A missing form of intelligence
Odyssey's CTO Jeff Hawke presents on Odyssey's research at RAAIS 2026
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-3
Our most powerful foundation world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Agora-2
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-2
PROWL-2 is the first framework in which a team of agents and its world model each learn from their own curriculum, continually and within a single training loop
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
Odyssey-3
Our most powerful foundation world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Agora-2
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-2
PROWL-2 is the first framework in which a team of agents and its world model each learn from their own curriculum, continually and within a single training loop
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
Odyssey-3
Our most powerful foundation world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Agora-2
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-2
PROWL-2 is the first framework in which a team of agents and its world model each learn from their own curriculum, continually and within a single training loop
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
Odyssey-3
Our most powerful foundation world model yet, materially advancing the state-of-the-art in physical accuracy of world models
World Model
Agora-2
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-2
PROWL-2 is the first framework in which a team of agents and its world model each learn from their own curriculum, continually and within a single training loop
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