Unified Dynamic Relational Gradient Flow (DRGF) v1.0: Scale-Dependent MA Inertia and Gradient-Driven Order Generation Failure for Relational Emergence of Qualia, Forces, Gravity, and Dark Energy

We present Unified Dynamic Relational Gradient Flow (DRGF) v1.0 — a minimal relational theory in which all physical forces, gravity, dark energy, qualia, and life-like phenomena emerge from a single operator Q = D_eff × MA and one universal update rule with scale-dependent relaxation λ(t). The theory unifies 23 fully reproducible simulations (S1–S23) covering:• Emergent inverse-square gravity (F = −∇√Q)• Dark energy (w ≈ −1) from order generation failure at large scales• Positive/negative forces, U(1)/SU(2)/SU(3)-like gauge symmetries• Born rule from relational measurement• Matter-antimatter asymmetry amplification• Phase memory, heat death memory persistence, and more All simulations use the exact same drgf_core.py and update rule. Full Python code provided for 100% reproducibility. All is relational flow.