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While graph-based methods represent the state-of-the-art for vector similarity search, existing graph indexes often suffer from poor navigability and clusterability in Out-Of-Distribution (OOD) scenarios (e.g., database and queries are sourced from different modalities). Recent studies have attempted to mitigate this issue by projecting and integrating query-derived auxiliary index structures, but these approaches remain largely heuristic and lack theoretical grounding. In this work, we propose Cross-Distribution Monotonic Graph (CDMG), a novel graph index designed to inherently support navigability and clusterability for OOD queries. CDMG introduces an integration strategy that bridges the distribution gap between database and query vectors, ensuring a key property\u2014cross-distribution monotonicity\u2014that guarantees monotonic search paths for OOD queries on graph indexes. Theoretical analysis demonstrates that CDMG achieves a lower search time complexity in OOD scenarios compared with existing graph indexes. Furthermore, we introduce CDMG+, a practical variant of CDMG that improves construction efficiency by introducing query synthesis, fusion-based distance computation, as well as optimized candidate acquisition and neighbor selection strategies. Extensive empirical evaluations demonstrate that our techniques outperform state-of-the-art methods, achieving up to a 3.6\u00d7 speedup on real-world OOD datasets.<\/jats:p>","DOI":"10.1145\/3786643","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:54:13Z","timestamp":1775584453000},"page":"1-29","source":"Crossref","is-referenced-by-count":0,"title":["Efficient and Robust Out-Of-Distribution Vector Similarity Search with Cross-Distribution Monotonic Graph"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9011-1019","authenticated-orcid":false,"given":"Qiang","family":"Yue","sequence":"first","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3806-1012","authenticated-orcid":false,"given":"Mengzhao","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8040-6809","authenticated-orcid":false,"given":"Xiaoliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6806-8405","authenticated-orcid":false,"given":"Cheng","family":"Long","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3240-2912","authenticated-orcid":false,"given":"Yuxiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5409-2812","authenticated-orcid":false,"given":"Jiahui","family":"Wang","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al., 2023. 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PmLR, 8748-8763."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736930"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.08.015"},{"key":"e_1_2_1_55_1","volume-title":"Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114","author":"Schuhmann Christoph","year":"2021","unstructured":"Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. 2021. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114 (2021)."},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1238"},{"key":"e_1_2_1_57_1","volume-title":"ICLR 2023 Workshop on Multimodal Representation Learning: Perks and Pitfalls.","author":"Shi Peiyang","year":"2023","unstructured":"Peiyang Shi, Michael C Welle, M\u00e5rten Bj\u00f6rkman, and Danica Kragic. 2023. Towards understanding the modality gap in CLIP. In ICLR 2023 Workshop on Multimodal Representation Learning: Perks and Pitfalls."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587638"},{"key":"e_1_2_1_59_1","volume-title":"Dmitry Baranchuk, Edo Liberty, Frank Liu, Ben Landrum, et al.","author":"Simhadri Harsha Vardhan","year":"2024","unstructured":"Harsha Vardhan Simhadri, Martin Aum\u00fcller, Amir Ingber, Matthijs Douze, George Williams, Magdalen Dobson Manohar, Dmitry Baranchuk, Edo Liberty, Frank Liu, Ben Landrum, et al., 2024. Results of the Big ANN: NeurIPS'23 competition. arXiv preprint arXiv:2409.17424 (2024)."