Cascadia extends the Casanovo architecture to work with DIA data. Unlike DeepNovo-DIA and Transformer-DIA, Cascadia does not rely on an initial precursor detection step. Instead, the model takes as input "augmented spectra," which include a series of temporally adjacent MS2 scans and all MS1 peaks within the corresponding isolation window. This approach enables the model to extract information from the MS1 data about the precursor isotope distribution, without excluding peptides with a weak MS1 signal but clear fragmentation pattern in the MS2. Cascadia encodes each input peak with a two-dimensional retention time-by-m/z embedding. Additionally, each peak is summed with a learned embedding that indicates whether it is derived from an MS1 or MS2 scan. This sequence of peak representations is then processed by a standard transformer encoder-decoder architecture akin to that of Casanovo. Furthermore, Cascadia employs an auxiliary fragment ion loss term that, in addition to predicting the amino acid sequence for a peptide, also predicts which peaks correspond to b/y fragment ions. This task helps stabilize model training on the much noisier DIA data, especially early in training when the model predicts few peptide sequences correctly.