Interpretable Language Model for Closed-Loop Type 1 Diabetes Control

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[Submitted on 19 Jun 2026]

Title:Interpretable Language Model for Closed-Loop Type 1 Diabetes Control

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Abstract:Type 1 Diabetes (T1D) is a chronic, life-threatening autoimmune condition characterized by the complete destruction of insulin-producing pancreatic beta cells. While Artificial Pancreas Systems (APS) powered by Reinforcement Learning (RL) have shown promise in automating insulin delivery, their ``black-box'' nature makes it hard for patients and doctors to trust them fully. This paper presents LLM-T1D, a promising approach that combines the precision of RL with the clear, human-like reasoning of Large Language Models (LLMs) to create a more transparent and reliable insulin pump controller. By training an expert RL system and distilling its knowledge into fine-tuned LLaMA 3.1 8B and Qwen3 8B models, we developed a controller that not only surpasses the RL system's performance but also explains its decisions in plain, understandable language. Tested on the FDA-approved UVA/Padova T1D simulator, the LLM controllers deliver excellent blood sugar control (73.5% Time in Range) while maintaining strict formal safety verification against hallucinations.

Comments: Accepted at the 2026 IEEE 22nd International Conference on Automation Science and Engineering conference (IEEE CASE 2026)

Subjects:

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)

Cite as: arXiv:2607.14126 [cs.AI]

(or arXiv:2607.14126v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.14126

arXiv-issued DOI via DataCite

Submission history

From: Maya Sarkar [view email] [v1] Fri, 19 Jun 2026 18:15:06 UTC (1,073 KB)

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