},{"key":"e_1_2_1_60_1","first-page":"177","article-title":"Results of the NeurIPS'21 challenge on billion-scale approximate nearest neighbor search. In NeurIPS 2021 Competitions and Demonstrations Track","author":"Simhadri Harsha Vardhan","year":"2022","unstructured":"Harsha Vardhan Simhadri, George Williams, Martin Aum\u00fcller, Matthijs Douze, Artem Babenko, Dmitry Baranchuk, Qi Chen, Lucas Hosseini, Ravishankar Krishnaswamny, Gopal Srinivasa, et al., 2022. Results of the NeurIPS'21 challenge on billion-scale approximate nearest neighbor search. In NeurIPS 2021 Competitions and Demonstrations Track. PMLR, 177-189.","journal-title":"PMLR"},{"key":"e_1_2_1_61_1","volume-title":"Ravishankar Krishnaswamy, and Harsha Vardhan Simhadri.","author":"Singh Aditi","year":"2021","unstructured":"Aditi Singh, Suhas Jayaram Subramanya, Ravishankar Krishnaswamy, and Harsha Vardhan Simhadri. 2021. Freshdiskann: A fast and accurate graph-based ann index for streaming similarity search. arXiv preprint arXiv:2105.09613 (2021)."},{"key":"e_1_2_1_62_1","volume-title":"Proceedings ninth IEEE international conference on computer vision. IEEE, 1470-1477","author":"Zisserman Sivic","year":"2003","unstructured":"Sivic and Zisserman. 2003. Video Google: A text retrieval approach to object matching in videos. In Proceedings ninth IEEE international conference on computer vision. IEEE, 1470-1477."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463257"},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783307"},{"key":"e_1_2_1_65_1","volume-title":"Cecilia Aguerrebere, Mark Hildebrand, and Theodore L. Willke.","author":"Tepper Mariano","year":"2024","unstructured":"Mariano Tepper, Ishwar Singh Bhati, Cecilia Aguerrebere, Mark Hildebrand, and Theodore L. Willke. 2024b. LeanVec: Searching vectors faster by making them fit. Transactions on Machine Learning Research (2024). Featured Certification."},{"key":"e_1_2_1_66_1","volume-title":"Cecilia Aguerrebere, and Ted Willke.","author":"Tepper Mariano","year":"2024","unstructured":"Mariano Tepper, Ishwar Singh Bhati, Cecilia Aguerrebere, and Ted Willke. 2024a. GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction. arXiv preprint arXiv:2410.22347 (2024)."},{"key":"e_1_2_1_67_1","volume-title":"Pattern recognition","author":"Theodoridis Sergios","unstructured":"Sergios Theodoridis and Konstantinos Koutroumbas. 2006. Pattern recognition. Elsevier."},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.5555\/3724648.3724659"},{"key":"e_1_2_1_69_1","first-page":"39","article-title":"Approximate Nearest Neighbor Search in High Dimensional Vector Databases: Current Research and Future Directions","volume":"46","author":"Tian Yao","year":"2023","unstructured":"Yao Tian, Ziyang Yue, Ruiyuan Zhang, Xi Zhao, Bolong Zheng, and Xiaofang Zhou. 2023. 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A note on graph-based nearest neighbor search. arXiv preprint arXiv:2012.11083 (2020)."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457550"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00361"},{"key":"e_1_2_1_73_1","first-page":"15738","article-title":"An efficient and robust framework for approximate nearest neighbor search with attribute constraint","volume":"36","author":"Wang Mengzhao","year":"2023","unstructured":"Mengzhao Wang, Lingwei Lv, Xiaoliang Xu, Yuxiang Wang, Qiang Yue, and Jiongkang Ni. 2023. An efficient and robust framework for approximate nearest neighbor search with attribute constraint. Advances in Neural Information Processing Systems, Vol. 36 (2023), 15738-15751.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3639269"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476255"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415541"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557610"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/1027527.1027665"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107305"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3698814"},{"key":"e_1_2_1_81_1","first-page":"103076","article-title":"CSPG: Crossing Sparse Proximity Graphs for Approximate Nearest Neighbor Search","volume":"37","author":"Yang Ming","year":"2024","unstructured":"Ming Yang, Yuzheng Cai, and Weiguo Zheng. 2024. 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Visrag: Vision-based retrieval-augmented generation on multi-modality documents. arXiv preprint arXiv:2410.10594 (2024)."