Experience Memory Graph Enables One-Shot Error Correction for LLM Agents

Jul 16, 2026

A new framework called Experience Memory Graph (EMG) is proposed to address error correction in large language model (LLM) agents. EMG reformulates agent failure recovery as a graph matching problem by converting both failed and successful trajectories into directed graphs, extracting correction patterns, and enabling one-shot error correction without iterative trial-and-error. Experiments on ALFWorld and ScienceWorld demonstrate that EMG outperforms state-of-the-art reflection-based baselines in both success rate and average reward, while eliminating the need for costly test-time trial-and-error loops.

Why it matters: This approach could make LLM agents more reliable and efficient in complex, long-horizon tasks by providing a generalizable and cost-effective error correction mechanism.

Full story at: arXiv AI/ML ↗