---
id: 20260814-T0-09
title: "扩散模型做无损压缩：新方法缩小文本文件体积"
title_en: "Diffuse to Compress: Diffusion LMs Achieve Lossless Compression"
url: https://ai.daily.yangsir.net/daily/20260814-T0-09
issue_date: 2026-08-14
publish_date: 2026-08-13T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2608.11249
---

# 扩散模型做无损压缩：新方法缩小文本文件体积

arXiv新论文探索利用扩散语言模型（Diffusion LMs）进行无损文本压缩。随着纯文本、源代码和XML等数字文本数据快速增长，存储需求激增，传统压缩方法面临瓶颈。该研究结合神经语言模型的最新进展，展示扩散模型在压缩效率上的潜力，可有效减小文件体积，适用于大数据存储和传输场景。

## English Version

**Diffuse to Compress: Diffusion LMs Achieve Lossless Compression**

An arXiv paper investigates leveraging diffusion language models for lossless text compression, addressing the explosive growth of digital text data like plain text, source code, and XML. Building on advances in neural LMs, the approach demonstrates improved compression efficiency, offering a viable solution for reducing storage and transmission costs in large-scale data environments.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2608.11249)

**详情页**：https://ai.daily.yangsir.net/daily/20260814-T0-09

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