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# Residual response-mirror baseline protocol
## Status and method boundary
Estimate-then-average WM passed its frozen capture gate but missed the short
accuracy gate by under one point. Its final feedback/forward cosine plateaued
near 0.81 in the deepest convolutional group despite frequent local probes.
The diagnosed issue is stationary estimator noise: a fresh finite-sample W
estimate is noisy even when Q already equals W.
Residual response mirroring (RRM) is a substantive update-rule change, not a
new cadence/batch/rate branch of failed WM. For each local Gaussian probe it
compares the observed forward child response with Q's predicted response and
updates from their difference. At Q=W, every individual stochastic update is
zero. The observation and update remain separated; the update never reads a
forward parameter. RRM is still inherited local predictive weight estimation,
not SDIL or Harnett-specific novelty.
A mechanics-only synthetic-image pilot was used to bound the mirror-rate grid.
No CIFAR development-prefix alignment or validation/test endpoint was observed
before this protocol and its executable selector were committed.
## RRM-0: mechanics gate
The convolutional smoke suite must verify:
- exact-symmetry response-residual fraction below `1e-14` for every probe;
- exact-symmetry parameter-update RMS below `1e-14`;
- changing all forward parameters after observations are fixed changes the
feedback update by exactly zero;
- all prior hierarchical and local-gradient checks remain green.
This passes at response-residual fraction `3.08e-17`, update RMS `2.75e-18`,
and forward-parameter independence error exactly zero.
## RRM-1: frozen-forward capture gate
Copy WM-1 exactly: seed-0 ResNet-20, random feedback scale 1, frozen 10k
development prefix, 64-example exact-gradient audit, convolutional mirror batch
1, Gaussian noise standard deviation 1, mirror seed 3000, 20 observations, and
no forward/readout/BatchNorm update. Record fixed HFA and cross
`eta_M in {0.03,0.1,0.3}`. Select early alignment, then all-layer alignment,
then lower rate.
All four records must be finite, and selected RRM must reach early alignment
0.65, all-layer alignment 0.70, mean feedback/forward cosine 0.93, norm ratios
in `[0.5,1.5]`, and zero task-loss queries. No extra observation, rate, batch,
noise, cadence, or layer-specific setting follows a failure.
## RRM-2: short accuracy gate
Only an RRM-1 pass opens two 10k-example, 20-epoch ResNet-20 validation jobs.
Copy WM-2 exactly: hidden LR `{0.03,0.1}`, output LR 0.1, batch 128, cosine
decay, no warmup, momentum 0.9, weight decay `1e-4`, 20 mirror warmup
observations, then one batch-1 observation every 16 task updates. The extra Q
response-prediction convolution is charged explicitly.
Select accuracy, then total MACs/rate. Both records must be finite; selected
RRM must reach 65%, lie within 10 points of matched BP 74.94%, retain early
alignment 0.50, use zero task-loss queries for feedback learning, and cost no
more than 1.15x matched BP MACs. Failure closes RRM.
## RRM-3: conditional full baseline
Only an RRM-2 pass opens one 200-epoch seed-0 validation run, copying selected
settings and the A1 epoch-100/150 step drops on all 45,000 development-training
examples. It must be finite, reach 88%, retain early alignment 0.50, and cost
at most 1.15x BP. It does not authorize test access. A pass instead freezes the
same scalable substrate for a raw-versus-innovation mixed-traffic experiment.
RRM is a baseline throughout. RRM-1/RRM-2 cannot raise the reviewer score;
only a later load-bearing innovation result on top of it can do so.
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