Furthermore, a key feature of the transformer is the "attention" mechanism, which enables the model to automatically learn important semantic relationships between pairs of input features. A key advantage of the attention mechanism is that it provides a global receptive field. This allows transformers to naturally model relationships between all peaks in the spectrum regardless of their distance on the m/z axis, in contrast to CNNs, which are constrained by a localized receptive field. This capacity for global context is one reason transformers have become the architecture of choice in recent de novo sequencing models.