传统嵌入向量要么全存要么全废，维度定死；嵌套表示（MRL）训练出的向量有个特殊性质——只取前 128 维依然可用，取前 256 维更好。本文讲清它的直觉原理、两种用法（截断降维与级联检索）、以及「普通模型向量不能随便截」这条红线。

「北京」和「首都」没有一个字相同，计算机却能算出它们语义相近——秘密是把文字映射成高维空间里的向量，让语义关系变成几何关系。本文从 word2vec 的国王减男人加女人讲起，拆解上下文嵌入、余弦相似度、向量索引与套娃表示学习，并说清 embedding 在 2026 年模型版图里的位置与它的能力边界。

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看榜单选 embedding 模型经常翻车，因为榜单是通用语料，你的数据不是。本文给出语言与领域的第一分水岭、维度的真实代价、可信评测的做法与工程参数对照表，五十条真实问题即可完成一次有依据的决策。

Cohere has released Embed 5, a new embedding model family. It targets enterprise search, RAG, and agentic retrieval. The…

Perplexity Research and turbopuffer have released **pplx-embed-v2-context-9b-preview**, a contextual embedding model for…

Extending a text embedding model to new modalities typically degrades text retrieval quality, and existing omni-modal em…

Google Research has introduced Retrieve-for-Train (R4T), a framework for search that returns coherent, diverse result se…

Recursive self-improvement is becoming increasingly vital for autonomous AI agents, where progress hinges on discovering…

Late-interaction retrieval is the state-of-the-art for visual document search, but it pays for its accuracy in storage. …