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Annotation reporting in NLP is improving, but critical details that affect validity are still frequently overlooked, especially in model evaluations.
LLMs' temporal reasoning crumbles in low-resource languages and rarer calendar formats, not due to a lack of reasoning ability, but because poor tokenization fragments dates and times.
LLMs struggle to provide reliable answers to Islamic queries, but Fanar-Sadiq's multi-agent architecture, with specialized modules for scripture, jurisprudence, and calculations, delivers grounded and verifiable responses.
