Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation

• URL: http://arxiv.org/abs/2508.03117v1
• Date: Tue, 05 Aug 2025 05:54:20 GMT
• Title: Toward a Trustworthy Optimization Modeling Agent via Verifiable Synthetic Data Generation
• Authors: Vinicius Lima, Dzung T. Phan, Jayant Kalagnanam, Dhaval Patel, Nianjun Zhou,
• Abstract summary: We present a framework for training trustworthy large language model (LLM) agents via a synthetic data generation pipeline.<n> OptiTrust is a modular LLM agent that performs multi-language translation from natural language to solver-ready code.<n>Our agent achieves state-of-the-art performance on standard benchmarks.
• Score: 11.988926173584154
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a framework for training trustworthy large language model (LLM) agents for optimization modeling via a verifiable synthetic data generation pipeline. Focusing on linear and mixed-integer linear programming, our approach begins with structured symbolic representations and systematically produces natural language descriptions, mathematical formulations, and solver-executable code. By programmatically constructing each instance with known optimal solutions, the pipeline ensures full verifiability and enables automatic filtering of low-quality demonstrations generated by teacher models. Each dataset instance includes a structured representation of the optimization problem, a corresponding natural language description, the verified optimal solution, and step-by-step demonstrations - generated by a teacher model - that show how to model and solve the problem across multiple optimization modeling languages. This enables supervised fine-tuning of open-source LLMs specifically tailored to optimization tasks. To operationalize this pipeline, we introduce OptiTrust, a modular LLM agent that performs multi-stage translation from natural language to solver-ready code, leveraging stepwise demonstrations, multi-language inference, and majority-vote cross-validation. Our agent achieves state-of-the-art performance on standard benchmarks. Out of 7 datasets, it achieves the highest accuracy on six and outperforms the next-best algorithm by at least 8 percentage on three of them. Our approach provides a scalable, verifiable, and principled path toward building reliable LLM agents for real-world optimization applications.

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