The Faiss Library

Distinctive Image Features from Scale-Invariant Keypoints

Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589 · cited 2× · see citing works

SciPy 1.0: fundamental algorithms for scientific computing in Python

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works

Least squares quantization in PCM

Lloyd, S. (1982) · IEEE Transactions on Information Theory · doi:10.1109/tit.1982.1056489 · Cited by 15,836 · see citing works

Enriching Word Vectors with Subword Information

Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645 · see citing works

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00482 · Cited by 7,781 · see citing works

Emerging Properties in Self-Supervised Vision Transformers

Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638 · see citing works

Billion-scale similarity search with GPUs

Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228 · see citing works

Locality-sensitive hashing scheme based on p-stable distributions

Datar, M., Immorlica, N., Indyk, P., & Mirrokni, V. (2004) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/997817.997857 · Cited by 2,971 · see citing works

Product Quantization for Nearest Neighbor Search

Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 8× · see citing works

Deep Clustering for Unsupervised Learning of Visual Features

Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_9 · Cited by 2,674 · see citing works

Measurement Reliability and Validity

(2000) · Research Methods in Applied Settings · doi:10.4324/9781410605337-29 · Cited by 2,541 · see citing works

Similarity estimation techniques from rounding algorithms

Charikar, M. (2002) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/509907.509965 · Cited by 2,262 · see citing works

Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs

Malkov, Y. A., & Yashunin, D. A. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889473 · Cited by 1,904 · see citing works

Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search

Jegou, H., Douze, M., & Schmid, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_24 · Cited by 1,901 · cited 1× · see citing works

Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval

Gong, Y., Lazebnik, S., Gordo, A., & Perronnin, F. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.193 · Cited by 1,834 · see citing works

Aggregating Local Image Descriptors into Compact Codes

Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., & Schmid, C. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.235 · Cited by 1,503 · see citing works

Scalable Nearest Neighbor Algorithms for High Dimensional Data

Muja, M., & Lowe, D. G. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2321376 · Cited by 1,401 · see citing works

Concept Decompositions for Large Sparse Text Data Using Clustering

Dhillon, I. S., & Modha, D. S. (2001) · Machine Learning · doi:10.1023/a:1007612920971 · Cited by 1,308 · see citing works

A Survey on Learning to Hash

Wang, J., Zhang, T., Song, J., Sebe, N., & Shen, H. T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699960 · Cited by 1,034 · see citing works

HashNet: Deep Learning to Hash by Continuation

Cao, Z., Long, M., Wang, J., & Yu, P. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.598 · Cited by 777 · see citing works

Efficient k-nearest neighbor graph construction for generic similarity measures

Dong, W., Charikar, M., & Li, K. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963487 · Cited by 703 · see citing works

Learning to Hash for Indexing Big Data—A Survey

Wang, J., Liu, W., Kumar, S., & Chang, S. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2487976 · Cited by 524 · see citing works

Untitled work

(2021) · doi:10.1145/3448016.3457550 · Cited by 416 · see citing works

Optimized Product Quantization for Approximate Nearest Neighbor Search

Ge, T., He, K., Ke, Q., & Sun, J. Z. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.379 · Cited by 401 · see citing works

Fast approximate nearest neighbor search with the navigating spreading-out graph

Fu, C., Xiang, C. J., Wang, C., & Cai, D. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3303753.3303754 · Cited by 384 · see citing works

KILT: a Benchmark for Knowledge Intensive Language Tasks

Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., et al. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.200 · Cited by 357 · see citing works

Embedding-based Retrieval in Facebook Search

Huang, J.-T., Sharma, A., Sun, S., Xia, L., Zhang, D., Pronin, P., Padmanabhan, J., Ottaviano, G., & Yang, L. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403305 · Cited by 331 · see citing works

Searching in one billion vectors: Re-rank with source coding

Jeǵou, H., Tavenard, R., Douze, M., & Amsaleg, L. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946540 · Cited by 317 · see citing works

ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms

Aumüller, M., Bernhardsson, E., & Faithfull, A. (2019) · Information Systems · doi:10.1016/j.is.2019.02.006 · Cited by 301 · see citing works

Locality sensitive hashing: A comparison of hash function types and querying mechanisms

Paulevé, L., Jeǵou, H., & Amsaleg, L. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.04.004 · Cited by 298 · see citing works

Additive Quantization for Extreme Vector Compression

Babenko, A., & Lempitsky, V. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.124 · Cited by 289 · see citing works

