Reliable Arabic hallucination detection requires not only strong ranking performance but also robustness to distribution shift, calibrated probabilities, and useful uncertainty for selective prediction. We present the NAMAA Community’s submission to HalluScoring 2026 Task ~1.1 and a set of post-submission diagnostic experiments. The scored, template-compliant system is a CAMeLBERT cross-encoder over the question and generated answer; it achieves 0.7596 ROC-AUC on the hidden test set, ranking third among seven teams. We compare this template-defined input path with reference-aware answer-pair models that use the gold answer. A CAMeLBERT input-framing ablation raises the unseen-question ROC-AUC from 0.8713 to 0.9272, while similar gains appear across ARBERT and MARBERTv2. Reference-free distillation recovers only a small portion of this advantage, whereas LoRA-InfoNCE substantially improves direct cosine discrimination. On a separately trained CAMeLBERT reliability replicate, temperature scaling reduces expected calibration error from 0.0993 to 0.0562, and MC-Dropout variance reduces retained-set error from 14.08% to 8.35% when the 30% most uncertain cases are deferred. These results motivate reporting distributional generalization, calibration, and selective risk alongside ROC-AUC. Code and checkpoints for the scored submission are publicly available. [<https://github.com/NAMAA-ORG/HalluScoring-Arabic-Hallucination-Detection>]