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awesome-vector-ANN-search-papers

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A list of papers in the field of approximate nearest neighbor search on high-dimensional vectors.

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# awesome-vector-ANN-search-papers
A list of papers in the field of approximate nearest neighbor search on high-dimensional vectors.

We refine papers according to **categories** for your reference :)

Let's dive into ANN search and vector database!

## Introduction of ANN search
In general, there are many great articles in the website of [Pinecone](https://www.pinecone.io/learn/) and [Zilliz](https://zilliz.com/learn), including core components, deep dives, user cases and ML foundations in the field of vector databases.

Recently, the 1st Workshop on Vector Databases is held along with ICML2025. Please refer to [VecDB](https://vecdb-ws.github.io/icml2025/) for detailed technical report.

If you are a Chinese developer and want to learn about the getting-started concepts and techniques in ANN search with vector datasets, here is a [blog](https://zhuanlan.zhihu.com/p/686251186) that contains most of the concepts for a beginner.

## Papers Refined with Categories
In this section, we provide papers refined with categories. We also provide common abbreviation of methods after the title with Abbr.

- [0. Upcoming papers](#0-upcoming-papers)
- [1. Graph-based](#1-graph-based)
- [2. Combining graph and other categories](#2-combining-graph-and-other-categories)
- [3. Partitions-based and Distributed](#3-partitions-based-and-distributed)
- [4. Quantization-based](#4-quantization-based)
- [5. Hash-based](#5-hash-based)
- [6. Tree-based](#6-tree-based)
- [7. Disk available](#7-disk-available)
- [8. Survey and benchmark](#8-survey-and-benchmark)
- [9. Hybrid query](#9-hybrid-query)
- [10. Computation acceleration](#10-computation-acceleration)
- [11. Vector database system](#11-vector-database-system)
- [12. GPU-based](#12-gpu-based)

### 0. Upcoming papers
We are excited to track the latest papers in vector search from major 2026 conferences cycle.
Our collection is presently being updated, and we look forward to sharing the full categorized list shortly.

| Title   | Venue   | Authors   | Link   |
|---------|---------|-----------|--------|
|OdinANN: Direct Insert for Consistently Stable Performance in Billion-Scale Graph-Based Vector Search | FAST2026| Guo et.al| [link](https://www.usenix.org/system/files/conf%C3%A9rence/fast26/fast26spring-prepub_guo.pdf) |
| Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search | SIGMOD2026 | Li et al. | [link](https://arxiv.org/abs/2508.08744) |
| AlayaLaser: Efficient Index Layout and Search Strategy for Large-scale High-dimensional Vector Similarity Search | SIGMOD2026 | Chen et al. | [link](https://arxiv.org/pdf/2602.23342) |
| Enhancing Graph-based Approximate Maximum Inner Product Search via Norm-Adaptive Partitioning | SIGMOD2026 | Zhao et al. | [link](https://dl.acm.org/doi/10.1145/3802051) |
| FGIM: a Fast Graph-based Indexes Merging Framework for Approximate Nearest Neighbor Search | SIGMOD2026 | Wu et al. | [link](https://dl.acm.org/doi/10.1145/3786651) |
| HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search | SIGMOD2026 | Xu et al. | [link](https://dl.acm.org/doi/10.1145/3749167) |
| TRIM: Accelerating High-Dimensional Vector Similarity Search with Enhanced Triangle-Inequality-Based Pruning | SIGMOD2026 | Song et al. | [link](https://dl.acm.org/doi/10.1145/3769838) |
| Accelerating High-Dimensional ANN Search via Skipping Redundant Distance Computations | SIGMOD2026 | Song et al. | [link](https://dl.acm.org/doi/10.1145/3769753) |
| DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search | SIGMOD2026 | Chatzakis et al. | [link](https://dl.acm.org/doi/10.1145/3749160) |
| TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search | SIGMOD2026 | Wei et al. | [link](https://dl.acm.org/doi/10.1145/3802118) |
| Through the Lens of Hubness: A Revisit on Graph-Based Approximate Nearest Neighbor Search: [Experiments & Analysis] | SIGMOD2026 | Xu et al. | [link](https://dl.acm.org/doi/10.1145/3802120) |
| ConANN: Conformal Approximate Nearest Neighbor Search | VLDB2026 | Horchidan et al. | [link](https://dl.acm.org/doi/10.14778/3772181.3772184) |
| JHQ: Johnson-Lindenstrauss Enhanced Hierarchical Quantization for High-Dimensional Approximate Nearest Neighbor Search | VLDB2026 | Han et al. | [link](https://www.vldb.org/pvldb/vol19/p1530-han.pdf) |
| I/O Optimizations for Graph-Based Disk-Resident Approximate Nearest Neighbor Search: A Design Space Exploration | VLDB2026 | Li et al. | [link](https://dl.acm.org/doi/10.14778/3801059.3801064) |
| Quantization Meets Projection: A Happy Marriage for Approximate k-Nearest Neighbor Search | VLDB2026 | Yang et al. | [link](https://dl.acm.org/doi/10.14778/3797919.3797931) |
| Distance Comparison Operations Are Not Silver Bullets in Vector Similarity Search: A Benchmark Study on Their Merits and Limits [Experiment, Analysis, and Benchmark] | ICDE2026 | Zheng et al. | [link](https://arxiv.org/pdf/2604.02801) |
| SINDI: An Efficient Index for Sparse Vector Approximate Maximum Inner Product Search | ICDE2026 | Li et al. | [link](https://arxiv.org/pdf/2509.08395) |
| Approximate Diverse k-nearest Neighbor Search in Vector Database | ICDE2026 | Zhao et al. | [link](https://arxiv.org/pdf/2510.27243) |
| GLIDE: GPU-Accelerated ANN Graph Index Construction via Data Locality | ICDE2026 | Ruan et al. | [link](https://icde2026.github.io/accepted-papers.html) |
| Trading Vector Data in Vector Databases | ICDE2026 | Cheng et al. | [link](https://arxiv.org/pdf/2511.07139) |
| Text2VectorSQL: Towards a Unified Interface for Vector Search and SQL Queries | ICDE2026 | Wang et al. | [link](https://arxiv.org/pdf/2506.23071) |
| MINT: Multi-Vector Search Index Tuning | ICDE2026 | Zhu et al. | [link](https://arxiv.org/pdf/2504.20018) |
| SQLVec: SQL-Based Vector Similarity Search | ICDE2026 | Zhang et al. | [link](https://icde2026.github.io/accepted-papers.html) |
| Generalized Range Filtering Approximate Nearest Neighbor Search: Containment and Overlap | KDD2026 | Liu et al. | [link](https://dl.acm.org/doi/10.1145/3770855.3817986) |
| Hitcher: Efficient GPU-based Vector Search via Cluster-Centric Kernel and Hitch-Ride Ordering | KDD2026 | Zhou et al. | [link](https://dl.acm.org/doi/10.1145/3770854.3780212) |

