Spectrum-Guided Label Refinement for Robust $\text{De Novo}$ Peptide Sequencing

Neural *de novo* peptide sequencing models learn from database-search labels that contain both false matches and systematic errors. We introduce Physics-Consistent Self-Correction (PCSC), which uses observed fragment peaks to refine these labels. A model predicts a peptide for each spectrum. The prediction replaces the current label when it receives a higher score from a fixed spectrum–peptide matcher. Models are then retrained on the updated labels. With 60% global corruption, five label updates recover 63.3% of corrupted labels, while four retraining rounds raise peptide recall on an independent nine-species benchmark from 0.3142 to 0.3970. On a separate corpus of over 35 million PSMs, one update improves recall in seven of nine species. The procedure also improves an autoregressive model, with gains of 6.88 percentage points under greedy decoding and 0.52 under mass-filtered beam search. These results show that fragment-based label refinement can improve sequencing accuracy across different models and training corpora.