正在补充深度解读，当前内容可以先阅读
论文解决了什么问题
Agents built around large language models continually accumulate interaction trajectories during deployment, yet their behavior typically remains fixed. Beyond updating model weights, these trajectories can improve the agent harness that constructs context, mediates tools, validates actions, and recovers execution. We ...
核心方法
任务场景：强化学习、检索与 RAG
适合谁阅读
上下文工程Agent 系统强化学习检索与 RAG
可核验的原论文来源和作者
- 作者
- 作者信息暂未从原始元数据中确认
- 来源
- arXiv
- 论文 ID
- 2608.02276