Add final report, plots, experiment guide, and complete NOTE.md

All experiments complete:
- Toy LQ: credit bridge matches state bridge (~0.94 costate cosine)
- CIFAR-10: credit bridge (29.6%) comparable to DFA (30.0%), both beat state bridge (18.5%)
- State bridge confirms core hypothesis: perfect state prediction != useful credit
- Terminal gradient matching is essential for credit bridge

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+# Experiment Guide
+
+## Requirements
+- Python 3.10+
+- PyTorch 2.x with CUDA
+- torchvision, numpy, scipy, matplotlib
+
+## Project Structure
+```
+models/
+  residual_mlp.py   - Deep residual MLP (pre-LayerNorm + GELU blocks)
+  value_net.py      - Scalar value network V_phi for credit bridge
+  state_bridge.py   - State predictor G_psi for state bridge
+
+experiments/
+  toy_lq_v2.py      - Phase A: Linear-quadratic sanity check
+  cifar_resmlp.py   - Phase B: CIFAR-10 main experiment
+  plot_toy_final.py  - Generate toy plots
+  plot_cifar_final.py - Generate CIFAR plots
+
+metrics/
+  credit_metrics.py  - Diagnostic metrics (cosine, rho, nudging, etc.)
+
+configs/             - YAML configs
+report/              - Plots and final report
+results/             - Experiment outputs
+```
+
+## Running Experiments
+
+### Phase A: Toy LQ Sanity Check
+```bash
+# Single seed
+CUDA_VISIBLE_DEVICES=0 python experiments/toy_lq_v2.py \
+  --gpu 0 --seed 42 --num_steps 8000 \
+  --sigma_bridge 0.1 --lam 0.1 \
+  --term_grad_weight 1.0 --fm_weight 0.0 \
+  --output_dir results/toy_lq_frozen
+
+# All 3 seeds
+for seed in 42 123 456; do
+  CUDA_VISIBLE_DEVICES=0 python experiments/toy_lq_v2.py \
+    --gpu 0 --seed $seed --num_steps 8000 \
+    --sigma_bridge 0.1 --lam 0.1 \
+    --term_grad_weight 1.0 --fm_weight 0.0 \
+    --output_dir results/toy_lq_frozen
+done
+```
+
+### Phase B: CIFAR-10 Main Experiment
+```bash
+# Single seed (runs BP, DFA, State Bridge, Credit Bridge sequentially)
+CUDA_VISIBLE_DEVICES=0 python experiments/cifar_resmlp.py \
+  --dataset cifar10 --d_hidden 512 --num_blocks 12 \
+  --epochs 100 --seeds 42 --gpu 0 \
+  --output_dir results/cifar10
+
+# Parallel across GPUs
+CUDA_VISIBLE_DEVICES=0 python experiments/cifar_resmlp.py --seeds 42 --output_dir results/cifar10 --gpu 0 &
+CUDA_VISIBLE_DEVICES=1 python experiments/cifar_resmlp.py --seeds 123 --output_dir results/cifar10_seed123 --gpu 0 &
+CUDA_VISIBLE_DEVICES=2 python experiments/cifar_resmlp.py --seeds 456 --output_dir results/cifar10_seed456 --gpu 0 &
+wait
+```
+
+### Generate Plots
+```bash
+python experiments/plot_toy_final.py
+python experiments/plot_cifar_final.py
+```
+
+## Key Parameters
+| Parameter | Toy LQ | CIFAR-10 | Description |
+|-----------|--------|----------|-------------|
+| d_hidden | 64 | 512 | Hidden dimension |
+| num_layers/blocks | 12 | 12 | Depth |
+| sigma_bridge | 0.1 | 0.05 | Bridge noise std |
+| lam | 0.1 | 0.1 | Temperature |
+| K | 8 | 4 | MC samples for bridge target |
+| term_grad_weight | 1.0 | 1.0 | Terminal gradient matching weight |
+| ema_momentum | 0.995 | 0.995 | EMA for target network |
+| lr_fb | 1e-3 | 1e-3 | Feedback net learning rate |
+
+## Implementation Notes
+- **No hidden BP anchor**: Non-BP methods never use exact backprop through hidden layers.
+- **Detached hidden copies**: All feedback/value net inputs use `detach().requires_grad_(True)`.
+- **Block-local updates**: Each block's parameters updated only from its local forward + credit signal.
+- **Output head**: Uses exact CE gradient with detached h_L.
+- **Terminal gradient matching**: Matches grad_h V at terminal layer to grad_{h_L} CE. This is output-layer-local information, not hidden BP.
+- **Credit bridge warmup**: First 20% epochs use DFA credits, then linearly blend to credit bridge credits.