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Paper · arXiv

Rotate the Loop: Blockwise Conformality for Looped Transformers

Looped transformers reuse one shared block across many recurrent steps, so depth becomes a quantity that can be spent at runtime rather than a stack of distinct layers. Those extra steps help only if a write made on one recurrent step is still readable on later ones, and that depends on the linear map applied to the residual between recurrent steps. We show that whether the loop learns to track state is a geometric question about the form of this map, and we propose blockwise conformal injections: the residual splits into coordinate-aligned groups and rotates inside each one, so every group has its own timescale while the geometry stays exact within it. Our experiments show a performance ...

AuthorsAlexey Zemtsov, Albina Klepach, Vadim Lomshakov et al.
Primary categorycs.LG
Published30 September 2026
Last updated at source30 September 2026
cs.LGcs.AIstat.ML
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