Running summarizing tools on a new article

Abstract: Large Language Models (LLMs) suffer from hallucination-generating plausible but factually incorrect content-posing significant threats to AI system reliability with documented${\$} \mathbf{1 0 0}$billion market losses and critical failures across healthcare, legal, and educational domains. Existing mitigation techniques achieve$\mathbf{1 2 - 3 5 \%}$improvements but lack comprehensive integration. This paper proposes a unified validation framework combining internal and external validation strategies. Internal validation uses semantic entropy, attention analysis, and representation probing to achieve 79 % AUROC in hallucination detection. External validation employs advanced RAG architectures and automated factchecking, demonstrating$28-95 \%$hallucination reduction. The framework uses internal signals as an efficient triage mechanism (95 % overhead reduction) to trigger external verification, achieving 1.82x cost-effectiveness ratios. Evaluation across 15+ benchmarks and production deployments validates effectiveness in critical applications.