Dancing in Chains: Strategic Persuasion in Academic Rebuttal via Theory of Mind
- URL: http://arxiv.org/abs/2601.15715v2
- Date: Tue, 27 Jan 2026 04:09:51 GMT
- Title: Dancing in Chains: Strategic Persuasion in Academic Rebuttal via Theory of Mind
- Authors: Zhitao He, Zongwei Lyu, Yi R Fung,
- Abstract summary: We introduce RebuttalAgent, the first framework to ground academic rebuttal in Theory of Mind (ToM)<n>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.<n>For reliable and efficient automated evaluation, we develop Rebuttal-RM, a specialized evaluator trained on over 100K samples of multi-source rebuttal data.
- Score: 4.964424546439509
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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.
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