逃離自我確認陷阱：一種面向智能體經驗學習的執行-提煉-驗證範式

摘要

經驗驅動的自我演化對於大型語言模型（LLM）智能體透過開放世界互動來提升能力至關重要。然而，現有的經驗學習方法大多依賴單智能體循環——同一智能體同時執行任務、總結結果並決定記憶內容。這種設定使智能體容易陷入「自我確認陷阱」：錯誤但自洽的軌跡被誤判為成功經驗，進而在檢索與重複使用時導致累積錯誤。為了解決此問題，我們提出EDV——一個用於可靠經驗學習的「執行-提煉-驗證」框架。在執行階段，多個異質智能體平行探索相同的任務空間，產生多樣化的候選軌跡。在提煉階段，一個專職的第三方智能體透過比較分析這些軌跡來產生候選經驗，從而減少以執行者為中心的總結偏差。在驗證階段，執行群體透過共識機制驗證候選經驗，只有通過驗證的經驗才會寫入共享或私有記憶。透過將三個階段解耦，EDV將經驗學習從孤立的自我反思轉變為協作建構，在寫入記憶前過濾錯誤與雜訊內容。我們在三個具有挑戰性的長時序基準上評估EDV：tau2-bench、Mind2Web與MMTB。結果顯示EDV持續優於強基線，驗證了可靠的經驗建構對於穩健的智能體自我演化至關重要。我們的程式碼已開源於https://github.com/shidingz/EDV。

English

Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly rely on single-agent loops, where the same agent executes tasks, summarizes outcomes, and determines memory content. This setup makes agents vulnerable to the Self-Confirmation Trap: wrong-but-self-consistent trajectories are misidentified as successful experience, leading to cumulative errors during retrieval and reuse. To address this issue, we propose EDV, an Execute-Distill-Verify framework for reliable experience learning. In the Execute stage, multiple heterogeneous agents explore the same task space in parallel to generate diverse candidate trajectories. In the Distill stage, a dedicated third-party agent comparatively analyzes these trajectories to produce candidate experiences, reducing executor-centric summarization bias. In the Verify stage, the execution group validates candidates via a consensus mechanism, and only approved experiences are written into shared or private memory. By decoupling the three stages, EDV transforms experience learning from isolated self-reflection into collaborative construction, filtering erroneous and noisy content before memory insertion. We evaluate EDV on three challenging long-horizon benchmarks: tau2-bench, Mind2Web and MMTB. Results show EDV consistently outperforms strong baselines, validating that reliable experience construction is essential for robust agent self-evolution. Our code is available at https://github.com/shidingz/EDV.