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arXiv AI By Zae Myung Kim, Young-Jun Lee, Seungyeon Jwa, Dongyeop Kang

Meta$^n$: Recursive Self-Improvement through Emergent Depth

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Meta$^n$ is a recursive self‑improvement framework for large language models that keeps a fixed meta‑operation Ω and repeatedly applies it to its own outputs, creating deeper layers that reason from higher perspectives. By avoiding changes to the meta‑operation, the system remains stable while the input grows, allowing depth to emerge through convergence and evolutionary search. Experiments on two backbone models show Meta$^n$ surpasses prior self‑improving agents across eight benchmark families, notably achieving positive scores on the ARC‑AGI‑2 benchmark designed to resist skill memorization.

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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