Progressive-Masking MIL Discovers Disruption Precursor Event Chains

Tokamak disruptions are abrupt losses of plasma confinement that can damage the device, which motivates the search for early precursor events. Reliable labels in disruption prediction, however, usually record only whether a discharge ultimately disrupts, while the timing of precursor events is rarely annotated. We therefore treat each discharge as a bag of 5-ms tokens and use multiple-instance learning (MIL) to place candidate precursors in time from the discharge label alone. A bag-level classifier can reach a good score from late-stage signatures, so progressive masking removes the evidence it has already identified along with every token that follows, and retraining on what remains drives each successive model toward earlier precursors. An event grammar converts the outputs of the frozen models into candidate events, and a shared vocabulary groups them into six families that are linked into event chains ending in either disruption or normal termination. On 1,816 J-TEXT discharges, twelve rounds place the earliest disruption-associated event at a median of 185 ms before the recording endpoint, against 65 ms for a single MIL model. The fraction of disruption-associated events inside the final 100 ms falls from 70% to 32%, and the share of multi-event chains rises from 3.9% to 25.8%. Using chain content for disruption classification, AUROC increases from 0.49 to 0.71 and held-out AUPRC from 0.857 to 0.903. The chains reveal how precursors evolve and can serve as candidate training labels for real-time disruption predictors, with no manual event annotation.