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# KP teaching-signal ablation
## Question
The dynamic SDIL image model and clean KP use the same reciprocal KP credit
transport. Their relevant local signals are
```
clean KP: u = s
raw KP: u = a = s + t
matched raw KP: u = ||r|| / ||a|| * a
dynamic SDIL: u = r = s + t - prediction(t).
```
Here `s` is the ordinary clean KP instruction and `t` is four-RMS
soma-predictable apical traffic. This experiment therefore changes neither
the forward architecture nor the feedback-learning substrate. It asks
whether the subtractive innovation operation is necessary to recover useful
credit when the same KP substrate receives mixed apical traffic.
Raw and norm-matched KP execute the same frozen 64-example slow fit and the
same instruction-off fast neutral projection on every ordinary minibatch.
They report the projection but do not apply its subtractive direction. Raw
uses the mixed apical vector unchanged. Matched raw preserves that vector's
direction and changes only its per-example norm to the projected innovation
norm. Thus a matched-raw deficit cannot be attributed to a larger update
norm, and neither deficit can be attributed to a cheaper observation budget
for SDIL.
This is a conditional robustness intervention, not a claim that dynamic SDIL
outperforms clean KP when no nuisance traffic is present.
## KTS-0: mechanics
Before any endpoint launch, the CPU mechanics test must establish:
- raw with the sham projection is bitwise equal to `s + t`;
- matched raw has projected innovation's per-example norm to relative error
below `1e-12` and raw's direction to error below `1e-12`;
- raw, matched, and innovation receive the identical instruction-off
projection report;
- the projection observes zero task-instruction examples and changes no slow
predictor parameter;
- all three rules form their local KP forward and reciprocal correlations from
the selected used vector without reverse-mode differentiation.
## KTS-1: frozen validation endpoint
KTS-1 inherits the passed
`results/kp_dynamic_projection_full_gate.json` endpoint without rerunning or
selecting it: clean KP is 91.26% and dynamic SDIL is 91.18% on the frozen
45,000/5,000 CIFAR-10 split. The only new endpoints are `raw` and `matched`.
Use ResNet-20, width 16, model/data-loader/traffic seed 0/0/4000, batch 128,
200 epochs, augmentation, batch normalization, SGD momentum 0.9, weight decay
`1e-4`, learning rate 0.1 with 0.1 drops at epochs 100 and 150, traffic ratio
4, one closed-form 64-example predictor fit, a frozen predictor thereafter,
and the fast instruction-off projection on every task batch. Evaluate the
validation split exactly once at the endpoint and never evaluate test.
Both jobs must share one clean tracked source revision containing this
protocol, its runner, analyzer, mechanics test, and the sham-control
implementation. No endpoint may be deleted, replaced, or relaunched under
the same name. A nonfinite trajectory is retained as a terminal control
collapse, not repaired by lowering the learning rate or traffic strength.
The mechanism advantage passes only if every provenance, split, argument,
traffic, observation, query, projection, norm-control, and cost invariant
passes and dynamic SDIL exceeds raw by at least 5 percentage points and
matched raw by at least 3 percentage points. A nonfinite control counts as a
performance/stability failure of that control, while the raw record and its
failure location remain part of the evidence. A finite control must have a
valid endpoint accuracy. Every job must use zero task-loss queries, one
64-example slow fit, one instruction-off projection observation per ordinary
example, no predictor updates during task learning, at most 1.34 times the
frozen BP affine-MAC estimate, and separately reported positive elementwise
work.
Passing KTS-1 establishes that innovation subtraction, rather than KP
transport alone or signal-norm rescaling, is necessary under the specified
soma-predictable traffic. It does not establish superiority to Dual Prop,
BP, or clean KP in the clean setting. Multi-seed and cross-architecture
confirmation remain subsequent gates.
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