MOF-Sleuth: Tool-Grounded Reward Alignment for Explainable Fine-Grained MOF CIF Auditing
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
Record details
Published: 22 July 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Transparency
Retrieved: 23 July 2026
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How to cite this record
ethics.ai (22 July 2026), “MOF-Sleuth: Tool-Grounded Reward Alignment for Explainable Fine-Grained MOF CIF Auditing,” evidence record 12688, https://ethics.ai/record/12688 (originally published by arXiv).
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