Symmetry Reveals Layerwise Dynamics
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Symmetry Reveals Layerwise Dynamics: How Transformers Perform In-Context Classification
arXiv:2604.11613v3 Announce Type: replace Abstract: Transformers can perform in-context classification from a few labeled examples, yet the inference-time algorithm remains opaque. We study multi-class linear classification in the hard no-margin regime and make the computation identifiable by enforcing feature- and label-permutation equivariance at every layer. This enables interpretability while maintaining functional equivalence and yields highly structured weights.