analysis: localize early-layer feedback bottlenecks

1 files changed, 9 insertions, 0 deletions

diff --git a/ROADMAP.md b/ROADMAP.md
index 75e606a..2ad919f 100644
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -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