Add Phase 10A.5: blend gain is implicit regularization, not learned credit

Dissection of 6 branches from same DFA checkpoint:
- blend_random_frozen: 12.6% (CATASTROPHIC — frozen noise destroys training)
- blend_random_trainable: 32.2% (+1.2% — trainable network helps)
- blend_shuffled_trainable: 32.5% (+1.4% — even wrong targets work!)
- blend_gaussian_noise: 30.8% (neutral)
- scaled_DFA_norm_match: 31.0% (neutral)

The gain comes from implicit regularization through a co-optimized auxiliary
network, NOT from learned credit quality. Phase 9A's +1.5% was an optimization
dynamics effect, not evidence of useful credit assignment.

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

1 files changed, 19 insertions, 1 deletions

diff --git a/NOTE.md b/NOTE.md
index e6ba41c..62deba6 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 10A — NO PREFIT THRESHOLD, BLEND ITSELF IS THE ACTIVE INGREDIENT
+## Status: PHASE 10A.5 — BLEND GAIN IS IMPLICIT REGULARIZATION, NOT LEARNED CREDIT
 
 ---
 
@@ -570,5 +570,23 @@ The +1.5% gain from 9A's blend(0.75) at t0=5 is the project's best online result
 Frozen Gamma/rho are near zero at all prefit levels. The benefit comes from the blend mechanism
 itself — blending DFA with any additional signal provides regularization/diversification.
 
+### Phase 10A.5: Blend Mechanism Dissection
+
+| Branch | final acc | diff vs DFA |
+|--------|-----------|-------------|
+| continue_DFA | 0.311 | baseline |
+| blend_random_**frozen** | **0.126** | **-18.5%** (catastrophic) |
+| blend_random_**trainable** | 0.322 | +1.2% |
+| blend_shuffled_trainable | 0.325 | +1.4% |
+| blend_gaussian_noise | 0.308 | -0.3% |
+| scaled_DFA_norm_match | 0.310 | -0.0% |
+
+**Mechanism identified**: The gain is from **implicit regularization through a trainable
+auxiliary network**, NOT from learned credit. Frozen random Vec crashes (12.6%).
+Trainable Vec helps even with shuffled targets. Gaussian noise and norm scaling don't help.
+
+Phase 9A's +1.5% was not evidence of useful credit — it was an optimization dynamics effect.
+
 ### Experiment IDs (Phase 10)
 - `prefit_threshold/`: Phase 10A prefit threshold curve
+- `blend_dissection/`: Phase 10A.5 blend mechanism dissection