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

Qianli Ma, Chang Guo, Zhiheng Tian, Siyu Wang, Jipeng Xiao, Yuanhao Yue, Zhipeng Zhang


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 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.
Anthology ID:
2026.acl-long.2140
Volume:
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2026
Address:
San Diego, California, United States
Editors:
Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
46134–46166
Language:
URL:
https://preview.aclanthology.org/ingest-acl/2026.acl-long.2140/
DOI:
Bibkey:
Cite (ACL):
Qianli Ma, Chang Guo, Zhiheng Tian, Siyu Wang, Jipeng Xiao, Yuanhao Yue, and Zhipeng Zhang. 2026. Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 46134–46166, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance (Ma et al., ACL 2026)
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PDF:
https://preview.aclanthology.org/ingest-acl/2026.acl-long.2140.pdf
Checklist:
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