DevLan/vispubs huggingface dataset:
Mixture of Cluster-guided Experts for Retrieval-Augmented Label Placement
Authors: Pingshun Zhang, Enyu Che, Yinan Chen, Bingyao Huang, Haibin Ling, Jingwei Qu
Published at Vis, 2025

Text labels are widely used to convey auxiliary information in visualization and graphic design. The substantial variability in the categories and structures of labeled objects leads to diverse label layouts. Recent single-model learning-based solutions in label placement struggle to capture fine-grained differences between these layouts, which in turn limits their performance. In addition, although human designers often consult previous works to gain design insights, existing label layouts typically serve merely as training data, limiting the extent to which embedded design knowledge can be exploited. To address these challenges, we propose a mixture of cluster-guided experts (MoCE) solution for label placement. In this design, multiple experts jointly refine layout features, with each expert responsible for a specific cluster of layouts. A cluster-based gating function assigns input samples to experts based on representation clustering. We implement this idea through the Label Placement Cluster-guided Experts (LPCE) model, in which a MoCE layer integrates multiple feed-forward networks (FFNs), with each expert composed of a pair of FFNs. Furthermore, we introduce a retrieval augmentation strategy into LPCE, which retrieves and encodes reference layouts for each input sample to enrich its representations. Extensive experiments demonstrate that LPCE achieves superior performance in label placement, both quantitatively and qualitatively, surpassing a range of state-of-the-art baselines. Our algorithm is available at https://github.com/PingshunZhang/LPCE.
MoE-INR: Implicit Neural Representation with Mixture-of-Experts for Time-Varying Volumetric Data Compression
Authors: Jun Han 0010, Kaiyuan Tang 0001, Chaoli Wang 0001
Published at Vis, 2025

Implicit neural representations (INRs) have emerged as a transformative paradigm for time-varying volumetric data compression and representation, owing to their ability to model high-dimensional signals effectively. INRs represent scalar fields based on sampled coordinates, typically using either a single network for the entire field or multiple networks across different spatial domains. However, these approaches often face challenges in modeling complex patterns and introducing boundary artifacts. To address these limitations, we propose MoE-INR, an INR architecture based on a mixture-of-experts (MoE) framework. MoE-INR automates irregular subdivisions of spatiotemporal fields and dynamically assigns them to different expert networks. The architecture comprises three key components: a policy network, a shared encoder, and multiple expert decoders. The policy network subdivides the field and determines which expert decoder is responsible for a given input coordinate. The shared encoder extracts hidden representations from the input coordinates, and the expert decoders transform these high-dimensional features into scalar values. This design results in a unified framework accommodating diverse INR types, including conventional, grid-based, and ensemble. We evaluate the effectiveness of MoE-INR on multiple time-varying datasets with varying characteristics. Experimental results demonstrate that MoE-INR significantly outperforms existing non-MoE and MoE-based INRs and traditional lossy compression methods across quantitative and qualitative metrics under various compression ratios.
MoMo - Combining Neuron Morphology and Connectivity for Interactive Motif Analysis in Connectomes
Authors: M. Shewarega, Jakob Troidl, Oliver Alvarado Rodriguez, Mohammad Dindoost, Philipp Harth, H. Haberkern, J. Stegmaier, David A. Bader, Hanspeter Pfister
Published at Vis, 2025

