The Trinity of Consistency as a Defining Principle for General World Models
The construction of World Models capable of learning, simulating, and reasoning about objective physical laws constitutes a foundational challenge in the pursuit of Artificial General Intelligence. Recent advancements represented by video generation models like Sora have demonstrated the potential of data-driven scaling laws to approximate physical dynamics, while the emerging Unified Multimodal Model (UMM) offers a promising architectural paradigm for integrating perception, language, and reasoning. Despite these advances, the field still lacks a principled theoretical framework that defines the essential properties requisite for a General World Model. In this paper, we propose that a World Model must be grounded in the Trinity of Consistency: Modal Consistency as the semantic interface, Spatial Consistency as the geometric basis, and Temporal Consistency as the causal engine. Through this tripartite lens, we systematically review the evolution of multimodal learning, revealing a trajectory from loosely coupled specialized modules toward unified architectures that enable the synergistic emergence of internal world simulators. To complement this conceptual framework, we introduce CoW-Bench, a benchmark centered on multi-frame reasoning and generation scenarios. CoW-Bench evaluates both video generation models and UMMs under a unified evaluation protocol. Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.
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Jingxuan Wei
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Siyuan Li
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2026-02-27T16:40:47.703Z
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Siyuan Li
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Yuhang Xu
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Zheng Sun
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Junjie Jiang
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Hexuan Jin
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Caijun Jia
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Honghao He
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Xinglong Xu
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Xi bai
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Chang Yu
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Yumou Liu
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Junnan Zhu
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Xuanhe Zhou
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2026-02-27T16:40:45.120Z
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xuanhe zhou
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Jintao Chen
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Xiaobin Hu
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Shancheng Pang
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Bihui Yu
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Ran He
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Zhen Lei
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Stan Z. Li
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Conghui He
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Shuicheng Yan
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Cheng Tan
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2026-02-27T16:40:37.726Z
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Cheng Tan
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The Trinity of Consistency as a Defining Principle for General World Models
The construction of World Models capable of learning, simulating, and reasoning about objective physical laws constitutes a foundational challenge in the pursuit of Artificial General Intelligence. Recent advancements represented by video generation models like Sora have demonstrated the potential of data-driven scaling laws to approximate physical dynamics, while the emerging Unified Multimodal Model (UMM) offers a promising architectural paradigm for integrating perception, language, and reasoning. Despite these advances, the field still lacks a principled theoretical framework that defines the essential properties requisite for a General World Model. In this paper, we propose that a World Model must be grounded in the Trinity of Consistency: Modal Consistency as the semantic interface, Spatial Consistency as the geometric basis, and Temporal Consistency as the causal engine. Through this tripartite lens, we systematically review the evolution of multimodal learning, revealing a trajectory from loosely coupled specialized modules toward unified architectures that enable the synergistic emergence of internal world simulators. To complement this conceptual framework, we introduce CoW-Bench, a benchmark centered on multi-frame reasoning and generation scenarios. CoW-Bench evaluates both video generation models and UMMs under a unified evaluation protocol. Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.
World Models require three consistency principles—modal, spatial, and temporal—for general artificial intelligence, with a proposed benchmark evaluating multimodal learning systems.
World Models video generation models Unified Multimodal Model multimodal learning multi-frame reasoning CoW-Bench modal consistency spatial consistency temporal consistency