基于轨迹的内存生成用于自改进代理系统
Trajectory-Informed Memory Generation for Self-Improving Agent Systems
AI总结：
本研究提出基于轨迹的内存生成框架，通过提取执行轨迹中的有效信息，提升代理在复杂任务中的性能表现。
摘要：
LLM驱动的代理面临持续存在的挑战：从执行经验中学习以提高未来性能。虽然代理可以成功完成许多任务，但它们往往重复低效的模式，无法从相似的错误中恢复，且错失了应用过去执行中成功策略的机会。我们提出了一种新的框架，用于自动从代理执行轨迹中提取可操作的见解，并利用它们通过上下文记忆检索来提高未来性能。我们的方法包括四个组成部分：（1）轨迹智能提取器，对代理推理模式进行语义分析；（2）决策归因分析器，识别导致失败、恢复或低效的决策和推理步骤；（3）上下文学习生成器，产生三种类型的指导——从成功模式中提取的策略提示、从失败处理中提取的恢复提示，以及从低效但成功的执行中提取的优化提示；（4）自适应记忆检索系统，根据多维相似性将相关见解注入代理提示中。与现有的存储通用对话事实的记忆系统不同，我们的框架理解执行模式，提取具有证明的结构化见解，并检索针对特定任务上下文的指导。在AppWorld基准测试中的评估显示了一致的改进，持有任务上的场景目标完成率提高了14.3个百分点，尤其在复杂任务上（28.5~pp场景目标改进，相对增加149%）
英文摘要：
LLM-powered agents face a persistent challenge: learning from their execution experiences to improve future performance. While agents can successfully complete many tasks, they often repeat inefficient patterns, fail to recover from similar errors, and miss opportunities to apply successful strategies from past executions. We present a novel framework for automatically extracting actionable learnings from agent execution trajectories and utilizing them to improve future performance through contextual memory retrieval. Our approach comprises four components: (1) a Trajectory Intelligence Extractor that performs semantic analysis of agent reasoning patterns, (2) a Decision Attribution Analyzer that identifies which decisions and reasoning steps led to failures, recoveries, or inefficiencies, (3) a Contextual Learning Generator that produces three types of guidance -- strategy tips from successful patterns, recovery tips from failure handling, and optimization tips from inefficient but successful executions, and (4) an Adaptive Memory Retrieval System that injects relevant learnings into agent prompts based on multi-dimensional similarity. Unlike existing memory systems that store generic conversational facts, our framework understands execution patterns, extracts structured learnings with provenance, and retrieves guidance tailored to specific task contexts. Evaluation on the AppWorld benchmark demonstrates consistent improvements, with up to 14.3 percentage point gains in scenario goal completion on held-out tasks and particularly strong benefits on complex tasks (28.5~pp scenario goal improvement, a 149\% relative increase).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
