Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings

No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval

arXiv:2605. 30120v3 Announce Type: replace-cross Abstract: Multi-vector retrieval (MVR) models, exemplified by ColBERT, have established new benchmarks in retrieval accuracy by preserving fine-grained token-level interactions.

RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Search

arXiv:2608.30384v1 Announce Type: new Abstract: By introducing RSLM (Rotated Scaled Lloyd-Max), a family of training-free vector quantization codecs compressing embeddings to 1--4 bits per dimension,...

NeuRoute: Logit-Guided Neural Routing for Billion-Scale Vector Search with Sub-Hour Index Construction

arXiv:2608. 15438v1 Announce Type: cross Abstract: Building approximate nearest neighbor (ANN) indexes at billion scale is often dominated by expensive global clustering or graph construction, making time-to-index a first-order systems concern.

Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

Projection and Quantisation: A Unifying View of Learning to Hash, from Random Projections to the RAG Era

arXiv:2510. 04127v2 Announce Type: replace-cross Abstract: Approximate nearest neighbour (ANN) search underpins large-scale retrieval, increasingly within the retrieval-augmented generation pipelines that ground large language models, yet the methods that address it have multiplied across communities until they are seldom read as a single field.

Matryoshka Hash Representations for Model-Aware Compact Semantic Retrieval

Matryoshka Hash Representations (MHR) propose a two‑stage quantization approach for retrieval‑augmented generation. First, a long binary code is learned; then, frozen, additional zero‑initialized residual adaptors are trained to produce searchable prefixes of varying byte budgets. Evaluated on MS MARCO and transferred to seven BEIR datasets, MHR achieves higher NDCG@10 and Recall@100 at 32‑byte budgets than baselines, especially in low‑budget regimes, and can also improve candidate shortlisting and graph‑index pruning.

PolarQuant: Leveraging Polar Transformation for Efficient Key Cache Quantization and Decoding Acceleration

arXiv:2502. 00527v2 Announce Type: replace Abstract: The KV cache in large language models is a dominant factor in memory usage, limiting their broader applicability.

VIBE: Vector Index Benchmark for Embeddings

arXiv:2505. 17810v2 Announce Type: replace Abstract: Approximate nearest neighbor (ANN) search is a performance-critical component of many machine learning pipelines, and rigorous benchmarking is essential for assessing the performance of vector indexes for ANN search.

Panorama: Fast-Track Nearest Neighbors

arXiv:2510. 00566v4 Announce Type: replace-cross Abstract: Approximate Nearest-Neighbor Search (ANNS) pipelines for high-dimensional neural embeddings spend the bulk of their query time in candidate verification, making it the primary bottleneck in the search process.

GEM-KMeans: Memory-Efficient and Accurate Clustering on Massive Scale with GPU Optimization

arXiv:2609.36074v1 Announce Type: new Abstract: Memory-efficient scaling on clustering problems without sacrificing statistical accuracy is of central interest for large-scale data analysis and machi...

Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering

arXiv:2606. 10896v1 Announce Type: new Abstract: We present \textbf{Flash-GMM}, a fused Triton kernel for efficient computation of Gaussian Mixture Models (GMMs) over large-scale data in a single GPU pass.

Fractal KV-Cache Archives: Lossless Symbolic Storage with In-Place Retrieval for Long-Context LLM Inference

arXiv:2607. 07144v1 Announce Type: new Abstract: The key-value (KV) cache dominates the memory cost of long-context autoregressive inference, and a growing body of work compresses it through quantization, eviction, or offloading.