You don't often see one-word titles in AI papers.

That aside, strong recommend this paper from Google DeepMind.

I think this is an interesting training-free approach to evolve model architectures by leveraging the model itself to inform architectural modifications.

Something like this could also inspire even more robust recursive self-improvement approaches.

Approach details below:

A feedforward transformer can only update its internal state as many times as it has layers. Long generations need more updates than that, so chain-of-thought ends up doing basic state tracking in text.

Recirculation adds recurrence at inference time.

The model feeds activations back through itself during prefill, which lets it act like a dynamical system and track belief states without any retraining.

Generation cost stays flat. All the serial work happens in prefill.

On the Gemma3 family, the adaptive variant cuts perplexity 23% and lifts GSM8k accuracy 21%, with the original weights frozen and only light hyperparameter tuning.

Paper: https://arxiv.org/abs/2608.17981

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