# Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges arXiv: https://arxiv.org/abs/2508.09561 ยท HTML: https://mlsanity.com/paper/2508.09561/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in World models for language agents, one of 11 directions in World models. - Builds on 97 of its 178 references that are in the map. - First posted on arXiv 13 Aug 2025. ## Placed in - World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/) - LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/) ## Builds on (most cited first, 30 of 97) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017) - [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024) - [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016) - [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019) - [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023) - [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019) - [Learning Latent Dynamics for Planning from Pixels](https://mlsanity.com/paper/1811.04551/) (2018) - [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023) - [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022) - [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021) - [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020) - [DeepMind Control Suite](https://mlsanity.com/paper/1801.00690/) (2018) - [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024) - [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025) - [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023) - [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023) - [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025) - [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022) - [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018) - [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017) - [VideoGPT: Video Generation using VQ-VAE and Transformers](https://mlsanity.com/paper/2104.10157/) (2021) - [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020) - [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023) - [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://mlsanity.com/paper/2412.14171/) (2024) - [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017) ## Built on by (most cited first, 3 of 3) - [3D and 4D World Modeling: A Survey](https://mlsanity.com/paper/2509.07996/) (2025) - [From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions](https://mlsanity.com/paper/2510.05596/) (2025) - [Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning](https://mlsanity.com/paper/2511.02748/) (2025) ## Abstract (arXiv) 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.