AI RESEARCH
OptArgus: A Multi-Agent System to Detect Hallucinations in LLM-based Optimization Modeling
arXiv CS.AI
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ArXi:2605.11738v1 Announce Type: new Large language models (LLMs) are increasingly used to translate natural-language optimization problems into mathematical formulations and solver code, but matching the reference objective value is not a reliable test of correctness: an artifact may agree numerically while still changing the underlying optimization semantics. We formulate this issue as \emph{optimization-modeling hallucination detection}, namely structural consistency auditing over the problem description, symbolic model, and solver implementation.