撰文丨王聪 编辑丨王多鱼 排版丨水成文 在科学研究的道路上，计算与实验如同双翼，共同推动着人类认知边界的扩展。然而，计算虽能高速模拟与预测，但实验却仍是瓶颈——操作依赖人力、细节易 出错、技能难传承。 有没有一种方法，能让 AI 不仅"想"得到，还能"看"得见、"做"得成？ 现在，来自斯坦福大学和普林斯顿大学的华人团队给出了答案——将 人工智能 （AI） 与 扩展现实 （XR） 技术结合，打造出了一个能真正与 人类科学家 （Human Scientist） 携手工作的" Al 协作科学家" （AI Co-Scientist） 。 该研究以： LabOS: The AI-XR Co-Scientist That Sees and Works With Humans 为题，发表在了 预印本平台 arXiv 上，斯坦福大学 丛乐 教授和普林斯顿大 学 王梦迪 教授为论文共同通讯作者。 该研究开发了一个 Al-XR Co-Scientist —— LabOS ，这是一个融合了 人工智能 （AI） 与 扩展现实 （XR） 的前沿科研协作平台，旨在通过智能感知与虚实交 互技术，重新定义科学研究的边界，让 AI 真正"看见 ...

Core Viewpoint - The article discusses the limitations of current AI agents, which rely heavily on static configurations and struggle to adapt to dynamic environments. It introduces the concept of "self-evolving AI agents" as a solution to these challenges, providing a systematic framework for their development and implementation [1][5][6]. Summary by Sections Need for Self-Evolving AI Agents - The rapid development of large language models (LLMs) has shown the potential of AI agents in various fields, but they are fundamentally limited by their dependence on manually designed static configurations [5][6]. Definition and Goals - Self-evolving AI agents are defined as autonomous systems that continuously and systematically optimize their internal components through interaction with their environment, adapting to changes in tasks, context, and resources while ensuring safety and performance [6][12]. Three Laws and Evolution Stages - The article outlines three laws for self-evolving AI agents, inspired by Asimov's laws, which serve as constraints during the design process [8][12]. It also describes a four-stage evolution process for LLM-driven agents, transitioning from static models to self-evolving systems [9]. Four-Component Feedback Loop - A unified technical framework is proposed, consisting of four components: system inputs, agent systems, environments, and optimizers, which work together in a feedback loop to facilitate the evolution of AI agents [10][11]. Technical Framework and Optimization - The article categorizes the optimization of self-evolving AI into three main directions: single-agent optimization, multi-agent optimization, and domain-specific optimization, detailing various techniques and methodologies for each [20][21][30]. Domain-Specific Applications - The paper highlights the application of self-evolving AI in specific fields such as biomedicine, programming, finance, and law, emphasizing the need for tailored approaches to meet the unique challenges of each domain [30][31][33]. Evaluation and Safety - The article discusses the importance of establishing evaluation methods to measure the effectiveness of self-evolving AI and addresses safety concerns associated with their evolution, proposing continuous monitoring and auditing mechanisms [34][40]. Future Challenges and Directions - The article identifies key challenges in the development of self-evolving AI, including balancing safety with evolution efficiency, improving evaluation systems, and enabling cross-domain adaptability [41][42]. Conclusion - The ultimate goal of self-evolving AI agents is to create systems that can collaborate with humans as partners rather than merely executing commands, marking a significant shift in the understanding and application of AI technology [42].