HaS accelerates RAG retrieval via homology-aware speculative retrieval and homologous query re-identification validation, cutting latency 24-37% with 1-2% accuracy drop on tested datasets.
Efficient and robust approxi- mate nearest neighbor search using hierarchical navigable small world graphs,
4 Pith papers cite this work. Polarity classification is still indexing.
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JZ-Tree introduces a flattened Morton plane-based tree hierarchy enabling collaborative dual-tree walks that deliver more than 10x faster exact k-NN search and FoF clustering on GPUs for N greater than 10 million particles, with multi-GPU scaling.
NasZip delivers up to 8.4x speedup over CPU baselines and 1.69x over prior NDP accelerators for ANNS by combining near-data processing with statistics-based PCA early exiting, dynamic-float encoding, and data-aware neighbor mapping.
Memanto delivers 89.8% and 87.1% accuracy on LongMemEval and LoCoMo benchmarks using typed semantic memory and information-theoretic retrieval, outperforming hybrid graph and vector systems with a single query and zero ingestion cost.
citing papers explorer
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HaS: Accelerating RAG through Homology-Aware Speculative Retrieval
HaS accelerates RAG retrieval via homology-aware speculative retrieval and homologous query re-identification validation, cutting latency 24-37% with 1-2% accuracy drop on tested datasets.
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JZ-Tree: GPU friendly neighbour search and friends-of-friends with dual tree walks in JAX plus CUDA
JZ-Tree introduces a flattened Morton plane-based tree hierarchy enabling collaborative dual-tree walks that deliver more than 10x faster exact k-NN search and FoF clustering on GPUs for N greater than 10 million particles, with multi-GPU scaling.
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NasZip: Software and Hardware Co-Design to Accelerate Approximate Nearest Neighbor Search with DIMM-Based Near-Data Processing
NasZip delivers up to 8.4x speedup over CPU baselines and 1.69x over prior NDP accelerators for ANNS by combining near-data processing with statistics-based PCA early exiting, dynamic-float encoding, and data-aware neighbor mapping.
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Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents
Memanto delivers 89.8% and 87.1% accuracy on LongMemEval and LoCoMo benchmarks using typed semantic memory and information-theoretic retrieval, outperforming hybrid graph and vector systems with a single query and zero ingestion cost.