@inproceedings{zhao-etal-2026-msearth,
    title = "{MSE}arth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with {MLLM}s",
    author = "Zhao, Xiangyu  and
      Xu, Wanghan  and
      Liu, Bo  and
      Zhou, Yuhao  and
      Ling, Fenghua  and
      Fei, Ben  and
      Yue, Xiaoyu  and
      Bai, Lei  and
      Zhang, Wenlong  and
      Wu, Xiao-Ming",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.239/",
    pages = "5270--5301",
    ISBN = "979-8-89176-390-6",
    abstract = "The rapid advancement of multimodal large language models (MLLMs) offers new opportunities for complex scientific challenges, yet their application in earth science{---}especially at the graduate level{---}remains underexplored due to a lack of benchmarks reflecting the depth and complexity of geoscientific reasoning. Existing datasets often rely on synthetic data or simple figure-caption pairs, failing to capture the nuanced reasoning required for real-world applications. To address this, we introduce MSEarth, a multimodal scientific dataset and benchmark curated from high-quality, open-access publications. Covering the five major spheres of Earth science{---}atmosphere, cryosphere, hydrosphere, lithosphere, and biosphere{---}MSEarth features over 289K figures with refined captions enriched by contextual discussions and reasoning from the original papers. The benchmark supports tasks such as scientific figure captioning, multiple choice questions, and open-ended reasoning, providing a scalable, high-fidelity resource for developing and evaluating MLLMs in scientific reasoning."
}