[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-world-model::en":3,"gloss-cluster-world-model::en":23,"gloss-next-world-model::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"world-model","core-ai","World Model","A world model is a learned internal simulator: a neural network that predicts how an environment will change in response to actions, letting a system \"imagine\" outcomes before acting. The idea comes from model-based reinforcement learning — Ha and Schmidhuber's 2018 work trained agents inside their own dream of the environment — and DeepMind's Dreamer line showed agents learning complex control almost entirely in imagination. The term now also labels large video-generation models like Sora and Genie, on the argument that predicting plausible future frames requires implicitly learning physics, object permanence, and cause and effect. Skeptics counter that generating convincing video is not the same as maintaining a consistent causal model, and glitches expose the gap. For builders, world models matter as the research bet behind robotics, autonomous driving, and interactive 3D environments — domains where trial and error in the real world is too slow or dangerous.","A world model is a learned simulator that predicts how an environment responds to actions — the research bet behind robotics and video-gen AI.",null,[11,14,17,20],{"slug":12,"name":13},"deep-learning","Deep Learning",{"slug":15,"name":16},"foundation-model","Foundation Model",{"slug":18,"name":19},"reinforcement-learning","Reinforcement Learning (RL)",{"slug":21,"name":22},"video-synthesis","Video Synthesis",[24,28,32,36,39,42,45,48,51,54,57,58],{"slug":25,"category":5,"name":26,"updated_at":27},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":33,"category":5,"name":34,"updated_at":35},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":37,"category":5,"name":38,"updated_at":27},"attention","Attention",{"slug":40,"category":5,"name":41,"updated_at":35},"beam-search","Beam Search",{"slug":43,"category":5,"name":44,"updated_at":31},"benchmark-contamination","Benchmark Contamination",{"slug":46,"category":5,"name":47,"updated_at":31},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":49,"category":5,"name":50,"updated_at":35},"computer-vision","Computer Vision",{"slug":52,"category":5,"name":53,"updated_at":31},"constitutional-ai","Constitutional AI",{"slug":55,"category":5,"name":56,"updated_at":27},"context-window","Context Window",{"slug":12,"category":5,"name":13,"updated_at":35},{"slug":59,"category":5,"name":60,"updated_at":27},"diffusion-model","Diffusion Model"]