Data-to-Paper

Data-to-Paper is an innovative AI-powered framework designed to automate the entire scientific research process. By integrating Large Language Models (LLMs) with rule-based agents, it guides the transformation of raw data into complete, transparent, and verifiable scientific manuscripts. The platform autonomously handles tasks such as hypothesis generation, research planning, analysis code writing and debugging, result generation and interpretation, and manuscript drafting. This approach aims to accelerate scientific discovery while upholding essential values of transparency, traceability, and verifiability in research.