Create README_stage5.md

README_stage5.md

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+# Stage Five — ViT-Small/B32 (ImageNet Subset) Energy-Scaling Validation
+
+**Rendered Frame Theory (RFT)**  
+Author: Liam S. Grinstead  
+Date: Oct‑2025
+
+---
+
+## 📄 Abstract
+Stage Five scales RFT from ViT‑Tiny to ViT‑Small/B32, testing whether coherence‑linked efficiency persists at higher depth and embedding dimension. Using a consistent telemetry schema (drift, flux, E_ret, coherence, J/step, ΔT), RFT (DCLR + Ψ–Ω) is compared with Adam under matched conditions. Results show reduced energy per step and stable drift/flux at comparable accuracy, confirming that RFT’s efficiency gains hold as model capacity increases.
+
+---
+
+## 🎯 Objective
+Validate that RFT’s energy and stability advantages generalise to ViT‑Small/B32 by measuring J/step, drift, flux, and accuracy on an ImageNet‑like workload, with bf16 autocast where available and identical hyperparameters across modes.
+
+---
+
+## ⚙️ Methodology
+- **Model:** ViT‑Small, patch size 32, dim 384, depth 12, heads 6, MLP ratio 4  
+- **Data:** ImageNet‑subset via ImageFolder (recommended), or synthetic fallback for quick verification  
+- **Setup:** Python 3.10, PyTorch ≥ 2.1, A100/H100 (bf16 autocast if available), seed 1234  
+- **Metrics:** Loss, accuracy, J/step (NVML if present; proxy otherwise), drift, flux, energy‑retention (E_ret), coherence (coh), ΔT  
+- **Parity:** Same batch size, learning rate, and number of steps across RFT and BASE  
+- **Orbital Coupler:** Ψ–Ω drift/flux synchronisation each iteration  
+- **Optimisers:** DCLR (RFT) vs Adam (BASE)
+
+---
+
+## 📊 Results
+- **RFT (DCLR + Ψ–Ω):** Reduced energy per step compared to Adam, with tightly bounded drift and smooth flux.  
+- **Baseline (Adam):** Higher J/step and less stable drift/flux behaviour at matched accuracy.  
+- **Synthetic fallback:** Reproduced the same qualitative efficiency pattern, confirming that gains arise from optimiser–telemetry dynamics rather than dataset artefacts.
+
+---
+
+## 💡 Discussion
+Scaling from ViT‑Tiny to ViT‑Small/B32 preserves RFT’s advantages in attention‑heavy architectures. The energy reduction with stable drift/flux strengthens the claim that coherence‑linked control is architecture‑agnostic and scales with depth and embedding dimension.
+
+---
+
+## ✅ Conclusion
+RFT maintains its efficiency and stability benefits at ViT‑Small/B32 scale, validating the energy‑scaling hypothesis and setting the stage for ViT‑Base and multi‑modal fusion in later stages.
+
+---
+
+## 📂 Reproducibility
+- **Script:** `stage5.py`  
+- **Log Output:** `stage5_vit_small_b32.jsonl`  
+- **Seed:** 1234  
+- **Hardware:** A100/H100 (CPU fallback supported)  
+- **Sealing:** All runs sealed with SHA‑512 hashes
+
+---
+
+## 🚀 Usage
+- **RFT mode:**  
+  ```bash
+  python stage5.py --mode RFT --steps 1000 --batch 256 --lr 5e-4 --data_dir /path/to/imagenet_subset