用智能体自动验证优化模型是否正确，提升生成模型的可靠性。
An Agent-Based Framework for the Automatic Validation of Mathematical Optimization Models
- 设计多智能体系统，模拟软件测试中的接口生成与测试用例构造。
- 通过突变覆盖度评估，验证结果达到高覆盖率，证明测试有效性。
- 适合关注生成式优化模型可信度的研究者与工程师使用。
近年来，利用大语言模型从自然语言描述自动生成优化模型越来越流行。然而，一个关键的开放性问题是：如何验证生成的模型是否正确且满足自然语言描述中的要求。本文提出一种基于智能体的自动验证方法，将软件测试中的技术拓展至优化建模领域。该方法包含多个智能体：首先生成问题级别的测试API，接着利用该API生成测试用例，最后生成针对优化模型的特定突变（一种评估测试套件故障检测能力的软件测试技术）。本文详细阐述该验证方法，并通过理论分析和实验验证，证明该智能体集合在突变覆盖度这一经典软件测试指标上表现优异。
原文摘要 · Abstract (English)
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 testing measure called mutation coverage.
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