Many de novo sequencing models employ convolutional neural networks (CNNs) . A CNN processes vector inputs using a set of sliding windows, where each sliding window (a "filter") learns to recognize specific patterns within the data (Figure ). CNNs were instrumental in the advent of deep learning methods, partly because they provide powerful and general pattern recognition capabilities and partly because their computations can be extremely efficiently carried out on a GPU. Furthermore, CNNs provide a strong inductive bias; the learned convolutional filters emphasize relationships between neighboring elements in the input vector. An important factor here, however, is that in standard CNNs, the receptive field is relatively small, particularly when using conventional convolutional layers combined with max-pooling operations. This limited receptive field may not adequately capture patterns between peaks that are far apart in the m/z axis, such as complementary b-and y-ions or peaks separated by multiple amino acids. One approach to address this limitation is the use of dilated convolutions, as implemented in models like PepNet . Dilated convolutions allow the network to expand its receptive field without losing resolution, enabling it to detect patterns across a broader range of m/z values while maintaining computational efficiency.