### 1. Graph-based

This category collects papers that propose graph-based methods, without combining other three types of categories (e.g., tree-based, hash-based, quantization-based).

| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| Approximate nearest neighbor algorithm based on navigable small world graphs (Abbr. NSW)| IS2014                | Malkov et al.                     | [link](https://www.researchgate.net/profile/Yu-Malkov/publication/259126397_Approximate_nearest_neighbor_algorithm_based_on_navigable_small_world_graphs/links/63733c302f4bca7fd06030b8/Approximate-nearest-neighbor-algorithm-based-on-navigable-small-world-graphs.pdf) |
| EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph | 2016                  | Fu et al.                         | [link](https://arxiv.org/pdf/1609.07228)                                                                  |
| FANNG: Fast Approximate Nearest Neighbour Graphs                      | CVPR2016              | Harwood et al.                    | [link](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Harwood_FANNG_Fast_Approximate_CVPR_2016_paper.pdf) |
| Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs (Abbr. HNSW) | TPAMI2018             | Malkov et al.                     | [link](https://www.researchgate.net/profile/Yu-Malkov/publication/259126397_Approximate_nearest_neighbor_algorithm_based_on_navigable_small_world_graphs/links/63733c302f4bca7fd06030b8/Approximate-nearest-neighbor-algorithm-based-on-navigable-small-world-graphs.pdf) |
| Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph (Abbr. NSG) | VLDB2019              | Fu et al.                         | [link](https://www.vldb.org/pvldb/vol12/p461-fu.pdf)                                                      |
| Learning to Route in Similarity Graphs                                | ICML2019              | Baranchuk et al.                  | [link](http://proceedings.mlr.press/v97/baranchuk19a/baranchuk19a.pdf)                                    |
| Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination | SIGMOD2020            | Li et al.                         | [link](https://dl.acm.org/doi/pdf/10.1145/3318464.3380600)                                                |
| Graph-based Nearest Neighbor Search:  From Practice to Theory | ICML2020 | Prokhorenkova et al. |[link](http://proceedings.mlr.press/v119/prokhorenkova20a/prokhorenkova20a.pdf) |
| High Dimensional Similarity Search with Satellite System Graph: Efficiency, Scalability, and Unindexed Query Compatibility  (Abbr. NSSG) | TPAMI2021             | Fu et al.                         | [link](https://arxiv.org/pdf/1907.06146)                                                                  |
| Reinforcement Routing on Proximity Graph for Efficient Recommendation | TOIS2023              | Feng et al.                       | [link](https://www.microsoft.com/en-us/research/uploads/prod/2022/03/paper.pdf)                          |
| ARKGraph: All-Range Approximate K-Nearest-Neighbor Graph               | VLDB2023              | Zuo et al.                        | [link](https://www.vldb.org/pvldb/vol16/p2645-deng.pdf)                                                    |
| Worst-case performance of popular approximate nearest neighbor search implementations: guarantees and limitations | NIPS2023 | Indyk et al. | [link](https://dl.acm.org/doi/10.5555/3666122.3669013) |
| Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search | 2024                  | Lu et al.                         | [link](https://arxiv.org/pdf/2402.11354)                                                                  |
| RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Search | VLDB2024              | Chen et al.                       | [link](https://www.vldb.org/pvldb/vol17/p2735-chen.pdf)                                                   |
