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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3 Pith papers cite this work. Polarity classification is still indexing.
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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.
EMA attaches Markers as compact summaries to graph edges for predicate-aware guidance in filtering ANN search, delivering 1.68x-12.25x speedups over prior general filtering methods while supporting dynamic updates.
citing papers explorer
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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.
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EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updates
EMA attaches Markers as compact summaries to graph edges for predicate-aware guidance in filtering ANN search, delivering 1.68x-12.25x speedups over prior general filtering methods while supporting dynamic updates.