Autonomous LLM-driven research from data to human-verifiable research papers
<|reference_start|>Autonomous LLM-driven research from data to human-verifiable research papers: 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 de novo quantitative insights from data. For simple research goals, a fully-autonomous cycle can create manuscripts which 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.<|reference_end|>
@article{ifargan2024autonomous,
    title={Autonomous LLM-driven research from data to human-verifiable research
  papers},
    author={Tal Ifargan, Lukas Hafner, Maor Kern, Ori Alcalay, Roy Kishony},
    journal={arXiv preprint arXiv:2404.17605},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17605},
    primaryClass={q-bio.OT cs.AI}
}