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@@ -364,6 +364,15 @@ was not launched; test and confirmation seeds remain untouched. This localizes
the remaining bottleneck to early-layer credit rather than merely spatial
projection variance.
+The subsequent no-training representation oracle further decomposes that
+bottleneck. The existing spatial fields reach `0.05494` early alignment with
+unconstrained per-example coefficients; the actual output-error-conditioned
+family reaches `0.02397` on a disjoint 32-example batch, while learned v2
+reaches `0.00721`. Naive local-average/channel-context fields fall slightly to
+`0.02246`. The next algorithmic branch should therefore address both causal
+sample efficiency and feedback context; simply lowering `eta_A`, adding the
+tested fields, or reopening V2-2 is not justified.
+
Prepare convolutional local-update primitives and ResNet-20/32/56 protocols early. Queue frozen
runs opportunistically on authorized idle GPUs. Because BurstCCN already reports CIFAR-10 and
ImageNet scaling, dataset scale alone is not novel. The oral-level target is a memorable joint