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# Oral-A preregistration: standard CIFAR residual networks
## Claim boundary and status
This protocol asks whether the low-query learned-feedback backbone can train a
standard deep convolutional family without reverse-mode credit assignment. It
does **not** rescue the failed Oral-B biological-signature gate. With
`nuisance_scale=0`, the predictor is inactive and SDIL reduces to learned
direct-feedback calibration in the sense of Lansdell et al.; standard-network
scaling is therefore an engineering/credit-assignment contribution, while the
Harnett-specific innovation claim remains supported only in the separately
audited mixed-traffic experiments.
No CIFAR image/ResNet accuracy was observed before this protocol was frozen.
Mechanics-only CPU checks are complete: standard ResNet parameter counts,
option-A shortcuts, augmentation determinism, local Conv+BatchNorm eligibility,
BatchNorm-coupled causal perturbations, running-stat isolation, and both apical
vectorizers pass `experiments/cifar_image_smoke.py` and
`experiments/conv_local_smoke.py`.
## Fixed data, architecture, and accounting
- CIFAR-10 uses the original Python batches under `/home/yurenh2/sdrn/data`.
- Development uses a 45,000/5,000 training-only stratified split with seed 2027,
validation-index SHA-256
`8328b206a97c420e49e54e3eca4abe3274c4756b084355784ea3fb8059e4515b` and
exactly 500 validation examples per class.
- Training augmentation is random 32x32 crop after four-pixel zero padding and
horizontal flip with probability 0.5. Validation/test are not augmented.
- The architecture is the canonical CIFAR `6n+2` ResNet topology with 3x3
convolutions, BatchNorm, ReLU, global average pooling, and parameter-free
option-A shortcuts. ResNet-20/32/56 contain respectively 269,722, 464,154,
and 853,018 forward parameters.
- BatchNorm's complete minibatch Jacobian is evaluated within its own layer.
No local update reads a downstream forward weight. Counterfactual `+/-`
trials are separate batches and do not update running statistics.
- Because BatchNorm couples examples, causal calibration uses the batch-mean
loss derivative multiplied by batch size. This is an unbiased estimate of
the summed-loss hidden gradient. Logical batch queries, observed causal
scalars, per-example CE terms, forward-equivalent examples, MAC components,
peak memory, and wall time are all recorded separately.
- SDIL uses one antithetic direction every four task updates (`K=1/every=4`),
selected before this project reached convolutional models by the passed C3
gate. `sigma=0.01`; no direction/cadence tuning is allowed here.
## A1: establish a valid exact-BP reference
The primary seed-0 ResNet-20 validation run uses 200 epochs, batch 128, SGD
momentum 0.9, weight decay `1e-4`, learning rate 0.1 with 10x drops at epochs
100 and 150, and no warmup. It must reach at least 90% final validation
accuracy with finite loss.
If and only if the primary fails, one predeclared architecture-recovery run is
allowed: cosine learning rate from 0.1, five warmup epochs, and weight decay
`5e-4`, with everything else fixed. The higher final validation endpoint is
selected; if neither reaches 90%, the current implementation is not a valid
standard-network testbed and all later Oral-A stages stop. This recovery tests
the forward architecture/optimizer, not SDIL.
## A2a: apical calibration screen with frozen forward weights
On seed 0, ResNet-20, the first 10,000 post-split training examples, and 100
feedback-only minibatches, cross:
- vectorizer: `spatial_template`, `channel_gated`;
- initial feedback scale: `0`, `1`;
- apical regression rate: `0.001`, `0.01`, `0.1`.
Forward weights, BN affine parameters, and the readout remain bitwise fixed.
Each run uses K1 antithetic BatchNorm-coupled calibration and a fixed 32-example
training-prefix alignment probe. A candidate is eligible only if all values are
finite and early-third teaching/negative-gradient cosine is positive. Within
each vectorizer family, select maximum early-third cosine, then maximum
all-layer mean cosine, then lower `eta_A`, then lower `a_scale`. Both families
must yield an eligible candidate or A2 stops.
The spatial template has one class vector per spatial unit. The channel-gated
candidate shares `A c + tanh(h) * G c` over positions; at ResNet-56 it has
40,640 rather than 5,324,800 vectorizer parameters. This capacity/cost choice
is frozen before accuracy is observed.
