On September 8, OpenAI announced a solution to the Navier-Stokes existence and smoothness problem, one of the seven Millennium Prize Problems offering $1 million for their solution. But twelve hours earlier, mathematician Tristan Buckmaster publicly raised concerns that unpublished work he and Levent Alpoge were doing on the Euler equations may have found its way to OpenAI. It turned out, both mathematicians had been using ChatGPT during their research, and Buckmaster was concerned that OpenAI's model could have been learning from their conversations. OpenAI denies the allegations.
The incident raises an important question for researchers navigating a growing set of AI tools. What happens to unpublished scientific work when we share it with AI models? Researchers are increasingly using AI throughout the manuscript lifecycle, from developing ideas, to preparing grants, analyzing data, and improving manuscripts. In doing so, they are sharing results that haven't been published or patented. In many cases, those results haven't even been shared with collaborators yet.
Until now, Reviewer3 has been working with frontier model providers, like OpenAI, Anthropic, or Google, under zero data retention. Manuscripts were processed for their review session, only, and never used to train or improve those models. However, given recent allegations around data privacy and intellectual property, we wanted to offer a new alternative for researchers that are no longer comfortable with that.
We launched Reviewer3 AI. Reviewer3 AI runs every Reviewer3 check on an open-weights model on dedicated inference, without calling OpenAI, Anthropic, or Google. Manuscripts are still never used to train models. Our goal is to build AI that both improves your research and protects it.
If you are evaluating Reviewer3 for a journal or publisher and would like to understand exactly how your manuscripts are processed, email us. We are happy to walk your security team through the entire workflow.