AI RESEARCH
EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle
arXiv CS.AI
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ArXi:2510.16079v2 Announce Type: replace-cross Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from their own experiences. While existing frameworks mainly focus on mitigating external knowledge gaps, they fail to address a fundamental limitation: the inability to iteratively refine problem-solving strategies. In this work, we