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

arXiv: https://arxiv.org/abs/2603.17420 · HTML: https://mlsanity.com/paper/2603.17420/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 22 of its 117 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 22 of 22)

- [Ditto: Building Digital Twins of Articulated Objects from Interaction](https://mlsanity.com/paper/2202.08227/) (2022)
- [Advancing Open-source World Models](https://mlsanity.com/paper/2601.20540/) (2026)
- [A Comprehensive Survey on World Models for Embodied AI](https://mlsanity.com/paper/2510.16732/) (2025)
- [A Survey: Learning Embodied Intelligence from Physical Simulators and World Models](https://mlsanity.com/paper/2507.00917/) (2025)
- [WorldRFT: Latent World Model Planning with Reinforcement Fine-Tuning for Autonomous Driving](https://mlsanity.com/paper/2512.19133/) (2025)
- [World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks](https://mlsanity.com/paper/2506.00417/) (2025)
- [Aerial World Model for Long-horizon Visual Generation and Navigation in 3D Space](https://mlsanity.com/paper/2512.21887/) (2025)
- [FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content](https://mlsanity.com/paper/2308.14256/) (2023)
- [Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment](https://mlsanity.com/paper/2501.03265/) (2025)
- [The Safety Challenge of World Models for Embodied AI Agents: A Review](https://mlsanity.com/paper/2510.05865/) (2025)
- [Towards Unraveling and Improving Generalization in World Models](https://mlsanity.com/paper/2501.00195/) (2024)
- [World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child](https://mlsanity.com/paper/2503.15168/) (2025)
- [Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models](https://mlsanity.com/paper/2601.01321/) (2026)
- [Remote Sensing-Oriented World Model](https://mlsanity.com/paper/2509.17808/) (2025)
- [Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons](https://mlsanity.com/paper/2510.01439/) (2025)
- [Towards Edge General Intelligence: Knowledge Distillation for Mobile Agentic AI](https://mlsanity.com/paper/2511.19947/) (2025)
- [Integrated Sensing, Computing, and Semantic Communication with Fluid Antenna for Metaverse](https://mlsanity.com/paper/2504.07656/) (2025)
- [Semantic Communications with World Models](https://mlsanity.com/paper/2510.24785/) (2025)
- [Distributed Integrated Sensing and Edge AI Exploiting Prior Information](https://mlsanity.com/paper/2512.00309/) (2025)
- [Integrated Sensing, Communication, Computing, and Control Meets UAV Swarms in 6G](https://mlsanity.com/paper/2512.07054/) (2025)
- [MetaWorld: Skill Transfer and Composition in a Hierarchical World Model for Grounding High-Level Instructions](https://mlsanity.com/paper/2601.17507/) (2026)
- [AI and World Models](https://mlsanity.com/paper/2601.17796/) (2026)

## Abstract (arXiv)

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.