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arXiv AI By Brian B. Moser, Stanislav Frolov, Tobias C. Nauen, Federico Raue, Andreas Dengel

The Self-Improvement Problem

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The paper examines the challenge of safely allowing autonomous AI agents to improve themselves. It formalizes the problem by modeling agents as programs and defining improvement via a potentially uncomputable semantic score. The authors prove that in general, no terminating procedure can reliably accept only genuine improvements while never missing any, but they identify a practical bounded regime where exhaustive simulation can achieve these guarantees, noting its limitations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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