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GlucoAgent: Personalized Glucose Forecasting from Multimodal Personal Context — arXiv:2611.04126

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04126
License (as archived): CC0-1.0
Archived: 2026-11-04T12:27:41.586Z
SHA-256 of the archived text: 2e50bdd1141e01664052ab7832a0c5e8d02f842e333f88acc6eb990fc2237c83
Stable URL of this page: https://metraipolis.com/documento/dc_caa5bbefcd5d

How to cite

GlucoAgent: Personalized Glucose Forecasting from Multimodal Personal Context — arXiv:2611.04126 — arXiv (en), https://arxiv.org/abs/2611.04126 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_caa5bbefcd5d (Metraipolis archive dc_caa5bbefcd5d, sha256 2e50bdd1141e01664052ab7832a0c5e8d02f842e333f88acc6eb990fc2237c83)

BibTeX

@misc{dccaa5bbefcd5d,
  title = {GlucoAgent: Personalized Glucose Forecasting from Multimodal Personal Context — arXiv:2611.04126},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04126},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_caa5bbefcd5d; sha256 2e50bdd1141e01664052ab7832a0c5e8d02f842e333f88acc6eb990fc2237c83},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04126. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Continuous glucose monitors, smartwatches, meal logs, and health profiles form a rich multimodal personal context that can significantly enhance glucose forecasting. However, existing methods struggle to effectively utilize these signals to improve glucose prediction because the relevance of different modalities varies drastically across individuals and time. We present GlucoAgent, an LLM-based agentic system that addresses this challenge through dynamic, case-by-case analysis at inference time. For each forecast, GlucoAgent writes and runs code over an individual's multimodal context and returns predictions with a reason and supporting evidence. The generated programs are highly diverse, ranging from retrieving similar past meals to fitting and back-testing physiological and statistical models. On CGMacros, a multimodal glucose dataset, GlucoAgent outperforms time series foundation models on every forecasting horizon, achieving an average RMSE of 22.5 mg/dL compared to 23.5 mg/dL of Chronos-2, the leading multimodal time series foundation model. Furthermore, GlucoAgent is highly effective at leveraging multimodal data. The addition of multimodal data reduces its average RMSE by 1.4 mg/dL (24.0 mg/dL to 22.5 mg/dL), compared to a marginal 0.3 mg/dL (23.8 mg/dL to 23.5 mg/dL) improvement for Chronos-2. This advantage is most pronounced in challenging post-meal windows, where GlucoAgent uses multimodal context to reduce 60-minute forecast RMSE by 4.6 mg/dL (31.0 mg/dL to 26.5 mg/dL), far exceeding the 0.8 mg/dL (31.9 mg/dL to 31.1 mg/dL) reduction achieved by Chronos-2.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.04126 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T12:26:47.128Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).