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本文提出一种针对小样本场景下缺失质量与未见物种计数估计的“脆弱性感知”正则化框架，通过严格凹的熵-KL优化提供闭式解，作为对 Good-Turing 等经典估计器的轻量级修正，在长尾、单例主导的小样本场景中显著提升稳定性。
Proposes Geometry-Spectral Rectification (GSR), a theoretically grounded framework that treats long-tailed learning as a spectral regularization problem, achieving new state-of-the-art results for analytic class-incremental learning.