We present DAggerCoref, our submission to the CRAC 2026 Shared Task on Multilingual Coreference Resolution. DAggerCoref is a three-stage cascade built on XLM-RoBERTa-large: a gap classifier for zero pronoun detection, a mention head classifier, and a coarse-to-fine antecedent scorer. Our central contribution is applying DAgger (Ross et al., 2011) to coreference resolution: after training the antecedent scorer on gold mentions, we fine-tune on a 50/50 mix of gold and pipeline-predicted mentions, closing the train/test distribution mismatch and improving development set macro CoNLL F1 by 1.10 points. We also introduce Otsu adaptive thresholding for zero pronoun detection, which matches gold-tuned per-dataset thresholds without requiring any gold supervision. Our system achieves a macro CoNLL F1 of 67.56 on the official test set across 27 datasets and 19 languages