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authorYurenHao0426 <Blackhao0426@gmail.com>2026-07-22 02:13:33 -0500
committerYurenHao0426 <Blackhao0426@gmail.com>2026-07-22 02:13:33 -0500
commit7e8d314c0fb8e82730e24da153e8b73c3af4ecd6 (patch)
tree49b57e987648bd9352aa2f65ee8d12ee208160c1 /RESULTS.md
parentb9a9524ca559cc30a584779ba39f7764887d7fcf (diff)
results: freeze predictor timescale choice
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@@ -131,6 +131,33 @@ SDIL matches DFA on easy MNIST and **beats DFA on FashionMNIST (+1–2 pts) and
On CIFAR the SDIL−DFA gap GROWS with depth (+1.8 → +2.7 → +5.0 at d5/10/20): DFA degrades with depth,
SDIL holds.
+## Endogenous top-down traffic timescale screen (validation only)
+
+This development screen used one model seed, a fixed 5,000-example stratified MNIST validation
+split drawn only from training data, and never evaluated the test set. All 18 runs share validation
+index hash `c4cec216...2e3d`, clean source revision `17bcd97`, and the same 15-epoch training
+protocol. The grid crossed `eta_P in {0.002, 0.01, 0.05}`, neutral warmup steps in
+`{0, 200, 1000}`, and endogenous top-down traffic scales in `{0.2, 0.5}`.
+
+| eta_P | warmup steps | rho=0.2 last val (%) | rho=0.5 last val (%) | mean (%) | worst (%) |
+|------:|-------------:|---------------------:|---------------------:|---------:|----------:|
+| 0.002 | 0 | 91.50 | 77.24 | 84.37 | 77.24 |
+| 0.002 | 200 | 91.02 | 89.10 | 90.06 | 89.10 |
+| 0.002 | 1000 | 93.10 | 88.82 | 90.96 | 88.82 |
+| 0.010 | 0 | 94.94 | 92.40 | 93.67 | 92.40 |
+| 0.010 | 200 | 92.88 | 91.12 | 92.00 | 91.12 |
+| 0.010 | 1000 | 95.04 | 91.06 | 93.05 | 91.06 |
+| 0.050 | 0 | 96.52 | 91.40 | 93.96 | 91.40 |
+| **0.050** | **200** | **94.20** | **95.26** | **94.73** | **94.20** |
+| 0.050 | 1000 | 94.70 | 93.62 | 94.16 | 93.62 |
+
+The selected development setting is `eta_P=0.05`, 200 warmup steps: it has the highest mean
+last-epoch validation accuracy and the highest worst-traffic endpoint. Its final mean traffic R2 is
+0.802 at rho=0.2 and 0.999 at rho=0.5. This reverses the initial assumption that `P` must be much
+slower than forward learning: once teaching and traffic are separated by neutral periods, fast
+tracking is beneficial, while a long warmup is not consistently useful. This is a frozen
+validation choice for the multi-seed C1 experiment, not evidence that C1 has passed.
+
## Legacy credit assignment / depth utility
Per-layer alignment cos(r_l, −∇_{h_l}L), early-third average, CIFAR no-BN residual:
| depth | DFA early-align | SDIL early-align |