There's one great example that shows how physics can help upgrade AI world models, and it's LeHami. @ylecun, @randall_balestr and @RominaYalov brought Hamiltonian mechanics into JEPA to preserve phase-space volume and prevent latent collapse during long-term predictions. Why exactly Hamiltonian mechanics? • Variables are coordinates q and momenta p (not velocities like in Lagrangian mechanics), giving LeHami paired first-order equations for updating its states. • This makes Hamiltonian H a central function. It's often total energy: kinetic + potential, in classical mechanics. • LeHami learns this H function with kinetic-like K(p) and potential-like V(q, u) components, where u represents actions. • A symplectic leapfrog integrator uses this function to predict how states evolve depending on the actions taken, while no constrains are violated. To understand this better it's useful to think of a pendulum. It can pass the same position in either direction. But physical position alone won’t predict what comes next, you also need momentum. LeHami follows a similar principle, but works with learned representations of position and momentum. → And most importantly, it builds a constraint into how those predictions evolve. Maybe that's exactly the kind of thing world models like JEPA need now - one less way to go wrong. We've taken a much deeper look at LeHami's design and results in this guide. Plus, there's a little mechanics lesson for AI and physical AI folks that's worth learning →