Core Insights - The article discusses the transition from static large language models (LLMs) to self-evolving agents capable of continuous learning and adaptation in dynamic environments, paving the way towards artificial superintelligence (ASI) [3][4][46] - It emphasizes the need for a structured framework to understand and design self-evolving agents, focusing on three fundamental questions: what to evolve, when to evolve, and how to evolve [6][46] Group 1: What to Evolve - Self-evolving agents can improve various components such as models, memory, tools, and architecture over time to enhance performance and adaptability [19][20] - The evolution of these components is crucial for the agent's ability to handle complex tasks and environments effectively [19][20] Group 2: When to Evolve - The article categorizes self-evolution into two time modes: intra-test-time self-evolution, which occurs during task execution, and inter-test-time self-evolution, which happens between tasks [22][23] - Intra-test-time self-evolution allows agents to adapt in real-time to specific challenges, while inter-test-time self-evolution leverages accumulated experiences for future performance improvements [22][23] Group 3: How to Evolve - Self-evolution emphasizes a continuous learning process where agents learn from real-world interactions, seek feedback, and adjust strategies dynamically [26][27] - Various methodologies for self-evolution include reward-based evolution, imitation learning, and population-based approaches, each with distinct feedback types and data sources [29][30] Group 4: Applications and Evaluation - Self-evolving agents have significant potential in various fields, including programming, education, and healthcare, where continuous adaptation is essential [6][34] - Evaluating self-evolving agents presents unique challenges, requiring metrics that capture adaptability, knowledge retention, and long-term generalization capabilities [34][36] Group 5: Future Directions - The article highlights the importance of addressing challenges such as catastrophic forgetting, knowledge transfer, and ensuring safety and controllability in self-evolving agents [40][43] - Future research should focus on developing scalable architectures, dynamic evaluation methods, and personalized agents that can adapt to individual user preferences [38][44]

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]