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World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
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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.
Forward citations
Cited by 9 Pith papers
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World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems
Introduces WM-CDT framework with causal information value metric to maximize long-term return-per-bit in semantic communications for physical AI with closed-loop sensing-inference-control.
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6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence
6G networks need LLM-based agents in a layered semantic control plane to achieve autonomous intelligence, with empirical results showing that heterogeneous deployment across device-edge-core is required due to inheren...
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Learning Ad Hoc Network Dynamics via Graph-Structured World Models
G-RSSM learns per-node dynamics in wireless ad hoc networks via graph attention and trains clustering policies through imagined rollouts, generalizing from N=50 training to larger networks.
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Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning
WM-MS3M adds a compact stochastic latent and dual decoders to a causal multi-scale SSM, improving KPI MAE by 1.69% over MS3M with 32% fewer parameters and enabling PRB what-if rollouts.
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Compact LLM Deployment and World Model Assisted Offloading in Mobile Edge Computing
A pruning-distillation-quantization pipeline with a world-model-augmented PPO controller claims 70-80% smaller edge LLMs and 12-30% lower inference latency, but one of its own model rows contradicts the accuracy/hallu...
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DWM-RO: Decentralized World Models with Reasoning Offloading for SWIPT-enabled Satellite-Terrestrial HetNets
A decentralized world-model MARL framework with uncertainty-gated edge offloading and latent-state mean subtraction improves simulated SWIPT beamforming and power-splitting performance.
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Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial
A survey and tutorial that organizes LLM-enabled wireless network optimization into formulation, solution, and verification stages, with case studies drawn from the authors' own prior papers.
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Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions
A survey that organizes agentic AI for 6G edge networks into four pillars, compactness, efficiency, knowledge and reasoning, and migration, and illustrates them with prior case studies.
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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges
A survey reviewing how world models and agentic AI could be combined to give edge devices predictive, proactive decision-making, with a taxonomy of methods, applications, and challenges.
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