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.
Nli4volvis: Natural language interaction for volume visualization via llm multi-agents and editable 3d gaussian splatting
7 Pith papers cite this work. Polarity classification is still indexing.
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Raiven mediates LLM visualization authoring via a formally defined DSL that unifies scientific and information visualization, producing deterministic, verifiable code from metadata-only inputs.
SASAV introduces the first fully autonomous multi-agent system for scientific data analysis and visualization that operates without external prompting or human-in-the-loop feedback.
SciVisAgentSkills provides reusable agent skills that raise mean task scores on a 108-task SciVis benchmark when paired with Codex and Claude Code agents.
Topo-GS repurposes 3D Gaussian Splatting with local geometric constraints and topology-aware losses to produce continuous volumetric embeddings of high-dimensional data.
Empirical comparison of domain-specific, computer-use, and general-purpose LLM agents plus CLI/GUI modalities on SciVis tasks reveals general-purpose agents highest in success rate but costliest, domain-specific agents more efficient, and persistent memory beneficial depending on mode.
VArify introduces a tree visualization to support human verification of GraphRAG evidence for LLM responses in food science, evaluated in a study with six domain experts.
citing papers explorer
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SciVisAgentSkills: Design and Evaluation of Agent Skills for Scientific Data Analysis and Visualization
SciVisAgentSkills provides reusable agent skills that raise mean task scores on a 108-task SciVis benchmark when paired with Codex and Claude Code agents.
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Exploring LLM Agent Designs and Interaction Modalities for Scientific Visualization
Empirical comparison of domain-specific, computer-use, and general-purpose LLM agents plus CLI/GUI modalities on SciVis tasks reveals general-purpose agents highest in success rate but costliest, domain-specific agents more efficient, and persistent memory beneficial depending on mode.