运筹学问题用自然语言描述就能自动建模生成代码，OptiAgent在多个基准上SOTA，迭代自纠正机制很靠谱。
OptiAgent是一个多智能体框架，能将运筹学问题的自然语言描述转换为求解器可用的数学公式和可执行代码。其架构包含专用智能体提取决策变量和约束，并支持迭代自纠正。引入多循环验证架构，包含四个专门反馈机制，分别针对误解、结构缺陷、数学不一致、验证失败和代码错误。在LP、MILP和非线性规划任务的4个基准中，OptiAgent在3个上取得SOTA，并在剩余数据集上具有竞争力。
OptiAgent: End-to-End Optimization Modeling via Multi-Agent Iterative Refinement
We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-ready mathematical formulation as well as executable code. Our architecture prioritizes the mathematical modeling step, where dedicated agents extract structures, such as decision variables and constraints, enabling iterative self-correction. We introduce a novel multi-loop validation architecture with four specialized feedback mechanisms, each targeting a distinct failure mode such as misinterpretation, structural defects, mathematical inconsistencies, validation failures, and code errors. Alongside accuracy, our modular design improves the process of solving optimization problems by improving transparency, as each agent exposes its reasoning and feedback, making the full modeling process auditable. Our framework achieves state-of-the-art performance on 3 out of 4 benchmarks across LP, MILP, and Nonlinear Programming tasks, while remaining highly competitive on the remaining dataset.