从物竞天择到智能进化，首篇自进化智能体综述的ASI之路

Core Insights - The article discusses the limitations of static large language models (LLMs) and introduces the concept of self-evolving agents as a new paradigm in artificial intelligence [2] - A comprehensive review has been published by researchers from Princeton University and other top institutions to establish a unified theoretical framework for self-evolving agents, aiming to pave the way for artificial general intelligence (AGI) and artificial superintelligence (ASI) [2][32] Definition and Framework - The review provides a formal definition of self-evolving agents, laying a mathematical foundation for research and discussion in the field [5] - It constructs a complete framework for analyzing and designing self-evolving agents based on four dimensions: What, When, How, and Where [8] What to Evolve? - The four core pillars for self-improvement within the agent system are identified: Models, Context, Tools, and Architecture [11] - Evolution can occur at two levels for models: optimizing decision policies and accumulating experience through interaction with the environment [13] - Context evolution involves dynamic management of memory and automated optimization of prompts [13] - Tools evolution includes the creation of new tools, mastery of existing tools, and efficient management of tool selection [13] - Architecture evolution can target both single-agent and multi-agent systems to optimize workflows and collaboration [14] When to Evolve? - Evolution timing determines the relationship between learning and task execution, categorized into two main modes: intra-test-time and inter-test-time self-evolution [17] How to Evolve? - Intra-test-time self-evolution occurs during task execution, allowing agents to adapt in real-time [20] - Inter-test-time self-evolution happens after task completion, where agents iterate on their capabilities based on accumulated experiences [20] - Evolution can be driven by various methodologies, including reward-based evolution, imitation learning, and population-based methods [21][22] Where to Evolve? - Self-evolving agents can evolve in general domains to enhance versatility or specialize in specific domains such as coding, GUI interaction, finance, medical applications, and education [25] Evaluation and Future Directions - The review emphasizes the need for dynamic evaluation metrics for self-evolving agents, focusing on adaptability, knowledge retention, generalization, efficiency, and safety [28] - Future challenges include developing personalized AI agents, enhancing generalization and cross-domain adaptability, ensuring safety and controllability, and exploring multi-agent ecosystems [32]