More Data Cannot Break a Symmetry: Identifiability by Design

The paper shows that unsupervised representational alignment can fail due to symmetry in the stimulus geometry, even before data are collected. By using a design-time diagnostic based on the automorphism group of the geometry, the authors demonstrate that dense sampling can create near-duplicates that make alignment degenerate. Applying this diagnostic to a colour design reduces catastrophic alignment failures from 75% to 2% without altering models, layers, or solvers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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