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

A new framework called EDV aims to make large language model agents more reliable by preventing them from learning from their own mistakes, a problem researchers call the Self-Confirmation Trap [1]. The framework, detailed in a paper submitted in 2026, proposes decoupling experience learning into three distinct stages: Execute, Distill, and Verify [1]. Existing methods typically rely on a single agent that both performs tasks and decides what it has learned, a closed loop that can reinforce errors [1]. When an agent generates a wrong-but-self-consistent trajectory, it may misidentify the sequence as a successful experience, leading to cumulative errors during later retrieval and reuse [1][2]. To break this cycle, the EDV framework introduces multiple heterogeneous agents that explore the same task space in parallel during the Execute stage, generating diverse candidate trajectories [1][2]. A dedicated third-party agent then comparatively analyzes these trajectories in the Distill stage to produce candidate experiences, a step designed to reduce executor-centric summarization bias [1][2]. In the final Verify stage, the execution group validates these candidates through a consensus mechanism, and only approved experiences are written into shared or private memory [1][2]. The researchers evaluated EDV on three long-horizon benchmarks: tau2-bench, Mind2Web, and MMTB [1][2]. The results showed EDV consistently outperformed strong baselines, supporting the argument that reliable experience construction is essential for robust agent self-evolution [1][2]. The code for the framework has been made publicly available on GitHub [1][2]. The work was posted on arXiv, a preprint server where research communities share findings before formal peer review [1]. The paper is associated with arXivLabs, a framework that allows collaborators to develop and share new features on the platform [1].