# Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings

arXiv: https://arxiv.org/abs/2608.14648 · HTML: https://mlsanity.com/paper/2608.14648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 82 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Builds on (most cited first, 28 of 28)

- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate](https://mlsanity.com/paper/2504.19874/) (2025)
- [PLAID: An Efficient Engine for Late Interaction Retrieval](https://mlsanity.com/paper/2205.09707/) (2022)
- [ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data](https://mlsanity.com/paper/2403.04871/) (2024)
- [SPFresh: Incremental In-Place Update for Billion-Scale Vector Search](https://mlsanity.com/paper/2410.14452/) (2024)
- [High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations](https://mlsanity.com/paper/2303.09855/) (2023)
- [ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms](https://mlsanity.com/paper/2305.04359/) (2023)
- [Similarity search in the blink of an eye with compressed indices](https://mlsanity.com/paper/2304.04759/) (2023)
- [Efficient Code Embeddings from Code Generation Models](https://mlsanity.com/paper/2508.21290/) (2025)
- [Locally-Adaptive Quantization for Streaming Vector Search](https://mlsanity.com/paper/2402.02044/) (2024)
- [PDX: A Data Layout for Vector Similarity Search](https://mlsanity.com/paper/2503.04422/) (2025)
- [Quake: Adaptive Indexing for Vector Search](https://mlsanity.com/paper/2506.03437/) (2025)
- [MicroNN: An On-device Disk-resident Updatable Vector Database](https://mlsanity.com/paper/2504.05573/) (2025)
- [VIBE: Vector Index Benchmark for Embeddings](https://mlsanity.com/paper/2505.17810/) (2025)
- [LeanVec: Searching vectors faster by making them fit](https://mlsanity.com/paper/2312.16335/) (2023)
- [AiSAQ: All-in-Storage ANNS with Product Quantization for DRAM-free Information Retrieval](https://mlsanity.com/paper/2404.06004/) (2024)
- [GPU-Native Approximate Nearest Neighbor Search with IVF-RaBitQ: Fast Index Build and Search](https://mlsanity.com/paper/2602.23999/) (2026)
- [Cloud-Native Vector Search: A Comprehensive Performance Analysis](https://mlsanity.com/paper/2511.14748/) (2025)
- [Curator: Efficient Vector Search with Low-Selectivity Filters](https://mlsanity.com/paper/2601.01291/) (2026)
- [PQk-means: Billion-scale Clustering for Product-quantized Codes](https://mlsanity.com/paper/1709.03708/) (2017)
- [Panorama: Fast-Track Nearest Neighbors](https://mlsanity.com/paper/2510.00566/) (2025)
- [B+ANN: A Fast Billion-Scale Disk-based Nearest-Neighbor Index](https://mlsanity.com/paper/2511.15557/) (2025)
- [A Super Fast K-means for Indexing Vector Embeddings](https://mlsanity.com/paper/2603.20009/) (2026)
- [An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments and Analysis]](https://mlsanity.com/paper/2603.23710/) (2026)
- [PDET-LSH: Scalable In-Memory Indexing for High-Dimensional Approximate Nearest Neighbor Search with Quality Guarantees](https://mlsanity.com/paper/2603.24920/) (2026)
- [Distance Comparison Operations Are Not Silver Bullets in Vector Similarity Search: A Benchmark Study on Their Merits and Limits](https://mlsanity.com/paper/2604.02801/) (2026)
- [Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing](https://mlsanity.com/paper/2604.28142/) (2026)

## Abstract (arXiv)

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 https://github.com/cwida/SuperKMeans.