AMAN Sentinel

AMAN Sentinel is an Arabic-first AI misinformation verification system designed around a simple principle: when an AI system is uncertain, it should verify or abstain rather than confidently guess. The system combines multiple Arabic text classifiers with semantic novelty detection and model-agreement signals to estimate whether a claim can be handled reliably. A selective router then decides whether to return the initial assessment or escalate the claim to a deeper verification pipeline. Escalated claims are matched against available evidence using hybrid retrieval and analyzed by an instruction-tuned language model, with an explicit insufficient-evidence outcome when the retrieved information cannot support a reliable conclusion. On our leakage-audited test set, the SVM baseline achieved 86.45% accuracy. At our selected routing point, AMAN Sentinel achieved 92.65% selective accuracy on the accepted subset while maintaining 76.90% coverage. When all three classifiers agreed, accuracy reached 92.6%. We validated the complete application end-to-end on university GPU infrastructure using Qwen2.5-7B, including the frontend, FastAPI backend, classification, novelty detection, routing, retrieval, and deep-verification path. The project is also prepared for AMD ROCm/MI300X deployment; AMD Developer Cloud access was requested, with MI300X execution pending account GPU-access approval at submission time. AMAN Sentinel aims to make Arabic AI verification more useful not by answering every claim, but by recognizing when stronger evidence is needed.