v2.3.0 - ARI-based SP_clu for all layout types

What's Changed

SP_clu Algorithm Update

• Adjusted Rand Index (ARI): SP_clu now uses ARI instead of simple label matching
• ARI is invariant to label permutation and corrects for chance agreement
• References: Hubert & Arabie (1985), Rand (1971)

SP_clu Scope Change

• Now computed for all layout types (previously returned 1.0 for non-"cluster" layouts)
• SP_total reflects true three-component average across all experiments

Updated Results (n=1,000 trials)

Experiment	Condition	SP (v2.2.0)	SP (v2.3.0)
sp12	Topology p=0.7	0.600	0.343
sp12	Metric k=0.7	0.816	0.719
sp30	λ=0.6 (Synth)	0.574	0.301

Key Findings Preserved ✅

• Topology dominance (topology < metric)
• SP-SSC independence
• λ trade-off (SP↓ as SSC↑)
• Rotation invariance

Migration Notes

• Breaking change: SP_total values differ from v2.2.0
• SP_adj and SP_ord unchanged
• Re-run experiments to obtain v2.3.0 results

---

Full Changelog: v2.2.0...v2.3.0

v2.2.0 - Rotation-Invariant SP_ord & THINGS Demo

What's New

🔧 SP_ord Rotation Invariance (Critical Fix)

• Refactored SP_ord to use pairwise distance rank comparison (Kendall's τ)
• Previous implementation used x-axis sorting, which failed under rotation
• Verified: SP ≈ 0.94–0.97 for all rotation angles (previously 0.66–0.86)

🧪 THINGS Demo

• New Colab notebook: demos/things/THINGS_SSC_SP_Demo.ipynb
• Analyzes human similarity embedding (1,854 object concepts, 120 dimensions)
• Compares MDS, t-SNE, UMAP across SSC and SP metrics

📊 Results Archive

• Complete experiment results (1,000 trials per condition)
• Summary CSVs in results/v2.2.0/ for all experiment series (I-2, O-2, O-3, O-4)

Key Results (n=1,000)

Rotation Angle	SP (v2.1.0)	SP (v2.2.0)
0°	0.859	1.000
90°	0.673	0.968
180°	0.761	0.967

Full Changelog

See CHANGELOG.md

v2.1.0 - SP Measurement System with Complete O-4 Validation

v2.1.0 - SP Measurement System with Complete O-4 Validation

Release Date: 2025-11-26

This release provides the complete empirical foundation for Intelligence Relativity, featuring dual measurement systems (SSC and SP) and comprehensive validation of four fundamental observations (O-1 through O-4).

---

🎉 Highlights

Two Complementary Measurement Systems

System	Version	Metrics	Purpose
SSC	v1.x	Semantic-Spatial Correlation	O-1 natural orthogonality
SP	v2.x	Structural Preservation	O-2, O-3, O-4 validation

Key Innovation: SSC and SP measure independent dimensions—validating that semantic correlation and structural preservation are orthogonal metrics.

SP (Structural Preservation) Framework

A complete measurement system for evaluating structural properties:

• SP_adj: Adjacency preservation (k-NN graph overlap)
• SP_ord: Order preservation (rank correlation)
• SP_clu: Clustering preservation (label stability)

O-4 Value-Gated Coupling: Complete Validation

Four experimental conditions demonstrating the SSC-SP trade-off:

Condition	Layout	Embeddings	Key Finding
sp30	Random	Synthetic	Baseline trade-off
sp31	Random	BERT	Real-world validation
sp50	Grid	Synthetic	Structured control
sp51	Grid	BERT	Dramatic trade-off from high SP

Key Discovery: The value gate parameter λ controls the emergence of meaning from structure, with clear linear and saturation regimes.

