Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models
By: Jialong Wu, Xiaoying Zhang, Hongyi Yuan, Xiangcheng Zhang, Tianhao Huang, Changjing He, Chaoyi Deng, Renrui Zhang, Youbin Wu, Mingsheng Long
Published: 2026-01-27
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Abstract
Researchers introduced a framework and benchmark to study visual world modeling in Unified Multimodal Models (UMMs), demonstrating that visual generation significantly improves reasoning on physical and spatial tasks, which are often challenging for current AI systems. This work clarifies the potential of multimodal world modeling for more powerful, human-like multimodal AI.