Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning
arXiv:2606.24428v1 Announce Type: new Abstract: 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-
The proliferation of LLM agents interacting in open-world environments necessitates robust mechanisms to prevent cumulative errors and improve learning safely.
Improving agentic learning processes is crucial for the reliability, scalability, and broader adoption of AI agents across industries.
This paper proposes a new paradigm (Execute-Distill-Verify) that enhances agent self-correction, mitigating the 'Self-Confirmation Trap' inherent in current single-agent learning loops.
- · AI agents developers
- · Businesses adopting AI agents
- · AI infrastructure providers
- · Legacy single-agent learning architectures
- · Applications vulnerable to self-confirming errors
More reliable and adaptable AI agents become deployable in complex, dynamic environments.
Reduced need for constant human supervision and intervention in agent operations enhances efficiency.
Accelerated development of fully autonomous systems capable of continuous self-improvement without human oversight.
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Read at arXiv cs.CL