The system uses multiple evaluation metrics to assess the quality of the model’s reasoning, including precision, comprehensiveness, clarity, creativity, and granularity, as well as pairwise comparisons between different models. A dedicated reward model evaluates the self-reflection stage, ensuring it introduces new insights and provides feedback to guide improvement. This approach offers explainability, critical self-evaluation, comprehensive evaluation, and iterative improvement, resulting in a sophisticated and explainable deepfake detection model.