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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 2polarities
background 2representative citing papers
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
-
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.
-
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.