@lateinteraction: Late-interaction sparse retrieval? With neuron-level inverted indexing, on top of unsupervised sparse autoencoders. Wor…
Summary
This paper presents a single-stage sparse coding method using unsupervised sparse autoencoders and natural inverted indexing to accelerate multi-vector retrieval, outperforming traditional k-means based approaches.
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Cached at: 05/31/26, 04:57 AM
Late-interaction sparse retrieval? 😁
With neuron-level inverted indexing, on top of unsupervised sparse autoencoders. Works much better than directly training sparse retrievers.
Lots of cool ideas developed & composed in here. Thanks for the insights @Veritas2026 @yifeiwang77! https://t.co/tPf2Mohuy9
Sumit (@_reachsumit): No More K-means:Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval
@Veritas2026 et al. replace vector clustering with efficient sparse autoencoders & natural inverted indexing to accelerate multi-vector retrieval.
📝https://t.co/jux1WjXkML 👨🏽💻
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