Add Phase 8: schedule timing test — online co-learning is the remaining bottleneck

Vec_only_from_0: 15.4% (cold-start failure, can't learn credit on random features)
DFA_only: 31.2% (remains best non-BP method)
DFA_then_Vec_T20: 12.9% (switching to Vec destroys DFA-built features)
Vec_T5_then_DFA: 26.6% (partial recovery but still worse than pure DFA)

Phase 7A's early-window finding doesn't transfer: it required offline-trained Vec
on frozen features. Online Vec estimator faces cold-start paradox — needs structured
features to learn credit, but structured features need good credit to form.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

1 files changed, 36 insertions, 1 deletions

diff --git a/NOTE.md b/NOTE.md
index 892cf1e..74b75f3 100644
--- a/NOTE.md
+++ b/NOTE.md
@@ -5,7 +5,7 @@
 - **pilot**: Controlled iteration (commits 0b9ebb2, 7baf7ae)
 - **frozen**: Code at commit 0b9ebb2 for all reported results
 
-## Status: PHASE 7A SNAPSHOT TIME SWEEP — EARLY SNAPSHOTS SHOW POSITIVE TRANSFER
+## Status: PHASE 8 SCHEDULE TIMING — ONLINE CO-LEARNING IS THE REMAINING BOTTLENECK
 
 ---
 
@@ -449,3 +449,38 @@ Credit bridge should be used from epoch 0.
 
 ### Experiment IDs (Phase 7)
 - `snapshot_time/`: Phase 7A snapshot time sweep with BP checkpoints
+
+---
+
+## Phase 8: Schedule Timing Hypothesis Test
+
+**Setup**: CIFAR-10, L=4, d=256, 100 epochs, seed=42
+
+| Schedule | acc@5 | acc@20 | final |
+|----------|-------|--------|-------|
+| DFA_only | **0.297** | **0.308** | **0.312** |
+| Vec_only_from_0 | 0.135 | 0.151 | 0.154 |
+| Vec_T5_then_DFA | 0.135 | 0.213 | 0.266 |
+| DFA_T20_then_Vec | 0.297 | 0.308 | 0.129 |
+
+**Phase 7A's timing hypothesis does NOT transfer to online training.**
+
+Vec from epoch 0 gets stuck at 15% (near chance). The online Vec estimator
+starts from random initialization and cannot learn useful credit fast enough
+when the forward net is also random (cold-start paradox).
+
+DFA alone remains the best non-BP method (31.2%).
+
+### The cold-start paradox:
+Vec credit is most useful on early features, but Vec can only learn useful credit
+from features with structure. DFA provides structure slowly, but by the time Vec
+is ready, the early window is closed.
+
+### Project conclusion at this point:
+- Vec estimator WORKS (synthetic + frozen CIFAR)
+- Local surrogate CAN exploit it (same-batch, Phase 6.5A)
+- Early snapshots show generalization (Phase 7A, offline-trained Vec)
+- But online co-learning of estimator + forward net is unsolved (cold-start)
+
+### Experiment IDs (Phase 8)
+- `schedule_timing/`: Phase 8 schedule comparison