A Unified Transformer-Based Framework for Multimodal AI Understanding and Cross-Domain Knowledge Integration

Abstract

This paper introduces a unified transformer-based framework designed to enhance multimodal understanding and enable seamless cross-domain knowledge integration. The model processes and aligns information from text, images, audio, and structured data using hierarchical attention and shared latent representations. By leveraging multimodal fusion and domain-adaptive training, the framework demonstrates strong performance on tasks involving visual question answering, sentiment analysis, and knowledge retrieval. Experimental results show significant gains in accuracy, contextual comprehension, and generalization across multiple benchmark datasets. The study provides a foundation for next-generation AI systems capable of richer perception and deeper reasoning across diverse information modalities.