---

✨ Package Contents

Core Modules

SSC System (src/core/)

• ssc_computation.py - SSC measurement
• deterministic.py - Reproducibility framework
• generators.py - Data generation
• statistics.py - Statistical tools

SP System (src/core_sp/)

• sp_metrics.py - SP computation engine
• ssc_wrapper.py - SSC integration for v2
• value_gate.py - Value-gated coupling (PCA-based morphing)
• generators.py - Semantic embedding generation
• topology_ops.py - Topological disruption operations
• metric_ops.py - Metric transformations
• deterministic.py - Environment verification & manifest generation

Experiments

SSC Experiments (experiments/) - 17 experiments

• O-1 baseline validation
• Layout/metric/seed robustness
• BERT and multilingual validation

SP Experiments (experiments_sp/) - 18 experiments

I-2: Measurement System Validation (4 experiments)

• sp00: Identity/isometry baseline
• sp01: Full destruction benchmark
• sp02: Topology rewire curves
• sp03: Layout robustness

O-2: Topological Dominance (4 experiments)

• sp10: Metric invariance
• sp11: Topology sensitivity
• sp12: Topology vs metric comparison
• sp13: Layout generalization

O-3: SP-SSC Independence (3 experiments)

• sp20: Coordinate noise tolerance
• sp21: Semantic noise tolerance
• sp22: Mixed noise grid

O-4: Value-Gated Coupling - Random Layout (2 experiments)

• sp30: λ-sweep (synthetic embeddings)
• sp31: λ-sweep (BERT embeddings)

O-4 Extra: Value-Gated Coupling - Grid Layout (2 experiments)

• sp50: λ-sweep (synthetic embeddings on grid)
• sp51: λ-sweep (BERT embeddings on grid)

Robustness (3 experiments)

• sp40: Dimension/sample size robustness
• sp41: Layout/topology robustness
• sp42: k-NN parameter robustness

Demos (demos/dr_evaluation/)

Dimensionality reduction evaluation on MNIST:

python demos/dr_evaluation/run_dr_demo.py

Outputs:

• results.csv - Quantitative comparison
• comparison_bar.png - SP/SSC comparison chart
• embeddings_scatter.png - Visual inspection

Finding: t-SNE shows highest SP (0.43), PCA shows highest SSC (0.49)—confirming metric independence.

Testing

• 170 tests covering both measurement systems
• Full cross-implementation validation
• Deterministic reproducibility verification

---

📊 Experiment Results

O-1: Natural Orthogonality (SSC System)

SSC: -0.0025 ± 0.0736
90% CI: [-0.0063, 0.0013]
✅ Natural orthogonality confirmed across layouts/metrics/seeds

O-2: Topological Dominance

Topology disruption: SP drops sharply
Metric distortion: SP remains stable
✅ Phase > Metric confirmed

O-3: SP-SSC Independence

Coordinate noise: SP↓, SSC stable
Semantic noise: SP stable, SSC varies
✅ Independent axes confirmed

O-4: Value-Gated Coupling

Random Layout (BERT - sp31):

λ	SP	SSC
0.0	0.816 ± 0.000	-0.000 ± 0.013
0.4	0.624 ± 0.012	0.076 ± 0.071
1.0	0.515 ± 0.017	0.718 ± 0.000

Grid Layout (BERT - sp51):

λ	SP	SSC
0.0	0.845 ± 0.000	0.002 ± 0.072
0.2	0.599 ± 0.010	0.198 ± 0.079
1.0	0.516 ± 0.017	0.521 ± 0.000

Key Insight: Grid layout reveals trade-off more dramatically—SP drops 0.25 at λ=0.2 while SSC jumps to 0.20.

Summary Files

All results available in outputs_sp/:

• summary_all_I2.csv - Instrument validation
• summary_all_O2.csv - Topological dominance
• summary_all_O3.csv - Independence
• summary_all_O4.csv - Value gate (random layout)
• summary_all_O4_extra.csv - Value gate (grid layout)
• summary_all_robust.csv - Robustness tests
• env.txt - Environment specification

---

🔧 Technical Specifications

Environment

• Python: 3.10.19
• NumPy: 1.24.3
• SciPy: 1.10.1
• scikit-learn: For PCA in value gate

Deterministic Execution

All experiments execute with perfect reproducibility:

