Metis: Bridging Text and Code Memory for Self-Evolving Agents

arXiv:2606.24151v1 Announce Type: new Abstract: Self-evolving agents improve over time by distilling experience from past executions and reusing it in future tasks. Existing systems represent such experience either as natural-language text injected into the agent context or as code exposed as callable tools. However, the choice between these representations is typically made at design time rather than derived from the characteristics of the experience itself, leaving the trade-offs between them ...