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.
AlphaOPT: Formulating optimization programs with self-improving LLM experience library
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Agora-Opt uses decentralized debate among LLM agent teams plus a read-write memory bank to produce more accurate optimization models from text than prior LLM methods.
citing papers explorer
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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.
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From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling
Agora-Opt uses decentralized debate among LLM agent teams plus a read-write memory bank to produce more accurate optimization models from text than prior LLM methods.