This paper introduces RebuttalAgent, an AI framework that grounds academic rebuttal in Theory of Mind (ToM) to generate strategic and persuasive responses. It proposes a ToM-Strategy-Response (TSR) pipeline, supported by a large-scale synthetic dataset (RebuttalBench) and a specialized evaluation model (Rebuttal-RM), significantly outperforming existing models in automated and human evaluations.

This paper proposes considering Theory of Mind (ToM) in Human-Robot Interaction (HRI) as a form of Explainable AI (XAI) and evaluates existing ToM studies using an XAI framework, identifying gaps in assessing explanation fidelity. It advocates for an integrated approach combining ToM's user focus with XAI's technical rigor.