Multics: Generative Multi-Player World Model with Hierarchical Communication
Multi-player interactive world models aim to simulate shared environments from multiple players' perspectives, but achieving robust generation while scaling to more players remains challenging. Existing approaches that rely on fully-connected communication (e.g., Solaris) incur quadratic attention costs as the number of players grows, while centralized approaches (e.g., γ-World
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Multi-player interactive world models aim to simulate shared environments from multiple players' perspectives, but achieving robust generation while scaling to more players remains challenging. Existing approaches that rely on fully-connected communication (e.g., Solaris) incur quadratic attention costs as the number of players grows, while centralized approaches (e.g., γ-World
This research introduces a novel hierarchical communication mechanism for multi-player world models, addressing scalability and interference issues in generative AI for shared environments. ML engineers can apply these principles to design more efficient and robust multi-agent systems and interactive simulations, particularly in gaming, robotics, and virtual reality.