From 71bea3e04485715e9fa4e375218bb349508a2c3f Mon Sep 17 00:00:00 2001
From: YurenHao0426 <Blackhao0426@gmail.com>
Date: Sat, 29 Aug 2026 18:38:47 -0500
Subject: docs: lead with the three-part imperfection story

---
 README.md | 55 +++++++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 49 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 666ecd0..4724cf2 100644
--- a/README.md
+++ b/README.md
@@ -6,11 +6,9 @@ algorithmic hypothesis is narrow: a mixed apical compartment should teach from
 the component that is unexpected given the same neuron's ordinary somatic
 state, rather than from raw apical activity.
 
-External A6000 collaborators running the complete 81-cell matched crossover
-should begin with
-[`COLLABORATOR_ONBOARDING.md`](COLLABORATOR_ONBOARDING.md). It contains the
-method boundary, current positive and negative results, environment/data
-setup, frozen matrix, restart policy, and the single entry-point command.
+The archived 81-cell digital crossover packet is documented in
+[`COLLABORATOR_ONBOARDING.md`](COLLABORATOR_ONBOARDING.md), including its
+method boundary, completed cells, environment, and restart policy.
 
 For hidden population `l`, the implemented rule is
 
@@ -21,6 +19,46 @@ r_l       = a_l - a_hat_l
 Delta W_l = eta (r_l * local postsynaptic gain) h_(l-1)^T.
 ```
 
+## Current paper direction
+
+The current paper studies predictable error in a local teaching channel. Its
+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;
+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.
+
+The current one-line method is
+
+```text
+r = measured local teaching signal - instruction-off local prediction
+Delta w = eta * r * local eligibility.
+```
+
+The predictor can be a fitted local function or a same-state neutral sample.
+It reads no BP gradient, device-error constant, downstream weight, or global
+task loss. Neutral measurements and their cost are counted.
+
+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
+25.86% mean classification error to 0%. 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.
+
+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.pdf`](results/figs/figure_physical_hardware_evidence.pdf).
+
 The standard-ResNet stability branch additionally uses a fast paired neutral
 observation to project any *remaining* affine neutral residual off current
 soma before plasticity. This local microphase is explicitly counted; it is not
@@ -33,7 +71,12 @@ novel. The candidate contribution is the neutral-period, per-cell innovation
 operation under mixed apical traffic, together with its theory, causal audit,
 and scaling behavior. See `NOVELTY.md` for the exact prior-art boundary.
 
-## Current audited evidence
+## Previous clean-scaling program (retained audit trail)
+
+The results below remain reproducible and useful as baseline evidence. Clean
+ResNet scaling in this program comes from the reciprocal Kolen--Pollack
+backbone, so it is not used as evidence that residualization itself causes
+scaling in the current paper.
 
 - On flattened CIFAR-10, SDIL loses only `0.214 +/- 0.349` accuracy points from
   hidden depth 5 to 60, while DFA's early-layer teaching alignment falls from
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