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

2026-03-18

AI Summary

Traditional digital twins are constrained in dynamic edge environments by limited autonomy, adaptability, and scalability, hindering their ability to support the advancement of ubiquitous mobile edge intelligence. This work proposes a paradigm shift from physics-driven, centralized digital twins toward data-driven, decentralized world models, clearly delineating the conceptual distinctions between the two for the first time. It introduces an agent-centric edge world model framework that integrates core components including perceptual modeling, latent state representation, dynamics learning, imagination-based planning, and memory mechanisms. The proposed framework offers a systematic design pathway for emerging 6G applications such as integrated sensing and communication, semantic communication, and air-ground collaboration, thereby advancing the development of scalable, reliable, and interoperable edge-native intelligent agents.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Modeling other Agents
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Digital Twins
• World Models
• Edge Intelligence
• 6G
• Autonomy

Innovation

• World Models
• Digital Twins
• Edge General Intelligence
• Agent-Centric AI
• 6G Edge Computing