Population-Scalable Multi-Agent World Modeling across Scene Geometries

World models have recently achieved impressive progress in visual prediction and interactive generation, yet existing multi-agent systems are typically tied to predefined agent configurations and scene-specific environments. We propose Khora, a scalable multi-agent world model that supports both inference-time population expansion and generalization across geometrically distinct environments. Khora decouples shared world-state evolution from visual rendering: an action-conditioned state model maintains dynamic agents in a persistent Spatio-temporal Board (STBoard), while each observation is generated independently through a projection-based rendering interface. The renderer is trained with one target view per sample and reused across agents at inference without retraining. Crucially, scene geometry is provided to the renderer as an explicit mesh condition, allowing the same trained model to render observations that conform to different input environments without changing the underlying world dynamics or model architecture. This design enables Khora to scale along two complementary dimensions: many agents can interact within the same evolving world, while the same model can render observations across many different mesh-defined environments. Experiments demonstrate multi-agent consistency, action-conditioned interaction, generalization to unseen meshes, and scalability to large agent populations.