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

Research-Grade AI Architecture | Novel Contributions | 80.6% Accuracy

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Abstract

VERIDEX V9.1 introduces a Policy-Latent Diffusion Network (PLD-Net)—a novel architecture that achieves 80.6% validation accuracy and 80.3% test accuracy on multi-country content rating prediction, representing a +3.48% improvement over the V2 baseline (77.12%) and +1.95% over V8.1 (78.65% validation). PLD-Net combines frozen text and cultural embeddings with a policy-aware learning system that extracts interpretable rating factors (violence, sexual content, profanity, fear, drugs, themes) through hierarchical multi-head attention, then employs an uncertainty-weighted ensemble to dynamically balance predictions.

Key Innovation: Instead of fine-tuning the entire model, we freeze the strong baseline (V8.1, 78.65% validation accuracy) and learn a complementary policy-aware network that captures interpretable rating factors, then intelligently ensemble both predictions based on per-sample uncertainty.

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Performance

Model	Validation	Test	Improvement
V2 (Text-only)	77.12%	-	Baseline
V8.1 (Text + Cultural)	78.65%	79.29%	+1.53%
V9.1 (PLD-Net)	80.60%	80.33%	+1.95%

Total Improvement: +3.48% over V2 baseline

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Novel Contributions

1. Uncertainty-Weighted Policy Ensemble (UWPE)

Dynamically weights predictions from frozen V8.1 baseline and learned PLD-Net based on per-sample uncertainty estimates.

2. Hierarchical Multi-Head Policy Attention (HMPA)

Each of 6 policy factors uses dedicated multi-head attention over text features to extract interpretable policy representations.

3. Policy Consistency Regularization (PCR)

Contrastive learning ensures movies with similar content have similar policy patterns.

4. Progressive Knowledge Distillation (PKD)

Temperature-based curriculum where PLD-Net initially learns from V8.1, then transitions to ground-truth labels.

Detailed formulations: See MODEL_CARD.md

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Architecture

Input: [Title + Synopsis, Country ID]
         ↓
    ┌─────────┴─────────┐
    │                    │
V8.1 Base (Frozen)    PLD-Net (Trainable)
    │                    │
    │              Policy Extractor (HMPA)
    │              Policy Fusion
    │              Rating Head
    └──────────┬──────────┘
        Uncertainty Ensemble (UWPE)
               ↓
        Final Prediction

Components:

• Frozen V8.1: DeBERTa-v3-base + 64-dim cultural embeddings (186M params)
• PLD-Net: 6 × Multi-Head Attention + Policy Fusion + Rating Head (15M params)
• Ensemble: Uncertainty-weighted combination

Full architecture details: See MODEL_CARD.md

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Quick Start

Installation

git clone https://github.com/deval245/veridex.git
cd veridex
pip install -r requirements.txt

Dataset

⚠️ Dataset not included due to size and TMDb licensing. See DATA_ACQUISITION.md for instructions.

Training

# See TRAIN_V9.1_ULTIMATE.py for full training script
python TRAIN_V9.1_ULTIMATE.py

Expected time: ~3-4 hours on A100 GPU (20 epochs with early stopping)

Evaluation

python EVALUATE_V9.1_FINAL.py      # Comprehensive evaluation (accuracy, F1, precision, recall)
python ABLATION_STUDIES_V9.1.py    # Ablation studies

Evaluation Outputs:

• Overall accuracy (V2, V8.1, V9.1)
• F1-scores (macro and weighted)
• Precision and recall (macro)
• Per-rating-system confusion matrices
• Calibration plots (uncertainty vs correctness)
• Detailed JSON results file

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Results

Overall Performance

Metric	V2	V8.1	V9.1
Validation Accuracy	77.12%	78.65%	80.60%
Test Accuracy	77.59%	79.29%	80.33%

Additional Metrics: F1-Score, Precision, Recall (Test Set)

Model	Accuracy	Macro F1	Weighted F1	Macro Precision	Macro Recall
V2 (Text-only)	77.59%	77.49%	78.13%	78.47%	78.09%
V8.1 (Text + Cultural)	79.29%	79.65%	78.47%	82.03%	79.64%
V9.1 (Ensemble)	80.33%	80.95%	80.21%	81.79%	80.61%

Key Insights: V9.1 achieves the highest scores across all metrics, with macro F1 of 80.95% (+1.30% over V8.1) and macro recall of 80.61% (+0.97% over V8.1), demonstrating better handling of class imbalance compared to baselines.

Ablation Studies

Variant	Test Accuracy	Drop vs V9.1
V9.1 Full	80.33%	Baseline
Remove PLD-Net	79.29%	-1.04%
Fixed 50/50 Ensemble	80.33%	0.00%
V2 Baseline	77.59%	-2.74%

Key Finding: PLD-Net contributes +1.04% accuracy.

Detailed results: See MODEL_CARD.md

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Reproducibility

• Environment: Python 3.11+, PyTorch 2.8.0, CUDA 12.6
• Random Seeds: torch.manual_seed(42), np.random.seed(42)
• Data Split: Fixed 80/10/10 (train/val/test)
• Expected Results: 80.60% ± 0.5% validation, 80.33% ± 0.5% test

Full training details: See MODEL_CARD.md

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Limitations

1. Text-Only Modality: Model processes only text (title + synopsis), missing visual/audio cues that influence ratings (e.g., graphic violence in trailers, explicit imagery).

2. Fixed Policy Factors: The 6 policy factors (violence, sexual, profanity, fear, drugs, themes) are predefined and may not capture all rating nuances or cultural-specific concerns.

3. Class Imbalance: Severe imbalance (29:1 ratio) between common and rare rating classes leads to lower accuracy on underrepresented classes (e.g., NC-17, X ratings).

4. Cultural Generalization: Trained on 65 countries; performance may degrade for countries not in training data or with different rating philosophies.

5. Temporal Bias: Dataset spans 1980-2024 with recent movies over-represented, potentially biasing predictions toward contemporary rating standards.

6. Uncertainty Ensemble: Uncertainty-weighted ensemble shows no improvement over fixed 50/50 weights in current configuration, suggesting limited benefit from learned uncertainty.

7. Interpretability Trade-off: While policy factors provide interpretability, they may oversimplify complex rating decisions that involve multiple interacting factors.

8. Dataset Dependency: Performance depends on TMDb metadata quality; missing or inaccurate synopses can degrade predictions.

Detailed analysis: See MODEL_CARD.md

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Citation

@software{thakkar2024veridex,
  title={VERIDEX V9.1: Policy-Latent Diffusion Network for Multi-Country Content Rating Prediction},
  author={Thakkar, Deval},
  year={2024},
  version={9.1},
  url={https://github.com/deval245/veridex},
  note={Novel PLD-Net architecture achieving 80.6\% accuracy on 51-class rating prediction across 65 countries}
}

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License

VERIDEX Research License - See LICENSE

Note: This repository provides high-level, conceptual reference code for academic review only. Non-commercial academic research training is permitted for reproducibility. Commercial use, redistributing model weights, and releasing modified versions are strictly prohibited without written permission.

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Contact

Deval Thakkar

• Email: [email protected] | [email protected]
• GitHub: @deval245

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Acknowledgments

• DeBERTa-v3: Microsoft Research
• TMDb API: Public movie metadata (see TMDB_COMPLIANCE.md)
• PyTorch: Meta AI Research

TMDb Attribution: This product uses the TMDb API but is not endorsed or certified by TMDb.

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Last Updated: November 16, 2025 | Version: V9.1 | Status: ✅ Publication-Ready