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.

翻译：世界模型已成为人工智能研究的关键前沿领域，其目标是通过注入物理动力学与世界知识来增强大模型。核心目标在于使智能体能够理解、预测并与复杂环境交互。然而，当前研究格局仍呈碎片化，方法主要集中于将世界知识注入孤立任务，如视觉预测、三维估计或符号接地，而非建立统一的定义或框架。尽管这些面向特定任务的整合带来了性能提升，但它们通常缺乏整体世界理解所需的系统性连贯性。本文分析了此类碎片化方法的局限性，并提出了世界模型的统一设计规范。我们认为，一个稳健的世界模型不应是能力的松散集合，而应是一个规范框架，有机整合交互、感知、符号推理与空间表征。本工作旨在提供一个结构化视角，以引导未来研究朝着更通用、稳健且具有原则性的世界模型方向发展。