OptiVerse is a new benchmark spanning neglected optimization domains that shows LLMs suffer sharp accuracy drops on hard problems due to modeling and logic errors, with a Dual-View Auditor Agent proposed to improve performance.
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RLBD trains a neural policy with REINFORCE to select cuts adaptively in Benders decomposition, yielding faster convergence and better generalization than standard BD or SVM-based LearnBD on an EV charging problem.
citing papers explorer
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OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving
OptiVerse is a new benchmark spanning neglected optimization domains that shows LLMs suffer sharp accuracy drops on hard problems due to modeling and logic errors, with a Dual-View Auditor Agent proposed to improve performance.
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Learning to Cut: Reinforcement Learning for Benders Decomposition
RLBD trains a neural policy with REINFORCE to select cuts adaptively in Benders decomposition, yielding faster convergence and better generalization than standard BD or SVM-based LearnBD on an EV charging problem.