Towards end-to-end automation of AI research

Checked 30 Sept 2026. Open license (CC-BY, CC-BY-SA, CC0, public domain): full text indexed and used in synthesis.

Abstract The automation of science is a long-standing ambition in artificial intelligence (AI) research 1,2 . Although the community has made substantial progress in automating individual components of the scientific process, a system that autonomously navigates the entire research life cycle—from conception to publication—has remained out of reach. Here we present a pipeline for automating the entire scientific process end to end. We present The AI Scientist, which creates research ideas, writes code, runs experiments, plots and analyses data, writes the entire scientific manuscript, and performs its own peer review. Its ideas, execution and presentation are of sufficient quality that the manuscript generated by this AI system passed the first round of peer review for a workshop of a top-tier machine learning conference. The workshop had an acceptance rate of 70%. Our system leverages modern foundation models 3–5 within a complex agentic system. We evaluate The AI Scientist in two settings: a focused mode using human-provided code templates as an initial scaffold for conducting research on a specific topic and a template-free, open-ended mode that leverages agentic search for wider scientific exploration 6,7 . Both settings produce diverse ideas and automatically test, report on and evaluate them. This achievement demonstrates the growing capacity of AI for making scientific contributions and signifies a potential paradigm shift in how research is conducted. As with any impactful new technology, there could be important risks, including taxing overwhelmed review systems and adding noise to the scientific literature. However, if developed responsibly, such autonomous systems could greatly accelerate scientific discovery.

• The field has shifted from automating narrow, isolated tasks toward general-purpose systems that span much of the research life cycle. Lu et al. 2026's AI Scientist runs from ideation to peer review, Gottweis et al. 2026 frame Co-Scientist as a general collaborator for scientists, and Huang et al. 2025 present Biomni as a general-purpose biomedical agent rather than a specialist workflow. Trace →
• Lu et al. 2026 report that a manuscript generated by The AI Scientist passed the first round of peer review at a workshop of a top-tier machine learning conference, a headline result for end-to-end automation. The same passage notes the workshop had a 70% acceptance rate, which tempers how strong a quality signal this provides. Trace →
• Both system builders and ethicists flag risks to the scientific literature itself: Lu et al. 2026 warn their own technology could tax overwhelmed review systems and add noise to the literature. Resnik et al. 2026 catalogue a broader set of ethical issues, including biased or deceptive research, overreliance on AI, and diffusion of responsibility. Trace →
• Lu et al. 2026 report that The AI Scientist autonomously performs ideation, experiments, analysis, writing and peer review, and that one generated manuscript passed the first round of peer review at a workshop of a top-tier machine learning conference — though the workshop had a 70% acceptance rate. Trace →
• Authors across the bundle flag risks of research automation: Lu et al. 2026 warn of taxing overwhelmed review systems and adding noise to the literature, Li et al. 2025 highlight hallucinations that appear valid but are false, and Resnik et al. 2026 enumerate ethical issues including increasing rates of biased, erroneous and deceptive research. Trace →

• AI for science and agentic researchanchor