🤖 AI Summary
This work proposes a multi-agent collaborative framework that automatically translates natural language descriptions of operations research problems into solvable mathematical models and executable code. To address common modeling challenges—such as semantic misinterpretation, structural flaws, and mathematical inconsistencies—the approach employs specialized agents to extract decision variables and constraints, integrating structured information extraction, iterative self-correction, and a fourfold feedback validation mechanism to achieve end-to-end modeling. Its modular architecture enhances transparency and auditability throughout the modeling process. Evaluated on four standard benchmarks encompassing linear programming (LP), mixed-integer linear programming (MILP), and nonlinear programming, the method achieves state-of-the-art performance on three and demonstrates highly competitive results on the fourth.
📝 Abstract
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.