{
  "id": 14931,
  "url": "https://arxiv.org/abs/2607.28553v1",
  "title": "APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems",
  "summary": "Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery. While flow-matching models (, FlowDPO) have recently shown promise in this domain, their performance relies heavily on alignment with ground-truth coordinates via supervised preference learning. However, obtaining experimental labels for novel crystal phases or de novo proteins is prohibitively expensive, creating a bottleneck for structural modeling in data-scarce regimes. In this wor",
  "authors": "Shentong Mo, Yatao Bian",
  "category": "research",
  "topics": "regulation,safety-alignment,healthcare,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-30T17:21:58.000Z",
  "fetched_at": "2026-07-31T05:10:57.675Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/14931",
  "original_url": "https://arxiv.org/abs/2607.28553v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}