Create README_stage4.md

README_stage4.md

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+# Stage Four — ViT-Tiny (ImageNet Subset) Validation
+
+**Rendered Frame Theory (RFT)**  
+Author: Liam S. Grinstead  
+Date: Oct‑2025
+
+---
+
+## Abstract
+Stage Four evaluates RFT on a transformer vision model (ViT‑Tiny), extending beyond CNN baselines. Using ImageNet‑1K subsets (or synthetic stand‑ins), we compare RFT (DCLR + Ψ–Ω coupling) against Adam. Results show lower energy per step and tighter drift/flux stability at matched accuracy, demonstrating that coherence‑aware optimisation benefits attention layers as well as convolutions.
+
+---
+
+## Objective
+Verify that RFT’s efficiency and coherence gains persist in transformer architectures by benchmarking ViT‑Tiny on an ImageNet‑like workload with identical hyperparameters across modes and unified telemetry.
+
+---
+
+## Methodology
+- **Model:** ViT‑Tiny (patch 16, dim 192, depth 12, heads 3) with CLS token  
+- **Data:** ImageNet‑subset via ImageFolder (train/val), or synthetic images/labels if no `--data_dir` supplied  
+- **Setup:** PyTorch 2.x, Python 3.10, 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, learning rate, and 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):** Lower energy‑per‑step at comparable loss/accuracy; drift remains tightly bounded, flux stable; coherence near unity throughout the run.  
+- **Baseline (Adam):** Higher J/step and looser drift/flux behaviour at matched settings.  
+- **Synthetic data fallback:** Reproduces the same qualitative efficiency pattern, indicating optimiser–telemetry interaction is the source of gains, not dataset artefacts.
+
+---
+
+## Discussion
+ViT‑Tiny confirms that RFT’s coherence mechanisms generalise to attention‑based architectures. The reduction in J/step with stable drift/flux mirrors Stage One (CNN behaviour) and Stage Two (multi‑agent dynamical stability), indicating architecture‑agnostic benefits driven by coherence‑aware optimisation.
+
+---
+
+## Conclusion
+RFT scales cleanly to transformers: energy efficiency improves without compromising learning signals, and coherence remains robust across iterations. This stage prepares for deeper vision models (ViT‑Small, ViT‑Base) and multi‑modal settings.
+
+---
+
+## Reproducibility
+- **Script:** `stage4.py`  
+- **Log Output:** `stage4_vit_tiny.jsonl`  
+- **Seed:** 1234  
+- **Hardware:** A100/H100 (CPU fallback supported)  
+- **Sealing:** All runs are sealed with SHA‑512 hashes
+
+---
+
+## Usage
+- **RFT mode:**  
+  `python stage4.py --mode RFT --steps 1000 --batch 256 --lr 5e-4 --data_dir /path/to/imagenet_subset`
+- **BASE mode (Adam):**  
+  `python stage4.py --mode BASE --steps 1000 --batch 256 --lr 5e-4 --data_dir /path/to/imagenet_subset`
+- **Synthetic fallback:** Omit `--data_dir` to auto‑generate data.