From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent
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Computer Science > Human-Computer Interaction
arXiv:2608.13581 (cs)
[Submitted on 7 Jul 2026]
Title:From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent
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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 they enable context-aware reasoning and iterative refinement. Inspired by this, we propose a physio-feedback agentic loop, a unified system that integrates individualized absorption modeling with dietary intervention to regulate glucose response. Specifically, we develop a Physiology-Aware Glucose Predictor to model individualized absorption dynamics through a learnable Temporal Physiological Absorption Decay Module. We then construct a Prediction-Driven Two-Stage Meal Optimization Agent that iteratively refines real-world meals using predicted outcomes as explicit feedback. Through extensive experiments on multiple public datasets, we demonstrate that our method not only improves prediction accuracy but also effectively reduces glucose excursions. To the best of our knowledge, this paper marks the first step in integrating physiological learning with an LLM-based agent for personalized glucose regulation.
| Comments: | Accepted in ACL 2026 Findings, 17 pages, 4 figures |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.13581 [cs.HC] |
| (or arXiv:2608.13581v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2608.13581
arXiv-issued DOI via DataCite
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| Journal reference: | Findings of the Association for Computational Linguistics ACL 2026, pages 21629 to 21645 |
| Related DOI: | https://doi.org/10.18653/v1/2026.findings-acl.1087
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