From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent

Excerpt

arXiv:2608.13581v1 Announce Type: cross Abstract: Personalized glucose regulation remains a central yet unresolved challenge in precision nutrition, as postprandial glucose response varies substantially across individuals. Existing approaches based on glycemic indices fail to adequately account for such heterogeneity and lack the mechanism to dynamically adjust meals based on personal physiological feedback. In this context, recent advances in LLM-based agents offer a promising direction, as the