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

arXiv: https://arxiv.org/abs/2511.20601 · HTML: https://mlsanity.com/paper/2511.20601/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth](https://mlsanity.com/paper/2103.03404/) (2021)
- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://mlsanity.com/paper/2304.08424/) (2023)
- [Deep learning generalizes because the parameter-function map is biased towards simple functions](https://mlsanity.com/paper/1805.08522/) (2018)
- [Gluformer: Transformer-Based Personalized Glucose Forecasting with Uncertainty Quantification](https://mlsanity.com/paper/2209.04526/) (2022)
- [Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response](https://mlsanity.com/paper/2402.17233/) (2024)
- [AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset](https://mlsanity.com/paper/2502.09919/) (2025)
- [Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks](https://mlsanity.com/paper/2106.00884/) (2021)
- [A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction](https://mlsanity.com/paper/2505.08821/) (2025)
- [SSM-CGM: Interpretable State-Space Forecasting Model of Continuous Glucose Monitoring for Personalized Diabetes Management](https://mlsanity.com/paper/2510.04386/) (2025)

## Built on by (most cited first, 2 of 2)

- [The Stationarity Bias: Stratified Stress-Testing for Time-Series Imputation in Regulated Dynamical Systems](https://mlsanity.com/paper/2602.15637/) (2026)
- [Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization](https://mlsanity.com/paper/2606.29386/) (2026)

## Abstract (arXiv)

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 Deltadrivers, the performance gain of multivariate models over matched univariate baselines. Across the literature, Deltadrivers 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 Deltadrivers to prevent driver-blind models from being considered state-of-the-art.