| Steiner-Hardness: A Query Hardness Measure for Graph-Based ANN Indexes | VLDB2025              | Wang et al.                       | [link](https://arxiv.org/pdf/2408.13899)                                                                  |
| UAccelerating Approximate Nearest Neighbor Search in Hierarchical Graphs: Efficient Level Navigation with Shortcuts | VLDB2025 | Gong et al. | [link](https://dl.acm.org/doi/abs/10.14778/3748191.3748212) |
| Select Edges Wisely: Monotonic Path Aware Graph Layout Optimization for Disk-based ANN Search | VLDB2025 | Yue et al. | [link](https://dl.acm.org/doi/abs/10.14778/3749646.3749697) |
| Empowering Graph-based Approximate Nearest Neighbor Search with Adaptive Awareness Capabilities | SIGKDD2025 | Ruan et al. | [link](https://dl.acm.org/doi/abs/10.1145/3711896.3736930) |
| Wolverine: Highly Efficient Monotonic Search Path Repair for Graph-based ANN Index Updates | VLDB2025 | Liu et al. | [link](https://dl.acm.org/doi/pdf/10.14778/3734839.3734860) |
| Revisiting the Index Construction of Proximity Graph-Based Approximate Nearest Neighbor Search | VLDB2025 | Yang et al. | [link](https://dl.acm.org/doi/abs/10.14778/3725688.3725709) |
| MIRAGE-ANNS: Mixed Approach Graph-based Indexing for Approximate Nearest Neighbor Search | SIGMOD2025 | Voruganti et al.| [link](https://dl.acm.org/doi/10.1145/3725325) |
| Fast-Convergent Proximity Graphs for Approximate Nearest Neighbor Search | SIGMOD2026 | Li et al. | [link](https://dl.acm.org/doi/10.1145/3786650) |
| Proximity Graphs for Similarity Search: Fast Construction, Lower Bounds, and Euclidean Separation | PODS2026 | Lu et al. | [link](https://dl.acm.org/doi/10.1145/3767716) |
| Enhancing Graph-based Approximate Maximum Inner Product Search via Norm-Adaptive Partitioning | SIGMOD2026 | Zhao et al. | [link](https://dl.acm.org/doi/10.1145/3802051) |
| FGIM: a Fast Graph-based Indexes Merging Framework for Approximate Nearest Neighbor Search | SIGMOD2026 | Wu et al. | [link](https://dl.acm.org/doi/10.1145/3786651) |
| VSAG: An Optimized Search Framework for Graph-Based Approximate Nearest Neighbor Search | VLDB2025 | Zhong et al. | [link](https://www.vldb.org/pvldb/vol18/p5017-cheng.pdf) |

### 2. Combining graph and other categories
| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| Locality-Sensitive Indexing for Graph-Based Approximate Nearest Neighbor Search | SIGIR2025 | Chung  et al. | [link](https://dl.acm.org/doi/pdf/10.1145/3726302.3730028) |
|SymphonyQG: towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search | SIGMOD2025 |Gou et al. | [link](https://dl.acm.org/doi/pdf/10.1145/3709730) |
| Routing-Guided Learned Product Quantization for Graph-Based Approximate Nearest Neighbor Search | ICDE2024              | Yue et al.                        | [link](https://arxiv.org/pdf/2311.18724)                                                                  |
| ELPIS: Graph-Based Similarity Search for Scalable Data Science         | VLDB2023              | Azizi et al.                      | [link](https://www.vldb.org/pvldb/vol16/p1548-azizi.pdf)                                                  |
| Towards Efficient Index Construction and Approximate Nearest Neighbor Search in High-Dimensional Spaces  (Abbr. LSH-APG)  | VLDB2023              | Zhao et al.                       | [link](https://www.vldb.org/pvldb/vol16/p1979-zhao.pdf)                                                   |
| HVS: hierarchical graph structure based on voronoi diagrams for solving approximate nearest neighbor search | VLDB2021              | Lu et al.                         | [link](http://www.vldb.org/pvldb/vol15/p246-lu.pdf)                                                       |

