{"id":230282,"date":"2026-02-01T06:06:21","date_gmt":"2026-02-01T12:06:21","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2026\/02\/geometry-behind-how-ai-agents-learn-revealed"},"modified":"2026-02-01T06:06:21","modified_gmt":"2026-02-01T12:06:21","slug":"geometry-behind-how-ai-agents-learn-revealed","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2026\/02\/geometry-behind-how-ai-agents-learn-revealed","title":{"rendered":"Geometry behind how AI agents learn revealed"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/geometry-behind-how-ai-agents-learn-revealed3.jpg\"><\/a><\/p>\n<p>A new study from the University at Albany shows that artificial intelligence systems may organize information in far more intricate ways than previously thought. The study, \u201c<a href=\"https:\/\/www.arxiv.org\/abs\/2507.22010\" target=\"_blank\">Exploring the Stratified Space Structure of an RL Game with the Volume Growth Transform<\/a>,\u201d has been published online through <i>arXiv<\/i>.<\/p>\n<p>For decades, scientists assumed that neural networks encoded data on smooth, low-dimensional surfaces known as manifolds. But UAlbany researchers found that a transformer-based reinforcement-learning model instead organizes its internal representations in stratified spaces\u2014geometric structures composed of multiple interconnected regions with different dimensions. Their findings mirror recent results in large language models, suggesting that stratified geometry might be a fundamental feature of modern AI systems.<\/p>\n<p>\u201cThese models are not living on simple surfaces,\u201d said Justin Curry, associate professor in the Department of Mathematics and Statistics in the College of Arts and Sciences. \u201cWhat we see instead is a patchwork of geometric layers, each with its own dimensionality. It\u2019s a much richer and more complex picture of how AI understands the world.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A new study from the University at Albany shows that artificial intelligence systems may organize information in far more intricate ways than previously thought. The study, \u201cExploring the Stratified Space Structure of an RL Game with the Volume Growth Transform,\u201d has been published online through arXiv. For decades, scientists assumed that neural networks encoded data [\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-230282","post","type-post","status-publish","format-standard","hentry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/230282","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=230282"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/230282\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=230282"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=230282"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=230282"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}