Abstract This report introduces a system-agnostic framework for modeling recursive dynamics between oppositional agents. The core structure consists of a dual-operator system: one representing differential tension (R), and another representing an asymptotic regulation function (R!) that trends toward balance over time. Collapse and divergence thresholds are defined in terms of signal deltas and derivative magnitude, enabling this model to generalize across logical, physical, biological, computational, and emergent systems. By treating resolution as a function of bounded difference and divergence as an unrestrained recursive acceleration, this framework captures the essential logic behind system stability, bifurcation, and convergence. Normed drift serves as a measure of deviation, while the reciprocal asymptotic operator models return-to-equilibrium trajectories. These components together allow the system to interpret, react, and stabilize through feedback-driven dynamics. The Stone Recursive Law A minimal, recursive equation for universal self-governance through tension resolution. R = R_1 - R_0 • R_0 = Baseline / inhibited force • R_1 = Active / opposing force • R = Recursive internal tension signal 1. Detects system imbalance 2. Resolves internal tension 3. Supports decision, adaptation, or collapse 4. Applies across domains (AI, biology, logic, ethics, engineering) Its novelties are extensive. Minimal form, recursive logic, Domain-agnostic, Enables systems to self-regulate, self-correct, and self-collapse, Combines biological inhibition with symbolic feedback logic, 3D Visualization of recursive tension over time, Drift and normalization logic from (0,0,0), Modular Python timeline tracker system A universal recursive law that gives any system the ability to measure, resolve, and adapt through internal tension—enabling self-governance, decision-making, and collapse.