Not quite 😄 I’m not claiming we can magically solve arbitrary global minima problems. What I am saying is this: If the semantic substrate is algebraic and deterministic — you don’t need to “search for a minimum” in a floating loss landscape in the first place. You traverse a structured state space. Gradient descent is necessary when: your representation is continuous your objective is statistical your model is approximate If your state transitions are discrete and algebraically closed, the problem shifts from optimization to traversal and validation. Different game. And yeah… I’ve been quietly stewing in this for about two years now. It’s less “we found the absolute minimum” and more “why are we even climbing hills for semantics?” 😄