A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search

Wang, M., Xu, X., Yue, Q., & Wang, Y. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3476249.3476255 · Cited by 273 · see citing works

REPLUG: Retrieval-Augmented Black-Box Language Models

Shi, W., Min, S., Yasunaga, M., Seo, M., James, R., Lewis, M., Zettlemoyer, L., & Yih, W.-T. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.463 · Cited by 209 · see citing works

Learning Joint Multilingual Sentence Representations with Neural Machine Translation

Schwenk, H., & Douze, M. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2619 · Cited by 207 · see citing works

Optimal Data-Dependent Hashing for Approximate Near Neighbors

Andoni, A., & Razenshteyn, I. (2015) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2746539.2746553 · Cited by 189 · see citing works

Approximate Nearest Neighbor Search by Residual Vector Quantization

Chen, Y., Guan, T., & Wang, C. (2010) · Sensors · doi:10.3390/s101211259 · Cited by 167 · see citing works

Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces

Bachrach, Y., Finkelstein, Y., Gilad-Bachrach, R., Katzir, L., Koenigstein, N., Nice, N., & Paquet, U. (2014) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2645710.2645741 · Cited by 163 · see citing works

Tree quantization for large-scale similarity search and classification

Babenko, A., & Lempitsky, V. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299052 · Cited by 106 · see citing works

Image similarity search with compact data structures

Lv, Q., Charikar, M., & Li, K. (2004) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1031171.1031213 · Cited by 105 · see citing works

A Self-Supervised Descriptor for Image Copy Detection

Pizzi, E., Roy, S. D., Ravindra, S. N., Goyal, P., & Douze, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01413 · Cited by 101 · see citing works

Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters

Gollapudi, S., Karia, N., Sivashankar, V., Krishnaswamy, R., Begwani, N., Raz, S., Lin, Y., Zhang, Y., Mahapatro, N., Srinivasan, P., et al. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583552 · Cited by 85 · see citing works

How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval

Lin, S.-C., Asai, A., Li, M., Oğuz, B., Lin, J., Mehdad, Y., Yih, W.-T., & Chen, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.423 · Cited by 83 · see citing works

CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs

Ootomo, H., Naruse, A., Nolet, C., Wang, H., Fehér, T., & Wang, Y. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00323 · Cited by 63 · see citing works

Revisiting Additive Quantization

Martínez, J., Clement, J., Hoos, H. H., & Little, J. J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_9 · Cited by 57 · see citing works

Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search

Boytsov, L., Novak, D., Malkov, Y., & Nyberg, E. (2016) · Proceedings of the 25th ACM International on Conference on Information and Knowledge Management · doi:10.1145/2983323.2983815 · Cited by 50 · see citing works

Polysemous Codes

Douze, M., Jeǵou, H., & Perronnin, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46475-6_48 · Cited by 46 · see citing works

Link and Code: Fast Indexing with Graphs and Compact Regression Codes

Douze, M., Sablayrolles, A., & Jeǵou, H. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00384 · Cited by 35 · see citing works

LSQ++: Lower Running Time and Higher Recall in Multi-codebook Quantization

Martínez, J., Zakhmi, S., Hoos, H. H., & Little, J. J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_30 · Cited by 31 · see citing works

The Yael Library

Douze, M., & Jeǵou, H. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654892 · Cited by 25 · see citing works

Unsupervised Neural Quantization for Compressed-Domain Similarity Search

Morozov, S., & Babenko, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00313 · Cited by 24 · see citing works

An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors

Bruch, S., Nardini, F. M., Ingber, A., & Liberty, E. (2023) · ACM Transactions on Information Systems · doi:10.1145/3609797 · Cited by 22 · see citing works

RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Search

Chen, M., Zhang, K., He, Z., Jing, Y., & Wang, X. S. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3681954.3681959 · Cited by 19 · see citing works

Balancing clusters to reduce response time variability in large scale image search

Tavenard, R., Jeǵou, H., & Amsaleg, L. (2011) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2011.5972514 · Cited by 18 · see citing works

Searching with expectations

Sandhawalia, H., & Jeǵou, H. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495403 · Cited by 14 · see citing works

Asymmetric Mapping Quantization for Nearest Neighbor Search

Hong, W., Tang, X., Meng, J., & Yuan, J. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2925347 · Cited by 12 · see citing works

DeDrift: Robust Similarity Search under Content Drift

Baranchuk, D., Douze, M., Upadhyay, Y., & Yalnız, İ. Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01012 · Cited by 11 · see citing works

TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s

Chern, F., Hechtman, B., Davis, A., Guo, R., Majnemer, D., & Kumar, S. (2022) · neural information processing systems · doi:10.52202/068431-1127 · Cited by 11 · see citing works

Bringing Semantic Knowledge Graph Technology to Your Data

van Luijt, B., & Verhagen, M. (2020) · IEEE Software · doi:10.1109/ms.2019.2957526 · Cited by 11 · see citing works

The NeurIPS '18 Competition

Escalera, S., & Hebrich, R. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-29135-8 · Cited by 7 · see citing works

The 2023 Video Similarity Dataset and ChallengePreprint

Pizzi, E., Kordopatis-Zilos, G., Patel, H., Postelnicua, G., Ravindra, S. N., Gupta, A., Papadopoulos, S., Tolias, G., & Douze, M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4597445 · Cited by 5 · see citing works

Nearest Neighbor Search with Compact Codes: A Decoder Perspective

Amara, K., Douze, M., Sablayrolles, A., & Jeǵou, H. (2022) · International Conference on Multimedia Retrieval · doi:10.1145/3512527.3531408 · Cited by 2 · see citing works

Memory Mosaics

Zhang, J., Nolte, N., Sadhukhan, R., Chen, B., & Bottou, L. (2024) · arXiv · doi:10.48550/arxiv.2405.06394 · cited 1×

Retrieval augmented generation for large language models in healthcare: A systematic review

Amugongo, L. M., Mascheroni, P., Brooks, S., Doering, S., & Seidel, J. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000877 · Cited by 267 · see citing works

The power of noise: Redefining retrieval for rag systems

Cuconasu, F., Trappolini, G., Siciliano, F., Filice, S., Campagnano, C., Maarek, Y., Tonellotto, N., & Silvestri, F. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3626772.3657834 · Cited by 236 · see citing works

Evaluating Retrieval Quality in Retrieval-Augmented Generation

Salemi, A., & Zamani, H. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657957 · Cited by 192 · see citing works

From Matching to Generation: A Survey on Generative Information Retrieval

Li, X., Jin, J., Zhou, Y., Zhang, Y., Zhang, P., Zhu, Y., & Dou, Z. (2025) · ACM Transactions on Information Systems · doi:10.1145/3722552 · Cited by 124 · see citing works

MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot

Zhao, X., Liu, S., Yang, S.-Y., & Miao, C. (2025) · Proceedings of the ACM on Web Conference 2025 · doi:10.1145/3696410.3714782 · Cited by 116 · see citing works

GIS Copilot: towards an autonomous GIS agent for spatial analysis

Akinboyewa, T., Li, Z., Ning, H., & Lessani, M. N. (2025) · International Journal of Digital Earth · doi:10.1080/17538947.2025.2497489 · Cited by 80 · see citing works

A survey on retrieval-augmentation generation (RAG) models for healthcare applications

El-Enen, M. A. A., Saad, S., & Nazmy, T. M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11666-9 · Cited by 78 · see citing works

SCORE: Story Coherence and Retrieval Enhancement for AI Narratives

Yi, Q., He, Y., Wang, J., Song, X., Lu, K., Qian, S., Yuan, X., Xin, Y., Wang, Y., Tang, J., et al. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774905.3795734 · Cited by 73 · see citing works

AIGC-empowered smart manufacturing: Prospects and challenges

Leng, J., Zheng, K., Li, R. C., Chen, C., Wang, B., Liu, Q., Chen, X., & Shen, W. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103076 · Cited by 70 · see citing works

Interoperable information modelling leveraging asset administration shell and large language model for quality control toward zero defect manufacturing

Shi, D., Liedl, P., & Bauernhansl, T. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.10.011 · Cited by 61 · see citing works

A trimodal protein language model enables advanced protein searches

Su, J., He, Y., You, S., Jiang, S. J., Zhou, X., Zhang, X., Wang, Y., Su, X., Tolstoy, I., Chang, X., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02836-0 · Cited by 52 · see citing works

Unifying Multimodal Retrieval via Document Screenshot Embedding

Ma, X., Lin, S.-C., Li, M., Chen, W., & Lin, J. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.373 · Cited by 47 · see citing works

PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation

Liu, Y., Sharma, P., Oswal, M., Xia, H., & Huang, Y. (2025) · Designing Interactive Systems Conference · doi:10.1145/3715336.3735789 · Cited by 45 · see citing works

A multimodal large language model for materials science

Tang, Y., Xu, W., Cao, J., Ma, J., Gao, W., Farrell, S., Erichson, N. B., Mahoney, M. W., Nonaka, A., & Yao, Z. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01214-y · Cited by 42 · see citing works

PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval

Zhuang, S., Ma, X., Koopman, B., Lin, J., & Zuccon, G. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.250 · Cited by 40 · see citing works

Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search

Gao, J., Gou, Y., Xu, Y., Yang, Y., Long, C., & Wong, R. C.-W. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725413 · Cited by 38 · see citing works

Dual data mapping with fine-tuned large language models and asset administration shells toward interoperable knowledge representation

Shi, D., Meyer, O., Oberle, M., & Bauernhansl, T. (2024) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2024.102837 · Cited by 36 · see citing works

Generating Long Semantic IDs in Parallel for Recommendation

Hou, Y., Li, J., Shin, A., Jeon, J., Santhanam, A., Shao, W. P., Hassani, K., Yao, N., & McAuley, J. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3736979 · Cited by 36 · see citing works

Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM

Hong, Z., Yuan, Z., Chen, H., Zhang, Q., Huang, F., & Huang, X. B. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.653 · Cited by 35 · see citing works

Towards Automated Multiple Choice Question Generation and Evaluation: Aligning with Bloom’s Taxonomy

Hwang, K., Wang, K., Alomair, M., Choa, F.-S., & Chen, L. K. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-64299-9_35 · Cited by 34 · see citing works

CyberRAG: An agentic RAG cyber attack classification and reporting tool

Blefari, F., Cosentino, C., Pironti, F. A., Furfaro, A., & Marozzo, F. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108186 · Cited by 33 · see citing works

Emerging trends: a gentle introduction to RAG

Church, K., Sun, J., Yue, R., Vickers, P., Saba, W., & Chandrasekar, R. (2024) · Natural Language Engineering · doi:10.1017/s1351324924000044 · Cited by 32 · see citing works

Enhancing retrieval-augmented generation for interoperable industrial knowledge representation and inference toward cognitive digital twins

Shi, D., Li, J., Meyer, O., & Bauernhansl, T. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104330 · Cited by 30 · see citing works

LLM-Powered Multimodal AI Conversations for Diabetes Prevention

Dao, D. K. T., Teo, J. Y. C., Wang, W., & Nguyen, H. D. (2024) · doi:10.1145/3643479.3662049 · Cited by 29 · see citing works

Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs

Singal, R., Patwa, P., Patwa, P., Chadha, A. R., & Das, A. (2024) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2024.fever-1.10 · Cited by 29 · see citing works

Navigating Labels and Vectors: A Unified Approach to Filtered Approximate Nearest Neighbor Search

Cai, Y., Shi, J., Chen, Y., & Zheng, W. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3698822 · Cited by 29 · see citing works

Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition

Gao, X., Ye, G., Chen, T., Zhang, W., Yu, J., & Yin, H. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714916 · Cited by 29 · see citing works

Intent-driven network management with multi-agent LLMs: the confucius framework

Wang, Z., Lin, S., Yan, G., Ghorbani, S., Yu, M., Zhou, J., Hu, N., Baruah, L., Peters, S., Kamath, S., et al. (2025) · Proceedings of the ACM SIGCOMM 2025 Conference · doi:10.1145/3718958.3750537 · Cited by 27 · see citing works

Collective intelligence for AI-assisted chemical synthesis

Li, H., Sarkar, S., Lu, W., Loftus, P. O., Qiu, T., Shee, Y., Cuomo, A. E., Webster, J.-P., Kelly, H. R., Manee, V., et al. (2026) · Nature · doi:10.1038/s41586-026-10131-4 · Cited by 27 · see citing works

SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search

Gou, Y., Gao, J., Xu, Y., & Long, C. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3709730 · Cited by 26 · see citing works

Protein and genomic language models uncover the unexplored diversity of bacterial immunity

Mordret, E., Hervé, A., Tesson, F., Vaysset, H., Clabby, T., Loubat, A., Shomar, H., Planel, R., Lavenir, R., Cury, J., et al. (2026) · Science · doi:10.1126/science.adv8275 · Cited by 26 · see citing works

A Survey of Personalization: From RAG to Agent

Li, X., Jia, P., Xu, D., Wen, Y., Zhang, Y., Zhang, W., Wang, W., Wang, Y., Du, Z., Li, X., et al. (2026) · ACM Transactions on Information Systems · doi:10.1145/3802586 · Cited by 24 · see citing works

Semantic Operators and Their Optimization: Enabling LLM-Based Data Processing with Accuracy Guarantees in LOTUS

