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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.
18 Oct 2026
