Infeasibility Aware Large Language Models for Combinatorial Optimization
Large language models (LLMs) are increasingly explored for NP-hard combinatorial optimization problems, but most existing methods emphasize feasible-instance solution generation and do not explicitly address infeasibility detection. We propose an infeasibility-aware framework that combines certifiable dataset construction, supervised fine-tuning, and LLM-assisted downstream search. For the minor-embedding problem, we introduce a new mathematical programming formulation together with provable zero-phase infeasibility screening, which enables scalable construction of training instances labeled either as feasible with structured certificates or as certifiably infeasible. Using training data ...
| Authors | Yakun Wang, Min Chen, Zeguan Wu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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