• Fixed seeds for all random operations
• Single-threaded BLAS (eliminates non-determinism)
• Automatic environment logging
• Word shuffling for BERT trial variability

from src.core_sp import set_deterministic_mode, verify_environment

set_deterministic_mode()
verify_environment("outputs_sp/env.txt")

Environment record:

{
  "python": "3.10.19",
  "numpy": "1.24.3",
  "scipy": "1.10.1",
  "blas_threads": {"OPENBLAS_NUM_THREADS": "1"}
}

---

📦 Installation

# Clone repository
git clone https://github.com/HIDEKI-SQ/intelligence-relativity.git
cd intelligence-relativity

# Install dependencies
pip install -r requirements.txt

# Run tests (170 tests)
pytest tests/ -v

# Run SSC baseline (O-1)
python -m src.experiments.exp_00_baseline

# Run SP baseline (I-2)
python -m src.experiments_sp.i2_sp_instrument.sp00_identity_isometry

# Run O-4 experiments
python -m src.experiments_sp.o4_value_gate_tradeoff_sp.sp30_lambda_sweep_synth
python -m src.experiments_sp.o4_extra_sp.sp51_lambda_tradeoff_grid_bert

# Run demo
python demos/dr_evaluation/run_dr_demo.py

---

🔬 Research Applications

This package provides paper-ready data for:

Paper	Observation	Data Source
I-1	Deterministic Reproducibility	SSC system validation
I-2	SP Measurement System	sp00-sp03
O-1	Natural Orthogonality	exp_00 + BERT validation
O-2	Topological Dominance	sp10-sp13
O-3	Stress Tolerance	sp20-sp22
O-4	Value-Gated Coupling	sp30-sp31, sp50-sp51

All experiments include:

• n=1000 trials
• 95% confidence intervals
• Environment specifications
• Deterministic reproducibility

---

📚 Documentation

• README: Complete package documentation
• CHANGELOG: Version history with technical details
• Experiments: Documented in each experiment file
• Demos: Self-contained with example outputs

---

📞 Contact

Author: Hideki
Email: hideki@r3776.jp
Repository: https://github.com/HIDEKI-SQ/intelligence-relativity

For questions or bug reports, please open an issue.

---

🔗 Version History

Version	Date	Highlights
v2.1.0	2025-11-26	O-4 grid layout, value gate refactoring
v2.0.0	2025-11-22	SP measurement system
v1.1.2	2025-11-16	Environment standardization
v1.0.0	2025-11-06	Initial SSC system

Full Changelog: https://github.com/HIDEKI-SQ/intelligence-relativity/blob/main/CHANGELOG.md

---

Complete empirical foundation for Intelligence Relativity

Deterministic reproducibility across 35 experiments (Python 3.10.19, NumPy 1.24.3)

v2.0.0 - SP Measurement System & Dimensionality Reduction Demo

v2.0.0 - Major Release: SP Measurement System

Release Date: 2025-11-22

This major release introduces the SP (Structural Preservation) measurement system, complementing the existing SSC system with comprehensive tools for measuring structural preservation in cognitive and spatial representations.

---

🎉 Highlights

New Measurement System: SP (Structural Preservation)

A complete measurement framework for evaluating how well transformations preserve structural properties:

• SP_adj: Adjacency preservation (k-NN graph overlap)
• SP_ord: Order preservation (rank correlation)
• SP_clu: Clustering preservation (label stability)

Key Innovation: SP and SSC measure independent dimensions—validating that structural preservation and semantic correlation are orthogonal metrics.

Dimensionality Reduction Demo

Real-world demonstration on MNIST comparing t-SNE, UMAP, and PCA:

• 📊 Publication-ready comparison figures
• 📈 Clear visualization of SP vs SSC tradeoffs
• ✅ Confirms metric independence empirically

Finding: PCA shows lowest SP (0.23) but highest SSC (0.49), while t-SNE shows highest SP (0.43) with moderate SSC (0.38)—proving the metrics capture distinct phenomena.

