Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings

[2608.14648] Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Databases arXiv:2608.14648 (cs) [Submitted on 30 Jul 2026] Title: Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings Authors: Leonardo Kuffo , Peter Boncz View a PDF of the paper titled Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings, by Leonardo Kuffo and 1 other authors View PDF HTML (experimental) Abstract: In this study, we revisit three widely used techniques in vector search and utilize them to optimize vector embedding indexing through clustering: dimensionality reduction, quantization, and dimension pruning. We propose an indexing pipeline in which these techniques are applied before clustering, and we focus on how they affect storage footprint, clustering time, and the quality of the resulting centroids for vector search tasks. Our results reveal that using full-precision vectors for clustering is excessive, as even 1-bit codes can achieve near-optimal clustering quality (within 1% of ideal) while reducing storage requirements by 60x and delivering attractive performance gains (Figure 1). We open-source our implementations at this https URL . Comments: VLDB 2026 Workshop: The 2nd Workshop on Vector Databases Subjects: Databases (cs.DB) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.14648 [cs.DB] (or arXiv:2608.14648v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2608.14648 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Leonardo Kuffo [ view email ] [v1] Thu, 30 Jul 2026 12:59:55 UTC (2,984 KB) Full-text links: Access Paper: View a PDF of the paper titled Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings, by Leonardo Kuffo and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.DB < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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