{
  "id": 17807,
  "url": "https://arxiv.org/abs/2608.07176v1",
  "title": "Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation",
  "summary": "Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training large Diffusion Transformers. Although representation alignment has improved efficiency in general computer vision, its role within the highly specialized endoscopic image space remains unclear. We introduce REVEAL (Representation-driven Endoscopic Visual Embedding Alignment), the largest generative foundation model for endoscopy to date, trained",
  "authors": "Francisco Caetano, Tim J. M. Jaspers, Haiko Middeljans, Martijn R. Jong, Rixta A. H. van Eijck van Heslinga, Floor Slooter et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T12:47:36.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/17807",
  "original_url": "https://arxiv.org/abs/2608.07176v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}