Agora-1: The Multi-Agent World Model

Agora-1 learns two distinct functions. First, it learns how the world state evolves over time in response to player interaction. To do this, we train a model directly on the internal state of one or more games—in the case of Agora-1, GoldenEye. This model learns the underlying gameplay dynamics and how state transitions occur from player actions. Second, Agora-1 learns how to render that shared state visually. This is accomplished using a DiT-based world model conditioned directly on the shared game state, rather than prompts, images, or other traditional conditioning signals.

You can think of this separation as loosely analogous to the structure of a modern game engine. The difference is that both components are entirely learned systems. They do not rely on hard-coded gameplay logic or rendering rules, but instead learn directly from data.

Both models introduce unique research challenges. Discrete game state is structurally different from the continuous visual domains that most DiT-based world models operate over, requiring architectures specifically designed for gameplay state modeling and large amounts of structured training data. At the same time, the rendering model must learn to generate consistent visual representations of the same shared state from multiple viewpoints simultaneously. One consequence of this architecture is that the underlying game state can be manipulated directly, allowing Agora-1 to generate entirely new levels while preserving gameplay dynamics consistent with the source games.