As AI agents become more autonomous, they are increasingly able to modify their own prompts, tools, middleware, routing, resources, and execution harnesses.
That raises a question we studied in our recent work:
What happens when a self-modification improves capability but cannot be safely reversed later?
We introduce EvoUndo, a framework that treats recoverability as an explicit constraint on self-evolving agents.
Across 600 unseen self-evolution tasks, we identified 197 capability-improving mutations that failed recoverability verification. Under the original recovery representation, conventional repair recovered 0/197.
Our experiments suggest that two important bottlenecks are state grounding and recovery-language expressivity.
The broader idea is that persistent self-modifications should not be judged only by whether they improve performance. They should also be tested for whether the previous state can be safely recovered across counterfactual states.
I’m one of the authors of the paper.
[link] [comments]