How Optimality Structures Sparse Dictionaries: Theory for Interpreting SAE Representations
Sparse Autoencoders (SAEs) have found success parsing neural network representations into interpretable concepts, providing a basis for understanding and control. However, what exactly SAEs extract and, hence, the scientific conclusions we can draw from them are not obvious. In short, if your SAE behaves strangely, does that reflect interesting neural network behaviour or an SAE-imposed distortion? Towards answering this, we use dictionary learning identifiability results to derive constraints that optimal dictionary learning features must satisfy. For example, an optimal feature will never turn on only while another is active. We use these conditions to explain various SAE oddities - hie...
| Authors | William Dorrell |
|---|---|
| Primary category | q-bio.NC |
| Published | 1 June 2026 |
| Last updated at source | 29 September 2026 |
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