Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence

Large language models are now being used as autonomous research agents that can conduct scientific studies, formulate hypotheses, run experiments, and write papers on their own. However, a critical problem has emerged: these AI-generated papers often contain errors that compound through the research process, including fake citations, misalignment between described methods and actual code, and unreproducible results. The Science One Framework is a new system designed to prevent these errors by building verifiable evidence chains throughout the research process, where every claim in a paper is linked back to its supporting evidence such as retrieved papers, experimental logs, or code. When tested against existing systems using an automated audit protocol, the Science One Framework eliminated phantom references and achieved perfect score verification while still matching or exceeding human expert performance on research tasks.