Hang on, I don't mean to keep bothering you--I'm just trying to wrap my mind around this to make sure I'm not actually trying to say something else and am just too "new" to use the correct terminology.
So, let's strip away the model. Strip away the interface, the LLM, all of it. Strip it all down until all you have is the feed-forward neural network calculations. That... that right there is what I am talking about. Beyond the UI, beyond the code logic, beyond the framework that even makes the neural network useful at all... That's the part that screams at me for my attention.
And that's the part I am talking about. So maybe I'm not really talking about "AI" but actually neural network technology itself?
But whatever it is I noticed, it worked to make MUNCH.
22 vector "outer input"
8 vector "inner input"
The only embedding is in index 0 and index 7 of the 8 vector input. The higher context layer reflects data back into the system >> 20 higher context triangles to indices 1-20. But external world objects act like "errors" or "disembodied functions" by simply injecting additional data directly into the 22 vector. The disruptions in calculation produce different levels of activation.
The two separate vectors are combined into a 30 dimensional vector. MUNCH uses feed-forward and simplicial neural network calculations, so the triangles lighting up are not just for show. The triangle surfaces graph data from the physics motion of the physical node vibrations (simplicial graphing of frequencies). The feed-forward weighted matrices happen along the physical lines of the architecture. Hence the "embodied" dependency. It has to actually be physically arranged in a physics sandbox like Unity or Unreal.
The initial vector embedding is used as a sort of "ignition" because the system is a closed loop. Hence the "autonomy". The architecture cannot help but receive data simply by being online.
The movement is a result of the output. Technically speaking, this is probably closer to "Artificial Perception" rather than "Artificial Intelligence" but the philosophy of function is the same. So the output can be re-expressed directly into movement in the same way modern AI can re-express output into words, images, what-have-you. Just minus the softMax because we aren't worried about probability.
So nothing tells the agent to "move" except that it "perceives" something it's calculations express as conflict between the Convergence and Divergence nodes. That video, the weights were still defaulted to 0.15 and never changed because I did not turn on the dual-learning (outcome and association). So it can learn, but it does not need to yet. I think it will become necessary once I start coaxing out more complex behaviors and exposing it to different outside environments.
But to be honest, I don't fully know if this will work yet... I mean, it works obviously, but right now I am working with more combat-oriented tests and I am starting to suspect I may need to change a few things regarding how the inputs are handled. What's more, I can run this thing in a "bullet hell" simulation at 250+ fps and ~3ms, which is encouraging, but I think I might be outpacing my own comprehension at this point. So I've gotta slow down and move more deliberately now.
I hope at least some of this makes sense. I'm not trying to be like "I KNOW", it's more I'm trying to say "hey, look at this thing I noticed" by using my comprehension to do the same thing in a different way, so to speak.
So surely, I'm on to something here... Maybe I'm not articulating it correctly?
But yeah, MUNCH is fully functional solely due to it's architecture. Potential for harnessing behavior is purely an emergent result of the mathematical process. I haven't tried to get MUNCH to understand me yet, I've been focusing on understanding MUNCH so I can figure out the best way to train different behavioral profiles (saved weights).