{
  "id": 18264,
  "url": "https://arxiv.org/abs/2608.09688v1",
  "title": "Confusion-Geometry Rebalancing for Long-Tailed Adversarial Training",
  "summary": "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 lo",
  "authors": "Mengnan Zhao, Geyong Min, Lihe Zhang, Tianhang Zheng, Jie Cui",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T14:54:00.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18264",
  "original_url": "https://arxiv.org/abs/2608.09688v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}