A world model is an AI system that learns a predictive representation of how an environment changes over time. It can use observations, actions, or language to estimate future states and possible outcomes before an agent acts. World models support planning in model-based reinforcement learning, robotics, embodied AI, autonomous driving, and interactive simulation. This site catalogs these systems with model records, research papers, benchmark context, and source links. Start with the world models database, compare systems on the leaderboard, or read the Performance Index methodology.
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A generative model that learns to produce data by reversing a gradual noising process. In world models, diffusion-based architectures (used in NVIDIA Cosmos and UniSim) generate high-fidelity video frames representing possible future states of the environment.
Diffusion Model is a glossary concept in the architecture layer of the world models knowledge base.
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| Attribute | Value |
|---|---|
| Term | Diffusion Model |
| Category | Architecture |
| Definition | A generative model that learns to produce data by reversing a gradual noising process. In world models, diffusion-based architectures (used in NVIDIA Cosmos and UniSim) generate high-fidelity video frames representing possible future states of the environment. |
| Related Models | 2 |
| Related Research | 1 |
| Model | Lab | Category |
|---|---|---|
| NVIDIA Cosmos | NVIDIA | Foundation World Model |
| UniSim | Google DeepMind | Generative World Model |
| Topic | Summary |
|---|---|
| Foundation World Models | How foundation world models such as Cosmos and Genie 2 bring large-scale learned simulation to robotics, autonomous driving, and physical AI. |
| Term | Category | Definition |
|---|---|---|
| Autoregressive Model | Architecture | A model that generates outputs one element at a time, where each output depends on previous elements. IRIS treats world modeling as autoregressive token prediction, bridging language modeling and world modeling techniques. |
| Foundation Model | Paradigms | A large-scale model trained on broad data that can be adapted to many downstream tasks. Foundation world models (like NVIDIA Cosmos and Genie 2) learn general-purpose representations of world dynamics, analogous to how GPT models serve as foundations for language tasks. |
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Published by world-models.io editorial board.
Lead editor Bernard Grenat.
This glossary page publishes stable definitions linked to related models, research topics, and primary-source context.
Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-09-27.
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