@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://aclanthology.org/2025.acl-long.220/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking
%A Li, Zhuoqun
%A Yu, Haiyang
%A Chen, Xuanang
%A Lin, Hongyu
%A Lu, Yaojie
%A Huang, Fei
%A Han, Xianpei
%A Li, Yongbin
%A Sun, Le
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F li-etal-2025-deepsolution
%X 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.
%R 10.18653/v1/2025.acl-long.220
%U https://aclanthology.org/2025.acl-long.220/
%U https://doi.org/10.18653/v1/2025.acl-long.220
%P 4380-4396
Markdown (Informal)
[DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking](https://aclanthology.org/2025.acl-long.220/) (Li et al., ACL 2025)
ACL
- Zhuoqun Li, Haiyang Yu, Xuanang Chen, Hongyu Lin, Yaojie Lu, Fei Huang, Xianpei Han, Yongbin Li, and Le Sun. 2025. DeepSolution: Boosting Complex Engineering Solution Design via Tree-based Exploration and Bi-point Thinking. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4380–4396, Vienna, Austria. Association for Computational Linguistics.