A machine-learned router predicts per-query recall for filtered ANN methods and selects the recall-QPS optimal one, outperforming fixed baselines on five unseen datasets.
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URNG and UG enable a single graph index for diverse interval-aware ANN queries by preserving monotonic searchability and structural heredity.
FAVOR achieves 1.3-5x higher QPS at 95% Recall@10 for arbitrary filtered ANNS by combining exclusion-distance reshaping in HNSW graphs with a selectivity-driven router that switches between brute-force and optimized search.
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.
citing papers explorer
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Query-aware Routing for Filtered Approximate Nearest Neighbors Search
A machine-learned router predicts per-query recall for filtered ANN methods and selects the recall-QPS optimal one, outperforming fixed baselines on five unseen datasets.
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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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FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances
FAVOR achieves 1.3-5x higher QPS at 95% Recall@10 for arbitrary filtered ANNS by combining exclusion-distance reshaping in HNSW graphs with a selectivity-driven router that switches between brute-force and optimized search.
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Opal: Private Memory for Personal AI
Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.