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arXiv Machine Learning By Haoyan Luo, Mateo Espinosa Zarlenga, Mateja Jamnik

Persistent Sparse Autoencoders: Learning Feature-Specific Timescales in Language Model Representations

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Persistent Sparse Autoencoders (Persistent SAEs) extend standard sparse autoencoders by learning a persistence coefficient for each feature, enabling the model to capture feature‑specific timescales from reconstruction alone. The study shows that these persistent features maintain competitive reconstruction quality while distinguishing between short‑timescale, locally interpretable features and long‑timescale, context‑accumulating features. In a prompt‑injection monitoring case study, slow features were found to preserve injection‑related signals and remain causally effective over long contexts.

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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