H-ANS: Hijoluminic Ansatz for Neural Superposition

Multi-Stream Fractal Resonance Architecture for Zero-Day Anomaly Detection and BCI Neural Superposition Modeling

---

âš¡ Quick Start

# Install
pip install torch numpy scipy matplotlib

# Run all 11 sanity checks
python -m tests.test_sanity

# Zero-Day Detector (no training needed)
python -m builds.build1_zeroday.detector

# BCI Neural Superposition Model
python -m builds.build6_bci.cortex

# Path Integral Interference Demo
python -m examples.demo_path_integral

# H-ANS Classifier Demo
python -m examples.demo_classifier

---

🔬 What Is H-ANS?

H-ANS is a novel neural architecture that replaces softmax with a fractal resonance collapse mechanism inspired by the path integral formulation of quantum mechanics. It maintains two parallel processing streams — a local Schrödinger-like field evolution and a global path integral evaluation — that are fused only at the collapse point.

The central innovation: a complex-valued fractal mass whose imaginary part encodes principled, learned uncertainty:

m = (1 + Σ_f i/Δ_f) · κ_H · c²

where Δ_f are learnable fold gaps (≈ [1.31, 2.13, 4.02, 8.00])
and Im(m) > 0 always — irreducible uncertainty is baked into the physics.

---

🛡� Build 1: Zero-Day Network Intrusion Detector

Detects novel attacks with zero training data. No signatures. No labeled examples. No thresholds to tune.

How It Works

Traffic → Hann Window → FFT → Power Spectrum → Branch Amplitudes → Collapse → Entropy → Score

Normal traffic (periodic, structured) → narrow frequency peaks → low entropy ✅
Anomalous traffic (random, noise-like) → flat frequency spectrum → high entropy 🚨

Results (synthetic validation)

Metric	Value
Separation (anomaly - normal)	+0.51
Detection rate	100%
False positive rate	6-14%
Learnable parameters	5 (optional folds)

Docker Deployment

docker build -t hijoluminic-firewall .
docker run -p 8080:8080 hijoluminic-firewall
# API: GET http://localhost:8080/status
#      GET http://localhost:8080/alerts
#      POST http://localhost:8080/analyze

---

🧠 Build 6: BCI Neural Superposition Model

Quantifies perceptual uncertainty in brain-computer interfaces. Maintains multiple simultaneous interpretations of ambiguous stimuli.

How It Works

Neural Signals → Site Encoding → H-ANS → Branch Generation → Collapse → Entropy

Results

Stimulus	Entropy	Meaning
Clear image	0.006	Collapsed — certain
Rubin vase	1.373	Superposition — uncertain
Necker cube	1.380	Superposition — uncertain

---

📊 Validation Summary

Test	Status
Clifford algebra (anticommutator)	✅
Complexified velocity map	✅
FractalMass complex mass	✅
HijoluminicOperator shapes	✅
PathIntegralLayer propagator	✅
Path integral constructive interference	✅
CollapseOfLight sums to 1	✅
Fractal resonance weights	✅
PathBundle propagator	✅
Full model forward+backward	✅
Light model forward+backward	✅

---

📄 IP & Licensing

This repository contains original research protected by:

• GitHub Release: v0.1.0 — timestamped prior art (June 2, 2026)
• Zenodo DOI: 10.5281/zenodo.20510175 — timestamped prior art
• Defensive Publication: Technical disclosure at DISCLOSURE.md — arXiv-ready
• Provisional Patent Application: Prepared at PROVISIONAL_PATENT.md — 15 claims covering:
  • Fractal resonance collapse for anomaly detection (Claims 1-6)
  • Perceptual uncertainty quantification for BCI (Claims 7-8)
  • Two-stream neural architecture with complex mass (Claims 9-10)
• Copyright: Source code © 2026. All rights reserved.

License: AGPL v3 (free for academic and non-commercial use. Commercial licensing available.)

---

🔮 Roadmap

• Core H-ANS architecture (two-stream, CollapseOfLight, complex mass)
• Build 1: Zero-Day Detector (frequency-domain + collapse entropy)
• Build 6: BCI Neural Superposition (perceptual uncertainty)
• Docker container + REST API
• Real PCAP capture interface
• Live entropy dashboard
• arXiv submission
• Provisional patent filing
• Clinical EEG dataset validation
• Commercial sensor deployment
• Platform licensing

---

👤 Inventor

[Name] — [Affiliation] Contact: [email] Date of first reduction to practice: June 2, 2026 Provenance dogtag: 3732a5a3-05c9-43c7-be45-4746df4962b1

---

This README is part of a defensive publication. No license is granted for commercial use without explicit agreement.