Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges | Paper Citation Record

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This paper cites A survey on mobility of edge computing networks in IoT: State-of-the-art, architectures, and challenges,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges A survey on mobility of edge computing networks in IoT: State-of-the-art, architectures, and challenges,

Reference 1

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This paper cites Edge-computing-driven internet of things: A survey,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Edge-computing-driven internet of things: A survey,

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This paper cites Distributed artificial intelligence empowered by end-edge-cloud com- puting: A survey,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Distributed artificial intelligence empowered by end-edge-cloud com- puting: A survey,

Reference 3

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This paper cites Disclosing edge intelligence: A systematic meta-survey,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Disclosing edge intelligence: A systematic meta-survey,

Reference 4

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Edge intelligence: Empowering intelligence to the edge of network,

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This paper cites Edge intelligence: Paving the last mile of artificial intelligence with edge computing,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Edge intelligence: Paving the last mile of artificial intelligence with edge computing,

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This paper cites Security and privacy on 6G network edge: A survey,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Security and privacy on 6G network edge: A survey,

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This paper cites Edge intelligence: The confluence of edge computing and artificial intelligence,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Edge intelligence: The confluence of edge computing and artificial intelligence,

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This paper cites Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

Reference 9

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This paper cites Towards edge general intelligence via large language models: Opportunities and challenges,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Towards edge general intelligence via large language models: Opportunities and challenges,

Reference 10

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Observation 7c9130f1-eff8-47d0-b0d3-e870d1351f25 · outbound

This paper cites Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Agentic AI: Autonomous intelligence for complex goals–a comprehensive survey,

Reference 11

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This paper cites AI agents vs. agentic AI: A conceptual taxonomy, applications and challenge,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges AI agents vs. agentic AI: A conceptual taxonomy, applications and challenge,

Reference 12

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This paper cites The road toward general edge intelligence: Standing on the shoulders of foundation models,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges The road toward general edge intelligence: Standing on the shoulders of foundation models,

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This paper cites Artificial general intelligence (AGI)-native wireless systems: A journey beyond 6G,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Artificial general intelligence (AGI)-native wireless systems: A journey beyond 6G,

Reference 14

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This paper cites Thinking in space: How multimodal large language models see, remember, and recall spaces,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Thinking in space: How multimodal large language models see, remember, and recall spaces,

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This paper cites A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27,

Reference 16

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This paper cites Recurrent world models facilitate policy evolution,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Recurrent world models facilitate policy evolution,

Reference 17

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This paper cites Mastering diverse control tasks through world models,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Mastering diverse control tasks through world models,

Reference 18

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This paper cites Understanding world or predicting future? a comprehensive survey of world models,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Understanding world or predicting future? a comprehensive survey of world models,

Reference 19

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This paper cites Learning and Leveraging World Models in Visual Representation Learning.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Learning and Leveraging World Models in Visual Representation Learning

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This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 21

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This paper cites A survey of recent advances in optimization methods for wireless communications,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges A survey of recent advances in optimization methods for wireless communications,

Reference 22

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Fully-decoupled RAN for feedback-free multi-base station transmission in MIMO-OFDM system,

Reference 23

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This paper cites Channel estimation for reconfigurable intelligent surface assisted high-mobility wireless systems,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Channel estimation for reconfigurable intelligent surface assisted high-mobility wireless systems,

Reference 24

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This paper cites Federated learning over fully-decoupled RAN architecture for two-tier computing acceleration,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Federated learning over fully-decoupled RAN architecture for two-tier computing acceleration,

Reference 25

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This paper cites Navigating the road ahead: A comprehensive survey of radio resource allocation for vehicle platooning in C-V2X communications,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Navigating the road ahead: A comprehensive survey of radio resource allocation for vehicle platooning in C-V2X communications,

Reference 26

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This paper cites World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks

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This paper cites Generative AI for secure physical layer communi- cations: A survey,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Generative AI for secure physical layer communi- cations: A survey,

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This paper cites Reasoning with Language Model is Planning with World Model.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Reasoning with Language Model is Planning with World Model

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This paper cites Vista: A generalizable driving world model with high fidelity and versatile controllability,.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Vista: A generalizable driving world model with high fidelity and versatile controllability,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Random 3D mobile UA V networks: Mobility modeling and coverage probability,

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This paper cites World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges A Survey of World Models for Autonomous Driving

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges World models for autonomous driving: An initial survey,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Edge Intelligence: Architectures, Challenges, and Applications

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Is sora a world simulator? a comprehensive survey on general world models and beyond,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Digital twin assisted intelligent network man- agement for vehicular applications,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges LLM-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness,

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This paper cites Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning.

Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Energy-Efficient RSMA-enabled Low-altitude MEC Optimization Via Generative AI-enhanced Deep Reinforcement Learning

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges LLaMA: Open and Efficient Foundation Language Models

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Leveraging edge intelligence and LLMs to advance 6G-enabled internet of automated defense vehicles,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Trustworthy distributed AI systems: Robustness, privacy, and governance,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Generative AI-enabled Wireless Communications for Robust Low-Altitude Economy Networking

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges MemGPT: Towards LLMs as Operating Systems

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Learning latent dynamics for planning from pixels,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Dream to Control: Learning Behaviors by Latent Imagination

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Tutorial overview of model predictive control,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Deep learning, reinforcement learning, and world models,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Dyna, an integrated architecture for learning, planning, and reacting,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Auto-encoders in deep learning—a review with new perspectives,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Tutorial on Variational Autoencoders

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Neural discrete representation learning,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Transformers in vision: A survey,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges A survey on vision transformer,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges GPT (generative pre-trained transformer)— a comprehensive review on enabling technologies, po- tential applications, emerging challenges, and future directions,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Vision-language models for vision tasks: A survey,

Reference 59

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges A Generalist Agent

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Generative adversarial net- works,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges World-GAN: A gener- ative model for minecraft worlds,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Generative adversarial networks: An overview,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Diffusion models: A comprehensive survey of methods and applications,

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Video generation models as world simulators,

Reference 67

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Mastering Atari with Discrete World Models

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Transformer-based World Models Are Happy With 100k Interactions

Reference 69

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 70

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges WorldGPT: Empowering LLM as multimodal world model,

Reference 71

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Adversarial Video Generation on Complex Datasets

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges World Models via Policy-Guided Trajectory Diffusion

Reference 73

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Prioritized sweeping: Reinforcement learning with less data and less time,

Reference 74

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Wireless networks design in the era of deep learning: Model-based, AI-based, or both?

Reference 75

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Autonomous navigation of UA V by using real-time model-based reinforcement learning,

Reference 76

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Texplore: real-time sample-efficient reinforce- ment learning for robots,

Reference 77

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges A model-based reinforcement learning protocol for routing in vehicular ad hoc network,

Reference 78

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Model-based reinforcement learning with kernels for resource allocation in RAN slices,

Reference 79

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Mastering atari, go, chess and shogi by planning with a learned model,

Reference 80

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges OpenAI Gym

Reference 81

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Machine learning for channel quality prediction: From concept to experimental validation,

Reference 82

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Deep learning based prediction of traffic peaks in mobile networks,

Reference 83

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Energy- efficient trajectory optimization with wireless charging in UA V-assisted MEC based on multi-objective reinforcement learning,

Reference 84

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Joint deployment and trajectory optimization in UA V-assisted vehicular edge computing networks,

Reference 85

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Joint communication and trajectory optimization for multi-UA V enabled mobile internet of vehicles,

Reference 86

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Uncertainty-aware model-based reinforce- ment learning: Methodology and application in autonomous driving,

Reference 87

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Uncertainty- aware multiview deep learning for internet of things applications,

Reference 88

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Rm-Gen: Conditional diffusion model-based radio map generation for wireless networks,

Reference 89

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Generative AI based Secure Wireless Sensing for ISAC Networks

Reference 91

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Generative AI for deep reinforcement learning: Framework, analysis, and use cases,

Reference 92

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Deep reinforcement learning for channel estimation in RIS-aided wireless networks,

Reference 93

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Model-based: End-to-end molecular communication system through deep reinforce- ment learning auto encoder,

Reference 94

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Model predictive control: Theory and practice—a survey,

Reference 95

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Model predictive path integral control for agile unmanned aerial vehicles,

Reference 96

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Model predictive control for the receiving-side DC–DC converter of dynamic wireless power transfer,

Reference 97

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges A low computational burden model predictive control for dynamic wireless charging,

Reference 98

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges A survey of monte carlo tree search methods,

Reference 99

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Edge General Intelligence Through World Models and Agentic AI: Fundamentals, Solutions, and Challenges Path planning for the dynamic UA V-aided wireless systems using monte carlo tree search,

Reference 100

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