SlideOn's sparse dims are vocabulary-independent and index-friendly
tomaarsen · x · 2026-10-20
Both models output 32,768-dim sparse vectors scored by dot product, but unlike many sparse approaches the dims don't correspond to vocabulary tokens. Active features store cleanly in an inverted index: query features select posting lists, then matching weights contribute to scores. In the comparison table SlideOn hits 56.68 BEIR-15 at 174M params, beating most sparse models regardless of size.
Related event: LightOn Open-Sources SOTA Sparse Retrievers SlideOn and mSlideOn(25 posts)→
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