Beyond Self-Reflection: An Execute-Distill-Verify Framework for Agentic Experience Learning

arXiv:2606.24428v2 Announce Type: replace Abstract: Experience learning lets large language model agents reuse lessons through external memory without updating model parameters. However, reflection can endorse execution errors and store them as future guidance, a failure mode we call the Self-Confirmation Trap. We propose EDV, an Execute-Distill-Verify framework for offline experience construction. Heterogeneous agents independently execute each task; a third-party distiller compares their trajectories and environmental feedback to extract candidate lessons; and the executors separately review each candidate, retaining only unanimously approved lessons. Evaluation uses a single solver retrieving from the frozen memory bank. Experiments on $\tau^2$-bench, Mind2Web, and MMTB show improved revisited-task performance, with additional evidence of within-domain transfer to held-out tasks on $\tau^2$-bench. On an annotated Retail subset, EDV reduces the Self-Confirmation Trap Rate from 11.0% with RB-MaTTS to 5.0%, indicating less retention of erroneous guidance.