A-MADiff: Attention-Guided Multi-Agent DRL with Diffusion Policies for Memory-Aware Task Orchestration in Mobile AIGC Networks

Chongzhi Wu; Zhengtao Li; Jiawen Kang; Jinbo Wen; Xiaohuan Li; Maomao Zhang; Ekram Hossain

cs.NIAug 29, 2026

A-MADiff: Attention-Guided Multi-Agent DRL with Diffusion Policies for Memory-Aware Task Orchestration in Mobile AIGC Networks

Authors: Chongzhi Wu, Zhengtao Li, Jiawen Kang, Jinbo Wen, Xiaohuan Li, Maomao Zhang, Ekram Hossain

Organizations: School of Automation, Guangdong University of Technology, Guangzhou 510006, China · Department of Computer Science, City University of Hong Kong, Hong Kong · School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China · Anhui Engineering Research Center for Agricultural Product Quality Safety Digital Intelligence, Fuyang Normal University, Fuyang 236037, China · School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236037, China · Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada

Abstract

Artificial Intelligence-Generated Content (AIGC) services employ Generative AI (GenAI) models to automatically generate diverse content. Mobile AIGC networks host GenAI models on edge-located AIGC Service Providers (ASPs) to deliver low-latency and personalized AIGC services for mobile users. However, AIGC inference tasks typically occupy GPU memory until task completion, causing GPU memory exhaustion at serving ASPs and triggering out-of-memory failures rather than merely increasing service latency. Existing studies on AIGC task orchestration have largely overlooked GPU memory feasibility constraints. To address this issue, we develop a cooperative multi-agent orchestration framework, in which each edge node is equipped with a scheduling agent to route tasks to local ASPs or neighboring edge nodes. Since scheduling agents make decisions based only on local observations, while peer offloading couples their resource states and long-term utilities, we formulate the orchestration process as a cooperative Decentralized Partially Observable Markov Decision Process (Dec-POMDP). To solve the Dec-POMDP, we propose an \underline{A}ttention-guided \underline{M}ulti-\underline{A}gent deep reinforcement learning algorithm with \underline{Diff}usion policies (A-MADiff) under the centralized training with a decentralized execution paradigm. A-MADiff employs diffusion-based decentralized actors to generate multi-modal preferences over feasible orchestration actions, and an attention-guided centralized critic to estimate per-agent values from cross-agent states under GPU memory heterogeneity. Numerical results demonstrate that A-MADiff significantly improves the cumulative reward over the state-of-the-art baseline.

Figures & tables

Fig. 1: Overview of the proposed GPU memory-aware task orchestration framework in distributed mobile AIGC networks.

Fig. 2: Overall architecture of the proposed A-MADiff algorithm for GPU memory-aware AIGC task orchestration.

Category	Parameters	Values
System	Scheduling agents $N$	$20$
	ASPs per agent $\|\mathcal{K}_{n}\|$	$5$
	Queue capacity $Q_{\max}$	$50$
	Max offloading hops $H_{\max}$	$2$
Task	Arrival rate $\lambda_{n}$ (per step)	$0.3$
	Pre-generated task pool per agent	$500$

TABLE I: Simulation parameters.

	Methods	Cumulative Rewards	Effective Success Rates	Rewards per Completed Task	Negative Completion Rates	OOM Rates
Heuristic Methods	Random	$-407.78\pm 19.96$	$8.18\%$	$-0.0577$	$42.46\%$	$5.78\%$
	RoundRobin	$-373.35\pm 19.41$	$7.94\%$	$-0.0923$	$45.97\%$	$4.85\%$
	Greedy	$281.84\pm 19.13$	$77.71\%$	$0.2414$	$22.29\%$	$2.07\%$
MADRL Methods	MADDPG	$355.81\pm 19.32$	$87.31\%$	$0.3260$	$12.68\%$	$1.56\%$
	MAPPO	$422.38\pm 31.35$	$90.87\%$	$0.4036$	$8.94\%$	$1.04\%$
	MASAC	$476.58\pm 16.59$	$91.29\%$	$0.4278$	$8.68\%$	$1.35\%$

TABLE II: Performance comparisons of A-MADiff and baselines. Bold values indicate the best result in each column.

Panel of Figure 5 — Fig. 3: Training performance of all evaluated methods. The heuristic baselines provide constant reference levels, the MADRL methods are evaluated at regular intervals on separate evaluation episodes during training, and the vertical axis in (c) uses a logarithmic scale.

Fig. 4: Action composition of different MADRL methods during evaluation, showing the ratios of local execution, peer offloading, and queue deferral.

Fig. 5: Reverse denoising process of the diffusion-based actors within one joint scheduling decision at decision step

t=58

. Arrow width and opacity encode the probabilities obtained by applying the masked softmax in ( 29 ) to the intermediate logit vector

x_{n,\ell}^{t}

at denoising step

\ell\in\{20,16,12,8,4,0\}

, and only the two largest offloading candidates of each agent are drawn. Node color encodes the available GPU memory ratio in ( 10b ). Blue, purple, and orange arrows denote the preferences of scheduling agents

n_{2}

n_{8}

, and

n_{3}

, respectively, and solid arrows in the final panel mark the selected actions.

Fig. 6: Mean attention weights of the centralized critic at decision step $t=7$ , averaged over the four attention heads and the two critics. Rows and columns correspond to the query agent $n$ and the key agent $n^{\prime}$ in ( 31 ). Green boxes mark the three largest weights in the row of $n_{2}$ , and the orange dashed box marks the offloading target selected by the actor.

Panel of Figure 9 — Fig. 7: Robustness comparison of the cumulative reward under three scaling dimensions. The GPU memory capacity scale uniformly scales the GPU memory capacity of all ASPs, the GPU memory requirement scale uniformly scales the GPU memory requirements of all tasks, and the task arrival scale uniformly scales the task arrival rate at all scheduling agents. Each experiment varies one dimension while the others remain at their default values.

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Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems

Mingzhou Fan, Siyuan Xu, Mingxuan Yuan

Multi-Agent System Optimization LLM Agent Orchestration LLM Agent Workflow Optimization Multi-Agent Systems Multi-Agent LLMs

Open paper AskPDF arXiv

Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use. Recent systems increasingly organize these agents as graphs of specialized, interconnected nodes. Although graph-based orchestration supports flexible decomposition and coordination, it creates a key challenge: \textbf{attention allocation}. As workflows grow, existing approaches often execute graph components uniformly, wasting resources on irrelevant or low-impact tasks. We introduce \textbf{Attention Orchestration}, a paradigm that extends Transformer-style attention from token representations to workflow-level agent coordination. Our framework, \textbf{Adaptive Goal-aware Attention Orchestration (AGAO)}, dynamically estimates agent importance based on user objectives, graph dependencies, and computational constraints. AGAO combines three components: (1) goal-aware attention, measuring semantic relevance between user goals and agent capabilities; (2) topology-aware attention, modeling structural dependencies in agent graphs; and (3) resource-aware attention, allocating budgets and execution priorities across heterogeneous agents. Together, these mechanisms transform static agent graphs into adaptive systems that focus computation on goal-critical reasoning paths. Experiments across diverse multi-agent workloads show that AGAO improves task effectiveness while reducing unnecessary computation, latency, and token consumption compared with existing graph-based execution strategies. Our work establishes \textbf{Attention Engineering} as a direction for scalable, intelligent multi-agent systems. Code: https://github.com/MingzhouFan97/AGAO.

Abstract

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