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DiabAgent: Toward Agentic Physiological Control — arXiv:2611.03601

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03601
License (as archived): CC0-1.0
Archived: 2026-11-03T10:24:20.695Z
SHA-256 of the archived text: 7d6a2bbdc5305b1e8edd1f07ae8fc57c210211be95d5f2c31bed7f30a7ee641a
Stable URL of this page: https://metraipolis.com/documento/dc_70167fec554f

How to cite

DiabAgent: Toward Agentic Physiological Control — arXiv:2611.03601 — arXiv (en), https://arxiv.org/abs/2611.03601 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_70167fec554f (Metraipolis archive dc_70167fec554f, sha256 7d6a2bbdc5305b1e8edd1f07ae8fc57c210211be95d5f2c31bed7f30a7ee641a)

BibTeX

@misc{dc70167fec554f,
  title = {DiabAgent: Toward Agentic Physiological Control — arXiv:2611.03601},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03601},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_70167fec554f; sha256 7d6a2bbdc5305b1e8edd1f07ae8fc57c210211be95d5f2c31bed7f30a7ee641a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03601. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As pretraining scales, medical language models have advanced rapidly in clinical question answering. Their ability to achieve effective physiological control through sustained intervention still lacks systematic quantitative evaluation. Type 1 diabetes provides a concrete setting: impaired insulin secretion creates dependence on exogenous insulin, with dosing continually adjusted using information about meals, exercise, illness, and glucose measurements. We introduce DiabAgent, a research suite for agentic physiological control comprising the Agentic Physiological Control (APC) framework, the DiabetSim simulator, and the SweetBench benchmark. APC controls glucose through medical-device tools, using ongoing observations to infer individual states, accumulate personal experience, and choose treatment actions. We build DiabetSim to simulate different patients, events, and device conditions for scalable virtual testing and training. Using DiabetSim, we construct SweetBench by instantiating 32 scenario designs as 212 glucose-control tasks. With Claude Opus 5.5 and a fixed decision interval of one hour, APC achieves a COGI of 73.5, compared with 71.2 for the open-source closed-loop baseline OpenAPS. Ablations further show that individual adaptation and contextual information improve glucose control. Combining high-frequency rule-based control with lower-frequency agent decisions shows potential for balancing control performance and computational cost. Our work provides a framework, simulator, and evaluation foundation for agents to participate in human physiological control.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2611.03601 — 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 2026-11-03T10:19:49.957Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).