},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00046"},{"key":"e_1_2_1_86_1","volume-title":"Routing-Guided Learned Product Quantization for Graph-Based Approximate Nearest Neighbor Search. In 2024 IEEE 40th International Conference on Data Engineering (ICDE). IEEE, 4870-4883","author":"Yue Qiang","year":"2024","unstructured":"Qiang Yue, Xiaoliang Xu, Yuxiang Wang, Yikun Tao, and Xuliyuan Luo. 2024. Routing-Guided Learned Product Quantization for Graph-Based Approximate Nearest Neighbor Search. In 2024 IEEE 40th International Conference on Data Engineering (ICDE). 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Efficient and effective retrieval of dense-sparse hybrid vectors using graph-based approximate nearest neighbor search. arXiv preprint arXiv:2410.20381 (2024)."},{"key":"e_1_2_1_89_1","first-page":"377","volume-title":"17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23)","author":"Zhang Qianxi","year":"2023","unstructured":"Qianxi Zhang, Shuotao Xu, Qi Chen, Guoxin Sui, Jiadong Xie, Zhizhen Cai, Yaoqi Chen, Yinxuan He, Yuqing Yang, Fan Yang, et al., 2023. : Unifying Online Vector Similarity Search and Relational Queries via Relaxed Monotonicity. In 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23). 377-395."},{"key":"e_1_2_1_90_1","volume-title":"International Conference on Machine Learning. PMLR, 838-846","author":"Zhang Ting","year":"2014","unstructured":"Ting Zhang, Chao Du, and Jingdong Wang. 2014. Composite quantization for approximate nearest neighbor search. In International Conference on Machine Learning. PMLR, 838-846."},{"key":"e_1_2_1_91_1","first-page":"1","article-title":"Bridging Modality Gap with Large Speech and Language Models for End-to-End Speech-to-Text Translation. In ICASSP 2025 - 2025 IEEE International Conference on Acoustics","author":"Zhang Weitai","year":"2025","unstructured":"Weitai Zhang, Simran Naagar, Zhongyi Ye, Peiwang Tang, Xinyuan Zhou, Junhua Liu, and Lirong Dai. 2025. Bridging Modality Gap with Large Speech and Language Models for End-to-End Speech-to-Text Translation. In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1-5.","journal-title":"Speech and Signal Processing (ICASSP)."},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.14778\/3594512.3594527"},{"key":"e_1_2_1_93_1","volume-title":"Heng Tao Shen, and Jingkuan Song","author":"Zhong Xiaoyao","year":"2025","unstructured":"Xiaoyao Zhong, Jiabao Jin, Peng Cheng, Mingyu Yang, Lei Chen, Haoyang Li, Zhitao Shen, Xuemin Lin, Heng Tao Shen, and Jingkuan Song. 2025a. EnhanceGraph: A Continuously Enhanced Graph-based Index for High-dimensional Approximate Nearest Neighbor Search. arXiv preprint arXiv:2506.13144 (2025)."},{"key":"e_1_2_1_94_1","first-page":"12","article-title":"VSAG","volume":"18","author":"Zhong Xiaoyao","year":"2025","unstructured":"Xiaoyao Zhong, Haotian Li, Jiabao Jin, Mingyu Yang, Deming Chu, Xiangyu Wang, Zhitao Shen, Wei Jia, George Gu, Yi Xie, Xuemin Lin, Heng Tao Shen, Jingkuan Song, and Peng Cheng. 2025b. VSAG: An Optimized Search Framework for Graph-Based Approximate Nearest Neighbor Search. Vol. 18, 12 (Sept. 2025), 5017\u20135030.","journal-title":"An Optimized Search Framework for Graph-Based Approximate Nearest Neighbor Search."},{"key":"e_1_2_1_95_1","volume-title":"Understanding and Generalizing Monotonic Proximity Graphs for Approximate Nearest Neighbor Search. arXiv preprint arXiv:2107.13052","author":"Zhu Dantong","year":"2021","unstructured":"Dantong Zhu and Minjia Zhang. 2021. Understanding and Generalizing Monotonic Proximity Graphs for Approximate Nearest Neighbor Search. arXiv preprint arXiv:2107.13052 (2021)."}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3786643","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:01:21Z","timestamp":1775592081000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3786643"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,2]]},"references-count":95,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,4,2]]}},"alternative-id":["10.1145\/3786643"],"URL":"https:\/\/doi.org\/10.1145\/3786643","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,2]]}}}