analysis: localize early-layer feedback bottlenecks

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diff --git a/RESULTS.md b/RESULTS.md
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@@ -663,6 +663,26 @@ per full hidden field, whereas structured targets are scored per channel-basis
 moment. No empirical 1133x variance-reduction claim is made from their raw
 ratio.
 
+A no-training oracle audit then separates feedback capacity from estimator
+efficiency on disjoint 32-example BatchNorm batches. Early-third alignment is
+`0.05494` when each example/channel receives unconstrained optimal coefficients
+for the existing `[1,tanh(h)]` spatial fields, but falls to `0.02397` when those
+coefficients must be predicted by the actual output-error-linear A/G family.
+The learned structured estimator reaches `0.00721`, about 30% of that
+cross-validated family oracle. Thus the current basis is not incapable of
+crossing the v2 threshold, but causal regression leaves a substantial gap and
+the output-error-only coefficient map itself discards over half of the spatial
+oracle's directional alignment.
+
+Adding fixed local-average and channel-mean somatic fields does not help
+(`0.02246` early-third cross-validated alignment). An independent spatial
+template reaches `0.04797`, but its prediction/target energy ratio is `0.458`
+despite low cosine, a warning that the high-capacity map amplifies
+out-of-sample error rather than solving credit assignment cleanly. These are
+post-failure oracle diagnostics, not trainable-method results. They rule out a
+naive basis expansion and point toward both better causal sample efficiency
+and a more informative hierarchical/high-level feedback context.
+
 ## How to run
 `experiments/run.py --mode {bp,fa,dfa,sdil} --dataset {mnist,fmnist,cifar10} --depth D --residual {0,1} --act {tanh,gelu,silu,relu}`
 Batteries: `experiments/run_v2.sh <ds> "<depths>" <res> <act> "<seeds>" <ep> <pfx>`.