[2511.16383] An Agent-Based Framework for the Automatic Validation of Mathematical Optimization Models

Computer Science > Artificial Intelligence arXiv:2511.16383 (cs) [Submitted on 20 Nov 2025 (v1), last revised 5 Apr 2026 (this version, v2)] Title:An Agent-Based Framework for the Automatic Validation of Mathematical Optimization Models Authors:Alexander Zadorojniy, Segev Wasserkrug, Eitan Farchi View a PDF of the paper titled An Agent-Based Framework for the Automatic Validation of Mathematical Optimization Models, by Alexander Zadorojniy and 2 other authors View PDF HTML (experimental) Abstract:Recently, using Large Language Models (LLMs) to generate optimization models from natural language descriptions has became increasingly popular. However, a major open question is how to validate that the generated models are correct and satisfy the requirements defined in the natural language description. In this work, we propose a novel agent-based method for automatic validation of optimization models that builds upon and extends methods from software testing to address optimization modeling . This method consists of several agents that initially generate a problem-level testing API, then generate tests utilizing this API, and, lastly, generate mutations specific to the optimization model (a well-known software testing technique assessing the fault detection power of the test suite). In this work, we detail this validation method and show, through both theory and experiments, the high quality of validation provided by this agent ensemble in terms of the well-known software testi...