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News/Beyond the Hard Budget: Sparsity Regularizers for More Interpretable Top-k Sparse Autoencoders
arxiv
PublishedJuly 15, 2026 at 4:00 AM

Beyond the Hard Budget: Sparsity Regularizers for More Interpretable Top-k Sparse Autoencoders

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arXiv:2606.27321v2 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) have become a leading tool for interpreting the representations of vision foundation models, decomposing their polysemantic activations into a larger set of sparse, more monosemantic features. The Top-$k$ SAE, a now

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