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arXiv — cs.AI preprintsInternational2 October 2026

Screw Attention: Rigid-Body Algebra Inside a Transformer

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arXiv:2610.00904v1 Announce Type: cross Abstract: Learned manipulation policies rediscover from data the spatial relations that rigid-body mechanics supplies in closed form. This costs data, and it leaves the policies fragile to geometric changes in the scene. We present Screw Attention, a transformer layer in which the relation between two bodies is a spatial transform rather than a graph edge. Every token is a body with a pose. Each pair of tokens carries the relative pose and, for robot joints, the joint screw. Messages are transported along this relation into the receiver's frame, while th
— arXiv — cs.AI preprints

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