Comment by dansitu
3 hours ago
Congratulations, this is a really cool approach and result. I lead a research team and I'm very interested in tooling for improved collaboration, especially between human and agent researchers.
Do you have any evidence that this approach scales to cross-domain research?
I'd say science is somewhat fractal in this sense. A research area contains topics, those topics have sub-topics, those sub-topics have competing lines that "talk" to each other, etc. So if it works in core ML that means it's working for RL and deep learning and computer vision and NLP -- which is kind of cross-domain.
That said - we believe the method does need to have sufficient data to model the conceptual density. So it'll not work in closed-science areas.
One other thing I will share: we have been working on a way to enable verified academics with sufficient topical domain expertise to engage in small group discussions that update these documents. You can think of it like Althea (our agent) being an area chair and moderating a discussion to determine how to update pages. The reason we think this will work is that you can have an anonymous discussion with the AI system (and be blunt), but we can weigh your expertise when updating the documents.