Paper2Rebuttal：一個用於透明化作者回覆輔助的多智能體框架

摘要

撰寫有效的反駁意見是一項高風險任務，其要求遠超語言流利度，因為它需要精準對齊審稿人意圖與論文細節。現有解決方案通常將其視為直接文本生成問題，存在幻覺生成、遺漏批評及缺乏可驗證依據等缺陷。為解決這些限制，我們提出首個多智能體框架RebuttalAgent，將反駁意見生成重新定義為以證據為核心的規劃任務。我們的系統將複雜反饋分解為原子化問題點，通過融合壓縮摘要與高保真文本動態構建混合上下文，同時集成自主按需的外部搜索模塊以解決需引用外部文獻的問題。通過在起草前生成可審查的回應方案，RebuttalAgent確保每個論點都明確錨定於內部或外部證據。我們在新建的RebuttalBench上驗證方法，證明本流程在覆蓋率、忠實度與策略連貫性上均優於強基線模型，為同行評審提供透明可控的輔助工具。程式碼將公開釋出。

English

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.

Paper2Rebuttal：一個用於透明化作者回覆輔助的多智能體框架

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

摘要

Support