Abstract
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.
Abstract (translated)
尽管人工智能（AI）已经在研究工作流程的各个阶段深度集成，并取得了显著进展，但在学术反驳方面仍面临着一个重要且未充分探索的挑战。这是因为反驳是一种在严重信息不对称下的策略性沟通过程，而不仅仅是简单的技术辩论。因此，现有的方法由于主要模仿表面语言层面的表达，未能捕捉到有效说服所需的从对方角度出发的关键元素。 本文介绍了RebuttalAgent框架，这是首个基于心智理论（Theory of Mind, ToM）进行学术反驳的研究框架，并通过一种ToM-策略-响应(TSR)管道实现，该管道模型化审稿人的心理状态、制定说服策略并生成与策略相契合的回应。为了训练我们的代理程序，我们构建了RebuttalBench，这是一个大规模的数据集，通过新颖的批评和细化方法合成而成。我们的培训过程分为两个阶段：首先是监督微调阶段，使代理人具备基于心智理论的分析和战略规划能力；其次是利用自我奖励机制进行可扩展自我改进的强化学习阶段。 为了实现可靠的自动评估，我们进一步开发了Rebuttal-RM，这是一个专门的评估器，在超过10万个多源反驳数据样本上进行了训练。它在自动化评分方面与人类偏好的一致性超过了强大的裁判模型GPT-4.1。 广泛的实验表明，RebuttalAgent在自动化指标上的表现比基础模型平均高出18.3%，并在自动和人工评价中均超越了先进的专有模型。 免责声明：生成的反驳内容仅供作者参考启发，并辅助草拟。它并非旨在替代作者本人的批判性分析与回应。
URL
https://arxiv.org/abs/2601.15715
