{
  "id": 3826,
  "url": "https://arxiv.org/abs/2605.24475v1",
  "title": "Robust Fuzzy Multi-view Learning under View Conflict",
  "summary": "Trusted multi-view classification aims to deliver reliable fusion for accurate predictions and has recently attracted substantial attention in both academia and industry. However, existing TMVC methods typically assume strict alignment across different views during both training and testing phases, which is often impractical in real-world scenarios. This limitation motivates us to revisit TMVC and extend it to a more challenging setting: how to mitigate the impact of view conflict (VC) during bo",
  "authors": "Siyuan Duan, Yuan Sun, Dezhong Peng, Yingke Chen, Xi Peng, Peng Hu",
  "category": "research",
  "topics": "safety-alignment,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-23T08:52:34.000Z",
  "fetched_at": "2026-07-14T16:30:31.922Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3826",
  "original_url": "https://arxiv.org/abs/2605.24475v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}