Stability Feedback as a Robustness Mechanism: Cross-Regime Validation via Monte Carlo Neural Training

This paper investigates whether regime-sensitive, closed-loop stability regulation enhances robustness and expands empirical stability boundaries in stochastic neural optimization. Across 350 paired Monte Carlo trials under increasing label noise, a feedback-regulated training procedure demonstrates consistent validation-error reduction relative to a fixed open-loop baseline. A paired sign test rejects the null hypothesis of no improvement (p < 10^-6), confirming that gains are systematic rather than seed-specific. A discrete stability phase-boundary analysis further shows outward expansion of the non-divergent learning-rate region under fixed hyperparameters, with a median five-fold increase in maximum stable learning rate across tested widths. Cross-dataset replication on a spirals benchmark confirms divergence suppression in high-stress regimes without artificial inflation of performance in already stable regions. The results provide controlled proxy evidence that instability-triggered feedback can induce structural robustness effects beyond static hyperparameter tuning within the evaluated experimental envelope.