Patel, L., Jha, S., Pan, M., Gupta, H., Asawa, P., Guestrin, C., & Zaharia, M. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3749646.3749685 · Cited by 23 · see citing works

Retrieval-Augmented Generation for Multiple-Choice Questions and Answers Generation

Pradeesh, N., Remya, T., Thushara, M. G., Krishna, K. A., & Pranav, V. S. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.352 · Cited by 23 · see citing works

iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search

Xu, Y., Gao, J., Gou, Y., Long, C., & Jensen, C. S. (2024) · Proceedings of the ACM on Management of Data · doi:10.1145/3698814 · Cited by 22 · see citing works

Memory$^3$: Language Modeling with Explicit Memory

Yang, H., Lin, Z., Wang, W., Wu, H., Li, Z., Tang, B., Wei, W., Wang, J., Tang, Z., Song, S., et al. (2024) · Journal of Machine Learning · doi:10.4208/jml.240708 · Cited by 22 · see citing works

OpsEval: A Comprehensive Benchmark Suite for Evaluating Large Language Models’ Capability in IT Operations Domain

Liu, Y., Pei, C., Xu, L., Chen, B., Sun, M., Zhang, Z., Sun, Y., Zhang, S., Kun, W., Zhang, H., et al. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3728572 · Cited by 22 · see citing works

MedPix 2.0: A Comprehensive Multimodal Biomedical Data Set for Advanced AI Applications with Retrieval Augmented Generation and Knowledge Graphs

Siragusa, I., Contino, S., Ciura, M. L., Alicata, R., & Pirrone, R. (2025) · Data Science and Engineering · doi:10.1007/s41019-025-00297-8 · Cited by 21 · see citing works

Federated reasoning LLMs: a survey

Wei, S., Tong, Y., Zhou, Z., Xu, Y., Gao, J., Wei, T., He, T., & Lv, W. (2025) · Frontiers of Computer Science · doi:10.1007/s11704-025-50480-3 · Cited by 20 · see citing works

Retrieval-Augmented Few-Shot Medical Image Segmentation With Foundation Models

Zhao, L., Chen, X., Chen, E. Z., Liu, Y., Chen, T., & Sun, S. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3568479 · Cited by 19 · see citing works

RAGing ahead in rheumatology: new language model architectures to tame artificial intelligence

Benavent, D., Venerito, V., & Vegas, X. M. (2025) · Therapeutic Advances in Musculoskeletal Disease · doi:10.1177/1759720x251331529 · Cited by 19 · see citing works

Aftina: enhancing stability and preventing hallucination in AI-based Islamic fatwa generation using LLMs and RAG

Mohammed, M. Y., Ali, S. A., Ali, S. K., Majeed, A. A., & Mohamed, E. H. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11229-y · Cited by 19 · see citing works

Demonstrating CAESURA: Language Models as Multi-Modal Query Planners

Urban, M., & Binnig, C. (2024) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3626246.3654732 · Cited by 19 · see citing works

Introducing ChatSQC: Enhancing statistical quality control with augmented AI

Megahed, F. M., Chen, Y., Zwetsloot, I. M., Knoth, S., Montgomery, D. C., & Jones‐Farmer, L. A. (2024) · Journal of Quality Technology · doi:10.1080/00224065.2024.2372328 · Cited by 19 · see citing works

Improving Medical Multi-modal Contrastive Learning with Expert Annotations

Kumar, Y., & Marttinen, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72661-3_27 · Cited by 19 · see citing works

EchoSight: Advancing Visual-Language Models with Wiki Knowledge

Yan, Y., & Xie, W. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.83 · Cited by 18 · see citing works

Evaluating Retrieval-Augmented Generation Variants for Clinical Decision Support: Hallucination Mitigation and Secure On-Premises Deployment

Wołk, K. (2025) · Electronics · doi:10.3390/electronics14214227 · Cited by 18 · see citing works

FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding OptimizatioN

Li, Z., He, Y., He, L., Wang, J., Shi, T., Lei, B., Li, Y., & Chen, Q. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11208959 · Cited by 18 · see citing works

A Systematic Evaluation of Large Language Models and Retrieval-Augmented Generation for the Task of Kazakh Question Answering

Mansurova, A., Tleubayeva, A., Nugumanova, A., Shomanov, A., & Şeker, Ş. E. (2025) · Information · doi:10.3390/info16110943 · Cited by 18 · see citing works

