<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Technical Directions on WorldSense Tech Blog</title><link>https://worldsensetech.com/en/tags/technical-directions/</link><description>Recent content in Technical Directions on WorldSense Tech Blog</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Tue, 04 Aug 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://worldsensetech.com/en/tags/technical-directions/index.xml" rel="self" type="application/rss+xml"/><item><title>VLA vs World Models: Which Will Prevail?</title><link>https://worldsensetech.com/en/articles/vla-vs-world-model/</link><pubDate>Tue, 04 Aug 2026 00:00:00 +0000</pubDate><guid>https://worldsensetech.com/en/articles/vla-vs-world-model/</guid><description>&lt;p&gt;Between 2025 and 2026, two distinctly different technical directions have emerged in robot AI. One is the VLA (Vision-Language-Action) approach, represented by RT-2, OpenVLA, and pi-0. The other is the World Models approach, represented by DreamerV3, Genie, and DIAMOND.&lt;/p&gt;
&lt;p&gt;Many colleagues have asked me: between these two directions, which one should I bet on? My answer is: the question itself is wrong.&lt;/p&gt;
&lt;p&gt;In today&amp;rsquo;s article, I want to break down and compare these two approaches, explain the logic, advantages, and bottlenecks of each, and then discuss why I believe they will ultimately converge.&lt;/p&gt;</description></item></channel></rss>