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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, Yiwen Tang, Yuanxing Zhang, Pengfei Wan, Xintao Wang, Wentao Zhang

TL;DR

The paper argues that current world-model research is fragmented, often injecting world knowledge into individual tasks rather than building a unified, proactive framework. It introduces a normative Unified World Model Framework with five core components—Interaction, Reasoning, Memory, Environment, and Multimodal Generation—to enable robust world simulation. The authors critique existing task-specific approaches across LLMs, VLMs, video/3D generation, and embodied AI, highlighting deficits in physical coherence, long-horizon memory, and real-world interaction. They outline critical directions—physically-grounded representations, embodied control, and autonomous modular evolution—aimed at producing general, robust agents capable of active exploration and accurate world understanding.

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.

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

TL;DR

The paper argues that current world-model research is fragmented, often injecting world knowledge into individual tasks rather than building a unified, proactive framework. It introduces a normative Unified World Model Framework with five core components—Interaction, Reasoning, Memory, Environment, and Multimodal Generation—to enable robust world simulation. The authors critique existing task-specific approaches across LLMs, VLMs, video/3D generation, and embodied AI, highlighting deficits in physical coherence, long-horizon memory, and real-world interaction. They outline critical directions—physically-grounded representations, embodied control, and autonomous modular evolution—aimed at producing general, robust agents capable of active exploration and accurate world understanding.

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.
Paper Structure (20 sections, 4 figures)

This paper contains 20 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Reasoning with World Knowledge
  4. World-Driven Content Generation
  5. Agents in Interactive Environments
  6. Unified World Model Framework
  7. Interaction.
  8. Reasoning.
  9. Memory.
  10. Environment.
  11. Multimodal Generation.
  12. Limitations of Existing Models Incorporating World Knowledge
  13. Discussion: Standardization and Feasibility
  14. Efficiency vs. Generalization.
  15. Diversity vs. Integration.
  16. ...and 5 more sections

Figures (4)

  • Figure 1: Comparison between current task-specific paradigms and the proposed unified framework. While current research often reduces World Models to the injection of knowledge into specific tasks, a holistic World Model aims to endow AI with general capabilities to tackle multifaceted real-world challenges.
  • Figure 2: Illustration of the advocated unified world model framework. Each component served as: (a) Interaction: Enabling the model to handle multi-format inputs from the complex physical world. (b) Reasoning: Conducting logical analysis and inference derived from complex inputs. (c) Memory: Supporting long-term retention and extensive context processing. (d) Multimodal Generation: Empowering the model to generate multimodal outputs, which serve both as environmental feedback and as a catalyst for superior reasoning.
  • Figure 3: Failure cases of various task-specific methods infused with world knowledge.
  • Figure 4: Illustration of the limitations of existing embodied AI and autonomous driving systems. Images sourced from internet search.