English(EN) Multi-Agentic AI for Fairness-Aware and Accelerated Multi-modal Large Model Inference in Real-world Mobile Edge Networks

多智能体AI框架优化移动边缘网络中的LLM推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

一篇新的研究论文提出了一种多智能体AI框架，旨在优化移动边缘网络中大语言模型（LLMs）的推理。该框架通过采用一个长期的全局规划智能体、一个短期的提示调度智能体以及节点上的LM部署智能体来降低延迟并提高公平性。这些智能体利用运行时数据的自然语言推理来管理提示路由和模型部署。在城市范围的测试平台上进行的实验表明，平均端到端延迟显著降低，比基线提高了80.6%，同时保持了高Jain指数的公平性。 AI

影响该框架可以使边缘设备上的AI服务更高效、更具响应性，从而改善移动应用程序的用户体验。

排序理由在arXiv上发表的研究论文，详细介绍了一个新的AI框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

Haiyuan Li

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 8 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 在arXiv上发表的研究论文，详细介绍了一个新的AI框架。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Haiyuan Li, Hari Madhukumar, Shuangyi Yan, Yulei Wu, Dimitra Simeonidou · 2026-10-27 04:00

面向真实移动边缘网络的公平感知和加速多模态大模型推理的多智能体AI

arXiv:2602.07215v2 Announce Type: replace-cross 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 l…

本文如何被排名

报道来源 [1]

面向真实移动边缘网络的公平感知和加速多模态大模型推理的多智能体AI

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