IVF-TQ replaces learned codebooks with a fixed random rotation and precomputed scalar quantizer in the residual layer of an IVF index, delivering streaming recall stability at fixed bit budgets via a uniform-over-sphere inner-product bound.
Ilyas, Theodoros Rekatsinas, and Shivaram Venkataraman
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 6roles
background 2polarities
background 2representative citing papers
Benchmark study shows DCO methods for vector similarity search are not reliable silver bullets due to high sensitivity to data properties and hardware, making them unsuitable for production deployment.
CLIP proposes a cosine-law-based pruning method for IVF vector search enabling O(1) cluster and log-time vector pruning with guarantees, plus variants for hierarchical and dynamic settings, showing up to 78% pruning and 69% efficiency gains.
Signal-triggered local repair in graph ANN indexes improves minimum recall@10 by 0.014-0.050 under bursty churn versus fixed-cadence repair at matched budget on SIFT-128 and Fashion-MNIST-784.
ACRONYM claims a CAM-accelerated platform for dynamic vector databases that delivers over 90% recall at 8 million queries per second using 32 MB memory and 2.56 uJ per query while supporting updates without stalling.
NAVIS improves concurrent search and update throughput in on-SSD graph vector search by up to 2.74x for insertions and 1.37x for searches through reduced position-seeking overhead.
citing papers explorer
-
IVF-TQ: Calibration-Free Streaming Vector Search via a Codebook-Free Residual Layer
IVF-TQ replaces learned codebooks with a fixed random rotation and precomputed scalar quantizer in the residual layer of an IVF index, delivering streaming recall stability at fixed bit budgets via a uniform-over-sphere inner-product bound.
-
CLIP: Lightweight Cosine-Law-Based Inverted-List Pruning for IVF-Based Vector Search
CLIP proposes a cosine-law-based pruning method for IVF vector search enabling O(1) cluster and log-time vector pruning with guarantees, plus variants for hierarchical and dynamic settings, showing up to 78% pruning and 69% efficiency gains.
-
When to Repair a Graph ANN Index: Navigability-Signal-Triggered Local Repair Protects Tail Recall Under Bursty Churn
Signal-triggered local repair in graph ANN indexes improves minimum recall@10 by 0.014-0.050 under bursty churn versus fixed-cadence repair at matched budget on SIFT-128 and Fashion-MNIST-784.
-
ACRONYM: Accelerated Approximate Nearest Neighbor Search in Memory for Dynamic Vector Databases
ACRONYM claims a CAM-accelerated platform for dynamic vector databases that delivers over 90% recall at 8 million queries per second using 32 MB memory and 2.56 uJ per query while supporting updates without stalling.
-
NAVIS: Concurrent Search and Update with Low Position-Seeking Overhead in On-SSD Graph-Based Vector Search
NAVIS improves concurrent search and update throughput in on-SSD graph vector search by up to 2.74x for insertions and 1.37x for searches through reduced position-seeking overhead.