Meta^n Escapes the Depth-2 Ceiling of Self-Improving Agents by Freezing the Meta-Operation and Recursing on Its Input
Self-improving agents that edit themselves must leave part of their own editing machinery untouched to stay stable, capping realized meta-depth at roughly two. Meta^n keeps a single meta-operation fixed and instead recurses on its input: the operation reads traces of the solver stack below along with the code that produced them, then writes the next layer as a strategic pre-process plus a library of callable helpers. Depth is set by convergence rather than fixed in advance, with an evolutionary archive searching over layer chains. Across two backbones it beats prior self-improving agents on all eight benchmark families, and on ARC-AGI-2, built specifically to resist skill memorization, it is the only method scoring above zero. Ablations attribute most of the gain to the conditioning each layer passes forward.
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