Conformal Influence-Driven Realignment (CIDR): A Framework for Restoring Model Reliability under Distribution Shift

This paper proposes CIDR, a three-stage framework integrating entropy-based drift detection, influence-guided targeted unlearning, and conformal recalibration to restore model reliability under distribution shift. CIDR restores coverage to 0.903 ± 0.034 against a target of 0.90 with no statistically significant accuracy degradation, outperforms full retraining in accuracy (0.770 vs. 0.669) and efficiency (30% smaller prediction sets, 1/10 the cost), and provides a practical solution for maintaining trustworthy AI under drift.