A Unified Framework for Metric Optimization Across Diverse AI Paradigms: From Classical Algorithms to Large-ScaleTransformers (standard AI improvement)

DescriptionThis article presents a unified framework for optimizing performance metrics across twelve major AI paradigms, ranging from interpretable classical algorithms to large-scale deep learning models such as transformers. For each paradigm—including decision trees, support vector machines, genetic algorithms, neural networks, and transformers—the paper formalizes key evaluation metrics and proposes advanced strategies for improvement, spanning hyperparameter tuning, architectural innovations, data-centric methods, and hybridization techniques. A special focus is given to cross-paradigm synergies, offering a systematic typology of hybrid AI systems (sequential, embedded, and co-learning). The work argues that the future of AI lies in the intelligent integration of diverse approaches rather than reliance on a single paradigm. Supported by illustrative examples and benchmark-style demonstrations, this paper serves as both a reference and a roadmap for researchers and practitioners aiming to push the boundaries of AI performance. 📄 DOI: 10.5281/zenodo.17009620 Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Transformers, Decision Trees, Support Vector Machines, Genetic Algorithms, Hybrid AI, Performance Optimization, Metric Enhancement, Cross-Paradigm Synergies