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Multi-Agentic AI framework optimizes LLM inference in mobile edge networks

By PulseAugur Editorial · [1 sources] ·

A new research paper proposes a Multi-Agentic AI framework designed to optimize the inference of large language models (LLMs) in mobile edge networks. This framework aims to reduce latency and improve fairness by employing a long-term Global Planning Agent, a short-term Prompt Scheduling Agent, and on-node LM Deployment Agents. These agents utilize natural language reasoning over runtime data to manage prompt routing and model deployment. Experiments on a city-wide testbed demonstrated a significant reduction in average end-to-end latency, achieving up to an 80.6% improvement over a baseline, while maintaining a high Jain's index for fairness. AI

IMPACT This framework could enable more efficient and responsive AI services on edge devices, improving user experience for mobile applications.

RANK_REASON Research paper published on arXiv detailing a new AI framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Multi-Agentic AI framework optimizes LLM inference in mobile edge networks

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Research paper published on arXiv detailing a new AI framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Haiyuan Li, Hari Madhukumar, Shuangyi Yan, Yulei Wu, Dimitra Simeonidou ·

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

    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…

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