ReLoop closes the feasibility-correctness gap in LLM optimization code via structured generation and behavioral verification with parameter perturbations, reaching 100% executability and accuracy gains on benchmarks while releasing RetailOpt-190.
ORLM: A customizable framework in training large models for automated optimization modeling.Operations Research, 73(6):2986–3009
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AutoREM augments LLMs with a structured memory of failed reformulation trajectories to improve accuracy and efficiency on robust optimization tasks without parameter updates or expert knowledge.
citing papers explorer
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ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization
ReLoop closes the feasibility-correctness gap in LLM optimization code via structured generation and behavioral verification with parameter perturbations, reaching 100% executability and accuracy gains on benchmarks while releasing RetailOpt-190.
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Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Models
AutoREM augments LLMs with a structured memory of failed reformulation trajectories to improve accuracy and efficiency on robust optimization tasks without parameter updates or expert knowledge.