Recursive AI Feedback Loop Simulator

Control Levers & Gains Math.js Matrix Integrator

Dario Amodei (Anthropic): "I don't think it's a moment in time. I think it's a continuous process... AI improving itself continuously."

Recursive Code Synthesis Speed ($K_c$) 1.40

Synthetic Data Filter Efficiency ($\eta_d$) 0.85

Evaluation Feedback Fidelity ($F_e$) 0.90

Hardware Scaling Decay ($\gamma_h$) 0.12

Benchmark Ceiling Saturation ($S_{max}$) 10.0

Feedback Latency Delay ($\tau$ steps) 1

State Equation: $C_{t+1} = C_t + \Delta t \cdot \left[ K_c \cdot \eta_d \cdot F_{e, t-\tau} \cdot C_t^{1-\gamma_h} \right] \cdot \left(1 - \frac{C_t}{S_{max}}\right)$

✔ Autonomous System Stability Verified

Continuous Loop Network Topology Animated Signal Signal-Flow Pulse

Final Capability Score 8.42 +742% gain over base

Primary Limiting Factor Hardware Scaling Decay exponent $\gamma_h = 0.12$

Loop System Stability Controlled Exponential Eigenvalue $\lambda = 1.08$

Saturation Step Threshold 38 Step where $\frac{dC}{dt} < 0.02$

Real-Time Phase Trajectory & Capability Acceleration 50 Continuous Simulation Cycles

Step-by-Step Telemetry Audit Log

Step	Capability ($C_t$)	Delta Velocity ($\Delta C$)	Synthetic Data Quality	Feedback Fidelity	Status