5dimension/sentinel-chronos-audit

🔬 Rigorous Experimental Audit: Sentinel Chronos Document

Subject: "THE SENTINEL CHRONOS: TEMPORAL DILATION & NEURO-LATTICE RESONANCE" by Romain Abdel-Aal

TL;DR Scorecard

Claim	Verdict
Root spacing = 2πe	✅ Confirmed (but known result from 1890s)
HSK Stable Attractor	❌ FALSE — gradient explodes to 10⁶⁹
1/e + 1/(2z) approx	⚠️ Rough — 47% error at z=0.1
3442× speedup	❌ FALSE — category error
17× learning (NLS)	❌ Unverifiable — no protocol
33-min model training	❌ Unverified — no code
Pre-emptive Shield	❌ Impossible — violates causality

Overall: 1/7 claims confirmed

Key Experiments

1. Root Analysis: Zeros of F(z) = Σ zⁿ/nⁿ confirmed at Im spacing ≈ 17.090 (vs 2πe = 17.079). This follows from Phragmén–Lindelöf theory for entire functions of order 1, type 1/e.

2. Gradient Flow: HSK activation gradient EXPLODES (10⁶⁹ after 100 layers) — the document's own benchmark code proves the "Stable Attractor" label is wrong.

3. Neural Network Test: HSK-F(z) diverges to NaN. HSK-z/e (linear scaling) gets only 70% on MNIST. GELU gets 97.3%.

4. Asymptotic: Ψ(N) = √(2πN/e)·exp(N/e) is valid (standard saddle-point result). The "3442× speedup" confuses having a formula with time dilation.

Files

• AUDIT_REPORT.md — Full 270-line report with all experimental data
• figures/ — 5 publication-quality plots
• scripts/ — Reproducible experiment scripts
• data/ — Raw experimental results (JSON)

Reproduce

pip install numpy scipy mpmath torch torchvision matplotlib
python scripts/exp1c_roots.py    # Root analysis
python scripts/exp2c.py          # Gradient analysis  
python scripts/exp3_nn.py        # Neural network comparison
python scripts/exp4b_asymptotic.py  # Asymptotic verification