Towards Next-Generation Architectures of Large Language Models: A Survey

Large Language Models (LLMs) have advanced rapidly through increases in model scale, data, and computation, while their underlying architectures have also undergone substantial evolution. Beyond the canonical Transformer architecture, recent designs seek greater efficiency, scalability, and flexibility by rethinking how sequence information is processed, represented, and transformed. We survey efficient attention and state management, modern recurrent and other linear-complexity sequence models, Mixture-of-Experts architectures, and advances in activation functions, normalization, residual connections, positional encoding, and tokenization. We also cover multi-token prediction and adaptive and recurrent depth. Across these areas, we highlight key design principles and trade-offs among representational power, optimization stability, and computational cost. Complementing this component-wise treatment, we take a system-level perspective. We trace representative open-weight LLM families to identify recurring patterns in how architectural choices are combined, and synthesize these developments along four dimensions—processing structure, transformation dynamics, state management, and output commitment—characterizing how recent designs preserve, refine, or relax the assumptions of the canonical decoder. Together, this survey aims to provide a systematic overview of LLM architectural evolution and to chart pathways toward next-generation designs.