Abstracts:Surgery and hospital stays for pediatric transplantation involve frequent interventions that require complete sedation, care and self-care, disease assimilation, and anxiety for the patient. This article presents the development of a comprehensive tool called virtual transplant reality (VTR) currently used in a hospital with actual patients. Our tool is intended to provide an aid to the psychological support of children who have undergone a liver transplant. VTR consists of two applications: a virtual reality application with a head mounted display worn by the patient and a desktop application for the therapist. After tests carried out at the Hospital Universitario La Paz (Madrid, Spain) over a period of one year with 65 patients, the results indicate that our system offers a series of advantages as a complement to the psychological therapy of pediatric transplant patients.

Abstracts:Large language models (LLMs) are now being applied to the tasks of visualization generation and understanding, demonstrating these models’ ability to be “visually literate.” On the generation side, LLMs have shown promise in powering natural languages’ interfaces for visualization authoring while also suffering from usability and inconsistency issues. On the interpretation side, models (especially vision–language models) can answer basic questions about visualizations, synthesize visual and textual information, and detect misleading visual designs. However, models also tend to struggle with certain analytic tasks, and their takeaways from reading visualizations often differ from those of humans. We aim to both illuminate the state of the art in LLMs’ visualization literacy and speculate on where such work may, and perhaps ought to, take us next.

Abstracts:The process of digital image generation has evolved significantly since the late 1960s. Driven originally by visualizing geometric models developed for computer-aided design and flight simulators, the search for image quality spans fields ranging from computer animation to electronic games to medicine to architecture. My involvement with rendering and computer graphics started in 1966 at Boeing and the University of Washington in Seattle and then moved to the movie wizards at Industrial Light and Magic and Pixar in 1980. This interview describes my career and much of the background that led to my achievements. I decided that a directed interview would be best to capture my career accomplishments and memories. The details in the interview cover a myriad of projects and many of the other technologies that inspired me. Taken as a whole, my work is a logical progression of continual improvement. Perhaps more important are the people with whom I consulted and collaborated. They provided inspiration and guidance and have led me to a lifetime of learning, something I continue to this day.

Abstracts:While it is necessary for most (if not all) visualization and visual analytics (VIS) publication venues to use peer review processes to assure the quality of the papers to be published, it is also necessary for the VIS community to appraise and improve the quality of peer review processes from time to time. In recent years, rejecting a VIS paper seems to have become rather easy, as many rejection reasons are available to criticize a given paper. In this article, we analyze possible causes of this phenomenon and recommend possible remedies. In particular, over the past decades, the visualization field has rapidly grown to include many types of contributions and specialized research areas. Given this large landscape of topics, we need to ensure that good contributions within each area are reviewed properly, published, and built upon to make significant advancement in the area concerned. Therefore, it is crucial that our review process applies specific criteria for each area and does not expect individual publications to satisfy many review criteria designed for other areas. In this way, we hope VIS review processes will enable more VIS research with X factors (original, innovative, significant, impactful, rigorous, insightful, or inspirational) to be published promptly, allowing VIS researchers and practitioners to make even more impactful contributions to data sciences.

Abstracts:Autonomous agents powered by large language models are transforming artificial intelligence (AI), creating an imperative for the visualization area. However, our field’s focus on a human in the sensemaking loop raises critical questions about autonomy, delegation, and coordination for such agentic visualization that preserve human agency while amplifying analytical capabilities. This article addresses these questions by reinterpreting existing visualization systems with semiautomated or fully automatic AI components through an agentic lens. Based on this analysis, we extract a collection of design patterns for agentic visualization, including agentic roles, communication, and coordination. These patterns provide a foundation for future agentic visualization systems that effectively harness AI agents while maintaining human insight and control.

Abstracts:Object recognition is a fundamental challenge in computer vision, particularly for fine-grained object classification, where classes differ in minor features. Improved fine-grained object classification requires a teaching system with numerous classes and instances of data. As the number of hierarchical levels and instances grows, debugging these models becomes increasingly complex. Moreover, different types of debugging tasks require varying approaches, explanations, and levels of detail. We present MuCHEx, a multimodal conversational system that blends natural language and visual interaction for interactive debugging of hierarchical object classification. Natural language allows users to flexibly express high-level questions or debugging goals without needing to navigate complex interfaces, while adaptive explanations surface only the most relevant visual or textual details based on the user’s current task. This multimodal approach combines the expressiveness of language with the precision of direct manipulation, enabling context-aware exploration during model debugging.

Abstracts:Fashion e-commerce design requires the integration of creativity, functionality, and responsiveness to user preferences. While AI offers valuable support, generative models often miss the nuances of user experience, and task-specific models, although more accurate, lack transparency and real-world adaptability—especially with complex multimodal data. These issues reduce designers’ trust and hinder effective AI integration. To address this, we present FashionCook, a visual analytics system designed to support human–AI collaboration in the context of fashion e-commerce. The system bridges communication among model builders, designers, and marketers by providing transparent model interpretations, “what-if” scenario exploration, and iterative feedback mechanisms. We validate the system through two real-world case studies and a user study, demonstrating how FashionCook enhances collaborative workflows and improves design outcomes in data-driven fashion e-commerce environments.

Abstracts:Image-based person reidentification aims to match individuals across multiple cameras. Despite advances in machine learning, their effectiveness in real-world scenarios remains limited, often leaving users to handle fine-grained matching manually. Recent work has explored textual information as auxiliary cues, but existing methods generate coarse descriptions and fail to integrate them effectively into retrieval workflows. To address these issues, we adopt a vision–language model fine-tuned with domain-specific knowledge to generate detailed textual descriptions and keywords for pedestrian images. We then create a joint search space combining visual and textual information, using image clustering and keyword co-occurrence to build a semantic layout. In addition, we introduce a dynamic spiral word cloud algorithm to improve visual presentation and enhance semantic associations. Finally, we conduct case studies, a user study, and expert feedback, demonstrating the usability and effectiveness of our system.

Abstracts:Visual comparison of text embeddings is crucial for analyzing semantic differences and comparing embedding models. Existing methods fail to maintain visual consistency in comparative regions and lack AI-assisted analysis, leading to high cognitive loads and time-consuming exploration processes. In this article, we propose AnchorTextVis, a visual analytics approach based on AnchorMap—our dynamic projection algorithm balancing spatial quality and temporal coherence and large language models (LLMs) to preserve users’ mental map and accelerate the exploration process. We introduce the use of comparable dimensionality reduction algorithms that maintain visual consistency, such as AnchorMap from our previous work and Joint t-SNE. Building on this foundation, we leverage LLMs to compare and summarize, offering users insights. For quantitative comparisons, we define two complementary metrics, Shared k-nearest neighbors (KNN) and Coordinate distance. Besides, we have also designed intuitive representation and rich interactive tools to compare clusters of texts and individual texts. We demonstrate the effectiveness and usefulness of our approach through three case studies and expert feedback.