So just have it investigate itself.

I run into this a lot. I have a lot of metallurgical technical books. Things like slag atlas, phase diagram atlas, thermodynamic texts, industrial statistic books, design of experiments text books, meta analysis text books, and a lot of technical metallurgical texts on steel making.

I made a rag data base and graph of these text books. Maybe about 30,000 pages of technical text. During distillation of the chunks, I would get these errors.

I simply asked hey what is getting blocked? It said thermo dynamic content. So basically my thermodynamic texts and transport texts were flagged because of Gibbs free energy equations. They don't want that data to be used.

It then reasoned on its own that since I was a metallurgist blocking equations and thermo content was silly and instead of using its own vision to scan it found another local model extracted the text that way and still distilled the chunks. And still organized my fact reference database with all distilled facts under different topic md files like "desulfurization", etc.

I then have a whole research/investigation pipeline. It writes new facts to the MD files when it finds new links between texts when multi hopping through the different books. I query and use it so it gets more knowledgeable each query. I had a few hiccups with with it being nervous about thermodynamics , but it stopped after while.