{
  "id": 7643,
  "url": "https://arxiv.org/abs/2603.05421v3",
  "title": "DARK: Diagonal-Anchored Repulsive Knowledge Distillation for Vision-Language Models under Extreme Compression",
  "summary": "Compressing vision-language models for on-device deployment is increasingly important in clinical settings, but knowledge distillation (KD) degrades sharply when the teacher-student capacity gap spans an order of magnitude or more. We argue that, under such gaps, strict imitation of the teacher is a poor objective: much of the teacher's pairwise similarity structure reflects its own architectural biases rather than information a compact student can efficiently represent. We propose \\textbf{Diago",
  "authors": "Numan Saeed, Asif Hanif, Fadillah Adamsyah Maani, Hussain Alasmawi, Mohammad Yaqub",
  "category": "research",
  "topics": "healthcare,children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-05T17:43:00.000Z",
  "fetched_at": "2026-07-14T16:33:21.051Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7643",
  "original_url": "https://arxiv.org/abs/2603.05421v3",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}