Exploring LLM Agent Designs and Interaction Modalities for Scientific Visualization
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 scripts, model context protocol (MCP) or API calls, command-line interfaces (CLI), and graphical user interfaces (GUI). Our goal is to characterize the tr...
| Authors | Jackson Vonderhorst, Kuangshi Ai, Haichao Miao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 April 2026 |
| Last updated at source | 26 June 2026 |
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