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Shandong University
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Deterministic dependency graphs severely misalign with stochastic residual structures in decoupled diffusion forecasters, but dynamically sparsifying edges via residual uncertainty estimation eliminates redundant conditioning and repairs calibration.
Population-level validation of CGM AI tools hides critical disparities, with T1D patients facing 6 mg/dL higher prediction errors than T2D counterparts.