Large-scale detection of multilingual coordinated activity on Telegram

Blas, L., Saraf, D., Salkar, T., Adadurova, N., Luceri, L., & Ferrara, E. (2025) · npj Complexity · doi:10.1038/s44260-025-00056-w · Cited by 18 · see citing works

AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT Applications

Shen, L., Yang, Q., Zheng, Y., & Li, M. (2025) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3680207.3723486 · Cited by 18 · see citing works

GUing: A Mobile GUI Search Engine using a Vision-Language Model

Wei, J., Courbis, A.-L., Lambolais, T., Xu, B., Bernard, P. L., Dray, G., & Maalej, W. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3702993 · Cited by 18 · see citing works

Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking

Kemper, S., Cui, J., Dicarlantonio, K., Lin, K., Tang, D., Korikov, A., & Sanner, S. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657670 · Cited by 17 · see citing works

Question-Based Retrieval using Atomic Units for Enterprise RAG

Raina, V., & Gales, M. (2024) · Fact Extraction and VERification Workshop (FEVER) · doi:10.18653/v1/2024.fever-1.25 · Cited by 17 · see citing works

Qwen3-VL Technical Report

Bai, S., Cai, Y., Chen, R., Chen, K., Chen, X., Cheng, Z., Deng, L., Ding, W., Gao, C., Ge, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2511.21631 · Cited by 27 · see citing works

REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing

Chen, K., Nadig, R., Frouzakis, M., Ghiasi, N. M., Liang, Y., Mao, H., Park, J., Sadrosadati, M., & Mutlu, O. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731116 · Cited by 16 · see citing works

CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval

Lülf, C., Martins, D. M. L., Salles, M. A. V., Zhou, Y., & Gieseke, F. C. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657678 · Cited by 16 · see citing works

VexIR2Vec : An Architecture-Neutral Embedding Framework for Binary Similarity

VenkataKeerthy, S., Banerjee, S., Dey, S., Andaluri, Y., Raghul, P. S., Kalyanasundaram, S., Pereira, F. M. Q., & Upadrasta, R. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3721481 · Cited by 16 · see citing works

Developing large language models for quantum chemistry simulation input generation

Jacobs, P. F., & Pollice, R. (2025) · Digital Discovery · doi:10.1039/d4dd00366g · Cited by 14 · see citing works

GaussDB-Vector: A Large-Scale Persistent Real-Time Vector Database for LLM Applications

Sun, J., Li, G., Pan, J., Wang, J., Xie, Y., Liu, R., & Nie, W. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3750601.3750619 · Cited by 13 · see citing works

Semantic IDs for Joint Generative Search and Recommendation

Penha, G., D’Amico, E., Nadai, M. D., Palumbo, E., Tamborrino, A., Vardasbi, A., Lefarov, M., Lin, S., Heath, T., Fabbri, F., et al. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3759300 · Cited by 13 · see citing works

MaRAG: a knowledge graph and retrieval-augmented framework for maritime accident analysis

Zou, Y., Wang, S., Li, G., & Ning, B. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.123622 · Cited by 13 · see citing works

M3DocVQA: Multi-Modal Multi-Page Multi-Document Understanding

Cho, J., Mahata, D., İrsoy, O., He, Y., & Bansal, M. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00649 · Cited by 13 · see citing works

PromptMap: An Alternative Interaction Style for AI-Based Image Generation

Adamkiewicz, K., Woźniak, P. W., Dominiak, J., Romanowski, A., Karolus, J., & Frolov, S. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712150 · Cited by 13 · see citing works

Mask-based Membership Inference Attacks for Retrieval-Augmented Generation

Liu, M., Zhang, S., & Long, C. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714771 · Cited by 13 · see citing works

Accelerating Graph Indexing for ANNS on Modern CPUs

Wang, M., Wu, H., Ke, X., Gao, Y., Zhu, Y., & Zhou, W. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725260 · Cited by 13 · see citing works

Question Decomposition for Retrieval-Augmented Generation

Ammann, P. J. L., Golde, J., & Akbik, A. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-srw.32 · Cited by 13 · see citing works

Protein Set Transformer: a protein-based genome language model to power high-diversity viromics

Martin, C., Gitter, A., & Anantharaman, K. (2025) · Nature Communications · doi:10.1038/s41467-025-66049-4 · Cited by 13 · see citing works

Quest for Orthologs in the era of Data Deluge and AI: Challenges and Innovations in Orthology Prediction and Data Integration

