arXiv (OAI Expanded)
open access
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:
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