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Self-evolving LLM agents can drastically reduce reasoning overhead by transforming atomic actions into reusable Standard Operating Procedures, leading to higher success rates and fewer interaction rounds.
LLMs can achieve remarkable out-of-distribution generalization by learning to self-update their context through a novel reinforcement learning framework.