### 3. Partitions-based and Distributed
| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| ConANN: Conformal Approximate Nearest Neighbor Search | VLDB2026 | Horchidan et al. | [link](https://dl.acm.org/doi/10.14778/3772181.3772184) |
| Tribase: A Vector Data Query Engine for Reliable and Lossless Pruning Compression using Triangle Inequalities | SIGMOD2025 | Xu et al. | [link](https://dl.acm.org/doi/pdf/10.1145/3709743) |
| LIRA: A Learning-based Query-aware Partition Framework for Large-scale ANN Search | WWW2025 | Zeng et al. |[link](https://arxiv.org/abs/2503.23409) |
| Optimizing the Number of Clusters for Billion-Scale Quantization-Based Nearest Neighbor Search | TKDE2024              | Fu et al.                         | [link](https://ieeexplore.ieee.org/abstract/document/10547412)                                            |
| DIMS: Distributed Index for Similarity Search in Metric Spaces        | TKDE2024              | Zhu et al.                        | [link](https://ieeexplore.ieee.org/abstract/document/10737368)                                            |
| Learning Balanced Tree Indexes for Large-Scale Vector Retrieval      | SIGKDD2023            | Li et al.                         | [link](https://dl.acm.org/doi/pdf/10.1145/3580305.3599406)                                                |
| BLISS: A Billion scale Index using Iterative Re-partitioning          | SIGKDD2022            | Gupta et al.                      | [link](https://dl.acm.org/doi/pdf/10.1145/3534678.3539414)                                                |
| Learned Probing Cardinality Estimation for High-Dimensional Approximate NN Search | ICDE2023              | Zheng et al.                      | [link](https://ieeexplore.ieee.org/abstract/document/10184837)                                            |
| Learning-based query optimization for multi-probe approximate nearest neighbor search | VLDBJ2023             | Zhang et al.                      | [link](https://link.springer.com/article/10.1007/s00778-022-00762-0)                                     |
| SOAR: Improved Indexing for Approximate Nearest Neighbor Search      | NIPS2023              | Sun et al.                        | [link](https://openreview.net/pdf?id=QvIvWMaQdX)                                                          |
| Efficient Distributed Approximate k-Nearest Neighbor Graph Construction by Multiway Random Division Forest | KDD2023              | Kim et al.                        | [link](https://dl.acm.org/doi/abs/10.1145/3580305.3599327)                                                |
| Odyssey: A Journey in the Land of Distributed Data Series Similarity Search | VLDB2023              | Chatzakis et al.                  | [link](https://dl.acm.org/doi/10.14778/3579075.3579087)                                                   |                                                       |
| Learning Space Partitions for Nearest Neighbor Search   (Abbr. Neural LSH)              | ICLR2020              | et al.                            | [link](https://arxiv.org/pdf/1901.08544)                                                                  |

