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

cs.AIMar 18, 2026

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

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

Organizations: State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services, College of Computer, Northwest University, Xian, 710127, Shaanxi, China. · College of Computing and Data Science, Nanyang Technological University, Singapore 639798. · the Automation of School, Guangdong University of Technology, Guangzhou 510006, China.

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.

Figures & tables

Fig. 1 : Conceptual architecture from digital twins to world models for edge general intelligence.

Fig. 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.

Name	Ref	Description	Technical Foundation	Advantages
World models	[ 9 ] [ 26 ] [ 1 ] [ 27 ]	• Learns compact latent representations from multi modal sensory inputs • Models temporal state transitions to capture environment physics • Forecasts future latent states conditioned on planned actions	• Representation learning • Dynamics models • Self-supervised learning • Latent-state compression	• Resource-efficient • Self-adaptive • Foresighted • Task-centric
Digital twins	[ 11 ] [ 16 ] [ 28 ] [ 29 ]	• Constructs physics-based virtual replicas of physical assets • Maintains bidirectional synchronization between physical and virtual states • Propagates state changes via real-time data streaming	• IoT sensory ingestion • Bidirectional synchronization • Physics-based simulation • Edge-cloud computing	• Cyber-physical fidelity • Life cycle governance • Anticipatory risk mitigation • Holistic visualization

TABLE I : Differences between digital twins and world models

Scope	Ref	Key insight	From digital twins to world models
Edge general intelligence	[ 32 ]	• Semi-synchronous edge intelligence using digital twins • Virtual-physical interaction for real-time decisions	Digital twins continuously supply boundary data, laying an online learning foundation for later integration with world models
	[ 18 ]	• Lightweight recurrent state-space model with fast reasoning • Real-time prediction for autonomous edge nodes	World models give edge nodes real-time look-ahead reasoning ability, providing a decision brain for subsequent connection to digital twins
	[ 33 ]	• Compress LLM knowledge into world models • Digital twins calibrate for trustworthy edge AI	World models compress and offload large model capabilities to the edge, while digital twins correct physical errors in real time, making edge general intelligence both efficient and trustworthy

TABLE II : From digital twins to world models: Enabling edge general intelligence

Fig. 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.

Feature	Characteristics	Replication to Abstraction	Rule-based to Data-driven	Passive to Active	System-centric to Agent-centric
Digital Twin	World Replication [ 16 ] Rule-driven [ 45 ] Passive Simulation [ 46 ] System-centric [ 47 ]	• Achieve high-fidelity mapping that reflects precise physical reality • Maintain structural isomorphism to ensure geometric consistency with objects • Reproduce intricate physical details and geometry for total synchronization	• Operate based on deterministic rules to ensure predictable outcomes • Models are strictly governed by explicit physical laws and mechanisms • Utilize complex mechanism equations to simulate exact physical behaviors	• Run simulations based on fixed parameters to verify designs • Simulations rely on specific initial states to predict results • Treat agent actions as external inputs within a static system	• Provide comprehensive global modeling for the entire system landscape • Create a complete virtual replica encompassing all system components • Ensure full coverage of complex system behaviors and interactions
World Model	World Abstraction [ 26 ] Data-driven [ 27 ] Active Imagination [ 18 ] Agent-internal cognitive component [ 9 ]	• Abstract complex environments into compact, task-oriented representations • Retain only the critical information necessary for reward prediction • Extract low-dimensional latent features to simplify environmental complexity	• Learn versatile environment representations through advanced generative AI • Evolve through continuous learning from massive environmental interaction data • Capture implicit state transition dynamics without explicit physical formulas	• Predict future environment states conditioned on specific agent actions • Imagine virtual trajectories within latent space to evaluate plans • Enable autonomous exploration through internal ”what-if” active simulations	• Focus on ego-centric local modeling relevant to the agent • Dynamically adapt modeling to match the agent’s real-time perception • Function as the internal cognitive brain for autonomous decision-making

TABLE III : Comparison between digital twin and world model

Fig. 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.

Fig. 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.

