https://arxiv.org/abs/2601.14171
🔥 Project Page: https://mqleet.github.io/Paper2Rebuttal_ProjectPage/
🕹️ Code: https://github.com/AutoLab-SAI-SJTU/Paper2Rebuttal (⭐️)
🤗 Huggingface Space: https://huggingface.co/spaces/Mqleet/RebuttalAgent

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This is an automated message from the Librarian Bot. I found the following papers similar to this paper.

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Papers

arxiv:2601.14171

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

Published on Jan 20

· Submitted by

Qianli Ma on Jan 22

#2 Paper of the day

· AutoLab

Upvote

Authors:

Qianli Ma ,

Abstract

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.

AI-generated summary

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.

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Mqleet

Paper author Paper submitter 29 days ago

RebuttalAgent is an AI-powered multi-agent system that helps researchers craft high-quality rebuttals for academic paper reviews. The system analyzes reviewer comments, searches relevant literature, generates rebuttal strategies, and produces formal rebuttal letters, all through an interactive human-in-the-loop workflow.

🌐 Paper: https://arxiv.org/abs/2601.14171
🔥 Project Page: https://mqleet.github.io/Paper2Rebuttal_ProjectPage/
🕹️ Code: https://github.com/AutoLab-SAI-SJTU/Paper2Rebuttal (⭐️)
🤗 Huggingface Space: https://huggingface.co/spaces/Mqleet/RebuttalAgent