reconstruction accuracy of the original data from the visualization itself. This reconstruction-based metric provides an autonomous and scalable proxy for thorough human evaluation, facilitating more efficient and reliable AI-driven visualization workflows.
Thinking Inside the Box: Considerations for Putting Data Physicalization Workshops in a Box
Authors: Derya Akbaba, C. Svensson, C. Torelli, M. Callmeryd, Miriah Meyer
Published at EuroVis, 2026

Visualization researchers utilize workshops both for applied research and to engage different populations with visualization-based activities. While there are many benefits to running visualization workshops, their utility and impact rely on the presence of a researcher who has deep knowledge about visualization theory and practice. In this work, we introduce workshop-in-a-box as a design concept intended to challenge the researcher-centric approach to data physicalization workshops. Through a design study with a socially innovative organization, we deployed several data physicalization workshops that our collaborator ran instead of us. Based on this experience, along with two accompanying case studies that validate the concept, we present material and procedural considerations for how to put data physicalization workshops into a box and the implications it has for extending visualization research outside the bounds of academia.
Topology-based Visual Analysis of Hydrothermal Plumes
Authors: Adhitya Kamakshidasan, Tushar Jain, Karen G. Bemis, Valerio Pascucci
Published at EuroVis, 2026

Hydrothermal plumes are turbulent structures of intense heat and mineral smoke that rise and disperse into the deep ocean. Existing models generally characterize these systems as a single axisymmetric plume originating from a point source. However, this assumption breaks down in weakly venting, spatially distributed systems, where low-flux discharge and strong background currents produce faint and distorted plumes that traditional centerline-based diagnostics fail to characterize. We present a topology-based visual analysis framework that treats plumes as time-varying three-dimensional scalar fields and captures their full structural variability. Using merge trees, we cluster plume snapshots into representative morphologies and uncover a clear coupling between plume structure and phases of the tidal cycle. To support exploration, we introduce a radial merge tree visualization that arranges plume similarity on a tidal clock, revealing periodic behaviors not discernible with existing techniques. We further develop an extremum graph based workflow that identifies discrete surface venting regions within a reconstructed porous rock, and use streamline visualizations to link subsurface transport pathways to the overlying plume.
TT4D: Tensor-Train-based 4D Time-Dependent Volume Rendering
Authors: Clara Hartmann, Rafael Ballester-Ripoll, Renato Pajarola
Published at EuroVis, 2026

Visualizing large-scale, time-varying volumetric data using direct volume rendering remains a significant challenge in scientific visualization, particularly as data sizes and resolutions continue to increase. One promising direction for addressing this challenge is the use of tensor decompositions, which have proven to be a useful tool for the compact representation of large, high-dimensional data. However, their integration into time-dependent interactive visualization pipelines remains limited. We introduce TT4D, a memory- and time-efficient lossy compression and decompression technique for four-dimensional (4D) time-varying volume data based on the tensor train (TT) decomposition. Our approach employs efficient subsampling during decomposition to reduce memory consumption and computational cost, enabling the processing of large datasets while avoiding unnecessarily large intermediate matrices and tensors. Beyond compression, TT4D provides a GPU-based, on-the-fly decoding scheme that avoids reconstructing the entire 4D volume, supporting interactive visualization. At equivalent error levels, TT4D outperforms other transform-based compressors at random-access decompression across datasets up to 64GB. Exploiting the structure of TT cores, our approach enables adaptive multiresolution rendering, fast spatio-temporal data exploration, and interactive filtering. Together, these capabilities make TT4D a scalable solution for interactive visualization of high-resolution, time-varying volume data.
Tubes or Ribbons? Comparing Texture-space Visualization for Multivariate Line Data
Authors: Benjamin Russig, Rufat Rzayev, Raimund Dachselt, Stefan Gumhold
Published at EuroVis, 2026

Multivariate line data is critical for analyzing flow fields, agent systems, and dynamic trajectories. Embedding secondary variables along spatial paths using surface-based primitives such as ribbons and circular tubes introduces challenges related to perspective, scale, and distortion. Perceptual trade-offs due to these challenges remain unclear. We address this gap through a controlled user study with 10 experts in computational fluid dynamics and visualization, performing four identical analysis tasks involving both spatial (requiring location-based relationships) and non-spatial (requiring attribute value comparisons) aspects. The tasks were performed using a prototype with interactivity limited to controlling the camera. While quantitative measures showed no significant performance differences between embedded visualizations on ribbons and tubes, we found a clear subjective preference for tubes among the study participants. Furthermore, their feedback indicates surface-based embeddings are generally helpful and should be utilized more often.
Uncertainty-Aware Visual Analysis of Force Networks in 2D Granular Materials
Authors: Marina Evers, Abrar Naseer, Tejas Gorur Murthy, Vijay Natarajan, T. Bin Masood, Daniel Weiskopf, Ingrid Hotz
Published at EuroVis, 2026

Uncertainty in experimental measurements makes it challenging to determine which features are intrinsic to the phenomenon and which are most stable and reliable. Granular materials, such as sand, form a complex system in which the forces between individual particles influence the material’s macroscopic behavior. However, these forces are also subject to uncertainty, as repeated measurements can yield different results. In this paper, we investigate how to model and visually analyze the uncertain structure of forces in granular materials. We adopt different perspectives on uncertainty by considering it as variance, probability, or additional variable. For a nuanced analysis of granular material data, we propose combining visualizations that represent these perspectives into a single visual analytics approach. We integrate uncertainty-aware spatial visualizations that convey the probability of features, visualizations of derived measures and their variance over changes in the packing fraction, and overviews of varying probability thresholds. Finally, we evaluate our proposed approach in a case study conducted together with geotechnical engineers for the example of a 2D ensemble of photoelastic disks.
URBANCLIPATLAS: A Visual Analytics Framework for Event and Scene Retrieval in Urban Videos
Authors: Joel Perca, Luis Sante, Juanpablo Heredia, João Rulff, Cláudio T. Silva, Jorge Poco
Published at EuroVis, 2026

Extracting actionable insights from long-duration urban videos is often labor-intensive: analysts must manually sift through raw footage to pinpoint target events or uncover broader behavioral trends. In this work, we present URBANCLIPATLAS, a visual analytics system for exploring long urban videos recorded at street intersections. URBANCLIPATLAS combines retrieval-augmented generation (RAG), taxonomy-aware entity extraction, and video grounding to support event retrieval and interpretation. The system segments extended recordings into short clips, generates textual descriptions with a vision–language model, and indexes them for semantic retrieval. A knowledge graph maps entities and relations from LLM answers onto a domain-specific taxonomy and aligns them with detected objects and trajectories to support visual grounding and verification. URBANCLIPATLAS supports scene retrieval through an augmented chat-based interface and improves scene interpretation by tightly aligning textual outputs with video evidence. This design strengthens the connection between textual reasoning and visual evidence, reducing the effort required to validate model outputs and refine hypotheses. We demonstrate the usefulness of URBANCLIPATLAS on the StreetAware dataset through two case studies involving hazardous scenarios and crossing dynamics at street intersections. URBANCLIPATLAS helps analysts reason about safety- and mobility-related patterns across large urban video collections.