Winning competitive debates requires sophisticated reasoning and argument skills. There are unique challenges in the competitive debate: (1) The time constraints force debaters to make strategic choices about which points to pursue rather than covering all possible arguments; (2) The persuasiveness of the debate relies on the back-and-forth interaction between arguments, which a single final game status cannot evaluate. To address these challenges, we propose TreeDebater, a novel debate framework that excels in competitive debate. We introduce two tree structures: the Rehearsal Tree and Debate Flow Tree. The Rehearsal Tree anticipates the attack and defenses to evaluate the strength of the claim, while the Debate Flow Tree tracks the debate status to identify the active actions. TreeDebater allocates its time budget among candidate actions and uses the speech time controller and feedback from the simulated audience to revise its statement. The human evaluation on both the stage-level and the debate-level comparison shows that our TreeDebater outperforms the state-of-the-art multi-agent debate system, with a +15.6% improvement in stage-level persuasiveness with DeepSeek and +10% debate-level opinion shift win. Further investigation shows that TreeDebater shows better strategies in limiting time to important debate actions, aligning with the strategies of human debate experts.

翻译：赢得竞争性辩论需要复杂的推理与论证技巧。竞争性辩论面临独特挑战：(1)时间限制迫使辩手必须对论点进行战略性选择，而非覆盖所有可能论证；(2)辩论的说服力依赖于论点间的往复互动，单一最终状态无法评估其效果。为解决这些挑战，我们提出TreeDebater这一在竞争性辩论中表现卓越的新型辩论框架。我们引入两种树结构：预演树与辩论流树。预演树通过预测攻防路径来评估主张强度，而辩论流树则通过追踪辩论状态来识别有效行动。TreeDebater将时间预算分配给候选行动，并利用发言时间控制器与模拟观众反馈来修正陈述。在阶段级与辩论级比较的人类评估表明，我们的TreeDebater优于最先进的多智能体辩论系统：使用DeepSeek时阶段级说服力提升15.6%，辩论级观点转变胜率提高10%。进一步研究发现，TreeDebater在重要辩论行动的时间分配上展现出更优策略，与人类辩论专家的战略思维高度吻合。