Open-source AI for science: OpenScholar and why replicability matters

Min was quoted in Science on the value of open, replicable AI for research: It’s very important to put this type of open-source research out there because it is replicable."

A Science article reports on OpenScholar, an AI built by the Allen Institute for AI (Ai2) and five universities to help researchers keep up with the flood of papers (over 4 million in 2024). Unlike general-purpose chatbots, it answers science questions by searching a database of 45 million open-access papers, synthesising multiple sources, and critiquing its own answers to cut down on hallucinated references. In a Nature study, it outperformed GPT-4o and Llama on domain benchmarks and was preferred over human experts in many cases. The team have made the code and data freely available—which Min highlights as crucial for peer review and replicability. The piece also notes caveats: paywalled literature is missing from the corpus, and over-relying on summaries can deskill researchers and obscure nuance. A follow-up model, DR Tulu-8B, was described in a November 2025 preprint.