世界模型研究并非简单将世界知识注入特定任务

摘要

世界模型已成为人工智能研究的关键前沿，其核心目标是通过融入物理动态与世界知识来增强大模型能力，使智能体能够理解、预测并交互复杂环境。然而当前研究格局仍显碎片化，现有方法主要集中于将世界知识注入孤立任务——如视觉预测、三维估计或符号落地——而非建立统一的理论定义或框架。尽管这些任务特定型整合能提升性能，但往往缺乏实现整体世界理解所需的系统性关联。本文剖析了此类碎片化方法的局限性，并提出世界模型的统一设计规范。我们认为稳健的世界模型不应是能力的松散集合，而应成为融合交互、感知、符号推理与空间表征的规范性框架。本研究旨在提供结构化视角，以引导未来研究朝着更具通用性、鲁棒性和原则性的世界模型方向发展。

English

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.

世界模型研究并非简单将世界知识注入特定任务

Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks

摘要

Support