Multi-vector retrieval methods such as ColBERT and its recent variant, the ConteXtualized Token Retriever (XTR), offer high accuracy but face efficiency challenges at scale. To address this, we present WARP, a retrieval engine that substantially improves the efficiency of retrievers trained with the XTR objective through three key innovations: (1) WARPSELECT for dynamic similarity imputation; (2) implicit decompression, avoiding costly vector reconstruction during retrieval; and (3) a two-stage reduction process for efficient score aggregation. Combined with highly-optimized C++ kernels, our system reduces end-to-end latency compared to XTR's reference implementation by 41x, and achieves a 3x speedup over the ColBERTv2/PLAID engine, while preserving retrieval quality.
@article{arxiv.2501.17788,
title = {WARP: An Efficient Engine for Multi-Vector Retrieval},
author = {Jan Luca Scheerer and Matei Zaharia and Christopher Potts and Gustavo Alonso and Omar Khattab},
journal= {arXiv preprint arXiv:2501.17788},
year = {2025}
}