Connectomics, a subfield of neuroscience, reconstructs structural and functional brain maps at synapse-level resolution. These complex spatial maps consist of tree-like neurons interconnected by synapses. Motif analysis is a widely used method for identifying recurring subgraph patterns in connectomes. These motifs, thus, potentially represent fundamental units of information processing. However, existing computational tools often oversimplify neurons as mere nodes in a graph, disregarding their intricate morphologies. In this paper, we introduce MoMo, a novel interactive visualization framework for analyzing neuron morphology-aware motifs in large connectome graphs. First, we propose an advanced graph data structure that integrates both neuronal morphology and synaptic connectivity. This enables highly efficient, parallel subgraph isomorphism searches, allowing for interactive morphological motif queries. Second, we develop a sketch-based interface that facilitates the intuitive exploration of morphology-based motifs within our new data structure. Users can conduct interactive motif searches on state-of-the-art connectomes and visualize results as interactive 3D renderings. We present a detailed goal and task analysis for motif exploration in connectomes, incorporating neuron morphology. Finally, we evaluate MoMo through case studies with four domain experts, who asses the tool's usefulness and effectiveness in motif exploration, and relevance to real-world neuroscience research. The source code for MoMo is available here.
Mosaic Selections: Managing and Optimizing User Selections for Scalable Data Visualization Systems
Authors: Jeffrey Heer, Dominik Moritz, Ron Pechuk
Published at Vis, 2025

Though powerful tools for analysis and communication, interactive visualizations often fail to support real-time interaction with large datasets with millions or more records. To highlight and filter data, users indicate values or intervals of interest. Such selections may span multiple components, combine in complex ways, and require optimizations to ensure low-latency updates. We describe Mosaic Selections, a model for representing, managing, and optimizing user selections, in which one or more filter predicates are added to queries that request data for visualizations and input widgets. By analyzing both queries and selection predicates, Mosaic Selections enable automatic optimizations, including pre-aggregating data to rapidly compute selection updates. We contribute a formal description of our selection model and optimization methods, and their implementation in the open-source Mosaic architecture. Benchmark results demonstrate orders-of-magnitude latency improvements for selection-based optimizations over unoptimized queries and existing optimizers for the Vega language. The Mosaic Selection model provides infrastructure for flexible, interoperable filtering across multiple visualizations, alongside automatic optimizations to scale to millions and even billions of records.
Motif Simplification for BioFabric Network Visualizations: Improving Pattern Recognition and Interpretation
Authors: Johannes Fuchs 0001, Cody Dunne, Maria-Viktoria Heinle, Daniel A. Keim, Sara Di Bartolomeo
Published at Vis, 2025

Detecting and interpreting common patterns in relational data is crucial for understanding complex topological structures across various domains. These patterns, or network motifs, can often be detected algorithmically. However, visual inspection remains vital for exploring and discovering patterns. This paper focuses on presenting motifs within BioFabric network visualizations—a unique technique that opens opportunities for research on scaling to larger networks, design variations, and layout algorithms to better expose motifs. Our goal is to show how highlighting motifs can assist users in identifying and interpreting patterns in BioFabric visualizations. To this end, we leverage existing motif simplification techniques. We replace edges with glyphs representing fundamental motifs such as staircases, cliques, paths, and connector nodes. The results of our controlled experiment and usage scenarios demonstrate that motif simplification for BioFabric is useful for detecting and interpreting network patterns. Our participants were faster and more confident using the simplified view without sacrificing accuracy. The efficacy of our current motif simplification approach depends on which extant layout algorithm is used. We hope our promising findings on user performance will motivate future research on layout algorithms tailored to maximizing motif presentation. Our supplemental material is available at https://osf.io/f8s3g/?view_only=7e2df9109dfd4e6c85b89ed828320843.
Natural Language-Driven Viewpoint Navigation for Volume Exploration via Semantic Block Representation
Authors: Xuan Zhao, Jun Tao 0002
Published at Vis, 2025

Exploring volumetric data is crucial for interpreting scientific datasets. However, selecting optimal viewpoints for effective navigation can be challenging, particularly for users without extensive domain expertise or familiarity with 3D navigation. In this paper, we propose a novel framework that leverages natural language interaction to enhance volumetric data exploration. Our approach encodes volumetric blocks to capture and differentiate underlying structures. It further incorporates a CLIP Score mechanism, which provides semantic information to the blocks to guide navigation. The navigation is empowered by a reinforcement learning framework that leverage these semantic cues to efficiently search for and identify desired viewpoints that align with the user's intent. The selected viewpoints are evaluated using CLIP Score to ensure that they best reflect the user queries. By automating viewpoint selection, our method improves the efficiency of volumetric data navigation and enhances the interpretability of complex scientific phenomena.
Neighborhood-Preserving Voronoi Treemaps
Authors: Patrick Paetzold, Rebecca Kehlbeck, Yumeng Xue, Bin Chen, Yunhai Wang, Oliver Deussen
Published at Vis, 2025

