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From Digital Twins to World Models:Opportunities, Challenges, and Applications for Mobile Edge General Intelligence

Jie Zheng, Dusit Niyato, Changyuan Zhao, Jiawen Kang, Jiacheng Wang

Abstract

The rapid evolution toward 6G and beyond communication systems is accelerating the convergence of digital twins and world models at the network edge. Traditional digital twins provide high-fidelity representations of physical systems and support monitoring, analysis, and offline optimization. However, in highly dynamic edge environments, they face limitations in autonomy, adaptability, and scalability. This paper presents a systematic survey of the transition from digital twins to world models and discusses its role in enabling edge general intelligence (EGI). First, the paper clarifies the conceptual differences between digital twins and world models and highlights the shift from physics-based, centralized, and system-centric replicas to data-driven, decentralized, and agent-centric internal models. This discussion helps readers gain a clear understanding of how this transition enables more adaptive, autonomous, and resource-efficient intelligence at the network edge. The paper reviews the design principles, architectures, and key components of world models, including perception, latent state representation, dynamics learning, imagination-based planning, and memory. In addition, it examines the integration of world models and digital twins in wireless EGI systems and surveys emerging applications in integrated sensing and communications, semantic communication, air-ground networks, and low-altitude wireless networks. Finally, this survey provides a systematic roadmap and practical insights for designing world-model-driven edge intelligence systems in wireless and edge computing environments. It also outlines key research challenges and future directions toward scalable, reliable, and interoperable world models for edge-native agentic AI.

From Digital Twins to World Models:Opportunities, Challenges, and Applications for Mobile Edge General Intelligence

Abstract

The rapid evolution toward 6G and beyond communication systems is accelerating the convergence of digital twins and world models at the network edge. Traditional digital twins provide high-fidelity representations of physical systems and support monitoring, analysis, and offline optimization. However, in highly dynamic edge environments, they face limitations in autonomy, adaptability, and scalability. This paper presents a systematic survey of the transition from digital twins to world models and discusses its role in enabling edge general intelligence (EGI). First, the paper clarifies the conceptual differences between digital twins and world models and highlights the shift from physics-based, centralized, and system-centric replicas to data-driven, decentralized, and agent-centric internal models. This discussion helps readers gain a clear understanding of how this transition enables more adaptive, autonomous, and resource-efficient intelligence at the network edge. The paper reviews the design principles, architectures, and key components of world models, including perception, latent state representation, dynamics learning, imagination-based planning, and memory. In addition, it examines the integration of world models and digital twins in wireless EGI systems and surveys emerging applications in integrated sensing and communications, semantic communication, air-ground networks, and low-altitude wireless networks. Finally, this survey provides a systematic roadmap and practical insights for designing world-model-driven edge intelligence systems in wireless and edge computing environments. It also outlines key research challenges and future directions toward scalable, reliable, and interoperable world models for edge-native agentic AI.
Paper Structure (30 sections, 8 figures, 5 tables)

This paper contains 30 sections, 8 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Motivation and Contribution
  4. WORLD MODELS
  5. Introduction to Digital Twins
  6. Overview of World Models
  7. Differences Between World Models and Digital Twins
  8. World-Model-Enabled Edge General Intelligence
  9. From Digital Twin to World Model
  10. Core Components of World Models
  11. From World Replication to World Abstraction
  12. From Rule-Driven Dynamics to Data-Driven World Evolution
  13. Action-Conditioned Prediction and Imagination
  14. Agent-Centric and Localized World Modeling
  15. Summary of the World Model Paradigm
  16. ...and 15 more sections

Figures (8)

  • Figure 1: Conceptual architecture from digital twins to world models for edge general intelligence.
  • Figure 2: A conceptual framework illustrating how digital twins and world models enable EGI. (A): Digital twin offline physics replica feeds online monitoring. (B): EGI agents compress data and pick actions via reward. (C): world model hierarchy bridges offline planning to online action.
  • Figure 3: Evolution from Digital Twin to World Model for EGI. (A) From world replication to world abstraction, (B) From rule-driven to data-driven, (C) From passive simulation to active imagination, (D) From system-centric to agent-centric.
  • Figure 4: Evolution from digital twin to world model for EGI. (A) From world replication to world abstraction, (B) From rule-driven to data-driven, (C) From passive simulation to active imagination, (D) From system-centric to agent-centric.
  • Figure 5: A conceptual framework illustrating how the optimized edge ISCC system enables intelligent edge computing and sensing through multi-modal data processing, world model-driven perception, and dynamic edge co-optimization.
  • ...and 3 more figures