A survey of 55 agentic VA systems proposes a co-evolutionary framework defining four agent roles (PLANNER, CREATOR, REVIEWER, CONTEXT MANAGER) mapped to visual analytics pipeline stages along with design guidelines.
Visualization generation with large language models: An evaluation
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ReVis parses image-based visualizations into a reusable DSL via an MLLM pipeline and supports reproduction, data updates, and customization through an interactive interface.
Athanor converts static visualizations to interactive versions via MLLMs, a multi-agent analyzer, and an abstraction transformer, allowing natural language authoring of interactions.
An LLM-based framework recommends drill-down paths in visual analytics by approximating a greedy algorithm, interpreting user intent, and managing exploration branches to reduce cognitive load.
citing papers explorer
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Exploring Agentic Visual Analytics: A Co-Evolutionary Framework of Roles and Workflows
A survey of 55 agentic VA systems proposes a co-evolutionary framework defining four agent roles (PLANNER, CREATOR, REVIEWER, CONTEXT MANAGER) mapped to visual analytics pipeline stages along with design guidelines.
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ReVis: Towards Reusable Image-Based Visualizations with MLLMs
ReVis parses image-based visualizations into a reusable DSL via an MLLM pipeline and supports reproduction, data updates, and customization through an interactive interface.
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From Static to Interactive: Authoring Interactive Visualizations via Natural Language
Athanor converts static visualizations to interactive versions via MLLMs, a multi-agent analyzer, and an abstraction transformer, allowing natural language authoring of interactions.
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Intelligent Drill-Down: Large Language Model-Driven Drill-Down Technique for Human-AI Collaborative Visual Exploration
An LLM-based framework recommends drill-down paths in visual analytics by approximating a greedy algorithm, interpreting user intent, and managing exploration branches to reduce cognitive load.