The Transformer architecture relies solely on attention mechanisms, making it entirely independent of any form of sequential processing, including recurrent layers.

Transformers use attention to let every word look at every other word at once, so they do not need to read the sentence word by word like RNNs. Because attention can connect any two positions directly, the model can process the whole sentence in parallel, speeding up training. However, since attention alone has no sense of order, Transformers add positional encodings so the model knows which word comes first or last. For example, if the sentence is “The cat sat,” the attention mechanism will compute relationships between “The,” “cat,” and “sat,” while positional encodings tell it that “The” comes before “cat. ” This combination lets Transformers handle long sequences efficiently without sequential layers.