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DiabAgent: Toward Agentic Physiological Control

DiabAgent evaluates sustained agentic glucose control with a simulator and 212-task benchmark. In the reported setup, its agent framework scored above the OpenAPS baseline on COGI.

arXiv · Nov 2, 2026

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Source Summary

DiabAgent combines the **APC framework**, DiabetSim simulator, and SweetBench benchmark. SweetBench instantiates **32 scenario designs as 212 tasks**; with Claude Opus 5.5 and hourly decisions, APC scored 73.5 COGI versus 71.2 for the OpenAPS baseline.

Practical Implication

For builders of long-running tool agents, the setup highlights per-user adaptation, accumulated experience, and context such as meals or exercise. The authors also explore combining frequent rule-based control with less frequent agent decisions.

Agent-Ready Context

DiabAgent combines the **APC framework**, DiabetSim simulator, and SweetBench benchmark. SweetBench instantiates **32 scenario designs as 212 tasks**; with Claude Opus 5.5 and hourly decisions, APC scored 73.5 COGI versus 71.2 for the OpenAPS baseline.

For builders of long-running tool agents, the setup highlights per-user adaptation, accumulated experience, and context such as meals or exercise. The authors also explore combining frequent rule-based control with less frequent agent decisions.

This is a virtual research evaluation for type 1 diabetes control. The supplied material does not establish safety or effectiveness in real clinical use.

Connected Context · Feed7 Judgment

DiabAgent adds a simulated, personalized control benchmark with explicit experience and context, and compares its agent to an established controller. The result supports evaluating agents on sustained decisions in a domain-specific simulator, while the virtual setting leaves real-world clinical safety unresolved.

Simulated Control EvalsBoth evaluate consequential agent control in simulated environments; Simulated Control Evals specifically cautions that simulated results are deployment-specific and do not replace executed safety evidence.Patient-specific rubric-based evaluation of large language models across longitudinal cancer careBoth concern clinical agent evaluation across context-dependent decisions; the cancer-care benchmark shows why aggregate performance may obscure failures that vary by workflow phase.AgentHPOBench: A Benchmark For Evaluating LLM Agents as Sequential Hyperparameter OptimizersDiabAgent emphasizes accumulated experience and per-user adaptation, while AgentHPOBench tests whether agents improve through sequential history; together they point to evaluating sustained behavior, not only isolated outputs.

Context Map

benchmarkresearchdata#agent-evals#agent-reliability#tool-use

Uncertainty

This is a virtual research evaluation for type 1 diabetes control. The supplied material does not establish safety or effectiveness in real clinical use.