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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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.
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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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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.