gpt-5.6-sol单次查询就能生成与专业方法相当的运筹算法，无需人工调优。
研究测试了gpt-5.6-sol模型在库存控制、排队网络控制和优化选择三类运筹问题上的表现。该模型在两级测试中均匹配或超越现有最佳方法，即使算法在评估实例前已固定。模型性能在八个月内发布的不同版本间显著提升，表明此能力发展迅速。
LLMs Can Design Near-Optimal OR Algorithms
We ask whether large language models (LLMs) can design effective algorithms for well-specified operations research (OR) problems. We study inventory control, queueing network control, and assortment optimization. We evaluate two levels of LLM use: at level 1, the model receives one problem instance and returns a solution for that instance; at level 2, it receives only the problem class description and broad parameter ranges, and returns an algorithm that maps instance parameters to solutions. Human input is minimal: we give one untuned prompt that describes the problem, and the model has access to a Python sandbox tool with a fixed compute budget. The strongest model we test, gpt-5.6-sol, matches or outperforms the best existing method on almost all evaluated instances. This holds even at level 2, where the returned algorithm is fixed before seeing the evaluation instances. Performance also improves sharply across models released less than eight months apart, suggesting that this capability is moving quickly. Thus, for the well-specified operations problems we study, a single untuned LLM query can already produce algorithms competitive with specialized methods. These results suggest that frontier LLMs can be a serious empirical baseline for algorithm design in well-specified OR problems.