LLMs Can Design Near-Optimal OR Algorithms
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TL;DR - This paper evaluates whether LLMs can design algorithms for well-specified operations research problems. Its strongest tested model, GPT-5.6 Sol, matched or exceeded leading specialized methods on nearly all evaluated instances, suggesting frontier LLMs are becoming credible baselines for OR algorithm design.
- Covers inventory control, queueing network control, and assortment optimization.
- Tests both instance-specific solutions and general algorithms created before evaluation instances are revealed.
- Uses a single untuned prompt, minimal human input, and a Python sandbox with a fixed compute budget.
- Reports sharp performance gains across models released within an eight-month period.
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LLMs Can Design Near-Optimal OR Algorithms
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TL;DR - This paper evaluates whether LLMs can design algorithms for well-specified operations research problems. Its strongest tested model, GPT-5.6 Sol, matched or exceeded leading specialized methods on nearly all evaluated instances, suggesting frontier LLMs are becoming credible baselines for OR algorithm design.
- Covers inventory control, queueing network control, and assortment optimization.
- Tests both instance-specific solutions and general algorithms created before evaluation instances are revealed.
- Uses a single untuned prompt, minimal human input, and a Python sandbox with a fixed compute budget.
- Reports sharp performance gains across models released within an eight-month period.