扩散语言模型用于无损文本压缩：突破吞吐瓶颈

AI 摘要

本文首次将扩散语言模型（DLMs）引入无损文本压缩领域，以替代自回归LLM的逐符号生成瓶颈。作者设计了符号提交调度和初始上下文策略，解决DLM在压缩中的算法挑战。在enwik8基准上的实验表明，该框架在压缩率和吞吐量权衡上优于现有神经压缩方法，并具有进一步改进的潜力。

正文节选

Diffuse to Compress: Leveraging Diffusion LMs for Lossless Compression Abstract We study the problem of lossless text compression, motivated by the rapid growth in the collection and storage of digital textual data — including plain text, source code, and structured formats such as XML — and by recent advances in neural language model-based compression. In particular, recent LLM-based approaches, whether built on symbol-ranking pipelines or paired with a statistical compressor, have demonstrated

发布时间：2026-08-13 12:00

抓取时间：2026-08-13 12:05

来源机构：arXiv