让多个智能体在视频生成中共享世界状态，提升一致性与连贯性。
Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers
- 用可学习的共享状态令牌追踪全局与个体状态，动态更新每帧结果。
- 在两人版我的世界视频生成中，逻辑一致性提升23%，视觉质量更优。
- 适合研究多智能体交互、持续世界建模的开发者与研究员。
多智能体交互的世界模型不仅需要生成一致的观测，还需保持跨智能体和视角持续演化的世界状态。现有自回归视频扩散流程通过条件上下文传递观测历史，难以在多智能体和多视角场景中维持共享状态。我们提出WorldWeaver（W²），一种流式多智能体视频扩散模型，通过引入跨智能体世界状态寄存器：可学习的令牌，用于存储共享世界信息、追踪个体智能体状态，并在每生成一个片段后动态更新。这些寄存器通过个体状态、全局视图（如鸟瞰图）及场景文本等监督信号进行锚定。此外，采用Mixture-of-Transformers架构，为世界状态建模与视觉帧建模分别使用独立权重。在双智能体我的世界视频生成任务上的大量实验表明，显式世界状态建模显著提升了逻辑一致性与生成质量。
原文摘要 · Abstract (English)
Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared state difficult to maintain in multi-agent and multi-view settings. We present WorldWeaver (W^2), a streaming multi-agent video diffusion model that augments rollout with cross-agent world state registers: learnable tokens that store shared world information, track individual agent status, and are dynamically updated after each generated chunk. We ground these registers with supervision signals spanning individual agent status, global state views including bird's-eye views, and scene text. We further improve the architecture with a Mixture-of-Transformers design that uses separate weights for world state modeling and visual frame modeling. Extensive experiments in two-agent Minecraft video generation show that explicit world-state modeling improves logical consistency and generation quality.
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