Vis Meets AI

Goals

Data visualization requires a thoughtful design process that heavily relies on both domain knowledge and familiarity with visualization techniques. Given the vast design space and inherent complexity, even experts often invest substantial effort to create effective visualizations for exploration or communication.

With the rapid advancement of artificial intelligence (AI)—particularly the rise of powerful foundation models such as large language models (LLMs), vision-language models (VLMs), and multimodal AI systems—the field of visualization is undergoing a significant transformation. These cutting-edge models present new opportunities to automate and augment the visualization process. For instance, LLMs can translate natural language queries into visual specifications, assist with data wrangling, and recommend appropriate visualization types. VLMs and multimodal models enable deeper data understanding and interaction by integrating textual, visual, and tabular information, further advancing the creation of intuitive and intelligent visualizations.

Concurrently, visualization plays an increasingly vital role in the development and deployment of advanced AI models. As these models grow in complexity and scale, the need for effective visual interfaces and techniques becomes more pressing—to interpret model behavior, debug outputs, and foster transparency, accountability, and human-AI collaboration.

This workshop, held in conjunction with IEEE PacificVis 2026, aims to explore this dynamic and rapidly evolving area by fostering communication between the visualization and AI communities. Attendees will engage with the latest research at the intersection of AI-enhanced visualization (AI4VIS) and visualization-enhanced AI (VIS4AI), with a particular focus on how cutting-edge models—such as LLMs, VLMs, and beyond—are reshaping the landscape.

Call for Participation

Submission

We welcome submissions in the form of full papers. All accepted papers will be published in a special issue of the Information Visualization journal.

Authors should follow the instructions under "Preparing your manuscript for submission" as outlined in the journal's submission guidelines. A LaTeX template is available under the "Article format" section at this link. While there is no strict page limit, we encourage authors to ensure that the length of the paper is appropriate for the scope and significance of the contribution.

Submissions must be made through PCS (Track Name: PacificVis 2026 Visualization Meets AI Workshop). Only double-blind (anonymized) submissions will be accepted. Please replace author names with the paper ID number to ensure anonymity.

Important Dates

December 22, 2025: Paper due

January 30, 2026: 1st cycle notification from workshop chairs

Conditionally accepted papers need to go through minor revisions and to be reviewed in the second review cycle.

February 13, 2026: Revision due

February 23, 2026: 2nd cycle notification from workshop chairs

Workshop chairs will recommend acceptance to the Editor-in-Chief (EIC) of Information Visualization if the revisions are deemed satisfactory. However, papers with inadequate revisions may still be rejected in this cycle.

March 2, 2026: Editable source files due

March 9, 2026: Final notification from the EIC of Information Visualization

April 20, 2026: Workshop

Topics of Interest

We invite high-quality research and application papers that integrate visualization and AI/machine learning. Submissions may address topics in both AI for Visualization (AI4VIS) and Visualization for AI (VIS4AI).

Example papers:

AI4VIS

P.-P. Vázquez. Are LLMs ready for Visualization? In 2024 IEEE 17th Pacific Visualization Conference (PacificVis), pp. 343-352, 2024.

J. Han and C. Wang. VCNet: A Generative Model for Volume Completion. Visual Informatics, 6(2): 62-73, 2022.

L. Giovannangeli, R. Bourqui, R. Giot, and D. Auber. Toward Automatic Comparison of Visualization Techniques: Application to Graph Visualization. Visual Informatics, 4(2): 86-98, 2020.

J. Shen, R. Wang, and H.-W. Shen. Visual Exploration of Latent Space for Traditional Chinese Music. Visual Informatics, 4(2): 99-108, 2020.

VIS4AI

Z. Liang, G. Li, R. Gu, Y. Wang, and G. Shan. SampleViz: Concept based Sampling for Policy Refinement in Deep Reinforcement Learning. In 2024 IEEE 17th Pacific Visualization Conference (PacificVis), pp. 359-368, 2024.

M. Gleicher, X. Yu, and Y. Chen. Trinary Tools for Continuously Valued Binary Classifiers. Visual Informatics, 6(2): 74-86, 2022.

X. Ji, Y. Tu, W. He, J. Wang, H.-W. Shen, and P.-Y. Yen. USEVis: Visual Analytics of Attention-Based Neural Embedding in Information Retrieval. Visual Informatics, 5(2): 1-12, 2021.

M. Wang, J. Wenskovitch, L. House, N. Polys, and C. North. Bridging Cognitive Gaps between User and Model in Interactive Dimension Reduction. Visual Informatics, 5(2): 13-25, 2021.