An academic-grade journal of artificial intelligence research — peer-reviewed analysis of large language models, machine learning frameworks, and the engineering behind today’s most consequential AI systems.
Introduction: The Imperative of Domain-Specific Adaptation The meteoric rise of general-purpose large language models (LLMs) has demonstrated remarkable capabilities across a broad spectrum of tasks. However, their deployment…
The convergence of artificial intelligence (AI) and biotechnology is catalyzing a paradigm shift in pharmaceutical research, promising to accelerate the historically arduous and costly journey from target identification…
The pursuit of unbiased, high-performing machine learning models is fundamentally constrained by the quality and composition of their training data. Dataset bias—systematic skews in data collection, annotation, or…
The convergence of federated learning (FL) and differential privacy (DP) represents a paradigm shift in privacy-preserving machine learning. Federated learning, which enables model training across decentralized devices without…
Introduction: The Imperative for Adaptive Supply Chains The modern global supply chain is a paradigm of complexity, characterized by volatility, uncertainty, and interconnectedness. Traditional optimization models, often reliant…
Introduction: The Imperative of Governing Intelligence The rapid ascent of artificial intelligence, particularly foundation models and generative AI, presents a profound governance challenge for democratic societies. These technologies…