AINeutralarXiv – CS AI · 6h ago6/10
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SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modeling
Researchers introduce SAC-Opt, a framework that improves how large language models generate optimization code by grounding corrections in semantic accuracy rather than solver feedback alone. The approach achieves 7.7% average improvement in modeling accuracy across datasets, with gains up to 21.9% on complex problems, addressing silent logical errors in LLM-generated optimization models.