### 4. Quantization-based
| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| JHQ: Johnson-Lindenstrauss Enhanced Hierarchical Quantization for High-Dimensional Approximate Nearest Neighbor Search | VLDB2026 | Han et al. | [link](https://www.vldb.org/pvldb/vol19/p1530-han.pdf) |
| Quantization Meets Projection: A Happy Marriage for Approximate k-Nearest Neighbor Search | VLDB2026 | Yang et al. | [link](https://dl.acm.org/doi/10.14778/3797919.3797931) |
| Boosting Deep Vector Quantization with Progressive Distribution Transformation | SIGKDD2025 | Wang et al. | [link](https://dl.acm.org/doi/pdf/10.1145/3711896.3736863) |
| Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search | SIGMOD2025 | Gao et al. | [link](https://arxiv.org/abs/2409.09913) |
| RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search | SIGMOD2024            | Gao et al.                        | [link](https://dl.acm.org/doi/pdf/10.1145/3654970)                                                       |
| Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search| 2024                  | Gao et al.                        | [link](https://arxiv.org/pdf/2409.09913)                                                                  |
| Residual quantization with implicit neural codebooks| ICML2024                  | Huijben et al.                        | [link](https://dl.acm.org/doi/abs/10.5555/3692070.3692900)                                                                  |
| Knowledge Distillation for High Dimensional Search Index             | NeurIPS2024            | Lu et al.                         | [link](https://proceedings.neurips.cc/paper_files/paper/2023/file/6a15378acabd1aef017ec79a3ed744d2-Paper-Conference.pdf) |
| Similarity Search in the Blink of an Eye with Compressed Indices      | VLDB2023              | Aguerrebere et al.                | [link](https://arxiv.org/pdf/2304.04759)                                                                  |
| Model-enhanced Vector Index                                           | NeurIPS2023            | Zhang et al.                      | [link](https://proceedings.neurips.cc/paper_files/paper/2023/file/ac112e8ffc4e5b9ece32070440a8ca43-Paper-Conference.pdf) |
| Accelerating Large-Scale Inference with Anisotropic Vector Quantization, ScaNN | ICML2020              | Guo et al.                        | [link](https://dl.acm.org/doi/abs/10.5555/3524938.3525302)                                                |
| Unsupervised Neural Quantization for Compressed-Domain Similarity Search | ICCV2019              | Morozov et al.                        | [link](https://openaccess.thecvf.com/content_ICCV_2019/papers/Morozov_Unsupervised_Neural_Quantization_for_Compressed-Domain_Similarity_Search_ICCV_2019_paper.pdf)                                                |

### 5. Hash-based
| Title | Venue | Authors | Link |
|-------|--------|----------|-----------|
| DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor Search | VLDB2024 | Wei et al. | [link](https://arxiv.org/pdf/2406.10938) |
| DB-LSH: Locality-Sensitive Hashing with Query-based Dynamic Bucketing | ICDE2023 | Tian et al. | [link](https://ieeexplore.ieee.org/abstract/document/9835575) |
| DB-LSH 2.0: Locality-Sensitive Hashing With Query-Based Dynamic Bucketing | TKDE2023 | Tian et al. | [link](https://ieeexplore.ieee.org/abstract/document/10184454) |
| PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search | VLDB2022 | Zheng et al. | [link](https://arxiv.org/pdf/2107.05537) |
| LIDER: an efficient high-dimensional learned index for large-scale dense passage retrieval | VLDB2022 | Wang et al. | [link](https://www.vldb.org/pvldb/vol16/p154-wang.pdf) |
| MP-RW-LSH: an efficient multi-probe LSH solution to ANNS-L1 | VLDB2021 | Wang et al. | [link](https://vldb.org/pvldb/vol14/p3267-wang.pdf) |
| Query-aware locality-sensitive hashing for approximate nearest neighbor search | VLDB2015 | Huang et al. | [link](https://www.researchgate.net/profile/Jianlin-Feng/publication/292671953_Query-aware_locality-sensitive_hashing_for_approximate_nearest_neighbor_search/links/59a41df1a6fdcc773a371efc/Query-aware-locality-sensitive-hashing-for-approximate-nearest-neighbor-search.pdf) |

### 6. Tree-based
| Title | Venue | Authors | Link |
|-------|--------|----------|-----------|
| DIDS: Double Indices and Double Summarizations for Fast Similarity Search | VLDB2024 | Hu et al. | [link](https://www.vldb.org/pvldb/vol17/p2198-wang.pdf) |
| Adaptive Indexing in High-Dimensional Metric Spaces | VLDB2023 | Lampropoulos et al. | [link](https://cs.au.dk/~karras/p2525-mamoulis.pdf) |
| Hercules Against Data Series Similarity Search | VLDB2022 | Echihabi et al. | [link](https://www.vldb.org/pvldb/vol15/p2005-echihabi.pdf) |
| Scalable Nearest Neighbor Algorithms for High Dimensional Data | TPAMI2020 | Muja et al. | [link](https://ieeexplore.ieee.org/iel7/34/4359286/06809191.pdf) |
| i SAX: indexing and mining terabyte sized time series | SIGKDD2008 | Shieh et al. | [link](http://alumni.cs.ucr.edu/~shiehj/files/iSAX.pdf) |

