AIRC: Action-Innovation Response Certificates for Risk-Aware Offline Policy Improvement in Blood Glucose Control

Offline insulin-control learning must infer which actions are supported by retrospective treatment data, which direction improves control, and when an increase becomes unsafe. We introduce Action-Innovation Response Certificates (AIRC), an evidence architecture that converts state-relative action innovations into signed response certificates. Agreement across complementary data folds and clinically relevant prediction horizons determines the direction and confidence of policy updates, while a separate short-horizon pathway estimates the risk attached to positive insulin shifts. In SimGlucose, AIRC achieves the highest mean cumulative reward among six offline reinforcement learning baselines and improves time in range by 4.86% relative to the strongest reward baseline, IQL, while reducing the glycemia risk index by 10.77%. At the 240-minute analysis horizon, 54.67% of eligible AIRC events meet the postprandial target-range criterion, 10.2 percentage points more than with IQL. Component ablations separate the roles of response direction, asymmetric risk weighting, and innovation scaling. On matched held-out states from the public Loop dataset, AIRC improves trend–action correlation by 0.139 and directional consistency by 4.51 percentage points over IQL. Together, these results establish AIRC as a response-certified framework for risk-aware offline policy improvement in basal-insulin control. The source code and datasets are submitted as Supplementary Materials for Reproducibility.