document confirmed digital scaling evidence

1 files changed, 13 insertions, 8 deletions

diff --git a/README.md b/README.md
index 6d4204d..9cae3cb 100644
--- a/README.md
+++ b/README.md
@@ -27,9 +27,10 @@ three experimental parts are:
 1. add the same innovation subtraction to digital Dual Propagation,
    Equilibrium Propagation, standard coupled learning, and overclamped coupled
    learning;
-2. scale a digital CLLN from 32 to 2,048 learnable edges under fixed component
-   imperfection, comparing clean learning, same-RMS zero-mean noise, raw
-   imperfection, static calibration, and SDIL;
+2. scale a digital Contrastive Local Learning Network (CLLN) from 32 to 2,048
+   learnable edges under fixed component imperfection, comparing clean
+   learning, same-RMS zero-mean noise, raw imperfection, static calibration,
+   and SDIL;
 3. test the update in the nonlinear CLLN with published component-error scales,
    nonideal local correlated-double-sampling, and a SPICE check of the local
    sample/hold/subtract primitive.
@@ -49,16 +50,20 @@ The completed transfer evidence is: Dual Propagation recovers from 9.40% to
 82.92% validation accuracy against 82.86% clean; EP recovers from 31.38% to
 74.52% against 76.26% clean; and the nonlinear 4-by-4 CLLN recovers from
 25.42% mean classification error to 0% on 120 untouched task/device pairs.
-The first fully confirmed digital
-CLLN size contains 40 tasks and three device draws: at 32 edges, clean and
-SDIL have 0% error, raw imperfection has 34.69%, same-RMS noise has 0.73%, and
-static calibration has 6.15%. The remaining five sizes are running under the
-frozen protocol.
+The confirmed digital CLLN ladder contains 40 tasks, three device draws, six
+sizes, and five methods. From 32 to 2,048 edges, static-calibration error grows
+from 6.15% to 27.19%, while SDIL grows from 0% to 2.29% against 1.56% clean at
+the largest size. SDIL reduces the excess final-error growth slope by 96.2%
+and stable-failure growth by 89.5% relative to static calibration. At 2,048
+edges it uses 23.3% fewer local updates to the censored target but 52.8% more
+local scalar reads because each update includes a neutral measurement.
 
 Start with [`THREE_PART_EVIDENCE.md`](THREE_PART_EVIDENCE.md) for the evidence
 map and [`CLLN_SCALING.md`](CLLN_SCALING.md) for the frozen scaling protocol.
 The current hardware-realistic figure is
 [`figure_physical_hardware_evidence_confirmation.pdf`](results/figs/figure_physical_hardware_evidence_confirmation.pdf).
+The confirmed digital scaling figure is
+[`figure_clln_scaling_confirmation.pdf`](results/figs/figure_clln_scaling_confirmation.pdf).
 
 The standard-ResNet stability branch additionally uses a fast paired neutral
 observation to project any *remaining* affine neutral residual off current