analysis: diagnose Oral-A calibration failure

1 files changed, 9 insertions, 1 deletions

diff --git a/ROADMAP.md b/ROADMAP.md
index e2dd375..3203e04 100644
--- a/ROADMAP.md
+++ b/ROADMAP.md
@@ -341,12 +341,20 @@ claim is explicitly computational rather than a fit to cortical data.
 `ORAL_A.md` froze a validation-only ResNet-20 development funnel. A1 passed with
 `91.62%` BP validation accuracy. A2b selected channel-gated SDIL at `41.98%`,
 ahead of tuned DFA at `37.16%`, on the 10k/20-epoch screen. In full A3, DFA
-ended finite at `33.06%`; SDIL became nonfinite at epoch 90 and ended at
+ended finite at `33.06%`; SDIL became nonfinite at epoch 89 and ended at
 `10.00%`, failing alignment, accuracy, and finiteness gates. The MAC gate alone
 passed. Per the stop rule, the five-seed ResNet-20/32/56 test panel was never
 run. The exact BatchNorm/local-gradient mechanics remain verified, but the
 current training recipe does not establish standard-network scaling.
 
+Post-failure diagnosis is recorded separately in
+`results/oral_a_failure_diagnosis.json`: prediction--target cosine remains
+below `8.51e-5` in magnitude, calibration MSE is indistinguishable from target
+power, and target power grows `2.13e9x` before nonfiniteness. The next
+development branch must therefore reduce causal-estimator variance in the
+representable channel-gated subspace; changing only the v1 threshold is not an
+allowed response.
+
 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