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| Rendered Frame Theory — Stabilising System Verification Panel | |
| Interactive verification panel for Rendered Frame Theory (RFT) harmonic stability under controlled synthetic noise. | |
| This Space is a reproducible test harness for anticipatory stability (QΩ) and synchronisation coherence (ζ_sync) across multiple domains. | |
| • Domains: AI/Neural, SpaceX/Aerospace, Energy/RHES, Extreme Perturbation | |
| • Noise control: slider for σ (0.00–0.30) to probe robustness | |
| • Outputs: JSON with mean QΩ / ζ_sync, status classification, and timestamp | |
| • Logging: Save Run Log downloads a timestamped .json record for audit trails | |
| • Reference DOI: https://doi.org/10.5281/zenodo.17466722 | |
| Live panel: https://rftsystems-rft-omega-api.hf.space | |
| ⸻ | |
| How to Use | |
| 1. Open the panel → https://rftsystems-rft-omega-api.hf.space | |
| 2. Select a System Profile (AI/Neural, SpaceX/Aerospace, Energy/RHES, Extreme Perturbation). | |
| 3. Choose Noise Distribution (gauss or uniform). | |
| 4. Adjust Synthetic Noise (σ) with the slider (0.00–0.30). | |
| 5. Click Run Simulation → JSON output appears with live QΩ/ζ_sync and status. | |
| 6. Click 💾 Save Run Log to download the result as a .json (timestamped). | |
| Example output | |
| { | |
| "profile": "AI / Neural", | |
| "noise_scale": 0.080, | |
| "distribution": "gauss", | |
| "QΩ_mean": 0.834, | |
| "ζ_sync_mean": 0.799, | |
| "status_majority": "perturbed", | |
| "timestamp_utc": "2025-10-29T14:04:05.114382Z", | |
| "rft_notice": "All Rights Reserved — RFT-IPURL v1.0 (UK / Berne). Research validation use only. No reverse-engineering without written consent." | |
| } | |
| What to Expect | |
| Typical stable ranges (nominal conditions) | |
| Metric | |
| Range | |
| Meaning | |
| QΩ | |
| 0.82–0.89 | |
| Harmonic stability factor (amplitude) | |
| ζ_sync | |
| 0.75–0.88 | |
| Synchronisation coherence (phase) | |
| Status classification (qualitative) | |
| • nominal — low variance; coherent equilibrium | |
| • perturbed — moderate variance; coherent but stressed | |
| • critical — high variance; edge-of-instability | |
| Noise guidance by profile (starting points) | |
| • AI / Neural: σ ≈ 0.01–0.10 (training drift / GPU jitter) | |
| • SpaceX / Aerospace: σ ≈ 0.03–0.12 (vibration / telemetry lag) | |
| • Energy / RHES: σ ≈ 0.02–0.10 (grid oscillations / load steps) | |
| • Extreme Perturbation: σ up to 0.30 (stress testing / failure modes) | |
| Notes | |
| • The panel applies domain-specific weighting (relative importance of QΩ vs ζ_sync). | |
| • Outputs are bounded to [0.00, 0.99] to prevent saturation artifacts and maintain comparability. | |
| • Repeated runs at fixed σ typically show < 0.05 variance in stable regimes. | |
| ⸻ | |
| Validation Purpose | |
| • Benchmark harmonic resilience under controlled perturbations (σ sweeps). | |
| • Study predictive drift signals: observe divergence/convergence of QΩ and ζ_sync as σ increases. | |
| • Profile-specific tuning: compare AI vs Aerospace vs Energy with identical σ to see weighting effects. | |
| For deeper collaboration (e.g., xAI / RobustBench / GLUE-style testing), this panel can be extended with dataset hooks and richer logging while keeping internal parameters sealed under RFT-IPURL. | |
| ⸻ | |
| Rights & Contact | |
| All Rights Reserved under RFT-IPURL v1.0 and the Berne Convention (UK Copyright Law). | |
| Author / Contact: Liam Grinstead — liamgrinstead2@gmail.com | |