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# Experiment Notes
## Experiment Phases
- **debug**: Initial implementation, rapid iteration (commits ce24e36)
- **pilot**: Controlled iteration (commits 0b9ebb2, 7baf7ae)
- **frozen**: Code at commit 0b9ebb2 for all reported results
## Status: COMPLETE
---
## Final Results Summary
### Toy LQ (3 seeds, 8000 steps)
| Method | Costate Cosine | ρ | Nudging |
|--------|---------------|---|---------|
| DFA | 0.001±0.003 | 0.001±0.007 | 0.000±0.001 |
| State Bridge | 0.945±0.002 | 0.931±0.003 | -0.344±0.019 |
| Credit Bridge | 0.944±0.001 | 0.930±0.002 | -0.342±0.019 |
### CIFAR-10 (3 seeds, 100 epochs)
| Method | Test Accuracy |
|--------|:------------:|
| BP | 59.2%±0.4% |
| DFA | 30.0%±0.3% |
| Credit Bridge | 29.6%±1.0% |
| State Bridge | 18.5%±1.8% |
### CIFAR-10 Diagnostics (seed 42)
| Method | BP Cosine | ρ | Nudge |
|--------|-----------|---|-------|
| BP | 0.940 | 0.990 | -0.027 |
| Credit Bridge | 0.056 | ~0 | ~0 |
| DFA | 0.030 | 0.005 | ~0 |
| State Bridge | 0.021 | 0.004 | ~0 |
---
## Key Findings
1. **Terminal gradient matching is essential** for credit bridge.
Without it, V learns correct values but uninformative gradients (cos → 0.03).
With it, credit bridge matches state bridge on toy (~0.94 cosine).
2. **State bridge fails on nonlinear systems** despite near-perfect state prediction.
State prediction error → 0.0000 but test accuracy = 18.5% (worst of all methods).
This confirms the core hypothesis: bridging state ≠ bridging credit.
3. **Credit bridge modestly outperforms DFA in BP cosine** (0.056 vs 0.030, ~2x)
but accuracy is comparable (29.6% vs 30.0%).
4. **All non-BP methods struggle** on the deep 12-block MLP architecture.
The gap to BP (59.2%) is large for all methods.
---
## Changes Log
- `ce24e36`: Initial implementation
- `0b9ebb2`: Sync state bridge to use normalized MSE in both toy and CIFAR
- `7baf7ae`: Add experiment notes and .gitignore
## Experiment IDs
- `toy_lq_frozen/`: Final toy results (3 seeds, synced state bridge)
- `cifar10/`, `cifar10_seed123/`, `cifar10_seed456/`: Final CIFAR results
- `toy_lq/`: Debug-phase toy results (raw state bridge, for ablation)
- `smoke_test/`, `smoke_test2/`: FashionMNIST debug runs
## Design Decisions
1. Terminal gradient matching (term_grad_weight=1.0): output-layer-local, not hidden BP
2. DFA warmup for credit bridge (20% of epochs): prevents value net bootstrap failure
3. Normalized MSE for state bridge: numerical stability
4. Credit normalization: a_norm = a / (RMS(a) + 1e-6)
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