Agreement rather than normalisation keeps depth reusable in looped language models
Tests normalization between passes through a looped transformer. Removing it from pretrained Ouro models breaks predictions, while small models trained without it generalize better across loop counts, showing why the computation used during training and inference must agree.
First public: · Added to this list: October 26, 2026
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Recurrent reasoning — Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.
Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.