An AI-driven framework for enhancing regulatory precision and efficiency in CRISPR-Cas gene-edited crops: challenges, opportunities, and global harmonization

Empirical validation demonstrates improved performance in regulatory alignment accuracy and resilience to policy drift across a diverse set of gene-editing scenarios. By tightly integrating formal representations of molecular edits with dynamic, multi-jurisdictional policy inference, our framework offers a scalable and interpretable path forward in enhancing regulatory precision and global harmonization in the oversight of CRISPR-Cas-edited crops.