---
id: 20260708-T0-01
title: "分层稀疏注意力新方法HSA-D，实现无限上下文建模"
title_en: "HSA-D Achieves Infinite Context Modeling with Hierarchical Sparse Attention"
url: https://ai.daily.yangsir.net/daily/20260708-T0-01
issue_date: 2026-07-08
publish_date: 2026-07-07T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2607.02980
---

# 分层稀疏注意力新方法HSA-D，实现无限上下文建模

arXiv 新论文提出分层稀疏注意力（HSA-D），解决了LLM处理长文本时计算成本高和长度外推差的问题。通过改进的分块稀疏注意力机制，该方法在保持线性计算复杂度的同时，实现了无限上下文建模。与现有稀疏注意力方法不同，HSA-D在效率和效果上达到了更优平衡，为长文档理解、代码生成等场景提供了新思路。

## English Version

**HSA-D Achieves Infinite Context Modeling with Hierarchical Sparse Attention**

A new arXiv paper introduces Hierarchical Sparse Attention (HSA-D), addressing the quadratic computation cost and poor length extrapolation of dense attention in LLMs. By improving chunk-wise sparse attention, HSA-D achieves infinite context modeling with linear complexity, striking a better balance between efficiency and effectiveness for long-document understanding and code generation.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2607.02980)

**详情页**：https://ai.daily.yangsir.net/daily/20260708-T0-01

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