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    <abstract>Continuous glucose monitors (CGMs) used in diabetes care collect rich personal health data that could improve day-to-day self-management. However, current patient platforms only offer static summaries which do not support inquisitive user queries. Large language models (LLMs) could enable free-form inquiries about continuous glucose data, but deploying them over sensitive health records raises privacy and accuracy concerns. In this paper, we present **CGM-Agent**, a privacy-preserving framework for question answering over personal glucose data. In our design, the LLM serves purely as a reasoning engine that selects analytical functions. All computation occurs locally, and personal health data never leaves the user’s device. For evaluation, we construct a benchmark of 4,180 questions combining parameterized question templates with real user queries and ground truth derived from deterministic program execution. Evaluating 6 leading LLMs, we find that top models achieve 94% value accuracy on synthetic queries and 88% on ambiguous real-world queries. Errors stem primarily from intent and temporal ambiguity rather than computational failures. Additionally, lightweight models achieve competitive performance in our agent design, suggesting opportunities for low-cost deployment. We release our code and benchmark to support future work on trustworthy health agents.</abstract>
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%0 Conference Proceedings
%T If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data
%A Cui, Yanjun
%A Emami, Ali
%A Prioleau, Temiloluwa
%A Singh, Nikhil
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F cui-etal-2026-cgm
%X Continuous glucose monitors (CGMs) used in diabetes care collect rich personal health data that could improve day-to-day self-management. However, current patient platforms only offer static summaries which do not support inquisitive user queries. Large language models (LLMs) could enable free-form inquiries about continuous glucose data, but deploying them over sensitive health records raises privacy and accuracy concerns. In this paper, we present **CGM-Agent**, a privacy-preserving framework for question answering over personal glucose data. In our design, the LLM serves purely as a reasoning engine that selects analytical functions. All computation occurs locally, and personal health data never leaves the user’s device. For evaluation, we construct a benchmark of 4,180 questions combining parameterized question templates with real user queries and ground truth derived from deterministic program execution. Evaluating 6 leading LLMs, we find that top models achieve 94% value accuracy on synthetic queries and 88% on ambiguous real-world queries. Errors stem primarily from intent and temporal ambiguity rather than computational failures. Additionally, lightweight models achieve competitive performance in our agent design, suggesting opportunities for low-cost deployment. We release our code and benchmark to support future work on trustworthy health agents.
%U https://aclanthology.org/2026.findings-acl.891/
%P 17941-17958

Markdown (Informal)

[If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data](https://aclanthology.org/2026.findings-acl.891/) (Cui et al., Findings 2026)

• If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data (Cui et al., Findings 2026)

ACL

• Yanjun Cui, Ali Emami, Temiloluwa Prioleau, and Nikhil Singh. 2026. If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17941–17958, San Diego, California, United States. Association for Computational Linguistics.