arXiv:2604.27996v3 Announce Type: replace Abstract: This paper examines how large language model (LLM) agents perform on scientific visualization (SciVis) tasks that require generating visualization workflows from natural-language instructions. We compare three representative agent designs: domain-specific agents with structured tool use, computer-use agents, and general-purpose coding agents, across 15 benchmark tasks, evaluating visualization quality, efficiency, robustness, computational cost, and the impact of persistent memory. We further study interaction modalities, including code scrip
The rapid advancement and integration of LLMs are pushing the boundaries of autonomous agency, making the exploration of their application in complex tasks like scientific visualization a timely and critical area of study.
The development of LLM agents for scientific visualization could significantly accelerate scientific discovery and data interpretation by automating complex, time-consuming workflows, empowering researchers with advanced analytical capabilities.
This research outlines methodological comparisons of LLM agent designs, providing a framework for developing more effective and robust AI agents for specialized scientific tasks, potentially redefining how data is analyzed and presented.
- · AI software developers
- · Scientific researchers
- · Data visualization platforms
- · Academic institutions
- · Manual data visualization providers
- · Disciplines resistant to AI integration
Automated generation of diverse and high-quality scientific visualizations becomes a standard practice.
Reduced time from raw data to scientific insights, accelerating research cycles across various domains.
Shift in scientific training towards understanding and guiding AI agents rather than performing manual visualization tasks.
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Read at arXiv cs.AI