<?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/en/tags/openai/</link><description>Recent content in Openai on Commentary of Takao</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>Commentary of Takao</copyright><lastBuildDate>Sat, 13 Jun 2026 23:11:50 +0900</lastBuildDate><atom:link href="https://takao.blog/en/tags/openai/index.xml" rel="self" type="application/rss+xml"/><item><title>Understanding OpenAI's New Reasoning Models and Their Inner Workings</title><link>https://takao.blog/en/web/ai-reasoning-models-openai/</link><pubDate>Wed, 05 Feb 2025 00:00:00 +0900</pubDate><guid>https://takao.blog/en/web/ai-reasoning-models-openai/</guid><description>&lt;img src="https://takao.blog/img/thumnail.webp" alt="Featured image of post Understanding OpenAI's New Reasoning Models and Their Inner Workings" /&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>