The Driver-Blindness Phenomenon: Why Deep Sequence Models Default to Autocorrelation in Blood Glucose Forecasting

Deep sequence models for blood glucose forecasting consistently fail to leverage clinically informative drivers–insulin, meals, and activity–despite well-understood physiological mechanisms. We term this Driver-Blindness and formalize it via ${\mathrm{\Delta }}_{\text{drivers}}$, the performance gain of multivariate models over matched univariate baselines. Across the literature, ${\mathrm{\Delta }}_{\text{drivers}}$ is typically near zero. We attribute this to three interacting factors: architectural biases favoring autocorrelation (C1), data fidelity gaps that render drivers noisy and confounded (C2), and physiological heterogeneity that undermines population-level models (C3). We synthesize strategies that partially mitigate Driver-Blindness–including physiological feature encoders, causal regularization, and personalization–and recommend that future work routinely report ${\mathrm{\Delta }}_{\text{drivers}}$ to prevent driver-blind models from being considered state-of-the-art.