arXiv AI By Yanwen Peng, Delvin Ce Zhang, Xi Wang, Nikolaos Aletras

BusMA: A Bus Communication Substrate for Multi-Agent Systems

Read the original on arXiv AI →

BusMA introduces a bus-based communication substrate for multi‑agent systems, enabling any agent to address others through a shared channel. The framework includes agent registration, message routing, and shared memory management, and defines four communication intents—discussion, challenge, guidance, and request for explanation—to facilitate fine‑grained interaction. Experiments with two leading LLMs on 13 tasks in visual reasoning, mathematical reasoning, and knowledge retrieval show that BusMA consistently outperforms existing hierarchical manager‑worker and router‑based message passing methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jul 13

Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks

arXiv:2607. 09330v1 Announce Type: new Abstract: Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in physical artificial intelligence such as smart factories, warehouses, and service robotics.

By Nuocheng Yang, Sihua Wang, Zihan Chen, Tony Q. S. Quek, Changchuan Yin