A dense, pretrained language model can be retrofitted with recurrent depth and learn an iterative latent transition that persists after outcome-only annealing.
Qwen2.5-0.5B-Instruct is split into a Prelude, a weight-tied Recurrent Block, and a Coda, with an identity-preserving one-loop path and a re-entry bridge on later loops.
At loop 1 the retrofit remains non-inferior to its base on a preregistered ARC battery.
Three findings
First, the mechanism is a reusable procedure rather than terminal-answer lookup, and installs at two budgets: 6M trained parameters over frozen base weights and 180M full-block.
With intermediate-step supervision, the model computes one task step per loop and persists when only final answers are graded.
The adapter matched the full block overall (83.8% versus 84.0%), led through depth 11, and trailed beyond.
Verbal fine-tuning reached 79-86% on controlled verbal renderings (zero-shot transfer was minimal), and adapter verbal training begun from the installed mechanism outpaced matched fresh training by 18.6 points, including on a held-out test set.
Second, the operation extrapolates to roughly 1.5 times its supervised depth, holding 70% accuracy through depth 18.
Third, a same-size scratchpad-trained model matched the recurrent model within its learned horizon but collapsed beyond it.
The recurrent model won overall, 84% versus 72%, retained 53% versus 2.5% beyond depth 10, and answered 7.6 times faster.
An iterative transformer can therefore perform deeper reasoning in latent space faster than comparable or larger models fine-tuned on the same task, in a system-level comparison.
A second task
running the rule in reverse, exposed the limits: the inverse was learnable in isolation, but no continuation acquired it while preserving the installed mechanism and general capability, a catastrophic-interference boundary.
Learned depth selection remains open.