most citedUniversal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment

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cs.CV2026★ 1 cited

Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment

Harrish Thasarathan, Julian Forsyth, Thomas Fel +2

We present Universal Sparse Autoencoders (USAEs), a framework for uncovering and aligning interpretable concepts spanning multiple pretrained deep neural networks. Unlike existing…