Where Reasoning Diverges: Localized Multi-Agent Debate for Multi-Hop Question Answering
Weijun Gao, Xiang Ding, Haoyang Liu, Tiancheng Xing
Why It Matters
What makes this one worth your time
This approach could improve the efficiency and accuracy of multi-hop question answering systems, which are crucial for complex information retrieval tasks.
LMAD enhances multi-hop question answering by focusing debates on localized conflicts.
Summary
The paper introduces Localized Multi-Agent Debate (LMAD), a protocol for multi-hop question answering that focuses on resolving disagreements at the earliest point of conflict between agent rationales. The method is evaluated on four benchmarks and shows improved performance over conventional baselines.
Key contributions
- Introduction of the LMAD protocol for localized conflict resolution in multi-agent debates.
- Demonstration of improved judge accuracy on multi-hop question answering benchmarks.
- Evaluation across multiple model families and backbones.
Notable insights
- The use of localized conflict resolution in multi-agent debates to improve inference efficiency.
- Guarded resolution allows for addressing later conflicts without revisiting previously accepted steps.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2608.01463v2 Announce Type: replace Abstract: Multi-agent debate commonly exchanges complete rationales even when disagreements concern only a few intermediate claims. We introduce Localized Multi-Agent Debate (LMAD), an inference-time protocol that represents agent rationales as nodes, locates their earliest conflict, and restricts debate to the corresponding local segments. Guarded resolution extends a shared committed state so that later conflicts can be addressed without reopening accepted steps. We evaluate LMAD on four multi-hop question-answering benchmarks using ten backbones from four model families. Our method achieves the highest macro-averaged judge accuracy across all ten backbones, outperforming the strongest conventional baseline by up to 7.20 percentage points.