VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification
Natural language interfaces can greatly benefit the accessibility and usability of optimization modeling, and recent advances in large language models (LLMs) show promise in automatically translating textual problem descriptions into executable solver formulations. However, a key challenge for existing approaches is to ensure that the inferred formulation correctly implements the intended task, even if it may execute without errors. We introduce VeriSimpl, a solver LLM framework for robust natural-language-to-optimization formalization. Our approach is based on the idea of simplification-based verification, where the optimization solver is leveraged to generate simplified diagnostic queri...
| Authors | Sumaya Abdul Rahman, Seckhen Ariel Andrade Cuellar, Ghani Raissov et al. |
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| Primary category | cs.AI |
| Published | 24 May 2026 |
| Last updated at source | 22 September 2026 |
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