Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses· 递归经验工作记忆架构解决长周期任务
Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We introduce Recuris, a recursive Experiential-Working Memory architecture for long-horizon agent harnesses, in which Working Memory tracks task progress and guides skill selection from Experiential Memory, grounding skill use in current needs rather than the full history. This coupling also turns execution into structured evidence that localizes failures to specific memory components. Across tasks, a fixed Meta-Agent turns that evidence into localized, validation-gated updates to Skill Memory that reshape execution and yield new evidence, forming a bounded recursive memory-evolution loop. Across four long-horizon benchmarks and ten models, Recu
递归经验工作记忆架构改善长周期任务中的技能调用与迭代。
- 核心方法
- 提出Recuris架构，通过工作记忆追踪任务进展并从经验记忆中选择技能，同时固定元代理根据执行证据更新技能记忆，形成递归记忆进化循环。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 长周期任务中，随着任务历史的增长，状态变得模糊，技能调用失误率增加。
- 关键实验
- 在四个长周期任务基准和十个模型上进行测试，展示方法的有效性。
- 主要贡献
- 解决长周期任务中状态追踪与技能选择的挑战，通过递归架构实现自适应和自改进。
- 意义与局限
- 该方法有助于开发更智能的代理来执行复杂和长时间的任务，但可能存在对特定类型任务的适用性限制。