Although artificial intelligence (AI) has become deeply integrated into various stages of the research workflow and achieved remarkable advancements, academic rebuttal remains a significant and underexplored challenge. This is because rebuttal is a complex process of strategic communication under severe information asymmetry rather than a simple technical debate. Consequently, current approaches struggle as they largely imitate surface-level linguistics, missing the essential element of perspective-taking required for effective persuasion. In this paper, we introduce RebuttalAgent, the first framework to ground academic rebuttal in Theory of Mind (ToM), operationalized through a ToM-Strategy-Response (TSR) pipeline that models reviewer mental state, formulates persuasion strategy, and generates strategy-grounded response. To train our agent, we construct RebuttalBench, a large-scale dataset synthesized via a novel critique-and-refine approach. Our training process consists of two stages, beginning with a supervised fine-tuning phase to equip the agent with ToM-based analysis and strategic planning capabilities, followed by a reinforcement learning phase leveraging the self-reward mechanism for scalable self-improvement. For reliable and efficient automated evaluation, we further develop Rebuttal-RM, a specialized evaluator trained on over 100K samples of multi-source rebuttal data, which achieves scoring consistency with human preferences surpassing powerful judge GPT-4.1. Extensive experiments show RebuttalAgent significantly outperforms the base model by an average of 18.3% on automated metrics, while also outperforming advanced proprietary models across both automated and human evaluations. Disclaimer: the generated rebuttal content is for reference only to inspire authors and assist in drafting. It is not intended to replace the author's own critical analysis and response.

翻译：尽管人工智能已深度融入科研工作流的各个环节并取得了显著进展，学术反驳仍是一个重要且尚未充分探索的挑战。这是因为反驳本质上是在严重信息不对称下进行的策略性沟通过程，而非简单的技术辩论。因此，现有方法大多仅模仿表层语言特征，难以胜任，因为它们缺失了有效说服所必需的换位思考这一核心要素。本文提出RebuttalAgent，这是首个将心智理论（Theory of Mind, ToM）作为基础的学术反驳框架，通过一个ToM-策略-响应（TSR）流程实现，该流程对审稿人的心智状态进行建模、制定说服策略并生成基于策略的回应。为训练我们的智能体，我们构建了RebuttalBench，一个通过新颖的“批判-精炼”方法合成的大规模数据集。训练过程包含两个阶段：首先进行监督微调，使智能体具备基于ToM的分析与策略规划能力；随后进行强化学习，利用自奖励机制实现可扩展的自我改进。为实现可靠高效的自动化评估，我们进一步开发了Rebuttal-RM，这是一个在超过10万份多来源反驳数据上训练的专业评估器，其评分与人类偏好的一致性超越了强大的评判模型GPT-4.1。大量实验表明，RebuttalAgent在自动化指标上平均显著优于基线模型18.3%，同时在自动化与人工评估中均超越了先进的专有模型。免责声明：所生成的反驳内容仅供作者参考以启发思路、辅助起草，并非旨在取代作者自身的批判性分析与回应。