@inproceedings{yan-etal-2026-errorradar,
    title = "{E}rror{R}adar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection",
    author = "Yan, Yibo  and
      Wang, Shen  and
      Huo, Jiahao  and
      Li, Hang  and
      Li, Boyan  and
      Su, Jiamin  and
      Gao, Xiong  and
      Zhang, YiFan  and
      Xu, Tianlong  and
      Chu, Zhendong  and
      Zhong, Aoxiao  and
      Wang, Kun  and
      Xiong, Hui  and
      Yu, Philip S.  and
      Hu, Xuming  and
      Wen, Qingsong",
    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.1217/",
    pages = "24297--24334",
    ISBN = "979-8-89176-395-1",
    abstract = "As the field of Multimodal Large Language Models (MLLMs) continues to evolve, their potential to handle mathematical reasoning tasks is promising, as they can handle multimodal questions via cross-modal understanding capabilities compared to text-only LLMs. Current mathematical benchmarks predominantly focus on evaluating MLLMs' problem-solving ability, yet there is a crucial gap in addressing more complex scenarios such as error detection, for enhancing reasoning capability in complicated settings. To fill this gap, we formally formulate the new task {---} multimodal error detection, and introduce **ErrorRadar, the first benchmark designed to assess MLLMs' capabilities in such a task. ErrorRadar evaluates two sub-tasks: error step identification and error categorization**, providing a framework for evaluating MLLMs' complex mathematical reasoning ability. It consists of 2,500 high-quality multimodal K-12 mathematical problems, collected from real-world student interactions in an educational organization, with expert-based annotation and metadata such as problem type and error category. Through extensive experiments, we evaluated both open-source and closed-source representative MLLMs, benchmarking their performance against educational expert evaluators. Results indicate challenges still remain, as GPT-4o with best model performance is still around 10{\%} behind human evaluation"
}