@inproceedings{huang-etal-2026-guess,
    title = "Do Not Guess, Verify: Logic-Guided Adaptive Reasoning for Multimodal Misinformation Detection",
    author = "Huang, Kun  and
      Qiu, Rui  and
      Li, Xiaoming  and
      Uddin, Salah",
    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.238/",
    pages = "4848--4861",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent advances in Large Vision{--}language Models (VLMs) suggest their potential for multimodal misinformation detection. However, existing multimodal misinformation detectors often fail to effectively integrate them, relying instead on passive aggregation of multimodal features and social signals. Such correlation-driven paradigms are vulnerable to spurious associations and multimodal noise, and lack explicit verification mechanisms. In this paper, we propose Logic-Guided Adaptive Reasoning (LoGAR), a verification-oriented framework that integrates VLMs into multimodal misinformation detection through explicit rationale-guided reasoning. LoGAR leverages a VLM to generate an explicit verification rationale, which serves as a global semantic anchor to condition the entire reasoning process. Concretely, the rationale functions as an active query to guide multimodal feature fusion and as a conditioning signal to modulate message passing over heterogeneous social graphs, enabling hypothesis-aware evidence aggregation. Furthermore, LoGAR introduces an instance-aware adaptive depth mechanism that dynamically determines the required reasoning depth. Experimental results on multiple multimodal misinformation benchmarks demonstrate that LoGAR consistently outperforms state-of-the-art methods while significantly reducing computational cost."
}