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arXiv:cs.AI· Yanwen Peng, Delvin Ce Zhang, Xi Wang, Nikolaos Aletras·· 4 小时前AI 评分42

BusMA:面向多智能体系统的总线通信底座

BusMA: A Bus Communication Substrate for Multi-Agent Systems

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受计算机总线架构启发,研究者提出 BusMA 通信框架,让任意智能体通过共享总线直接寻址其他智能体,包含智能体注册、消息路由和共享内存管理组件。

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Abstract:Multi-Agent (MA) systems are effective at solving complex tasks that demand planning, tool use, and the synthesis of evidence from multiple sources. Existing systems typically adopt Hierarchical Manager-Worker (HMW) or Router-based Message Passing (RMP) structures as their communication protocol. However, these designs restrict agent autonomy: Worker agents cannot directly consult specific "peers", and misrouted messages can propagate errors. Inspired by bus architectures in computer systems, we propose BusMA, a communication framework that allows any agent to address other agents through a shared channel, i.e., the Bus. It consists of agent registration, message routing, and shared memory management components. Worker agents, each equipped with tools, have their own local memory and can reason, act (tool usage), and communicate by posting shared messages with specific intents. We introduce four intents: discussion, challenge, guidance, and request for explanation, which support fine-grained communication among agents. A Chair agent monitors the shared memory to coordinate interactions and facilitate convergence among Workers. To evaluate the effectiveness of BusMA, we conduct extensive experiments with two frontier LLMs across 13 tasks spanning visual reasoning, mathematical reasoning, and knowledge retrieval. The results demonstrate that BusMA consistently outperforms state-of-the-art HMW and RMP methods.
Comments: Camera-ready version accepted to AACL 2026. 23 pages
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.15054 [cs.AI]
  (or arXiv:2609.15054v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2609.15054

arXiv-issued DOI via DataCite

Submission history

From: Yanwen Peng [view email]
[v1] Mon, 14 Sep 2026 05:19:55 UTC (2,286 KB)
[v2] Fri, 2 Oct 2026 11:17:09 UTC (2,324 KB)

来源:arXiv:cs.AI · arxiv.org