MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution 事件

MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution arXiv:2606.07603v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions. Existing experience-driven methods often rely on memory or heuristics without enhancing the model's ability to learn, treating it as a passive executor and leading to early performance plateaus and limited