AI 自省会越学越错，得找别人来挑刺
让 AI 自己执行任务、自己总结经验、自己记住教训——听起来很合理，但论文发现这其实是个陷阱：AI 会把自己犯的错也当成正确经验存下来，越学越偏。研究者提出一个三阶段框架：先让多个不同版本的 AI 并行执行同一任务，再让一个独立的第三方 AI 对比这些轨迹、提炼候选经验，最后让执行组投票验证，只有通过共识的经验才写入记忆。在三个长周期任务基准上，这套方法一致优于传统自省式学习。它不是你明天能用上的，但它揭示了一个反直觉的事实：AI 的自我进化需要引入外部视角，否则就是闭门造车。
📄 原文摘要(英文)
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.