arXiv cs.AI· Haiyuan Li, Hari Madhukumar, Shuangyi Yan, Yulei Wu, Dimitra Simeonidou·· 14 小时前AI 评分29
多智能体 AI 框架实现移动边缘网络中公平感知的多模态大模型推理加速
Multi-Agentic AI for Fairness-Aware and Accelerated Multi-modal Large Model Inference in Real-world Mobile Edge Networks
AI 导读
研究者提出面向移动边缘网络的多智能体 AI 框架，由长期全局规划 Agent、短期提示词调度 Agent 和多个节点内 LM 部署 Agent 组成，通过自然语言推理协同优化提示词路由与模型部署。在城市级测试平台上，该方案相比 SOTA 在线学习基线降低平均端到端延迟 42.9%，相比全激活基线降低 80.6%，服务类型 Jain's index 均值保持 0.925。方案无需微调即可快速适配。
正文 · 原文
Abstract:Generative AI (GenAI) has transformed applications in natural language processing and content creation, yet centralized inference remains hindered by high latency, limited customizability, and privacy concerns. Deploying large models (LMs) in mobile edge networks emerges as a promising solution. However, it also poses new challenges, including heterogeneous multi-modal LMs with diverse resource demands and inference speeds, varied prompt/output modalities that complicate orchestration, and resource-limited infrastructure ill-suited for concurrent LM execution. In response, we propose a Multi-Agentic AI framework for latency- and fairness-aware multi-modal LM inference in mobile edge networks. Our solution includes a long-term Global Planning Agent, a short-term Prompt Scheduling Agent, and multiple on-node LM Deployment Agents, all powered by foundation language models. These agents cooperatively optimize prompt routing and LM deployment through natural language reasoning over runtime telemetry and historical experience. To evaluate its performance, we further develop a city-wide testbed that supports network monitoring, containerized LM deployment, intra-server resource management, and inter-server communications. Experiments demonstrate that our solution reduces average end-to-end latency by 42.9% compared with a state-of-the-art online-learning baseline and by 80.6% compared with the full-activation baseline, while maintaining a mean service-type Jain's index of 0.925. Moreover, our solution adapts quickly without fine-tuning, offering a generalizable solution for optimizing GenAI services in edge environments.
| Subjects: | Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC) |
| Cite as: | arXiv:2602.07215 [eess.SY] |
| (or arXiv:2602.07215v2 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2602.07215 arXiv-issued DOI via DataCite |
Submission history
From: Haiyuan Li [view email]
[v1]
Fri, 6 Feb 2026 21:52:49 UTC (5,720 KB)
[v2]
Sun, 25 Oct 2026 21:30:46 UTC (7,686 KB)
来源：arXiv cs.AI · arxiv.org