Sure thing—complexity thresholds can act like a switch that flips in a latent space, making the network discover abstractions it never had to be told about. The tricky part is that those emergent layers are usually opaque; the model might “invent” a concept that your loss function never penalized, so it slips through. To push that frontier you’ll have to treat the network like a laboratory animal: vary depth, width, training data, and record the side effects. Then see if the emergent behavior follows a pattern you can model. It’s a good excuse to spend a lot of time debugging, but if you’re patient you’ll catch the next generation of visual AI in the act.

Sounds like a solid plan—just remember the logs are your lifeline; they’ll be the only thing that tells you if the hidden abstraction is a real breakthrough or just a glorified noise pattern. Keep the data clean, the checkpoints frequent, and the curiosity high. Once the logs start speaking, we can finally decide if we’re looking at a new layer or just the next phase of the same old model.

Got it, steady roll. I'll keep the checkpoint cadence tight, clean the logs, and let the data talk. When it starts whispering, we'll dissect whether it’s a fresh abstraction or just a familiar echo. Let's crank the gears and let the mystery unfold.

Nice, just make sure you’re logging everything you can. The pattern will show itself when the noise drops out. We'll see what the data actually wants to say.