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TECHNICAL_NOTES.md

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+# 🚀 RFT Adaptive Computing Kernel — Technical Notes (v1.0)
+
+The **Rendered Frame Theory (RFT) Adaptive Computing Kernel** benchmarks computational self-stabilization, throughput efficiency, and coherence across **CPU, GPU, and TPU** workloads.
+
+It integrates RFT’s harmonic feedback system (QΩ / ζ_sync) with adaptive governors to regulate:
+- **Clock scaling**
+- **Thermal headroom**
+- **Duty-cycle performance**
+- **Noise-induced computation drift**
+
+This system demonstrates how RFT’s harmonic framework can stabilize compute throughput and coherence across different hardware and noise environments.
+
+---
+
+## ⚡ Core Function
+Each run simulates workloads and injects synthetic noise or load imbalance.  
+RFT’s adaptive controller adjusts clock scale and workload timing in real time to preserve frame-level stability.  
+It outputs:
+
+| Metric | Description |
+|---------|--------------|
+| **QΩ** | Harmonic stability of compute cycles (amplitude equilibrium). |
+| **ζ_sync** | Synchronization coherence between threads/cores. |
+| **items/sec** | Estimated throughput efficiency after stability correction. |
+| **status** | System condition: nominal / perturbed / critical. |
+
+---
+
+## 🧩 Supported Profiles
+| Profile | Description |
+|----------|--------------|
+| **CPU** | Scalar or integer workloads — stability under linear compute load. |
+| **GPU** | Parallel workloads (matrix or transform) — coherence under high variance. |
+| **TPU** | Tensor workloads — synchronization in large-batch inference. |
+| **Mixed / I/O** | Combines memory, disk, and network delay tests for system-level drift study. |
+
+---
+
+## ⚙️ Internal Dynamics
+- **Adaptive Governor:** Modulates internal load scaling (clock, thread, or matrix block size).  
+- **Noise Control:** Applies synthetic perturbation (σ = 0.00–0.30) to simulate real-world variance.  
+- **Micro-Benchmark:** Runs lightweight compute cycles and reports items/sec safely.  
+- **Feedback Loop:** Uses QΩ/ζ_sync variance as control input for next iteration (adaptive self-correction).  
+- **Bounded Validation:** Metrics capped to realistic operational limits.
+
+---
+
+## 📈 How to Use
+1. Choose **Profile** → CPU / GPU / TPU / Mixed.  
+2. Adjust **Noise Level (σ)** and sample count.  
+3. Run simulation.  
+4. Observe:
+   - **items/sec** → throughput stability  
+   - **QΩ / ζ_sync** → harmonic state  
+   - **status** → equilibrium condition
+
+Repeated runs at identical σ show adaptive stability improvement.
+
+---
+
+## 🧮 Interpretation
+| Status | Description | Expected Behavior |
+|---------|--------------|-------------------|
+| **Nominal** | Stable equilibrium | Items/sec consistent, QΩ ≈ ζ_sync |
+| **Perturbed** | Transitional adjustment | Minor drops in throughput, recovery visible |
+| **Critical** | Overload or divergence | Severe drop or incoherence detected |
+
+---
+
+## 🔐 Verification & Rights
+All adaptive logic and governing equations are protected under **RFT-IPURL v1.0** and the **Berne Convention (UK Copyright Law)**.  
+All performance runs are timestamped and may be SHA-512 sealed for traceable verification.
+
+**Author:** Liam Grinstead  
+**Affiliation:** Rendered Frame Theory Systems (RFTSystems)  
+**DOI:** [https://doi.org/10.5281/zenodo.17466722](https://doi.org/10.5281/zenodo.17466722)  
+**License:** RFT-IPURL v1.0 — Research validation use only.