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    "story_id": "mp-2026-11-03-014",
    "source_story_id": "tmp-story-diabagent-physiological-control",
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    "headline": "The Medical Agent Enters a Glucose Simulator",
    "slug": "the-medical-agent-enters-a-glucose-simulator",
    "dek": "DiabAgent evaluates sustained tool-using glucose control across 212 simulated tasks rather than limiting medical models to question answering.",
    "summary": "DiabAgent evaluates sustained tool-using glucose control across 212 simulated tasks rather than limiting medical models to question answering.",
    "body_text": "DiabAgent combines an agentic-control framework, a diabetes simulator and a 212-task benchmark built from 32 scenario designs. The agent observes glucose and contextual events, accumulates patient-specific experience and uses medical-device tools to choose actions. With one named frontier model and hourly decisions, the framework reached a reported COGI score of 73.5 versus 71.2 for the OpenAPS baseline; ablations credited individual adaptation and contextual information, and a hybrid of frequent rules with slower agent decisions reduced the decision burden. All results come from a virtual environment. They do not establish safety, clinical benefit or permission for a language model to dose insulin in real patients.",
    "why_it_matters": "DiabAgent evaluates sustained tool-using glucose control across 212 simulated tasks rather than limiting medical models to question answering.",
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      "They do not establish safety, clinical benefit or permission for a language model to dose insulin in real patients."
    ],
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    "tags": [
      "diabetes",
      "medical agents",
      "simulation",
      "physiological control"
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  "sources": [
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      "source_id": "source-2026-11-03-014",
      "title": "arXiv preprint 2611.03601, original v1",
      "publisher": "arXiv",
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      "published_at": "2026-11-02T13:41:02.000-05:00",
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    "description": "A daily newspaper for the age of artificial intelligence."
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    "title": "The Medical Agent Enters a Glucose Simulator",
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