@inproceedings{li-etal-2026-enhancing,
    title = "Enhancing Multilingual Reasoning via Steerable Model Merging",
    author = "Li, Zhuoran  and
      Xu, Rui  and
      Yang, Jian  and
      Liu, Junnan  and
      Chen, Zhijun  and
      Mao, Qianren  and
      Guo, Hongcheng  and
      Liu, Jiaheng  and
      Xiao, Likang  and
      LI, Ming  and
      Wang, Xiaojie",
    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.1856/",
    pages = "37266--37277",
    ISBN = "979-8-89176-395-1",
    abstract = "Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reasoning tasks by aligning feature spaces of different models. However, the merged single model often fails to address the conflicts between source models, leading to suboptimal performance. In other words, the one-size-fits-all merging strategy may not align with the characteristics of different inputs which may require prioritizing certain models over others. To this end, we propose a Steerable Model Merging (**ST-Merge**) framework to modulate the contribution of each source model. To realize this idea, we introduce a gated cross-attention mechanism to weight or filter the two attended source models in an adaptive manner. Extensive experiments demonstrate that ST-Merge consistently outperforms multiple strong baselines on four multilingual reasoning benchmarks across 21 different languages."
}