World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks

• URL: http://arxiv.org/abs/2506.00417v1
• Date: Sat, 31 May 2025 06:43:00 GMT
• Title: World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
• Authors: Changyuan Zhao, Ruichen Zhang, Jiacheng Wang, Gaosheng Zhao, Dusit Niyato, Geng Sun, Shiwen Mao, Dong In Kim,
• Abstract summary: We present a comprehensive overview of world models, highlighting their architecture, training paradigms, and applications across prediction, generation, planning, and causal reasoning.<n>We propose Wireless Dreamer, a novel world model-based reinforcement learning framework tailored for wireless edge intelligence optimization.
• Score: 55.90051810762702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: World models are emerging as a transformative paradigm in artificial intelligence, enabling agents to construct internal representations of their environments for predictive reasoning, planning, and decision-making. By learning latent dynamics, world models provide a sample-efficient framework that is especially valuable in data-constrained or safety-critical scenarios. In this paper, we present a comprehensive overview of world models, highlighting their architecture, training paradigms, and applications across prediction, generation, planning, and causal reasoning. We compare and distinguish world models from related concepts such as digital twins, the metaverse, and foundation models, clarifying their unique role as embedded cognitive engines for autonomous agents. We further propose Wireless Dreamer, a novel world model-based reinforcement learning framework tailored for wireless edge intelligence optimization, particularly in low-altitude wireless networks (LAWNs). Through a weather-aware UAV trajectory planning case study, we demonstrate the effectiveness of our framework in improving learning efficiency and decision quality.

Related papers

• Assessing Adaptive World Models in Machines with Novel Games [77.94506923046423]
  We argue that rapid adaptation is linked to the efficient construction and refinement of internal representations of the environment.<n>Current understanding and evaluation of world models in artificial intelligence remains narrow.<n>We propose a new benchmarking paradigm based on suites of carefully designed games with genuine, deep and continually refreshing novelty.
  arXiv  Detail & Related papers  (2025-07-17T06:28:14Z)
• Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy [0.0]
  We argue for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and intuitive human-AI interfaces.<n>Through illustrative examples and conceptual frameworks, we advocate that inverse design in manufacturing should evolve into a unified ecosystem.
  arXiv  Detail & Related papers  (2025-05-29T08:15:27Z)
• AI in a vat: Fundamental limits of efficient world modelling for agent sandboxing and interpretability [84.52205243353761]
  Recent work proposes using world models to generate controlled virtual environments in which AI agents can be tested before deployment.<n>We investigate ways of simplifying world models that remain agnostic to the AI agent under evaluation.
  arXiv  Detail & Related papers  (2025-04-06T20:35:44Z)
• A Survey of World Models for Autonomous Driving [63.33363128964687]
  Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling.<n>This paper systematically reviews recent advances in world models for autonomous driving.
  arXiv  Detail & Related papers  (2025-01-20T04:00:02Z)
• Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
  This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer.<n>By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.
  arXiv  Detail & Related papers  (2025-01-17T10:39:09Z)
• Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey [61.39993881402787]
  World models and video generation are pivotal technologies in the domain of autonomous driving. This paper investigates the relationship between these two technologies. By analyzing the interplay between video generation and world models, this survey identifies critical challenges and future research directions.
  arXiv  Detail & Related papers  (2024-11-05T08:58:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.