Paper Overview
- Research area: Computer Vision (CV)
- Authors: Sicheng Mo, Yuheng Li, Ziyang Leng
- Published: 2026-07-25
- arXiv: 2507.20487
Abstract
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.
The authors present WorldWeaver (W²), 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. These registers are grounded with supervision signals spanning individual agent status, global state views including bird's-eye views, and scene text. The architecture is further improved through a hybrid Transformer design that uses independent weights for world state modeling and visual frame modeling. Extensive experiments on dual-agent Minecraft video generation demonstrate that explicit world state modeling improves logical consistency and generation quality.
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*Auto-collected on 2026-07-26*