arXiv:2510.05115v3 Announce Type: replace 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 undetected semantic errors that silently produce syntactically correct but logically flawed models. To address this challenge, we propose SAC-Opt, a backward-guided correction framework th
The proliferation of LLMs capable of generating executable code for optimization tasks highlights the immediate need for robust correction frameworks to ensure reliability and accuracy.
This development addresses a critical weakness in LLM-driven optimization, preventing the silent generation of flawed models and enabling broader, more trustworthy application of AI in complex problem-solving.
The shift from solver-driven, post-hoc fixes to backward-guided, semantic correction improves the quality and reliability of AI-generated optimization models, moving towards more autonomous and accurate agentic systems.
- · AI developers (LLM, agents)
- · Optimization software providers
- · Industries relying on complex optimization (logistics, finance, manufacturing)
- · Companies relying on manual optimization model development
- · Limited-scope rule-based AI solutions
Improved accuracy and reliability of AI-generated optimization models will accelerate their adoption across various sectors.
Increased trust in AI-driven modeling could lead to more complex, multi-modal optimization challenges being tackled by autonomous agents.
The integration of such sophisticated correction mechanisms could enable a new class of self-improving AI agents capable of identifying and fixing their own 'semantic errors' across broader tasks.
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Read at arXiv cs.AI