SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modeling

ArXi:2510.05115v3 Announce Type: replace Large language models (LLMs) have opened new paradigms in optimization modeling by enabling the generation of executable solver code from natural language descriptions. Despite this promise, existing approaches typically remain solver-driven: they rely on single-pass forward generation and apply limited post-hoc fixes based on solver error messages, leaving undetected semantic errors that silently produce syntactically correct but logically flawed models.