SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents
arXiv:2603.29139v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have enabled agentic systems to translate natural-language intent into executable scientific visualization (SciVis) tasks. Despite rapid progress, the community lacks a principled and reproducible benchmark for evaluating these emerging SciVis agents in realistic, multi-step analysis settings. We present SciVisAgentBench, a comprehensive and extensible benchmark for evaluating scientific data analysis and visualization agents. Our benchmark is grounded in a structured taxonomy spanning four dime
The rapid advancement of large language models (LLMs) has enabled new agentic capabilities, creating an urgent need for robust evaluation methodologies in specialized domains like scientific visualization.
A standardized benchmark for SciVis agents will accelerate the development and adoption of AI systems capable of autonomously executing complex scientific data analysis, impacting research and development across many fields.
The introduction of SciVisAgentBench provides a formal framework to compare, validate, and improve autonomous scientific visualization agents, moving the field towards more reliable and potent AI assistants for scientific discovery.
- · AI researchers and developers
- · Scientific research institutions
- · Data visualization software companies
- · Industries relying on scientific data analysis
- · Manual data analysts (long-term)
- · Companies with proprietary, non-agentic SciVis solutions
Improved performance and reliability of AI agents in scientific visualization.
Faster scientific discovery and a reduction in the time needed for complex data interpretation across various scientific disciplines.
The democratization of advanced scientific data analysis, allowing researchers with less specialized training to leverage sophisticated visualization techniques.
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Read at arXiv cs.AI