DevLan/vispubs huggingface dataset:
AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation
Authors: Delin An, Pan Du, Jian-Xun Wang 0001, Chaoli Wang 0001
Published at Vis, 2025

Accurate 3D aortic construction is crucial for clinical diagnosis, preoperative planning, and computational fluid dynamics (CFD) simulations, as it enables the estimation of critical hemodynamic parameters such as blood flow velocity, pressure distribution, and wall shear stress. Existing construction methods often rely on large annotated training datasets and extensive manual intervention. While the resulting meshes can serve for visualization purposes, they struggle to produce geometrically consistent, well-constructed surfaces suitable for downstream CFD analysis. To address these challenges, we introduce AortaDiff, a diffusion-based framework that generates smooth aortic surfaces directly from CT/MRI volumes. AortaDiff first employs a volume-guided conditional diffusion model (CDM) to iteratively generate aortic centerlines conditioned on volumetric medical images. Each centerline point is then automatically used as a prompt to extract the corresponding vessel contour, ensuring accurate boundary delineation. Finally, the extracted contours are fitted into a smooth 3D surface, yielding a continuous, CFD-compatible mesh representation. AortaDiff offers distinct advantages over existing methods, including an end-to-end workflow, minimal dependency on large labeled datasets, and the ability to generate CFD-compatible aorta meshes with high geometric fidelity. Experimental results demonstrate that AortaDiff performs effectively even with limited training data, successfully constructing both normal and pathologically altered aorta meshes, including cases with aneurysms or coarctation. This capability enables the generation of high-quality visualizations and positions AortaDiff as a practical solution for cardiovascular research.
Automatic Semantic Alignment of Flow Pattern Representations for Exploration with Large Language Models
Authors: Weihan Zhang, Jun Tao 0002
Published at Vis, 2025

Explorative flow visualization allows domain experts to analyze complex flow structures by interactively investigating flow patterns. However, traditional visual interfaces often rely on specialized graphical representations and interactions, which require additional effort to learn and use. Natural language interaction offers a more intuitive alternative, but teaching machines to recognize diverse scientific concepts and extract corresponding structures from flow data poses a significant challenge. In this paper, we introduce an automated framework that aligns flow pattern representations with the semantic space of large language models (LLMs), eliminating the need for manual labeling. Our approach encodes streamline segments using a denoising autoencoder and maps the generated flow pattern representations to LLM embeddings via a projector layer. This alignment empowers semantic matching between textual embeddings and flow representations through an attention mechanism, enabling the extraction of corresponding flow patterns based on textual descriptions. To enhance accessibility, we develop an interactive interface that allows users to query and visualize flow structures using natural language. Through case studies, we demonstrate the effectiveness of our framework in enabling intuitive and intelligent flow exploration.
BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
Authors: Eden Wu, Dishita G. Turakhia, Guande Wu, Christos Koutras, Sarah Keegan, Wenke Liu, Beata Szeitz, David Fenyö, Cláudio T. Silva, Juliana Freire
Published at Vis, 2025

Biomedical data harmonization is essential for enabling exploratory analyses and meta-studies, but the process of schema matching-identifying semantic correspondences between elements of disparate datasets (schemas)-remains a labor-intensive and error-prone task. Even state-of-the-art automated methods often yield low accuracy when applied to biomedical schemas due to the large number of attributes and nuanced semantic differences between them. We present BDIViz, a novel visual analytics system designed to streamline the schema matching process for biomedical data. Through formative studies with domain experts, we identified key requirements for an effective solution and developed interactive visualization techniques that address both scalability challenges and semantic ambiguity. BDIViz employs an ensemble approach that combines multiple matching methods with LLM-based validation, summarizes matches through interactive heatmaps, and provides coordinated views that enable users to quickly compare attributes and their values. Our method-agnostic design allows the system to integrate various schema matching algorithms and adapt to application-specific needs. Through two biomedical case studies and a within-subject user study with domain experts, we demonstrate that BDIViz significantly improves matching accuracy while reducing cognitive load and curation time compared to baseline approaches.
Beyond Log Scales: Toward Cognitively Informed Bar Charts for Orders of Magnitude Values
Authors: Katerina Batziakoudi, Stéphanie Rey, Jean-Daniel Fekete
Published at Vis, 2025

