URNG and UG enable a single graph index for diverse interval-aware ANN queries by preserving monotonic searchability and structural heredity.
Fast approximate nearest neighbor search with the navigating spreading-out graph
11 Pith papers cite this work, alongside 25 external citations. Polarity classification is still indexing.
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25
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UNVERDICTED 11representative citing papers
Slipstream exploits continuity in vector streams to reduce insertion costs in graph ANNS indexes via prior-insertion candidates and an adaptive controller, delivering up to 30.8x higher throughput at >=0.95 recall@10 on five datasets.
LAANN introduces I/O-aware look-ahead techniques for disk-based ANNS and reports 1.41x-4.66x higher throughput with fewer I/O operations at Recall@10=0.9 on large datasets.
LSM-VEC integrates hierarchical graphs with LSM-tree levels for out-of-place dynamic updates, sampling-based search, and connectivity-aware reordering, outperforming prior disk-based ANN systems on billion-scale data with higher recall, lower latency, and over 66% memory reduction.
MINT defines multi-vector search index tuning and provides algorithms that achieve 2.1X to 8.3X latency speedup over baselines under storage and recall constraints.
RACORN-1 adds adaptive search fallback to ACORN-1 to fix recall collapse at low selectivity in filtered vector search, achieving 9-26x speedups over HNSW with recovered recall on 1M-40M datasets.
Co-design of 14.5x compacted index, asynchronous scheduler, and multiplication-free kernel for PIM-based graph ANNS delivers up to 20x CPU and 17.1x GPU throughput on billion-scale benchmarks.
AlayaLaser uses a SIMD-optimized on-disk graph layout plus caching and search strategies to outperform prior on-disk ANNS systems and match or exceed in-memory performance on large high-dimensional datasets.
IID-Nav enables progressive retrieval in large-scale recommenders by treating it as iterative goal-driven graph traversal with recursive state evolution supporting unlimited depth without rising inference cost.
ScaleGANN accelerates graph-based ANN index construction up to 9x faster and 6x cheaper than DiskANN by using divide-and-merge on distributed low-cost spot GPUs with optimized partitioning and a cost-aware scheduler.
Faiss is a library offering indexing methods and primitives for efficient vector similarity search, a core need in vector databases for AI applications.
citing papers explorer
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Efficient Graph Indexing for Interval-Aware Vector Search
URNG and UG enable a single graph index for diverse interval-aware ANN queries by preserving monotonic searchability and structural heredity.
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Slipstream: Locality-Aware Graph Index Construction for Streaming Approximate Nearest Neighbor Search
Slipstream exploits continuity in vector streams to reduce insertion costs in graph ANNS indexes via prior-insertion candidates and an adaptive controller, delivering up to 30.8x higher throughput at >=0.95 recall@10 on five datasets.
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LAANN: I/O-Aware Look-Ahead Search for Disk-Based Approximate Nearest Neighbor Search
LAANN introduces I/O-aware look-ahead techniques for disk-based ANNS and reports 1.41x-4.66x higher throughput with fewer I/O operations at Recall@10=0.9 on large datasets.
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LSM-VEC: A Large-Scale Disk-Based System for Dynamic Vector Search
LSM-VEC integrates hierarchical graphs with LSM-tree levels for out-of-place dynamic updates, sampling-based search, and connectivity-aware reordering, outperforming prior disk-based ANN systems on billion-scale data with higher recall, lower latency, and over 66% memory reduction.
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MINT: Multi-Vector Search Index Tuning
MINT defines multi-vector search index tuning and provides algorithms that achieve 2.1X to 8.3X latency speedup over baselines under storage and recall constraints.
-
RACORN-1: Adaptive Recall-Preserving Speedup for Low-Selectivity Filtered Vector Search
RACORN-1 adds adaptive search fallback to ACORN-1 to fix recall collapse at low selectivity in filtered vector search, achieving 9-26x speedups over HNSW with recovered recall on 1M-40M datasets.
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Co-Designing Graph-based Approximate Nearest Neighbor Search at Billion Scale for Processing-in-Memory
Co-design of 14.5x compacted index, asynchronous scheduler, and multiplication-free kernel for PIM-based graph ANNS delivers up to 20x CPU and 17.1x GPU throughput on billion-scale benchmarks.
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AlayaLaser: Efficient Index Layout and Search Strategy for Large-scale High-dimensional Vector Similarity Search
AlayaLaser uses a SIMD-optimized on-disk graph layout plus caching and search strategies to outperform prior on-disk ANNS systems and match or exceed in-memory performance on large high-dimensional datasets.
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From Extraction to Navigation: Progressive Retrieval with Indirectly Infinite Depth
IID-Nav enables progressive retrieval in large-scale recommenders by treating it as iterative goal-driven graph traversal with recursive state evolution supporting unlimited depth without rising inference cost.
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ScaleGANN: Accelerate Large-Scale ANN Indexing by Cost-effective Cloud GPUs
ScaleGANN accelerates graph-based ANN index construction up to 9x faster and 6x cheaper than DiskANN by using divide-and-merge on distributed low-cost spot GPUs with optimized partitioning and a cost-aware scheduler.
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The Faiss library
Faiss is a library offering indexing methods and primitives for efficient vector similarity search, a core need in vector databases for AI applications.