近年来，大语言模型（LLM）已展现出卓越的通用能力，但其核心仍是静态的。面对日新月异的任务、知识领域和交互环境，模型无法实时调整其内部参数，这 一根本性瓶颈日益凸显。 当我们将视野从提升静态模型的规模，转向构建能够实时学习和适应的动态智能体时，一个全新的范式—— 自进化智能体（Self-evolving Agents） ——正引领着 人工智能领域的变革。 核心框架：四大维度定义智能体演化 然而，尽管学术界与工业界对自进化智能体的兴趣与日俱增，但整个领域仍缺乏一个系统性的梳理与顶层设计。多数研究将「演化」作为智能体整体框架的一个 子集，未能深入回答该领域三个最根本的问题：智能体的哪些部分应该演化（What）？演化何时发生（When）？以及，演化如何实现（How）？ 为应对上述挑战，普林斯顿大学联合多所顶尖机构的研究者们联合发布了首个全面且系统的自进化智能体综述。该综述旨在为这一新兴领域建立一个统一的理论 框架和清晰的路线图，最终为实现通用人工智能（AGI）乃至人工超级智能（ASI）铺平道路。 自进化智能体的形式化定义 为确保研究的严谨性，该综述首先为「自进化智能体」提供了一套形式化的定义，为整个领域的研究和讨论 ...

Core Insights - The article discusses the transition from static large language models (LLMs) to self-evolving agents that can adapt and learn continuously from interactions with their environment, aiming for artificial superintelligence (ASI) [3][5][52] - It emphasizes three fundamental questions regarding self-evolving agents: what to evolve, when to evolve, and how to evolve, providing a structured framework for understanding and designing these systems [6][52] Group 1: What to Evolve - Self-evolving agents can improve various components such as models, memory, tools, and workflows to enhance performance and adaptability [14][22] - The evolution of agents is categorized into four pillars: cognitive core (model), context (instructions and memory), external capabilities (tool creation), and system architecture [22][24] Group 2: When to Evolve - Self-evolution occurs in two main time modes: intra-test-time self-evolution, which happens during task execution, and inter-test-time self-evolution, which occurs between tasks [26][27] - The article outlines three basic learning paradigms relevant to self-evolution: in-context learning (ICL), supervised fine-tuning (SFT), and reinforcement learning (RL) [27][28] Group 3: How to Evolve - The article discusses various methods for self-evolution, including reward-based evolution, imitation and demonstration learning, and population-based approaches [32][36] - It highlights the importance of continuous learning from real-world interactions, seeking feedback, and adjusting strategies based on dynamic environments [30][32] Group 4: Evaluation of Self-evolving Agents - Evaluating self-evolving agents presents unique challenges, requiring assessments that capture adaptability, knowledge retention, and long-term generalization capabilities [40] - The article calls for dynamic evaluation methods that reflect the ongoing evolution and diverse contributions of agents in multi-agent systems [51][40] Group 5: Future Directions - The deployment of personalized self-evolving agents is identified as a critical goal, focusing on accurately capturing user behavior and preferences over time [43] - Challenges include ensuring that self-evolving agents do not reinforce existing biases and developing adaptive evaluation metrics that reflect their dynamic nature [44][45]