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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)

Paper ID
7438
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,247 | 29.70%
DOI
10.1145/3802065

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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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