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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.22979 |
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| _version_ | 1866909990194774016 |
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| author | Zhang, Xinzhi Chen, Zeyi Zope, Humishka Barbalho, Hugo Mellou, Konstantina Molinaro, Marco Kulkarni, Janardhan Menache, Ishai Li, Sirui |
| author_facet | Zhang, Xinzhi Chen, Zeyi Zope, Humishka Barbalho, Hugo Mellou, Konstantina Molinaro, Marco Kulkarni, Janardhan Menache, Ishai Li, Sirui |
| contents | Mathematical programming -- the task of expressing operations and decision-making problems in precise mathematical language -- is fundamental across domains, yet remains a skill-intensive process requiring operations research expertise. Recent advances in large language models for complex reasoning have spurred interest in automating this task, translating natural language into executable optimization models. Current approaches, however, achieve limited accuracy, hindered by scarce and noisy training data without leveraging domain knowledge. In this work, we systematically integrate optimization expertise to improve formulation accuracy for mixed-integer linear programming, a key family of mathematical programs. Our OptiMind framework leverages semi-automated, class-based error analysis to guide both training and inference, explicitly preventing common mistakes within each optimization class. Our resulting fine-tuned LLM significantly improves formulation accuracy by 20.7% across multiple optimization benchmarks, with consistent gains under test-time scaling methods such as self-consistency and multi-turn feedback, enabling further progress toward robust LLM-assisted optimization formulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OptiMind: Teaching LLMs to Think Like Optimization Experts Zhang, Xinzhi Chen, Zeyi Zope, Humishka Barbalho, Hugo Mellou, Konstantina Molinaro, Marco Kulkarni, Janardhan Menache, Ishai Li, Sirui Machine Learning Mathematical programming -- the task of expressing operations and decision-making problems in precise mathematical language -- is fundamental across domains, yet remains a skill-intensive process requiring operations research expertise. Recent advances in large language models for complex reasoning have spurred interest in automating this task, translating natural language into executable optimization models. Current approaches, however, achieve limited accuracy, hindered by scarce and noisy training data without leveraging domain knowledge. In this work, we systematically integrate optimization expertise to improve formulation accuracy for mixed-integer linear programming, a key family of mathematical programs. Our OptiMind framework leverages semi-automated, class-based error analysis to guide both training and inference, explicitly preventing common mistakes within each optimization class. Our resulting fine-tuned LLM significantly improves formulation accuracy by 20.7% across multiple optimization benchmarks, with consistent gains under test-time scaling methods such as self-consistency and multi-turn feedback, enabling further progress toward robust LLM-assisted optimization formulation. |
| title | OptiMind: Teaching LLMs to Think Like Optimization Experts |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.22979 |