Veda and EffVeda partition vectors into disjoint role-combination blocks, apply lattice-based copy and merge operations within a storage budget, index large nodes with HNSW, and use coordinated search with distance bounds to deliver higher throughput at high recall.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
Semantic Recall is a new evaluation metric for approximate nearest neighbor search that focuses only on semantically relevant results, with Tolerant Recall as a proxy when relevance labels are unavailable.
CubeGraph uses hierarchical spatial grids and on-the-fly stitching of per-cell vector graphs to enable single-pass nearest-neighbor search for hybrid vector-spatial queries.
AlayaLaser uses a SIMD-optimized on-disk graph layout plus caching and search strategies to outperform prior on-disk ANNS systems and match or exceed in-memory performance on large high-dimensional datasets.
citing papers explorer
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Don't Be a Pot Stirrer! Authorized Vector Data Retrieval via Access-Aware Indexing
Veda and EffVeda partition vectors into disjoint role-combination blocks, apply lattice-based copy and merge operations within a storage budget, index large nodes with HNSW, and use coordinated search with distance bounds to deliver higher throughput at high recall.
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Semantic Recall for Vector Search
Semantic Recall is a new evaluation metric for approximate nearest neighbor search that focuses only on semantically relevant results, with Tolerant Recall as a proxy when relevance labels are unavailable.
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CubeGraph: Efficient Retrieval-Augmented Generation for Spatial and Temporal Data
CubeGraph uses hierarchical spatial grids and on-the-fly stitching of per-cell vector graphs to enable single-pass nearest-neighbor search for hybrid vector-spatial queries.
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AlayaLaser: Efficient Index Layout and Search Strategy for Large-scale High-dimensional Vector Similarity Search
AlayaLaser uses a SIMD-optimized on-disk graph layout plus caching and search strategies to outperform prior on-disk ANNS systems and match or exceed in-memory performance on large high-dimensional datasets.