---

✨ What's New

Core Modules (src/core_sp/)

• sp_metrics.py - SP computation engine
• ssc_wrapper.py - SSC integration for v2
• value_gate.py - Value-gated coupling mechanism (O-4)
• generators.py - Semantic embedding generation
• topology_ops.py - Topological disruption operations
• metric_ops.py - Metric transformations
• deterministic.py - Environment verification & manifest generation

Experiments (experiments_sp/)

16 new experiments validating O-2, O-3, O-4, and I-2:

I-2: Measurement System Validation (4 experiments)

• sp00: Identity/isometry baseline
• sp01: Full destruction benchmark
• sp02: Topology rewire curves
• sp03: Layout robustness

O-2: Topological Dominance (4 experiments)

• sp10: Metric invariance
• sp11: Topology sensitivity
• sp12: Topology vs metric comparison
• sp13: Layout generalization

O-3: SP-SSC Independence (3 experiments)

• sp20: Coordinate noise tolerance
• sp21: Semantic noise tolerance
• sp22: Mixed noise grid

O-4: Value-Gated Coupling (2 experiments)

• sp30: λ-sweep (synthetic embeddings)
• sp31: λ-sweep (BERT embeddings)

Robustness (3 experiments)

• sp40: Dimension/sample size robustness
• sp41: Layout/topology robustness
• sp42: k-NN parameter robustness

Demos (demos/dr_evaluation/)

New: Complete dimensionality reduction evaluation demo:

python demos/dr_evaluation/run_dr_demo.py

Outputs:

• results.csv - Quantitative comparison
• comparison_bar.png - SP/SSC comparison chart
• embeddings_scatter.png - Visual inspection

Testing & CI/CD

• 101 new tests for SP system
• 175 total tests (100% pass rate)
• New workflow: run_experiments_sp.yml
• Demo testing: test_demo.yml

Documentation

• NEW: CHANGELOG.md - Complete version history
• UPDATED: README.md - v2 system documentation
• UPDATED: Comprehensive experiment documentation

---

🔧 Technical Improvements

Environment Standardization

All experiments now run on Python 3.10.19 with NumPy 1.24.3:

• ✅ Unified environment across v1 and v2
• ✅ Automatic env.txt generation
• ✅ Perfect reproducibility (std = 0.00)

Deterministic Execution

Enhanced reproducibility features:

from src.core_sp import set_deterministic_mode, verify_environment

set_deterministic_mode()  # Lock all random operations
verify_environment("outputs_sp/env.txt")  # Record environment

Environment record example:

{
  "python": "3.10.19",
  "numpy": "1.24.3",
  "scipy": "1.10.1",
  "blas_threads": {"OPENBLAS_NUM_THREADS": "1"}
}

---

📦 Installation

# Clone repository
git clone https://github.com/HIDEKI-SQ/intelligence-relativity.git
cd intelligence-relativity

# Install dependencies
pip install -r requirements.txt

# Run tests
pytest tests/ -v

# Run demo
python demos/dr_evaluation/run_dr_demo.py

---

📊 Experiment Results

SP System Validation

All 16 experiments completed successfully with deterministic reproducibility:

Series	Experiments	Status
I-2 (Instrument)	4	✅ Complete
O-2 (Topology)	4	✅ Complete
O-3 (Independence)	3	✅ Complete
O-4 (Value Gate)	2	✅ Complete
Robustness	3	✅ Complete

Results available in: outputs_sp/summary_all_*.csv

Demo Results (MNIST)

Method	SP_total	SSC	Key Strength
t-SNE	0.43	0.38	Clustering
UMAP	0.27	0.35	Order preservation
PCA	0.23	0.49	Semantic correlation

---

🔬 Research Impact

This release provides the complete empirical foundation for:

• I-2 Paper: SP measurement system validation
• O-2 Paper: Topological dominance in structure preservation
• O-3 Paper: SP-SSC independence demonstration
• O-4 Paper: Value-gated coupling with SP-SSC tradeoff

Key Theoretical Contribution:
Empirical proof that structural preservation (SP) and semantic correlation (SSC) are orthogonal dimensions, validating the independence assumption in the Relativity Theory of Intelligence.

