WorldCast: Exploring Intelligent Visual Generation in World State Prediction
Humans interpret multimodal observations of the world and forecast how it will evolve. Recent multimodal generative systems have demonstrated similar capabilities: they can integrate multimodal context and synthesize visual outputs that depict changes in the world. To characterize this emerging trajectory, we organize model capabilities into a four-level hierarchy and discover that higher-level tasks remain relatively underexplored despite model progress. To bridge this gap, we explore model performance on multi-step world state prediction, a high-level task that requires models to forecast successive future world states from multimodal observations and diverse references. Such a task req...
| Authors | Wenhao Yan, Haodong Li, Xiaotong Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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- 16 Oct 2026 Description: Humans interpret multimodal observations of the world and forecast how it will evolve. Recent multimodal generative systems have demonstrated similar capabilities: they can integrate multimodal context and synthesize visual outputs that dep → Humans interpret multimodal observations of the world and forecast how it will evolve. Recent multimodal generative systems have demonstrated similar capabilities: they can integrate multimodal context and synthesize visual outputs that dep
- 16 Oct 2026 Topics added cs.CV, cs.AI removed hf-daily-papers, multimodal generative systems, world state prediction, entangled understanding and generation, world knowledge, worldcast benchmark, visual state prediction, dynamic planning and reasoning
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