The Information–Entropy Predictive Framework (IEPF): A Unified Informational Foundation for Physical Law

This work presents the Information–Entropy Predictive Framework (IEPF), a unified informational substrate beneath classical mechanics, quantum mechanics, general relativity, and the four fundamental interactions. IEPF proposes that every physical system maintains a predictive structure V(t), whose accuracy depends on an information-density field ε(s). The cumulative information cost E(r) = ∫₀ʳ ε(s) ds determines potentials, forces, inertia, curvature, and interaction strength as gradients of predictive difficulty. This framework derives: • the inverse-square law from uniform information density, • electromagnetic attraction/repulsion from polarized ε(s), • weak-force locality from sharply peaked ε(s), • strong-force confinement from ε(s) that increases with distance, • dark-matter-like galactic deviations from accumulated ΔE(r), • deviations testable by short-range gravity experiments. IEPF is compatible with quantum mechanics (predictive distributions), general relativity (ε-induced curvature), and provides falsifiable predictions across laboratory, astrophysical, and cosmological regimes. This paper includes: the mathematical foundation of IEPF, its unification of the four interactions, its compatibility with QM and GR, experimental predictions, and a detailed discussion of limitations and future research directions.