World Models Should Prioritize the Unification of Physical and Social Dynamics
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914111613304832 |
|---|---|
| author | Zhang, Xiaoyuan Ma, Chengdong Huang, Yizhe Huang, Weidong Qi, Siyuan Zhu, Song-Chun Feng, Xue Yang, Yaodong |
| author_facet | Zhang, Xiaoyuan Ma, Chengdong Huang, Yizhe Huang, Weidong Qi, Siyuan Zhu, Song-Chun Feng, Xue Yang, Yaodong |
| contents | World models, which explicitly learn environmental dynamics to lay the foundation for planning, reasoning, and decision-making, are rapidly advancing in predicting both physical dynamics and aspects of social behavior, yet predominantly in separate silos. This division results in a systemic failure to model the crucial interplay between physical environments and social constructs, rendering current models fundamentally incapable of adequately addressing the true complexity of real-world systems where physical and social realities are inextricably intertwined. This position paper argues that the systematic, bidirectional unification of physical and social predictive capabilities is the next crucial frontier for world model development. We contend that comprehensive world models must holistically integrate objective physical laws with the subjective, evolving, and context-dependent nature of social dynamics. Such unification is paramount for AI to robustly navigate complex real-world challenges and achieve more generalizable intelligence. This paper substantiates this imperative by analyzing core impediments to integration, proposing foundational guiding principles (ACE Principles), and outlining a conceptual framework alongside a research roadmap towards truly holistic world models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21219 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | World Models Should Prioritize the Unification of Physical and Social Dynamics Zhang, Xiaoyuan Ma, Chengdong Huang, Yizhe Huang, Weidong Qi, Siyuan Zhu, Song-Chun Feng, Xue Yang, Yaodong Computers and Society World models, which explicitly learn environmental dynamics to lay the foundation for planning, reasoning, and decision-making, are rapidly advancing in predicting both physical dynamics and aspects of social behavior, yet predominantly in separate silos. This division results in a systemic failure to model the crucial interplay between physical environments and social constructs, rendering current models fundamentally incapable of adequately addressing the true complexity of real-world systems where physical and social realities are inextricably intertwined. This position paper argues that the systematic, bidirectional unification of physical and social predictive capabilities is the next crucial frontier for world model development. We contend that comprehensive world models must holistically integrate objective physical laws with the subjective, evolving, and context-dependent nature of social dynamics. Such unification is paramount for AI to robustly navigate complex real-world challenges and achieve more generalizable intelligence. This paper substantiates this imperative by analyzing core impediments to integration, proposing foundational guiding principles (ACE Principles), and outlining a conceptual framework alongside a research roadmap towards truly holistic world models. |
| title | World Models Should Prioritize the Unification of Physical and Social Dynamics |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2510.21219 |