Multiprobe grid ANN maintains roughly constant d-scaling on GloVe while graph/tree/partitioning methods degrade, with near-linear N scaling and lower indexing cost.
Enhancing hnsw index for real-time up- dates: Addressing unreachable points and performance degradation
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
CHRONOS is a three-layer system for evolving data marketplaces that applies neural-ODE temporal decay, changepoint-aware Shapley valuation, and EXP3-IX private coordination to achieve 0.937 recall, 2.74 qps, 161 ms latency, and epsilon 4.25 at delta 10^-6.
SARAD is a hybrid LLM-DRL framework for autonomous driving that replaces random exploration with RAG-enhanced LLM guidance, an attention discriminator, and a collision predictor, reporting performance gains in the Highway-Env simulator.
VGGT-SLAM++ improves on prior transformer SLAM by adding dense DEM submap graphs and high-cadence local optimization, achieving SOTA accuracy with reduced drift and bounded memory on benchmarks.
citing papers explorer
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Scaling Laws for Grid-Based Approximate Nearest Neighbor Search in High Dimensions
Multiprobe grid ANN maintains roughly constant d-scaling on GloVe while graph/tree/partitioning methods degrade, with near-linear N scaling and lower indexing cost.
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CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces
CHRONOS is a three-layer system for evolving data marketplaces that applies neural-ODE temporal decay, changepoint-aware Shapley valuation, and EXP3-IX private coordination to achieve 0.937 recall, 2.74 qps, 161 ms latency, and epsilon 4.25 at delta 10^-6.
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SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving
SARAD is a hybrid LLM-DRL framework for autonomous driving that replaces random exploration with RAG-enhanced LLM guidance, an attention discriminator, and a collision predictor, reporting performance gains in the Highway-Env simulator.
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VGGT-SLAM++
VGGT-SLAM++ improves on prior transformer SLAM by adding dense DEM submap graphs and high-cadence local optimization, achieving SOTA accuracy with reduced drift and bounded memory on benchmarks.