Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks
Bohan Zeng, Kaixin Zhu, Daili Hua, Bozhou Li, Chengzhuo Tong, Yuran Wang, Xinyi Huang, Yifan Dai, Zixiang Zhang, Yifan Yang, Zhou Liu, Hao Liang, Xiaochen Ma, Ruichuan An, Tianyi Bai, Hongcheng Gao, Junbo Niu, Yang Shi, Xinlong Chen, Yue Ding, Minglei Shi, Kai Zeng
2026-02-04
Summary
This paper discusses the growing field of 'world models' in artificial intelligence, which are attempts to give AI systems a better understanding of how the world works, beyond just processing data.
What's the problem?
Currently, building world models is a bit messy. Researchers are focusing on teaching AI specific skills like predicting what they 'see' or understanding 3D shapes, but these skills aren't connected. It's like learning individual facts without understanding how they all fit together to form a complete picture of the world, leading to AI that isn't truly 'understanding' its environment in a comprehensive way.
What's the solution?
The paper proposes a more organized approach to building world models. Instead of focusing on isolated skills, they suggest a framework that combines how an AI interacts with the world, how it perceives things, how it uses logic and symbols, and how it understands spatial relationships. Essentially, they want a world model to be a unified system, not just a collection of parts.
Why it matters?
This research is important because it provides a roadmap for creating more intelligent and adaptable AI. By focusing on a unified framework for world models, we can move beyond AI that excels at specific tasks and towards AI that can genuinely understand and navigate the complexities of the real world, leading to more robust and generally capable systems.
Abstract
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.