“Autonomous LLM-Driven Research from Data to Human-Verifiable Research Papers”, 2024-04-24 ():
As AI promises to accelerate scientific discovery, it remains unclear whether fully AI-driven research is possible and whether it can adhere to key scientific values, such as transparency, traceability, and verifiability.
Mimicking human scientific practices, we built data-to-paper, an automation platform that guides interacting LLM agents through a complete stepwise research process, while programmatically back-tracing information flow and allowing human oversight and interactions. In autopilot mode, provided with annotated data alone, data-to-paper raised hypotheses, designed research plans, wrote and debugged analysis codes, generated and interpreted results, and created complete and information-traceable research papers.
Even though research novelty was relatively limited, the process demonstrated autonomous generation of novel quantitative insights from data. For simple research goals, a fully-autonomous cycle can create manuscripts that recapitulate peer-reviewed publications without major errors in about 80–90%; yet as goal complexity increases, human co-piloting becomes critical for assuring accuracy.
Beyond the process itself, created manuscripts too are inherently verifiable, as information-tracing allows to programmatically chain results, methods, and data. Our work thereby demonstrates a potential for AI-driven acceleration of scientific discovery while enhancing, rather than jeopardizing, traceability, transparency, and verifiability.