让大模型智能选择记忆还是参数更新，实现更稳更快的自我进化。
Learning What to Remember and What to Internalize in LLM Self-Evolution via Adaptive Memory-Parameter Coordination
- 根据任务特点自动分配记忆或参数更新路径
- 在多类任务中提升自进化效率与鲁棒性
- 适合需要持续适应动态环境的智能体应用
大型语言模型代理在部署后常面临工具接口、API及用户需求动态变化的问题。现有自进化方法主要分为两类：基于工具箱的策略将反馈外化为可编辑的记忆或技能，实现快速适应；基于参数的策略则将经验内化至模型参数，促进能力深层提升。但单一使用任一机制会带来灵活性与性能之间的权衡。本文提出COVE框架，通过任务感知路由、阶段感知调度与知识优化，融合两种学习机制，使自进化不再是经验的无差别积累，而是依据任务与知识类型匹配合适学习方式的协同过程。跨多个任务类别实验表明，相较于单通道进化策略，COVE在动态环境中展现出更强的鲁棒性与更高的效率提升。
原文摘要 · Abstract (English)
Large language model agents increasingly operate in dynamic environments where tool interfaces, APIs, and user requirements change after deployment. Existing self-evolution methods mainly follow two paradigms: harness-based approaches, which externalize feedback into editable memories or skills for rapid adaptation, and parameter-based approaches, which internalize experience into model parameters for deeper capability improvement. However, using either mechanism alone creates a trade-off between flexibility and performance. This paper asks how an agent can coordinate both channels to achieve robust self-evolution. We present COVE, a unified agent self-evolution framework that combines harness-based and parameter-based learning through task-aware routing, stage-aware scheduling, and knowledge optimization. Through this design, COVE treats self-evolution not as indiscriminate accumulation of experience, but as a coordinated process that matches tasks and knowledge types to appropriate learning mechanisms. Experiments across multiple task categories show that COVE outperforms single-channel evolution strategies, demonstrating more robust and efficient improvement under changing environments.
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