<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Transformer on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/transformer/</link><description>Recent content in Transformer on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Mon, 13 Jul 2026 07:40:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/transformer/index.xml" rel="self" type="application/rss+xml"/><item><title>从GPT到Transformer：架构创新的时间线</title><link>https://guijiagi.com/posts/article-89/</link><pubDate>Mon, 13 Jul 2026 07:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-89/</guid><description>回顾从Transformer诞生到2026年的架构演进，梳理每一次关键突破的技术脉络</description></item><item><title>深度解析RoPE位置编码及其变体：从原理到演进</title><link>https://guijiagi.com/posts/article-80/</link><pubDate>Mon, 13 Jul 2026 06:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-80/</guid><description>系统解析Rotary Position Embedding的数学原理、关键变体与工程实现，理解为什么它成为主流位置编码方案</description></item><item><title>从BERT到GPT：语言模型的进化史</title><link>https://guijiagi.com/posts/article-65/</link><pubDate>Mon, 13 Jul 2026 03:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-65/</guid><description>回顾从BERT到GPT再到大模型时代的语言模型演进历程，梳理关键技术节点和思想脉络</description></item><item><title>深度解析注意力机制的变体与演进</title><link>https://guijiagi.com/posts/article-55/</link><pubDate>Mon, 13 Jul 2026 02:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-55/</guid><description>从标准Self-Attention到Linear Attention、Flash Attention、Sparse 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+0800</pubDate><guid>https://guijiagi.com/posts/rope-rotation-position-encoding/</guid><description>深入解析RoPE旋转位置编码的数学原理、长度外推方法及2026年最新改进</description></item><item><title>Transformer注意力机制深度剖析</title><link>https://guijiagi.com/posts/transformer-attention-mechanism-deep/</link><pubDate>Thu, 02 Jul 2026 10:50:00 +0800</pubDate><guid>https://guijiagi.com/posts/transformer-attention-mechanism-deep/</guid><description>从数学原理到工程实现，全面拆解Transformer注意力机制的核心细节</description></item><item><title>后LLM时代：什么将取代Transformer</title><link>https://guijiagi.com/posts/post-llm-era-paradigm/</link><pubDate>Thu, 02 Jul 2026 10:32:00 +0800</pubDate><guid>https://guijiagi.com/posts/post-llm-era-paradigm/</guid><description>LLM时代终将过去，什么架构将取代Transformer？</description></item><item><title>Transformer架构2026：从注意力机制到混合专家的演进</title><link>https://guijiagi.com/posts/transformer-architecture-2026-attention-to-moe-evolution/</link><pubDate>Tue, 30 Jun 2026 09:20:00 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+0800</pubDate><guid>https://guijiagi.com/posts/transformer-architecture-2026-evolution/</guid><description>深入解析Transformer架构在2026年的最新演进，涵盖Attention机制优化、MoE混合专家架构、Mamba状态空间模型三大方向</description></item><item><title>Transformer替代架构Survey</title><link>https://guijiagi.com/posts/transformer-alternatives-survey/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/transformer-alternatives-survey/</guid><description>Transformer替代架构Survey</description></item><item><title>注意力机制变体对比分析</title><link>https://guijiagi.com/posts/attention-mechanism-variants-2026/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/attention-mechanism-variants-2026/</guid><description>系统对比分析主流注意力机制变体，从标准Self-Attention到Flash Attention的技术演进</description></item><item><title>超越 Transformer：Mamba/SSM/RWKV 架构深度对比</title><link>https://guijiagi.com/posts/transformer-alternatives-2026/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/transformer-alternatives-2026/</guid><description>深度解析 Mamba、S4/S6、RWKV 等替代 Transformer 的新一代架构，从状态空间模型到线性注意力，全面对比优劣。</description></item><item><title>注意力机制演进史：从 Bahdanau 到 Flash Attention 3</title><link>https://guijiagi.com/posts/attention-mechanism-evolution/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/attention-mechanism-evolution/</guid><description>全面梳理注意力机制从 2014 年到 2025 年的演进历程，涵盖 Bahdanau Attention、Self-Attention、Multi-Head、Linear Attention、Flash Attention 等关键技术。</description></item><item><title>Attention 机制详解：从 Self-Attention 到 Multi-Query Attention</title><link>https://guijiagi.com/posts/attention-mechanism-explained/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/attention-mechanism-explained/</guid><description>深入解析 Transformer Attention 机制的数学原理、变体演进与复杂度分析</description></item><item><title>Transformer 架构深度解析：从 Attention 到 GPT</title><link>https://guijiagi.com/posts/transformer-architecture-deep/</link><pubDate>Wed, 24 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/transformer-architecture-deep/</guid><description>深入剖析 Transformer 架构的每个组件，从自注意力机制到 GPT 解码器-only 架构的演进</description></item></channel></rss>