The course explores recent and emerging research directions in large language models, from their foundations and training methods to their deployment as autonomous agents. Topics span model foundations (model architecture, optimizer) and scaling laws, test-time reasoning, efficient inference, and self-improving agent systems. The course emphasizes critical evaluation of recent findings, connections between empirical advances and theoretical understanding, and the exploration of new research questions in the field.