exp: freeze raw KP traffic scaling control

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+# Raw KP under four-RMS mixed traffic
+
+## Question
+
+The A6000 crossover shows that SDIL remains close to clean reciprocal
+Kolen--Pollack (KP) learning as ResNet depth increases.  This control asks
+whether the same KP substrate remains trainable when it receives SDIL's
+four-RMS soma-predictable apical traffic without subtracting the predictable
+component.
+
+The compared teaching signals are
+
+```text
+clean KP:  u = s
+raw KP:    u = s + t
+SDIL:      u = s + t - prediction(t)
+```
+
+Here `s` is the reciprocal KP instruction and `t` is the frozen traffic field.
+Raw KP executes the same 64-example closed-form predictor fit and the same
+instruction-off neutral projection as SDIL, but uses the mixed apical vector
+unchanged.  The predictor and projection are therefore charged equally while
+the subtractive direction is the only effective difference.
+
+## Frozen validation panel
+
+Run raw KP at ResNet-20, ResNet-32, and ResNet-56 with the settings used by the
+A6000 crossover:
+
+- CIFAR-10 45,000/5,000 train/validation split; test remains untouched;
+- standard CIFAR `6n+2` ResNet, base width 16, option-A shortcuts, BatchNorm;
+- model, loader, split, and traffic seeds `0`, `0`, `2027`, and `5000`;
+- batch size 128, 200 epochs, standard crop/flip augmentation;
+- SGD momentum 0.9, weight decay `1e-4`, learning rate 0.1, drops at epochs
+  100 and 150;
+- reciprocal KP feedback, four-RMS traffic, one 64-example closed-form neutral
+  fit, and one paired neutral projection per ordinary minibatch;
+- validation evaluation after every epoch and at the final endpoint.
+
+The learning rate and traffic strength stay fixed across depth.  Every depth
+is retained whether it completes, becomes nonfinite, or reaches the 48-hour
+timeout.  The primary metric is final validation accuracy; stability and the
+first nonfinite event are reported alongside it.
+
+## Interpretation
+
+The panel is paired with the clean-KP and SDIL cells from the A6000 crossover.
+A growing raw-KP deficit with depth, together with stable SDIL endpoints,
+supports the claim that subtracting soma-predictable traffic preserves KP's
+scaling behavior under mixed feedback.  Clean KP remains the clean-feedback
+reference.