---

📚 Documentation

• README: https://github.com/HIDEKI-SQ/intelligence-relativity/blob/main/README.md
• Changelog: https://github.com/HIDEKI-SQ/intelligence-relativity/blob/main/CHANGELOG.md
• Experiments: See experiments_sp/ directory
• Demos: See demos/ directory

---

🙏 Acknowledgments

• Complete SP measurement framework validated across 16 experiments
• MNIST demo confirms SP-SSC independence on real data
• Perfect deterministic reproducibility maintained
• Unified environment (Python 3.10.19, NumPy 1.24.3) across all systems

---

📞 Contact

Author: Hideki
Email: hideki@r3776.jp
Repository: https://github.com/HIDEKI-SQ/intelligence-relativity

For questions or bug reports, please open an issue.

---

🔗 Related Releases

• v1.1.2 - SSC system with environment fix
• v1.1.1 - Version-pinned dependencies
• v1.0.0 - Initial SSC system

---

Full Changelog: https://github.com/HIDEKI-SQ/intelligence-relativity/blob/main/CHANGELOG.md

v1.1.2 – Deterministic Reproducibility Package (Publication-Ready)

This release provides the publication-ready, deterministic measurement
infrastructure for semantic–spatial correlation experiments.

Key updates in v1.1.2:

• Finalized repository structure and workflow organization
• Exact dependency pinning (requirements.txt)
• Machine-precision reproducibility (|ΔSSC| < 1e−16)
• All CI tests passing (16/16)
• Full experiment pipeline verified (baseline, dimensionality sweeps,
  sample-size sweeps, distance metrics, spatial layouts, multilingual)
• Regenerable figures and archived datasets

All numerical results remain identical across versions (v1.1.0 → v1.1.2).
This version is recommended for reproducibility and citation.

Zenodo Concept DOI: 10.5281/zenodo.17615992
Version DOI: 10.5281/zenodo.17666805

v1.1.1 - Deterministic Reproducibility Standard for O-1

v1.1.1 - Deterministic Reproducibility Standard for O-1

🎯 Purpose

This release establishes the deterministic reproducibility standard for the O-1 (Natural Orthogonality) study, ensuring bit-for-bit reproducibility of all reported results across computing environments.

🔧 Changes

Core Dependencies - Exact Version Pinning

Pinned exact versions for all dependencies affecting SSC computation determinism:

• numpy: 1.24.3 (was ≥1.24.0)
• scipy: 1.10.1 (was ≥1.10.0)
• pandas: 2.0.3 (was ≥2.0.0)
• matplotlib: 3.7.2 (was ≥3.7.0)
• scikit-learn: 1.3.0 (was ≥1.3.0)

Supplementary Dependencies - Controlled Ranges

Maintained version ranges for non-deterministic components:

• transformers: ≥4.30.0, <5.0.0 (BERT embedding extraction only)
• torch: ≥2.0.0, <3.0.0 (supplementary multilingual validation)

These components are used only in Section 3.4 (multilingual validation) and do not affect the core deterministic SSC measurement pipeline.

Documentation Enhancements

• Added comprehensive reproducibility notes
• Documented CI configuration requirements
• Clarified the distinction between deterministic core and flexible supplementary dependencies

📊 Reproducibility Guarantees

This release ensures:

• ✅ Bit-for-bit reproducibility of all SSC computations
• ✅ Zero standard deviation (std = 0.00) across repeated executions
• ✅ 16/16 CI test passage under deterministic execution contract
• ✅ Cross-implementation drift < 10⁻¹⁶

🔬 Associated Publication

Preprint: Natural Orthogonality Between Semantic and Spatial Structure: Evidence from Large-Scale Null-Correlation Studies

Concept DOI: 10.5281/zenodo.17615992

Version DOI: 10.5281/zenodo.17621148 (to be updated)

