The idea of this research is based on a novel paradigm for solving complex, NP-hard problems (e.g., schedul- ing, routing) by leveraging Large Language Models (LLMs) as dynamic orchestrators in Agentic solvers, as proposed in a position Paper by professor Roberto Amadini and Simone Gazza.
Selección de solvers para SAT y Job Shop Scheduling con redes convolucionales: cada instancia se codifica como imagen o tensor y una CNN predice qué solver la resuelve dentro del límite de tiempo. Pipelines de clasificación, multietiqueta y regresión. Práctica de investigación, Universidad del Valle.
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