Scenario	Paradigm	References	Key Functions and Contributions
ISCC	Digital Twin	[ 64 , 65 , 18 ]	Supports high-fidelity simulation for offline sensing calibration, link configuration, and resource pre-allocation.
	World Model	[ 66 , 67 , 68 , 69 ]	Enables online coordination via latent dynamics learning and adaptive sampling and offloading.
Semantic Communication	Digital Twin	[ 72 , 73 , 74 ]	Provides semantic policy calibration through priority setting and channel loss simulation.
	World Model	[ 75 , 76 , 77 ]	Supports dynamic semantic adaptation through implicit channel learning and encoding adjustment.
A2G Networks	Digital Twin	[ 78 , 79 , 80 ]	Facilitates global multi-layer scheduling and cross-layer system optimization.
	World Model	[ 37 , 81 ]	Enables low-latency local control via action–link modeling and latent roll out.

TABLE IV : Functional comparison of digital twin and world model in edge intelligence

Fig. 6 : A conceptual framework depicting the integration of world model processing, digital twin pre-optimization, and real-time adaptation for enabling robust and intelligent semantic communication.

Fig. 7 : Overview of world model–enabled framework for LEO–A2G networks: Latent simulation and causal learning with digital twin–edge hierarchical optimization supporting mobility, low-latency operations, and link adaptation.

Fig. 8 : Overview of the world model–driven framework for LAWNs: Onboard latent prediction, physics-aware control, and digital twin–guided edge adaptation.

Field	Method	Characteristic	Related Resource Link
Digital Twin	FaceChain [ 93 ]	Training-free and compatible	https://github.com/modelscope/facechain
	PsyDT [ 94 ]	LLMs and psychological counseling	https://github.com/scutcyr/SoulChat2.0
	DTTD2 [ 95 ]	Robust and object tracking	https://github.com/augcog/DTTD2
	DTaaS [ 96 ]	Management and services	https://github.com/INTO-CPS-Association/DTaaS
	Ditto [ 97 ]	PointNet++ and articulated object	https://github.com/UT-Austin-RPL/Ditto
World Model	LWM [ 98 ]	Context understanding and training	https://github.com/LargeWorldModel/LWM

TABLE V : A summary of frameworks for digital twin, world model, edge general intelligence

World Models

World models represent and predict how environments change, helping agents plan, act, and reason about possible futures.

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Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
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Apr 16, 2026Julian Jimenez Agudelo, Paola Soto, Ayat Zaki-Hindi +56G Networks Digital Twins
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Apr 8, 2026J/K move · Enter open · S save

Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G

Hang Zou, Yuzhi Yang, Lina Bariah, Yu Tian+14

Research Institute for Digital Future, Khalifa University, 127788 Abu Dhabi, UAE · College of Information Science and Electronic Engineering, Zhejiang University, 310027, Hangzhou, China · University of Oulu and Aalto University+2

Network Slicing World Model-Based Planning Action-Conditioned World Models 6G Networks

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The integration of machine learning tools into telecom networks, has led to two prevailing paradigms, namely, language-based systems, such as Large Language Models (LLMs), and physics-based systems, such as Digital Twins (DTs). While LLM-based approaches enable flexible interaction and automation, they lack explicit representations of network dynamics. DTs, in contrast, offer a high-fidelity network simulation, but remain scenario-specific and are not designed for learning or decision-making under uncertainty. This gap becomes critical for 6G systems, where decisions must take into account the evolving network states, uncertainty, and the cascading effects of control actions across multiple layers. In this article, we introduce the {Telecom World Model}~(TWM) concept, an architecture for learned, action-conditioned, uncertainty-aware modeling of telecom system dynamics. We decompose the problem into two interacting worlds, a controllable system world consisting of operator-configurable settings and an external world that captures propagation, mobility, traffic, and failures. We propose a three-layer architecture, comprising a field world model for spatial environment prediction, a control/dynamics world model for action-conditioned Key Performance Indicator (KPI) trajectory prediction, and a telecom foundation model layer for intent translation and orchestration. We showcase a comparative analysis between existing paradigms, which demonstrates that TWM jointly provides telecom state grounding, fast action-conditioned roll-outs, calibrated uncertainty, multi-timescale dynamics, model-based planning, and LLM-integrated guardrails. Furthermore, we present a proof-of-concept on network slicing to validate the proposed architecture, showing that the full three-layer pipeline outperforms single-world baselines and accurately predicts KPI trajectories.

Abstract

World Models

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