<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Openai on Commentary of Takao</title><link>https://takao.blog/zh/tags/openai/</link><description>Recent content in Openai on Commentary of Takao</description><generator>Hugo -- gohugo.io</generator><language>zh</language><copyright>Commentary of Takao</copyright><lastBuildDate>Sun, 12 Jul 2026 04:12:51 +0900</lastBuildDate><atom:link href="https://takao.blog/zh/tags/openai/index.xml" rel="self" type="application/rss+xml"/><item><title>AI推理模型与OpenAI</title><link>https://takao.blog/zh/web/ai-reasoning-models-openai/</link><pubDate>Wed, 05 Feb 2025 00:00:00 +0900</pubDate><guid>https://takao.blog/zh/web/ai-reasoning-models-openai/</guid><description>&lt;img src="https://takao.blog/img/thumbnail/ai-reasoning-models-openai-zh.png" alt="Featured image of post AI推理模型与OpenAI" /&gt;&lt;h2 id="introduction"&gt;Introduction
&lt;/h2&gt;&lt;p&gt;In recent years, the evolutionary pace of generative AI has been nothing short of extraordinary. Among these developments, the new reasoning models released by OpenAI (such as the o1 and o3 series) employ a fundamentally different architecture and approach compared to conventional large language models like GPT-4o.&lt;/p&gt;
&lt;p&gt;Traditional Large Language Models (LLMs) excel at predicting and generating the &amp;ldquo;most likely next word&amp;rdquo; at high speeds. However, when faced with tasks demanding deep logical deduction—such as complex logic puzzles, advanced mathematics, or refactoring large-scale codebases—they often rely on intuitive leaps, leading to logical inconsistencies known as hallucinations.&lt;/p&gt;</description></item></channel></rss>