Examines the evolution of sequence modeling, focusing on the impact, advantages, and disadvantages of the Transformer architecture.

It contrasts Transformers with earlier models like Recurrent Neural Networks (RNNs) and their variants (LSTMs, GRUs), highlighting the Transformer's key innovation of self-attention which enables superior handling of long-range dependencies and parallel processing.

Crucially, the report identifies the Transformer's quadratic complexity for long sequences as its main limitation, driving the development of more efficient alternatives like State Space Models (SSMs) such as Mamba and modern RNNs like RWKV and RetNet. It also explores hybrid architectures that combine elements from different paradigms and discusses the broad applications and ethical considerations of these models across various fields.