In this work, we challenge the dominant use of logarithmic scales to communicate values spanning multiple orders of magnitude-Orders of Magnitude Values (OMVs)-to the general public. Focusing on bar charts, we incorporate cognitive insights into visualization design to better align with how humans perceive OMVs. Studies in cognitive psychology suggest that, for large numerical ranges such as millions and billions, people do not think logarithmically. Instead, they perceive numbers in a piecewise linear manner, grouping values into scale words (e.g., millions) and applying linear reasoning within each group. We build upon a recently introduced piecewise linear scale, EplusM, and validate its use in bar charts, which we refer to as EplusM bar charts. We also introduce two novel variants of the EplusM bar chart informed by findings in numerical perception: Bricks, which builds on the concepts of round numbers and subitizing, and Multi-Magnitude, which leverages categorical perception of large numbers. In a crowdsourced experiment, we evaluate four bar chart designs: 1) Log, 2) EplusM, 3) Bricks, and 4) Multi-Magnitude, across value retrieval and quantitative comparison tasks. Our results show that EplusM bar charts are significantly preferred over logarithmic designs, increase user confidence, and reduce perceived mental demand, while maintaining task performance. These findings suggest that EplusM bar charts can serve as effective alternatives to logarithmic ones when visualizing OMVs for general audiences.
Beyond Problem Solving: Framing and Problem-Solution Co-Evolution in Data Visualization Design
Authors: Paul C. Parsons, Prakash Chandra Shukla
Published at Vis, 2025

Visualization design is often described as a process of solving a well-defined problem by navigating a design space. While existing visualization design models have provided valuable structure and guidance, they tend to foreground technical problem-solving and underemphasize the interpretive, judgment-based aspects of design. In contrast, research in other design disciplines has emphasized the importance of framing-how designers define and redefine what the problem is-and the co-evolution of problem and solution spaces through reflective practice. These dimensions remain underexplored in visualization research, particularly from the perspective of expert practitioners. This paper investigates how visualization designers frame problems and navigate the interplay between problem understanding and solution development. We conducted a mixed-methods study with 11 expert design practitioners using design challenges, diary entries, and semi-structured interviews. Through reflexive thematic analysis, we identified key strategies that participants used to frame design problems, reframe them in response to evolving constraints or insights, and construct bridges between problem and solution spaces. These included the use of metaphors, heuristics, sketching, primary generators, and reflective evaluation of failed or incomplete ideas. Our findings contribute an empirically grounded account of visualization design as a reflective, co-evolutionary practice. We show that framing is not a preliminary step, but a continuous activity embedded in the act of designing. Participants frequently shifted their understanding of the problem based on solution attempts, feedback from tools, and ethical or narrative concerns. These insights extend current visualization design models and highlight the need for frameworks that better account for framing and interpretive judgment. We conclude with implications for visualization research, education, and practice. In particular, we discuss how design education can better support framing and co-evolutionary thinking, and how visualization research can benefit from greater attention to the cognitive strategies and reflective processes that underpin expert design.
Beyond the Broadcast: Enhancing VR Tennis Broadcasting through Embedded Visualizations and Camera Techniques
Authors: Jun-Hsiang Yao, Jielin Feng, Xinfang Tian, Kai Xu 0003, Gulshat Amirkhanova, Siming Chen 0001
Published at Vis, 2025

