README_stage5.md

# Stage Five — ViT-Small/B32 (ImageNet Subset) Energy-Scaling Validation

**Rendered Frame Theory (RFT)**  
Author: Liam S. Grinstead  
Date: Oct‑2025

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## 📄 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.

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## 🎯 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.

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## ⚙️ 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)

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## 📊 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.

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## 💡 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.

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## ✅ 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.

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## 📂 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

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## 🚀 Usage
- **RFT mode:**  
  ```bash
  python stage5.py --mode RFT --steps 1000 --batch 256 --lr 5e-4 --data_dir /path/to/imagenet_subset