### 7. Disk available
| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node | NeurIPS2019           | Subramanya et al.                 | [link](https://proceedings.neurips.cc/paper_files/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf) |
| DiskANN++: Efficient Page-based Search over Isomorphic Mapped Graph Index using Query-sensitivity Entry Vertex | 2023                  | Ni et al.                         | [link](https://arxiv.org/pdf/2310.00402)                                                                  |
| SPFresh: Incremental In-Place Update for Billion-Scale Vector Search   | SOSP2023              | Xu et al.                         | [link](https://dl.acm.org/doi/abs/10.1145/3600006.3613166)                                                |
| Filtered − DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters | Web2023               | Gollapudi et al.                  | [link](https://dl.acm.org/doi/pdf/10.1145/3543507.3583552)                                                |
| FreshDiskANN: A Fast and Accurate Graph-Based ANN Index for Streaming Similarity Search | 2021                  | Singh et al.                      | [link](https://dcreager.net/pdf/Singh2021.pdf)                                                            |
| SPANN: Highly-efﬁcient Billion-scale Approximate Nearest Neighbor Search | NeurIPS2021           | Chen et al.                       | [link](https://proceedings.neurips.cc/paper_files/paper/2021/file/299dc35e747eb77177d9cea10a802da2-Paper.pdf) |
| I/O Optimizations for Graph-Based Disk-Resident Approximate Nearest Neighbor Search: A Design Space Exploration | VLDB2026 | Li et al. | [link](https://dl.acm.org/doi/10.14778/3801059.3801064) |

### 8. Survey and benchmark
| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| Filtered Vector Search: State-of-the-Art and Research Opportunities | VLDB2025 | Chronis et al. | [link](https://dl.acm.org/doi/10.14778/3750601.3750700) |
| Through the Lens of Hubness: A Revisit on Graph-Based Approximate Nearest Neighbor Search: [Experiments & Analysis] | SIGMOD2026 | Xu et al. | [link](https://dl.acm.org/doi/10.1145/3802120) |
|An Experimental Evaluation of Hybrid Querying on Vectors | VLDB2025| Zhu et.al| [link](https://www.vldb.org/pvldb/vol19/p183-zheng.pdf) |
| Distance Comparison Operations Are Not Silver Bullets in Vector Similarity Search: A Benchmark Study on Their Merits and Limits [Experiment, Analysis, and Benchmark] | ICDE2026 | Zheng et al. | [link](https://arxiv.org/pdf/2604.02801) |
| Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art | SIGMOD2025 | Azizi et al. | [link](https://dl.acm.org/doi/pdf/10.1145/3709693) |
| Evaluating and Generating Query Workloads for High Dimensional Vector Similarity Search | KDD2025 | Ceccarello et al. | [link](https://dl.acm.org/doi/10.1145/3711896.3737383) |
| Survey of Vector Database Management Systems                         | VLDB2024             | Pan et al.                       | [link](https://arxiv.org/pdf/2310.14021)                                                                 |
| ParlayANN: Scalable and Deterministic Parallel GraphBased Approximate Nearest Neighbor Search Algorithms | PPoPP2024            | Manohar et al.                   | [link](https://dl.acm.org/doi/pdf/10.1145/3627535.3638475)                                               |
| Deep Learning for Approximate Nearest Neighbour Search: A Survey and Future Directions | TKDE2022             | Li et al.                        | [link](https://ieeexplore.ieee.org/abstract/document/9942356)                                             |
| A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search | VLDB2021             | Wang et al.                      | [link](https://www.vldb.org/pvldb/vol14/p1964-wang.pdf)                                                   |

