Analysis Surveys Alternative Architectures to Standard Transformer-Based LLMs

A comprehensive analysis examines alternatives to the standard transformer-based large language models that currently dominate the field, including text diffusion models, linear attention hybrid architectures, and specialized approaches such as code world models. These alternative approaches are evaluated for their potential to improve efficiency or modeling performance beyond what traditional autoregressive transformer designs offer, with recent examples from late 2024 through 2026.