arXiv:2607. 14818v1 Announce Type: cross Abstract: We report on CoreForge, an experience in using large language models (LLMs) to build an unweighted MaxSAT solver from research papers rather than from an existing solver codebase.
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arXiv:2606. 15577v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly involved in complex mathematical optimization, even if the pragmatic user who triggers them is unaware of it.
arXiv:2609.10226v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in...
arXiv:2507. 22876v2 Announce Type: replace Abstract: The Satisfiability problem (SAT) is fundamental in computational complexity theory and has a wide range of industrial applications.
arXiv:2605. 25246v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines.
The paper introduces SOVER, a framework that uses Large Language Models (LLMs) to extract semantic mappings between optimization reformulations and then formally verifies these mappings with SMT solvers. Z3 is employed to check domain cross-feasibility and objective-order preservation for mixed-integer linear problems, while dReal handles tolerance-aware feasibility and ε-argmin checks for continuous nonlinear problems. The authors also present NLEquiv-150, a benchmark of 150 nonlinear reformulation pairs, and report that SOVER correctly classifies 149 out of 150 pairs, including all 50 hard negatives, with the single error due to incomplete mapping extraction.
arXiv:2607. 20474v1 Announce Type: new Abstract: 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.