# DESSERT: An Efficient Algorithm for Vector Set Search with Vector Set Queries

arXiv: https://arxiv.org/abs/2210.15748 · HTML: https://mlsanity.com/paper/2210.15748/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2022.

## Built on by (most cited first, 10 of 10)

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- [Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval](https://mlsanity.com/paper/2512.12458/) (2025)
- [The Virtuous Cycle: AI-Powered Vector Search and Vector Search-Augmented AI](https://mlsanity.com/paper/2603.09347/) (2026)
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## Abstract (arXiv)

We study the problem of textitvector set search with textitvector set queries. This task is analogous to traditional near-neighbor search, with the exception that both the query and each element in the collection are textitsets of vectors. We identify this problem as a core subroutine for semantic search applications and find that existing solutions are unacceptably slow. Towards this end, we present a new approximate search algorithm, DESSERT (bf DESSERT bf Effeciently bf Searches bf Sets of bf Embeddings via bf Retrieval bf Tables). DESSERT is a general tool with strong theoretical guarantees and excellent empirical performance. When we integrate DESSERT into ColBERT, a state-of-the-art semantic search model, we find a 2-5x speedup on the MS MARCO and LoTTE retrieval benchmarks with minimal loss in recall, underscoring the effectiveness and practical applicability of our proposal.