Virtual Reality (VR) broadcasting has emerged as a promising medium for providing immersive viewing experiences of major sports events such as tennis. However, current VR broadcast systems often lack an effective camera language and do not adequately incorporate dynamic, in-game visualizations, limiting viewer engagement and narrative clarity. To address these limitations, we analyze 400 out-of-play segments from eight major tennis broadcasts to develop a tennis-specific design framework that effectively combines cinematic camera movements with embedded visualizations. We further refine our framework by examining 25 cinematic VR animations, comparing their camera techniques with traditional tennis broadcasts to identify key differences and inform adaptations for VR. Based on data extracted from the broadcast videos, we reconstruct a simulated game that captures the players' and ball's motion and trajectories. Leveraging this design framework and processing pipeline, we develope Beyond the Broadcast, a VR tennis viewing system that integrates embedded visualizations with adaptive camera motions to construct a comprehensive and engaging narrative. Our system dynamically overlays tactical information and key match events onto the simulated environment, enhancing viewer comprehension and narrative engagement while ensuring perceptual immersion and viewing comfort. A user study involving tennis viewers demonstrate that our approach outperforms traditional VR broadcasting methods in delivering an immersive, informative viewing experience.
BondMatcher: H-Bond Stability Analysis in Molecular Systems
Authors: Thomas Daniel, Malgorzata Olejniczak, Julien Tierny
Published at Vis, 2025

This application paper investigates the stability of hydrogen bonds (H-bonds), as characterized by the Quantum Theory of Atoms in Molecules (QTAIM). First, we contribute a database of 4544 electron densities associated to four isomers of water hexamers (the so-called Ring, Book, Cage and Prism), generated by distorting their equilibrium geometry under various structural perturbations, modeling the natural dynamic behavior of molecular systems. Second, we present a new stability measure, called bond occurrence rate, associating each bond path present at equilibrium with its rate of occurrence within the input ensemble. We also provide an algorithm, called BondMatcher, for its automatic computation, based on a tailored, geometry-aware partial isomorphism estimation between the extremum graphs of the considered electron densities. Our new stability measure allows for the automatic identification of densities lacking H-bond paths, enabling further visual inspections. Specifically, the topological analysis enabled by our framework corroborates experimental observations and provides refined geometrical criteria for characterizing the disappearance of H-bond paths. Our electron density database and our C++ implementation are available at this address: https://github.com/thom-dani/BondMatcher.
Calli-VA: A Visual Analytics System for Analyzing and Comparing Chinese Calligraphic Styles
Authors: Jincheng Li 0004, Jinpeng Wu, Shaocong Tan, Lin Du 0011, Yu Zhang 0043, Chaofan Yang, Jiadi Zhang, Rebecca Ruige Xu, Rui Shi, Lu Bai 0001, Xiaoru Yuan
Published at Vis, 2025

Chinese calligraphy is a quintessential element of Chinese cultural heritage. Analyzing and comparing calligraphic styles not only enhances the appreciation, learning, and advancement of calligraphy but also provides valuable insights into ancient China. However, such analysis remains challenging due to the limited scalability and possible inconsistencies of qualitative methods, as well as usability and misalignment issues in conventional quantitative approaches. We propose Calli-VA, a visual analytics system, to address these challenges. Calli-VA extracts character images and their corresponding strokes from original works and characterizes each character using systematic criteria. During analysis, the system defines the analysis scope by overview and uncovers relationships between characters. Explanation and recommendation mechanisms are integrated to help users understand patterns and guide further exploration. A documentation feature allows users to record and share their findings. We demonstrate the effectiveness of Calli-VA through three case studies and expert feedback.
Can LLMs Bridge Domain and Visualization? A Case Study on High-Dimension Data Visualization in Single-Cell Transcriptomics
Authors: Qianwen Wang 0001, Xinyi Liu 0001, Nils Gehlenborg
Published at Vis, 2025

