Add CIFAR deltaL test (failed) and pivot design memo

- CIFAR deltaL: s=grad_hL CE (dim=512) -> acc=17.2%, Gamma≈0
  Confirms scalar value field has dimensionality bottleneck on CIFAR
- Pivot memo: direct vector credit field a_phi(h,t,s) -> R^d
  Trained with perturbation-based target, avoids curvature problem
  Still satisfies no hidden BP anchor constraint

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

1 files changed, 12 insertions, 0 deletions

diff --git a/NOTE.md b/NOTE.md
index 8d6091d..774b72a 100644
--- a/NOTE.md
+++ b/NOTE.md
@@ -153,3 +153,15 @@ wr=0.5 -> worst Gamma (0.23) but best acc (0.66).
 Clear tradeoff between credit quality and accuracy.
 
 Best single config: deltaL + tgw=1.0 + wr=0.05 -> **Gamma=0.768, rho=0.691**
+
+### CIFAR deltaL Test
+deltaL conditioning (s=grad_{h_L} CE, dim=512) on CIFAR L=4: FAILED.
+Acc=17.2%, Gamma≈0, rho≈0. The 512-dim conditioning is too high-dimensional
+for the value net. Confirms the scalar V approach has a dimensionality bottleneck.
+
+### Pivot Recommendation: Direct Vector Credit Field
+See `report_explore/MEMO_pivot_vector_field.md`.
+Instead of V_phi -> grad_h V, learn a_phi(h_l, t_l, s) -> R^d directly.
+Train with perturbation-based target: match <a, v> to actual loss change.
+Still satisfies no hidden BP anchor constraint.
+Minimal test: synthetic alpha=1.0, L=4 with M=4 perturbation directions.