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Even worse, the chosen parameters may produce good recall for some queries, but bad recall for hard queries. To solve these problems, we present DARTH, a method that uses target declarative recall. DARTH uses a novel method for providing target declarative recall on top of an ANNS index by employing an adaptive early termination strategy integrated into the search algorithm. Through a wide range of experiments, we demonstrate that DARTH effectively meets user-defined recall targets while achieving significant speedups, up to 14.6x (average: 6.8x; median: 5.7x) faster than the search without early termination for HNSW and up to 41.8x (average: 13.6x; median: 8.1x) for IVF.<\/jats:p>","DOI":"10.1145\/3749160","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:17:03Z","timestamp":1758647823000},"page":"1-26","source":"Crossref","is-referenced-by-count":2,"title":["DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9616-6210","authenticated-orcid":false,"given":"Manos","family":"Chatzakis","sequence":"first","affiliation":[{"name":"LIPADE, Universite Paris Cite, Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6360-9496","authenticated-orcid":false,"given":"Yannis","family":"Papakonstantinou","sequence":"additional","affiliation":[{"name":"Google Cloud, San Diego, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8031-0265","authenticated-orcid":false,"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[{"name":"LIPADE, Universite Paris Cite, Paris, France"}]}],"member":"320","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2024. pgvector. https:\/\/github.com\/pgvector\/pgvector"},{"key":"e_1_2_1_2_1","unstructured":"2025. 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ANN Benchmarks HNSW parameters. https:\/\/github.com\/erikbern\/ann-benchmarks\/blob\/main\/ann_benchmarks\/algorithms\/hnswlib\/config.yml."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2628071.2628092"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2019.02.006"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2021.101807"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/3583140.3583166"},{"key":"e_1_2_1_10_1","volume-title":"Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art. PACMMOD","author":"Azizi Ilias","year":"2025","unstructured":"Ilias Azizi, Karima Echihabi, and Themis Palpanas. 2025. Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art. 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Return of the lernaean hydra: Experimental evaluation of data series approximate similarity search. arXiv preprint arXiv:2006.11459 (2020)."},{"key":"e_1_2_1_34_1","unstructured":"Elastic. [n.d.]. Elasticsearch. https:\/\/www.elastic.co\/. Accessed: 2024-11-26."},{"key":"e_1_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Panagiota Fatourou Eleftherios Kosmas Themis Palpanas and George Paterakis. 2023. FreSh: A Lock-Free Data Series Index. In SRDS.","DOI":"10.1109\/SRDS60354.2023.00029"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2001.914864"},{"key":"e_1_2_1_37_1","volume-title":"Greedy function approximation: a gradient boosting machine. Annals of statistics","author":"Friedman Jerome H","year":"2001","unstructured":"Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189-1232."},{"key":"e_1_2_1_38_1","volume-title":"Stochastic gradient boosting. 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Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997 (2023)."},{"key":"e_1_2_1_42_1","unstructured":"GASS. [n.d.]. GASS HNSW parameters. https:\/\/github.com\/zeraph6\/GASS_Repo\/blob\/main\/code\/README.md."},{"key":"e_1_2_1_43_1","volume-title":"Optimized product quantization","author":"Ge Tiezheng","year":"2013","unstructured":"Tiezheng Ge, Kaiming He, Qifa Ke, and Jian Sun. 2013. Optimized product quantization. IEEE transactions on pattern analysis and machine intelligence, Vol. 36, 4 (2013), 744-755."},{"key":"e_1_2_1_44_1","unstructured":"Anna Gogolou Theophanis Tsandilas Themis Palpanas and Anastasia Bezerianos. 2019. Progressive similarity search on time series data. In BigVis 2019-2nd International Workshop on Big Data Visual Exploration and Analytics."