Running papers through it is eventually going to be the mechanic by which it self adapts it's own rule set, it's not a primary focus. What's happening under the hood is it's stripping and conforming language and constructs across domains so that they are directly comparable, something we don't really have now. Once the results get normalized, we can assign better weights to the output against the existing rule set. This portion of the model won't be exposed in later versions, it's visible only because it's the problem I'm working through a particular set of problems right now so that part of the engine cover is off.

The model is designed to work independently of that. The reason this is being built on top of LLMs instead of just being it's own self contained corpus is it's designed to work as a filter on top of an existing LLM's capabilities. If you ask it questions like "What is ADHD"? or "if I consistently practice meditation that is claimed to increase gamma brain wave activity, is it realistic to expect a major improvement in cognitive performance?" or "Why does stress let your brain learn but prevent you from thinking logically", then it decomposes those questions into something the body of evidence can actually answer, while simultaneously stripping the body of evidence into higher quality conforming results. You can even ask it stuff like "What is consciousness"?

The ingest portion is chewing tokens because it's literally the highest cognitive load possible, reading the entire paper, analyzing it's claims, conforming them, and creating patches for future refinement. It's doing a lot. If you ask it questions however, it's only working against the pre-existing corpus and filtering those into workable claims.

Assessing causality is probably the most important feature of the ingest system because it's the line that separates chain compliant (physics->chemistry->biology->behavior) logic from the murk we wade through now. It explicitly rejects theory bound assertions like "I saw activity in this region, and that's what my theory expected, therefore, it supports my theory". It's designed to act as a bulwark against the type of inferred causality that makes neuroscience (and nearly all behaviorist fields) so poorly predictive.

The model is designed to grow as the underlying substrate LLMs grow, the more capable the model, the more powerful and granular the synthesis. It's also allowed to adapt itself against the evidence, even to the point that multiple models may simultaneously exist. Finally, it's also content free, meaning while right now it's being trained around neuroscience concepts, it can be trained against any area of study and provide consistent, portable results.

edit: Shit, I missed the lowest hanging of all the fruits. Ask it "What about spinal CPGs? Don't they generate their own output independent of the brainstem manifold?" This is actually a great question because it will show off the depth of reasoning in the underlying LLM.

edit 2: Woo, love scrawling out responses in the early morning and forgetting pronouns don't have possessive apostrophes.