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LatComm: Reusable Context Compression for Latent Communication between Large Language Models

Huawei Lin, Jinting Liu, Tan Yu, Weijie Zhao · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26840 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

将多个大型语言模型(LLM)结合在一起的系统通常会使用一个背景来处理许多问题。当背景在模型之间作为文本传递时,接收模型会对每个问题重新处理所有代码。避免这种成本需要一个紧凑的、可重复使用的语境表示,并且可以通过不同的LLM读取。现有的压缩和隐形通信方法每种都只提供一些这些属性。我们引入了LatComm,一个隐形通信界面,通过将编码与阅读分开来提供这三个:语境被编码一次,而没有问题,接收者会选择每个问题要读什么。一个冷冻的语境编码器LLM会将语境压缩成一个固定尺寸的包,并将其发送到不同的冷冻接收器LLM,该接收器会通过交叉注意

Machine translation (Meta M2M100); the English original follows.

Abstract

Systems that combine several large language models (LLMs) often use one context for many questions. When the context is passed between models as text, the receiving model processes all of its tokens again for every question. Avoiding this cost requires a context representation that is compact, reusable across questions, and readable by a different LLM. Existing compression and latent communication methods each provide only some of these properties. We introduce LatComm, a Latent Communication interface that provides all three by separating encoding from reading: the context is encoded once without the question, and the receiver selects what to read for each question. A frozen context encoder LLM compresses the context into a fixed-size packet and sends it to a different frozen receiver LLM, which reads the packet through cross-attention, with gates computed from its prompt. On 12 multiple-choice question-answering datasets, LatComm improves average accuracy over single-vector baselines by up to 10.2 points. On QuALITY, it reduces the receiver's input by 57x and answers the questions of an article 3x faster.

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Topics: Computer science, Context (archaeology), Encoding (memory), Context model, Network packet, Language model

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