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583552"},{"key":"e_1_2_1_46_1","unstructured":"Google Cloud. [n.d.]. Vertex AI. https:\/\/cloud.google.com\/vertex-ai\/docs\/vector-search\/overview. Accessed: 2024-12-19."},{"key":"e_1_2_1_47_1","volume-title":"SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search. arXiv preprint arXiv:2411.12229","author":"Gou Yutong","year":"2024","unstructured":"Yutong Gou, Jianyang Gao, Yuexuan Xu, and Cheng Long. 2024. SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search. arXiv preprint arXiv:2411.12229 (2024)."},{"key":"e_1_2_1_48_1","volume-title":"International Conference on Machine Learning. PMLR, 3887-3896","author":"Guo Ruiqi","year":"2020","unstructured":"Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar. 2020. Accelerating large-scale inference with anisotropic vector quantization. In International Conference on Machine Learning. 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Ood-diskann: Efficient and scalable graph anns for out-of-distribution queries. arXiv preprint arXiv:2211.12850 (2022)."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2023.102166"},{"key":"e_1_2_1_54_1","volume-title":"Ravishankar Krishnawamy, and Rohan Kadekodi.","author":"Subramanya Suhas Jayaram","year":"2019","unstructured":"Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri, Ravishankar Krishnawamy, and Rohan Kadekodi. 2019. Diskann: Fast accurate billion-point nearest neighbor search on a single node. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_2_1_55_1","volume-title":"Product quantization for nearest neighbor search","author":"Jegou Herve","year":"2010","unstructured":"Herve Jegou, Matthijs Douze, and Cordelia Schmid. 2010. Product quantization for nearest neighbor search. IEEE transactions on pattern analysis and machine intelligence, Vol. 33, 1 (2010), 117-128."},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5946540"},{"key":"e_1_2_1_57_1","volume-title":"When large language models meet vector databases: a survey. arXiv preprint arXiv:2402.01763","author":"Jing Zhi","year":"2024","unstructured":"Zhi Jing, Yongye Su, Yikun Han, Bo Yuan, Haiyun Xu, Chunjiang Liu, Kehai Chen, and Min Zhang. 2024. When large language models meet vector databases: a survey. arXiv preprint arXiv:2402.01763 (2024)."},{"key":"e_1_2_1_58_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_2_1_59_1","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","volume":"33","author":"Lewis Patrick","year":"2020","unstructured":"Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K\u00fcttler, Mike Lewis, Wen-tau Yih, Tim Rockt\u00e4schel, et al., 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9459-9474.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380600"},{"key":"e_1_2_1_61_1","volume-title":"A survey on retrieval-augmented text generation. arXiv preprint arXiv:2202.01110","author":"Li Huayang","year":"2022","unstructured":"Huayang Li, Yixuan Su, Deng Cai, Yan Wang, and Lemao Liu. 2022. A survey on retrieval-augmented text generation. arXiv preprint arXiv:2202.01110 (2022)."},{"key":"e_1_2_1_62_1","volume-title":"Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs","author":"Malkov Yu A","year":"2018","unstructured":"Yu A Malkov and Dmitry A Yashunin. 2018. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE transactions on pattern analysis and machine intelligence, Vol. 42, 4 (2018), 824-836."},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.3169\/mta.6.2"},{"key":"e_1_2_1_64_1","unstructured":"Microsoft. [n.d.]. Vectors in Azure AI Search. https:\/\/learn.microsoft.com\/en-us\/azure\/search\/vector-search-overview. Accessed: 2024-11-26."},{"key":"e_1_2_1_65_1","unstructured":"Microsoft Azure. [n.d.]. Azure Cosmos DB. https:\/\/learn.microsoft.com\/en-us\/azure\/cosmos-db\/vector-database. Accessed: 2024-12-19."},{"key":"e_1_2_1_66_1","unstructured":"MongoDB Inc. [n.d.]. MongoDB. https:\/\/www.mongodb.com\/. Accessed: 2024-12-19."},{"key":"e_1_2_1_67_1","volume-title":"Gradient boosting machines, a tutorial. Frontiers in neurorobotics","author":"Natekin Alexey","year":"2013","unstructured":"Alexey Natekin and Alois Knoll. 