# 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.