Majidian, S., Hadziahmetovic, A., Langschied, F., Pascarelli, S., Prieto-Baños, S., Rojas-Vargas, J., Arvestad, L., Cheema, J., Cosentino, S., Ebersberger, I., et al. (2025) · Journal of Molecular Evolution · doi:10.1007/s00239-025-10272-6 · Cited by 12 · see citing works

GATSim: Urban mobility simulation with generative agents

Liu, Q., Li, C., & Ma, W. (2025) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2025.103234 · Cited by 12 · see citing works

SnapSeek 3.0: Interactive Lifelog Search System with Enhanced Scene Understanding

Ho-Le, M.-Q., Ho, D.-K., Ninh, V.-T., Nguyen, T.-T., Gurrin, C. G., & Tran, M.-T. (2025) · ACM Workshop on the Lifelog Search Challenge (LSC) · doi:10.1145/3729459.3748697 · Cited by 12 · see citing works

Revealing a coherent cell-state landscape across single-cell datasets with CONCORD

Zhu, Q., Jiang, Z., Zuckerman, B., Weinberger, L. S., Thomson, M. W., & Gartner, Z. J. (2026) · Nature Biotechnology · doi:10.1038/s41587-025-02950-z · Cited by 12 · see citing works

Enhancing large language models for knowledge graph question answering via multi-granularity knowledge injection and structured reasoning path-augmented prompting

Gong, C., Wei, Z., Tao, W., & Miao, D. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104614 · Cited by 12 · see citing works

LAPIS: Language Model-Augmented Police Investigation System

Kim, H., Kim, D., Lee, J., Yoon, C., Choi, D., Gim, M., & Kang, J. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3680044 · Cited by 12 · see citing works

Fair-CO2: Fair Attribution for Cloud Carbon Emissions

Han, L., Kakadia, J., Lee, B. C., & Gupta, U. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731023 · Cited by 12 · see citing works

W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering

Nian, J., Peng, Z., Wang, Q., & Fang, Y. (2025) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3731120.3744578 · Cited by 12 · see citing works

DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search

Chatzakis, M., Yannis Papakonstantinou, D., & Palpanas, T. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3749160 · Cited by 12 · see citing works

Nanostructured Material Design via a Retrieval-Augmented Generation (RAG) Approach: Bridging Laboratory Practice and Scientific Literature

Krotkov, N. A., Sbytov, D. A., Chakhoyan, A. A., Kornienko, P. I., Starikova, A. A., Stepanov, M. G., Piven, A. O., Aliev, T. A., Orlova, T., Rafayelyan, M. S., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01897 · Cited by 12 · see citing works

RAR: Retrieving and Ranking Augmented MLLMs for Visual Recognition

Liu, Z., Sun, Z., Zang, Y., Li, W., Zhang, P., Dong, X., Xiong, Y., Lin, D., & Wang, J. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3644175 · Cited by 12 · see citing works

Decoding the Molecular Language of Proteins with EvollaPreprint

Zhou, X., Han, C., Zhang, Y., Du, H., Tian, J., Su, J., Liu, R., Zhuang, K., Jiang, S., Gitter, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.05.630192 · Cited by 12 · see citing works

MIRAGE-ANNS: Mixed Approach Graph-based Indexing for Approximate Nearest Neighbor Search

Voruganti, S., & ÖZSU, M. T. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725325 · Cited by 11 · see citing works

ANSMET: Approximate Nearest Neighbor Search with Near-Memory Processing and Hybrid Early Termination

Li, Y., Jin, Y., Tian, B., Zhang, H., & Gao, M. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731013 · Cited by 11 · see citing works

Hierarchical Knowledge Injection for Improving LLM-based Program Repair

Ehsani, R., Parra, E., Haiduc, S., & Chatterjee, P. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00122 · Cited by 11 · see citing works

Leveraging Retrieval Augmented Generative LLMs for Automated Metadata Description Generation to Enhance Data Catalogs

Singh, M. P., Kumar, A., Donaparthi, S., & Karambelkar, G. (2025) · doi:10.5121/csit.2025.150303 · Cited by 11 · see citing works

HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights

Gökdemir, O., Siebenschuh, C., Brace, A., Wells, A. I., Hsu, B., Hippe, K., Setty, P. V., Ajith, A., Pauloski, J. G., Sastry, V. K., et al. (2025) · Platform for Advanced Scientific Computing Conference (PASC) · doi:10.1145/3732775.3733586 · Cited by 11 · see citing works

CITYWALK : Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge

Zhang, Y., Lu, Q., Liu, K., Dou, W., Zhu, J., Li, Q., Zhang, C., Lin, Z., & Wei, J. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3763791 · Cited by 11 · see citing works

