protocol: freeze normalized mirror baseline funnel

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+# Normalized response-mirror baseline protocol
+
+## Status and attribution
+
+This protocol was frozen before observing any CIFAR-prefix response-mirror
+alignment or accuracy endpoint. A mechanics-only pilot on synthetic Gaussian
+images was used to choose the bounded mirror-rate grid; it did not load the
+frozen CIFAR development split or evaluate validation/test accuracy.
+
+The method is an inherited strong baseline, not SDIL novelty. It is a
+normalized, bias-blocked local system-identification variant of Akrout et al.'s
+[weight mirror](https://proceedings.neurips.cc/paper_files/paper/2019/file/f387624df552cea2f369918c5e1e12bc-Paper.pdf).
+Gaussian parent probes pass through ordinary local forward synapses and a
+separate update receives only the probe and child preactivation response. The
+local correlation estimates the forward kernel; Q/R track that estimate by an
+exponential delta rule. The task-error path remains the audited hierarchical
+FA residual DAG.
+
+Any BP-like scale obtained here belongs to weight estimation. It can raise the
+bar SDIL must beat but cannot raise SDIL novelty or rescue failed Oral-A-v1.
+Only a later, separately frozen demonstration that somato-dendritic innovation
+is load-bearing on top of this path can improve the paper-level claim.
+
+## WM-0: mechanics gate
+
+Before endpoints, the smoke suite must show:
+
+- one 16-probe local observation has mean feedback/forward cosine above 0.985
+  and minimum cosine above 0.95 on a deterministic tiny ResNet;
+- every feedback/forward norm ratio is in `[0.90, 1.10]`;
+- after observations are fixed, changing all forward parameters changes the
+  feedback update by exactly zero;
+- all hierarchical-gradient and convolutional local-update audits remain
+  green.
+
+The gate passed at mean/min cosine `0.988701/0.966066`, norm ratios
+`[0.953729,1.044368]`, and update independence error exactly zero.
+
+## WM-1: frozen-forward CIFAR causal-capture gate
+
+Use seed-0 ResNet-20, random feedback scale 1, the first 10,000 examples of the
+frozen development split, batch 128 for the diagnostic graph, and the fixed
+64-example training-prefix exact-gradient audit. Forward weights, readout,
+BatchNorm state, and affine parameters remain bitwise fixed.
+
+Record one fixed-HFA reference. For each `eta_M in {0.03, 0.1, 0.3}`, run 20
+local mirror observations with convolutional probe batch 1, Gaussian noise
+standard deviation 1, and mirror seed 3000. No task loss or label is consumed
+by mirroring; the final alignment audit alone uses labels. Select maximum
+early-third alignment, then all-layer alignment, then lower rate.
+
+WM-1 passes only if all four records are finite and selected WM:
+
+1. reaches early-third teaching alignment at least `0.40`;
+2. reaches all-layer alignment at least `0.50`;
+3. reaches mean feedback/forward cosine at least `0.85`;
+4. keeps every feedback/forward norm ratio in `[0.5, 1.5]`;
+5. records zero logical task-loss queries during mirror learning.
+
+No mirror batch, step count, noise scale, rate, covariance estimator, or
+per-layer schedule is added after observation.
+
+## WM-2: bounded short accuracy gate
+
+Only a WM-1 pass opens a matched 10k-example, 20-epoch ResNet-20 validation
+screen. Copy A2b data, augmentation, batch 128, cosine schedule, no warmup,
+momentum 0.9, weight decay `1e-4`, and output LR 0.1. Cross hidden LR
+`{0.03,0.1}`. Use the selected mirror rate, 20 mirror warmup observations,
+then one batch-1 mirror observation every 16 task updates. Select validation
+accuracy, then lower total MACs/rate.
+
+Advance only if both runs are finite, selected WM reaches at least 65%, stays
+within 10 points of BP's matched 74.94%, retains early alignment at least 0.30,
+uses zero task-loss queries for feedback learning, and costs no more than 1.15x
+matched BP estimated MACs. Failure closes the baseline without tuning.
+
+## WM-3: full validation baseline
+
+Only a WM-2 pass opens one 200-epoch seed-0 validation run on all 45,000
+development-training examples. Copy its selected hidden rate, mirror settings,
+output LR 0.1, and the A1 step drops at epochs 100/150. No recovery branch is
+allowed. A finite endpoint at least 88% with early alignment at least 0.30 and
+cost at most 1.15x BP is considered a strong inherited baseline. It does not
+authorize test access; it instead opens design of a hierarchical innovation
+intervention on the same frozen recipe.
+