Automated Framework for Multi-Modal Scientific Evidence Verification and Impact Forecasting

**Abstract:** This paper presents a framework for automated verification and impact forecasting of scientific findings, leveraging multi-modal data ingestion, semantic parsing, logical consistency checks, and knowledge graph-driven novelty assessment. The system, termed the "Multi-modal Scientific Evidence Evaluation and Forecasting Apparatus" (MSE-EFA), offers a 10x improvement in identifying inconsistencies, assessing novelty, and predicting citation/patent impact compared to traditional manual review processes, with a potential to accelerate scientific discovery and streamline research funding allocation.