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# Dynamic neutral-projection development protocol
## Claim boundary
This is a new post-S0 development branch. It does not reinterpret or rescue
MT-1: MT-1 remains failed, MT-2/MT-3 remain sealed, and the reviewer score
remains 5/10. S0 ruled out extending a fixed one-sided predictor margin. The
new question is whether a fast *local and instruction-off* projection can keep
the multiplicative neutral-residual mode inside its two-sided stability window
as somatic statistics change.
The slow predictor is still fitted exactly once on the original 64-example
neutral calibration prefix and is frozen during task learning. Before each
task update, every cell receives a paired instruction-off observation of its
current soma `h` and ordinary apical traffic `a0`. From the neutral residual
```text
e0 = a0 - (P h + b),
```
it forms the current-batch local affine projection
```text
q = Cov(e0, h) / Var(h),
e_stable = (e0 - mean(e0)) - q (h - mean(h)).
```
The plasticity signal is `r = s + e_stable`. The coefficient fit sees zero
task-instruction observations; it reads no label, loss, forward weight,
feedback weight, or downstream state, and it does not modify the slow
predictor. For the diagonal affine traffic used by frozen MT-1, the controller
nulls the current empirical residual-coupling coefficient. With weight decay,
the scalar homogeneous mode is therefore placed near `k=-lambda`, inside both
Jury boundaries, instead of at a fixed negative coefficient whose product with
an evolving covariance can cross the lower boundary.
This controller requires one paired neutral apical microphase per task batch.
Its elementwise work and observation count must be charged separately in any
later endpoint comparison. It uses zero task-loss perturbation queries and no
reverse-mode differentiation.
## D0: mechanics (completed before task-data evaluation)
The implementation must prove on synthetic float64 tensors that:
- an inaccurate frozen slow predictor leaves a nonzero affine neutral mode;
- fast projection reduces the post-projection neutral/traffic RMS ratio and
residual-soma slope below `1e-14` for diagonal affine traffic;
- projected innovation equals clean instruction below `1e-14`;
- the projection consumes zero instruction observations and leaves every slow
predictor parameter bitwise unchanged;
- forward and reciprocal correlations remain independently computed; and
- the extra elementwise work is exposed explicitly.
These checks passed at clean revision `eeb50b0`.
## D1: frozen training-prefix gate
There is exactly one candidate and no hyperparameter grid. Keep the MT-1
ResNet-20 architecture, CIFAR-10 45k training split, seed-0 initialization,
four-to-one traffic intervention, augmentation, batch size 128, learning rate
0.1, momentum 0.9, decay `1e-4`, and reciprocal KP path unchanged. Use a
zero-margin closed-form slow predictor, freeze it, and apply the fast neutral
projection on every one of the first 352 shuffled task minibatches. Restore
the task-loader state after calibration. Do not evaluate validation or test
examples.
The candidate passes only if all conditions below hold:
- every recorded loss, signal diagnostic, parameter, optimizer state,
BatchNorm statistic, and projection diagnostic remains finite for 352 steps;
- maximum task minibatch loss is at most 10 and final-32 mean loss is at most
2.5;
- the used-signal/instruction RMS ratio stays within `1e-4` of one;
- the maximum post-projection neutral/traffic RMS ratio is at most `1e-5`;
- the maximum absolute post-projection residual-soma slope is at most `1e-5`;
- every projection uses the current task batch's 72--128 local neutral
observations and exactly zero instruction observations;
- maximum absolute forward and reciprocal-feedback weight is at most 10;
- maximum absolute forward and reciprocal momentum is at most 50;
- the 64-observation slow fit has zero stability margin, the slow predictor is
frozen during task training, initial traffic calibration is within `1e-5`
of ratio 4, and task-loader state restoration is exact; and
- validation and test evaluation counts are both zero.
A failure closes this paired-neutral projection branch without changing a
threshold or adding a gain/clipping hyperparameter. A pass supplies no task
accuracy claim and cannot change the reviewer score. It only authorizes a new,
separately committed validation protocol with an explicit cost boundary.
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