Add Phase 10A.6: gain requires trainable depth-aware aux, not semantic credit

9-branch dissection results:
- zero_target crashes (-9.1%): aux must output non-zero
- constant_input neutral (+0.0%): needs at least depth info
- time_only works (+1.0%): h_l not needed, just depth index
- shuffled/fresh_random work (+1.3-1.4%): no semantic content needed
- prefit60_trainable ≈ random_trainable: prefit adds nothing
- All frozen branches crash: trainability is essential

Mechanism: depth-aware trainable auxiliary perturbation that diversifies
block-local updates. Not semantic credit, not pure trainability.

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 62deba6..0e8de0a 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.5 — BLEND GAIN IS IMPLICIT REGULARIZATION, NOT LEARNED CREDIT
+## Status: PHASE 10A.6 — GAIN REQUIRES TRAINABLE DEPTH-AWARE AUX, NOT SEMANTIC CREDIT
 
 ---
 
@@ -587,6 +587,24 @@ Trainable Vec helps even with shuffled targets. Gaussian noise and norm scaling
 
 Phase 9A's +1.5% was not evidence of useful credit — it was an optimization dynamics effect.
 
+### Phase 10A.6: Structured vs Semantic Auxiliary
+
+| Branch | final | diff | Key insight |
+|--------|-------|------|-------------|
+| random_trainable | 0.324 | +1.2% | works |
+| shuffled_trainable | 0.325 | +1.4% | no semantics needed |
+| **zero_target** | **0.221** | **-9.1%** | must output non-zero |
+| fresh_random_target | 0.325 | +1.3% | stable targets not needed |
+| time_only | 0.321 | +1.0% | h_l not needed, just depth |
+| **constant_input** | **0.312** | **+0.0%** | needs at least depth info |
+| prefit60_frozen | 0.127 | -18.4% | frozen = crash |
+| prefit60_trainable | 0.321 | +1.0% | prefit ≈ random init |
+
+**Mechanism**: depth-aware trainable auxiliary perturbation that diversifies block-local updates.
+Not semantic credit. Not pure trainability (zero_target crashes). Not state-dependent (time_only works).
+Depth-awareness is the minimal requirement (constant_input fails).
+
 ### Experiment IDs (Phase 10)
 - `prefit_threshold/`: Phase 10A prefit threshold curve
 - `blend_dissection/`: Phase 10A.5 blend mechanism dissection
+- `structured_aux/`: Phase 10A.6 structured vs semantic auxiliary