arXiv (All)
open access
No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval
Multi-vector retrieval (MVR) models, exemplified by ColBERT, have established new benchmarks in retrieval accuracy by preserving fine-grained token-level interactions. However, this granularity imposes prohibitive storage and retrieval efficiency bottlenecks: to manage the immense memory footprint a
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
- When Does Low-Bit Quantization Preserve the Decisions of Vector Search?#996671
- RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Search#799588
- When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation#625100
- When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation#622827
- Non-standard memory models with indexed retrieval#780797
Record · ID 1128604
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