EffiOR: Post-Model Refinement for Efficient Solving

Large language models (LLMs) have made substantial progress in automatically generating mathematical models and solver implementations for operations research problems. However, correct modeling does not necessarily imply efficient solving, as the same optimization problem may admit multiple valid formulations with substantially different computational performance. We propose EffiOR, a framework for improving existing optimization models and solver implementations rather than rebuilding them from scratch. EffiOR formalizes 29 reusable Optimization Skills across Model-Structure, Formulation-Refinement, and Solver-Implementation, enabling existing models and solver implementations to be transformed for more efficient solving while preserving the original problem semantics. Experiments on FrontierOR-179 show consistent efficiency gains, while four additional optimization-modeling benchmarks demonstrate high transformation consistency.