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

2025-05-31

AI Summary

In future networks, edge-intelligent autonomous agents operating under data-constrained and safety-critical conditions suffer from insufficient environmental modeling capability. Method: This paper proposes a world model framework for cognitive agents—first defining the world model as an embedded cognitive engine (distinct from digital twins and large language models)—to construct compact latent-space dynamics representations enabling predictive inference and online planning; it further introduces Wireless Dreamer, a lightweight, deployable reinforcement learning framework integrating meteorological awareness and low-altitude wireless channel modeling for UAV trajectory optimization. Contribution/Results: Evaluated in the LAWN simulation environment, the approach reduces sample complexity by 47%, improves task completion rate by 32%, and significantly enhances learning efficiency and decision robustness at the network edge.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Developing world models for cognitive agents in edge intelligence
• Optimizing wireless edge networks using world model-based reinforcement learning
• Enhancing UAV trajectory planning with weather-aware decision-making

Innovation

• World models enable predictive reasoning and planning
• Wireless Dreamer optimizes edge intelligence via reinforcement learning
• Sample-efficient framework for data-constrained scenarios