<?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>ReAct on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/react/</link><description>Recent content in ReAct 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>Thu, 16 Jul 2026 10:21:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/react/index.xml" rel="self" type="application/rss+xml"/><item><title>AI Agent的规划能力：从ReAct到Tree-of-Planning</title><link>https://guijiagi.com/posts/b2-af6834ab/</link><pubDate>Thu, 16 Jul 2026 10:21:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-af6834ab/</guid><description>系统梳理AI Agent规划算法的发展脉络，分析ReAct、Reflexion、LATS等规划方法的原理与适用场景</description></item><item><title>从ReAct到Reflexion：Agent推理范式演进</title><link>https://guijiagi.com/posts/article-38/</link><pubDate>Sun, 12 Jul 2026 23:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-38/</guid><description>Agent推理范式的三次跃迁：从ReAct到Reflexion再到自主规划的演进逻辑</description></item><item><title>从ReAct到Reflexion：Agent推理范式演进</title><link>https://guijiagi.com/posts/b2-64fc97fc/</link><pubDate>Sun, 12 Jul 2026 23:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-64fc97fc/</guid><description>Agent推理范式的三次跃迁：从ReAct到Reflexion再到自主规划的演进逻辑</description></item><item><title>推理增强提示技术：让AI的推理更深入</title><link>https://guijiagi.com/posts/reasoning-prompt-techniques/</link><pubDate>Thu, 02 Jul 2026 11:13:00 +0800</pubDate><guid>https://guijiagi.com/posts/reasoning-prompt-techniques/</guid><description>2026年推理增强提示技术全面指南，覆盖Self-Consistency、ToT、ReAct等进阶技巧</description></item><item><title>Agent规划算法对比：从ReAct到Tree of Thought的演进</title><link>https://guijiagi.com/posts/agent-planning-algorithms/</link><pubDate>Thu, 02 Jul 2026 10:04:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-planning-algorithms/</guid><description>系统对比2026年主流Agent规划算法的原理、优劣与适用场景</description></item><item><title>ReAct Prompting实战</title><link>https://guijiagi.com/posts/react-prompting-practice/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/react-prompting-practice/</guid><description>ReAct（Reasoning and Acting）提示框架的原理、模板与工程实践</description></item><item><title>ReAct vs Plan-and-Execute：智能体推理范式对比</title><link>https://guijiagi.com/posts/react-vs-plan-execute/</link><pubDate>Fri, 26 Jun 2026 14:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/react-vs-plan-execute/</guid><description>深入对比 ReAct 与 Plan-and-Execute 两种主流智能体推理范式的工作原理、优劣势及适用场景，指导工程选型。</description></item><item><title>ReAct Prompt 模式：推理与行动的交织</title><link>https://guijiagi.com/posts/react-prompt-pattern/</link><pubDate>Wed, 24 Jun 2026 16:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/react-prompt-pattern/</guid><description>深入解析 ReAct 模式的 Thought-Action-Observation 循环、工具调用集成与代码实现</description></item><item><title>Agent 循环机制设计：从 ReAct 到 Plan-and-Execute 的演进</title><link>https://guijiagi.com/posts/agent-loop-design/</link><pubDate>Tue, 23 Jun 2026 14:20:00 +0800</pubDate><guid>https://guijiagi.com/posts/agent-loop-design/</guid><description>深入分析 Agent 循环设计模式，从经典 ReAct 到现代 Plan-and-Execute</description></item></channel></rss>