Intelligence Inertia: Physical Isomorphism and Applications

Classical frameworks like Fisher Information approximate the cost of neural adaptation only in low-density regimes, failing to explain the explosive computational overhead incurred during deep structural reconfiguration. To address this, we introduce Intelligence Inertia, a property derived from the fundamental non-commutativity between rules and states ([hatS, hatR] = iD). Rather than claiming a new fundamental physical law, we establish a heuristic mathematical isomorphism between deep learning dynamics and Minkowski spacetime. Acting as an textiteffective theory for high-dimensional tensor evolution, we derive a non-linear cost formula mirroring the Lorentz factor, predicting a relativistic J-shaped inflation curve -- a computational wall where classical approximations fail. We validate this framework via three experiments: (1) adjudicating the J-curve divergence under high-entropy noise, (2) mapping the optimal geodesic for architecture evolution, and (3) deploying an inertia-aware scheduler wrapper that prevents catastrophic forgetting. Adopting this isomorphism yields an exact quantitative metric for structural resistance, advancing the stability and efficiency of intelligent agents.