@inproceedings{she-etal-2026-crossroads,
    title = "Crossroads of Optimization under Uncertainty: How to Choose the Optimal Model",
    author = "She, Chengxi  and
      Chen, Zhiqiang  and
      Lu, Xingyu  and
      Chen, Caihua  and
      Zhao, Piaoyang  and
      Wang, Xuedong",
    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.172/",
    pages = "3508--3539",
    ISBN = "979-8-89176-395-1",
    abstract = "To address two correlated question in Optimization under Uncertainty (OuU): Expertise Threshold and Selection Conundrum, we propose LLM4OuU, a multi-agent framework that automates both the modeling and solving of six distinct types of uncertainty models and generates mapping pairs to explore the potential relationship between optimization problems and optimal models. Firstly, we decompose the complex modeling process into five sequential steps and design specialized LLM agents combining high-level domain expertise. Secondly, we introduce a hybrid dataset spanning various industries based on Retrieval-Augmented Generation (RAG) to benchmark performance. Extensive experiments demonstrate that LLM4OuU achieves superior performance compared to baselines, even reaching up to 99{\%} on specific model types. Finally, we establish a mapping from problem features to optimal models, with correlation analysis revealing that not only data scale but also the specific scenario significantly influence model selection."
}