arXiv AI
Sep 24

Do We Need Complex Topology Control? Distinct-Peer Random Routing Improves Cost-Efficiency in Sparse Multi-Agent Debate

The paper investigates whether complex communication topologies are necessary for effective multi‑agent debate (MAD) among large language models. It demonstrates that a simple random-without-replacement routing policy—where each agent debates with two newly sampled peers each round—consistently improves the accuracy‑cost trade‑off in sparse MAD setups. Additionally, the study shows that lightweight deliberation stopping can further reduce inference costs without sacrificing accuracy.

By Boxuan Wang, Zhuoyun Li, Xiaowei Huang, Yi Dong
arXiv AI
Jun 30

Mixture of Debaters: Learn to Debate at Architectural Level in Multi-Agent Reasoning

arXiv:2606. 29425v1 Announce Type: new Abstract: Existing multi-agent debate frameworks suffer from two critical limitations: they rely on static architectures where agent roles and coordination patterns are fixed at design time, and they require instantiating multiple model copies, incurring substantial computational overhead.

By Dayong Liang, Kaisong Gong, Yi Cai, Changmeng Zheng, Xiao-Yong Wei
arXiv AI
Jul 7

Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration

arXiv:2511. 02200v2 Announce Type: replace Abstract: The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique expertise, collaborate flexibly, and address challenges unattainable for individual models.

By Jingbo Wang, Sendong Zhao, Haochun Wang, Yuzheng Fan, Ting Liu