Comprehensive review synthesizes the evolution and future of large language models, exploring their capabilities and limitations.
This preprint presents a comprehensive review of the evolution, capabilities, limitations, and future directions of Large Language Models (LLMs). The paper surveys transformer architectures, scaling laws, reasoning capabilities, coding performance, educational applications, enterprise adoption, multimodal systems, AI agents, and emerging trends through 2026. It synthesizes findings from major model families including GPT, Claude, Gemini, Llama, Qwen, and DeepSeek, while discussing technical challenges, governance considerations, and future research opportunities.
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Divyansh Shukla (2026) studied this question.
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