LangNCO: Compiling Natural-Language Constraints into Inference-Time Interventions for Neural Combinatorial Optimization
In brief
- LangNCO converts natural-language constraints into executable rules for a pre-trained NCO solver.
- An LLM generates validated rules, applied through action masks without altering the model.
- Results: 100% feasibility on four main datasets with average gaps of 1.63 to 3.26%.
LangNCO introduces a framework to translate natural-language constraints into executable rules for a pre-trained neural combinatorial optimization solver. The system uses an LLM to generate validated rules, applied via action masks without modifying the model. Results show 100% feasibility on four main datasets with mean gaps of 1.63–3.26%.
What it changes for you
- Industry & ManufacturingEnables dynamic adaptation of delivery routes or production planning through natural language constraints, without retraining.
- Transport & LogisticsImproves the robustness of routing algorithms against unexpected operational constraints like road closures or time windows.
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