Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models
Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opaque, and the field lacks a principled procedure for verifying that an apparent ``concept'' inside a model is real rather than an artifact of sequence composition. We introduce a framework that combines sparse dictionary learning with causal intervention to extract, validate, and causally test interpretable features in genomic foundation models. Training top-$k$ sparse autoencoders on the hidden activations of two architecturally distinct models, Nucleotide Transformer ($6$-mer tokenization) and DNABERT-2 (byte-pair encoding), we recover thousands of mo...
| Authors | Sarwan Ali |
|---|---|
| Primary category | q-bio.GN |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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