@inproceedings{hou-etal-2026-chromic,
    title = "{CHROMIC}: Chronological Reasoning Across Multi-Panel Comics",
    author = "Hou, Bingxuan  and
      Lin, Jiayi  and
      Zhang, Chenyang  and
      Yin, Dapeng  and
      Zhu, Shuyue  and
      Hong, Qingqing  and
      Gao, Mengna  and
      Wang, Junli",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.205/",
    pages = "4384--4400",
    ISBN = "979-8-89176-380-7",
    abstract = "Large-scale vision{--}language models (LVLMs) have achieved remarkable progress on various reasoning tasks. However, most studies focus on natural photographic images and pay limited attention to multi-panel visual narratives such as comics. This leaves a clear gap in our understanding of how well LVLMs perform chronological reasoning across comic panels. To address this, we introduce **ChrOMIC**, a new benchmark dataset for **chro**nological reasoning in multi-panel **comic**s. It covers six types of reasoning questions and spans both Western and Japanese comic styles. To ensure high-quality annotations, we customized a human{--}AI collaborative annotation process tailored to the characteristics of the two comic styles. We further introduce three core tasks: Description Reordering and Panel Reordering, which jointly assess models' ability to understand chronological order in panel sequences, and Multiple-Choice Question Answering (MCQA), which evaluates narrative-level reasoning. We evaluate a range of open-source and commercial LVLMs on ChrOMIC, and find that even the leading models struggle with panel-based chronological reasoning. Further analysis reveals key limitations, including weak visual action understanding and frequent hallucinations in fine-grained visual interpretation."
}