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Life sciences · Preprint
arXiv · August 10, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed computational preprint proposing CGRm, a plug-in algorithm for improving robustness in long-tailed adversarial training by leveraging confusion geometry and directed robust errors. The authors report consistent empirical gains on benchmarks and provide ablation studies, but the work has not undergone peer review and is not applicable to clinical practice.
Preprint. Intervention: Confusion Geometry Rebalancing method (CGRm): a plug-in framework using periodic robust evaluations to derive source class loss weights, class-wise robust coefficients, and a directed confusion geometry graph, coupled with feedback-weighte…. Compared with: Existing methods for long-tailed adversarial training that correct class priors or adapt class-wise robust supervision..
CGRm achieves consistent robust performance gains over existing methods on long-tailed benchmarks. The method couples feedback-weighted robust optimization with graph-guided margin correction to boost robustness of vulnerable classes. Ablation studies validate the contribution of each component.
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This is an unrefereed arXiv preprint describing a machine learning algorithm for adversarial training; it reports experimental benchmarks but has not undergone peer review.
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Adversarial training under long tailed distributions suffers from a dual imbalance: the class imbalance skews the training objective toward head classes, and the adversarial inner maximization may further amplify this bias. Existing methods mitigate this issue by correcting class priors or adapting class wise robust supervision, yet they treat each class in isolation and fail to identify which boundaries drive long tailed collapse. We propose a Confusion Geometry Rebalancing method (CGRm) for long tail adversarial training, a plug in framework that leverages directed robust errors as training signals. CGRm leverages periodic robust evaluations to derive source class loss weights, class wise robust coefficients, and a directed confusion geometry graph. The method then couples feedback weighted robust optimization with graph guided margin correction, thereby boosting the robustness of vulnerable classes and sharpening the critical boundaries that drive long tailed performance degradation. Experiments on long tailed benchmarks show that CGRm achieves consistent robust performance gains over existing methods, with ablations validating the contribution of each component. We provide the code in the supplement.
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