results: close no-KP causal bootstrap screen

1 files changed, 15 insertions, 0 deletions

diff --git a/RESULTS.md b/RESULTS.md
index 6da95a0..9bf803e 100644
--- a/RESULTS.md
+++ b/RESULTS.md
@@ -1132,6 +1132,21 @@ its survival under the four-times-RMS mixed-traffic intervention.
 `results/figs/figure6_standard_depth_scaling.{pdf,png}` is regenerated from all
 60 records and verifies their hashes plus the historical training source.
 
+The separate Oral-A-v5 feasibility branch asks whether scalable feedback can
+be learned without KP or forward-weight mirroring.  It replaces V4's one
+batch-level scalar for all 267,904 feedback parameters with 128 independent
+per-example node-perturbation observations for one hierarchical edge at a
+time.  At a matched 760-query frozen-forward budget, the run is finite,
+preserves every forward/BatchNorm/optimizer tensor bitwise, and keeps feedback
+norm ratios in `[0.8778,1.0036]`.  All-layer alignment improves from fixed
+HFA's `0.011279` to `0.086687`, with late-layer alignments as high as `0.3837`.
+The preregistered gate nevertheless fails: early-third alignment is only
+`0.001147` versus the required `0.10`, and mean feedback/forward cosine is
+`0.013162`.  No accuracy run is opened.  This is evidence that the causal
+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.
+
 ## 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>`.