Automated Verification and Enhancement of Deep Learning Models via Meta-Self-Evaluation (AVH-MSE)

**Abstract:** Deep learning models, while achieving state-of-the-art results in numerous domains, frequently suffer from vulnerabilities including logical inconsistencies, reproducibility issues, limited generalization, and unexpected edge-case failures. This paper introduces Automated Verification and Enhancement of Deep Learning Models via Meta-Self-Evaluation (AVH-MSE), a framework that uses a multi-layered pipeline to rigorously evaluate deep learning models and autonomously refine their performance. AVH-MSE leverages a novel HyperScore metric fused from multiple evaluation streams—logical consistency, code/formula verification, novelty analysis, impact forecasting, and reproducibility—yielding a robust and actionable evaluation result. The system autonomously adapts its learning process through a reinforcement learning-guided feedback loop, leading to significant improvement in model reliability and generalization capabilities. AVH-MSE is designed for immediate commercialization within 5-10 years and is poised to dramatically improve the trustworthiness and applicability of deep learning across various industries.