Sample-level reliability heterogeneity is common in deep time series learning.
Standard training pipelines apply a uniform regularization setting to all samples, which can under-regularize corrupted samples and over-restrict clean samples.
Common robustness approaches filter observations in data space or impose priors on latent representations.
We propose Capacity-Centric Modulation (CCM) as a complementary, sample-adaptive regularization principle.
Under this principle, we introduce SACM (Sample-Adaptive Capacity Modulation), a task-agnostic framework that exploits spectral sparsity to assign sample-wise dropout probabilities along internal activation paths.
SACM integrates into existing backbones without architectural redesign and preserves the deterministic inference pipeline.
Across 301 real-world dataset-backbone pairs covering 9 forecasting, 32 classification, and 4 anomaly-detection datasets, SACM reduces forecasting MSE by 6.7% on average and improves classification accuracy and point-adjusted F1 by 3.04% and 17.05%, respectively, relative to unmodified backbones, with zero test-time overhead.