Isolated sign language recognition is commonly formulated as supervised classification, but this formulation is challenging when nearly every target gloss has only one labeled video. We study the Isolated Sign Language Recognition track of the WSLP 2026 Shared Task on Indian Sign Language Processing, a predominantly one-shot target setting with 4,398 training videos spanning 4,361 glosses, over 99% of which are singletons. We introduce CARE, a Coverage-Aware Retrieval Ensemble that augments a complete local visual dictionary with a CISLR reference bank covering a subset of the target vocabulary. A frozen Video-Swin encoder ranks all target labels locally, while frozen I3D and SignRep encoders provide external retrieval evidence for lexically matched labels. CARE estimates orthogonal Procrustes mappings from shared-class centroids, retains both raw and aligned representation geometries, and preserves external videos as individual nearest-neighbor references. Query-wise score standardization and a deterministic coverage mask restrict external evidence to supported labels. The method requires neither target-specific backbone fine-tuning nor a large-vocabulary classification head. CARE obtains 74.31%/76.15%/76.15% Top-1/5/10 accuracy on the WSLP 2026 validation split and 65.59%/67.11%/68.25% on the official test split. The 8.72% Top-1 gap warrants caution when interpreting performance on the small 109-query validation split used for model selection.

Hallucination detection in Arabic Islamic question answering is challenging because fluent answers may misstate a verse, narration, ruling, attribution, or historical fact. The task jointly requires detecting hallucination and recovering the factually correct option. We introduce C3R, a Counterfactual Cross-Check Recognition framework that models this relation with two role-conditioned predictions from one Arabic language model: a truth role selects the factual option, while a match role selects the candidate that best represents the generated response. Training uses targeted counterfactual responses, hard distractors, option-order permutations, and completion-likelihood supervision without a cross-view consistency loss. At inference, C3R scores valid structured completions, maps predictions from permuted presentations back to semantic option identities, and derives the hallucination label from argmax agreement between the two roles. The cross-check is applied only at inference. With a QLoRA-adapted ALLaM backbone, C3R ranks first on the official test leaderboard with a final score of 0.8824, comprising 0.8673 macro-F1 and 0.8975 option accuracy. These results demonstrate the effectiveness of structured cross-role agreement for knowledge-sensitive hallucination detection and answer verification.