Highly recommended read from Google DeepMind and colleagues.

There is a lot of interest in self-evolving agents, but they're hard to get right.

This work highlights how important and difficult memory and retrieval are in agents.

The big question in this paper is whether self-evolving agents keep learning without forgetting.

They tested ReasoningBank, GEPA, and Meta-Harness on four task streams that drift in topic, workflow, or domain, built from BIRD, BFCL, and ScienceWorld.

The weights stay frozen, and the agents adapt by rewriting prompts, memory, and skills.

Self-evolution improves overall scores on most streams. But more adaptation consistently means agents forget more.

Methods that rewrite a shared prompt or workspace make global edits, so they interfere with unseen tasks and overwrite older knowledge when the stream shifts.

Memory retrieval keeps updates more local, but it still pulls in memories from other domains and collides when similar queries need different workflows.

Static benchmarks hide both problems, so test self-evolving agents on a stream that changes over time.

https://arxiv.org/abs/2610.25858

Chat with Paper: https://academy.dair.ai/papers/are-self-evolving-agents-continual-learners-2610.25858