results: close stagewise no-KP feedback screen

1 files changed, 14 insertions, 0 deletions

diff --git a/RESULTS.md b/RESULTS.md
index 9bf803e..6cd5166 100644
--- a/RESULTS.md
+++ b/RESULTS.md
@@ -1147,6 +1147,20 @@ observations are informative near the output, not evidence that the no-KP
 vectorizer scales; the existing positive ResNet scaling result still belongs
 to the inherited KP substrate.
 
+The final bounded no-KP follow-up, Oral-A-v6, tests whether strict stagewise
+timing and locally whitened fits repair V5 without increasing its 760-query
+budget.  The dense readout fit works: feedback/forward cosine reaches
+`0.81319` and last-hidden teaching alignment reaches `0.87219`.  The
+convolutional diagonal fit does not.  Correlated child contexts make the
+diagonal inverse unstable: finite feedback norm ratios reach `49.08`, parent
+field MSE grows from `0.0258` at edge 18 to `6.84e30` at edge 9, and edge 9
+through the input become nonfinite.  All forward and BatchNorm state remains
+bitwise fixed, and no validation or test endpoint is evaluated.  This closes
+the present task-loss-only no-KP causal-vectorizer route.  A full covariance
+solver or local response measurement would be system identification; the
+latter is already represented by the response-mirror baseline, while KP is
+the substrate behind the positive standard-depth scaling result.
+
 ## How to run
 `experiments/run.py --mode {bp,fa,dfa,sdil} --dataset {mnist,fmnist,cifar10} --depth D --residual {0,1} --act {tanh,gelu,silu,relu}`
 Batteries: `experiments/run_v2.sh <ds> "<depths>" <res> <act> "<seeds>" <ep> <pfx>`.