@inproceedings{yang-etal-2026-reasonany,
    title = "{R}eason{A}ny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging",
    author = "Yang, Junyao  and
      Qian, Chen  and
      Shen, Wen  and
      Liu, Yong  and
      Shao, Jing  and
      Liu, Dongrui",
    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.2201/",
    pages = "47650--47675",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Reasoning Models (LRMs) with long chain-of-thought reasoning have recently achieved remarkable success. Yet, equipping domain-specialized models with such reasoning capabilities, referred to as ``Reasoning + X'', remains a significant challenge. While model merging offers a promising training-free solution, existing methods often suffer from a destructive performance collapse: existing methods tend to both weaken reasoning depth and compromise domain-specific utility. Interestingly, we identify a counter-intuitive phenomenon underlying this failure: \textit{reasoning ability predominantly resides in parameter regions with low gradient sensitivity, contrary to the common assumption that domain capabilities correspond to high-magnitude parameters}. Motivated by this insight, we propose \textbf{ReasonAny}, a novel merging framework that resolves the {reasoning{--}domain performance collapse} through Contrastive Gradient Identification. Experiments across safety, biomedicine, and finance domains show that ReasonAny effectively synthesizes ``Reasoning + X'' capabilities, significantly outperforming state-of-the-art baselines while retaining robust reasoning performance."
}