SOVER: Formal Certification of Optimization Reformulations via LLM-Assisted SMT Verification

Abstract:Large Language Models (LLMs) have shown remarkable promise in translating and reformulating complex mathematical optimization problems across modeling languages. However, validating such transformations through empirical solver executions alone is unreliable, as solver outcomes may be affected by local minima, structural timeouts, numerical artifacts, and subtle semantic divergence between formulations. We introduce SOVER, an LLM-assisted SMT framework that separates semantic mapping from formal certification: Z3 checks domain cross-feasibility and global objective-order preservation for mixed-integer linear formulations, while dReal provides tolerance-aware feasibility/range and $\epsilon$-argmin checks for continuous nonlinear formulations. We also introduce NLEquiv-150, a public benchmark of 100 equivalent and 50 deliberately hard non-equivalent nonlinear reformulation pairs. With LLM-extracted mappings, SOVER classifies 149/150 pairs (99.33%) correctly, including all 50 hard negatives; the sole error is an incomplete mapping extraction.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2609.00728 [cs.AI]

(or arXiv:2609.00728v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.00728 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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