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 $\textbf{RebuttalAgent}$, 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, $\textbf{RebuttalAgent}$ ensures that every argument is explicitly anchored in internal or external evidence. We validate our approach on the proposed $\textbf{RebuttalBench}$ 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.

翻译：撰写有效的反驳是一项高风险任务，它需要的不仅仅是语言流畅性，更要求审稿人意图与稿件细节之间的精确对齐。当前的解决方案通常将其视为一个直接到文本的生成问题，因而存在幻觉、忽视批评以及缺乏可验证依据等缺陷。为应对这些局限性，我们引入了 $\textbf{RebuttalAgent}$，这是首个将反驳生成重新定义为以证据为中心的规划任务的多智能体框架。我们的系统将复杂的审稿意见分解为原子化的关切点，并通过综合压缩摘要与高保真文本动态构建混合上下文，同时集成了一个自主的、按需调用的外部搜索模块，以解决需要外部文献支持的关切。通过在起草前生成一个可检查的回复计划，$\textbf{RebuttalAgent}$ 确保每一个论点都明确地锚定在内部或外部证据之上。我们在提出的 $\textbf{RebuttalBench}$ 上验证了我们的方法，并证明我们的流程在覆盖度、忠实度和策略连贯性方面均优于强基线，为同行评审过程提供了一个透明且可控的辅助工具。代码将予以发布。