Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
This addresses the challenge of writing transparent and verifiable rebuttals for researchers in academic peer review, though it appears incremental as it builds on existing text generation approaches with a novel planning method.
The paper tackles the problem of generating effective rebuttals in peer review by introducing RebuttalAgent, a multi-agent framework that reframes rebuttal generation as an evidence-centric planning task, and demonstrates that it outperforms strong baselines in coverage, faithfulness, and strategic coherence on the proposed RebuttalBench.
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.