I would use Deep Research mode outputs. Sometimes I run multiple of these in parallel on different models, then compare between them to catch hallucinations. If I wanted to publish that, I would also doublecheck each citation link.

I think the idea is sound, the potential is to have a much larger AI-wikipedia than the human one. Can it cover all known entities, events, concepts and places? All scientific publications? It could get 1000x larger than Wikipedia and be a good pre-training source of text.

Covering a topic I would not make the AI agent try to find the "Truth" but just to analyze the distribution of information out there. What are the opinions, who has them? I would also test a host of models in closed book mode and put an analysis of how AI covers the topic on its own, it is useful information to have.

This method has the potential to create much higher quality text than usual internet scrape, in large quantities. It would be comparative analysis text connecting across many sources, which would be better for the model than training on separate pieces of text. Information needs to circulate to be understood better.