---
id: 20260418-T0-14
title: "压缩感知指导的LLM结构化剪枝新方法"
title_en: "Compressed-Sensing-Guided Structured Pruning for LLMs"
url: https://ai.daily.yangsir.net/daily/20260418-T0-14
issue_date: 2026-04-18
publish_date: 2026-04-17T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2604.14156
---

# 压缩感知指导的LLM结构化剪枝新方法

arXiv论文提出基于压缩感知的LLM结构化剪枝方法。该方法在保持精度的同时，可压缩模型40%参数并降低30%解码延迟。研究通过保留关键神经元连接结构，实现推理加速与性能平衡，适用于资源受限场景部署。

## English Version

**Compressed-Sensing-Guided Structured Pruning for LLMs**

An arXiv paper introduces compressed-sensing-guided structured pruning for LLMs. It achieves 40% parameter reduction and 30% lower decoding latency while maintaining accuracy. By preserving critical neuron connections, it balances speed and performance for resource-constrained deployments.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2604.14156)

**详情页**：https://ai.daily.yangsir.net/daily/20260418-T0-14

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