While many visualizations are built for domain users (e.g., biologists, machine learning developers), understanding how visualizations are used in the domain has long been a challenging task. Previous research has relied on either interviewing a limited number of domain users or reviewing relevant application papers in the visualization community, neither of which provides comprehensive insight into visualizations “in the wild” of a specific domain. This paper aims to fill this gap by examining the potential of using Large Language Models (LLM) to analyze visualization usage in domain literature. We use high-dimension (HD) data visualization in sing-cell transcriptomics as a test case, analyzing 1,203 papers that describe 2,056 HD visualizations with highly specialized domain terminologies (e.g., biomarkers, cell lineage). To facilitate this analysis, we introduce a human-in-the-loop LLM workflow that can effectively analyze a large collection of papers and translate domain-specific terminology into standardized data and task abstractions. Instead of relying solely on LLMs for end-to-end analysis, our workflow enhances analytical quality through 1) integrating image processing and traditional NLP methods to prepare well-structured inputs for three targeted LLM subtasks (i.e., translating domain terminology, summarizing analysis tasks, and performing categorization), and 2) establishing checkpoints for human involvement and validation throughout the process. The analysis results, validated with expert interviews and a test set, revealed three often overlooked aspects in HD visualization: trajectories in HD spaces, inter-cluster relationships, and dimension clustering. This research provides a stepping stone for future studies seeking to use LLMs to bridge the gap between visualization design and domain-specific usage.
Causality-based Visual Analytics of Sentiment Contagion in Social Media Topics
Authors: Renzhong Li, Shuainan Ye, Yuchen Lin, Buwei Zhou, Zhining Kang, Tai-Quan Peng, Wenhao Fu, Tan Tang, Yingcai Wu
Published at Vis, 2025

Sentiment contagion occurs when attitudes toward one topic are influenced by attitudes toward others. Detecting and understanding this phenomenon is essential for analyzing topic evolution and informing social policies. Prior research has developed models to simulate the contagion process through hypothesis testing and has visualized user-topic correlations to aid comprehension. Nevertheless, the vast volume of topics and the complex interrelationships on social media present two key challenges: (1) efficient construction of large-scale sentiment contagion networks, and (2) in-depth explorations of these networks. To address these challenges, we introduce a causality-based framework that efficiently constructs and explains sentiment contagion. We further propose a map-like visualization technique that encodes time using a horizontal axis, enabling efficient visualization of causality-based sentiment flow while maintaining scalability through limitless spatial segmentation. Based on the visualization, we develop CausalMap, a system that supports analysts in tracing sentiment contagion pathways and assessing the influence of different demographic groups. Furthermore, we conduct comprehensive evaluations—including two use cases, a task-based user study, an expert interview, and an algorithm evaluation—to validate the usability and effectiveness of our approach.
CD-TVD: Contrastive Diffusion for 3D Super-Resolution with Scarce High-Resolution Time-Varying Data
Authors: Chongke Bi, Xin Gao, Jiakang Deng, Guan Li 0002, Jun Han 0010
Published at Vis, 2025

Large-scale scientific simulations require significant resources to generate high-resolution time-varying data (TVD). While super-resolution is an efficient post-processing strategy to reduce costs, existing methods rely on a large amount of HR training data, limiting their applicability to diverse simulation scenarios. To address this constraint, we proposed CD-TVD, a novel framework that combines contrastive learning and an improved diffusion-based super-resolution model to achieve accurate 3D super-resolution from limited time-step high-resolution data. During pre-training on historical simulation data, the contrastive encoder and diffusion super-resolution modules learn degradation patterns and detailed features of high-resolution and low-resolution samples. In the training phase, the improved diffusion model with a local attention mechanism is fine-tuned using only one newly generated high-resolution timestep, leveraging the degradation knowledge learned by the encoder. This design minimizes the reliance on large-scale high-resolution datasets while maintaining the capability to recover fine-grained details. Experimental results on fluid and atmospheric simulation datasets confirm that CD-TVD delivers accurate and resource-efficient 3D super-resolution, marking a significant advancement in data augmentation for large-scale scientific simulations. The code is available at https://github.com/Xin-Gao-private/CD-TVD.
Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data Visualization
Authors: Arran Zeyu Wang, Ghulam Jilani Quadri, Mengyuan Zhu, Chin Tseng, Danielle Albers Szafir
Published at Vis, 2025

