Research on World Models Is Not Merely Injecting World Knowledge into Specific Tasks
This work addresses the problem of lack of systematic coherence in world models for AI researchers, but it is incremental as it focuses on proposing a framework rather than demonstrating new empirical results.
The paper tackles the fragmented approach in world model research by analyzing limitations of task-specific integrations and proposing a unified design specification to guide future work toward more general and principled models.
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.