@inproceedings{li-etal-2025-deepsolution,
    title = "{D}eep{S}olution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking",
    author = "Li, Zhuoqun  and
      Yu, Haiyang  and
      Chen, Xuanang  and
      Lin, Hongyu  and
      Lu, Yaojie  and
      Huang, Fei  and
      Han, Xianpei  and
      Li, Yongbin  and
      Sun, Le",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.220/",
    pages = "4380--4396",
    ISBN = "979-8-89176-251-0",
    abstract = "Designing solutions for complex engineering challenges is crucial in human production activities. However, previous research in the retrieval-augmented generation (RAG) field has not sufficiently addressed tasks related to the design of complex engineering solutions. To fill this gap, we introduce a new benchmark, SolutionBench, to evaluate a system{'}s ability to generate complete and feasible solutions for engineering problems with multiple complex constraints. To further advance the design of complex engineering solutions, we propose a novel system, SolutionRAG, that leverages the tree-based exploration and bi-point thinking mechanism to generate reliable solutions. Extensive experimental results demonstrate that SolutionRAG achieves state-of-the-art (SOTA) performance on the SolutionBench, highlighting its potential to enhance the automation and reliability of complex engineering solution design in real-world applications."
}