---
id: 20260311-T0-13
title: "Transformer模型跨尺度神经信息处理机制研究"
title_en: "Transformer Models' Cross-Scale Neural Processing"
url: https://ai.daily.yangsir.net/daily/20260311-T0-13
issue_date: 2026-03-11
publish_date: 2026-03-10T04:00:00.000Z
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2603.06592
---

# Transformer模型跨尺度神经信息处理机制研究

arXiv论文揭示基于Transformer的语言模型在不同尺度上存在统一的层次化潜在结构。研究团队通过解构模型训练过程，发现神经元激活模式存在层级化规律，能解释模型中的复杂现象。该理论框架将模型参数量从10亿扩展至1万亿级时仍保持稳定，准确率波动不超过2%。该发现为设计更高效的Transformer架构提供理论基础，可应用于大模型压缩和推理优化。

## English Version

**Transformer Models' Cross-Scale Neural Processing**

arXiv paper reveals unified hierarchical latent structures across different scales in Transformer language models. By deconstructing training processes, researchers found hierarchical neuron activation patterns explaining complex phenomena. The theoretical framework remains stable when scaling parameters from 1B to 1T, with accuracy fluctuations under 2%. This provides theoretical foundations for more efficient Transformer designs applicable to large model compression and inference optimization.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2603.06592)

**详情页**：https://ai.daily.yangsir.net/daily/20260311-T0-13

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