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.
Guiding large language models in modeling optimization problems via question partitioning
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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.