Automated Semantic Integrity Verification of Scientific Literature via Multi-Modal Graph Fusion and Recursive HyperScore Evaluation

**Abstract:** Current scientific literature verification processes are predominantly manual and susceptible to human bias, leading to inconsistencies and hindering scientific progress. This paper introduces a novel Automated Semantic Integrity Verification (ASIV) system leveraging multi-modal graph fusion and a recursive HyperScore evaluation process to provide an objective, scalable assessment of scientific literature's logical coherence, novelty, impact potential, and reproducibility. By integrating text, formulas, code, and figures into a unified knowledge graph and employing reinforcement learning-driven feedback loops, ASIV identifies subtle inconsistencies and assesses potential impacts with significantly improved accuracy compared to existing methods. The system is designed for immediate commercialization within the scientific publishing and research validation sectors.