{
  "id": 10206,
  "url": "https://arxiv.org/abs/2607.11257v1",
  "title": "LaGuadia: Language-Guided Adaptive Distillation from Pathology Foundation Models",
  "summary": "Pathology Foundation Models (PFMs) offer powerful Whole Slide Image (WSI) representations but suffer from massive computational costs. While Knowledge Distillation (KD) can create efficient student models, existing multi-teacher methods often use suboptimal uniform weighting that ignores tissue heterogeneity. We propose LaGuadia (Language-Guided Adaptive DistillAtion), a framework that develops a compact pathology image encoder by dynamically integrating expertise from multiple PFMs under clinic",
  "authors": "Gangsu Kim, Won-Ki Jeong",
  "category": "research",
  "topics": "children-education",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-13T08:38:21.000Z",
  "fetched_at": "2026-07-14T16:55:59.928Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10206",
  "original_url": "https://arxiv.org/abs/2607.11257v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}