LLM-Guided Graph Generation for Structure-Based Local Improvement Methods

Excerpt

arXiv:2608.13333v2 Announce Type: replace Abstract: Large neighborhood search normally selects a random subset of decision variables for iterative optimization. To efficiently solve various problems, researchers tend to design variable selection strategies that take into account structural features across different domains. In this paper, we build an automatic pipeline that is problem-agnostic to all problems in the MiniZinc format. By prompting an LLM with our semantic guidelines, we guide the