COMPASS: Dual Recalibration for Long-Tailed Adversarial Training

Adversarial training (AT) has demonstrated remarkable success on balanced datasets, yet its effectiveness under long-tailed distributions remains challenging, as it necessitates simultaneously improving clean accuracy, overall robustness, and tail-class robustness. Existing methods for long-tailed robustness commonly mitigate class imbalance via prior-based corrections or class-wise perturbation schedules. Nevertheless, they frequently overlook two critical issues: whether a sample is sufficiently reliable in the clean domain to warrant strong adversarial supervision, and whether the final classifier still suffers from bias inherited from the long-tailed training prior. To address these issues, we propose COMPASS, a framework that navigates long-tailed AT through reliable robust supervision and classifier recalibration. COMPASS consists of two modules. Robust Supervision Recalibration (RSR) strengthens adversarial supervision only when class-level robust deficiency is supported by sample-level clean reliability, avoiding excessive perturbations on unstable tail samples. Train-only Classifier Recalibration (TCR) then freezes robust representations and interpolates the original adversarial classifier with a class-balanced recalibrated classifier selected solely from training data. Extensive experiments show that COMPASS substantially improves tail-class robustness and achieves competitive overall performance.