Analytical Review of Large Language Model Architectures

Large Language Models (LLMs) have become the foundation of modern Artificial Intelligence systems, enabling breakthroughs in natural language understanding, reasoning, code generation, multimodal learning, and autonomous agents. Recent advances in Transformer-based architectures have significantly improved model capabilities, scalability, and generalization performance. This paper presents a comprehensive analytical review of modern LLM architectures, tracing their evolution from early neural language models to contemporary frontier systems such as GPT, Claude, Gemini, LLaMA, DeepSeek, and Mistral. The study examines core architectural components including attention mechanisms, positional encoding, Mixture-of-Experts (MoE), retrieval-augmented generation (RAG), multimodal extensions, and reasoning-enhanced designs. Furthermore, the paper discusses the strengths and limitations of current architectures and highlights future research directions toward efficient, trustworthy, and autonomous AI systems.