FrontierOR benchmark shows frontier LLMs outperform Gurobi on solution quality and efficiency in only 31% of one-shot cases and 50% with test-time evolution on hard large-scale optimization tasks.
arXiv preprint arXiv:2505.16952 (2025)
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NLCO benchmark shows LLMs achieve reasonable feasibility on small natural-language CO tasks but degrade on larger instances, with set-based problems easier than graph-structured or bottleneck-objective ones.
CAM is an unsupervised training method for discrete diffusion models on combinatorial optimization problems that uses discrete adjoint dynamics to supply low-variance trajectory-level signals.
A survey compiling roles, applications, benchmarks, challenges, and future directions for large language models in operations research.
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