# Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses arXiv: https://arxiv.org/abs/2505.12710 · HTML: https://mlsanity.com/paper/2505.12710/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 48 references that are in the map. - First posted on arXiv 19 May 2025. ## Builds on (most cited first, 30 of 32) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023) - [Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning](https://mlsanity.com/paper/2208.06193/) (2022) - [Learning a Diffusion Model Policy from Rewards via Q-Score Matching](https://mlsanity.com/paper/2312.11752/) (2023) - [Policy Representation via Diffusion Probability Model for Reinforcement Learning](https://mlsanity.com/paper/2305.13122/) (2023) - [Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization](https://mlsanity.com/paper/2405.16173/) (2024) - [Diffusion Actor-Critic with Entropy Regulator](https://mlsanity.com/paper/2405.15177/) (2024) - [Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design](https://mlsanity.com/paper/2410.13643/) (2024) - [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023) - [Learning Multimodal Behaviors from Scratch with Diffusion Policy Gradient](https://mlsanity.com/paper/2406.00681/) (2024) - [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022) - [Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services](https://mlsanity.com/paper/2303.13052/) (2023) - [Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning](https://mlsanity.com/paper/2405.20555/) (2024) - [Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks](https://mlsanity.com/paper/2304.03446/) (2023) - [Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses](https://mlsanity.com/paper/2302.08418/) (2023) - [Reparameterized Policy Learning for Multimodal Trajectory Optimization](https://mlsanity.com/paper/2307.10710/) (2023) - [When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worlds](https://mlsanity.com/paper/2208.13501/) (2022) - [Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction](https://mlsanity.com/paper/2306.14683/) (2023) - [Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaverses](https://mlsanity.com/paper/2309.00477/) (2023) - [Blockchain-assisted Twin Migration for Vehicular Metaverses: A Game Theory Approach](https://mlsanity.com/paper/2309.02173/) (2023) - [Task Freshness-aware Incentive Mechanism for Vehicle Twin Migration in Vehicular Metaverses](https://mlsanity.com/paper/2309.04703/) (2023) - [Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses](https://mlsanity.com/paper/2407.11036/) (2024) - [A Survey of Moving Target Defenses for Network Security](https://mlsanity.com/paper/1905.00964/) (2019) - [Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse](https://mlsanity.com/paper/2301.07636/) (2023) - [MetaSlicing: A Novel Resource Allocation Framework for Metaverse](https://mlsanity.com/paper/2205.11087/) (2022) - [Beyond Reality: The Pivotal Role of Generative AI in the Metaverse](https://mlsanity.com/paper/2308.06272/) (2023) - [When Metaverses Meet Vehicle Road Cooperation: Multi-Agent DRL-Based Stackelberg Game for Vehicular Twins Migration](https://mlsanity.com/paper/2312.17081/) (2023) - [Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications](https://mlsanity.com/paper/1806.05134/) (2018) - [The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions](https://mlsanity.com/paper/2304.09240/) (2023) ## Built on by (most cited first, 2 of 2) - [RadioDiff-3D: A 3D×3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication](https://mlsanity.com/paper/2507.12166/) (2025) - [RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization](https://mlsanity.com/paper/2607.08045/) (2026) ## Abstract (arXiv) Vehicular metaverses are an emerging paradigm that merges intelligent transportation systems with virtual spaces, leveraging advanced digital twin and Artificial Intelligence (AI) technologies to seamlessly integrate vehicles, users, and digital environments. In this paradigm, vehicular AI agents are endowed with environment perception, decision-making, and action execution capabilities, enabling real-time processing and analysis of multi-modal data to provide users with customized interactive services. Since vehicular AI agents require substantial resources for real-time decision-making, given vehicle mobility and network dynamics conditions, the AI agents are deployed in RoadSide Units (RSUs) with sufficient resources and dynamically migrated among them. However, AI agent migration requires frequent data exchanges, which may expose vehicular metaverses to potential cyber attacks. To this end, we propose a reliable vehicular AI agent migration framework, achieving reliable dynamic migration and efficient resource scheduling through cooperation between vehicles and RSUs. Additionally, we design a trust evaluation model based on the theory of planned behavior to dynamically quantify the reputation of RSUs, thereby better accommodating the personalized trust preferences of users. We then model the vehicular AI agent migration process as a partially observable markov decision process and develop a Confidence-regulated Generative Diffusion Model (CGDM) to efficiently generate AI agent migration decisions. Numerical results demonstrate that the CGDM algorithm significantly outperforms baseline methods in reducing system latency and enhancing robustness against cyber attacks.