Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
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| _version_ | 1866911726926036992 |
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| author | Liu, Haoyang Wang, Jie Niu, Boxuan Han, Xiongwei Xu, Yian Ye, Mingxuan Geng, Zijie Zhu, Fangzhou Zhong, Tao Yuan, Mingxuan Hao, Jianye |
| author_facet | Liu, Haoyang Wang, Jie Niu, Boxuan Han, Xiongwei Xu, Yian Ye, Mingxuan Geng, Zijie Zhu, Fangzhou Zhong, Tao Yuan, Mingxuan Hao, Jianye |
| contents | Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements have utilized large language models (LLMs) to automate this modeling process. However, existing works often struggle to verify the correctness of the generated optimization models, without checking the rationality of the constraints and variables or the validity of solutions to the generated models. This hampers the subsequent verification and correction steps, and thus it severely hurts the modeling accuracy. To address this challenge, we propose a novel LLM-based framework with Dual-side Verification (Opt-Verifier) from both structure and solution perspectives, thereby improving the modeling accuracy. The structure-side verification ensures that the modeling structure of the generated optimization models aligns with the original problem description, accurately capturing the problem's constraints and requirements. Meanwhile, the solution-side verification interprets and evaluates the solutions' validity, confirming that the optimization models are logically and mathematically sound. Experiments on popular benchmarks demonstrate that our approach achieves over 20\% improvement in accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_29556 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification Liu, Haoyang Wang, Jie Niu, Boxuan Han, Xiongwei Xu, Yian Ye, Mingxuan Geng, Zijie Zhu, Fangzhou Zhong, Tao Yuan, Mingxuan Hao, Jianye Artificial Intelligence Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements have utilized large language models (LLMs) to automate this modeling process. However, existing works often struggle to verify the correctness of the generated optimization models, without checking the rationality of the constraints and variables or the validity of solutions to the generated models. This hampers the subsequent verification and correction steps, and thus it severely hurts the modeling accuracy. To address this challenge, we propose a novel LLM-based framework with Dual-side Verification (Opt-Verifier) from both structure and solution perspectives, thereby improving the modeling accuracy. The structure-side verification ensures that the modeling structure of the generated optimization models aligns with the original problem description, accurately capturing the problem's constraints and requirements. Meanwhile, the solution-side verification interprets and evaluates the solutions' validity, confirming that the optimization models are logically and mathematically sound. Experiments on popular benchmarks demonstrate that our approach achieves over 20\% improvement in accuracy. |
| title | Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.29556 |