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# KP mixed-traffic innovation protocol
## Question and attribution boundary
This protocol asks whether the Harnett-specific somato-dendritic innovation is
load-bearing on a standard ResNet once a strong non-backpropagating credit path
is available. The reciprocal Kolen--Pollack (KP) pathway is inherited from
Akrout et al. and receives no SDIL novelty credit. The candidate contribution
is only the neutral-period subtraction used in the mixed apical compartment.
At every hidden population, first compute the hierarchical KP instruction
`s_l`. Add fixed soma-predictable traffic
```text
t_l = g_l B_l h_l,
a_l = s_l + t_l,
a_hat_l = P_l h_l + b_l,
r_l = a_l - a_hat_l.
```
`B_l` is a per-unit log-normal coefficient drawn once from traffic seed 4000.
The positive gain `g_l` is fixed once at initialization using the first 64
unaugmented development-training examples so that every layer has
`RMS(t_l)/RMS(s_l)=4`. Labels enter only this one training-prefix scale
calibration through `s_l`; no validation endpoint is used. The target ratio is
not retuned across signals, epochs, or seeds.
The diagonal affine predictor is trained only with instruction switched off.
All three conditions receive exactly 20 neutral warmup minibatches at
`eta_P=0.1` and one neutral predictor update every 16 task minibatches. Raw and
norm-matched controls pay the same predictor-learning cost even though raw
does not consume its prediction. The three used signals are
```text
raw: u_l = a_l,
matched: u_l = ||r_l||_example / ||a_l||_example * a_l,
innovation: u_l = r_l.
```
Norms are taken over every non-batch coordinate separately for each example
and layer. Consequently the matched control has the innovation's update-signal
norm but the raw signal's direction at the same network state. Every condition
uses its own used signal for both the forward local correlation and the
independently recomputed reciprocal KP correlation; matched optimizer and
decay dynamics are otherwise unchanged.
This is a controlled predictable-traffic test, not the Oral-B natural-traffic
claim. The broad endogenous top-down traffic and desired-velocity branches
remain failed and are not reopened by this protocol.
## MT-0: mechanics and cost gate
Before any task endpoint, the float64/CPU smoke suite must establish all of:
- setting traffic gain to zero makes raw, matched, and innovation exactly the
clean KP instruction when `P=b=0`;
- setting `P_l=g_l B_l, b_l=0` removes the predictable traffic exactly;
- layerwise gain calibration realizes ratio 4 to relative error below `1e-12`;
- matched raw has the innovation norm to relative error below `1e-12` and
preserves raw cosine direction to absolute error below `1e-12`;
- the predictor update depends only on local soma and instruction-off apical
traffic, not labels, output errors, or forward/feedback weights;
- independently recomputed forward and reciprocal correlations remain equal
under every used signal, and no parameter acquires an autograd graph;
- affine MACs, predictor/traffic elementwise operations, loss queries, memory,
and wall time are reported separately. All three conditions execute the
same calibration and predictor schedule.
Failure closes this branch. Passing mechanics does not change the reviewer
score.
## MT-1: frozen 20-epoch load-bearing screen
This screen is conditional on a KP-2 pass. Run exactly raw, matched, and
innovation at seed 0 on the frozen 45,000/5,000 CIFAR-10 split. Copy KP-1:
ResNet-20 width 16, batch 128, standard augmentation, BatchNorm, hidden/output
LR 0.1, epoch-100/150 step drops (hence constant in this screen), no optimizer
warmup, momentum 0.9, and weight decay `1e-4`. Use the traffic and predictor
settings above and a 32-example final alignment audit. There is no traffic,
predictor, learning-rate, decay, or signal-rule grid.
MT-1 passes only if all of the following hold:
- all three records, epoch losses, tracking trajectories, and diagnostics are
finite, clean, and validation-only;
- innovation accuracy is at least 75% and no more than 5 points below the
frozen clean KP-1 accuracy of 82.66%;
- innovation exceeds raw by at least 5 accuracy points and norm-matched raw by
at least 3 points;
- innovation's used-signal early-third alignment is at least 0.70, and on the
same final innovation network its innovation alignment exceeds raw-apical
alignment by at least 0.15;
- innovation's final feedback/forward cosine is at least 0.80 and its
epoch-11--20 mean is at least 0.70;
- every initial layer ratio is within `1e-5` of 4, the post-warmup predictor
traffic-residual RMS ratio is at most 0.25, and matched norm relative error
is at most `1e-6`;
- feedback learning uses zero task-loss queries and each condition's affine
MAC estimate is at most `1.40x` the frozen epoch-20 BP reference.
The accuracy gaps and same-state alignment rotation are conjunctive: clipping
an unstable raw update is not enough. MT-1 failure closes the branch without a
weaker traffic ratio. A pass opens MT-2 but leaves the reviewer score at 5/10.
## MT-2: frozen full validation gate
Copy MT-1 exactly for 200 epochs. The three jobs remain seed 0 and do not touch
the test set. In addition to finite/clean provenance, exact ratio calibration,
zero queries, matched norms, and the `1.40x` BP MAC ceiling, require:
- innovation validation accuracy at least 88% and within 3 points of KP-2;
- innovation gains of at least 5 points over raw and 3 over matched raw;
- innovation used-signal early alignment at least 0.80 and a same-network
advantage of at least 0.15 over raw-apical alignment;
- final and epoch-151--200 mean feedback/forward cosine both at least 0.95;
- post-training traffic-residual RMS ratio at most 0.05.
MT-2 failure closes the confirmation panel. A pass is strong development
evidence but one seed is insufficient for acceptance, so the formal score
remains 5/10.
## MT-3: untouched five-seed test confirmation
Only an MT-2 pass opens seeds 10--14. For each seed, pair an independently
drawn traffic field with seed `5000 + model_seed`. Freeze the chosen method and
run clean KP, raw, matched, and innovation after refitting on all 50,000
CIFAR-10 training examples. Evaluate the test set exactly once per run; no
validation endpoint, seed deletion, or replacement is permitted.
The confirmation passes only if all 20 records are finite and clean, mean
innovation test accuracy is at least 88%, and:
- the mean paired innovation deficit to clean KP is at most 3 points;
- mean paired innovation gains are at least 5 points over raw and 3 over
matched raw;
- the one-sided 95% lower confidence bound of each paired innovation gain is
above zero;
- at least four of five paired gains are positive against each control;
- all mechanism, query, and cost invariants from MT-2 remain satisfied.
A complete pass raises the strict reviewer estimate from 5/10 to 6/10 (weak
accept): it would establish a Harnett-specific, norm-controlled innovation
effect on a standard ResNet across untouched network and traffic draws. It
would not establish natural biological traffic or broad architecture scaling;
those remain Oral-B and Oral-A work respectively.
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