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arXiv AI

Diffusion Language Models for Mobile Edge Agentic AI: Foundations, Applications, and Challenges

Diffusion language models (DLMs) provide a non‑autoregressive approach for mobile edge agentic AI, refining tokens through iterative denoising instead of left‑to‑right decoding. They can update multiple uncertain tokens in parallel and use bidirectional context, allowing flexible quality‑latency trade‑offs and early exits that reduce response delay and communication overhead. The survey reviews DLM foundations, resource‑efficient architectures, training and inference acceleration, compression, deployment strategies, and discusses open issues such as long‑context management, split inference, and trustworthy execution.

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arXiv AI
Oct 27

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

The paper proposes a Multi-Agentic AI framework to enable fairness‑aware and accelerated inference of large, heterogeneous multi‑modal language models on mobile edge networks. It introduces a Global Planning Agent, a Prompt Scheduling Agent, and multiple on‑node LM Deployment Agents that jointly optimize prompt routing and model deployment using natural language reasoning over telemetry and historical data. Experiments on a city‑wide testbed show a 42.9% reduction in end‑to‑end latency versus a state‑of‑the‑art baseline and an 80.6% reduction versus a full‑activation baseline, while preserving high fairness (Jain's index 0.925).

By Haiyuan Li, Hari Madhukumar, Shuangyi Yan, Yulei Wu, Dimitra Simeonidou
llmsagentsnlpfine-tuningmultimodalbenchmarkssafety
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Hugging Face Trending Papers
Aug 6

BALANCE: Hybrid Autoregressive-Speculative LLM Inference in Wireless Edge Networks

Edge inference is a promising paradigm to provide large language model (LLM) inference services in next-generation mobile networks. LLM inference mainly relies on two approaches: Autoregressive decoding (AD) generates output tokens sequentially, resulting in long latency; Speculative decoding (SD) accelerates inference by using a small language model (SLM) to generate multiple draft tokens for LLM verification, but incurs extra memory costs.

llmsefficiency
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arXiv Machine Learning
Aug 27

Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing

The paper introduces COMLLM, a generative framework that combines Group Relative Policy Optimization with a Look‑Ahead Collaborative Simulation to enable multi‑turn reasoning for task offloading in Mobile Edge Computing. By performing multi‑step Monte Carlo rollouts that jointly model server queue dynamics, COMLLM incorporates long‑term system evolution into its reward design, achieving near‑optimal latency and improved load‑balancing fairness. The framework demonstrates zero‑shot scalability to larger network topologies, outperforming supervised fine‑tuning, deep reinforcement learning, and heuristic baselines without requiring retraining.

By Ning Yang, Chuangxin Cheng, Haijun Zhang
llmsreinforcement-learningfine-tuningsafety
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arXiv Machine Learning
Sep 14

The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices

The paper investigates the environmental impact of running large language models (LLMs) on mobile devices. It evaluates 18 different LLM configurations on two smartphones and a server, measuring energy per token, latency, accuracy, and battery-cycle consumption. Findings reveal that on-device inference is about three times less energy‑efficient than batched server inference, that energy consumption varies non‑monotonically with quantization bit‑width, and that most models are not on the Pareto front of accuracy and energy efficiency. The study concludes that local AI is not inherently more sustainable than cloud inference, with the majority of environmental impact stemming from device embodied carbon.

By \'Edouard Gu\'egain, Tristan Coignion
llmsdiffusionroboticsefficiencybenchmarkssafety
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