Persistent Sparse Autoencoders: Learning Feature-Specific Timescales in Language Model Representations
Sparse autoencoders (SAEs) decompose language model activations into sparse features, yet these models traditionally encode each token independently, failing to expose information that persists across a sequence. We first show that temporal persistence can naturally emerge in standard SAE features: after a feature activates, the hidden state remains aligned with its direction, and past activations help reconstruct later hidden states. How long this lasts varies widely across features. We therefore introduce Persistent Sparse Autoencoders (Persistent SAEs), an extension of standard SAEs that learns a persistence coefficient for each feature, allowing the model to learn feature-specific tim...
| Authors | Haoyan Luo, Mateo Espinosa Zarlenga, Mateja Jamnik |
|---|---|
| Primary category | cs.LG |
| Published | 19 July 2026 |
| Last updated at source | 2 September 2026 |
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