LLMs for Explainable Few-shot Deception Detection

Boumber, D., Tuck, B. E., Verma, R. M., & Qachfar, F. Z. (2024) · ACM International Workshop on Security and Privacy Analytics (IWSPA) · doi:10.1145/3643651.3659898 · Cited by 11 · see citing works

MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation

Wang, Z. J., & Chau, D. H. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657662 · Cited by 10 · see citing works

Lightning IR: Straightforward Fine-tuning and Inference of Transformer-based Language Models for Information Retrieval

Schlatt, F., Fröbe, M., & Hagen, M. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3704118 · Cited by 10 · see citing works

Leveraging Approximate Caching for Faster Retrieval-Augmented Generation

Bergman, S. A., Zhang, J., Kermarrec, A.-M., Petrescu, D., Pires, R. P., Randl, M., & de Vos, M. (2025) · Workshop on Machine Learning and Systems (EuroMLSys) · doi:10.1145/3721146.3721941 · Cited by 10 · see citing works

Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph

Prahlad, D., Lee, C., Kim, D., & Kim, H. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715473 · Cited by 10 · see citing works

Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective

Zeng, S., Zhang, J., Li, B., Lin, Y., Zheng, T., Everaert, D., Lu, H., Liu, H., Liu, H., Xing, Y., et al. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.151 · Cited by 10 · see citing works

Towards Secure Code Generation With LLMs: A Study on Common Weakness Enumeration

Zhao, J., Sun, Y., Huang, C., Liu, C., Guan, Y., Zeng, Y., & Liu, Y. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3619281 · Cited by 10 · see citing works

s3: You Don’t Need That Much Data to Train a Search Agent via RL

Jiang, P., Xu, X., Lin, J., Xiao, J., Wang, Z., Sun, J., & Han, J. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1095 · Cited by 10 · see citing works

Human-scATAC-Corpus: a comprehensive database of scATAC-seq data

Chen, X., Gao, Z., Li, K., Wang, Z., Jiang, Q., Cui, X., Li, Z., & Jiang, R. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1216 · Cited by 10 · see citing works

Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation

Pesl, R. D., Mathew, J. G., Mecella, M., & Aiello, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-94571-7_8 · Cited by 10 · see citing works

IMPLEMENTASI QUESTION ANSWERING SYSTEM TAFSIR AL-AZHAR MENGGUNAKAN LANGCHAIN DAN LARGE LANGUAGE MODEL BERBASIS CHATBOT TELEGRAM

Permadi, A. B. P., Nazruddin Safaat, H., Handayani, L., & Yusra, Y. (2024) · Jurnal Teknoif Teknik Informatika Institut Teknologi Padang · doi:10.21063/jtif.2024.v12.1.62-69 · Cited by 9 · see citing works

Efficient Dynamic Indexing for Range Filtered Approximate Nearest Neighbor Search

Zhang, F., Jiang, M., Hou, G., Shi, J., Fan, H. T., Zhou, W., Li, F., & Wang, S. (2025) · Proceedings of the ACM on Management of Data · doi:10.1145/3725401 · Cited by 9 · see citing works

KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis

Liu, Z., Tang, Q., Nie, S., Wu, S., Zhang, L. F., & Tang, Y. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728911 · Cited by 9 · see citing works

Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action

Kim, T., Lee, J., Yoon, S., Kim, S., & Lee, D. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.275 · Cited by 9 · see citing works

Optimizing Vector Embedding Storage and Indexing for AI at Scale

Mohantyz, S. (2026) · Journal of Artificial Intelligence & Cloud Computing · doi:10.47363/jaicc/icmlaids2026/2026(5)1

A Comparative Analysis of Efficient Similarity Search in Qdrant as a Vector Database and FAISS as a Vector Search LibraryPreprint

Hovsepyan, D. (2026) · Research Square · doi:10.21203/rs.3.rs-11169373/v1

A Layered Approximate Search Approach over Clustering for Scalable Vector Databases

Choi, W., Baek, S., & Lee, E. (2026) · Journal of Korean institute of intelligent systems · doi:10.5391/jkiis.2026.36.1.7

Vector Databases and High-Dimensional Search: Semantic Similarity at Scale

Koos, G. (2026) · Apress eBooks · doi:10.1007/979-8-8688-2709-9_7

Winnex Madhava Direct: Production-Grade Deterministic Vector Search Benchmark

Padilha, K. A. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.21011419