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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.
This paper proposes Single-stage Sparse Retrieval (SSR), which replaces K-means clustering with sparse autoencoders and inverted indexing, achieving 15x faster indexing and halved retrieval latency while improving accuracy on the BEIR benchmark.