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This paper disentangles the roles of evolver and agent in self-improving LLM agents, showing that a small evolver can write sufficiently good updates, while a mid-tier agent benefits most from using them. It recommends using the strongest model as the task executor, not the update writer.
The article discusses the growing importance of memory architecture in LLMs, suggesting that reliability of memory may matter more than raw model intelligence as models improve.