GEM: A Native Graph-based Index for Multi-Vector Retrieval
Summary: GEM natively indexes vector sets via set-level clustering and bridged local proximity graphs, preserving fine-grained multi-vector semantics. Metric decoupling, semantic shortcuts, multi-entry beam search, and quantized distances deliver up to 16× speedups at comparable or better accuracy. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Yao Tian (Hong Kong University of Science and Technology)
- 2. Zhoujin Tian (Hong Kong University of Science and Technology)
- 3. Xi Zhao (Hong Kong University of Science and Technology)
- 4. Ruiyuan Zhang (Hong Kong Generative AI Research & Development Center)
- 5. Xiaofang Zhou (Hong Kong University of Science and Technology)
BibTeX Citation
@inproceedings{tian_sigmod26,
title = {{GEM: A Native Graph-based Index for Multi-Vector Retrieval}},
author = {Tian, Yao and Tian, Zhoujin and Zhao, Xi and Zhang, Ruiyuan and Zhou, Xiaofang},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802065},
url = {https://dl.acm.org/doi/10.1145/3802065},
year = {2026}
}
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