LLM从工具变科研自主体，重塑科学发现流程
From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery
- 按科研流程分三类角色：工具、分析师、科学家
- 推动研究自动化向自主决策演进
- 适合关注AI科研协作与伦理的学者
大型语言模型（LLMs）正推动科学发现范式转变，从特定任务自动化工具逐步发展为具备更高自主性的智能体，深刻重构科研流程与人机协作模式。本文系统梳理该领域进展，提出基于科学方法论的三层分类框架——工具、分析师、科学家，清晰界定其在研究生命周期中自主性与职责的递进关系。同时识别关键挑战与未来方向，包括机器人自动化、自我改进能力及伦理治理机制。本综述构建了概念架构与战略前瞻，旨在引导人工智能驱动的科学发现实现快速创新与负责任发展。相关资源见GitHub仓库：https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundamentally redefining research processes and human-AI collaboration. This survey systematically charts this burgeoning field, placing a central focus on the changing roles and escalating capabilities of LLMs in science. Through the lens of the scientific method, we introduce a foundational three-level taxonomy-Tool, Analyst, and Scientist-to delineate their escalating autonomy and evolving responsibilities within the research lifecycle. We further identify pivotal challenges and future research trajectories such as robotic automation, self-improvement, and ethical governance. Overall, this survey provides a conceptual architecture and strategic foresight to navigate and shape the future of AI-driven scientific discovery, fostering both rapid innovation and responsible advancement. Github Repository: https://github.com/HKUST-KnowComp/Awesome-LLM-Scientific-Discovery.
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