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

• URL: http://arxiv.org/abs/2601.14171v1
• Date: Tue, 20 Jan 2026 17:23:51 GMT
• Title: Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
• Authors: Qianli Ma, Chang Guo, Zhiheng Tian, Siyu Wang, Jipeng Xiao, Yuanhao Yue, Zhipeng Zhang,
• Abstract summary: $textbfRebuttalAgent$ is a new multi-agents framework that reframes rebuttal generation as an evidence-centric planning task.<n>Our system decomposes complex feedback into atomic concerns and dynamically constructs hybrid contexts.<n>By generating an inspectable response plan before drafting, $textbfRebuttalAgent$ ensures that every argument is explicitly anchored in internal or external evidence.
• Score: 23.470768802111007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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