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Rubén Darío Guerrero

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Preprint Sep 2026

Directions That Don't Drift: Stiefel Manifold Routing for Transformer Attention

Four propositions prove steepest descent in the embedded metric, gradient-scale independence, well-conditioning, well-conditioning, and exact $\mathrm{O}(d)$-equivariance; two five-seed grokking studies confirm the constrained arm does not grok better than the baseline and the weight-decay exemption has no grokking con...

Rubén Darío Guerrero · 0 citations

#machine learning Preprint Sep 2026

Stiefel Attention: When the Geometry of Transformer Projection Matrices Dominates Optimizer Choice---and When It Does Not

The query and key projections $\WQ,\WK$ in attention are almost always trained by Euclidean optimizers with no geometric constraint. We constrain them to the Stiefel manifold and optimize with a Riemannian Adam carrying one scalar second moment per frame---the form of \citet{becigneul2019}, here extended to the compact...

Rubén Darío Guerrero · 0 citations