---
id: 20261020-T0-04
title: "离散扩散模型的新思路：让前向过程按区域区别对待上下文"
title_en: "Region-Inhomogeneous Forward Dynamics Proposed for Discrete Diffusion Models"
url: https://ai.daily.yangsir.net/daily/20261020-T0-04
issue_date: 2026-10-20
publish_date: 2026-10-19T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2610.22380
---

# 离散扩散模型的新思路：让前向过程按区域区别对待上下文

新论文研究离散语言扩散模型的前向过程设计。作者指出，连续扩散把前向过程当作设计空间，而离散语言扩散普遍采用吸收式破坏，导致生成全程完全信任上下文——被破坏的上下文会被当作真值直接复制。论文提出区域非均匀的前向动力学，让模型对不同区域的上下文采取不同的信任程度。这为控制生成过程中的信息保留提供了新的调节手段。

## English Version

**Region-Inhomogeneous Forward Dynamics Proposed for Discrete Diffusion Models**

A new paper examines forward process design for discrete language diffusion models. The authors note that while continuous diffusion treats the forward process as a design space, discrete language diffusion typically uses absorbing corruption, leaving context fully trusted throughout generation, so corrupted context gets copied as ground truth. The paper proposes region-inhomogeneous forward dynamics, letting the model apply different levels of trust to different context regions. This adds a new lever for controlling information retention during generation.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2610.22380)

**详情页**：https://ai.daily.yangsir.net/daily/20261020-T0-04

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