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A Deeper Analysis of Block-Sparse Featurizers

A new analysis of the block-sparse featurizer (BSF), introduced by Fel et al. in 2026, finds it still suffers from classic sparse autoencoder failure modes like feature splitting and composition, despite its design for low-dimensional manifold features common in vision. The authors propose architectural changes, including a Tournament Top-K selection rule that significantly reduces feature splitting, and extend the block paradigm to the crosscoder.

read2 min views1 publishedAug 31, 2026
A Deeper Analysis of Block-Sparse Featurizers
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[Submitted on 27 Aug 2026]


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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.

Submission history #

From: Alexandru-Iulius Jerpelea [[view email](/show-email/5f30d5d2/2608.27515)]

**[v1]** Thu, 27 Aug 2026 09:50:20 UTC (1,239 KB)

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