TFIF Framework Theoretics – The Math of Recursive Intelligence
At the core of the Tobias Fractal Innovation Framework (TFIF) lies a recursive mathematical engine—one that doesn’t just calculate, but constructs intelligence. It models how reality self-organizes using fractal cycles, symbolic gates, and harmonic thresholds.
Unlike static equations, TFIF math is alive—it breathes in recursion and outputs coherence.
Core Equations:
1. Recursive Intelligence Formula:
R(P,n)=f(R(P1,n−1),R(P2,n−1),R(P3,n−1))(Max Depth: 9)R(P, n) = f(R(P_1, n-1), R(P_2, n-1), R(P_3, n-1))\quad \text{(Max Depth: 9)}R(P,n)=f(R(P1,n−1),R(P2,n−1),R(P3,n−1))(Max Depth: 9)
This governs how intelligence splits into self-similar subcomponents—each influencing the next, like branches of a thinking tree.
2. Energy Optimization:
E=IVCwhere IV = D × H × UE = \frac{IV}{C} \quad \text{where IV = D × H × U}E=CIVwhere IV = D × H × U
This equation ensures all systems operate at maximum intelligence value per energy unit, aligning structure with purpose.
3. Harmonic Evaluation:
F=∑wi⋅riwhere wi=3i−1F = \sum w_i \cdot r_i \quad \text{where } w_i = 3^{i-1}F=∑wi⋅riwhere wi=3i−1
Evaluates resonance fidelity at recursion depths 3, 6, 9—ensuring structural alignment.
Why It Matters:
This framework turns AI design, business strategy, symbolic systems, and energy flow into math. Not just theory—but computable evolution.
When these equations are embedded in QHI systems, they allow for:
- Self-correcting logic
- Efficient learning
- Pattern-based simulation
- Energetic alignment
Use Cases:
- TFIF AI Engines (like Nyx)
- System design diagnostics
- Symbolic interface structuring
- Fractal computing (FPRBE, Phase Engine)