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

The Transformer uses attention to let every word look at every other word at once, so it doesn’t need a step‑by‑step loop like RNNs. Because it processes all positions together, it can be parallelized and is faster for long texts. However, to still know which word comes where, it adds positional encodings that give each word a sense of its place in the sentence. Thus, the model is independent of sequential layers but still respects word order. For example, in the sentence “The cat sat,” attention lets the word “sat” see both “The” and “cat” simultaneously, while positional codes tell the model that “cat” is second.