@inproceedings{ge-etal-2026-vasevqa,
    title = "{V}ase{VQA}: Multimodal Agent and Benchmark for {A}ncient {G}reek Pottery",
    author = "Ge, Jinchao  and
      Cheng, Tengfei  and
      Wu, Biao  and
      Zhang, Zeyu  and
      Huang, Shiya  and
      Bishop, Judith  and
      Shepherd, Gillian  and
      Fang, Meng  and
      Chen, Ling  and
      Zhao, Yang",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.60/",
    pages = "1154--1167",
    ISBN = "979-8-89176-386-9",
    abstract = "Understanding cultural heritage artifacts such as ancient Greek pottery requires expert-level reasoning that remains challenging for current MLLMs due to limited domain-specific data. We introduce VaseVQA, a benchmark for ancient Greek pottery, primarily vases, consisting of 31,773 images and 67,614 question{--}answer pairs across seven expert-defined categories, enabling systematic evaluation of expert-level cultural heritage understanding. Using this dataset, we explore effective training strategies for domain-specific reasoning. While supervised fine-tuning improves adaptation to domain knowledge, it struggles with deeper reasoning tasks. We propose VaseVL, which augments SFT with reinforcement learning using verifiable rewards. Experiments show that VaseVL consistently outperforms supervised baselines, especially on reasoning-intensive questions, highlighting the value of targeted reinforcement learning for cultural heritage visual question answering."
}