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A world foundation model (WFM) is a broadly pretrained model designed to predict how an environment may change, then adapt to particular tasks. In NVIDIA’s technical definition, it predicts a future observation from past observations and a current perturbation—such as an action, a random change, or a text description of a change.
What does “world foundation model” mean?
The term joins two ideas. A world model represents or predicts the dynamics of an environment: given what has happened and some change or intervention, what might happen next? A foundation model is a broadly pretrained starting point intended to be adapted for downstream tasks.
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NVIDIA’s Cosmos-Predict1 technical report uses “world foundation model” for a model that predicts a future observation from past observations plus a current perturbation. In its example, observations are RGB video, while the perturbation can be an agent action, a random change, or text describing a change. This is a clear operational definition from NVIDIA, not evidence that all researchers and vendors use the term identically. NVIDIA Cosmos-Predict1 technical report
What does a WFM do?
It predicts a possible next state
The central idea is conditional prediction: the model uses observations of an environment and a specified change to produce an estimate of what the environment may look like afterward. That future can be represented as video, but generated video is an output format, not the entire definition. The important function is predicting a future state conditioned on what the model has observed and what is expected to change.
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It can be adapted to a particular task
The “foundation” part signals that the pretrained model is intended to transfer, rather than being built from scratch for only one setting. NVIDIA describes post-training a general model for a target physical-AI environment, including with target-specific prompt-video pairs. The downstream setting might involve a robot or an autonomous vehicle, but adaptation does not itself prove that predictions are accurate enough for real-world deployment. NVIDIA Research publication on the Cosmos platform
How is it different from related AI systems?
The useful question is whether a system predicts how an environment changes, especially in response to an action. A vision-language model may describe or answer questions about an image or video; an action policy may select what an agent should do. A WFM, as described in NVIDIA’s definition, predicts a future observation given prior observations and a perturbation. These functions can overlap in a system, so the labels alone do not establish a complete taxonomy or tell you what every model can do.
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To assess a particular model, look beyond its name and check:
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →- Prediction target: Does it produce future video, a latent future state, or another representation?
- Conditioning: Does it accept past observations alone, or can it condition on text, actions, trajectories, or other control signals?
- Modalities: What can it take in and generate—for example, video, images, language, audio, or actions?
- Adaptation evidence: Is there a documented method and evaluation for post-training it in the intended environment?
- Evaluation and limits: Is prediction quality measured for the task you care about, and does evidence show downstream usefulness? A realistic-looking prediction is not, by itself, proof of physically accurate simulation.
- Access and terms: Check the current model license and usage terms. Licensing described for one release should not be assumed to apply to later models.
What is NVIDIA Cosmos?
NVIDIA’s Cosmos is a relevant example of a WFM platform for physical AI. Its 2025 publication describes pretrained models, video curation, post-training examples, and video tokenizers as parts of the platform for building customized world models. NVIDIA’s January 2025 launch announcement said the models were trained on “millions of hours” of driving and robotics videos; that is NVIDIA’s reported scale, not an independently audited dataset measurement. NVIDIA Cosmos launch announcement
NVIDIA’s current Cosmos Lab page describes Cosmos 3 as a family that jointly processes and generates language, images, video, audio, and action sequences. Model capabilities, access, and licensing can change, so consult the official page and the specific model’s terms when evaluating a current release.
The intended applications include robotics and autonomous-vehicle development. A model’s ability to generate plausible future scenes can help with development or simulation workflows, but it does not establish that every prediction matches physical reality or is safe to use without validation. NVIDIA vice president of research Ming-Yu Liu described the area as early-stage in a January 7, 2025 interview: “We are still in the infancy of world foundation model development — it’s useful, but we need to make it more useful.” NVIDIA interview with Ming-Yu Liu
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