Turn-Averaged SAEs for Feature Discovery and Long-Context Attribution
Sparse autoencoders (SAEs) have become a useful tool for extracting interpretable features in language models. However, standard SAE architectures operate on individual token activations, meaning that the number of active features scales linearly with context length, and studying long model transcripts becomes difficult. We introduce turn-averaged SAEs, which represent a single Human or Assistant turn with a fixed number of features by learning to reconstruct the average model activation across the turn. We find that turn-averaged features describe a single turn's high-level characteristics more completely than per-token features when judged by an LLM. We also demonstrate that turn-averag...
| Authors | Kevin Der, Harish Kamath, Ben Thompson |
|---|---|
| Primary category | cs.CL |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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