提出统一世界模型框架，打破任务碎片化研究困局
Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks
- 构建整合感知、交互、符号推理与空间表征的统一框架
- 指出当前研究多聚焦孤立任务，缺乏系统性协同机制
- 适合追求通用智能与具身认知的模型研发者参考
世界模型已成为人工智能研究的关键前沿，旨在通过注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、预测并交互于复杂环境。然而，当前研究仍呈碎片化状态，多数方法仅将世界知识注入视觉预测、3D估计或符号锚定等孤立任务，未能建立统一定义或架构。尽管这些任务特定集成带来性能提升，但往往缺乏实现整体世界理解所需的系统一致性。本文分析此类碎片化方法的局限，并提出世界模型的统一设计规范：一个强健的世界模型不应是能力的松散集合，而应是一个内嵌交互、感知、符号推理与空间表征的规范性框架。本工作旨在为未来更具通用性、鲁棒性和原则性的世界建模研究提供结构化视角。
原文摘要 · Abstract (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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。