Voronoi treemaps are used to depict nodes and their hierarchical relationships simultaneously. However, in addition to the hierarchical structure, data attributes, such as co-occurring features or similarities, frequently exist. Examples include geographical attributes like shared borders between countries or contextualized semantic information such as embedding vectors derived from large language models. In this work, we introduce a Voronoi treemap algorithm that leverages data similarity to generate neighborhood-preserving treemaps. First, we extend the treemap layout pipeline to consider similarity during data preprocessing. We then use a Kuhn-Munkres matching of similarities to centroidal Voronoi tessellation (CVT) cells to create initial Voronoi diagrams with equal cell sizes for each level. Greedy swapping is used to improve the neighborhoods of cells to match the data's similarity further. During optimization, cell areas are iteratively adjusted to their respective sizes while preserving the existing neighborhoods. We demonstrate the practicality of our approach through multiple real-world examples drawn from infographics and linguistics. To quantitatively assess the resulting treemaps, we employ treemap metrics and measure neighborhood preservation.
NLI4VolVis: Natural Language Interaction for Volume Visualization via LLM Multi-Agents and Editable 3D Gaussian Splatting
Authors: Kuangshi Ai, Kaiyuan Tang 0001, Chaoli Wang 0001
Published at Vis, 2025

Traditional volume visualization (VolVis) methods, like direct volume rendering, suffer from rigid transfer function designs and high computational costs. Although novel view synthesis approaches enhance rendering efficiency, they require additional learning effort for non-experts and lack support for semantic-level interaction. To bridge this gap, we propose NLI4VolVis, an interactive system that enables users to explore, query, and edit volumetric scenes using natural language. NLI4VolVis integrates multi-view semantic segmentation and vision-language models to extract and understand semantic components in a scene. We introduce a multi-agent large language model architecture equipped with extensive function-calling tools to interpret user intents and execute visualization tasks. The agents leverage external tools and declarative VolVis commands to interact with the VolVis engine powered by 3D editable Gaussians, enabling open-vocabulary object querying, real-time scene editing, best-view selection, and 2D stylization. We validate our system through case studies and a user study, highlighting its improved accessibility and usability in volumetric data exploration. We strongly recommend readers check out our case studies, demo video, and source code at https://nli4volvis.github.io/.
OW-CLIP: Data-Efficient Visual Supervision for Open-World Object Detection via Human-AI Collaboration
Authors: Junwen Duan, Wei Xue, Ziyao Kang, Shixia Liu, Jiazhi Xia
Published at Vis, 2025

Open-world object detection (OWOD) extends traditional object detection to identifying both known and unknown object, necessitating continuous model adaptation as new annotations emerge. Current approaches face significant limitations: 1) data-hungry training due to reliance on a large number of crowdsourced annotations, 2) susceptibility to “partial feature overfitting,” and 3) limited flexibility due to required model architecture modifications. To tackle these issues, we present OW-CLIP, a visual analytics system that provides curated data and enables data-efficient OWOD model incremental training. OW-CLIP implements plug-and-play multimodal prompt tuning tailored for OWOD settings and introduces a novel “Crop-Smoothing” technique to mitigate partial feature overfitting. To meet the data requirements for the training methodology, we propose dual-modal data refinement methods that leverage large language models and cross-modal similarity for data generation and filtering. Simultaneously, we develope a visualization interface that enables users to explore and deliver high-quality annotations—including class-specific visual feature phrases and fine-grained differentiated images. Quantitative evaluation demonstrates that OW-CLIP achieves competitive performance at 89% of state-of-the-art performance while requiring only 3.8% self-generated data, while outperforming SOTA approach when trained with equivalent data volumes. A case study shows the effectiveness of the developed method and the improved annotation quality of our visualization system.
OwnershipTracker: A Visual Analytics Approach to Uncovering Historical Book Ownership Patterns
Authors: Yiwen Xing, Meilai Ji, Cristina Dondi, Alfie Abdul-Rahman
Published at Vis, 2025

