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Qingcan Kang

3 accepted papers

2026

From Intent to Solver Code: Semantic Alignment in Optimization Modeling

ICML 2026poster

Large language models (LLMs) have opened new paradigms in optimization modeling by enabling the generation of executable solver code from natural language descriptions. Despite this promise, existing approaches typically remain solver-driven: they rely on single-pass forward generation and apply lim…

Cited by 0SourceScholar
2025

A Survey of Optimization Modeling Meets LLMs: Progress and Future Directions

IJCAI 2025

By virtue of its great utility in solving real-world problems, optimization modeling has been widely employed for optimal decision-making across various sectors, but it requires substantial expertise from operations research professionals. With the advent of large language models (LLMs), new opportu

Cited by 0SourcePDFScholar
2025

Decision Information Meets Large Language Models: The Future of Explainable Operations Research

ICLR 2025poster

Operations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integrating Large Language Models (LLMs), they still struggle to produce meaningful explanations. This lack of clarity raises conc…