用大模型驱动的智能体，自动完成科研全流程。
Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics
- 构建可自主协作的人机语言代码物理系统
- 覆盖假设生成到结果分析全链条流程
- 适合希望加速科研的跨学科研究者
计算长期是科学发现的基石。近年来，大型语言模型（LLMs）兴起，催生了自主系统——智能体，它们在不同自主水平下加速发现进程。这些语言智能体提供了一个灵活且通用的框架，协调与人类科学家、自然语言、编程语言及代码、物理世界的交互。本文阐述我们对基于LLM的科学智能体的见解与愿景，及其在重塑科学发现生命周期中的日益重要角色：从假设生成、实验设计与执行，到结果分析与优化。我们批判性地审视当前方法，强调关键创新、实际成果和未解挑战。此外，识别出开放的研究问题，并提出构建更稳健、可泛化、自适应科学智能体的前景方向。我们的分析凸显了自主智能体在多个领域加速科学发现的巨大潜力。
原文摘要 · Abstract (English)
Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous systems, referred to as agents, that accelerate discovery across varying levels of autonomy. These language agents provide a flexible and versatile framework that orchestrates interactions with human scientists, natural language, computer language and code, and physics. This paper presents our view and vision of LLM-based scientific agents and their growing role in transforming the scientific discovery lifecycle, from hypothesis discovery, experimental design and execution, to result analysis and refinement. We critically examine current methodologies, emphasizing key innovations, practical achievements, and outstanding limitations. Additionally, we identify open research challenges and outline promising directions for building more robust, generalizable, and adaptive scientific agents. Our analysis highlights the transformative potential of autonomous agents to accelerate scientific discovery across diverse domains.
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