这篇论文用图匹配让智能体一次纠正错误，不用反复试错，在ALFWorld和ScienceWorld上效果比现有方法更好。
提出Experience Memory Graph (EMG)框架，将智能体失败恢复转化为图匹配问题。训练时将失败轨迹和专家轨迹转化为有向动作决策图，通过图匹配提取公共子图（成功工作流）和图编辑路径，存储于记忆图中。测试时无需试错循环，单次执行即可纠正错误。在ALFWorld和ScienceWorld基准上，EMG在成功率和平均奖励上持续优于现有反思基线。
Experience Memory Graph: One-Shot Error Correction for Agents
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.