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This paper introduces DFSC, a PyTorch environment built around a Mittag-Leffler Spectral Layer (MLSL) that enables error-controlled, differentiable fractional propagation in hybrid neural models. It separates known fractional dynamics from data-driven corrections, supports batched/dense/sparse operator paths, and provides certified error bounds in certain regimes.