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News/Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders
arxiv
PublishedJune 12, 2026 at 4:00 AM
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Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders

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arXiv:2606.12138v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are widely used to interpret neural network representations, but their utility depends on whether the learned features are reproducible across training runs. We study this question through \emph{feature stability}: for each S

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