From Paper to Prototype: An End-to-End AI System for Automated Research Implementation

Abstract: Bridging the gap between academic research and real-world implementation remains a complex challenge in the technological ecosystem. While research papers propose algorithms and frameworks, converting them into runnable prototypes requires significant time, effort, and programming knowledge. This paper presents Research to Reality (PROTOGEN), an intelligent, end-to-end AI-driven system that automates the translation of academic research into executable software. By leveraging Natural Language Processing (NLP), a multi-model Large Language Model (LLM) pipeline powered by NVIDIA NIM, Groq (LLaMA-3.3-70B), and Google Gemini 2.0 Flash, the system performs text extraction, summarization, ideation, code generation, and live execution via Docker containerization. The system achieves a prototype generation success rate of 78.4% across 50 research papers spanning 6 domains, with an average end-to-end pipeline latency of 42 seconds. The work combines theoretical understanding with practical implementation, illustrating how AI systems can accelerate innovation, enhance collaboration, and make research outputs more actionable.

Keywords: AI code generation, NLP, LLM, software prototyping, research automation, human-in-the-loop, program synthesis, Docker containerization, NVIDIA NIM, multi-model pipeline .