Mobile metaverses are envisioned as a transformative digital ecosystem that delivers immersive, intelligent, and ubiquitous services through mobile devices. Driven by Large Language Models (LLMs) and Vision-Language Models (VLMs), Artificial Intelligence (AI) agents hold the potential to empower the creation, maintenance, and evolution of mobile metaverses, enabling seamless human-machine interaction and dynamic service adaptation. Currently, AI agents are primarily built upon cloud-based LLMs and VLMs. However, several challenges hinder their efficient deployment, including high service latency and a risk of sensitive data leakage during perception and processing. In this paper, we develop an edge-cloud collaboration-based federated AI agent construction framework in mobile metaverses. Specifically, Edge Servers (ESs), as agent infrastructures, first create agent modules in a distributed manner. The cloud server then integrates these modules into AI agents and deploys them at the edge, thereby enabling low-latency AI agent services for users. Considering that ESs may exhibit dynamic levels of willingness to participate in federated AI agent construction, we design a two-period dynamic contract model to continuously incentivize ESs to participate in agent module creation, effectively addressing the dynamic information asymmetry between the cloud server and ESs. Furthermore, we propose an Enhanced Diffusion Model-based Soft Actor-Critic (EDMSAC) algorithm to effectively generate optimal dynamic contracts. In the algorithm, we apply dynamic structured pruning to DM-based actor networks to enhance denoising efficiency and policy learning performance. Simulation results demonstrate that the EDMSAC algorithm outperforms the DMSAC algorithm by up to $23\%$ in optimal dynamic contract generation.

翻译：移动元宇宙被设想为一个变革性的数字生态系统，通过移动设备提供沉浸式、智能化且无处不在的服务。在大型语言模型（LLMs）和视觉语言模型（VLMs）的驱动下，人工智能（AI）智能体具备赋能移动元宇宙创建、维护与演进的潜力，能够实现无缝的人机交互与动态服务适配。目前，AI智能体主要构建于基于云的LLMs和VLMs之上。然而，其高效部署面临若干挑战，包括高服务延迟以及在感知与处理过程中存在的敏感数据泄露风险。本文提出了一种基于边云协同的移动元宇宙联邦AI智能体构建框架。具体而言，边缘服务器（ESs）作为智能体基础设施，首先以分布式方式创建智能体模块。随后，云服务器将这些模块集成为AI智能体并部署在边缘，从而为用户提供低延迟的AI智能体服务。考虑到ESs参与联邦AI智能体构建的意愿可能呈现动态变化，我们设计了一个两阶段动态合约模型，以持续激励ESs参与智能体模块创建，有效应对云服务器与ESs之间的动态信息不对称问题。此外，我们提出了一种基于增强扩散模型的软演员-评论家（EDMSAC）算法，以有效生成最优动态合约。在该算法中，我们对基于扩散模型的演员网络应用动态结构化剪枝，以提升去噪效率与策略学习性能。仿真结果表明，在最优动态合约生成方面，EDMSAC算法相比DMSAC算法的性能提升最高可达$23\%$。