Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
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.
69710b60c1c7409747bf9431
Qianli Ma
claimed_verified
2026-01-22T08:46:28.336Z
6448b2f53e7b3c11be684348
https://cdn-avatars.huggingface.co/v1/production/uploads/6448b2f53e7b3c11be684348/QvlUQG3pWf8ZyEVBV6F7w.jpeg
Qianli Ma
Mqleet
user
Mqleet
69710b60c1c7409747bf9432
Chang Guo
69710b60c1c7409747bf9433
Zhiheng Tian
69710b60c1c7409747bf9434
Siyu Wang
69710b60c1c7409747bf9435
Jipeng Xiao
69710b60c1c7409747bf9436
Yuanhao Yue
69710b60c1c7409747bf9437
Zhipeng Zhang
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
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.
RebuttalAgent is a multi-agent framework that reframes rebuttal generation as an evidence-centric planning task, improving coverage, faithfulness, and strategic coherence in academic peer review.
multi-agents framework evidence-centric planning rebuttal generation peer review strategic coherence faithful generation external search module