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A Deeper Analysis of Block-Sparse Featurizers

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Computer Science > Machine Learning

arXiv:2608.27515 (cs)
[Submitted on 27 Aug 2026]

Title:A Deeper Analysis of Block-Sparse Featurizers

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Abstract:The recently introduced block-sparse featurizer (BSF; Fel et al., 2026) is similar to a sparse autoencoder (SAE), but its atomic unit is a small subspace (a block of directions) rather than a single direction. It is designed for features that live on low-dimensional manifolds, which are especially frequent in vision. This work studies the BSF's strengths and weaknesses, finding how it still somewhat suffers from classic SAE failure modes, like feature splitting and composition. We propose several architectural changes to the BSF, including a Tournament Top-K selection rule that significantly reduces feature splitting, and we also extend the block paradigm to the crosscoder.
Comments: 9 pages, 12 figures, 2 tables
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.27515 [cs.LG]
  (or arXiv:2608.27515v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.27515
arXiv-issued DOI via DataCite

Submission history

From: Alexandru-Iulius Jerpelea [view email]
[v1] Thu, 27 Aug 2026 09:50:20 UTC (1,239 KB)
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