Size Doesn't Matter: Cosine-Scored Sparse Autoencoders
Sparse autoencoders (SAEs) detect features via inner product, so a feature's activation scales with both its directional alignment and the input's norm. Features that fire on token norm therefore claim dictionary slots regardless of content alignment. This matters because sublayer normalization has already discarded the magnitude the score measures, so the encoder detects a quantity the model does not read. We replace the score with a learned blend of cosine similarity and input magnitude, letting the optimizer choose how much norm to use; a per-feature extension lets each feature decide independently. In both regimes, training is free to recover inner product but never does, with no feat...
| Authors | Silen Naihin, Lev Stambler |
|---|---|
| Primary category | cs.LG |
| Published | 13 June 2026 |
| Last updated at source | 29 June 2026 |
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