Dynamically Detect and Fix Hardness for Efficient Approximate Nearest Neighbor Search
Summary: Propose Escape Hardness, a per-query metric to locate defective regions in graph-based ANNS and a two-stage online repair that fixes them on-the-fly without bipartite graphs or costly rebuilds. RFix (entry) and NGFix (vicinity) improve OOD robustness and speed (up to 2.25×). (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Zhiyuan Hua (Nankai University)
- 2. Qiji Mo (Nankai University)
- 3. Zebin Yao (Nankai University)
- 4. Lixiao Cui (Nankai University)
- 5. Xiaoguang Liu (Nankai University)
- 6. Gang Wang (Nankai University)
- 7. Zijing Wei (Alibaba)
- 8. Xinyu Liu (Alibaba)
- 9. Tianxiao Tang (Alibaba)
- 10. Shaozhi Liu (Alibaba)
- 11. Lin Qu (Alibaba)
BibTeX Citation
@inproceedings{hua_sigmod26,
title = {{Dynamically Detect and Fix Hardness for Efficient Approximate Nearest Neighbor Search}},
author = {Hua, Zhiyuan and Mo, Qiji and Yao, Zebin and Cui, Lixiao and Liu, Xiaoguang and Wang, Gang and Wei, Zijing and Liu, Xinyu and Tang, Tianxiao and Liu, Shaozhi and Qu, Lin},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769783},
url = {https://dl.acm.org/doi/10.1145/3769783},
year = {2026}
}
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