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.

翻译：世界模型通过显式学习环境动态，为规划、推理与决策奠定基础，其在物理动态和社会行为预测方面均取得快速进展，但当前研究大多处于相互割裂的状态。这种分野导致现有模型系统性地无法刻画物理环境与社会结构之间的关键交互作用，使其从根本上难以充分应对现实世界系统的真实复杂性——在现实系统中，物理现实与社会现实密不可分。本立场论文论证，系统化、双向统一物理与社会预测能力将成为世界模型发展的下一个关键前沿。我们主张，完备的世界模型必须将客观物理定律与主观性、演化性及情境依赖性的社会动态本质进行整体性整合。此类统一对于人工智能稳健应对复杂现实挑战、实现更具泛化能力的智能至关重要。本文通过分析当前整合面临的核心障碍，提出基础性指导原则（ACE原则），并勾勒出概念框架与研究路线图，从而论证迈向真正整体性世界模型的必要性。