Training-Free and Interpretable Hateful Video Detection via Multi-stage Adversarial Reasoning

Shuonan Yang; Yuchen Zhang; Zeyu Fu

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arXiv · 2601.15115

Training-Free and Interpretable Hateful Video Detection via Multi-stage Adversarial Reasoning

Shuonan Yang·Yuchen Zhang·Zeyu Fu

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Abstract

Hateful videos pose serious risks by amplifying discrimination, inciting violence, and undermining online safety. Existing training-based hateful video detection methods are constrained by limited training data and lack of interpretability, while directly prompting large vision-language models often struggle to deliver reliable hate detection. To address these challenges, this paper introduces MARS, a training-free Multi-stage Adversarial ReaSoning framework that enables reliable and interpretable hateful content detection. MARS begins with the objective description of video content, establishing a neutral foundation for subsequent analysis. Building on this, it develops evidence-based reasoning that supports potential hateful interpretations, while in parallel incorporating counter-evidence reasoning to capture plausible non-hateful perspectives. Finally, these perspectives are synthesized into a conclusive and explainable decision. Extensive evaluation on two real-world datasets shows that MARS achieves up to 10% improvement under certain backbones and settings compared to other training-free approaches and outperforms state-of-the-art training-based methods on one dataset. In addition, MARS produces human-understandable justifications, thereby supporting compliance oversight and enhancing the transparency of content moderation workflows. The code is available at https://github.com/Multimodal-Intelligence-Lab-MIL/MARS.

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Shuonan Yang, Yuchen Zhang, Zeyu Fu. 2026-01-21. Training-Free and Interpretable Hateful Video Detection via Multi-stage Adversarial Reasoning. https://arxiv.org/abs/2601.15115

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