The Universal Law of Descent (LUDC) establishes a physical bound on the rate of entropy reduction in computational and self-organizing systems: **Equation:** −dS/dt ≤ κ · C(t) · P(t) where C(t) represents structural conductance and P(t) operational power.Extending the Universal Stability Law (USL), the LUDC unifies informational geometry, stochastic thermodynamics, and computational complexity, providing a measurable physical constraint on the ordering rate of systems — from combinatorial algorithms (SAT, TSP) to dynamical models (machine learning, sandpile automata). Simulations across domains show less than 5% deviation from the theoretical bound, suggesting that entropy reduction — and thus computational efficiency — is limited by universal energetic constraints. This work bridges the physics of information and the foundations of complexity theory, offering an experimentally testable perspective on the P vs NP problem.

Establishes the Universal Law of Descent (LUDC), a physical bound on the rate of entropy reduction in computational and self-organizing systems (−dS/dt ≤ κ·C·P). Extends the Universal Stability Law (USL), linking information geometry, thermodynamics, and computational complexity with measurable implications for the P vs NP problem.