Feasibility of fully AI-driven research that adheres to scientific values

Determine whether fully AI-driven scientific research—executed autonomously end-to-end by artificial intelligence systems—can be conducted while adhering to the key scientific values of transparency, traceability, and verifiability.

Background

The paper investigates whether LLM agents can autonomously execute the complete scientific research workflow—from hypothesis generation and data analysis to writing and assembling a manuscript—while maintaining rigorous standards of transparency, traceability, and verifiability.

To explore this question, the authors developed data-to-paper, a platform that orchestrates multi-agent LLM interactions with rule-based checks, code execution, literature retrieval, and information tracing. Their case studies show promise for simple research goals but highlight failures and the need for human co-piloting for more complex tasks, leaving the broader feasibility across diverse contexts as an unresolved question.