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>]

We present NAMAA Community’s systems for the four closed-track Arabic sentence-segmentation subtasks of AraSeg 2026. Heterogeneous ensembles combining LoRA-adapted bidirectional language models, XLM-R variants, a character-level SaT model, and punctuation-aware models outperform their development-selected single-model baselines. Large weights assigned to weaker but architecturally distinct members are consistent with complementary errors, although they do not establish causality. A training-only logistic MEMM improves F1 by +0.9 and +0.6 points on the punctuation-free subtasks but degrades both punctuated ones; the same directional pattern appears on the blind split. Aligning punctuated and punctuation-free inputs reveals F1 points of oracle headroom against the contemporaneous NoPnx locks, while only 52.5% of removed tokens are covered by the restoration label inventory. Genre-proxy analysis localizes the largest degradation to scriptural text. On the blind split, in the closed track, our systems achieved F1 scores of 94.4/91.3/89.9/87.0 on PA, NP, NoPnx-PA, and NoPnx-NP, respectively, ranking 6th/3rd/4th/3rd. The same systems, entered unchanged in the open track, rank 4th/2nd/3rd/3rd. Implementation and experimental resources are publicly available.[<https://github.com/NAMAA-ORG/NAMAA-Community-AraSeg-2026>]