Worldscape-MoE: A Unified Mixture-of-Experts World Model for Scalable Heterogeneous Action Control
arXiv:2607.03964v1 Announce Type: cross Abstract: World models are rapidly becoming a core infrastructure for embodied intelligence and interactive agents: they provide controllable simulators in which agents can perceive, act, forecast, and acquire scalable experience. Yet current video generation world models are still organized around isolated control interfaces, such as camera trajectories, robot actions, or hand-joint signals. This fragmentation is increasingly a scaling bottleneck. The central challenge is not the absence of controllable generators, but the lack of a unified and extensib
The accelerating development of advanced AI models and embodied intelligence necessitates more unified approaches to world model control, as fragmented interfaces become a limiting factor.
This work introduces a scalable solution for heterogeneous action control in world models, moving towards more capable and autonomous AI agents that can operate across diverse environments and control systems.
Current fragmented world models are being replaced by unified, scalable mixture-of-experts architectures, enabling a more coherent and extensible control over virtual and embodied agents.
- · AI research labs
- · Embodied AI developers
- · Robotics integrators
- · Simulation platforms
- · Developers relying on siloed control systems
- · Specialized, narrow world model providers
- · Companies slow to adopt unified AI architectures
More sophisticated and versatile embodied AI agents become feasible.
Accelerated development of general-purpose robots and autonomous systems that can perform complex, multi-modal tasks.
Potential for AI to manage and interact with highly complex real-world systems, from manufacturing to logistics, with greater autonomy.
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Read at arXiv cs.AI