## A2b: bounded short accuracy screen
Use seed 0, ResNet-20, 10,000 training examples, 20 epochs, final validation
only, cosine decay, no warmup, momentum 0.9, and weight decay `1e-4`:
- one exact-BP run at hidden/output LR 0.1;
- DFA at hidden LR `{0.01, 0.03, 0.1}`, output LR 0.1;
- each A2a-selected SDIL vectorizer at the same three hidden rates, output LR
0.1, its selected A hyperparameters, and 100 A-only warmup minibatches.
Select DFA by maximum final validation accuracy, then lower MAC cost/rate.
Select SDIL by maximum accuracy, except that a channel-gated candidate within
0.5 points of the global maximum wins; remaining ties use lower total MACs,
then lower hidden rate. Advancement requires BP at least 50%, SDIL at least
35%, SDIL no more than 10 points below selected DFA, and all trajectories
finite. Failure stops Oral-A rather than expanding the grid.
## A3: full seed-0 ResNet-20 development gate
Train the A1-selected BP schedule, A2b-selected DFA, and A2b-selected SDIL for
200 epochs on all 45,000 training examples. DFA/SDIL retain their selected
hidden base rate, use output base rate 0.1 and the same epoch-100/150 10x drops;
SDIL retains its 100-step A-only warmup and K1/every4 joint calibration. No
nuisance traffic or predictor learning is present.
All of the following must hold to open the independent test panel:
1. BP final validation accuracy is at least 90%.
2. SDIL is within 5 points of BP and at least 2 points above tuned DFA.
3. SDIL early-third teaching alignment is at least 0.05.
4. Every loss/metric is finite and SDIL's estimated total training MACs do not
exceed exact BP's estimate.
No recovery branch follows an A3 failure.
## A4: independent ResNet-20/32/56 confirmation
Only after A3 passes, run exact BP, selected DFA, and selected SDIL at depths
20, 32, and 56 for model/data-loader seeds 10--14. Hyperparameters and epoch
budgets are copied without depth-specific tuning. Confirmation refits on all
50,000 original training examples (`val_examples=0`) after development has
closed. The CIFAR-10 test set is
evaluated exactly once at the end of every run; no intermediate test metric is
logged or used for selection.
The Oral-A gate requires:
1. all 45 trajectories are finite;
2. at every depth, mean SDIL test accuracy is within 2 points of exact BP;
3. at depth 56, SDIL exceeds DFA by at least 2 points;
4. paired SDIL depth-20 to depth-56 accuracy changes by no less than -2 points;
5. depth-56 early-third alignment is at least 0.05 and at least 30% of the
depth-20 value;
6. SDIL remains nondominated among matched local methods in accuracy versus
estimated MACs, logical causal queries, and peak memory. Wall time is shown
but cannot substitute for these hardware-independent coordinates.
Native BurstCCN and Dual Prop runs remain separate executable-fidelity
comparisons because their architectures, epochs, and objectives differ. Their
published multi-seed values are not pooled with this matched panel.
## Stop rules
- No test seed 10--14 may be touched before A3 passes.
- No failed threshold may be weakened after observing an endpoint.
- No extra depth, direction count, perturbation frequency, vectorizer, or
optimizer branch is added after its corresponding screen closes.
- A strong standard-network result cannot be described as evidence that the
natural Harnett causal-role/error-derivative signature emerged.
## Audited outcome (2026-07-22)
- A1 passed the primary schedule at `91.62%` validation accuracy; the recovery
branch was not used.
- A2a selected both vectorizer families. A2b selected channel-gated SDIL at
hidden LR `0.03`: `41.98%` versus tuned DFA's `37.16%` and BP's `74.94%`.
- In A3, BP ended at `91.62%` and DFA ended finite at `33.06%`. SDIL became
nonfinite at epoch 89 and ended at `10.00%`; its early-third alignment was
nonfinite. It passed the BP and MAC checks but failed the four remaining
checks.
- A3 status is `failed`. A4 was not opened, no confirmation JSON was produced,
and test seeds 10--14 remain untouched. No recovery or threshold change is
permitted by this protocol.
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