SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modeling
A new framework called SAC-Opt addresses semantic errors in optimization modeling with large language models. Unlike existing solver-driven approaches that rely on single-pass generation and post-hoc fixes based on solver error messages, SAC-Opt uses backward-guided correction grounded in problem semantics. It aligns original semantic anchors with those reconstructed from generated code and selectively corrects mismatched components. This method enables fine-grained refinement of constraint and objective logic, driving convergence toward semantically faithful models. The work is published on arXiv under identifier 2510.05115.
Key facts
- SAC-Opt is a backward-guided correction framework for optimization modeling with LLMs.
- It uses semantic anchors rather than solver feedback for correction.
- The framework aligns original semantic anchors with reconstructed ones from generated code.
- Selective correction is applied only to mismatched components.
- The approach enables fine-grained refinement of constraint and objective logic.
- It addresses undetected semantic errors in syntactically correct but logically flawed models.
- The paper is available on arXiv with identifier 2510.05115.
- Existing approaches are solver-driven and rely on single-pass forward generation.
Entities
Institutions
- arXiv