VERIDEX V9.1: Policy-Latent Diffusion Network for Multi-Country Content Rating Prediction

This preprint introduces VERIDEX V9.1, a Policy-Latent Diffusion Network (PLD-Net) designed for multi-country content rating prediction across 65 countries and 51 rating classes. The model achieves 80.6% validation accuracy and 80.3% test accuracy on a dataset of 12,264 movies, improving baseline performance by +3.48%.

VERIDEX V9.1 introduces four novel contributions:
(1) Uncertainty-Weighted Policy Ensemble (UWPE),
(2) Hierarchical Multi-Head Policy Attention (HMPA),
(3) Policy Consistency Regularization (PCR),
(4) Progressive Knowledge Distillation (PKD).

This version includes complete reproducibility details, architecture diagrams, dataset specifications, TMDb-compliance notes, ablation studies, and evaluation metrics. The work is released as a research preprint for academic visibility and citation.