LLM-Based Multi-Agent Systems over Wireless Networks: A Joint Agent--Network Design Perspective

Chao Hu; Yuan Guo; Guanlin Wu; Yueling Che; Han Hu; Jie Xu

cs.MASep 29, 2026

LLM-Based Multi-Agent Systems over Wireless Networks: A Joint Agent--Network Design Perspective

Authors: Chao Hu, Yuan Guo, Guanlin Wu, Yueling Che, Han Hu, Jie Xu

Organizations: College of Computer Science and Software Engineering (CSSE), Shenzhen University, Shenzhen 518060, China · School of Science and Engineering (SSE), the Shenzhen Future Network of Intelligence Institute (FNii), and the Guangdong Provincial Key Laboratory of Future Networks of Intelligence, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518172, China · School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Abstract

As large language models (LLMs) evolve from standalone models into collaborative agents embedded in physical systems, their reasoning and execution are increasingly distributed across wireless edge nodes. In this setting, wireless networks are experiencing a paradigm shift from only providing data connectivity to supporting the multi-agent reasoning workflow itself. The task performance of such network-constrained LLM-based multi-agent systems (MASs) is jointly affected by the multi-agent reasoning dependencies as well as the underlying network connectivity and edge resources. This coupling gives rise to various technical challenges, including the metric misalignment and message redundancy, state inconsistency and topology mismatch, as well as resource limitation and trust discontinuity. To address these challenges, this article develops a novel joint agent--network design perspective that coordinates decisions on both sides of the system. Specifically, we present the joint design of agent--interaction scheduling and resource allocation, the message selection-transmission co-design, as well as the joint agent--network topology design and workload--resource allocation. Furthermore, we consider the network-verified provenance that is linked with agent-side information-flow control to constrain how received information affects subsequent operations. An illustrative vehicle-to-everything (V2X) case study shows that jointly adapting agent-side interaction decisions and network operations improves task completion under communication and edge-resource constraints, outperforming the conventional agent-only and wireless-only separate designs.

Figures & tables

Fig. 1: From single-agent operation to multi-agent collaboration over wireless networks. Task planning forms inter-agent collaboration, while its runtime execution and adaptation depend on wireless connectivity and edge resources.

Challenge	Agent Side	Network/Edge Side	Coupling	Joint Decision
Metric misalignment	Interaction priority [ 11 ]	Resource allocation	Interaction credit $\leftrightarrow$ service feasibility	Interaction–resource scheduling
Message redundancy and state inconsistency	Message selection/compression [ 9 ]	Delivery fidelity/timeliness [ 6 , 8 ]	Receiver-context-conditioned decision distortion	Message selection and adaptive delivery
Topology mismatch	Logical edge/endpoint selection [ 10 , 9 , 15 ]	Association/path selection [ 5 , 8 ]	Interaction credit $\leftrightarrow$ support cost	Joint edge–network mapping
Resource limitation	Reasoning budget/workload	Communication/edge-resource allocation [ 7 , 4 ]	Workload footprint $\leftrightarrow$ resource feedback	Workload–resource allocation
Trust discontinuity	Information-flow control [ 13 ]	Provenance verification/admission	Provenance $\leftrightarrow$ trust requirement	Provenance-aware information flow

TABLE I: Joint design space for network-constrained LLM-based MASs.

Fig. 2: Key challenges and representative applications of LLM-based MASs over wireless networks.

Fig. 3: Illustration of case scenario and collaboration workflow.

Fig. 4: Task completion rate under two bottleneck regimes. Under the information-limited regime (left), $\kappa$ measures the relative decision relevance of the key agent; under the bandwidth-limited regime (right), $B_{\max}$ denotes the available uplink bandwidth.

Fig. 5: Compute and memory bottlenecks under different agent–network designs (OOM is out of memory).

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A Token/KV-Cache Communication Media Selection and Resource Allocation Strategy for Multi-Agent Collaboration

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State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, 210008, China · Institute of Intelligent Networks and Communications (NINE), Nanjing University (Suzhou Campus), Suzhou, 215163, China

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The convergence of large language models (LLMs) with 6G networks is fostering a paradigm of autonomous multi-agent cooperation, which in turn is expected to substantially increase east-west traffic. Although latent-space interaction mechanisms can enable more efficient collaboration than symbolic natural-language (NL) exchanges, prior work often abstracts away the associated communication overhead under practical wireless constraints. In embodied multi-agent settings, heterogeneous interaction media incur disparate inference and transmission costs, thereby inducing an inherent end-to-end (E2E) latency trade-off. To address this, we propose a joint design that integrates communication-media selection with wireless resource allocation. Through analytical characterization and simulation-based evaluation, we show that neither token-based transmission nor key-value (KV) cache-based transmission is uniformly optimal across operating regimes, as performance depends critically on system parameters such as available computational resources and channel conditions. Accordingly, we formulate a joint optimization problem aimed at minimizing the E2E latency of multi-agent collaboration and develop a low-complexity joint media selection and resource allocation (JMSRA) algorithm. Numerical results further confirm that, by adaptively coordinating the interaction media and bandwidth allocation over heterogeneous links, the proposed scheme achieves markedly reduced E2E latency relative to conventional NL-only and KV-cache-only baselines, enabling efficient and robust multi-agent collaboration in future wireless networks.

Abstract

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