Dense vs multi-vector in one line: one vector per text vs one per token
tomaarsen · x · 2026-08-18
A dense model compresses a whole text into one vector; a multi-vector model keeps one vector per token and scores query against document with MaxSim — for each query token, take its best match in the document, then sum. Nothing gets averaged away. This is part of a thread accompanying a blog post by Tom Aarsen, Antoine Chaffin and Raphael Sourty covering checkpoint loading, scoring, search stacks, page-image retrieval and keeping the index affordable.
Related event: Sentence Transformers v6.0 ships with first-class late interaction models(33 posts)→
More from coding & agent
- PHAROS: An Open-Source npm for MCP Servers, Written in Go — Nofear001 · 2026-08-19
- Using AI Agents to draft release reports from evidence collections — CodeByPoonam · 2026-08-19
- MUON: An Open-Source Shared Brain for Parallel Coding Agents — Virtual_Gift_5327 · 2026-08-19
- Netlify integrates OpenRouter to enable model swapping without code changes — thisiskp_ · 2026-08-19
- Dev runs three Codex accounts plus Claude to parallelize coding agents — ChanceKelch · 2026-08-19
- DeepSeek open sources 'deepseek-harness' agent framework with 130k+ stars — alex_verem · 2026-08-19