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.

翻译：随着前沿多模态大语言模型（MLLMs）在数据理解与视觉推理方面取得最新进展，大语言模型（LLMs）的角色已从被动的"LLM即接口"演变为主动的"LLM即评判官"，从而能够更深入地融入科学数据分析与可视化流程。然而，现有科学可视化智能体仍依赖领域专家提供特定数据集先验知识，或需借助面向可视化的目标函数通过迭代反馈引导工作流。这种被动响应、依赖数据、人类参与循环（HITL）的范式耗时且难以有效扩展至大规模科学数据。本研究提出面向科学分析与可视化的自主智能体（SASAV），这是首个无需任何外部提示或HITL反馈即可完全自主进行科学数据分析并生成洞见性可视化的人工智能体。SASAV是一个多智能体系统，通过我们提出的组件（包括自动化数据剖析、上下文感知知识检索、以及推理驱动的可视化参数探索）自动编排数据探索工作流，同时支持下游交互式可视化任务。本研究为未来"人工智能驱动科学"（AI for Science）规模化加速科学发现与创新奠定了基础构件。