The Universal Descent Principle (LUDC): A Recurrent Kinetic Bound on Entropy Reduction in Adaptive Systems

The Universal Descent Principle (LUDC) proposes a recurrent physical bound on the rate of entropy reduction in computational and self-organizing systems. **Key Inequality:** -dS/dt ≤ κ * C(t) * P(t) where C(t) represents structural conductance and P(t) operational power. Extending the Universal Stability Principle (USL), LUDC provides a measurable kinetic constraint on ordering rates across domains—from combinatorial algorithms (SAT, TSP) to dynamical models (machine learning, sandpile automata). Simulations show the inequality holds with violations below 5% and κ ≈ 1, suggesting universal energetic limits on entropy reduction and computational efficiency. This work bridges informational geometry, stochastic thermodynamics, and computational complexity, offering a testable physical perspective on problems like P vs NP. Here, "universal" refers to the consistent emergence of this bound in disparate domains (thermodynamic, combinatorial, learning, dissipative) without specialized tuning.