State-of-the-art single-agent claim verification methods struggle with complex claims that require nuanced analysis of multifaceted evidence. Inspired by real-world professional fact-checkers, we propose \textbf{DebateCV}, the first debate-driven claim verification framework powered by multiple LLM agents. In DebateCV, two \textit{Debaters} argue opposing stances to surface subtle errors in single-agent assessments. A decisive \textit{Moderator} is then required to weigh the evidential strength of conflicting arguments to deliver an accurate verdict. Yet, zero-shot Moderators are biased toward neutral judgments, and no datasets exist for training them. To bridge this gap, we propose \textbf{Debate-SFT}, a post-training framework that leverages synthetic data to enhance agents' ability to effectively adjudicate debates for claim verification. Results show that our methods surpass state-of-the-art non-debate approaches in both accuracy (across various evidence conditions) and justification quality.

翻译：现有单代理事实核查方法在处理需要多维度证据细致分析的复杂声明时表现不足。受现实世界专业事实核查员的启发，我们提出\textbf{DebateCV}——首个由多个大语言模型代理驱动的辩论式声明验证框架。在DebateCV中，两位\textit{辩论者}就对立立场进行辩论，以暴露单代理评估中的细微错误。随后需要具有裁决能力的\textit{主持人}权衡矛盾论证的证据强度，从而给出准确判定。然而，零样本主持人存在中立判断偏差，且缺乏用于训练主持人的数据集。为弥补这一不足，我们提出\textbf{Debate-SFT}——一种利用合成数据增强代理有效裁决声明验证辩论能力的后训练框架。实验结果表明，我们的方法在准确率（跨多种证据条件）和论证质量两方面均超越现有最先进的非辩论式方法。