Radial Suppression Accelerates Algorithmic Generalization: A Geometric Analysis of Delayed Generalization

arXiv:2606.32000v1 Announce Type: new Abstract: Why do neural networks memorize algorithmic training data long before they generalize? We present a geometric case study demonstrating that, on tasks where generalization requires discovering structured low-dimensional circuits, the memorization-generalization delay is driven by radial inflation of hidden representations under cross-entropy optimization. We formalize a radial-angular decomposition of activation-space dynamics and derive three testable propositions: (i) that penalizing radial inflation induces anisotropic, data-dependent weight re

Source: arXiv cs.LG — read the full report at the original publisher.

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