[2604.06205] Tool-MCoT: Tool Augmented Multimodal Chain-of-Thought for Content Safety Moderation

Computer Science > Computation and Language arXiv:2604.06205 (cs) [Submitted on 15 Mar 2026] Title:Tool-MCoT: Tool Augmented Multimodal Chain-of-Thought for Content Safety Moderation Authors:Shutong Zhang, Dylan Zhou, Yinxiao Liu, Yang Yang, Huiwen Luo, Wenfei Zou View a PDF of the paper titled Tool-MCoT: Tool Augmented Multimodal Chain-of-Thought for Content Safety Moderation, by Shutong Zhang and 5 other authors View PDF HTML (experimental) Abstract:The growth of online platforms and user content requires strong content moderation systems that can handle complex inputs from various media types. While large language models (LLMs) are effective, their high computational cost and latency present significant challenges for scalable deployment. To address this, we introduce Tool-MCoT, a small language model (SLM) fine-tuned for content safety moderation leveraging external framework. By training our model on tool-augmented chain-of-thought data generated by LLM, we demonstrate that the SLM can learn to effectively utilize these tools to improve its reasoning and decision-making. Our experiments show that the fine-tuned SLM achieves significant performance gains. Furthermore, we show that the model can learn to use these tools selectively, achieving a balance between moderation accuracy and inference efficiency by calling tools only when necessary. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.06205 [cs.CL]   (or arXiv:2604.062...