Ownership relationships of early printed books from the 15th century reveal complex patterns of distribution and possession, offering valuable insights for historical research. This paper presents OwnershipTracker, a visual analytics application developed to explore and trace these relationships using data from the Material Evidence in Incunabula (MEI) database. OwnershipTracker integrates bibliographic records, copy-specific data, and book provenance and ownership details, enabling users to uncover intricate ownership sequences over time. The application combines several visualization techniques, including network graphs to map connections between owners, timelines for temporal analysis, chord diagrams to quantify transfer patterns, and a distinctive, collaboratively designed spiderweb-like diagram highlighting converging and dispersing ownership transfers through specific owners. Developed iteratively with input from historical book researchers, the application underwent multiple refinements to align with domain research requirements. A summative evaluation with domain experts showcased the tool's ability to address the defined requirements and tasks. The final version of OwnershipTracker is deployed and accessible at: https://booktracker.nms.kcl.ac.uk/ownership.
Perceiving Slope and Acceleration: Evidence for Variable Tempo Sampling in Pitch-Based Sonification of Functions
Authors: Danyang Fan, Walker Smith, Takako Fujioka, Chris Chafe, Sile O'Modhrain, Diana Deutsch, Sean Follmer
Published at Vis, 2025

Sonification offers a non-visual way to understand data, with pitch-based encodings being the most common. Yet, how well people perceive slope and acceleration—key features of data trends—remains poorly understood. Drawing on people's natural abilities to perceive tempo, we introduce a novel sampling method for pitch-based sonification to enhance the perception of slope and acceleration in univariate functions. While traditional sonification methods often sample data at uniform x-spacing, yielding notes played at a fixed tempo with variable pitch intervals (Variable Pitch Interval), our approach samples at uniform y-spacing, producing notes with consistent pitch intervals but variable tempo (Variable Tempo). We conducted psychoacoustic experiments to understand slope and acceleration perception across three sampling methods: Variable Pitch Interval, Variable Tempo, and a Continuous (no sampling) baseline. In slope comparison tasks, Variable Tempo was more accurate than the other methods when modulated by the magnitude ratio between slopes. For acceleration perception, just-noticeable differences under Variable Tempo were over 13 times finer than with other methods. Participants also commonly reported higher confidence, lower mental effort, and a stronger preference for Variable Tempo compared to other methods. This work contributes models of slope and acceleration perception across pitch-based sonification techniques, introduces Variable Tempo as a novel and preferred sampling method, and provides promising initial evidence that leveraging timing can lead to more sensitive, accurate, and precise interpretation of derivative-based data features.
PiCCL: Data-Driven Composition of Bespoke Pictorial Charts
Authors: Haoyan Shi, Yunhai Wang, Junhao Chen, Chenglong Wang 0005, Bongshin Lee
Published at Vis, 2025

We present PiCCL (Pictorial Chart Composition Language), a new language that enables users to easily create pictorial charts using a set of simple operators. To support systematic construction while addressing the main challenge of expressive pictorial chart authoring-manual composition and fine-tuning of visual properties-PiCCL introduces a parametric representation that integrates data-driven chart generation with graphical composition. It also employs a lazy data-binding mechanism that automatically synthesizes charts. PiCCL is grounded in a comprehensive analysis of real-world pictorial chart examples. We describe PiCCL's design and its implementation as piccl.js, a JavaScript-based library. To evaluate PiCCL, we showcase a gallery that demonstrates its expressiveness and report findings from a user study assessing the usability of piccl.js. We conclude with a discussion of PiCCL's limitations and potential, as well as future research directions.
PixelatedScatter: Arbitrary-level Visual Abstraction for Large-scale Multiclass Scatterplots
Authors: Ziheng Guo, Tianxiang Wei, Zeyu Li 0003, Lianghao Zhang 0001, Sisi Li 0001, Jiawan Zhang
Published at Vis, 2025

