Comparing Sparsified MLPs and Transcoders: Many Transcoder Features Are Neurons in Disguise
MLP neurons in transformer language models are widely understood to be polysemantic, motivating transcoders - sparse, learned approximations of MLP layers - as interpretable replacement models for mechanistic analysis. However, recent work shows that MLP neurons can be used to recover sparse, interpretable circuits (Arora et al. 2026, Ye et al. 2025), raising the question of whether transcoders are necessary for circuit analysis. We define neuron features as transcoder features whose weights are strongly aligned with individual MLP neurons, and show that they account for a substantial fraction of active features in late layers across 13 open-source transcoders. Only neuron features have a...
| Authors | Stepan Shabalin, Mateusz Piotrowski |
|---|---|
| Primary category | cs.AI |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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