### 9. Hybrid and filtered search
| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| Generalized Range Filtering Approximate Nearest Neighbor Search: Containment and Overlap | KDD2026 | Liu et al. | [link](https://dl.acm.org/doi/10.1145/3770855.3817986) |
| SIEVE: Effective Filtered Vector Search with Collection of Indexes | VLDB2025 | Li et al. | [link](https://dl.acm.org/doi/10.14778/3749646.3749725) |
| Dynamic Range-Filtering Approximate Nearest Neighbor Search | VLDB2025 | Peng et al. | [link](https://www.vldb.org/pvldb/vol18/p3256-deng.pdf) |
| UNIFY: Unified Index for Range Filtered Approximate Nearest Neighbors Search | VLDB2025 | Liang et al. | [link](https://dl.acm.org/doi/10.14778/3717755.3717770) |
| DEG: Efficient Hybrid Vector Search Using the Dynamic Edge Navigation Graph | SIGMOD2025 | Yin et al. | [link](https://dl.acm.org/doi/pdf/10.1145/3709679) |
| Navigating Labels and Vectors: A Unified Approach to Filtered Approximate Nearest Neighbor Search | SIGMOD2025 | Cai et al. | [link](https://dl.acm.org/doi/pdf/10.1145/3698822) |
| iRangeGraph: Improvising Range-dedicated Graphs for Range-filtering Nearest Neighbor Search | SIGMOD2025 | Xu et al. | [link](https://dl.acm.org/doi/pdf/10.1145/3698814) |
| Timestamp Approximate Nearest Neighbor Search over High-Dimensional Vector Data | ICDE2025 | Wang et al. | [link](https://zhouzimu.github.io/paper/icde25-wang.pdf) |
| SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search | SIGMOD2024           | Zuo et al.                       | [link](https://dl.acm.org/doi/pdf/10.1145/3639324)                                                      |
| Bridging Dense and Sparse Maximum Inner Product Search | TOIS2024           | Bruch et al.                       | [link](https://dl.acm.org/doi/full/10.1145/3665324)                                                      |
| An Efficient and Robust Framework for Approximate Nearest Neighbor Search with Attribute Constraint (NHQ)| NeurIPS2023 | Wang et al. | [link](https://proceedings.neurips.cc/paper_files/paper/2023/file/32e41d6b0a51a63a9a90697da19d235d-Paper-Conference.pdf) |
|ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data | SIGMOD2024 | Patel et al. |  [link](https://dl.acm.org/doi/10.1145/3654923) |
| Filtered − DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters | Web2023              | Gollapudi et al.                 | [link](https://dl.acm.org/doi/pdf/10.1145/3543507.3583552)                                              |
| High-Throughput Vector Similarity Search in Knowledge Graphs  (HQI)  | SIGMOD2023           | Mohoney et al.                   | [link](https://arxiv.org/pdf/2304.01926) |
| Navigable Proximity Graph-Driven Native Hybrid Queries with Structured and Unstructured Constraints | 2022                 | Wang et al.                      | [link](https://arxiv.org/pdf/2203.13601)  |
| HQANN: Efficient and Robust Similarity Search for Hybrid Queries with Structured and Unstructured Constraints | CIKM2022 | Wu et al.| [link](https://dl-acm-org.remotexs.ntu.edu.sg/doi/abs/10.1145/3511808.3557610) |
| AnalyticDB-V: a hybrid analytical engine towards query fusion for structured and unstructured data | VLDB2020             | Wei et al.                        | [link](https://vldb.org/pvldb/vol13/p3152-wei.pdf)                                                      |

### 10. Computation acceleration
| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| DARTH: Declarative Recall Through Early Termination for Approximate Nearest Neighbor Search | SIGMOD2026 | Chatzakis et al. | [link](https://dl.acm.org/doi/10.1145/3749160) |
| TRIM: Accelerating High-Dimensional Vector Similarity Search with Enhanced Triangle-Inequality-Based Pruning | SIGMOD2026 | Song et al. | [link](https://dl.acm.org/doi/10.1145/3769838) |
| Effective and General Distance Computation for Approximate Nearest Neighbor Search | ICDE2025 | Yang et al. | [link](https://ieeexplore.ieee.org/document/11113031/) |
| Accelerating Graph Indexing for ANNS on Modern CPUs | SIGMOD2025 | Wang et al. | [link](https://arxiv.org/abs/2502.18113) |
| Accelerating Graph-based Vector Search via Delayed-Synchronization Traversal | 2024                 | Jiang et al.                      | [link](https://arxiv.org/abs/2406.12385)                                                                |
| Juno: Optimizing High-Dimensional Approximate Nearest Neighbour Search with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping | ASPLOS2024            | Liu et al.                       | [link](https://horizon-lab.org/pubs/asplos24-juno.pdf)                                                  |
| High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations (Abbr. ADSampline) | SIGMOD2023           | Gao et al.                       | [link](https://dl.acm.org/doi/pdf/10.1145/3589282)                                                      |
| FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search | Web2023              | Chen et al.                      | [link](https://dl.acm.org/doi/pdf/10.1145/3543507.3583318)                                              |
| Relative NN-Descent: A Fast Index Construction for Graph-Based Approximate Nearest Neighbor Search | MM2023               | Ono et al.                       | [link](https://arxiv.org/pdf/2310.20419)                                                                |
| AdANNS: A Framework for Adaptive Semantic Search                        | NIPS2023             | Rege et al.                      | [link](https://proceedings.neurips.cc/paper_files/paper/2023/file/f062da1973ac9ac61fc6d44dd7fa309f-Paper-Conference.pdf) |

