@inproceedings{wang-etal-2026-csi,
    title = "{CSI}: An Investigative Multi-Agent Framework for Explainable Short Video Fake News Detection",
    author = "Wang, Yuxin  and
      Yang, Yang  and
      Zhang, Huaiwen",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1274/",
    pages = "25508--25528",
    ISBN = "979-8-89176-395-1",
    abstract = "The proliferation of short video fake news threatens social stability. Current detection methods rely either on black-box Multimodal Small Language Models (MSLMs), which suffer from poor explainability and superficial understanding, or on specific prompt strategies for Multimodal Large Language Models (MLLMs) that underutilize their reasoning capabilities and knowledge. To address these challenges, we propose a novel multi-agent framework named CSI for short video fake news detection. CSI implements two key units: 1) Multimodal Forensics Unit (MFU), which performs synchronous multimodal deconstruction and external knowledge retrieval to collect comprehensive evidence. 2) Case Review Unit (CRU), which first employs collaborative discussion to facilitate viewpoint interaction to obtain the review result. Subsequently, the Adjudicator integrates evidence and the review result via multiple attention mechanisms to interact with the news, ensuring a robust verdict.Extensive experiments on two real-world datasets demonstrate that CSI provides rigorous explanations while achieving state-of-the-art performance. Our code is available at: https://github.com/VFCenter/CSI."
}