In a nutshell: EDV uses multiple heterogeneous agents to generate diverse solution approaches, an independent verifier, and a consensus mechanism to filter out erroneous experiences before they are stored.
Researchers have developed a new framework called EDV (Execute-Distill-Verify) that prevents LLM agents from storing erroneous experiences as successful and falling into a chain of errors. The problem arises when an agent independently performs tasks, evaluates its own results, and stores the insights gained—without external oversight.
The Problem: Self-Confirmation Trap
When LLM agents are meant to learn, they perform tasks, evaluate their own results, and store the insights gained. This cycle has a critical weakness: if an agent goes down a wrong path, it can evaluate that deviation as consistent and correct in its own assessment. The erroneous experience is stored and retrieved again for similar tasks—leading to cumulative errors. Researchers call this phenomenon the “Self-Confirmation Trap”.
EDV Framework with Three Phases
The EDV framework decouples the learning process into three stages: In the Execute phase, multiple distinct agents explore the same task space in parallel and generate diverse solution candidates. Subsequently, in the Distill phase, a dedicated third-party agent comparatively analyzes these trajectories and creates experience candidates—without the one-sided perspective of the executing agent. Finally, the Verification phase validates the candidates through a consensus mechanism among the executing agents. Only approved experiences are written to shared or private memory.
Validation on Three Benchmarks
The method was tested on three demanding long-horizon benchmarks: tau2-bench, Mind2Web, and MMTB. EDV consistently outperformed strong baselines. The code is available at https://github.com/shidingz/EDV. The framework transforms experience-based learning from isolated self-reflection to collaborative construction with upstream error filtering.
Source: arxiv.org · Published 22 June 2026
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