Overdraw is inevitable in large-scale scatterplots. Current scatterplot abstraction methods lose features in medium-to-low density regions. We propose a visual abstraction method designed to provide better feature preservation across arbitrary abstraction levels for large-scale scatterplots, particularly in medium-to-low density regions. The method consists of three closely interconnected steps: first, we partition the scatterplot into iso-density regions and equalize visual density; then, we allocate pixels for different classes within each region; finally, we reconstruct the data distribution based on pixels. User studies, quantitative and qualitative evaluations demonstrate that, compared to previous methods, our approach better preserves features and exhibits a special advantage when handling ultra-high dynamic range data distributions.
ProactiveVA: Proactive Visual Analytics with LLM-based UI Agent
Authors: Yuheng Zhao, Xueli Shu, Liwen Fan, Lin Gao, Yu Zhang 0043, Siming Chen 0001
Published at Vis, 2025

Visual analytics (VA) is typically applied to complex data, thus requiring complex tools. While visual analytics empowers analysts in data analysis, analysts may get lost in the complexity occasionally. This highlights the need for intelligent assistance mechanisms. However, even the latest LLM-assisted VA systems only provide help when explicitly requested by the user, making them insufficiently intelligent to offer suggestions when analysts need them the most. We propose a ProactiveVA framework in which LLM-powered UI agent monitors user interactions and delivers context-aware assistance proactively. To design effective proactive assistance, we first conducted a formative study analyzing help-seeking behaviors in user interaction logs, identifying when users need proactive help, what assistance they require, and how the agent should intervene. Based on this analysis, we distilled key design requirements in terms of intent recognition, solution generation, interpretability and controllability. Guided by these requirements, we develop a three-stage UI agent pipeline including perception, reasoning, and acting. The agent autonomously perceives users' needs from VA interaction logs, providing tailored suggestions and intuitive guidance through interactive exploration of the system. We implemented the framework in two representative types of VA systems, demonstrating its generalizability, and evaluated the effectiveness through an algorithm evaluation, case and expert study and a user study. We also discuss current design trade-offs of proactive VA and areas for further exploration.
Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Ugly
Authors: Lianghan Dong, Anamaria Crisan
Published at Vis, 2025

Vision Language Models (VLMs) demonstrate promising chart comprehension capabilities. Yet, prior explorations of their visualization literacy have been limited to assessing their response correctness and fail to explore their internal reasoning. To address this gap, we adapted attention-guided class activation maps (AG-CAM) for VLMs, to visualize the influence and importance of input features (image and text) on model responses. Using this approach, we conducted an examination of four open-source (ChartGemma, Janus 1B and 7B, and LLaVA) and two closed-source (GPT-4o, Gemini) models comparing their performance and, for the open-source models, their AG-CAM results. Overall, we found that ChartGemma, a 3B parameter VLM fine-tuned for chart question-answering (QA), outperformed other open-source models and exhibited performance on par with significantly larger closed-source VLMs. We also found that VLMs exhibit spatial reasoning by accurately localizing key chart features, and semantic reasoning by associating visual elements with corresponding data values and query tokens. Our approach is the first to demonstrate the use of AG-CAM on early fusion VLM architectures, which are widely used, and for chart QA. We also show preliminary evidence that these results can align with human reasoning. Our promising open-source VLMs results pave the way for transparent and reproducible research in AI visualization literacy. Code and Supplemental Materials: https://osf.io/fp3rg