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

SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modeling arXiv:2510.05115v3 Announce Type: replace-cross Abstract: 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 undet