---
id: 20260326-T0-12
title: "稀疏特征技术将注意力计算成本降低90%"
title_en: "Sparse Features Reduce Attention Cost by 90%"
url: https://ai.daily.yangsir.net/daily/20260326-T0-12
issue_date: 2026-03-26
publish_date: 2026-03-25T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2603.22300
---

# 稀疏特征技术将注意力计算成本降低90%

最新研究提出通过特征稀疏化技术，将Transformer模型处理超长文本的计算成本从O(n²d)降至O(n)，实现了90%的性能提升。该方法在保持模型精度的同时，显著降低了内存占用和推理时间。研究人员已在多个基准测试中验证了其有效性，这一突破将为长文本处理提供更高效的解决方案。

## English Version

**Sparse Features Reduce Attention Cost by 90%**

A new study introduces feature sparsity techniques that reduce Transformer's computation cost from O(n²d) to O(n) for long-context processing, achieving 90% performance improvement. The method maintains model accuracy while significantly lowering memory usage and inference time. The approach has been validated across multiple benchmarks, offering a more efficient solution for long-text processing.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2603.22300)

**详情页**：https://ai.daily.yangsir.net/daily/20260326-T0-12

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