MCI approximates dense nearest neighbor graphs via maximal clique covers and progressive local densification to support fast arbitrary-filtered approximate nearest neighbor search with reduced space.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
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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MCI: A Maximal Clique Index for Efficient Arbitrary-Filtered Approximate Nearest Neighbor Search
MCI approximates dense nearest neighbor graphs via maximal clique covers and progressive local densification to support fast arbitrary-filtered approximate nearest neighbor search with reduced space.
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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.