Version: 1.0 (MIC v5 compatible)
Author: Yuragi Mito
Affiliation: Rokkotsu Bar
License: CC BY 4.0
MINIS is a theoretical framework consisting of three interdependent formal models:
- MIC (Mirror-Inversion Cycle): Hamiltonian cycles in XOR difference space (Z₂)^n
- ICM (Inversion-Closure Model): Depth-limited inversion structures
- NSPM (Non-Stochastic Probability Model): Deterministic probability emergence
These models independently specify structural behavior in binary state spaces, with observed interdependence.
Latest Release: January 2026
MIC v5 introduces fundamental units theory with three generators (k=1,2,3):
- Fundamental units: k ∈ {1, 2, 3}
- Scale invariance: Verified across 2-15 bits
- XOR difference space: (Z₂)^n algebraic structure
- Convergence: k=3 patterns stabilize at ~85.7% smoothness
Complete ORDER sequences (2-9 bits):
- Available in
data/directory - All states enumerated
- Computationally verified
Statistical verification (12-15 bits):
- ΔH distribution confirmed
- Convergence demonstrated
- See
docs/MIC_v5.pdffor details
minis/
├── README.md # This file
├── LICENSE # CC BY 4.0
│
├── docs/ # Papers (PDF)
│ ├── MIC_v5.pdf # Latest (Jan 2026)
│ ├── ICM.pdf
│ ├── NSPM.pdf
│ └── Structural_Notes.pdf
│
├── data/ # Complete ORDER data
│ ├── mic_2bit_v5.txt # 4 states
│ ├── mic_3bit_v5.txt # 8 states
│ ├── mic_4bit_v5.txt # 16 states
│ ├── mic_5bit_23_v5.txt # 32 states (2-3 pattern)
│ ├── mic_6bit_v5.txt # 64 states
│ ├── mic_7bit_v5.txt # 128 states (2-2-3 pattern)
│ ├── mic_8bit_v5.txt # 256 states
│ └── mic_9bit_v5.txt # 512 states
│
├── core/ # Core implementations
│ ├── mic_model.py # MIC v5 implementation
│ ├── icm_model.py # ICM implementation
│ ├── nspm_model.py # NSPM implementation
│ └── coupled_minimal.py # Coupled system
│
├── examples/ # Usage examples
│ ├── verify_mic_v5.py # Verify v5 data
│ ├── generate_mic_v5.py # Generate patterns
│ ├── core_structure_demo.py # Core concepts
│ └── ...
│
└── tests/ # Test suite
├── test_mic_v5.py
├── test_icm.py
└── test_nspm.py
python examples/verify_mic_v5.pyfrom core.mic_model import MICv5
# Generate 6-bit pattern
mic = MICv5(n_bits=6)
pattern = mic.generate()
print(f"Generated {len(pattern)} states")# Load from data files
with open('data/mic_6bit_v5.txt', 'r') as f:
order = [line.strip() for line in f]
# Verify
from examples.verify_mic_v5 import verify_pattern
verify_pattern(order)git clone https://github.com/yourusername/minis.git
cd minis
pip install -r requirements.txt # pytest onlyNo installation required for basic use.
- MIC v5:
docs/MIC_v5.pdf- Complete theory with 2-15 bit verification - ICM:
docs/ICM.pdf- Inversion-closure framework - NSPM:
docs/NSPM.pdf- Deterministic probability model - Notes:
docs/Structural_Notes.pdf- Additional technical details
MIC v5 Theory:
- XOR difference space (Z₂)^n
- Fundamental units: k ∈ {1,2,3}
- Scale invariance
- Hamming distance patterns
Data Format:
- Plain text files
- One state per line
- Binary strings (e.g., "111111")
- Circular ordering (last → first)
pytest tests/All patterns verified with:
- ✓ Complete state coverage
- ✓ Hamming distance constraints
- ✓ Cycle closure
- ✓ Statistical properties
- v5 (Jan 2026): Fundamental units theory, 2-15 bit verification
- v4 (2025): Complete mathematical proof for 6-bit
- v3 (2025): Corrected distance set {1,2,3,4}
- v1-v2 (2025): Initial 6-bit patterns
- v1.0 (2026): MIC v5 integration, complete data
If you use MINIS in your research, please cite:
@software{mito2026minis,
author = {Mito, Yuragi},
title = {MINIS: MIC-ICM-NSPM Interdependent System},
year = {2026},
publisher = {Zenodo},
version = {2.0},
doi = {10.5281/zenodo.18367614},
url = {https://github.com/yourusername/minis}
}
@article{mito2026mic,
author = {Mito, Yuragi},
title = {Mirror-Inversion Cycle (MIC) v5: Scale-Invariant Hamiltonian Patterns in XOR Difference Space},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.YYYYYYY}
}Creative Commons Attribution 4.0 International (CC BY 4.0)
You are free to:
- Share: copy and redistribute
- Adapt: remix, transform, and build upon
Under the terms:
- Attribution: give appropriate credit
See LICENSE file for details.
- Author: Yuragi Mito (三統揺)
- Affiliation: Rokkotsu Bar
- GitHub: [Repository URL]
Development supported by extensive dialogue with AI systems (ChatGPT, Claude, Gemini) serving as a networked exploratory framework for structural pattern analysis.