Paper2Rebuttal：面向作者回复辅助的多智能体透明化框架

摘要

撰写有效的反驳意见是一项高难度任务，其要求远不止语言流畅性，更需要精准把握审稿人意图与论文细节之间的对应关系。现有解决方案通常将其视为端到端文本生成问题，存在虚构内容、遗漏批评要点及缺乏可验证依据等缺陷。为突破这些局限，我们提出首个多智能体框架RebuttalAgent，将反驳生成重新定义为以证据为核心的规划任务。该系统将复杂审阅意见分解为原子化问题点，通过融合压缩摘要与高保真原文构建动态混合上下文，同时集成自主按需的外部检索模块以解决需借助外部文献的质疑。通过在起草前生成可审查的响应方案，RebuttalAgent确保每个论点都明确锚定于内部或外部证据。我们在提出的RebuttalBench基准上验证方法，证明本流水线在覆盖度、忠实度与策略连贯性上均优于强基线模型，为同行评审流程提供了透明可控的辅助工具。代码将开源发布。

English

Writing effective rebuttals is a high-stakes task that demands more than linguistic fluency, as it requires precise alignment between reviewer intent and manuscript details. Current solutions typically treat this as a direct-to-text generation problem, suffering from hallucination, overlooked critiques, and a lack of verifiable grounding. To address these limitations, we introduce RebuttalAgent, the first multi-agents framework that reframes rebuttal generation as an evidence-centric planning task. Our system decomposes complex feedback into atomic concerns and dynamically constructs hybrid contexts by synthesizing compressed summaries with high-fidelity text while integrating an autonomous and on-demand external search module to resolve concerns requiring outside literature. By generating an inspectable response plan before drafting, RebuttalAgent ensures that every argument is explicitly anchored in internal or external evidence. We validate our approach on the proposed RebuttalBench and demonstrate that our pipeline outperforms strong baselines in coverage, faithfulness, and strategic coherence, offering a transparent and controllable assistant for the peer review process. Code will be released.

Paper2Rebuttal：面向作者回复辅助的多智能体透明化框架

Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance

摘要

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