Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915768459853824 |
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| author | Zeng, Bohan Zhu, Kaixin Hua, Daili Li, Bozhou Tong, Chengzhuo Wang, Yuran Huang, Xinyi Dai, Yifan Zhang, Zixiang Yang, Yifan Liu, Zhou Liang, Hao Ma, Xiaochen An, Ruichuan Bai, Tianyi Gao, Hongcheng Niu, Junbo Shi, Yang Chen, Xinlong Ding, Yue Shi, Minglei Zeng, Kai Tang, Yiwen Zhang, Yuanxing Wan, Pengfei Wang, Xintao Zhang, Wentao |
| author_facet | Zeng, Bohan Zhu, Kaixin Hua, Daili Li, Bozhou Tong, Chengzhuo Wang, Yuran Huang, Xinyi Dai, Yifan Zhang, Zixiang Yang, Yifan Liu, Zhou Liang, Hao Ma, Xiaochen An, Ruichuan Bai, Tianyi Gao, Hongcheng Niu, Junbo Shi, Yang Chen, Xinlong Ding, Yue Shi, Minglei Zeng, Kai Tang, Yiwen Zhang, Yuanxing Wan, Pengfei Wang, Xintao Zhang, Wentao |
| contents | World models have emerged as a critical frontier in AI research, aiming to enhance large models by infusing them with physical dynamics and world knowledge. The core objective is to enable agents to understand, predict, and interact with complex environments. However, current research landscape remains fragmented, with approaches predominantly focused on injecting world knowledge into isolated tasks, such as visual prediction, 3D estimation, or symbol grounding, rather than establishing a unified definition or framework. While these task-specific integrations yield performance gains, they often lack the systematic coherence required for holistic world understanding. In this paper, we analyze the limitations of such fragmented approaches and propose a unified design specification for world models. We suggest that a robust world model should not be a loose collection of capabilities but a normative framework that integrally incorporates interaction, perception, symbolic reasoning, and spatial representation. This work aims to provide a structured perspective to guide future research toward more general, robust, and principled models of the world. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_01630 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks Zeng, Bohan Zhu, Kaixin Hua, Daili Li, Bozhou Tong, Chengzhuo Wang, Yuran Huang, Xinyi Dai, Yifan Zhang, Zixiang Yang, Yifan Liu, Zhou Liang, Hao Ma, Xiaochen An, Ruichuan Bai, Tianyi Gao, Hongcheng Niu, Junbo Shi, Yang Chen, Xinlong Ding, Yue Shi, Minglei Zeng, Kai Tang, Yiwen Zhang, Yuanxing Wan, Pengfei Wang, Xintao Zhang, Wentao Computer Vision and Pattern Recognition World models have emerged as a critical frontier in AI research, aiming to enhance large models by infusing them with physical dynamics and world knowledge. The core objective is to enable agents to understand, predict, and interact with complex environments. However, current research landscape remains fragmented, with approaches predominantly focused on injecting world knowledge into isolated tasks, such as visual prediction, 3D estimation, or symbol grounding, rather than establishing a unified definition or framework. While these task-specific integrations yield performance gains, they often lack the systematic coherence required for holistic world understanding. In this paper, we analyze the limitations of such fragmented approaches and propose a unified design specification for world models. We suggest that a robust world model should not be a loose collection of capabilities but a normative framework that integrally incorporates interaction, perception, symbolic reasoning, and spatial representation. This work aims to provide a structured perspective to guide future research toward more general, robust, and principled models of the world. |
| title | Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.01630 |