Core Insights - The article discusses the development of LabOS, an AI-XR Co-Scientist platform that integrates artificial intelligence with extended reality technology to enhance scientific research collaboration between AI and human scientists [3][6][29] Group 1: LabOS Overview - LabOS is the first AI Co-Scientist that combines computational reasoning with real-world experiments, utilizing multimodal perception and XR-supported human-machine collaboration [6][9] - The platform consists of four types of AI agents: planning agents, development agents, critique agents, and tool creation agents, enabling a complete research workflow from hypothesis generation to data analysis [9][12] Group 2: Functionality and Applications - LabOS allows AI to "see" what human scientists see, providing real-time assistance during experiments, which transforms laboratories into intelligent collaborative spaces [7][27] - The platform has demonstrated its capabilities in three biomedical scenarios: cancer immunotherapy target discovery, cell fusion mechanism research, and guidance in stem cell engineering [21][23][25] Group 3: Technological Innovations - LabOS incorporates LabSuperVision (LSV) for visual understanding of laboratory environments, achieving over 90% accuracy in error detection during experiments [14][18] - The use of XR glasses facilitates seamless interaction between human scientists and AI, allowing for real-time video transmission and structured guidance [17][20] Group 4: Future Implications - The emergence of LabOS signifies a new era of human-AI collaboration in laboratories, enhancing the speed of discovery and the reproducibility of research [29] - As AI and XR technologies continue to evolve, LabOS is expected to become a standard tool in laboratories, fostering a co-evolution of human intuition and machine learning [29]

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].