2013. Gradient boosting machines, a tutorial. Frontiers in neurorobotics, Vol. 7 (2013), 21."},{"key":"e_1_2_1_68_1","unstructured":"OpenAI. 2024. ChatGPT (November 2024 version). https:\/\/openai.com Accessed: 2024-11-30."},{"key":"e_1_2_1_69_1","unstructured":"Oracle Corporation. [n.d.]. Oracle AI Vector Search. https:\/\/www.oracle.com\/database\/ai-vector-search\/. Accessed: 2024-11-26."},{"key":"e_1_2_1_70_1","volume-title":"ADS, ADS, ADS-Full, ParIS, ParIS, MESSI, DPiSAX, ULISSE, Coconut-Trie\/Tree, Coconut-LSM. In Information Search, Integration, and Personalization: 13th International Workshop, ISIP","author":"Palpanas Themis","year":"2019","unstructured":"Themis Palpanas. 2020. Evolution of a Data Series Index: The iSAX Family of Data Series Indexes: iSAX, iSAX2. 0, iSAX2, ADS, ADS, ADS-Full, ParIS, ParIS, MESSI, DPiSAX, ULISSE, Coconut-Trie\/Tree, Coconut-LSM. In Information Search, Integration, and Personalization: 13th International Workshop, ISIP 2019, Heraklion, Greece, May 9-10, 2019, Revised Selected Papers 13. Springer, 68-83."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-024-00864-x"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/SISAP.2008.18"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00036"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00171"},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_2_1_76_1","unstructured":"Pinecone Inc. [n.d.]. Pinecone. https:\/\/www.pinecone.io\/. Accessed: 2024-12-19."},{"key":"e_1_2_1_77_1","first-page":"10299","article-title":"Recommender systems with generative retrieval","volume":"36","author":"Rajput Shashank","year":"2023","unstructured":"Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al., 2023. Recommender systems with generative retrieval. Advances in Neural Information Processing Systems, Vol. 36 (2023), 10299-10315.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_78_1","first-page":"10672","article-title":"Hm-ann: Efficient billion-point nearest neighbor search on heterogeneous memory","volume":"33","author":"Ren Jie","year":"2020","unstructured":"Jie Ren, Minjia Zhang, and Dong Li. 2020. Hm-ann: Efficient billion-point nearest neighbor search on heterogeneous memory. Advances in Neural Information Processing Systems, Vol. 33 (2020), 10672-10684.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_79_1","volume-title":"Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological methods","author":"Rights Jason D","year":"2019","unstructured":"Jason D Rights and Sonya K Sterba. 2019. Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological methods, Vol. 24, 3 (2019), 309."},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","unstructured":"Viktor Sanca Manos Chatzakis and Anastasia Ailamaki. 2024. Optimizing Context-Enhanced Relational Joins. (2024) 501-515. https:\/\/doi.org\/10.1109\/ICDE60146.2024.00045","DOI":"10.1109\/ICDE60146.2024.00045"},{"key":"e_1_2_1_81_1","first-page":"9","article-title":"WE-LDA: a word embeddings augmented LDA model for web services clustering. In 2017 ieee international conference on web services (icws)","author":"Shi Min","year":"2017","unstructured":"Min Shi, Jianxun Liu, Dong Zhou, Mingdong Tang, and Buqing Cao. 2017. WE-LDA: a word embeddings augmented LDA model for web services clustering. In 2017 ieee international conference on web services (icws). 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A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search. arXiv preprint arXiv:2101.12631 (2021)."},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1145\/3709701"},{"key":"e_1_2_1_89_1","first-page":"3","article-title":"Graph-and Tree-based Indexes for High-dimensional Vector Similarity Search: Analyses, Comparisons, and Future Directions","volume":"46","author":"Wang Zeyu","year":"2023","unstructured":"Zeyu Wang, Peng Wang, Themis Palpanas, and Wei Wang. 2023a. Graph-and Tree-based Indexes for High-dimensional Vector Similarity Search: Analyses, Comparisons, and Future Directions. IEEE Data Eng. Bull., Vol. 46, 3 (2023), 3-21.","journal-title":"IEEE Data Eng. 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