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.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A multi-agent harness autonomously generates functional single-page VIS apps with linked views for scientific data tasks using coordinated skills for analysis, planning, implementation, and evaluation.
General-purpose coding agents achieve highest success on SciVis tasks but cost more compute, while domain-specific agents are efficient yet less flexible and computer-use agents falter on long workflows.
citing papers explorer
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SASAV: Self-Directed Agent for Scientific Analysis and Visualization
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.
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Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks
A multi-agent harness autonomously generates functional single-page VIS apps with linked views for scientific data tasks using coordinated skills for analysis, planning, implementation, and evaluation.
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Exploring Interaction Paradigms for LLM Agents in Scientific Visualization
General-purpose coding agents achieve highest success on SciVis tasks but cost more compute, while domain-specific agents are efficient yet less flexible and computer-use agents falter on long workflows.