@inproceedings{gao-etal-2026-med,
    title = "{MED}-{COREASONER}: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning",
    author = "Gao, Fan  and
      Tong, Sherry T.  and
      Sohn, Jiwoong  and
      Huang, Jiahao  and
      Jiang, Junfeng  and
      Xia, Ding  and
      Ittichaiwong, Piyalitt  and
      Veerakanjana, Kanyakorn  and
      Kim, Hyunjae  and
      Chen, Qingyu  and
      Marrese-Taylor, Edison  and
      Kobayashi, Kazuma  and
      Aizawa, Akiko  and
      Li, Irene",
    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.1140/",
    pages = "24868--24888",
    ISBN = "979-8-89176-390-6",
    abstract = "While reasoning-enhanced large language models perform strongly on English medical tasks, a persistent multilingual gap remains, with substantially weaker reasoning in local languages, limiting equitable global medical deployment. To bridge this gap, we introduce Med-CoReasoner, a language-informed co-reasoning framework that elicits parallel English and local-language reasoning, abstracts them into structured concepts, and integrates local clinical knowledge into an English logical scaffold via concept-level alignment and retrieval. This design combines the structural robustness of English reasoning with the practice-grounded expertise encoded in local languages. To evaluate multilingual medical reasoning beyond multiple-choice settings, we construct MultiMed-X, a benchmark covering seven languages with expert-annotated long-form question answering and natural language inference tasks, comprising 350 instances per language. Experiments across three benchmarks show that Med-CoReasoner improves multilingual reasoning performance by an average of 5{\%}, with particularly substantial gains in low-resource languages. Moreover, model distillation and expert evaluation analysis further confirm that Med-CoReasoner produces clinically sound and culturally grounded reasoning traces."
}