Building frontier world models
The next frontier after language models is world models, and learning to predict the next state of a world. Hear from our researchers how these models really work at a low-level and the mountain we're climbing
Our most recent news
What we've been working on recently
Our most recent news
What we've been working on recently
World Model
Odyssey-2
Our most powerful general purpose world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Starchild-1
A step beyond world models that learn only from visual observation, toward systems that learn from richer multimodal interaction with the world
World Model
Agora-1
A multi-agent world model, enabling multiple participants—human or AI—to share and interact within the same world simulation in real-time
Reinforcement Learning
PROWL
A novel RL-driven adversarial framework where an RL agent explores game environments with the objective to improve world model performance
World Model
Odyssey-2
Our most powerful general purpose world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Starchild-1
A step beyond world models that learn only from visual observation, toward systems that learn from richer multimodal interaction with the world
World Model
Agora-1
A multi-agent world model, enabling multiple participants—human or AI—to share and interact within the same world simulation in real-time
Reinforcement Learning
PROWL
A novel RL-driven adversarial framework where an RL agent explores game environments with the objective to improve world model performance
World Model
Odyssey-2
Our most powerful general purpose world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Starchild-1
A step beyond world models that learn only from visual observation, toward systems that learn from richer multimodal interaction with the world
World Model
Agora-1
A multi-agent world model, enabling multiple participants—human or AI—to share and interact within the same world simulation in real-time
Reinforcement Learning
PROWL
A novel RL-driven adversarial framework where an RL agent explores game environments with the objective to improve world model performance
World Model
Odyssey-2
Our most powerful general purpose world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Starchild-1
A step beyond world models that learn only from visual observation, toward systems that learn from richer multimodal interaction with the world
World Model
Agora-1
A multi-agent world model, enabling multiple participants—human or AI—to share and interact within the same world simulation in real-time
Reinforcement Learning
PROWL
A novel RL-driven adversarial framework where an RL agent explores game environments with the objective to improve world model performance