Definitely not MCP, as that pulls info into the context. Unless contexts become REALLY big so that I can add 10B in swift knowledge, that's not gonna help me.
Possible RAG? I don't know enough about how that works, but I think that's not quite it either. I don't want to import facts like "the swift standard library contains a reverse array function", i more want to import knowledge - e.g. the parameters used to generate the text to reverse an array in swift.
Tool calling wouldn't do it either. You'd have to encode every single possible bit of useful info into the tool call, and the tool response would have to encode every piece as well (variable names, function scopes, types defined in other files, etc). E.g. how does it find a bug, if you have to pass understanding back and forth between the brain that understands debugging and the brain that understands THIS code?
I think having unused or rarely used weights doesn’t influence the results as poorly as RAG injecting irrelevant facts.
It sounds to me like some sort of “dynamic MoE” where you can add/create or remove experts on the fly.
I think what you’re describing is the closest approximation we reasonably have right now though.
There is nothing optimal about needing a few billion more parameters to be able to piece together probable answers that can be asserted by querying an oracle.
> I think having unused or rarely used weights doesn’t influence the results as poorly as RAG injecting irrelevant facts.
Those aren't free. The more parameters you add, the higher the computational cost required to train and prompt a mode.
And all for what? To piece together info that you can just query from a data source?