<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Multimodal AI — The Latent Daily</title><description>Models that combine text, images, audio and video.</description><link>https://thelatentdaily.com/</link><language>en-us</language><atom:link href="https://thelatentdaily.com/rss/multimodal-ai.xml" rel="self" type="application/rss+xml"/><item><title>Vision Transformer (ViT) explained: an image is worth 16×16 words</title><link>https://thelatentdaily.com/research/vision-transformer-vit-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/vision-transformer-vit-explained/</guid><description>In 2020, Google showed that a plain transformer applied to image patches can rival convolutional networks. How ViT works, why it needs lots of data and what it enabled.</description><pubDate>Sun, 18 Oct 2026 05:17:00 GMT</pubDate><category>Computer Vision</category><category>research</category><category>vision-transformer</category><category>computer-vision</category><category>transformer</category><author>editors@thelatentdaily.com</author></item><item><title>CLIP explained: how OpenAI taught a model to connect images and words</title><link>https://thelatentdaily.com/research/clip-contrastive-language-image-pretraining-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/clip-contrastive-language-image-pretraining-explained/</guid><description>CLIP learned from 400 million image-caption pairs to match pictures with text, enabling zero-shot image classification and powering text-to-image generators.</description><pubDate>Sun, 18 Oct 2026 05:16:00 GMT</pubDate><category>Multimodal AI</category><category>research</category><category>clip</category><category>multimodal</category><category>computer-vision</category><author>editors@thelatentdaily.com</author></item><item><title>LLaVA explained: how to give a language model eyes cheaply</title><link>https://thelatentdaily.com/research/llava-visual-instruction-tuning-explained/</link><guid isPermaLink="true">https://thelatentdaily.com/research/llava-visual-instruction-tuning-explained/</guid><description>The 2023 LLaVA paper connected a vision encoder to an open language model and trained it on AI-generated visual instructions, creating an open multimodal assistant.</description><pubDate>Sun, 18 Oct 2026 05:15:00 GMT</pubDate><category>Multimodal AI</category><category>research</category><category>llava</category><category>multimodal</category><category>open-source</category><author>editors@thelatentdaily.com</author></item><item><title>How foundation models are changing robotics: vision-language-action models</title><link>https://thelatentdaily.com/articles/vision-language-action-models-robotics/</link><guid isPermaLink="true">https://thelatentdaily.com/articles/vision-language-action-models-robotics/</guid><description>Robots that follow instructions like &quot;pick up the extinct animal&quot;: how vision-language-action models such as RT-2 connect web knowledge to physical actions.</description><pubDate>Sun, 18 Oct 2026 04:33:00 GMT</pubDate><category>Robotics &amp; Humanoids</category><category>robotics</category><category>vla</category><category>embodied-ai</category><category>research</category><author>editors@thelatentdaily.com</author></item></channel></rss>