SASAV: Self-Directed Agent for Scientific Analysis and Visualization

TL;DR

SASAV is a fully autonomous AI agent that independently conducts scientific data analysis and visualization, eliminating the need for human intervention and enabling scalable scientific discovery.

Contribution

This work introduces SASAV, the first fully autonomous agent capable of performing scientific analysis and visualization without external prompts or human-in-the-loop feedback.

Findings

SASAV can automatically explore data and generate visualizations without human input.

The system integrates automated data profiling, knowledge retrieval, and reasoning-driven visualization.

SASAV accelerates scientific discovery by scaling data analysis workflows autonomously.

Abstract

With recent advances in frontier multimodal large language models (MLLMs) for data understanding and visual reasoning, the role of LLMs has evolved from passive LLM-as-an-interface to proactive LLM-as-a-judge, enabling deeper integration into the scientific data analysis and visualization pipelines. However, existing scientific visualization agents still rely on domain experts to provide prior knowledge for specific datasets or visualization-oriented objective functions to guide the workflow through iterative feedback. This reactive, data-dependent, human-in-the-loop (HITL) paradigm is time-consuming and does not scale effectively to large-scale scientific data. In this work, we propose a Self-Directed Agent for Scientific Analysis and Visualization (SASAV), the first fully autonomous AI agent to perform scientific data analysis and generate insightful visualizations without any external prompting or HITL feedback. SASAV is a multi-agent system that automatically orchestrates data exploration workflows through our proposed components, including automated data profiling, context-aware knowledge retrieval, and reasoning-driven visualization parameter exploration, while supporting downstream interactive visualization tasks. This work establishes a foundational building block for the future AI for Science to accelerate scientific discovery and innovation at scale.