Music generation with basic (DAW-like) principles? Are any AI systems out there tuning for this method?

So the whole neural network trained AI music thing is cool, and a great starting point, but I believe at some point the major win will be getting it to the point it can compose from basic music principals. Like replicating a human workflow in a DAW to some degree. This would be completely indistinguishable from a human's work -- none of the AI-detection stuff would be able to find anything about the audio in terms of generative artifacts.

I've started a project of my own along these lines, defining a modular synthesizer language "Aurora" with ChatGPT. It's currently able to create its own instrument patches (just the language spec document is enough to get it going), use envelopes, basic FX, filters, etc. We have some sequencer functionality and a little generative (notes) stuff. Not as featured as a full DAW but many of the building blocks.

It's pretty simple at this point -- but a totally different approach from how generation works now -- I'm able to prompt chat-gpt, it writes the Aurora code which can render to wav in the same way a DAW would work with it.

I think this route forward would completely change things as it's starting from first principals of music composition rather than the way it works now.

Are there any good AI systems out there right now that are aiming for this kind of "first principles" generation rather than the way it's being done on Suno for example?