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

Forum topic · 小凯 · 2026-09-25

Summary

A paper on arXiv (2609.27150) by Boxuan Wang, Zhuoyun Li, Xiaowei Huang, and Yi Dong examines whether complex topology control is necessary in multi-agent debate (MAD) for improving large language model reasoning. While sophisticated mechanisms that learn, adapt, or dynamically reconfigure agent communication have become popular, the authors find that a simple random-without-replacement routing policy—in which each agent debates two distinct, newly sampled peers per round—serves as an unexpectedly strong baseline that consistently improves the accuracy-cost trade-off of sparse MAD. They further study deliberation stopping mechanisms, showing that lightweight stopping rules can substantially reduce inference cost while maintaining competitive accuracy. The results suggest that sophisticated topology control methods, such as learned topological adaptation, should first be compared against strong simple routing and stopping baselines before their added complexity is justified. The paper was posted on 2026-09-25 in the machine learning category.

Paper Overview

Field: Machine Learning Authors: Boxuan Wang, Zhuoyun Li, Xiaowei Huang, Yi Dong Published: 2026-09-25 arXiv: 2609.27150

Abstract

Multi-agent debate (MAD) has emerged as a promising paradigm for improving the reasoning accuracy of large language models (LLMs) through iterative peer interaction. Communication topology plays a central role in this process, motivating increasingly sophisticated mechanisms that learn, adapt, or dynamically reconfigure agent interactions to improve accuracy or reasoning reliability. Meanwhile, prior studies suggest that much simpler sparse communication can already achieve competitive performance at substantially lower cost.

In this work, the authors take a closer look at sparse MAD and ask whether complex topology control is actually necessary to improve collective reasoning. They find that a simple random-without-replacement routing policy—which lets each agent debate with two distinct and newly sampled peers each round—provides an unexpectedly strong baseline that consistently improves the accuracy-cost trade-off of sparse MAD.

Building on this observation, they further study deliberation stopping mechanisms, showing that lightweight stopping can substantially reduce inference cost while maintaining competitive accuracy.

Key Takeaways

Complex topology control (e.g., learned topological adaptation) may not be necessary for effective multi-agent debate.
A simple random-without-replacement routing policy is a strong baseline for sparse MAD.
Lightweight stopping mechanisms significantly cut inference costs with competitive accuracy.
Sophisticated methods should be benchmarked against strong simple baselines before claiming benefits from added complexity.

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*Auto-collected on 2026-09-25*