🚀 Quick Start

# Clone repository
git clone https://github.com/HIDEKI-SQ/intelligence-relativity.git
cd intelligence-relativity

# Checkout this release
git checkout v1.1.1

# Install exact dependencies
pip install -r requirements.txt

# Verify reproducibility
python -m pytest tests/ --verbose

# Reproduce all experiments (one command)
make reproduce

📋 System Requirements

• Python: 3.10.12 (recommended for exact reproducibility)
• BLAS: Single-threaded (OPENBLAS_NUM_THREADS=1, MKL_NUM_THREADS=1)
• Floating-point: IEEE-754 double precision (float64)

🔗 Related Releases

• v1.1.0: Initial O-1 experimental implementation
• v1.0.0: I-1 measurement infrastructure baseline

📝 Citation

If you use this release in your research, please cite:

@software{hideki2025o1,
  author    = {HIDEKI},
  title     = {Intelligence Relativity: Natural Orthogonality Study (v1.1.1)},
  year      = {2025},
  version   = {v1.1.1},
  doi       = {10.5281/zenodo.17621148},
  url       = {https://github.com/HIDEKI-SQ/intelligence-relativity}
}

✅ Validation Status

• All 16 CI tests passing
• Cross-platform verification (Linux, macOS, Windows)
• Deterministic execution contract verified
• Documentation completeness checked
• Compatible with O-1 manuscript submission

🙏 Acknowledgments

This reproducibility standard builds upon the measurement protocols established in I-1 and extends them to large-scale observational studies.

---

Full Changelog: v1.1.0...v1.1.1

I-1 SSC Instrument v1.0.1 (requirements pinned)

This release provides the reference implementation of the SSC instrument
used in preprint 2025-150-I1 ("Deterministic Reproducibility of
Semantic–Spatial Correlation at 10⁻¹⁶ Precision").

What’s new compared to v1.0.0

• The SSC implementation (v1/v2/v3) and test suite remain identical to v1.0.0.

• Dependencies are now fully version-pinned in requirements.txt:

  • numpy==2.2.6
  • scipy==1.15.3
  • pandas==2.3.3
  • matplotlib==3.10.0

This change freezes the execution environment for I-1 and guarantees
deterministic reproducibility of the reported cross-implementation drift
(|Δ|max = 1.25 × 10⁻¹⁶) under the deterministic execution contract.

How to reproduce I-1 (Test Suite 5)

python -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate
pip install -r requirements.txt
python tests/i1/test_ssc_full_pipeline.py
The workflow .github/workflows/run_test_suite_5.yml runs the same
pipeline on GitHub Actions and uploads the results under outputs/i1/.

Intelligence Relativity v1.1.0 - Complete Experimental Validation

Intelligence Relativity v1.1.0

Complete Experimental Validation of the Relativity Theory of Intelligence

🎯 Overview

This release establishes the complete empirical foundation for Intelligence Relativity, validating all four fundamental observations (O-1 through O-4) across 17 comprehensive experiments.

✅ Key Achievements

Experimental Validation

• 17/17 experiments completed with 100% success rate
• Perfect reproducibility confirmed (deterministic execution, std = 0.00)
• Real-world validation using BERT embeddings
• Cross-linguistic universality demonstrated (English, Japanese, Chinese)

Observation Framework

• O-1: Natural Orthogonality (SSC ≈ 0 at λ=0) - Validated across 11 experiments
• O-2: Topological Dominance (Phase > Metric) - Validated in 2 experiments
• O-3: Stress Tolerance (Independent axes) - Validated in 2 experiments
• O-4: Value-Gated Coupling (λ controls SSC) - Validated in 2 experiments

Infrastructure

• Automated CI/CD with GitHub Actions
• Complete test suite with 80%+ coverage
• Comprehensive documentation with detailed README
• Deterministic reproducibility guarantees

📊 Experimental Results

Core Experiments (EXP-00 to EXP-13)

All 14 core experiments confirmed natural orthogonality and robustness across:

• Multiple spatial arrangements (grid, line, circle, 3D cube, random)
• Various embedding dimensions (50, 100, 200, 500)
• Different sample sizes (N = 10, 20, 40, 80)
• Multiple distance metrics (correlation, euclidean, cosine)

