Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning

Experience-driven self-evolution is essential for large language model (LLM) agents to improve through interaction with open-world environments. However, existing experience learning methods largely rely on single-agent loops, in which the same agent executes tasks, summarizes outcomes, and decides what should be written into memory. In such settings, agents are prone to the Self-Confirmation Trap, where wrong-but-self-consistent trajectories are mistakenly treated as successful experience, leading to error accumulation through later retrieval and reuse. To address this challenge, 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, generating diverse candidate trajectories. In the Distill stage, a designated third-party distillation agent comparatively analyzes these trajectories and produces candidate experiences, reducing the bias of executor-centric self-summarization. In the Verify stage, the execution group jointly validates candidate experiences through a consensus-based mechanism, and only experiences that pass strict validation are written into shared or private memory. By decoupling execution, distillation, and validation, EDV turns experience learning from an isolated self-reflection loop into a collaborative experience construction process that suppresses erroneous and noisy experi- ence before memory insertion. We evaluate EDV on challenging long-horizon benchmarks, including τ2-bench, Mind2Web, and MMTB. Experimental results show that EDV consis- tently outperforms strong baselines, demonstrating the value of improving the reliability of experience construction for agent self-evolution. These findings suggest that robust agent improvement depends not only on richer memory, but also on how experience is constructed before it enters memory. Our code is available at https://github.com/shidingz/EDV.