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.pdf for 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.py

from 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 only

No 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.