@inproceedings{wu-etal-2026-seeing,
    title = "Seeing Beyond Words: {M}at{VQA} for Challenging Visual-Scientific Reasoning in Materials Science",
    author = "Wu, Sifan  and
      Zhang, Huan  and
      Li, Yizhan  and
      Effaty, Farshid  and
      Mei, Hongyuan  and
      Ataei, Amirreza  and
      Liu, Bang",
    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.1181/",
    pages = "23599--23614",
    ISBN = "979-8-89176-395-1",
    abstract = "The emergence of Multimodal Large Language Models (MLLMs) that integrate vision and language modalities has unlocked new potentials for scientific reasoning, outperforming prior benchmarks in both natural language and coding domains. Current materials science evaluation datasets such as MaScQA and SciQA remain largely text-based and fail to capture the visual and research-level analytic complexity required in materials discovery and design. We introduce MatVQA, a scalable benchmark specifically designed to address this gap. Generated via an automated pipeline, MArxivAgent, from recent materials literature, MatVQA features 1672 questions across four critical structure-property-performance (SPP) reasoning tasks. Uniquely, MatVQA employs an iterative process to eliminate textual shortcuts, compelling MLLMs to perform fine-grained, low-level visual analysis of material imagery (e.g., microscopy, diffraction patterns) integrated with multi-step scientific reasoning. Benchmarking 19 open- and closed-source MLLMs on MatVQA reveals substantial gaps in current multimodal reasoning capabilities. The MatVQA benchmark is publicly available[{\ensuremath{<}}https://huggingface.co/datasets/trqcbf/matvqa{\_}v2{\ensuremath{>}}] to facilitate further research on applying MLLMs to complex materials science problems."
}