When Better Classifiers Make Worse Tuners: IonTrapBench and History-Gated Stopping for Closed-Loop Mass Spectrometer Tuning

In autonomous instrument tuning systems based on learned classifiers, spectral diagnoses determine when to stop or continue adjustment. However, classification errors not only affect the assessment of the current signal but may also change subsequent observations through parameter updates. Window-level classification metrics therefore cannot directly substitute for evaluation of the complete tuning process. We reveal this prediction–decision misalignment in autonomous mass spectrometer tuning: retraining that improves window-level fault recall reduces the tuning success rate; false negatives lead to premature stopping, whereas false positives may introduce new faults through unnecessary parameter adjustments. To systematically investigate this problem, we propose IonTrapBench, which extends conventional window-level fault classification evaluation to a trajectory-level closed-loop decision evaluation paradigm that includes parameter states, action consequences, and final outcomes. On this basis, we propose History-Gated Stopping, which uses historical fault risk at a parameter state to adjust stopping decisions, combining predictions with state-level decision risk without retraining the classifier. Under sequential history, the method reduces false negatives by 88.2%; with full-history assistance, it increases the average tuning success rate from 84.2% to 91.6%. We plan to release the data and evaluation materials to support joint research on spectral diagnosis, parameter adjustment, and their final outcomes.