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

Hugging Face Daily Papers Papers
llm-agents experience-learning self-evolution collaborative-construction benchmark open-source

Summary

This paper proposes the EDV framework, which uses multiple heterogeneous agents in execute-distill-verify stages to build reliable experiences for LLM agents, preventing self-confirmatory errors and improving performance on long-horizon benchmarks.

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

Source: https://huggingface.co/papers/2606.24428

Abstract

EDV is a three-stage framework that uses multiple heterogeneous agents to collaboratively construct reliable experiences for LLM agents, preventing self-confirmatory errors through execute-distill-verify processes.

Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existingexperience learningmethods 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, anExecute-Distill-Verifyframework for reliableexperience learning. In the Execute stage, multipleheterogeneous agentsexplore 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 transformsexperience learningfrom isolated self-reflection intocollaborative construction, filtering erroneous and noisy content beforememory insertion. We evaluate EDV on three challenginglong-horizon benchmarks:tau2-bench,Mind2WebandMMTB. 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.

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