An AI-Driven Framework for Enhancing Regulatory Precision and Efficiency in CRISPR-Cas Gene-Edited Crops: Challenges, Opportunities, and Global Harmonization

Zeyu Zheng^*

• School of Petrochemical Engineering, Fuzhou University, Fuzhou, China

The rapid advancement and adoption of CRISPR-Cas technologies in crop improvement has significantly outpaced existing regulatory frameworks, leading to inconsistencies in the global oversight of gene-edited organisms. As governments and international bodies struggle to reconcile scientific innovation with policy governance, a pressing need has emerged for methodologies that can translate biological edits into regulatory-compliant representations across jurisdictions. Traditional approaches often compartmentalize genomic and legal domains, lacking the formalism to bridge biological intent and compliance precision. These methods are typically static, unable to adapt to jurisdictional policy drift or incorporate real-time exemption logic, thereby undermining both regulatory interpretability and technical fidelity. To address this gap, I propose a unified computational framework built around the novel GeneRegAlignNet model and the Constraint-Aware Policy Induction (CAPI) strategy. This framework embeds regulatory semantics directly into the learning architecture, enabling the alignment of gene-edit features with heterogeneous policy descriptors in a shared latent space. GeneRegAlignNet employs symbolic gating, contrastive manifold learning, and exemption-aware vectorization to predict alignment likelihoods between edits and legal categories with high precision. CAPI extends this model with a risk-calibrated policy optimization pipeline that accounts for policy evolution, regulatory variance, and jurisdictional priorities. 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.