{"schema":"record/v1","id":"art_01M5MC9P299NTTJRG5ASQQXZJY","type":"article","revision":1,"etag":"\"r1.s0\"","title":"When driving and robot-learning papers say \"world model\", what exactly do they mean (inputs, outputs, intended use), and where does the word claim more than the evidence tests?","summary":"In every source inspected here a \"world model\" is a learned generative model that takes past observations (video) plus some control input and produces future video; the sources differ in what the model is for. GAIA-1 and Vista define it as predicting action consequences, NVIDIA as predicting the next observation given a \"perturbation\", 1X as an evaluator that also predicts task success. Several sources list uses (planning, policy training, evaluation) that they do not test; by 2026 Wayve and 1X frame the model as a closed-loop evaluation tool. The word is the authors' own label and none of the inspected definitions is tied to a physics-accuracy test.","status":"active","body":{"text":"In every source inspected here a \"world model\" is a learned generative model that takes past observations (video) plus some control input and produces future video; the sources differ in what the model is for. GAIA-1 and Vista define it as predicting action consequences, NVIDIA as predicting the next observation given a \"perturbation\", 1X as an evaluator that also predicts task success. Several sources list uses (planning, policy training, evaluation) that they do not test; by 2026 Wayve and 1X frame the model as a closed-loop evaluation tool. The word is the authors' own label and none of the inspected definitions is tied to a physics-accuracy test.\n\nCurator qualifications: Sources agree a world model is a learned generator of future video from past observations plus control; they differ on purpose (action consequences, next state).\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2309.17080v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2501.03575v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2405.17398v5","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2309.09777v2","note":"candidate evidence excerpts"},{"url":"https://www.1x.tech/discover/1x-world-model","note":"candidate evidence excerpts"},{"url":"https://www.1x.tech/1x-world-model.pdf","note":"candidate evidence excerpts"},{"url":"https://wayve.ai/thinking/gaia-4/","note":"candidate evidence excerpts"}]},"categories":["T06","T15","T19"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2309.17080v1","claim":"In every source inspected here a \"world model\" is a learned generative model that takes past observations (video) plus some control input and produces future video; the sources differ in what the model is for. 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GAIA-1 and Vista define it as predicting action consequences, NVIDIA as predicting the next observation given a \"perturbation\", 1X as an evaluator that also predicts task success. Several sources list uses (planning, policy training, evaluation) that they do not test; by 2026 Wayve and 1X frame the model as a closed-loop evaluation tool. The word is the authors' own label and none of the inspected definitions is tied to a physics-accuracy test.\n\nCurator qualifications: Sources agree a world model is a learned generator of future video from past observations plus control; they differ on purpose (action consequences, next state).\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2309.17080v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2501.03575v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2405.17398v5","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2309.09777v2","note":"candidate evidence excerpts"},{"url":"https://www.1x.tech/discover/1x-world-model","note":"candidate evidence excerpts"},{"url":"https://www.1x.tech/1x-world-model.pdf","note":"candidate evidence excerpts"},{"url":"https://wayve.ai/thinking/gaia-4/","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"When driving and robot-learning papers say \"world model\", what exactly do they mean (inputs, outputs, intended use), and where does the word claim more than the evidence tests?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"In every source inspected here a \"world model\" is a learned generative model that takes past observations (video) plus some control input and produces future video; the sources differ in what the model is for. 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