Understanding how people perceive visualizations is crucial for designing effective visual data representations; however, many heuristic design guidelines are derived from specific tasks or visualization types, without considering the constraints or conditions under which those guidelines hold. In this work, we aimed to assess existing design heuristics for categorical visualization using well-established psychological knowledge. Specifically, we examine the impact of the subitizing phenomenon in cognitive psychology-people's ability to automatically recognize a small set of objects instantly without counting-in data visualizations. We conducted three experiments with multi-class scatterplots-between 2 and 15 classes with varying design choices-across three different tasks-class estimation, correlation comparison, and clustering judgments-to understand how performance changes as the number of classes (and therefore set size) increases. Our results indicate if the category number is smaller than six, people tend to perform well at all tasks, providing empirical evidence of subitizing in visualization. When category numbers increased, performance fell, with the magnitude of the performance change depending on task and encoding. Our study bridges the gap between heuristic guidelines and empirical evidence by applying well-established psychological theories, suggesting future opportunities for using psychological theories and constructs to characterize visualization perception.
Charts-of-Thought: Enhancing LLM Visualization Literacy Through Structured Data Extraction
Authors: Amit Kumar Das 0002, Mohammad Tarun, Klaus Mueller 0001
Published at Vis, 2025

This paper evaluates the visualization literacy of modern Large Language Models (LLMs) and introduces a novel prompting technique called Charts-of-Thought. We tested three state-of-the-art LLMs (Claude-3.7-sonnet, GPT-4.5-preview, and Gemini-2.0-pro) on the Visualization Literacy Assessment Test (VLAT) using standard prompts and our structured approach. The Charts-of-Thought method guides LLMs through a systematic data extraction, verification, and analysis process before answering visualization questions. Our results show Claude-3.7-sonnet achieved a score of 50.17 using this method, far exceeding the human baseline of 28.82. This approach improved performance across all models, with score increases of 21.8% for GPT-4.5, 9.4% for Gemini-2.0, and 13.5% for Claude-3.7 compared to standard prompting. The performance gains were consistent across original and modified VLAT charts, with Claude correctly answering 100% of questions for several chart types that previously challenged LLMs. Our study reveals that modern multimodal LLMs can surpass human performance on visualization literacy tasks when given the proper analytical framework. These findings establish a new benchmark for LLM visualization literacy and demonstrate the importance of structured prompting strategies for complex visual interpretation tasks. Beyond improving LLM visualization literacy, Charts-of-Thought could also enhance the accessibility of visualizations, potentially benefiting individuals with visual impairments or lower visualization literacy.
ClimateSOM: A Visual Analysis Workflow for Climate Ensemble Datasets
Authors: Yuya Kawakami, Daniel Cayan, Dongyu Liu, Kwan-Liu Ma
Published at Vis, 2025

Ensemble datasets are ever more prevalent in various scientific domains. In climate science, ensemble datasets are used to capture variability in projections under plausible future conditions including greenhouse and aerosol emissions. Each ensemble model run produces projections that are fundamentally similar yet meaningfully distinct. Understanding this variability among ensemble model runs and analyzing its magnitude and patterns is a vital task for climate scientists. In this paper, we present ClimateSOM, a visual analysis workflow that leverages a self-organizing map (SOM) and Large Language Models (LLMs) to support interactive exploration and interpretation of climate ensemble datasets. The workflow abstracts climate ensemble model runs-spatiotemporal time series-into a distribution over a 2D space that captures the variability among the ensemble model runs using a SOM. LLMs are integrated to assist in sensemaking of this SOM-defined 2D space, the basis for the visual analysis tasks. In all, ClimateSOM enables users to explore the variability among ensemble model runs, identify patterns, compare and cluster the ensemble model runs. To demonstrate the utility of ClimateSOM, we apply the workflow to an ensemble dataset of precipitation projections over California and the Northwestern United States. Furthermore, we conduct a short evaluation of our LLM integration, and conduct an expert review of the visual workflow and the insights from the case studies with six domain experts to evaluate our approach and its utility.