---
id: 20261023-T0-10
title: "DiffResearch用扩散语言模型加速深度研究，压缩端到端延迟"
title_en: "DiffResearch Uses Diffusion Language Models to Cut Deep Research Latency"
url: https://ai.daily.yangsir.net/daily/20261023-T0-10
issue_date: 2026-10-23
publish_date: 2026-10-22T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2610.28909
---

# DiffResearch用扩散语言模型加速深度研究，压缩端到端延迟

arXiv新论文DiffResearch指出，深度研究系统需要反复规划、收集证据、综合和修订，端到端延迟是交互式使用的关键瓶颈。现有生产系统几乎都基于自回归语言模型，继承了从左到右的解码方式。DiffResearch改用扩散语言模型来加速这一流程。论文摘要未给出具体加速倍数。

## English Version

**DiffResearch Uses Diffusion Language Models to Cut Deep Research Latency**

A new arXiv paper, DiffResearch, notes that deep research systems iteratively plan, gather evidence, synthesize and revise, making end-to-end latency a key bottleneck for interactive use. Nearly all production systems rely on autoregressive language models and inherit left-to-right decoding. DiffResearch instead applies diffusion language models to speed up the pipeline. The abstract does not state a specific speedup figure.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2610.28909)

**详情页**：https://ai.daily.yangsir.net/daily/20261023-T0-10

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