## Introduction Researchers have developed a new technology that enables language models to better understand context and relationships between concepts. This innovation could revolutionize the approach to text comprehension problems. The technology is called MegaRAG and uses a knowledge graph to provide a more complete structure for information. This allows language models to achieve better results in text comprehension tasks, both on single-text and multimedia documents. MegaRAG's innovative approach is based on integrating visual and spatial signs with the knowledge graph structure. This enables language models to better understand relationships between concepts and achieve better results in text comprehension tasks. ## Experimental Results Researchers conducted a series of experiments to evaluate MegaRAG's effectiveness. The results show that the technology allows language models to achieve better results in text comprehension tasks, both on single-text and multimedia documents. ## Conclusion The new technology MegaRAG could revolutionize the approach to text comprehension problems. Its ability to integrate visual and spatial signs with the knowledge graph structure enables language models to better understand relationships between concepts and achieve better results in text comprehension tasks. ## Implications MegaRAG's technology could have a significant impact on research and application of language models. It could be used to improve text understanding in various fields, such as medicine, law, and science. ## Future Research Directions Researchers are already working on new applications of MegaRAG technology. They are interested in exploring the use of this technology to improve text understanding in various fields and developing new applications for language models.