Oasis-World: Toward Open-Domain Multi-Agent Interactive World Model

World models capable of predicting environmental dynamics conditioned on agent actions form a cornerstone of embodied intelligence and interactive simulation. Although single-agent formulations have witnessed substantial progress, extending world modeling to multi-agent environments introduces fundamental challenges including identity persistence under severe mutual occlusion, dynamic spatial tracking across co-present entities, and the grounding of concurrent behaviors to distinct visual outcomes, all of which substantially exceed the capacity of existing datasets. To bridge this data bottleneck, we present **OasisData-Engine**, an automated generation pipeline that integrates scene preparation, coordinated trajectory planning, offline photorealistic rendering, and streaming annotation to facilitate scalable parallel capture of multi-agent interactions within high-fidelity Unreal Engine environments. Leveraging this infrastructure, we establish **OasisData**, a comprehensive benchmark encompassing over 130 environments and 80 distinct character identities with configurations supporting from one to ten concurrent participants, accumulating more than 4,900 hours of video strictly paired with fine-grained frame-level action commands and structured descriptions. Furthermore, we develop **Oasis-World** as a baseline for open-domain multi-agent world modeling, conducting systematic empirical evaluations across spatial reasoning, action responsiveness, and occluded interactions to delineate critical challenges and future trajectories for interactive world modeling. **We will open source the model weights, code and data**.