Persistent SAEs learn per-feature persistence coefficients from reconstruction, splitting features into fast local detectors and slow topic-tracking states that retain prompt-injection signals over long contexts.
Gardner and Geoff Pleiss and Robert Pless and Noah Snavely and Kavita Bala and Kilian Q
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Persistent Sparse Autoencoders: Learning Feature Timescales in Language Models
Persistent SAEs learn per-feature persistence coefficients from reconstruction, splitting features into fast local detectors and slow topic-tracking states that retain prompt-injection signals over long contexts.