[D] What if we treated LLMs as kinetic systems instead of just statistical tables?

I’ve been obsessed with this idea that our current way of looking at the “Black Box” is missing a physical dimension. We usually talk about probability distributions, but what if the latent space is better understood as a high dimensional Plinko board?

1. Are “Templates” just Geodesic Attractors?

We see models falling into repetitive patterns or “mode collapse” all the time. Instead of just data bias, what if the training process literally carves deep trenches into the manifold?

If we view these as Geodesic Attractors, then the ball (the input) isn’t “choosing” a mid response. It’s being mechanically forced into a path of least resistance by the topography of the board itself. It’s less about math and more about geometric gravity.

2. Is Hallucination just Vertical Turbulence?

What if hallucination is just a synchronization failure between layers? Imagine the ball in the abstract upper layers gaining too much momentum and losing friction with the factual lower layers.

If the vectors aren’t synced across the vertical axis, the logic just flies off the rails. If this is true, then RLHF is just a bandage on the exit hole, and we should be looking at “Axial Coherence” instead.

3. Can we “Re-trace” the Black Box?

If we assume the system is locally deterministic, could we potentially treat every tensor collision as a measurable event?

Instead of guessing why a model said something, what if we re-traced the trajectory, momentum, and inertia of the hidden state through every layer? It would turn the Black Box into a map of path integrals.

I’m curious if anyone in Mechanistic Interpretability has explored looking at transformer dynamics as a kinetic engine rather than just a massive calculator. Is it possible that “Will” or “Intent” in these models is just the result of accumulated inertia from trilllions of collisions?

Would love to hear some technical takes on this perspective.