OptArgus deploys a conductor-plus-specialist multi-agent architecture and a new four-category hallucination taxonomy to detect structural errors in LLM-generated optimization models more reliably than single-agent baselines on a three-part benchmark.
A pattern of`c = -c_ref`, followed by solving, followed by`objective_value = -result.fun`for a maximize objective is a standard faithful wrapper, not a hallucination
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OptArgus: A Multi-Agent System to Detect Hallucinations in LLM-based Optimization Modeling
OptArgus deploys a conductor-plus-specialist multi-agent architecture and a new four-category hallucination taxonomy to detect structural errors in LLM-generated optimization models more reliably than single-agent baselines on a three-part benchmark.