Currently, most deep learning tools generate peptides in an autoregressive manner, predicting each amino acid sequentially. This approach poses several challenges: it limits the ability to correct early mistakes in the amino acid sequence, shows diverging behavior when discriminative peaks are absent (often for middle amino acids in longer peptides), requires heuristics to ensure that the predicted peptide matches the observed precursor m/z, and suffers from computational inefficiencies because autoregressive decoding cannot be parallelized. Although some tools like PepNet and π-PrimeNovo attempt to predict the entire peptide sequence at once, they can face limitations such as an inflexible maximum peptide length. Similarly, while the diffusion-based decoder in InstaNovo is an interesting conceptual contribution , it is only used as a postprocessor after an initial peptide sequence has been obtained via autoregressive decoding, and this postprocessor does not significantly improve the predictive performance. These approaches highlight the need for innovative decoding strategies that could significantly enhance the performance of de novo sequencing by better leveraging the information available in specific mass spectra.