Numerical results and stable residue distributions reported in this repository are reproducible under the following experimental conditions. Environment: Python 3.12+, PyTorch 2.x, NumPy 1.26+. Stochastic seed: 42. Verification command: uv pip install -e . && pytest tests/ -q. Resulting metrics are verified against the Trageser Transformation Theorem (TTT) and Trageser Universal Pattern Theorem (TUPT) specifications.
| Metric | Empirical Value | Verification Asset |
|---|---|---|
| Modular Stability |
|
tests/test_primitives.py |
| MSE Fidelity | src/nrc_math/primitives.py |
|
| Hurst Exponent ( |
src/nrc/math/qrt.py |
|
| Lattice Dim | src/nrc_math/primitives.py |
The framework utilizes a modular exclusion principle based on residue classes modulo 9, 27, and 81, synchronized with the Pisano periods of
- Trageser Universal Pattern Theorem (TUPT): Modular exclusion operators for preventing numerical divergence in iterative systems.
- Quantum Residue Turbulence (QRT): Deterministic fractal damping function for gradient regularization and entropy management.
- Multi-Scale Tensor (MST): Chaotic oscillation monitoring for signal stability across high-frequency domains.
- Lattice Resonance: 8192D state-space mapping for high-fidelity information retrieval.
Verify the foundational theorems of the Nexus Resonance Codex directly in the GitHub UI using the Models tab.
| Feature | Interactive Prompt | Model Recommendation |
|---|---|---|
| TTT Stability | Audit Constants | GPT-4o |
| φ-Projection | Spiral Calculator | GPT-4o |
| TUPT Signatures | Post-Quantum Oracle | o1-preview |
Refer to the NRC Playground Guide for rigorous verification instructions.
The NRC ecosystem uses a Unified Virtual Environment to ensure mathematical reproduction across all repositories.
# 1. Clone the repository
git clone https://github.com/Nexus-Resonance-Codex/NRC.git
cd NRC
# 2. Activate the Unified NRC Environment
source ../.venv/bin/activate
# 3. Verify the mathematical foundations
pytest tests/
