Reflection Pretraining Enables Token-Level Self-Correction in De Novo Peptide Sequencing

De novo peptide sequencing infers a peptide's amino-acid sequence directly from its tandem mass spectrum (MS/MS). State-of-the-art models decode the peptide autoregressively, one residue at a time, and cannot revise a residue once it has been emitted: a single misread residue, caused for instance by a missing fragment ion, corrupts the whole peptide. We propose reflection pretraining, which augments the amino-acid vocabulary with a reflection token <reflect> and trains the decoder to revise its own residues during generation. De novo sequencing is unusually well suited to this: every training spectrum is paired with a gold-label peptide, so the position and identity of an injected error are known exactly, and the spectrum itself (the fragment-ion ladder and the precursor mass) contains the evidence needed to verify each residue. During pretraining, we inject synthetic residue errors into target peptides and require the model to emit <reflect> followed by the correct residue. Errors are drawn either uniformly or from later residues of the same peptide, which mimics the residue-order confusions typical of real spectra. Gradients are blocked at error positions, so the injected error serves as context rather than as a learning target. On the 9-species benchmark, reflection-pretrained Transformers detect and correct their own mistakes at inference time and improve amino-acid and peptide-level precision over a standard Transformer and, on average, over domain-specific de novo methods, without any task-specific architectural modules. The same mechanism regularizes training and lets experts insert <reflect> at suspect residues. Code, data and pre-trained model weights can be found in the Anonymous GitHub.