Supplementary Experiments

• SUP-14 (BERT): Real-world validation with O-1 (SSC = 0.0031 ± 0.0614) and O-4 (λ-sweep: 0.0006 → 0.1345)
• SUP-15 (Multilingual): Cross-linguistic validation across English, Japanese, and Chinese

🔬 Technical Specifications

Reproducibility

• Deterministic execution: Fixed seeds, locked dependencies
• CI verification: 16/16 tests passing
• Execution time: Complete suite runs in <2 minutes
• Standard deviation: 0.00 (perfect reproducibility)

Test Coverage

• Core measurement toolkit: ✅ Validated
• SSC computation: ✅ Validated
• Deterministic guarantees: ✅ Validated
• Experiment structure: ✅ Validated

📦 What's Included

Source Code

• Complete measurement toolkit (src/core/)
• 17 validated experiments (src/experiments/)
• Comprehensive test suite (tests/)

Infrastructure

• GitHub Actions workflows for tests and experiments
• Automated coverage reporting
• Complete environment specifications

Documentation

• Detailed README with quick start guide
• Complete API documentation
• Reproducibility instructions

🎓 Citation

@software{intelligence_relativity_2025,
  author = {Hideki},
  title = {Intelligence Relativity: Empirical Validation},
  version = {1.1.0},
  year = {2025},
  url = {https://github.com/HIDEKI-SQ/intelligence-relativity},
  doi = {10.5281/zenodo.XXXXXXX}
}

📝 Next Steps

This release provides the complete empirical foundation for:

1. I-series papers: Instrument validation (I-1, I-2)
2. O-series papers: Observation reporting (O-1 through O-4)
3. L-series papers: Theoretical integration (L-1, L-2)

🙏 Acknowledgments

This work represents the first complete experimental validation of the Intelligence Relativity framework, establishing a new paradigm for understanding the relationship between structure, value, and meaning.

---

Repository: https://github.com/HIDEKI-SQ/intelligence-relativity
Framework: Optics of Intelligence
License: MIT

v1.0.0 - Instrument Core (I-1) Release

Instrument Core Release (I-1)

This release contains the validated SSC measurement toolkit and Test Suite 5, forming the foundation for the Optics of Intelligence research program.

Contents

Core Measurement Toolkit (/src/core/)

• ssc_computation.py - SSC calculation (validated at 10⁻¹⁶ precision)
• deterministic.py - Reproducibility utilities
• generators.py - Data generation
• statistics.py - Statistical analysis
• visualization.py - Plotting functions
• README.md - Complete documentation

Validation (/tests/i1/)

• test_ssc_full_pipeline.py - Test Suite 5 (20/20 tests passed)

Results (/outputs/i1/)

• test_suite_5_summary.json - Complete results
• test_suite_5_summary.csv - Summary statistics
• sha256_manifest.json - File checksums

CI/CD

• .github/workflows/run_test_suite_5.yml - Automated validation

Validation Results

• Cross-implementation agreement: 1.25×10⁻¹⁶ (max)
• Specification: |Δ| < 0.07
• Status: ✅ PASS (all metrics)
• Environment: Python 3.10.19, NumPy 2.2.6, SciPy 1.15.3

Associated Publication

Paper: Deterministic Reproducibility of Semantic-Spatial Correlation: Cross-Implementation Validation at 10⁻¹⁶ Precision

Preprint: [DOI pending - add after Zenodo sync]

Reproducibility

One-command reproduction:

git clone https://github.com/HIDEKI-SQ/intelligence-relativity.git
cd intelligence-relativity
git checkout v1.0.0
python tests/i1/test_ssc_full_pipeline.py

Expected runtime: ~60 seconds

Citation

@software{hideki2025_intelligence_relativity_v1,
  author = {HIDEKI},
  title = {Intelligence Relativity: Instrument Core (I-1)},
  year = {2025},
  version = {v1.0.0},
  url = {https://github.com/HIDEKI-SQ/intelligence-relativity},
  doi = {[Zenodo DOI - add after sync]}
}

License

MIT License - See LICENSE file for details