Feed
Meta$^n$: Recursive Self-Improvement through Emergent Depth
One fixed improver rewrites the agent layer by layer, each time seeing everything below it.
0.331
score
ARC-AGI-2 score, GPT-5.2 backbone
Benchmark built to resist memorized skills; baselines stay near zero.
How it works
Improvers stay shallow
Self-improving agents fix answers, and self-editing ones freeze part of their machinery, so real meta-depth stops near two.
Recurse on the input
A fixed prompt Ω reads all task traces plus earlier layers' code, then writes a new layer of guidance and helpers.
Wins on eight families
Beats OpenEvolve and Gödel Agent on every benchmark family. Layers take on distinct roles, like rollback, without being told.
From the paper