{
  "id": 12688,
  "url": "https://arxiv.org/abs/2607.19935v1",
  "title": "MOF-Sleuth: Tool-Grounded Reward Alignment for Explainable Fine-Grained MOF CIF Auditing",
  "summary": "Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information files (CIFs). Subtle chemical and structural errors in these inputs can compromise downstream results and hinder manual inspection. LLM advances in computational chemistry offer paths beyond predictive screening toward fine-grained diagnosis with evidence-grounded explanations. However, two challenges remain: (i) limited fine-grained attribution: MOF-specific val",
  "authors": "Yu Liu, Zhiwei Yang, Diandian Guo, Kun Peng, Fangfang Yuan, Cong Cao et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-22T09:05:43.000Z",
  "fetched_at": "2026-07-23T05:10:49.458Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/12688",
  "original_url": "https://arxiv.org/abs/2607.19935v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}