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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.09561 |
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| _version_ | 1866909735721107456 |
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| author | Zhao, Changyuan Liu, Guangyuan Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Kang, Jiawen Niyato, Dusit Li, Zan Xuemin Shen Han, Zhu Sun, Sumei Yuen, Chau Kim, Dong In |
| author_facet | Zhao, Changyuan Liu, Guangyuan Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Kang, Jiawen Niyato, Dusit Li, Zan Xuemin Shen Han, Zhu Sun, Sumei Yuen, Chau Kim, Dong In |
| contents | Edge General Intelligence (EGI) represents a transformative evolution of edge computing, where distributed agents possess the capability to perceive, reason, and act autonomously across diverse, dynamic environments. Central to this vision are world models, which act as proactive internal simulators that not only predict but also actively imagine future trajectories, reason under uncertainty, and plan multi-step actions with foresight. This proactive nature allows agents to anticipate potential outcomes and optimize decisions ahead of real-world interactions. While prior works in robotics and gaming have showcased the potential of world models, their integration into the wireless edge for EGI remains underexplored. This survey bridges this gap by offering a comprehensive analysis of how world models can empower agentic artificial intelligence (AI) systems at the edge. We first examine the architectural foundations of world models, including latent representation learning, dynamics modeling, and imagination-based planning. Building on these core capabilities, we illustrate their proactive applications across EGI scenarios such as vehicular networks, unmanned aerial vehicle (UAV) networks, the Internet of Things (IoT) systems, and network functions virtualization, thereby highlighting how they can enhance optimization under latency, energy, and privacy constraints. We then explore their synergy with foundation models and digital twins, positioning world models as the cognitive backbone of EGI. Finally, we highlight open challenges, such as safety guarantees, efficient training, and constrained deployment, and outline future research directions. This survey provides both a conceptual foundation and a practical roadmap for realizing the next generation of intelligent, autonomous edge systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09561 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Zhao, Changyuan Liu, Guangyuan Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Kang, Jiawen Niyato, Dusit Li, Zan Xuemin Shen Han, Zhu Sun, Sumei Yuen, Chau Kim, Dong In Machine Learning Edge General Intelligence (EGI) represents a transformative evolution of edge computing, where distributed agents possess the capability to perceive, reason, and act autonomously across diverse, dynamic environments. Central to this vision are world models, which act as proactive internal simulators that not only predict but also actively imagine future trajectories, reason under uncertainty, and plan multi-step actions with foresight. This proactive nature allows agents to anticipate potential outcomes and optimize decisions ahead of real-world interactions. While prior works in robotics and gaming have showcased the potential of world models, their integration into the wireless edge for EGI remains underexplored. This survey bridges this gap by offering a comprehensive analysis of how world models can empower agentic artificial intelligence (AI) systems at the edge. We first examine the architectural foundations of world models, including latent representation learning, dynamics modeling, and imagination-based planning. Building on these core capabilities, we illustrate their proactive applications across EGI scenarios such as vehicular networks, unmanned aerial vehicle (UAV) networks, the Internet of Things (IoT) systems, and network functions virtualization, thereby highlighting how they can enhance optimization under latency, energy, and privacy constraints. We then explore their synergy with foundation models and digital twins, positioning world models as the cognitive backbone of EGI. Finally, we highlight open challenges, such as safety guarantees, efficient training, and constrained deployment, and outline future research directions. This survey provides both a conceptual foundation and a practical roadmap for realizing the next generation of intelligent, autonomous edge systems. |
| title | Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.09561 |