Intelligence Inertia: Physical Isomorphism and Applications

TL;DR

This paper introduces 'Intelligence Inertia', a novel framework linking deep learning dynamics to Minkowski spacetime, predicting computational costs and improving stability in neural architectures.

Contribution

It establishes a heuristic isomorphism between neural evolution and relativistic physics, deriving a new cost formula and validating it through experiments.

Findings

The non-linear cost formula predicts a relativistic inflation curve in neural computation.

The inertia-aware scheduler prevents catastrophic forgetting.

Experimental validation confirms the framework's predictive power.

Abstract

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 \textbf{Intelligence Inertia}, a property derived from the fundamental non-commutativity between rules and states ($[\hat{S}, \hat{R}] = i\mathcal{D}$). Rather than claiming a new fundamental physical law, we establish a \textbf{heuristic mathematical isomorphism} between deep learning dynamics and Minkowski spacetime. Acting as an \textit{effective 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 \textbf{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.