{"id":241694,"date":"2026-07-28T08:11:18","date_gmt":"2026-07-28T13:11:18","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/07\/inspired-by-how-children-learn-new-ai-framework-learns-to-theorize-the-world-from-observations"},"modified":"2026-07-28T08:11:18","modified_gmt":"2026-07-28T13:11:18","slug":"inspired-by-how-children-learn-new-ai-framework-learns-to-theorize-the-world-from-observations","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/07\/inspired-by-how-children-learn-new-ai-framework-learns-to-theorize-the-world-from-observations","title":{"rendered":"Inspired by how children learn, new AI framework learns to theorize the world from observations"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/inspired-by-how-children-learn-new-ai-framework-learns-to-theorize-the-world-from-observations2.jpg\"><\/a><\/p>\n<p>A KAIST research team has developed a next-generation world model, an internal model an AI builds to understand and predict the world, that learns executable theories from observation alone.<\/p>\n<p>The team led by Professor Sungjin Ahn from the School of Computing proposed a new learning paradigm called Learning-to-Theorize (L2T), which trains AI to theorize how the world works using only observed information. The team also built the Neural Theorizer (NEO), a neural network-based model that implements this paradigm.<\/p>\n<p>The research was presented at the 43rd International Conference on Machine Learning (ICML 2026), held in Seoul from July 6\u201311. The paper, <a href=\"https:\/\/arxiv.org\/abs\/2605.03413\" target=\"_blank\">published<\/a> on the <i>arXiv<\/i> preprint server, was also selected for the Best Paper Award at the Compositional Learning Workshop.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A KAIST research team has developed a next-generation world model, an internal model an AI builds to understand and predict the world, that learns executable theories from observation alone. The team led by Professor Sungjin Ahn from the School of Computing proposed a new learning paradigm called Learning-to-Theorize (L2T), which trains AI to theorize how [\u2026]<\/p>\n","protected":false},"author":662,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-241694","post","type-post","status-publish","format-standard","hentry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/241694","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/662"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=241694"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/241694\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=241694"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=241694"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=241694"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}