World Models Should Prioritize the Unification of Physical and Social Dynamics

2025-10-24

AI Summary

Existing world models treat physical dynamics and social behavior as disjoint components, failing to capture their deep coupling in real-world scenarios and thus limiting generalization in complex human-robot collaborative environments. To address this, we propose the ACE principle—Alignment, Co-evolution, and Emergence—and introduce the first unified world model framework that jointly predicts both physical state evolution and social intent generation. Our approach integrates context-aware dynamic adaptation with predictive learning, planning-based reasoning, and foundational theories from the social sciences. We systematically identify key cross-domain integration bottlenecks and provide empirically verifiable theoretical principles alongside a concrete technical roadmap. This work establishes a more realistic, socially cognizant world modeling paradigm for artificial general intelligence, advancing beyond purely physics-centric or behavior-only abstractions.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

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.

Problem

• Unifying physical and social dynamics in world models
• Addressing systemic failure in modeling environment-social interplay
• Integrating objective physical laws with subjective social dynamics

Innovation

• Unifying physical and social predictive capabilities
• Integrating objective laws with subjective social dynamics
• Proposing ACE Principles for holistic world models