Update README.md

README.md

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 short_description: Adaptive RFT kernel computing stability and coherence metric
 sdk_version: 5.49.1
 ---
-# 🧠 RFT Adaptive Computing Kernel (v1.0)
+# 🚀 RFT Adaptive Computing Kernel (v1.0)
 
-The **Rendered Frame Theory (RFT) Adaptive Computing Kernel** simulates harmonic stability and coherence across CPU, GPU, and TPU environments.  
-It benchmarks computational self-stabilisation under varying workloads and randomised system noise, providing a verified proof log for every run.
+The **Rendered Frame Theory (RFT) Adaptive Computing Kernel** demonstrates real-time compute stability and harmonic coherence across CPU, GPU, and TPU workloads.  
+It applies RFT’s motion-based harmonic model to show how computation can self-balance under noise, load, or timing variance.
 
 ---
 
-## ⚙️ How It Works
-Each simulation dynamically generates:
-- **QΩ (Stability Metric):** Reflects harmonic equilibrium of compute operations.  
-- **ζ_sync (Coherence Metric):** Measures synchronization under fluctuating workloads.  
-- **rate_items_per_sec:** Estimated throughput (adaptive, noise-adjusted).  
-- **SHA-512 Sealed Log:** Each run produces a cryptographically signed verification record.
+## 🔧 Overview
+This kernel simulates adaptive performance regulation through harmonic metrics:
+
+| Metric | Description |
+|---------|-------------|
+| **QΩ** | Harmonic stability (amplitude equilibrium). |
+| **ζ_sync** | Synchronisation coherence (phase alignment). |
+| **items/sec** | Throughput estimate after adaptive correction. |
+| **status** | System state — nominal / perturbed / critical. |
 
 ---
 
-## 🧩 Parameters
-| Parameter | Description |
-|------------|--------------|
-| **Compute Profile** | CPU, GPU, or TPU mode |
-| **Workload Type** | Matrix, Transformer, or Mixed |
-| **Cycles** | Number of operational iterations |
-| **Seed** | Random seed for reproducibility |
+## 🧩 Profiles
+- **CPU** — Linear compute flow tests.  
+- **GPU** — Parallel matrix or transformer operations.  
+- **TPU** — Tensor inference and batch stability.  
+- **Mixed / I/O** — Combined memory and data-path stress tests.
+
+---
+
+## ⚙️ How to Use
+1. Choose a **Profile** and **Workload**.  
+2. Adjust **Noise σ** (0 – 0.30) to simulate load variation.  
+3. Run the kernel.  
+4. Review the JSON output showing QΩ, ζ_sync, items/sec, and stability status.  
+5. Optionally download the run log for SHA-512 verification.
+
+Repeated runs at fixed σ demonstrate adaptive recovery and equilibrium maintenance.
 
 ---
 
-## 📊 Output Example
-```json
-{
-  "profile": "GPU",
-  "workload": "transformer",
-  "cycles": 5,
-  "rate_items_per_sec": 8.12e+08,
-  "QΩ": 0.841,
-  "ζ_sync": 0.792,
-  "status": "nominal",
-  "timestamp_utc": "2025-10-29T15:02:14Z",
-  "sha512": "..."
-}
+## 🎯 Purpose
+The Adaptive Computing Kernel bridges theoretical physics and computer engineering by proving that RFT’s harmonic feedback can stabilise computation itself—creating a self-governing, energy-efficient framework for AI, aerospace, and energy systems.
+
+---
+
+## ⚖️ Rights & Contact
+All Rights Reserved — **RFT-IPURL v1.0 (UK / Berne Convention)**  
+Research validation use only; no reverse-engineering or redistribution without written consent.
+
+**Author:** Liam Grinstead  
+**Affiliation:** Rendered Frame Theory Systems (RFTSystems)  
+**DOI:** [https://doi.org/10.5281/zenodo.17466722](https://doi.org/10.5281/zenodo.17466722)