### 11. Vector database system

| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| HARMONY: A Scalable Distributed Vector Database for High-Throughput Approximate Nearest Neighbor Search | SIGMOD2026 | Xu et al. | [link](https://dl.acm.org/doi/10.1145/3749167) |
|OdinANN: Direct Insert for Consistently Stable Performance in Billion-Scale Graph-Based Vector Search | FAST2026| Guo et.al| [link](https://www.usenix.org/system/files/conf%C3%A9rence/fast26/fast26spring-prepub_guo.pdf) |
| VStream: A Distributed Streaming Vector Search System | VLDB2025 | Gong et al. | [link](https://www.vldb.org/pvldb/vol18/p1593-gao.pdf) |
| MicroNN: An On-device Disk-resident Updatable Vector Database | SIGMOD2025 | Pound et al. | [link](https://dl.acm.org/doi/abs/10.1145/3722212.3724444) |
| CAGRA: Highly Parallel Graph Construction and  Approximate Nearest Neighbor Search for GPUs | ICDE2024 | Ootomo et al. | [link](https://arxiv.org/pdf/2308.15136) |
| Vexless: A Serverless Vector Data Management System Using Cloud Functions | SIGMOD2024             | Su et al.                        | [link](https://dl.acm.org/doi/pdf/10.1145/3654990)                                                      |
| SingleStore-V: An Integrated Vector Database System in SingleStore    | VLDB2024             | Chen et al.                      | [link](https://cs.purdue.edu/homes/csjgwang/pubs/VLDB24_SingleStoreVec.pdf)                              |
| Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment | SIGMOD2024           | Wang et al.                      | [link](https://arxiv.org/pdf/2401.02116)                                                                |
| Efficient Approximate Nearest Neighbor Search in Multi-dimensional Databases | SIGMOD2023           | Peng et al.                      | [link](https://www.comp.hkbu.edu.hk/~edisonchan/publication/tau-MNG.pdf)                                |
| VBASE: Unifying Online Vector Similarity Search and Relational Queries via Relaxed Monotonicity | OSDI2023             | Zhang et al.                     | [link](https://www.usenix.org/system/files/osdi23-zhang-qianxi_1.pdf)                                   |
| Manu: A Cloud Native Vector Database Management System               | VLDB2022             | Guo et al.                       | [link](https://www.vldb.org/pvldb/vol15/p3548-yan.pdf)                                                   |
| Milvus: A Purpose-Built Vector Data Management System                | SIGMOD2021           | Wang et al.                      | [link](https://dl.acm.org/doi/pdf/10.1145/3448016.3457550)                                                |
| LANNS: a web-scale approximate nearest neighbor lookup system        | VLDB2021             | Doshi et al.                     | [link](https://arxiv.org/pdf/2010.09426)                                                                |

### 12. GPU-based

| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| GLIDE: GPU-Accelerated ANN Graph Index Construction via Data Locality | ICDE2026 | Ruan et al. | [link](https://icde2026.github.io/accepted-papers.html) |
| Hitcher: Efficient GPU-based Vector Search via Cluster-Centric Kernel and Hitch-Ride Ordering | KDD2026 | Zhou et al. | [link](https://dl.acm.org/doi/10.1145/3770854.3780212) |
| Scalable Graph Indexing using GPUs for Approximate Nearest Neighbor Search | SIGMOD2026 | Li et al. | [link](https://arxiv.org/abs/2508.08744) |
| AlayaLaser: Efficient Index Layout and Search Strategy for Large-scale High-dimensional Vector Similarity Search | SIGMOD2026 | Chen et al. | [link](https://arxiv.org/pdf/2602.23342) |
| Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration | VLDBJ2026 | Li et al. |[link](https://link.springer.com/article/10.1007/s00778-026-00974-8) |
| CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs | ICDE2024 | Ootomo et al. |[link](https://ieeexplore.ieee.org/abstract/document/10597683) |
| GGNN: Graph-based GPU Nearest Neighbor Search | TBD2023 | Groh et al. | [link](https://ieeexplore.ieee.org/abstract/document/9739943) |
| GPU-accelerated Proximity Graph Approximate Nearest Neighbor Search and Construction | ICDE2022 | Yu et al. | [link](https://ieeexplore.ieee.org/document/9835618) |
| SONG: Approximate Nearest Neighbor Search on GPU | ICDE2020 | Zhao et al. | [link](https://ieeexplore.ieee.org/document/9101583) |

### 13. Maximum Inner Product
| Title   | Venue   | Authors   | Link   |
|-------|--------|----------|-----------|
| FARGO: Fast Maximum Inner Product Search via Global Multi-Probing | VLDB2023 | Zhao et al. | [link](https://www.vldb.org/pvldb/vol16/p1100-zheng.pdf) |
| SINDI: An Efficient Index for Sparse Vector Approximate Maximum Inner Product Search | ICDE2026 | Li et al. | [link](https://arxiv.org/pdf/2509.08395) |

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ximuzeng@std.uestc.edu.cn

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