The rapid growth of scientific publications has created a significant gap between theoretical research and practical implementation, as many academic papers lack accessible, reproducible software artifacts. This work presents an end-to-end artificial intelligence system that automatically transforms academic research papers into runnable software prototypes. The proposed framework leverages large language models and a multi-agent architecture to perform structured document understanding, methodological decomposition, code generation, and iterative validation. By integrating natural language processing, program synthesis, and automated debugging, the system extracts key algorithmic components, reconstructs experimental workflows, and generates executable code with minimal human intervention. Additionally, a feedback-driven refinement loop ensures improved correctness and reproducibility of the generated prototypes. Experimental evaluation on a diverse set of machine learning papers demonstrates the system's ability to produce functional and coherent implementations, significantly reducing the time required to transition from research concepts to working software. This approach contributes toward bridging the reproducibility gap in scientific research and enabling faster innovation cycles through automated research-to-reality transformation.