We present R-Debater, an agentic framework for generating multi-turn debates built on argumentative memory. Grounded in rhetoric and memory studies, the system views debate as a process of recalling and adapting prior arguments to maintain stance consistency, respond to opponents, and support claims with evidence. Specifically, R-Debater integrates a debate knowledge base for retrieving case-like evidence and prior debate moves with a role-based agent that composes coherent utterances across turns. We evaluate on standardized ORCHID debates, constructing a 1,000-item retrieval corpus and a held-out set of 32 debates across seven domains. Two tasks are evaluated: next-utterance generation, assessed by InspireScore (subjective, logical, and factual), and adversarial multi-turn simulations, judged by Debatrix (argument, source, language, and overall). Compared with strong LLM baselines, R-Debater achieves higher single-turn and multi-turn scores. Human evaluation with 20 experienced debaters further confirms its consistency and evidence use, showing that combining retrieval grounding with structured planning yields more faithful, stance-aligned, and coherent debates across turns.

翻译：我们提出了R-Debater，一个基于论证记忆生成多轮辩论的智能体框架。该系统植根于修辞学与记忆研究，将辩论视为一个回忆并调整先前论点以保持立场一致性、回应对手、并用证据支持主张的过程。具体而言，R-Debater整合了一个用于检索类案例证据及先前辩论策略的辩论知识库，以及一个基于角色的智能体，该智能体负责跨轮次组织连贯的发言。我们在标准化的ORCHID辩论数据集上进行评估，构建了一个包含1000个条目的检索语料库以及一个涵盖七个领域的、包含32场辩论的保留测试集。评估包含两项任务：下一轮发言生成（通过InspireScore在主观性、逻辑性和事实性方面进行评估）和对抗性多轮模拟（通过Debatrix在论点、来源、语言和整体表现方面进行评判）。与强大的大语言模型基线相比，R-Debater在单轮和多轮评分上均取得了更高的分数。由20位经验丰富的辩手进行的人工评估进一步证实了其立场一致性和证据使用能力，表明将检索基础与结构化规划相结合，能够生成跨轮次更忠实、立场更一致且更连贯的辩论。