README_stage4.md

Stage Four — ViT-Tiny (ImageNet Subset) Validation

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

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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.

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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.

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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)

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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.

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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.

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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.

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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

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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.