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.
arXiv preprint arXiv:2505.16952 (2025)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
dataset 1polarities
background 1representative citing papers
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.
citing papers explorer
-
Large Language Models for Operations Research: A Comprehensive Survey
A survey compiling roles, applications, benchmarks, challenges, and future directions for large language models in operations research.