{"ID":2876611,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21324","arxiv_id":"2508.21324","title":"Distribution-Aware Feature Selection for SAEs","abstract":"Sparse autoencoders (SAEs) decompose neural activations into interpretable features. A widely adopted variant, the TopK SAE, reconstructs each token from its K most active latents. However, this approach is inefficient, as some tokens carry more information than others. BatchTopK addresses this limitation by selecting top activations across a batch of tokens. This improves average reconstruction but risks an \"activation lottery,\" where rare high-magnitude features crowd out more informative but lower-magnitude ones. To address this issue, we introduce Sampled-SAE: we score the columns (representing features) of the batch activation matrix (via $L_2$ norm or entropy), forming a candidate pool of size $Kl$, and then apply Top-$K$ to select tokens across the batch from the restricted pool of features. Varying $l$ traces a spectrum between batch-level and token-specific selection. At $l=1$, tokens draw only from $K$ globally influential features, while larger $l$ expands the pool toward standard BatchTopK and more token-specific features across the batch. Small $l$ thus enforces global consistency; large $l$ favors fine-grained reconstruction. On Pythia-160M, no single value optimizes $l$ across all metrics: the best choice depends on the trade-off between shared structure, reconstruction fidelity, and downstream performance. Sampled-SAE thus reframes BatchTopK as a tunable, distribution-aware family.","short_abstract":"Sparse autoencoders (SAEs) decompose neural activations into interpretable features. A widely adopted variant, the TopK SAE, reconstructs each token from its K most active latents. However, this approach is inefficient, as some tokens carry more information than others. BatchTopK addresses this limitation by selecting...","url_abs":"https://arxiv.org/abs/2508.21324","url_pdf":"https://arxiv.org/pdf/2508.21324v1","authors":"[\"Narmeen Oozeer\",\"Nirmalendu Prakash\",\"Michael Lan\",\"Alice Rigg\",\"Amirali Abdullah\"]","published":"2025-08-29T04:42:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}