results: close normalized mirror short accuracy gate

1 files changed, 22 insertions, 0 deletions

diff --git a/RESULTS.md b/RESULTS.md
index 2669dc1..06c70bb 100644
--- a/RESULTS.md
+++ b/RESULTS.md
@@ -806,6 +806,28 @@ task-scalar queries did not calibrate it. WM-2 therefore opens. The result is
 credited entirely to an inherited weight-estimation baseline; it cannot raise
 the SDIL novelty score by itself.
 
+The conditional WM-2 task screen is much stronger than fixed HFA but narrowly
+fails its frozen advancement gate:
+
+| method | validation accuracy | early alignment | all-layer alignment | estimated MACs |
+|:--|--:|--:|--:|--:|
+| WM, hidden LR 0.03 | 62.68% | 0.827404 | 0.825811 | 2.4252e13 |
+| WM, hidden LR 0.10 | **64.04%** | **0.939256** | **0.916222** | 2.4252e13 |
+| matched BP | 74.94% | n/a | n/a | 2.4331e13 |
+
+Both WM runs are finite, use zero task-loss queries for feedback learning, and
+cost `0.9968x` matched BP MACs. Selected WM improves fixed HFA by 20.52 points,
+but misses 65% by 0.96 points and the within-10-point BP window by 0.90 points.
+Thus four check families pass--finite, alignment, query, and cost--while both
+accuracy checks fail. WM-3 is closed without extra mirror tuning.
+
+This is an informative optimization negative: a hidden teaching cosine of
+`0.939` and feedback/forward cosine `0.936` still leave a 10.90-point short-
+horizon accuracy gap. Alignment is necessary here but not sufficient; local
+gain errors and the remaining compounding angular errors can materially change
+the trajectory. It would be misleading to present the capture result alone as
+BP-equivalent learning.
+
 ## 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>`.