Paper 2601.14171
Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
- Published
- Jan 2026
- Research lab
- Independent
- Citations
- 6
- GitHub
- 553 stars
01 In brief
Summary
The paper introduces REBUTTALAGENT, a multi-agent framework for assisting authors in writing peer-review rebuttals.
It reframes rebuttal generation as an evidence-centric planning task, decomposing feedback into atomic concerns, constructing hybrid contexts from compressed and raw manuscript text, and integrating on-demand external literature search.
The system generates an inspectable response plan before drafting, with human-in-the-loop checkpoints, to ensure arguments are grounded in verifiable evidence.
The authors propose REBUTTALBENCH, a benchmark derived from ICLR 2023 OpenReview data, and evaluate using an LLM-as-judge rubric measuring relevance, argumentation quality, and communication quality.
Experiments show REBUTTALAGENT outperforms direct-to-text baselines and chat-LLMs across all dimensions, with larger gains for weaker base models.
Ablations reveal that external evidence construction is the most critical component, while checkers prevent verbose over-generation.
The work addresses limitations of prior approaches, such as hallucination and lack of transparency, and emphasizes author control and verifiability.
02 From the paper
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.