LLM-Guided Graph Generation for Structure-Based Local Improvement Methods
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 LLM to produce a graph generator that maps any instance of a problem type to a uniform weighted graph, where nodes represent decision variables and edges represent constraint relationships. These problem-agnostic graphs guide our structure-based loc...
| Authors | Hai Xia, Vaidyanathan Peruvemba Ramaswamy, Stefan Szeider |
|---|---|
| Primary category | cs.AI |
| Published | 13 August 2026 |
| Last updated at source | 17 August 2026 |
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