TL;DR A new framework safely repairs the "hallucinations" that happen when LLMs translate natural-language optimization problems into math models and solver code. It lifts accuracy by up to 20 points while keeping regressions below 1%. Title: Fix What's Wrong, Keep What's Right: Hallucination Repair for LLM-Based Optimization Modeling URL: Points 🔍 Cross-checks the problem text, math model, code, and data in five directions to pinpoint exactly where and why something is wrong ✂️ Picks the smallest necessary fix scope, from code-only to a partial model edit to a full structural rebuild, based on evidence 📜 24 reusable "RepairCards" spell out, as contracts, what can be changed, what must be protected, and what counts as acceptance ✅ A fix is only kept if it passes both an execution test and an audit; otherwise the original artifact is preserved 📊 Tested across 6 benchmarks and multiple upstream generators, it improves accuracy by 6.3 to 20.1 points while capping regression at 1.0% 🧩 Removing the diagnosis step alone drops the rescue rate by 24.5 points, confirming each mechanism's contribution Turning "repair" into a systematic post-processing step, rather than just better generation, feels genuinely practical and could extend well beyond optimization into other structured-output generation tasks. #LLM# #OptimizationModeling#