Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification
arXiv:2605.29556v1 Announce Type: new Abstract: Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements have utilized large language models (LLMs) to automate this modeling process. However, existing works often struggle to verify the correctness of the generated optimization models, without checking the rationality of the constraints and variables or the validity of solutions to the generated models. This hampers the subsequent verification and correction steps, and thus it severely hurts the modelin
The proliferation of LLMs creates an immediate need for robust verification tools to ensure their outputs, particularly in complex domains like optimization modeling, are reliable and correct.
Improving the accuracy and reliability of LLM-generated optimization models will significantly accelerate automation in operations research, impacting various industries that rely on complex decision-making.
The ability to automatically verify and correct LLM-generated optimization models will reduce the need for extensive human expertise, making sophisticated modeling more accessible and efficient.
- · Operations Research (OR) software providers
- · Companies adopting AI for supply chain optimization
- · LLM developers focusing on enterprise solutions
- · Academics in applied mathematics and computer science
- · Manual optimization consultants
- · Traditional OR software with high human intervention
Increased adoption of LLM-driven optimization solutions across industries due to enhanced reliability.
Shortage of skilled human modelers could accelerate further as LLMs handle more complex tasks, driving demand for new AI-centric roles.
Enhanced optimization capabilities could lead to unforeseen efficiencies and new business models in resource allocation and logistics.
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