# Dynamic Superblock Pruning for Fast Learned Sparse Retrieval

arXiv: https://arxiv.org/abs/2504.17045 · HTML: https://mlsanity.com/paper/2504.17045/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [SINDI: An Efficient Index for Sparse Vector Approximate Maximum Inner Product Search](https://mlsanity.com/paper/2509.08395/) (2025)

## Abstract (arXiv)

This paper proposes superblock pruning (SP) during top-k online document retrieval for learned sparse representations. SP structures the sparse index as a set of superblocks on a sequence of document blocks and conducts a superblock-level selection to decide if some superblocks can be pruned before visiting their child blocks. SP generalizes the previous flat block or cluster-based pruning, allowing the early detection of groups of documents that cannot or are less likely to appear in the final top-k list. SP can accelerate sparse retrieval in a rank-safe or approximate manner under a high-relevance competitiveness constraint. Our experiments show that the proposed scheme significantly outperforms state-of-the-art baselines on MS MARCO passages on a single-threaded CPU.