arXiv (All)
open access
When Does Low-Bit Quantization Preserve the Decisions of Vector Search?
Low-bit quantization can achieve high recall on some vector representations and fail sharply on others, while average distortion and global rank correlation do not explain the difference. We study quantized vector search at the level of the comparisons consumed by ranking and graph-pruning algorithm
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Search#799588
- No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval#1128604
- Attention Calibration for Position-Fair Dense Retrieval#1123110
- Centering before Pruning: Lightweight Geometry Correction for Diversity-Based Visual Token Pruning in LVLMs#799479
- Centering before Pruning: Lightweight Geometry Correction for Diversity-Based Visual Token Pruning in LVLMs#798056
Record · ID 996671
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive