We describe the ZHR system for both tracks of StanceEval-2026, Arabic stance detection on Mawqif-XT, where Track 1 evaluates on seen targets and Track 2 on unseen ones. The system combines fine-tuned Arabic encoders, a few-shot classifier that retrieves labelled examples for a served instruction model, and generative models adapted with LoRA, and turns their blended probabilities into labels with a threshold rule that abstains to None unless one stance class is probable enough, with thresholds fixed on the development split. We report the whole sequence of experiments, including eight that lowered F_avg2 and were not adopted. The main finding is that transfer to a new target is carried by the demonstrations, the labelled examples retrieved into the prompt, rather than by the model itself. Generated on-target tweets in the example pool gave the largest gain on both tracks, matching the surface form of the test tweets helped where the target has a distinctive style, and doubling the examples hurt. Our official results are F_avg2 0.896 with accuracy 0.855 on Track 1, and F_avg2 0.940 with accuracy 0.922 on Track 2. The strongest official baselines reach 0.8425 on the Track 1 development set and 0.7475 on the Track 2 test set.

Two-phase Whisper fine-tuning (broad dialect pre-training + low-LR target annealing) reaches 57.0 WER on NADI 2026 Subtask 1.1, with 8 WER points from the schedule alone.

We describe the ZHR systems for the two sentence-level tracks of the BAREC 2026 Shared Task on Arabic readability assessment: Strict, where training is restricted to the BAREC training split, and Open, which allows any public data. Each sentence receives one of 19 ordinal levels, and systems are ranked by quadratic weighted kappa (QWK). Both tracks use one recipe. Arabic encoders are fine-tuned under five ordinal objectives and averaged with weights from greedy selection, and the averaged score is discretized by 18 thresholds fitted to maximize QWK; every fitted quantity uses the development split. The official Strict system, an ensemble of ten train-split models, scores 85.0 QWK on the blind set. The official Open system adds pretraining on the silver-labeled BAREC-10M corpus and restricts selection to members whose early stopping never touched the calibration split; it scores 85.2. Both clear the official baseline (82.5) by 2.5 and 2.7 QWK while trailing it in exact accuracy, a trade-off that comes from the metric itself and that we measure directly. Negative results are part of the record: a catalog of 19 changes that did not help, each measured under a held-out protocol.