经验记忆图：智能体的一次性错误纠正
Experience Memory Graph: One-Shot Error Correction for Agents
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中文总结 AI 辅助
研究针对大语言模型智能体在复杂任务中易出错且难恢复的问题，提出经验记忆图框架，将失败恢复转化为图匹配问题，训练时提取相关子图和路径存储，测试时指导智能体，实验证明其性能优于现有基线且无需测试时反复试验。
中文摘要 AI 辅助
大语言模型智能体在自主决策中展现出显著能力，但在复杂的长期任务中常出现复合错误且难以从失败中恢复。现有自我纠正机制存在缺陷。为此，我们提出经验记忆图（EMG）框架，将智能体失败恢复重新表述为图匹配问题。训练时，把失败探索轨迹和成功专家轨迹转换为有向行动决策图，匹配后提取公共子图和图编辑路径存储在记忆图中。测试时，EMG 能在无循环的单次执行中检索相关见解并指导智能体。在 ALFWorld 和 ScienceWorld 上的实验表明，EMG 在成功率和平均奖励方面始终优于现有反射基线，且无需测试时的反复试验。
英文摘要
Large Language Model (LLM) agents have shown remarkable capabilities in autonomous decision-making by generating sequential trajectories of states, actions, and observations. However, in complex, long-horizon tasks, these agents frequently suffer from compounding errors and struggle to recover from failures. Existing self-correction mechanisms rely on prompt-based reflection, which is inherently brittle, incurs heavy time and API costs due to iterative trial-and-error loops, and produces task-specific memory that may be hard to generalize to new scenarios. To address this, we propose Experience Memory Graph (EMG), a framework that reformulates agent failure recovery as a graph matching problem. At training time, we convert both failed exploration trajectories and successful expert trajectories into directed action decision graphs. By matching these graphs, we extract common subgraphs (successful workflows) and graph edit paths that explicitly indicate how to correct failures (e.g., which actions to add, delete, or relabel under a given observation), and store them in a memory graph with intra-task nodes and cross-task edges. At test time, EMG retrieves relevant insights and guides the agent in a single, loop-free execution. Experiments on ALFWorld and ScienceWorld show that EMG consistently outperforms state-of-the-art reflection baselines in success rate and average reward, while requiring no test-time trial-and-error.
发表机构
- University of Electronic Science and Technology of China（电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。
